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merge develop to master for 0.16.1 release (pre-work for ccxt into use)

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Samuel Husso 2018-05-12 09:48:40 +03:00
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9
.dockerignore
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@ -4,3 +4,12 @@ Dockerfile
.dockerignore
config.json*
*.sqlite
.coveragerc
.eggs
.github
.pylintrc
.travis.yml
CONTRIBUTING.md
MANIFEST.in
README.md
freqtrade.service

5
.gitignore vendored
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@ -5,6 +5,9 @@ config.json
*.sqlite
.hyperopt
logfile.txt
hyperopt_trials.pickle
user_data/
freqtrade-plot.html
# Byte-compiled / optimized / DLL files
__pycache__/
@ -86,4 +89,4 @@ target/
.idea
.vscode
hyperopt_trials.pickle
.pytest_cache/

4
.travis.yml
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@ -13,7 +13,7 @@ addons:
install:
- ./install_ta-lib.sh
- export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
- pip install --upgrade flake8 coveralls
- pip install --upgrade flake8 coveralls pytest-random-order
- pip install -r requirements.txt
- pip install -e .
jobs:
@ -34,4 +34,4 @@ notifications:
cache:
directories:
- $HOME/.cache/pip
- ta-lib
- ta-lib

4
Dockerfile
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@ -1,7 +1,7 @@
FROM python:3.6.2
FROM python:3.6.5-slim-stretch
# Install TA-lib
RUN apt-get update && apt-get -y install build-essential && apt-get clean
RUN apt-get update && apt-get -y install curl build-essential && apt-get clean
RUN curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz | \
tar xzvf - && \
cd ta-lib && \

25
README.md
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@ -2,6 +2,7 @@
[![Build Status](https://travis-ci.org/gcarq/freqtrade.svg?branch=develop)](https://travis-ci.org/gcarq/freqtrade)
[![Coverage Status](https://coveralls.io/repos/github/gcarq/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/gcarq/freqtrade?branch=develop)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/gcarq/freqtrade/maintainability)
Simple High frequency trading bot for crypto currencies designed to
@ -80,6 +81,13 @@ bot in dry-run. We invite you to read the
[bot documentation](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md)
to ensure you understand how the bot is working.
### Easy installation
The script below will install all dependencies and help you to configure the bot.
```bash
./setup.sh --install
```
### Manual installation
The following steps are made for Linux/MacOS environment
**1. Clone the repo**
@ -97,7 +105,7 @@ vi config.json
**3. Build your docker image and run it**
```bash
docker build -t freqtrade .
docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
@ -136,8 +144,8 @@ to understand the requirements before sending your pull-requests.
### Bot commands
```bash
usage: main.py [-h] [-c PATH] [-v] [--version] [--dynamic-whitelist [INT]]
[--dry-run-db]
usage: main.py [-h] [-v] [--version] [-c PATH] [--dry-run-db] [--datadir PATH]
[--dynamic-whitelist [INT]]
{backtesting,hyperopt} ...
Simple High Frequency Trading Bot for crypto currencies
@ -149,16 +157,17 @@ positional arguments:
optional arguments:
-h, --help show this help message and exit
-c PATH, --config PATH
specify configuration file (default: config.json)
-v, --verbose be verbose
--version show program's version number and exit
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
-c PATH, --config PATH
specify configuration file (default: config.json)
--dry-run-db Force dry run to use a local DB
"tradesv3.dry_run.sqlite" instead of memory DB. Work
only if dry_run is enabled.
--datadir PATH path to backtest data (default freqdata/tests/testdata
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
```
More details on:
- [How to run the bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)

7
bin/freqtrade
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@ -1,4 +1,7 @@
#!/usr/bin/env python3
from freqtrade.main import main
main()
import sys
from freqtrade.main import main, set_loggers
set_loggers()
main(sys.argv[1:])

15
config.json.example
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@ -4,21 +4,14 @@
"stake_amount": 0.05,
"fiat_display_currency": "USD",
"dry_run": false,
"minimal_roi": {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
},
"stoploss": -0.10,
"unfilledtimeout": 600,
"bid_strategy": {
"ask_last_balance": 0.0
},
"exchange": {
"name": "bittrex",
"key": "key",
"secret": "secret",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"pair_whitelist": [
"BTC_ETH",
"BTC_LTC",
@ -41,8 +34,8 @@
},
"telegram": {
"enabled": true,
"token": "token",
"chat_id": "chat_id"
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"internals": {

54
config_full.json.example Normal file
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@ -0,0 +1,54 @@
{
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"fiat_display_currency": "USD",
"dry_run": false,
"ticker_interval": 5,
"minimal_roi": {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
},
"stoploss": -0.10,
"unfilledtimeout": 600,
"bid_strategy": {
"ask_last_balance": 0.0
},
"exchange": {
"name": "bittrex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"pair_whitelist": [
"BTC_ETH",
"BTC_LTC",
"BTC_ETC",
"BTC_DASH",
"BTC_ZEC",
"BTC_XLM",
"BTC_NXT",
"BTC_POWR",
"BTC_ADA",
"BTC_XMR"
],
"pair_blacklist": [
"BTC_DOGE"
]
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false
},
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"internals": {
"process_throttle_secs": 5
},
"strategy": "DefaultStrategy",
"strategy_path": "/some/folder/"
}

57
docs/backtesting.md
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@ -14,7 +14,7 @@ real data. This is what we call
Backtesting will use the crypto-currencies (pair) from your config file
and load static tickers located in
[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).
[/freqtrade/tests/testdata](https://github.com/gcarq/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
automatically. Testdata files will not be updated until you specify it.
@ -51,6 +51,61 @@ python3 ./freqtrade/main.py backtesting --realistic-simulation --live
python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
```
**With a (custom) strategy file**
```bash
python3 ./freqtrade/main.py -s currentstrategy backtesting
```
Where `-s currentstrategy` refers to a filename `currentstrategy.py` in `freqtrade/user_data/strategies`
**Exporting trades to file**
```bash
python3 ./freqtrade/main.py backtesting --export trades
```
**Running backtest 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:
```bash
python3 ./freqtrade/main.py backtesting --timerange=-200
```
***Advanced use of timerange***
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.
The full timerange specification:
- Use last 123 tickframes of data: `--timerange=-123`
- Use first 123 tickframes of data: `--timerange=123-`
- Use tickframes from line 123 through 456: `--timerange=123-456`
Incoming feature, not implemented yet:
- `--timerange=-20180131`
- `--timerange=20180101-`
- `--timerange=20180101-20181231`
**Update testdata directory**
To update your testdata directory, or download into another testdata directory:
```bash
mkdir -p user_data/data/testdata-20180113
cp freqtrade/tests/testdata/pairs.json user_data/data-20180113
cd user_data/data-20180113
```
Possibly edit pairs.json file to include/exclude pairs
```bash
python3 freqtrade/tests/testdata/download_backtest_data.py -p pairs.json
```
The script will read your pairs.json file, and download ticker data
into the current working directory.
For help about backtesting usage, please refer to
[Backtesting commands](#backtesting-commands).

105
docs/bot-optimization.md
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@ -3,21 +3,62 @@ This page explains where to customize your strategies, and add new
indicators.
## Table of Contents
- [Change your strategy](#change-your-strategy)
- [Install a custom strategy file](#install-a-custom-strategy-file)
- [Customize your strategy](#change-your-strategy)
- [Add more Indicator](#add-more-indicator)
- [Where is the default strategy](#where-is-the-default-strategy)
Since the version `0.16.0` the bot allows using custom strategy file.
## Install a custom strategy file
This is very simple. Copy paste your strategy file into the folder
`user_data/strategies`.
Let assume you have a class called `AwesomeStrategy` in the file `awesome-strategy.py`:
1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy
```
## Change your strategy
The bot is using buy and sell strategies to buy and sell your trades.
Both are customizable.
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`.
### Buy strategy
The default buy strategy is located in the file
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73-L92).
Edit the function `populate_buy_trend()` to update your buy strategy.
A strategy file contains all the information needed to build a good strategy:
- Buy strategy rules
- Sell strategy rules
- Minimal ROI recommended
- Stoploss recommended
- Hyperopt parameter
Sample:
The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
You can test it with the parameter: `--strategy TestStrategy`
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy
```
### Specify custom strategy location
If you want to use a strategy from a different folder you can pass `--strategy-path`
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
```
**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
file as reference.**
### Buy strategy
Edit the method `populate_buy_trend()` into your strategy file to
update your buy strategy.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@ -25,14 +66,9 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
@ -40,41 +76,31 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
```
### Sell strategy
The default buy strategy is located in the file
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115)
Edit the function `populate_sell_trend()` to update your buy strategy.
Edit the method `populate_sell_trend()` into your strategy file to
update your sell strategy.
Sample:
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(
(crossed_above(dataframe['rsi'], 70)) |
(crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
(dataframe['tema'] > dataframe['blower']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
## Add more Indicator
As you have seen, buy and sell strategies need indicators. You can see
the indicators in the file
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115).
Of course you can add more indicators by extending the list contained in
the function `populate_indicators()`.
As you have seen, 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.
Sample:
```python
@ -111,6 +137,15 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
return dataframe
```
**Want more indicators example?**
Look into the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
Then uncomment indicators you need.
### Where is the default strategy?
The default buy strategy is located in the file
[freqtrade/default_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py).
## Next step
Now you have a perfect strategy you probably want to backtesting it.

50
docs/bot-usage.md
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@ -22,19 +22,21 @@ positional arguments:
optional arguments:
-h, --help show this help message and exit
-c PATH, --config PATH
specify configuration file (default: config.json)
-v, --verbose be verbose
--version show program's version number and exit
-dd PATH, --datadir PATH
Path is from where backtesting and hyperopt will load the
ticker data files (default freqdata/tests/testdata).
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
-c PATH, --config PATH
specify configuration file (default: config.json)
-s NAME, --strategy NAME
specify strategy class name (default: DefaultStrategy)
--strategy-path PATH specify additional strategy lookup path
--dry-run-db Force dry run to use a local DB
"tradesv3.dry_run.sqlite" instead of memory DB. Work
only if dry_run is enabled.
--datadir PATH
path to backtest data (default freqdata/tests/testdata
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
```
### How to use a different config file?
@ -45,6 +47,38 @@ default, the bot will load the file `./config.json`
python3 ./freqtrade/main.py -c path/far/far/away/config.json
```
### How to use --strategy?
This parameter will allow you to load your custom strategy class.
Per default without `--strategy` or `-s` the bot will load the
`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
**Example:**
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
```
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](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.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!):
```bash
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
```
#### How to install a strategy?
This is very simple. Copy paste your strategy file into the folder
`user_data/strategies` or use `--strategy-path`. And voila, the bot is ready to use it.
### How to use --dynamic-whitelist?
Per default `--dynamic-whitelist` will retrieve the 20 currencies based
on BaseVolume. This value can be changed when you run the script.

20
docs/configuration.md
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@ -17,10 +17,11 @@ The table below will list all configuration parameters.
| `max_open_trades` | 3 | Yes | Number of trades open your bot will have.
| `stake_currency` | BTC | Yes | Crypto-currency used for trading.
| `stake_amount` | 0.05 | Yes | 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.
| `ticker_interval` | [1, 5, 30, 60, 1440] | No | The ticker interval to use (1min, 5 min, 30 min, 1 hour or 1 day). Defaut is 5 minutes
| `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below.
| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
| `minimal_roi` | See below | Yes | Set the threshold in percent the bot will use to sell a trade. More information below.
| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below.
| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file.
| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file.
| `unfilledtimeout` | 0 | No | How long (in minutes) the bot will wait for an unfilled order to complete, after which the order will be cancelled.
| `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
| `exchange.name` | bittrex | Yes | Name of the exchange class to use.
@ -29,10 +30,13 @@ The table below will list all configuration parameters.
| `exchange.pair_whitelist` | [] | No | List of currency to use by the bot. Can be overrided with `--dynamic-whitelist` param.
| `exchange.pair_blacklist` | [] | No | List of currency the bot must avoid. Useful when using `--dynamic-whitelist` param.
| `experimental.use_sell_signal` | false | No | Use your sell strategy in addition of the `minimal_roi`.
| `experimental.sell_profit_only` | false | No | waits until you have made a positive profit before taking a sell decision.
| `telegram.enabled` | true | Yes | Enable or not the usage of Telegram.
| `telegram.token` | token | No | Your Telegram bot token. Only required is `enable` is `true`.
| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required is `enable` is `true`.
| `telegram.token` | token | No | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required if `telegram.enabled` is `true`.
| `initial_state` | running | No | Defines the initial application state. More information below.
| `strategy` | DefaultStrategy | No | Defines Strategy class to use.
| `strategy_path` | null | No | Adds an additional strategy lookup path (must be a folder).
| `internals.process_throttle_secs` | 5 | Yes | Set the process throttle. Value in second.
The definition of each config parameters is in
@ -51,11 +55,19 @@ See the example below:
},
```
Most of the strategy files already include the optimal `minimal_roi`
value. This parameter is optional. If you use it, it will take over the
`minimal_roi` value from the strategy file.
### Understand stoploss
`stoploss` 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, it will take over the
`stoploss` value from the strategy file.
### Understand initial_state
`initial_state` is an optional field that defines the initial application state.
Possible values are `running` or `stopped`. (default=`running`)

64
docs/faq.md
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@ -2,20 +2,70 @@
#### 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!
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 understand your disappointment but unfortunately 12 trades is just not enough to say anything. If you run backtesting, you can see that our current algorithm does leave you on the plus side, but that is after thousands of trades and even there, you will be left with losses on specific coins that you have traded tens if not hundreds of times. We 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 understand your disappointment but unfortunately 12 trades is just
not enough to say anything. If you run backtesting, you can see that our
current algorithm does leave you on the plus side, but that is after
thousands of trades and even there, you will be left with losses on
specific coins that you have traded tens if not hundreds of times. We
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.
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
That's great. We have a nice backtesting and hyperoptimizing setup. See the tutorial [[here|Testing-new-strategies-with-Hyperopt]].
That's great. We have a nice backtesting and hyperoptimizing setup. See
the tutorial [here|Testing-new-strategies-with-Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/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.
### 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
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
```
or if you want intermediate result to see
```bash
for i in {1..100}; do python3 ./freqtrade/main.py hyperopt -e 100; done
```
#### 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
:
- 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
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
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.
You can use the `/forcesell all` command from Telegram.

93
docs/hyperopt.md
View File

@ -14,14 +14,13 @@ parameters with Hyperopt.
## Prepare Hyperopt
Before we start digging in Hyperopt, we recommend you to take a look at
out Hyperopt file
[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
your strategy file located into [user_data/strategies/](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
### 1. Configure your Guards and Triggers
There are two places you need to change to add a new buy strategy for
testing:
- Inside the [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L167-L207).
- Inside the [SPACE dict](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L47-L94).
There are two places you need to change in your strategy file to add a
new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
- Inside [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
There you have two different type of indicators: 1. `guards` and 2.
`triggers`.
@ -38,10 +37,10 @@ ADX > 10*".
If you have updated the buy strategy, means change the content of
`populate_buy_trend()` function you have to update the `guards` and
`populate_buy_trend()` method you have to update the `guards` and
`triggers` hyperopts must used.
As for an example if your `populate_buy_trend()` function is:
As for an example if your `populate_buy_trend()` method is:
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
dataframe.loc[
@ -52,14 +51,14 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
return dataframe
```
Your hyperopt file must contains `guards` to find the right value for
Your hyperopt file must contain `guards` to find the right value for
`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That
means you will need to enable/disable triggers.
In our case the `SPACE` and `populate_buy_trend` in hyperopt.py file
will be look like:
In our case the `SPACE` and `populate_buy_trend` in your strategy file
will look like:
```python
SPACE = {
space = {
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
@ -82,7 +81,7 @@ SPACE = {
...
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if params['adx']['enabled']:
@ -106,18 +105,18 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
### 2. Update the hyperopt config file
Hyperopt is using a dedicated config file. At this moment hyperopt
Hyperopt is using a dedicated config file. Currently hyperopt
cannot use your config file. It is also made on purpose to allow you
testing your strategy with different configurations.
The Hyperopt configuration is located in
[freqtrade/optimize/hyperopt_conf.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt_conf.py).
[user_data/hyperopt_conf.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopt_conf.py).
## Advanced notions
### Understand the Guards and Triggers
When you need to add the new guards and triggers to be hyperopt
parameters, you do this by adding them into the [SPACE dict](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L47-L94).
parameters, you do this by adding them into the [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
If it's a trigger, you add one line to the 'trigger' choice group and that's it.
@ -128,19 +127,21 @@ If it's a guard, you will add a line like this:
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
```
This says, "*one of guards is RSI, it can have two values, enabled or
This says, "*one of the guards is RSI, it can have two values, enabled or
disabled. If it is enabled, try different values for it between 20 and 40*".
So, the part of the strategy builder using the above setting looks like
this:
```
if params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
```
It checks if Hyperopt wants the RSI guard to be enabled for this
round `params['rsi']['enabled']` and if it is, then it will add a
condition that says RSI must be < than the value hyperopt picked
for this evaluation, that is given in the `params['rsi']['value']`.
condition that says RSI must be smaller than the value hyperopt picked
for this evaluation, which is given in the `params['rsi']['value']`.
That's it. Now you can add new parts of strategies to Hyperopt and it
will try all the combinations with all different values in the search
@ -149,9 +150,7 @@ for best working algo.
### Add a new Indicators
If you want to test an indicator that isn't used by the bot currently,
you need to add it to
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L40-L70)
inside the `populate_indicators` function.
you need to add it to the `populate_indicators()` method in `hyperopt.py`.
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
@ -160,13 +159,40 @@ it will take time you will have the result (more than 30 mins).
We strongly recommend to use `screen` to prevent any connection loss.
```bash
python3 ./freqtrade/main.py -c config.json hyperopt
python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
```
The `-e` flag will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.
### Execute hyperopt with different ticker-data source
If you would like to learn parameters using an alternate ticke-data that
you have on-disk, use the --datadir PATH option. Default hyperopt will
use data from directory freqtrade/tests/testdata.
If you would like to hyperopt parameters using an alternate ticker data that
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
use data from directory `user_data/data`.
### Running hyperopt with smaller testset
Use the `--timeperiod` argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
```bash
python3 ./freqtrade/main.py hyperopt --timeperiod -200
```
### Running hyperopt with smaller search space
Use the `--spaces` argument to limit the search space used by hyperopt.
Letting Hyperopt optimize everything is a huuuuge search space. Often it
might make more sense to start by just searching for initial buy algorithm.
Or maybe you just want to optimize your stoploss or roi table for that awesome
new buy strategy you have.
Legal values are:
- `all`: optimize everything
- `buy`: just search for a new buy 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`
### Hyperopt with MongoDB
Hyperopt with MongoDB, is like Hyperopt under steroids. As you saw by
@ -259,24 +285,19 @@ customizable value.
- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`)
- and so on...
You have to look from
[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L170-L200)
what those values match to.
You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to.
So for example you had `adx:` with the `value: 15.0` so we would look
at `adx`-block from
[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L178-L179).
That translates to the following code block to
[analyze.populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73)
at `adx`-block, that translates to the following code block:
```
(dataframe['adx'] > 15.0)
```
So translating your whole hyperopt result to as the new buy-signal
Translating your whole hyperopt result to as the new buy-signal
would be the following:
```
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 15.0) & # adx-value

320
docs/installation.md
View File

@ -1,111 +1,167 @@
# Install the bot
# Installation
This page explains how to prepare your environment for running the bot.
To understand how to set up the bot please read the Bot
[Bot configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
page.
To understand how to set up the bot please read the [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md) page.
## Table of Contents
- [Docker Automatic Installation](#docker)
- [Linux or Mac manual Installation](#linux--mac)
- [Linux - Ubuntu 16.04](#21-linux---ubuntu-1604)
- [Linux - Other distro](#22-linux---other-distro)
- [MacOS installation](#23-macos-installation)
- [Advanced Linux ](#advanced-linux)
- [Windows manual Installation](#windows)
# Docker
* [Table of Contents](#table-of-contents)
* [Easy Installation - Linux Script](#easy-installation---linux-script)
* [Automatic Installation - Docker](#automatic-installation---docker)
* [Custom Linux MacOS Installation](#custom-installation)
- [Requirements](#requirements)
- [Linux - Ubuntu 16.04](#linux---ubuntu-1604)
- [MacOS](#macos)
- [Setup Config and virtual env](#setup-config-and-virtual-env)
* [Windows](#windows)
<!-- /TOC -->
------
## 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.
```bash
$ ./setup.sh
usage:
-i,--install Install freqtrade from scratch
-u,--update Command git pull to update.
-r,--reset Hard reset your develop/master branch.
-c,--config Easy config generator (Will override your existing file).
```
### --install
This script will install everything you need to run the bot:
* Mandatory software as: `Python3`, `ta-lib`, `wget`
* Setup your virtualenv
* Configure your `config.json` file
This script is a combination of `install script` `--reset`, `--config`
### --update
Update parameter will pull the last version of your current branch and update your virtualenv.
### --reset
Reset parameter will hard reset your branch (only if you are on `master` or `develop`) and recreate your virtualenv.
### --config
Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
------
## Automatic Installation - Docker
## Easy installation
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.
* [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
### 1. Prepare the bot
1. Clone the git
```bash
git clone https://github.com/gcarq/freqtrade.git
```
2. (Optional) Checkout the develop branch
#### 1.2. (Optional) Checkout the develop branch
```bash
git checkout develop
```
3. Go into the new directory
#### 1.3. Go into the new directory
```bash
cd freqtrade
```
4. Copy `config.sample` to `config.json`
```bash
cp config.json.example config.json
```
To edit the config please refer to the [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md) page
5. Create your DB file (Optional, the bot will create it if it is missing)
```bash
# For Production
touch tradesv3.sqlite
# For Dry-run
#### 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](https://github.com/gcarq/freqtrade/blob/develop/docs/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. Build the docker image
### 2. Build the Docker image
```bash
cd freqtrade
docker build -t freqtrade .
```
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
a sqlite database file (see the "5. Run a restartable docker image"
section) to keep it between updates.
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 build process you can verify that the image was created with:
```
### 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):
```
docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
### 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
```
In this example, the database will be created inside the docker instance
and will be lost when you will refresh your image.
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**
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**
#### 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
```
If you are using `dry_run=True` it's not necessary to mount
`tradesv3.sqlite`, but you can mount `tradesv3.dryrun.sqlite` if you
plan to use the dry run mode with the param `--dry-run-db`.
If you are using `dry_run=True` it's not necessary to mount `tradesv3.sqlite`, but you can mount `tradesv3.dryrun.sqlite` if you plan to use the dry run mode with the param `--dry-run-db`.
### 6. Monitor your Docker instance
You can then use the following commands to monitor and manage your container:
```bash
@ -116,35 +172,39 @@ docker stop freqtrade
docker start freqtrade
```
You do not need to rebuild the image for configuration changes, it will
suffice to edit `config.json` and restart the container.
You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
------
## 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.
### Requirements
# Linux / MacOS
## 1. Requirements
Click each one for install guide:
- [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/),
note the bot was not tested on Python >= 3.7.x
- [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)
* [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/), note the bot was not tested on Python >= 3.7.x
* [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)
## 2. First install required packages
This bot require Python 3.6 and TA-LIB
### 2.1 Linux - Ubuntu 16.04
### Linux - Ubuntu 16.04
#### 1. Install Python 3.6, Git, and wget
**2.1.1. Install Python 3.6, Git, and wget**
```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
```
**2.1.2. Install TA-LIB**
#### 2. Install TA-Lib
Official webpage: https://mrjbq7.github.io/ta-lib/install.html
```
```bash
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar xvzf ta-lib-0.4.0-src.tar.gz
cd ta-lib
@ -155,29 +215,58 @@ cd ..
rm -rf ./ta-lib*
```
**2.1.3. [Optional] Install MongoDB**
#### 3. [Optional] Install MongoDB
Install MongoDB if you plan to optimize your strategy with Hyperopt.
```bash
sudo apt-get install mongodb-org
```
Complete tutorial on [Digital Ocean: How to Install MongoDB on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-install-mongodb-on-ubuntu-16-04)
### 2.2. Linux - Other distro
If you are on a different Linux OS you maybe have to adapt things like:
> Complete tutorial from Digital Ocean: [How to Install MongoDB on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-install-mongodb-on-ubuntu-16-04).
- package manager (for example yum instead of apt-get)
- package names
#### 4. Install FreqTrade
### 2.3. MacOS installation
Clone the git repository:
**2.3.1. Install Python 3.6, git and wget**
```bash
brew install python3 git wget
git clone https://github.com/gcarq/freqtrade.git
```
**2.3.2. [Optional] Install MongoDB**
Optionally checkout the develop branch:
```bash
git checkout develop
```
#### 5. Configure `freqtrade` as a `systemd` service
From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
After that you can start the daemon with:
```bash
systemctl --user start freqtrade
```
For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
```bash
sudo loginctl enable-linger "$USER"
```
### MacOS
#### 1. Install Python 3.6, git, wget and ta-lib
```bash
brew install python3 git wget ta-lib
```
#### 2. [Optional] Install MongoDB
Install MongoDB if you plan to optimize your strategy with Hyperopt.
```bash
curl -O https://fastdl.mongodb.org/osx/mongodb-osx-ssl-x86_64-3.4.10.tgz
tar -zxvf mongodb-osx-ssl-x86_64-3.4.10.tgz
@ -186,49 +275,63 @@ cp -R -n mongodb-osx-x86_64-3.4.10/ <path_freqtrade>/env/mongodb
export PATH=<path_freqtrade>/env/mongodb/bin:$PATH
```
## 3. Clone the repo
The following steps are made for Linux/mac environment
1. Clone the git `git clone https://github.com/gcarq/freqtrade.git`
2. (Optional) Checkout the develop branch `git checkout develop`
#### 3. Install FreqTrade
Clone the git repository:
```bash
git clone https://github.com/gcarq/freqtrade.git
```
Optionally checkout the develop branch:
```bash
git checkout develop
```
### Setup Config and virtual env
#### 1. Initialize the configuration
## 4. Prepare the bot
```bash
cd freqtrade
cp config.json.example config.json
```
To edit the config please refer to [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
## 5. Setup your virtual env
> *To edit the config please refer to [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md).*
#### 2. Setup your Python virtual environment (virtualenv)
```bash
python3.6 -m venv .env
source .env/bin/activate
pip3.6 install --upgrade pip
pip3.6 install -r requirements.txt
pip3.6 install -e .
```
## 6. Run the bot
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.
#### 3. Run the Bot
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
```
### Advanced Linux
**systemd service file**
Copy `./freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`)
and update `WorkingDirectory` and `ExecStart` to match your setup.
After that you can start the daemon with:
```bash
systemctl --user start freqtrade
```
------
# Windows
We do recommend Windows users to use [Docker](#docker) this will work
much easier and smoother (also safer).
## Windows
We recommend that Windows users use [Docker](#docker) as this will work
much easier and smoother (also more secure).
### Install freqtrade
copy paste `config.json` to ``\path\freqtrade-develop\freqtrade`
```cmd
#copy paste config.json to \path\freqtrade-develop\freqtrade
>cd \path\freqtrade-develop
>python -m venv .env
>cd .env\Scripts
@ -239,8 +342,9 @@ much easier and smoother (also safer).
>cd freqtrade
>python main.py
```
*Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/gcarq/freqtrade/issues/222)*
## Next step
Now you have an environment ready, the next step is to
[configure your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md).
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/gcarq/freqtrade/issues/222)
Now you have an environment ready, the next step is
[Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)...

77
docs/plotting.md Normal file
View File

@ -0,0 +1,77 @@
# Plotting
This page explains how to plot prices, indicator, profits.
## Table of Contents
- [Plot price and indicators](#plot-price-and-indicators)
- [Plot profit](#plot-profit)
## Installation
Plotting scripts use Plotly library. Install/upgrade it with:
```
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 pair] [--live]
```
Example
```
python scripts/plot_dataframe.py -p BTC_ETH
```
The `-p` pair argument, can be used to specify what
pair you would like to plot.
**Advanced use**
To plot the current live price use the `--live` flag:
```
python scripts/plot_dataframe.py -p BTC_ETH --live
```
To plot a timerange (to zoom in):
```
python scripts/plot_dataframe.py -p BTC_ETH --timerange=100-200
```
Timerange doesn't work with live data.
## Plot profit
The profit plotter show 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
more of an estimate.
3) Each pair individually profit
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.
Perhaps you want an algorithm that steadily makes small profits,
or one that acts less seldom, but makes big swings.
The third graph can be useful to spot outliers, events in pairs
that makes profit spikes.
Usage for the profit plotter:
```
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
```

6
docs/pre-requisite.md
View File

@ -15,7 +15,7 @@ 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.
### 1. Create your instagram bot
### 1. Create your Telegram bot
**1.1. Start a chat with https://telegram.me/BotFather**
**1.2. Send the message** `/newbot`
*BotFather response:*
@ -39,8 +39,10 @@ Use this token to access the HTTP API:
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`.**
`chat_id`.**

12
docs/sql_cheatsheet.md
View File

@ -67,6 +67,18 @@ SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, c
WHERE id=31;
```
## Insert manually a new trade
```sql
INSERT
INTO trades (exchange, pair, is_open, fee, open_rate, stake_amount, amount, open_date)
VALUES ('BITTREX', 'BTC_<COIN>', 1, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
```
**Example:**
```sql
INSERT INTO trades (exchange, pair, is_open, fee, open_rate, stake_amount, amount, open_date) VALUES ('BITTREX', 'BTC_ETC', 1, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
```
## Fix wrong fees in the table
If your DB was created before

15
docs/telegram-usage.md
View File

@ -15,7 +15,7 @@ official commands. You can ask at any moment for help with `/help`.
| Command | Default | Description |
|----------|---------|-------------|
| `/start` | | Starts the trader
| `/stop` | | Starts the trader
| `/stop` | | Stops the trader
| `/status` | | Lists all open trades
| `/status table` | | List all open trades in a table format
| `/count` | | Displays number of trades used and available
@ -126,4 +126,15 @@ Day Profit BTC Profit USD
```
## /version
> **Version:** `0.14.3`
> **Version:** `0.14.3`
### using proxy with telegram
in [freqtrade/freqtrade/rpc/telegram.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/rpc/telegram.py) replace
```
self._updater = Updater(token=self._config['telegram']['token'], workers=0)
```
with
```
self._updater = Updater(token=self._config['telegram']['token'], request_kwargs={'proxy_url': 'socks5://127.0.0.1:1080/'}, workers=0)
```

441
freqtrade/analyze.py
View File

@ -2,314 +2,213 @@
Functions to analyze ticker data with indicators and produce buy and sell signals
"""
import logging
from datetime import timedelta
from datetime import datetime, timedelta
from enum import Enum
from typing import Dict, List
from typing import Dict, List, Tuple
import arrow
import talib.abstract as ta
from pandas import DataFrame, to_datetime
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import get_ticker_history
from freqtrade.persistence import Trade
from freqtrade.strategy.resolver import StrategyResolver
logger = logging.getLogger(__name__)
class SignalType(Enum):
""" Enum to distinguish between buy and sell signals """
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
def parse_ticker_dataframe(ticker: list) -> DataFrame:
class Analyze(object):
"""
Analyses the trend for the given ticker history
:param ticker: See exchange.get_ticker_history
:return: DataFrame
Analyze class contains everything the bot need to determine if the situation is good for
buying or selling.
"""
columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
frame = DataFrame(ticker) \
.drop('BV', 1) \
.rename(columns=columns)
frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
frame.sort_values('date', inplace=True)
return frame
def __init__(self, config: dict) -> None:
"""
Init Analyze
:param config: Bot configuration (use the one from Configuration())
"""
self.config = config
self.strategy = StrategyResolver(self.config).strategy
@staticmethod
def parse_ticker_dataframe(ticker: list) -> DataFrame:
"""
Analyses the trend for the given ticker history
:param ticker: See exchange.get_ticker_history
:return: DataFrame
"""
columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
frame = DataFrame(ticker).rename(columns=columns)
if 'BV' in frame:
frame.drop('BV', axis=1, inplace=True)
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# group by index and aggregate results to eliminate duplicate ticks
frame = frame.groupby(by='date', as_index=False, sort=True).agg({
'close': 'last',
'high': 'max',
'low': 'min',
'open': 'first',
'volume': 'max',
})
return frame
# Momentum Indicator
# ------------------------------------
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
# ADX
dataframe['adx'] = ta.ADX(dataframe)
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
return self.strategy.populate_indicators(dataframe=dataframe)
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
"""
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.strategy.populate_buy_trend(dataframe=dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.strategy.populate_sell_trend(dataframe=dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
def get_ticker_interval(self) -> int:
"""
Return ticker interval to use
:return: Ticker interval value to use
"""
return self.strategy.ticker_interval
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
"""
# ROC
dataframe['roc'] = ta.ROC(dataframe)
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
"""
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
"""
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
"""
dataframe = self.parse_ticker_dataframe(ticker_history)
dataframe = self.populate_indicators(dataframe)
dataframe = self.populate_buy_trend(dataframe)
dataframe = self.populate_sell_trend(dataframe)
return dataframe
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
def get_signal(self, pair: str, interval: int) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:param interval: Interval to use (in min)
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
ticker_hist = get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
"""
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
"""
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
try:
dataframe = self.analyze_ticker(ticker_hist)
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
pair,
str(error)
)
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s',
pair,
str(error)
)
return False, False
# Overlap Studies
# ------------------------------------
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
# Previous Bollinger bands
# Because ta.BBANDS implementation is broken with small numbers, it actually
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
# and use middle band instead.
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
"""
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
"""
latest = dataframe.iloc[-1]
# EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
if signal_date < arrow.utcnow() - timedelta(minutes=(interval + 5)):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
return buy, sell
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool, sell: bool) -> bool:
"""
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.
:return: True if trade should be sold, False otherwise
"""
# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
if self.min_roi_reached(trade=trade, current_rate=rate, current_time=date):
logger.debug('Required profit reached. Selling..')
return True
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
if self.config.get('experimental', {}).get('sell_profit_only', False):
logger.debug('Checking if trade is profitable..')
if trade.calc_profit(rate=rate) <= 0:
return False
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
if sell and not buy and self.config.get('experimental', {}).get('use_sell_signal', False):
logger.debug('Sell signal received. Selling..')
return True
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
"""
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
"""
return dataframe
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
),
'buy'] = 1
return dataframe
def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
),
'sell'] = 1
return dataframe
def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
"""
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
"""
dataframe = parse_ticker_dataframe(ticker_history)
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
dataframe = populate_sell_trend(dataframe)
return dataframe
def get_signal(pair: str, signal: SignalType) -> bool:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: True if pair is good for buying, False otherwise
"""
ticker_hist = get_ticker_history(pair)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False
try:
dataframe = analyze_ticker(ticker_hist)
except ValueError as ex:
logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
return False
except Exception as ex:
logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
def min_roi_reached(self, trade: Trade, current_rate: float, current_time: datetime) -> bool:
"""
Based an earlier trade and current price and ROI configuration, decides whether bot should
sell
:return True if bot should sell at current rate
"""
current_profit = trade.calc_profit_percent(current_rate)
if self.strategy.stoploss is not None and current_profit < self.strategy.stoploss:
logger.debug('Stop loss hit.')
return True
# Check if time matches and current rate is above threshold
time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
for duration, threshold in self.strategy.minimal_roi.items():
if time_diff <= duration:
return False
if current_profit > threshold:
return True
return False
if dataframe.empty:
return False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
if signal_date < arrow.now() - timedelta(minutes=10):
return False
result = latest[signal.value] == 1
logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
return result
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {pair: self.populate_indicators(self.parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()}

257
freqtrade/arguments.py Normal file
View File

@ -0,0 +1,257 @@
"""
This module contains the argument manager class
"""
import argparse
import logging
import os
import re
from typing import List, Tuple, Optional
from freqtrade import __version__, constants
class Arguments(object):
"""
Arguments Class. Manage the arguments received by the cli
"""
def __init__(self, args: List[str], description: str):
self.args = args
self.parsed_arg = 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)
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='be verbose',
action='store_const',
dest='loglevel',
const=logging.DEBUG,
default=logging.INFO,
)
self.parser.add_argument(
'--version',
action='version',
version='%(prog)s {}'.format(__version__),
)
self.parser.add_argument(
'-c', '--config',
help='specify configuration file (default: %(default)s)',
dest='config',
default='config.json',
type=str,
metavar='PATH',
)
self.parser.add_argument(
'-d', '--datadir',
help='path to backtest data (default: %(default)s',
dest='datadir',
default=os.path.join('freqtrade', 'tests', 'testdata'),
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 20 currencies)', # noqa
dest='dynamic_whitelist',
const=constants.DYNAMIC_WHITELIST,
type=int,
metavar='INT',
nargs='?',
)
self.parser.add_argument(
'--dry-run-db',
help='Force dry run to use a local DB "tradesv3.dry_run.sqlite" \
instead of memory DB. Work only if dry_run is enabled.',
action='store_true',
dest='dry_run_db',
)
@staticmethod
def backtesting_options(parser: argparse.ArgumentParser) -> None:
"""
Parses given arguments for Backtesting scripts.
"""
parser.add_argument(
'-l', '--live',
help='using 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 Bittrex. \
Use it if you want to run your backtesting with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
parser.add_argument(
'--export',
help='export backtest results, argument are: trades\
Example --export=trades',
type=str,
default=None,
dest='export',
)
@staticmethod
def optimizer_shared_options(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (1, 5, 30, 60, 1440)',
dest='ticker_interval',
type=int,
metavar='INT',
)
parser.add_argument(
'--realistic-simulation',
help='uses max_open_trades from config to simulate real world limitations',
action='store_true',
dest='realistic_simulation',
)
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(
'-e', '--epochs',
help='specify number of epochs (default: %(default)d)',
dest='epochs',
default=constants.HYPEROPT_EPOCH,
type=int,
metavar='INT',
)
parser.add_argument(
'--use-mongodb',
help='parallelize evaluations with mongodb (requires mongod in PATH)',
dest='mongodb',
action='store_true',
)
parser.add_argument(
'-s', '--spaces',
help='Specify which parameters to hyperopt. Space separate list. \
Default: %(default)s',
choices=['all', 'buy', '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
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 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: str) -> Optional[Tuple[List, int, int]]:
"""
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 None
syntax = [(r'^-(\d{8})$', (None, 'date')),
(r'^(\d{8})-$', ('date', None)),
(r'^(\d{8})-(\d{8})$', ('date', 'date')),
(r'^(-\d+)$', (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 = None
stop = None
if stype[0]:
start = rvals[index]
if stype[0] != 'date':
start = int(start)
index += 1
if stype[1]:
stop = rvals[index]
if stype[1] != 'date':
stop = int(stop)
return stype, start, stop
raise Exception('Incorrect syntax for timerange "%s"' % text)
def scripts_options(self) -> None:
"""
Parses given arguments for plot scripts.
"""
self.parser.add_argument(
'-p', '--pair',
help='Show profits for only this pairs. Pairs are comma-separated.',
dest='pair',
default=None
)

208
freqtrade/configuration.py Normal file
View File

@ -0,0 +1,208 @@
"""
This module contains the configuration class
"""
import json
import logging
from argparse import Namespace
from typing import Dict, Any
from jsonschema import Draft4Validator, validate
from jsonschema.exceptions import ValidationError, best_match
from freqtrade import constants
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) -> None:
self.args = args
self.config = None
def load_config(self) -> Dict[str, Any]:
"""
Extract information for sys.argv and load the bot configuration
:return: Configuration dictionary
"""
logger.info('Using config: %s ...', self.args.config)
config = self._load_config_file(self.args.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 Hyperopt
config = self._load_hyperopt_config(config)
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:
logger.critical(
'Config file "%s" not found. Please create your config file',
path
)
exit(0)
if 'internals' not in conf:
conf['internals'] = {}
logger.info('Validating configuration ...')
return self._validate_config(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({'loglevel': self.args.loglevel})
logging.basicConfig(
level=config['loglevel'],
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
logger.info('Log level set to %s', logging.getLevelName(config['loglevel']))
# Add dynamic_whitelist if found
if 'dynamic_whitelist' in self.args and self.args.dynamic_whitelist:
config.update({'dynamic_whitelist': self.args.dynamic_whitelist})
logger.info(
'Parameter --dynamic-whitelist detected. '
'Using dynamically generated whitelist. '
'(not applicable with Backtesting and Hyperopt)'
)
# Add dry_run_db if found and the bot in dry run
if self.args.dry_run_db and config.get('dry_run', False):
config.update({'dry_run_db': True})
logger.info('Parameter --dry-run-db detected ...')
if config.get('dry_run_db', False):
if config.get('dry_run', False):
logger.info('Dry_run will use the DB file: "tradesv3.dry_run.sqlite"')
else:
logger.info('Dry run is disabled. (--dry_run_db ignored)')
return config
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: %d ...', 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 --realistic-simulation is used we add it to the configuration
if 'realistic_simulation' in self.args and self.args.realistic_simulation:
config.update({'realistic_simulation': True})
logger.info('Parameter --realistic-simulation detected ...')
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.args.datadir})
logger.info('Parameter --datadir detected: %s ...', self.args.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 --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)
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 --realistic-simulation 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 --mongodb is used we add it to the configuration
if 'mongodb' in self.args and self.args.mongodb:
config.update({'mongodb': self.args.mongodb})
logger.info('Parameter --use-mongodb detected ...')
# 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(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)
return conf
except ValidationError as exception:
logger.fatal(
'Invalid configuration. See config.json.example. Reason: %s',
exception
)
raise ValidationError(
best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
)
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

116
freqtrade/constants.py Normal file
View File

@ -0,0 +1,116 @@
# pragma pylint: disable=too-few-public-methods
"""
bot constants
"""
DYNAMIC_WHITELIST = 20 # pairs
PROCESS_THROTTLE_SECS = 5 # sec
TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
# Required json-schema for user specified config
CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': 1},
'ticker_interval': {'type': 'integer', 'enum': [1, 5, 30, 60, 1440]},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'ETH', 'USDT']},
'stake_amount': {'type': 'number', 'minimum': 0.0005},
'fiat_display_currency': {'type': 'string', 'enum': ['AUD', 'BRL', 'CAD', 'CHF',
'CLP', 'CNY', 'CZK', 'DKK',
'EUR', 'GBP', 'HKD', 'HUF',
'IDR', 'ILS', 'INR', 'JPY',
'KRW', 'MXN', 'MYR', 'NOK',
'NZD', 'PHP', 'PKR', 'PLN',
'RUB', 'SEK', 'SGD', 'THB',
'TRY', 'TWD', 'ZAR', 'USD']},
'dry_run': {'type': 'boolean'},
'minimal_roi': {
'type': 'object',
'patternProperties': {
'^[0-9.]+$': {'type': 'number'}
},
'minProperties': 1
},
'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
'unfilledtimeout': {'type': 'integer', 'minimum': 0},
'bid_strategy': {
'type': 'object',
'properties': {
'ask_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False
},
},
'required': ['ask_last_balance']
},
'exchange': {'$ref': '#/definitions/exchange'},
'experimental': {
'type': 'object',
'properties': {
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'}
}
},
'telegram': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
},
'required': ['enabled', 'token', 'chat_id']
},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'internals': {
'type': 'object',
'properties': {
'process_throttle_secs': {'type': 'number'},
'interval': {'type': 'integer'}
}
}
},
'definitions': {
'exchange': {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'key': {'type': 'string'},
'secret': {'type': 'string'},
'pair_whitelist': {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
},
'uniqueItems': True
},
'pair_blacklist': {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
},
'uniqueItems': True
}
},
'required': ['name', 'key', 'secret', 'pair_whitelist']
}
},
'anyOf': [
{'required': ['exchange']}
],
'required': [
'max_open_trades',
'stake_currency',
'stake_amount',
'fiat_display_currency',
'dry_run',
'bid_strategy',
'telegram'
]
}

2
freqtrade/exchange/__init__.py
View File

@ -139,7 +139,7 @@ def get_ticker(pair: str, refresh: Optional[bool] = True) -> dict:
@cached(TTLCache(maxsize=100, ttl=30))
def get_ticker_history(pair: str, tick_interval: Optional[int] = 5) -> List[Dict]:
def get_ticker_history(pair: str, tick_interval) -> List[Dict]:
return _API.get_ticker_history(pair, tick_interval)

55
freqtrade/exchange/bittrex.py
View File

@ -1,9 +1,8 @@
import logging
import requests
from typing import Dict, List, Optional
from bittrex.bittrex import Bittrex as _Bittrex
from bittrex.bittrex import API_V1_1, API_V2_0
from bittrex.bittrex import Bittrex as _Bittrex
from requests.exceptions import ContentDecodingError
from freqtrade import OperationalException
@ -15,20 +14,6 @@ _API: _Bittrex = None
_API_V2: _Bittrex = None
_EXCHANGE_CONF: dict = {}
# API socket timeout
API_TIMEOUT = 60
def custom_requests(request_url, apisign):
"""
Set timeout for requests
"""
return requests.get(
request_url,
headers={"apisign": apisign},
timeout=API_TIMEOUT
).json()
class Bittrex(Exchange):
"""
@ -47,14 +32,12 @@ class Bittrex(Exchange):
api_secret=_EXCHANGE_CONF['secret'],
calls_per_second=1,
api_version=API_V1_1,
dispatch=custom_requests
)
_API_V2 = _Bittrex(
api_key=_EXCHANGE_CONF['key'],
api_secret=_EXCHANGE_CONF['secret'],
calls_per_second=1,
api_version=API_V2_0,
dispatch=custom_requests
)
self.cached_ticker = {}
@ -69,7 +52,7 @@ class Bittrex(Exchange):
'MIN_TRADE_REQUIREMENT_NOT_MET',
]
if response['message'] in temp_error_messages:
raise ContentDecodingError('Got {}'.format(response['message']))
raise ContentDecodingError(response['message'])
@property
def fee(self) -> float:
@ -122,13 +105,11 @@ class Bittrex(Exchange):
raise OperationalException('{message} params=({pair})'.format(
message=data['message'],
pair=pair))
if not data.get('result') \
or not data['result'].get('Bid') \
or not data['result'].get('Ask') \
or not data['result'].get('Last'):
raise ContentDecodingError('{message} params=({pair})'.format(
message='Got invalid response from bittrex',
keys = ['Bid', 'Ask', 'Last']
if not data.get('result') or\
not all(key in data.get('result', {}) for key in keys) or\
not all(data.get('result', {})[key] is not None for key in keys):
raise ContentDecodingError('Invalid response from Bittrex params=({pair})'.format(
pair=pair))
# Update the pair
self.cached_ticker[pair] = {
@ -143,23 +124,27 @@ class Bittrex(Exchange):
interval = 'oneMin'
elif tick_interval == 5:
interval = 'fiveMin'
elif tick_interval == 30:
interval = 'thirtyMin'
elif tick_interval == 60:
interval = 'hour'
elif tick_interval == 1440:
interval = 'Day'
else:
raise ValueError('Cannot parse tick_interval: {}'.format(tick_interval))
raise ValueError('Unknown tick_interval: {}'.format(tick_interval))
data = _API_V2.get_candles(pair.replace('_', '-'), interval)
# These sanity check are necessary because bittrex cannot keep their API stable.
if not data.get('result'):
raise ContentDecodingError('{message} params=({pair})'.format(
message='Got invalid response from bittrex',
raise ContentDecodingError('Invalid response from Bittrex params=({pair})'.format(
pair=pair))
for prop in ['C', 'V', 'O', 'H', 'L', 'T']:
for tick in data['result']:
if prop not in tick.keys():
raise ContentDecodingError('{message} params=({pair})'.format(
message='Required property {} not present in response'.format(prop),
pair=pair))
raise ContentDecodingError('Required property {} not present '
'in response params=({})'.format(prop, pair))
if not data['success']:
Bittrex._validate_response(data)
@ -203,21 +188,21 @@ class Bittrex(Exchange):
data = _API.get_markets()
if not data['success']:
Bittrex._validate_response(data)
raise OperationalException('{message}'.format(message=data['message']))
raise OperationalException(data['message'])
return [m['MarketName'].replace('-', '_') for m in data['result']]
def get_market_summaries(self) -> List[Dict]:
data = _API.get_market_summaries()
if not data['success']:
Bittrex._validate_response(data)
raise OperationalException('{message}'.format(message=data['message']))
raise OperationalException(data['message'])
return data['result']
def get_wallet_health(self) -> List[Dict]:
data = _API_V2.get_wallet_health()
if not data['success']:
Bittrex._validate_response(data)
raise OperationalException('{message}'.format(message=data['message']))
raise OperationalException(data['message'])
return [{
'Currency': entry['Health']['Currency'],
'IsActive': entry['Health']['IsActive'],

50
freqtrade/fiat_convert.py
View File

@ -1,12 +1,20 @@
"""
Module that define classes to convert Crypto-currency to FIAT
e.g BTC to USD
"""
import logging
import time
from pymarketcap import Pymarketcap
from coinmarketcap import Market
logger = logging.getLogger(__name__)
class CryptoFiat():
class CryptoFiat(object):
"""
Object to describe what is the price of Crypto-currency in a FIAT
"""
# Constants
CACHE_DURATION = 6 * 60 * 60 # 6 hours
@ -48,7 +56,15 @@ class CryptoFiat():
return self._expiration - time.time() <= 0
class CryptoToFiatConverter():
class CryptoToFiatConverter(object):
"""
Main class to initiate Crypto to FIAT.
This object contains a list of pair Crypto, FIAT
This object is also a Singleton
"""
__instance = None
_coinmarketcap = None
# Constants
SUPPORTED_FIAT = [
"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
@ -57,12 +73,22 @@ class CryptoToFiatConverter():
"RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD"
]
def __init__(self) -> None:
try:
self._coinmarketcap = Pymarketcap()
except BaseException:
self._coinmarketcap = None
CRYPTOMAP = {
'BTC': 'bitcoin',
'ETH': 'ethereum',
'USDT': 'thether'
}
def __new__(cls):
if CryptoToFiatConverter.__instance is None:
CryptoToFiatConverter.__instance = object.__new__(cls)
try:
CryptoToFiatConverter._coinmarketcap = Market()
except BaseException:
CryptoToFiatConverter._coinmarketcap = None
return CryptoToFiatConverter.__instance
def __init__(self) -> None:
self._pairs = []
def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
@ -152,12 +178,16 @@ class CryptoToFiatConverter():
# Check if the fiat convertion you want is supported
if not self._is_supported_fiat(fiat=fiat_symbol):
raise ValueError('The fiat {} is not supported.'.format(fiat_symbol))
if crypto_symbol not in self.CRYPTOMAP:
raise ValueError(
'The crypto symbol {} is not supported.'.format(crypto_symbol))
try:
return float(
self._coinmarketcap.ticker(
currency=crypto_symbol,
currency=self.CRYPTOMAP[crypto_symbol],
convert=fiat_symbol
)['price_' + fiat_symbol.lower()]
)[0]['price_' + fiat_symbol.lower()]
)
except BaseException:
return 0.0

526
freqtrade/freqtradebot.py Normal file
View File

@ -0,0 +1,526 @@
"""
Freqtrade is the main module of this bot. It contains the class Freqtrade()
"""
import copy
import json
import logging
import time
import traceback
from datetime import datetime
from typing import Dict, List, Optional, Any, Callable
import arrow
import requests
from cachetools import cached, TTLCache
from freqtrade import (
DependencyException, OperationalException, exchange, persistence, __version__
)
from freqtrade.analyze import Analyze
from freqtrade import constants
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.persistence import Trade
from freqtrade.rpc.rpc_manager import RPCManager
from freqtrade.state import State
logger = logging.getLogger(__name__)
class FreqtradeBot(object):
"""
Freqtrade is the main class of the bot.
This is from here the bot start its logic.
"""
def __init__(self, config: Dict[str, Any], db_url: Optional[str] = None):
"""
Init all variables and object the bot need to work
:param config: configuration dict, you can use the Configuration.get_config()
method to get the config dict.
:param db_url: database connector string for sqlalchemy (Optional)
"""
logger.info(
'Starting freqtrade %s',
__version__,
)
# Init bot states
self.state = State.STOPPED
# Init objects
self.config = config
self.analyze = None
self.fiat_converter = None
self.rpc = None
self.persistence = None
self.exchange = None
self._init_modules(db_url=db_url)
def _init_modules(self, db_url: Optional[str] = None) -> None:
"""
Initializes all modules and updates the config
:param db_url: database connector string for sqlalchemy (Optional)
:return: None
"""
# Initialize all modules
self.analyze = Analyze(self.config)
self.fiat_converter = CryptoToFiatConverter()
self.rpc = RPCManager(self)
persistence.init(self.config, db_url)
exchange.init(self.config)
# Set initial application state
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
def clean(self) -> bool:
"""
Cleanup the application state und finish all pending tasks
:return: None
"""
self.rpc.send_msg('*Status:* `Stopping trader...`')
logger.info('Stopping trader and cleaning up modules...')
self.state = State.STOPPED
self.rpc.cleanup()
persistence.cleanup()
return True
def worker(self, old_state: None) -> State:
"""
Trading routine that must be run at each loop
:param old_state: the previous service state from the previous call
:return: current service state
"""
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg('*Status:* `{}`'.format(state.name.lower()))
logger.info('Changing state to: %s', state.name)
if state == State.STOPPED:
time.sleep(1)
elif state == State.RUNNING:
min_secs = self.config.get('internals', {}).get(
'process_throttle_secs',
constants.PROCESS_THROTTLE_SECS
)
nb_assets = self.config.get('dynamic_whitelist', None)
self._throttle(func=self._process,
min_secs=min_secs,
nb_assets=nb_assets)
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, nb_assets: Optional[int] = 0) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
# Refresh whitelist based on wallet maintenance
sanitized_list = self._refresh_whitelist(
self._gen_pair_whitelist(
self.config['stake_currency']
) if nb_assets else self.config['exchange']['pair_whitelist']
)
# Keep only the subsets of pairs wanted (up to nb_assets)
final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
self.config['exchange']['pair_whitelist'] = final_list
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
# 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(self.config['unfilledtimeout'])
Trade.session.flush()
except (requests.exceptions.RequestException, json.JSONDecodeError) as error:
logger.warning('%s, retrying in 30 seconds...', error)
time.sleep(constants.RETRY_TIMEOUT)
except OperationalException:
self.rpc.send_msg(
'*Status:* OperationalException:\n```\n{traceback}```{hint}'
.format(
traceback=traceback.format_exc(),
hint='Issue `/start` if you think it is safe to restart.'
)
)
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self, base_currency: str, key: str = 'BaseVolume') -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'BaseVolume')
:return: List of pairs
"""
summaries = sorted(
(s for s in exchange.get_market_summaries() if
s['MarketName'].startswith(base_currency)),
key=lambda s: s.get(key) or 0.0,
reverse=True
)
return [s['MarketName'].replace('-', '_') for s in summaries]
def _refresh_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check wallet health 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
black_listed
"""
sanitized_whitelist = whitelist
health = exchange.get_wallet_health()
known_pairs = set()
for status in health:
pair = '{}_{}'.format(self.config['stake_currency'], status['Currency'])
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.config['exchange'].get('pair_blacklist', []):
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not status['IsActive']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist (reason: %s).',
pair, status.get('Notice') or 'wallet is not active'
)
# We need to remove pairs that are unknown
final_list = [x for x in sanitized_whitelist if x in known_pairs]
return final_list
def get_target_bid(self, ticker: Dict[str, float]) -> float:
"""
Calculates bid target between current ask price and last price
:param ticker: Ticker to use for getting Ask and Last Price
:return: float: Price
"""
if ticker['ask'] < ticker['last']:
return ticker['ask']
balance = self.config['bid_strategy']['ask_last_balance']
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def create_trade(self) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
:param interval: Ticker interval used for Analyze
:return: True if a trade object has been created and persisted, False otherwise
"""
stake_amount = self.config['stake_amount']
interval = self.analyze.get_ticker_interval()
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount
)
whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(self.config['stake_currency']) < stake_amount:
raise DependencyException(
'stake amount is not fulfilled (currency={})'.format(self.config['stake_currency'])
)
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
(buy, sell) = self.analyze.get_signal(_pair, interval)
if buy and not sell:
pair = _pair
break
else:
return False
# Calculate amount
buy_limit = self.get_target_bid(exchange.get_ticker(pair))
amount = stake_amount / buy_limit
order_id = exchange.buy(pair, buy_limit, amount)
stake_amount_fiat = self.fiat_converter.convert_amount(
stake_amount,
self.config['stake_currency'],
self.config['fiat_display_currency']
)
# Create trade entity and return
self.rpc.send_msg(
'*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '
.format(
exchange.get_name().upper(),
pair.replace('_', '/'),
exchange.get_pair_detail_url(pair),
buy_limit,
stake_amount,
self.config['stake_currency'],
stake_amount_fiat,
self.config['fiat_display_currency']
)
)
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
trade = Trade(
pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=exchange.get_fee(),
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id
)
Trade.session.add(trade)
Trade.session.flush()
return True
def process_maybe_execute_buy(self) -> bool:
"""
Tries to execute a buy trade in a safe way
:return: True if executed
"""
try:
# Create entity and execute trade
if self.create_trade():
return True
logger.info('Found no buy signals for whitelisted currencies. Trying again..')
return False
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
return False
def process_maybe_execute_sell(self, trade: Trade) -> bool:
"""
Tries to execute a sell trade
:return: True if executed
"""
# 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)
trade.update(exchange.get_order(trade.open_order_id))
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return self.handle_trade(trade)
return False
def handle_trade(self, trade: Trade) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold, False otherwise
"""
if not trade.is_open:
raise ValueError('attempt to handle closed trade: {}'.format(trade))
logger.debug('Handling %s ...', trade)
current_rate = exchange.get_ticker(trade.pair)['bid']
(buy, sell) = (False, False)
if self.config.get('experimental', {}).get('use_sell_signal'):
(buy, sell) = self.analyze.get_signal(trade.pair, self.analyze.get_ticker_interval())
if self.analyze.should_sell(trade, current_rate, datetime.utcnow(), buy, sell):
self.execute_sell(trade, current_rate)
return True
logger.info('Found no sell signals for whitelisted currencies. Trying again..')
return False
def check_handle_timedout(self, timeoutvalue: int) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
timeoutthreashold = arrow.utcnow().shift(minutes=-timeoutvalue).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
try:
order = exchange.get_order(trade.open_order_id)
except requests.exceptions.RequestException:
logger.info(
'Cannot query order for %s due to %s',
trade,
traceback.format_exc())
continue
ordertime = arrow.get(order['opened'])
# Check if trade is still actually open
if int(order['remaining']) == 0:
continue
if order['type'] == "LIMIT_BUY" and ordertime < timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
elif order['type'] == "LIMIT_SELL" and ordertime < timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
# handle_timedout_limit_sell()?
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""Buy timeout - cancel order
:return: True if order was fully cancelled
"""
exchange.cancel_order(trade.open_order_id)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
# FIX? do we really need to flush, caller of
# check_handle_timedout will flush afterwards
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
self.rpc.send_msg('*Timeout:* Unfilled buy order for {} cancelled'.format(
trade.pair.replace('_', '/')))
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg('*Timeout:* Remaining buy order for {} cancelled'.format(
trade.pair.replace('_', '/')))
return False
# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
"""
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
"""
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
exchange.cancel_order(trade.open_order_id)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
self.rpc.send_msg('*Timeout:* Unfilled sell order for {} cancelled'.format(
trade.pair.replace('_', '/')))
logger.info('Sell order timeout for %s.', trade)
return True
# TODO: figure out how to handle partially complete sell orders
return False
def execute_sell(self, trade: Trade, limit: float) -> None:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:return: None
"""
# Execute sell and update trade record
order_id = exchange.sell(str(trade.pair), limit, trade.amount)
trade.open_order_id = order_id
fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
profit_trade = trade.calc_profit(rate=limit)
current_rate = exchange.get_ticker(trade.pair, False)['bid']
profit = trade.calc_profit_percent(current_rate)
message = "*{exchange}:* Selling\n" \
"*Current Pair:* [{pair}]({pair_url})\n" \
"*Limit:* `{limit}`\n" \
"*Amount:* `{amount}`\n" \
"*Open Rate:* `{open_rate:.8f}`\n" \
"*Current Rate:* `{current_rate:.8f}`\n" \
"*Profit:* `{profit:.2f}%`" \
"".format(
exchange=trade.exchange,
pair=trade.pair,
pair_url=exchange.get_pair_detail_url(trade.pair),
limit=limit,
open_rate=trade.open_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
profit=round(profit * 100, 2),
)
# For regular case, when the configuration exists
if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
fiat_converter = CryptoToFiatConverter()
profit_fiat = fiat_converter.convert_amount(
profit_trade,
self.config['stake_currency'],
self.config['fiat_display_currency']
)
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f} {coin}`' \
'` / {profit_fiat:.3f} {fiat})`' \
''.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade,
coin=self.config['stake_currency'],
profit_fiat=profit_fiat,
fiat=self.config['fiat_display_currency'],
)
# Because telegram._forcesell does not have the configuration
# Ignore the FIAT value and does not show the stake_currency as well
else:
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade
)
# Send the message
self.rpc.send_msg(message)
Trade.session.flush()

40
freqtrade/indicator_helpers.py Normal file
View File

@ -0,0 +1,40 @@
from math import exp, pi, sqrt, cos
import numpy as np
import talib as ta
from pandas import Series
def went_up(series: Series) -> bool:
return series > series.shift(1)
def went_down(series: Series) -> bool:
return series < series.shift(1)
def ehlers_super_smoother(series: Series, smoothing: float = 6) -> type(Series):
magic = pi * sqrt(2) / smoothing
a1 = exp(-magic)
coeff2 = 2 * a1 * cos(magic)
coeff3 = -a1 * a1
coeff1 = (1 - coeff2 - coeff3) / 2
filtered = series.copy()
for i in range(2, len(series)):
filtered.iloc[i] = coeff1 * (series.iloc[i] + series.iloc[i-1]) + \
coeff2 * filtered.iloc[i-1] + coeff3 * filtered.iloc[i-2]
return filtered
def fishers_inverse(series: Series, smoothing: float = 0) -> np.ndarray:
""" Does a smoothed fishers inverse transformation.
Can be used with any oscillator that goes from 0 to 100 like RSI or MFI """
v1 = 0.1 * (series - 50)
if smoothing > 0:
v2 = ta.WMA(v1.values, timeperiod=smoothing)
else:
v2 = v1
return (np.exp(2 * v2)-1) / (np.exp(2 * v2) + 1)

493
freqtrade/main.py
View File

@ -1,472 +1,69 @@
#!/usr/bin/env python3
import copy
import json
"""
Main Freqtrade bot script.
Read the documentation to know what cli arguments you need.
"""
import logging
import sys
import time
import traceback
from datetime import datetime
from typing import Dict, List, Optional
from typing import List
import arrow
import requests
from cachetools import cached, TTLCache
from freqtrade import (DependencyException, OperationalException, __version__,
exchange, persistence, rpc)
from freqtrade.analyze import SignalType, get_signal
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.misc import (State, get_state, load_config, parse_args,
throttle, update_state)
from freqtrade.persistence import Trade
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.freqtradebot import FreqtradeBot
logger = logging.getLogger('freqtrade')
_CONF = {}
def refresh_whitelist(whitelist: List[str]) -> List[str]:
def main(sysargv: List[str]) -> None:
"""
Check wallet health 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 black_listed
"""
sanitized_whitelist = whitelist
health = exchange.get_wallet_health()
known_pairs = set()
for status in health:
pair = '{}_{}'.format(_CONF['stake_currency'], status['Currency'])
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in _CONF['exchange'].get('pair_blacklist', []):
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not status['IsActive']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist (reason: %s).',
pair, status.get('Notice') or 'wallet is not active'
)
# We need to remove pairs that are unknown
final_list = [x for x in sanitized_whitelist if x in known_pairs]
return final_list
def _process(nb_assets: Optional[int] = 0) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if a trade has been created or closed, False otherwise
"""
state_changed = False
try:
# Refresh whitelist based on wallet maintenance
sanitized_list = refresh_whitelist(
gen_pair_whitelist(
_CONF['stake_currency']
) if nb_assets else _CONF['exchange']['pair_whitelist']
)
# Keep only the subsets of pairs wanted (up to nb_assets)
final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
_CONF['exchange']['pair_whitelist'] = final_list
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if len(trades) < _CONF['max_open_trades']:
try:
# Create entity and execute trade
state_changed = create_trade(float(_CONF['stake_amount']))
if not state_changed:
logger.info(
'Checked all whitelisted currencies. '
'Found no suitable entry positions for buying. Will keep looking ...'
)
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
for trade in trades:
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Got open order for %s', trade)
trade.update(exchange.get_order(trade.open_order_id))
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
state_changed = handle_trade(trade) or state_changed
if 'unfilledtimeout' in _CONF:
# Check and handle any timed out open orders
check_handle_timedout(_CONF['unfilledtimeout'])
Trade.session.flush()
except (requests.exceptions.RequestException, json.JSONDecodeError) as error:
logger.warning(
'Got %s in _process(), retrying in 30 seconds...',
error
)
time.sleep(30)
except OperationalException:
rpc.send_msg('*Status:* Got OperationalException:\n```\n{traceback}```{hint}'.format(
traceback=traceback.format_exc(),
hint='Issue `/start` if you think it is safe to restart.'
))
logger.exception('Got OperationalException. Stopping trader ...')
update_state(State.STOPPED)
return state_changed
def check_handle_timedout(timeoutvalue: int) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
This function will initiate the bot and start the trading loop.
:return: None
"""
timeoutthreashold = arrow.utcnow().shift(minutes=-timeoutvalue).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
order = exchange.get_order(trade.open_order_id)
ordertime = arrow.get(order['opened'])
if order['type'] == "LIMIT_BUY" and ordertime < timeoutthreashold:
# Buy timeout - cancel order
exchange.cancel_order(trade.open_order_id)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
elif order['type'] == "LIMIT_SELL" and ordertime < timeoutthreashold:
# Sell timeout - cancel order and update trade
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
exchange.cancel_order(trade.open_order_id)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
logger.info('Sell order timeout for %s.', trade)
return True
else:
# TODO: figure out how to handle partially complete sell orders
pass
def execute_sell(trade: Trade, limit: float) -> None:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:return: None
"""
# Execute sell and update trade record
order_id = exchange.sell(str(trade.pair), limit, trade.amount)
trade.open_order_id = order_id
fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
profit_trade = trade.calc_profit(rate=limit)
message = '*{exchange}:* Selling [{pair}]({pair_url}) with limit `{limit:.8f}`'.format(
exchange=trade.exchange,
pair=trade.pair.replace('_', '/'),
pair_url=exchange.get_pair_detail_url(trade.pair),
limit=limit
arguments = Arguments(
sysargv,
'Simple High Frequency Trading Bot for crypto currencies'
)
args = arguments.get_parsed_arg()
# For regular case, when the configuration exists
if 'stake_currency' in _CONF and 'fiat_display_currency' in _CONF:
fiat_converter = CryptoToFiatConverter()
profit_fiat = fiat_converter.convert_amount(
profit_trade,
_CONF['stake_currency'],
_CONF['fiat_display_currency']
)
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f} {coin}`' \
'` / {profit_fiat:.3f} {fiat})`'.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade,
coin=_CONF['stake_currency'],
profit_fiat=profit_fiat,
fiat=_CONF['fiat_display_currency'],
)
# Because telegram._forcesell does not have the configuration
# Ignore the FIAT value and does not show the stake_currency as well
else:
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade
)
# Send the message
rpc.send_msg(message)
Trade.session.flush()
def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -> bool:
"""
Based an earlier trade and current price and ROI configuration, decides whether bot should sell
:return True if bot should sell at current rate
"""
current_profit = trade.calc_profit_percent(current_rate)
if 'stoploss' in _CONF and current_profit < float(_CONF['stoploss']):
logger.debug('Stop loss hit.')
return True
# Check if time matches and current rate is above threshold
time_diff = (current_time - trade.open_date).total_seconds() / 60
for duration, threshold in sorted(_CONF['minimal_roi'].items()):
if time_diff > float(duration) and current_profit > threshold:
return True
logger.debug('Threshold not reached. (cur_profit: %1.2f%%)', float(current_profit) * 100.0)
return False
def handle_trade(trade: Trade) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold, False otherwise
"""
if not trade.is_open:
raise ValueError('attempt to handle closed trade: {}'.format(trade))
logger.debug('Handling %s ...', trade)
current_rate = exchange.get_ticker(trade.pair)['bid']
# Check if minimal roi has been reached
if min_roi_reached(trade, current_rate, datetime.utcnow()):
logger.debug('Executing sell due to ROI ...')
execute_sell(trade, current_rate)
return True
# Experimental: Check if sell signal has been enabled and triggered
if _CONF.get('experimental', {}).get('use_sell_signal'):
# Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
if _CONF.get('experimental', {}).get('sell_profit_only'):
logger.debug('Checking if trade is profitable ...')
if trade.calc_profit(rate=current_rate) <= 0:
return False
logger.debug('Checking sell_signal ...')
if get_signal(trade.pair, SignalType.SELL):
logger.debug('Executing sell due to sell signal ...')
execute_sell(trade, current_rate)
return True
return False
def get_target_bid(ticker: Dict[str, float]) -> float:
""" Calculates bid target between current ask price and last price """
if ticker['ask'] < ticker['last']:
return ticker['ask']
balance = _CONF['bid_strategy']['ask_last_balance']
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def create_trade(stake_amount: float) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
:return: True if a trade object has been created and persisted, False otherwise
"""
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount
)
whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
raise DependencyException(
'stake amount is not fulfilled (currency={})'.format(_CONF['stake_currency'])
)
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No pair in whitelist')
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
if get_signal(_pair, SignalType.BUY):
pair = _pair
break
else:
return False
# Calculate amount
buy_limit = get_target_bid(exchange.get_ticker(pair))
amount = stake_amount / buy_limit
order_id = exchange.buy(pair, buy_limit, amount)
fiat_converter = CryptoToFiatConverter()
stake_amount_fiat = fiat_converter.convert_amount(
stake_amount,
_CONF['stake_currency'],
_CONF['fiat_display_currency']
)
# Create trade entity and return
rpc.send_msg('*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '.format(
exchange.get_name().upper(),
pair.replace('_', '/'),
exchange.get_pair_detail_url(pair),
buy_limit, stake_amount, _CONF['stake_currency'],
stake_amount_fiat, _CONF['fiat_display_currency']
))
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
trade = Trade(
pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=exchange.get_fee(),
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id
)
Trade.session.add(trade)
Trade.session.flush()
return True
def init(config: dict, db_url: Optional[str] = None) -> None:
"""
Initializes all modules and updates the config
:param config: config as dict
:param db_url: database connector string for sqlalchemy (Optional)
:return: None
"""
# Initialize all modules
rpc.init(config)
persistence.init(config, db_url)
exchange.init(config)
# Set initial application state
initial_state = config.get('initial_state')
if initial_state:
update_state(State[initial_state.upper()])
else:
update_state(State.STOPPED)
@cached(TTLCache(maxsize=1, ttl=1800))
def gen_pair_whitelist(base_currency: str, key: str = 'BaseVolume') -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'BaseVolume')
:return: List of pairs
"""
summaries = sorted(
(s for s in exchange.get_market_summaries() if s['MarketName'].startswith(base_currency)),
key=lambda s: s.get(key) or 0.0,
reverse=True
)
return [s['MarketName'].replace('-', '_') for s in summaries]
def cleanup() -> None:
"""
Cleanup the application state und finish all pending tasks
:return: None
"""
rpc.send_msg('*Status:* `Stopping trader...`')
logger.info('Stopping trader and cleaning up modules...')
update_state(State.STOPPED)
persistence.cleanup()
rpc.cleanup()
exit(0)
def main(sysargv=sys.argv[1:]) -> None:
"""
Loads and validates the config and handles the main loop
:return: None
"""
global _CONF
args = parse_args(sysargv,
'Simple High Frequency Trading Bot for crypto currencies')
# A subcommand has been issued
# A subcommand has been issued.
# Means if Backtesting or Hyperopt have been called we exit the bot
if hasattr(args, 'func'):
args.func(args)
exit(0)
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
logger.info(
'Starting freqtrade %s (loglevel=%s)',
__version__,
logging.getLevelName(args.loglevel)
)
# Load and validate configuration
_CONF = load_config(args.config)
# Initialize all modules and start main loop
if args.dynamic_whitelist:
logger.info('Using dynamically generated whitelist. (--dynamic-whitelist detected)')
# If the user ask for Dry run with a local DB instead of memory
if args.dry_run_db:
if _CONF.get('dry_run', False):
_CONF.update({'dry_run_db': True})
logger.info(
'Dry_run will use the DB file: "tradesv3.dry_run.sqlite". (--dry_run_db detected)'
)
else:
logger.info('Dry run is disabled. (--dry_run_db ignored)')
return
freqtrade = None
return_code = 1
try:
init(_CONF)
old_state = None
while True:
new_state = get_state()
# Log state transition
if new_state != old_state:
rpc.send_msg('*Status:* `{}`'.format(new_state.name.lower()))
logger.info('Changing state to: %s', new_state.name)
# Load and validate configuration
config = Configuration(args).get_config()
# Init the bot
freqtrade = FreqtradeBot(config)
state = None
while 1:
state = freqtrade.worker(old_state=state)
if new_state == State.STOPPED:
time.sleep(1)
elif new_state == State.RUNNING:
throttle(
_process,
min_secs=_CONF['internals'].get('process_throttle_secs', 10),
nb_assets=args.dynamic_whitelist,
)
old_state = new_state
except KeyboardInterrupt:
logger.info('Got SIGINT, aborting ...')
logger.info('SIGINT received, aborting ...')
return_code = 0
except BaseException:
logger.exception('Got fatal exception!')
logger.exception('Fatal exception!')
finally:
cleanup()
if freqtrade:
freqtrade.clean()
sys.exit(return_code)
def set_loggers() -> None:
"""
Set the logger level for Third party libs
:return: None
"""
logging.getLogger('requests.packages.urllib3').setLevel(logging.INFO)
logging.getLogger('telegram').setLevel(logging.INFO)
if __name__ == '__main__':
main()
set_loggers()
main(sys.argv[1:])

364
freqtrade/misc.py
View File

@ -1,318 +1,74 @@
import argparse
import enum
"""
Various tool function for Freqtrade and scripts
"""
import json
import logging
import time
import os
from typing import Any, Callable, Dict, List
import re
from datetime import datetime
from typing import Dict
from jsonschema import Draft4Validator, validate
from jsonschema.exceptions import ValidationError, best_match
from wrapt import synchronized
from freqtrade import __version__
import numpy as np
from pandas import DataFrame
logger = logging.getLogger(__name__)
class State(enum.Enum):
RUNNING = 0
STOPPED = 1
# Current application state
_STATE = State.STOPPED
@synchronized
def update_state(state: State) -> None:
def shorten_date(_date: str) -> str:
"""
Updates the application state
:param state: new state
:return: None
Trim the date so it fits on small screens
"""
global _STATE
_STATE = state
new_date = re.sub('seconds?', 'sec', _date)
new_date = re.sub('minutes?', 'min', new_date)
new_date = re.sub('hours?', 'h', new_date)
new_date = re.sub('days?', 'd', new_date)
new_date = re.sub('^an?', '1', new_date)
return new_date
@synchronized
def get_state() -> State:
############################################
# Used by scripts #
# Matplotlib doesn't support ::datetime64, #
# so we need to convert it into ::datetime #
############################################
def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
"""
Gets the current application state
Convert an pandas-array of timestamps into
An numpy-array of datetimes
:return: numpy-array of datetime
"""
times = []
dates = dates.astype(datetime)
for index in range(0, dates.size):
date = dates[index].to_pydatetime()
times.append(date)
return np.array(times)
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) -> None:
"""
Dump JSON data into a file
:param filename: file to create
:param data: JSON Data to save
:return:
"""
return _STATE
def load_config(path: str) -> Dict:
"""
Loads a config file from the given path
:param path: path as str
:return: configuration as dictionary
"""
with open(path) as file:
conf = json.load(file)
if 'internals' not in conf:
conf['internals'] = {}
logger.info('Validating configuration ...')
try:
validate(conf, CONF_SCHEMA)
return conf
except ValidationError as exception:
logger.fatal('Invalid configuration. See config.json.example. Reason: %s', exception)
raise ValidationError(
best_match(Draft4Validator(CONF_SCHEMA).iter_errors(conf)).message
)
def throttle(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 common_args_parser(description: str):
"""
Parses given common arguments and returns them as a parsed object.
"""
parser = argparse.ArgumentParser(
description=description
)
parser.add_argument(
'-v', '--verbose',
help='be verbose',
action='store_const',
dest='loglevel',
const=logging.DEBUG,
default=logging.INFO,
)
parser.add_argument(
'--version',
action='version',
version='%(prog)s {}'.format(__version__),
)
parser.add_argument(
'-c', '--config',
help='specify configuration file (default: config.json)',
dest='config',
default='config.json',
type=str,
metavar='PATH',
)
return parser
def parse_args(args: List[str], description: str):
"""
Parses given arguments and returns an argparse Namespace instance.
Returns None if a sub command has been selected and executed.
"""
parser = common_args_parser(description)
parser.add_argument(
'--dry-run-db',
help='Force dry run to use a local DB "tradesv3.dry_run.sqlite" \
instead of memory DB. Work only if dry_run is enabled.',
action='store_true',
dest='dry_run_db',
)
parser.add_argument(
'-dd', '--datadir',
help='path to backtest data (default freqdata/tests/testdata',
dest='datadir',
default=os.path.join('freqtrade', 'tests', 'testdata'),
type=str,
metavar='PATH',
)
parser.add_argument(
'--dynamic-whitelist',
help='dynamically generate and update whitelist \
based on 24h BaseVolume (Default 20 currencies)', # noqa
dest='dynamic_whitelist',
const=20,
type=int,
metavar='INT',
nargs='?',
)
build_subcommands(parser)
return parser.parse_args(args)
def build_subcommands(parser: argparse.ArgumentParser) -> None:
""" Builds and attaches all subcommands """
from freqtrade.optimize import backtesting, hyperopt
subparsers = parser.add_subparsers(dest='subparser')
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
backtesting_cmd.set_defaults(func=backtesting.start)
backtesting_cmd.add_argument(
'-l', '--live',
action='store_true',
dest='live',
help='using live data',
)
backtesting_cmd.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (default: 5)',
dest='ticker_interval',
default=5,
type=int,
metavar='INT',
)
backtesting_cmd.add_argument(
'--realistic-simulation',
help='uses max_open_trades from config to simulate real world limitations',
action='store_true',
dest='realistic_simulation',
)
backtesting_cmd.add_argument(
'-r', '--refresh-pairs-cached',
help='refresh the pairs files in tests/testdata with the latest data from Bittrex. \
Use it if you want to run your backtesting with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
hyperopt_cmd.set_defaults(func=hyperopt.start)
hyperopt_cmd.add_argument(
'-e', '--epochs',
help='specify number of epochs (default: 100)',
dest='epochs',
default=100,
type=int,
metavar='INT',
)
hyperopt_cmd.add_argument(
'--use-mongodb',
help='parallelize evaluations with mongodb (requires mongod in PATH)',
dest='mongodb',
action='store_true',
)
hyperopt_cmd.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (default: 5)',
dest='ticker_interval',
default=5,
type=int,
metavar='INT',
)
# Required json-schema for user specified config
CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': 1},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'ETH', 'USDT']},
'stake_amount': {'type': 'number', 'minimum': 0.0005},
'fiat_display_currency': {'type': 'string', 'enum': ['AUD', 'BRL', 'CAD', 'CHF',
'CLP', 'CNY', 'CZK', 'DKK',
'EUR', 'GBP', 'HKD', 'HUF',
'IDR', 'ILS', 'INR', 'JPY',
'KRW', 'MXN', 'MYR', 'NOK',
'NZD', 'PHP', 'PKR', 'PLN',
'RUB', 'SEK', 'SGD', 'THB',
'TRY', 'TWD', 'ZAR', 'USD']},
'dry_run': {'type': 'boolean'},
'minimal_roi': {
'type': 'object',
'patternProperties': {
'^[0-9.]+$': {'type': 'number'}
},
'minProperties': 1
},
'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
'unfilledtimeout': {'type': 'integer', 'minimum': 0},
'bid_strategy': {
'type': 'object',
'properties': {
'ask_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False
},
},
'required': ['ask_last_balance']
},
'exchange': {'$ref': '#/definitions/exchange'},
'experimental': {
'type': 'object',
'properties': {
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'}
}
},
'telegram': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
},
'required': ['enabled', 'token', 'chat_id']
},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'internals': {
'type': 'object',
'properties': {
'process_throttle_secs': {'type': 'number'}
}
}
},
'definitions': {
'exchange': {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'key': {'type': 'string'},
'secret': {'type': 'string'},
'pair_whitelist': {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
},
'uniqueItems': True
},
'pair_blacklist': {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
},
'uniqueItems': True
}
},
'required': ['name', 'key', 'secret', 'pair_whitelist']
}
},
'anyOf': [
{'required': ['exchange']}
],
'required': [
'max_open_trades',
'stake_currency',
'stake_amount',
'fiat_display_currency',
'dry_run',
'minimal_roi',
'bid_strategy',
'telegram'
]
}
with open(filename, 'w') as fp:
json.dump(data, fp, default=str)

141
freqtrade/optimize/__init__.py
View File

@ -1,44 +1,69 @@
# pragma pylint: disable=missing-docstring
import logging
import gzip
import json
import logging
import os
from typing import Optional, List, Dict
from pandas import DataFrame
from typing import Optional, List, Dict, Tuple
from freqtrade import misc
from freqtrade.exchange import get_ticker_history
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
from user_data.hyperopt_conf import hyperopt_optimize_conf
logger = logging.getLogger(__name__)
def load_tickerdata_file(datadir, pair, ticker_interval):
def trim_tickerlist(tickerlist: List[Dict], timerange: Tuple[Tuple, int, int]) -> List[Dict]:
stype, start, stop = timerange
if stype == (None, 'line'):
return tickerlist[stop:]
elif stype == ('line', None):
return tickerlist[0:start]
elif stype == ('index', 'index'):
return tickerlist[start:stop]
return tickerlist
def load_tickerdata_file(
datadir: str, pair: str,
ticker_interval: int,
timerange: Optional[Tuple[Tuple, int, int]] = None) -> Optional[List[Dict]]:
"""
Load a pair from file,
:return dict OR empty if unsuccesful
"""
path = make_testdata_path(datadir)
file = '{abspath}/{pair}-{ticker_interval}.json'.format(
abspath=path,
file = os.path.join(path, '{pair}-{ticker_interval}.json'.format(
pair=pair,
ticker_interval=ticker_interval,
)
# The file does not exist we download it
if not os.path.isfile(file):
))
gzipfile = file + '.gz'
# If the file does not exist we download it when None is returned.
# If file exists, read the file, load the json
if os.path.isfile(gzipfile):
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json.load(tickerdata)
elif os.path.isfile(file):
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json.load(tickerdata)
else:
return None
# Read the file, load the json
with open(file) as tickerdata:
pairdata = json.load(tickerdata)
if timerange:
pairdata = trim_tickerlist(pairdata, timerange)
return pairdata
def load_data(datadir: str, ticker_interval: int = 5, pairs: Optional[List[str]] = None,
refresh_pairs: Optional[bool] = False) -> Dict[str, List]:
def load_data(datadir: str, ticker_interval: int,
pairs: Optional[List[str]] = None,
refresh_pairs: Optional[bool] = False,
timerange: Optional[Tuple[Tuple, int, int]] = None) -> Dict[str, List]:
"""
Loads ticker history data for the given parameters
:param ticker_interval: ticker interval in minutes
:param pairs: list of pairs
:return: dict
"""
result = {}
@ -48,86 +73,76 @@ def load_data(datadir: str, ticker_interval: int = 5, pairs: Optional[List[str]]
# If the user force the refresh of pairs
if refresh_pairs:
logger.info('Download data for all pairs and store them in %s', datadir)
download_pairs(datadir, _pairs)
download_pairs(datadir, _pairs, ticker_interval)
for pair in _pairs:
pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
if not pairdata:
# download the tickerdata from exchange
download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
# and retry reading the pair
pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
result[pair] = pairdata
return result
def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""Creates a dataframe and populates indicators for given ticker data"""
return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()}
def make_testdata_path(datadir: str) -> str:
"""Return the path where testdata files are stored"""
return datadir or os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', 'tests', 'testdata'))
return datadir or os.path.abspath(
os.path.join(
os.path.dirname(__file__), '..', 'tests', 'testdata'
)
)
def download_pairs(datadir, pairs: List[str]) -> bool:
"""For each pairs passed in parameters, download 1 and 5 ticker intervals"""
def download_pairs(datadir, pairs: List[str], ticker_interval: int) -> bool:
"""For each pairs passed in parameters, download the ticker intervals"""
for pair in pairs:
try:
for interval in [1, 5]:
download_backtesting_testdata(datadir, pair=pair, interval=interval)
download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
except BaseException:
logger.info('Failed to download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
))
logger.info(
'Failed to download the pair: "%s", Interval: %s min',
pair,
ticker_interval
)
return False
return True
def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> bool:
# FIX: 20180110, suggest rename interval to tick_interval
def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> None:
"""
Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pairs: list of pairs to download
:return: bool
"""
path = make_testdata_path(datadir)
logger.info('Download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
))
logger.info(
'Download the pair: "%s", Interval: %s min', pair, interval
)
filepair = pair.replace("-", "_")
filename = os.path.join(path, '{pair}-{interval}.json'.format(
pair=filepair,
pair=pair.replace("-", "_"),
interval=interval,
))
filename = filename.replace('USDT_BTC', 'BTC_FAKEBULL')
if os.path.isfile(filename):
with open(filename, "rt") as fp:
data = json.load(fp)
logger.debug("Current Start: {}".format(data[1]['T']))
logger.debug("Current End: {}".format(data[-1:][0]['T']))
with open(filename, "rt") as file:
data = json.load(file)
else:
data = []
logger.debug("Current Start: None")
logger.debug("Current End: None")
new_data = get_ticker_history(pair=pair, tick_interval=int(interval))
for row in new_data:
if row not in data:
data.append(row)
logger.debug("New Start: {}".format(data[1]['T']))
logger.debug("New End: {}".format(data[-1:][0]['T']))
data = sorted(data, key=lambda data: data['T'])
logger.debug('Current Start: %s', data[1]['T'] if data else None)
logger.debug('Current End: %s', data[-1:][0]['T'] if data else None)
with open(filename, "wt") as fp:
json.dump(data, fp)
# Extend data with new ticker history
data.extend([
row for row in get_ticker_history(pair=pair, tick_interval=int(interval))
if row not in data
])
return True
data = sorted(data, key=lambda _data: _data['T'])
logger.debug('New Start: %s', data[1]['T'])
logger.debug('New End: %s', data[-1:][0]['T'])
misc.file_dump_json(filename, data)

432
freqtrade/optimize/backtesting.py
View File

@ -1,197 +1,307 @@
# pragma pylint: disable=missing-docstring,W0212
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
"""
This module contains the backtesting logic
"""
import logging
from typing import Dict, Tuple
import operator
from argparse import Namespace
from typing import Dict, Tuple, Any, List, Optional
import arrow
from pandas import DataFrame, Series
from pandas import DataFrame
from tabulate import tabulate
import freqtrade.misc as misc
import freqtrade.optimize as optimize
from freqtrade import exchange
from freqtrade.analyze import populate_buy_trend, populate_sell_trend
from freqtrade.analyze import Analyze
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.exchange import Bittrex
from freqtrade.main import min_roi_reached
from freqtrade.optimize import preprocess
from freqtrade.misc import file_dump_json
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
class Backtesting(object):
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
all_dates = Series([])
for pair, pair_data in data.items():
all_dates = all_dates.append(pair_data['date'])
all_dates.sort_values(inplace=True)
return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = None
self.ticker_interval = None
self.tickerdata_to_dataframe = None
self.populate_buy_trend = None
self.populate_sell_trend = None
self._init()
def generate_text_table(
data: Dict[str, Dict], results: DataFrame, stake_currency, ticker_interval) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data:
result = results[results.currency == pair]
def _init(self) -> None:
"""
Init objects required for backtesting
:return: None
"""
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
exchange._API = Bittrex({'key': '', 'secret': ''})
@staticmethod
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(min(frame.date)), arrow.get(max(frame.date)))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = self.config.get('stake_currency')
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data:
result = results[results.currency == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(),
result.duration.mean(),
len(result[result.profit_BTC > 0]),
len(result[result.profit_BTC < 0])
])
# Append Total
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(),
result.duration.mean() * ticker_interval,
result.profit.sum(),
result.loss.sum()
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean(),
len(results[results.profit_BTC > 0]),
len(results[results.profit_BTC < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean() * ticker_interval,
results.profit.sum(),
results.loss.sum()
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
open_rate=buy_row.close,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee=exchange.get_fee()
)
def backtest(stake_amount: float, processed: Dict[str, DataFrame],
max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
"""
Implements backtesting functionality
:param stake_amount: btc amount to use for each trade
:param processed: a processed dictionary with format {pair, data}
:param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
:param realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame
"""
trades = []
trade_count_lock: dict = {}
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0
ticker = populate_sell_trend(populate_buy_trend(pair_data))
# for each buy point
lock_pair_until = None
buy_subset = ticker[ticker.buy == 1][['buy', 'open', 'close', 'date', 'sell']]
for row in buy_subset.itertuples(index=True):
if realistic:
if lock_pair_until is not None and row.Index <= lock_pair_until:
continue
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
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
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
if max_open_trades > 0:
# Increase lock
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
buy_signal = sell_row.buy
if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
sell_row.sell):
return \
sell_row, \
(
pair,
trade.calc_profit_percent(rate=sell_row.close),
trade.calc_profit(rate=sell_row.close),
(sell_row.date - buy_row.date).seconds // 60
), \
sell_row.date
return None
trade = Trade(
open_rate=row.close,
open_date=row.date,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
def backtest(self, args: Dict) -> DataFrame:
"""
Implements backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
sell_profit_only: sell if profit only
use_sell_signal: act on sell-signal
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
record = args.get('record', None)
records = []
trades = []
trade_count_lock = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(self.populate_buy_trend(pair_data))[headers]
ticker = [x for x in ticker_data.itertuples()]
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if lock_pair_until is not None and row.date <= lock_pair_until:
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
ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_count_lock, args)
if ret:
row2, trade_entry, next_date = ret
lock_pair_until = next_date
trades.append(trade_entry)
if record:
# Note, need to be json.dump friendly
# record a tuple of pair, current_profit_percent,
# entry-date, duration
records.append((pair, trade_entry[1],
row.date.strftime('%s'),
row2.date.strftime('%s'),
index, trade_entry[3]))
# For now export inside backtest(), maybe change so that backtest()
# returns a tuple like: (dataframe, records, logs, etc)
if record and record.find('trades') >= 0:
logger.info('Dumping backtest results')
file_dump_json('backtest-result.json', records)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
return DataFrame.from_records(trades, columns=labels)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
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 ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(self.config.get('timerange'))
data = optimize.load_data(
self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
timerange=timerange
)
# calculate win/lose forwards from buy point
sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
for row2 in sell_subset.itertuples(index=True):
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
max_open_trades = 0
current_profit_percent = trade.calc_profit_percent(rate=row2.close)
if (sell_profit_only and current_profit_percent < 0):
continue
if min_roi_reached(trade, row2.close, row2.date) or \
(row2.sell == 1 and use_sell_signal) or \
current_profit_percent <= stoploss:
current_profit_btc = trade.calc_profit(rate=row2.close)
lock_pair_until = row2.Index
preprocessed = self.tickerdata_to_dataframe(data)
trades.append(
(
pair,
current_profit_percent,
current_profit_btc,
row2.Index - row.Index,
current_profit_btc > 0,
current_profit_btc < 0
)
)
break
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
return DataFrame.from_records(trades, columns=labels)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
# Execute backtest and print results
sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False)
use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False)
results = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'realistic': self.config.get('realistic_simulation', False),
'sell_profit_only': sell_profit_only,
'use_sell_signal': use_sell_signal,
'record': self.config.get('export')
}
)
logger.info(
'\n==================================== '
'BACKTESTING REPORT'
' ====================================\n'
'%s',
self._generate_text_table(
data,
results
)
)
def start(args):
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
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)
config = configuration.get_config()
exchange._API = Bittrex({'key': '', 'secret': ''})
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
return config
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
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')
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = preprocess(data)
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
)
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], args.ticker_interval)
)
# Initialize backtesting object
backtesting = Backtesting(config)
backtesting.start()

824
freqtrade/optimize/hyperopt.py
View File

@ -1,318 +1,608 @@
# pragma pylint: disable=missing-docstring,W0212,W0603
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
"""
This module contains the hyperopt logic
"""
import json
import logging
import sys
import os
import pickle
import signal
import os
import sys
from argparse import Namespace
from functools import reduce
from math import exp
from operator import itemgetter
from typing import Dict, Any, Callable
import numpy
import talib.abstract as ta
from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
from hyperopt.mongoexp import MongoTrials
from pandas import DataFrame
from freqtrade import main # noqa
from freqtrade import exchange, optimize
from freqtrade.exchange import Bittrex
from freqtrade.misc import load_config
from freqtrade.optimize.backtesting import backtest
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from freqtrade.vendor.qtpylib.indicators import crossed_above
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.optimize import load_data
from freqtrade.optimize.backtesting import Backtesting
from user_data.hyperopt_conf import hyperopt_optimize_conf
# Remove noisy log messages
logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
TARGET_TRADES = 1100
TOTAL_TRIES = 0
_CURRENT_TRIES = 0
CURRENT_BEST_LOSS = 100
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
MAX_ACCEPTED_TRADE_DURATION = 240
class Hyperopt(Backtesting):
"""
Hyperopt class, this class contains all the logic to run a hyperopt simulation
# this is expexted avg profit * expected trade count
# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
EXPECTED_MAX_PROFIT = 3.85
To run a backtest:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
# Configuration and data used by hyperopt
PROCESSED = None # optimize.preprocess(optimize.load_data())
OPTIMIZE_CONFIG = hyperopt_optimize_conf()
super().__init__(config)
# 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.current_tries = 0
self.current_best_loss = 100
# Hyperopt Trials
TRIALS_FILE = os.path.join('freqtrade', 'optimize', 'hyperopt_trials.pickle')
TRIALS = Trials()
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
self.max_accepted_trade_duration = 300
# Monkey patch config
from freqtrade import main # noqa
main._CONF = OPTIMIZE_CONFIG
# 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
# Configuration and data used by hyperopt
self.processed = None
SPACE = {
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
'green_candle': hp.choice('green_candle', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema5_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'stochf_cross'},
{'type': 'ht_sine'},
]),
'stoploss': hp.uniform('stoploss', -0.5, -0.02),
}
# Hyperopt Trials
self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle')
self.trials = Trials()
@staticmethod
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['adx'] = ta.ADX(dataframe)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['roc'] = ta.ROC(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabolic
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
def save_trials(trials, trials_path=TRIALS_FILE):
"""Save hyperopt trials to file"""
logger.info('Saving Trials to \'{}\''.format(trials_path))
pickle.dump(trials, open(trials_path, 'wb'))
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
def read_trials(trials_path=TRIALS_FILE):
"""Read hyperopt trials file"""
logger.info('Reading Trials from \'{}\''.format(trials_path))
trials = pickle.load(open(trials_path, 'rb'))
os.remove(trials_path)
return trials
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
def log_trials_result(trials):
vals = json.dumps(trials.best_trial['misc']['vals'], indent=4)
results = trials.best_trial['result']['result']
logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
def log_results(results):
""" log results if it is better than any previous evaluation """
global CURRENT_BEST_LOSS
if results['loss'] < CURRENT_BEST_LOSS:
CURRENT_BEST_LOSS = results['loss']
logger.info('{:5d}/{}: {}'.format(
results['current_tries'],
results['total_tries'],
results['result']))
else:
print('.', end='')
sys.stdout.flush()
def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
""" objective function, returns smaller number for more optimal results """
trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
return trade_loss + profit_loss + duration_loss
def optimizer(params):
global _CURRENT_TRIES
from freqtrade.optimize import backtesting
backtesting.populate_buy_trend = buy_strategy_generator(params)
results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, stoploss=params['stoploss'])
result_explanation = format_results(results)
total_profit = results.profit_percent.sum()
trade_count = len(results.index)
trade_duration = results.duration.mean() * 5
if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION:
print('.', end='')
return {
'status': STATUS_FAIL,
'loss': float('inf')
}
loss = calculate_loss(total_profit, trade_count, trade_duration)
_CURRENT_TRIES += 1
log_results({
'loss': loss,
'current_tries': _CURRENT_TRIES,
'total_tries': TOTAL_TRIES,
'result': result_explanation,
})
return {
'loss': loss,
'status': STATUS_OK,
'result': result_explanation,
}
def format_results(results: DataFrame):
return ('{:6d} trades. Avg profit {: 5.2f}%. '
'Total profit {: 11.8f} BTC. Avg duration {:5.1f} mins.').format(
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean() * 5,
)
def buy_strategy_generator(params):
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if params['uptrend_long_ema']['enabled']:
conditions.append(dataframe['ema50'] > dataframe['ema100'])
if params['uptrend_short_ema']['enabled']:
conditions.append(dataframe['ema5'] > dataframe['ema10'])
if params['mfi']['enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value'])
if params['fastd']['enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value'])
if params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
if params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
if params['green_candle']['enabled']:
conditions.append(dataframe['close'] > dataframe['open'])
if params['uptrend_sma']['enabled']:
prevsma = dataframe['sma'].shift(1)
conditions.append(dataframe['sma'] > prevsma)
# TRIGGERS
triggers = {
'lower_bb': dataframe['tema'] <= dataframe['blower'],
'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
}
conditions.append(triggers.get(params['trigger']['type']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
# Chart type
# ------------------------------------
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
return dataframe
return populate_buy_trend
def save_trials(self) -> None:
"""
Save hyperopt trials to file
"""
logger.info('Saving Trials to \'%s\'', self.trials_file)
pickle.dump(self.trials, open(self.trials_file, 'wb'))
def start(args):
global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
def read_trials(self) -> Trials:
"""
Read hyperopt trials file
"""
logger.info('Reading Trials from \'%s\'', self.trials_file)
trials = pickle.load(open(self.trials_file, 'rb'))
os.remove(self.trials_file)
return trials
TOTAL_TRIES = args.epochs
def log_trials_result(self) -> None:
"""
Display Best hyperopt result
"""
vals = json.dumps(self.trials.best_trial['misc']['vals'], indent=4)
results = self.trials.best_trial['result']['result']
logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
exchange._API = Bittrex({'key': '', 'secret': ''})
def log_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
"""
if results['loss'] < self.current_best_loss:
self.current_best_loss = results['loss']
log_msg = '\n{:5d}/{}: {}. Loss {:.5f}'.format(
results['current_tries'],
results['total_tries'],
results['result'],
results['loss']
)
print(log_msg)
else:
print('.', end='')
sys.stdout.flush()
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='\n%(message)s',
)
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)
return trade_loss + profit_loss + duration_loss
logger.info('Using config: %s ...', args.config)
config = load_config(args.config)
pairs = config['exchange']['pair_whitelist']
PROCESSED = optimize.preprocess(optimize.load_data(
args.datadir, pairs=pairs, ticker_interval=args.ticker_interval))
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table thqt 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
if args.mongodb:
logger.info('Using mongodb ...')
logger.info('Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!')
return roi_table
db_name = 'freqtrade_hyperopt'
TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name), exp_key='exp1')
else:
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, signal_handler)
# read trials file if we have one
if os.path.exists(TRIALS_FILE):
TRIALS = read_trials()
@staticmethod
def roi_space() -> Dict[str, Any]:
"""
Values to search for each ROI steps
"""
return {
'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
}
_CURRENT_TRIES = len(TRIALS.results)
TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
logger.info(
'Continuing with trials. Current: {}, Total: {}'
.format(_CURRENT_TRIES, TOTAL_TRIES))
@staticmethod
def stoploss_space() -> Dict[str, Any]:
"""
Stoploss Value to search
"""
return {
'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
}
try:
best_parameters = fmin(
fn=optimizer,
space=SPACE,
algo=tpe.suggest,
max_evals=TOTAL_TRIES,
trials=TRIALS
@staticmethod
def indicator_space() -> Dict[str, Any]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return {
'macd_below_zero': hp.choice('macd_below_zero', [
{'enabled': False},
{'enabled': True}
]),
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
]),
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
'green_candle': hp.choice('green_candle', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'lower_bb_tema'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema3_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'ht_sine'},
{'type': 'heiken_reversal_bull'},
{'type': 'di_cross'},
]),
}
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
def hyperopt_space(self) -> Dict[str, Any]:
"""
Return the space to use during Hyperopt
"""
spaces = {}
if self.has_space('buy'):
spaces = {**spaces, **Hyperopt.indicator_space()}
if self.has_space('roi'):
spaces = {**spaces, **Hyperopt.roi_space()}
if self.has_space('stoploss'):
spaces = {**spaces, **Hyperopt.stoploss_space()}
return spaces
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
conditions.append(dataframe['ema50'] > dataframe['ema100'])
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
conditions.append(dataframe['macd'] < 0)
if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
conditions.append(dataframe['ema5'] > dataframe['ema10'])
if 'mfi' in params and params['mfi']['enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value'])
if 'fastd' in params and params['fastd']['enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value'])
if 'adx' in params and params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if 'rsi' in params and params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
if 'over_sar' in params and params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
if 'green_candle' in params and params['green_candle']['enabled']:
conditions.append(dataframe['close'] > dataframe['open'])
if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
prevsma = dataframe['sma'].shift(1)
conditions.append(dataframe['sma'] > prevsma)
# TRIGGERS
triggers = {
'lower_bb': (
dataframe['close'] < dataframe['bb_lowerband']
),
'lower_bb_tema': (
dataframe['tema'] < dataframe['bb_lowerband']
),
'faststoch10': (qtpylib.crossed_above(
dataframe['fastd'], 10.0
)),
'ao_cross_zero': (qtpylib.crossed_above(
dataframe['ao'], 0.0
)),
'ema3_cross_ema10': (qtpylib.crossed_above(
dataframe['ema3'], dataframe['ema10']
)),
'macd_cross_signal': (qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
)),
'sar_reversal': (qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
)),
'ht_sine': (qtpylib.crossed_above(
dataframe['htleadsine'], dataframe['htsine']
)),
'heiken_reversal_bull': (
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
(dataframe['ha_low'] == dataframe['ha_open'])
),
'di_cross': (qtpylib.crossed_above(
dataframe['plus_di'], dataframe['minus_di']
)),
}
conditions.append(triggers.get(params['trigger']['type']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
def generate_optimizer(self, params: Dict) -> Dict:
if self.has_space('roi'):
self.analyze.strategy.minimal_roi = self.generate_roi_table(params)
if self.has_space('buy'):
self.populate_buy_trend = self.buy_strategy_generator(params)
if self.has_space('stoploss'):
self.analyze.strategy.stoploss = params['stoploss']
results = self.backtest(
{
'stake_amount': self.config['stake_amount'],
'processed': self.processed,
'realistic': self.config.get('realistic_simulation', False),
}
)
result_explanation = self.format_results(results)
total_profit = results.profit_percent.sum()
trade_count = len(results.index)
trade_duration = results.duration.mean()
if trade_count == 0 or trade_duration > self.max_accepted_trade_duration:
print('.', end='')
return {
'status': STATUS_FAIL,
'loss': float('inf')
}
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
self.current_tries += 1
self.log_results(
{
'loss': loss,
'current_tries': self.current_tries,
'total_tries': self.total_tries,
'result': result_explanation,
}
)
results = sorted(TRIALS.results, key=itemgetter('loss'))
best_result = results[0]['result']
return {
'loss': loss,
'status': STATUS_OK,
'result': result_explanation,
}
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
@staticmethod
def format_results(results: DataFrame) -> str:
"""
Return the format result in a string
"""
return ('{:6d} trades. Avg profit {: 5.2f}%. '
'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.profit_percent.sum(),
results.duration.mean(),
)
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(SPACE, best_parameters)
def start(self) -> None:
timerange = Arguments.parse_timerange(self.config.get('timerange'))
data = load_data(
datadir=self.config.get('datadir'),
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.ticker_interval,
timerange=timerange
)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
logger.info('Best Result:\n%s', best_result)
if self.has_space('buy'):
self.analyze.populate_indicators = Hyperopt.populate_indicators
self.processed = self.tickerdata_to_dataframe(data)
# Store trials result to file to resume next time
save_trials(TRIALS)
if self.config.get('mongodb'):
logger.info('Using mongodb ...')
logger.info(
'Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!'
)
db_name = 'freqtrade_hyperopt'
self.trials = MongoTrials(
arg='mongo://127.0.0.1:1234/{}/jobs'.format(db_name),
exp_key='exp1'
)
else:
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, self.signal_handler)
# read trials file if we have one
if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
self.trials = self.read_trials()
self.current_tries = len(self.trials.results)
self.total_tries += self.current_tries
logger.info(
'Continuing with trials. Current: %d, Total: %d',
self.current_tries,
self.total_tries
)
try:
best_parameters = fmin(
fn=self.generate_optimizer,
space=self.hyperopt_space(),
algo=tpe.suggest,
max_evals=self.total_tries,
trials=self.trials
)
results = sorted(self.trials.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(
self.hyperopt_space(),
best_parameters
)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters:
logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time
self.save_trials()
def signal_handler(self, sig, frame) -> None:
"""
Hyperopt SIGINT handler
"""
logger.info(
'Hyperopt received %s',
signal.Signals(sig).name
)
self.save_trials()
self.log_trials_result()
sys.exit(0)
def signal_handler(sig, frame):
"""Hyperopt SIGINT handler"""
logger.info('Hyperopt received {}'.format(signal.Signals(sig).name))
def start(args: Namespace) -> None:
"""
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
"""
save_trials(TRIALS)
log_trials_result(TRIALS)
sys.exit(0)
# Remove noisy log messages
logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
# Initialize configuration
# Monkey patch the configuration with hyperopt_conf.py
configuration = Configuration(args)
logger.info('Starting freqtrade in Hyperopt mode')
optimize_config = hyperopt_optimize_conf()
config = configuration._load_common_config(optimize_config)
config = configuration._load_backtesting_config(config)
config = configuration._load_hyperopt_config(config)
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
# Initialize backtesting object
hyperopt = Hyperopt(config)
hyperopt.start()

31
freqtrade/persistence.py
View File

@ -1,3 +1,7 @@
"""
This module contains the class to persist trades into SQLite
"""
import logging
from datetime import datetime
from decimal import Decimal, getcontext
@ -47,6 +51,10 @@ def init(config: dict, engine: Optional[Engine] = None) -> None:
Trade.query = session.query_property()
_DECL_BASE.metadata.create_all(engine)
# Clean dry_run DB
if _CONF.get('dry_run', False) and _CONF.get('dry_run_db', False):
clean_dry_run_db()
def cleanup() -> None:
"""
@ -56,7 +64,21 @@ def cleanup() -> None:
Trade.session.flush()
def clean_dry_run_db() -> None:
"""
Remove open_order_id from a Dry_run DB
:return: None
"""
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
# Check we are updating only a dry_run order not a prod one
if 'dry_run' in trade.open_order_id:
trade.open_order_id = None
class Trade(_DECL_BASE):
"""
Class used to define a trade structure
"""
__tablename__ = 'trades'
id = Column(Integer, primary_key=True)
@ -172,8 +194,8 @@ class Trade(_DECL_BASE):
"""
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
rate=Decimal(rate or self.close_rate),
fee=Decimal(fee or self.fee)
rate=(rate or self.close_rate),
fee=(fee or self.fee)
)
return float("{0:.8f}".format(close_trade_price - open_trade_price))
@ -185,14 +207,15 @@ class Trade(_DECL_BASE):
Calculates the profit in percentage (including fee).
:param rate: rate to compare with (optional).
If rate is not set self.close_rate will be used
:param fee: fee to use on the close rate (optional).
:return: profit in percentage as float
"""
getcontext().prec = 8
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
rate=Decimal(rate or self.close_rate),
fee=Decimal(fee or self.fee)
rate=(rate or self.close_rate),
fee=(fee or self.fee)
)
return float("{0:.8f}".format((close_trade_price / open_trade_price) - 1))

42
freqtrade/rpc/__init__.py
View File

@ -1,42 +0,0 @@
import logging
from . import telegram
logger = logging.getLogger(__name__)
REGISTERED_MODULES = []
def init(config: dict) -> None:
"""
Initializes all enabled rpc modules
:param config: config to use
:return: None
"""
if config['telegram'].get('enabled', False):
logger.info('Enabling rpc.telegram ...')
REGISTERED_MODULES.append('telegram')
telegram.init(config)
def cleanup() -> None:
"""
Stops all enabled rpc modules
:return: None
"""
if 'telegram' in REGISTERED_MODULES:
logger.debug('Cleaning up rpc.telegram ...')
telegram.cleanup()
def send_msg(msg: str) -> None:
"""
Send given markdown message to all registered rpc modules
:param msg: message
:return: None
"""
logger.info(msg)
if 'telegram' in REGISTERED_MODULES:
telegram.send_msg(msg)

383
freqtrade/rpc/rpc.py Normal file
View File

@ -0,0 +1,383 @@
"""
This module contains class to define a RPC communications
"""
import logging
from datetime import datetime, timedelta
from decimal import Decimal
from typing import Tuple, Any
import arrow
import sqlalchemy as sql
from pandas import DataFrame
from freqtrade import exchange
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.state import State
logger = logging.getLogger(__name__)
class RPC(object):
"""
RPC class can be used to have extra feature, like bot data, and access to DB data
"""
def __init__(self, freqtrade) -> None:
"""
Initializes all enabled rpc modules
:param freqtrade: Instance of a freqtrade bot
:return: None
"""
self.freqtrade = freqtrade
def rpc_trade_status(self) -> Tuple[bool, Any]:
"""
Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
a remotely exposed function
:return:
"""
# Fetch open trade
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if self.freqtrade.state != State.RUNNING:
return True, '*Status:* `trader is not running`'
elif not trades:
return True, '*Status:* `no active trade`'
else:
result = []
for trade in trades:
order = None
if trade.open_order_id:
order = exchange.get_order(trade.open_order_id)
# calculate profit and send message to user
current_rate = exchange.get_ticker(trade.pair, False)['bid']
current_profit = trade.calc_profit_percent(current_rate)
fmt_close_profit = '{:.2f}%'.format(
round(trade.close_profit * 100, 2)
) if trade.close_profit else None
message = "*Trade ID:* `{trade_id}`\n" \
"*Current Pair:* [{pair}]({market_url})\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(
trade_id=trade.id,
pair=trade.pair,
market_url=exchange.get_pair_detail_url(trade.pair),
date=arrow.get(trade.open_date).humanize(),
open_rate=trade.open_rate,
close_rate=trade.close_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
close_profit=fmt_close_profit,
current_profit=round(current_profit * 100, 2),
open_order='({} rem={:.8f})'.format(
order['type'], order['remaining']
) if order else None,
)
result.append(message)
return False, result
def rpc_status_table(self) -> Tuple[bool, Any]:
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if self.freqtrade.state != State.RUNNING:
return True, '*Status:* `trader is not running`'
elif not trades:
return True, '*Status:* `no active order`'
else:
trades_list = []
for trade in trades:
# calculate profit and send message to user
current_rate = exchange.get_ticker(trade.pair, False)['bid']
trades_list.append([
trade.id,
trade.pair,
shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
'{:.2f}%'.format(100 * trade.calc_profit_percent(current_rate))
])
columns = ['ID', 'Pair', 'Since', 'Profit']
df_statuses = DataFrame.from_records(trades_list, columns=columns)
df_statuses = df_statuses.set_index(columns[0])
# The style used throughout is to return a tuple
# consisting of (error_occured?, result)
# Another approach would be to just return the
# result, or raise error
return False, df_statuses
def rpc_daily_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Tuple[bool, Any]:
today = datetime.utcnow().date()
profit_days = {}
if not (isinstance(timescale, int) and timescale > 0):
return True, '*Daily [n]:* `must be an integer greater than 0`'
fiat = self.freqtrade.fiat_converter
for day in range(0, timescale):
profitday = today - timedelta(days=day)
trades = Trade.query \
.filter(Trade.is_open.is_(False)) \
.filter(Trade.close_date >= profitday)\
.filter(Trade.close_date < (profitday + timedelta(days=1)))\
.order_by(Trade.close_date)\
.all()
curdayprofit = sum(trade.calc_profit() for trade in trades)
profit_days[profitday] = {
'amount': format(curdayprofit, '.8f'),
'trades': len(trades)
}
stats = [
[
key,
'{value:.8f} {symbol}'.format(
value=float(value['amount']),
symbol=stake_currency
),
'{value:.3f} {symbol}'.format(
value=fiat.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
),
symbol=fiat_display_currency
),
'{value} trade{s}'.format(
value=value['trades'],
s='' if value['trades'] < 2 else 's'
),
]
for key, value in profit_days.items()
]
return False, stats
def rpc_trade_statistics(
self, stake_currency: str, fiat_display_currency: str) -> Tuple[bool, Any]:
"""
:return: cumulative profit statistics.
"""
trades = Trade.query.order_by(Trade.id).all()
profit_all_coin = []
profit_all_percent = []
profit_closed_coin = []
profit_closed_percent = []
durations = []
for trade in trades:
current_rate = None
if not trade.open_rate:
continue
if trade.close_date:
durations.append((trade.close_date - trade.open_date).total_seconds())
if not trade.is_open:
profit_percent = trade.calc_profit_percent()
profit_closed_coin.append(trade.calc_profit())
profit_closed_percent.append(profit_percent)
else:
# Get current rate
current_rate = exchange.get_ticker(trade.pair, False)['bid']
profit_percent = trade.calc_profit_percent(rate=current_rate)
profit_all_coin.append(
trade.calc_profit(rate=Decimal(trade.close_rate or current_rate))
)
profit_all_percent.append(profit_percent)
best_pair = Trade.session.query(
Trade.pair, sql.func.sum(Trade.close_profit).label('profit_sum')
).filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(sql.text('profit_sum DESC')).first()
if not best_pair:
return True, '*Status:* `no closed trade`'
bp_pair, bp_rate = best_pair
# FIX: we want to keep fiatconverter in a state/environment,
# doing this will utilize its caching functionallity, instead we reinitialize it here
fiat = self.freqtrade.fiat_converter
# Prepare data to display
profit_closed_coin = round(sum(profit_closed_coin), 8)
profit_closed_percent = round(sum(profit_closed_percent) * 100, 2)
profit_closed_fiat = fiat.convert_amount(
profit_closed_coin,
stake_currency,
fiat_display_currency
)
profit_all_coin = round(sum(profit_all_coin), 8)
profit_all_percent = round(sum(profit_all_percent) * 100, 2)
profit_all_fiat = fiat.convert_amount(
profit_all_coin,
stake_currency,
fiat_display_currency
)
num = float(len(durations) or 1)
return (
False,
{
'profit_closed_coin': profit_closed_coin,
'profit_closed_percent': profit_closed_percent,
'profit_closed_fiat': profit_closed_fiat,
'profit_all_coin': profit_all_coin,
'profit_all_percent': profit_all_percent,
'profit_all_fiat': profit_all_fiat,
'trade_count': len(trades),
'first_trade_date': arrow.get(trades[0].open_date).humanize(),
'latest_trade_date': arrow.get(trades[-1].open_date).humanize(),
'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
'best_pair': bp_pair,
'best_rate': round(bp_rate * 100, 2)
}
)
def rpc_balance(self, fiat_display_currency: str) -> Tuple[bool, Any]:
"""
:return: current account balance per crypto
"""
balances = [
c for c in exchange.get_balances()
if c['Balance'] or c['Available'] or c['Pending']
]
if not balances:
return True, '`All balances are zero.`'
output = []
total = 0.0
for currency in balances:
coin = currency['Currency']
if coin == 'BTC':
currency["Rate"] = 1.0
else:
if coin == 'USDT':
currency["Rate"] = 1.0 / exchange.get_ticker('USDT_BTC', False)['bid']
else:
currency["Rate"] = exchange.get_ticker('BTC_' + coin, False)['bid']
currency['BTC'] = currency["Rate"] * currency["Balance"]
total = total + currency['BTC']
output.append(
{
'currency': currency['Currency'],
'available': currency['Available'],
'balance': currency['Balance'],
'pending': currency['Pending'],
'est_btc': currency['BTC']
}
)
fiat = self.freqtrade.fiat_converter
symbol = fiat_display_currency
value = fiat.convert_amount(total, 'BTC', symbol)
return False, (output, total, symbol, value)
def rpc_start(self) -> (bool, str):
"""
Handler for start.
"""
if self.freqtrade.state == State.RUNNING:
return True, '*Status:* `already running`'
self.freqtrade.state = State.RUNNING
return False, '`Starting trader ...`'
def rpc_stop(self) -> (bool, str):
"""
Handler for stop.
"""
if self.freqtrade.state == State.RUNNING:
self.freqtrade.state = State.STOPPED
return False, '`Stopping trader ...`'
return True, '*Status:* `already stopped`'
# FIX: no test for this!!!!
def rpc_forcesell(self, trade_id) -> Tuple[bool, Any]:
"""
Handler for forcesell <id>.
Sells the given trade at current price
:return: error or None
"""
def _exec_forcesell(trade: Trade) -> None:
# Check if there is there is an open order
if trade.open_order_id:
order = exchange.get_order(trade.open_order_id)
# Cancel open LIMIT_BUY orders and close trade
if order and not order['closed'] and order['type'] == 'LIMIT_BUY':
exchange.cancel_order(trade.open_order_id)
trade.close(order.get('rate') or trade.open_rate)
# TODO: sell amount which has been bought already
return
# Ignore trades with an attached LIMIT_SELL order
if order and not order['closed'] and order['type'] == 'LIMIT_SELL':
return
# Get current rate and execute sell
current_rate = exchange.get_ticker(trade.pair, False)['bid']
self.freqtrade.execute_sell(trade, current_rate)
# ---- EOF def _exec_forcesell ----
if self.freqtrade.state != State.RUNNING:
return True, '`trader is not running`'
if trade_id == 'all':
# Execute sell for all open orders
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
_exec_forcesell(trade)
return False, ''
# Query for trade
trade = Trade.query.filter(
sql.and_(
Trade.id == trade_id,
Trade.is_open.is_(True)
)
).first()
if not trade:
logger.warning('forcesell: Invalid argument received')
return True, 'Invalid argument.'
_exec_forcesell(trade)
return False, ''
def rpc_performance(self) -> Tuple[bool, Any]:
"""
Handler for performance.
Shows a performance statistic from finished trades
"""
if self.freqtrade.state != State.RUNNING:
return True, '`trader is not running`'
pair_rates = Trade.session.query(Trade.pair,
sql.func.sum(Trade.close_profit).label('profit_sum'),
sql.func.count(Trade.pair).label('count')) \
.filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(sql.text('profit_sum DESC')) \
.all()
trades = []
for (pair, rate, count) in pair_rates:
trades.append({'pair': pair, 'profit': round(rate * 100, 2), 'count': count})
return False, trades
def rpc_count(self) -> Tuple[bool, Any]:
"""
Returns the number of trades running
:return: None
"""
if self.freqtrade.state != State.RUNNING:
return True, '`trader is not running`'
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
return False, trades

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freqtrade/rpc/rpc_manager.py Normal file
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"""
This module contains class to manage RPC communications (Telegram, Slack, ...)
"""
import logging
from freqtrade.rpc.telegram import Telegram
logger = logging.getLogger(__name__)
class RPCManager(object):
"""
Class to manage RPC objects (Telegram, Slack, ...)
"""
def __init__(self, freqtrade) -> None:
"""
Initializes all enabled rpc modules
:param config: config to use
:return: None
"""
self.freqtrade = freqtrade
self.registered_modules = []
self.telegram = None
self._init()
def _init(self) -> None:
"""
Init RPC modules
:return:
"""
if self.freqtrade.config['telegram'].get('enabled', False):
logger.info('Enabling rpc.telegram ...')
self.registered_modules.append('telegram')
self.telegram = Telegram(self.freqtrade)
def cleanup(self) -> None:
"""
Stops all enabled rpc modules
:return: None
"""
if 'telegram' in self.registered_modules:
logger.info('Cleaning up rpc.telegram ...')
self.registered_modules.remove('telegram')
self.telegram.cleanup()
def send_msg(self, msg: str) -> None:
"""
Send given markdown message to all registered rpc modules
:param msg: message
:return: None
"""
logger.info(msg)
if 'telegram' in self.registered_modules:
self.telegram.send_msg(msg)

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freqtrade/state.py Normal file
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# pragma pylint: disable=too-few-public-methods
"""
Bot state constant
"""
import enum
class State(enum.Enum):
"""
Bot running states
"""
RUNNING = 0
STOPPED = 1

0
freqtrade/strategy/__init__.py Normal file
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240
freqtrade/strategy/default_strategy.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.indicator_helpers import fishers_inverse
from freqtrade.strategy.interface import IStrategy
class DefaultStrategy(IStrategy):
"""
Default Strategy provided by freqtrade bot.
You can override it with your own strategy
"""
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = 5
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
"""
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
"""
# ROC
dataframe['roc'] = ta.ROC(dataframe)
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
"""
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
# Previous Bollinger bands
# Because ta.BBANDS implementation is broken with small numbers, it actually
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
# and use middle band instead.
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
),
'sell'] = 1
return dataframe

44
freqtrade/strategy/interface.py Normal file
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"""
IStrategy interface
This module defines the interface to apply for strategies
"""
from abc import ABC, abstractmethod
from pandas import DataFrame
class IStrategy(ABC):
"""
Interface for freqtrade strategies
Defines the mandatory structure must follow any custom strategies
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
@abstractmethod
def populate_indicators(self, dataframe: DataFrame) -> 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
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with sell column
"""

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freqtrade/strategy/resolver.py Normal file
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# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom strategies
"""
import importlib.util
import inspect
import logging
import os
from collections import OrderedDict
from typing import Optional, Dict, Type
from freqtrade import constants
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class StrategyResolver(object):
"""
This class contains all the logic to load custom strategy class
"""
__slots__ = ['strategy']
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 strategy is in the configuration, otherwise fallback to the default strategy
strategy_name = config.get('strategy') or constants.DEFAULT_STRATEGY
self.strategy = self._load_strategy(strategy_name, extra_dir=config.get('strategy_path'))
# Set attributes
# Check if we need to override configuration
if 'minimal_roi' in config:
self.strategy.minimal_roi = config['minimal_roi']
logger.info("Override strategy \'minimal_roi\' with value in config file.")
if 'stoploss' in config:
self.strategy.stoploss = config['stoploss']
logger.info(
"Override strategy \'stoploss\' with value in config file: %s.", config['stoploss']
)
if 'ticker_interval' in config:
self.strategy.ticker_interval = config['ticker_interval']
logger.info(
"Override strategy \'ticker_interval\' with value in config file: %s.",
config['ticker_interval']
)
# Sort and apply type conversions
self.strategy.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),
key=lambda t: t[0]))
self.strategy.stoploss = float(self.strategy.stoploss)
self.strategy.ticker_interval = int(self.strategy.ticker_interval)
def _load_strategy(
self, strategy_name: str, extra_dir: Optional[str] = None) -> Optional[IStrategy]:
"""
Search and loads the specified strategy.
:param strategy_name: name of the module to import
:param extra_dir: additional directory to search for the given strategy
:return: Strategy instance or None
"""
current_path = os.path.dirname(os.path.realpath(__file__))
abs_paths = [
os.path.join(current_path, '..', '..', 'user_data', 'strategies'),
current_path,
]
if extra_dir:
# Add extra strategy directory on top of search paths
abs_paths.insert(0, extra_dir)
for path in abs_paths:
strategy = self._search_strategy(path, strategy_name)
if strategy:
logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
return strategy
raise ImportError(
"Impossible to load Strategy '{}'. This class does not exist"
" or contains Python code errors".format(strategy_name)
)
@staticmethod
def _get_valid_strategies(module_path: str, strategy_name: str) -> Optional[Type[IStrategy]]:
"""
Returns a list of all possible strategies for the given module_path
:param module_path: absolute path to the module
:param strategy_name: Class name of the strategy
:return: Tuple with (name, class) or None
"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('user_data.strategies', module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
valid_strategies_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if strategy_name == name and IStrategy in obj.__bases__
)
return next(valid_strategies_gen, None)
@staticmethod
def _search_strategy(directory: str, strategy_name: str) -> Optional[IStrategy]:
"""
Search for the strategy_name in the given directory
:param directory: relative or absolute directory path
:return: name of the strategy class
"""
logger.debug('Searching for strategy %s in \'%s\'', strategy_name, directory)
for entry in os.listdir(directory):
# Only consider python files
if not entry.endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
strategy = StrategyResolver._get_valid_strategies(
os.path.abspath(os.path.join(directory, entry)), strategy_name
)
if strategy:
return strategy()
return None

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freqtrade/tests/conftest.py
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@ -1,16 +1,52 @@
# pragma pylint: disable=missing-docstring
import json
import logging
from datetime import datetime
from functools import reduce
from unittest.mock import MagicMock
import arrow
import pytest
from jsonschema import validate
from sqlalchemy import create_engine
from telegram import Chat, Message, Update
from freqtrade.misc import CONF_SCHEMA
from freqtrade.analyze import Analyze
from freqtrade import constants
from freqtrade.freqtradebot import FreqtradeBot
logging.getLogger('').setLevel(logging.INFO)
@pytest.fixture(scope="module")
def log_has(line, logs):
# caplog mocker returns log as a tuple: ('freqtrade.analyze', logging.WARNING, 'foobar')
# and we want to match line against foobar in the tuple
return reduce(lambda a, b: a or b,
filter(lambda x: x[2] == line, logs),
False)
# Functions for recurrent object patching
def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
"""
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
"""
mocker.patch('freqtrade.fiat_convert.Market', {'price_usd': 12345.0})
mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager', MagicMock())
mocker.patch('freqtrade.freqtradebot.persistence.init', MagicMock())
mocker.patch('freqtrade.freqtradebot.exchange.init', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager._init', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager.send_msg', MagicMock())
mocker.patch('freqtrade.freqtradebot.Analyze.get_signal', MagicMock())
return FreqtradeBot(config, create_engine('sqlite://'))
@pytest.fixture(scope="function")
def default_conf():
""" Returns validated configuration suitable for most tests """
configuration = {
@ -18,6 +54,7 @@ def default_conf():
"stake_currency": "BTC",
"stake_amount": 0.001,
"fiat_display_currency": "USD",
"ticker_interval": 5,
"dry_run": True,
"minimal_roi": {
"40": 0.0,
@ -48,9 +85,10 @@ def default_conf():
"token": "token",
"chat_id": "0"
},
"initial_state": "running"
"initial_state": "running",
"loglevel": logging.DEBUG
}
validate(configuration, CONF_SCHEMA)
validate(configuration, constants.CONF_SCHEMA)
return configuration
@ -216,3 +254,178 @@ def ticker_history():
"BV": 0.7039405
}
]
@pytest.fixture
def ticker_history_without_bv():
return [
{
"O": 8.794e-05,
"H": 8.948e-05,
"L": 8.794e-05,
"C": 8.88e-05,
"V": 991.09056638,
"T": "2017-11-26T08:50:00"
},
{
"O": 8.88e-05,
"H": 8.942e-05,
"L": 8.88e-05,
"C": 8.893e-05,
"V": 658.77935965,
"T": "2017-11-26T08:55:00"
},
{
"O": 8.891e-05,
"H": 8.893e-05,
"L": 8.875e-05,
"C": 8.877e-05,
"V": 7920.73570705,
"T": "2017-11-26T09:00:00"
}
]
# FIX: Perhaps change result fixture to use BTC_UNITEST instead?
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
return Analyze.parse_ticker_dataframe(json.load(data_file))
# FIX:
# Create an fixture/function
# that inserts a trade of some type and open-status
# return the open-order-id
# See tests in rpc/main that could use this
@pytest.fixture
def get_market_summaries_data():
"""
This fixture is a real result from exchange.get_market_summaries() but reduced to only
8 entries. 4 BTC, 4 USTD
:return: JSON market summaries
"""
return [
{
'Ask': 1.316e-05,
'BaseVolume': 5.72599471,
'Bid': 1.3e-05,
'Created': '2014-04-14T00:00:00',
'High': 1.414e-05,
'Last': 1.298e-05,
'Low': 1.282e-05,
'MarketName': 'BTC-XWC',
'OpenBuyOrders': 2000,
'OpenSellOrders': 1484,
'PrevDay': 1.376e-05,
'TimeStamp': '2018-02-05T01:32:40.493',
'Volume': 424041.21418375
},
{
'Ask': 0.00627051,
'BaseVolume': 93.23302388,
'Bid': 0.00618192,
'Created': '2016-10-20T04:48:30.387',
'High': 0.00669897,
'Last': 0.00618192,
'Low': 0.006,
'MarketName': 'BTC-XZC',
'OpenBuyOrders': 343,
'OpenSellOrders': 2037,
'PrevDay': 0.00668229,
'TimeStamp': '2018-02-05T01:32:43.383',
'Volume': 14863.60730702
},
{
'Ask': 0.01137247,
'BaseVolume': 383.55922657,
'Bid': 0.01136006,
'Created': '2016-11-15T20:29:59.73',
'High': 0.012,
'Last': 0.01137247,
'Low': 0.01119883,
'MarketName': 'BTC-ZCL',
'OpenBuyOrders': 1332,
'OpenSellOrders': 5317,
'PrevDay': 0.01179603,
'TimeStamp': '2018-02-05T01:32:42.773',
'Volume': 33308.07358285
},
{
'Ask': 0.04155821,
'BaseVolume': 274.75369074,
'Bid': 0.04130002,
'Created': '2016-10-28T17:13:10.833',
'High': 0.04354429,
'Last': 0.041585,
'Low': 0.0413,
'MarketName': 'BTC-ZEC',
'OpenBuyOrders': 863,
'OpenSellOrders': 5579,
'PrevDay': 0.0429,
'TimeStamp': '2018-02-05T01:32:43.21',
'Volume': 6479.84033259
},
{
'Ask': 210.99999999,
'BaseVolume': 615132.70989532,
'Bid': 210.05503736,
'Created': '2017-07-21T01:08:49.397',
'High': 257.396,
'Last': 211.0,
'Low': 209.05333589,
'MarketName': 'USDT-XMR',
'OpenBuyOrders': 180,
'OpenSellOrders': 1203,
'PrevDay': 247.93528899,
'TimeStamp': '2018-02-05T01:32:43.117',
'Volume': 2688.17410793
},
{
'Ask': 0.79589979,
'BaseVolume': 9349557.01853031,
'Bid': 0.789226,
'Created': '2017-07-14T17:10:10.737',
'High': 0.977,
'Last': 0.79589979,
'Low': 0.781,
'MarketName': 'USDT-XRP',
'OpenBuyOrders': 1075,
'OpenSellOrders': 6508,
'PrevDay': 0.93300218,
'TimeStamp': '2018-02-05T01:32:42.383',
'Volume': 10801663.00788851
},
{
'Ask': 0.05154982,
'BaseVolume': 2311087.71232136,
'Bid': 0.05040107,
'Created': '2017-12-29T19:29:18.357',
'High': 0.06668561,
'Last': 0.0508,
'Low': 0.05006731,
'MarketName': 'USDT-XVG',
'OpenBuyOrders': 655,
'OpenSellOrders': 5544,
'PrevDay': 0.0627,
'TimeStamp': '2018-02-05T01:32:41.507',
'Volume': 40031424.2152716
},
{
'Ask': 332.65500022,
'BaseVolume': 562911.87455665,
'Bid': 330.00000001,
'Created': '2017-07-14T17:10:10.673',
'High': 401.59999999,
'Last': 332.65500019,
'Low': 330.0,
'MarketName': 'USDT-ZEC',
'OpenBuyOrders': 161,
'OpenSellOrders': 1731,
'PrevDay': 391.42,
'TimeStamp': '2018-02-05T01:32:42.947',
'Volume': 1571.09647946
}
]

115
freqtrade/tests/exchange/test_exchange.py
View File

@ -1,26 +1,37 @@
# pragma pylint: disable=missing-docstring,C0103
from unittest.mock import MagicMock
from requests.exceptions import RequestException
from random import randint
# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
# pragma pylint: disable=protected-access
import logging
import pytest
from random import randint
from unittest.mock import MagicMock
import pytest
from requests.exceptions import RequestException
import freqtrade.exchange as exchange
from freqtrade import OperationalException
from freqtrade.exchange import init, validate_pairs, buy, sell, get_balance, get_balances, \
get_ticker, cancel_order, get_name, get_fee
get_ticker, get_ticker_history, cancel_order, get_name, get_fee
from freqtrade.tests.conftest import log_has
API_INIT = False
def maybe_init_api(conf, mocker, force=False):
global API_INIT
if force or not API_INIT:
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
init(config=conf)
API_INIT = True
def test_init(default_conf, mocker, caplog):
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
init(config=default_conf)
assert ('freqtrade.exchange',
logging.INFO,
'Instance is running with dry_run enabled'
) in caplog.record_tuples
caplog.set_level(logging.INFO)
maybe_init_api(default_conf, mocker, True)
assert log_has('Instance is running with dry_run enabled', caplog.record_tuples)
def test_init_exception(default_conf, mocker):
def test_init_exception(default_conf):
default_conf['exchange']['name'] = 'wrong_exchange_name'
with pytest.raises(
@ -60,16 +71,15 @@ def test_validate_pairs_not_compatible(default_conf, mocker):
def test_validate_pairs_exception(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
api_mock = MagicMock()
api_mock.get_markets = MagicMock(side_effect=RequestException())
mocker.patch('freqtrade.exchange._API', api_mock)
# with pytest.raises(RequestException, match=r'Unable to validate pairs'):
validate_pairs(default_conf['exchange']['pair_whitelist'])
assert ('freqtrade.exchange',
logging.WARNING,
'Unable to validate pairs (assuming they are correct). Reason: '
) in caplog.record_tuples
assert log_has('Unable to validate pairs (assuming they are correct). Reason: ',
caplog.record_tuples)
def test_buy_dry_run(default_conf, mocker):
@ -159,8 +169,10 @@ def test_get_balances_prod(default_conf, mocker):
assert get_balances()[0]['Pending'] == 0.0
def test_get_ticker(mocker, ticker):
# This test is somewhat redundant with
# test_exchange_bittrex.py::test_exchange_bittrex_get_ticker
def test_get_ticker(default_conf, mocker):
maybe_init_api(default_conf, mocker)
api_mock = MagicMock()
tick = {"success": True, 'result': {'Bid': 0.00001098, 'Ask': 0.00001099, 'Last': 0.0001}}
api_mock.get_ticker = MagicMock(return_value=tick)
@ -177,6 +189,7 @@ def test_get_ticker(mocker, ticker):
mocker.patch('freqtrade.exchange.bittrex._API', api_mock)
# if not caching the result we should get the same ticker
# if not fetching a new result we should get the cached ticker
ticker = get_ticker(pair='BTC_ETH', refresh=False)
assert ticker['bid'] == 0.00001098
assert ticker['ask'] == 0.00001099
@ -187,6 +200,26 @@ def test_get_ticker(mocker, ticker):
assert ticker['ask'] == 1
def test_get_ticker_history(default_conf, mocker):
api_mock = MagicMock()
tick = 123
api_mock.get_ticker_history = MagicMock(return_value=tick)
mocker.patch('freqtrade.exchange._API', api_mock)
# retrieve original ticker
ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
assert ticks == 123
# change the ticker
tick = 999
api_mock.get_ticker_history = MagicMock(return_value=tick)
mocker.patch('freqtrade.exchange._API', api_mock)
# ensure caching will still return the original ticker
ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
assert ticks == 123
def test_cancel_order_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
@ -194,6 +227,33 @@ def test_cancel_order_dry_run(default_conf, mocker):
assert cancel_order(order_id='123') is None
# Ensure that if not dry_run, we should call API
def test_cancel_order(default_conf, mocker):
default_conf['dry_run'] = False
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
api_mock = MagicMock()
api_mock.cancel_order = MagicMock(return_value=123)
mocker.patch('freqtrade.exchange._API', api_mock)
assert cancel_order(order_id='_') == 123
def test_get_order(default_conf, mocker):
default_conf['dry_run'] = True
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
order = MagicMock()
order.myid = 123
exchange._DRY_RUN_OPEN_ORDERS['X'] = order
print(exchange.get_order('X'))
assert exchange.get_order('X').myid == 123
default_conf['dry_run'] = False
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
api_mock = MagicMock()
api_mock.get_order = MagicMock(return_value=456)
mocker.patch('freqtrade.exchange._API', api_mock)
assert exchange.get_order('X') == 456
def test_get_name(default_conf, mocker):
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
@ -209,3 +269,18 @@ def test_get_fee(default_conf, mocker):
init(default_conf)
assert get_fee() == 0.0025
def test_exchange_misc(mocker):
api_mock = MagicMock()
mocker.patch('freqtrade.exchange._API', api_mock)
exchange.get_markets()
assert api_mock.get_markets.call_count == 1
exchange.get_market_summaries()
assert api_mock.get_market_summaries.call_count == 1
api_mock.name = 123
assert exchange.get_name() == 123
api_mock.fee = 456
assert exchange.get_fee() == 456
exchange.get_wallet_health()
assert api_mock.get_wallet_health.call_count == 1

48
freqtrade/tests/exchange/test_exchange_bittrex.py
View File

@ -1,11 +1,12 @@
# pragma pylint: disable=missing-docstring,C0103
# pragma pylint: disable=missing-docstring, C0103, protected-access, unused-argument
from unittest.mock import MagicMock
import pytest
from unittest.mock import MagicMock
from requests.exceptions import ContentDecodingError
from freqtrade.exchange.bittrex import Bittrex
import freqtrade.exchange.bittrex as btx
from freqtrade.exchange.bittrex import Bittrex
# Eat this flake8
@ -88,8 +89,7 @@ class FakeBittrex():
'PricePerUnit': 1,
'Quantity': 1,
'QuantityRemaining': 1,
'Closed': True
},
'Closed': True},
'message': 'lost'}
def fake_cancel_order(self, uuid):
@ -143,7 +143,7 @@ def test_exchange_bittrex_fee():
assert fee >= 0 and fee < 0.1 # Fee is 0-10 %
def test_exchange_bittrex_buy_good(mocker):
def test_exchange_bittrex_buy_good():
wb = make_wrap_bittrex()
fb = FakeBittrex()
uuid = wb.buy('BTC_ETH', 1, 1)
@ -154,7 +154,7 @@ def test_exchange_bittrex_buy_good(mocker):
wb.buy('BAD', 1, 1)
def test_exchange_bittrex_sell_good(mocker):
def test_exchange_bittrex_sell_good():
wb = make_wrap_bittrex()
fb = FakeBittrex()
uuid = wb.sell('BTC_ETH', 1, 1)
@ -165,7 +165,7 @@ def test_exchange_bittrex_sell_good(mocker):
uuid = wb.sell('BAD', 1, 1)
def test_exchange_bittrex_get_balance(mocker):
def test_exchange_bittrex_get_balance():
wb = make_wrap_bittrex()
fb = FakeBittrex()
bal = wb.get_balance('BTC_ETH')
@ -211,28 +211,40 @@ def test_exchange_bittrex_get_ticker():
def test_exchange_bittrex_get_ticker_bad():
wb = make_wrap_bittrex()
fb = FakeBittrex()
fb.result = {'success': True,
'result': {'Bid': 1}} # incomplete result
with pytest.raises(ContentDecodingError, match=r'.*Got invalid response from bittrex params.*'):
fb.result = {'success': True, 'result': {'Bid': 1, 'Ask': 0}} # incomplete result
with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex params.*'):
wb.get_ticker('BTC_ETH')
fb.result = {'success': False,
'message': 'gone bad'
}
fb.result = {'success': False, 'message': 'gone bad'}
with pytest.raises(btx.OperationalException, match=r'.*gone bad.*'):
wb.get_ticker('BTC_ETH')
fb.result = {'success': True, 'result': {}} # incomplete result
with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex params.*'):
wb.get_ticker('BTC_ETH')
fb.result = {'success': False, 'message': 'gone bad'}
with pytest.raises(btx.OperationalException, match=r'.*gone bad.*'):
wb.get_ticker('BTC_ETH')
def test_exchange_bittrex_get_ticker_history_one():
fb.result = {'success': True,
'result': {'Bid': 1, 'Ask': 0, 'Last': None}} # incomplete result
with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex params.*'):
wb.get_ticker('BTC_ETH')
def test_exchange_bittrex_get_ticker_history_intervals():
wb = make_wrap_bittrex()
FakeBittrex()
assert wb.get_ticker_history('BTC_ETH', 1)
for tick_interval in [1, 5, 30, 60, 1440]:
assert ([{'C': 0, 'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}] ==
wb.get_ticker_history('BTC_ETH', tick_interval))
def test_exchange_bittrex_get_ticker_history():
wb = make_wrap_bittrex()
fb = FakeBittrex()
assert wb.get_ticker_history('BTC_ETH', 5)
with pytest.raises(ValueError, match=r'.*Cannot parse tick_interval.*'):
with pytest.raises(ValueError, match=r'.*Unknown tick_interval.*'):
wb.get_ticker_history('BTC_ETH', 2)
fb.success = False
@ -240,7 +252,7 @@ def test_exchange_bittrex_get_ticker_history():
wb.get_ticker_history('BTC_ETH', 5)
fb.success = True
with pytest.raises(ContentDecodingError, match=r'.*Got invalid response from bittrex.*'):
with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex.*'):
fb.result = {'bad': 0}
wb.get_ticker_history('BTC_ETH', 5)

610
freqtrade/tests/optimize/test_backtesting.py
View File

@ -1,74 +1,40 @@
# pragma pylint: disable=missing-docstring,W0212
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
import logging
import json
import math
import pandas as pd
import random
from copy import deepcopy
from typing import List
from unittest.mock import MagicMock
from freqtrade import exchange, optimize
from freqtrade.exchange import Bittrex
from freqtrade.optimize import preprocess
from freqtrade.optimize.backtesting import backtest, generate_text_table, get_timeframe
import freqtrade.optimize.backtesting as backtesting
import numpy as np
import pandas as pd
from arrow import Arrow
from freqtrade import optimize
from freqtrade.analyze import Analyze
from freqtrade.arguments import Arguments
from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration
from freqtrade.tests.conftest import default_conf, log_has
# Avoid to reinit the same object again and again
_BACKTESTING = Backtesting(default_conf())
def test_generate_text_table():
results = pd.DataFrame(
{
'currency': ['BTC_ETH', 'BTC_ETH'],
'profit_percent': [0.1, 0.2],
'profit_BTC': [0.2, 0.4],
'duration': [10, 30],
'profit': [2, 0],
'loss': [0, 0]
}
)
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
'------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
'BTC_ETH 2 15.00 0.60000000 100.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
def get_args(args) -> List[str]:
return Arguments(args, '').get_parsed_arg()
def test_get_timeframe():
data = preprocess(optimize.load_data(
None, ticker_interval=1, pairs=['BTC_UNITEST']))
min_date, max_date = get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def test_backtest(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
assert not results.empty
def test_backtest_1min_ticker_interval(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 1, True)
assert not results.empty
def trim_dictlist(dl, num):
def trim_dictlist(dict_list, num):
new = {}
for pair, pair_data in dl.items():
for pair, pair_data in dict_list.items():
new[pair] = pair_data[num:]
return new
def load_data_test(what):
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -100)
timerange = ((None, 'line'), None, -100)
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'], timerange=timerange)
pair = data['BTC_UNITEST']
datalen = len(pair)
# Depending on the what parameter we now adjust the
@ -109,31 +75,404 @@ def load_data_test(what):
return data
def simple_backtest(config, contour, num_results):
def simple_backtest(config, contour, num_results) -> None:
backtesting = _BACKTESTING
data = load_data_test(contour)
processed = optimize.preprocess(data)
processed = backtesting.tickerdata_to_dataframe(data)
assert isinstance(processed, dict)
results = backtest(config['stake_amount'], processed, 1, True)
results = backtesting.backtest(
{
'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'realistic': True
}
)
# results :: <class 'pandas.core.frame.DataFrame'>
assert len(results) == num_results
# Test backtest on offline data
# loaded by freqdata/optimize/__init__.py::load_data()
def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False, timerange=None):
tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1, timerange=timerange)
pairdata = {'BTC_UNITEST': tickerdata}
return pairdata
def test_backtest2(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
# use for mock freqtrade.exchange.get_ticker_history'
def _load_pair_as_ticks(pair, tickfreq):
ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
ticks = trim_dictlist(ticks, -200)
return ticks[pair]
# FIX: fixturize this?
def _make_backtest_conf(conf=None, pair='BTC_UNITEST', record=None):
data = optimize.load_data(None, ticker_interval=8, pairs=[pair])
data = trim_dictlist(data, -200)
return {
'stake_amount': conf['stake_amount'],
'processed': _BACKTESTING.tickerdata_to_dataframe(data),
'max_open_trades': 10,
'realistic': True,
'record': record
}
def _trend(signals, buy_value, sell_value):
n = len(signals['low'])
buy = np.zeros(n)
sell = np.zeros(n)
for i in range(0, len(signals['buy'])):
if random.random() > 0.5: # Both buy and sell signals at same timeframe
buy[i] = buy_value
sell[i] = sell_value
signals['buy'] = buy
signals['sell'] = sell
return signals
def _trend_alternate(dataframe=None):
signals = dataframe
low = signals['low']
n = len(low)
buy = np.zeros(n)
sell = np.zeros(n)
for i in range(0, len(buy)):
if i % 2 == 0:
buy[i] = 1
else:
sell[i] = 1
signals['buy'] = buy
signals['sell'] = sell
return dataframe
def _run_backtest_1(fun, backtest_conf):
# strategy is a global (hidden as a singleton), so we
# emulate strategy being pure, by override/restore here
# if we dont do this, the override in strategy will carry over
# to other tests
old_buy = _BACKTESTING.populate_buy_trend
old_sell = _BACKTESTING.populate_sell_trend
_BACKTESTING.populate_buy_trend = fun # Override
_BACKTESTING.populate_sell_trend = fun # Override
results = _BACKTESTING.backtest(backtest_conf)
_BACKTESTING.populate_buy_trend = old_buy # restore override
_BACKTESTING.populate_sell_trend = old_sell # restore override
return results
# Unit tests
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'backtesting'
]
config = setup_configuration(get_args(args))
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 log_has(
'Parameter --datadir detected: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert 'live' not in config
assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation' not in config
assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert 'refresh_pairs' not in config
assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
assert 'timerange' not in config
assert 'export' not in config
def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'--datadir', '/foo/bar',
'backtesting',
'--ticker-interval', '1',
'--live',
'--realistic-simulation',
'--refresh-pairs-cached',
'--timerange', ':100',
'--export', '/bar/foo'
]
config = setup_configuration(get_args(args))
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 log_has(
'Parameter --datadir detected: {} ...'.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: 1 ...',
caplog.record_tuples
)
assert 'live' in config
assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation'in config
assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples)
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']),
caplog.record_tuples
)
assert 'export' in config
assert log_has(
'Parameter --export detected: {} ...'.format(config['export']),
caplog.record_tuples
)
def test_start(mocker, default_conf, caplog) -> None:
"""
Test start() function
"""
start_mock = MagicMock()
mocker.patch('freqtrade.optimize.backtesting.Backtesting.start', start_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'backtesting'
]
args = get_args(args)
start(args)
assert log_has(
'Starting freqtrade in Backtesting mode',
caplog.record_tuples
)
assert start_mock.call_count == 1
def test_backtesting__init__(mocker, default_conf) -> None:
"""
Test Backtesting.__init__() method
"""
init_mock = MagicMock()
mocker.patch('freqtrade.optimize.backtesting.Backtesting._init', init_mock)
backtesting = Backtesting(default_conf)
assert backtesting.config == default_conf
assert backtesting.analyze is None
assert backtesting.ticker_interval is None
assert backtesting.tickerdata_to_dataframe is None
assert backtesting.populate_buy_trend is None
assert backtesting.populate_sell_trend is None
assert init_mock.call_count == 1
def test_backtesting_init(default_conf) -> None:
"""
Test Backtesting._init() method
"""
backtesting = Backtesting(default_conf)
assert backtesting.config == default_conf
assert isinstance(backtesting.analyze, Analyze)
assert backtesting.ticker_interval == 5
assert callable(backtesting.tickerdata_to_dataframe)
assert callable(backtesting.populate_buy_trend)
assert callable(backtesting.populate_sell_trend)
def test_tickerdata_to_dataframe(default_conf) -> None:
"""
Test Backtesting.tickerdata_to_dataframe() method
"""
timerange = ((None, 'line'), None, -100)
tick = optimize.load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
tickerlist = {'BTC_UNITEST': tick}
backtesting = _BACKTESTING
data = backtesting.tickerdata_to_dataframe(tickerlist)
assert len(data['BTC_UNITEST']) == 100
# Load Analyze to compare the result between Backtesting function and Analyze are the same
analyze = Analyze(default_conf)
data2 = analyze.tickerdata_to_dataframe(tickerlist)
assert data['BTC_UNITEST'].equals(data2['BTC_UNITEST'])
def test_get_timeframe() -> None:
"""
Test Backtesting.get_timeframe() method
"""
backtesting = _BACKTESTING
data = backtesting.tickerdata_to_dataframe(
optimize.load_data(
None,
ticker_interval=1,
pairs=['BTC_UNITEST']
)
)
min_date, max_date = backtesting.get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def test_generate_text_table():
"""
Test Backtesting.generate_text_table() method
"""
backtesting = _BACKTESTING
results = pd.DataFrame(
{
'currency': ['BTC_ETH', 'BTC_ETH'],
'profit_percent': [0.1, 0.2],
'profit_BTC': [0.2, 0.4],
'duration': [10, 30],
'profit': [2, 0],
'loss': [0, 0]
}
)
result_str = (
'pair buy count avg profit % '
'total profit BTC avg duration profit loss\n'
'------- ----------- -------------- '
'------------------ -------------- -------- ------\n'
'BTC_ETH 2 15.00 '
'0.60000000 20.0 2 0\n'
'TOTAL 2 15.00 '
'0.60000000 20.0 2 0'
)
assert backtesting._generate_text_table(data={'BTC_ETH': {}}, results=results) == result_str
def test_backtesting_start(default_conf, mocker, caplog) -> None:
"""
Test Backtesting.start() method
"""
def get_timeframe(input1, input2):
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.freqtradebot.Analyze', MagicMock())
mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
mocker.patch('freqtrade.exchange.get_ticker_history')
mocker.patch.multiple(
'freqtrade.optimize.backtesting.Backtesting',
backtest=MagicMock(),
_generate_text_table=MagicMock(return_value='1'),
get_timeframe=get_timeframe,
)
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
conf['ticker_interval'] = 1
conf['live'] = False
conf['datadir'] = None
conf['export'] = None
conf['timerange'] = '-100'
backtesting = Backtesting(conf)
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 '
'up to 2017-11-14T22:59:00+00:00 (0 days)..'
]
for line in exists:
assert log_has(line, caplog.record_tuples)
def test_backtest(default_conf) -> None:
"""
Test Backtesting.backtest() method
"""
backtesting = _BACKTESTING
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
data = trim_dictlist(data, -200)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'max_open_trades': 10,
'realistic': True
}
)
assert not results.empty
def test_processed(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
def test_backtest_1min_ticker_interval(default_conf) -> None:
"""
Test Backtesting.backtest() method with 1 min ticker
"""
backtesting = _BACKTESTING
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -200)
results = backtesting.backtest(
{
'stake_amount': default_conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'max_open_trades': 1,
'realistic': True
}
)
assert not results.empty
def test_processed() -> None:
"""
Test Backtesting.backtest() method with offline data
"""
backtesting = _BACKTESTING
dict_of_tickerrows = load_data_test('raise')
dataframes = optimize.preprocess(dict_of_tickerrows)
dataframes = backtesting.tickerdata_to_dataframe(dict_of_tickerrows)
dataframe = dataframes['BTC_UNITEST']
cols = dataframe.columns
# assert the dataframe got some of the indicator columns
@ -142,36 +481,129 @@ def test_processed(default_conf, mocker):
assert col in cols
def test_backtest_pricecontours(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
def test_backtest_pricecontours(default_conf) -> None:
tests = [['raise', 17], ['lower', 0], ['sine', 17]]
for [contour, numres] in tests:
simple_backtest(default_conf, contour, numres)
def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False):
tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1)
pairdata = {'BTC_UNITEST': tickerdata}
return trim_dictlist(pairdata, -100)
# Test backtest using offline data (testdata directory)
def test_backtest_ticks(default_conf):
ticks = [1, 5]
fun = _BACKTESTING.populate_buy_trend
for tick in ticks:
backtest_conf = _make_backtest_conf(conf=default_conf)
results = _run_backtest_1(fun, backtest_conf)
assert not results.empty
def test_backtest_start(default_conf, mocker, caplog):
def test_backtest_clash_buy_sell(default_conf):
# Override the default buy trend function in our DefaultStrategy
def fun(dataframe=None):
buy_value = 1
sell_value = 1
return _trend(dataframe, buy_value, sell_value)
backtest_conf = _make_backtest_conf(conf=default_conf)
results = _run_backtest_1(fun, backtest_conf)
assert results.empty
def test_backtest_only_sell(default_conf):
# Override the default buy trend function in our DefaultStrategy
def fun(dataframe=None):
buy_value = 0
sell_value = 1
return _trend(dataframe, buy_value, sell_value)
backtest_conf = _make_backtest_conf(conf=default_conf)
results = _run_backtest_1(fun, backtest_conf)
assert results.empty
def test_backtest_alternate_buy_sell(default_conf):
backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
results = _run_backtest_1(_trend_alternate, backtest_conf)
assert len(results) == 3
def test_backtest_record(default_conf, mocker):
names = []
records = []
mocker.patch(
'freqtrade.optimize.backtesting.file_dump_json',
new=lambda n, r: (names.append(n), records.append(r))
)
backtest_conf = _make_backtest_conf(
conf=default_conf,
pair='BTC_UNITEST',
record="trades"
)
results = _run_backtest_1(_trend_alternate, backtest_conf)
assert len(results) == 3
# Assert file_dump_json was only called once
assert names == ['backtest-result.json']
records = records[0]
# Ensure records are of correct type
assert len(records) == 3
# ('BTC_UNITEST', 0.00331158, '1510684320', '1510691700', 0, 117)
# Below follows just a typecheck of the schema/type of trade-records
oix = None
for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
assert pair == 'BTC_UNITEST'
isinstance(profit, float)
# FIX: buy/sell should be converted to ints
isinstance(date_buy, str)
isinstance(date_sell, str)
isinstance(buy_index, pd._libs.tslib.Timestamp)
if oix:
assert buy_index > oix
oix = buy_index
assert dur > 0
def test_backtest_start_live(default_conf, mocker, caplog):
default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
mocker.patch.multiple('freqtrade.optimize',
load_data=mocked_load_data)
mocker.patch('freqtrade.exchange.get_ticker_history',
new=lambda n, i: _load_pair_as_ticks(n, i))
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)
))
args = MagicMock()
args.ticker_interval = 1
args.level = 10
args.live = False
args.live = True
args.datadir = None
backtesting.start(args)
args.export = None
args.strategy = 'DefaultStrategy'
args.timerange = '-100' # needed due to MagicMock malleability
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'backtesting',
'--ticker-interval', '1',
'--live',
'--timerange', '-100'
]
args = get_args(args)
start(args)
# check the logs, that will contain the backtest result
exists = ['Using max_open_trades: 1 ...',
'Using stake_amount: 0.001 ...',
'Measuring data from 2017-11-14T21:17:00+00:00 up to 2017-11-14T22:59:00+00:00 ...']
exists = [
'Parameter -i/--ticker-interval detected ...',
'Using ticker_interval: 1 ...',
'Parameter -l/--live detected ...',
'Using max_open_trades: 1 ...',
'Parameter --timerange detected: -100 ..',
'Parameter --datadir detected: freqtrade/tests/testdata ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Downloading data for all pairs in whitelist ...',
'Measuring data from 2017-11-14T19:32:00+00:00 up to 2017-11-14T22:59:00+00:00 (0 days)..'
]
for line in exists:
assert ('freqtrade.optimize.backtesting',
logging.INFO,
line) in caplog.record_tuples
log_has(line, caplog.record_tuples)

587
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -1,112 +1,143 @@
# pragma pylint: disable=missing-docstring,W0212,C0103
from freqtrade.optimize.hyperopt import calculate_loss, TARGET_TRADES, EXPECTED_MAX_PROFIT, start, \
log_results, save_trials, read_trials
import json
import os
from copy import deepcopy
from unittest.mock import MagicMock
import pandas as pd
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.optimize.hyperopt import Hyperopt, start
from freqtrade.strategy.resolver import StrategyResolver
from freqtrade.tests.conftest import default_conf, log_has
from freqtrade.tests.optimize.test_backtesting import get_args
def test_loss_calculation_prefer_correct_trade_count():
correct = calculate_loss(1, TARGET_TRADES, 20)
over = calculate_loss(1, TARGET_TRADES + 100, 20)
under = calculate_loss(1, TARGET_TRADES - 100, 20)
assert over > correct
assert under > correct
# Avoid to reinit the same object again and again
_HYPEROPT = Hyperopt(default_conf())
def test_loss_calculation_prefer_shorter_trades():
shorter = calculate_loss(1, 100, 20)
longer = calculate_loss(1, 100, 30)
assert shorter < longer
def test_loss_calculation_has_limited_profit():
correct = calculate_loss(EXPECTED_MAX_PROFIT, TARGET_TRADES, 20)
over = calculate_loss(EXPECTED_MAX_PROFIT * 2, TARGET_TRADES, 20)
under = calculate_loss(EXPECTED_MAX_PROFIT / 2, TARGET_TRADES, 20)
assert over == correct
assert under > correct
def create_trials(mocker):
# Functions for recurrent object patching
def create_trials(mocker) -> None:
"""
When creating trials, mock the hyperopt Trials so that *by default*
- we don't create any pickle'd files in the filesystem
- we might have a pickle'd file so make sure that we return
false when looking for it
"""
mocker.patch('freqtrade.optimize.hyperopt.TRIALS_FILE',
return_value='freqtrade/tests/optimize/ut_trials.pickle')
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists',
return_value=False)
mocker.patch('freqtrade.optimize.hyperopt.save_trials',
return_value=None)
mocker.patch('freqtrade.optimize.hyperopt.read_trials',
return_value=None)
mocker.patch('freqtrade.optimize.hyperopt.os.remove',
return_value=True)
_HYPEROPT.trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=False)
mocker.patch('freqtrade.optimize.hyperopt.os.path.getsize', return_value=1)
mocker.patch('freqtrade.optimize.hyperopt.os.remove', return_value=True)
mocker.patch('freqtrade.optimize.hyperopt.pickle.dump', return_value=None)
return mocker.Mock(
results=[{
'loss': 1,
'result': 'foo',
'status': 'ok'
}],
results=[
{
'loss': 1,
'result': 'foo',
'status': 'ok'
}
],
best_trial={'misc': {'vals': {'adx': 999}}}
)
def test_start_calls_fmin(mocker):
trials = create_trials(mocker)
mocker.patch('freqtrade.optimize.hyperopt.TRIALS', return_value=trials)
mocker.patch('freqtrade.optimize.hyperopt.sorted',
return_value=trials.results)
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.load_data')
mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
args = mocker.Mock(epochs=1, config='config.json.example', mongodb=False)
# Unit tests
def test_start(mocker, default_conf, caplog) -> None:
"""
Test start() function
"""
start_mock = MagicMock()
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'hyperopt',
'--epochs', '5'
]
args = get_args(args)
StrategyResolver({'strategy': 'DefaultStrategy'})
start(args)
mock_fmin.assert_called_once()
import pprint
pprint.pprint(caplog.record_tuples)
assert log_has(
'Starting freqtrade in Hyperopt mode',
caplog.record_tuples
)
assert start_mock.call_count == 1
def test_start_uses_mongotrials(mocker):
mock_mongotrials = mocker.patch('freqtrade.optimize.hyperopt.MongoTrials',
return_value=create_trials(mocker))
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.load_data')
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
def test_loss_calculation_prefer_correct_trade_count() -> None:
"""
Test Hyperopt.calculate_loss()
"""
hyperopt = _HYPEROPT
StrategyResolver({'strategy': 'DefaultStrategy'})
args = mocker.Mock(epochs=1, config='config.json.example', mongodb=True)
start(args)
mock_mongotrials.assert_called_once()
correct = hyperopt.calculate_loss(1, hyperopt.target_trades, 20)
over = hyperopt.calculate_loss(1, hyperopt.target_trades + 100, 20)
under = hyperopt.calculate_loss(1, hyperopt.target_trades - 100, 20)
assert over > correct
assert under > correct
def test_log_results_if_loss_improves(mocker):
logger = mocker.patch('freqtrade.optimize.hyperopt.logger.info')
global CURRENT_BEST_LOSS
CURRENT_BEST_LOSS = 2
log_results({
'loss': 1,
'current_tries': 1,
'total_tries': 2,
'result': 'foo'
})
def test_loss_calculation_prefer_shorter_trades() -> None:
"""
Test Hyperopt.calculate_loss()
"""
hyperopt = _HYPEROPT
logger.assert_called_once()
shorter = hyperopt.calculate_loss(1, 100, 20)
longer = hyperopt.calculate_loss(1, 100, 30)
assert shorter < longer
def test_no_log_if_loss_does_not_improve(mocker):
logger = mocker.patch('freqtrade.optimize.hyperopt.logger.info')
global CURRENT_BEST_LOSS
CURRENT_BEST_LOSS = 2
log_results({
'loss': 3,
})
def test_loss_calculation_has_limited_profit() -> None:
hyperopt = _HYPEROPT
assert not logger.called
correct = hyperopt.calculate_loss(hyperopt.expected_max_profit, hyperopt.target_trades, 20)
over = hyperopt.calculate_loss(hyperopt.expected_max_profit * 2, hyperopt.target_trades, 20)
under = hyperopt.calculate_loss(hyperopt.expected_max_profit / 2, hyperopt.target_trades, 20)
assert over == correct
assert under > correct
def test_fmin_best_results(mocker, caplog):
def test_log_results_if_loss_improves(capsys) -> None:
hyperopt = _HYPEROPT
hyperopt.current_best_loss = 2
hyperopt.log_results(
{
'loss': 1,
'current_tries': 1,
'total_tries': 2,
'result': 'foo'
}
)
out, err = capsys.readouterr()
assert ' 1/2: foo. Loss 1.00000'in out
def test_no_log_if_loss_does_not_improve(caplog) -> None:
hyperopt = _HYPEROPT
hyperopt.current_best_loss = 2
hyperopt.log_results(
{
'loss': 3,
}
)
assert caplog.record_tuples == []
def test_fmin_best_results(mocker, default_conf, caplog) -> None:
fmin_result = {
"macd_below_zero": 0,
"adx": 1,
"adx-value": 15.0,
"fastd": 1,
@ -121,37 +152,73 @@ def test_fmin_best_results(mocker, caplog):
"uptrend_short_ema": 0,
"uptrend_sma": 0,
"stoploss": -0.1,
"roi_t1": 1,
"roi_t2": 2,
"roi_t3": 3,
"roi_p1": 1,
"roi_p2": 2,
"roi_p3": 3,
}
mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.load_data')
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
args = mocker.Mock(epochs=1, config='config.json.example')
start(args)
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
mocker.patch('freqtrade.optimize.hyperopt.hyperopt_optimize_conf', return_value=conf)
StrategyResolver({'strategy': 'DefaultStrategy'})
hyperopt = Hyperopt(conf)
hyperopt.trials = create_trials(mocker)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
exists = [
'Best parameters',
'Best parameters:',
'"adx": {\n "enabled": true,\n "value": 15.0\n },',
'"fastd": {\n "enabled": true,\n "value": 40.0\n },',
'"green_candle": {\n "enabled": true\n },',
'"macd_below_zero": {\n "enabled": false\n },',
'"mfi": {\n "enabled": false\n },',
'"trigger": {\n "type": "ao_cross_zero"\n },',
'"stoploss": -0.1',
'"over_sar": {\n "enabled": false\n },',
'"roi_p1": 1.0,',
'"roi_p2": 2.0,',
'"roi_p3": 3.0,',
'"roi_t1": 1.0,',
'"roi_t2": 2.0,',
'"roi_t3": 3.0,',
'"rsi": {\n "enabled": true,\n "value": 37.0\n },',
'"stoploss": -0.1,',
'"trigger": {\n "type": "faststoch10"\n },',
'"uptrend_long_ema": {\n "enabled": true\n },',
'"uptrend_short_ema": {\n "enabled": false\n },',
'"uptrend_sma": {\n "enabled": false\n }',
'ROI table:\n{0: 6.0, 3.0: 3.0, 5.0: 1.0, 6.0: 0}',
'Best Result:\nfoo'
]
for line in exists:
assert line in caplog.text
def test_fmin_throw_value_error(mocker, caplog):
mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.load_data')
def test_fmin_throw_value_error(mocker, default_conf, caplog) -> None:
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError())
args = mocker.Mock(epochs=1, config='config.json.example')
start(args)
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
mocker.patch('freqtrade.optimize.hyperopt.hyperopt_optimize_conf', return_value=conf)
StrategyResolver({'strategy': 'DefaultStrategy'})
hyperopt = Hyperopt(conf)
hyperopt.trials = create_trials(mocker)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
exists = [
'Best Result:',
@ -163,61 +230,305 @@ def test_fmin_throw_value_error(mocker, caplog):
assert line in caplog.text
def test_resuming_previous_hyperopt_results_succeeds(mocker):
import freqtrade.optimize.hyperopt as hyperopt
def test_resuming_previous_hyperopt_results_succeeds(mocker, default_conf) -> None:
trials = create_trials(mocker)
mocker.patch('freqtrade.optimize.hyperopt.TRIALS',
return_value=trials)
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists',
return_value=True)
mocker.patch('freqtrade.optimize.hyperopt.len',
return_value=len(trials.results))
mock_read = mocker.patch('freqtrade.optimize.hyperopt.read_trials',
return_value=trials)
mock_save = mocker.patch('freqtrade.optimize.hyperopt.save_trials',
return_value=None)
mocker.patch('freqtrade.optimize.hyperopt.sorted',
return_value=trials.results)
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.load_data')
mocker.patch('freqtrade.optimize.hyperopt.fmin',
return_value={})
args = mocker.Mock(epochs=1,
config='config.json.example',
mongodb=False)
start(args)
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'mongodb': False})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=True)
mocker.patch('freqtrade.optimize.hyperopt.len', return_value=len(trials.results))
mock_read = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.read_trials',
return_value=trials
)
mock_save = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.save_trials',
return_value=None
)
mocker.patch('freqtrade.optimize.hyperopt.sorted', return_value=trials.results)
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
mocker.patch('freqtrade.optimize.hyperopt.hyperopt_optimize_conf', return_value=conf)
StrategyResolver({'strategy': 'DefaultStrategy'})
hyperopt = Hyperopt(conf)
hyperopt.trials = trials
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
mock_read.assert_called_once()
mock_save.assert_called_once()
current_tries = hyperopt._CURRENT_TRIES
total_tries = hyperopt.TOTAL_TRIES
current_tries = hyperopt.current_tries
total_tries = hyperopt.total_tries
assert current_tries == len(trials.results)
assert total_tries == (current_tries + len(trials.results))
def test_save_trials_saves_trials(mocker):
def test_save_trials_saves_trials(mocker, caplog) -> None:
create_trials(mocker)
mock_dump = mocker.patch('freqtrade.optimize.hyperopt.pickle.dump', return_value=None)
hyperopt = _HYPEROPT
mocker.patch('freqtrade.optimize.hyperopt.open', return_value=hyperopt.trials_file)
hyperopt.save_trials()
assert log_has(
'Saving Trials to \'freqtrade/tests/optimize/ut_trials.pickle\'',
caplog.record_tuples
)
mock_dump.assert_called_once()
def test_read_trials_returns_trials_file(mocker, caplog) -> None:
trials = create_trials(mocker)
mock_dump = mocker.patch('freqtrade.optimize.hyperopt.pickle.dump',
return_value=None)
trials_path = mocker.patch('freqtrade.optimize.hyperopt.TRIALS_FILE',
return_value='ut_trials.pickle')
mocker.patch('freqtrade.optimize.hyperopt.open',
return_value=trials_path)
save_trials(trials, trials_path)
mock_load = mocker.patch('freqtrade.optimize.hyperopt.pickle.load', return_value=trials)
mock_open = mocker.patch('freqtrade.optimize.hyperopt.open', return_value=mock_load)
mock_dump.assert_called_once_with(trials, trials_path)
def test_read_trials_returns_trials_file(mocker):
trials = create_trials(mocker)
mock_load = mocker.patch('freqtrade.optimize.hyperopt.pickle.load',
return_value=trials)
mock_open = mocker.patch('freqtrade.optimize.hyperopt.open',
return_value=mock_load)
assert read_trials() == trials
hyperopt = _HYPEROPT
hyperopt_trial = hyperopt.read_trials()
assert log_has(
'Reading Trials from \'freqtrade/tests/optimize/ut_trials.pickle\'',
caplog.record_tuples
)
assert hyperopt_trial == trials
mock_open.assert_called_once()
mock_load.assert_called_once()
def test_roi_table_generation() -> None:
params = {
'roi_t1': 5,
'roi_t2': 10,
'roi_t3': 15,
'roi_p1': 1,
'roi_p2': 2,
'roi_p3': 3,
}
hyperopt = _HYPEROPT
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_fmin(mocker, default_conf) -> None:
trials = create_trials(mocker)
mocker.patch('freqtrade.optimize.hyperopt.sorted', return_value=trials.results)
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'mongodb': False})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
hyperopt = Hyperopt(conf)
hyperopt.trials = trials
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
mock_fmin.assert_called_once()
def test_start_uses_mongotrials(mocker, default_conf) -> None:
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
mock_mongotrials = mocker.patch(
'freqtrade.optimize.hyperopt.MongoTrials',
return_value=create_trials(mocker)
)
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'mongodb': True})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
mocker.patch('freqtrade.optimize.hyperopt.hyperopt_optimize_conf', return_value=conf)
hyperopt = Hyperopt(conf)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
mock_mongotrials.assert_called_once()
mock_fmin.assert_called_once()
# test log_trials_result
# test buy_strategy_generator def populate_buy_trend
# test optimizer if 'ro_t1' in params
def test_format_results():
"""
Test Hyperopt.format_results()
"""
trades = [
('BTC_ETH', 2, 2, 123),
('BTC_LTC', 1, 1, 123),
('BTC_XRP', -1, -2, -246)
]
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
df = pd.DataFrame.from_records(trades, columns=labels)
x = Hyperopt.format_results(df)
assert x.find(' 66.67%')
def test_signal_handler(mocker):
"""
Test Hyperopt.signal_handler()
"""
m = MagicMock()
mocker.patch('sys.exit', m)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.save_trials', m)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.log_trials_result', m)
hyperopt = _HYPEROPT
hyperopt.signal_handler(9, None)
assert m.call_count == 3
def test_has_space():
"""
Test Hyperopt.has_space() method
"""
_HYPEROPT.config.update({'spaces': ['buy', 'roi']})
assert _HYPEROPT.has_space('roi')
assert _HYPEROPT.has_space('buy')
assert not _HYPEROPT.has_space('stoploss')
_HYPEROPT.config.update({'spaces': ['all']})
assert _HYPEROPT.has_space('buy')
def test_populate_indicators() -> None:
"""
Test Hyperopt.populate_indicators()
"""
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1)
tickerlist = {'BTC_UNITEST': tick}
dataframes = _HYPEROPT.tickerdata_to_dataframe(tickerlist)
dataframe = _HYPEROPT.populate_indicators(dataframes['BTC_UNITEST'])
# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
assert 'ao' in dataframe
assert 'cci' in dataframe
def test_buy_strategy_generator() -> None:
"""
Test Hyperopt.buy_strategy_generator()
"""
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1)
tickerlist = {'BTC_UNITEST': tick}
dataframes = _HYPEROPT.tickerdata_to_dataframe(tickerlist)
dataframe = _HYPEROPT.populate_indicators(dataframes['BTC_UNITEST'])
populate_buy_trend = _HYPEROPT.buy_strategy_generator(
{
'uptrend_long_ema': {
'enabled': True
},
'macd_below_zero': {
'enabled': True
},
'uptrend_short_ema': {
'enabled': True
},
'mfi': {
'enabled': True,
'value': 20
},
'fastd': {
'enabled': True,
'value': 20
},
'adx': {
'enabled': True,
'value': 20
},
'rsi': {
'enabled': True,
'value': 20
},
'over_sar': {
'enabled': True,
},
'green_candle': {
'enabled': True,
},
'uptrend_sma': {
'enabled': True,
},
'trigger': {
'type': 'lower_bb'
}
}
)
result = populate_buy_trend(dataframe)
# Check if some indicators are generated. We will not test all of them
assert 'buy' in result
assert 1 in result['buy']
def test_generate_optimizer(mocker, default_conf) -> None:
"""
Test Hyperopt.generate_optimizer() function
"""
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
trades = [
('BTC_POWR', 0.023117, 0.000233, 100)
]
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
backtest_result = pd.DataFrame.from_records(trades, columns=labels)
mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.backtest',
MagicMock(return_value=backtest_result)
)
optimizer_param = {
'adx': {'enabled': False},
'fastd': {'enabled': True, 'value': 35.0},
'green_candle': {'enabled': True},
'macd_below_zero': {'enabled': True},
'mfi': {'enabled': False},
'over_sar': {'enabled': False},
'roi_p1': 0.01,
'roi_p2': 0.01,
'roi_p3': 0.1,
'roi_t1': 60.0,
'roi_t2': 30.0,
'roi_t3': 20.0,
'rsi': {'enabled': False},
'stoploss': -0.4,
'trigger': {'type': 'macd_cross_signal'},
'uptrend_long_ema': {'enabled': False},
'uptrend_short_ema': {'enabled': True},
'uptrend_sma': {'enabled': True}
}
response_expected = {
'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'(0.0231Σ%). Avg duration 100.0 mins.',
'status': 'ok'
}
hyperopt = Hyperopt(conf)
generate_optimizer_value = hyperopt.generate_optimizer(optimizer_param)
assert generate_optimizer_value == response_expected

2
freqtrade/tests/optimize/test_hyperopt_config.py
View File

@ -1,6 +1,6 @@
# pragma pylint: disable=missing-docstring,W0212
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from user_data.hyperopt_conf import hyperopt_optimize_conf
def test_hyperopt_optimize_conf():

214
freqtrade/tests/optimize/test_optimize.py
View File

@ -1,15 +1,18 @@
# pragma pylint: disable=missing-docstring,W0212
# pragma pylint: disable=missing-docstring, protected-access, C0103
import json
import os
import logging
import uuid
from shutil import copyfile
from freqtrade import exchange, optimize
from freqtrade.exchange import Bittrex
from freqtrade.optimize.__init__ import make_testdata_path, download_pairs,\
download_backtesting_testdata, load_tickerdata_file
from freqtrade import optimize
from freqtrade.misc import file_dump_json
from freqtrade.optimize.__init__ import make_testdata_path, download_pairs, \
download_backtesting_testdata, load_tickerdata_file, trim_tickerlist
from freqtrade.tests.conftest import log_has
# Change this if modifying BTC_UNITEST testdatafile
_btc_unittest_length = 13681
_BTC_UNITTEST_LENGTH = 13681
def _backup_file(file: str, copy_file: bool = False) -> None:
@ -43,65 +46,68 @@ def _clean_test_file(file: str) -> None:
os.rename(file_swp, file)
def test_load_data_5min_ticker(default_conf, ticker_history, mocker, caplog):
def test_load_data_30min_ticker(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 30 min ticker
"""
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file = 'freqtrade/tests/testdata/BTC_ETH-5.json'
file = 'freqtrade/tests/testdata/BTC_UNITTEST-30.json'
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['BTC_ETH'])
optimize.load_data(None, pairs=['BTC_UNITTEST'], ticker_interval=30)
assert os.path.isfile(file) is True
assert ('freqtrade.optimize',
logging.INFO,
'Download the pair: "BTC_ETH", Interval: 5 min'
) not in caplog.record_tuples
assert not log_has('Download the pair: "BTC_ETH", Interval: 30 min', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_1min_ticker(default_conf, ticker_history, mocker, caplog):
def test_load_data_5min_ticker(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 5 min ticker
"""
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file = 'freqtrade/tests/testdata/BTC_ETH-5.json'
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['BTC_ETH'], ticker_interval=5)
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "BTC_ETH", Interval: 5 min', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
file = 'freqtrade/tests/testdata/BTC_ETH-1.json'
_backup_file(file, copy_file=True)
optimize.load_data(None, ticker_interval=1, pairs=['BTC_ETH'])
assert os.path.isfile(file) is True
assert ('freqtrade.optimize',
logging.INFO,
'Download the pair: "BTC_ETH", Interval: 1 min'
) not in caplog.record_tuples
assert not log_has('Download the pair: "BTC_ETH", Interval: 1 min', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_with_new_pair_1min(default_conf, ticker_history, mocker, caplog):
def test_load_data_with_new_pair_1min(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file = 'freqtrade/tests/testdata/BTC_MEME-1.json'
_backup_file(file)
optimize.load_data(None, ticker_interval=1, pairs=['BTC_MEME'])
assert os.path.isfile(file) is True
assert ('freqtrade.optimize',
logging.INFO,
'Download the pair: "BTC_MEME", Interval: 1 min'
) in caplog.record_tuples
assert log_has('Download the pair: "BTC_MEME", Interval: 1 min', caplog.record_tuples)
_clean_test_file(file)
def test_testdata_path():
def test_testdata_path() -> None:
assert os.path.join('freqtrade', 'tests', 'testdata') in make_testdata_path(None)
def test_download_pairs(default_conf, ticker_history, mocker):
def test_download_pairs(ticker_history, mocker) -> None:
mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file1_1 = 'freqtrade/tests/testdata/BTC_MEME-1.json'
file1_5 = 'freqtrade/tests/testdata/BTC_MEME-5.json'
@ -113,46 +119,50 @@ def test_download_pairs(default_conf, ticker_history, mocker):
_backup_file(file2_1)
_backup_file(file2_5)
assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI']) is True
assert os.path.isfile(file1_1) is False
assert os.path.isfile(file2_1) is False
assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI'], ticker_interval=1) is True
assert os.path.isfile(file1_1) is True
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_1) is True
assert os.path.isfile(file2_5) is True
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
_clean_test_file(file2_1)
assert os.path.isfile(file1_5) is False
assert os.path.isfile(file2_5) is False
assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI'], ticker_interval=5) is True
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_5) is True
# clean files freshly downloaded
_clean_test_file(file1_5)
_clean_test_file(file2_5)
def test_download_pairs_exception(default_conf, ticker_history, mocker, caplog):
def test_download_pairs_exception(ticker_history, mocker, caplog) -> None:
mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
mocker.patch('freqtrade.optimize.__init__.download_backtesting_testdata',
side_effect=BaseException('File Error'))
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file1_1 = 'freqtrade/tests/testdata/BTC_MEME-1.json'
file1_5 = 'freqtrade/tests/testdata/BTC_MEME-5.json'
_backup_file(file1_1)
_backup_file(file1_5)
download_pairs(None, pairs=['BTC-MEME'])
download_pairs(None, pairs=['BTC-MEME'], ticker_interval=1)
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
assert ('freqtrade.optimize.__init__',
logging.INFO,
'Failed to download the pair: "BTC-MEME", Interval: 1 min'
) in caplog.record_tuples
assert log_has('Failed to download the pair: "BTC-MEME", Interval: 1 min', caplog.record_tuples)
def test_download_backtesting_testdata(default_conf, ticker_history, mocker):
def test_download_backtesting_testdata(ticker_history, mocker) -> None:
mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# Download a 1 min ticker file
file1 = 'freqtrade/tests/testdata/BTC_XEL-1.json'
@ -170,7 +180,105 @@ def test_download_backtesting_testdata(default_conf, ticker_history, mocker):
_clean_test_file(file2)
def test_load_tickerdata_file():
def test_download_backtesting_testdata2(mocker) -> None:
tick = [{'T': 'bar'}, {'T': 'foo'}]
json_dump_mock = mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=tick)
download_backtesting_testdata(None, pair="BTC-UNITEST", interval=1)
download_backtesting_testdata(None, pair="BTC-UNITEST", interval=3)
assert json_dump_mock.call_count == 2
def test_load_tickerdata_file() -> None:
# 7 does not exist in either format.
assert not load_tickerdata_file(None, 'BTC_UNITEST', 7)
# 1 exists only as a .json
tickerdata = load_tickerdata_file(None, 'BTC_UNITEST', 1)
assert _btc_unittest_length == len(tickerdata)
assert _BTC_UNITTEST_LENGTH == len(tickerdata)
# 8 .json is empty and will fail if it's loaded. .json.gz is a copy of 1.json
tickerdata = load_tickerdata_file(None, 'BTC_UNITEST', 8)
assert _BTC_UNITTEST_LENGTH == len(tickerdata)
def test_init(default_conf, mocker) -> None:
conf = {'exchange': {'pair_whitelist': []}}
mocker.patch('freqtrade.optimize.hyperopt_optimize_conf', return_value=conf)
assert {} == optimize.load_data(
'',
pairs=[],
refresh_pairs=True,
ticker_interval=int(default_conf['ticker_interval'])
)
def test_trim_tickerlist() -> None:
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
ticker_list = json.load(data_file)
ticker_list_len = len(ticker_list)
# Test the pattern ^(-\d+)$
# This pattern remove X element from the beginning
timerange = ((None, 'line'), None, 5)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_list_len == ticker_len + 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[-1] is ticker[-1] # The last element must be the same
# Test the pattern ^(\d+)-$
# This pattern keep X element from the end
timerange = (('line', None), 5, None)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is ticker[0] # The first element must be the same
assert ticker_list[-1] is not ticker[-1] # The last element should be different
# Test the pattern ^(\d+)-(\d+)$
# This pattern extract a window
timerange = (('index', 'index'), 5, 10)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_len == 5
assert ticker_list[0] is not ticker[0] # The first element should be different
assert ticker_list[5] is ticker[0] # The list starts at the index 5
assert ticker_list[9] is ticker[-1] # The list ends at the index 9 (5 elements)
# Test a wrong pattern
# This pattern must return the list unchanged
timerange = ((None, None), None, 5)
ticker = trim_tickerlist(ticker_list, timerange)
ticker_len = len(ticker)
assert ticker_list_len == ticker_len
def test_file_dump_json() -> None:
"""
Test file_dump_json()
:return: None
"""
file = 'freqtrade/tests/testdata/test_{id}.json'.format(id=str(uuid.uuid4()))
data = {'bar': 'foo'}
# check the file we will create does not exist
assert os.path.isfile(file) is False
# Create the Json file
file_dump_json(file, data)
# Check the file was create
assert os.path.isfile(file) is True
# Open the Json file created and test the data is in it
with open(file) as data_file:
json_from_file = json.load(data_file)
assert 'bar' in json_from_file
assert json_from_file['bar'] == 'foo'
# Remove the file
_clean_test_file(file)

569
freqtrade/tests/rpc/test_rpc.py
View File

@ -1,57 +1,544 @@
# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, C0103
from copy import deepcopy
# pragma pylint: disable=invalid-sequence-index, invalid-name, too-many-arguments
"""
Unit test file for rpc/rpc.py
"""
from datetime import datetime
from unittest.mock import MagicMock
from freqtrade.rpc import init, cleanup, send_msg
from sqlalchemy import create_engine
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import Trade
from freqtrade.rpc.rpc import RPC
from freqtrade.state import State
from freqtrade.tests.test_freqtradebot import patch_get_signal, patch_coinmarketcap
def test_init_telegram_enabled(default_conf, mocker):
module_list = []
mocker.patch('freqtrade.rpc.REGISTERED_MODULES', module_list)
telegram_mock = mocker.patch('freqtrade.rpc.telegram.init', MagicMock())
init(default_conf)
assert telegram_mock.call_count == 1
assert 'telegram' in module_list
# Functions for recurrent object patching
def prec_satoshi(a, b) -> float:
"""
:return: True if A and B differs less than one satoshi.
"""
return abs(a - b) < 0.00000001
def test_init_telegram_disabled(default_conf, mocker):
module_list = []
mocker.patch('freqtrade.rpc.REGISTERED_MODULES', module_list)
telegram_mock = mocker.patch('freqtrade.rpc.telegram.init', MagicMock())
# Unit tests
def test_rpc_trade_status(default_conf, ticker, mocker) -> None:
"""
Test rpc_trade_status() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker
)
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
init(conf)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
assert telegram_mock.call_count == 0
assert 'telegram' not in module_list
freqtradebot.state = State.STOPPED
(error, result) = rpc.rpc_trade_status()
assert error
assert 'trader is not running' in result
freqtradebot.state = State.RUNNING
(error, result) = rpc.rpc_trade_status()
assert error
assert 'no active trade' in result
freqtradebot.create_trade()
(error, result) = rpc.rpc_trade_status()
assert not error
trade = result[0]
result_message = [
'*Trade ID:* `1`\n'
'*Current Pair:* '
'[BTC_ETH](https://www.bittrex.com/Market/Index?MarketName=BTC-ETH)\n'
'*Open Since:* `just now`\n'
'*Amount:* `90.99181074`\n'
'*Open Rate:* `0.00001099`\n'
'*Close Rate:* `None`\n'
'*Current Rate:* `0.00001098`\n'
'*Close Profit:* `None`\n'
'*Current Profit:* `-0.59%`\n'
'*Open Order:* `(LIMIT_BUY rem=0.00000000)`'
]
assert result == result_message
assert trade.find('[BTC_ETH]') >= 0
def test_cleanup_telegram_enabled(mocker):
mocker.patch('freqtrade.rpc.REGISTERED_MODULES', ['telegram'])
telegram_mock = mocker.patch('freqtrade.rpc.telegram.cleanup', MagicMock())
cleanup()
assert telegram_mock.call_count == 1
def test_rpc_status_table(default_conf, ticker, mocker) -> None:
"""
Test rpc_status_table() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
(error, result) = rpc.rpc_status_table()
assert error
assert '*Status:* `trader is not running`' in result
freqtradebot.state = State.RUNNING
(error, result) = rpc.rpc_status_table()
assert error
assert '*Status:* `no active order`' in result
freqtradebot.create_trade()
(error, result) = rpc.rpc_status_table()
assert 'just now' in result['Since'].all()
assert 'BTC_ETH' in result['Pair'].all()
assert '-0.59%' in result['Profit'].all()
def test_cleanup_telegram_disabled(mocker):
mocker.patch('freqtrade.rpc.REGISTERED_MODULES', [])
telegram_mock = mocker.patch('freqtrade.rpc.telegram.cleanup', MagicMock())
cleanup()
assert telegram_mock.call_count == 0
def test_rpc_daily_profit(default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker)\
-> None:
"""
Test rpc_daily_profit() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker, value={'price_usd': 15000.0})
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
stake_currency = default_conf['stake_currency']
fiat_display_currency = default_conf['fiat_display_currency']
rpc = RPC(freqtradebot)
# Create some test data
freqtradebot.create_trade()
trade = Trade.query.first()
assert trade
# Simulate buy & sell
trade.update(limit_buy_order)
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
# Try valid data
update.message.text = '/daily 2'
(error, days) = rpc.rpc_daily_profit(7, stake_currency, fiat_display_currency)
assert not error
assert len(days) == 7
for day in days:
# [datetime.date(2018, 1, 11), '0.00000000 BTC', '0.000 USD']
assert (day[1] == '0.00000000 BTC' or
day[1] == '0.00006217 BTC')
assert (day[2] == '0.000 USD' or
day[2] == '0.933 USD')
# ensure first day is current date
assert str(days[0][0]) == str(datetime.utcnow().date())
# Try invalid data
(error, days) = rpc.rpc_daily_profit(0, stake_currency, fiat_display_currency)
assert error
assert days.find('must be an integer greater than 0') >= 0
def test_send_msg_telegram_enabled(mocker):
mocker.patch('freqtrade.rpc.REGISTERED_MODULES', ['telegram'])
telegram_mock = mocker.patch('freqtrade.rpc.telegram.send_msg', MagicMock())
send_msg('test')
assert telegram_mock.call_count == 1
def test_rpc_trade_statistics(
default_conf, ticker, ticker_sell_up, limit_buy_order, limit_sell_order, mocker) -> None:
"""
Test rpc_trade_statistics() method
"""
patch_get_signal(mocker, (True, False))
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
stake_currency = default_conf['stake_currency']
fiat_display_currency = default_conf['fiat_display_currency']
rpc = RPC(freqtradebot)
(error, stats) = rpc.rpc_trade_statistics(stake_currency, fiat_display_currency)
assert error
assert stats.find('no closed trade') >= 0
# Create some test data
freqtradebot.create_trade()
trade = Trade.query.first()
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Update the ticker with a market going up
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker_sell_up
)
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
(error, stats) = rpc.rpc_trade_statistics(stake_currency, fiat_display_currency)
assert not error
assert prec_satoshi(stats['profit_closed_coin'], 6.217e-05)
assert prec_satoshi(stats['profit_closed_percent'], 6.2)
assert prec_satoshi(stats['profit_closed_fiat'], 0.93255)
assert prec_satoshi(stats['profit_all_coin'], 6.217e-05)
assert prec_satoshi(stats['profit_all_percent'], 6.2)
assert prec_satoshi(stats['profit_all_fiat'], 0.93255)
assert stats['trade_count'] == 1
assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] == '0:00:00'
assert stats['best_pair'] == 'BTC_ETH'
assert prec_satoshi(stats['best_rate'], 6.2)
def test_send_msg_telegram_disabled(mocker):
mocker.patch('freqtrade.rpc.REGISTERED_MODULES', [])
telegram_mock = mocker.patch('freqtrade.rpc.telegram.send_msg', MagicMock())
send_msg('test')
assert telegram_mock.call_count == 0
# Test that rpc_trade_statistics can handle trades that lacks
# trade.open_rate (it is set to None)
def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, ticker_sell_up, limit_buy_order,
limit_sell_order):
"""
Test rpc_trade_statistics() method
"""
patch_get_signal(mocker, (True, False))
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
stake_currency = default_conf['stake_currency']
fiat_display_currency = default_conf['fiat_display_currency']
rpc = RPC(freqtradebot)
# Create some test data
freqtradebot.create_trade()
trade = Trade.query.first()
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Update the ticker with a market going up
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker_sell_up
)
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
for trade in Trade.query.order_by(Trade.id).all():
trade.open_rate = None
(error, stats) = rpc.rpc_trade_statistics(stake_currency, fiat_display_currency)
assert not error
assert prec_satoshi(stats['profit_closed_coin'], 0)
assert prec_satoshi(stats['profit_closed_percent'], 0)
assert prec_satoshi(stats['profit_closed_fiat'], 0)
assert prec_satoshi(stats['profit_all_coin'], 0)
assert prec_satoshi(stats['profit_all_percent'], 0)
assert prec_satoshi(stats['profit_all_fiat'], 0)
assert stats['trade_count'] == 1
assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now'
assert stats['avg_duration'] == '0:00:00'
assert stats['best_pair'] == 'BTC_ETH'
assert prec_satoshi(stats['best_rate'], 6.2)
def test_rpc_balance_handle(default_conf, mocker):
"""
Test rpc_balance() method
"""
mock_balance = [
{
'Currency': 'BTC',
'Balance': 10.0,
'Available': 12.0,
'Pending': 0.0,
'CryptoAddress': 'XXXX',
},
{
'Currency': 'ETH',
'Balance': 0.0,
'Available': 0.0,
'Pending': 0.0,
'CryptoAddress': 'XXXX',
}
]
patch_get_signal(mocker, (True, False))
mocker.patch.multiple(
'freqtrade.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_balances=MagicMock(return_value=mock_balance)
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
(error, res) = rpc.rpc_balance(default_conf['fiat_display_currency'])
assert not error
(trade, x, y, z) = res
assert prec_satoshi(x, 10)
assert prec_satoshi(z, 150000)
assert 'USD' in y
assert len(trade) == 1
assert 'BTC' in trade[0]['currency']
assert prec_satoshi(trade[0]['available'], 12)
assert prec_satoshi(trade[0]['balance'], 10)
assert prec_satoshi(trade[0]['pending'], 0)
assert prec_satoshi(trade[0]['est_btc'], 10)
def test_rpc_start(mocker, default_conf) -> None:
"""
Test rpc_start() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock()
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
(error, result) = rpc.rpc_start()
assert not error
assert '`Starting trader ...`' in result
assert freqtradebot.state == State.RUNNING
(error, result) = rpc.rpc_start()
assert error
assert '*Status:* `already running`' in result
assert freqtradebot.state == State.RUNNING
def test_rpc_stop(mocker, default_conf) -> None:
"""
Test rpc_stop() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock()
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
freqtradebot.state = State.RUNNING
(error, result) = rpc.rpc_stop()
assert not error
assert '`Stopping trader ...`' in result
assert freqtradebot.state == State.STOPPED
(error, result) = rpc.rpc_stop()
assert error
assert '*Status:* `already stopped`' in result
assert freqtradebot.state == State.STOPPED
def test_rpc_forcesell(default_conf, ticker, mocker) -> None:
"""
Test rpc_forcesell() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
cancel_order_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
cancel_order=cancel_order_mock,
get_order=MagicMock(
return_value={
'closed': True,
'type': 'LIMIT_BUY',
}
)
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
freqtradebot.state = State.STOPPED
(error, res) = rpc.rpc_forcesell(None)
assert error
assert res == '`trader is not running`'
freqtradebot.state = State.RUNNING
(error, res) = rpc.rpc_forcesell(None)
assert error
assert res == 'Invalid argument.'
(error, res) = rpc.rpc_forcesell('all')
assert not error
assert res == ''
freqtradebot.create_trade()
(error, res) = rpc.rpc_forcesell('all')
assert not error
assert res == ''
(error, res) = rpc.rpc_forcesell('1')
assert not error
assert res == ''
freqtradebot.state = State.STOPPED
(error, res) = rpc.rpc_forcesell(None)
assert error
assert res == '`trader is not running`'
(error, res) = rpc.rpc_forcesell('all')
assert error
assert res == '`trader is not running`'
freqtradebot.state = State.RUNNING
assert cancel_order_mock.call_count == 0
# make an limit-buy open trade
mocker.patch(
'freqtrade.freqtradebot.exchange.get_order',
return_value={
'closed': None,
'type': 'LIMIT_BUY'
}
)
# check that the trade is called, which is done
# by ensuring exchange.cancel_order is called
(error, res) = rpc.rpc_forcesell('1')
assert not error
assert res == ''
assert cancel_order_mock.call_count == 1
freqtradebot.create_trade()
# make an limit-sell open trade
mocker.patch(
'freqtrade.freqtradebot.exchange.get_order',
return_value={
'closed': None,
'type': 'LIMIT_SELL'
}
)
(error, res) = rpc.rpc_forcesell('2')
assert not error
assert res == ''
# status quo, no exchange calls
assert cancel_order_mock.call_count == 1
def test_performance_handle(default_conf, ticker, limit_buy_order,
limit_sell_order, mocker) -> None:
"""
Test rpc_performance() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_balances=MagicMock(return_value=ticker),
get_ticker=ticker
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
# Create some test data
freqtradebot.create_trade()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
(error, res) = rpc.rpc_performance()
assert not error
assert len(res) == 1
assert res[0]['pair'] == 'BTC_ETH'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit'], 6.2)
def test_rpc_count(mocker, default_conf, ticker) -> None:
"""
Test rpc_count() method
"""
patch_get_signal(mocker, (True, False))
patch_coinmarketcap(mocker)
mocker.patch('freqtrade.rpc.rpc_manager.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
validate_pairs=MagicMock(),
get_balances=MagicMock(return_value=ticker),
get_ticker=ticker
)
freqtradebot = FreqtradeBot(default_conf, create_engine('sqlite://'))
rpc = RPC(freqtradebot)
(error, trades) = rpc.rpc_count()
nb_trades = len(trades)
assert not error
assert nb_trades == 0
# Create some test data
freqtradebot.create_trade()
(error, trades) = rpc.rpc_count()
nb_trades = len(trades)
assert not error
assert nb_trades == 1

139
freqtrade/tests/rpc/test_rpc_manager.py Normal file
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@ -0,0 +1,139 @@
"""
Unit test file for rpc/rpc_manager.py
"""
import logging
from copy import deepcopy
from unittest.mock import MagicMock
from freqtrade.rpc.rpc_manager import RPCManager
from freqtrade.rpc.telegram import Telegram
from freqtrade.tests.conftest import log_has, get_patched_freqtradebot
def test_rpc_manager_object() -> None:
"""
Test the Arguments object has the mandatory methods
:return: None
"""
assert hasattr(RPCManager, '_init')
assert hasattr(RPCManager, 'send_msg')
assert hasattr(RPCManager, 'cleanup')
def test__init__(mocker, default_conf) -> None:
"""
Test __init__() method
"""
init_mock = mocker.patch('freqtrade.rpc.rpc_manager.RPCManager._init', MagicMock())
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
assert rpc_manager.freqtrade == freqtradebot
assert rpc_manager.registered_modules == []
assert rpc_manager.telegram is None
assert init_mock.call_count == 1
def test_init_telegram_disabled(mocker, default_conf, caplog) -> None:
"""
Test _init() method with Telegram disabled
"""
caplog.set_level(logging.DEBUG)
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
freqtradebot = get_patched_freqtradebot(mocker, conf)
rpc_manager = RPCManager(freqtradebot)
assert not log_has('Enabling rpc.telegram ...', caplog.record_tuples)
assert rpc_manager.registered_modules == []
assert rpc_manager.telegram is None
def test_init_telegram_enabled(mocker, default_conf, caplog) -> None:
"""
Test _init() method with Telegram enabled
"""
caplog.set_level(logging.DEBUG)
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
assert log_has('Enabling rpc.telegram ...', caplog.record_tuples)
len_modules = len(rpc_manager.registered_modules)
assert len_modules == 1
assert 'telegram' in rpc_manager.registered_modules
assert isinstance(rpc_manager.telegram, Telegram)
def test_cleanup_telegram_disabled(mocker, default_conf, caplog) -> None:
"""
Test cleanup() method with Telegram disabled
"""
caplog.set_level(logging.DEBUG)
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock())
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
freqtradebot = get_patched_freqtradebot(mocker, conf)
rpc_manager = RPCManager(freqtradebot)
rpc_manager.cleanup()
assert not log_has('Cleaning up rpc.telegram ...', caplog.record_tuples)
assert telegram_mock.call_count == 0
def test_cleanup_telegram_enabled(mocker, default_conf, caplog) -> None:
"""
Test cleanup() method with Telegram enabled
"""
caplog.set_level(logging.DEBUG)
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock())
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
# Check we have Telegram as a registered modules
assert 'telegram' in rpc_manager.registered_modules
rpc_manager.cleanup()
assert log_has('Cleaning up rpc.telegram ...', caplog.record_tuples)
assert 'telegram' not in rpc_manager.registered_modules
assert telegram_mock.call_count == 1
def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None:
"""
Test send_msg() method with Telegram disabled
"""
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
conf = deepcopy(default_conf)
conf['telegram']['enabled'] = False
freqtradebot = get_patched_freqtradebot(mocker, conf)
rpc_manager = RPCManager(freqtradebot)
rpc_manager.send_msg('test')
assert log_has('test', caplog.record_tuples)
assert telegram_mock.call_count == 0
def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None:
"""
Test send_msg() method with Telegram disabled
"""
telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
rpc_manager = RPCManager(freqtradebot)
rpc_manager.send_msg('test')
assert log_has('test', caplog.record_tuples)
assert telegram_mock.call_count == 1

1436
freqtrade/tests/rpc/test_rpc_telegram.py
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34
freqtrade/tests/strategy/test_default_strategy.py Normal file
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@ -0,0 +1,34 @@
import json
import pytest
from pandas import DataFrame
from freqtrade.analyze import Analyze
from freqtrade.strategy.default_strategy import DefaultStrategy
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
return Analyze.parse_ticker_dataframe(json.load(data_file))
def test_default_strategy_structure():
assert hasattr(DefaultStrategy, 'minimal_roi')
assert hasattr(DefaultStrategy, 'stoploss')
assert hasattr(DefaultStrategy, 'ticker_interval')
assert hasattr(DefaultStrategy, 'populate_indicators')
assert hasattr(DefaultStrategy, 'populate_buy_trend')
assert hasattr(DefaultStrategy, 'populate_sell_trend')
def test_default_strategy(result):
strategy = DefaultStrategy()
assert type(strategy.minimal_roi) is dict
assert type(strategy.stoploss) is float
assert type(strategy.ticker_interval) is int
indicators = strategy.populate_indicators(result)
assert type(indicators) is DataFrame
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
assert type(strategy.populate_sell_trend(indicators)) is DataFrame

118
freqtrade/tests/strategy/test_strategy.py Normal file
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@ -0,0 +1,118 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import logging
import os
import pytest
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.resolver import StrategyResolver
def test_search_strategy():
default_location = os.path.join(os.path.dirname(
os.path.realpath(__file__)), '..', '..', 'strategy'
)
assert isinstance(
StrategyResolver._search_strategy(default_location, 'DefaultStrategy'), IStrategy
)
assert StrategyResolver._search_strategy(default_location, 'NotFoundStrategy') is None
def test_load_strategy(result):
resolver = StrategyResolver()
resolver._load_strategy('TestStrategy')
assert hasattr(resolver.strategy, 'populate_indicators')
assert 'adx' in resolver.strategy.populate_indicators(result)
def test_load_strategy_custom_directory(result):
resolver = StrategyResolver()
extra_dir = os.path.join('some', 'path')
with pytest.raises(
FileNotFoundError,
match=r".*No such file or directory: '{}'".format(extra_dir)):
resolver._load_strategy('TestStrategy', extra_dir)
assert hasattr(resolver.strategy, 'populate_indicators')
assert 'adx' in resolver.strategy.populate_indicators(result)
def test_load_not_found_strategy():
strategy = StrategyResolver()
with pytest.raises(ImportError,
match=r'Impossible to load Strategy \'NotFoundStrategy\'.'
r' This class does not exist or contains Python code errors'):
strategy._load_strategy('NotFoundStrategy')
def test_strategy(result):
resolver = StrategyResolver({'strategy': 'DefaultStrategy'})
assert hasattr(resolver.strategy, 'minimal_roi')
assert resolver.strategy.minimal_roi[0] == 0.04
assert hasattr(resolver.strategy, 'stoploss')
assert resolver.strategy.stoploss == -0.10
assert hasattr(resolver.strategy, 'populate_indicators')
assert 'adx' in resolver.strategy.populate_indicators(result)
assert hasattr(resolver.strategy, 'populate_buy_trend')
dataframe = resolver.strategy.populate_buy_trend(resolver.strategy.populate_indicators(result))
assert 'buy' in dataframe.columns
assert hasattr(resolver.strategy, 'populate_sell_trend')
dataframe = resolver.strategy.populate_sell_trend(resolver.strategy.populate_indicators(result))
assert 'sell' in dataframe.columns
def test_strategy_override_minimal_roi(caplog):
caplog.set_level(logging.INFO)
config = {
'strategy': 'DefaultStrategy',
'minimal_roi': {
"0": 0.5
}
}
resolver = StrategyResolver(config)
assert hasattr(resolver.strategy, 'minimal_roi')
assert resolver.strategy.minimal_roi[0] == 0.5
assert ('freqtrade.strategy.resolver',
logging.INFO,
'Override strategy \'minimal_roi\' with value in config file.'
) in caplog.record_tuples
def test_strategy_override_stoploss(caplog):
caplog.set_level(logging.INFO)
config = {
'strategy': 'DefaultStrategy',
'stoploss': -0.5
}
resolver = StrategyResolver(config)
assert hasattr(resolver.strategy, 'stoploss')
assert resolver.strategy.stoploss == -0.5
assert ('freqtrade.strategy.resolver',
logging.INFO,
'Override strategy \'stoploss\' with value in config file: -0.5.'
) in caplog.record_tuples
def test_strategy_override_ticker_interval(caplog):
caplog.set_level(logging.INFO)
config = {
'strategy': 'DefaultStrategy',
'ticker_interval': 60
}
resolver = StrategyResolver(config)
assert hasattr(resolver.strategy, 'ticker_interval')
assert resolver.strategy.ticker_interval == 60
assert ('freqtrade.strategy.resolver',
logging.INFO,
'Override strategy \'ticker_interval\' with value in config file: 60.'
) in caplog.record_tuples

97
freqtrade/tests/test_acl_pair.py
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@ -1,4 +1,6 @@
from freqtrade.main import refresh_whitelist, gen_pair_whitelist
# pragma pylint: disable=missing-docstring,C0103,protected-access
import freqtrade.tests.conftest as tt # test tools
# whitelist, blacklist, filtering, all of that will
# eventually become some rules to run on a generic ACL engine
@ -6,21 +8,22 @@ from freqtrade.main import refresh_whitelist, gen_pair_whitelist
def whitelist_conf():
return {
'stake_currency': 'BTC',
'exchange': {
'pair_whitelist': [
'BTC_ETH',
'BTC_TKN',
'BTC_TRST',
'BTC_SWT',
'BTC_BCC'
],
'pair_blacklist': [
'BTC_BLK'
],
},
}
config = tt.default_conf()
config['stake_currency'] = 'BTC'
config['exchange']['pair_whitelist'] = [
'BTC_ETH',
'BTC_TKN',
'BTC_TRST',
'BTC_SWT',
'BTC_BCC'
]
config['exchange']['pair_blacklist'] = [
'BTC_BLK'
]
return config
def get_market_summaries():
@ -73,16 +76,9 @@ def get_market_summaries():
def get_health():
return [{'Currency': 'ETH',
'IsActive': True
},
{'Currency': 'TKN',
'IsActive': True
},
{'Currency': 'BLK',
'IsActive': True
}
]
return [{'Currency': 'ETH', 'IsActive': True},
{'Currency': 'TKN', 'IsActive': True},
{'Currency': 'BLK', 'IsActive': True}]
def get_health_empty():
@ -91,11 +87,13 @@ def get_health_empty():
def test_refresh_market_pair_not_in_whitelist(mocker):
conf = whitelist_conf()
mocker.patch.dict('freqtrade.main._CONF', conf)
mocker.patch.multiple('freqtrade.main.exchange',
get_wallet_health=get_health)
refreshedwhitelist = refresh_whitelist(
conf['exchange']['pair_whitelist'] + ['BTC_XXX'])
freqtradebot = tt.get_patched_freqtradebot(mocker, conf)
mocker.patch('freqtrade.freqtradebot.exchange.get_wallet_health', get_health)
refreshedwhitelist = freqtradebot._refresh_whitelist(
conf['exchange']['pair_whitelist'] + ['BTC_XXX']
)
# List ordered by BaseVolume
whitelist = ['BTC_ETH', 'BTC_TKN']
# Ensure all except those in whitelist are removed
@ -104,10 +102,11 @@ def test_refresh_market_pair_not_in_whitelist(mocker):
def test_refresh_whitelist(mocker):
conf = whitelist_conf()
mocker.patch.dict('freqtrade.main._CONF', conf)
mocker.patch.multiple('freqtrade.main.exchange',
get_wallet_health=get_health)
refreshedwhitelist = refresh_whitelist(conf['exchange']['pair_whitelist'])
freqtradebot = tt.get_patched_freqtradebot(mocker, conf)
mocker.patch('freqtrade.freqtradebot.exchange.get_wallet_health', get_health)
refreshedwhitelist = freqtradebot._refresh_whitelist(conf['exchange']['pair_whitelist'])
# List ordered by BaseVolume
whitelist = ['BTC_ETH', 'BTC_TKN']
# Ensure all except those in whitelist are removed
@ -116,26 +115,32 @@ def test_refresh_whitelist(mocker):
def test_refresh_whitelist_dynamic(mocker):
conf = whitelist_conf()
mocker.patch.dict('freqtrade.main._CONF', conf)
mocker.patch.multiple('freqtrade.main.exchange',
get_wallet_health=get_health)
mocker.patch.multiple('freqtrade.main.exchange',
get_market_summaries=get_market_summaries)
freqtradebot = tt.get_patched_freqtradebot(mocker, conf)
mocker.patch.multiple(
'freqtrade.freqtradebot.exchange',
get_wallet_health=get_health,
get_market_summaries=get_market_summaries
)
# argument: use the whitelist dynamically by exchange-volume
whitelist = ['BTC_TKN', 'BTC_ETH']
refreshedwhitelist = refresh_whitelist(
gen_pair_whitelist(conf['stake_currency']))
refreshedwhitelist = freqtradebot._refresh_whitelist(
freqtradebot._gen_pair_whitelist(conf['stake_currency'])
)
assert whitelist == refreshedwhitelist
def test_refresh_whitelist_dynamic_empty(mocker):
conf = whitelist_conf()
mocker.patch.dict('freqtrade.main._CONF', conf)
mocker.patch.multiple('freqtrade.main.exchange',
get_wallet_health=get_health_empty)
freqtradebot = tt.get_patched_freqtradebot(mocker, conf)
mocker.patch('freqtrade.freqtradebot.exchange.get_wallet_health', get_health_empty)
# argument: use the whitelist dynamically by exchange-volume
whitelist = []
conf['exchange']['pair_whitelist'] = []
refresh_whitelist(whitelist)
freqtradebot._refresh_whitelist(whitelist)
pairslist = conf['exchange']['pair_whitelist']
assert set(whitelist) == set(pairslist)

194
freqtrade/tests/test_analyze.py
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@ -1,74 +1,194 @@
# pragma pylint: disable=missing-docstring,W0621
import json
# pragma pylint: disable=missing-docstring, C0103
"""
Unit test file for analyse.py
"""
import datetime
import logging
from unittest.mock import MagicMock
import arrow
import pytest
from pandas import DataFrame
from freqtrade.analyze import (SignalType, get_signal, parse_ticker_dataframe,
populate_buy_trend, populate_indicators,
populate_sell_trend)
from freqtrade.analyze import Analyze, SignalType
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.tests.conftest import log_has
# Avoid to reinit the same object again and again
_ANALYZE = Analyze({'strategy': 'DefaultStrategy'})
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
return parse_ticker_dataframe(json.load(data_file))
def test_signaltype_object() -> None:
"""
Test the SignalType object has the mandatory Constants
:return: None
"""
assert hasattr(SignalType, 'BUY')
assert hasattr(SignalType, 'SELL')
def test_analyze_object() -> None:
"""
Test the Analyze object has the mandatory methods
:return: None
"""
assert hasattr(Analyze, 'parse_ticker_dataframe')
assert hasattr(Analyze, 'populate_indicators')
assert hasattr(Analyze, 'populate_buy_trend')
assert hasattr(Analyze, 'populate_sell_trend')
assert hasattr(Analyze, 'analyze_ticker')
assert hasattr(Analyze, 'get_signal')
assert hasattr(Analyze, 'should_sell')
assert hasattr(Analyze, 'min_roi_reached')
def test_dataframe_correct_length(result):
dataframe = Analyze.parse_ticker_dataframe(result)
assert len(result.index) == len(dataframe.index)
def test_dataframe_correct_columns(result):
assert result.columns.tolist() == \
['close', 'high', 'low', 'open', 'date', 'volume']
def test_dataframe_correct_length(result):
assert len(result.index) == 14395
['date', 'close', 'high', 'low', 'open', 'volume']
def test_populates_buy_trend(result):
dataframe = populate_buy_trend(populate_indicators(result))
# Load the default strategy for the unit test, because this logic is done in main.py
dataframe = _ANALYZE.populate_buy_trend(_ANALYZE.populate_indicators(result))
assert 'buy' in dataframe.columns
def test_populates_sell_trend(result):
dataframe = populate_sell_trend(populate_indicators(result))
# Load the default strategy for the unit test, because this logic is done in main.py
dataframe = _ANALYZE.populate_sell_trend(_ANALYZE.populate_indicators(result))
assert 'sell' in dataframe.columns
def test_returns_latest_buy_signal(mocker):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
)
assert get_signal('BTC-ETH', SignalType.BUY)
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
)
)
assert not get_signal('BTC-ETH', SignalType.BUY)
assert _ANALYZE.get_signal('BTC-ETH', 5) == (True, False)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
)
)
assert _ANALYZE.get_signal('BTC-ETH', 5) == (False, True)
def test_returns_latest_sell_signal(mocker):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'sell': 1, 'date': arrow.utcnow()}])
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
)
)
assert get_signal('BTC-ETH', SignalType.SELL)
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'sell': 0, 'date': arrow.utcnow()}])
assert _ANALYZE.get_signal('BTC-ETH', 5) == (False, True)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
)
)
assert _ANALYZE.get_signal('BTC-ETH', 5) == (True, False)
def test_get_signal_empty(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=None)
assert (False, False) == _ANALYZE.get_signal('foo', int(default_conf['ticker_interval']))
assert log_has('Empty ticker history for pair foo', caplog.record_tuples)
def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
side_effect=ValueError('xyz')
)
)
assert (False, False) == _ANALYZE.get_signal('foo', int(default_conf['ticker_interval']))
assert log_has('Unable to analyze ticker for pair foo: xyz', caplog.record_tuples)
def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame([])
)
)
assert (False, False) == _ANALYZE.get_signal('xyz', int(default_conf['ticker_interval']))
assert log_has('Empty dataframe for pair xyz', caplog.record_tuples)
def test_get_signal_old_dataframe(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
# FIX: The get_signal function has hardcoded 10, which we must inturn hardcode
oldtime = arrow.utcnow() - datetime.timedelta(minutes=11)
ticks = DataFrame([{'buy': 1, 'date': oldtime}])
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
return_value=DataFrame(ticks)
)
)
assert (False, False) == _ANALYZE.get_signal('xyz', int(default_conf['ticker_interval']))
assert log_has(
'Outdated history for pair xyz. Last tick is 11 minutes old',
caplog.record_tuples
)
assert not get_signal('BTC-ETH', SignalType.SELL)
def test_get_signal_handles_exceptions(mocker):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
mocker.patch('freqtrade.analyze.analyze_ticker',
side_effect=Exception('invalid ticker history '))
mocker.patch.multiple(
'freqtrade.analyze.Analyze',
analyze_ticker=MagicMock(
side_effect=Exception('invalid ticker history ')
)
)
assert not get_signal('BTC-ETH', SignalType.BUY)
assert _ANALYZE.get_signal('BTC-ETH', 5) == (False, False)
def test_parse_ticker_dataframe(ticker_history, ticker_history_without_bv):
columns = ['date', 'close', 'high', 'low', 'open', 'volume']
# Test file with BV data
dataframe = Analyze.parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns
# Test file without BV data
dataframe = Analyze.parse_ticker_dataframe(ticker_history_without_bv)
assert dataframe.columns.tolist() == columns
def test_tickerdata_to_dataframe(default_conf) -> None:
"""
Test Analyze.tickerdata_to_dataframe() method
"""
analyze = Analyze(default_conf)
timerange = ((None, 'line'), None, -100)
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
tickerlist = {'BTC_UNITEST': tick}
data = analyze.tickerdata_to_dataframe(tickerlist)
assert len(data['BTC_UNITEST']) == 100

154
freqtrade/tests/test_arguments.py Normal file
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@ -0,0 +1,154 @@
# pragma pylint: disable=missing-docstring, C0103
"""
Unit test file for arguments.py
"""
import argparse
import logging
import pytest
from freqtrade.arguments import Arguments
def test_arguments_object() -> None:
"""
Test the Arguments object has the mandatory methods
:return: None
"""
assert hasattr(Arguments, 'get_parsed_arg')
assert hasattr(Arguments, 'parse_args')
assert hasattr(Arguments, 'parse_timerange')
assert hasattr(Arguments, 'scripts_options')
# Parse common command-line-arguments. Used for all tools
def test_parse_args_none() -> None:
arguments = Arguments([], '')
assert isinstance(arguments, Arguments)
assert isinstance(arguments.parser, argparse.ArgumentParser)
assert isinstance(arguments.parser, argparse.ArgumentParser)
def test_parse_args_defaults() -> None:
args = Arguments([], '').get_parsed_arg()
assert args.config == 'config.json'
assert args.dynamic_whitelist is None
assert args.loglevel == logging.INFO
def test_parse_args_config() -> None:
args = Arguments(['-c', '/dev/null'], '').get_parsed_arg()
assert args.config == '/dev/null'
args = Arguments(['--config', '/dev/null'], '').get_parsed_arg()
assert args.config == '/dev/null'
def test_parse_args_verbose() -> None:
args = Arguments(['-v'], '').get_parsed_arg()
assert args.loglevel == logging.DEBUG
args = Arguments(['--verbose'], '').get_parsed_arg()
assert args.loglevel == logging.DEBUG
def test_scripts_options() -> None:
arguments = Arguments(['-p', 'BTC_ETH'], '')
arguments.scripts_options()
args = arguments.get_parsed_arg()
assert args.pair == 'BTC_ETH'
def test_parse_args_version() -> None:
with pytest.raises(SystemExit, match=r'0'):
Arguments(['--version'], '').get_parsed_arg()
def test_parse_args_invalid() -> None:
with pytest.raises(SystemExit, match=r'2'):
Arguments(['-c'], '').get_parsed_arg()
def test_parse_args_strategy() -> None:
args = Arguments(['--strategy', 'SomeStrategy'], '').get_parsed_arg()
assert args.strategy == 'SomeStrategy'
def test_parse_args_strategy_invalid() -> None:
with pytest.raises(SystemExit, match=r'2'):
Arguments(['--strategy'], '').get_parsed_arg()
def test_parse_args_strategy_path() -> None:
args = Arguments(['--strategy-path', '/some/path'], '').get_parsed_arg()
assert args.strategy_path == '/some/path'
def test_parse_args_strategy_path_invalid() -> None:
with pytest.raises(SystemExit, match=r'2'):
Arguments(['--strategy-path'], '').get_parsed_arg()
def test_parse_args_dynamic_whitelist() -> None:
args = Arguments(['--dynamic-whitelist'], '').get_parsed_arg()
assert args.dynamic_whitelist == 20
def test_parse_args_dynamic_whitelist_10() -> None:
args = Arguments(['--dynamic-whitelist', '10'], '').get_parsed_arg()
assert args.dynamic_whitelist == 10
def test_parse_args_dynamic_whitelist_invalid_values() -> None:
with pytest.raises(SystemExit, match=r'2'):
Arguments(['--dynamic-whitelist', 'abc'], '').get_parsed_arg()
def test_parse_timerange_incorrect() -> None:
assert ((None, 'line'), None, -200) == Arguments.parse_timerange('-200')
assert (('line', None), 200, None) == Arguments.parse_timerange('200-')
with pytest.raises(Exception, match=r'Incorrect syntax.*'):
Arguments.parse_timerange('-')
def test_parse_args_backtesting_invalid() -> None:
with pytest.raises(SystemExit, match=r'2'):
Arguments(['backtesting --ticker-interval'], '').get_parsed_arg()
with pytest.raises(SystemExit, match=r'2'):
Arguments(['backtesting --ticker-interval', 'abc'], '').get_parsed_arg()
def test_parse_args_backtesting_custom() -> None:
args = [
'-c', 'test_conf.json',
'backtesting',
'--live',
'--ticker-interval', '1',
'--refresh-pairs-cached']
call_args = Arguments(args, '').get_parsed_arg()
assert call_args.config == 'test_conf.json'
assert call_args.live is True
assert call_args.loglevel == logging.INFO
assert call_args.subparser == 'backtesting'
assert call_args.func is not None
assert call_args.ticker_interval == 1
assert call_args.refresh_pairs is True
def test_parse_args_hyperopt_custom() -> None:
args = [
'-c', 'test_conf.json',
'hyperopt',
'--epochs', '20',
'--spaces', 'buy'
]
call_args = Arguments(args, '').get_parsed_arg()
assert call_args.config == 'test_conf.json'
assert call_args.epochs == 20
assert call_args.loglevel == logging.INFO
assert call_args.subparser == 'hyperopt'
assert call_args.spaces == ['buy']
assert call_args.func is not None

336
freqtrade/tests/test_configuration.py Normal file
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@ -0,0 +1,336 @@
# pragma pylint: disable=protected-access, invalid-name
"""
Unit test file for configuration.py
"""
import json
from copy import deepcopy
from unittest.mock import MagicMock
import pytest
from jsonschema import ValidationError
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.tests.conftest import log_has
def test_configuration_object() -> None:
"""
Test the Constants object has the mandatory Constants
"""
assert hasattr(Configuration, 'load_config')
assert hasattr(Configuration, '_load_config_file')
assert hasattr(Configuration, '_validate_config')
assert hasattr(Configuration, '_load_common_config')
assert hasattr(Configuration, '_load_backtesting_config')
assert hasattr(Configuration, '_load_hyperopt_config')
assert hasattr(Configuration, 'get_config')
def test_load_config_invalid_pair(default_conf, mocker) -> None:
"""
Test the configuration validator with an invalid PAIR format
"""
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'].append('BTC-ETH')
with pytest.raises(ValidationError, match=r'.*does not match.*'):
configuration = Configuration([])
configuration._validate_config(conf)
def test_load_config_missing_attributes(default_conf, mocker) -> None:
"""
Test the configuration validator with a missing attribute
"""
conf = deepcopy(default_conf)
conf.pop('exchange')
with pytest.raises(ValidationError, match=r'.*\'exchange\' is a required property.*'):
configuration = Configuration([])
configuration._validate_config(conf)
def test_load_config_file(default_conf, mocker, caplog) -> None:
"""
Test Configuration._load_config_file() method
"""
file_mock = mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
configuration = Configuration([])
validated_conf = configuration._load_config_file('somefile')
assert file_mock.call_count == 1
assert validated_conf.items() >= default_conf.items()
assert 'internals' in validated_conf
assert log_has('Validating configuration ...', caplog.record_tuples)
def test_load_config_file_exception(mocker, caplog) -> None:
"""
Test Configuration._load_config_file() method
"""
mocker.patch(
'freqtrade.configuration.open',
MagicMock(side_effect=FileNotFoundError('File not found'))
)
configuration = Configuration([])
with pytest.raises(SystemExit):
configuration._load_config_file('somefile')
assert log_has(
'Config file "somefile" not found. Please create your config file',
caplog.record_tuples
)
def test_load_config(default_conf, mocker) -> None:
"""
Test Configuration.load_config() without any cli params
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = Arguments([], '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
assert validated_conf.get('strategy') == 'DefaultStrategy'
assert validated_conf.get('strategy_path') is None
assert 'dynamic_whitelist' not in validated_conf
assert 'dry_run_db' not in validated_conf
def test_load_config_with_params(default_conf, mocker) -> None:
"""
Test Configuration.load_config() with cli params used
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
'--strategy-path', '/some/path',
'--dry-run-db',
]
args = Arguments(args, '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
assert validated_conf.get('dynamic_whitelist') == 10
assert validated_conf.get('strategy') == 'TestStrategy'
assert validated_conf.get('strategy_path') == '/some/path'
assert validated_conf.get('dry_run_db') is True
def test_load_custom_strategy(default_conf, mocker) -> None:
"""
Test Configuration.load_config() without any cli params
"""
custom_conf = deepcopy(default_conf)
custom_conf.update({
'strategy': 'CustomStrategy',
'strategy_path': '/tmp/strategies',
})
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(custom_conf)
))
args = Arguments([], '').get_parsed_arg()
configuration = Configuration(args)
validated_conf = configuration.load_config()
assert validated_conf.get('strategy') == 'CustomStrategy'
assert validated_conf.get('strategy_path') == '/tmp/strategies'
def test_show_info(default_conf, mocker, caplog) -> None:
"""
Test Configuration.show_info()
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--dynamic-whitelist', '10',
'--strategy', 'TestStrategy',
'--dry-run-db'
]
args = Arguments(args, '').get_parsed_arg()
configuration = Configuration(args)
configuration.get_config()
assert log_has(
'Parameter --dynamic-whitelist detected. '
'Using dynamically generated whitelist. '
'(not applicable with Backtesting and Hyperopt)',
caplog.record_tuples
)
assert log_has(
'Parameter --dry-run-db detected ...',
caplog.record_tuples
)
assert log_has(
'Dry_run will use the DB file: "tradesv3.dry_run.sqlite"',
caplog.record_tuples
)
# Test the Dry run condition
configuration.config.update({'dry_run': False})
configuration._load_common_config(configuration.config)
assert log_has(
'Dry run is disabled. (--dry_run_db ignored)',
caplog.record_tuples
)
def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'backtesting'
]
args = Arguments(args, '').get_parsed_arg()
configuration = Configuration(args)
config = configuration.get_config()
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 log_has(
'Parameter --datadir detected: {} ...'.format(config['datadir']),
caplog.record_tuples
)
assert 'ticker_interval' in config
assert not log_has('Parameter -i/--ticker-interval detected ...', caplog.record_tuples)
assert 'live' not in config
assert not log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation' not in config
assert not log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert 'refresh_pairs' not in config
assert not log_has('Parameter -r/--refresh-pairs-cached detected ...', caplog.record_tuples)
assert 'timerange' not in config
assert 'export' not in config
def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
'--datadir', '/foo/bar',
'backtesting',
'--ticker-interval', '1',
'--live',
'--realistic-simulation',
'--refresh-pairs-cached',
'--timerange', ':100',
'--export', '/bar/foo'
]
args = Arguments(args, '').get_parsed_arg()
configuration = Configuration(args)
config = configuration.get_config()
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 log_has(
'Parameter --datadir detected: {} ...'.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: 1 ...',
caplog.record_tuples
)
assert 'live' in config
assert log_has('Parameter -l/--live detected ...', caplog.record_tuples)
assert 'realistic_simulation'in config
assert log_has('Parameter --realistic-simulation detected ...', caplog.record_tuples)
assert log_has('Using max_open_trades: 1 ...', caplog.record_tuples)
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']),
caplog.record_tuples
)
assert 'export' in config
assert log_has(
'Parameter --export detected: {} ...'.format(config['export']),
caplog.record_tuples
)
def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
"""
Test setup_configuration() function
"""
mocker.patch('freqtrade.configuration.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
args = [
'hyperopt',
'--epochs', '10',
'--use-mongodb',
'--spaces', 'all',
]
args = Arguments(args, '').get_parsed_arg()
configuration = Configuration(args)
config = configuration.get_config()
assert 'epochs' in config
assert int(config['epochs']) == 10
assert log_has('Parameter --epochs detected ...', caplog.record_tuples)
assert log_has('Will run Hyperopt with for 10 epochs ...', caplog.record_tuples)
assert 'mongodb' in config
assert config['mongodb'] is True
assert log_has('Parameter --use-mongodb detected ...', caplog.record_tuples)
assert 'spaces' in config
assert config['spaces'] == ['all']
assert log_has('Parameter -s/--spaces detected: [\'all\']', caplog.record_tuples)

25
freqtrade/tests/test_constants.py Normal file
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@ -0,0 +1,25 @@
"""
Unit test file for constants.py
"""
from freqtrade import constants
def test_constant_object() -> None:
"""
Test the Constants object has the mandatory Constants
"""
assert hasattr(constants, 'CONF_SCHEMA')
assert hasattr(constants, 'DYNAMIC_WHITELIST')
assert hasattr(constants, 'PROCESS_THROTTLE_SECS')
assert hasattr(constants, 'TICKER_INTERVAL')
assert hasattr(constants, 'HYPEROPT_EPOCH')
assert hasattr(constants, 'RETRY_TIMEOUT')
assert hasattr(constants, 'DEFAULT_STRATEGY')
def test_conf_schema() -> None:
"""
Test the CONF_SCHEMA is from the right type
"""
assert isinstance(constants.CONF_SCHEMA, dict)

13
freqtrade/tests/test_dataframe.py
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@ -1,26 +1,33 @@
# pragma pylint: disable=missing-docstring, C0103
import pandas
import freqtrade.optimize
from freqtrade import analyze
from freqtrade.analyze import Analyze
from freqtrade.optimize import load_data
from freqtrade.strategy.resolver import StrategyResolver
_pairs = ['BTC_ETH']
def load_dataframe_pair(pairs):
ld = freqtrade.optimize.load_data(None, ticker_interval=5, pairs=pairs)
ld = load_data(None, ticker_interval=5, pairs=pairs)
assert isinstance(ld, dict)
assert isinstance(pairs[0], str)
dataframe = ld[pairs[0]]
analyze = Analyze({'strategy': 'DefaultStrategy'})
dataframe = analyze.analyze_ticker(dataframe)
return dataframe
def test_dataframe_load():
StrategyResolver({'strategy': 'DefaultStrategy'})
dataframe = load_dataframe_pair(_pairs)
assert isinstance(dataframe, pandas.core.frame.DataFrame)
def test_dataframe_columns_exists():
StrategyResolver({'strategy': 'DefaultStrategy'})
dataframe = load_dataframe_pair(_pairs)
assert 'high' in dataframe.columns
assert 'low' in dataframe.columns

30
freqtrade/tests/test_fiat_convert.py
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@ -1,4 +1,5 @@
# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, C0103
# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors,
# pragma pylint: disable=protected-access, C0103
import time
from unittest.mock import MagicMock
@ -47,16 +48,19 @@ def test_fiat_convert_is_supported():
def test_fiat_convert_add_pair():
fiat_convert = CryptoToFiatConverter()
assert len(fiat_convert._pairs) == 0
pair_len = len(fiat_convert._pairs)
assert pair_len == 0
fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='usd', price=12345.0)
assert len(fiat_convert._pairs) == 1
pair_len = len(fiat_convert._pairs)
assert pair_len == 1
assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
assert fiat_convert._pairs[0].fiat_symbol == 'USD'
assert fiat_convert._pairs[0].price == 12345.0
fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='Eur', price=13000.2)
assert len(fiat_convert._pairs) == 2
pair_len = len(fiat_convert._pairs)
assert pair_len == 2
assert fiat_convert._pairs[1].crypto_symbol == 'BTC'
assert fiat_convert._pairs[1].fiat_symbol == 'EUR'
assert fiat_convert._pairs[1].price == 13000.2
@ -67,12 +71,15 @@ def test_fiat_convert_find_price(mocker):
'price_usd': 12345.0,
'price_eur': 13000.2
})
mocker.patch('freqtrade.fiat_convert.Pymarketcap.ticker', api_mock)
mocker.patch('freqtrade.fiat_convert.Market.ticker', api_mock)
fiat_convert = CryptoToFiatConverter()
with pytest.raises(ValueError, match=r'The fiat ABC is not supported.'):
fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='ABC')
with pytest.raises(ValueError, match=r'The crypto symbol XRP is not supported.'):
fiat_convert.get_price(crypto_symbol='XRP', fiat_symbol='USD')
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=12345.0)
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 12345.0
assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 12345.0
@ -86,7 +93,7 @@ def test_fiat_convert_get_price(mocker):
'price_usd': 28000.0,
'price_eur': 15000.0
})
mocker.patch('freqtrade.fiat_convert.Pymarketcap.ticker', api_mock)
mocker.patch('freqtrade.fiat_convert.Market.ticker', api_mock)
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=28000.0)
fiat_convert = CryptoToFiatConverter()
@ -95,7 +102,8 @@ def test_fiat_convert_get_price(mocker):
fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='US Dollar')
# Check the value return by the method
assert len(fiat_convert._pairs) == 0
pair_len = len(fiat_convert._pairs)
assert pair_len == 0
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 28000.0
assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
assert fiat_convert._pairs[0].fiat_symbol == 'USD'
@ -116,10 +124,12 @@ def test_fiat_convert_get_price(mocker):
assert fiat_convert._pairs[0]._expiration is not expiration
def test_fiat_convert_without_network(mocker):
pymarketcap = MagicMock(side_effect=ImportError('Oh boy, you have no network!'))
mocker.patch('freqtrade.fiat_convert.Pymarketcap', pymarketcap)
def test_fiat_convert_without_network():
# Because CryptoToFiatConverter is a Singleton we reset the value of _coinmarketcap
fiat_convert = CryptoToFiatConverter()
CryptoToFiatConverter._coinmarketcap = None
assert fiat_convert._coinmarketcap is None
assert fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='USD') == 0.0

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freqtrade/tests/test_freqtradebot.py Normal file
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13
freqtrade/tests/test_indicator_helpers.py Normal file
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@ -0,0 +1,13 @@
import pandas as pd
from freqtrade.indicator_helpers import went_up, went_down
def test_went_up():
series = pd.Series([1, 2, 3, 1])
assert went_up(series).equals(pd.Series([False, True, True, False]))
def test_went_down():
series = pd.Series([1, 2, 3, 1])
assert went_down(series).equals(pd.Series([False, False, False, True]))

703
freqtrade/tests/test_main.py
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@ -1,31 +1,23 @@
# pragma pylint: disable=missing-docstring,C0103
import copy
"""
Unit test file for main.py
"""
import logging
from unittest.mock import MagicMock
import arrow
import pytest
import requests
from sqlalchemy import create_engine
import freqtrade.main as main
from freqtrade import DependencyException, OperationalException
from freqtrade.analyze import SignalType
from freqtrade.exchange import Exchanges
from freqtrade.main import (_process, check_handle_timedout, create_trade,
execute_sell, get_target_bid, handle_trade, init)
from freqtrade.misc import State, get_state
from freqtrade.persistence import Trade
from freqtrade.main import main, set_loggers
from freqtrade.tests.conftest import log_has
def test_parse_args_backtesting(mocker):
""" Test that main() can start backtesting or hyperopt.
and also ensure we can pass some specific arguments
argument parsing is done in test_misc.py """
backtesting_mock = mocker.patch(
'freqtrade.optimize.backtesting.start', MagicMock())
with pytest.raises(SystemExit, match=r'0'):
main.main(['backtesting'])
def test_parse_args_backtesting(mocker) -> None:
"""
Test that main() can start backtesting and also ensure we can pass some specific arguments
further argument parsing is done in test_arguments.py
"""
backtesting_mock = mocker.patch('freqtrade.optimize.backtesting.start', MagicMock())
main(['backtesting'])
assert backtesting_mock.call_count == 1
call_args = backtesting_mock.call_args[0][0]
assert call_args.config == 'config.json'
@ -33,14 +25,15 @@ def test_parse_args_backtesting(mocker):
assert call_args.loglevel == 20
assert call_args.subparser == 'backtesting'
assert call_args.func is not None
assert call_args.ticker_interval == 5
assert call_args.ticker_interval is None
def test_main_start_hyperopt(mocker):
hyperopt_mock = mocker.patch(
'freqtrade.optimize.hyperopt.start', MagicMock())
with pytest.raises(SystemExit, match=r'0'):
main.main(['hyperopt'])
def test_main_start_hyperopt(mocker) -> None:
"""
Test that main() can start hyperopt
"""
hyperopt_mock = mocker.patch('freqtrade.optimize.hyperopt.start', MagicMock())
main(['hyperopt'])
assert hyperopt_mock.call_count == 1
call_args = hyperopt_mock.call_args[0][0]
assert call_args.config == 'config.json'
@ -49,628 +42,52 @@ def test_main_start_hyperopt(mocker):
assert call_args.func is not None
def test_process_trade_creation(default_conf, ticker, limit_buy_order, health, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_wallet_health=health,
buy=MagicMock(return_value='mocked_limit_buy'),
get_order=MagicMock(return_value=limit_buy_order))
init(default_conf, create_engine('sqlite://'))
def test_set_loggers() -> None:
"""
Test set_loggers() update the logger level for third-party libraries
"""
previous_value1 = logging.getLogger('requests.packages.urllib3').level
previous_value2 = logging.getLogger('telegram').level
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert not trades
set_loggers()
result = _process()
assert result is True
value1 = logging.getLogger('requests.packages.urllib3').level
assert previous_value1 is not value1
assert value1 is logging.INFO
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert len(trades) == 1
trade = trades[0]
assert trade is not None
assert trade.stake_amount == default_conf['stake_amount']
assert trade.is_open
assert trade.open_date is not None
assert trade.exchange == Exchanges.BITTREX.name
assert trade.open_rate == 0.00001099
assert trade.amount == 90.99181073703367
value2 = logging.getLogger('telegram').level
assert previous_value2 is not value2
assert value2 is logging.INFO
def test_process_exchange_failures(default_conf, ticker, health, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
sleep_mock = mocker.patch('time.sleep', side_effect=lambda _: None)
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_wallet_health=health,
buy=MagicMock(side_effect=requests.exceptions.RequestException))
init(default_conf, create_engine('sqlite://'))
result = _process()
assert result is False
assert sleep_mock.has_calls()
def test_process_operational_exception(default_conf, ticker, health, mocker):
msg_mock = MagicMock()
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=msg_mock)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_wallet_health=health,
buy=MagicMock(side_effect=OperationalException))
init(default_conf, create_engine('sqlite://'))
assert get_state() == State.RUNNING
result = _process()
assert result is False
assert get_state() == State.STOPPED
assert 'OperationalException' in msg_mock.call_args_list[-1][0][0]
def test_process_trade_handling(default_conf, ticker, limit_buy_order, health, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal',
side_effect=lambda *args: False if args[1] == SignalType.SELL else True)
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_wallet_health=health,
buy=MagicMock(return_value='mocked_limit_buy'),
get_order=MagicMock(return_value=limit_buy_order))
init(default_conf, create_engine('sqlite://'))
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert not trades
result = _process()
assert result is True
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert len(trades) == 1
result = _process()
assert result is False
def test_create_trade(default_conf, ticker, limit_buy_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
# Save state of current whitelist
whitelist = copy.deepcopy(default_conf['exchange']['pair_whitelist'])
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
assert trade is not None
assert trade.stake_amount == 0.001
assert trade.is_open
assert trade.open_date is not None
assert trade.exchange == Exchanges.BITTREX.name
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
assert trade.open_rate == 0.00001099
assert trade.amount == 90.99181073
assert whitelist == default_conf['exchange']['pair_whitelist']
def test_create_trade_minimal_amount(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
buy_mock = mocker.patch(
'freqtrade.main.exchange.buy', MagicMock(return_value='mocked_limit_buy')
def test_main(mocker, caplog) -> None:
"""
Test main() function
In this test we are skipping the while True loop by throwing an exception.
"""
mocker.patch.multiple(
'freqtrade.freqtradebot.FreqtradeBot',
_init_modules=MagicMock(),
worker=MagicMock(
side_effect=KeyboardInterrupt
),
clean=MagicMock(),
)
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
init(default_conf, create_engine('sqlite://'))
min_stake_amount = 0.0005
create_trade(min_stake_amount)
rate, amount = buy_mock.call_args[0][1], buy_mock.call_args[0][2]
assert rate * amount >= min_stake_amount
args = ['-c', 'config.json.example']
# Test Main + the KeyboardInterrupt exception
with pytest.raises(SystemExit) as pytest_wrapped_e:
main(args)
log_has('Starting freqtrade', caplog.record_tuples)
log_has('Got SIGINT, aborting ...', caplog.record_tuples)
assert pytest_wrapped_e.type == SystemExit
assert pytest_wrapped_e.value.code == 42
def test_create_trade_no_stake_amount(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'),
get_balance=MagicMock(return_value=default_conf['stake_amount'] * 0.5))
with pytest.raises(DependencyException, match=r'.*stake amount.*'):
create_trade(default_conf['stake_amount'])
def test_create_trade_no_pairs(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
with pytest.raises(DependencyException, match=r'.*No pair in whitelist.*'):
conf = copy.deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = []
mocker.patch.dict('freqtrade.main._CONF', conf)
create_trade(default_conf['stake_amount'])
def test_create_trade_no_pairs_after_blacklist(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
with pytest.raises(DependencyException, match=r'.*No pair in whitelist.*'):
conf = copy.deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = ["BTC_ETH"]
conf['exchange']['pair_blacklist'] = ["BTC_ETH"]
mocker.patch.dict('freqtrade.main._CONF', conf)
create_trade(default_conf['stake_amount'])
def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock(return_value={
'bid': 0.00001172,
'ask': 0.00001173,
'last': 0.00001172
}),
buy=MagicMock(return_value='mocked_limit_buy'),
sell=MagicMock(return_value='mocked_limit_sell'))
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
assert trade
trade.update(limit_buy_order)
assert trade.is_open is True
handle_trade(trade)
assert trade.open_order_id == 'mocked_limit_sell'
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
assert trade.close_rate == 0.00001173
assert trade.close_profit == 0.06201057
assert trade.calc_profit() == 0.00006217
assert trade.close_date is not None
def test_handle_trade_roi(default_conf, ticker, mocker, caplog):
default_conf.update({'experimental': {'use_sell_signal': True}})
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
mocker.patch('freqtrade.main.min_roi_reached', return_value=True)
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
trade.is_open = True
# FIX: sniffing logs, suggest handle_trade should not execute_sell
# instead that responsibility should be moved out of handle_trade(),
# we might just want to check if we are in a sell condition without
# executing
# if ROI is reached we must sell
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: False)
assert handle_trade(trade)
assert ('freqtrade', logging.DEBUG, 'Executing sell due to ROI ...') in caplog.record_tuples
# if ROI is reached we must sell even if sell-signal is not signalled
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
assert handle_trade(trade)
assert ('freqtrade', logging.DEBUG, 'Executing sell due to ROI ...') in caplog.record_tuples
def test_handle_trade_experimental(default_conf, ticker, mocker, caplog):
default_conf.update({'experimental': {'use_sell_signal': True}})
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
trade.is_open = True
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: False)
value_returned = handle_trade(trade)
assert ('freqtrade', logging.DEBUG, 'Checking sell_signal ...') in caplog.record_tuples
assert value_returned is False
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
assert handle_trade(trade)
s = 'Executing sell due to sell signal ...'
assert ('freqtrade', logging.DEBUG, s) in caplog.record_tuples
def test_close_trade(default_conf, ticker, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
# Create trade and sell it
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
assert trade
trade.update(limit_buy_order)
trade.update(limit_sell_order)
assert trade.is_open is False
with pytest.raises(ValueError, match=r'.*closed trade.*'):
handle_trade(trade)
def test_check_handle_timedout_buy(default_conf, ticker, limit_buy_order_old, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
cancel_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_order=MagicMock(return_value=limit_buy_order_old),
cancel_order=cancel_order_mock)
init(default_conf, create_engine('sqlite://'))
trade_buy = Trade(
pair='BTC_ETH',
open_rate=0.00001099,
exchange='BITTREX',
open_order_id='123456789',
amount=90.99181073,
fee=0.0,
stake_amount=1,
open_date=arrow.utcnow().shift(minutes=-601).datetime,
is_open=True
# Test the BaseException case
mocker.patch(
'freqtrade.freqtradebot.FreqtradeBot.worker',
MagicMock(side_effect=BaseException)
)
Trade.session.add(trade_buy)
# check it does cancel buy orders over the time limit
check_handle_timedout(600)
assert cancel_order_mock.call_count == 1
trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
assert len(trades) == 0
def test_check_handle_timedout_sell(default_conf, ticker, limit_sell_order_old, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
cancel_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_order=MagicMock(return_value=limit_sell_order_old),
cancel_order=cancel_order_mock)
init(default_conf, create_engine('sqlite://'))
trade_sell = Trade(
pair='BTC_ETH',
open_rate=0.00001099,
exchange='BITTREX',
open_order_id='123456789',
amount=90.99181073,
fee=0.0,
stake_amount=1,
open_date=arrow.utcnow().shift(hours=-5).datetime,
close_date=arrow.utcnow().shift(minutes=-601).datetime,
is_open=False
)
Trade.session.add(trade_sell)
# check it does cancel sell orders over the time limit
check_handle_timedout(600)
assert cancel_order_mock.call_count == 1
assert trade_sell.is_open is True
def test_check_handle_timedout_partial(default_conf, ticker, limit_buy_order_old_partial,
mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
cancel_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
get_order=MagicMock(return_value=limit_buy_order_old_partial),
cancel_order=cancel_order_mock)
init(default_conf, create_engine('sqlite://'))
trade_buy = Trade(
pair='BTC_ETH',
open_rate=0.00001099,
exchange='BITTREX',
open_order_id='123456789',
amount=90.99181073,
fee=0.0,
stake_amount=1,
open_date=arrow.utcnow().shift(minutes=-601).datetime,
is_open=True
)
Trade.session.add(trade_buy)
# check it does cancel buy orders over the time limit
# note this is for a partially-complete buy order
check_handle_timedout(600)
assert cancel_order_mock.call_count == 1
trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
assert len(trades) == 1
assert trades[0].amount == 23.0
assert trades[0].stake_amount == trade_buy.open_rate * trades[0].amount
def test_balance_fully_ask_side(mocker):
mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 0.0}})
assert get_target_bid({'ask': 20, 'last': 10}) == 20
def test_balance_fully_last_side(mocker):
mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
assert get_target_bid({'ask': 20, 'last': 10}) == 10
def test_balance_bigger_last_ask(mocker):
mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
assert get_target_bid({'ask': 5, 'last': 10}) == 5
def test_execute_sell_up(default_conf, ticker, ticker_sell_up, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
trade = Trade.query.first()
assert trade
# Increase the price and sell it
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker_sell_up)
execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
def test_execute_sell_down(default_conf, ticker, ticker_sell_down, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
init=MagicMock(),
send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
trade = Trade.query.first()
assert trade
# Decrease the price and sell it
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker_sell_down)
execute_sell(trade=trade, limit=ticker_sell_down()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
def test_execute_sell_without_conf(default_conf, ticker, ticker_sell_up, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
trade = Trade.query.first()
assert trade
# Increase the price and sell it
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker_sell_up)
mocker.patch('freqtrade.main._CONF', {})
execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
assert '(profit: 6.11%, 0.00006126)' in rpc_mock.call_args_list[-1][0][0]
assert 'USD' not in rpc_mock.call_args_list[-1][0][0]
def test_sell_profit_only_enable_profit(default_conf, limit_buy_order, mocker):
default_conf['experimental'] = {
'use_sell_signal': True,
'sell_profit_only': True,
}
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock(return_value={
'bid': 0.00002172,
'ask': 0.00002173,
'last': 0.00002172
}),
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is True
def test_sell_profit_only_disable_profit(default_conf, limit_buy_order, mocker):
default_conf['experimental'] = {
'use_sell_signal': True,
'sell_profit_only': False,
}
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock(return_value={
'bid': 0.00002172,
'ask': 0.00002173,
'last': 0.00002172
}),
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is True
def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, mocker):
default_conf['experimental'] = {
'use_sell_signal': True,
'sell_profit_only': True,
}
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock(return_value={
'bid': 0.00000172,
'ask': 0.00000173,
'last': 0.00000172
}),
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is False
def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, mocker):
default_conf['experimental'] = {
'use_sell_signal': True,
'sell_profit_only': False,
}
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=MagicMock(return_value={
'bid': 0.00000172,
'ask': 0.00000173,
'last': 0.00000172
}),
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is True
with pytest.raises(SystemExit):
main(args)
log_has('Got fatal exception!', caplog.record_tuples)

207
freqtrade/tests/test_misc.py
View File

@ -1,164 +1,71 @@
# pragma pylint: disable=missing-docstring,C0103
import argparse
import json
import time
from copy import deepcopy
import pytest
from jsonschema import ValidationError
"""
Unit test file for misc.py
"""
from freqtrade.misc import (common_args_parser, load_config, parse_args,
throttle)
import datetime
from unittest.mock import MagicMock
from freqtrade.analyze import Analyze
from freqtrade.misc import (shorten_date, datesarray_to_datetimearray,
common_datearray, file_dump_json)
from freqtrade.optimize.__init__ import load_tickerdata_file
def test_throttle():
def func():
return 42
start = time.time()
result = throttle(func, min_secs=0.1)
end = time.time()
assert result == 42
assert end - start > 0.1
result = throttle(func, min_secs=-1)
assert result == 42
def test_shorten_date() -> None:
"""
Test shorten_date() function
:return: None
"""
str_data = '1 day, 2 hours, 3 minutes, 4 seconds ago'
str_shorten_data = '1 d, 2 h, 3 min, 4 sec ago'
assert shorten_date(str_data) == str_shorten_data
def test_throttle_with_assets():
def test_datesarray_to_datetimearray(ticker_history):
"""
Test datesarray_to_datetimearray() function
:return: None
"""
dataframes = Analyze.parse_ticker_dataframe(ticker_history)
dates = datesarray_to_datetimearray(dataframes['date'])
def func(nb_assets=-1):
return nb_assets
assert isinstance(dates[0], datetime.datetime)
assert dates[0].year == 2017
assert dates[0].month == 11
assert dates[0].day == 26
assert dates[0].hour == 8
assert dates[0].minute == 50
result = throttle(func, min_secs=0.1, nb_assets=666)
assert result == 666
result = throttle(func, min_secs=0.1)
assert result == -1
date_len = len(dates)
assert date_len == 3
# Parse common command-line-arguments. Used for all tools
def test_common_datearray(default_conf, mocker) -> None:
"""
Test common_datearray()
:return: None
"""
analyze = Analyze(default_conf)
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1)
tickerlist = {'BTC_UNITEST': tick}
dataframes = analyze.tickerdata_to_dataframe(tickerlist)
def test_parse_args_none():
args = common_args_parser('')
assert isinstance(args, argparse.ArgumentParser)
dates = common_datearray(dataframes)
assert dates.size == dataframes['BTC_UNITEST']['date'].size
assert dates[0] == dataframes['BTC_UNITEST']['date'][0]
assert dates[-1] == dataframes['BTC_UNITEST']['date'][-1]
def test_parse_args_defaults():
args = parse_args([], '')
assert args.config == 'config.json'
assert args.dynamic_whitelist is None
assert args.loglevel == 20
def test_parse_args_config():
args = parse_args(['-c', '/dev/null'], '')
assert args.config == '/dev/null'
args = parse_args(['--config', '/dev/null'], '')
assert args.config == '/dev/null'
def test_parse_args_verbose():
args = parse_args(['-v'], '')
assert args.loglevel == 10
args = parse_args(['--verbose'], '')
assert args.loglevel == 10
def test_parse_args_version():
with pytest.raises(SystemExit, match=r'0'):
parse_args(['--version'], '')
def test_parse_args_invalid():
with pytest.raises(SystemExit, match=r'2'):
parse_args(['-c'], '')
# Parse command-line-arguments
# used for main, backtesting and hyperopt
def test_parse_args_dynamic_whitelist():
args = parse_args(['--dynamic-whitelist'], '')
assert args.dynamic_whitelist == 20
def test_parse_args_dynamic_whitelist_10():
args = parse_args(['--dynamic-whitelist', '10'], '')
assert args.dynamic_whitelist == 10
def test_parse_args_dynamic_whitelist_invalid_values():
with pytest.raises(SystemExit, match=r'2'):
parse_args(['--dynamic-whitelist', 'abc'], '')
def test_parse_args_backtesting_invalid():
with pytest.raises(SystemExit, match=r'2'):
parse_args(['backtesting --ticker-interval'], '')
with pytest.raises(SystemExit, match=r'2'):
parse_args(['backtesting --ticker-interval', 'abc'], '')
def test_parse_args_backtesting_custom():
args = [
'-c', 'test_conf.json',
'backtesting',
'--live',
'--ticker-interval', '1',
'--refresh-pairs-cached']
call_args = parse_args(args, '')
assert call_args.config == 'test_conf.json'
assert call_args.live is True
assert call_args.loglevel == 20
assert call_args.subparser == 'backtesting'
assert call_args.func is not None
assert call_args.ticker_interval == 1
assert call_args.refresh_pairs is True
def test_parse_args_hyperopt_custom(mocker):
args = ['-c', 'test_conf.json', 'hyperopt', '--epochs', '20']
call_args = parse_args(args, '')
assert call_args.config == 'test_conf.json'
assert call_args.epochs == 20
assert call_args.loglevel == 20
assert call_args.subparser == 'hyperopt'
assert call_args.func is not None
def test_load_config(default_conf, mocker):
file_mock = mocker.patch('freqtrade.misc.open', mocker.mock_open(
read_data=json.dumps(default_conf)
))
validated_conf = load_config('somefile')
assert file_mock.call_count == 1
assert validated_conf.items() >= default_conf.items()
def test_load_config_invalid_pair(default_conf, mocker):
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'].append('BTC-ETH')
mocker.patch(
'freqtrade.misc.open',
mocker.mock_open(
read_data=json.dumps(conf)))
with pytest.raises(ValidationError, match=r'.*does not match.*'):
load_config('somefile')
def test_load_config_missing_attributes(default_conf, mocker):
conf = deepcopy(default_conf)
conf.pop('exchange')
mocker.patch(
'freqtrade.misc.open',
mocker.mock_open(
read_data=json.dumps(conf)))
with pytest.raises(ValidationError, match=r'.*\'exchange\' is a required property.*'):
load_config('somefile')
def test_file_dump_json(mocker) -> None:
"""
Test file_dump_json()
:return: None
"""
file_open = mocker.patch('freqtrade.misc.open', MagicMock())
json_dump = mocker.patch('json.dump', MagicMock())
file_dump_json('somefile', [1, 2, 3])
assert file_open.call_count == 1
assert json_dump.call_count == 1

76
freqtrade/tests/test_persistence.py
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@ -1,10 +1,16 @@
# pragma pylint: disable=missing-docstring
# pragma pylint: disable=missing-docstring, C0103
import os
import pytest
from sqlalchemy import create_engine
from freqtrade.exchange import Exchanges
from freqtrade.persistence import Trade, init
from freqtrade.persistence import Trade, init, clean_dry_run_db
@pytest.fixture(scope='function')
def init_persistence(default_conf):
init(default_conf)
def test_init_create_session(default_conf, mocker):
@ -13,7 +19,7 @@ def test_init_create_session(default_conf, mocker):
# Check if init create a session
init(default_conf)
assert hasattr(Trade, 'session')
assert type(Trade.session).__name__ is 'Session'
assert 'Session' in type(Trade.session).__name__
def test_init_dry_run_db(default_conf, mocker):
@ -89,6 +95,7 @@ def test_init_prod_db(default_conf, mocker):
os.rename(prod_db_swp, prod_db)
@pytest.mark.usefixtures("init_persistence")
def test_update_with_bittrex(limit_buy_order, limit_sell_order):
"""
On this test we will buy and sell a crypto currency.
@ -143,6 +150,7 @@ def test_update_with_bittrex(limit_buy_order, limit_sell_order):
assert trade.close_date is not None
@pytest.mark.usefixtures("init_persistence")
def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order):
trade = Trade(
pair='BTC_ETH',
@ -165,6 +173,7 @@ def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order):
assert trade.calc_profit_percent() == 0.06201057
@pytest.mark.usefixtures("init_persistence")
def test_calc_close_trade_price_exception(limit_buy_order):
trade = Trade(
pair='BTC_ETH',
@ -178,6 +187,7 @@ def test_calc_close_trade_price_exception(limit_buy_order):
assert trade.calc_close_trade_price() == 0.0
@pytest.mark.usefixtures("init_persistence")
def test_update_open_order(limit_buy_order):
trade = Trade(
pair='BTC_ETH',
@ -200,6 +210,7 @@ def test_update_open_order(limit_buy_order):
assert trade.close_date is None
@pytest.mark.usefixtures("init_persistence")
def test_update_invalid_order(limit_buy_order):
trade = Trade(
pair='BTC_ETH',
@ -212,6 +223,7 @@ def test_update_invalid_order(limit_buy_order):
trade.update(limit_buy_order)
@pytest.mark.usefixtures("init_persistence")
def test_calc_open_trade_price(limit_buy_order):
trade = Trade(
pair='BTC_ETH',
@ -229,6 +241,7 @@ def test_calc_open_trade_price(limit_buy_order):
assert trade.calc_open_trade_price(fee=0.003) == 0.001003000
@pytest.mark.usefixtures("init_persistence")
def test_calc_close_trade_price(limit_buy_order, limit_sell_order):
trade = Trade(
pair='BTC_ETH',
@ -250,6 +263,7 @@ def test_calc_close_trade_price(limit_buy_order, limit_sell_order):
assert trade.calc_close_trade_price(fee=0.005) == 0.0010619972
@pytest.mark.usefixtures("init_persistence")
def test_calc_profit(limit_buy_order, limit_sell_order):
trade = Trade(
pair='BTC_ETH',
@ -272,10 +286,6 @@ def test_calc_profit(limit_buy_order, limit_sell_order):
# Lower than open rate
assert trade.calc_profit(rate=0.00000123, fee=0.003) == -0.00089092
# Only custom fee without sell order applied
with pytest.raises(TypeError):
trade.calc_profit(fee=0.003)
# Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
trade.update(limit_sell_order)
assert trade.calc_profit() == 0.00006217
@ -284,6 +294,7 @@ def test_calc_profit(limit_buy_order, limit_sell_order):
assert trade.calc_profit(fee=0.003) == 0.00006163
@pytest.mark.usefixtures("init_persistence")
def test_calc_profit_percent(limit_buy_order, limit_sell_order):
trade = Trade(
pair='BTC_ETH',
@ -300,13 +311,56 @@ def test_calc_profit_percent(limit_buy_order, limit_sell_order):
# Get percent of profit with a custom rate (Lower than open rate)
assert trade.calc_profit_percent(rate=0.00000123) == -0.88863827
# Only custom fee without sell order applied
with pytest.raises(TypeError):
trade.calc_profit_percent(fee=0.003)
# Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
trade.update(limit_sell_order)
assert trade.calc_profit_percent() == 0.06201057
# Test with a custom fee rate on the close trade
assert trade.calc_profit_percent(fee=0.003) == 0.0614782
def test_clean_dry_run_db(default_conf):
init(default_conf, create_engine('sqlite://'))
# Simulate dry_run entries
trade = Trade(
pair='BTC_ETH',
stake_amount=0.001,
amount=123.0,
fee=0.0025,
open_rate=0.123,
exchange='BITTREX',
open_order_id='dry_run_buy_12345'
)
Trade.session.add(trade)
trade = Trade(
pair='BTC_ETC',
stake_amount=0.001,
amount=123.0,
fee=0.0025,
open_rate=0.123,
exchange='BITTREX',
open_order_id='dry_run_sell_12345'
)
Trade.session.add(trade)
# Simulate prod entry
trade = Trade(
pair='BTC_ETC',
stake_amount=0.001,
amount=123.0,
fee=0.0025,
open_rate=0.123,
exchange='BITTREX',
open_order_id='prod_buy_12345'
)
Trade.session.add(trade)
# We have 3 entries: 2 dry_run, 1 prod
assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 3
clean_dry_run_db()
# We have now only the prod
assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 1

14
freqtrade/tests/test_state.py Normal file
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@ -0,0 +1,14 @@
"""
Unit test file for constants.py
"""
from freqtrade.state import State
def test_state_object() -> None:
"""
Test the State object has the mandatory states
:return: None
"""
assert hasattr(State, 'RUNNING')
assert hasattr(State, 'STOPPED')

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@ -0,0 +1,3 @@
[
{"O": 0.00162008, "H": 0.00162008, "L": 0.00162008, "C": 0.00162008, "V": 108.14853839, "T": "2017-11-04T23:02:00", "BV": 0.17520927}
]

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freqtrade/tests/testdata/download_backtest_data.py vendored
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@ -2,28 +2,37 @@
"""This script generate json data from bittrex"""
import json
from os import path
import sys
from freqtrade import exchange
from freqtrade import misc
from freqtrade.exchange import Bittrex
PAIRS = [
'BTC_BCC', 'BTC_ETH', 'BTC_MER', 'BTC_POWR', 'BTC_ETC',
'BTC_OK', 'BTC_NEO', 'BTC_EMC2', 'BTC_DASH', 'BTC_LSK',
'BTC_LTC', 'BTC_XZC', 'BTC_OMG', 'BTC_STRAT', 'BTC_XRP',
'BTC_QTUM', 'BTC_WAVES', 'BTC_VTC', 'BTC_XLM', 'BTC_MCO'
]
TICKER_INTERVAL = 5 # ticker interval in minutes (currently implemented: 1 and 5)
OUTPUT_DIR = path.dirname(path.realpath(__file__))
parser = misc.common_args_parser('download utility')
parser.add_argument(
'-p', '--pair',
help='JSON file containing pairs to download',
dest='pair',
default=None
)
args = parser.parse_args(sys.argv[1:])
TICKER_INTERVALS = [1, 5] # ticker interval in minutes (currently implemented: 1 and 5)
PAIRS = []
if args.pair:
with open(args.pair) as file:
PAIRS = json.load(file)
PAIRS = list(set(PAIRS))
print('About to download pairs:', PAIRS)
# Init Bittrex exchange
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair in PAIRS:
data = exchange.get_ticker_history(pair, TICKER_INTERVAL)
filename = path.join(OUTPUT_DIR, '{}-{}.json'.format(
pair,
TICKER_INTERVAL,
))
with open(filename, 'w') as fp:
json.dump(data, fp)
for tick_interval in TICKER_INTERVALS:
print('downloading pair %s, interval %s' % (pair, tick_interval))
data = exchange.get_ticker_history(pair, tick_interval)
filename = '{}-{}.json'.format(pair, tick_interval)
misc.file_dump_json(filename, data)

26
freqtrade/tests/testdata/pairs.json vendored Normal file
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@ -0,0 +1,26 @@
[
"BTC_ADA",
"BTC_BAT",
"BTC_DASH",
"BTC_ETC",
"BTC_ETH",
"BTC_GBYTE",
"BTC_LSK",
"BTC_LTC",
"BTC_NEO",
"BTC_NXT",
"BTC_POWR",
"BTC_STORJ",
"BTC_QTUM",
"BTC_WAVES",
"BTC_VTC",
"BTC_XLM",
"BTC_XMR",
"BTC_XVG",
"BTC_XRP",
"BTC_ZEC",
"USDT_BTC",
"USDT_LTC",
"USDT_ETH"
]

23
requirements.txt
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@ -1,26 +1,25 @@
python-bittrex==0.2.2
SQLAlchemy==1.2.0
python-telegram-bot==9.0.0
arrow==0.12.0
python-bittrex==0.3.0
SQLAlchemy==1.2.7
python-telegram-bot==10.1.0
arrow==0.12.1
cachetools==2.0.1
requests==2.18.4
urllib3==1.22
wrapt==1.10.11
pandas==0.22.0
scikit-learn==0.19.1
scipy==1.0.0
scipy==1.1.0
jsonschema==2.6.0
numpy==1.14.0
TA-Lib==0.4.15
pytest==3.3.2
pytest-mock==1.6.3
numpy==1.14.3
TA-Lib==0.4.17
pytest==3.5.1
pytest-mock==1.10.0
pytest-cov==2.5.1
hyperopt==0.1
# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
networkx==1.11
tabulate==0.8.2
pymarketcap==3.3.147
coinmarketcap==5.0.1
# Required for plotting data
#matplotlib==2.1.0
#PYQT5==5.9
#plotly==2.3.0

212
scripts/plot_dataframe.py
View File

@ -1,70 +1,182 @@
#!/usr/bin/env python3
"""
Script to display when the bot will buy a specific pair
Mandatory Cli parameters:
-p / --pair: pair to examine
Optional Cli parameters
-s / --strategy: strategy to use
-d / --datadir: path to pair backtest data
--timerange: specify what timerange of data to use.
-l / --live: Live, to download the latest ticker for the pair
"""
import logging
import sys
import argparse
import matplotlib # Install PYQT5 manually if you want to test this helper function
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
from freqtrade import exchange, analyze
from freqtrade.misc import common_args_parser
from argparse import Namespace
from typing import List
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
from freqtrade.arguments import Arguments
from freqtrade.analyze import Analyze
from freqtrade import exchange
import freqtrade.optimize as optimize
def plot_parse_args(args ):
parser = common_args_parser(description='Graph utility')
parser.add_argument(
'-p', '--pair',
help = 'What currency pair',
dest = 'pair',
default = 'BTC_ETH',
type = str,
)
return parser.parse_args(args)
logger = logging.getLogger(__name__)
def plot_analyzed_dataframe(args) -> None:
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
Calls analyze() and plots the returned dataframe
:param pair: pair as str
:return: None
"""
pair = args.pair
pair = args.pair.replace('-', '_')
timerange = Arguments.parse_timerange(args.timerange)
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
dataframe = analyze.analyze_ticker(ticker)
# Init strategy
try:
analyze = Analyze({'strategy': args.strategy})
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy
)
exit()
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
tick_interval = analyze.strategy.ticker_interval
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
tickers = {}
if args.live:
logger.info('Downloading pair.')
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
else:
tickers = optimize.load_data(
datadir=args.datadir,
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=False,
timerange=timerange
)
dataframes = analyze.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
if len(dataframe.index) > 750:
logger.warning('Ticker contained more than 750 candles, clipping.')
data = dataframe.tail(750)
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
candles = go.Candlestick(
x=data.date,
open=data.open,
high=data.high,
low=data.low,
close=data.close,
name='Price'
)
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
df_buy = data[data['buy'] == 1]
buys = go.Scattergl(
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',
)
)
df_sell = data[data['sell'] == 1]
sells = go.Scattergl(
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',
)
)
bb_lower = go.Scatter(
x=data.date,
y=data.bb_lowerband,
name='BB lower',
line={'color': "transparent"},
)
bb_upper = go.Scatter(
x=data.date,
y=data.bb_upperband,
name='BB upper',
fill="tonexty",
fillcolor="rgba(0,176,246,0.2)",
line={'color': "transparent"},
)
macd = go.Scattergl(x=data['date'], y=data['macd'], name='MACD')
macdsignal = go.Scattergl(x=data['date'], y=data['macdsignal'], name='MACD signal')
volume = go.Bar(x=data['date'], y=data['volume'], name='Volume')
fig = tools.make_subplots(
rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 4],
vertical_spacing=0.0001,
)
fig.append_trace(candles, 1, 1)
fig.append_trace(bb_lower, 1, 1)
fig.append_trace(bb_upper, 1, 1)
fig.append_trace(buys, 1, 1)
fig.append_trace(sells, 1, 1)
fig.append_trace(volume, 2, 1)
fig.append_trace(macd, 3, 1)
fig.append_trace(macdsignal, 3, 1)
fig['layout'].update(title=args.pair)
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Volume')
fig['layout']['yaxis3'].update(title='MACD')
plot(fig, filename='freqtrade-plot.html')
def plot_parse_args(args: List[str]) -> Namespace:
"""
Parse args passed to the script
:param args: Cli arguments
:return: args: Array with all arguments
"""
arguments = Arguments(args, 'Graph dataframe')
arguments.scripts_options()
arguments.common_args_parser()
arguments.optimizer_shared_options(arguments.parser)
arguments.backtesting_options(arguments.parser)
return arguments.parse_args()
def main(sysargv: List[str]) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
logger.info('Starting Plot Dataframe')
plot_analyzed_dataframe(
plot_parse_args(sysargv)
)
if __name__ == '__main__':
args = plot_parse_args(sys.argv[1:])
plot_analyzed_dataframe(args)
main(sys.argv[1:])

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scripts/plot_profit.py Executable file
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@ -0,0 +1,223 @@
#!/usr/bin/env python3
"""
Script to display profits
Mandatory Cli parameters:
-p / --pair: pair to examine
Optional Cli parameters
-c / --config: specify configuration file
-s / --strategy: strategy to use
--timerange: specify what timerange of data to use.
"""
import logging
import sys
import json
from argparse import Namespace
from typing import List, Optional
import numpy as np
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.analyze import Analyze
import freqtrade.optimize as optimize
import freqtrade.misc as misc
logger = logging.getLogger(__name__)
# data:: [ pair, profit-%, enter, exit, time, duration]
# data:: ["BTC_ETH", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
def make_profit_array(
data: List, px: int, min_date: int,
interval: int, filter_pairs: Optional[List] = None) -> np.ndarray:
pg = np.zeros(px)
filter_pairs = filter_pairs or []
# Go through the trades
# and make an total profit
# array
for trade in data:
pair = trade[0]
if filter_pairs and pair not in filter_pairs:
continue
profit = trade[1]
trade_sell_time = int(trade[3])
ix = define_index(min_date, trade_sell_time, interval)
if ix < px:
logger.debug('[%s]: Add profit %s on %s', pair, profit, trade[4])
pg[ix] += profit
# rewrite the pg array to go from
# total profits at each timeframe
# to accumulated profits
pa = 0
for x in range(0, len(pg)):
p = pg[x] # Get current total percent
pa += p # Add to the accumulated percent
pg[x] = pa # write back to save memory
return pg
def plot_profit(args: Namespace) -> None:
"""
Plots the total profit for all pairs.
Note, the profit calculation isn't realistic.
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
# We need to use the same pairs, same tick_interval
# and same timeperiod as used in backtesting
# to match the tickerdata against the profits-results
timerange = Arguments.parse_timerange(args.timerange)
config = Configuration(args).get_config()
# Init strategy
try:
analyze = Analyze({'strategy': config.get('strategy')})
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
config.get('strategy')
)
exit()
# Take pairs from the cli otherwise switch to the pair in the config file
if args.pair:
filter_pairs = args.pair
filter_pairs = filter_pairs.split(',')
else:
filter_pairs = config['exchange']['pair_whitelist']
tick_interval = analyze.strategy.ticker_interval
pairs = config['exchange']['pair_whitelist']
if filter_pairs:
pairs = list(set(pairs) & set(filter_pairs))
logger.info('Filter, keep pairs %s' % pairs)
tickers = optimize.load_data(
datadir=args.datadir,
pairs=pairs,
ticker_interval=tick_interval,
refresh_pairs=False,
timerange=timerange
)
dataframes = analyze.tickerdata_to_dataframe(tickers)
# NOTE: the dataframes are of unequal length,
# 'dates' is an merged date array of them all.
dates = misc.common_datearray(dataframes)
min_date = int(min(dates).timestamp())
max_date = int(max(dates).timestamp())
num_iterations = define_index(min_date, max_date, tick_interval) + 1
# Make an average close price of all the pairs that was involved.
# this could be useful to gauge the overall market trend
# We are essentially saying:
# array <- sum dataframes[*]['close'] / num_items dataframes
# FIX: there should be some onliner numpy/panda for this
avgclose = np.zeros(num_iterations)
num = 0
for pair, pair_data in dataframes.items():
close = pair_data['close']
maxprice = max(close) # Normalize price to [0,1]
logger.info('Pair %s has length %s' % (pair, len(close)))
for x in range(0, len(close)):
avgclose[x] += close[x] / maxprice
# avgclose += close
num += 1
avgclose /= num
# Load the profits results
# And make an profits-growth array
try:
filename = 'backtest-result.json'
with open(filename) as file:
data = json.load(file)
except FileNotFoundError:
logger.critical('File "backtest-result.json" not found. This script require backtesting '
'results to run.\nPlease run a backtesting with the parameter --export.')
exit(0)
pg = make_profit_array(data, num_iterations, min_date, tick_interval, filter_pairs)
#
# Plot the pairs average close prices, and total profit growth
#
avgclose = go.Scattergl(
x=dates,
y=avgclose,
name='Avg close price',
)
profit = go.Scattergl(
x=dates,
y=pg,
name='Profit',
)
fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
fig.append_trace(avgclose, 1, 1)
fig.append_trace(profit, 2, 1)
for pair in pairs:
pg = make_profit_array(data, num_iterations, min_date, tick_interval, pair)
pair_profit = go.Scattergl(
x=dates,
y=pg,
name=pair,
)
fig.append_trace(pair_profit, 3, 1)
plot(fig, filename='freqtrade-profit-plot.html')
def define_index(min_date: int, max_date: int, interval: int) -> int:
"""
Return the index of a specific date
"""
return int((max_date - min_date) / (interval * 60))
def plot_parse_args(args: List[str]) -> Namespace:
"""
Parse args passed to the script
:param args: Cli arguments
:return: args: Array with all arguments
"""
arguments = Arguments(args, 'Graph profits')
arguments.scripts_options()
arguments.common_args_parser()
arguments.optimizer_shared_options(arguments.parser)
arguments.backtesting_options(arguments.parser)
return arguments.parse_args()
def main(sysargv: List[str]) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
logger.info('Starting Plot Dataframe')
plot_profit(
plot_parse_args(sysargv)
)
if __name__ == '__main__':
main(sys.argv[1:])

2
setup.py
View File

@ -35,7 +35,7 @@ setup(name='freqtrade',
'TA-Lib',
'tabulate',
'cachetools',
'pymarketcap',
'coinmarketcap',
],
include_package_data=True,
zip_safe=False,

208
setup.sh Executable file
View File

@ -0,0 +1,208 @@
#!/usr/bin/env bash
#encoding=utf8
function updateenv () {
echo "
-------------------------
Update your virtual env
-------------------------
"
source .env/bin/activate
pip3.6 install --upgrade pip
pip3 install -r requirements.txt --upgrade
pip3 install -r requirements.txt
pip3 install -e .
}
# Install tab lib
function install_talib () {
curl -O -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib && ./configure --prefix=/usr && make && sudo make install
cd .. && rm -rf ./ta-lib*
}
# Install bot MacOS
function install_macos () {
if [ ! -x "$(command -v brew)" ]
then
echo "-------------------------"
echo "Install Brew"
echo "-------------------------"
echo
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
fi
brew install python3 wget ta-lib
}
# Install bot Debian_ubuntu
function install_debian () {
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
install_talib
}
# Upgrade the bot
function update () {
git pull
updateenv
}
# Reset Develop or Master branch
function reset () {
echo "----------------------------"
echo "Reset branch and virtual env"
echo "----------------------------"
echo
if [ "1" == $(git branch -vv |grep -cE "\* develop|\* master") ]
then
if [ -d ".env" ]; then
echo "- Delete your previous virtual env"
rm -rf .env
fi
git fetch -a
if [ "1" == $(git branch -vv |grep -c "* develop") ]
then
echo "- Hard resetting of 'develop' branch."
git reset --hard origin/develop
elif [ "1" == $(git branch -vv |grep -c "* master") ]
then
echo "- Hard resetting of 'master' branch."
git reset --hard origin/master
fi
else
echo "Reset ignored because you are not on 'master' or 'develop'."
fi
python3.6 -m venv .env
updateenv
}
function config_generator () {
echo "Starting to generate config.json"
echo "-------------------------"
echo "General configuration"
echo "-------------------------"
echo
read -p "Max open trades: (Default: 3) " max_trades
read -p "Stake amount: (Default: 0.05) " stake_amount
read -p "Stake currency: (Default: BTC) " stake_currency
read -p "Fiat currency: (Default: USD) " fiat_currency
echo "------------------------"
echo "Bittrex config generator"
echo "------------------------"
echo
read -p "Exchange API key: " api_key
read -p "Exchange API Secret: " api_secret
echo "-------------------------"
echo "Telegram config generator"
echo "-------------------------"
read -p "Telegram Token: " token
read -p "Telegram Chat_id: " chat_id
sed -e "s/\"max_open_trades\": 3,/\"max_open_trades\": $max_trades,/g" \
-e "s/\"stake_amount\": 0.05,/\"stake_amount\": $stake_amount,/g" \
-e "s/\"stake_currency\": \"BTC\",/\"stake_currency\": \"$stake_currency\",/g" \
-e "s/\"fiat_display_currency\": \"USD\",/\"fiat_display_currency\": \"$fiat_currency\",/g" \
-e "s/\"your_exchange_key\"/\"$api_key\"/g" \
-e "s/\"your_exchange_secret\"/\"$api_secret\"/g" \
-e "s/\"your_telegram_token\"/\"$token\"/g" \
-e "s/\"your_telegram_chat_id\"/\"$chat_id\"/g" \
-e "s/\"dry_run\": false,/\"dry_run\": true,/g" config.json.example > config.json
}
function config () {
if [ -f config.json ]
then
read -p "A config file already exist, do you want to override it [Y/N]? "
if [[ $REPLY =~ ^[Yy]$ ]]
then
config_generator
else
echo "Configuration of config.json ignored."
fi
else
config_generator
fi
echo "Edit ./config.json to modify Pair and other configurations."
}
function install () {
echo "-------------------------"
echo "Install mandatory dependencies"
echo "-------------------------"
echo
if [ "$(uname -s)" == "Darwin" ]
then
echo "- You are on macOS"
install_macos
elif [ -x "$(command -v apt-get)" ]
then
echo "- You are on Debian/Ubuntu"
install_debian
else
echo "This script does not support your OS."
echo "If you have Python3.6, pip, virtualenv, ta-lib you can continue."
echo "Wait 10 seconds to continue the next install steps or use ctrl+c to interrupt this shell."
sleep 10
fi
reset
echo "
- Install complete.
"
config
echo "You can now use the bot by executing 'source .env/bin/activate; python3 freqtrade/main.py'."
}
function plot () {
echo "
-----------------------------------------
Install dependencies for Plotting scripts
-----------------------------------------
"
pip install plotly --upgrade
}
function help () {
echo "usage:"
echo " -i,--install Install freqtrade from scratch"
echo " -u,--update Command git pull to update."
echo " -r,--reset Hard reset your develop/master branch."
echo " -c,--config Easy config generator (Will override your existing file)."
echo " -p,--plot Install dependencies for Plotting scripts."
}
case $* in
--install|-i)
install
;;
--config|-c)
config
;;
--update|-u)
update
;;
--reset|-r)
reset
;;
--plot|-p)
plot
;;
*)
help
;;
esac
exit 0

0
user_data/data/.gitkeep Normal file
View File

0
freqtrade/optimize/hyperopt_conf.py → user_data/hyperopt_conf.py
View File

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