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1
.github/ISSUE_TEMPLATE.md vendored
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@ -5,6 +5,7 @@ If it hasn't been reported, please create a new issue.
## Step 2: Describe your environment
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Branch: Master | Develop

4
.gitignore vendored
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@ -81,7 +81,6 @@ target/
# Jupyter Notebook
.ipynb_checkpoints
*.ipynb
# pyenv
.python-version
@ -93,3 +92,6 @@ target/
.pytest_cache/
.mypy_cache/
#exceptions
!user_data/noteboks/*example.ipynb

6
.pyup.yml
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@ -11,7 +11,10 @@ update: all
# allowed: True, False
pin: True
schedule: "every day"
# update schedule
# default: empty
# allowed: "every day", "every week", ..
schedule: "every week"
search: False
@ -22,6 +25,7 @@ requirements:
- requirements.txt
- requirements-dev.txt
- requirements-plot.txt
- requirements-common.txt
# configure the branch prefix the bot is using

27
.travis.yml
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@ -10,16 +10,11 @@ services:
env:
global:
- IMAGE_NAME=freqtradeorg/freqtrade
addons:
apt:
packages:
- libelf-dev
- libdw-dev
- binutils-dev
install:
- cd build_helpers && ./install_ta-lib.sh; cd ..
- export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
- pip install --upgrade pytest-random-order
- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
- export TA_LIBRARY_PATH=${HOME}/dependencies/lib
- export TA_INCLUDE_PATH=${HOME}/dependencies/lib/include
- pip install -r requirements-dev.txt
- pip install -e .
jobs:
@ -27,21 +22,21 @@ jobs:
include:
- stage: tests
script:
- pytest --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
- pytest --random-order --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
# Allow failure for coveralls
- coveralls || true
- coveralls || true
name: pytest
- script:
- cp config.json.example config.json
- python freqtrade/main.py --datadir freqtrade/tests/testdata backtesting
- freqtrade --datadir freqtrade/tests/testdata backtesting
name: backtest
- script:
- cp config.json.example config.json
- python freqtrade/main.py --datadir freqtrade/tests/testdata hyperopt -e 5
- freqtrade --datadir freqtrade/tests/testdata hyperopt -e 5
name: hyperopt
- script: flake8 freqtrade
- script: flake8 freqtrade scripts
name: flake8
- script: mypy freqtrade
- script: mypy freqtrade scripts
name: mypy
- stage: docker
@ -56,4 +51,4 @@ notifications:
cache:
pip: True
directories:
- /usr/local/lib
- $HOME/dependencies

2
CONTRIBUTING.md
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@ -11,7 +11,7 @@ Few pointers for contributions:
- Create your PR against the `develop` branch, not `master`.
- New features need to contain unit tests and must be PEP8 conformant (max-line-length = 100).
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE)
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg)
or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
## Getting started

4
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.7.2-slim-stretch
FROM python:3.7.3-slim-stretch
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \
@ -16,7 +16,7 @@ RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt /freqtrade/
COPY requirements.txt requirements-common.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements.txt --no-cache-dir

40
Dockerfile.pi Normal file
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@ -0,0 +1,40 @@
FROM balenalib/raspberrypi3-debian:stretch
RUN [ "cross-build-start" ]
RUN apt-get update \
&& apt-get -y install wget curl build-essential libssl-dev libffi-dev \
&& apt-get clean
# Prepare environment
RUN mkdir /freqtrade
WORKDIR /freqtrade
# Install TA-lib
COPY build_helpers/ta-lib-0.4.0-src.tar.gz /freqtrade/
RUN tar -xzf /freqtrade/ta-lib-0.4.0-src.tar.gz \
&& cd /freqtrade/ta-lib/ \
&& ./configure \
&& make \
&& make install \
&& rm /freqtrade/ta-lib-0.4.0-src.tar.gz
ENV LD_LIBRARY_PATH /usr/local/lib
# Install berryconda
RUN wget https://github.com/jjhelmus/berryconda/releases/download/v2.0.0/Berryconda3-2.0.0-Linux-armv7l.sh \
&& bash ./Berryconda3-2.0.0-Linux-armv7l.sh -b \
&& rm Berryconda3-2.0.0-Linux-armv7l.sh
# Install dependencies
COPY requirements-common.txt /freqtrade/
RUN ~/berryconda3/bin/conda install -y numpy pandas scipy \
&& ~/berryconda3/bin/pip install -r requirements-common.txt --no-cache-dir
# Install and execute
COPY . /freqtrade/
RUN ~/berryconda3/bin/pip install -e . --no-cache-dir
RUN [ "cross-build-end" ]
ENTRYPOINT ["/root/berryconda3/bin/python","./freqtrade/main.py"]

2
Dockerfile.technical
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@ -3,4 +3,4 @@ FROM freqtradeorg/freqtrade:develop
RUN apt-get update \
&& apt-get -y install git \
&& apt-get clean \
&& pip install git+https://github.com/berlinguyinca/technical
&& pip install git+https://github.com/freqtrade/technical

13
README.md
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@ -68,9 +68,9 @@ For any other type of installation please refer to [Installation doc](https://ww
### Bot commands
```
usage: freqtrade [-h] [-v] [--version] [-c PATH] [-d PATH] [-s NAME]
[--strategy-path PATH] [--dynamic-whitelist [INT]]
[--db-url PATH]
usage: freqtrade [-h] [-v] [--logfile FILE] [--version] [-c PATH] [-d PATH]
[-s NAME] [--strategy-path PATH] [--dynamic-whitelist [INT]]
[--db-url PATH] [--sd-notify]
{backtesting,edge,hyperopt} ...
Free, open source crypto trading bot
@ -84,6 +84,7 @@ positional arguments:
optional arguments:
-h, --help show this help message and exit
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified
--version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: None). Multiple
@ -100,6 +101,7 @@ optional arguments:
--db-url PATH Override trades database URL, this is useful if
dry_run is enabled or in custom deployments (default:
None).
--sd-notify Notify systemd service manager.
```
### Telegram RPC commands
@ -127,7 +129,6 @@ The project is currently setup in two main branches:
- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
## A note on Binance
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
@ -140,7 +141,7 @@ Accounts having BNB accounts use this to pay for fees - if your first trade happ
For any questions not covered by the documentation or for further
information about the bot, we encourage you to join our slack channel.
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE).
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg).
### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
@ -171,7 +172,7 @@ to understand the requirements before sending your pull-requests.
Coding is not a neccessity to contribute - maybe start with improving our documentation?
Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Important:** Always create your PR against the `develop` branch, not `master`.

8
bin/freqtrade
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@ -1,7 +1,11 @@
#!/usr/bin/env python3
import sys
import warnings
from freqtrade.main import main, set_loggers
set_loggers()
from freqtrade.main import main
warnings.warn(
"Deprecated - To continue to run the bot like this, please run `pip install -e .` again.",
DeprecationWarning)
main(sys.argv[1:])

10
build_helpers/install_ta-lib.sh
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@ -1,8 +1,14 @@
if [ ! -f "/usr/local/lib/libta_lib.a" ]; then
if [ -z "$1" ]; then
INSTALL_LOC=/usr/local
else
INSTALL_LOC=${1}
fi
echo "Installing to ${INSTALL_LOC}"
if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib \
&& sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
&& ./configure \
&& ./configure --prefix=${INSTALL_LOC}/ \
&& make \
&& which sudo && sudo make install || make install \
&& cd ..

3
config.json.example
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@ -30,7 +30,8 @@
"secret": "your_exchange_secret",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false
"enableRateLimit": true,
"rateLimit": 500
},
"pair_whitelist": [
"ETH/BTC",

5
config_binance.json.example
View File

@ -11,8 +11,8 @@
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0,
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
@ -30,7 +30,8 @@
"secret": "your_exchange_secret",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
"AST/BTC",

18
config_full.json.example
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@ -22,8 +22,8 @@
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0,
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
@ -39,12 +39,12 @@
"buy": "limit",
"sell": "limit",
"stoploss": "market",
"stoploss_on_exchange": "false",
"stoploss_on_exchange": false,
"stoploss_on_exchange_interval": 60
},
"order_time_in_force": {
"buy": "gtc",
"sell": "gtc",
"sell": "gtc"
},
"pairlist": {
"method": "VolumePairList",
@ -56,11 +56,14 @@
},
"exchange": {
"name": "bittrex",
"sandbox": false,
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"password": "",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false,
"rateLimit": 500,
"aiohttp_trust_env": false
},
"pair_whitelist": [
@ -106,6 +109,13 @@
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"username": "freqtrader",
"password": "SuperSecurePassword"
},
"db_url": "sqlite:///tradesv3.sqlite",
"initial_state": "running",
"forcebuy_enable": false,
@ -113,5 +123,5 @@
"process_throttle_secs": 5
},
"strategy": "DefaultStrategy",
"strategy_path": "/some/folder/"
"strategy_path": "user_data/strategies/"
}

7
config_kraken.json.example
View File

@ -5,15 +5,14 @@
"fiat_display_currency": "EUR",
"ticker_interval" : "5m",
"dry_run": true,
"db_url": "sqlite:///tradesv3.dryrun.sqlite",
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0,
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
@ -60,8 +59,8 @@
},
"telegram": {
"enabled": false,
"token": "",
"chat_id": ""
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"forcebuy_enable": false,

59
docs/backtesting.md
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@ -13,7 +13,7 @@ Backtesting will use the crypto-currencies (pair) from your config file
and load static tickers located in
[/freqtrade/tests/testdata](https://github.com/freqtrade/freqtrade/tree/develop/freqtrade/tests/testdata).
If the 5 min and 1 min ticker for the crypto-currencies to test is not
already in the `testdata` folder, backtesting will download them
already in the `testdata` directory, backtesting will download them
automatically. Testdata files will not be updated until you specify it.
The result of backtesting will confirm you if your bot has better odds of making a profit than a loss.
@ -24,53 +24,61 @@ The backtesting is very easy with freqtrade.
#### With 5 min tickers (Per default)
```bash
python3 ./freqtrade/main.py backtesting
freqtrade backtesting
```
#### With 1 min tickers
```bash
python3 ./freqtrade/main.py backtesting --ticker-interval 1m
freqtrade backtesting --ticker-interval 1m
```
#### Update cached pairs with the latest data
```bash
python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
freqtrade backtesting --refresh-pairs-cached
```
#### With live data (do not alter your testdata files)
```bash
python3 ./freqtrade/main.py backtesting --live
freqtrade backtesting --live
```
#### Using a different on-disk ticker-data source
```bash
python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
freqtrade backtesting --datadir freqtrade/tests/testdata-20180101
```
#### With a (custom) strategy file
```bash
python3 ./freqtrade/main.py -s TestStrategy backtesting
freqtrade -s TestStrategy backtesting
```
Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory
Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory.
#### Comparing multiple Strategies
```bash
freqtrade backtesting --strategy-list TestStrategy1 AwesomeStrategy --ticker-interval 5m
```
Where `TestStrategy1` and `AwesomeStrategy` refer to class names of strategies.
#### Exporting trades to file
```bash
python3 ./freqtrade/main.py backtesting --export trades
freqtrade backtesting --export trades
```
The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts folder.
The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
#### Exporting trades to file specifying a custom filename
```bash
python3 ./freqtrade/main.py backtesting --export trades --export-filename=backtest_teststrategy.json
freqtrade backtesting --export trades --export-filename=backtest_teststrategy.json
```
#### Running backtest with smaller testset
@ -81,7 +89,7 @@ you want to use. The last N ticks/timeframes will be used.
Example:
```bash
python3 ./freqtrade/main.py backtesting --timerange=-200
freqtrade backtesting --timerange=-200
```
#### Advanced use of timerange
@ -107,7 +115,7 @@ To download new set of backtesting ticker data, you can use a download script.
If you are using Binance for example:
- create a folder `user_data/data/binance` and copy `pairs.json` in that folder.
- create a directory `user_data/data/binance` and copy `pairs.json` in that directory.
- update the `pairs.json` to contain the currency pairs you are interested in.
```bash
@ -123,11 +131,12 @@ python scripts/download_backtest_data.py --exchange binance
This will download ticker data for all the currency pairs you defined in `pairs.json`.
- To use a different folder than the exchange specific default, use `--export user_data/data/some_directory`.
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
- To change the exchange used to download the tickers, use `--exchange`. Default is `bittrex`.
- To use `pairs.json` from some other folder, use `--pairs-file some_other_dir/pairs.json`.
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download ticker data for only 10 days, use `--days 10`.
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with other options.
For help about backtesting usage, please refer to [Backtesting commands](#backtesting-commands).
@ -220,24 +229,8 @@ strategies, your configuration, and the crypto-currency you have set up.
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis.
You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file.
``` python
from freqtrade.data.btanalysis import load_backtest_data
df = load_backtest_data("user_data/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output unreadable.
If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a PR so the community can benefit from it.
## Backtesting multiple strategies
@ -246,7 +239,7 @@ To backtest multiple strategies, a list of Strategies can be provided.
This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
strategies you'd like to compare, this should give a nice runtime boost.
All listed Strategies need to be in the same folder.
All listed Strategies need to be in the same directory.
``` bash
freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades

144
docs/bot-usage.md
View File

@ -2,13 +2,16 @@
This page explains the different parameters of the bot and how to run it.
!Note:
If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
## Bot commands
```
usage: freqtrade [-h] [-v] [--version] [-c PATH] [-d PATH] [-s NAME]
[--strategy-path PATH] [--dynamic-whitelist [INT]]
[--db-url PATH]
usage: freqtrade [-h] [-v] [--logfile FILE] [--version] [-c PATH] [-d PATH]
[-s NAME] [--strategy-path PATH] [--db-url PATH]
[--sd-notify]
{backtesting,edge,hyperopt} ...
Free, open source crypto trading bot
@ -22,19 +25,18 @@ positional arguments:
optional arguments:
-h, --help show this help message and exit
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--version show program's version number and exit
--logfile FILE Log to the file specified
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: None). Multiple
--config options may be used.
--config options may be used. Can be set to '-' to
read config from stdin.
-d PATH, --datadir PATH
Path to backtest data.
-s NAME, --strategy NAME
Specify strategy class name (default:
DefaultStrategy).
--strategy-path PATH Specify additional strategy lookup path.
--dynamic-whitelist [INT]
Dynamically generate and update whitelist based on 24h
BaseVolume (default: 20). DEPRECATED.
--db-url PATH Override trades database URL, this is useful if
dry_run is enabled or in custom deployments (default:
None).
@ -47,7 +49,7 @@ The bot allows you to select which configuration file you want to use. Per
default, the bot will load the file `./config.json`
```bash
python3 ./freqtrade/main.py -c path/far/far/away/config.json
freqtrade -c path/far/far/away/config.json
```
### How to use multiple configuration files?
@ -63,13 +65,13 @@ empty key and secrete values while running in the Dry Mode (which does not actua
require them):
```bash
python3 ./freqtrade/main.py -c ./config.json
freqtrade -c ./config.json
```
and specify both configuration files when running in the normal Live Trade Mode:
```bash
python3 ./freqtrade/main.py -c ./config.json -c path/to/secrets/keys.config.json
freqtrade -c ./config.json -c path/to/secrets/keys.config.json
```
This could help you hide your private Exchange key and Exchange secrete on you local machine
@ -78,7 +80,7 @@ prevent unintended disclosure of sensitive private data when you publish example
of your configuration in the project issues or in the Internet.
See more details on this technique with examples in the documentation page on
[configuration](bot-configuration.md).
[configuration](configuration.md).
### How to use **--strategy**?
@ -95,39 +97,28 @@ In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
a strategy class called `AwesomeStrategy` to load it:
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy
freqtrade --strategy AwesomeStrategy
```
If the bot does not find your strategy file, it will display in an error
message the reason (File not found, or errors in your code).
Learn more about strategy file in
[optimize your bot](bot-optimization.md).
[Strategy Customization](strategy-customization.md).
### How to use **--strategy-path**?
This parameter allows you to add an additional strategy lookup path, which gets
checked before the default locations (The passed path must be a folder!):
checked before the default locations (The passed path must be a directory!):
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
```
#### How to install a strategy?
This is very simple. Copy paste your strategy file into the folder
This is very simple. Copy paste your strategy file into the directory
`user_data/strategies` or use `--strategy-path`. And voila, the bot is ready to use it.
### How to use **--dynamic-whitelist**?
!!! danger "DEPRECATED"
This command line option is deprecated. Please move your configurations using it
to the configurations that utilize the `StaticPairList` or `VolumePairList` methods set
in the configuration file
as outlined [here](configuration/#dynamic-pairlists)
Description of this deprecated feature was moved to [here](deprecated.md).
Please no longer use it.
### How to use **--db-url**?
When you run the bot in Dry-run mode, per default no transactions are
@ -136,7 +127,7 @@ using `--db-url`. This can also be used to specify a custom database
in production mode. Example command:
```bash
python3 ./freqtrade/main.py -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
```
## Backtesting commands
@ -145,9 +136,11 @@ Backtesting also uses the config specified via `-c/--config`.
```
usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--eps] [--dmmp] [-l] [-r]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
[--max_open_trades MAX_OPEN_TRADES]
[--stake_amount STAKE_AMOUNT] [-r] [--eps] [--dmmp]
[-l]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
optional arguments:
-h, --help show this help message and exit
@ -155,6 +148,14 @@ optional arguments:
Specify ticker interval (1m, 5m, 30m, 1h, 1d).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades MAX_OPEN_TRADES
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
-r, --refresh-pairs-cached
Refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your optimization commands with up-to-date data.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
@ -163,12 +164,8 @@ optional arguments:
(same as setting `max_open_trades` to a very high
number).
-l, --live Use live data.
-r, --refresh-pairs-cached
Refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your backtesting with up-to-date data.
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a commaseparated list of strategies to
Provide a space-separated list of strategies to
backtest Please note that ticker-interval needs to be
set either in config or via command line. When using
this together with --export trades, the strategy-name
@ -187,7 +184,7 @@ optional arguments:
### How to use **--refresh-pairs-cached** parameter?
The first time your run Backtesting, it will take the pairs you have
set in your config file and download data from Bittrex.
set in your config file and download data from the Exchange.
If for any reason you want to update your data set, you use
`--refresh-pairs-cached` to force Backtesting to update the data it has.
@ -205,30 +202,66 @@ to find optimal parameter values for your stategy.
```
usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--customhyperopt NAME] [--eps] [--dmmp] [-e INT]
[--max_open_trades INT]
[--stake_amount STAKE_AMOUNT] [-r]
[--customhyperopt NAME] [--hyperopt-path PATH]
[--eps] [-e INT]
[-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
[--dmmp] [--print-all] [-j JOBS]
[--random-state INT] [--min-trades INT] [--continue]
[--hyperopt-loss NAME]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
Specify ticker interval (1m, 5m, 30m, 1h, 1d).
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades INT
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
-r, --refresh-pairs-cached
Refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your optimization commands with up-to-date data.
--customhyperopt NAME
Specify hyperopt class name (default:
DefaultHyperOpts).
`DefaultHyperOpts`).
--hyperopt-path PATH Specify additional lookup path for Hyperopts and
Hyperopt Loss functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
-e INT, --epochs INT Specify number of epochs (default: 100).
-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
Specify which parameters to hyperopt. Space-separated
list. Default: `all`.
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
-e INT, --epochs INT Specify number of epochs (default: 100).
-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
Specify which parameters to hyperopt. Space separate
list. Default: all.
--print-all Print all results, not only the best ones.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
(default), all CPUs are used, for -2, all CPUs but one
are used, etc. If 1 is given, no parallel computing
code is used at all.
--random-state INT Set random state to some positive integer for
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--continue Continue hyperopt from previous runs. By default,
temporary files will be removed and hyperopt will
start from scratch.
--hyperopt-loss NAME
Specify the class name of the hyperopt loss function
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. (default:
`DefaultHyperOptLoss`).
```
## Edge commands
@ -236,8 +269,10 @@ optional arguments:
To know your trade expectacny and winrate against historical data, you can use Edge.
```
usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE] [-r]
[--stoplosses STOPLOSS_RANGE]
usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades MAX_OPEN_TRADES]
[--stake_amount STAKE_AMOUNT] [-r]
[--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
@ -245,10 +280,14 @@ optional arguments:
Specify ticker interval (1m, 5m, 30m, 1h, 1d).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades MAX_OPEN_TRADES
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
-r, --refresh-pairs-cached
Refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your edge with up-to-date data.
run your optimization commands with up-to-date data.
--stoplosses STOPLOSS_RANGE
Defines a range of stoploss against which edge will
assess the strategy the format is "min,max,step"
@ -258,12 +297,7 @@ optional arguments:
To understand edge and how to read the results, please read the [edge documentation](edge.md).
## A parameter missing in the configuration?
All parameters for `main.py`, `backtesting`, `hyperopt` are referenced
in [misc.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/misc.py#L84)
## Next step
The optimal strategy of the bot will change with time depending of the market trends. The next step is to
[optimize your bot](bot-optimization.md).
[Strategy Customization](strategy-customization.md).

107
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@ -14,12 +14,13 @@ Mandatory Parameters are marked as **Required**.
| Command | Default | Description |
|----------|---------|-------------|
| `max_open_trades` | 3 | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades)
| `stake_currency` | BTC | **Required.** Crypto-currency used for trading.
| `stake_amount` | 0.05 | **Required.** Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to `"unlimited"` to allow the bot to use all available balance.
| `stake_currency` | BTC | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy).
| `stake_amount` | 0.05 | **Required.** Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to `"unlimited"` to allow the bot to use all available balance. [Strategy Override](#parameters-in-the-strategy).
| `amount_reserve_percent` | 0.05 | Reserve some amount in min pair stake amount. Default is 5%. The bot will reserve `amount_reserve_percent` + stop-loss value when calculating min pair stake amount in order to avoid possible trade refusals.
| `ticker_interval` | [1m, 5m, 15m, 30m, 1h, 1d, ...] | The ticker interval to use (1min, 5 min, 15 min, 30 min, 1 hour or 1 day). Default is 5 minutes. [Strategy Override](#parameters-in-the-strategy).
| `fiat_display_currency` | USD | **Required.** Fiat currency used to show your profits. More information below.
| `dry_run` | true | **Required.** Define if the bot must be in Dry-run or production mode.
| `dry_run_wallet` | 999.9 | Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason.
| `process_only_new_candles` | false | If set to true indicators are processed only once a new candle arrives. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy).
| `minimal_roi` | See below | Set the threshold in percent the bot will use to sell a trade. More information below. [Strategy Override](#parameters-in-the-strategy).
| `stoploss` | -0.10 | Value of the stoploss in percent used by the bot. More information below. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
@ -39,13 +40,12 @@ Mandatory Parameters are marked as **Required**.
| `ask_strategy.order_book_max` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `order_types` | None | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).
| `order_time_in_force` | None | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy).
| `exchange.name` | bittrex | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
| `exchange.name` | | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
| `exchange.sandbox` | false | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.
| `exchange.key` | key | API key to use for the exchange. Only required when you are in production mode.
| `exchange.secret` | secret | API secret to use for the exchange. Only required when you are in production mode.
| `exchange.pair_whitelist` | [] | List of currency to use by the bot. Can be overrided with `--dynamic-whitelist` param.
| `exchange.pair_blacklist` | [] | List of currency the bot must avoid. Useful when using `--dynamic-whitelist` param.
| `exchange.ccxt_rate_limit` | True | DEPRECATED!! Have CCXT handle Exchange rate limits. Depending on the exchange, having this to false can lead to temporary bans from the exchange.
| `exchange.key` | '' | API key to use for the exchange. Only required when you are in production mode.
| `exchange.secret` | '' | API secret to use for the exchange. Only required when you are in production mode.
| `exchange.pair_whitelist` | [] | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
| `exchange.pair_blacklist` | [] | List of pairs the bot must absolutely avoid for trading and backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
| `exchange.ccxt_config` | None | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
| `exchange.ccxt_async_config` | None | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
| `exchange.markets_refresh_interval` | 60 | The interval in minutes in which markets are reloaded.
@ -53,7 +53,7 @@ Mandatory Parameters are marked as **Required**.
| `experimental.use_sell_signal` | false | Use your sell strategy in addition of the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy).
| `experimental.sell_profit_only` | false | Waits until you have made a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy).
| `experimental.ignore_roi_if_buy_signal` | false | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy).
| `pairlist.method` | StaticPairList | Use Static whitelist. [More information below](#dynamic-pairlists).
| `pairlist.method` | StaticPairList | Use static or dynamic volume-based pairlist. [More information below](#dynamic-pairlists).
| `pairlist.config` | None | Additional configuration for dynamic pairlists. [More information below](#dynamic-pairlists).
| `telegram.enabled` | true | **Required.** Enable or not the usage of Telegram.
| `telegram.token` | token | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
@ -67,17 +67,20 @@ Mandatory Parameters are marked as **Required**.
| `initial_state` | running | Defines the initial application state. More information below.
| `forcebuy_enable` | false | Enables the RPC Commands to force a buy. More information below.
| `strategy` | DefaultStrategy | Defines Strategy class to use.
| `strategy_path` | null | Adds an additional strategy lookup path (must be a folder).
| `strategy_path` | null | Adds an additional strategy lookup path (must be a directory).
| `internals.process_throttle_secs` | 5 | **Required.** Set the process throttle. Value in second.
| `internals.sd_notify` | false | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details.
| `logfile` | | Specify Logfile. Uses a rolling strategy of 10 files, with 1Mb per file.
### Parameters in the strategy
The following parameters can be set in either configuration file or strategy.
Values set in the configuration file always overwrite values set in the strategy.
* `minimal_roi`
* `stake_currency`
* `stake_amount`
* `ticker_interval`
* `minimal_roi`
* `stoploss`
* `trailing_stop`
* `trailing_stop_positive`
@ -128,17 +131,11 @@ If it is not set in either Strategy or Configuration, a default of 1000% `{"0":
### Understand stoploss
The `stoploss` configuration parameter is loss in percentage that should trigger a sale.
For example, value `-0.10` will cause immediate sell if the
profit dips below -10% for a given trade. This parameter is optional.
Most of the strategy files already include the optimal `stoploss`
value. This parameter is optional. If you use it in the configuration file, it will take over the
`stoploss` value from the strategy file.
Go to the [stoploss documentation](stoploss.md) for more details.
### Understand trailing stoploss
Go to the [trailing stoploss Documentation](stoploss.md) for details on trailing stoploss.
Go to the [trailing stoploss Documentation](stoploss.md#trailing-stop-loss) for details on trailing stoploss.
### Understand initial_state
@ -188,14 +185,28 @@ If this is configured, all 4 values (`buy`, `sell`, `stoploss` and
`stoploss_on_exchange`) need to be present, otherwise the bot will warn about it and fail to start.
The below is the default which is used if this is not configured in either strategy or configuration file.
Syntax for Strategy:
```python
"order_types": {
order_types = {
"buy": "limit",
"sell": "limit",
"stoploss": "market",
"stoploss_on_exchange": False,
"stoploss_on_exchange_interval": 60
},
}
```
Configuration:
```json
"order_types": {
"buy": "limit",
"sell": "limit",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_on_exchange_interval": 60
}
```
!!! Note
@ -208,7 +219,12 @@ The below is the default which is used if this is not configured in either strat
unsure of what you are doing. For more information about how stoploss works please
read [the stoploss documentation](stoploss.md).
!!! Note
In case of stoploss on exchange if the stoploss is cancelled manually then
the bot would recreate one.
### Understand order_time_in_force
The `order_time_in_force` configuration parameter defines the policy by which the order
is executed on the exchange. Three commonly used time in force are:
@ -244,9 +260,9 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
This is an ongoing work. For now it is supported only for binance and only for buy orders.
Please don't change the default value unless you know what you are doing.
### What values for exchange.name?
### Exchange configuration
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports 115 cryptocurrency
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
with only Bittrex and Binance.
@ -258,6 +274,49 @@ The bot was tested with the following exchanges:
Feel free to test other exchanges and submit your PR to improve the bot.
#### Sample exchange configuration
A exchange configuration for "binance" would look as follows:
```json
"exchange": {
"name": "binance",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
```
This configuration enables binance, as well as rate limiting to avoid bans from the exchange.
`"rateLimit": 200` defines a wait-event of 0.2s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
!!! Note
Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
#### Advanced FreqTrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.
### What values can be used for fiat_display_currency?
The `fiat_display_currency` configuration parameter sets the base currency to use for the
@ -321,8 +380,6 @@ section of the configuration.
* `StaticPairList`
* It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
* `VolumePairList`
* Formerly available as `--dynamic-whitelist [<number_assets>]`. This command line
option is deprecated and should no longer be used.
* It selects `number_assets` top pairs based on `sort_key`, which can be one of
`askVolume`, `bidVolume` and `quoteVolume`, defaults to `quoteVolume`.
* There is a possibility to filter low-value coins that would not allow setting a stop loss

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@ -0,0 +1,114 @@
# Analyzing bot data
You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation).
*Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
## Example snippets
### Load backtest results into a pandas dataframe
```python
from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
df = load_backtest_data("user_data/backtest_data/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.
### Load live trading results into a pandas dataframe
``` python
from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
df.groupby("pair")["sell_reason"].value_counts()
```
## Strategy debugging example
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
### Import requirements and define variables used in analyses
```python
# Imports
from pathlib import Path
import os
from freqtrade.data.history import load_pair_history
from freqtrade.resolvers import StrategyResolver
# You can override strategy settings as demonstrated below.
# Customize these according to your needs.
# Define some constants
ticker_interval = "5m"
# Name of the strategy class
strategy_name = 'AwesomeStrategy'
# Path to user data
user_data_dir = 'user_data'
# Location of the strategy
strategy_location = Path(user_data_dir, 'strategies')
# Location of the data
data_location = Path(user_data_dir, 'data', 'binance')
# Pair to analyze
# Only use one pair here
pair = "BTC_USDT"
```
### Load exchange data
```python
# Load data using values set above
bt_data = load_pair_history(datadir=Path(data_location),
ticker_interval=ticker_interval,
pair=pair)
# Confirm success
print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")
```
### Load and run strategy
* Rerun each time the strategy file is changed
```python
# Load strategy using values set above
strategy = StrategyResolver({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location}).strategy
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(bt_data, {'pair': pair})
```
### Display the trade details
* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
#### Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units
#### Comparison with full backtest
having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29).
The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
```python
# Report results
print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=True)
data.tail()
```
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

34
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@ -4,28 +4,16 @@ This page contains description of the command line arguments, configuration para
and the bot features that were declared as DEPRECATED by the bot development team
and are no longer supported. Please avoid their usage in your configuration.
### the `--live` command line option
`--live` in the context of backtesting allows to download the latest tick data for backtesting.
Since this only downloads one set of data (by default 500 candles) - this is not really suitable for extendet backtesting, and has therefore been deprecated.
This command was deprecated in `2019.6-dev` and will be removed after the next release.
## Removed features
### The **--dynamic-whitelist** command line option
Per default `--dynamic-whitelist` will retrieve the 20 currencies based
on BaseVolume. This value can be changed when you run the script.
**By Default**
Get the 20 currencies based on BaseVolume.
```bash
python3 ./freqtrade/main.py --dynamic-whitelist
```
**Customize the number of currencies to retrieve**
Get the 30 currencies based on BaseVolume.
```bash
python3 ./freqtrade/main.py --dynamic-whitelist 30
```
**Exception**
`--dynamic-whitelist` must be greater than 0. If you enter 0 or a
negative value (e.g -2), `--dynamic-whitelist` will use the default
value (20).
This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
and in freqtrade 2019.7 (master branch).

70
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@ -2,7 +2,7 @@
This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE) where you can ask questions.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg) where you can ask questions.
## Documentation
@ -12,8 +12,8 @@ Special fields for the documentation (like Note boxes, ...) can be found [here](
## Developer setup
To configure a development environment, use best use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
Alternatively (if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -r requirements-dev.txt`.
To configure a development environment, best use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
Alternatively (if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
@ -81,11 +81,56 @@ Please also run `self._validate_whitelist(pairs)` and to check and remove pairs
This is a simple method used by `VolumePairList` - however serves as a good example.
It implements caching (`@cached(TTLCache(maxsize=1, ttl=1800))`) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.
## Implement a new Exchange (WIP)
!!! Note
This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
Most exchanges supported by CCXT should work out of the box.
### Stoploss On Exchange
Check if the new exchange supports Stoploss on Exchange orders through their API.
Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselfs. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
### Incomplete candles
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
To check how the new exchange behaves, you can use the following snippet:
``` python
import ccxt
from datetime import datetime
from freqtrade.data.converter import parse_ticker_dataframe
ct = ccxt.binance()
timeframe = "1d"
pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange!
raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
# convert to dataframe
df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
print(df1["date"].tail(1))
print(datetime.utcnow())
```
``` output
19 2019-06-08 00:00:00+00:00
2019-06-09 12:30:27.873327
```
The output will show the last entry from the Exchange as well as the current UTC date.
If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
## Creating a release
This part of the documentation is aimed at maintainers, and shows how to create a release.
### create release branch
### Create release branch
``` bash
# make sure you're in develop branch
@ -95,11 +140,14 @@ git checkout develop
git checkout -b new_release
```
* edit `freqtrade/__init__.py` and add the desired version (for example `0.18.0`)
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
* Commit this part
* push that branch to the remote and create a PR
* push that branch to the remote and create a PR against the master branch
### create changelog from git commits
### Create changelog from git commits
!!! Note
Make sure that both master and develop are up-todate!.
``` bash
# Needs to be done before merging / pulling that branch.
@ -108,10 +156,14 @@ git log --oneline --no-decorate --no-merges master..develop
### Create github release / tag
Once the PR against master is merged (best right after merging):
* Use the button "Draft a new release" in the Github UI (subsection releases)
* Use the version-number specified as tag.
* Use "master" as reference (this step comes after the above PR is merged).
* use the above changelog as release comment (as codeblock)
* Use the above changelog as release comment (as codeblock)
### After-release
* update version in develop to next valid version and postfix that with `-dev` (`0.18.0 -> 0.18.1-dev`)
* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
* Create a PR against develop to update that branch.

204
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@ -0,0 +1,204 @@
# Using FreqTrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Download the official FreqTrade docker image
Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:develop
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:develop freqtrade
```
To update the image, simply run the above commands again and restart your running container.
Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
### Prepare the configuration files
Even though you will use docker, you'll still need some files from the github repository.
#### Clone the git repository
Linux/Mac/Windows with WSL
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
Windows with docker
```bash
git clone --config core.autocrlf=input https://github.com/freqtrade/freqtrade.git
```
#### Copy `config.json.example` to `config.json`
```bash
cd freqtrade
cp -n config.json.example config.json
```
> To understand the configuration options, please refer to the [Bot Configuration](configuration.md) page.
#### Create your database file
Production
```bash
touch tradesv3.sqlite
````
Dry-Run
```bash
touch tradesv3.dryrun.sqlite
```
!!! Note
Make sure to use the path to this file when starting the bot in docker.
### Build your own Docker image
Best start by pulling the official docker image from dockerhub as explained [here](#download-the-official-docker-image) to speed up building.
To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
```bash
docker build -t freqtrade -f Dockerfile.technical .
```
If you are developing using Docker, use `Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
```bash
docker build -f Dockerfile.develop -t freqtrade-dev .
```
!!! Note
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between updates.
#### Verify the Docker image
After the build process you can verify that the image was created with:
```bash
docker images
```
The output should contain the freqtrade image.
### Run the Docker image
You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
```bash
docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
!!! Warning
In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
#### Adjust timezone
By default, the container will use UTC timezone.
Should you find this irritating please add the following to your docker commands:
##### Linux
``` bash
-v /etc/timezone:/etc/timezone:ro
# Complete command:
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
##### MacOS
There is known issue in OSX Docker versions after 17.09.1, whereby `/etc/localtime` cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
### Run a restartable docker image
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
#### Move your config file and database
The following will assume that you place your configuration / database files to `~/.freqtrade`, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
```bash
mkdir ~/.freqtrade
mv config.json ~/.freqtrade
mv tradesv3.sqlite ~/.freqtrade
```
#### Run the docker image
```bash
docker run -d \
--name freqtrade \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/user_data/:/freqtrade/user_data \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
```
!!! Note
db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
!!! Note
All available bot command line parameters can be added to the end of the `docker run` command.
### Monitor your Docker instance
You can use the following commands to monitor and manage your container:
```bash
docker logs freqtrade
docker logs -f freqtrade
docker restart freqtrade
docker stop freqtrade
docker start freqtrade
```
For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
!!! Note
You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
### Backtest with docker
The following assumes that the download/setup of the docker image have been completed successfully.
Also, backtest-data should be available at `~/.freqtrade/user_data/`.
```bash
docker run -d \
--name freqtrade \
-v /etc/localtime:/etc/localtime:ro \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
freqtrade --strategy AwsomelyProfitableStrategy backtesting
```
Head over to the [Backtesting Documentation](backtesting.md) for more details.
!!! Note
Additional bot command line parameters can be appended after the image name (`freqtrade` in the above example).

24
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@ -3,7 +3,7 @@
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
!!! Warning
Edge positioning is not compatible with dynamic whitelist. If enabled, it overrides the dynamic whitelist option.
Edge positioning is not compatible with dynamic (volume-based) whitelist.
!!! Note
Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
@ -146,16 +146,19 @@ Percentage of allowed risk per trade.
(defaults to 0.01 so 1%)
#### stoploss_range_min
Minimum stoploss.
(defaults to -0.01)
#### stoploss_range_max
Maximum stoploss.
(defaults to -0.10)
#### stoploss_range_step
As an example if this is set to -0.01 then Edge will test the strategy for \[-0.01, -0,02, -0,03 ..., -0.09, -0.10\] ranges.
Note than having a smaller step means having a bigger range which could lead to slow calculation.
@ -164,6 +167,7 @@ If you set this parameter to -0.001, you then slow down the Edge calculation by
(defaults to -0.01)
#### minimum_winrate
It filters out pairs which don't have at least minimum_winrate.
This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio.
@ -171,6 +175,7 @@ This comes handy if you want to be conservative and don't comprise win rate in f
(defaults to 0.60)
#### minimum_expectancy
It filters out pairs which have the expectancy lower than this number.
Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
@ -178,6 +183,7 @@ Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ re
(defaults to 0.20)
#### min_trade_number
When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable.
Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something.
@ -185,6 +191,7 @@ Having a win rate of 100% on a single trade doesn't mean anything at all. But ha
(defaults to 10, it is highly recommended not to decrease this number)
#### max_trade_duration_minute
Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.
**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).
@ -192,15 +199,17 @@ Edge will filter out trades with long duration. If a trade is profitable after 1
(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes)
#### remove_pumps
Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.
(defaults to false)
## Running Edge independently
You can run Edge independently in order to see in details the result. Here is an example:
```bash
python3 ./freqtrade/main.py edge
freqtrade edge
```
An example of its output:
@ -224,18 +233,21 @@ An example of its output:
| NEBL/BTC | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
### Update cached pairs with the latest data
```bash
python3 ./freqtrade/main.py edge --refresh-pairs-cached
freqtrade edge --refresh-pairs-cached
```
### Precising stoploss range
```bash
python3 ./freqtrade/main.py edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
```
### Advanced use of timerange
```bash
python3 ./freqtrade/main.py edge --timerange=20181110-20181113
freqtrade edge --timerange=20181110-20181113
```
Doing `--timerange=-200` will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop.

74
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@ -1,14 +1,25 @@
# Freqtrade FAQ
### Freqtrade commons
## Freqtrade common issues
#### I have waited 5 minutes, why hasn't the bot made any trades yet?!
### The bot does not start
Running the bot with `freqtrade --config config.json` does show the output `freqtrade: command not found`.
This could have the following reasons:
* The virtual environment is not active
* run `source .env/bin/activate` to activate the virtual environment
* The installation did not work correctly.
* Please check the [Installation documentation](installation.md).
### I have waited 5 minutes, why hasn't the bot made any trades yet?!
Depending on the buy strategy, the amount of whitelisted coins, the
situation of the market etc, it can take up to hours to find good entry
position for a trade. Be patient!
#### I have made 12 trades already, why is my total profit negative?!
### I have made 12 trades already, why is my total profit negative?!
I understand your disappointment but unfortunately 12 trades is just
not enough to say anything. If you run backtesting, you can see that our
@ -19,53 +30,64 @@ of course constantly aim to improve the bot but it will _always_ be a
gamble, which should leave you with modest wins on monthly basis but
you can't say much from few trades.
#### I’d like to change the stake amount. Can I just stop the bot with
/stop and then change the config.json and run it again?
### I’d like to change the stake amount. Can I just stop the bot with /stop and then change the config.json and run it again?
Not quite. Trades are persisted to a database but the configuration is
currently only read when the bot is killed and restarted. `/stop` more
like pauses. You can stop your bot, adjust settings and start it again.
#### I want to improve the bot with a new strategy
### I want to improve the bot with a new strategy
That's great. We have a nice backtesting and hyperoptimizing setup. See
the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
#### Is there a setting to only SELL the coins being held and not
perform anymore BUYS?
### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
You can use the `/forcesell all` command from Telegram.
### Hyperopt module
### I get the message "RESTRICTED_MARKET"
Currently known to happen for US Bittrex users.
Bittrex split its exchange into US and International versions.
The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair.
If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
## Hyperopt module
### How many epoch do I need to get a good Hyperopt result?
#### How many epoch do I need to get a good Hyperopt result?
Per default Hyperopts without `-e` or `--epochs` parameter will only
run 100 epochs, means 100 evals of your triggers, guards, .... Too few
run 100 epochs, means 100 evals of your triggers, guards, ... Too few
to find a great result (unless if you are very lucky), so you probably
have to run it for 10.000 or more. But it will take an eternity to
compute.
We recommend you to run it at least 10.000 epochs:
```bash
python3 ./freqtrade/main.py hyperopt -e 10000
freqtrade hyperopt -e 10000
```
or if you want intermediate result to see
```bash
for i in {1..100}; do python3 ./freqtrade/main.py hyperopt -e 100; done
for i in {1..100}; do freqtrade hyperopt -e 100; done
```
#### Why it is so long to run hyperopt?
### Why it is so long to run hyperopt?
Finding a great Hyperopt results takes time.
If you wonder why it takes a while to find great hyperopt results
This answer was written during the under the release 0.15.1, when we had
:
This answer was written during the under the release 0.15.1, when we had:
- 8 triggers
- 9 guards: let's say we evaluate even 10 values from each
- 1 stoploss calculation: let's say we want 10 values from that too to
be evaluated
- 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
The following calculation is still very rough and not very precise
but it will give the idea. With only these triggers and guards there is
@ -73,17 +95,17 @@ already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
of the search space.
### Edge module
## Edge module
#### Edge implements interesting approach for controlling position size, is there any theory behind it?
### Edge implements interesting approach for controlling position size, is there any theory behind it?
The Edge module is mostly a result of brainstorming of [@mishaker](https://github.com/mishaker) and [@creslinux](https://github.com/creslinux) freqtrade team members.
You can find further info on expectancy, winrate, risk management and position size in the following sources:
* https://www.tradeciety.com/ultimate-math-guide-for-traders/
* http://www.vantharp.com/tharp-concepts/expectancy.asp
* https://samuraitradingacademy.com/trading-expectancy/
* https://www.learningmarkets.com/determining-expectancy-in-your-trading/
* http://www.lonestocktrader.com/make-money-trading-positive-expectancy/
* https://www.babypips.com/trading/trade-expectancy-matter
- https://www.tradeciety.com/ultimate-math-guide-for-traders/
- http://www.vantharp.com/tharp-concepts/expectancy.asp
- https://samuraitradingacademy.com/trading-expectancy/
- https://www.learningmarkets.com/determining-expectancy-in-your-trading/
- http://www.lonestocktrader.com/make-money-trading-positive-expectancy/
- https://www.babypips.com/trading/trade-expectancy-matter

236
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@ -12,29 +12,34 @@ and still take a long time.
## Prepare Hyperopting
Before we start digging into Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py)
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
### Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required.
Depending on the space you want to optimize, only some of the below are required:
* fill `populate_indicators` - probably a copy from your strategy
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimzation
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimzation
* fill `roi_space` - for ROI optimization
* fill `generate_roi_table` - for ROI optimization (if you need more than 3 entries)
* fill `stoploss_space` - stoploss optimization
* Optional but recommended
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
Optional, but recommended:
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
### 1. Install a Custom Hyperopt File
Put your hyperopt file into the folder`user_data/hyperopts`.
Put your hyperopt file into the directory `user_data/hyperopts`.
Let assume you want a hyperopt file `awesome_hyperopt.py`:
Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
@ -62,7 +67,7 @@ If you have updated the buy strategy, ie. changed the contents of
#### Sell optimization
Similar to the buy-signal above, sell-signals can also be optimized.
Similar to the buy-signal above, sell-signals can also be optimized.
Place the corresponding settings into the following methods
* Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
@ -71,6 +76,11 @@ Place the corresponding settings into the following methods
The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
#### Using ticker-interval as part of the Strategy
The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
In the case of the linked sample-value this would be `SampleHyperOpts.ticker_interval`.
## Solving a Mystery
Let's say you are curious: should you use MACD crossings or lower Bollinger
@ -122,9 +132,10 @@ So let's write the buy strategy using these values:
dataframe['macd'], dataframe['macdsignal']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
@ -138,21 +149,90 @@ it will end with telling you which paramter combination produced the best profit
The search for best parameters starts with a few random combinations and then uses a
regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
that minimizes the value of the objective function `calculate_loss` in `hyperopt.py`.
that minimizes the value of the [loss function](#loss-functions).
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in `hyperopt.py`.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
This class should be in its own file within the `user_data/hyperopts/` directory.
Currently, the following loss functions are builtin: `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function), `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns) and `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration).
### Creating and using a custom loss function
To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
``` python
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600
EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300
class SuperDuperHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade).
Weights are distributed as follows:
* 0.4 to trade duration
* 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result
```
Currently, the arguments are:
* `results`: DataFrame containing the result
The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):
`pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
* `trade_count`: Amount of trades (identical to `len(results)`)
* `min_date`: Start date of the hyperopting TimeFrame
* `min_date`: End date of the hyperopting TimeFrame
This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
!!! Note
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
!!! Note
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
Because hyperopt tries a lot of combinations to find the best parameters it will take time you will have the result (more than 30 mins).
Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
python3 ./freqtrade/main.py -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all
freqtrade -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.
@ -162,8 +242,11 @@ running at least several thousand evaluations.
The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
!!! Warning
When switching parameters or changing configuration options, the file `user_data/hyperopt_results.pickle` should be removed. It's used to be able to continue interrupted calculations, but does not detect changes to settings or the hyperopt file.
When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
### Execute Hyperopt with Different Ticker-Data Source
@ -173,12 +256,11 @@ use data from directory `user_data/data`.
### Running Hyperopt with Smaller Testset
Use the `--timerange` argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
Use the `--timerange` argument to change how much of the testset you want to use.
For example, to use one month of data, pass the following parameter to the hyperopt call:
```bash
python3 ./freqtrade/main.py hyperopt --timerange -200
freqtrade hyperopt --timerange 20180401-20180501
```
### Running Hyperopt with Smaller Search Space
@ -191,12 +273,33 @@ new buy strategy you have.
Legal values are:
- `all`: optimize everything
- `buy`: just search for a new buy strategy
- `sell`: just search for a new sell strategy
- `roi`: just optimize the minimal profit table for your strategy
- `stoploss`: search for the best stoploss value
- space-separated list of any of the above values for example `--spaces roi stoploss`
* `all`: optimize everything
* `buy`: just search for a new buy strategy
* `sell`: just search for a new sell strategy
* `roi`: just optimize the minimal profit table for your strategy
* `stoploss`: search for the best stoploss value
* space-separated list of any of the above values for example `--spaces roi stoploss`
### Position stacking and disabling max market positions
In some situations, you may need to run Hyperopt (and Backtesting) with the
`--eps`/`--enable-position-staking` and `--dmmp`/`--disable-max-market-positions` arguments.
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
open trade is allowed for every traded pair. The total number of trades open for all pairs
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
some potential trades to be hidden (or masked) by previosly open trades.
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
during Hyperopt/Backtesting (which is equal to setting `max_open_trades` to a very high
number).
!!! Note
Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
You can also enable position stacking in the configuration file by explicitly setting
`"position_stacking"=true`.
## Understand the Hyperopt Result
@ -205,8 +308,10 @@ Given the following result from hyperopt:
```
Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
@ -225,7 +330,7 @@ method, what those values match to.
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
```
``` python
(dataframe['rsi'] < 29.0)
```
@ -245,25 +350,19 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
### Understand Hyperopt ROI results
If you are optimizing ROI, you're result will look as follows and include a ROI table.
If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
```
Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': false,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower',
'roi_t1': 40,
'roi_t2': 57,
'roi_t3': 21,
'roi_p1': 0.03634636907306948,
'roi_p2': 0.055237357937802885,
'roi_p3': 0.015163796015548354,
'stoploss': -0.37996664668703606
}
'trigger': 'bb_lower'}
ROI table:
{ 0: 0.10674752302642071,
21: 0.09158372701087236,
@ -274,27 +373,54 @@ ROI table:
This would translate to the following ROI table:
``` python
minimal_roi = {
minimal_roi = {
"118": 0,
"78": 0.0363463,
"78": 0.0363,
"21": 0.0915,
"0": 0.106
}
```
### Validate backtest result
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps) with the values that can vary in the following ranges:
| # | minutes | ROI percentage |
|---|---|---|
| 1 | always 0 | 0.03...0.31 |
| 2 | 10...40 | 0.02...0.11 |
| 3 | 20...100 | 0.01...0.04 |
| 4 | 30...220 | always 0 |
This structure of the ROI table is sufficient in most cases. Override the `roi_space()` method defining the ranges desired if you need components of the ROI tables to vary in other ranges.
Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization in these methods if you need a different structure of the ROI table or other amount of rows (steps) in the ROI tables.
### Understand Hyperopt Stoploss results
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
```
Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
Stoploss: -0.37996664668703606
```
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.5...-0.02, which is sufficient in most cases.
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization.
### Validate backtesting results
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
To archive the same results (number of trades, ...) than during hyperopt, please use the command line flags `--disable-max-market-positions` and `--enable-position-stacking` for backtesting.
This configuration is the default in hyperopt for performance reasons.
You can overwrite position stacking in the configuration by explicitly setting `"position_stacking"=false` or by changing the relevant line in your hyperopt file [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L191).
Enabling the market-position for hyperopt is currently not possible.
!!! Note:
Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
## Next Step

14
docs/index.md
View File

@ -21,8 +21,8 @@ Freqtrade is a cryptocurrency trading bot written in Python.
We strongly recommend you to have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms and techniques implemented in it.
## Features
- Based on Python 3.6+: For botting on any operating system — Windows, macOS and Linux.
- Persistence: Persistence is achieved through sqlite database.
- Dry-run mode: Run the bot without playing money.
@ -31,17 +31,19 @@ Freqtrade is a cryptocurrency trading bot written in Python.
- Edge position sizing: Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market.
- Whitelist crypto-currencies: Select which crypto-currency you want to trade or use dynamic whitelists based on market (pair) trade volume.
- Blacklist crypto-currencies: Select which crypto-currency you want to avoid.
- Manageable via Telegram: Manage the bot with Telegram.
- Manageable via Telegram or REST APi: Manage the bot with Telegram or via the builtin REST API.
- Display profit/loss in fiat: Display your profit/loss in any of 33 fiat currencies supported.
- Daily summary of profit/loss: Receive the daily summary of your profit/loss.
- Performance status report: Receive the performance status of your current trades.
## Requirements
### Up to date clock
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
### Hardware requirements
To run this bot we recommend you a cloud instance with a minimum of:
- 2GB RAM
@ -49,6 +51,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
- 2vCPU
### Software requirements
- Python 3.6.x
- pip (pip3)
- git
@ -56,12 +59,13 @@ To run this bot we recommend you a cloud instance with a minimum of:
- virtualenv (Recommended)
- Docker (Recommended)
## Support
Help / Slack
For any questions not covered by the documentation or for further information about the bot, we encourage you to join our Slack channel.
Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE) to join Slack channel.
Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg) to join Slack channel.
## Ready to try?
Begin by reading our installation guide [here](installation).

317
docs/installation.md
View File

@ -1,72 +1,49 @@
# Installation
This page explains how to prepare your environment for running the bot.
## Prerequisite
Before running your bot in production you will need to setup few
external API. In production mode, the bot required valid Bittrex API
credentials and a Telegram bot (optional but recommended).
- [Setup your exchange account](#setup-your-exchange-account)
- [Backtesting commands](#setup-your-telegram-bot)
### Requirements
Click each one for install guide:
* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
* [pip](https://pip.pypa.io/en/stable/installing/)
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
### API keys
Before running your bot in production you will need to setup few
external API. In production mode, the bot will require valid Exchange API
credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
### Setup your exchange account
*To be completed, please feel free to complete this section.*
### Setup your Telegram bot
The only things you need is a working Telegram bot and its API token.
Below we explain how to create your Telegram Bot, and how to get your
Telegram user id.
You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
### 1. Create your Telegram bot
**1.1. Start a chat with https://telegram.me/BotFather**
**1.2. Send the message `/newbot`. ** *BotFather response:*
```
Alright, a new bot. How are we going to call it? Please choose a name for your bot.
```
**1.3. Choose the public name of your bot (e.x. `Freqtrade bot`)**
*BotFather response:*
```
Good. Now let's choose a username for your bot. It must end in `bot`. Like this, for example: TetrisBot or tetris_bot.
```
**1.4. Choose the name id of your bot (e.x "`My_own_freqtrade_bot`")**
**1.5. Father bot will return you the token (API key)**<br/>
Copy it and keep it you will use it for the config parameter `token`.
*BotFather response:*
```hl_lines="4"
Done! Congratulations on your new bot. You will find it at t.me/My_own_freqtrade_bot. You can now add a description, about section and profile picture for your bot, see /help for a list of commands. By the way, when you've finished creating your cool bot, ping our Bot Support if you want a better username for it. Just make sure the bot is fully operational before you do this.
Use this token to access the HTTP API:
521095879:AAEcEZEL7ADJ56FtG_qD0bQJSKETbXCBCi0
For a description of the Bot API, see this page: https://core.telegram.org/bots/api
```
**1.6. Don't forget to start the conversation with your bot, by clicking /START button**
### 2. Get your user id
**2.1. Talk to https://telegram.me/userinfobot**
**2.2. Get your "Id", you will use it for the config parameter
`chat_id`.**
<hr/>
## Quick start
Freqtrade provides a Linux/MacOS script to install all dependencies and help you to configure the bot.
!!! Note
Python3.6 or higher and the corresponding pip are assumed to be available. The install-script will warn and stop if that's not the case.
```bash
git clone git@github.com:freqtrade/freqtrade.git
cd freqtrade
git checkout develop
./setup.sh --install
```
!!! Note
Windows installation is explained [here](#windows).
<hr/>
## Easy Installation - Linux Script
If you are on Debian, Ubuntu or MacOS a freqtrade provides a script to Install, Update, Configure, and Reset your bot.
If you are on Debian, Ubuntu or MacOS freqtrade provides a script to Install, Update, Configure, and Reset your bot.
```bash
$ ./setup.sh
@ -81,7 +58,7 @@ usage:
This script will install everything you need to run the bot:
* Mandatory software as: `Python3`, `ta-lib`, `wget`
* Mandatory software as: `ta-lib`
* Setup your virtualenv
* Configure your `config.json` file
@ -101,212 +78,21 @@ Config parameter is a `config.json` configurator. This script will ask you quest
------
## Automatic Installation - Docker
Start by downloading Docker for your platform:
* [Mac](https://www.docker.com/products/docker#/mac)
* [Windows](https://www.docker.com/products/docker#/windows)
* [Linux](https://www.docker.com/products/docker#/linux)
Once you have Docker installed, simply create the config file (e.g. `config.json`) and then create a Docker image for `freqtrade` using the Dockerfile in this repo.
### 1. Prepare the Bot
**1.1. Clone the git repository**
Linux/Mac/Windows with WSL
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
Windows with docker
```bash
git clone --config core.autocrlf=input https://github.com/freqtrade/freqtrade.git
```
**1.2. (Optional) Checkout the develop branch**
```bash
git checkout develop
```
**1.3. Go into the new directory**
```bash
cd freqtrade
```
**1.4. Copy `config.json.example` to `config.json`**
```bash
cp -n config.json.example config.json
```
> To edit the config please refer to the [Bot Configuration](configuration.md) page.
**1.5. Create your database file *(optional - the bot will create it if it is missing)**
Production
```bash
touch tradesv3.sqlite
````
Dry-Run
```bash
touch tradesv3.dryrun.sqlite
```
### 2. Download or build the docker image
Either use the prebuilt image from docker hub - or build the image yourself if you would like more control on which version is used.
Branches / tags available can be checked out on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
**2.1. Download the docker image**
Pull the image from docker hub and (optionally) change the name of the image
```bash
docker pull freqtradeorg/freqtrade:develop
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:develop freqtrade
```
To update the image, simply run the above commands again and restart your running container.
**2.2. Build the Docker image**
```bash
cd freqtrade
docker build -t freqtrade .
```
If you are developing using Docker, use `Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
```bash
docker build -f ./Dockerfile.develop -t freqtrade-dev .
```
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between updates.
### 3. Verify the Docker image
After the build process you can verify that the image was created with:
```bash
docker images
```
### 4. Run the Docker image
You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
```bash
docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
There is known issue in OSX Docker versions after 17.09.1, whereby /etc/localtime cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
### 5. Run a restartable docker image
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
**5.1. Move your config file and database**
```bash
mkdir ~/.freqtrade
mv config.json ~/.freqtrade
mv tradesv3.sqlite ~/.freqtrade
```
**5.2. Run the docker image**
```bash
docker run -d \
--name freqtrade \
-v /etc/localtime:/etc/localtime:ro \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
freqtrade --db-url sqlite:///tradesv3.sqlite
```
!!! Note
db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
### 6. Monitor your Docker instance
You can then use the following commands to monitor and manage your container:
```bash
docker logs freqtrade
docker logs -f freqtrade
docker restart freqtrade
docker stop freqtrade
docker start freqtrade
```
For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
!!! Note
You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
### 7. Backtest with docker
The following assumes that the above steps (1-4) have been completed successfully.
Also, backtest-data should be available at `~/.freqtrade/user_data/`.
```bash
docker run -d \
--name freqtrade \
-v /etc/localtime:/etc/localtime:ro \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
freqtrade --strategy AwsomelyProfitableStrategy backtesting
```
Head over to the [Backtesting Documentation](backtesting.md) for more details.
!!! Note
Additional parameters can be appended after the image name (`freqtrade` in the above example).
------
## Custom Installation
We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros.
OS Specific steps are listed first, the [Common](#common) section below is necessary for all systems.
### Requirements
Click each one for install guide:
* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
* [pip](https://pip.pypa.io/en/stable/installing/)
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
!!! Note
Python3.6 or higher and the corresponding pip are assumed to be available.
### Linux - Ubuntu 16.04
#### Install Python 3.6, Git, and wget
#### Install necessary dependencies
```bash
sudo add-apt-repository ppa:jonathonf/python-3.6
sudo apt-get update
sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git
sudo apt-get install build-essential git
```
#### Raspberry Pi / Raspbian
@ -315,7 +101,6 @@ Before installing FreqTrade on a Raspberry Pi running the official Raspbian Imag
The following assumes that miniconda3 is installed and available in your environment. Last miniconda3 installation file use python 3.4, we will update to python 3.6 on this installation.
It's recommended to use (mini)conda for this as installation/compilation of `numpy`, `scipy` and `pandas` takes a long time.
If you have installed it from (mini)conda, you can remove `numpy`, `scipy`, and `pandas` from `requirements.txt` before you install it with `pip`.
Additional package to install on your Raspbian, `libffi-dev` required by cryptography (from python-telegram-bot).
@ -327,18 +112,10 @@ conda activate freqtrade
conda install scipy pandas numpy
sudo apt install libffi-dev
python3 -m pip install -r requirements.txt
python3 -m pip install -r requirements-common.txt
python3 -m pip install -e .
```
### MacOS
#### Install Python 3.6, git and wget
```bash
brew install python3 git wget
```
### Common
#### 1. Install TA-Lib
@ -379,7 +156,7 @@ git clone https://github.com/freqtrade/freqtrade.git
```
Optionally checkout the stable/master branch:
Optionally checkout the master branch to get the latest stable release:
```bash
git checkout master
@ -397,9 +174,9 @@ cp config.json.example config.json
#### 5. Install python dependencies
``` bash
pip3 install --upgrade pip
pip3 install -r requirements.txt
pip3 install -e .
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
python3 -m pip install -e .
```
#### 6. Run the Bot
@ -407,10 +184,10 @@ pip3 install -e .
If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
```bash
python3.6 ./freqtrade/main.py -c config.json
freqtrade -c config.json
```
*Note*: If you run the bot on a server, you should consider using [Docker](#automatic-installation---docker) a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
*Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
#### 7. [Optional] Configure `freqtrade` as a `systemd` service
@ -437,15 +214,25 @@ when it changes.
The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd
as the watchdog.
!!! Note:
The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a
Docker container.
!!! Note
The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
------
## Using Conda
Freqtrade can also be installed using Anaconda (or Miniconda).
``` bash
conda env create -f environment.yml
```
!!! Note:
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first.
## Windows
We recommend that Windows users use [Docker](#docker) as this will work much easier and smoother (also more secure).
We recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
If that is not available on your system, feel free to try the instructions below, which led to success for some.
@ -458,8 +245,6 @@ If that is not available on your system, feel free to try the instructions below
git clone https://github.com/freqtrade/freqtrade.git
```
copy paste `config.json` to ``\path\freqtrade-develop\freqtrade`
#### Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
@ -489,7 +274,7 @@ error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++
Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or docker first.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
---

79
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View File

@ -1,63 +1,83 @@
# Plotting
This page explains how to plot prices, indicator, profits.
This page explains how to plot prices, indicators and profits.
## Installation
Plotting scripts use Plotly library. Install/upgrade it with:
``` bash
pip install -U -r requirements-plot.txt
```
pip install --upgrade plotly
```
At least version 2.3.0 is required.
## Plot price and indicators
Usage for the price plotter:
```
script/plot_dataframe.py [-h] [-p pairs] [--live]
``` bash
python3 script/plot_dataframe.py [-h] [-p pairs] [--live]
```
Example
```
python scripts/plot_dataframe.py -p BTC/ETH
``` bash
python3 scripts/plot_dataframe.py -p BTC/ETH
```
The `-p` pairs argument, can be used to specify
pairs you would like to plot.
The `-p` pairs argument can be used to specify pairs you would like to plot.
**Advanced use**
Specify custom indicators.
Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
``` bash
python3 scripts/plot_dataframe.py -p BTC/ETH --indicators1 sma,ema --indicators2 macd
```
### Advanced use
To plot multiple pairs, separate them with a comma:
```
python scripts/plot_dataframe.py -p BTC/ETH,XRP/ETH
``` bash
python3 scripts/plot_dataframe.py -p BTC/ETH,XRP/ETH
```
To plot the current live price use the `--live` flag:
```
python scripts/plot_dataframe.py -p BTC/ETH --live
``` bash
python3 scripts/plot_dataframe.py -p BTC/ETH --live
```
To plot a timerange (to zoom in):
``` bash
python3 scripts/plot_dataframe.py -p BTC/ETH --timerange=100-200
```
python scripts/plot_dataframe.py -p BTC/ETH --timerange=100-200
```
Timerange doesn't work with live data.
To plot trades stored in a database use `--db-url` argument:
```
python scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH
``` bash
python3 scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
```
To plot a test strategy the strategy should have first be backtested.
The results may then be plotted with the -s argument:
To plot trades from a backtesting result, use `--export-filename <filename>`
``` bash
python3 scripts/plot_dataframe.py --export-filename user_data/backtest_data/backtest-result.json -p BTC/ETH
```
python scripts/plot_dataframe.py -s Strategy_Name -p BTC/ETH --datadir user_data/data/<exchange_name>/
To plot a custom strategy the strategy should have first be backtested.
The results may then be plotted with the -s argument:
``` bash
python3 scripts/plot_dataframe.py -s Strategy_Name -p BTC/ETH --datadir user_data/data/<exchange_name>/
```
## Plot profit
The profit plotter show a picture with three plots:
The profit plotter shows a picture with three plots:
1) Average closing price for all pairs
2) The summarized profit made by backtesting.
Note that this is not the real-world profit, but
@ -67,7 +87,7 @@ The profit plotter show a picture with three plots:
The first graph is good to get a grip of how the overall market
progresses.
The second graph will show how you algorithm works or doesnt.
The second graph will show how your algorithm works or doesn't.
Perhaps you want an algorithm that steadily makes small profits,
or one that acts less seldom, but makes big swings.
@ -76,13 +96,14 @@ that makes profit spikes.
Usage for the profit plotter:
```
script/plot_profit.py [-h] [-p pair] [--datadir directory] [--ticker_interval num]
``` bash
python3 script/plot_profit.py [-h] [-p pair] [--datadir directory] [--ticker_interval num]
```
The `-p` pair argument, can be used to plot a single pair
Example
```
python3 scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p BTC_LTC
``` bash
python3 scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p LTC/BTC
```

2
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View File

@ -1 +1 @@
mkdocs-material==3.1.0
mkdocs-material==4.4.0

193
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View File

@ -0,0 +1,193 @@
# REST API Usage
## Configuration
Enable the rest API by adding the api_server section to your configuration and setting `api_server.enabled` to `true`.
Sample configuration:
``` json
"api_server": {
"enabled": true,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"username": "Freqtrader",
"password": "SuperSecret1!"
},
```
!!! Danger Security warning
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger Password selection
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/version` to check if the API is running correctly.
To generate a secure password, either use a password manager, or use the below code snipped.
``` python
import secrets
secrets.token_hex()
```
### Configuration with docker
If you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
``` json
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080
},
```
Add the following to your docker command:
``` bash
-p 127.0.0.1:8080:8080
```
A complete sample-command may then look as follows:
```bash
docker run -d \
--name freqtrade \
-v ~/.freqtrade/config.json:/freqtrade/config.json \
-v ~/.freqtrade/user_data/:/freqtrade/user_data \
-v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-p 127.0.0.1:8080:8080 \
freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
```
!!! Danger "Security warning"
By using `-p 8080:8080` the API is available to everyone connecting to the server under the correct port, so others may be able to control your bot.
## Consuming the API
You can consume the API by using the script `scripts/rest_client.py`.
The client script only requires the `requests` module, so FreqTrade does not need to be installed on the system.
``` bash
python3 scripts/rest_client.py <command> [optional parameters]
```
By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be used, however you can specify a configuration file to override this behaviour.
### Minimalistic client config
``` json
{
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080
}
}
```
``` bash
python3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]
```
## Available commands
| Command | Default | Description |
|----------|---------|-------------|
| `start` | | Starts the trader
| `stop` | | Stops the trader
| `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_conf` | | Reloads the configuration file
| `status` | | Lists all open trades
| `status table` | | List all open trades in a table format
| `count` | | Displays number of trades used and available
| `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
| `forcesell <trade_id>` | | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | | Instantly sells all open trades (Ignoring `minimum_roi`).
| `forcebuy <pair> [rate]` | | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `performance` | | Show performance of each finished trade grouped by pair
| `balance` | | Show account balance per currency
| `daily <n>` | 7 | Shows profit or loss per day, over the last n days
| `whitelist` | | Show the current whitelist
| `blacklist [pair]` | | Show the current blacklist, or adds a pair to the blacklist.
| `edge` | | Show validated pairs by Edge if it is enabled.
| `version` | | Show version
Possible commands can be listed from the rest-client script using the `help` command.
``` bash
python3 scripts/rest_client.py help
```
``` output
Possible commands:
balance
Get the account balance
:returns: json object
blacklist
Show the current blacklist
:param add: List of coins to add (example: "BNB/BTC")
:returns: json object
count
Returns the amount of open trades
:returns: json object
daily
Returns the amount of open trades
:returns: json object
edge
Returns information about edge
:returns: json object
forcebuy
Buy an asset
:param pair: Pair to buy (ETH/BTC)
:param price: Optional - price to buy
:returns: json object of the trade
forcesell
Force-sell a trade
:param tradeid: Id of the trade (can be received via status command)
:returns: json object
performance
Returns the performance of the different coins
:returns: json object
profit
Returns the profit summary
:returns: json object
reload_conf
Reload configuration
:returns: json object
start
Start the bot if it's in stopped state.
:returns: json object
status
Get the status of open trades
:returns: json object
stop
Stop the bot. Use start to restart
:returns: json object
stopbuy
Stop buying (but handle sells gracefully).
use reload_conf to reset
:returns: json object
version
Returns the version of the bot
:returns: json object containing the version
whitelist
Show the current whitelist
:returns: json object
```

12
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View File

@ -1,5 +1,5 @@
# SQL Helper
This page constains some help if you want to edit your sqlite db.
This page contains some help if you want to edit your sqlite db.
## Install sqlite3
**Ubuntu/Debian installation**
@ -65,12 +65,12 @@ SELECT * FROM trades;
## Fix trade still open after a manual sell on the exchange
!!! Warning:
Manually selling on the exchange should not be done by default, since the bot does not detect this and will try to sell anyway.
/foresell <tradeid> should accomplish the same thing.
!!! Warning
Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.
It is strongly advised to backup your database file before making any manual changes.
!!! Note:
This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
!!! Note
This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
```sql
UPDATE trades

24
docs/stoploss.md
View File

@ -1,4 +1,13 @@
# Stop Loss support
# Stop Loss
The `stoploss` configuration parameter is loss in percentage that should trigger a sale.
For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
Most of the strategy files already include the optimal `stoploss`
value. This parameter is optional. If you use it in the configuration file, it will take over the
`stoploss` value from the strategy file.
## Stop Loss support
At this stage the bot contains the following stoploss support modes:
@ -16,13 +25,12 @@ In case of stoploss on exchange there is another parameter called `stoploss_on_e
!!! Note
Stoploss on exchange is only supported for Binance as of now.
## Static Stop Loss
This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which
will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined loss.
## Trail Stop Loss
## Trailing Stop Loss
The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally.
To enable this Feauture all you have to do is to define the configuration element:
@ -63,3 +71,13 @@ The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
You should also make sure to have this value (`trailing_stop_positive_offset`) lower than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.
If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured`stoploss`.
## Changing stoploss on open trades
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_conf` command (alternatively, completely stopping and restarting the bot also works).
The new stoploss value will be applied to open trades (and corresponding log-messages will be generated).
### Limitations
Stoploss values cannot be changed if `trailing_stop` is enabled and the stoploss has already been adjusted, or if [Edge](edge.md) is enabled (since Edge would recalculate stoploss based on the current market situation).

93
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View File

@ -5,8 +5,7 @@ indicators.
## Install a custom strategy file
This is very simple. Copy paste your strategy file into the folder
`user_data/strategies`.
This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
Let assume you have a class called `AwesomeStrategy` in the file `awesome-strategy.py`:
@ -14,7 +13,7 @@ Let assume you have a class called `AwesomeStrategy` in the file `awesome-strate
2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy
freqtrade --strategy AwesomeStrategy
```
## Change your strategy
@ -22,7 +21,7 @@ python3 ./freqtrade/main.py --strategy AwesomeStrategy
The bot includes a default strategy file. However, we recommend you to
use your own file to not have to lose your parameters every time the default
strategy file will be updated on Github. Put your custom strategy file
into the folder `user_data/strategies`.
into the directory `user_data/strategies`.
Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes.
`cp user_data/strategies/test_strategy.py user_data/strategies/awesome-strategy.py`
@ -41,18 +40,24 @@ The bot also include a sample strategy called `TestStrategy` you can update: `us
You can test it with the parameter: `--strategy TestStrategy`
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy
freqtrade --strategy AwesomeStrategy
```
**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
file as reference.**
!!! Note: Strategies and Backtesting
!!! Note Strategies and Backtesting
To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
!!! Warning Using future data
Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
needs to take care to avoid having the strategy utilize data from the future.
Samples for usage of future data are `dataframe.shift(-1)`, `dataframe.resample("1h")` (this uses the left border of the interval, so moves data from an hour to the start of the hour).
They all use data which is not available during regular operations, so these strategies will perform well during backtesting, but will fail / perform badly in dry-runs.
### Customize Indicators
Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
@ -212,9 +217,12 @@ stoploss = -0.10
```
This would signify a stoploss of -10%.
For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order dict, so your stoploss is on the exchange and cannot be missed for network-problems (or other problems).
For more information on order_types please look [here](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md#understand-order_types).
For more information on order_types please look [here](configuration.md#understand-order_types).
### Ticker interval
@ -250,22 +258,19 @@ class Awesomestrategy(IStrategy):
self.cust_info[metadata["pair"]["crosstime"] = 1
```
!!! Warning:
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note:
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
### Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
!!!Note:
The DataProvier is currently not available during backtesting / hyperopt, but this is planned for the future.
All methods return `None` in case of failure (do not raise an exception).
Please always check if the `DataProvider` is available to avoid failures during backtesting.
Please always check the mode of operation to select the correct method to get data (samples see below).
#### Possible options for DataProvider
@ -278,20 +283,35 @@ Please always check if the `DataProvider` is available to avoid failures during
``` python
if self.dp:
if dp.runmode == 'live':
if ('ETH/BTC', ticker_interval) in self.dp.available_pairs:
data_eth = self.dp.ohlcv(pair='ETH/BTC',
ticker_interval=ticker_interval)
if self.dp.runmode in ('live', 'dry_run'):
if (f'{self.stake_currency}/BTC', self.ticker_interval) in self.dp.available_pairs:
data_eth = self.dp.ohlcv(pair='{self.stake_currency}/BTC',
ticker_interval=self.ticker_interval)
else:
# Get historic ohlcv data (cached on disk).
history_eth = self.dp.historic_ohlcv(pair='ETH/BTC',
history_eth = self.dp.historic_ohlcv(pair='{self.stake_currency}/BTC',
ticker_interval='1h')
```
!!! Warning: Warning about backtesting
!!! Warning Warning about backtesting
Be carefull when using dataprovider in backtesting. `historic_ohlcv()` provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
!!! Warning Warning in hyperopt
This option cannot currently be used during hyperopt.
#### Orderbook
``` python
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
```
!Warning The order book is not part of the historic data which means backtesting and hyperopt will not work if this
method is used.
#### Available Pairs
``` python
@ -300,6 +320,7 @@ if self.dp:
print(f"available {pair}, {ticker}")
```
#### Get data for non-tradeable pairs
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
@ -317,7 +338,7 @@ def informative_pairs(self):
]
```
!!! Warning:
!!! Warning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer time-intervals when possible
@ -327,7 +348,7 @@ def informative_pairs(self):
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
!!!NOTE:
!!! Note
Wallets is not available during backtesting / hyperopt.
Please always check if `Wallets` is available to avoid failures during backtesting.
@ -345,6 +366,30 @@ if self.wallets:
- `get_used(asset)` - currently tied up balance (open orders)
- `get_total(asset)` - total available balance - sum of the 2 above
### Print created dataframe
To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
You may also want to print the pair so it's clear what data is currently shown.
``` python
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
#>> whatever condition<<<
),
'buy'] = 1
# Print the Analyzed pair
print(f"result for {metadata['pair']}")
# Inspect the last 5 rows
print(dataframe.tail())
return dataframe
```
Printing more than a few rows is also possible (simply use `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
### Where is the default strategy?
The default buy strategy is located in the file
@ -352,10 +397,10 @@ The default buy strategy is located in the file
### Specify custom strategy location
If you want to use a strategy from a different folder you can pass `--strategy-path`
If you want to use a strategy from a different directory you can pass `--strategy-path`
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
```
### Further strategy ideas
@ -364,7 +409,7 @@ To get additional Ideas for strategies, head over to our [strategy repository](h
Feel free to use any of them as inspiration for your own strategies.
We're happy to accept Pull Requests containing new Strategies to that repo.
We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE) which is a great place to get and/or share ideas.
We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LWEyODBiNzkzNzcyNzU0MWYyYzE5NjIyOTQxMzBmMGUxOTIzM2YyN2Y4NWY1YTEwZDgwYTRmMzE2NmM5ZmY2MTg) which is a great place to get and/or share ideas.
## Next step

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@ -1,13 +1,48 @@
# Telegram usage
This page explains how to command your bot with Telegram.
## Setup your Telegram bot
## Prerequisite
To control your bot with Telegram, you need first to
[set up a Telegram bot](installation.md)
and add your Telegram API keys into your config file.
Below we explain how to create your Telegram Bot, and how to get your
Telegram user id.
### 1. Create your Telegram bot
Start a chat with the [Telegram BotFather](https://telegram.me/BotFather)
Send the message `/newbot`.
*BotFather response:*
> Alright, a new bot. How are we going to call it? Please choose a name for your bot.
Choose the public name of your bot (e.x. `Freqtrade bot`)
*BotFather response:*
> Good. Now let's choose a username for your bot. It must end in `bot`. Like this, for example: TetrisBot or tetris_bot.
Choose the name id of your bot and send it to the BotFather (e.g. "`My_own_freqtrade_bot`")
*BotFather response:*
> Done! Congratulations on your new bot. You will find it at `t.me/yourbots_name_bot`. You can now add a description, about section and profile picture for your bot, see /help for a list of commands. By the way, when you've finished creating your cool bot, ping our Bot Support if you want a better username for it. Just make sure the bot is fully operational before you do this.
> Use this token to access the HTTP API: `22222222:APITOKEN`
> For a description of the Bot API, see this page: https://core.telegram.org/bots/api Father bot will return you the token (API key)
Copy the API Token (`22222222:APITOKEN` in the above example) and keep use it for the config parameter `token`.
Don't forget to start the conversation with your bot, by clicking `/START` button
### 2. Get your user id
Talk to the [userinfobot](https://telegram.me/userinfobot)
Get your "Id", you will use it for the config parameter `chat_id`.
## Telegram commands
Per default, the Telegram bot shows predefined commands. Some commands
are only available by sending them to the bot. The table below list the
official commands. You can ask at any moment for help with `/help`.
@ -28,6 +63,9 @@ official commands. You can ask at any moment for help with `/help`.
| `/performance` | | Show performance of each finished trade grouped by pair
| `/balance` | | Show account balance per currency
| `/daily <n>` | 7 | Shows profit or loss per day, over the last n days
| `/whitelist` | | Show the current whitelist
| `/blacklist [pair]` | | Show the current blacklist, or adds a pair to the blacklist.
| `/edge` | | Show validated pairs by Edge if it is enabled.
| `/help` | | Show help message
| `/version` | | Show version
@ -55,23 +93,21 @@ Once all positions are sold, run `/stop` to completely stop the bot.
`/reload_conf` resets "max_open_trades" to the value set in the configuration and resets this command.
!!! warning:
The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
!!! warning
The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
### /status
For each open trade, the bot will send you the following message.
> **Trade ID:** `123`
> **Current Pair:** CVC/BTC
> **Open Since:** `1 days ago`
> **Amount:** `26.64180098`
> **Open Rate:** `0.00007489`
> **Close Rate:** `None`
> **Current Rate:** `0.00007489`
> **Close Profit:** `None`
> **Current Profit:** `12.95%`
> **Open Order:** `None`
> **Trade ID:** `123` `(since 1 days ago)`
> **Current Pair:** CVC/BTC
> **Open Since:** `1 days ago`
> **Amount:** `26.64180098`
> **Open Rate:** `0.00007489`
> **Current Rate:** `0.00007489`
> **Current Profit:** `12.95%`
> **Stoploss:** `0.00007389 (-0.02%)`
### /status table
@ -96,18 +132,18 @@ current max
Return a summary of your profit/loss and performance.
> **ROI:** Close trades
> ∙ `0.00485701 BTC (258.45%)`
> ∙ `62.968 USD`
> **ROI:** All trades
> ∙ `0.00255280 BTC (143.43%)`
> ∙ `33.095 EUR`
>
> **Total Trade Count:** `138`
> **First Trade opened:** `3 days ago`
> **Latest Trade opened:** `2 minutes ago`
> **Avg. Duration:** `2:33:45`
> **Best Performing:** `PAY/BTC: 50.23%`
> **ROI:** Close trades
> ∙ `0.00485701 BTC (258.45%)`
> ∙ `62.968 USD`
> **ROI:** All trades
> ∙ `0.00255280 BTC (143.43%)`
> ∙ `33.095 EUR`
>
> **Total Trade Count:** `138`
> **First Trade opened:** `3 days ago`
> **Latest Trade opened:** `2 minutes ago`
> **Avg. Duration:** `2:33:45`
> **Best Performing:** `PAY/BTC: 50.23%`
### /forcesell <trade_id>
@ -115,7 +151,7 @@ Return a summary of your profit/loss and performance.
### /forcebuy <pair>
> **BITTREX**: Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
> **BITTREX:** Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
Note that for this to work, `forcebuy_enable` needs to be set to true.
@ -125,26 +161,26 @@ Note that for this to work, `forcebuy_enable` needs to be set to true.
Return the performance of each crypto-currency the bot has sold.
> Performance:
> 1. `RCN/BTC 57.77%`
> 2. `PAY/BTC 56.91%`
> 3. `VIB/BTC 47.07%`
> 4. `SALT/BTC 30.24%`
> 5. `STORJ/BTC 27.24%`
> ...
> 1. `RCN/BTC 57.77%`
> 2. `PAY/BTC 56.91%`
> 3. `VIB/BTC 47.07%`
> 4. `SALT/BTC 30.24%`
> 5. `STORJ/BTC 27.24%`
> ...
### /balance
Return the balance of all crypto-currency your have on the exchange.
> **Currency:** BTC
> **Available:** 3.05890234
> **Balance:** 3.05890234
> **Pending:** 0.0
> **Currency:** BTC
> **Available:** 3.05890234
> **Balance:** 3.05890234
> **Pending:** 0.0
> **Currency:** CVC
> **Available:** 86.64180098
> **Balance:** 86.64180098
> **Pending:** 0.0
> **Currency:** CVC
> **Available:** 86.64180098
> **Balance:** 86.64180098
> **Pending:** 0.0
### /daily <n>
@ -160,6 +196,38 @@ Day Profit BTC Profit USD
2018-01-01 0.00269130 BTC 34.986 USD
```
### /whitelist
Shows the current whitelist
> Using whitelist `StaticPairList` with 22 pairs
> `IOTA/BTC, NEO/BTC, TRX/BTC, VET/BTC, ADA/BTC, ETC/BTC, NCASH/BTC, DASH/BTC, XRP/BTC, XVG/BTC, EOS/BTC, LTC/BTC, OMG/BTC, BTG/BTC, LSK/BTC, ZEC/BTC, HOT/BTC, IOTX/BTC, XMR/BTC, AST/BTC, XLM/BTC, NANO/BTC`
### /blacklist [pair]
Shows the current blacklist.
If Pair is set, then this pair will be added to the pairlist.
Also supports multiple pairs, seperated by a space.
Use `/reload_conf` to reset the blacklist.
> Using blacklist `StaticPairList` with 2 pairs
>`DODGE/BTC`, `HOT/BTC`.
### /edge
Shows pairs validated by Edge along with their corresponding winrate, expectancy and stoploss values.
> **Edge only validated following pairs:**
```
Pair Winrate Expectancy Stoploss
-------- --------- ------------ ----------
DOCK/ETH 0.522727 0.881821 -0.03
PHX/ETH 0.677419 0.560488 -0.03
HOT/ETH 0.733333 0.490492 -0.03
HC/ETH 0.588235 0.280988 -0.02
ARDR/ETH 0.366667 0.143059 -0.01
```
### /version
> **Version:** `0.14.3`

42
docs/webhook-config.md
View File

@ -1,7 +1,5 @@
# Webhook usage
This page explains how to configure your bot to talk to webhooks.
## Configuration
Enable webhooks by adding a webhook-section to your configuration file, and setting `webhook.enabled` to `true`.
@ -39,32 +37,32 @@ Different payloads can be configured for different events. Not all fields are ne
The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
Possible parameters are:
* exchange
* pair
* limit
* stake_amount
* stake_amount_fiat
* stake_currency
* fiat_currency
* `exchange`
* `pair`
* `limit`
* `stake_amount`
* `stake_currency`
* `fiat_currency`
* `order_type`
### Webhooksell
The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
Possible parameters are:
* exchange
* pair
* gain
* limit
* amount
* open_rate
* current_rate
* profit_amount
* profit_percent
* profit_fiat
* stake_currency
* fiat_currency
* sell_reason
* `exchange`
* `pair`
* `gain`
* `limit`
* `amount`
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
* `order_type`
### Webhookstatus

59
environment.yml Normal file
View File

@ -0,0 +1,59 @@
name: freqtrade
channels:
- defaults
- conda-forge
dependencies:
# Required for app
- python>=3.6
- pip
- wheel
- numpy
- pandas
- scipy
- SQLAlchemy
- scikit-learn
- arrow
- requests
- urllib3
- wrapt
- joblib
- jsonschema
- tabulate
- python-rapidjson
- filelock
- flask
- python-dotenv
- cachetools
- scikit-optimize
- python-telegram-bot
# Optional for plotting
- plotly
# Optional for development
- flake8
- pytest
- pytest-mock
- pytest-asyncio
- pytest-cov
- coveralls
- mypy
# Useful for jupyter
- jupyter
- ipykernel
- isort
- yapf
- pip:
# Required for app
- cython
- coinmarketcap
- ccxt
- TA-Lib
- py_find_1st
- sdnotify
# Optional for develpment
- flake8-tidy-imports
- flake8-type-annotations
- pytest-random-order
- -e .

18
freqtrade/__init__.py
View File

@ -1,15 +1,15 @@
""" FreqTrade bot """
__version__ = '0.18.2-dev'
__version__ = '2019.7-dev'
class DependencyException(BaseException):
class DependencyException(Exception):
"""
Indicates that a assumed dependency is not met.
Indicates that an assumed dependency is not met.
This could happen when there is currently not enough money on the account.
"""
class OperationalException(BaseException):
class OperationalException(Exception):
"""
Requires manual intervention.
This happens when an exchange returns an unexpected error during runtime
@ -17,7 +17,15 @@ class OperationalException(BaseException):
"""
class TemporaryError(BaseException):
class InvalidOrderException(Exception):
"""
This is returned when the order is not valid. Example:
If stoploss on exchange order is hit, then trying to cancel the order
should return this exception.
"""
class TemporaryError(Exception):
"""
Temporary network or exchange related error.
This could happen when an exchange is congested, unavailable, or the user

5
freqtrade/__main__.py
View File

@ -6,10 +6,7 @@ To launch Freqtrade as a module
> python -m freqtrade (with Python >= 3.6)
"""
import sys
from freqtrade import main
if __name__ == '__main__':
main.set_loggers()
main.main(sys.argv[1:])
main.main()

433
freqtrade/arguments.py
View File

@ -1,433 +0,0 @@
"""
This module contains the argument manager class
"""
import argparse
import os
import re
from typing import List, NamedTuple, Optional
import arrow
from freqtrade import __version__, constants
class TimeRange(NamedTuple):
"""
NamedTuple Defining timerange inputs.
[start/stop]type defines if [start/stop]ts shall be used.
if *type is none, don't use corresponding startvalue.
"""
starttype: Optional[str] = None
stoptype: Optional[str] = None
startts: int = 0
stopts: int = 0
class Arguments(object):
"""
Arguments Class. Manage the arguments received by the cli
"""
def __init__(self, args: List[str], description: str) -> None:
self.args = args
self.parsed_arg: Optional[argparse.Namespace] = None
self.parser = argparse.ArgumentParser(description=description)
def _load_args(self) -> None:
self.common_args_parser()
self._build_subcommands()
def get_parsed_arg(self) -> argparse.Namespace:
"""
Return the list of arguments
:return: List[str] List of arguments
"""
if self.parsed_arg is None:
self._load_args()
self.parsed_arg = self.parse_args()
return self.parsed_arg
def parse_args(self) -> argparse.Namespace:
"""
Parses given arguments and returns an argparse Namespace instance.
"""
parsed_arg = self.parser.parse_args(self.args)
# Workaround issue in argparse with action='append' and default value
# (see https://bugs.python.org/issue16399)
if parsed_arg.config is None:
parsed_arg.config = [constants.DEFAULT_CONFIG]
return parsed_arg
def common_args_parser(self) -> None:
"""
Parses given common arguments and returns them as a parsed object.
"""
self.parser.add_argument(
'-v', '--verbose',
help='Verbose mode (-vv for more, -vvv to get all messages).',
action='count',
dest='loglevel',
default=0,
)
self.parser.add_argument(
'--version',
action='version',
version=f'%(prog)s {__version__}'
)
self.parser.add_argument(
'-c', '--config',
help='Specify configuration file (default: %(default)s). '
'Multiple --config options may be used.',
dest='config',
action='append',
type=str,
metavar='PATH',
)
self.parser.add_argument(
'-d', '--datadir',
help='Path to backtest data.',
dest='datadir',
default=None,
type=str,
metavar='PATH',
)
self.parser.add_argument(
'-s', '--strategy',
help='Specify strategy class name (default: %(default)s).',
dest='strategy',
default='DefaultStrategy',
type=str,
metavar='NAME',
)
self.parser.add_argument(
'--strategy-path',
help='Specify additional strategy lookup path.',
dest='strategy_path',
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--dynamic-whitelist',
help='Dynamically generate and update whitelist'
' based on 24h BaseVolume (default: %(const)s).'
' DEPRECATED.',
dest='dynamic_whitelist',
const=constants.DYNAMIC_WHITELIST,
type=int,
metavar='INT',
nargs='?',
)
self.parser.add_argument(
'--db-url',
help='Override trades database URL, this is useful if dry_run is enabled'
' or in custom deployments (default: %(default)s).',
dest='db_url',
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--sd-notify',
help='Notify systemd service manager.',
action='store_true',
dest='sd_notify',
)
@staticmethod
def backtesting_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Backtesting scripts.
"""
parser.add_argument(
'--eps', '--enable-position-stacking',
help='Allow buying the same pair multiple times (position stacking).',
action='store_true',
dest='position_stacking',
default=False
)
parser.add_argument(
'--dmmp', '--disable-max-market-positions',
help='Disable applying `max_open_trades` during backtest '
'(same as setting `max_open_trades` to a very high number).',
action='store_false',
dest='use_max_market_positions',
default=True
)
parser.add_argument(
'-l', '--live',
help='Use live data.',
action='store_true',
dest='live',
)
parser.add_argument(
'-r', '--refresh-pairs-cached',
help='Refresh the pairs files in tests/testdata with the latest data from the '
'exchange. Use it if you want to run your backtesting with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
parser.add_argument(
'--strategy-list',
help='Provide a commaseparated list of strategies to backtest '
'Please note that ticker-interval needs to be set either in config '
'or via command line. When using this together with --export trades, '
'the strategy-name is injected into the filename '
'(so backtest-data.json becomes backtest-data-DefaultStrategy.json',
nargs='+',
dest='strategy_list',
)
parser.add_argument(
'--export',
help='Export backtest results, argument are: trades. '
'Example --export=trades',
type=str,
default=None,
dest='export',
)
parser.add_argument(
'--export-filename',
help='Save backtest results to this filename \
requires --export to be set as well\
Example --export-filename=user_data/backtest_data/backtest_today.json\
(default: %(default)s)',
type=str,
default=os.path.join('user_data', 'backtest_data', 'backtest-result.json'),
dest='exportfilename',
metavar='PATH',
)
@staticmethod
def edge_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Backtesting scripts.
"""
parser.add_argument(
'-r', '--refresh-pairs-cached',
help='Refresh the pairs files in tests/testdata with the latest data from the '
'exchange. Use it if you want to run your edge with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
parser.add_argument(
'--stoplosses',
help='Defines a range of stoploss against which edge will assess the strategy '
'the format is "min,max,step" (without any space).'
'example: --stoplosses=-0.01,-0.1,-0.001',
type=str,
dest='stoploss_range',
)
@staticmethod
def optimizer_shared_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given common arguments for Backtesting and Hyperopt scripts.
:param parser:
:return:
"""
parser.add_argument(
'-i', '--ticker-interval',
help='Specify ticker interval (1m, 5m, 30m, 1h, 1d).',
dest='ticker_interval',
type=str,
)
parser.add_argument(
'--timerange',
help='Specify what timerange of data to use.',
default=None,
type=str,
dest='timerange',
)
@staticmethod
def hyperopt_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Hyperopt scripts.
"""
parser.add_argument(
'--customhyperopt',
help='Specify hyperopt class name (default: %(default)s).',
dest='hyperopt',
default=constants.DEFAULT_HYPEROPT,
type=str,
metavar='NAME',
)
parser.add_argument(
'--eps', '--enable-position-stacking',
help='Allow buying the same pair multiple times (position stacking).',
action='store_true',
dest='position_stacking',
default=False
)
parser.add_argument(
'--dmmp', '--disable-max-market-positions',
help='Disable applying `max_open_trades` during backtest '
'(same as setting `max_open_trades` to a very high number).',
action='store_false',
dest='use_max_market_positions',
default=True
)
parser.add_argument(
'-e', '--epochs',
help='Specify number of epochs (default: %(default)d).',
dest='epochs',
default=constants.HYPEROPT_EPOCH,
type=int,
metavar='INT',
)
parser.add_argument(
'-s', '--spaces',
help='Specify which parameters to hyperopt. Space separate list. \
Default: %(default)s.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
default='all',
nargs='+',
dest='spaces',
)
def _build_subcommands(self) -> None:
"""
Builds and attaches all subcommands
:return: None
"""
from freqtrade.optimize import backtesting, hyperopt, edge_cli
subparsers = self.parser.add_subparsers(dest='subparser')
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.')
backtesting_cmd.set_defaults(func=backtesting.start)
self.optimizer_shared_options(backtesting_cmd)
self.backtesting_options(backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.')
edge_cmd.set_defaults(func=edge_cli.start)
self.optimizer_shared_options(edge_cmd)
self.edge_options(edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.')
hyperopt_cmd.set_defaults(func=hyperopt.start)
self.optimizer_shared_options(hyperopt_cmd)
self.hyperopt_options(hyperopt_cmd)
@staticmethod
def parse_timerange(text: Optional[str]) -> TimeRange:
"""
Parse the value of the argument --timerange to determine what is the range desired
:param text: value from --timerange
:return: Start and End range period
"""
if text is None:
return TimeRange(None, None, 0, 0)
syntax = [(r'^-(\d{8})$', (None, 'date')),
(r'^(\d{8})-$', ('date', None)),
(r'^(\d{8})-(\d{8})$', ('date', 'date')),
(r'^-(\d{10})$', (None, 'date')),
(r'^(\d{10})-$', ('date', None)),
(r'^(\d{10})-(\d{10})$', ('date', 'date')),
(r'^(-\d+)$', (None, 'line')),
(r'^(\d+)-$', ('line', None)),
(r'^(\d+)-(\d+)$', ('index', 'index'))]
for rex, stype in syntax:
# Apply the regular expression to text
match = re.match(rex, text)
if match: # Regex has matched
rvals = match.groups()
index = 0
start: int = 0
stop: int = 0
if stype[0]:
starts = rvals[index]
if stype[0] == 'date' and len(starts) == 8:
start = arrow.get(starts, 'YYYYMMDD').timestamp
else:
start = int(starts)
index += 1
if stype[1]:
stops = rvals[index]
if stype[1] == 'date' and len(stops) == 8:
stop = arrow.get(stops, 'YYYYMMDD').timestamp
else:
stop = int(stops)
return TimeRange(stype[0], stype[1], start, stop)
raise Exception('Incorrect syntax for timerange "%s"' % text)
def scripts_options(self) -> None:
"""
Parses given arguments for scripts.
"""
self.parser.add_argument(
'-p', '--pairs',
help='Show profits for only this pairs. Pairs are comma-separated.',
dest='pairs',
default=None
)
def testdata_dl_options(self) -> None:
"""
Parses given arguments for testdata download
"""
self.parser.add_argument(
'--pairs-file',
help='File containing a list of pairs to download.',
dest='pairs_file',
default=None,
metavar='PATH',
)
self.parser.add_argument(
'--export',
help='Export files to given dir.',
dest='export',
default=None,
metavar='PATH',
)
self.parser.add_argument(
'-c', '--config',
help='Specify configuration file (default: %(default)s). '
'Multiple --config options may be used.',
dest='config',
action='append',
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--days',
help='Download data for given number of days.',
dest='days',
type=int,
metavar='INT',
default=None
)
self.parser.add_argument(
'--exchange',
help='Exchange name (default: %(default)s). Only valid if no config is provided.',
dest='exchange',
type=str,
default='bittrex'
)
self.parser.add_argument(
'-t', '--timeframes',
help='Specify which tickers to download. Space separated list. \
Default: %(default)s.',
choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
'6h', '8h', '12h', '1d', '3d', '1w'],
default=['1m', '5m'],
nargs='+',
dest='timeframes',
)
self.parser.add_argument(
'--erase',
help='Clean all existing data for the selected exchange/pairs/timeframes.',
dest='erase',
action='store_true'
)

378
freqtrade/configuration.py
View File

@ -1,378 +0,0 @@
"""
This module contains the configuration class
"""
import json
import logging
import os
from argparse import Namespace
from typing import Any, Dict, Optional
import ccxt
from jsonschema import Draft4Validator, validate
from jsonschema.exceptions import ValidationError, best_match
from freqtrade import OperationalException, constants
from freqtrade.state import RunMode
from freqtrade.misc import deep_merge_dicts
logger = logging.getLogger(__name__)
def set_loggers(log_level: int = 0) -> None:
"""
Set the logger level for Third party libs
:return: None
"""
logging.getLogger('requests').setLevel(logging.INFO if log_level <= 1 else logging.DEBUG)
logging.getLogger("urllib3").setLevel(logging.INFO if log_level <= 1 else logging.DEBUG)
logging.getLogger('ccxt.base.exchange').setLevel(
logging.INFO if log_level <= 2 else logging.DEBUG)
logging.getLogger('telegram').setLevel(logging.INFO)
class Configuration(object):
"""
Class to read and init the bot configuration
Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
"""
def __init__(self, args: Namespace, runmode: RunMode = None) -> None:
self.args = args
self.config: Optional[Dict[str, Any]] = None
self.runmode = runmode
def load_config(self) -> Dict[str, Any]:
"""
Extract information for sys.argv and load the bot configuration
:return: Configuration dictionary
"""
config: Dict[str, Any] = {}
# Now expecting a list of config filenames here, not a string
for path in self.args.config:
logger.info('Using config: %s ...', path)
# Merge config options, overwriting old values
config = deep_merge_dicts(self._load_config_file(path), config)
if 'internals' not in config:
config['internals'] = {}
logger.info('Validating configuration ...')
self._validate_config_schema(config)
self._validate_config_consistency(config)
# Set strategy if not specified in config and or if it's non default
if self.args.strategy != constants.DEFAULT_STRATEGY or not config.get('strategy'):
config.update({'strategy': self.args.strategy})
if self.args.strategy_path:
config.update({'strategy_path': self.args.strategy_path})
# Load Common configuration
config = self._load_common_config(config)
# Load Backtesting
config = self._load_backtesting_config(config)
# Load Edge
config = self._load_edge_config(config)
# Load Hyperopt
config = self._load_hyperopt_config(config)
# Set runmode
if not self.runmode:
# Handle real mode, infer dry/live from config
self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
config.update({'runmode': self.runmode})
return config
def _load_config_file(self, path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path
:param path: path as str
:return: configuration as dictionary
"""
try:
with open(path) as file:
conf = json.load(file)
except FileNotFoundError:
raise OperationalException(
f'Config file "{path}" not found!'
' Please create a config file or check whether it exists.')
return conf
def _load_common_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract information for sys.argv and load common configuration
:return: configuration as dictionary
"""
# Log level
if 'loglevel' in self.args and self.args.loglevel:
config.update({'verbosity': self.args.loglevel})
else:
config.update({'verbosity': 0})
logging.basicConfig(
level=logging.INFO if config['verbosity'] < 1 else logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
set_loggers(config['verbosity'])
logger.info('Verbosity set to %s', config['verbosity'])
# Support for sd_notify
if self.args.sd_notify:
config['internals'].update({'sd_notify': True})
# Add dynamic_whitelist if found
if 'dynamic_whitelist' in self.args and self.args.dynamic_whitelist:
# Update to volumePairList (the previous default)
config['pairlist'] = {'method': 'VolumePairList',
'config': {'number_assets': self.args.dynamic_whitelist}
}
logger.warning(
'Parameter --dynamic-whitelist has been deprecated, '
'and will be completely replaced by the whitelist dict in the future. '
'For now: using dynamically generated whitelist based on VolumePairList. '
'(not applicable with Backtesting and Hyperopt)'
)
if self.args.db_url and self.args.db_url != constants.DEFAULT_DB_PROD_URL:
config.update({'db_url': self.args.db_url})
logger.info('Parameter --db-url detected ...')
if config.get('dry_run', False):
logger.info('Dry run is enabled')
if config.get('db_url') in [None, constants.DEFAULT_DB_PROD_URL]:
# Default to in-memory db for dry_run if not specified
config['db_url'] = constants.DEFAULT_DB_DRYRUN_URL
else:
if not config.get('db_url', None):
config['db_url'] = constants.DEFAULT_DB_PROD_URL
logger.info('Dry run is disabled')
if config.get('forcebuy_enable', False):
logger.warning('`forcebuy` RPC message enabled.')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
logger.info(f'Using DB: "{config["db_url"]}"')
# Check if the exchange set by the user is supported
self.check_exchange(config)
return config
def _create_datadir(self, config: Dict[str, Any], datadir: Optional[str] = None) -> str:
if not datadir:
# set datadir
exchange_name = config.get('exchange', {}).get('name').lower()
datadir = os.path.join('user_data', 'data', exchange_name)
if not os.path.isdir(datadir):
os.makedirs(datadir)
logger.info(f'Created data directory: {datadir}')
return datadir
def _load_backtesting_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract information for sys.argv and load Backtesting configuration
:return: configuration as dictionary
"""
# If -i/--ticker-interval is used we override the configuration parameter
# (that will override the strategy configuration)
if 'ticker_interval' in self.args and self.args.ticker_interval:
config.update({'ticker_interval': self.args.ticker_interval})
logger.info('Parameter -i/--ticker-interval detected ...')
logger.info('Using ticker_interval: %s ...', config.get('ticker_interval'))
# If -l/--live is used we add it to the configuration
if 'live' in self.args and self.args.live:
config.update({'live': True})
logger.info('Parameter -l/--live detected ...')
# If --enable-position-stacking is used we add it to the configuration
if 'position_stacking' in self.args and self.args.position_stacking:
config.update({'position_stacking': True})
logger.info('Parameter --enable-position-stacking detected ...')
# If --disable-max-market-positions is used we add it to the configuration
if 'use_max_market_positions' in self.args and not self.args.use_max_market_positions:
config.update({'use_max_market_positions': False})
logger.info('Parameter --disable-max-market-positions detected ...')
logger.info('max_open_trades set to unlimited ...')
else:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
# If --timerange is used we add it to the configuration
if 'timerange' in self.args and self.args.timerange:
config.update({'timerange': self.args.timerange})
logger.info('Parameter --timerange detected: %s ...', self.args.timerange)
# If --datadir is used we add it to the configuration
if 'datadir' in self.args and self.args.datadir:
config.update({'datadir': self._create_datadir(config, self.args.datadir)})
else:
config.update({'datadir': self._create_datadir(config, None)})
logger.info('Using data folder: %s ...', config.get('datadir'))
# If -r/--refresh-pairs-cached is used we add it to the configuration
if 'refresh_pairs' in self.args and self.args.refresh_pairs:
config.update({'refresh_pairs': True})
logger.info('Parameter -r/--refresh-pairs-cached detected ...')
if 'strategy_list' in self.args and self.args.strategy_list:
config.update({'strategy_list': self.args.strategy_list})
logger.info('Using strategy list of %s Strategies', len(self.args.strategy_list))
if 'ticker_interval' in self.args and self.args.ticker_interval:
config.update({'ticker_interval': self.args.ticker_interval})
logger.info('Overriding ticker interval with Command line argument')
# If --export is used we add it to the configuration
if 'export' in self.args and self.args.export:
config.update({'export': self.args.export})
logger.info('Parameter --export detected: %s ...', self.args.export)
# If --export-filename is used we add it to the configuration
if 'export' in config and 'exportfilename' in self.args and self.args.exportfilename:
config.update({'exportfilename': self.args.exportfilename})
logger.info('Storing backtest results to %s ...', self.args.exportfilename)
return config
def _load_edge_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract information for sys.argv and load Edge configuration
:return: configuration as dictionary
"""
# If --timerange is used we add it to the configuration
if 'timerange' in self.args and self.args.timerange:
config.update({'timerange': self.args.timerange})
logger.info('Parameter --timerange detected: %s ...', self.args.timerange)
# If --timerange is used we add it to the configuration
if 'stoploss_range' in self.args and self.args.stoploss_range:
txt_range = eval(self.args.stoploss_range)
config['edge'].update({'stoploss_range_min': txt_range[0]})
config['edge'].update({'stoploss_range_max': txt_range[1]})
config['edge'].update({'stoploss_range_step': txt_range[2]})
logger.info('Parameter --stoplosses detected: %s ...', self.args.stoploss_range)
# If -r/--refresh-pairs-cached is used we add it to the configuration
if 'refresh_pairs' in self.args and self.args.refresh_pairs:
config.update({'refresh_pairs': True})
logger.info('Parameter -r/--refresh-pairs-cached detected ...')
return config
def _load_hyperopt_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract information for sys.argv and load Hyperopt configuration
:return: configuration as dictionary
"""
if "hyperopt" in self.args:
# Add the hyperopt file to use
config.update({'hyperopt': self.args.hyperopt})
# If --epochs is used we add it to the configuration
if 'epochs' in self.args and self.args.epochs:
config.update({'epochs': self.args.epochs})
logger.info('Parameter --epochs detected ...')
logger.info('Will run Hyperopt with for %s epochs ...', config.get('epochs'))
# If --spaces is used we add it to the configuration
if 'spaces' in self.args and self.args.spaces:
config.update({'spaces': self.args.spaces})
logger.info('Parameter -s/--spaces detected: %s', config.get('spaces'))
return config
def _validate_config_schema(self, conf: Dict[str, Any]) -> Dict[str, Any]:
"""
Validate the configuration follow the Config Schema
:param conf: Config in JSON format
:return: Returns the config if valid, otherwise throw an exception
"""
try:
validate(conf, constants.CONF_SCHEMA, Draft4Validator)
return conf
except ValidationError as exception:
logger.critical(
'Invalid configuration. See config.json.example. Reason: %s',
exception
)
raise ValidationError(
best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
)
def _validate_config_consistency(self, conf: Dict[str, Any]) -> None:
"""
Validate the configuration consistency
:param conf: Config in JSON format
:return: Returns None if everything is ok, otherwise throw an OperationalException
"""
# validating trailing stoploss
self._validate_trailing_stoploss(conf)
def _validate_trailing_stoploss(self, conf: Dict[str, Any]) -> None:
# Skip if trailing stoploss is not activated
if not conf.get('trailing_stop', False):
return
tsl_positive = float(conf.get('trailing_stop_positive', 0))
tsl_offset = float(conf.get('trailing_stop_positive_offset', 0))
tsl_only_offset = conf.get('trailing_only_offset_is_reached', False)
if tsl_only_offset:
if tsl_positive == 0.0:
raise OperationalException(
f'The config trailing_only_offset_is_reached needs '
'trailing_stop_positive_offset to be more than 0 in your config.')
if tsl_positive > 0 and 0 < tsl_offset <= tsl_positive:
raise OperationalException(
f'The config trailing_stop_positive_offset needs '
'to be greater than trailing_stop_positive_offset in your config.')
def get_config(self) -> Dict[str, Any]:
"""
Return the config. Use this method to get the bot config
:return: Dict: Bot config
"""
if self.config is None:
self.config = self.load_config()
return self.config
def check_exchange(self, config: Dict[str, Any]) -> bool:
"""
Check if the exchange name in the config file is supported by Freqtrade
:return: True or raised an exception if the exchange if not supported
"""
exchange = config.get('exchange', {}).get('name').lower()
if exchange not in ccxt.exchanges:
exception_msg = f'Exchange "{exchange}" not supported.\n' \
f'The following exchanges are supported: {", ".join(ccxt.exchanges)}'
logger.critical(exception_msg)
raise OperationalException(
exception_msg
)
# Depreciation warning
if 'ccxt_rate_limit' in config.get('exchange', {}):
logger.warning("`ccxt_rate_limit` has been deprecated in favor of "
"`ccxt_config` and `ccxt_async_config` and will be removed "
"in a future version.")
logger.debug('Exchange "%s" supported', exchange)
return True

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freqtrade/configuration/__init__.py Normal file
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from freqtrade.configuration.arguments import Arguments, TimeRange # noqa: F401
from freqtrade.configuration.configuration import Configuration # noqa: F401

176
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"""
This module contains the argument manager class
"""
import argparse
import re
from typing import List, NamedTuple, Optional
import arrow
from freqtrade.configuration.cli_options import AVAILABLE_CLI_OPTIONS
from freqtrade import constants
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir"]
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_MAIN = ARGS_COMMON + ARGS_STRATEGY + ["db_url", "sd_notify"]
ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
"max_open_trades", "stake_amount", "refresh_pairs"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"live", "strategy_list", "export", "exportfilename"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "epochs", "spaces",
"use_max_market_positions", "print_all", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
"hyperopt_continue", "hyperopt_loss"]
ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
ARGS_LIST_EXCHANGES = ["print_one_column"]
ARGS_DOWNLOADER = ARGS_COMMON + ["pairs", "pairs_file", "days", "exchange", "timeframes", "erase"]
ARGS_PLOT_DATAFRAME = (ARGS_COMMON + ARGS_STRATEGY +
["pairs", "indicators1", "indicators2", "plot_limit", "db_url",
"trade_source", "export", "exportfilename", "timerange",
"refresh_pairs", "live"])
ARGS_PLOT_PROFIT = (ARGS_COMMON + ARGS_STRATEGY +
["pairs", "timerange", "export", "exportfilename", "db_url", "trade_source"])
class TimeRange(NamedTuple):
"""
NamedTuple defining timerange inputs.
[start/stop]type defines if [start/stop]ts shall be used.
if *type is None, don't use corresponding startvalue.
"""
starttype: Optional[str] = None
stoptype: Optional[str] = None
startts: int = 0
stopts: int = 0
class Arguments(object):
"""
Arguments Class. Manage the arguments received by the cli
"""
def __init__(self, args: Optional[List[str]], description: str,
no_default_config: bool = False) -> None:
self.args = args
self._parsed_arg: Optional[argparse.Namespace] = None
self.parser = argparse.ArgumentParser(description=description)
self._no_default_config = no_default_config
def _load_args(self) -> None:
self._build_args(optionlist=ARGS_MAIN)
self._build_subcommands()
def get_parsed_arg(self) -> argparse.Namespace:
"""
Return the list of arguments
:return: List[str] List of arguments
"""
if self._parsed_arg is None:
self._load_args()
self._parsed_arg = self._parse_args()
return self._parsed_arg
def _parse_args(self) -> argparse.Namespace:
"""
Parses given arguments and returns an argparse Namespace instance.
"""
parsed_arg = self.parser.parse_args(self.args)
# Workaround issue in argparse with action='append' and default value
# (see https://bugs.python.org/issue16399)
if not self._no_default_config and parsed_arg.config is None:
parsed_arg.config = [constants.DEFAULT_CONFIG]
return parsed_arg
def _build_args(self, optionlist, parser=None):
parser = parser or self.parser
for val in optionlist:
opt = AVAILABLE_CLI_OPTIONS[val]
parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
def _build_subcommands(self) -> None:
"""
Builds and attaches all subcommands.
:return: None
"""
from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
from freqtrade.utils import start_list_exchanges
subparsers = self.parser.add_subparsers(dest='subparser')
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.')
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.')
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.')
hyperopt_cmd.set_defaults(func=start_hyperopt)
self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.'
)
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
@staticmethod
def parse_timerange(text: Optional[str]) -> TimeRange:
"""
Parse the value of the argument --timerange to determine what is the range desired
:param text: value from --timerange
:return: Start and End range period
"""
if text is None:
return TimeRange(None, None, 0, 0)
syntax = [(r'^-(\d{8})$', (None, 'date')),
(r'^(\d{8})-$', ('date', None)),
(r'^(\d{8})-(\d{8})$', ('date', 'date')),
(r'^-(\d{10})$', (None, 'date')),
(r'^(\d{10})-$', ('date', None)),
(r'^(\d{10})-(\d{10})$', ('date', 'date')),
(r'^(-\d+)$', (None, 'line')),
(r'^(\d+)-$', ('line', None)),
(r'^(\d+)-(\d+)$', ('index', 'index'))]
for rex, stype in syntax:
# Apply the regular expression to text
match = re.match(rex, text)
if match: # Regex has matched
rvals = match.groups()
index = 0
start: int = 0
stop: int = 0
if stype[0]:
starts = rvals[index]
if stype[0] == 'date' and len(starts) == 8:
start = arrow.get(starts, 'YYYYMMDD').timestamp
else:
start = int(starts)
index += 1
if stype[1]:
stops = rvals[index]
if stype[1] == 'date' and len(stops) == 8:
stop = arrow.get(stops, 'YYYYMMDD').timestamp
else:
stop = int(stops)
return TimeRange(stype[0], stype[1], start, stop)
raise Exception('Incorrect syntax for timerange "%s"' % text)

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import logging
from typing import Any, Dict
from freqtrade import OperationalException
from freqtrade.exchange import (is_exchange_bad, is_exchange_available,
is_exchange_officially_supported, available_exchanges)
logger = logging.getLogger(__name__)
def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
"""
Check if the exchange name in the config file is supported by Freqtrade
:param check_for_bad: if True, check the exchange against the list of known 'bad'
exchanges
:return: False if exchange is 'bad', i.e. is known to work with the bot with
critical issues or does not work at all, crashes, etc. True otherwise.
raises an exception if the exchange if not supported by ccxt
and thus is not known for the Freqtrade at all.
"""
logger.info("Checking exchange...")
exchange = config.get('exchange', {}).get('name').lower()
if not is_exchange_available(exchange):
raise OperationalException(
f'Exchange "{exchange}" is not supported by ccxt '
f'and therefore not available for the bot.\n'
f'The following exchanges are supported by ccxt: '
f'{", ".join(available_exchanges())}'
)
if check_for_bad and is_exchange_bad(exchange):
logger.warning(f'Exchange "{exchange}" is known to not work with the bot yet. '
f'Use it only for development and testing purposes.')
return False
if is_exchange_officially_supported(exchange):
logger.info(f'Exchange "{exchange}" is officially supported '
f'by the Freqtrade development team.')
else:
logger.warning(f'Exchange "{exchange}" is supported by ccxt '
f'and therefore available for the bot but not officially supported '
f'by the Freqtrade development team. '
f'It may work flawlessly (please report back) or have serious issues. '
f'Use it at your own discretion.')
return True

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"""
Definition of cli arguments used in arguments.py
"""
import argparse
import os
from freqtrade import __version__, constants
def check_int_positive(value: str) -> int:
try:
uint = int(value)
if uint <= 0:
raise ValueError
except ValueError:
raise argparse.ArgumentTypeError(
f"{value} is invalid for this parameter, should be a positive integer value"
)
return uint
class Arg:
# Optional CLI arguments
def __init__(self, *args, **kwargs):
self.cli = args
self.kwargs = kwargs
# List of available command line options
AVAILABLE_CLI_OPTIONS = {
# Common options
"verbosity": Arg(
'-v', '--verbose',
help='Verbose mode (-vv for more, -vvv to get all messages).',
action='count',
default=0,
),
"logfile": Arg(
'--logfile',
help='Log to the file specified.',
metavar='FILE',
),
"version": Arg(
'-V', '--version',
action='version',
version=f'%(prog)s {__version__}',
),
"config": Arg(
'-c', '--config',
help=f'Specify configuration file (default: `{constants.DEFAULT_CONFIG}`). '
f'Multiple --config options may be used. '
f'Can be set to `-` to read config from stdin.',
action='append',
metavar='PATH',
),
"datadir": Arg(
'-d', '--datadir',
help='Path to backtest data.',
metavar='PATH',
),
# Main options
"strategy": Arg(
'-s', '--strategy',
help='Specify strategy class name (default: `%(default)s`).',
metavar='NAME',
default='DefaultStrategy',
),
"strategy_path": Arg(
'--strategy-path',
help='Specify additional strategy lookup path.',
metavar='PATH',
),
"db_url": Arg(
'--db-url',
help=f'Override trades database URL, this is useful in custom deployments '
f'(default: `{constants.DEFAULT_DB_PROD_URL}` for Live Run mode, '
f'`{constants.DEFAULT_DB_DRYRUN_URL}` for Dry Run).',
metavar='PATH',
),
"sd_notify": Arg(
'--sd-notify',
help='Notify systemd service manager.',
action='store_true',
),
# Optimize common
"ticker_interval": Arg(
'-i', '--ticker-interval',
help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
),
"timerange": Arg(
'--timerange',
help='Specify what timerange of data to use.',
),
"max_open_trades": Arg(
'--max_open_trades',
help='Specify max_open_trades to use.',
type=int,
metavar='INT',
),
"stake_amount": Arg(
'--stake_amount',
help='Specify stake_amount.',
type=float,
),
"refresh_pairs": Arg(
'-r', '--refresh-pairs-cached',
help='Refresh the pairs files in tests/testdata with the latest data from the '
'exchange. Use it if you want to run your optimization commands with '
'up-to-date data.',
action='store_true',
),
# Backtesting
"position_stacking": Arg(
'--eps', '--enable-position-stacking',
help='Allow buying the same pair multiple times (position stacking).',
action='store_true',
default=False,
),
"use_max_market_positions": Arg(
'--dmmp', '--disable-max-market-positions',
help='Disable applying `max_open_trades` during backtest '
'(same as setting `max_open_trades` to a very high number).',
action='store_false',
default=True,
),
"live": Arg(
'-l', '--live',
help='Use live data.',
action='store_true',
),
"strategy_list": Arg(
'--strategy-list',
help='Provide a space-separated list of strategies to backtest. '
'Please note that ticker-interval needs to be set either in config '
'or via command line. When using this together with `--export trades`, '
'the strategy-name is injected into the filename '
'(so `backtest-data.json` becomes `backtest-data-DefaultStrategy.json`',
nargs='+',
),
"export": Arg(
'--export',
help='Export backtest results, argument are: trades. '
'Example: `--export=trades`',
),
"exportfilename": Arg(
'--export-filename',
help='Save backtest results to the file with this filename (default: `%(default)s`). '
'Requires `--export` to be set as well. '
'Example: `--export-filename=user_data/backtest_data/backtest_today.json`',
metavar='PATH',
default=os.path.join('user_data', 'backtest_data',
'backtest-result.json'),
),
# Edge
"stoploss_range": Arg(
'--stoplosses',
help='Defines a range of stoploss values against which edge will assess the strategy. '
'The format is "min,max,step" (without any space). '
'Example: `--stoplosses=-0.01,-0.1,-0.001`',
),
# Hyperopt
"hyperopt": Arg(
'--customhyperopt',
help='Specify hyperopt class name (default: `%(default)s`).',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT,
),
"hyperopt_path": Arg(
'--hyperopt-path',
help='Specify additional lookup path for Hyperopts and Hyperopt Loss functions.',
metavar='PATH',
),
"epochs": Arg(
'-e', '--epochs',
help='Specify number of epochs (default: %(default)d).',
type=check_int_positive,
metavar='INT',
default=constants.HYPEROPT_EPOCH,
),
"spaces": Arg(
'-s', '--spaces',
help='Specify which parameters to hyperopt. Space-separated list. '
'Default: `%(default)s`.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
nargs='+',
default='all',
),
"print_all": Arg(
'--print-all',
help='Print all results, not only the best ones.',
action='store_true',
default=False,
),
"hyperopt_jobs": Arg(
'-j', '--job-workers',
help='The number of concurrently running jobs for hyperoptimization '
'(hyperopt worker processes). '
'If -1 (default), all CPUs are used, for -2, all CPUs but one are used, etc. '
'If 1 is given, no parallel computing code is used at all.',
type=int,
metavar='JOBS',
default=-1,
),
"hyperopt_random_state": Arg(
'--random-state',
help='Set random state to some positive integer for reproducible hyperopt results.',
type=check_int_positive,
metavar='INT',
),
"hyperopt_min_trades": Arg(
'--min-trades',
help="Set minimal desired number of trades for evaluations in the hyperopt "
"optimization path (default: 1).",
type=check_int_positive,
metavar='INT',
default=1,
),
"hyperopt_continue": Arg(
"--continue",
help="Continue hyperopt from previous runs. "
"By default, temporary files will be removed and hyperopt will start from scratch.",
default=False,
action='store_true',
),
"hyperopt_loss": Arg(
'--hyperopt-loss',
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. (default: `%(default)s`).',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT_LOSS,
),
# List exchanges
"print_one_column": Arg(
'-1', '--one-column',
help='Print exchanges in one column.',
action='store_true',
),
# Script options
"pairs": Arg(
'-p', '--pairs',
help='Show profits for only these pairs. Pairs are comma-separated.',
),
# Download data
"pairs_file": Arg(
'--pairs-file',
help='File containing a list of pairs to download.',
metavar='FILE',
),
"days": Arg(
'--days',
help='Download data for given number of days.',
type=check_int_positive,
metavar='INT',
),
"exchange": Arg(
'--exchange',
help=f'Exchange name (default: `{constants.DEFAULT_EXCHANGE}`). '
f'Only valid if no config is provided.',
),
"timeframes": Arg(
'-t', '--timeframes',
help=f'Specify which tickers to download. Space-separated list. '
f'Default: `{constants.DEFAULT_DOWNLOAD_TICKER_INTERVALS}`.',
choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
'6h', '8h', '12h', '1d', '3d', '1w'],
nargs='+',
),
"erase": Arg(
'--erase',
help='Clean all existing data for the selected exchange/pairs/timeframes.',
action='store_true',
),
# Plot dataframe
"indicators1": Arg(
'--indicators1',
help='Set indicators from your strategy you want in the first row of the graph. '
'Comma-separated list. Example: `ema3,ema5`. Default: `%(default)s`.',
default='sma,ema3,ema5',
),
"indicators2": Arg(
'--indicators2',
help='Set indicators from your strategy you want in the third row of the graph. '
'Comma-separated list. Example: `fastd,fastk`. Default: `%(default)s`.',
default='macd,macdsignal',
),
"plot_limit": Arg(
'--plot-limit',
help='Specify tick limit for plotting. Notice: too high values cause huge files. '
'Default: %(default)s.',
type=check_int_positive,
metavar='INT',
default=750,
),
"trade_source": Arg(
'--trade-source',
help='Specify the source for trades (Can be DB or file (backtest file)) '
'Default: %(default)s',
choices=["DB", "file"],
default="file",
),
}

328
freqtrade/configuration/configuration.py Normal file
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@ -0,0 +1,328 @@
"""
This module contains the configuration class
"""
import logging
import warnings
from argparse import Namespace
from typing import Any, Callable, Dict, Optional
from freqtrade import OperationalException, constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.create_datadir import create_datadir
from freqtrade.configuration.json_schema import validate_config_schema
from freqtrade.configuration.load_config import load_config_file
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
class Configuration(object):
"""
Class to read and init the bot configuration
Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
"""
def __init__(self, args: Namespace, runmode: RunMode = None) -> None:
self.args = args
self.config: Optional[Dict[str, Any]] = None
self.runmode = runmode
def get_config(self) -> Dict[str, Any]:
"""
Return the config. Use this method to get the bot config
:return: Dict: Bot config
"""
if self.config is None:
self.config = self.load_config()
return self.config
def _load_config_files(self) -> Dict[str, Any]:
"""
Iterate through the config files passed in the args,
loading all of them and merging their contents.
"""
config: Dict[str, Any] = {}
# We expect here a list of config filenames
for path in self.args.config:
logger.info('Using config: %s ...', path)
# Merge config options, overwriting old values
config = deep_merge_dicts(load_config_file(path), config)
return config
def _normalize_config(self, config: Dict[str, Any]) -> None:
"""
Make config more canonical -- i.e. for example add missing parts that we expect
to be normally in it...
"""
if 'internals' not in config:
config['internals'] = {}
def load_config(self) -> Dict[str, Any]:
"""
Extract information for sys.argv and load the bot configuration
:return: Configuration dictionary
"""
# Load all configs
config: Dict[str, Any] = self._load_config_files()
# Make resulting config more canonical
self._normalize_config(config)
logger.info('Validating configuration ...')
validate_config_schema(config)
self._validate_config_consistency(config)
self._process_common_options(config)
self._process_optimize_options(config)
self._process_plot_options(config)
self._process_runmode(config)
return config
def _process_logging_options(self, config: Dict[str, Any]) -> None:
"""
Extract information for sys.argv and load logging configuration:
the -v/--verbose, --logfile options
"""
# Log level
if 'verbosity' in self.args and self.args.verbosity:
config.update({'verbosity': self.args.verbosity})
else:
config.update({'verbosity': 0})
if 'logfile' in self.args and self.args.logfile:
config.update({'logfile': self.args.logfile})
setup_logging(config)
def _process_strategy_options(self, config: Dict[str, Any]) -> None:
# Set strategy if not specified in config and or if it's non default
if self.args.strategy != constants.DEFAULT_STRATEGY or not config.get('strategy'):
config.update({'strategy': self.args.strategy})
self._args_to_config(config, argname='strategy_path',
logstring='Using additional Strategy lookup path: {}')
def _process_common_options(self, config: Dict[str, Any]) -> None:
self._process_logging_options(config)
self._process_strategy_options(config)
if ('db_url' in self.args and self.args.db_url and
self.args.db_url != constants.DEFAULT_DB_PROD_URL):
config.update({'db_url': self.args.db_url})
logger.info('Parameter --db-url detected ...')
if config.get('dry_run', False):
logger.info('Dry run is enabled')
if config.get('db_url') in [None, constants.DEFAULT_DB_PROD_URL]:
# Default to in-memory db for dry_run if not specified
config['db_url'] = constants.DEFAULT_DB_DRYRUN_URL
else:
if not config.get('db_url', None):
config['db_url'] = constants.DEFAULT_DB_PROD_URL
logger.info('Dry run is disabled')
logger.info(f'Using DB: "{config["db_url"]}"')
if config.get('forcebuy_enable', False):
logger.warning('`forcebuy` RPC message enabled.')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
# Support for sd_notify
if 'sd_notify' in self.args and self.args.sd_notify:
config['internals'].update({'sd_notify': True})
# Check if the exchange set by the user is supported
check_exchange(config)
def _process_datadir_options(self, config: Dict[str, Any]) -> None:
"""
Extract information for sys.argv and load datadir configuration:
the --datadir option
"""
if 'datadir' in self.args and self.args.datadir:
config.update({'datadir': create_datadir(config, self.args.datadir)})
else:
config.update({'datadir': create_datadir(config, None)})
logger.info('Using data directory: %s ...', config.get('datadir'))
def _process_optimize_options(self, config: Dict[str, Any]) -> None:
# This will override the strategy configuration
self._args_to_config(config, argname='ticker_interval',
logstring='Parameter -i/--ticker-interval detected ... '
'Using ticker_interval: {} ...')
self._args_to_config(config, argname='live',
logstring='Parameter -l/--live detected ...',
deprecated_msg='--live will be removed soon.')
self._args_to_config(config, argname='position_stacking',
logstring='Parameter --enable-position-stacking detected ...')
if 'use_max_market_positions' in self.args and not self.args.use_max_market_positions:
config.update({'use_max_market_positions': False})
logger.info('Parameter --disable-max-market-positions detected ...')
logger.info('max_open_trades set to unlimited ...')
elif 'max_open_trades' in self.args and self.args.max_open_trades:
config.update({'max_open_trades': self.args.max_open_trades})
logger.info('Parameter --max_open_trades detected, '
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
else:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
self._args_to_config(config, argname='stake_amount',
logstring='Parameter --stake_amount detected, '
'overriding stake_amount to: {} ...')
self._args_to_config(config, argname='timerange',
logstring='Parameter --timerange detected: {} ...')
self._process_datadir_options(config)
self._args_to_config(config, argname='refresh_pairs',
logstring='Parameter -r/--refresh-pairs-cached detected ...')
self._args_to_config(config, argname='strategy_list',
logstring='Using strategy list of {} Strategies', logfun=len)
self._args_to_config(config, argname='ticker_interval',
logstring='Overriding ticker interval with Command line argument')
self._args_to_config(config, argname='export',
logstring='Parameter --export detected: {} ...')
self._args_to_config(config, argname='exportfilename',
logstring='Storing backtest results to {} ...')
# Edge section:
if 'stoploss_range' in self.args and self.args.stoploss_range:
txt_range = eval(self.args.stoploss_range)
config['edge'].update({'stoploss_range_min': txt_range[0]})
config['edge'].update({'stoploss_range_max': txt_range[1]})
config['edge'].update({'stoploss_range_step': txt_range[2]})
logger.info('Parameter --stoplosses detected: %s ...', self.args.stoploss_range)
# Hyperopt section
self._args_to_config(config, argname='hyperopt',
logstring='Using Hyperopt file {}')
self._args_to_config(config, argname='hyperopt_path',
logstring='Using additional Hyperopt lookup path: {}')
self._args_to_config(config, argname='epochs',
logstring='Parameter --epochs detected ... '
'Will run Hyperopt with for {} epochs ...'
)
self._args_to_config(config, argname='spaces',
logstring='Parameter -s/--spaces detected: {}')
self._args_to_config(config, argname='print_all',
logstring='Parameter --print-all detected ...')
self._args_to_config(config, argname='hyperopt_jobs',
logstring='Parameter -j/--job-workers detected: {}')
self._args_to_config(config, argname='hyperopt_random_state',
logstring='Parameter --random-state detected: {}')
self._args_to_config(config, argname='hyperopt_min_trades',
logstring='Parameter --min-trades detected: {}')
self._args_to_config(config, argname='hyperopt_continue',
logstring='Hyperopt continue: {}')
self._args_to_config(config, argname='hyperopt_loss',
logstring='Using loss function: {}')
def _process_plot_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='pairs',
logstring='Using pairs {}')
self._args_to_config(config, argname='indicators1',
logstring='Using indicators1: {}')
self._args_to_config(config, argname='indicators2',
logstring='Using indicators2: {}')
self._args_to_config(config, argname='plot_limit',
logstring='Limiting plot to: {}')
self._args_to_config(config, argname='trade_source',
logstring='Using trades from: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
if not self.runmode:
# Handle real mode, infer dry/live from config
self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
logger.info("Runmode set to {self.runmode}.")
config.update({'runmode': self.runmode})
def _validate_config_consistency(self, conf: Dict[str, Any]) -> None:
"""
Validate the configuration consistency
:param conf: Config in JSON format
:return: Returns None if everything is ok, otherwise throw an OperationalException
"""
# validating trailing stoploss
self._validate_trailing_stoploss(conf)
def _validate_trailing_stoploss(self, conf: Dict[str, Any]) -> None:
# Skip if trailing stoploss is not activated
if not conf.get('trailing_stop', False):
return
tsl_positive = float(conf.get('trailing_stop_positive', 0))
tsl_offset = float(conf.get('trailing_stop_positive_offset', 0))
tsl_only_offset = conf.get('trailing_only_offset_is_reached', False)
if tsl_only_offset:
if tsl_positive == 0.0:
raise OperationalException(
f'The config trailing_only_offset_is_reached needs '
'trailing_stop_positive_offset to be more than 0 in your config.')
if tsl_positive > 0 and 0 < tsl_offset <= tsl_positive:
raise OperationalException(
f'The config trailing_stop_positive_offset needs '
'to be greater than trailing_stop_positive_offset in your config.')
def _args_to_config(self, config: Dict[str, Any], argname: str,
logstring: str, logfun: Optional[Callable] = None,
deprecated_msg: Optional[str] = None) -> None:
"""
:param config: Configuration dictionary
:param argname: Argumentname in self.args - will be copied to config dict.
:param logstring: Logging String
:param logfun: logfun is applied to the configuration entry before passing
that entry to the log string using .format().
sample: logfun=len (prints the length of the found
configuration instead of the content)
"""
if argname in self.args and getattr(self.args, argname):
config.update({argname: getattr(self.args, argname)})
if logfun:
logger.info(logstring.format(logfun(config[argname])))
else:
logger.info(logstring.format(config[argname]))
if deprecated_msg:
warnings.warn(f"DEPRECATED: {deprecated_msg}", DeprecationWarning)

20
freqtrade/configuration/create_datadir.py Normal file
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@ -0,0 +1,20 @@
import logging
from typing import Any, Dict, Optional
from pathlib import Path
logger = logging.getLogger(__name__)
def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str:
folder = Path(datadir) if datadir else Path('user_data/data')
if not datadir:
# set datadir
exchange_name = config.get('exchange', {}).get('name').lower()
folder = folder.joinpath(exchange_name)
if not folder.is_dir():
folder.mkdir(parents=True)
logger.info(f'Created data directory: {datadir}')
return str(folder)

53
freqtrade/configuration/json_schema.py Normal file
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@ -0,0 +1,53 @@
import logging
from typing import Any, Dict
from jsonschema import Draft4Validator, validators
from jsonschema.exceptions import ValidationError, best_match
from freqtrade import constants
logger = logging.getLogger(__name__)
def _extend_validator(validator_class):
"""
Extended validator for the Freqtrade configuration JSON Schema.
Currently it only handles defaults for subschemas.
"""
validate_properties = validator_class.VALIDATORS['properties']
def set_defaults(validator, properties, instance, schema):
for prop, subschema in properties.items():
if 'default' in subschema:
instance.setdefault(prop, subschema['default'])
for error in validate_properties(
validator, properties, instance, schema,
):
yield error
return validators.extend(
validator_class, {'properties': set_defaults}
)
FreqtradeValidator = _extend_validator(Draft4Validator)
def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
"""
Validate the configuration follow the Config Schema
:param conf: Config in JSON format
:return: Returns the config if valid, otherwise throw an exception
"""
try:
FreqtradeValidator(constants.CONF_SCHEMA).validate(conf)
return conf
except ValidationError as e:
logger.critical(
f"Invalid configuration. See config.json.example. Reason: {e}"
)
raise ValidationError(
best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
)

30
freqtrade/configuration/load_config.py Normal file
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@ -0,0 +1,30 @@
"""
This module contain functions to load the configuration file
"""
import json
import logging
import sys
from typing import Any, Dict
from freqtrade import OperationalException
logger = logging.getLogger(__name__)
def load_config_file(path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path
:param path: path as str
:return: configuration as dictionary
"""
try:
# Read config from stdin if requested in the options
with open(path) if path != '-' else sys.stdin as file:
config = json.load(file)
except FileNotFoundError:
raise OperationalException(
f'Config file "{path}" not found!'
' Please create a config file or check whether it exists.')
return config

53
freqtrade/constants.py
View File

@ -4,13 +4,15 @@
bot constants
"""
DEFAULT_CONFIG = 'config.json'
DEFAULT_EXCHANGE = 'bittrex'
DYNAMIC_WHITELIST = 20 # pairs
PROCESS_THROTTLE_SECS = 5 # sec
TICKER_INTERVAL = 5 # min
DEFAULT_TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
DEFAULT_HYPEROPT = 'DefaultHyperOpts'
DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite://'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
@ -21,23 +23,13 @@ ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
DRY_RUN_WALLET = 999.9
DEFAULT_DOWNLOAD_TICKER_INTERVALS = '1m 5m'
TICKER_INTERVAL_MINUTES = {
'1m': 1,
'3m': 3,
'5m': 5,
'15m': 15,
'30m': 30,
'1h': 60,
'2h': 120,
'4h': 240,
'6h': 360,
'8h': 480,
'12h': 720,
'1d': 1440,
'3d': 4320,
'1w': 10080,
}
TICKER_INTERVALS = [
'1m', '3m', '5m', '15m', '30m',
'1h', '2h', '4h', '6h', '8h', '12h',
'1d', '3d', '1w',
]
SUPPORTED_FIAT = [
"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
@ -52,7 +44,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': list(TICKER_INTERVAL_MINUTES.keys())},
'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'stake_amount': {
"type": ["number", "string"],
@ -167,6 +159,21 @@ CONF_SCHEMA = {
'webhookstatus': {'type': 'object'},
},
},
'api_server': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'listen_ip_address': {'format': 'ipv4'},
'listen_port': {
'type': 'integer',
"minimum": 1024,
"maximum": 65535
},
'username': {'type': 'string'},
'password': {'type': 'string'},
},
'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password']
},
'db_url': {'type': 'string'},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'forcebuy_enable': {'type': 'boolean'},
@ -184,10 +191,10 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'sandbox': {'type': 'boolean'},
'key': {'type': 'string'},
'secret': {'type': 'string'},
'password': {'type': 'string'},
'sandbox': {'type': 'boolean', 'default': False},
'key': {'type': 'string', 'default': ''},
'secret': {'type': 'string', 'default': ''},
'password': {'type': 'string', 'default': ''},
'uid': {'type': 'string'},
'pair_whitelist': {
'type': 'array',
@ -210,7 +217,7 @@ CONF_SCHEMA = {
'ccxt_config': {'type': 'object'},
'ccxt_async_config': {'type': 'object'}
},
'required': ['name', 'key', 'secret', 'pair_whitelist']
'required': ['name', 'pair_whitelist']
},
'edge': {
'type': 'object',

104
freqtrade/data/btanalysis.py
View File

@ -1,12 +1,19 @@
"""
Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict
import numpy as np
import pandas as pd
import pytz
from freqtrade import persistence
from freqtrade.misc import json_load
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
@ -17,13 +24,13 @@ def load_backtest_data(filename) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
:return a dataframe with the analysis results
:return: a dataframe with the analysis results
"""
if isinstance(filename, str):
filename = Path(filename)
if not filename.is_file():
raise ValueError("File {filename} does not exist.")
raise ValueError(f"File {filename} does not exist.")
with filename.open() as file:
data = json_load(file)
@ -60,8 +67,99 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:
"""
Load trades from a DB (using dburl)
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
:return: Dataframe containing Trades
"""
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
persistence.init(db_url, clean_open_orders=False)
columns = ["pair", "open_time", "close_time", "profit", "profitperc",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=pytz.UTC),
t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
t.calc_profit(), t.calc_profit_percent(),
t.open_rate, t.close_rate, t.amount,
(t.close_date.timestamp() - t.open_date.timestamp()
if t.close_date else None),
t.sell_reason,
t.fee_open, t.fee_close,
t.open_rate_requested,
t.close_rate_requested,
t.stake_amount,
t.max_rate,
t.min_rate,
t.id, t.exchange,
t.stop_loss, t.initial_stop_loss,
t.strategy, t.ticker_interval
)
for t in Trade.query.all()],
columns=columns)
return trades
def load_trades(config) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
* loads data from backtestfile (using `exportfilename`)
"""
if config["trade_source"] == "DB":
return load_trades_from_db(config["db_url"])
elif config["trade_source"] == "file":
return load_backtest_data(Path(config["exportfilename"]))
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
"""
Compare trades and backtested pair DataFrames to get trades performed on backtested period
:return: the DataFrame of a trades of period
"""
trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
(trades['close_time'] <= dataframe.iloc[-1]['date'])]
return trades
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
"""
Combine multiple dataframes "column"
:param tickers: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return: DataFrame with the column renamed to the dict key, and a column
named mean, containing the mean of all pairs.
"""
df_comb = pd.concat([tickers[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in tickers], axis=1)
df_comb['mean'] = df_comb.mean(axis=1)
return df_comb
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:return: Returns df with one additional column, col_name, containing the cumulative profit.
"""
df[col_name] = trades.set_index('close_time')['profitperc'].cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous
df[col_name] = df[col_name].ffill()
return df

30
freqtrade/data/converter.py
View File

@ -2,22 +2,25 @@
Functions to convert data from one format to another
"""
import logging
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import TICKER_INTERVAL_MINUTES
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, ticker_interval: str,
fill_missing: bool = True) -> DataFrame:
def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
:param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
(see ohlcv_fill_up_missing_data for details)
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
@ -43,21 +46,25 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: str,
'close': 'last',
'volume': 'max',
})
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
logger.debug('Dropping last candle')
# eliminate partial candle
if drop_incomplete:
frame.drop(frame.tail(1).index, inplace=True)
logger.debug('Dropping last candle')
if fill_missing:
return ohlcv_fill_up_missing_data(frame, ticker_interval)
return ohlcv_fill_up_missing_data(frame, ticker_interval, pair)
else:
return frame
def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str) -> DataFrame:
def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair: str) -> DataFrame:
"""
Fills up missing data with 0 volume rows,
using the previous close as price for "open", "high" "low" and "close", volume is set to 0
"""
from freqtrade.exchange import timeframe_to_minutes
ohlc_dict = {
'open': 'first',
'high': 'max',
@ -65,9 +72,9 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str) -> Da
'close': 'last',
'volume': 'sum'
}
tick_mins = TICKER_INTERVAL_MINUTES[ticker_interval]
ticker_minutes = timeframe_to_minutes(ticker_interval)
# Resample to create "NAN" values
df = dataframe.resample(f'{tick_mins}min', on='date').agg(ohlc_dict)
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
# Forwardfill close for missing columns
df['close'] = df['close'].fillna(method='ffill')
@ -78,7 +85,10 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str) -> Da
'low': df['close'],
})
df.reset_index(inplace=True)
logger.debug(f"Missing data fillup: before: {len(dataframe)} - after: {len(df)}")
len_before = len(dataframe)
len_after = len(df)
if len_before != len_after:
logger.info(f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}")
return df

22
freqtrade/data/dataprovider.py
View File

@ -17,7 +17,7 @@ from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
class DataProvider(object):
class DataProvider():
def __init__(self, config: dict, exchange: Exchange) -> None:
self._config = config
@ -37,23 +37,23 @@ class DataProvider(object):
@property
def available_pairs(self) -> List[Tuple[str, str]]:
"""
Return a list of tuples containing pair, tick_interval for which data is currently cached.
Return a list of tuples containing pair, ticker_interval for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, tick_interval: str = None, copy: bool = True) -> DataFrame:
def ohlcv(self, pair: str, ticker_interval: str = None, copy: bool = True) -> DataFrame:
"""
get ohlcv data for the given pair as DataFrame
Please check `available_pairs` to verify which pairs are currently cached.
:param pair: pair to get the data for
:param tick_interval: ticker_interval to get pair for
:param ticker_interval: ticker_interval to get pair for
:param copy: copy dataframe before returning.
Use false only for RO operations (where the dataframe is not modified)
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
if tick_interval:
pairtick = (pair, tick_interval)
if ticker_interval:
pairtick = (pair, ticker_interval)
else:
pairtick = (pair, self._config['ticker_interval'])
@ -65,7 +65,7 @@ class DataProvider(object):
"""
get stored historic ohlcv data
:param pair: pair to get the data for
:param tick_interval: ticker_interval to get pair for
:param ticker_interval: ticker_interval to get pair for
"""
return load_pair_history(pair=pair,
ticker_interval=ticker_interval,
@ -81,12 +81,14 @@ class DataProvider(object):
# TODO: Implement me
pass
def orderbook(self, pair: str, max: int):
def orderbook(self, pair: str, maximum: int):
"""
return latest orderbook data
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
# TODO: Implement me
pass
return self._exchange.get_order_book(pair, maximum)
@property
def runmode(self) -> RunMode:

171
freqtrade/data/history.py
View File

@ -1,21 +1,24 @@
"""
Handle historic data (ohlcv).
includes:
Includes:
* load data for a pair (or a list of pairs) from disk
* download data from exchange and store to disk
"""
import logging
import operator
from datetime import datetime
from pathlib import Path
from typing import Optional, List, Dict, Tuple, Any
from typing import Any, Dict, List, Optional, Tuple
import arrow
from pandas import DataFrame
from freqtrade import misc, constants, OperationalException
from freqtrade import OperationalException, misc
from freqtrade.configuration import TimeRange
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exchange import Exchange
from freqtrade.arguments import TimeRange
from freqtrade.exchange import Exchange, timeframe_to_minutes
logger = logging.getLogger(__name__)
@ -60,14 +63,10 @@ def load_tickerdata_file(
timerange: Optional[TimeRange] = None) -> Optional[list]:
"""
Load a pair from file, either .json.gz or .json
:return tickerlist or None if unsuccesful
:return: tickerlist or None if unsuccesful
"""
path = make_testdata_path(datadir)
pair_s = pair.replace('/', '_')
file = path.joinpath(f'{pair_s}-{ticker_interval}.json')
pairdata = misc.file_load_json(file)
filename = pair_data_filename(datadir, pair, ticker_interval)
pairdata = misc.file_load_json(filename)
if not pairdata:
return None
@ -82,24 +81,29 @@ def load_pair_history(pair: str,
timerange: TimeRange = TimeRange(None, None, 0, 0),
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
fill_up_missing: bool = True
fill_up_missing: bool = True,
drop_incomplete: bool = True
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
:param pair: Pair to load data for
:param ticker_interval: Ticker-interval (e.g. "5m")
:param datadir: Path to the data storage location.
:param timerange: Limit data to be loaded to this timerange
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
:param exchange: Exchange object (needed when using "refresh_pairs")
:param fill_up_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:return: DataFrame with ohlcv data
"""
# If the user force the refresh of pairs
# The user forced the refresh of pairs
if refresh_pairs:
if not exchange:
raise OperationalException("Exchange needs to be initialized when "
"calling load_data with refresh_pairs=True")
logger.info('Download data for pair and store them in %s', datadir)
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
tick_interval=ticker_interval,
ticker_interval=ticker_interval,
timerange=timerange)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
@ -112,11 +116,15 @@ def load_pair_history(pair: str,
logger.warning('Missing data at end for pair %s, data ends at %s',
pair,
arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
return parse_ticker_dataframe(pairdata, ticker_interval, fill_up_missing)
return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning('No data for pair: "%s", Interval: %s. '
'Use --refresh-pairs-cached to download the data',
pair, ticker_interval)
logger.warning(
f'No history data for pair: "{pair}", interval: {ticker_interval}. '
'Use --refresh-pairs-cached option or download_backtest_data.py '
'script to download the data'
)
return None
@ -126,21 +134,34 @@ def load_data(datadir: Optional[Path],
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
timerange: TimeRange = TimeRange(None, None, 0, 0),
fill_up_missing: bool = True) -> Dict[str, DataFrame]:
fill_up_missing: bool = True,
live: bool = False
) -> Dict[str, DataFrame]:
"""
Loads ticker history data for a list of pairs the given parameters
:return: dict(<pair>:<tickerlist>)
"""
result = {}
result: Dict[str, DataFrame] = {}
if live:
if exchange:
logger.info('Live: Downloading data for all defined pairs ...')
exchange.refresh_latest_ohlcv([(pair, ticker_interval) for pair in pairs])
result = {key[0]: value for key, value in exchange._klines.items() if value is not None}
else:
raise OperationalException(
"Exchange needs to be initialized when using live data."
)
else:
logger.info('Using local backtesting data ...')
for pair in pairs:
hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange,
fill_up_missing=fill_up_missing)
if hist is not None:
result[pair] = hist
for pair in pairs:
hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange,
fill_up_missing=fill_up_missing)
if hist is not None:
result[pair] = hist
return result
@ -149,7 +170,14 @@ def make_testdata_path(datadir: Optional[Path]) -> Path:
return datadir or (Path(__file__).parent.parent / "tests" / "testdata").resolve()
def load_cached_data_for_updating(filename: Path, tick_interval: str,
def pair_data_filename(datadir: Optional[Path], pair: str, ticker_interval: str) -> Path:
path = make_testdata_path(datadir)
pair_s = pair.replace("/", "_")
filename = path.joinpath(f'{pair_s}-{ticker_interval}.json')
return filename
def load_cached_data_for_updating(filename: Path, ticker_interval: str,
timerange: Optional[TimeRange]) -> Tuple[List[Any],
Optional[int]]:
"""
@ -163,7 +191,7 @@ def load_cached_data_for_updating(filename: Path, tick_interval: str,
if timerange.starttype == 'date':
since_ms = timerange.startts * 1000
elif timerange.stoptype == 'line':
num_minutes = timerange.stopts * constants.TICKER_INTERVAL_MINUTES[tick_interval]
num_minutes = timerange.stopts * timeframe_to_minutes(ticker_interval)
since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
# read the cached file
@ -188,9 +216,9 @@ def load_cached_data_for_updating(filename: Path, tick_interval: str,
def download_pair_history(datadir: Optional[Path],
exchange: Exchange,
exchange: Optional[Exchange],
pair: str,
tick_interval: str = '5m',
ticker_interval: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
"""
Download the latest ticker intervals from the exchange for the pair passed in parameters
@ -199,26 +227,32 @@ def download_pair_history(datadir: Optional[Path],
the full data will be redownloaded
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param tick_interval: ticker interval
:param ticker_interval: ticker interval
:param timerange: range of time to download
:return: bool with success state
"""
if not exchange:
raise OperationalException(
"Exchange needs to be initialized when downloading pair history data"
)
try:
path = make_testdata_path(datadir)
filepair = pair.replace("/", "_")
filename = path.joinpath(f'{filepair}-{tick_interval}.json')
filename = pair_data_filename(datadir, pair, ticker_interval)
logger.info('Download the pair: "%s", Interval: %s', pair, tick_interval)
logger.info(
f'Download history data for pair: "{pair}", interval: {ticker_interval} '
f'and store in {datadir}.'
)
data, since_ms = load_cached_data_for_updating(filename, tick_interval, timerange)
data, since_ms = load_cached_data_for_updating(filename, ticker_interval, timerange)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_history(pair=pair, tick_interval=tick_interval,
new_data = exchange.get_history(pair=pair, ticker_interval=ticker_interval,
since_ms=since_ms if since_ms
else
int(arrow.utcnow().shift(days=-30).float_timestamp) * 1000)
@ -229,7 +263,46 @@ def download_pair_history(datadir: Optional[Path],
misc.file_dump_json(filename, data)
return True
except BaseException:
logger.info('Failed to download the pair: "%s", Interval: %s',
pair, tick_interval)
except Exception as e:
logger.error(
f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
f'Error: {e}'
)
return False
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
max_date: datetime, ticker_interval_mins: int) -> bool:
"""
Validates preprocessed backtesting data for missing values and shows warnings about it that.
:param data: preprocessed backtesting data (as DataFrame)
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param ticker_interval_mins: ticker interval in minutes
"""
# total difference in minutes / interval-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
found_missing = False
dflen = len(data)
if dflen < expected_frames:
found_missing = True
logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
pair, expected_frames, dflen, expected_frames - dflen)
return found_missing

36
freqtrade/edge/__init__.py
View File

@ -10,10 +10,8 @@ import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade import constants, OperationalException
from freqtrade.arguments import Arguments
from freqtrade.arguments import TimeRange
from freqtrade.configuration import Arguments, TimeRange
from freqtrade.data import history
from freqtrade.optimize import get_timeframe
from freqtrade.strategy.interface import SellType
@ -47,11 +45,6 @@ class Edge():
self.config = config
self.exchange = exchange
self.strategy = strategy
self.ticker_interval = self.strategy.ticker_interval
self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
self.get_timeframe = get_timeframe
self.advise_sell = self.strategy.advise_sell
self.advise_buy = self.strategy.advise_buy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
@ -102,7 +95,7 @@ class Edge():
data = history.load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
ticker_interval=self.strategy.ticker_interval,
refresh_pairs=self._refresh_pairs,
exchange=self.exchange,
timerange=self._timerange
@ -114,10 +107,10 @@ class Edge():
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.tickerdata_to_dataframe(data)
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
min_date, max_date = history.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
@ -132,13 +125,14 @@ class Edge():
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
ticker_data = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
logger.info("No trades found.")
return False
# Fill missing, calculable columns, profit, duration , abs etc.
@ -203,6 +197,22 @@ class Edge():
return self._final_pairs
def accepted_pairs(self) -> list:
"""
return a list of accepted pairs along with their winrate, expectancy and stoploss
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
final.append({
'Pair': pair,
'Winrate': info.winrate,
'Expectancy': info.expectancy,
'Stoploss': info.stoploss,
})
return final
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
"""
The result frame contains a number of columns that are calculable

7
freqtrade/exchange/__init__.py
View File

@ -1,3 +1,10 @@
from freqtrade.exchange.exchange import Exchange # noqa: F401
from freqtrade.exchange.exchange import (is_exchange_bad, # noqa: F401
is_exchange_available,
is_exchange_officially_supported,
available_exchanges)
from freqtrade.exchange.exchange import (timeframe_to_seconds, # noqa: F401
timeframe_to_minutes,
timeframe_to_msecs)
from freqtrade.exchange.kraken import Kraken # noqa: F401
from freqtrade.exchange.binance import Binance # noqa: F401

1
freqtrade/exchange/binance.py
View File

@ -11,6 +11,7 @@ class Binance(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"order_time_in_force": ['gtc', 'fok', 'ioc'],
}
def get_order_book(self, pair: str, limit: int = 100) -> dict:

277
freqtrade/exchange/exchange.py
View File

@ -1,33 +1,32 @@
# pragma pylint: disable=W0603
""" Cryptocurrency Exchanges support """
import logging
"""
Cryptocurrency Exchanges support
"""
import asyncio
import inspect
from random import randint
from typing import List, Dict, Tuple, Any, Optional
import logging
from copy import deepcopy
from datetime import datetime
from math import floor, ceil
from math import ceil, floor
from random import randint
from typing import Any, Dict, List, Optional, Tuple
import arrow
import asyncio
import ccxt
import ccxt.async_support as ccxt_async
from pandas import DataFrame
from freqtrade import constants, OperationalException, DependencyException, TemporaryError
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError, constants)
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.misc import deep_merge_dicts
logger = logging.getLogger(__name__)
API_RETRY_COUNT = 4
# Urls to exchange markets, insert quote and base with .format()
_EXCHANGE_URLS = {
ccxt.bittrex.__name__: '/Market/Index?MarketName={quote}-{base}',
ccxt.binance.__name__: '/tradeDetail.html?symbol={base}_{quote}',
}
def retrier_async(f):
async def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
@ -70,11 +69,15 @@ class Exchange(object):
_params: Dict = {}
# Dict to specify which options each exchange implements
# TODO: this should be merged with attributes from subclasses
# To avoid having to copy/paste this to all subclasses.
_ft_has = {
# This defines defaults, which can be selectively overridden by subclasses using _ft_has
# or by specifying them in the configuration.
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["gtc"],
"ohlcv_candle_limit": 500,
"ohlcv_partial_candle": True,
}
_ft_has: Dict = {}
def __init__(self, config: dict) -> None:
"""
@ -82,6 +85,9 @@ class Exchange(object):
it does basic validation whether the specified exchange and pairs are valid.
:return: None
"""
self._api: ccxt.Exchange = None
self._api_async: ccxt_async.Exchange = None
self._config.update(config)
self._cached_ticker: Dict[str, Any] = {}
@ -101,9 +107,22 @@ class Exchange(object):
logger.info('Instance is running with dry_run enabled')
exchange_config = config['exchange']
self._api: ccxt.Exchange = self._init_ccxt(
# Deep merge ft_has with default ft_has options
self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
if exchange_config.get("_ft_has_params"):
self._ft_has = deep_merge_dicts(exchange_config.get("_ft_has_params"),
self._ft_has)
logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
# Assign this directly for easy access
self._ohlcv_candle_limit = self._ft_has['ohlcv_candle_limit']
self._ohlcv_partial_candle = self._ft_has['ohlcv_partial_candle']
# Initialize ccxt objects
self._api = self._init_ccxt(
exchange_config, ccxt_kwargs=exchange_config.get('ccxt_config'))
self._api_async: ccxt_async.Exchange = self._init_ccxt(
self._api_async = self._init_ccxt(
exchange_config, ccxt_async, ccxt_kwargs=exchange_config.get('ccxt_async_config'))
logger.info('Using Exchange "%s"', self.name)
@ -140,15 +159,14 @@ class Exchange(object):
# Find matching class for the given exchange name
name = exchange_config['name']
if name not in ccxt_module.exchanges:
raise OperationalException(f'Exchange {name} is not supported')
if not is_exchange_available(name, ccxt_module):
raise OperationalException(f'Exchange {name} is not supported by ccxt')
ex_config = {
'apiKey': exchange_config.get('key'),
'secret': exchange_config.get('secret'),
'password': exchange_config.get('password'),
'uid': exchange_config.get('uid', ''),
'enableRateLimit': exchange_config.get('ccxt_rate_limit', True)
}
if ccxt_kwargs:
logger.info('Applying additional ccxt config: %s', ccxt_kwargs)
@ -156,8 +174,10 @@ class Exchange(object):
try:
api = getattr(ccxt_module, name.lower())(ex_config)
except (KeyError, AttributeError):
raise OperationalException(f'Exchange {name} is not supported')
except (KeyError, AttributeError) as e:
raise OperationalException(f'Exchange {name} is not supported') from e
except ccxt.BaseError as e:
raise OperationalException(f"Initialization of ccxt failed. Reason: {e}") from e
self.set_sandbox(api, exchange_config, name)
@ -224,8 +244,11 @@ class Exchange(object):
> arrow.utcnow().timestamp):
return None
logger.debug("Performing scheduled market reload..")
self._api.load_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().timestamp
try:
self._api.load_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().timestamp
except ccxt.BaseError:
logger.exception("Could not reload markets.")
def validate_pairs(self, pairs: List[str]) -> None:
"""
@ -237,24 +260,44 @@ class Exchange(object):
if not self.markets:
logger.warning('Unable to validate pairs (assuming they are correct).')
# return
return
stake_cur = self._config['stake_currency']
for pair in pairs:
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
# TODO: add a support for having coins in BTC/USDT format
if not pair.endswith(stake_cur):
raise OperationalException(
f'Pair {pair} not compatible with stake_currency: {stake_cur}')
if self.markets and pair not in self.markets:
raise OperationalException(
f'Pair {pair} is not available on {self.name}. '
f'Please remove {pair} from your whitelist.')
elif self.markets[pair].get('info', {}).get('IsRestricted', False):
# Warn users about restricted pairs in whitelist.
# We cannot determine reliably if Users are affected.
logger.warning(f"Pair {pair} is restricted for some users on this exchange."
f"Please check if you are impacted by this restriction "
f"on the exchange and eventually remove {pair} from your whitelist.")
def get_valid_pair_combination(self, curr_1, curr_2) -> str:
"""
Get valid pair combination of curr_1 and curr_2 by trying both combinations.
"""
for pair in [f"{curr_1}/{curr_2}", f"{curr_2}/{curr_1}"]:
if pair in self.markets and self.markets[pair].get('active'):
return pair
raise DependencyException(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
def validate_timeframes(self, timeframe: List[str]) -> None:
"""
Checks if ticker interval from config is a supported timeframe on the exchange
"""
if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
# If timeframes attribute is missing (or is None), the exchange probably
# has no fetchOHLCV method.
# Therefore we also show that.
raise OperationalException(
f"The ccxt library does not provide the list of timeframes "
f"for the exchange \"{self.name}\" and this exchange "
f"is therefore not supported. ccxt fetchOHLCV: {self.exchange_has('fetchOHLCV')}")
timeframes = self._api.timeframes
if timeframe not in timeframes:
raise OperationalException(
@ -279,10 +322,10 @@ class Exchange(object):
"""
Checks if order time in force configured in strategy/config are supported
"""
if any(v != 'gtc' for k, v in order_time_in_force.items()):
if self.name != 'Binance':
raise OperationalException(
f'Time in force policies are not supporetd for {self.name} yet.')
if any(v not in self._ft_has["order_time_in_force"]
for k, v in order_time_in_force.items()):
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
def exchange_has(self, endpoint: str) -> bool:
"""
@ -350,7 +393,9 @@ class Exchange(object):
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
rate = self.symbol_price_prec(pair, rate) if ordertype != 'market' else None
needs_price = (ordertype != 'market'
or self._api.options.get("createMarketBuyOrderRequiresPrice", False))
rate = self.symbol_price_prec(pair, rate) if needs_price else None
return self._api.create_order(pair, ordertype, side,
amount, rate, params)
@ -358,18 +403,18 @@ class Exchange(object):
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} {side} order on market {pair}.'
f'Tried to {side} amount {amount} at rate {rate} (total {rate*amount}).'
f'Message: {e}')
f'Tried to {side} amount {amount} at rate {rate} (total {rate * amount}).'
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise DependencyException(
f'Could not create {ordertype} {side} order on market {pair}.'
f'Tried to {side} amount {amount} at rate {rate} (total {rate*amount}).'
f'Message: {e}')
f'Tried to {side} amount {amount} at rate {rate} (total {rate * amount}).'
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}')
f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
def buy(self, pair: str, ordertype: str, amount: float,
rate: float, time_in_force) -> Dict:
@ -454,9 +499,9 @@ class Exchange(object):
return balances
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get balance due to {e.__class__.__name__}. Message: {e}')
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
@retrier
def get_tickers(self) -> Dict:
@ -465,18 +510,18 @@ class Exchange(object):
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch.'
f'Message: {e}')
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load tickers due to {e.__class__.__name__}. Message: {e}')
f'Could not load tickers due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
@retrier
def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
if refresh or pair not in self._cached_ticker.keys():
try:
if pair not in self._api.markets:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
raise DependencyException(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
try:
@ -489,33 +534,35 @@ class Exchange(object):
return data
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}')
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
else:
logger.info("returning cached ticker-data for %s", pair)
return self._cached_ticker[pair]
def get_history(self, pair: str, tick_interval: str,
def get_history(self, pair: str, ticker_interval: str,
since_ms: int) -> List:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_history(pair=pair, tick_interval=tick_interval,
self._async_get_history(pair=pair, ticker_interval=ticker_interval,
since_ms=since_ms))
async def _async_get_history(self, pair: str,
tick_interval: str,
ticker_interval: str,
since_ms: int) -> List:
# Assume exchange returns 500 candles
_LIMIT = 500
one_call = constants.TICKER_INTERVAL_MINUTES[tick_interval] * 60 * _LIMIT * 1000
logger.debug("one_call: %s", one_call)
one_call = timeframe_to_msecs(ticker_interval) * self._ohlcv_candle_limit
logger.debug(
"one_call: %s msecs (%s)",
one_call,
arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
)
input_coroutines = [self._async_get_candle_history(
pair, tick_interval, since) for since in
pair, ticker_interval, since) for since in
range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
@ -544,7 +591,10 @@ class Exchange(object):
or self._now_is_time_to_refresh(pair, ticker_interval)):
input_coroutines.append(self._async_get_candle_history(pair, ticker_interval))
else:
logger.debug("Using cached ohlcv data for %s, %s ...", pair, ticker_interval)
logger.debug(
"Using cached ohlcv data for pair %s, interval %s ...",
pair, ticker_interval
)
tickers = asyncio.get_event_loop().run_until_complete(
asyncio.gather(*input_coroutines, return_exceptions=True))
@ -555,35 +605,40 @@ class Exchange(object):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
pair = res[0]
tick_interval = res[1]
ticker_interval = res[1]
ticks = res[2]
# keeping last candle time as last refreshed time of the pair
if ticks:
self._pairs_last_refresh_time[(pair, tick_interval)] = ticks[-1][0] // 1000
self._pairs_last_refresh_time[(pair, ticker_interval)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, tick_interval)] = parse_ticker_dataframe(
ticks, tick_interval, fill_missing=True)
self._klines[(pair, ticker_interval)] = parse_ticker_dataframe(
ticks, ticker_interval, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
def _now_is_time_to_refresh(self, pair: str, ticker_interval: str) -> bool:
# Calculating ticker interval in seconds
interval_in_sec = constants.TICKER_INTERVAL_MINUTES[ticker_interval] * 60
interval_in_sec = timeframe_to_seconds(ticker_interval)
return not ((self._pairs_last_refresh_time.get((pair, ticker_interval), 0)
+ interval_in_sec) >= arrow.utcnow().timestamp)
@retrier_async
async def _async_get_candle_history(self, pair: str, tick_interval: str,
async def _async_get_candle_history(self, pair: str, ticker_interval: str,
since_ms: Optional[int] = None) -> Tuple[str, str, List]:
"""
Asyncronously gets candle histories using fetch_ohlcv
returns tuple: (pair, tick_interval, ohlcv_list)
returns tuple: (pair, ticker_interval, ohlcv_list)
"""
try:
# fetch ohlcv asynchronously
logger.debug("fetching %s, %s since %s ...", pair, tick_interval, since_ms)
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
pair, ticker_interval, since_ms, s
)
data = await self._api_async.fetch_ohlcv(pair, timeframe=tick_interval,
data = await self._api_async.fetch_ohlcv(pair, timeframe=ticker_interval,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
@ -595,19 +650,19 @@ class Exchange(object):
data = sorted(data, key=lambda x: x[0])
except IndexError:
logger.exception("Error loading %s. Result was %s.", pair, data)
return pair, tick_interval, []
logger.debug("done fetching %s, %s ...", pair, tick_interval)
return pair, tick_interval, data
return pair, ticker_interval, []
logger.debug("Done fetching pair %s, interval %s ...", pair, ticker_interval)
return pair, ticker_interval, data
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical candlestick data.'
f'Message: {e}')
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker history due to {e.__class__.__name__}. Message: {e}')
raise TemporaryError(f'Could not load ticker history due to {e.__class__.__name__}. '
f'Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data. Msg: {e}')
raise OperationalException(f'Could not fetch ticker data. Msg: {e}') from e
@retrier
def cancel_order(self, order_id: str, pair: str) -> None:
@ -617,13 +672,13 @@ class Exchange(object):
try:
return self._api.cancel_order(order_id, pair)
except ccxt.InvalidOrder as e:
raise DependencyException(
f'Could not cancel order. Message: {e}')
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}')
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
@retrier
def get_order(self, order_id: str, pair: str) -> Dict:
@ -633,13 +688,13 @@ class Exchange(object):
try:
return self._api.fetch_order(order_id, pair)
except ccxt.InvalidOrder as e:
raise DependencyException(
f'Could not get order. Message: {e}')
raise InvalidOrderException(
f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}')
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
@retrier
def get_order_book(self, pair: str, limit: int = 100) -> dict:
@ -655,12 +710,12 @@ class Exchange(object):
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching order book.'
f'Message: {e}')
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order book due to {e.__class__.__name__}. Message: {e}')
f'Could not get order book due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
@retrier
def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
@ -670,16 +725,17 @@ class Exchange(object):
return []
try:
# Allow 5s offset to catch slight time offsets (discovered in #1185)
my_trades = self._api.fetch_my_trades(pair, since.timestamp() - 5)
# since needs to be int in milliseconds
my_trades = self._api.fetch_my_trades(pair, int((since.timestamp() - 5) * 1000))
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
return matched_trades
except ccxt.NetworkError as e:
raise TemporaryError(
f'Could not get trades due to networking error. Message: {e}')
f'Could not get trades due to networking error. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
@retrier
def get_fee(self, symbol='ETH/BTC', type='', side='', amount=1,
@ -693,6 +749,45 @@ class Exchange(object):
price=price, takerOrMaker=taker_or_maker)['rate']
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get fee info due to {e.__class__.__name__}. Message: {e}')
f'Could not get fee info due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e)
raise OperationalException(e) from e
def is_exchange_bad(exchange: str) -> bool:
return exchange in ['bitmex', 'bitstamp']
def is_exchange_available(exchange: str, ccxt_module=None) -> bool:
return exchange in available_exchanges(ccxt_module)
def is_exchange_officially_supported(exchange: str) -> bool:
return exchange in ['bittrex', 'binance']
def available_exchanges(ccxt_module=None) -> List[str]:
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def timeframe_to_seconds(ticker_interval: str) -> int:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval)
def timeframe_to_minutes(ticker_interval: str) -> int:
"""
Same as above, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval) // 60
def timeframe_to_msecs(ticker_interval: str) -> int:
"""
Same as above, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval) * 1000

411
freqtrade/freqtradebot.py
View File

@ -4,20 +4,19 @@ Freqtrade is the main module of this bot. It contains the class Freqtrade()
import copy
import logging
import time
import traceback
from datetime import datetime
from typing import Any, Callable, Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional, Tuple
import arrow
from requests.exceptions import RequestException
import sdnotify
from freqtrade import (DependencyException, OperationalException,
TemporaryError, __version__, constants, persistence)
from freqtrade import (DependencyException, OperationalException, InvalidOrderException,
__version__, constants, persistence)
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.resolvers import ExchangeResolver, StrategyResolver, PairListResolver
@ -42,26 +41,19 @@ class FreqtradeBot(object):
to get the config dict.
"""
logger.info(
'Starting freqtrade %s',
__version__,
)
logger.info('Starting freqtrade %s', __version__)
# Init bot states
# Init bot state
self.state = State.STOPPED
# Init objects
self.config = config
self._sd_notify = sdnotify.SystemdNotifier() if \
self.config.get('internals', {}).get('sd_notify', False) else None
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
exchange_name = self.config.get('exchange', {}).get('name', 'bittrex').title()
self.exchange = ExchangeResolver(exchange_name, self.config).exchange
self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
self.wallets = Wallets(self.config, self.exchange)
self.dataprovider = DataProvider(self.config, self.exchange)
@ -79,29 +71,13 @@ class FreqtradeBot(object):
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist']
self._init_modules()
# Tell the systemd that we completed initialization phase
if self._sd_notify:
logger.debug("sd_notify: READY=1")
self._sd_notify.notify("READY=1")
persistence.init(self.config.get('db_url', None),
clean_open_orders=self.config.get('dry_run', False))
def _init_modules(self) -> None:
"""
Initializes all modules and updates the config
:return: None
"""
# Initialize all modules
persistence.init(self.config)
# Set initial application state
# Set initial bot state from config
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
self.state = State[initial_state.upper()] if initial_state else State.STOPPED
def cleanup(self) -> None:
"""
@ -113,130 +89,60 @@ class FreqtradeBot(object):
self.rpc.cleanup()
persistence.cleanup()
def stopping(self) -> None:
# Tell systemd that we are exiting now
if self._sd_notify:
logger.debug("sd_notify: STOPPING=1")
self._sd_notify.notify("STOPPING=1")
def reconfigure(self) -> None:
# Tell systemd that we initiated reconfiguring
if self._sd_notify:
logger.debug("sd_notify: RELOADING=1")
self._sd_notify.notify("RELOADING=1")
def worker(self, old_state: State = None) -> State:
def startup(self) -> None:
"""
Trading routine that must be run at each loop
:param old_state: the previous service state from the previous call
:return: current service state
Called on startup and after reloading the bot - triggers notifications and
performs startup tasks
"""
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'{state.name.lower()}'
})
logger.info('Changing state to: %s', state.name)
if state == State.RUNNING:
self.rpc.startup_messages(self.config, self.pairlists)
self.rpc.startup_messages(self.config, self.pairlists)
if not self.edge:
# Adjust stoploss if it was changed
Trade.stoploss_reinitialization(self.strategy.stoploss)
throttle_secs = self.config.get('internals', {}).get(
'process_throttle_secs',
constants.PROCESS_THROTTLE_SECS
)
if state == State.STOPPED:
# Ping systemd watchdog before sleeping in the stopped state
if self._sd_notify:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: STOPPED.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
time.sleep(throttle_secs)
elif state == State.RUNNING:
# Ping systemd watchdog before throttling
if self._sd_notify:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: RUNNING.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
self._throttle(func=self._process, min_secs=throttle_secs)
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
"""
start = time.time()
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
return result
def _process(self) -> bool:
def process(self) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Refresh whitelist
self.pairlists.refresh_pairlist()
self.active_pair_whitelist = self.pairlists.whitelist
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Refresh whitelist
self.pairlists.refresh_pairlist()
self.active_pair_whitelist = self.pairlists.whitelist
# Query trades from persistence layer
trades = Trade.get_open_trades()
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
self._extend_whitelist_with_trades(self.active_pair_whitelist, trades)
# Query trades from persistence layer
trades = Trade.get_open_trades()
# Refreshing candles
self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
self.strategy.informative_pairs())
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
self._extend_whitelist_with_trades(self.active_pair_whitelist, trades)
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
# Refreshing candles
self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
self.strategy.informative_pairs())
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
state_changed = self.process_maybe_execute_buy()
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
state_changed = self.process_maybe_execute_buy()
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
except TemporaryError as error:
logger.warning(f"Error: {error}, retrying in {constants.RETRY_TIMEOUT} seconds...")
time.sleep(constants.RETRY_TIMEOUT)
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'OperationalException:\n```\n{tb}```{hint}'
})
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
def _extend_whitelist_with_trades(self, whitelist: List[str], trades: List[Any]):
@ -298,19 +204,19 @@ class FreqtradeBot(object):
else:
stake_amount = self.config['stake_amount']
avaliable_amount = self.wallets.get_free(self.config['stake_currency'])
available_amount = self.wallets.get_free(self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(Trade.get_open_trades())
if open_trades >= self.config['max_open_trades']:
logger.warning('Can\'t open a new trade: max number of trades is reached')
return None
return avaliable_amount / (self.config['max_open_trades'] - open_trades)
return available_amount / (self.config['max_open_trades'] - open_trades)
# Check if stake_amount is fulfilled
if avaliable_amount < stake_amount:
if available_amount < stake_amount:
raise DependencyException(
f"Available balance({avaliable_amount} {self.config['stake_currency']}) is "
f"Available balance({available_amount} {self.config['stake_currency']}) is "
f"lower than stake amount({stake_amount} {self.config['stake_currency']})"
)
@ -356,6 +262,10 @@ class FreqtradeBot(object):
interval = self.strategy.ticker_interval
whitelist = copy.deepcopy(self.active_pair_whitelist)
if not whitelist:
logger.warning("Whitelist is empty.")
return False
# Remove currently opened and latest pairs from whitelist
for trade in Trade.get_open_trades():
if trade.pair in whitelist:
@ -363,7 +273,8 @@ class FreqtradeBot(object):
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
logger.info("No currency pair in whitelist, but checking to sell open trades.")
return False
# running get_signal on historical data fetched
for _pair in whitelist:
@ -433,8 +344,8 @@ class FreqtradeBot(object):
return False
amount = stake_amount / buy_limit_requested
order = self.exchange.buy(pair=pair, ordertype=self.strategy.order_types['buy'],
order_type = self.strategy.order_types['buy']
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
order_id = order['id']
@ -444,7 +355,6 @@ class FreqtradeBot(object):
buy_limit_filled_price = buy_limit_requested
if order_status == 'expired' or order_status == 'rejected':
order_type = self.strategy.order_types['buy']
order_tif = self.strategy.order_time_in_force['buy']
# return false if the order is not filled
@ -472,13 +382,13 @@ class FreqtradeBot(object):
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
order_id = None
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'limit': buy_limit_filled_price,
'order_type': order_type,
'stake_amount': stake_amount,
'stake_currency': stake_currency,
'fiat_currency': fiat_currency
@ -498,9 +408,13 @@ class FreqtradeBot(object):
exchange=self.exchange.id,
open_order_id=order_id,
strategy=self.strategy.get_strategy_name(),
ticker_interval=constants.TICKER_INTERVAL_MINUTES[self.config['ticker_interval']]
ticker_interval=timeframe_to_minutes(self.config['ticker_interval'])
)
# Update fees if order is closed
if order_status == 'closed':
self.update_trade_state(trade, order)
Trade.session.add(trade)
Trade.session.flush()
@ -531,24 +445,7 @@ class FreqtradeBot(object):
:return: True if executed
"""
try:
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
order = self.exchange.get_order(trade.open_order_id, trade.pair)
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if order['amount'] != new_amount:
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except OperationalException as exception:
logger.warning("Could not update trade amount: %s", exception)
# This handles both buy and sell orders!
trade.update(order)
self.update_trade_state(trade)
if self.strategy.order_types.get('stoploss_on_exchange') and trade.is_open:
result = self.handle_stoploss_on_exchange(trade)
@ -573,7 +470,7 @@ class FreqtradeBot(object):
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Get real amount for the trade
Necessary for self.exchanges which charge fees in base currency (e.g. binance)
Necessary for exchanges which charge fees in base currency (e.g. binance)
"""
order_amount = order['amount']
# Only run for closed orders
@ -581,8 +478,11 @@ class FreqtradeBot(object):
return order_amount
# use fee from order-dict if possible
if 'fee' in order and order['fee'] and (order['fee'].keys() >= {'currency', 'cost'}):
if trade.pair.startswith(order['fee']['currency']):
if ('fee' in order and order['fee'] is not None and
(order['fee'].keys() >= {'currency', 'cost'})):
if (order['fee']['currency'] is not None and
order['fee']['cost'] is not None and
trade.pair.startswith(order['fee']['currency'])):
new_amount = order_amount - order['fee']['cost']
logger.info("Applying fee on amount for %s (from %s to %s) from Order",
trade, order['amount'], new_amount)
@ -599,9 +499,12 @@ class FreqtradeBot(object):
fee_abs = 0
for exectrade in trades:
amount += exectrade['amount']
if "fee" in exectrade and (exectrade['fee'].keys() >= {'currency', 'cost'}):
if ("fee" in exectrade and exectrade['fee'] is not None and
(exectrade['fee'].keys() >= {'currency', 'cost'})):
# only applies if fee is in quote currency!
if trade.pair.startswith(exectrade['fee']['currency']):
if (exectrade['fee']['currency'] is not None and
exectrade['fee']['cost'] is not None and
trade.pair.startswith(exectrade['fee']['currency'])):
fee_abs += exectrade['fee']['cost']
if amount != order_amount:
@ -613,6 +516,36 @@ class FreqtradeBot(object):
f"(from {order_amount} to {real_amount}) from Trades")
return real_amount
def update_trade_state(self, trade, action_order: dict = None):
"""
Checks trades with open orders and updates the amount if necessary
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', trade.open_order_id, exception)
return
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if order['amount'] != new_amount:
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except OperationalException as exception:
logger.warning("Could not update trade amount: %s", exception)
trade.update(order)
# Updating wallets when order is closed
if not trade.is_open:
self.wallets.update()
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using get-ticker bid or first bid based on orderbook
@ -663,13 +596,13 @@ class FreqtradeBot(object):
logger.info(' order book asks top %s: %0.8f', i, order_book_rate)
sell_rate = order_book_rate
if self.check_sell(trade, sell_rate, buy, sell):
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
else:
logger.debug('checking sell')
sell_rate = self.get_sell_rate(trade.pair, True)
if self.check_sell(trade, sell_rate, buy, sell):
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
logger.debug('Found no sell signal for %s.', trade)
@ -682,11 +615,25 @@ class FreqtradeBot(object):
is enabled.
"""
result = False
logger.debug('Handling stoploss on exchange %s ...', trade)
# If trade is open and the buy order is fulfilled but there is no stoploss,
# then we add a stoploss on exchange
if not trade.open_order_id and not trade.stoploss_order_id:
stoploss_order = None
try:
# First we check if there is already a stoploss on exchange
stoploss_order = self.exchange.get_order(trade.stoploss_order_id, trade.pair) \
if trade.stoploss_order_id else None
except InvalidOrderException as exception:
logger.warning('Unable to fetch stoploss order: %s', exception)
# If trade open order id does not exist: buy order is fulfilled
buy_order_fulfilled = not trade.open_order_id
# Limit price threshold: As limit price should always be below price
limit_price_pct = 0.99
# If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
if (buy_order_fulfilled and not stoploss_order):
if self.edge:
stoploss = self.edge.stoploss(pair=trade.pair)
else:
@ -695,32 +642,47 @@ class FreqtradeBot(object):
stop_price = trade.open_rate * (1 + stoploss)
# limit price should be less than stop price.
# 0.99 is arbitrary here.
limit_price = stop_price * 0.99
limit_price = stop_price * limit_price_pct
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair, amount=trade.amount, stop_price=stop_price, rate=limit_price
)['id']
trade.stoploss_order_id = str(stoploss_order_id)
trade.stoploss_last_update = datetime.now()
try:
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair, amount=trade.amount, stop_price=stop_price, rate=limit_price
)['id']
trade.stoploss_order_id = str(stoploss_order_id)
trade.stoploss_last_update = datetime.now()
return False
# Or the trade open and there is already a stoploss on exchange.
# so we check if it is hit ...
elif trade.stoploss_order_id:
logger.debug('Handling stoploss on exchange %s ...', trade)
order = self.exchange.get_order(trade.stoploss_order_id, trade.pair)
if order['status'] == 'closed':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(order)
self.notify_sell(trade)
result = True
elif self.config.get('trailing_stop', False):
# if trailing stoploss is enabled we check if stoploss value has changed
# in which case we cancel stoploss order and put another one with new
# value immediately
self.handle_trailing_stoploss_on_exchange(trade, order)
except DependencyException as exception:
logger.warning('Unable to place a stoploss order on exchange: %s', exception)
return result
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] == 'canceled':
try:
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair, amount=trade.amount,
stop_price=trade.stop_loss, rate=trade.stop_loss * limit_price_pct
)['id']
trade.stoploss_order_id = str(stoploss_order_id)
return False
except DependencyException as exception:
logger.warning('Stoploss order was cancelled, '
'but unable to recreate one: %s', exception)
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] == 'closed':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(stoploss_order)
self._notify_sell(trade)
return True
# Finally we check if stoploss on exchange should be moved up because of trailing.
if stoploss_order and self.config.get('trailing_stop', False):
# if trailing stoploss is enabled we check if stoploss value has changed
# in which case we cancel stoploss order and put another one with new
# value immediately
self.handle_trailing_stoploss_on_exchange(trade, stoploss_order)
return False
def handle_trailing_stoploss_on_exchange(self, trade: Trade, order):
"""
@ -736,23 +698,34 @@ class FreqtradeBot(object):
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() > update_beat:
# cancelling the current stoploss on exchange first
logger.info('Trailing stoploss: cancelling current stoploss on exchange '
'in order to add another one ...')
if self.exchange.cancel_order(order['id'], trade.pair):
logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s})'
'in order to add another one ...', order['id'])
try:
self.exchange.cancel_order(order['id'], trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
f"for pair {trade.pair}")
try:
# creating the new one
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair, amount=trade.amount,
stop_price=trade.stop_loss, rate=trade.stop_loss * 0.99
)['id']
trade.stoploss_order_id = str(stoploss_order_id)
except DependencyException:
logger.exception(f"Could create trailing stoploss order "
f"for pair {trade.pair}.")
def check_sell(self, trade: Trade, sell_rate: float, buy: bool, sell: bool) -> bool:
if self.edge:
stoploss = self.edge.stoploss(trade.pair)
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell, force_stoploss=stoploss)
else:
should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell)
def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
buy: bool, sell: bool) -> bool:
"""
Check and execute sell
"""
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_sell.sell_flag:
self.execute_sell(trade, sell_rate, should_sell.sell_type)
@ -780,7 +753,7 @@ class FreqtradeBot(object):
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id, trade.pair)
except (RequestException, DependencyException):
except (RequestException, DependencyException, InvalidOrderException):
logger.info(
'Cannot query order for %s due to %s',
trade,
@ -890,7 +863,10 @@ class FreqtradeBot(object):
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
try:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
# Execute sell and update trade record
order_id = self.exchange.sell(pair=str(trade.pair),
@ -903,9 +879,9 @@ class FreqtradeBot(object):
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
Trade.session.flush()
self.notify_sell(trade)
self._notify_sell(trade)
def notify_sell(self, trade: Trade):
def _notify_sell(self, trade: Trade):
"""
Sends rpc notification when a sell occured.
"""
@ -922,6 +898,7 @@ class FreqtradeBot(object):
'pair': trade.pair,
'gain': gain,
'limit': trade.close_rate_requested,
'order_type': self.strategy.order_types['sell'],
'amount': trade.amount,
'open_rate': trade.open_rate,
'current_rate': current_rate,

50
freqtrade/loggers.py Normal file
View File

@ -0,0 +1,50 @@
import logging
import sys
from logging.handlers import RotatingFileHandler
from typing import Any, Dict, List
logger = logging.getLogger(__name__)
def _set_loggers(verbosity: int = 0) -> None:
"""
Set the logging level for third party libraries
:return: None
"""
logging.getLogger('requests').setLevel(
logging.INFO if verbosity <= 1 else logging.DEBUG
)
logging.getLogger("urllib3").setLevel(
logging.INFO if verbosity <= 1 else logging.DEBUG
)
logging.getLogger('ccxt.base.exchange').setLevel(
logging.INFO if verbosity <= 2 else logging.DEBUG
)
logging.getLogger('telegram').setLevel(logging.INFO)
def setup_logging(config: Dict[str, Any]) -> None:
"""
Process -v/--verbose, --logfile options
"""
# Log level
verbosity = config['verbosity']
# Log to stdout, not stderr
log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stdout)]
if config.get('logfile'):
log_handlers.append(RotatingFileHandler(config['logfile'],
maxBytes=1024 * 1024, # 1Mb
backupCount=10))
logging.basicConfig(
level=logging.INFO if verbosity < 1 else logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=log_handlers
)
_set_loggers(verbosity)
logger.info('Verbosity set to %s', verbosity)

94
freqtrade/main.py
View File

@ -3,90 +3,66 @@
Main Freqtrade bot script.
Read the documentation to know what cli arguments you need.
"""
import logging
import sys
# check min. python version
if sys.version_info < (3, 6):
sys.exit("Freqtrade requires Python version >= 3.6")
# flake8: noqa E402
import logging
from argparse import Namespace
from typing import List
from typing import Any, List
from freqtrade import OperationalException
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration, set_loggers
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.state import State
from freqtrade.rpc import RPCMessageType
from freqtrade.configuration import Arguments
from freqtrade.worker import Worker
logger = logging.getLogger('freqtrade')
def main(sysargv: List[str]) -> None:
def main(sysargv: List[str] = None) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
arguments = Arguments(
sysargv,
'Free, open source crypto trading bot'
)
args = arguments.get_parsed_arg()
# A subcommand has been issued.
# Means if Backtesting or Hyperopt have been called we exit the bot
if hasattr(args, 'func'):
args.func(args)
return
freqtrade = None
return_code = 1
return_code: Any = 1
worker = None
try:
# Load and validate configuration
config = Configuration(args, None).get_config()
arguments = Arguments(
sysargv,
'Free, open source crypto trading bot'
)
args: Namespace = arguments.get_parsed_arg()
# Init the bot
freqtrade = FreqtradeBot(config)
state = None
while True:
state = freqtrade.worker(old_state=state)
if state == State.RELOAD_CONF:
freqtrade = reconfigure(freqtrade, args)
# A subcommand has been issued.
# Means if Backtesting or Hyperopt have been called we exit the bot
if hasattr(args, 'func'):
args.func(args)
# TODO: fetch return_code as returned by the command function here
return_code = 0
else:
# Load and run worker
worker = Worker(args)
worker.run()
except SystemExit as e:
return_code = e
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
return_code = 0
except OperationalException as e:
logger.error(str(e))
return_code = 2
except BaseException:
except Exception:
logger.exception('Fatal exception!')
finally:
if freqtrade:
freqtrade.stopping()
freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'process died'
})
freqtrade.cleanup()
if worker:
worker.exit()
sys.exit(return_code)
def reconfigure(freqtrade: FreqtradeBot, args: Namespace) -> FreqtradeBot:
"""
Cleans up current instance, reloads the configuration and returns the new instance
"""
freqtrade.reconfigure()
# Clean up current modules
freqtrade.cleanup()
# Create new instance
freqtrade = FreqtradeBot(Configuration(args, None).get_config())
freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'config reloaded'
})
return freqtrade
if __name__ == '__main__':
set_loggers()
main(sys.argv[1:])
main()

24
freqtrade/misc.py
View File

@ -1,17 +1,15 @@
"""
Various tool function for Freqtrade and scripts
"""
import gzip
import logging
import re
from datetime import datetime
from typing import Dict
import numpy as np
from pandas import DataFrame
import rapidjson
logger = logging.getLogger(__name__)
@ -41,24 +39,6 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
return dates.dt.to_pydatetime()
def common_datearray(dfs: Dict[str, DataFrame]) -> np.ndarray:
"""
Return dates from Dataframe
:param dfs: Dict with format pair: pair_data
:return: List of dates
"""
alldates = {}
for pair, pair_data in dfs.items():
dates = datesarray_to_datetimearray(pair_data['date'])
for date in dates:
alldates[date] = 1
lst = []
for date, _ in alldates.items():
lst.append(date)
arr = np.array(lst)
return np.sort(arr, axis=0)
def file_dump_json(filename, data, is_zip=False) -> None:
"""
Dump JSON data into a file
@ -117,6 +97,8 @@ def format_ms_time(date: int) -> str:
def deep_merge_dicts(source, destination):
"""
Values from Source override destination, destination is returned (and modified!!)
Sample:
>>> a = { 'first' : { 'rows' : { 'pass' : 'dog', 'number' : '1' } } }
>>> b = { 'first' : { 'rows' : { 'fail' : 'cat', 'number' : '5' } } }
>>> merge(b, a) == { 'first' : { 'rows' : { 'pass' : 'dog', 'fail' : 'cat', 'number' : '5' } } }

132
freqtrade/optimize/__init__.py
View File

@ -1,49 +1,111 @@
# pragma pylint: disable=missing-docstring
import logging
from datetime import datetime
from typing import Dict, Tuple
import operator
from argparse import Namespace
from typing import Any, Dict
import arrow
from pandas import DataFrame
from filelock import FileLock, Timeout
from freqtrade import DependencyException, constants
from freqtrade.state import RunMode
from freqtrade.utils import setup_utils_configuration
from freqtrade.optimize.default_hyperopt import DefaultHyperOpts # noqa: F401
logger = logging.getLogger(__name__)
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
def setup_configuration(args: Namespace, method: RunMode) -> Dict[str, Any]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
Prepare the configuration for the Hyperopt module
:param args: Cli args from Arguments()
:return: Configuration
"""
timeframe = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
config = setup_utils_configuration(args, method)
if method == RunMode.BACKTEST:
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
raise DependencyException('stake amount could not be "%s" for backtesting' %
constants.UNLIMITED_STAKE_AMOUNT)
if method == RunMode.HYPEROPT:
# Special cases for Hyperopt
if config.get('strategy') and config.get('strategy') != 'DefaultStrategy':
logger.error("Please don't use --strategy for hyperopt.")
logger.error(
"Read the documentation at "
"https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md "
"to understand how to configure hyperopt.")
raise DependencyException("--strategy configured but not supported for hyperopt")
return config
def validate_backtest_data(data: Dict[str, DataFrame], min_date: datetime,
max_date: datetime, ticker_interval_mins: int) -> bool:
def start_backtesting(args: Namespace) -> None:
"""
Validates preprocessed backtesting data for missing values and shows warnings about it that.
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
"""
# Import here to avoid loading backtesting module when it's not used
from freqtrade.optimize.backtesting import Backtesting
:param data: dictionary with preprocessed backtesting data
:param min_date: start-date of the data
:param max_date: end-date of the data
:param ticker_interval_mins: ticker interval in minutes
# Initialize configuration
config = setup_configuration(args, RunMode.BACKTEST)
logger.info('Starting freqtrade in Backtesting mode')
# Initialize backtesting object
backtesting = Backtesting(config)
backtesting.start()
def start_hyperopt(args: Namespace) -> None:
"""
# total difference in minutes / interval-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
found_missing = False
for pair, df in data.items():
dflen = len(df)
if dflen < expected_frames:
found_missing = True
logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
pair, expected_frames, dflen, expected_frames - dflen)
return found_missing
Start hyperopt script
:param args: Cli args from Arguments()
:return: None
"""
# Import here to avoid loading hyperopt module when it's not used
from freqtrade.optimize.hyperopt import Hyperopt, HYPEROPT_LOCKFILE
# Initialize configuration
config = setup_configuration(args, RunMode.HYPEROPT)
logger.info('Starting freqtrade in Hyperopt mode')
lock = FileLock(HYPEROPT_LOCKFILE)
try:
with lock.acquire(timeout=1):
# Remove noisy log messages
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
logging.getLogger('filelock').setLevel(logging.WARNING)
# Initialize backtesting object
hyperopt = Hyperopt(config)
hyperopt.start()
except Timeout:
logger.info("Another running instance of freqtrade Hyperopt detected.")
logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
"Hyperopt module is resource hungry. Please run your Hyperopts sequentially "
"or on separate machines.")
logger.info("Quitting now.")
# TODO: return False here in order to help freqtrade to exit
# with non-zero exit code...
# Same in Edge and Backtesting start() functions.
def start_edge(args: Namespace) -> None:
"""
Start Edge script
:param args: Cli args from Arguments()
:return: None
"""
from freqtrade.optimize.edge_cli import EdgeCli
# Initialize configuration
config = setup_configuration(args, RunMode.EDGE)
logger.info('Starting freqtrade in Edge mode')
# Initialize Edge object
edge_cli = EdgeCli(config)
edge_cli.start()

243
freqtrade/optimize/backtesting.py
View File

@ -4,25 +4,24 @@
This module contains the backtesting logic
"""
import logging
from argparse import Namespace
from copy import deepcopy
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
from pandas import DataFrame
from tabulate import tabulate
from freqtrade import optimize
from freqtrade import DependencyException, constants
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade import OperationalException
from freqtrade.configuration import Arguments
from freqtrade.data import history
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import file_dump_json
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import SellType, IStrategy
from freqtrade.strategy.interface import IStrategy, SellType
from tabulate import tabulate
logger = logging.getLogger(__name__)
@ -64,32 +63,38 @@ class Backtesting(object):
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
self.fee = self.exchange.get_fee()
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
if self.config.get('strategy_list', None):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver(stratconf).strategy)
else:
# only one strategy
# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
# Load one strategy
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
exchange_name = self.config.get('exchange', {}).get('name', 'bittrex').title()
self.exchange = ExchangeResolver(exchange_name, self.config).exchange
self.fee = self.exchange.get_fee()
def _set_strategy(self, strategy):
"""
Load strategy into backtesting
"""
self.strategy = strategy
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.ticker_interval = self.config.get('ticker_interval')
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
# Set stoploss_on_exchange to false for backtesting,
@ -202,12 +207,37 @@ class Backtesting(object):
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed tickerlist into a list for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
ticker: Dict = {}
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
partial_ticker: List, trade_count_lock: Dict,
stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
open_rate=buy_row.open,
open_date=buy_row.date,
@ -223,26 +253,23 @@ class Backtesting(object):
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
buy_signal = sell_row.buy
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, sell_row.buy,
sell_row.sell, low=sell_row.low, high=sell_row.high)
if sell.sell_flag:
trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
# Set close_rate to stoploss
closerate = trade.stop_loss
elif sell.sell_type == (SellType.ROI):
# get next entry in min_roi > to trade duration
# Interface.py skips on trade_duration <= duration
roi_entry = max(list(filter(lambda x: trade_dur >= x,
self.strategy.minimal_roi.keys())))
roi = self.strategy.minimal_roi[roi_entry]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
closerate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None:
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
closerate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
else:
# This should not be reached...
closerate = sell_row.open
else:
closerate = sell_row.open
@ -296,74 +323,75 @@ class Backtesting(object):
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
# Arguments are long and noisy, so this is commented out.
# Uncomment if you need to debug the backtest() method.
# logger.debug(f"Start backtest, args: {args}")
processed = args['processed']
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args['start_date']
end_date = args['end_date']
trades = []
trade_count_lock: Dict = {}
ticker: Dict = {}
pairs = []
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker[pair] = [x for x in ticker_data.itertuples()]
pairs.append(pair)
# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
ticker: Dict = self._get_ticker_list(processed)
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
index = 0
# Loop timerange and test per pair
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
# print(f"time: {tmp}")
for i, pair in enumerate(ticker):
if pair not in indexes:
indexes[pair] = 0
try:
row = ticker[pair][index]
row = ticker[pair][indexes[pair]]
except IndexError:
# missing Data for one pair ...
# Warnings for this are shown by `validate_backtest_data`
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
continue
# Waits until the time-counter reaches the start of the data for this pair.
if row.date > tmp.datetime:
continue
indexes[pair] += 1
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if not position_stacking:
if pair in lock_pair_until and row.date <= lock_pair_until[pair]:
continue
if (not position_stacking and pair in lock_pair_until
and row.date <= lock_pair_until[pair]):
# without positionstacking, we can only have one open trade per pair.
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][index + 1:],
trade_count_lock, args)
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
trade_count_lock, stake_amount,
max_open_trades)
if trade_entry:
lock_pair_until[pair] = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until[pair] = end_date
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.ticker_interval_mins)
index += 1
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
@ -376,24 +404,17 @@ class Backtesting(object):
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
self.exchange.refresh_latest_ohlcv([(pair, self.ticker_interval) for pair in pairs])
data = {key[0]: value for key, value in self.exchange._klines.items()}
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
exchange=self.exchange,
timerange=timerange
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
exchange=self.exchange,
timerange=timerange,
live=self.config.get('live', False)
)
if not data:
logger.critical("No data found. Terminating.")
@ -406,20 +427,19 @@ class Backtesting(object):
max_open_trades = 0
all_results = {}
min_date, max_date = history.get_timeframe(data)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
min_date, max_date = optimize.get_timeframe(data)
# Validate dataframe for missing values (mainly at start and end, as fillup is called)
optimize.validate_backtest_data(data, min_date, max_date,
constants.TICKER_INTERVAL_MINUTES[self.ticker_interval])
logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
@ -456,38 +476,3 @@ class Backtesting(object):
print(' Strategy Summary '.center(133, '='))
print(self._generate_text_table_strategy(all_results))
print('\nFor more details, please look at the detail tables above')
def setup_configuration(args: Namespace) -> Dict[str, Any]:
"""
Prepare the configuration for the backtesting
:param args: Cli args from Arguments()
:return: Configuration
"""
configuration = Configuration(args, RunMode.BACKTEST)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
raise DependencyException('stake amount could not be "%s" for backtesting' %
constants.UNLIMITED_STAKE_AMOUNT)
return config
def start(args: Namespace) -> None:
"""
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
"""
# Initialize configuration
config = setup_configuration(args)
logger.info('Starting freqtrade in Backtesting mode')
# Initialize backtesting object
backtesting = Backtesting(config)
backtesting.start()

63
freqtrade/optimize/default_hyperopt.py
View File

@ -1,24 +1,21 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'DefaultHyperOpts'
class DefaultHyperOpts(IHyperOpt):
"""
Default hyperopt provided by freqtrade bot.
Default hyperopt provided by the Freqtrade bot.
You can override it with your own hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
@ -70,9 +67,10 @@ class DefaultHyperOpts(IHyperOpt):
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
@ -129,9 +127,10 @@ class DefaultHyperOpts(IHyperOpt):
dataframe['sar'], dataframe['close']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
@ -156,42 +155,6 @@ class DefaultHyperOpts(IHyperOpt):
'sell-sar_reversal'], name='sell-trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy

52
freqtrade/optimize/default_hyperopt_loss.py Normal file
View File

@ -0,0 +1,52 @@
"""
DefaultHyperOptLoss
This module defines the default HyperoptLoss class which is being used for
Hyperoptimization.
"""
from math import exp
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
# Set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
TARGET_TRADES = 600
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# expected max profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
EXPECTED_MAX_PROFIT = 3.0
# Max average trade duration in minutes.
# If eval ends with higher value, we consider it a failed eval.
MAX_ACCEPTED_TRADE_DURATION = 300
class DefaultHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the Default algorithm
Weights are distributed as follows:
* 0.4 to trade duration
* 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result

45
freqtrade/optimize/edge_cli.py
View File

@ -4,16 +4,14 @@
This module contains the edge backtesting interface
"""
import logging
from argparse import Namespace
from typing import Dict, Any
from tabulate import tabulate
from freqtrade import constants
from freqtrade.edge import Edge
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.configuration import Arguments
from freqtrade.exchange import Exchange
from freqtrade.resolvers import StrategyResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@ -35,6 +33,7 @@ class EdgeCli(object):
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.strategy = StrategyResolver(self.config).strategy
@ -73,37 +72,7 @@ class EdgeCli(object):
floatfmt=floatfmt, tablefmt="pipe")
def start(self) -> None:
self.edge.calculate()
print('') # blank like for readability
print(self._generate_edge_table(self.edge._cached_pairs))
def setup_configuration(args: Namespace) -> Dict[str, Any]:
"""
Prepare the configuration for edge backtesting
:param args: Cli args from Arguments()
:return: Configuration
"""
configuration = Configuration(args, RunMode.EDGECLI)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
return config
def start(args: Namespace) -> None:
"""
Start Edge script
:param args: Cli args from Arguments()
:return: None
"""
# Initialize configuration
config = setup_configuration(args)
logger.info('Starting freqtrade in Edge mode')
# Initialize Edge object
edge_cli = EdgeCli(config)
edge_cli.start()
result = self.edge.calculate()
if result:
print('') # blank line for readability
print(self._generate_edge_table(self.edge._cached_pairs))

297
freqtrade/optimize/hyperopt.py
View File

@ -5,33 +5,35 @@ This module contains the hyperopt logic
"""
import logging
import multiprocessing
import os
import sys
from argparse import Namespace
from math import exp
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.data.history import load_data
from freqtrade.optimize import get_timeframe
from freqtrade.configuration import Arguments
from freqtrade.data.history import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.state import RunMode
from freqtrade.resolvers import HyperOptResolver
# Import IHyperOptLoss to allow users import from this file
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver
logger = logging.getLogger(__name__)
INITIAL_POINTS = 30
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
TRIALSDATA_PICKLE = os.path.join('user_data', 'hyperopt_results.pickle')
HYPEROPT_LOCKFILE = os.path.join('user_data', 'hyperopt.lock')
class Hyperopt(Backtesting):
@ -44,28 +46,54 @@ class Hyperopt(Backtesting):
"""
def __init__(self, config: Dict[str, Any]) -> None:
super().__init__(config)
self.config = config
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
self.target_trades = 600
self.total_tries = config.get('epochs', 0)
self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
self.total_epochs = config.get('epochs', 0)
self.current_best_loss = 100
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
self.max_accepted_trade_duration = 300
# this is expexted avg profit * expected trade count
# for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
# check that the reported Σ% values do not exceed this!
self.expected_max_profit = 3.0
if not self.config.get('hyperopt_continue'):
self.clean_hyperopt()
else:
logger.info("Continuing on previous hyperopt results.")
# Previous evaluations
self.trials_file = os.path.join('user_data', 'hyperopt_results.pickle')
self.trials_file = TRIALSDATA_PICKLE
self.trials: List = []
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
self.max_open_trades = self.config['max_open_trades']
else:
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False),
if self.has_space('sell'):
# Make sure experimental is enabled
if 'experimental' not in self.config:
self.config['experimental'] = {}
self.config['experimental']['use_sell_signal'] = True
def clean_hyperopt(self):
"""
Remove hyperopt pickle files to restart hyperopt.
"""
for f in [TICKERDATA_PICKLE, TRIALSDATA_PICKLE]:
p = Path(f)
if p.is_file():
logger.info(f"Removing `{p}`.")
p.unlink()
def get_args(self, params):
dimensions = self.hyperopt_space()
# Ensure the number of dimensions match
@ -102,140 +130,157 @@ class Hyperopt(Backtesting):
"""
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
logger.info(
'Best result:\n%s\nwith values:\n',
best_result['result']
)
pprint(best_result['params'], indent=4)
if 'roi_t1' in best_result['params']:
logger.info('ROI table:')
pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4)
params = best_result['params']
log_str = self.format_results_logstring(best_result)
print(f"\nBest result:\n\n{log_str}\n")
if self.has_space('buy'):
print('Buy hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
indent=4)
if self.has_space('sell'):
print('Sell hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
indent=4)
if self.has_space('roi'):
print("ROI table:")
pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
if self.has_space('stoploss'):
print(f"Stoploss: {params.get('stoploss')}")
def log_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
"""
if results['loss'] < self.current_best_loss:
current = results['current_tries']
total = results['total_tries']
res = results['result']
loss = results['loss']
self.current_best_loss = results['loss']
log_msg = f'\n{current:5d}/{total}: {res}. Loss {loss:.5f}'
print(log_msg)
print_all = self.config.get('print_all', False)
if print_all or results['loss'] < self.current_best_loss:
log_str = self.format_results_logstring(results)
if print_all:
print(log_str)
else:
print('\n' + log_str)
else:
print('.', end='')
sys.stdout.flush()
def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:
"""
Objective function, returns smaller number for more optimal results
"""
trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
result = trade_loss + profit_loss + duration_loss
return result
def format_results_logstring(self, results) -> str:
# Output human-friendly index here (starting from 1)
current = results['current_epoch'] + 1
total = self.total_epochs
res = results['results_explanation']
loss = results['loss']
self.current_best_loss = results['loss']
log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
return log_str
def has_space(self, space: str) -> bool:
"""
Tell if a space value is contained in the configuration
"""
if space in self.config['spaces'] or 'all' in self.config['spaces']:
return True
return False
return any(s in self.config['spaces'] for s in [space, 'all'])
def hyperopt_space(self) -> List[Dimension]:
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
"""
Return the space to use during Hyperopt
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
"""
spaces: List[Dimension] = []
if self.has_space('buy'):
if space == 'buy' or (space is None and self.has_space('buy')):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
if self.has_space('sell'):
if space == 'sell' or (space is None and self.has_space('sell')):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
# Make sure experimental is enabled
if 'experimental' not in self.config:
self.config['experimental'] = {}
self.config['experimental']['use_sell_signal'] = True
if self.has_space('roi'):
if space == 'roi' or (space is None and self.has_space('roi')):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
if self.has_space('stoploss'):
if space == 'stoploss' or (space is None and self.has_space('stoploss')):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
return spaces
def generate_optimizer(self, _params: Dict) -> Dict:
"""
Used Optimize function. Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
params = self.get_args(_params)
if self.has_space('roi'):
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('buy'):
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
elif hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
if self.has_space('sell'):
self.advise_sell = self.custom_hyperopt.sell_strategy_generator(params)
elif hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
processed = load(TICKERDATA_PICKLE)
min_date, max_date = get_timeframe(processed)
results = self.backtest(
{
'stake_amount': self.config['stake_amount'],
'processed': processed,
'position_stacking': self.config.get('position_stacking', True),
'max_open_trades': self.max_open_trades,
'position_stacking': self.position_stacking,
'start_date': min_date,
'end_date': max_date,
}
)
result_explanation = self.format_results(results)
results_explanation = self.format_results(results)
total_profit = results.profit_percent.sum()
trade_count = len(results.index)
trade_duration = results.trade_duration.mean()
if trade_count == 0:
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
# in order to cast this hyperspace point away from optimization
# path. We do not want to optimize 'hodl' strategies.
if trade_count < self.config['hyperopt_min_trades']:
return {
'loss': MAX_LOSS,
'params': params,
'result': result_explanation,
'results_explanation': results_explanation,
}
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
loss = self.calculate_loss(results=results, trade_count=trade_count,
min_date=min_date.datetime, max_date=max_date.datetime)
return {
'loss': loss,
'params': params,
'result': result_explanation,
'results_explanation': results_explanation,
}
def format_results(self, results: DataFrame) -> str:
"""
Return the format result in a string
Return the formatted results explanation in a string
"""
trades = len(results.index)
avg_profit = results.profit_percent.mean() * 100.0
total_profit = results.profit_abs.sum()
stake_cur = self.config['stake_currency']
profit = results.profit_percent.sum()
profit = results.profit_percent.sum() * 100.0
duration = results.trade_duration.mean()
return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
f'Total profit {total_profit: 11.8f} {stake_cur} '
f'({profit:.4f}Σ%). Avg duration {duration:5.1f} mins.')
f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
def get_optimizer(self, cpu_count) -> Optimizer:
return Optimizer(
self.hyperopt_space(),
base_estimator="ET",
acq_optimizer="auto",
n_initial_points=30,
acq_optimizer_kwargs={'n_jobs': cpu_count}
n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.config.get('hyperopt_random_state', None)
)
def run_optimizer_parallel(self, parallel, asked) -> List:
@ -258,69 +303,61 @@ class Hyperopt(Backtesting):
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
exchange=self.exchange,
timerange=timerange
)
if self.has_space('buy') or self.has_space('sell'):
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
if not data:
logger.critical("No data found. Terminating.")
return
min_date, max_date = get_timeframe(data)
logger.info(
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
preprocessed = self.strategy.tickerdata_to_dataframe(data)
dump(preprocessed, TICKERDATA_PICKLE)
# We don't need exchange instance anymore while running hyperopt
self.exchange = None # type: ignore
self.load_previous_results()
cpus = multiprocessing.cpu_count()
cpus = cpu_count()
logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
opt = self.get_optimizer(cpus)
EVALS = max(self.total_tries // cpus, 1)
opt = self.get_optimizer(config_jobs)
try:
with Parallel(n_jobs=cpus) as parallel:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()
logger.info(f'Effective number of parallel workers used: {jobs}')
EVALS = max(self.total_epochs // jobs, 1)
for i in range(EVALS):
asked = opt.ask(n_points=cpus)
asked = opt.ask(n_points=jobs)
f_val = self.run_optimizer_parallel(parallel, asked)
opt.tell(asked, [i['loss'] for i in f_val])
self.trials += f_val
for j in range(cpus):
self.log_results({
'loss': f_val[j]['loss'],
'current_tries': i * cpus + j,
'total_tries': self.total_tries,
'result': f_val[j]['result'],
})
opt.tell(asked, [v['loss'] for v in f_val])
for j in range(jobs):
current = i * jobs + j
val = f_val[j]
val['current_epoch'] = current
val['is_initial_point'] = current < INITIAL_POINTS
self.log_results(val)
self.trials.append(val)
logger.debug(f"Optimizer epoch evaluated: {val}")
except KeyboardInterrupt:
print('User interrupted..')
self.save_trials()
self.log_trials_result()
def start(args: Namespace) -> None:
"""
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
"""
# Remove noisy log messages
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
# Initialize configuration
# Monkey patch the configuration with hyperopt_conf.py
configuration = Configuration(args, RunMode.HYPEROPT)
logger.info('Starting freqtrade in Hyperopt mode')
config = configuration.load_config()
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
if config.get('strategy') and config.get('strategy') != 'DefaultStrategy':
logger.error("Please don't use --strategy for hyperopt.")
logger.error(
"Read the documentation at "
"https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md "
"to understand how to configure hyperopt.")
raise ValueError("--strategy configured but not supported for hyperopt")
# Initialize backtesting object
hyperopt = Hyperopt(config)
hyperopt.start()

53
freqtrade/optimize/hyperopt_interface.py
View File

@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
from typing import Dict, Any, Callable, List
from pandas import DataFrame
from skopt.space import Dimension
from skopt.space import Dimension, Integer, Real
class IHyperOpt(ABC):
@ -20,61 +20,86 @@ class IHyperOpt(ABC):
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
ticker_interval: str
@staticmethod
@abstractmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:return: a Dataframe with all mandatory indicators for the strategies
Populate indicators that will be used in the Buy and Sell strategy.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe().
:return: A Dataframe with all mandatory indicators for the strategies.
"""
@staticmethod
@abstractmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator
Create a buy strategy generator.
"""
@staticmethod
@abstractmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator
Create a sell strategy generator.
"""
@staticmethod
@abstractmethod
def indicator_space() -> List[Dimension]:
"""
Create an indicator space
Create an indicator space.
"""
@staticmethod
@abstractmethod
def sell_indicator_space() -> List[Dimension]:
"""
Create a sell indicator space
Create a sell indicator space.
"""
@staticmethod
@abstractmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Create an roi table
Create a ROI table.
Generates the ROI table that will be used by Hyperopt.
You may override it in your custom Hyperopt class.
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
@abstractmethod
def stoploss_space() -> List[Dimension]:
"""
Create a stoploss space
Create a stoploss space.
Defines range of stoploss values to search.
You may override it in your custom Hyperopt class.
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
@abstractmethod
def roi_space() -> List[Dimension]:
"""
Create a roi space
Create a ROI space.
Defines values to search for each ROI steps.
You may override it in your custom Hyperopt class.
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]

25
freqtrade/optimize/hyperopt_loss_interface.py Normal file
View File

@ -0,0 +1,25 @@
"""
IHyperOptLoss interface
This module defines the interface for the loss-function for hyperopts
"""
from abc import ABC, abstractmethod
from datetime import datetime
from pandas import DataFrame
class IHyperOptLoss(ABC):
"""
Interface for freqtrade hyperopts Loss functions.
Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
"""
ticker_interval: str
@staticmethod
@abstractmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
"""

38
freqtrade/optimize/hyperopt_loss_onlyprofit.py Normal file
View File

@ -0,0 +1,38 @@
"""
OnlyProfitHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# expected max profit = 3.85
#
# Note, this is ratio. 3.85 stated above means 385Σ%, 3.0 means 300Σ%.
#
# In this implementation it's only used in calculation of the resulting value
# of the objective function as a normalization coefficient and does not
# represent any limit for profits as in the Freqtrade legacy default loss function.
EXPECTED_MAX_PROFIT = 3.0
class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation takes only profit into account.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results.
"""
total_profit = results.profit_percent.sum()
return 1 - total_profit / EXPECTED_MAX_PROFIT

45
freqtrade/optimize/hyperopt_loss_sharpe.py Normal file
View File

@ -0,0 +1,45 @@
"""
SharpeHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SharpeHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sharpe Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
"""
total_profit = results.profit_percent
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_yearly_return = total_profit.sum() / days_period
if (np.std(total_profit) != 0.):
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
return -sharp_ratio

37
freqtrade/pairlist/IPairList.py
View File

@ -60,34 +60,27 @@ class IPairList(ABC):
def _validate_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
trade
:return: the list of pairs the user wants to trade without the one unavailable or
:param whitelist: the sorted list of pairs the user might want to trade
:return: the list of pairs the user wants to trade without those unavailable or
black_listed
"""
sanitized_whitelist = whitelist
markets = self._freqtrade.exchange.markets
# Filter to markets in stake currency
markets = [markets[pair] for pair in markets if
markets[pair]['quote'] == self._config['stake_currency']]
known_pairs = set()
# TODO: we should loop over whitelist instead of all markets
for market in markets:
pair = market['symbol']
sanitized_whitelist = set()
for pair in whitelist:
# pair is not in the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.blacklist:
if (pair in self.blacklist or pair not in markets
or not pair.endswith(self._config['stake_currency'])):
logger.warning(f"Pair {pair} is not compatible with exchange "
f"{self._freqtrade.exchange.name} or contained in "
f"your blacklist. Removing it from whitelist..")
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
# Check if market is active
market = markets[pair]
if not market['active']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist. Market is not active.',
pair
)
logger.info(f"Ignoring {pair} from whitelist. Market is not active.")
continue
sanitized_whitelist.add(pair)
# We need to remove pairs that are unknown
return [x for x in sanitized_whitelist if x in known_pairs]
return list(sanitized_whitelist)

96
freqtrade/persistence.py
View File

@ -25,15 +25,16 @@ _DECL_BASE: Any = declarative_base()
_SQL_DOCS_URL = 'http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls'
def init(config: Dict) -> None:
def init(db_url: str, clean_open_orders: bool = False) -> None:
"""
Initializes this module with the given config,
registers all known command handlers
and starts polling for message updates
:param config: config to use
:param db_url: Database to use
:param clean_open_orders: Remove open orders from the database.
Useful for dry-run or if all orders have been reset on the exchange.
:return: None
"""
db_url = config.get('db_url', None)
kwargs = {}
# Take care of thread ownership if in-memory db
@ -57,7 +58,7 @@ def init(config: Dict) -> None:
check_migrate(engine)
# Clean dry_run DB if the db is not in-memory
if config.get('dry_run', False) and db_url != 'sqlite://':
if clean_open_orders and db_url != 'sqlite://':
clean_dry_run_db()
@ -83,7 +84,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'stoploss_last_update'):
if not has_column(cols, 'stop_loss_pct'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@ -91,10 +92,13 @@ def check_migrate(engine) -> None:
open_rate_requested = get_column_def(cols, 'open_rate_requested', 'null')
close_rate_requested = get_column_def(cols, 'close_rate_requested', 'null')
stop_loss = get_column_def(cols, 'stop_loss', '0.0')
stop_loss_pct = get_column_def(cols, 'stop_loss_pct', 'null')
initial_stop_loss = get_column_def(cols, 'initial_stop_loss', '0.0')
initial_stop_loss_pct = get_column_def(cols, 'initial_stop_loss_pct', 'null')
stoploss_order_id = get_column_def(cols, 'stoploss_order_id', 'null')
stoploss_last_update = get_column_def(cols, 'stoploss_last_update', 'null')
max_rate = get_column_def(cols, 'max_rate', '0.0')
min_rate = get_column_def(cols, 'min_rate', 'null')
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
@ -112,8 +116,9 @@ def check_migrate(engine) -> None:
(id, exchange, pair, is_open, fee_open, fee_close, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stop_loss, initial_stop_loss, stoploss_order_id, stoploss_last_update,
max_rate, sell_reason, strategy,
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, strategy,
ticker_interval
)
select id, lower(exchange),
@ -128,9 +133,11 @@ def check_migrate(engine) -> None:
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {initial_stop_loss} initial_stop_loss,
{stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
{initial_stop_loss} initial_stop_loss,
{initial_stop_loss_pct} initial_stop_loss_pct,
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {sell_reason} sell_reason,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{strategy} strategy, {ticker_interval} ticker_interval
from {table_back_name}
""")
@ -183,14 +190,20 @@ class Trade(_DECL_BASE):
open_order_id = Column(String)
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
strategy = Column(String, nullable=True)
ticker_interval = Column(Integer, nullable=True)
@ -201,8 +214,42 @@ class Trade(_DECL_BASE):
return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
f'open_rate={self.open_rate:.8f}, open_since={open_since})')
def to_json(self) -> Dict[str, Any]:
return {
'trade_id': self.id,
'pair': self.pair,
'open_date_hum': arrow.get(self.open_date).humanize(),
'open_date': self.open_date.strftime("%Y-%m-%d %H:%M:%S"),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime("%Y-%m-%d %H:%M:%S")
if self.close_date else None),
'open_rate': self.open_rate,
'close_rate': self.close_rate,
'amount': round(self.amount, 8),
'stake_amount': round(self.stake_amount, 8),
'stop_loss': self.stop_loss,
'stop_loss_pct': (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
'initial_stop_loss': self.initial_stop_loss,
'initial_stop_loss_pct': (self.initial_stop_loss_pct * 100
if self.initial_stop_loss_pct else None),
}
def adjust_min_max_rates(self, current_price: float):
"""
Adjust the max_rate and min_rate.
"""
self.max_rate = max(current_price, self.max_rate or self.open_rate)
self.min_rate = min(current_price, self.min_rate or self.open_rate)
def adjust_stop_loss(self, current_price: float, stoploss: float, initial: bool = False):
"""this adjusts the stop loss to it's most recently observed setting"""
"""
This adjusts the stop loss to it's most recently observed setting
:param current_price: Current rate the asset is traded
:param stoploss: Stoploss as factor (sample -0.05 -> -5% below current price).
:param initial: Called to initiate stop_loss.
Skips everything if self.stop_loss is already set.
"""
if initial and not (self.stop_loss is None or self.stop_loss == 0):
# Don't modify if called with initial and nothing to do
@ -210,24 +257,20 @@ class Trade(_DECL_BASE):
new_loss = float(current_price * (1 - abs(stoploss)))
# keeping track of the highest observed rate for this trade
if self.max_rate is None:
self.max_rate = current_price
else:
if current_price > self.max_rate:
self.max_rate = current_price
# no stop loss assigned yet
if not self.stop_loss:
logger.debug("assigning new stop loss")
self.stop_loss = new_loss
self.stop_loss_pct = -1 * abs(stoploss)
self.initial_stop_loss = new_loss
self.initial_stop_loss_pct = -1 * abs(stoploss)
self.stoploss_last_update = datetime.utcnow()
# evaluate if the stop loss needs to be updated
else:
if new_loss > self.stop_loss: # stop losses only walk up, never down!
self.stop_loss = new_loss
self.stop_loss_pct = -1 * abs(stoploss)
self.stoploss_last_update = datetime.utcnow()
logger.debug("adjusted stop loss")
else:
@ -379,3 +422,22 @@ class Trade(_DECL_BASE):
Query trades from persistence layer
"""
return Trade.query.filter(Trade.is_open.is_(True)).all()
@staticmethod
def stoploss_reinitialization(desired_stoploss):
"""
Adjust initial Stoploss to desired stoploss for all open trades.
"""
for trade in Trade.get_open_trades():
logger.info("Found open trade: %s", trade)
# skip case if trailing-stop changed the stoploss already.
if (trade.stop_loss == trade.initial_stop_loss
and trade.initial_stop_loss_pct != desired_stoploss):
# Stoploss value got changed
logger.info(f"Stoploss for {trade} needs adjustment.")
# Force reset of stoploss
trade.stop_loss = None
trade.adjust_stop_loss(trade.open_rate, desired_stoploss)
logger.info(f"new stoploss: {trade.stop_loss}, ")

0
freqtrade/plot/__init__.py Normal file
View File

324
freqtrade/plot/plotting.py Normal file
View File

@ -0,0 +1,324 @@
import logging
from pathlib import Path
from typing import Dict, List, Optional
import pandas as pd
from freqtrade.configuration import Arguments
from freqtrade.data import history
from freqtrade.data.btanalysis import (combine_tickers_with_mean,
create_cum_profit, load_trades)
from freqtrade.exchange import Exchange
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
logger = logging.getLogger(__name__)
try:
from plotly.subplots import make_subplots
from plotly.offline import plot
import plotly.graph_objects as go
except ImportError:
logger.exception("Module plotly not found \n Please install using `pip install plotly`")
exit(1)
def init_plotscript(config):
"""
Initialize objects needed for plotting
:return: Dict with tickers, trades, pairs and strategy
"""
exchange: Optional[Exchange] = None
# Exchange is only needed when downloading data!
if config.get("live", False) or config.get("refresh_pairs", False):
exchange = ExchangeResolver(config.get('exchange', {}).get('name'),
config).exchange
strategy = StrategyResolver(config).strategy
if "pairs" in config:
pairs = config["pairs"].split(',')
else:
pairs = config["exchange"]["pair_whitelist"]
# Set timerange to use
timerange = Arguments.parse_timerange(config.get("timerange"))
tickers = history.load_data(
datadir=Path(str(config.get("datadir"))),
pairs=pairs,
ticker_interval=config['ticker_interval'],
refresh_pairs=config.get('refresh_pairs', False),
timerange=timerange,
exchange=exchange,
live=config.get("live", False),
)
trades = load_trades(config)
return {"tickers": tickers,
"trades": trades,
"pairs": pairs,
"strategy": strategy,
}
def add_indicators(fig, row, indicators: List[str], data: pd.DataFrame) -> make_subplots:
"""
Generator all the indicator selected by the user for a specific row
:param fig: Plot figure to append to
:param row: row number for this plot
:param indicators: List of indicators present in the dataframe
:param data: candlestick DataFrame
"""
for indicator in indicators:
if indicator in data:
# TODO: Figure out why scattergl causes problems
scattergl = go.Scatter(
x=data['date'],
y=data[indicator].values,
mode='lines',
name=indicator
)
fig.add_trace(scattergl, row, 1)
else:
logger.info(
'Indicator "%s" ignored. Reason: This indicator is not found '
'in your strategy.',
indicator
)
return fig
def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_subplots:
"""
Add profit-plot
:param fig: Plot figure to append to
:param row: row number for this plot
:param data: candlestick DataFrame
:param column: Column to use for plot
:param name: Name to use
:return: fig with added profit plot
"""
profit = go.Scattergl(
x=data.index,
y=data[column],
name=name,
)
fig.add_trace(profit, row, 1)
return fig
def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
"""
Add trades to "fig"
"""
# Trades can be empty
if trades is not None and len(trades) > 0:
trade_buys = go.Scatter(
x=trades["open_time"],
y=trades["open_rate"],
mode='markers',
name='trade_buy',
marker=dict(
symbol='square-open',
size=11,
line=dict(width=2),
color='green'
)
)
# Create description for sell summarizing the trade
desc = trades.apply(lambda row: f"{round(row['profitperc'], 3)}%, {row['sell_reason']}, "
f"{row['duration']}min",
axis=1)
trade_sells = go.Scatter(
x=trades["close_time"],
y=trades["close_rate"],
text=desc,
mode='markers',
name='trade_sell',
marker=dict(
symbol='square-open',
size=11,
line=dict(width=2),
color='red'
)
)
fig.add_trace(trade_buys, 1, 1)
fig.add_trace(trade_sells, 1, 1)
else:
logger.warning("No trades found.")
return fig
def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None,
indicators1: List[str] = [],
indicators2: List[str] = [],) -> go.Figure:
"""
Generate the graph from the data generated by Backtesting or from DB
Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
:param pair: Pair to Display on the graph
:param data: OHLCV DataFrame containing indicators and buy/sell signals
:param trades: All trades created
:param indicators1: List containing Main plot indicators
:param indicators2: List containing Sub plot indicators
:return: None
"""
# Define the graph
fig = make_subplots(
rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 4],
vertical_spacing=0.0001,
)
fig['layout'].update(title=pair)
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Volume')
fig['layout']['yaxis3'].update(title='Other')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
# Common information
candles = go.Candlestick(
x=data.date,
open=data.open,
high=data.high,
low=data.low,
close=data.close,
name='Price'
)
fig.add_trace(candles, 1, 1)
if 'buy' in data.columns:
df_buy = data[data['buy'] == 1]
if len(df_buy) > 0:
buys = go.Scatter(
x=df_buy.date,
y=df_buy.close,
mode='markers',
name='buy',
marker=dict(
symbol='triangle-up-dot',
size=9,
line=dict(width=1),
color='green',
)
)
fig.add_trace(buys, 1, 1)
else:
logger.warning("No buy-signals found.")
if 'sell' in data.columns:
df_sell = data[data['sell'] == 1]
if len(df_sell) > 0:
sells = go.Scatter(
x=df_sell.date,
y=df_sell.close,
mode='markers',
name='sell',
marker=dict(
symbol='triangle-down-dot',
size=9,
line=dict(width=1),
color='red',
)
)
fig.add_trace(sells, 1, 1)
else:
logger.warning("No sell-signals found.")
if 'bb_lowerband' in data and 'bb_upperband' in data:
bb_lower = go.Scattergl(
x=data.date,
y=data.bb_lowerband,
name='BB lower',
line={'color': 'rgba(255,255,255,0)'},
)
bb_upper = go.Scattergl(
x=data.date,
y=data.bb_upperband,
name='BB upper',
fill="tonexty",
fillcolor="rgba(0,176,246,0.2)",
line={'color': 'rgba(255,255,255,0)'},
)
fig.add_trace(bb_lower, 1, 1)
fig.add_trace(bb_upper, 1, 1)
# Add indicators to main plot
fig = add_indicators(fig=fig, row=1, indicators=indicators1, data=data)
fig = plot_trades(fig, trades)
# Volume goes to row 2
volume = go.Bar(
x=data['date'],
y=data['volume'],
name='Volume'
)
fig.add_trace(volume, 2, 1)
# Add indicators to seperate row
fig = add_indicators(fig=fig, row=3, indicators=indicators2, data=data)
return fig
def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
trades: pd.DataFrame) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_tickers_with_mean(tickers, "close")
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
# Plot the pairs average close prices, and total profit growth
avgclose = go.Scattergl(
x=df_comb.index,
y=df_comb['mean'],
name='Avg close price',
)
fig = make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
fig['layout'].update(title="Profit plot")
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
for pair in pairs:
profit_col = f'cum_profit_{pair}'
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
return fig
def generate_plot_filename(pair, ticker_interval) -> str:
"""
Generate filenames per pair/ticker_interval to be used for storing plots
"""
pair_name = pair.replace("/", "_")
file_name = 'freqtrade-plot-' + pair_name + '-' + ticker_interval + '.html'
logger.info('Generate plot file for %s', pair)
return file_name
def store_plot_file(fig, filename: str, auto_open: bool = False) -> None:
"""
Generate a plot html file from pre populated fig plotly object
:param fig: Plotly Figure to plot
:param pair: Pair to plot (used as filename and Plot title)
:param ticker_interval: Used as part of the filename
:return: None
"""
Path("user_data/plots").mkdir(parents=True, exist_ok=True)
_filename = Path('user_data/plots').joinpath(filename)
plot(fig, filename=str(_filename),
auto_open=auto_open)
logger.info(f"Stored plot as {_filename}")

3
freqtrade/resolvers/__init__.py
View File

@ -1,5 +1,6 @@
from freqtrade.resolvers.iresolver import IResolver # noqa: F401
from freqtrade.resolvers.exchange_resolver import ExchangeResolver # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver # noqa: F401
# Don't import HyperoptResolver to avoid loading the whole Optimize tree
# from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver # noqa: F401
from freqtrade.resolvers.pairlist_resolver import PairListResolver # noqa: F401
from freqtrade.resolvers.strategy_resolver import StrategyResolver # noqa: F401

8
freqtrade/resolvers/exchange_resolver.py
View File

@ -22,11 +22,13 @@ class ExchangeResolver(IResolver):
Load the custom class from config parameter
:param config: configuration dictionary
"""
exchange_name = exchange_name.title()
try:
self.exchange = self._load_exchange(exchange_name, kwargs={'config': config})
except ImportError:
logger.info(
f"No {exchange_name} specific subclass found. Using the generic class instead.")
if not hasattr(self, "exchange"):
self.exchange = Exchange(config)
def _load_exchange(
@ -43,13 +45,13 @@ class ExchangeResolver(IResolver):
exchange = ex_class(kwargs['config'])
if exchange:
logger.info("Using resolved exchange %s", exchange_name)
logger.info(f"Using resolved exchange '{exchange_name}'...")
return exchange
except AttributeError:
# Pass and raise ImportError instead
pass
raise ImportError(
"Impossible to load Exchange '{}'. This class does not exist"
" or contains Python code errors".format(exchange_name)
f"Impossible to load Exchange '{exchange_name}'. This class does not exist "
"or contains Python code errors."
)

92
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -7,8 +7,10 @@ import logging
from pathlib import Path
from typing import Optional, Dict
from freqtrade.constants import DEFAULT_HYPEROPT
from freqtrade import OperationalException
from freqtrade.constants import DEFAULT_HYPEROPT, DEFAULT_HYPEROPT_LOSS
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.resolvers import IResolver
logger = logging.getLogger(__name__)
@ -21,17 +23,19 @@ class HyperOptResolver(IResolver):
__slots__ = ['hyperopt']
def __init__(self, config: Optional[Dict] = None) -> None:
def __init__(self, config: Dict) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
:param config: configuration dictionary
"""
config = config or {}
# Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
# Assign ticker_interval to be used in hyperopt
self.hyperopt.__class__.ticker_interval = str(config['ticker_interval'])
if not hasattr(self.hyperopt, 'populate_buy_trend'):
logger.warning("Custom Hyperopt does not provide populate_buy_trend. "
"Using populate_buy_trend from DefaultStrategy.")
@ -50,25 +54,75 @@ class HyperOptResolver(IResolver):
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = [
current_path.parent.parent.joinpath('user_data/hyperopts'),
Path.cwd().joinpath('user_data/hyperopts'),
current_path,
]
if extra_dir:
# Add extra hyperopt directory on top of search paths
abs_paths.insert(0, Path(extra_dir))
abs_paths.insert(0, Path(extra_dir).resolve())
for _path in abs_paths:
try:
hyperopt = self._search_object(directory=_path, object_type=IHyperOpt,
object_name=hyperopt_name)
if hyperopt:
logger.info("Using resolved hyperopt %s from '%s'", hyperopt_name, _path)
return hyperopt
except FileNotFoundError:
logger.warning('Path "%s" does not exist', _path.relative_to(Path.cwd()))
raise ImportError(
"Impossible to load Hyperopt '{}'. This class does not exist"
" or contains Python code errors".format(hyperopt_name)
hyperopt = self._load_object(paths=abs_paths, object_type=IHyperOpt,
object_name=hyperopt_name)
if hyperopt:
return hyperopt
raise OperationalException(
f"Impossible to load Hyperopt '{hyperopt_name}'. This class does not exist "
"or contains Python code errors."
)
class HyperOptLossResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt loss class
"""
__slots__ = ['hyperoptloss']
def __init__(self, config: Optional[Dict] = None) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
"""
config = config or {}
# Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
self.hyperoptloss = self._load_hyperoptloss(
hyperopt_name, extra_dir=config.get('hyperopt_path'))
# Assign ticker_interval to be used in hyperopt
self.hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
if not hasattr(self.hyperoptloss, 'hyperopt_loss_function'):
raise OperationalException(
f"Found hyperopt {hyperopt_name} does not implement `hyperopt_loss_function`.")
def _load_hyperoptloss(
self, hyper_loss_name: str, extra_dir: Optional[str] = None) -> IHyperOptLoss:
"""
Search and loads the specified hyperopt loss class.
:param hyper_loss_name: name of the module to import
:param extra_dir: additional directory to search for the given hyperopt
:return: HyperOptLoss instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = [
Path.cwd().joinpath('user_data/hyperopts'),
current_path,
]
if extra_dir:
# Add extra hyperopt directory on top of search paths
abs_paths.insert(0, Path(extra_dir).resolve())
hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
object_name=hyper_loss_name)
if hyperoptloss:
return hyperoptloss
raise OperationalException(
f"Impossible to load HyperoptLoss '{hyper_loss_name}'. This class does not exist "
"or contains Python code errors."
)

32
freqtrade/resolvers/iresolver.py
View File

@ -7,7 +7,7 @@ import importlib.util
import inspect
import logging
from pathlib import Path
from typing import Optional, Type, Any
from typing import Any, List, Optional, Tuple, Type, Union
logger = logging.getLogger(__name__)
@ -45,7 +45,7 @@ class IResolver(object):
@staticmethod
def _search_object(directory: Path, object_type, object_name: str,
kwargs: dict = {}) -> Optional[Any]:
kwargs: dict = {}) -> Union[Tuple[Any, Path], Tuple[None, None]]:
"""
Search for the objectname in the given directory
:param directory: relative or absolute directory path
@ -57,9 +57,33 @@ class IResolver(object):
if not str(entry).endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
module_path = Path.resolve(directory.joinpath(entry))
obj = IResolver._get_valid_object(
object_type, Path.resolve(directory.joinpath(entry)), object_name
object_type, module_path, object_name
)
if obj:
return obj(**kwargs)
return (obj(**kwargs), module_path)
return (None, None)
@staticmethod
def _load_object(paths: List[Path], object_type, object_name: str,
kwargs: dict = {}) -> Optional[Any]:
"""
Try to load object from path list.
"""
for _path in paths:
try:
(module, module_path) = IResolver._search_object(directory=_path,
object_type=object_type,
object_name=object_name,
kwargs=kwargs)
if module:
logger.info(
f"Using resolved {object_type.__name__.lower()[1:]} {object_name} "
f"from '{module_path}'...")
return module
except FileNotFoundError:
logger.warning('Path "%s" does not exist.', _path.resolve())
return None

24
freqtrade/resolvers/pairlist_resolver.py
View File

@ -6,6 +6,7 @@ This module load custom hyperopts
import logging
from pathlib import Path
from freqtrade import OperationalException
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import IResolver
@ -38,22 +39,15 @@ class PairListResolver(IResolver):
current_path = Path(__file__).parent.parent.joinpath('pairlist').resolve()
abs_paths = [
current_path.parent.parent.joinpath('user_data/pairlist'),
Path.cwd().joinpath('user_data/pairlist'),
current_path,
]
for _path in abs_paths:
try:
pairlist = self._search_object(directory=_path, object_type=IPairList,
object_name=pairlist_name,
kwargs=kwargs)
if pairlist:
logger.info("Using resolved pairlist %s from '%s'", pairlist_name, _path)
return pairlist
except FileNotFoundError:
logger.warning('Path "%s" does not exist', _path.relative_to(Path.cwd()))
raise ImportError(
"Impossible to load Pairlist '{}'. This class does not exist"
" or contains Python code errors".format(pairlist_name)
pairlist = self._load_object(paths=abs_paths, object_type=IPairList,
object_name=pairlist_name, kwargs=kwargs)
if pairlist:
return pairlist
raise OperationalException(
f"Impossible to load Pairlist '{pairlist_name}'. This class does not exist "
"or contains Python code errors."
)

37
freqtrade/resolvers/strategy_resolver.py
View File

@ -11,7 +11,7 @@ from inspect import getfullargspec
from pathlib import Path
from typing import Dict, Optional
from freqtrade import constants
from freqtrade import constants, OperationalException
from freqtrade.resolvers import IResolver
from freqtrade.strategy import import_strategy
from freqtrade.strategy.interface import IStrategy
@ -56,6 +56,8 @@ class StrategyResolver(IResolver):
("process_only_new_candles", None, False),
("order_types", None, False),
("order_time_in_force", None, False),
("stake_currency", None, False),
("stake_amount", None, False),
("use_sell_signal", False, True),
("sell_profit_only", False, True),
("ignore_roi_if_buy_signal", False, True),
@ -130,7 +132,7 @@ class StrategyResolver(IResolver):
abs_paths.insert(0, Path(extra_dir).resolve())
if ":" in strategy_name:
logger.info("loading base64 endocded strategy")
logger.info("loading base64 encoded strategy")
strat = strategy_name.split(":")
if len(strat) == 2:
@ -145,22 +147,21 @@ class StrategyResolver(IResolver):
# register temp path with the bot
abs_paths.insert(0, temp.resolve())
for _path in abs_paths:
strategy = self._load_object(paths=abs_paths, object_type=IStrategy,
object_name=strategy_name, kwargs={'config': config})
if strategy:
strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
try:
strategy = self._search_object(directory=_path, object_type=IStrategy,
object_name=strategy_name, kwargs={'config': config})
if strategy:
logger.info("Using resolved strategy %s from '%s'", strategy_name, _path)
strategy._populate_fun_len = len(
getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
return import_strategy(strategy, config=config)
except TypeError as e:
logger.warning(
f"Impossible to load strategy '{strategy_name}'. "
f"Error: {e}")
return import_strategy(strategy, config=config)
except FileNotFoundError:
logger.warning('Path "%s" does not exist', _path.relative_to(Path.cwd()))
raise ImportError(
"Impossible to load Strategy '{}'. This class does not exist"
" or contains Python code errors".format(strategy_name)
raise OperationalException(
f"Impossible to load Strategy '{strategy_name}'. This class does not exist "
"or contains Python code errors."
)

375
freqtrade/rpc/api_server.py Normal file
View File

@ -0,0 +1,375 @@
import logging
import threading
from datetime import date, datetime
from ipaddress import IPv4Address
from typing import Dict
from arrow import Arrow
from flask import Flask, jsonify, request
from flask.json import JSONEncoder
from werkzeug.serving import make_server
from freqtrade.__init__ import __version__
from freqtrade.rpc.rpc import RPC, RPCException
logger = logging.getLogger(__name__)
BASE_URI = "/api/v1"
class ArrowJSONEncoder(JSONEncoder):
def default(self, obj):
try:
if isinstance(obj, Arrow):
return obj.for_json()
elif isinstance(obj, date):
return obj.strftime("%Y-%m-%d")
elif isinstance(obj, datetime):
return obj.strftime("%Y-%m-%d %H:%M:%S")
iterable = iter(obj)
except TypeError:
pass
else:
return list(iterable)
return JSONEncoder.default(self, obj)
class ApiServer(RPC):
"""
This class runs api server and provides rpc.rpc functionality to it
This class starts a none blocking thread the api server runs within
"""
def rpc_catch_errors(func):
def func_wrapper(self, *args, **kwargs):
try:
return func(self, *args, **kwargs)
except RPCException as e:
logger.exception("API Error calling %s: %s", func.__name__, e)
return self.rest_error(f"Error querying {func.__name__}: {e}")
return func_wrapper
def check_auth(self, username, password):
return (username == self._config['api_server'].get('username') and
password == self._config['api_server'].get('password'))
def require_login(func):
def func_wrapper(self, *args, **kwargs):
auth = request.authorization
if auth and self.check_auth(auth.username, auth.password):
return func(self, *args, **kwargs)
else:
return jsonify({"error": "Unauthorized"}), 401
return func_wrapper
def __init__(self, freqtrade) -> None:
"""
Init the api server, and init the super class RPC
:param freqtrade: Instance of a freqtrade bot
:return: None
"""
super().__init__(freqtrade)
self._config = freqtrade.config
self.app = Flask(__name__)
self.app.json_encoder = ArrowJSONEncoder
# Register application handling
self.register_rest_rpc_urls()
thread = threading.Thread(target=self.run, daemon=True)
thread.start()
def cleanup(self) -> None:
logger.info("Stopping API Server")
self.srv.shutdown()
def run(self):
"""
Method that runs flask app in its own thread forever.
Section to handle configuration and running of the Rest server
also to check and warn if not bound to a loopback, warn on security risk.
"""
rest_ip = self._config['api_server']['listen_ip_address']
rest_port = self._config['api_server']['listen_port']
logger.info(f'Starting HTTP Server at {rest_ip}:{rest_port}')
if not IPv4Address(rest_ip).is_loopback:
logger.warning("SECURITY WARNING - Local Rest Server listening to external connections")
logger.warning("SECURITY WARNING - This is insecure please set to your loopback,"
"e.g 127.0.0.1 in config.json")
if not self._config['api_server'].get('password'):
logger.warning("SECURITY WARNING - No password for local REST Server defined. "
"Please make sure that this is intentional!")
# Run the Server
logger.info('Starting Local Rest Server.')
try:
self.srv = make_server(rest_ip, rest_port, self.app)
self.srv.serve_forever()
except Exception:
logger.exception("Api server failed to start.")
logger.info('Local Rest Server started.')
def send_msg(self, msg: Dict[str, str]) -> None:
"""
We don't push to endpoints at the moment.
Take a look at webhooks for that functionality.
"""
pass
def rest_dump(self, return_value):
""" Helper function to jsonify object for a webserver """
return jsonify(return_value)
def rest_error(self, error_msg):
return jsonify({"error": error_msg}), 502
def register_rest_rpc_urls(self):
"""
Registers flask app URLs that are calls to functonality in rpc.rpc.
First two arguments passed are /URL and 'Label'
Label can be used as a shortcut when refactoring
:return:
"""
self.app.register_error_handler(404, self.page_not_found)
# Actions to control the bot
self.app.add_url_rule(f'{BASE_URI}/start', 'start',
view_func=self._start, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/stop', 'stop', view_func=self._stop, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/stopbuy', 'stopbuy',
view_func=self._stopbuy, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/reload_conf', 'reload_conf',
view_func=self._reload_conf, methods=['POST'])
# Info commands
self.app.add_url_rule(f'{BASE_URI}/balance', 'balance',
view_func=self._balance, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/count', 'count', view_func=self._count, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/daily', 'daily', view_func=self._daily, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/edge', 'edge', view_func=self._edge, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/profit', 'profit',
view_func=self._profit, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/performance', 'performance',
view_func=self._performance, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/status', 'status',
view_func=self._status, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/version', 'version',
view_func=self._version, methods=['GET'])
# Combined actions and infos
self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
methods=['GET', 'POST'])
self.app.add_url_rule(f'{BASE_URI}/whitelist', 'whitelist', view_func=self._whitelist,
methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/forcebuy', 'forcebuy',
view_func=self._forcebuy, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/forcesell', 'forcesell', view_func=self._forcesell,
methods=['POST'])
# TODO: Implement the following
# help (?)
@require_login
def page_not_found(self, error):
"""
Return "404 not found", 404.
"""
return self.rest_dump({
'status': 'error',
'reason': f"There's no API call for {request.base_url}.",
'code': 404
}), 404
@require_login
@rpc_catch_errors
def _start(self):
"""
Handler for /start.
Starts TradeThread in bot if stopped.
"""
msg = self._rpc_start()
return self.rest_dump(msg)
@require_login
@rpc_catch_errors
def _stop(self):
"""
Handler for /stop.
Stops TradeThread in bot if running
"""
msg = self._rpc_stop()
return self.rest_dump(msg)
@require_login
@rpc_catch_errors
def _stopbuy(self):
"""
Handler for /stopbuy.
Sets max_open_trades to 0 and gracefully sells all open trades
"""
msg = self._rpc_stopbuy()
return self.rest_dump(msg)
@require_login
@rpc_catch_errors
def _version(self):
"""
Prints the bot's version
"""
return self.rest_dump({"version": __version__})
@require_login
@rpc_catch_errors
def _reload_conf(self):
"""
Handler for /reload_conf.
Triggers a config file reload
"""
msg = self._rpc_reload_conf()
return self.rest_dump(msg)
@require_login
@rpc_catch_errors
def _count(self):
"""
Handler for /count.
Returns the number of trades running
"""
msg = self._rpc_count()
return self.rest_dump(msg)
@require_login
@rpc_catch_errors
def _daily(self):
"""
Returns the last X days trading stats summary.
:return: stats
"""
timescale = request.args.get('timescale', 7)
timescale = int(timescale)
stats = self._rpc_daily_profit(timescale,
self._config['stake_currency'],
self._config['fiat_display_currency']
)
return self.rest_dump(stats)
@require_login
@rpc_catch_errors
def _edge(self):
"""
Returns information related to Edge.
:return: edge stats
"""
stats = self._rpc_edge()
return self.rest_dump(stats)
@require_login
@rpc_catch_errors
def _profit(self):
"""
Handler for /profit.
Returns a cumulative profit statistics
:return: stats
"""
logger.info("LocalRPC - Profit Command Called")
stats = self._rpc_trade_statistics(self._config['stake_currency'],
self._config['fiat_display_currency']
)
return self.rest_dump(stats)
@require_login
@rpc_catch_errors
def _performance(self):
"""
Handler for /performance.
Returns a cumulative performance statistics
:return: stats
"""
logger.info("LocalRPC - performance Command Called")
stats = self._rpc_performance()
return self.rest_dump(stats)
@require_login
@rpc_catch_errors
def _status(self):
"""
Handler for /status.
Returns the current status of the trades in json format
"""
results = self._rpc_trade_status()
return self.rest_dump(results)
@require_login
@rpc_catch_errors
def _balance(self):
"""
Handler for /balance.
Returns the current status of the trades in json format
"""
results = self._rpc_balance(self._config.get('fiat_display_currency', ''))
return self.rest_dump(results)
@require_login
@rpc_catch_errors
def _whitelist(self):
"""
Handler for /whitelist.
"""
results = self._rpc_whitelist()
return self.rest_dump(results)
@require_login
@rpc_catch_errors
def _blacklist(self):
"""
Handler for /blacklist.
"""
add = request.json.get("blacklist", None) if request.method == 'POST' else None
results = self._rpc_blacklist(add)
return self.rest_dump(results)
@require_login
@rpc_catch_errors
def _forcebuy(self):
"""
Handler for /forcebuy.
"""
asset = request.json.get("pair")
price = request.json.get("price", None)
trade = self._rpc_forcebuy(asset, price)
if trade:
return self.rest_dump(trade.to_json())
else:
return self.rest_dump({"status": f"Error buying pair {asset}."})
@require_login
@rpc_catch_errors
def _forcesell(self):
"""
Handler for /forcesell.
"""
tradeid = request.json.get("tradeid")
results = self._rpc_forcesell(tradeid)
return self.rest_dump(results)

70
freqtrade/rpc/rpc.py
View File

@ -48,6 +48,11 @@ class RPCException(Exception):
def __str__(self):
return self.message
def __json__(self):
return {
'msg': self.message
}
class RPC(object):
"""
@ -100,20 +105,17 @@ class RPC(object):
current_profit = trade.calc_profit_percent(current_rate)
fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
if trade.close_profit else None)
results.append(dict(
trade_id=trade.id,
pair=trade.pair,
date=arrow.get(trade.open_date),
open_rate=trade.open_rate,
close_rate=trade.close_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
trade_dict = trade.to_json()
trade_dict.update(dict(
base_currency=self._freqtrade.config['stake_currency'],
close_profit=fmt_close_profit,
current_rate=current_rate,
current_profit=round(current_profit * 100, 2),
open_order='({} {} rem={:.8f})'.format(
order['type'], order['side'], order['remaining']
order['type'], order['side'], order['remaining']
) if order else None,
))
results.append(trade_dict)
return results
def _rpc_status_table(self) -> DataFrame:
@ -279,11 +281,13 @@ class RPC(object):
rate = 1.0
else:
try:
if coin == 'USDT':
rate = 1.0 / self._freqtrade.get_sell_rate('BTC/USDT', False)
pair = self._freqtrade.exchange.get_valid_pair_combination(coin, "BTC")
if pair.startswith("BTC"):
rate = 1.0 / self._freqtrade.get_sell_rate(pair, False)
else:
rate = self._freqtrade.get_sell_rate(coin + '/BTC', False)
rate = self._freqtrade.get_sell_rate(pair, False)
except (TemporaryError, DependencyException):
logger.warning(f" Could not get rate for pair {coin}.")
continue
est_btc: float = rate * balance['total']
total = total + est_btc
@ -295,7 +299,10 @@ class RPC(object):
'est_btc': est_btc,
})
if total == 0.0:
raise RPCException('all balances are zero')
if self._freqtrade.config.get('dry_run', False):
raise RPCException('Running in Dry Run, balances are not available.')
else:
raise RPCException('All balances are zero.')
symbol = fiat_display_currency
value = self._fiat_converter.convert_amount(total, 'BTC',
@ -338,7 +345,7 @@ class RPC(object):
return {'status': 'No more buy will occur from now. Run /reload_conf to reset.'}
def _rpc_forcesell(self, trade_id) -> None:
def _rpc_forcesell(self, trade_id) -> Dict[str, str]:
"""
Handler for forcesell <id>.
Sells the given trade at current price
@ -378,7 +385,7 @@ class RPC(object):
for trade in Trade.get_open_trades():
_exec_forcesell(trade)
Trade.session.flush()
return
return {'result': 'Created sell orders for all open trades.'}
# Query for trade
trade = Trade.query.filter(
@ -393,6 +400,7 @@ class RPC(object):
_exec_forcesell(trade)
Trade.session.flush()
return {'result': f'Created sell order for trade {trade_id}.'}
def _rpc_forcebuy(self, pair: str, price: Optional[float]) -> Optional[Trade]:
"""
@ -446,17 +454,43 @@ class RPC(object):
for pair, rate, count in pair_rates
]
def _rpc_count(self) -> List[Trade]:
def _rpc_count(self) -> Dict[str, float]:
""" Returns the number of trades running """
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
return Trade.get_open_trades()
trades = Trade.get_open_trades()
return {
'current': len(trades),
'max': float(self._freqtrade.config['max_open_trades']),
'total_stake': sum((trade.open_rate * trade.amount) for trade in trades)
}
def _rpc_whitelist(self) -> Dict:
""" Returns the currently active whitelist"""
res = {'method': self._freqtrade.pairlists.name,
'length': len(self._freqtrade.pairlists.whitelist),
'length': len(self._freqtrade.active_pair_whitelist),
'whitelist': self._freqtrade.active_pair_whitelist
}
return res
def _rpc_blacklist(self, add: List[str] = None) -> Dict:
""" Returns the currently active blacklist"""
if add:
stake_currency = self._freqtrade.config.get('stake_currency')
for pair in add:
if (pair.endswith(stake_currency)
and pair not in self._freqtrade.pairlists.blacklist):
self._freqtrade.pairlists.blacklist.append(pair)
res = {'method': self._freqtrade.pairlists.name,
'length': len(self._freqtrade.pairlists.blacklist),
'blacklist': self._freqtrade.pairlists.blacklist,
}
return res
def _rpc_edge(self) -> List[Dict[str, Any]]:
""" Returns information related to Edge """
if not self._freqtrade.edge:
raise RPCException(f'Edge is not enabled.')
return self._freqtrade.edge.accepted_pairs()

6
freqtrade/rpc/rpc_manager.py
View File

@ -29,6 +29,12 @@ class RPCManager(object):
from freqtrade.rpc.webhook import Webhook
self.registered_modules.append(Webhook(freqtrade))
# Enable local rest api server for cmd line control
if freqtrade.config.get('api_server', {}).get('enabled', False):
logger.info('Enabling rpc.api_server')
from freqtrade.rpc.api_server import ApiServer
self.registered_modules.append(ApiServer(freqtrade))
def cleanup(self) -> None:
""" Stops all enabled rpc modules """
logger.info('Cleaning up rpc modules ...')

119
freqtrade/rpc/telegram.py
View File

@ -4,7 +4,7 @@
This module manage Telegram communication
"""
import logging
from typing import Any, Callable, Dict
from typing import Any, Callable, Dict, List
from tabulate import tabulate
from telegram import Bot, ParseMode, ReplyKeyboardMarkup, Update
@ -20,7 +20,10 @@ logger = logging.getLogger(__name__)
logger.debug('Included module rpc.telegram ...')
def authorized_only(command_handler: Callable[[Any, Bot, Update], None]) -> Callable[..., Any]:
MAX_TELEGRAM_MESSAGE_LENGTH = 4096
def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
"""
Decorator to check if the message comes from the correct chat_id
:param command_handler: Telegram CommandHandler
@ -93,6 +96,8 @@ class Telegram(RPC):
CommandHandler('reload_conf', self._reload_conf),
CommandHandler('stopbuy', self._stopbuy),
CommandHandler('whitelist', self._whitelist),
CommandHandler('blacklist', self._blacklist, pass_args=True),
CommandHandler('edge', self._edge),
CommandHandler('help', self._help),
CommandHandler('version', self._version),
]
@ -127,7 +132,7 @@ class Telegram(RPC):
msg['stake_amount_fiat'] = 0
message = ("*{exchange}:* Buying {pair}\n"
"with limit `{limit:.8f}\n"
"at rate `{limit:.8f}\n"
"({stake_amount:.6f} {stake_currency}").format(**msg)
if msg.get('fiat_currency', None):
@ -139,7 +144,7 @@ class Telegram(RPC):
msg['profit_percent'] = round(msg['profit_percent'] * 100, 2)
message = ("*{exchange}:* Selling {pair}\n"
"*Limit:* `{limit:.8f}`\n"
"*Rate:* `{limit:.8f}`\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
@ -188,25 +193,36 @@ class Telegram(RPC):
try:
results = self._rpc_trade_status()
# pre format data
for result in results:
result['date'] = result['date'].humanize()
messages = [
"*Trade ID:* `{trade_id}`\n"
"*Current Pair:* {pair}\n"
"*Open Since:* `{date}`\n"
"*Amount:* `{amount}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Close Rate:* `{close_rate}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Profit:* `{close_profit}`\n"
"*Current Profit:* `{current_profit:.2f}%`\n"
"*Open Order:* `{open_order}`".format(**result)
for result in results
]
messages = []
for r in results:
lines = [
"*Trade ID:* `{trade_id}` `(since {open_date_hum})`",
"*Current Pair:* {pair}",
"*Amount:* `{amount} ({stake_amount} {base_currency})`",
"*Open Rate:* `{open_rate:.8f}`",
"*Close Rate:* `{close_rate}`" if r['close_rate'] else "",
"*Current Rate:* `{current_rate:.8f}`",
"*Close Profit:* `{close_profit}`" if r['close_profit'] else "",
"*Current Profit:* `{current_profit:.2f}%`",
# Adding initial stoploss only if it is different from stoploss
"*Initial Stoploss:* `{initial_stop_loss:.8f}` " +
("`({initial_stop_loss_pct:.2f}%)`" if r['initial_stop_loss_pct'] else "")
if r['stop_loss'] != r['initial_stop_loss'] else "",
# Adding stoploss and stoploss percentage only if it is not None
"*Stoploss:* `{stop_loss:.8f}` " +
("`({stop_loss_pct:.2f}%)`" if r['stop_loss_pct'] else ""),
"*Open Order:* `{open_order}`" if r['open_order'] else ""
]
# Filter empty lines using list-comprehension
messages.append("\n".join([l for l in lines if l]).format(**r))
for msg in messages:
self._send_msg(msg, bot=bot)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -251,7 +267,8 @@ class Telegram(RPC):
headers=[
'Day',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}'
f'Profit {fiat_disp_cur}',
f'Trades'
],
tablefmt='simple')
message = f'<b>Daily Profit over the last {timescale} days</b>:\n<pre>{stats_tab}</pre>'
@ -312,13 +329,20 @@ class Telegram(RPC):
output = ''
for currency in result['currencies']:
if currency['est_btc'] > 0.0001:
output += "*{currency}:*\n" \
curr_output = "*{currency}:*\n" \
"\t`Available: {available: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
"\t`Pending: {pending: .8f}`\n" \
"\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
else:
output += "*{currency}:* not showing <1$ amount \n".format(**currency)
curr_output = "*{currency}:* not showing <1$ amount \n".format(**currency)
# Handle overflowing messsage length
if len(output + curr_output) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, bot=bot)
output = curr_output
else:
output += curr_output
output += "\n*Estimated Value*:\n" \
"\t`BTC: {total: .8f}`\n" \
@ -387,7 +411,9 @@ class Telegram(RPC):
trade_id = update.message.text.replace('/forcesell', '').strip()
try:
self._rpc_forcesell(trade_id)
msg = self._rpc_forcesell(trade_id)
self._send_msg('Forcesell Result: `{result}`'.format(**msg), bot=bot)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@ -441,12 +467,10 @@ class Telegram(RPC):
:return: None
"""
try:
trades = self._rpc_count()
message = tabulate({
'current': [len(trades)],
'max': [self._config['max_open_trades']],
'total stake': [sum((trade.open_rate * trade.amount) for trade in trades)]
}, headers=['current', 'max', 'total stake'], tablefmt='simple')
counts = self._rpc_count()
message = tabulate({k: [v] for k, v in counts.items()},
headers=['current', 'max', 'total stake'],
tablefmt='simple')
message = "<pre>{}</pre>".format(message)
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML)
@ -470,6 +494,38 @@ class Telegram(RPC):
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _blacklist(self, bot: Bot, update: Update, args: List[str]) -> None:
"""
Handler for /blacklist
Shows the currently active blacklist
"""
try:
blacklist = self._rpc_blacklist(args)
message = f"Blacklist contains {blacklist['length']} pairs\n"
message += f"`{', '.join(blacklist['blacklist'])}`"
logger.debug(message)
self._send_msg(message)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _edge(self, bot: Bot, update: Update) -> None:
"""
Handler for /edge
Shows information related to Edge
"""
try:
edge_pairs = self._rpc_edge()
edge_pairs_tab = tabulate(edge_pairs, headers='keys', tablefmt='simple')
message = f'<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>'
self._send_msg(message, bot=bot, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e), bot=bot)
@authorized_only
def _help(self, bot: Bot, update: Update) -> None:
"""
@ -497,6 +553,9 @@ class Telegram(RPC):
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" \
"*/reload_conf:* `Reload configuration file` \n" \
"*/whitelist:* `Show current whitelist` \n" \
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " \
"to the blacklist.` \n" \
"*/edge:* `Shows validated pairs by Edge if it is enabeld` \n" \
"*/help:* `This help message`\n" \
"*/version:* `Show version`"

4
freqtrade/state.py
View File

@ -18,11 +18,11 @@ class State(Enum):
class RunMode(Enum):
"""
Bot running mode (backtest, hyperopt, ...)
can be "live", "dry-run", "backtest", "edgecli", "hyperopt".
can be "live", "dry-run", "backtest", "edge", "hyperopt".
"""
LIVE = "live"
DRY_RUN = "dry_run"
BACKTEST = "backtest"
EDGECLI = "edgecli"
EDGE = "edge"
HYPEROPT = "hyperopt"
OTHER = "other" # Used for plotting scripts and test

3
freqtrade/strategy/__init__.py
View File

@ -6,6 +6,7 @@ from freqtrade.strategy.interface import IStrategy
# Import Default-Strategy to have hyperopt correctly resolve
from freqtrade.strategy.default_strategy import DefaultStrategy # noqa: F401
logger = logging.getLogger(__name__)
@ -16,7 +17,6 @@ def import_strategy(strategy: IStrategy, config: dict) -> IStrategy:
"""
# Copy all attributes from base class and class
comb = {**strategy.__class__.__dict__, **strategy.__dict__}
# Delete '_abc_impl' from dict as deepcopy fails on 3.7 with
@ -26,6 +26,7 @@ def import_strategy(strategy: IStrategy, config: dict) -> IStrategy:
del comb['_abc_impl']
attr = deepcopy(comb)
# Adjust module name
attr['__module__'] = 'freqtrade.strategy'

141
freqtrade/strategy/interface.py
View File

@ -6,17 +6,18 @@ import logging
from abc import ABC, abstractmethod
from datetime import datetime
from enum import Enum
from typing import Dict, List, NamedTuple, Tuple
from typing import Dict, List, NamedTuple, Optional, Tuple
import warnings
import arrow
from pandas import DataFrame
from freqtrade import constants
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -157,7 +158,24 @@ class IStrategy(ABC):
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
:param dataframe: Dataframe containing ticker data
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
return dataframe
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing ticker data
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
"""
pair = str(metadata.get('pair'))
@ -167,10 +185,7 @@ class IStrategy(ABC):
if (not self.process_only_new_candles or
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
# Defs that only make change on new candle data.
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
dataframe = self.analyze_ticker(dataframe, metadata)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
@ -197,7 +212,7 @@ class IStrategy(ABC):
return False, False
try:
dataframe = self.analyze_ticker(dataframe, {'pair': pair})
dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
@ -221,7 +236,7 @@ class IStrategy(ABC):
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
interval_minutes = timeframe_to_minutes(interval)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
logger.warning(
@ -247,6 +262,9 @@ class IStrategy(ABC):
"""
This function evaluate if on the condition required to trigger a sell has been reached
if the threshold is reached and updates the trade record.
:param low: Only used during backtesting to simulate stoploss
:param high: Only used during backtesting, to simulate ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be sold, False otherwise
"""
@ -254,14 +272,16 @@ class IStrategy(ABC):
current_rate = low or rate
current_profit = trade.calc_profit_percent(current_rate)
trade.adjust_min_max_rates(high or current_rate)
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss)
force_stoploss=force_stoploss, high=high)
if stoplossflag.sell_flag:
return stoplossflag
# Set current rate to low for backtesting sell
# Set current rate to high for backtesting sell
current_rate = high or rate
current_profit = trade.calc_profit_percent(current_rate)
experimental = self.config.get('experimental', {})
@ -285,8 +305,9 @@ class IStrategy(ABC):
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
current_profit: float, force_stoploss: float) -> SellCheckTuple:
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
force_stoploss: float, high: float = None) -> SellCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
@ -294,16 +315,39 @@ class IStrategy(ABC):
"""
trailing_stop = self.config.get('trailing_stop', False)
trade.adjust_stop_loss(trade.open_rate, force_stoploss if force_stoploss
else self.stoploss, initial=True)
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
# Initiate stoploss with open_rate. Does nothing if stoploss is already set.
trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
if trailing_stop:
# trailing stoploss handling
sl_offset = self.config.get('trailing_stop_positive_offset') or 0.0
tsl_only_offset = self.config.get('trailing_only_offset_is_reached', False)
# Make sure current_profit is calculated using high for backtesting.
high_profit = current_profit if not high else trade.calc_profit_percent(high)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (tsl_only_offset and high_profit < sl_offset):
# Specific handling for trailing_stop_positive
if 'trailing_stop_positive' in self.config and high_profit > sl_offset:
# Ignore mypy error check in configuration that this is a float
stop_loss_value = self.config.get('trailing_stop_positive') # type: ignore
logger.debug(f"using positive stop loss: {stop_loss_value} "
f"offset: {sl_offset:.4g} profit: {current_profit:.4f}%")
trade.adjust_stop_loss(high or current_rate, stop_loss_value)
# evaluate if the stoploss was hit if stoploss is not on exchange
if ((self.stoploss is not None) and
(trade.stop_loss >= current_rate) and
(not self.order_types.get('stoploss_on_exchange'))):
(trade.stop_loss >= current_rate) and
(not self.order_types.get('stoploss_on_exchange'))):
selltype = SellType.STOP_LOSS
# If Trailing stop (and max-rate did move above open rate)
if trailing_stop and trade.open_rate != trade.max_rate:
# If initial stoploss is not the same as current one then it is trailing.
if trade.initial_stop_loss != trade.stop_loss:
selltype = SellType.TRAILING_STOP_LOSS
logger.debug(
f"HIT STOP: current price at {current_rate:.6f}, "
@ -315,48 +359,34 @@ class IStrategy(ABC):
logger.debug('Stop loss hit.')
return SellCheckTuple(sell_flag=True, sell_type=selltype)
# update the stop loss afterwards, after all by definition it's supposed to be hanging
if trailing_stop:
# check if we have a special stop loss for positive condition
# and if profit is positive
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
sl_offset = self.config.get('trailing_stop_positive_offset') or 0.0
if 'trailing_stop_positive' in self.config and current_profit > sl_offset:
# Ignore mypy error check in configuration that this is a float
stop_loss_value = self.config.get('trailing_stop_positive') # type: ignore
logger.debug(f"using positive stop loss mode: {stop_loss_value} "
f"with offset {sl_offset:.4g} "
f"since we have profit {current_profit:.4f}%")
# if trailing_only_offset_is_reached is true,
# we update trailing stoploss only if offset is reached.
tsl_only_offset = self.config.get('trailing_only_offset_is_reached', False)
if not (tsl_only_offset and current_profit < sl_offset):
trade.adjust_stop_loss(current_rate, stop_loss_value)
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def min_roi_reached_entry(self, trade_dur: int) -> Optional[float]:
"""
Based on trade duration defines the ROI entry that may have been reached.
:param trade_dur: trade duration in minutes
:return: minimal ROI entry value or None if none proper ROI entry was found.
"""
# Get highest entry in ROI dict where key <= trade-duration
roi_list = list(filter(lambda x: x <= trade_dur, self.minimal_roi.keys()))
if not roi_list:
return None
roi_entry = max(roi_list)
return self.minimal_roi[roi_entry]
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
Based an earlier trade and current price and ROI configuration, decides whether bot should
Based on trade duration, current price and ROI configuration, decides whether bot should
sell. Requires current_profit to be in percent!!
:return True if bot should sell at current rate
:return: True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
trade_dur = (current_time.timestamp() - trade.open_date.timestamp()) / 60
# Get highest entry in ROI dict where key >= trade-duration
roi_entry = max(list(filter(lambda x: trade_dur >= x, self.minimal_roi.keys())))
threshold = self.minimal_roi[roi_entry]
if current_profit > threshold:
return True
return False
trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
roi = self.min_roi_reached_entry(trade_dur)
if roi is None:
return False
else:
return current_profit > roi
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""
@ -373,6 +403,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
if self._populate_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
@ -388,6 +419,7 @@ class IStrategy(ABC):
:param pair: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
logger.debug(f"Populating buy signals for pair {metadata.get('pair')}.")
if self._buy_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
@ -403,6 +435,7 @@ class IStrategy(ABC):
:param pair: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
logger.debug(f"Populating sell signals for pair {metadata.get('pair')}.")
if self._sell_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)

142
freqtrade/tests/conftest.py
View File

@ -2,21 +2,25 @@
import json
import logging
import re
from copy import deepcopy
from datetime import datetime
from functools import reduce
from typing import Dict, Optional
from pathlib import Path
from unittest.mock import MagicMock, PropertyMock
import arrow
import pytest
from telegram import Chat, Message, Update
from freqtrade import constants
from freqtrade import constants, persistence
from freqtrade.configuration import Arguments
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exchange import Exchange
from freqtrade.edge import Edge, PairInfo
from freqtrade.exchange import Exchange
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.resolvers import ExchangeResolver
from freqtrade.worker import Worker
logging.getLogger('').setLevel(logging.INFO)
@ -35,6 +39,17 @@ def log_has_re(line, logs):
False)
def get_args(args):
return Arguments(args, '').get_parsed_arg()
def patched_configuration_load_config_file(mocker, config) -> None:
mocker.patch(
'freqtrade.configuration.configuration.load_config_file',
lambda *args, **kwargs: config
)
def patch_exchange(mocker, api_mock=None, id='bittrex') -> None:
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
@ -53,7 +68,7 @@ def get_patched_exchange(mocker, config, api_mock=None, id='bittrex') -> Exchang
patch_exchange(mocker, api_mock, id)
config["exchange"]["name"] = id
try:
exchange = ExchangeResolver(id.title(), config).exchange
exchange = ExchangeResolver(id, config).exchange
except ImportError:
exchange = Exchange(config)
return exchange
@ -88,24 +103,54 @@ def get_patched_edge(mocker, config) -> Edge:
# Functions for recurrent object patching
def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
def patch_freqtradebot(mocker, config) -> None:
"""
This function patch _init_modules() to not call dependencies
:param mocker: a Mocker object to apply patches
:param config: Config to pass to the bot
:return: None
"""
patch_coinmarketcap(mocker, {'price_usd': 12345.0})
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
mocker.patch('freqtrade.freqtradebot.persistence.init', MagicMock())
persistence.init(config['db_url'])
patch_exchange(mocker, None)
mocker.patch('freqtrade.freqtradebot.RPCManager._init', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager.send_msg', MagicMock())
def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
"""
This function patches _init_modules() to not call dependencies
:param mocker: a Mocker object to apply patches
:param config: Config to pass to the bot
:return: FreqtradeBot
"""
patch_freqtradebot(mocker, config)
return FreqtradeBot(config)
def patch_coinmarketcap(mocker, value: Optional[Dict[str, float]] = None) -> None:
def get_patched_worker(mocker, config) -> Worker:
"""
This function patches _init_modules() to not call dependencies
:param mocker: a Mocker object to apply patches
:param config: Config to pass to the bot
:return: Worker
"""
patch_freqtradebot(mocker, config)
return Worker(args=None, config=config)
def patch_get_signal(freqtrade: FreqtradeBot, value=(True, False)) -> None:
"""
:param mocker: mocker to patch IStrategy class
:param value: which value IStrategy.get_signal() must return
:return: None
"""
freqtrade.strategy.get_signal = lambda e, s, t: value
freqtrade.exchange.refresh_latest_ohlcv = lambda p: None
@pytest.fixture(autouse=True)
def patch_coinmarketcap(mocker) -> None:
"""
Mocker to coinmarketcap to speed up tests
:param mocker: mocker to patch coinmarketcap class
@ -126,6 +171,11 @@ def patch_coinmarketcap(mocker, value: Optional[Dict[str, float]] = None) -> Non
)
@pytest.fixture(scope='function')
def init_persistence(default_conf):
persistence.init(default_conf['db_url'], default_conf['dry_run'])
@pytest.fixture(scope="function")
def default_conf():
""" Returns validated configuration suitable for most tests """
@ -171,6 +221,10 @@ def default_conf():
"LTC/BTC",
"XRP/BTC",
"NEO/BTC"
],
"pair_blacklist": [
"DOGE/BTC",
"HOT/BTC",
]
},
"telegram": {
@ -180,7 +234,7 @@ def default_conf():
},
"initial_state": "running",
"db_url": "sqlite://",
"loglevel": logging.DEBUG,
"verbosity": 3,
}
return configuration
@ -250,7 +304,7 @@ def markets():
'max': 500000,
},
},
'info': '',
'info': {},
},
'TKN/BTC': {
'id': 'tknbtc',
@ -275,7 +329,7 @@ def markets():
'max': 500000,
},
},
'info': '',
'info': {},
},
'BLK/BTC': {
'id': 'blkbtc',
@ -300,7 +354,7 @@ def markets():
'max': 500000,
},
},
'info': '',
'info': {},
},
'LTC/BTC': {
'id': 'ltcbtc',
@ -325,7 +379,7 @@ def markets():
'max': 500000,
},
},
'info': '',
'info': {},
},
'XRP/BTC': {
'id': 'xrpbtc',
@ -350,7 +404,7 @@ def markets():
'max': 500000,
},
},
'info': '',
'info': {},
},
'NEO/BTC': {
'id': 'neobtc',
@ -375,7 +429,7 @@ def markets():
'max': 500000,
},
},
'info': '',
'info': {},
},
'BTT/BTC': {
'id': 'BTTBTC',
@ -403,7 +457,7 @@ def markets():
'max': None
}
},
'info': "",
'info': {},
},
'ETH/USDT': {
'id': 'USDT-ETH',
@ -425,7 +479,7 @@ def markets():
}
},
'active': True,
'info': ""
'info': {},
},
'LTC/USDT': {
'id': 'USDT-LTC',
@ -447,7 +501,7 @@ def markets():
'max': None
}
},
'info': ""
'info': {},
}
}
@ -627,7 +681,7 @@ def ticker_history_list():
@pytest.fixture
def ticker_history(ticker_history_list):
return parse_ticker_dataframe(ticker_history_list, "5m", True)
return parse_ticker_dataframe(ticker_history_list, "5m", pair="UNITTEST/BTC", fill_missing=True)
@pytest.fixture
@ -831,8 +885,9 @@ def tickers():
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/UNITTEST_BTC-1m.json') as data_file:
return parse_ticker_dataframe(json.load(data_file), '1m', True)
with Path('freqtrade/tests/testdata/UNITTEST_BTC-1m.json').open('r') as data_file:
return parse_ticker_dataframe(json.load(data_file), '1m', pair="UNITTEST/BTC",
fill_missing=True)
# FIX:
# Create an fixture/function
@ -930,9 +985,10 @@ def buy_order_fee():
@pytest.fixture(scope="function")
def edge_conf(default_conf):
default_conf['max_open_trades'] = -1
default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
default_conf['edge'] = {
conf = deepcopy(default_conf)
conf['max_open_trades'] = -1
conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
conf['edge'] = {
"enabled": True,
"process_throttle_secs": 1800,
"calculate_since_number_of_days": 14,
@ -948,4 +1004,40 @@ def edge_conf(default_conf):
"remove_pumps": False
}
return default_conf
return conf
@pytest.fixture
def rpc_balance():
return {
'BTC': {
'total': 12.0,
'free': 12.0,
'used': 0.0
},
'ETH': {
'total': 0.0,
'free': 0.0,
'used': 0.0
},
'USDT': {
'total': 10000.0,
'free': 10000.0,
'used': 0.0
},
'LTC': {
'total': 10.0,
'free': 10.0,
'used': 0.0
},
'XRP': {
'total': 1.0,
'free': 1.0,
'used': 0.0
},
'EUR': {
'total': 10.0,
'free': 10.0,
'used': 0.0
},
}

121
freqtrade/tests/data/test_btanalysis.py
View File

@ -1,8 +1,19 @@
import pytest
from pandas import DataFrame
from unittest.mock import MagicMock
from freqtrade.data.btanalysis import BT_DATA_COLUMNS, load_backtest_data
from freqtrade.data.history import make_testdata_path
import pytest
from arrow import Arrow
from pandas import DataFrame, to_datetime
from freqtrade.configuration import Arguments, TimeRange
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
combine_tickers_with_mean,
create_cum_profit,
extract_trades_of_period,
load_backtest_data, load_trades,
load_trades_from_db)
from freqtrade.data.history import (load_data, load_pair_history,
make_testdata_path)
from freqtrade.tests.test_persistence import create_mock_trades
def test_load_backtest_data():
@ -19,3 +30,105 @@ def test_load_backtest_data():
with pytest.raises(ValueError, match=r"File .* does not exist\."):
load_backtest_data(str("filename") + "nofile")
@pytest.mark.usefixtures("init_persistence")
def test_load_trades_db(default_conf, fee, mocker):
create_mock_trades(fee)
# remove init so it does not init again
init_mock = mocker.patch('freqtrade.persistence.init', MagicMock())
trades = load_trades_from_db(db_url=default_conf['db_url'])
assert init_mock.call_count == 1
assert len(trades) == 3
assert isinstance(trades, DataFrame)
assert "pair" in trades.columns
assert "open_time" in trades.columns
assert "profitperc" in trades.columns
for col in BT_DATA_COLUMNS:
if col not in ['index', 'open_at_end']:
assert col in trades.columns
def test_extract_trades_of_period():
pair = "UNITTEST/BTC"
timerange = TimeRange(None, 'line', 0, -1000)
data = load_pair_history(pair=pair, ticker_interval='1m',
datadir=None, timerange=timerange)
# timerange = 2017-11-14 06:07 - 2017-11-14 22:58:00
trades = DataFrame(
{'pair': [pair, pair, pair, pair],
'profit_percent': [0.0, 0.1, -0.2, -0.5],
'profit_abs': [0.0, 1, -2, -5],
'open_time': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
Arrow(2017, 11, 14, 9, 41, 0).datetime,
Arrow(2017, 11, 14, 14, 20, 0).datetime,
Arrow(2017, 11, 15, 3, 40, 0).datetime,
], utc=True
),
'close_time': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime,
Arrow(2017, 11, 14, 10, 41, 0).datetime,
Arrow(2017, 11, 14, 15, 25, 0).datetime,
Arrow(2017, 11, 15, 3, 55, 0).datetime,
], utc=True)
})
trades1 = extract_trades_of_period(data, trades)
# First and last trade are dropped as they are out of range
assert len(trades1) == 2
assert trades1.iloc[0].open_time == Arrow(2017, 11, 14, 9, 41, 0).datetime
assert trades1.iloc[0].close_time == Arrow(2017, 11, 14, 10, 41, 0).datetime
assert trades1.iloc[-1].open_time == Arrow(2017, 11, 14, 14, 20, 0).datetime
assert trades1.iloc[-1].close_time == Arrow(2017, 11, 14, 15, 25, 0).datetime
def test_load_trades(default_conf, mocker):
db_mock = mocker.patch("freqtrade.data.btanalysis.load_trades_from_db", MagicMock())
bt_mock = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
default_conf['trade_source'] = "DB"
load_trades(default_conf)
assert db_mock.call_count == 1
assert bt_mock.call_count == 0
db_mock.reset_mock()
bt_mock.reset_mock()
default_conf['trade_source'] = "file"
default_conf['exportfilename'] = "testfile.json"
load_trades(default_conf)
assert db_mock.call_count == 0
assert bt_mock.call_count == 1
def test_combine_tickers_with_mean():
pairs = ["ETH/BTC", "XLM/BTC"]
tickers = load_data(datadir=None,
pairs=pairs,
ticker_interval='5m'
)
df = combine_tickers_with_mean(tickers)
assert isinstance(df, DataFrame)
assert "ETH/BTC" in df.columns
assert "XLM/BTC" in df.columns
assert "mean" in df.columns
def test_create_cum_profit():
filename = make_testdata_path(None) / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
timerange = Arguments.parse_timerange("20180110-20180112")
df = load_pair_history(pair="POWR/BTC", ticker_interval='5m',
datadir=None, timerange=timerange)
cum_profits = create_cum_profit(df.set_index('date'),
bt_data[bt_data["pair"] == 'POWR/BTC'],
"cum_profits")
assert "cum_profits" in cum_profits.columns
assert cum_profits.iloc[0]['cum_profits'] == 0
assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005

71
freqtrade/tests/data/test_converter.py
View File

@ -2,8 +2,7 @@
import logging
from freqtrade.data.converter import parse_ticker_dataframe, ohlcv_fill_up_missing_data
from freqtrade.data.history import load_pair_history
from freqtrade.optimize import validate_backtest_data, get_timeframe
from freqtrade.data.history import load_pair_history, validate_backtest_data, get_timeframe
from freqtrade.tests.conftest import log_has
@ -16,7 +15,8 @@ def test_parse_ticker_dataframe(ticker_history_list, caplog):
caplog.set_level(logging.DEBUG)
# Test file with BV data
dataframe = parse_ticker_dataframe(ticker_history_list, '5m', fill_missing=True)
dataframe = parse_ticker_dataframe(ticker_history_list, '5m',
pair="UNITTEST/BTC", fill_missing=True)
assert dataframe.columns.tolist() == columns
assert log_has('Parsing tickerlist to dataframe', caplog.record_tuples)
@ -28,18 +28,19 @@ def test_ohlcv_fill_up_missing_data(caplog):
pair='UNITTEST/BTC',
fill_up_missing=False)
caplog.set_level(logging.DEBUG)
data2 = ohlcv_fill_up_missing_data(data, '1m')
data2 = ohlcv_fill_up_missing_data(data, '1m', 'UNITTEST/BTC')
assert len(data2) > len(data)
# Column names should not change
assert (data.columns == data2.columns).all()
assert log_has(f"Missing data fillup: before: {len(data)} - after: {len(data2)}",
assert log_has(f"Missing data fillup for UNITTEST/BTC: before: "
f"{len(data)} - after: {len(data2)}",
caplog.record_tuples)
# Test fillup actually fixes invalid backtest data
min_date, max_date = get_timeframe({'UNITTEST/BTC': data})
assert validate_backtest_data({'UNITTEST/BTC': data}, min_date, max_date, 1)
assert not validate_backtest_data({'UNITTEST/BTC': data2}, min_date, max_date, 1)
assert validate_backtest_data(data, 'UNITTEST/BTC', min_date, max_date, 1)
assert not validate_backtest_data(data2, 'UNITTEST/BTC', min_date, max_date, 1)
def test_ohlcv_fill_up_missing_data2(caplog):
@ -79,10 +80,10 @@ def test_ohlcv_fill_up_missing_data2(caplog):
]
# Generate test-data without filling missing
data = parse_ticker_dataframe(ticks, ticker_interval, fill_missing=False)
data = parse_ticker_dataframe(ticks, ticker_interval, pair="UNITTEST/BTC", fill_missing=False)
assert len(data) == 3
caplog.set_level(logging.DEBUG)
data2 = ohlcv_fill_up_missing_data(data, ticker_interval)
data2 = ohlcv_fill_up_missing_data(data, ticker_interval, "UNITTEST/BTC")
assert len(data2) == 4
# 3rd candle has been filled
row = data2.loc[2, :]
@ -95,5 +96,55 @@ def test_ohlcv_fill_up_missing_data2(caplog):
# Column names should not change
assert (data.columns == data2.columns).all()
assert log_has(f"Missing data fillup: before: {len(data)} - after: {len(data2)}",
assert log_has(f"Missing data fillup for UNITTEST/BTC: before: "
f"{len(data)} - after: {len(data2)}",
caplog.record_tuples)
def test_ohlcv_drop_incomplete(caplog):
ticker_interval = '1d'
ticks = [[
1559750400000, # 2019-06-04
8.794e-05, # open
8.948e-05, # high
8.794e-05, # low
8.88e-05, # close
2255, # volume (in quote currency)
],
[
1559836800000, # 2019-06-05
8.88e-05,
8.942e-05,
8.88e-05,
8.893e-05,
9911,
],
[
1559923200000, # 2019-06-06
8.891e-05,
8.893e-05,
8.875e-05,
8.877e-05,
2251
],
[
1560009600000, # 2019-06-07
8.877e-05,
8.883e-05,
8.895e-05,
8.817e-05,
123551
]
]
caplog.set_level(logging.DEBUG)
data = parse_ticker_dataframe(ticks, ticker_interval, pair="UNITTEST/BTC",
fill_missing=False, drop_incomplete=False)
assert len(data) == 4
assert not log_has("Dropping last candle", caplog.record_tuples)
# Drop last candle
data = parse_ticker_dataframe(ticks, ticker_interval, pair="UNITTEST/BTC",
fill_missing=False, drop_incomplete=True)
assert len(data) == 3
assert log_has("Dropping last candle", caplog.record_tuples)

40
freqtrade/tests/data/test_dataprovider.py
View File

@ -9,31 +9,31 @@ from freqtrade.tests.conftest import get_patched_exchange
def test_ohlcv(mocker, default_conf, ticker_history):
default_conf["runmode"] = RunMode.DRY_RUN
tick_interval = default_conf["ticker_interval"]
ticker_interval = default_conf["ticker_interval"]
exchange = get_patched_exchange(mocker, default_conf)
exchange._klines[("XRP/BTC", tick_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", tick_interval)] = ticker_history
exchange._klines[("XRP/BTC", ticker_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ticker_history
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.DRY_RUN
assert ticker_history.equals(dp.ohlcv("UNITTEST/BTC", tick_interval))
assert isinstance(dp.ohlcv("UNITTEST/BTC", tick_interval), DataFrame)
assert dp.ohlcv("UNITTEST/BTC", tick_interval) is not ticker_history
assert dp.ohlcv("UNITTEST/BTC", tick_interval, copy=False) is ticker_history
assert not dp.ohlcv("UNITTEST/BTC", tick_interval).empty
assert dp.ohlcv("NONESENSE/AAA", tick_interval).empty
assert ticker_history.equals(dp.ohlcv("UNITTEST/BTC", ticker_interval))
assert isinstance(dp.ohlcv("UNITTEST/BTC", ticker_interval), DataFrame)
assert dp.ohlcv("UNITTEST/BTC", ticker_interval) is not ticker_history
assert dp.ohlcv("UNITTEST/BTC", ticker_interval, copy=False) is ticker_history
assert not dp.ohlcv("UNITTEST/BTC", ticker_interval).empty
assert dp.ohlcv("NONESENSE/AAA", ticker_interval).empty
# Test with and without parameter
assert dp.ohlcv("UNITTEST/BTC", tick_interval).equals(dp.ohlcv("UNITTEST/BTC"))
assert dp.ohlcv("UNITTEST/BTC", ticker_interval).equals(dp.ohlcv("UNITTEST/BTC"))
default_conf["runmode"] = RunMode.LIVE
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.LIVE
assert isinstance(dp.ohlcv("UNITTEST/BTC", tick_interval), DataFrame)
assert isinstance(dp.ohlcv("UNITTEST/BTC", ticker_interval), DataFrame)
default_conf["runmode"] = RunMode.BACKTEST
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.BACKTEST
assert dp.ohlcv("UNITTEST/BTC", tick_interval).empty
assert dp.ohlcv("UNITTEST/BTC", ticker_interval).empty
def test_historic_ohlcv(mocker, default_conf, ticker_history):
@ -54,15 +54,15 @@ def test_historic_ohlcv(mocker, default_conf, ticker_history):
def test_available_pairs(mocker, default_conf, ticker_history):
exchange = get_patched_exchange(mocker, default_conf)
tick_interval = default_conf["ticker_interval"]
exchange._klines[("XRP/BTC", tick_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", tick_interval)] = ticker_history
ticker_interval = default_conf["ticker_interval"]
exchange._klines[("XRP/BTC", ticker_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ticker_history
dp = DataProvider(default_conf, exchange)
assert len(dp.available_pairs) == 2
assert dp.available_pairs == [
("XRP/BTC", tick_interval),
("UNITTEST/BTC", tick_interval),
("XRP/BTC", ticker_interval),
("UNITTEST/BTC", ticker_interval),
]
@ -71,10 +71,10 @@ def test_refresh(mocker, default_conf, ticker_history):
mocker.patch("freqtrade.exchange.Exchange.refresh_latest_ohlcv", refresh_mock)
exchange = get_patched_exchange(mocker, default_conf, id="binance")
tick_interval = default_conf["ticker_interval"]
pairs = [("XRP/BTC", tick_interval), ("UNITTEST/BTC", tick_interval)]
ticker_interval = default_conf["ticker_interval"]
pairs = [("XRP/BTC", ticker_interval), ("UNITTEST/BTC", ticker_interval)]
pairs_non_trad = [("ETH/USDT", tick_interval), ("BTC/TUSD", "1h")]
pairs_non_trad = [("ETH/USDT", ticker_interval), ("BTC/TUSD", "1h")]
dp = DataProvider(default_conf, exchange)
dp.refresh(pairs)

155
freqtrade/tests/data/test_history.py
View File

@ -2,24 +2,27 @@
import json
import os
from pathlib import Path
import uuid
from pathlib import Path
from shutil import copyfile
from unittest.mock import MagicMock
import arrow
from pandas import DataFrame
import pytest
from pandas import DataFrame
from freqtrade import OperationalException
from freqtrade.arguments import TimeRange
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.history import (download_pair_history,
load_cached_data_for_updating,
load_tickerdata_file,
make_testdata_path,
load_tickerdata_file, make_testdata_path,
trim_tickerlist)
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import file_dump_json
from freqtrade.tests.conftest import get_patched_exchange, log_has
from freqtrade.strategy.default_strategy import DefaultStrategy
from freqtrade.tests.conftest import (get_patched_exchange, log_has,
patch_exchange)
# Change this if modifying UNITTEST/BTC testdatafile
_BTC_UNITTEST_LENGTH = 13681
@ -59,7 +62,11 @@ def _clean_test_file(file: str) -> None:
def test_load_data_30min_ticker(mocker, caplog, default_conf) -> None:
ld = history.load_pair_history(pair='UNITTEST/BTC', ticker_interval='30m', datadir=None)
assert isinstance(ld, DataFrame)
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 30m', caplog.record_tuples)
assert not log_has(
'Download history data for pair: "UNITTEST/BTC", interval: 30m '
'and store in None.',
caplog.record_tuples
)
def test_load_data_7min_ticker(mocker, caplog, default_conf) -> None:
@ -67,8 +74,11 @@ def test_load_data_7min_ticker(mocker, caplog, default_conf) -> None:
assert not isinstance(ld, DataFrame)
assert ld is None
assert log_has(
'No data for pair: "UNITTEST/BTC", Interval: 7m. '
'Use --refresh-pairs-cached to download the data', caplog.record_tuples)
'No history data for pair: "UNITTEST/BTC", interval: 7m. '
'Use --refresh-pairs-cached option or download_backtest_data.py '
'script to download the data',
caplog.record_tuples
)
def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
@ -77,7 +87,11 @@ def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
_backup_file(file, copy_file=True)
history.load_data(datadir=None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 1m', caplog.record_tuples)
assert not log_has(
'Download history data for pair: "UNITTEST/BTC", interval: 1m '
'and store in None.',
caplog.record_tuples
)
_clean_test_file(file)
@ -96,9 +110,12 @@ def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog, defau
refresh_pairs=False,
pair='MEME/BTC')
assert os.path.isfile(file) is False
assert log_has('No data for pair: "MEME/BTC", Interval: 1m. '
'Use --refresh-pairs-cached to download the data',
caplog.record_tuples)
assert log_has(
'No history data for pair: "MEME/BTC", interval: 1m. '
'Use --refresh-pairs-cached option or download_backtest_data.py '
'script to download the data',
caplog.record_tuples
)
# download a new pair if refresh_pairs is set
history.load_pair_history(datadir=None,
@ -107,7 +124,11 @@ def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog, defau
exchange=exchange,
pair='MEME/BTC')
assert os.path.isfile(file) is True
assert log_has('Download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
assert log_has(
'Download history data for pair: "MEME/BTC", interval: 1m '
'and store in None.',
caplog.record_tuples
)
with pytest.raises(OperationalException, match=r'Exchange needs to be initialized when.*'):
history.load_pair_history(datadir=None,
ticker_interval='1m',
@ -117,6 +138,31 @@ def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog, defau
_clean_test_file(file)
def test_load_data_live(default_conf, mocker, caplog) -> None:
refresh_mock = MagicMock()
mocker.patch("freqtrade.exchange.Exchange.refresh_latest_ohlcv", refresh_mock)
exchange = get_patched_exchange(mocker, default_conf)
history.load_data(datadir=None, ticker_interval='5m',
pairs=['UNITTEST/BTC', 'UNITTEST2/BTC'],
live=True,
exchange=exchange)
assert refresh_mock.call_count == 1
assert len(refresh_mock.call_args_list[0][0][0]) == 2
assert log_has('Live: Downloading data for all defined pairs ...', caplog.record_tuples)
def test_load_data_live_noexchange(default_conf, mocker, caplog) -> None:
with pytest.raises(OperationalException,
match=r'Exchange needs to be initialized when using live data.'):
history.load_data(datadir=None, ticker_interval='5m',
pairs=['UNITTEST/BTC', 'UNITTEST2/BTC'],
exchange=None,
live=True,
)
def test_testdata_path() -> None:
assert str(Path('freqtrade') / 'tests' / 'testdata') in str(make_testdata_path(None))
@ -242,10 +288,10 @@ def test_download_pair_history(ticker_history_list, mocker, default_conf) -> Non
assert download_pair_history(datadir=None, exchange=exchange,
pair='MEME/BTC',
tick_interval='1m')
ticker_interval='1m')
assert download_pair_history(datadir=None, exchange=exchange,
pair='CFI/BTC',
tick_interval='1m')
ticker_interval='1m')
assert not exchange._pairs_last_refresh_time
assert os.path.isfile(file1_1) is True
assert os.path.isfile(file2_1) is True
@ -259,10 +305,10 @@ def test_download_pair_history(ticker_history_list, mocker, default_conf) -> Non
assert download_pair_history(datadir=None, exchange=exchange,
pair='MEME/BTC',
tick_interval='5m')
ticker_interval='5m')
assert download_pair_history(datadir=None, exchange=exchange,
pair='CFI/BTC',
tick_interval='5m')
ticker_interval='5m')
assert not exchange._pairs_last_refresh_time
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_5) is True
@ -280,14 +326,14 @@ def test_download_pair_history2(mocker, default_conf) -> None:
json_dump_mock = mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
mocker.patch('freqtrade.exchange.Exchange.get_history', return_value=tick)
exchange = get_patched_exchange(mocker, default_conf)
download_pair_history(None, exchange, pair="UNITTEST/BTC", tick_interval='1m')
download_pair_history(None, exchange, pair="UNITTEST/BTC", tick_interval='3m')
download_pair_history(None, exchange, pair="UNITTEST/BTC", ticker_interval='1m')
download_pair_history(None, exchange, pair="UNITTEST/BTC", ticker_interval='3m')
assert json_dump_mock.call_count == 2
def test_download_backtesting_data_exception(ticker_history, mocker, caplog, default_conf) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history',
side_effect=BaseException('File Error'))
side_effect=Exception('File Error'))
exchange = get_patched_exchange(mocker, default_conf)
@ -298,11 +344,15 @@ def test_download_backtesting_data_exception(ticker_history, mocker, caplog, def
assert not download_pair_history(datadir=None, exchange=exchange,
pair='MEME/BTC',
tick_interval='1m')
ticker_interval='1m')
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
assert log_has('Failed to download the pair: "MEME/BTC", Interval: 1m', caplog.record_tuples)
assert log_has(
'Failed to download history data for pair: "MEME/BTC", interval: 1m. '
'Error: File Error',
caplog.record_tuples
)
def test_load_tickerdata_file() -> None:
@ -473,3 +523,62 @@ def test_file_dump_json_tofile() -> None:
# Remove the file
_clean_test_file(file)
def test_get_timeframe(default_conf, mocker) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
data = strategy.tickerdata_to_dataframe(
history.load_data(
datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC']
)
)
min_date, max_date = history.get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
def test_validate_backtest_data_warn(default_conf, mocker, caplog) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
data = strategy.tickerdata_to_dataframe(
history.load_data(
datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC'],
fill_up_missing=False
)
)
min_date, max_date = history.get_timeframe(data)
caplog.clear()
assert history.validate_backtest_data(data['UNITTEST/BTC'], 'UNITTEST/BTC',
min_date, max_date, timeframe_to_minutes('1m'))
assert len(caplog.record_tuples) == 1
assert log_has(
"UNITTEST/BTC has missing frames: expected 14396, got 13680, that's 716 missing values",
caplog.record_tuples)
def test_validate_backtest_data(default_conf, mocker, caplog) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
timerange = TimeRange('index', 'index', 200, 250)
data = strategy.tickerdata_to_dataframe(
history.load_data(
datadir=None,
ticker_interval='5m',
pairs=['UNITTEST/BTC'],
timerange=timerange
)
)
min_date, max_date = history.get_timeframe(data)
caplog.clear()
assert not history.validate_backtest_data(data['UNITTEST/BTC'], 'UNITTEST/BTC',
min_date, max_date, timeframe_to_minutes('5m'))
assert len(caplog.record_tuples) == 0

138
freqtrade/tests/edge/test_edge.py
View File

@ -10,10 +10,11 @@ import numpy as np
import pytest
from pandas import DataFrame, to_datetime
from freqtrade import OperationalException
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.edge import Edge, PairInfo
from freqtrade.strategy.interface import SellType
from freqtrade.tests.conftest import get_patched_freqtradebot
from freqtrade.tests.conftest import get_patched_freqtradebot, log_has
from freqtrade.tests.optimize import (BTContainer, BTrade,
_build_backtest_dataframe,
_get_frame_time_from_offset)
@ -30,7 +31,50 @@ ticker_start_time = arrow.get(2018, 10, 3)
ticker_interval_in_minute = 60
_ohlc = {'date': 0, 'buy': 1, 'open': 2, 'high': 3, 'low': 4, 'close': 5, 'sell': 6, 'volume': 7}
# Helpers for this test file
def _validate_ohlc(buy_ohlc_sell_matrice):
for index, ohlc in enumerate(buy_ohlc_sell_matrice):
# if not high < open < low or not high < close < low
if not ohlc[3] >= ohlc[2] >= ohlc[4] or not ohlc[3] >= ohlc[5] >= ohlc[4]:
raise Exception('Line ' + str(index + 1) + ' of ohlc has invalid values!')
return True
def _build_dataframe(buy_ohlc_sell_matrice):
_validate_ohlc(buy_ohlc_sell_matrice)
tickers = []
for ohlc in buy_ohlc_sell_matrice:
ticker = {
'date': ticker_start_time.shift(
minutes=(
ohlc[0] *
ticker_interval_in_minute)).timestamp *
1000,
'buy': ohlc[1],
'open': ohlc[2],
'high': ohlc[3],
'low': ohlc[4],
'close': ohlc[5],
'sell': ohlc[6]}
tickers.append(ticker)
frame = DataFrame(tickers)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return frame
def _time_on_candle(number):
return np.datetime64(ticker_start_time.shift(
minutes=(number * ticker_interval_in_minute)).timestamp * 1000, 'ms')
# End helper functions
# Open trade should be removed from the end
tc0 = BTContainer(data=[
# D O H L C V B S
@ -122,8 +166,8 @@ def test_edge_results(edge_conf, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.exit_type == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick)
assert arrow.get(res.open_time) == _get_frame_time_from_offset(trade.open_tick)
assert arrow.get(res.close_time) == _get_frame_time_from_offset(trade.close_tick)
def test_adjust(mocker, edge_conf):
@ -203,46 +247,6 @@ def test_nonexisting_stake_amount(mocker, edge_conf):
assert edge.stake_amount('N/O', 1, 2, 1) == 0.15
def _validate_ohlc(buy_ohlc_sell_matrice):
for index, ohlc in enumerate(buy_ohlc_sell_matrice):
# if not high < open < low or not high < close < low
if not ohlc[3] >= ohlc[2] >= ohlc[4] or not ohlc[3] >= ohlc[5] >= ohlc[4]:
raise Exception('Line ' + str(index + 1) + ' of ohlc has invalid values!')
return True
def _build_dataframe(buy_ohlc_sell_matrice):
_validate_ohlc(buy_ohlc_sell_matrice)
tickers = []
for ohlc in buy_ohlc_sell_matrice:
ticker = {
'date': ticker_start_time.shift(
minutes=(
ohlc[0] *
ticker_interval_in_minute)).timestamp *
1000,
'buy': ohlc[1],
'open': ohlc[2],
'high': ohlc[3],
'low': ohlc[4],
'close': ohlc[5],
'sell': ohlc[6]}
tickers.append(ticker)
frame = DataFrame(tickers)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return frame
def _time_on_candle(number):
return np.datetime64(ticker_start_time.shift(
minutes=(number * ticker_interval_in_minute)).timestamp * 1000, 'ms')
def test_edge_heartbeat_calculate(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
@ -259,7 +263,7 @@ def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=Fals
hz = 0.1
base = 0.001
ETHBTC = [
NEOBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
@ -281,8 +285,8 @@ def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=Fals
123.45
] for x in range(0, 500)]
pairdata = {'NEO/BTC': parse_ticker_dataframe(ETHBTC, '1h', fill_missing=True),
'LTC/BTC': parse_ticker_dataframe(LTCBTC, '1h', fill_missing=True)}
pairdata = {'NEO/BTC': parse_ticker_dataframe(NEOBTC, '1h', pair="NEO/BTC", fill_missing=True),
'LTC/BTC': parse_ticker_dataframe(LTCBTC, '1h', pair="LTC/BTC", fill_missing=True)}
return pairdata
@ -298,6 +302,40 @@ def test_edge_process_downloaded_data(mocker, edge_conf):
assert edge._last_updated <= arrow.utcnow().timestamp + 2
def test_edge_process_no_data(mocker, edge_conf, caplog):
edge_conf['datadir'] = None
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.001))
mocker.patch('freqtrade.data.history.load_data', MagicMock(return_value={}))
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
assert not edge.calculate()
assert len(edge._cached_pairs) == 0
assert log_has("No data found. Edge is stopped ...", caplog.record_tuples)
assert edge._last_updated == 0
def test_edge_process_no_trades(mocker, edge_conf, caplog):
edge_conf['datadir'] = None
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.001))
mocker.patch('freqtrade.data.history.load_data', mocked_load_data)
# Return empty
mocker.patch('freqtrade.edge.Edge._find_trades_for_stoploss_range', MagicMock(return_value=[]))
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
assert not edge.calculate()
assert len(edge._cached_pairs) == 0
assert log_has("No trades found.", caplog.record_tuples)
def test_edge_init_error(mocker, edge_conf,):
edge_conf['stake_amount'] = 0.5
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.001))
with pytest.raises(OperationalException, match='Edge works only with unlimited stake amount'):
get_patched_freqtradebot(mocker, edge_conf)
def test_process_expectancy(mocker, edge_conf):
edge_conf['edge']['min_trade_number'] = 2
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
@ -360,3 +398,11 @@ def test_process_expectancy(mocker, edge_conf):
assert round(final['TEST/BTC'].risk_reward_ratio, 10) == 306.5384615384
assert round(final['TEST/BTC'].required_risk_reward, 10) == 2.0
assert round(final['TEST/BTC'].expectancy, 10) == 101.5128205128
# Pop last item so no trade is profitable
trades.pop()
trades_df = DataFrame(trades)
trades_df = edge._fill_calculable_fields(trades_df)
final = edge._process_expectancy(trades_df)
assert len(final) == 0
assert isinstance(final, dict)

260
freqtrade/tests/exchange/test_exchange.py
View File

@ -2,7 +2,7 @@
# pragma pylint: disable=protected-access
import copy
import logging
from datetime import datetime
from datetime import datetime, timezone
from random import randint
from unittest.mock import MagicMock, Mock, PropertyMock
@ -11,7 +11,8 @@ import ccxt
import pytest
from pandas import DataFrame
from freqtrade import DependencyException, OperationalException, TemporaryError
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Binance, Exchange, Kraken
from freqtrade.exchange.exchange import API_RETRY_COUNT
from freqtrade.resolvers.exchange_resolver import ExchangeResolver
@ -32,13 +33,13 @@ def get_mock_coro(return_value):
def ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
fun, mock_ccxt_fun, **kwargs):
with pytest.raises(TemporaryError):
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError)
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError("DeaDBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
getattr(exchange, fun)(**kwargs)
assert api_mock.__dict__[mock_ccxt_fun].call_count == API_RETRY_COUNT + 1
with pytest.raises(OperationalException):
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError)
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
getattr(exchange, fun)(**kwargs)
assert api_mock.__dict__[mock_ccxt_fun].call_count == 1
@ -46,13 +47,13 @@ def ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
async def async_ccxt_exception(mocker, default_conf, api_mock, fun, mock_ccxt_fun, **kwargs):
with pytest.raises(TemporaryError):
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError)
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
await getattr(exchange, fun)(**kwargs)
assert api_mock.__dict__[mock_ccxt_fun].call_count == API_RETRY_COUNT + 1
with pytest.raises(OperationalException):
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError)
api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
await getattr(exchange, fun)(**kwargs)
assert api_mock.__dict__[mock_ccxt_fun].call_count == 1
@ -123,14 +124,14 @@ def test_exchange_resolver(default_conf, mocker, caplog):
caplog.record_tuples)
caplog.clear()
exchange = ExchangeResolver('Kraken', default_conf).exchange
exchange = ExchangeResolver('kraken', default_conf).exchange
assert isinstance(exchange, Exchange)
assert isinstance(exchange, Kraken)
assert not isinstance(exchange, Binance)
assert not log_has_re(r"No .* specific subclass found. Using the generic class instead.",
caplog.record_tuples)
exchange = ExchangeResolver('Binance', default_conf).exchange
exchange = ExchangeResolver('binance', default_conf).exchange
assert isinstance(exchange, Exchange)
assert isinstance(exchange, Binance)
assert not isinstance(exchange, Kraken)
@ -139,6 +140,28 @@ def test_exchange_resolver(default_conf, mocker, caplog):
caplog.record_tuples)
def test_validate_order_time_in_force(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
# explicitly test bittrex, exchanges implementing other policies need seperate tests
ex = get_patched_exchange(mocker, default_conf, id="bittrex")
tif = {
"buy": "gtc",
"sell": "gtc",
}
ex.validate_order_time_in_force(tif)
tif2 = {
"buy": "fok",
"sell": "ioc",
}
with pytest.raises(OperationalException, match=r"Time in force.*not supported for .*"):
ex.validate_order_time_in_force(tif2)
# Patch to see if this will pass if the values are in the ft dict
ex._ft_has.update({"order_time_in_force": ["gtc", "fok", "ioc"]})
ex.validate_order_time_in_force(tif2)
def test_symbol_amount_prec(default_conf, mocker):
'''
Test rounds down to 4 Decimal places
@ -233,13 +256,13 @@ def test__load_async_markets(default_conf, mocker, caplog):
def test__load_markets(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
api_mock = MagicMock()
api_mock.load_markets = MagicMock(side_effect=ccxt.BaseError())
api_mock.load_markets = MagicMock(side_effect=ccxt.BaseError("SomeError"))
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange._load_async_markets', MagicMock())
Exchange(default_conf)
assert log_has('Unable to initialize markets. Reason: ', caplog.record_tuples)
assert log_has('Unable to initialize markets. Reason: SomeError', caplog.record_tuples)
expected_return = {'ETH/BTC': 'available'}
api_mock = MagicMock()
@ -278,10 +301,24 @@ def test__reload_markets(default_conf, mocker, caplog):
assert log_has('Performing scheduled market reload..', caplog.record_tuples)
def test__reload_markets_exception(default_conf, mocker, caplog):
caplog.set_level(logging.DEBUG)
api_mock = MagicMock()
api_mock.load_markets = MagicMock(side_effect=ccxt.NetworkError("LoadError"))
default_conf['exchange']['markets_refresh_interval'] = 10
exchange = get_patched_exchange(mocker, default_conf, api_mock, id="binance")
# less than 10 minutes have passed, no reload
exchange._reload_markets()
assert exchange._last_markets_refresh == 0
assert log_has_re(r"Could not reload markets.*", caplog.record_tuples)
def test_validate_pairs(default_conf, mocker): # test exchange.validate_pairs directly
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
'ETH/BTC': {}, 'LTC/BTC': {}, 'XRP/BTC': {}, 'NEO/BTC': {}
})
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
@ -295,7 +332,7 @@ def test_validate_pairs(default_conf, mocker): # test exchange.validate_pairs d
def test_validate_pairs_not_available(default_conf, mocker):
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'XRP/BTC': 'inactive'
'XRP/BTC': {'inactive': True}
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
@ -305,19 +342,6 @@ def test_validate_pairs_not_available(default_conf, mocker):
Exchange(default_conf)
def test_validate_pairs_not_compatible(default_conf, mocker):
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': '', 'TKN/BTC': '', 'TRST/BTC': '', 'SWT/BTC': '', 'BCC/BTC': ''
})
default_conf['stake_currency'] = 'ETH'
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange._load_async_markets', MagicMock())
with pytest.raises(OperationalException, match=r'not compatible'):
Exchange(default_conf)
def test_validate_pairs_exception(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
api_mock = MagicMock()
@ -337,20 +361,21 @@ def test_validate_pairs_exception(default_conf, mocker, caplog):
caplog.record_tuples)
def test_validate_pairs_stake_exception(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
default_conf['stake_currency'] = 'ETH'
def test_validate_pairs_restricted(default_conf, mocker, caplog):
api_mock = MagicMock()
api_mock.name = MagicMock(return_value='binance')
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', api_mock)
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {}, 'LTC/BTC': {}, 'NEO/BTC': {},
'XRP/BTC': {'info': {'IsRestricted': True}}
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange._load_async_markets', MagicMock())
with pytest.raises(
OperationalException,
match=r'Pair ETH/BTC not compatible with stake_currency: ETH'
):
Exchange(default_conf)
Exchange(default_conf)
assert log_has(f"Pair XRP/BTC is restricted for some users on this exchange."
f"Please check if you are impacted by this restriction "
f"on the exchange and eventually remove XRP/BTC from your whitelist.",
caplog.record_tuples)
def test_validate_timeframes(default_conf, mocker):
@ -388,6 +413,45 @@ def test_validate_timeframes_failed(default_conf, mocker):
Exchange(default_conf)
def test_validate_timeframes_emulated_ohlcv_1(default_conf, mocker):
default_conf["ticker_interval"] = "3m"
api_mock = MagicMock()
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
# delete timeframes so magicmock does not autocreate it
del api_mock.timeframes
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
with pytest.raises(OperationalException,
match=r'The ccxt library does not provide the list of timeframes '
r'for the exchange ".*" and this exchange '
r'is therefore not supported. *'):
Exchange(default_conf)
def test_validate_timeframes_emulated_ohlcvi_2(default_conf, mocker):
default_conf["ticker_interval"] = "3m"
api_mock = MagicMock()
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
# delete timeframes so magicmock does not autocreate it
del api_mock.timeframes
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange._load_markets',
MagicMock(return_value={'timeframes': None}))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
with pytest.raises(OperationalException,
match=r'The ccxt library does not provide the list of timeframes '
r'for the exchange ".*" and this exchange '
r'is therefore not supported. *'):
Exchange(default_conf)
def test_validate_timeframes_not_in_config(default_conf, mocker):
del default_conf["ticker_interval"]
api_mock = MagicMock()
@ -496,15 +560,17 @@ def test_dry_run_order(default_conf, mocker, side, exchange_name):
("buy"),
("sell")
])
@pytest.mark.parametrize("ordertype,rate", [
("market", None),
("limit", 200),
("stop_loss_limit", 200)
@pytest.mark.parametrize("ordertype,rate,marketprice", [
("market", None, None),
("market", 200, True),
("limit", 200, None),
("stop_loss_limit", 200, None)
])
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_create_order(default_conf, mocker, side, ordertype, rate, exchange_name):
def test_create_order(default_conf, mocker, side, ordertype, rate, marketprice, exchange_name):
api_mock = MagicMock()
order_id = 'test_prod_{}_{}'.format(side, randint(0, 10 ** 6))
api_mock.options = {} if not marketprice else {"createMarketBuyOrderRequiresPrice": True}
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
@ -545,6 +611,7 @@ def test_buy_prod(default_conf, mocker, exchange_name):
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
order_type = 'market'
time_in_force = 'gtc'
api_mock.options = {}
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
@ -584,25 +651,25 @@ def test_buy_prod(default_conf, mocker, exchange_name):
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("Not enough funds"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
with pytest.raises(TemporaryError):
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError("Network disconnect"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
with pytest.raises(OperationalException):
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.buy(pair='ETH/BTC', ordertype=order_type,
amount=1, rate=200, time_in_force=time_in_force)
@ -612,6 +679,7 @@ def test_buy_prod(default_conf, mocker, exchange_name):
def test_buy_considers_time_in_force(default_conf, mocker, exchange_name):
api_mock = MagicMock()
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
api_mock.options = {}
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
@ -672,6 +740,7 @@ def test_sell_prod(default_conf, mocker, exchange_name):
api_mock = MagicMock()
order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
order_type = 'market'
api_mock.options = {}
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
@ -706,22 +775,22 @@ def test_sell_prod(default_conf, mocker, exchange_name):
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(TemporaryError):
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError("No Connection"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(OperationalException):
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
@ -736,6 +805,7 @@ def test_sell_considers_time_in_force(default_conf, mocker, exchange_name):
'foo': 'bar'
}
})
api_mock.options = {}
default_conf['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
@ -793,7 +863,7 @@ def test_get_balance_prod(default_conf, mocker, exchange_name):
assert exchange.get_balance(currency='BTC') == 123.4
with pytest.raises(OperationalException):
api_mock.fetch_balance = MagicMock(side_effect=ccxt.BaseError)
api_mock.fetch_balance = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.get_balance(currency='BTC')
@ -866,7 +936,7 @@ def test_get_tickers(default_conf, mocker, exchange_name):
"get_tickers", "fetch_tickers")
with pytest.raises(OperationalException):
api_mock.fetch_tickers = MagicMock(side_effect=ccxt.NotSupported)
api_mock.fetch_tickers = MagicMock(side_effect=ccxt.NotSupported("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.get_tickers()
@ -885,7 +955,7 @@ def test_get_ticker(default_conf, mocker, exchange_name):
'last': 0.0001,
}
api_mock.fetch_ticker = MagicMock(return_value=tick)
api_mock.markets = {'ETH/BTC': {}}
api_mock.markets = {'ETH/BTC': {'active': True}}
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
# retrieve original ticker
ticker = exchange.get_ticker(pair='ETH/BTC')
@ -947,8 +1017,8 @@ def test_get_history(default_conf, mocker, caplog, exchange_name):
]
pair = 'ETH/BTC'
async def mock_candle_hist(pair, tick_interval, since_ms):
return pair, tick_interval, tick
async def mock_candle_hist(pair, ticker_interval, since_ms):
return pair, ticker_interval, tick
exchange._async_get_candle_history = Mock(wraps=mock_candle_hist)
# one_call calculation * 1.8 should do 2 calls
@ -1008,7 +1078,7 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
exchange.refresh_latest_ohlcv([('IOTA/ETH', '5m'), ('XRP/ETH', '5m')])
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert log_has(f"Using cached ohlcv data for {pairs[0][0]}, {pairs[0][1]} ...",
assert log_has(f"Using cached ohlcv data for pair {pairs[0][0]}, interval {pairs[0][1]} ...",
caplog.record_tuples)
@ -1044,11 +1114,11 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
# exchange = Exchange(default_conf)
await async_ccxt_exception(mocker, default_conf, MagicMock(),
"_async_get_candle_history", "fetch_ohlcv",
pair='ABCD/BTC', tick_interval=default_conf['ticker_interval'])
pair='ABCD/BTC', ticker_interval=default_conf['ticker_interval'])
api_mock = MagicMock()
with pytest.raises(OperationalException, match=r'Could not fetch ticker data*'):
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.BaseError)
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
await exchange._async_get_candle_history(pair, "5m",
(arrow.utcnow().timestamp - 2000) * 1000)
@ -1120,15 +1190,15 @@ def test_get_order_book(default_conf, mocker, order_book_l2, exchange_name):
def test_get_order_book_exception(default_conf, mocker, exchange_name):
api_mock = MagicMock()
with pytest.raises(OperationalException):
api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.NotSupported)
api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.NotSupported("Not supported"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.get_order_book(pair='ETH/BTC', limit=50)
with pytest.raises(TemporaryError):
api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.NetworkError)
api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.NetworkError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.get_order_book(pair='ETH/BTC', limit=50)
with pytest.raises(OperationalException):
api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.BaseError)
api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.get_order_book(pair='ETH/BTC', limit=50)
@ -1240,11 +1310,11 @@ def test_cancel_order(default_conf, mocker, exchange_name):
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
assert exchange.cancel_order(order_id='_', pair='TKN/BTC') == 123
with pytest.raises(DependencyException):
api_mock.cancel_order = MagicMock(side_effect=ccxt.InvalidOrder)
with pytest.raises(InvalidOrderException):
api_mock.cancel_order = MagicMock(side_effect=ccxt.InvalidOrder("Did not find order"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.cancel_order(order_id='_', pair='TKN/BTC')
assert api_mock.cancel_order.call_count == API_RETRY_COUNT + 1
assert api_mock.cancel_order.call_count == 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
"cancel_order", "cancel_order",
@ -1267,11 +1337,11 @@ def test_get_order(default_conf, mocker, exchange_name):
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
assert exchange.get_order('X', 'TKN/BTC') == 456
with pytest.raises(DependencyException):
api_mock.fetch_order = MagicMock(side_effect=ccxt.InvalidOrder)
with pytest.raises(InvalidOrderException):
api_mock.fetch_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.get_order(order_id='_', pair='TKN/BTC')
assert api_mock.fetch_order.call_count == API_RETRY_COUNT + 1
assert api_mock.fetch_order.call_count == 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
'get_order', 'fetch_order',
@ -1291,7 +1361,7 @@ def test_name(default_conf, mocker, exchange_name):
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_get_trades_for_order(default_conf, mocker, exchange_name):
order_id = 'ABCD-ABCD'
since = datetime(2018, 5, 5)
since = datetime(2018, 5, 5, tzinfo=timezone.utc)
default_conf["dry_run"] = False
mocker.patch('freqtrade.exchange.Exchange.exchange_has', return_value=True)
api_mock = MagicMock()
@ -1321,6 +1391,13 @@ def test_get_trades_for_order(default_conf, mocker, exchange_name):
orders = exchange.get_trades_for_order(order_id, 'LTC/BTC', since)
assert len(orders) == 1
assert orders[0]['price'] == 165
assert api_mock.fetch_my_trades.call_count == 1
# since argument should be
assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
assert api_mock.fetch_my_trades.call_args[0][0] == 'LTC/BTC'
# Same test twice, hardcoded number and doing the same calculation
assert api_mock.fetch_my_trades.call_args[0][1] == 1525478395000
assert api_mock.fetch_my_trades.call_args[0][1] == int(since.timestamp() - 5) * 1000
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
'get_trades_for_order', 'fetch_my_trades',
@ -1384,22 +1461,22 @@ def test_stoploss_limit_order(default_conf, mocker):
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
with pytest.raises(DependencyException):
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
with pytest.raises(TemporaryError):
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError("No connection"))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
with pytest.raises(OperationalException):
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.stoploss_limit(pair='ETH/BTC', amount=1, stop_price=220, rate=200)
@ -1427,3 +1504,46 @@ def test_stoploss_limit_order_dry_run(default_conf, mocker):
assert order['type'] == order_type
assert order['price'] == 220
assert order['amount'] == 1
def test_merge_ft_has_dict(default_conf, mocker):
mocker.patch.multiple('freqtrade.exchange.Exchange',
_init_ccxt=MagicMock(return_value=MagicMock()),
_load_async_markets=MagicMock(),
validate_pairs=MagicMock(),
validate_timeframes=MagicMock())
ex = Exchange(default_conf)
assert ex._ft_has == Exchange._ft_has_default
ex = Kraken(default_conf)
assert ex._ft_has == Exchange._ft_has_default
# Binance defines different values
ex = Binance(default_conf)
assert ex._ft_has != Exchange._ft_has_default
assert ex._ft_has['stoploss_on_exchange']
assert ex._ft_has['order_time_in_force'] == ['gtc', 'fok', 'ioc']
conf = copy.deepcopy(default_conf)
conf['exchange']['_ft_has_params'] = {"DeadBeef": 20,
"stoploss_on_exchange": False}
# Use settings from configuration (overriding stoploss_on_exchange)
ex = Binance(conf)
assert ex._ft_has != Exchange._ft_has_default
assert not ex._ft_has['stoploss_on_exchange']
assert ex._ft_has['DeadBeef'] == 20
def test_get_valid_pair_combination(default_conf, mocker, markets):
mocker.patch.multiple('freqtrade.exchange.Exchange',
_init_ccxt=MagicMock(return_value=MagicMock()),
_load_async_markets=MagicMock(),
validate_pairs=MagicMock(),
validate_timeframes=MagicMock(),
markets=PropertyMock(return_value=markets))
ex = Exchange(default_conf)
assert ex.get_valid_pair_combination("ETH", "BTC") == "ETH/BTC"
assert ex.get_valid_pair_combination("BTC", "ETH") == "ETH/BTC"
with pytest.raises(DependencyException, match=r"Could not combine.* to get a valid pair."):
ex.get_valid_pair_combination("NOPAIR", "ETH")

2
freqtrade/tests/exchange/test_kraken.py
View File

@ -11,6 +11,7 @@ def test_buy_kraken_trading_agreement(default_conf, mocker):
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
order_type = 'limit'
time_in_force = 'ioc'
api_mock.options = {}
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
@ -42,6 +43,7 @@ def test_sell_kraken_trading_agreement(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
order_type = 'market'
api_mock.options = {}
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {

13
freqtrade/tests/optimize/__init__.py
View File

@ -3,11 +3,11 @@ from typing import NamedTuple, List
import arrow
from pandas import DataFrame
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.strategy.interface import SellType
from freqtrade.constants import TICKER_INTERVAL_MINUTES
ticker_start_time = arrow.get(2018, 10, 3)
tests_ticker_interval = "1h"
tests_ticker_interval = '1h'
class BTrade(NamedTuple):
@ -28,11 +28,16 @@ class BTContainer(NamedTuple):
roi: float
trades: List[BTrade]
profit_perc: float
trailing_stop: bool = False
trailing_only_offset_is_reached: bool = False
trailing_stop_positive: float = None
trailing_stop_positive_offset: float = 0.0
use_sell_signal: bool = False
def _get_frame_time_from_offset(offset):
return ticker_start_time.shift(minutes=(offset * TICKER_INTERVAL_MINUTES[tests_ticker_interval])
).datetime.replace(tzinfo=None)
return ticker_start_time.shift(minutes=(offset * timeframe_to_minutes(tests_ticker_interval))
).datetime
def _build_backtest_dataframe(ticker_with_signals):

218
freqtrade/tests/optimize/test_backtest_detail.py
View File

@ -2,22 +2,35 @@
import logging
from unittest.mock import MagicMock
from pandas import DataFrame
import pytest
from pandas import DataFrame
from freqtrade.optimize import get_timeframe
from freqtrade.data.history import get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.strategy.interface import SellType
from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
_get_frame_time_from_offset, tests_ticker_interval)
from freqtrade.tests.conftest import patch_exchange
from freqtrade.tests.optimize import (BTContainer, BTrade,
_build_backtest_dataframe,
_get_frame_time_from_offset,
tests_ticker_interval)
# Test 0 Minus 8% Close
# Test 0: Sell with signal sell in candle 3
# Test with Stop-loss at 1%
# TC1: Stop-Loss Triggered 1% loss
tc0 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4986, 4600, 6172, 0, 0], # exit with stoploss hit
[3, 5010, 5000, 4980, 5010, 6172, 0, 1],
[4, 5010, 4987, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi=1, profit_perc=0.002, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=4)]
)
# Test 1: Stop-Loss Triggered 1% loss
# Test with Stop-loss at 1%
tc1 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
@ -30,10 +43,9 @@ tc0 = BTContainer(data=[
)
# Test 1 Minus 4% Low, minus 1% close
# Test 2: Minus 4% Low, minus 1% close
# Test with Stop-Loss at 3%
# TC2: Stop-Loss Triggered 3% Loss
tc1 = BTContainer(data=[
tc2 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
@ -46,14 +58,13 @@ tc1 = BTContainer(data=[
)
# Test 3 Candle drops 4%, Recovers 1%.
# Entry Criteria Met
# Candle drops 20%
# Candle Data for test 3
# Test with Stop-Loss at 2%
# TC3: Trade-A: Stop-Loss Triggered 2% Loss
# Trade-B: Stop-Loss Triggered 2% Loss
tc2 = BTContainer(data=[
# Test 3: Multiple trades.
# Candle drops 4%, Recovers 1%.
# Entry Criteria Met
# Candle drops 20%
# Trade-A: Stop-Loss Triggered 2% Loss
# Trade-B: Stop-Loss Triggered 2% Loss
tc3 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
@ -67,11 +78,11 @@ tc2 = BTContainer(data=[
BTrade(sell_reason=SellType.STOP_LOSS, open_tick=4, close_tick=5)]
)
# Test 4 Minus 3% / recovery +15%
# Test 4: Minus 3% / recovery +15%
# Candle Data for test 3 – Candle drops 3% Closed 15% up
# Test with Stop-loss at 2% ROI 6%
# TC4: Stop-Loss Triggered 2% Loss
tc3 = BTContainer(data=[
# Stop-Loss Triggered 2% Loss
tc4 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
@ -83,10 +94,9 @@ tc3 = BTContainer(data=[
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 4 / Drops 0.5% Closes +20%
# Set stop-loss at 1% ROI 3%
# TC5: ROI triggers 3% Gain
tc4 = BTContainer(data=[
# Test 5: Drops 0.5% Closes +20%, ROI triggers 3% Gain
# stop-loss: 1%, ROI: 3%
tc5 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4980, 4987, 6172, 1, 0],
[1, 5000, 5025, 4980, 4987, 6172, 0, 0], # enter trade (signal on last candle)
@ -98,11 +108,9 @@ tc4 = BTContainer(data=[
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
)
# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
# Candle Data for test 6
# Set stop-loss at 2% ROI at 5%
# TC6: Stop-Loss triggers 2% Loss
tc5 = BTContainer(data=[
# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positve, Stop-Loss triggers 2% Loss
# stop-loss: 2% ROI: 5%
tc6 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
@ -114,11 +122,9 @@ tc5 = BTContainer(data=[
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
# Candle Data for test 7
# Set stop-loss at 2% ROI at 3%
# TC7: ROI Triggers 3% Gain
tc6 = BTContainer(data=[
# Test 7: 6% Positive / 1% Negative / Close 1% Positve, ROI Triggers 3% Gain
# stop-loss: 2% ROI: 3%
tc7 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
@ -130,6 +136,123 @@ tc6 = BTContainer(data=[
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=2)]
)
# Test 8: trailing_stop should raise so candle 3 causes a stoploss.
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted in candle 2
tc8 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5000, 6172, 0, 0],
[2, 5000, 5250, 4750, 4850, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi=0.10, profit_perc=-0.055, trailing_stop=True,
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
)
# Test 9: trailing_stop should raise - high and low in same candle.
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted in candle 3
tc9 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5000, 6172, 0, 0],
[2, 5000, 5050, 4950, 5000, 6172, 0, 0],
[3, 5000, 5200, 4550, 4850, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi=0.10, profit_perc=-0.064, trailing_stop=True,
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
)
# Test 10: trailing_stop should raise so candle 3 causes a stoploss
# without applying trailing_stop_positive since stoploss_offset is at 10%.
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
tc10 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi=0.10, profit_perc=-0.1, trailing_stop=True,
trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.10,
trailing_stop_positive=0.03,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=4)]
)
# Test 11: trailing_stop should raise so candle 3 causes a stoploss
# applying a positive trailing stop of 3% since stop_positive_offset is reached.
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
tc11 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi=0.10, profit_perc=0.019, trailing_stop=True,
trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05,
trailing_stop_positive=0.03,
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
)
# Test 12: trailing_stop should raise in candle 2 and cause a stoploss in the same candle
# applying a positive trailing stop of 3% since stop_positive_offset is reached.
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
tc12 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 4650, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi=0.10, profit_perc=0.019, trailing_stop=True,
trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05,
trailing_stop_positive=0.03,
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 13: Buy and sell ROI on same candle
# stop-loss: 10% (should not apply), ROI: 1%
tc13 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 4850, 5100, 6172, 0, 0],
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
[4, 4750, 4950, 4850, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi=0.01, profit_perc=0.01,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=1)]
)
# Test 14 - Buy and Stoploss on same candle
# stop-loss: 5%, ROI: 10% (should not apply)
tc14 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5100, 4600, 5100, 6172, 0, 0],
[2, 5100, 5251, 4850, 5100, 6172, 0, 0],
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.05, roi=0.10, profit_perc=-0.05,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
# Test 15 - Buy and ROI on same candle, followed by buy and Stoploss on next candle
# stop-loss: 5%, ROI: 10% (should not apply)
tc15 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5100, 4900, 5100, 6172, 1, 0],
[2, 5100, 5251, 4650, 5100, 6172, 0, 0],
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.05, roi=0.01, profit_perc=-0.04,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=1),
BTrade(sell_reason=SellType.STOP_LOSS, open_tick=2, close_tick=2)]
)
TESTS = [
tc0,
tc1,
@ -138,6 +261,15 @@ TESTS = [
tc4,
tc5,
tc6,
tc7,
tc8,
tc9,
tc10,
tc11,
tc12,
tc13,
tc14,
tc15,
]
@ -148,8 +280,16 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
"""
default_conf["stoploss"] = data.stop_loss
default_conf["minimal_roi"] = {"0": data.roi}
default_conf['ticker_interval'] = tests_ticker_interval
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
default_conf["ticker_interval"] = tests_ticker_interval
default_conf["trailing_stop"] = data.trailing_stop
default_conf["trailing_only_offset_is_reached"] = data.trailing_only_offset_is_reached
# Only add this to configuration If it's necessary
if data.trailing_stop_positive:
default_conf["trailing_stop_positive"] = data.trailing_stop_positive
default_conf["trailing_stop_positive_offset"] = data.trailing_stop_positive_offset
default_conf["experimental"] = {"use_sell_signal": data.use_sell_signal}
mocker.patch("freqtrade.exchange.Exchange.get_fee", MagicMock(return_value=0.0))
patch_exchange(mocker)
frame = _build_backtest_dataframe(data.data)
backtesting = Backtesting(default_conf)
@ -157,7 +297,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
backtesting.advise_sell = lambda a, m: frame
caplog.set_level(logging.DEBUG)
pair = 'UNITTEST/BTC'
pair = "UNITTEST/BTC"
# Dummy data as we mock the analyze functions
data_processed = {pair: DataFrame()}
min_date, max_date = get_timeframe({pair: frame})

160
freqtrade/tests/optimize/test_backtesting.py
View File

@ -1,9 +1,7 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
import json
import math
import random
from typing import List
from unittest.mock import MagicMock
import numpy as np
@ -11,28 +9,27 @@ import pandas as pd
import pytest
from arrow import Arrow
from freqtrade import DependencyException, constants
from freqtrade.arguments import Arguments, TimeRange
from freqtrade import DependencyException, OperationalException, constants
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import evaluate_result_multi
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import (Backtesting, setup_configuration,
start)
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import get_timeframe
from freqtrade.optimize import setup_configuration, start_backtesting
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.state import RunMode
from freqtrade.strategy.default_strategy import DefaultStrategy
from freqtrade.strategy.interface import SellType
from freqtrade.tests.conftest import log_has, patch_exchange
def get_args(args) -> List[str]:
return Arguments(args, '').get_parsed_arg()
from freqtrade.tests.conftest import (get_args, log_has, log_has_re,
patch_exchange,
patched_configuration_load_config_file)
def trim_dictlist(dict_list, num):
new = {}
for pair, pair_data in dict_list.items():
new[pair] = pair_data[num:]
new[pair] = pair_data[num:].reset_index()
return new
@ -77,7 +74,8 @@ def load_data_test(what):
pair[x][5] # Keep old volume
] for x in range(0, datalen)
]
return {'UNITTEST/BTC': parse_ticker_dataframe(data, '1m', fill_missing=True)}
return {'UNITTEST/BTC': parse_ticker_dataframe(data, '1m', pair="UNITTEST/BTC",
fill_missing=True)}
def simple_backtest(config, contour, num_results, mocker) -> None:
@ -104,9 +102,10 @@ def simple_backtest(config, contour, num_results, mocker) -> None:
def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
timerange=None, exchange=None):
timerange=None, exchange=None, live=False):
tickerdata = history.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange)
pairdata = {'UNITTEST/BTC': parse_ticker_dataframe(tickerdata, '1m', fill_missing=True)}
pairdata = {'UNITTEST/BTC': parse_ticker_dataframe(tickerdata, '1m', pair="UNITTEST/BTC",
fill_missing=True)}
return pairdata
@ -167,9 +166,7 @@ def _trend_alternate(dataframe=None, metadata=None):
# Unit tests
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
patched_configuration_load_config_file(mocker, default_conf)
args = [
'--config', 'config.json',
@ -177,7 +174,7 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
'backtesting'
]
config = setup_configuration(get_args(args))
config = setup_configuration(get_args(args), RunMode.BACKTEST)
assert 'max_open_trades' in config
assert 'stake_currency' in config
assert 'stake_amount' in config
@ -185,11 +182,11 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
assert 'pair_whitelist' in config['exchange']
assert 'datadir' in config
assert log_has(
'Using data folder: {} ...'.format(config['datadir']),
'Using data directory: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert not log_has_re('Parameter -i/--ticker-interval detected .*', caplog.record_tuples)
assert 'live' not in config
assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
@ -206,11 +203,13 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
assert config['runmode'] == RunMode.BACKTEST
@pytest.mark.filterwarnings("ignore:DEPRECATED")
def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
mocker.patch('freqtrade.configuration.Configuration._create_datadir', lambda s, c, x: x)
patched_configuration_load_config_file(mocker, default_conf)
mocker.patch(
'freqtrade.configuration.configuration.create_datadir',
lambda c, x: x
)
args = [
'--config', 'config.json',
@ -227,7 +226,7 @@ def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) ->
'--export-filename', 'foo_bar.json'
]
config = setup_configuration(get_args(args))
config = setup_configuration(get_args(args), RunMode.BACKTEST)
assert 'max_open_trades' in config
assert 'stake_currency' in config
assert 'stake_amount' in config
@ -237,15 +236,12 @@ def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) ->
assert config['runmode'] == RunMode.BACKTEST
assert log_has(
'Using data folder: {} ...'.format(config['datadir']),
'Using data directory: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert log_has(
'Using ticker_interval: 1m ...',
caplog.record_tuples
)
assert log_has('Parameter -i/--ticker-interval detected ... Using ticker_interval: 1m ...',
caplog.record_tuples)
assert 'live' in config
assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
@ -259,6 +255,7 @@ def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) ->
assert 'refresh_pairs' in config
assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
assert 'timerange' in config
assert log_has(
'Parameter --timerange detected: {} ...'.format(config['timerange']),
@ -280,9 +277,7 @@ def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) ->
def test_setup_configuration_unlimited_stake_amount(mocker, default_conf, caplog) -> None:
default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
patched_configuration_load_config_file(mocker, default_conf)
args = [
'--config', 'config.json',
@ -291,7 +286,7 @@ def test_setup_configuration_unlimited_stake_amount(mocker, default_conf, caplog
]
with pytest.raises(DependencyException, match=r'.*stake amount.*'):
setup_configuration(get_args(args))
setup_configuration(get_args(args), RunMode.BACKTEST)
def test_start(mocker, fee, default_conf, caplog) -> None:
@ -299,16 +294,15 @@ def test_start(mocker, fee, default_conf, caplog) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
patched_configuration_load_config_file(mocker, default_conf)
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'backtesting'
]
args = get_args(args)
start(args)
start_backtesting(args)
assert log_has(
'Starting freqtrade in Backtesting mode',
caplog.record_tuples
@ -346,16 +340,35 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
assert callable(backtesting.strategy.tickerdata_to_dataframe)
assert callable(backtesting.advise_buy)
assert callable(backtesting.advise_sell)
assert isinstance(backtesting.strategy.dp, DataProvider)
get_fee.assert_called()
assert backtesting.fee == 0.5
assert not backtesting.strategy.order_types["stoploss_on_exchange"]
def test_backtesting_init_no_ticker_interval(mocker, default_conf, caplog) -> None:
"""
Check that stoploss_on_exchange is set to False while backtesting
since backtesting assumes a perfect stoploss anyway.
"""
patch_exchange(mocker)
del default_conf['ticker_interval']
default_conf['strategy_list'] = ['DefaultStrategy',
'TestStrategy']
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
with pytest.raises(OperationalException):
Backtesting(default_conf)
log_has("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`", caplog.record_tuples)
def test_tickerdata_to_dataframe_bt(default_conf, mocker) -> None:
patch_exchange(mocker)
timerange = TimeRange(None, 'line', 0, -100)
tick = history.load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick, '1m', fill_missing=True)}
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick, '1m', pair="UNITTEST/BTC",
fill_missing=True)}
backtesting = Backtesting(default_conf)
data = backtesting.strategy.tickerdata_to_dataframe(tickerlist)
@ -472,7 +485,7 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.data.history.load_data', mocked_load_data)
mocker.patch('freqtrade.optimize.get_timeframe', get_timeframe)
mocker.patch('freqtrade.data.history.get_timeframe', get_timeframe)
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', MagicMock())
patch_exchange(mocker)
mocker.patch.multiple(
@ -492,10 +505,9 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
backtesting.start()
# check the logs, that will contain the backtest result
exists = [
'Using local backtesting data (using whitelist in given config) ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Measuring data from 2017-11-14T21:17:00+00:00 '
'Backtesting with data from 2017-11-14T21:17:00+00:00 '
'up to 2017-11-14T22:59:00+00:00 (0 days)..'
]
for line in exists:
@ -507,7 +519,7 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog) -> None:
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.data.history.load_data', MagicMock(return_value={}))
mocker.patch('freqtrade.optimize.get_timeframe', get_timeframe)
mocker.patch('freqtrade.data.history.get_timeframe', get_timeframe)
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', MagicMock())
patch_exchange(mocker)
mocker.patch.multiple(
@ -624,8 +636,9 @@ def test_processed(default_conf, mocker) -> None:
def test_backtest_pricecontours(default_conf, fee, mocker) -> None:
# TODO: Evaluate usefullness of this, the patterns and buy-signls are unrealistic
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
tests = [['raise', 19], ['lower', 0], ['sine', 18]]
tests = [['raise', 19], ['lower', 0], ['sine', 35]]
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
@ -683,25 +696,32 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
assert len(results.loc[results.open_at_end]) == 0
def test_backtest_multi_pair(default_conf, fee, mocker):
@pytest.mark.parametrize("pair", ['ADA/BTC', 'LTC/BTC'])
@pytest.mark.parametrize("tres", [0, 20, 30])
def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair):
def _trend_alternate_hold(dataframe=None, metadata=None):
"""
Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit)
Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
"""
multi = 8
if metadata['pair'] in('ETH/BTC', 'LTC/BTC'):
multi = 20
else:
multi = 18
dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0)
dataframe['sell'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
if metadata['pair'] in('ETH/BTC', 'LTC/BTC'):
dataframe['buy'] = dataframe['buy'].shift(-4)
dataframe['sell'] = dataframe['sell'].shift(-4)
return dataframe
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
data = history.load_data(datadir=None, ticker_interval='5m', pairs=pairs)
# Only use 500 lines to increase performance
data = trim_dictlist(data, -500)
# Remove data for one pair from the beginning of the data
data[pair] = data[pair][tres:].reset_index()
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '5m'
@ -812,6 +832,7 @@ def test_backtest_record(default_conf, fee, mocker):
assert dur > 0
@pytest.mark.filterwarnings("ignore:DEPRECATED")
def test_backtest_start_live(default_conf, mocker, caplog):
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
@ -824,9 +845,7 @@ def test_backtest_start_live(default_conf, mocker, caplog):
patch_exchange(mocker, api_mock)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock())
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
patched_configuration_load_config_file(mocker, default_conf)
args = [
'--config', 'config.json',
@ -840,19 +859,19 @@ def test_backtest_start_live(default_conf, mocker, caplog):
'--disable-max-market-positions'
]
args = get_args(args)
start(args)
start_backtesting(args)
# check the logs, that will contain the backtest result
exists = [
'Parameter -i/--ticker-interval detected ...',
'Using ticker_interval: 1m ...',
'Parameter -i/--ticker-interval detected ... Using ticker_interval: 1m ...',
'Parameter -l/--live detected ...',
'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
'Parameter --timerange detected: -100 ...',
'Using data folder: freqtrade/tests/testdata ...',
'Using data directory: freqtrade/tests/testdata ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Downloading data for all pairs in whitelist ...',
'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Live: Downloading data for all defined pairs ...',
'Backtesting with data from 2017-11-14T19:31:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Parameter --enable-position-stacking detected ...'
]
@ -860,6 +879,7 @@ def test_backtest_start_live(default_conf, mocker, caplog):
assert log_has(line, caplog.record_tuples)
@pytest.mark.filterwarnings("ignore:DEPRECATED")
def test_backtest_start_multi_strat(default_conf, mocker, caplog):
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
@ -876,9 +896,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog):
gen_strattable_mock = MagicMock()
mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table_strategy',
gen_strattable_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
patched_configuration_load_config_file(mocker, default_conf)
args = [
'--config', 'config.json',
@ -894,7 +912,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog):
'TestStrategy',
]
args = get_args(args)
start(args)
start_backtesting(args)
# 2 backtests, 4 tables
assert backtestmock.call_count == 2
assert gen_table_mock.call_count == 4
@ -902,16 +920,16 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog):
# check the logs, that will contain the backtest result
exists = [
'Parameter -i/--ticker-interval detected ...',
'Using ticker_interval: 1m ...',
'Parameter -i/--ticker-interval detected ... Using ticker_interval: 1m ...',
'Parameter -l/--live detected ...',
'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
'Parameter --timerange detected: -100 ...',
'Using data folder: freqtrade/tests/testdata ...',
'Using data directory: freqtrade/tests/testdata ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Downloading data for all pairs in whitelist ...',
'Measuring data from 2017-11-14T19:31:00+00:00 up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Live: Downloading data for all defined pairs ...',
'Backtesting with data from 2017-11-14T19:31:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategy',

57
freqtrade/tests/optimize/test_edge_cli.py
View File

@ -2,23 +2,17 @@
# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
from unittest.mock import MagicMock
import json
from typing import List
from freqtrade.edge import PairInfo
from freqtrade.arguments import Arguments
from freqtrade.optimize.edge_cli import (EdgeCli, setup_configuration, start)
from freqtrade.optimize import setup_configuration, start_edge
from freqtrade.optimize.edge_cli import EdgeCli
from freqtrade.state import RunMode
from freqtrade.tests.conftest import log_has, patch_exchange
def get_args(args) -> List[str]:
return Arguments(args, '').get_parsed_arg()
from freqtrade.tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
patched_configuration_load_config_file)
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
patched_configuration_load_config_file(mocker, default_conf)
args = [
'--config', 'config.json',
@ -26,8 +20,8 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
'edge'
]
config = setup_configuration(get_args(args))
assert config['runmode'] == RunMode.EDGECLI
config = setup_configuration(get_args(args), RunMode.EDGE)
assert config['runmode'] == RunMode.EDGE
assert 'max_open_trades' in config
assert 'stake_currency' in config
@ -36,11 +30,11 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
assert 'pair_whitelist' in config['exchange']
assert 'datadir' in config
assert log_has(
'Using data folder: {} ...'.format(config['datadir']),
'Using data directory: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert not log_has_re('Parameter -i/--ticker-interval detected .*', caplog.record_tuples)
assert 'refresh_pairs' not in config
assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
@ -50,10 +44,11 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
def test_setup_edge_configuration_with_arguments(mocker, edge_conf, caplog) -> None:
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(edge_conf)
))
mocker.patch('freqtrade.configuration.Configuration._create_datadir', lambda s, c, x: x)
patched_configuration_load_config_file(mocker, edge_conf)
mocker.patch(
'freqtrade.configuration.configuration.create_datadir',
lambda c, x: x
)
args = [
'--config', 'config.json',
@ -66,24 +61,21 @@ def test_setup_edge_configuration_with_arguments(mocker, edge_conf, caplog) -> N
'--stoplosses=-0.01,-0.10,-0.001'
]
config = setup_configuration(get_args(args))
config = setup_configuration(get_args(args), RunMode.EDGE)
assert 'max_open_trades' in config
assert 'stake_currency' in config
assert 'stake_amount' in config
assert 'exchange' in config
assert 'pair_whitelist' in config['exchange']
assert 'datadir' in config
assert config['runmode'] == RunMode.EDGECLI
assert config['runmode'] == RunMode.EDGE
assert log_has(
'Using data folder: {} ...'.format(config['datadir']),
'Using data directory: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert log_has(
'Using ticker_interval: 1m ...',
caplog.record_tuples
)
assert log_has('Parameter -i/--ticker-interval detected ... Using ticker_interval: 1m ...',
caplog.record_tuples)
assert 'refresh_pairs' in config
assert log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
@ -99,16 +91,15 @@ def test_start(mocker, fee, edge_conf, caplog) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.edge_cli.EdgeCli.start', start_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(edge_conf)
))
patched_configuration_load_config_file(mocker, edge_conf)
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'edge'
]
args = get_args(args)
start(args)
start_edge(args)
assert log_has(
'Starting freqtrade in Edge mode',
caplog.record_tuples
@ -118,8 +109,10 @@ def test_start(mocker, fee, edge_conf, caplog) -> None:
def test_edge_init(mocker, edge_conf) -> None:
patch_exchange(mocker)
edge_conf['stake_amount'] = 20
edge_cli = EdgeCli(edge_conf)
assert edge_cli.config == edge_conf
assert edge_cli.config['stake_amount'] == 'unlimited'
assert callable(edge_cli.edge.calculate)

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