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Merge branch 'develop' of https://github.com/freqtrade/freqtrade into Bo

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This commit is contained in:
Bo van Hasselt 2021-04-22 09:31:41 +02:00
commit 2b7a223bc9
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12
.dockerignore
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@ -1,9 +1,8 @@
.git
.gitignore
Dockerfile
Dockerfile.armhf
.dockerignore
config.json*
*.sqlite
.coveragerc
.eggs
.github
@ -13,4 +12,13 @@ CONTRIBUTING.md
MANIFEST.in
README.md
freqtrade.service
freqtrade.egg-info
config.json*
*.sqlite
user_data
*.log
.vscode
.mypy_cache
.ipynb_checkpoints

3
.gitattributes vendored Normal file
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@ -0,0 +1,3 @@
*.py eol=lf
*.sh eol=lf
*.ps1 eol=crlf

26
.github/workflows/ci.yml vendored
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@ -102,7 +102,7 @@ jobs:
mypy freqtrade scripts
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -148,6 +148,7 @@ jobs:
- name: Installation - macOS
run: |
brew update
brew install hdf5 c-blosc
python -m pip install --upgrade pip
export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
@ -194,7 +195,7 @@ jobs:
mypy freqtrade scripts
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -257,7 +258,7 @@ jobs:
mypy freqtrade scripts
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -288,7 +289,7 @@ jobs:
mkdocs build
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
@ -300,7 +301,7 @@ jobs:
runs-on: ubuntu-20.04
steps:
- name: Cleanup previous runs on this branch
uses: rokroskar/workflow-run-cleanup-action@v0.2.2
uses: rokroskar/workflow-run-cleanup-action@v0.3.2
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
env:
GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
@ -310,9 +311,18 @@ jobs:
needs: [ build_linux, build_macos, build_windows, docs_check ]
runs-on: ubuntu-20.04
steps:
- name: Check user permission
id: check
uses: scherermichael-oss/action-has-permission@1.0.6
with:
required-permission: write
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
uses: lazy-actions/slatify@v3.0.0
if: always() && steps.check.outputs.has-permission && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}
job_name: '*Freqtrade CI*'
@ -398,7 +408,7 @@ jobs:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
uses: lazy-actions/slatify@v3.0.0
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
with:
type: ${{ job.status }}

28
Dockerfile
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@ -1,14 +1,23 @@
FROM python:3.9.2-slim-buster as base
FROM python:3.9.4-slim-buster as base
# Setup env
ENV LANG C.UTF-8
ENV LC_ALL C.UTF-8
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONFAULTHANDLER 1
ENV PATH=/root/.local/bin:$PATH
ENV PATH=/home/ftuser/.local/bin:$PATH
ENV FT_APP_ENV="docker"
# Prepare environment
RUN mkdir /freqtrade
RUN mkdir /freqtrade \
&& apt update \
&& apt install -y sudo \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m ftuser \
&& chown ftuser:ftuser /freqtrade \
# Allow sudoers
&& echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
WORKDIR /freqtrade
# Install dependencies
@ -24,7 +33,8 @@ RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt requirements-hyperopt.txt /freqtrade/
COPY --chown=ftuser:ftuser requirements.txt requirements-hyperopt.txt /freqtrade/
USER ftuser
RUN pip install --user --no-cache-dir numpy \
&& pip install --user --no-cache-dir -r requirements-hyperopt.txt
@ -33,13 +43,13 @@ FROM base as runtime-image
COPY --from=python-deps /usr/local/lib /usr/local/lib
ENV LD_LIBRARY_PATH /usr/local/lib
COPY --from=python-deps /root/.local /root/.local
COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
USER ftuser
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir \
COPY --chown=ftuser:ftuser . /freqtrade/
RUN pip install -e . --user --no-cache-dir \
&& mkdir /freqtrade/user_data/ \
&& freqtrade install-ui

31
Dockerfile.armhf
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@ -1,19 +1,24 @@
FROM --platform=linux/arm/v7 python:3.7.9-slim-buster as base
FROM --platform=linux/arm/v7 python:3.7.10-slim-buster as base
# Setup env
ENV LANG C.UTF-8
ENV LC_ALL C.UTF-8
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONFAULTHANDLER 1
ENV PATH=/root/.local/bin:$PATH
ENV PATH=/home/ftuser/.local/bin:$PATH
ENV FT_APP_ENV="docker"
# Prepare environment
RUN mkdir /freqtrade
WORKDIR /freqtrade
RUN mkdir /freqtrade \
&& apt-get update \
&& apt-get -y install libatlas3-base curl sqlite3 libhdf5-serial-dev sudo \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m ftuser \
&& chown ftuser:ftuser /freqtrade \
# Allow sudoers
&& echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
RUN apt-get update \
&& apt-get -y install libatlas3-base curl sqlite3 \
&& apt-get clean
WORKDIR /freqtrade
# Install dependencies
FROM base as python-deps
@ -28,7 +33,8 @@ RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt /freqtrade/
COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
USER ftuser
RUN pip install --user --no-cache-dir numpy \
&& pip install --user --no-cache-dir -r requirements.txt
@ -37,11 +43,14 @@ FROM base as runtime-image
COPY --from=python-deps /usr/local/lib /usr/local/lib
ENV LD_LIBRARY_PATH /usr/local/lib
COPY --from=python-deps /root/.local /root/.local
COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
USER ftuser
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir \
COPY --chown=ftuser:ftuser . /freqtrade/
RUN pip install -e . --user --no-cache-dir \
&& mkdir /freqtrade/user_data/ \
&& freqtrade install-ui
ENTRYPOINT ["freqtrade"]

2
README.md
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@ -1,4 +1,4 @@
# Freqtrade
# ![freqtrade](docs/assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)

5
config_full.json.example
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@ -50,6 +50,7 @@
"sell": "limit",
"emergencysell": "market",
"forcesell": "market",
"forcebuy": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_on_exchange_interval": 60
@ -112,7 +113,7 @@
"password": "",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": false,
"enableRateLimit": true,
"rateLimit": 500,
"aiohttp_trust_env": false
},
@ -162,7 +163,9 @@
"warning": "on",
"startup": "on",
"buy": "on",
"buy_fill": "on",
"sell": "on",
"sell_fill": "on",
"buy_cancel": "on",
"sell_cancel": "on"
}

2
docker-compose.yml
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@ -9,7 +9,7 @@ services:
# Build step - only needed when additional dependencies are needed
# build:
# context: .
# dockerfile: "./docker/Dockerfile.technical"
# dockerfile: "./docker/Dockerfile.custom"
restart: unless-stopped
container_name: freqtrade
volumes:

10
docker/Dockerfile.custom Normal file
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@ -0,0 +1,10 @@
FROM freqtradeorg/freqtrade:develop
# Switch user to root if you must install something from apt
# Don't forget to switch the user back below!
# USER root
# The below dependency - pyti - serves as an example. Please use whatever you need!
RUN pip install --user pyti
# USER ftuser

4
docker/Dockerfile.develop
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@ -3,8 +3,8 @@ FROM freqtradeorg/freqtrade:develop
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements-dev.txt --no-cache-dir
RUN pip install numpy --user --no-cache-dir \
&& pip install -r requirements-dev.txt --user --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

2
docker/Dockerfile.jupyter
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@ -1,7 +1,7 @@
FROM freqtradeorg/freqtrade:develop_plot
RUN pip install jupyterlab --no-cache-dir
RUN pip install jupyterlab --user --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

2
docker/Dockerfile.plot
View File

@ -4,4 +4,4 @@ FROM freqtradeorg/freqtrade:${sourceimage}
# Install dependencies
COPY requirements-plot.txt /freqtrade/
RUN pip install -r requirements-plot.txt --no-cache-dir
RUN pip install -r requirements-plot.txt --user --no-cache-dir

6
docker/Dockerfile.technical
View File

@ -1,6 +0,0 @@
FROM freqtradeorg/freqtrade:develop
RUN apt-get update \
&& apt-get -y install git \
&& apt-get clean \
&& pip install git+https://github.com/freqtrade/technical

340
docs/advanced-hyperopt.md
View File

@ -4,34 +4,6 @@ This page explains some advanced Hyperopt topics that may require higher
coding skills and Python knowledge than creation of an ordinal hyperoptimization
class.
## Derived hyperopt classes
Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
```python
class MyAwesomeHyperOpt(IHyperOpt):
...
# Uses default stoploss dimension
class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
@staticmethod
def stoploss_space() -> List[Dimension]:
# Override boundaries for stoploss
return [
Real(-0.33, -0.01, name='stoploss'),
]
```
and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
```
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
```
## Creating and using a custom loss function
To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
@ -97,3 +69,315 @@ This function needs to return a floating point number (`float`). Smaller numbers
!!! Note
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
## Overriding pre-defined spaces
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
def stoploss_space(self):
return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]
```
## Space options
For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
* `Categorical` - Pick from a list of categories (e.g. `Categorical(['a', 'b', 'c'], name="cat")`)
* `Integer` - Pick from a range of whole numbers (e.g. `Integer(1, 10, name='rsi')`)
* `SKDecimal` - Pick from a range of decimal numbers with limited precision (e.g. `SKDecimal(0.1, 0.5, decimals=3, name='adx')`). *Available only with freqtrade*.
* `Real` - Pick from a range of decimal numbers with full precision (e.g. `Real(0.1, 0.5, name='adx')`
You can import all of these from `freqtrade.optimize.space`, although `Categorical`, `Integer` and `Real` are only aliases for their corresponding scikit-optimize Spaces. `SKDecimal` is provided by freqtrade for faster optimizations.
``` python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
```
!!! Hint "SKDecimal vs. Real"
We recommend to use `SKDecimal` instead of the `Real` space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.
Assuming the definition of a rather small space (`SKDecimal(0.10, 0.15, decimals=2, name='xxx')`) - SKDecimal will have 5 possibilities (`[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]`).
A corresponding real space `Real(0.10, 0.15 name='xxx')` on the other hand has an almost unlimited number of possibilities (`[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]`).
---
## Legacy Hyperopt
This Section explains the configuration of an explicit Hyperopt file (separate to the strategy).
!!! Warning "Deprecated / legacy mode"
Since the 2021.4 release you no longer have to write a separate hyperopt class, but all strategies can be hyperopted.
Please read the [main hyperopt page](hyperopt.md) for more details.
### Prepare hyperopt file
Configuring an explicit hyperopt file is similar to writing your own strategy, and many tasks will be similar.
!!! Tip "About this page"
For this page, we will be using a fictional strategy called `AwesomeStrategy` - which will be optimized using the `AwesomeHyperopt` class.
#### Create a Custom Hyperopt File
The simplest way to get started is to use the following command, which will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
Let assume you want a hyperopt file `AwesomeHyperopt.py`:
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
#### Legacy Hyperopt checklist
Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required:
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimization
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimization
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
Optional in hyperopt - can also be loaded from a strategy (recommended):
* `populate_indicators` - fallback to create indicators
* `populate_buy_trend` - fallback if not optimizing for buy space. should come from strategy
* `populate_sell_trend` - fallback if not optimizing for sell space. should come from strategy
!!! Note
You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
#### Defining a buy signal optimization
Let's say you are curious: should you use MACD crossings or lower Bollinger
Bands to trigger your buys. And you also wonder should you use RSI or ADX to
help with those buy decisions. If you decide to use RSI or ADX, which values
should I use for them? So let's use hyperparameter optimization to solve this
mystery.
We will start by defining a search space:
```python
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(20, 40, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
]
```
Above definition says: I have five parameters I want you to randomly combine
to find the best combination. Two of them are integer values (`adx-value` and `rsi-value`) and I want you test in the range of values 20 to 40.
Then we have three category variables. First two are either `True` or `False`.
We use these to either enable or disable the ADX and RSI guards.
The last one we call `trigger` and use it to decide which buy trigger we want to use.
So let's write the buy strategy generator using these values:
```python
@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, metadata: dict) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
```
Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.
It will use the given historical data and make buys based on the buy signals generated with the above function.
Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
!!! Note
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in your strategy or hyperopt file.
#### Sell optimization
Similar to the buy-signal above, sell-signals can also be optimized.
Place the corresponding settings into the following methods
* Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
* Within `sell_strategy_generator()` - populate the nested method `populate_sell_trend()` to apply the parameters.
The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
### Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.
The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.
Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
Hyperopt will store hyperopt results with the timestamp of the hyperopt start time.
Reading commands (`hyperopt-list`, `hyperopt-show`) can use `--hyperopt-filename <filename>` to read and display older hyperopt results.
You can find a list of filenames with `ls -l user_data/hyperopt_results/`.
#### Running Hyperopt using methods from a strategy
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --hyperopt AwesomeHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy AwesomeStrategy
```
### Understand the Hyperopt Result
Once Hyperopt is completed you can use the result to create a new strategy.
Given the following result from hyperopt:
```
Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
```
You should understand this result like:
* The buy trigger that worked best was `bb_lower`.
* You should not use ADX because `adx-enabled: False`)
* You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to.
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
```python
(dataframe['rsi'] < 29.0)
```
Translating your whole hyperopt result as the new buy-signal would then look like:
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 29.0) & # rsi-value
dataframe['close'] < dataframe['bb_lowerband'] # trigger
),
'buy'] = 1
return dataframe
```
### Validate backtesting results
Once the optimized parameters and conditions have been implemented into your strategy, you should backtest the strategy to make sure everything is working as expected.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
Should results don't match, please double-check to make sure you transferred all conditions correctly.
Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
### Sharing methods with your strategy
Hyperopt classes provide access to the Strategy via the `strategy` class attribute.
This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.
``` python
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
buy_params = {
'rsi-value': 30,
'adx-value': 35,
}
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return self.buy_strategy_generator(self.buy_params, dataframe, metadata)
@staticmethod
def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
qtpylib.crossed_above(dataframe['rsi'], params['rsi-value']) &
dataframe['adx'] > params['adx-value']) &
dataframe['volume'] > 0
)
, 'buy'] = 1
return dataframe
class MyAwesomeHyperOpt(IHyperOpt):
...
@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, metadata: dict) -> DataFrame:
# Call strategy's buy strategy generator
return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)
return populate_buy_trend
```

3
docs/assets/ccxt-logo.svg Normal file
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@ -15,15 +15,16 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--eps] [--dmmp] [--enable-protections]
[-p PAIRS [PAIRS ...]] [--eps] [--dmmp]
[--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
@ -37,6 +38,9 @@ optional arguments:
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
@ -48,6 +52,9 @@ optional arguments:
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a space-separated list of strategies to
backtest. Please note that ticker-interval needs to be
@ -91,8 +98,7 @@ Strategy arguments:
## Test your strategy with Backtesting
Now you have good Buy and Sell strategies and some historic data, you want to test it against
real data. This is what we call
[backtesting](https://en.wikipedia.org/wiki/Backtesting).
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
@ -100,6 +106,8 @@ For details on downloading, please refer to the [Data Downloading](data-download
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
!!! Warning "Using dynamic pairlists for backtesting"
Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
@ -107,38 +115,56 @@ The result of backtesting will confirm if your bot has better odds of making a p
To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
### Run a backtesting against the currencies listed in your config file
### Starting balance
#### With 5 min candle (OHLCV) data (per default)
Backtesting will require a starting balance, which can be provided as `--dry-run-wallet <balance>` or `--starting-balance <balance>` command line argument, or via `dry_run_wallet` configuration setting.
This amount must be higher than `stake_amount`, otherwise the bot will not be able to simulate any trade.
### Dynamic stake amount
Backtesting supports [dynamic stake amount](configuration.md#dynamic-stake-amount) by configuring `stake_amount` as `"unlimited"`, which will split the starting balance into `max_open_trades` pieces.
Profits from early trades will result in subsequent higher stake amounts, resulting in compounding of profits over the backtesting period.
### Example backtesting commands
With 5 min candle (OHLCV) data (per default)
```bash
freqtrade backtesting
freqtrade backtesting --strategy AwesomeStrategy
```
#### With 1 min candle (OHLCV) data
Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
---
With 1 min candle (OHLCV) data
```bash
freqtrade backtesting --timeframe 1m
freqtrade backtesting --strategy AwesomeStrategy --timeframe 1m
```
#### Using a different on-disk historical candle (OHLCV) data source
---
Providing a custom starting balance of 1000 (in stake currency)
```bash
freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
```
---
Using a different on-disk historical candle (OHLCV) data source
Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
You can then use this data for backtesting as follows:
```bash
freqtrade --datadir user_data/data/bittrex-20180101 backtesting
freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/bittrex-20180101
```
#### With a (custom) strategy file
---
```bash
freqtrade backtesting -s SampleStrategy
```
Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
#### Comparing multiple Strategies
Comparing multiple Strategies
```bash
freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
@ -146,23 +172,29 @@ freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timefram
Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
#### Exporting trades to file
---
Exporting trades to file
```bash
freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
freqtrade backtesting --strategy backtesting --export trades --config config.json
```
The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
#### Exporting trades to file specifying a custom filename
---
Exporting trades to file specifying a custom filename
```bash
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
freqtrade backtesting --strategy backtesting --export trades --export-filename=backtest_samplestrategy.json
```
Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
#### Supplying custom fee value
---
Supplying custom fee value
Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
@ -177,26 +209,26 @@ freqtrade backtesting --fee 0.001
!!! Note
Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
#### Running backtest with smaller testset by using timerange
---
Use the `--timerange` argument to change how much of the testset you want to use.
Running backtest with smaller test-set by using timerange
Use the `--timerange` argument to change how much of the test-set you want to use.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your input data.
```bash
freqtrade backtesting --timerange=20190501-
```
You can also specify particular dates or a range span indexed by start and stop.
You can also specify particular date ranges.
The full timerange specification:
- Use tickframes till 2018/01/31: `--timerange=-20180131`
- Use tickframes since 2018/01/31: `--timerange=20180131-`
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use tickframes between POSIX timestamps 1527595200 1527618600:
`--timerange=1527595200-1527618600`
- Use data until 2018/01/31: `--timerange=-20180131`
- Use data since 2018/01/31: `--timerange=20180131-`
- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use data between POSIX / epoch timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
## Understand the backtesting result
@ -248,19 +280,30 @@ A backtesting result will look like that:
| Max open trades | 3 |
| | |
| Total trades | 429 |
| Total Profit % | 152.41% |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| Trades per day | 3.575 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 25.27% |
| Worst day | -30.67% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Drawdown | 50.63% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
@ -281,9 +324,9 @@ here:
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums up all the profits/losses.
The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config so `tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
The column `Avg Profit %` shows the average profit for all trades made while the column `Cum Profit %` sums up all the profits/losses.
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
@ -324,19 +367,30 @@ It contains some useful key metrics about performance of your strategy on backte
| Max open trades | 3 |
| | |
| Total trades | 429 |
| Total Profit % | 152.41% |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| Trades per day | 3.575 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 25.27% |
| Worst day | -30.67% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Drawdown | 50.63% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
@ -347,13 +401,21 @@ It contains some useful key metrics about performance of your strategy on backte
- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
- `Total trades`: Identical to the total trades of the backtest output table.
- `Total Profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table.
- `Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
- `Final balance`: Final balance - starting balance + absolute profit.
- `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.
- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
- `Best Trade` / `Worst Trade`: Biggest winning trade and biggest losing trade
- `Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
@ -362,6 +424,7 @@ It contains some useful key metrics about performance of your strategy on backte
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Buys happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Sell-signal sells happen at open-price of the consecutive candle
- Sell-signal is favored over Stoploss, because sell-signals are assumed to trigger on candle's open
- ROI
@ -418,6 +481,5 @@ Detailed output for all strategies one after the other will be available, so mak
## Next step
Great, your strategy is profitable. What if the bot can give your the
optimal parameters to use for your strategy?
Great, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy?
Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)

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@ -53,6 +53,7 @@ This loop will be repeated again and again until the bot is stopped.
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair)
* Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy)
* Loops per candle simulating entry and exit points.
* Generate backtest report output

4
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@ -56,6 +56,7 @@ optional arguments:
usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[--db-url PATH] [--sd-notify] [--dry-run]
[--dry-run-wallet DRY_RUN_WALLET]
optional arguments:
-h, --help show this help message and exit
@ -66,6 +67,9 @@ optional arguments:
--sd-notify Notify systemd service manager.
--dry-run Enforce dry-run for trading (removes Exchange secrets
and simulates trades).
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

62
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@ -40,8 +40,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
| `stake_currency` | **Required.** Crypto-currency used for trading. <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float or `"unlimited"`.
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
@ -49,7 +49,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `timeframe` | The timeframe (former ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
@ -58,15 +58,17 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
| `ask_strategy.bid_last_balance` | Interpolate the selling price. More information [below](#sell-price-without-orderbook-enabled).
| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
@ -142,8 +144,6 @@ Values set in the configuration file always overwrite values set in the strategy
* `process_only_new_candles`
* `order_types`
* `order_time_in_force`
* `stake_currency`
* `stake_amount`
* `unfilledtimeout`
* `disable_dataframe_checks`
* `protections`
@ -157,6 +157,23 @@ Values set in the configuration file always overwrite values set in the strategy
There are several methods to configure how much of the stake currency the bot will use to enter a trade. All methods respect the [available balance configuration](#available-balance) as explained below.
#### Minimum trade stake
The minimum stake amount will depend by exchange and pair, and is usually listed in the exchange support pages.
Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.4$.
The minimum stake amount to buy this pair is therefore `20 * 0.6 ~= 12`.
This exchange has also a limit on USD - where all orders must be > 10$ - which however does not apply in this case.
To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
With a reserve of 5%, the minimum stake amount would be ~12.6$ (`12 * (1 + 0.05)`). If we take in account a stoploss of 10% on top of that - we'd end up with a value of ~14$ (`12.6 / (1 - 0.1)`).
To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
!!! Warning
Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange.
#### Available balance
By default, the bot assumes that the `complete amount - 1%` is at it's disposal, and when using [dynamic stake amount](#dynamic-stake-amount), it will split the complete balance into `max_open_trades` buckets per trade.
@ -219,11 +236,12 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
"tradable_balance_ratio": 0.99,
```
!!! Note
This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available).
!!! Tip "Compounding profits"
This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available), and will result in profit compounding.
!!! Note "When using Dry-Run Mode"
When using `"stake_amount" : "unlimited",` in combination with Dry-Run, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time. It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time.
It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
--8<-- "includes/pricing.md"
@ -278,7 +296,7 @@ For example, if your strategy is using a 1h timeframe, and you only want to buy
### Understand order_types
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`, `forcesell`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`, `emergencysell`, `forcesell`, `forcebuy`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
This allows to buy using limit orders, sell using
limit-orders, and create stoplosses using market orders. It also allows to set the
@ -290,7 +308,7 @@ the buy order is fulfilled.
If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`stoploss_on_exchange`) need to be present, otherwise the bot will fail to start.
For information on (`emergencysell`,`forcesell`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
For information on (`emergencysell`,`forcesell`, `forcebuy`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
Syntax for Strategy:
@ -299,6 +317,7 @@ order_types = {
"buy": "limit",
"sell": "limit",
"emergencysell": "market",
"forcebuy": "market",
"forcesell": "market",
"stoploss": "market",
"stoploss_on_exchange": False,
@ -314,6 +333,7 @@ Configuration:
"buy": "limit",
"sell": "limit",
"emergencysell": "market",
"forcebuy": "market",
"forcesell": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
@ -415,26 +435,6 @@ This configuration enables binance, as well as rate limiting to avoid bans from
Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
#### Advanced Freqtrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.
### What values can be used for fiat_display_currency?
The `fiat_display_currency` configuration parameter sets the base currency to use for the

6
docs/data-download.md
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@ -30,7 +30,7 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
Limit command to these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
@ -48,10 +48,10 @@ optional arguments:
exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
(default: `None`).
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
`None`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

8
docs/docker_quickstart.md
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@ -14,7 +14,7 @@ To simplify running freqtrade, please install [`docker-compose`](https://docs.do
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/stable/docker-compose.yml) ready for usage.
!!! Note
- The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
@ -22,7 +22,7 @@ Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.co
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
Create a new directory and place the [docker-compose file](https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml) in this directory.
=== "PC/MAC/Linux"
``` bash
@ -156,8 +156,8 @@ Head over to the [Backtesting Documentation](backtesting.md) to learn more.
### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) for an example).
If your strategy requires dependencies not included in the default image - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.

15
docs/edge.md
View File

@ -3,7 +3,7 @@
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.
!!! Warning
`Edge positioning` is not compatible with dynamic (volume-based) whitelist.
WHen using `Edge positioning` with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.
!!! Note
`Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
@ -215,16 +215,20 @@ Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet.
usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TIMEFRAME] [--timerange TIMERANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
[--fee FLOAT] [-p PAIRS [PAIRS ...]]
[--stoplosses STOPLOSS_RANGE]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `None`).
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
@ -233,6 +237,9 @@ optional arguments:
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--stoplosses STOPLOSS_RANGE
Defines a range of stoploss values against which edge
will assess the strategy. The format is "min,max,step"

45
docs/exchanges.md
View File

@ -7,10 +7,10 @@ This page combines common gotchas and informations which are exchange-specific a
!!! Tip "Stoploss on Exchange"
Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
### Blacklists
### Binance Blacklist
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
### Binance sites
@ -44,6 +44,10 @@ Due to the heavy rate-limiting applied by Kraken, the following configuration se
Downloading kraken data will require significantly more memory (RAM) than any other exchange, as the trades-data needs to be converted into candles on your machine.
It will also take a long time, as freqtrade will need to download every single trade that happened on the exchange for the pair / timerange combination, therefore please be patient.
!!! Warning "rateLimit tuning"
Please pay attention that rateLimit configuration entry holds delay in milliseconds between requests, NOT requests\sec rate.
So, in order to mitigate Kraken API "Rate limit exceeded" exception, this configuration should be increased, NOT decreased.
## Bittrex
### Order types
@ -96,6 +100,23 @@ To use subaccounts with FTX, you need to edit the configuration and add the foll
}
```
## Kucoin
Kucoin requries a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
```json
"exchange": {
"name": "kucoin",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"password": "your_exchange_api_key_password",
```
### Kucoin Blacklists
For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
@ -118,3 +139,23 @@ Whether your exchange returns incomplete candles or not can be checked using [th
Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.
### Advanced Freqtrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behavior.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.

16
docs/faq.md
View File

@ -1,5 +1,19 @@
# Freqtrade FAQ
## Supported Markets
Freqtrade supports spot trading only.
### Can I open short positions?
No, Freqtrade does not support trading with margin / leverage, and cannot open short positions.
In some cases, your exchange may provide leveraged spot tokens which can be traded with Freqtrade eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD, etc...
### Can I trade options or futures?
No, options and futures trading are not supported.
## Beginner Tips & Tricks
* When you work with your strategy & hyperopt file you should use a proper code editor like VSCode or PyCharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely pointed out by Freqtrade during startup).
@ -142,7 +156,7 @@ freqtrade hyperopt --hyperopt SampleHyperopt --hyperopt-loss SharpeHyperOptLossD
### Why does it take a long time to run hyperopt?
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/zt-mm786y93-Fxo37glxMY9g8OQC5AoOIw) - or the Freqtrade [discord community](https://discord.gg/MA9v74M). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:

401
docs/hyperopt.md
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@ -1,19 +1,22 @@
# Hyperopt
This page explains how to tune your strategy by finding the optimal
parameters, a process called hyperparameter optimization. The bot uses several
algorithms included in the `scikit-optimize` package to accomplish this. The
search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time.
parameters, a process called hyperparameter optimization. The bot uses algorithms included in the `scikit-optimize` package to accomplish this.
The search will burn all your CPU cores, make your laptop sound like a fighter jet and still take a long time.
In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
Hyperopt requires historic data to be available, just as backtesting does.
Hyperopt requires historic data to be available, just as backtesting does (hyperopt runs backtesting many times with different parameters).
To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
!!! Bug
Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
!!! Note
Since 2021.4 release you no longer have to write a separate hyperopt class, but can configure the parameters directly in the strategy.
The legacy method is still supported, but it is no longer the recommended way of setting up hyperopt.
The legacy documentation is available at [Legacy Hyperopt](advanced-hyperopt.md#legacy-hyperopt).
## Install hyperopt dependencies
Since Hyperopt dependencies are not needed to run the bot itself, are heavy, can not be easily built on some platforms (like Raspberry PI), they are not installed by default. Before you run Hyperopt, you need to install the corresponding dependencies, as described in this section below.
@ -34,7 +37,6 @@ pip install -r requirements-hyperopt.txt
## Hyperopt command reference
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
@ -42,8 +44,10 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
[--dmmp] [--enable-protections] [-e INT]
[-p PAIRS [PAIRS ...]] [--hyperopt NAME]
[--hyperopt-path PATH] [--eps] [--dmmp]
[--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
@ -52,8 +56,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
optional arguments:
-h, --help show this help message and exit
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--data-format-ohlcv {json,jsongz,hdf5}
@ -67,6 +70,9 @@ optional arguments:
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--hyperopt NAME Specify hyperopt class name which will be used by the
bot.
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
@ -82,6 +88,9 @@ optional arguments:
Enable protections for backtesting.Will slow
backtesting down by a considerable amount, but will
include configured protections
--dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
Starting balance, used for backtesting / hyperopt and
dry-runs.
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
Specify which parameters to hyperopt. Space-separated
@ -100,7 +109,8 @@ optional arguments:
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--hyperopt-loss NAME Specify the class name of the hyperopt loss function
--hyperopt-loss NAME, --hyperoptloss NAME
Specify the class name of the hyperopt loss function
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
@ -133,47 +143,19 @@ Strategy arguments:
```
## Prepare Hyperopting
Before we start digging into Hyperopt, we recommend you to take a look at
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt.py).
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar.
!!! Tip "About this page"
For this page, we will be using a fictional strategy called `AwesomeStrategy` - which will be optimized using the `AwesomeHyperopt` class.
The simplest way to get started is to use the following, command, which will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
### Hyperopt checklist
Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required:
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimization
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimization
* define parameters with `space='buy'` - for buy signal optimization
* define parameters with `space='sell'` - for sell signal optimization
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
`populate_indicators` needs to create all indicators any of the spaces may use, otherwise hyperopt will not work.
Optional in hyperopt - can also be loaded from a strategy (recommended):
* `populate_indicators` - fallback to create indicators
* `populate_buy_trend` - fallback if not optimizing for buy space. should come from strategy
* `populate_sell_trend` - fallback if not optimizing for sell space. should come from strategy
!!! Note
You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
Rarely you may also need to override:
Rarely you may also need to create a [nested class](advanced-hyperopt.md#overriding-pre-defined-spaces) named `HyperOpt` and implement
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
@ -181,31 +163,19 @@ Rarely you may also need to override:
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations.
You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
```python
# Have a working strategy at hand.
freqtrade new-hyperopt --hyperopt EmptyHyperopt
freqtrade hyperopt --hyperopt EmptyHyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
```
### Create a Custom Hyperopt File
Let assume you want a hyperopt file `AwesomeHyperopt.py`:
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
This command will create a new hyperopt file from a template, allowing you to get started quickly.
### Configure your Guards and Triggers
There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
There are two places you need to change in your strategy file to add a new buy hyperopt for testing:
* Inside `indicator_space()` - the parameters hyperopt shall be optimizing.
* Within `buy_strategy_generator()` - populate the nested `populate_buy_trend()` to apply the parameters.
* Define the parameters at the class level hyperopt shall be optimizing.
* Within `populate_buy_trend()` - use defined parameter values instead of raw constants.
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
@ -221,24 +191,46 @@ Hyper-optimization will, for each epoch round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like "*buy exactly when close price touches lower Bollinger band, BUT only if
ADX > 10*".
If you have updated the buy strategy, i.e. changed the contents of `populate_buy_trend()` method, you have to update the `guards` and `triggers` your hyperopt must use correspondingly.
```python
from freqtrade.strategy import IntParameter, IStrategy
class MyAwesomeStrategy(IStrategy):
# If parameter is prefixed with `buy_` or `sell_` then specifying `space` parameter is optional
# and space is inferred from parameter name.
buy_adx_min = IntParameter(0, 100, default=10)
def populate_buy_trend(self, dataframe: 'DataFrame', metadata: dict) -> 'DataFrame':
dataframe.loc[
(
(dataframe['adx'] > self.buy_adx_min.value)
), 'buy'] = 1
return dataframe
```
#### Sell optimization
Similar to the buy-signal above, sell-signals can also be optimized.
Place the corresponding settings into the following methods
* Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
* Within `sell_strategy_generator()` - populate the nested method `populate_sell_trend()` to apply the parameters.
* Define the parameters at the class level hyperopt shall be optimizing.
* Within `populate_sell_trend()` - use defined parameter values instead of raw constants.
The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
#### Using timeframe as a part of the Strategy
```python
class MyAwesomeStrategy(IStrategy):
# There is no strict parameter naming scheme. If you do not use `buy_` or `sell_` prefixes -
# please specify to which space parameter belongs using `space` parameter. Possible values:
# 'buy' or 'sell'.
adx_max = IntParameter(0, 100, default=50, space='sell')
The Strategy class exposes the timeframe value as the `self.timeframe` attribute.
The same value is available as class-attribute `HyperoptName.timeframe`.
In the case of the linked sample-value this would be `AwesomeHyperopt.timeframe`.
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] < self.adx_max.value)
), 'buy'] = 1
return dataframe
```
## Solving a Mystery
@ -248,50 +240,38 @@ help with those buy decisions. If you decide to use RSI or ADX, which values
should I use for them? So let's use hyperparameter optimization to solve this
mystery.
We will start by defining a search space:
We will start by defining hyperoptable parameters:
```python
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(20, 40, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
]
class MyAwesomeStrategy(IStrategy):
buy_adx = IntParameter(20, 40, default=30)
buy_rsi = IntParameter(20, 40, default=30)
buy_adx_enabled = CategoricalParameter([True, False]),
buy_rsi_enabled = CategoricalParameter([True, False]),
buy_trigger = CategoricalParameter(['bb_lower', 'macd_cross_signal']),
```
Above definition says: I have five parameters I want you to randomly combine
to find the best combination. Two of them are integer values (`adx-value`
and `rsi-value`) and I want you test in the range of values 20 to 40.
Above definition says: I have five parameters I want to randomly combine to find the best combination.
Two of them are integer values (`buy_adx` and `buy_rsi`) and I want you test in the range of values 20 to 40.
Then we have three category variables. First two are either `True` or `False`.
We use these to either enable or disable the ADX and RSI guards. The last
one we call `trigger` and use it to decide which buy trigger we want to use.
We use these to either enable or disable the ADX and RSI guards.
The last one we call `trigger` and use it to decide which buy trigger we want to use.
So let's write the buy strategy using these values:
```python
@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:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
if self.buy_adx_enabled.value:
conditions.append(dataframe['adx'] > self.buy_adx.value)
if self.buy_rsi_enabled.value:
conditions.append(dataframe['rsi'] < self.buy_rsi.value)
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
if self.buy_trigger.value == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
if self.buy_trigger.value == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
@ -305,8 +285,6 @@ So let's write the buy strategy using these values:
'buy'] = 1
return dataframe
return populate_buy_trend
```
Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.
@ -318,6 +296,24 @@ Based on the results, hyperopt will tell you which parameter combination produce
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in your strategy or hyperopt file.
## Parameter types
There are four parameter types each suited for different purposes.
* `IntParameter` - defines an integral parameter with upper and lower boundaries of search space.
* `DecimalParameter` - defines a floating point parameter with a limited number of decimals (default 3). Should be preferred instead of `RealParameter` in most cases.
* `RealParameter` - defines a floating point parameter with upper and lower boundaries and no precision limit. Rarely used as it creates a space with a near infinite number of possibilities.
* `CategoricalParameter` - defines a parameter with a predetermined number of choices.
!!! Tip "Disabling parameter optimization"
Each parameter takes two boolean parameters:
* `load` - when set to `False` it will not load values configured in `buy_params` and `sell_params`.
* `optimize` - when set to `False` parameter will not be included in optimization process.
Use these parameters to quickly prototype various ideas.
!!! Warning
Hyperoptable parameters cannot be used in `populate_indicators` - as hyperopt does not recalculate indicators for each epoch, so the starting value would be used in this case.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
@ -339,16 +335,14 @@ Creation of a custom loss function is covered in the [Advanced Hyperopt](advance
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result.
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
freqtrade hyperopt --config config.json --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.
The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.
Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
@ -362,24 +356,17 @@ The `--spaces all` option determines that all possible parameters should be opti
### Execute Hyperopt with different historical data source
If you would like to hyperopt parameters using an alternate historical data set that
you have on-disk, use the `--datadir PATH` option. By default, hyperopt
uses data from directory `user_data/data`.
you have on-disk, use the `--datadir PATH` option. By default, hyperopt uses data from directory `user_data/data`.
### Running Hyperopt with a smaller test-set
Use the `--timerange` argument to change how much of the test-set you want to use.
For example, to use one month of data, pass the following parameter to the hyperopt call:
For example, to use one month of data, pass `--timerange 20210101-20210201` (from january 2021 - february 2021) to the hyperopt call.
Full command:
```bash
freqtrade hyperopt --hyperopt <hyperoptname> --strategy <strategyname> --timerange 20180401-20180501
```
### Running Hyperopt using methods from a strategy
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --hyperopt AwesomeHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy AwesomeStrategy
freqtrade hyperopt --hyperopt <hyperoptname> --strategy <strategyname> --timerange 20210101-20210201
```
### Running Hyperopt with Smaller Search Space
@ -402,40 +389,9 @@ Legal values are:
The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
### Position stacking and disabling max market positions
In some situations, you may need to run Hyperopt (and Backtesting) with the
`--eps`/`--enable-position-staking` and `--dmmp`/`--disable-max-market-positions` arguments.
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
open trade is allowed for every traded pair. The total number of trades open for all pairs
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
some potential trades to be hidden (or masked) by previously open trades.
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
during Hyperopt/Backtesting (which is equal to setting `max_open_trades` to a very high
number).
!!! Note
Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
You can also enable position stacking in the configuration file by explicitly setting
`"position_stacking"=true`.
### Reproducible results
The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyper-optimization results with same random state value used.
## Understand the Hyperopt Result
Once Hyperopt is completed you can use the result to create a new strategy.
Once Hyperopt is completed you can use the result to update your strategy.
Given the following result from hyperopt:
```
@ -443,49 +399,38 @@ Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
# Buy hyperspace params:
buy_params = {
'buy_adx': 44,
'buy_rsi': 29,
'buy_adx_enabled': False,
'buy_rsi_enabled': True,
'buy_trigger': 'bb_lower'
}
```
You should understand this result like:
- The buy trigger that worked best was `bb_lower`.
- You should not use ADX because `adx-enabled: False`)
- You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
* The buy trigger that worked best was `bb_lower`.
* You should not use ADX because `'buy_adx_enabled': False`.
* You should **consider** using the RSI indicator (`'buy_rsi_enabled': True`) and the best value is `29.0` (`'buy_rsi': 29.0`)
You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to.
Your strategy class can immediately take advantage of these results. Simply copy hyperopt results block and paste them at class level, replacing old parameters (if any). New parameters will automatically be loaded next time strategy is executed.
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
Transferring your whole hyperopt result to your strategy would then look like:
```python
(dataframe['rsi'] < 29.0)
class MyAwesomeStrategy(IStrategy):
# Buy hyperspace params:
buy_params = {
'buy_adx': 44,
'buy_rsi': 29,
'buy_adx_enabled': False,
'buy_rsi_enabled': True,
'buy_trigger': 'bb_lower'
}
```
Translating your whole hyperopt result as the new buy-signal would then look like:
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 29.0) & # rsi-value
dataframe['close'] < dataframe['bb_lowerband'] # trigger
),
'buy'] = 1
return dataframe
```
By default, hyperopt prints colorized results -- epochs with positive profit are printed in the green color. This highlighting helps you find epochs that can be interesting for later analysis. Epochs with zero total profit or with negative profits (losses) are printed in the normal color. If you do not need colorization of results (for instance, when you are redirecting hyperopt output to a file) you can switch colorization off by specifying the `--no-color` option in the command line.
You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
!!! Note "Windows and color output"
Windows does not support color-output natively, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
### Understand Hyperopt ROI results
If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
@ -495,11 +440,13 @@ Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
ROI table:
{ 0: 0.10674,
# ROI table:
minimal_roi = {
0: 0.10674,
21: 0.09158,
78: 0.03634,
118: 0}
118: 0
}
```
In order to use this best ROI table found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `minimal_roi` attribute of your custom strategy:
@ -519,13 +466,13 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
#### Default ROI Search Space
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the timeframe used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the timeframe used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 3 digits after the decimal point):
| # step | 1m | | 5m | | 1h | | 1d | |
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
| 1 | 0 | 0.01161...0.11992 | 0 | 0.03...0.31 | 0 | 0.06883...0.71124 | 0 | 0.12178...1.25835 |
| 2 | 2...8 | 0.00774...0.04255 | 10...40 | 0.02...0.11 | 120...480 | 0.04589...0.25238 | 2880...11520 | 0.08118...0.44651 |
| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
| ------ | ------ | ------------- | -------- | ----------- | ---------- | ------------- | ------------ | ------------- |
| 1 | 0 | 0.011...0.119 | 0 | 0.03...0.31 | 0 | 0.068...0.711 | 0 | 0.121...1.258 |
| 2 | 2...8 | 0.007...0.042 | 10...40 | 0.02...0.11 | 120...480 | 0.045...0.252 | 2880...11520 | 0.081...0.446 |
| 3 | 4...20 | 0.003...0.015 | 20...100 | 0.01...0.04 | 240...1200 | 0.022...0.091 | 5760...28800 | 0.040...0.162 |
| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
@ -536,6 +483,9 @@ Override the `roi_space()` method if you need components of the ROI tables to va
A sample for these methods can be found in [sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
!!! Note "Reduced search space"
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
### Understand Hyperopt Stoploss results
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
@ -545,13 +495,16 @@ Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
Stoploss: -0.27996
# Buy hyperspace params:
buy_params = {
'buy_adx': 44,
'buy_rsi': 29,
'buy_adx_enabled': False,
'buy_rsi_enabled': True,
'buy_trigger': 'bb_lower'
}
stoploss: -0.27996
```
In order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `stoploss` attribute of your custom strategy:
@ -572,6 +525,9 @@ If you have the `stoploss_space()` method in your custom hyperopt file, remove i
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
!!! Note "Reduced search space"
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
### Understand Hyperopt Trailing Stop results
If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
@ -581,11 +537,11 @@ Best result:
45/100: 606 trades. Avg profit 1.04%. Total profit 0.31555614 BTC ( 630.48Σ%). Avg duration 150.3 mins. Objective: -1.10161
Trailing stop:
{ 'trailing_only_offset_is_reached': True,
'trailing_stop': True,
'trailing_stop_positive': 0.02001,
'trailing_stop_positive_offset': 0.06038}
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.02001
trailing_stop_positive_offset = 0.06038
trailing_only_offset_is_reached = True
```
In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
@ -607,6 +563,49 @@ If you are optimizing trailing stop values, Freqtrade creates the 'trailing' opt
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
!!! Note "Reduced search space"
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
### Reproducible results
The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyper-optimization results with same random state value used.
## Output formatting
By default, hyperopt prints colorized results -- epochs with positive profit are printed in the green color. This highlighting helps you find epochs that can be interesting for later analysis. Epochs with zero total profit or with negative profits (losses) are printed in the normal color. If you do not need colorization of results (for instance, when you are redirecting hyperopt output to a file) you can switch colorization off by specifying the `--no-color` option in the command line.
You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
!!! Note "Windows and color output"
Windows does not support color-output natively, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
## Position stacking and disabling max market positions
In some situations, you may need to run Hyperopt (and Backtesting) with the
`--eps`/`--enable-position-staking` and `--dmmp`/`--disable-max-market-positions` arguments.
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
open trade is allowed for every traded pair. The total number of trades open for all pairs
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
some potential trades to be hidden (or masked) by previously open trades.
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
during Hyperopt/Backtesting (which is equal to setting `max_open_trades` to a very high
number).
!!! Note
Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
You can also enable position stacking in the configuration file by explicitly setting
`"position_stacking"=true`.
## Show details of Hyperopt results
After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` sub-commands. The usage of these sub-commands is described in the [Utils](utils.md#list-hyperopt-results) chapter.

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@ -4,7 +4,7 @@ Pairlist Handlers define the list of pairs (pairlist) that the bot should trade.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
Additionally, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
Additionally, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter), [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
@ -29,6 +29,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
* [`ShuffleFilter`](#shufflefilter)
* [`SpreadFilter`](#spreadfilter)
* [`RangeStabilityFilter`](#rangestabilityfilter)
* [`VolatilityFilter`](#volatilityfilter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility sub-command to test your configuration quickly.
@ -59,6 +60,8 @@ When used in the chain of Pairlist Handlers in a non-leading position (after Sta
When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
The pairlist cache (`refresh_period`) on `VolumePairList` is only applicable to generating pairlists.
Filtering instances (not the first position in the list) will not apply any cache and will always use up-to-date data.
`VolumePairList` is based on the ticker data from exchange, as reported by the ccxt library:
@ -89,6 +92,7 @@ This filter allows freqtrade to ignore pairs until they have been listed for at
#### PerformanceFilter
Sorts pairs by past trade performance, as follows:
1. Positive performance.
2. No closed trades yet.
3. Negative performance.
@ -164,9 +168,32 @@ If the trading range over the last 10 days is <1%, remove the pair from the whit
!!! Tip
This Filter can be used to automatically remove stable coin pairs, which have a very low trading range, and are therefore extremely difficult to trade with profit.
#### VolatilityFilter
Volatility is the degree of historical variation of a pairs over time, is is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
This filter can be used to narrow down your pairs to a certain volatility or avoid very volatile pairs.
In the below example:
If the volatility over the last 10 days is not in the range of 0.05-0.50, remove the pair from the whitelist. The filter is applied every 24h.
```json
"pairlists": [
{
"method": "VolatilityFilter",
"lookback_days": 10,
"min_volatility": 0.05,
"max_volatility": 0.50,
"refresh_period": 86400
}
]
```
### Full example of Pairlist Handlers
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
```json
"exchange": {
@ -189,6 +216,13 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
"min_rate_of_change": 0.01,
"refresh_period": 1440
},
{
"method": "VolatilityFilter",
"lookback_days": 10,
"min_volatility": 0.05,
"max_volatility": 0.50,
"refresh_period": 86400
},
{"method": "ShuffleFilter", "seed": 42}
],
```

4
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@ -103,6 +103,10 @@ A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
The `ask_strategy.bid_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
### Market order pricing
When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.

5
docs/index.md
View File

@ -1,4 +1,5 @@
# Freqtrade
![freqtrade](assets/freqtrade_poweredby.svg)
[![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
@ -39,7 +40,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com)
- [X] [Kraken](https://kraken.com/)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
- [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Community tested

12
docs/plotting.md
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@ -37,7 +37,7 @@ usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
Limit command to these pairs. Pairs are space-
separated.
--indicators1 INDICATORS1 [INDICATORS1 ...]
Set indicators from your strategy you want in the
@ -66,8 +66,7 @@ optional arguments:
--timerange TIMERANGE
Specify what timerange of data to use.
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
--no-trades Skip using trades from backtesting file and DB.
Common arguments:
@ -91,6 +90,7 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
Example:
@ -245,7 +245,7 @@ usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
Limit command to these pairs. Pairs are space-
separated.
--timerange TIMERANGE
Specify what timerange of data to use.
@ -264,8 +264,7 @@ optional arguments:
Specify the source for trades (Can be DB or file
(backtest file)) Default: file
-i TIMEFRAME, --timeframe TIMEFRAME, --ticker-interval TIMEFRAME
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -288,6 +287,7 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
The `-p/--pairs` argument, can be used to limit the pairs that are considered for this calculation.

2
docs/requirements-docs.txt
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@ -1,3 +1,3 @@
mkdocs-material==7.0.3
mkdocs-material==7.1.2
mdx_truly_sane_lists==1.2
pymdown-extensions==8.1.1

22
docs/rest-api.md
View File

@ -125,12 +125,14 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_config` | Reloads the configuration file.
| `trades` | List last trades.
| `trade/<tradeid>` | Get specific trade.
| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `show_config` | Shows part of the current configuration with relevant settings to operation.
| `logs` | Shows last log messages.
| `status` | Lists all open trades.
| `count` | Displays number of trades used and available.
| `locks` | Displays currently locked pairs.
| `delete_lock <lock_id>` | Deletes (disables) the lock by id.
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
| `forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
@ -180,7 +182,12 @@ count
Return the amount of open trades.
daily
Return the amount of open trades.
Return the profits for each day, and amount of trades.
delete_lock
Delete (disable) lock from the database.
:param lock_id: ID for the lock to delete
delete_trade
Delete trade from the database.
@ -202,10 +209,13 @@ forcesell
:param tradeid: Id of the trade (can be received via status command)
locks
Return current locks
logs
Show latest logs.
:param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.
:param limit: Limits log messages to the last <limit> logs. No limit to get the entire log.
pair_candles
Return live dataframe for <pair><timeframe>.
@ -225,6 +235,9 @@ pair_history
performance
Return the performance of the different coins.
ping
simple ping
plot_config
Return plot configuration if the strategy defines one.
@ -261,6 +274,11 @@ strategy
:param strategy: Strategy class name
trade
Return specific trade
:param trade_id: Specify which trade to get.
trades
Return trades history.

4
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@ -6,6 +6,10 @@ With some configuration, freqtrade (in combination with ccxt) provides access to
This document is an overview to configure Freqtrade to be used with sandboxes.
This can be useful to developers and trader alike.
!!! Warning
Sandboxes usually have very low volume, and either a very wide spread, or no orders available at all.
Therefore, sandboxes will usually not do a good job of showing you how a strategy would work in real trading.
## Exchanges known to have a sandbox / testnet
* [binance](https://testnet.binance.vision/)

4
docs/stoploss.md
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@ -55,6 +55,10 @@ This same logic will reapply a stoploss order on the exchange should you cancel
`forcesell` is an optional value, which defaults to the same value as `sell` and is used when sending a `/forcesell` command from Telegram or from the Rest API.
### forcebuy
`forcebuy` is an optional value, which defaults to the same value as `buy` and is used when sending a `/forcebuy` command from Telegram or from the Rest API.
### emergencysell
`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.

166
docs/strategy-advanced.md
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@ -11,14 +11,73 @@ If you're just getting started, please be familiar with the methods described in
!!! Tip
You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
## Storing information
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
```python
class AwesomeStrategy(IStrategy):
# Create custom dictionary
custom_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Check if the entry already exists
if not metadata["pair"] in self.custom_info:
# Create empty entry for this pair
self.custom_info[metadata["pair"]] = {}
if "crosstime" in self.custom_info[metadata["pair"]]:
self.custom_info[metadata["pair"]]["crosstime"] += 1
else:
self.custom_info[metadata["pair"]]["crosstime"] = 1
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
***
### Storing custom information using DatetimeIndex from `dataframe`
Imagine you need to store an indicator like `ATR` or `RSI` into `custom_info`. To use this in a meaningful way, you will not only need the raw data of the indicator, but probably also need to keep the right timestamps.
```python
import talib.abstract as ta
class AwesomeStrategy(IStrategy):
# Create custom dictionary
custom_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# using "ATR" here as example
dataframe['atr'] = ta.ATR(dataframe)
if self.dp.runmode.value in ('backtest', 'hyperopt'):
# add indicator mapped to correct DatetimeIndex to custom_info
self.custom_info[metadata['pair']] = dataframe[['date', 'atr']].set_index('date')
return dataframe
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
See `custom_stoploss` examples below on how to access the saved dataframe columns
## Custom stoploss
A stoploss can only ever move upwards - so if you set it to an absolute profit of 2%, you can never move it below this price.
Also, the traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
The method must return a stoploss value (float / number) with a relative ratio below the current price.
E.g. `current_profit = 0.05` (5% profit) - stoploss returns `0.02` - then you "locked in" a profit of 3% (`0.05 - 0.02 = 0.03`).
The method must return a stoploss value (float / number) as a percentage of the current price.
E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
@ -87,9 +146,9 @@ class AwesomeStrategy(IStrategy):
current_rate: float, current_profit: float, **kwargs) -> float:
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date:
if current_time - timedelta(minutes=120) > trade.open_date_utc:
return -0.05
elif current_time - timedelta(minutes=60) > trade.open_date:
elif current_time - timedelta(minutes=60) > trade.open_date_utc:
return -0.10
return 1
```
@ -148,18 +207,26 @@ class AwesomeStrategy(IStrategy):
return max(min(desired_stoploss, 0.05), 0.025)
```
#### Absolute stoploss
#### Calculating stoploss relative to open price
The below example sets absolute profit levels based on the current profit.
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
#### Stepped stoploss
Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
* Use the regular stoploss until 20% profit is reached
* Once profit is > 40%, stoploss will be at 25%, locking in at least 25% of the profit.
* Once profit is > 25% - stoploss will be 15%.
* Once profit is > 20% - stoploss will be set to 7%.
* Once profit is > 20% - set stoploss to 7% above open price.
* Once profit is > 25% - set stoploss to 15% above open price.
* Once profit is > 40% - set stoploss to 25% above open price.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open
class AwesomeStrategy(IStrategy):
@ -170,15 +237,66 @@ class AwesomeStrategy(IStrategy):
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Calculate as `-desired_stop_from_open + current_profit` to get the distance between current_profit and initial price
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40:
return (-0.25 + current_profit)
if current_profit > 0.25:
return (-0.15 + current_profit)
if current_profit > 0.20:
return (-0.07 + current_profit)
return stoploss_from_open(0.25, current_profit)
elif current_profit > 0.25:
return stoploss_from_open(0.15, current_profit)
elif current_profit > 0.20:
return stoploss_from_open(0.07, current_profit)
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
#### Custom stoploss using an indicator from dataframe example
Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
See: "Storing custom information using DatetimeIndex from `dataframe`" example above) on how to store the indicator into `custom_info`
!!! Warning
only use .iat[-1] in live mode, not in backtesting/hyperopt
otherwise you will look into the future
see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
``` python
from freqtrade.persistence import Trade
from freqtrade.state import RunMode
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
result = 1
if self.custom_info and pair in self.custom_info and trade:
# using current_time directly (like below) will only work in backtesting.
# so check "runmode" to make sure that it's only used in backtesting/hyperopt
if self.dp and self.dp.runmode.value in ('backtest', 'hyperopt'):
relative_sl = self.custom_info[pair].loc[current_time]['atr']
# in live / dry-run, it'll be really the current time
else:
# but we can just use the last entry from an already analyzed dataframe instead
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
# WARNING
# only use .iat[-1] in live mode, not in backtesting/hyperopt
# otherwise you will look into the future
# see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies
relative_sl = dataframe['atr'].iat[-1]
if (relative_sl is not None):
# new stoploss relative to current_rate
new_stoploss = (current_rate-relative_sl)/current_rate
# turn into relative negative offset required by `custom_stoploss` return implementation
result = new_stoploss - 1
return result
```
---
@ -199,7 +317,7 @@ It applies a tight timeout for higher priced assets, while allowing more time to
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
@ -213,21 +331,21 @@ class AwesomeStrategy(IStrategy):
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
```

92
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@ -300,38 +300,7 @@ The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `p
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
The Metadata-dict should not be modified and does not persist information across multiple calls.
Instead, have a look at the section [Storing information](#Storing-information)
### Storing information
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
```python
class AwesomeStrategy(IStrategy):
# Create custom dictionary
cust_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Check if the entry already exists
if not metadata["pair"] in self.cust_info:
# Create empty entry for this pair
self.cust_info[metadata["pair"]] = {}
if "crosstime" in self.cust_info[metadata["pair"]]:
self.cust_info[metadata["pair"]]["crosstime"] += 1
else:
self.cust_info[metadata["pair"]]["crosstime"] = 1
```
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
***
Instead, have a look at the section [Storing information](strategy-advanced.md#Storing-information)
## Additional data (informative_pairs)
@ -467,6 +436,26 @@ if self.dp:
dataframe['best_ask'] = ob['asks'][0][0]
```
The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
``` js
{
'bids': [
[ price, amount ], // [ float, float ]
[ price, amount ],
...
],
'asks': [
[ price, amount ],
[ price, amount ],
//...
],
//...
}
```
Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
!!! Warning "Warning about backtesting"
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
@ -618,6 +607,43 @@ All columns of the informative dataframe will be available on the returning data
***
### *stoploss_from_open()*
Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the open price instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired percentage above the open price.
??? Example "Returning a stoploss relative to the open price from the custom stoploss function"
Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).
If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit)` which will return `0.1157024793`. 11.57% below $121 is $107, which is the same as 7% above $100.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, stoploss_from_open
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# once the profit has risin above 10%, keep the stoploss at 7% above the open price
if current_profit > 0.10:
return stoploss_from_open(0.07, current_profit)
return 1
```
Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -709,7 +735,7 @@ To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
!!! Warning
Locking pairs is not available during backtesting.
Manually locking pairs is not available during backtesting, only locks via Protections are allowed.
#### Pair locking example

10
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@ -82,12 +82,19 @@ Example configuration showing the different settings:
"buy": "silent",
"sell": "on",
"buy_cancel": "silent",
"sell_cancel": "on"
"sell_cancel": "on",
"buy_fill": "off",
"sell_fill": "off"
},
"balance_dust_level": 0.01
},
```
`buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange.
`sell` notifications are sent when the order is placed, while `sell_fill` notifications are sent when the order is filled on the exchange.
`*_fill` notifications are off by default and must be explicitly enabled.
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
## Create a custom keyboard (command shortcut buttons)
@ -146,6 +153,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/count` | Displays number of trades used and available
| `/locks` | Show currently locked pairs.
| `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
| `/profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `/forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).

199
docs/utils.md
View File

@ -253,18 +253,211 @@ optional arguments:
* Example: see exchanges available for the bot:
```
$ freqtrade list-exchanges
Exchanges available for Freqtrade: _1btcxe, acx, allcoin, bequant, bibox, binance, binanceje, binanceus, bitbank, bitfinex, bitfinex2, bitkk, bitlish, bitmart, bittrex, bitz, bleutrade, btcalpha, btcmarkets, btcturk, buda, cex, cobinhood, coinbaseprime, coinbasepro, coinex, cointiger, coss, crex24, digifinex, dsx, dx, ethfinex, fcoin, fcoinjp, gateio, gdax, gemini, hitbtc2, huobipro, huobiru, idex, kkex, kraken, kucoin, kucoin2, kuna, lbank, mandala, mercado, oceanex, okcoincny, okcoinusd, okex, okex3, poloniex, rightbtc, theocean, tidebit, upbit, zb
Exchanges available for Freqtrade:
Exchange name Valid reason
--------------- ------- --------------------------------------------
aax True
ascendex True missing opt: fetchMyTrades
bequant True
bibox True
bigone True
binance True
binanceus True
bitbank True missing opt: fetchTickers
bitcoincom True
bitfinex True
bitforex True missing opt: fetchMyTrades, fetchTickers
bitget True
bithumb True missing opt: fetchMyTrades
bitkk True missing opt: fetchMyTrades
bitmart True
bitmax True missing opt: fetchMyTrades
bitpanda True
bittrex True
bitvavo True
bitz True missing opt: fetchMyTrades
btcalpha True missing opt: fetchTicker, fetchTickers
btcmarkets True missing opt: fetchTickers
buda True missing opt: fetchMyTrades, fetchTickers
bw True missing opt: fetchMyTrades, fetchL2OrderBook
bybit True
bytetrade True
cdax True
cex True missing opt: fetchMyTrades
coinbaseprime True missing opt: fetchTickers
coinbasepro True missing opt: fetchTickers
coinex True
crex24 True
deribit True
digifinex True
equos True missing opt: fetchTicker, fetchTickers
eterbase True
fcoin True missing opt: fetchMyTrades, fetchTickers
fcoinjp True missing opt: fetchMyTrades, fetchTickers
ftx True
gateio True
gemini True
gopax True
hbtc True
hitbtc True
huobijp True
huobipro True
idex True
kraken True
kucoin True
lbank True missing opt: fetchMyTrades
mercado True missing opt: fetchTickers
ndax True missing opt: fetchTickers
novadax True
okcoin True
okex True
probit True
qtrade True
stex True
timex True
upbit True missing opt: fetchMyTrades
vcc True
zb True missing opt: fetchMyTrades
```
!!! Note "missing opt exchanges"
Values with "missing opt:" might need special configuration (e.g. using orderbook if `fetchTickers` is missing) - but should in theory work (although we cannot guarantee they will).
* Example: see all exchanges supported by the ccxt library (including 'bad' ones, i.e. those that are known to not work with Freqtrade):
```
$ freqtrade list-exchanges -a
All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpro, bcex, bequant, bibox, bigone, binance, binanceje, binanceus, bit2c, bitbank, bitbay, bitfinex, bitfinex2, bitflyer, bitforex, bithumb, bitkk, bitlish, bitmart, bitmex, bitso, bitstamp, bitstamp1, bittrex, bitz, bl3p, bleutrade, braziliex, btcalpha, btcbox, btcchina, btcmarkets, btctradeim, btctradeua, btcturk, buda, bxinth, cex, chilebit, cobinhood, coinbase, coinbaseprime, coinbasepro, coincheck, coinegg, coinex, coinexchange, coinfalcon, coinfloor, coingi, coinmarketcap, coinmate, coinone, coinspot, cointiger, coolcoin, coss, crex24, crypton, deribit, digifinex, dsx, dx, ethfinex, exmo, exx, fcoin, fcoinjp, flowbtc, foxbit, fybse, gateio, gdax, gemini, hitbtc, hitbtc2, huobipro, huobiru, ice3x, idex, independentreserve, indodax, itbit, kkex, kraken, kucoin, kucoin2, kuna, lakebtc, latoken, lbank, liquid, livecoin, luno, lykke, mandala, mercado, mixcoins, negociecoins, nova, oceanex, okcoincny, okcoinusd, okex, okex3, paymium, poloniex, rightbtc, southxchange, stronghold, surbitcoin, theocean, therock, tidebit, tidex, upbit, vaultoro, vbtc, virwox, xbtce, yobit, zaif, zb
All exchanges supported by the ccxt library:
Exchange name Valid reason
------------------ ------- ---------------------------------------------------------------------------------------
aax True
aofex False missing: fetchOrder
ascendex True missing opt: fetchMyTrades
bequant True
bibox True
bigone True
binance True
binanceus True
bit2c False missing: fetchOrder, fetchOHLCV
bitbank True missing opt: fetchTickers
bitbay False missing: fetchOrder
bitcoincom True
bitfinex True
bitfinex2 False missing: fetchOrder
bitflyer False missing: fetchOrder, fetchOHLCV
bitforex True missing opt: fetchMyTrades, fetchTickers
bitget True
bithumb True missing opt: fetchMyTrades
bitkk True missing opt: fetchMyTrades
bitmart True
bitmax True missing opt: fetchMyTrades
bitmex False Various reasons.
bitpanda True
bitso False missing: fetchOHLCV
bitstamp False Does not provide history. Details in https://github.com/freqtrade/freqtrade/issues/1983
bitstamp1 False missing: fetchOrder, fetchOHLCV
bittrex True
bitvavo True
bitz True missing opt: fetchMyTrades
bl3p False missing: fetchOrder, fetchOHLCV
bleutrade False missing: fetchOrder
braziliex False missing: fetchOHLCV
btcalpha True missing opt: fetchTicker, fetchTickers
btcbox False missing: fetchOHLCV
btcmarkets True missing opt: fetchTickers
btctradeua False missing: fetchOrder, fetchOHLCV
btcturk False missing: fetchOrder
buda True missing opt: fetchMyTrades, fetchTickers
bw True missing opt: fetchMyTrades, fetchL2OrderBook
bybit True
bytetrade True
cdax True
cex True missing opt: fetchMyTrades
chilebit False missing: fetchOrder, fetchOHLCV
coinbase False missing: fetchOrder, cancelOrder, createOrder, fetchOHLCV
coinbaseprime True missing opt: fetchTickers
coinbasepro True missing opt: fetchTickers
coincheck False missing: fetchOrder, fetchOHLCV
coinegg False missing: fetchOHLCV
coinex True
coinfalcon False missing: fetchOHLCV
coinfloor False missing: fetchOrder, fetchOHLCV
coingi False missing: fetchOrder, fetchOHLCV
coinmarketcap False missing: fetchOrder, cancelOrder, createOrder, fetchBalance, fetchOHLCV
coinmate False missing: fetchOHLCV
coinone False missing: fetchOHLCV
coinspot False missing: fetchOrder, cancelOrder, fetchOHLCV
crex24 True
currencycom False missing: fetchOrder
delta False missing: fetchOrder
deribit True
digifinex True
equos True missing opt: fetchTicker, fetchTickers
eterbase True
exmo False missing: fetchOrder
exx False missing: fetchOHLCV
fcoin True missing opt: fetchMyTrades, fetchTickers
fcoinjp True missing opt: fetchMyTrades, fetchTickers
flowbtc False missing: fetchOrder, fetchOHLCV
foxbit False missing: fetchOrder, fetchOHLCV
ftx True
gateio True
gemini True
gopax True
hbtc True
hitbtc True
hollaex False missing: fetchOrder
huobijp True
huobipro True
idex True
independentreserve False missing: fetchOHLCV
indodax False missing: fetchOHLCV
itbit False missing: fetchOHLCV
kraken True
kucoin True
kuna False missing: fetchOHLCV
lakebtc False missing: fetchOrder, fetchOHLCV
latoken False missing: fetchOrder, fetchOHLCV
lbank True missing opt: fetchMyTrades
liquid False missing: fetchOHLCV
luno False missing: fetchOHLCV
lykke False missing: fetchOHLCV
mercado True missing opt: fetchTickers
mixcoins False missing: fetchOrder, fetchOHLCV
ndax True missing opt: fetchTickers
novadax True
oceanex False missing: fetchOHLCV
okcoin True
okex True
paymium False missing: fetchOrder, fetchOHLCV
phemex False Does not provide history.
poloniex False missing: fetchOrder
probit True
qtrade True
rightbtc False missing: fetchOrder
ripio False missing: fetchOHLCV
southxchange False missing: fetchOrder, fetchOHLCV
stex True
surbitcoin False missing: fetchOrder, fetchOHLCV
therock False missing: fetchOHLCV
tidebit False missing: fetchOrder
tidex False missing: fetchOHLCV
timex True
upbit True missing opt: fetchMyTrades
vbtc False missing: fetchOrder, fetchOHLCV
vcc True
wavesexchange False missing: fetchOrder
whitebit False missing: fetchOrder, cancelOrder, createOrder, fetchBalance
xbtce False missing: fetchOrder, fetchOHLCV
xena False missing: fetchOrder
yobit False missing: fetchOHLCV
zaif False missing: fetchOrder, fetchOHLCV
zb True missing opt: fetchMyTrades
```
## List Timeframes
Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
Use the `list-timeframes` subcommand to see the list of timeframes available for the exchange.
```
usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]

46
docs/webhook-config.md
View File

@ -19,6 +19,11 @@ Sample configuration (tested using IFTTT).
"value1": "Cancelling Open Buy Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhookbuyfill": {
"value1": "Buy Order for {pair} filled",
"value2": "at {open_rate:8f}",
"value3": ""
},
"webhooksell": {
"value1": "Selling {pair}",
@ -30,6 +35,11 @@ Sample configuration (tested using IFTTT).
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhooksellfill": {
"value1": "Sell Order for {pair} filled",
"value2": "at {close_rate:8f}.",
"value3": ""
},
"webhookstatus": {
"value1": "Status: {status}",
"value2": "",
@ -91,6 +101,21 @@ Possible parameters are:
* `order_type`
* `current_rate`
### Webhookbuyfill
The fields in `webhook.webhookbuyfill` are filled when the bot filled a buy order. Parameters are filled using string.format.
Possible parameters are:
* `trade_id`
* `exchange`
* `pair`
* `open_rate`
* `amount`
* `open_date`
* `stake_amount`
* `stake_currency`
* `fiat_currency`
### Webhooksell
The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
@ -103,6 +128,27 @@ Possible parameters are:
* `limit`
* `amount`
* `open_rate`
* `profit_amount`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
* `order_type`
* `open_date`
* `close_date`
### Webhooksellfill
The fields in `webhook.webhooksellfill` are filled when the bot fills a sell order (closes a Trae). Parameters are filled using string.format.
Possible parameters are:
* `trade_id`
* `exchange`
* `pair`
* `gain`
* `close_rate`
* `amount`
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_ratio`

2
environment.yml
View File

@ -4,7 +4,7 @@ channels:
# - defaults
dependencies:
# 1/4 req main
- python>=3.7
- python>=3.7,<3.9
- numpy
- pandas
- pip

8
freqtrade/commands/arguments.py
View File

@ -14,18 +14,18 @@ ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_dat
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
"max_open_trades", "stake_amount", "fee"]
"max_open_trades", "stake_amount", "fee", "pairs"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"enable_protections",
"enable_protections", "dry_run_wallet",
"strategy_list", "export", "exportfilename"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "use_max_market_positions",
"enable_protections",
"enable_protections", "dry_run_wallet",
"epochs", "spaces", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",

32
freqtrade/commands/build_config_commands.py
View File

@ -1,9 +1,11 @@
import logging
import secrets
from pathlib import Path
from typing import Any, Dict, List
from questionary import Separator, prompt
from freqtrade.configuration.directory_operations import chown_user_directory
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, available_exchanges
@ -138,6 +140,32 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Insert Telegram chat id",
"when": lambda x: x['telegram']
},
{
"type": "confirm",
"name": "api_server",
"message": "Do you want to enable the Rest API (includes FreqUI)?",
"default": False,
},
{
"type": "text",
"name": "api_server_listen_addr",
"message": "Insert Api server Listen Address (best left untouched default!)",
"default": "127.0.0.1",
"when": lambda x: x['api_server']
},
{
"type": "text",
"name": "api_server_username",
"message": "Insert api-server username",
"default": "freqtrader",
"when": lambda x: x['api_server']
},
{
"type": "text",
"name": "api_server_password",
"message": "Insert api-server password",
"when": lambda x: x['api_server']
},
]
answers = prompt(questions)
@ -145,6 +173,9 @@ def ask_user_config() -> Dict[str, Any]:
# Interrupted questionary sessions return an empty dict.
raise OperationalException("User interrupted interactive questions.")
# Force JWT token to be a random string
answers['api_server_jwt_key'] = secrets.token_hex()
return answers
@ -186,6 +217,7 @@ def start_new_config(args: Dict[str, Any]) -> None:
"""
config_path = Path(args['config'][0])
chown_user_directory(config_path.parent)
if config_path.exists():
overwrite = ask_user_overwrite(config_path)
if overwrite:

13
freqtrade/commands/cli_options.py
View File

@ -110,10 +110,15 @@ AVAILABLE_CLI_OPTIONS = {
help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
action='store_true',
),
"dry_run_wallet": Arg(
'--dry-run-wallet', '--starting-balance',
help='Starting balance, used for backtesting / hyperopt and dry-runs.',
type=float,
),
# Optimize common
"timeframe": Arg(
'-i', '--timeframe', '--ticker-interval',
help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
help='Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).',
),
"timerange": Arg(
'--timerange',
@ -128,7 +133,6 @@ AVAILABLE_CLI_OPTIONS = {
"stake_amount": Arg(
'--stake-amount',
help='Override the value of the `stake_amount` configuration setting.',
type=float,
),
# Backtesting
"position_stacking": Arg(
@ -191,6 +195,7 @@ AVAILABLE_CLI_OPTIONS = {
'--hyperopt',
help='Specify hyperopt class name which will be used by the bot.',
metavar='NAME',
required=False,
),
"hyperopt_path": Arg(
'--hyperopt-path',
@ -262,7 +267,7 @@ AVAILABLE_CLI_OPTIONS = {
default=1,
),
"hyperopt_loss": Arg(
'--hyperopt-loss',
'--hyperopt-loss', '--hyperoptloss',
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
@ -345,7 +350,7 @@ AVAILABLE_CLI_OPTIONS = {
# Script options
"pairs": Arg(
'-p', '--pairs',
help='Show profits for only these pairs. Pairs are space-separated.',
help='Limit command to these pairs. Pairs are space-separated.',
nargs='+',
),
# Download data

19
freqtrade/commands/hyperopt_commands.py
View File

@ -17,7 +17,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
"""
List hyperopt epochs previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
@ -47,7 +47,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
config.get('hyperoptexportfilename'))
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
@ -57,18 +57,19 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if not export_csv:
try:
print(Hyperopt.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'], print_colorized, 0))
print(HyperoptTools.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'],
print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')
if epochs and not no_details:
sorted_epochs = sorted(epochs, key=itemgetter('loss'))
results = sorted_epochs[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
HyperoptTools.print_epoch_details(results, total_epochs, print_json, no_header)
if epochs and export_csv:
Hyperopt.export_csv_file(
HyperoptTools.export_csv_file(
config, epochs, total_epochs, not filteroptions['only_best'], export_csv
)
@ -77,7 +78,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
Show details of a hyperopt epoch previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
@ -105,7 +106,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
}
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
@ -124,7 +125,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
if epochs:
val = epochs[n]
Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")

18
freqtrade/commands/list_commands.py
View File

@ -13,7 +13,7 @@ from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import available_exchanges, ccxt_exchanges, market_is_active
from freqtrade.exchange import market_is_active, validate_exchanges
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@ -28,14 +28,18 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
:param args: Cli args from Arguments()
:return: None
"""
exchanges = ccxt_exchanges() if args['list_exchanges_all'] else available_exchanges()
exchanges = validate_exchanges(args['list_exchanges_all'])
if args['print_one_column']:
print('\n'.join(exchanges))
print('\n'.join([e[0] for e in exchanges]))
else:
if args['list_exchanges_all']:
print(f"All exchanges supported by the ccxt library: {', '.join(exchanges)}")
print("All exchanges supported by the ccxt library:")
else:
print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
print("Exchanges available for Freqtrade:")
exchanges = [e for e in exchanges if e[1] is not False]
print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
@ -99,7 +103,7 @@ def start_list_hyperopts(args: Dict[str, Any]) -> None:
def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print ticker intervals (timeframes) available on Exchange
Print timeframes available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use timeframe set in the config
@ -177,7 +181,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
# human-readable formats.
print()
if len(pairs):
if pairs:
if args.get('print_list', False):
# print data as a list, with human-readable summary
print(f"{summary_str}: {', '.join(pairs.keys())}.")

15
freqtrade/commands/optimize_commands.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value
from freqtrade.state import RunMode
@ -22,11 +23,13 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if (method in no_unlimited_runmodes.keys() and
config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
raise DependencyException(
f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
f'for {no_unlimited_runmodes[method]}')
if method in no_unlimited_runmodes.keys():
if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
and config['stake_amount'] > config['dry_run_wallet']):
wallet = round_coin_value(config['dry_run_wallet'], config['stake_currency'])
stake = round_coin_value(config['stake_amount'], config['stake_currency'])
raise OperationalException(f"Starting balance ({wallet}) "
f"is smaller than stake_amount {stake}.")
return config

14
freqtrade/configuration/check_exchange.py
View File

@ -2,8 +2,8 @@ import logging
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported)
from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
is_exchange_officially_supported, validate_exchange)
from freqtrade.state import RunMode
@ -57,9 +57,13 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
f'{", ".join(available_exchanges())}'
)
if check_for_bad and is_exchange_bad(exchange):
raise OperationalException(f'Exchange "{exchange}" is known to not work with the bot yet. '
f'Reason: {get_exchange_bad_reason(exchange)}')
valid, reason = validate_exchange(exchange)
if not valid:
if check_for_bad:
raise OperationalException(f'Exchange "{exchange}" will not work with Freqtrade. '
f'Reason: {reason}')
else:
logger.warning(f'Exchange "{exchange}" will not work with Freqtrade. Reason: {reason}')
if is_exchange_officially_supported(exchange):
logger.info(f'Exchange "{exchange}" is officially supported '

21
freqtrade/configuration/config_validation.py
View File

@ -47,6 +47,8 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
conf_schema = deepcopy(constants.CONF_SCHEMA)
if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
elif conf.get('runmode', RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
else:
conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
try:
@ -72,6 +74,7 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
# validating trailing stoploss
_validate_trailing_stoploss(conf)
_validate_price_config(conf)
_validate_edge(conf)
_validate_whitelist(conf)
_validate_protections(conf)
@ -93,6 +96,19 @@ def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
def _validate_price_config(conf: Dict[str, Any]) -> None:
"""
When using market orders, price sides must be using the "other" side of the price
"""
if (conf.get('order_types', {}).get('buy') == 'market'
and conf.get('bid_strategy', {}).get('price_side') != 'ask'):
raise OperationalException('Market buy orders require bid_strategy.price_side = "ask".')
if (conf.get('order_types', {}).get('sell') == 'market'
and conf.get('ask_strategy', {}).get('price_side') != 'bid'):
raise OperationalException('Market sell orders require ask_strategy.price_side = "bid".')
def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
if conf.get('stoploss') == 0.0:
@ -133,11 +149,6 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
if not conf.get('edge', {}).get('enabled'):
return
if conf.get('pairlist', {}).get('method') == 'VolumePairList':
raise OperationalException(
"Edge and VolumePairList are incompatible, "
"Edge will override whatever pairs VolumePairlist selects."
)
if not conf.get('ask_strategy', {}).get('use_sell_signal', True):
raise OperationalException(
"Edge requires `use_sell_signal` to be True, otherwise no sells will happen."

28
freqtrade/configuration/configuration.py
View File

@ -11,10 +11,10 @@ from freqtrade import constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
from freqtrade.configuration.load_config import load_config_file
from freqtrade.configuration.load_config import load_config_file, load_file
from freqtrade.exceptions import OperationalException
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.misc import deep_merge_dicts
from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
@ -214,9 +214,6 @@ class Configuration:
self._args_to_config(
config, argname='enable_protections',
logstring='Parameter --enable-protections detected, enabling Protections. ...')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
config.update({'use_max_market_positions': False})
@ -228,11 +225,23 @@ class Configuration:
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
elif config['runmode'] in NON_UTIL_MODES:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if self.args.get('stake_amount', None):
# Convert explicitly to float to support CLI argument for both unlimited and value
try:
self.args['stake_amount'] = float(self.args['stake_amount'])
except ValueError:
pass
self._args_to_config(config, argname='stake_amount',
logstring='Parameter --stake-amount detected, '
'overriding stake_amount to: {} ...')
self._args_to_config(config, argname='dry_run_wallet',
logstring='Parameter --dry-run-wallet detected, '
'overriding dry_run_wallet to: {} ...')
self._args_to_config(config, argname='fee',
logstring='Parameter --fee detected, '
'setting fee to: {} ...')
@ -436,6 +445,7 @@ class Configuration:
"""
if "pairs" in config:
config['exchange']['pair_whitelist'] = config['pairs']
return
if "pairs_file" in self.args and self.args["pairs_file"]:
@ -445,8 +455,7 @@ class Configuration:
# or if pairs file is specified explicitely
if not pairs_file.exists():
raise OperationalException(f'No pairs file found with path "{pairs_file}".')
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)
config['pairs'] = load_file(pairs_file)
config['pairs'].sort()
return
@ -457,7 +466,6 @@ class Configuration:
# Fall back to /dl_path/pairs.json
pairs_file = config['datadir'] / 'pairs.json'
if pairs_file.exists():
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)
config['pairs'] = load_file(pairs_file)
if 'pairs' in config:
config['pairs'].sort()

17
freqtrade/configuration/directory_operations.py
View File

@ -24,6 +24,21 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
return folder
def chown_user_directory(directory: Path) -> None:
"""
Use Sudo to change permissions of the home-directory if necessary
Only applies when running in docker!
"""
import os
if os.environ.get('FT_APP_ENV') == 'docker':
try:
import subprocess
subprocess.check_output(
['sudo', 'chown', '-R', 'ftuser:', str(directory.resolve())])
except Exception:
logger.warning(f"Could not chown {directory}")
def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
"""
Create userdata directory structure.
@ -37,6 +52,7 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
"notebooks", "plot", "strategies", ]
folder = Path(directory)
chown_user_directory(folder)
if not folder.is_dir():
if create_dir:
folder.mkdir(parents=True)
@ -72,6 +88,5 @@ def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
if not overwrite:
logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
continue
else:
logger.warning(f"File `{targetfile}` exists already, overwriting.")
shutil.copy(str(sourcedir / source), str(targetfile))

9
freqtrade/configuration/load_config.py
View File

@ -38,6 +38,15 @@ def log_config_error_range(path: str, errmsg: str) -> str:
return ''
def load_file(path: Path) -> Dict[str, Any]:
try:
with path.open('r') as file:
config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
except FileNotFoundError:
raise OperationalException(f'File file "{path}" not found!')
return config
def load_config_file(path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path

7
freqtrade/configuration/timerange.py
View File

@ -7,6 +7,8 @@ from typing import Optional
import arrow
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -103,5 +105,8 @@ class TimeRange:
stop = int(stops) // 1000
else:
stop = int(stops)
if start > stop > 0:
raise OperationalException(
f'Start date is after stop date for timerange "{text}"')
return TimeRange(stype[0], stype[1], start, stop)
raise Exception('Incorrect syntax for timerange "%s"' % text)
raise OperationalException(f'Incorrect syntax for timerange "{text}"')

36
freqtrade/constants.py
View File

@ -26,7 +26,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'PerformanceFilter', 'PrecisionFilter',
'PriceFilter', 'RangeStabilityFilter', 'ShuffleFilter',
'SpreadFilter']
'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
DRY_RUN_WALLET = 1000
@ -165,12 +165,18 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
'bid_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
},
'use_order_book': {'type': 'boolean'},
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'sell_profit_offset': {'type': 'number', 'minimum': 0.0},
'sell_profit_offset': {'type': 'number'},
'ignore_roi_if_buy_signal': {'type': 'boolean'}
}
},
@ -179,6 +185,8 @@ CONF_SCHEMA = {
'properties': {
'buy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
@ -238,14 +246,24 @@ CONF_SCHEMA = {
'balance_dust_level': {'type': 'number', 'minimum': 0.0},
'notification_settings': {
'type': 'object',
'default': {},
'properties': {
'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}
'buy_fill': {'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
}
}
},
@ -376,6 +394,16 @@ SCHEMA_TRADE_REQUIRED = [
'dataformat_trades',
]
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
'dataformat_ohlcv',
'dataformat_trades',
]
SCHEMA_MINIMAL_REQUIRED = [
'exchange',
'dry_run',

21
freqtrade/data/btanalysis.py
View File

@ -10,7 +10,7 @@ import pandas as pd
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
from freqtrade.persistence import Trade, init_db
from freqtrade.persistence import LocalTrade, Trade, init_db
logger = logging.getLogger(__name__)
@ -224,7 +224,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def trade_list_to_dataframe(trades: List[Trade]) -> pd.DataFrame:
def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
"""
Convert list of Trade objects to pandas Dataframe
:param trades: List of trade objects
@ -360,13 +360,14 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
high and low time and high and low value.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
@ -382,13 +383,17 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
raise ValueError("No losing trade, therefore no drawdown.")
high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
low_date = profit_results.loc[idxmin, date_col]
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
['high_value'].idxmax(), 'cumulative']
low_val = max_drawdown_df.loc[idxmin, 'cumulative']
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
def calculate_csum(trades: pd.DataFrame) -> Tuple[float, float]:
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
"""
Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param starting_balance: Add starting balance to results, to show the wallets high / low points
:return: Tuple (float, float) with cumsum of profit_abs
:raise: ValueError if trade-dataframe was found empty.
"""
@ -397,7 +402,7 @@ def calculate_csum(trades: pd.DataFrame) -> Tuple[float, float]:
csum_df = pd.DataFrame()
csum_df['sum'] = trades['profit_abs'].cumsum()
csum_min = csum_df['sum'].min()
csum_max = csum_df['sum'].max()
csum_min = csum_df['sum'].min() + starting_balance
csum_max = csum_df['sum'].max() + starting_balance
return csum_min, csum_max

19
freqtrade/data/converter.py
View File

@ -110,19 +110,32 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
df.reset_index(inplace=True)
len_before = len(dataframe)
len_after = len(df)
pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
if len_before != len_after:
logger.info(f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}")
message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
f" - {round(pct_missing * 100, 2)} %")
if pct_missing > 0.01:
logger.info(message)
else:
# Don't be verbose if only a small amount is missing
logger.debug(message)
return df
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
startup_candles: int = 0) -> DataFrame:
"""
Trim dataframe based on given timerange
:param df: Dataframe to trim
:param timerange: timerange (use start and end date if available)
:param: df_date_col: Column in the dataframe to use as Date column
:param df_date_col: Column in the dataframe to use as Date column
:param startup_candles: When not 0, is used instead the timerange start date
:return: trimmed dataframe
"""
if startup_candles:
# Trim candles instead of timeframe in case of given startup_candle count
df = df.iloc[startup_candles:, :]
else:
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]

2
freqtrade/data/dataprovider.py
View File

@ -170,6 +170,6 @@ class DataProvider:
"""
if self._pairlists:
return self._pairlists.whitelist
return self._pairlists.whitelist.copy()
else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.")

5
freqtrade/edge/edge_positioning.py
View File

@ -84,9 +84,8 @@ class Edge:
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
def calculate(self) -> bool:
pairs = expand_pairlist(self.config['exchange']['pair_whitelist'],
list(self.exchange.markets))
def calculate(self, pairs: List[str]) -> bool:
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (

5
freqtrade/exchange/__init__.py
View File

@ -8,10 +8,11 @@ from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin

2
freqtrade/exchange/binance.py
View File

@ -52,7 +52,7 @@ class Binance(Exchange):
'In stoploss limit order, stop price should be more than limit price')
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order

4
freqtrade/exchange/bittrex.py
View File

@ -12,10 +12,6 @@ class Bittrex(Exchange):
"""
Bittrex exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {

95
freqtrade/exchange/common.py
View File

@ -18,78 +18,8 @@ BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "
"Details in https://github.com/freqtrade/freqtrade/issues/1983",
"hitbtc": "This API cannot be used with Freqtrade. "
"Use `hitbtc2` exchange id to access this exchange.",
"phemex": "Does not provide history. ",
"poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
**dict.fromkeys([
'adara',
'anxpro',
'bigone',
'coinbase',
'coinexchange',
'coinmarketcap',
'lykke',
'xbtce',
], "Does not provide timeframes. ccxt fetchOHLCV: False"),
**dict.fromkeys([
'bcex',
'bit2c',
'bitbay',
'bitflyer',
'bitforex',
'bithumb',
'bitso',
'bitstamp1',
'bl3p',
'braziliex',
'btcbox',
'btcchina',
'btctradeim',
'btctradeua',
'bxinth',
'chilebit',
'coincheck',
'coinegg',
'coinfalcon',
'coinfloor',
'coingi',
'coinmate',
'coinone',
'coinspot',
'coolcoin',
'crypton',
'deribit',
'exmo',
'exx',
'flowbtc',
'foxbit',
'fybse',
# 'hitbtc',
'ice3x',
'independentreserve',
'indodax',
'itbit',
'lakebtc',
'latoken',
'liquid',
'livecoin',
'luno',
'mixcoins',
'negociecoins',
'nova',
'paymium',
'southxchange',
'stronghold',
'surbitcoin',
'therock',
'tidex',
'vaultoro',
'vbtc',
'virwox',
'yobit',
'zaif',
], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
}
MAP_EXCHANGE_CHILDCLASS = {
@ -98,6 +28,29 @@ MAP_EXCHANGE_CHILDCLASS = {
}
EXCHANGE_HAS_REQUIRED = [
# Required / private
'fetchOrder',
'cancelOrder',
'createOrder',
# 'createLimitOrder', 'createMarketOrder',
'fetchBalance',
# Public endpoints
'loadMarkets',
'fetchOHLCV',
]
EXCHANGE_HAS_OPTIONAL = [
# Private
'fetchMyTrades', # Trades for order - fee detection
# Public
'fetchOrderBook', 'fetchL2OrderBook', 'fetchTicker', # OR for pricing
'fetchTickers', # For volumepairlist?
'fetchTrades', # Downloading trades data
]
def calculate_backoff(retrycount, max_retries):
"""
Calculate backoff
@ -140,7 +93,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
logger.warning('retrying %s() still for %s times', f.__name__, count)
count -= 1
kwargs.update({'count': count})
if isinstance(ex, DDosProtection) or isinstance(ex, RetryableOrderError):
if isinstance(ex, (DDosProtection, RetryableOrderError)):
# increasing backoff
backoff_delay = calculate_backoff(count + 1, retries)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")

112
freqtrade/exchange/exchange.py
View File

@ -14,6 +14,7 @@ from typing import Any, Dict, List, Optional, Tuple
import arrow
import ccxt
import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE,
decimal_to_precision)
from pandas import DataFrame
@ -23,7 +24,8 @@ from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, RetryableOrderError,
TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES, retrier,
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED, retrier,
retrier_async)
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@ -62,6 +64,7 @@ class Exchange:
"trades_pagination": "time", # Possible are "time" or "id"
"trades_pagination_arg": "since",
"l2_limit_range": None,
"l2_limit_range_required": True, # Allow Empty L2 limit (kucoin)
}
_ft_has: Dict = {}
@ -82,6 +85,9 @@ class Exchange:
# Timestamp of last markets refresh
self._last_markets_refresh: int = 0
# Cache for 10 minutes ...
self._fetch_tickers_cache: TTLCache = TTLCache(maxsize=1, ttl=60 * 10)
# Holds candles
self._klines: Dict[Tuple[str, str], DataFrame] = {}
@ -147,6 +153,9 @@ class Exchange:
"""
Destructor - clean up async stuff
"""
self.close()
def close(self):
logger.debug("Exchange object destroyed, closing async loop")
if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
asyncio.get_event_loop().run_until_complete(self._api_async.close())
@ -308,8 +317,8 @@ class Exchange:
self._markets = self._api.load_markets()
self._load_async_markets()
self._last_markets_refresh = arrow.utcnow().int_timestamp
except ccxt.BaseError as e:
logger.warning('Unable to initialize markets. Reason: %s', e)
except ccxt.BaseError:
logger.exception('Unable to initialize markets.')
def reload_markets(self) -> None:
"""Reload markets both sync and async if refresh interval has passed """
@ -528,18 +537,20 @@ class Exchange:
return None
# reserve some percent defined in config (5% default) + stoploss
amount_reserve_percent = 1.0 - self._config.get('amount_reserve_percent',
amount_reserve_percent = 1.0 + self._config.get('amount_reserve_percent',
DEFAULT_AMOUNT_RESERVE_PERCENT)
amount_reserve_percent += stoploss
amount_reserve_percent = (
amount_reserve_percent / (1 - abs(stoploss)) if abs(stoploss) != 1 else 1.5
)
# it should not be more than 50%
amount_reserve_percent = max(amount_reserve_percent, 0.5)
amount_reserve_percent = max(min(amount_reserve_percent, 1.5), 1)
# The value returned should satisfy both limits: for amount (base currency) and
# for cost (quote, stake currency), so max() is used here.
# See also #2575 at github.
return max(min_stake_amounts) / amount_reserve_percent
return max(min_stake_amounts) * amount_reserve_percent
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
_amount = self.amount_to_precision(pair, amount)
@ -614,7 +625,7 @@ class Exchange:
rate: float, time_in_force: str) -> Dict:
if self._config['dry_run']:
dry_order = self.dry_run_order(pair, ordertype, "buy", amount, rate)
dry_order = self.create_dry_run_order(pair, ordertype, "buy", amount, rate)
return dry_order
params = self._params.copy()
@ -627,7 +638,7 @@ class Exchange:
rate: float, time_in_force: str = 'gtc') -> Dict:
if self._config['dry_run']:
dry_order = self.dry_run_order(pair, ordertype, "sell", amount, rate)
dry_order = self.create_dry_run_order(pair, ordertype, "sell", amount, rate)
return dry_order
params = self._params.copy()
@ -658,8 +669,6 @@ class Exchange:
@retrier
def get_balance(self, currency: str) -> float:
if self._config['dry_run']:
return self._config['dry_run_wallet']
# ccxt exception is already handled by get_balances
balances = self.get_balances()
@ -671,8 +680,6 @@ class Exchange:
@retrier
def get_balances(self) -> dict:
if self._config['dry_run']:
return {}
try:
balances = self._api.fetch_balance()
@ -692,9 +699,19 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_tickers(self) -> Dict:
def get_tickers(self, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
"""
if cached:
tickers = self._fetch_tickers_cache.get('fetch_tickers')
if tickers:
return tickers
try:
return self._api.fetch_tickers()
tickers = self._api.fetch_tickers()
self._fetch_tickers_cache['fetch_tickers'] = tickers
return tickers
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch. '
@ -803,7 +820,7 @@ class Exchange:
# Gather coroutines to run
for pair, timeframe in set(pair_list):
if (not ((pair, timeframe) in self._klines)
if (((pair, timeframe) not in self._klines)
or self._now_is_time_to_refresh(pair, timeframe)):
input_coroutines.append(self._async_get_candle_history(pair, timeframe,
since_ms=since_ms))
@ -955,7 +972,7 @@ class Exchange:
while True:
t = await self._async_fetch_trades(pair,
params={self._trades_pagination_arg: from_id})
if len(t):
if t:
# Skip last id since its the key for the next call
trades.extend(t[:-1])
if from_id == t[-1][1] or t[-1][0] > until:
@ -987,7 +1004,7 @@ class Exchange:
# DEFAULT_TRADES_COLUMNS: 1 -> id
while True:
t = await self._async_fetch_trades(pair, since=since)
if len(t):
if t:
since = t[-1][0]
trades.extend(t)
# Reached the end of the defined-download period
@ -1053,7 +1070,8 @@ class Exchange:
:param order: Order dict as returned from fetch_order()
:return: True if order has been cancelled without being filled, False otherwise.
"""
return order.get('status') in ('closed', 'canceled') and order.get('filled') == 0.0
return (order.get('status') in ('closed', 'canceled', 'cancelled')
and order.get('filled') == 0.0)
@retrier
def cancel_order(self, order_id: str, pair: str) -> Dict:
@ -1153,14 +1171,20 @@ class Exchange:
return self.fetch_order(order_id, pair)
@staticmethod
def get_next_limit_in_list(limit: int, limit_range: Optional[List[int]]):
def get_next_limit_in_list(limit: int, limit_range: Optional[List[int]],
range_required: bool = True):
"""
Get next greater value in the list.
Used by fetch_l2_order_book if the api only supports a limited range
"""
if not limit_range:
return limit
return min([x for x in limit_range if limit <= x] + [max(limit_range)])
result = min([x for x in limit_range if limit <= x] + [max(limit_range)])
if not range_required and limit > result:
# Range is not required - we can use None as parameter.
return None
return result
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
@ -1170,7 +1194,8 @@ class Exchange:
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
limit1 = self.get_next_limit_in_list(limit, self._ft_has['l2_limit_range'])
limit1 = self.get_next_limit_in_list(limit, self._ft_has['l2_limit_range'],
self._ft_has['l2_limit_range_required'])
try:
return self._api.fetch_l2_order_book(pair, limit1)
@ -1228,6 +1253,8 @@ class Exchange:
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: str = 'maker') -> float:
try:
if self._config['dry_run'] and self._config.get('fee', None) is not None:
return self._config['fee']
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
self._api.load_markets()
@ -1300,14 +1327,6 @@ class Exchange:
self.calculate_fee_rate(order))
def is_exchange_bad(exchange_name: str) -> bool:
return exchange_name in BAD_EXCHANGES
def get_exchange_bad_reason(exchange_name: str) -> str:
return BAD_EXCHANGES.get(exchange_name, "")
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
@ -1328,7 +1347,36 @@ def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
exchanges = ccxt_exchanges(ccxt_module)
return [x for x in exchanges if not is_exchange_bad(x)]
return [x for x in exchanges if validate_exchange(x)[0]]
def validate_exchange(exchange: str) -> Tuple[bool, str]:
ex_mod = getattr(ccxt, exchange.lower())()
if not ex_mod or not ex_mod.has:
return False, ''
missing = [k for k in EXCHANGE_HAS_REQUIRED if ex_mod.has.get(k) is not True]
if missing:
return False, f"missing: {', '.join(missing)}"
missing_opt = [k for k in EXCHANGE_HAS_OPTIONAL if not ex_mod.has.get(k)]
if exchange.lower() in BAD_EXCHANGES:
return False, BAD_EXCHANGES.get(exchange.lower(), '')
if missing_opt:
return True, f"missing opt: {', '.join(missing_opt)}"
return True, ''
def validate_exchanges(all_exchanges: bool) -> List[Tuple[str, bool, str]]:
"""
:return: List of tuples with exchangename, valid, reason.
"""
exchanges = ccxt_exchanges() if all_exchanges else available_exchanges()
exchanges_valid = [
(e, *validate_exchange(e)) for e in exchanges
]
return exchanges_valid
def timeframe_to_seconds(timeframe: str) -> int:

5
freqtrade/exchange/ftx.py
View File

@ -53,7 +53,7 @@ class Ftx(Exchange):
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order
@ -63,10 +63,11 @@ class Ftx(Exchange):
# set orderPrice to place limit order, otherwise it's a market order
params['orderPrice'] = limit_rate
params['stopPrice'] = stop_price
amount = self.amount_to_precision(pair, amount)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
amount=amount, params=params)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order

2
freqtrade/exchange/kraken.py
View File

@ -92,7 +92,7 @@ class Kraken(Exchange):
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order

24
freqtrade/exchange/kucoin.py Normal file
View File

@ -0,0 +1,24 @@
""" Kucoin exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Kucoin(Exchange):
"""
Kucoin exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"l2_limit_range": [20, 100],
"l2_limit_range_required": False,
}

83
freqtrade/freqtradebot.py
View File

@ -113,7 +113,7 @@ class FreqtradeBot(LoggingMixin):
via RPC about changes in the bot status.
"""
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'type': RPCMessageType.STATUS,
'status': msg
})
@ -187,7 +187,7 @@ class FreqtradeBot(LoggingMixin):
if self.get_free_open_trades():
self.enter_positions()
Trade.session.flush()
Trade.query.session.flush()
def process_stopped(self) -> None:
"""
@ -205,7 +205,7 @@ class FreqtradeBot(LoggingMixin):
if len(open_trades) != 0:
msg = {
'type': RPCMessageType.WARNING_NOTIFICATION,
'type': RPCMessageType.WARNING,
'status': f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
@ -225,7 +225,7 @@ class FreqtradeBot(LoggingMixin):
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.edge.calculate(_whitelist)
_whitelist = self.edge.adjust(_whitelist)
if trades:
@ -410,9 +410,7 @@ class FreqtradeBot(LoggingMixin):
bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in bid_strategy and bid_strategy.get('use_order_book', False):
logger.info(
f"Getting price from order book {bid_strategy['price_side'].capitalize()} side."
)
order_book_top = bid_strategy.get('order_book_top', 1)
order_book = self.exchange.fetch_l2_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
@ -425,14 +423,15 @@ class FreqtradeBot(LoggingMixin):
f"Orderbook: {order_book}"
)
raise PricingError from e
logger.info(f'...top {order_book_top} order book buy rate {rate_from_l2:.8f}')
logger.info(f"Buy price from orderbook {bid_strategy['price_side'].capitalize()} side "
f"- top {order_book_top} order book buy rate {rate_from_l2:.8f}")
used_rate = rate_from_l2
else:
logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
ticker = self.exchange.fetch_ticker(pair)
ticker_rate = ticker[bid_strategy['price_side']]
if ticker['last'] and ticker_rate > ticker['last']:
balance = self.config['bid_strategy']['ask_last_balance']
balance = bid_strategy['ask_last_balance']
ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
used_rate = ticker_rate
@ -479,19 +478,17 @@ class FreqtradeBot(LoggingMixin):
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
return False
logger.info(f"Buy signal found: about create a new trade with stake_amount: "
logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
f"{stake_amount} ...")
bid_check_dom = self.config.get('bid_strategy', {}).get('check_depth_of_market', {})
if ((bid_check_dom.get('enabled', False)) and
(bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
if self._check_depth_of_market_buy(pair, bid_check_dom):
logger.info(f'Executing Buy for {pair}.')
return self.execute_buy(pair, stake_amount)
else:
return False
logger.info(f'Executing Buy for {pair}')
return self.execute_buy(pair, stake_amount)
else:
return False
@ -520,7 +517,8 @@ class FreqtradeBot(LoggingMixin):
logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
return False
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None) -> bool:
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None,
forcebuy: bool = False) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
@ -548,6 +546,10 @@ class FreqtradeBot(LoggingMixin):
amount = stake_amount / buy_limit_requested
order_type = self.strategy.order_types['buy']
if forcebuy:
# Forcebuy can define a different ordertype
order_type = self.strategy.order_types.get('forcebuy', order_type)
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force):
@ -616,8 +618,8 @@ class FreqtradeBot(LoggingMixin):
if order_status == 'closed':
self.update_trade_state(trade, order_id, order)
Trade.session.add(trade)
Trade.session.flush()
Trade.query.session.add(trade)
Trade.query.session.flush()
# Updating wallets
self.wallets.update()
@ -632,7 +634,7 @@ class FreqtradeBot(LoggingMixin):
"""
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_NOTIFICATION,
'type': RPCMessageType.BUY,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
@ -656,7 +658,7 @@ class FreqtradeBot(LoggingMixin):
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'type': RPCMessageType.BUY_CANCEL,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
@ -673,6 +675,21 @@ class FreqtradeBot(LoggingMixin):
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_fill(self, trade: Trade) -> None:
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_FILL,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'open_rate': trade.open_rate,
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': trade.amount,
'open_date': trade.open_date,
}
self.rpc.send_msg(msg)
#
# SELL / exit positions / close trades logic and methods
#
@ -740,7 +757,13 @@ class FreqtradeBot(LoggingMixin):
logger.warning("Sell Price at location from orderbook could not be determined.")
raise PricingError from e
else:
rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
ticker = self.exchange.fetch_ticker(pair)
ticker_rate = ticker[ask_strategy['price_side']]
if ticker['last'] and ticker_rate < ticker['last']:
balance = ask_strategy.get('bid_last_balance', 0.0)
ticker_rate = ticker_rate - balance * (ticker_rate - ticker['last'])
rate = ticker_rate
if rate is None:
raise PricingError(f"Sell-Rate for {pair} was empty.")
self._sell_rate_cache[pair] = rate
@ -932,7 +955,7 @@ class FreqtradeBot(LoggingMixin):
Check and execute sell
"""
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell,
trade, sell_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
@ -1018,13 +1041,13 @@ class FreqtradeBot(LoggingMixin):
was_trade_fully_canceled = False
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('canceled', 'closed'):
if order['status'] not in ('cancelled', 'canceled', 'closed'):
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
# Simply bailing here is the only safe way - as this order will then be
# handled in the next iteration.
if corder.get('status') not in ('canceled', 'closed'):
if corder.get('status') not in ('cancelled', 'canceled', 'closed'):
logger.warning(f"Order {trade.open_order_id} for {trade.pair} not cancelled.")
return False
else:
@ -1194,7 +1217,7 @@ class FreqtradeBot(LoggingMixin):
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
self.update_trade_state(trade, trade.open_order_id, order)
Trade.session.flush()
Trade.query.session.flush()
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
@ -1204,19 +1227,20 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_sell(self, trade: Trade, order_type: str) -> None:
def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
"""
Sends rpc notification when a sell occured.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached rates here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
current_rate = self.get_sell_rate(trade.pair, False) if not fill else None
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'type': (RPCMessageType.SELL_FILL if fill
else RPCMessageType.SELL),
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
@ -1225,6 +1249,7 @@ class FreqtradeBot(LoggingMixin):
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
'close_rate': trade.close_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_ratio': profit_ratio,
@ -1259,7 +1284,7 @@ class FreqtradeBot(LoggingMixin):
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
'type': RPCMessageType.SELL_CANCEL,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
@ -1336,9 +1361,15 @@ class FreqtradeBot(LoggingMixin):
# Updating wallets when order is closed
if not trade.is_open:
if not stoploss_order and not trade.open_order_id:
self._notify_sell(trade, '', True)
self.protections.stop_per_pair(trade.pair)
self.protections.global_stop()
self.wallets.update()
elif not trade.open_order_id:
# Buy fill
self._notify_buy_fill(trade)
return False
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,

2
freqtrade/misc.py
View File

@ -81,7 +81,7 @@ def json_load(datafile: IO) -> Any:
"""
load data with rapidjson
Use this to have a consistent experience,
sete number_mode to "NM_NATIVE" for greatest speed
set number_mode to "NM_NATIVE" for greatest speed
"""
return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)

124
freqtrade/optimize/backtesting.py
View File

@ -17,17 +17,18 @@ from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.mixins import LoggingMixin
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
store_backtest_stats)
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence import LocalTrade, PairLocks, Trade
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -114,6 +115,8 @@ class Backtesting:
if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
self.wallets = Wallets(self.config, self.exchange, log=False)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
@ -124,7 +127,7 @@ class Backtesting:
PairLocks.use_db = True
Trade.use_db = True
def _set_strategy(self, strategy):
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
@ -171,8 +174,6 @@ class Backtesting:
PairLocks.use_db = False
PairLocks.timeframe = self.config['timeframe']
Trade.use_db = False
if enable_protections:
# Reset persisted data - used for protections only
PairLocks.reset_locks()
Trade.reset_trades()
@ -203,10 +204,10 @@ class Backtesting:
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = [x for x in df_analyzed.itertuples(index=False, name=None)]
data[pair] = df_analyzed.values.tolist()
return data
def _get_close_rate(self, sell_row: Tuple, trade: Trade, sell: SellCheckTuple,
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
@ -238,7 +239,7 @@ class Backtesting:
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row[LOW_IDX])
return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
else:
# This should not be reached...
@ -246,24 +247,67 @@ class Backtesting:
else:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[Trade]:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
sell_row[BUY_IDX], sell_row[SELL_IDX],
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = sell.sell_type.value
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = sell.sell_type
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_type.value):
return None
trade.close(closerate, show_msg=False)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[Trade]],
data: Dict[str, List[Tuple]]) -> List[Trade]:
def _enter_trade(self, pair: str, row: List, max_open_trades: int,
open_trade_count: int) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(
pair, max_open_trades - open_trade_count, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force):
return None
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
# Enter trade
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
exchange='backtesting',
)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
Handling of left open trades at the end of backtesting
"""
@ -274,13 +318,16 @@ class Backtesting:
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = SellType.FORCE_SELL
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
trade.is_open = True
trades.append(trade)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def backtest(self, processed: Dict, stake_amount: float,
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> DataFrame:
@ -292,7 +339,6 @@ class Backtesting:
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}
:param stake_amount: amount to use for each trade
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@ -300,11 +346,7 @@ class Backtesting:
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
f"start_date: {start_date}, end_date: {end_date}, "
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades: List[Trade] = []
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Use dict of lists with data for performance
@ -315,7 +357,7 @@ class Backtesting:
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List] = defaultdict(list)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
# Loop timerange and get candle for each pair at that point in time
@ -346,28 +388,18 @@ class Backtesting:
and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
# Enter trade
trade = Trade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
trade = self._enter_trade(pair, row, max_open_trades, open_trade_count_start)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
Trade.trades.append(trade)
LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured
@ -375,6 +407,8 @@ class Backtesting:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
@ -384,6 +418,7 @@ class Backtesting:
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
return trade_list_to_dataframe(trades)
@ -408,7 +443,8 @@ class Backtesting:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.required_startup)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
@ -417,7 +453,6 @@ class Backtesting:
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
@ -428,7 +463,8 @@ class Backtesting:
self.all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.locks,
'locks': PairLocks.get_all_locks(),
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
}

2
freqtrade/optimize/edge_cli.py
View File

@ -44,7 +44,7 @@ class EdgeCli:
'timerange') is None else str(self.config.get('timerange')))
def start(self) -> None:
result = self.edge.calculate()
result = self.edge.calculate(self.config['exchange']['pair_whitelist'])
if result:
print('') # blank line for readability
print(generate_edge_table(self.edge._cached_pairs))

307
freqtrade/optimize/hyperopt.py
View File

@ -4,36 +4,32 @@
This module contains the hyperopt logic
"""
import io
import locale
import logging
import random
import warnings
from collections import OrderedDict
from datetime import datetime
from math import ceil
from operator import itemgetter
from pathlib import Path
from pprint import pformat
from typing import Any, Dict, List, Optional
import progressbar
import rapidjson
import tabulate
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
from pandas import DataFrame, isna, json_normalize
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import file_dump_json, plural, round_dict
from freqtrade.misc import file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
@ -66,19 +62,26 @@ class Hyperopt:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.backtesting = Backtesting(self.config)
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' / f'hyperopt_results_{time_now}.pickle')
'hyperopt_results' /
f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@ -166,15 +169,6 @@ class Hyperopt:
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
def _get_params_details(self, params: Dict) -> Dict:
"""
Return the params for each space
@ -197,102 +191,16 @@ class Hyperopt:
return result
@staticmethod
def print_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
"""
Display details of the hyperopt result
"""
params = results.get('params_details', {})
# Default header string
if header_str is None:
header_str = "Best result"
if not no_header:
explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
print(f"\n{header_str}:\n\n{explanation_str}\n")
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
Hyperopt._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
def _params_update_for_json(result_dict, params, space: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space)
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in space_params.items()
)
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
def print_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
TODO: this should be moved to HyperoptTools too
"""
is_best = results['is_best']
if self.print_all or is_best:
print(
self.get_result_table(
HyperoptTools.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
@ -300,164 +208,6 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")
def has_space(self, space: str) -> bool:
"""
Tell if the space value is contained in the configuration
@ -537,7 +287,6 @@ class Hyperopt:
backtesting_results = self.backtesting.backtest(
processed=processed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=self.max_open_trades,
@ -622,22 +371,6 @@ class Hyperopt:
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = Hyperopt._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with Hyperopt results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@ -651,7 +384,8 @@ class Hyperopt:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.backtesting.required_startup)
min_date, max_date = get_timerange(preprocessed)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
@ -661,7 +395,10 @@ class Hyperopt:
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.exchange.close()
self.backtesting.exchange._api = None # type: ignore
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
@ -727,7 +464,7 @@ class Hyperopt:
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best_loss(val, self.current_best_loss)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
@ -755,7 +492,7 @@ class Hyperopt:
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

89
freqtrade/optimize/hyperopt_auto.py Normal file
View File

@ -0,0 +1,89 @@
"""
HyperOptAuto class.
This module implements a convenience auto-hyperopt class, which can be used together with strategies
that implement IHyperStrategy interface.
"""
from contextlib import suppress
from typing import Any, Callable, Dict, List
from pandas import DataFrame
with suppress(ImportError):
from skopt.space import Dimension
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class HyperOptAuto(IHyperOpt):
"""
This class delegates functionality to Strategy(IHyperStrategy) and Strategy.HyperOpt classes.
Most of the time Strategy.HyperOpt class would only implement indicator_space and
sell_indicator_space methods, but other hyperopt methods can be overridden as well.
"""
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_buy_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('buy'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_buy_trend(dataframe, metadata)
return populate_buy_trend
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_sell_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('sell'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_sell_trend(dataframe, metadata)
return populate_sell_trend
def _get_func(self, name) -> Callable:
"""
Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
:param name: function name.
:return: a requested function.
"""
hyperopt_cls = getattr(self.strategy, 'HyperOpt', None)
default_func = getattr(super(), name)
if hyperopt_cls:
return getattr(hyperopt_cls, name, default_func)
else:
return default_func
def _generate_indicator_space(self, category):
for attr_name, attr in self.strategy.enumerate_parameters(category):
if attr.optimize:
yield attr.get_space(attr_name)
def _get_indicator_space(self, category, fallback_method_name):
indicator_space = list(self._generate_indicator_space(category))
if len(indicator_space) > 0:
return indicator_space
else:
return self._get_func(fallback_method_name)()
def indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy', 'indicator_space')
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell', 'sell_indicator_space')
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)
def roi_space(self) -> List['Dimension']:
return self._get_func('roi_space')()
def stoploss_space(self) -> List['Dimension']:
return self._get_func('stoploss_space')()
def generate_trailing_params(self, params: Dict) -> Dict:
return self._get_func('generate_trailing_params')(params)
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()

59
freqtrade/optimize/hyperopt_interface.py
View File

@ -7,11 +7,13 @@ import math
from abc import ABC
from typing import Any, Callable, Dict, List
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Categorical, Dimension, Integer
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
@ -30,10 +32,11 @@ class IHyperOpt(ABC):
Defines the mandatory structure must follow any custom hyperopt
Class attributes you can use:
ticker_interval -> int: value of the ticker interval to use for the strategy
timeframe -> int: value of the timeframe to use for the strategy
"""
ticker_interval: str # DEPRECATED
timeframe: str
strategy: IStrategy
def __init__(self, config: dict) -> None:
self.config = config
@ -42,36 +45,31 @@ class IHyperOpt(ABC):
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator.
"""
raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator.
"""
raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
@staticmethod
def indicator_space() -> List[Dimension]:
def indicator_space(self) -> List[Dimension]:
"""
Create an indicator space.
"""
raise OperationalException(_format_exception_message('indicator_space', 'buy'))
@staticmethod
def sell_indicator_space() -> List[Dimension]:
def sell_indicator_space(self) -> List[Dimension]:
"""
Create a sell indicator space.
"""
raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
"""
Create a ROI table.
@ -86,8 +84,7 @@ class IHyperOpt(ABC):
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
def roi_space(self) -> List[Dimension]:
"""
Create a ROI space.
@ -95,7 +92,7 @@ class IHyperOpt(ABC):
This method implements adaptive roi hyperspace with varied
ranges for parameters which automatically adapts to the
ticker interval used.
timeframe used.
It's used by Freqtrade by default, if no custom roi_space method is defined.
"""
@ -107,7 +104,7 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_min = timeframe_to_minutes(self.timeframe)
# We define here limits for the ROI space parameters automagically adapted to the
# timeframe used by the bot:
@ -117,7 +114,7 @@ class IHyperOpt(ABC):
# * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
# method for the 5m timeframe.
roi_t_scale = timeframe_min / 5
roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
roi_limits = {
@ -143,7 +140,7 @@ class IHyperOpt(ABC):
'roi_p2': roi_limits['roi_p2_min'],
'roi_p3': roi_limits['roi_p3_min'],
}
logger.info(f"Min roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 3)}")
p = {
'roi_t1': roi_limits['roi_t1_max'],
'roi_t2': roi_limits['roi_t2_max'],
@ -152,19 +149,21 @@ class IHyperOpt(ABC):
'roi_p2': roi_limits['roi_p2_max'],
'roi_p3': roi_limits['roi_p3_max'],
}
logger.info(f"Max roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 3)}")
return [
Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
Real(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], name='roi_p1'),
Real(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], name='roi_p2'),
Real(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], name='roi_p3'),
SKDecimal(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], decimals=3,
name='roi_p1'),
SKDecimal(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], decimals=3,
name='roi_p2'),
SKDecimal(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], decimals=3,
name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
def stoploss_space(self) -> List[Dimension]:
"""
Create a stoploss space.
@ -172,11 +171,10 @@ class IHyperOpt(ABC):
You may override it in your custom Hyperopt class.
"""
return [
Real(-0.35, -0.02, name='stoploss'),
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
]
@staticmethod
def generate_trailing_params(params: Dict) -> Dict:
def generate_trailing_params(self, params: Dict) -> Dict:
"""
Create dict with trailing stop parameters.
"""
@ -188,8 +186,7 @@ class IHyperOpt(ABC):
'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
}
@staticmethod
def trailing_space() -> List[Dimension]:
def trailing_space(self) -> List[Dimension]:
"""
Create a trailing stoploss space.
@ -204,14 +201,14 @@ class IHyperOpt(ABC):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.01, 0.35, name='trailing_stop_positive'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

294
freqtrade/optimize/hyperopt_tools.py Normal file
View File

@ -0,0 +1,294 @@
import io
import logging
from collections import OrderedDict
from pathlib import Path
from pprint import pformat
from typing import Dict, List
import rapidjson
import tabulate
from colorama import Fore, Style
from joblib import load
from pandas import isna, json_normalize
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_dict
logger = logging.getLogger(__name__)
class HyperoptTools():
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with HyperoptTools results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
@staticmethod
def print_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
"""
Display details of the hyperopt result
"""
params = results.get('params_details', {})
# Default header string
if header_str is None:
header_str = "Best result"
if not no_header:
explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
print(f"\n{header_str}:\n\n{explanation_str}\n")
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
HyperoptTools._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
def _params_update_for_json(result_dict, params, space: str) -> None:
if space in params:
space_params = HyperoptTools._space_params(params, space)
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in space_params.items()
)
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = HyperoptTools._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
'Stake currency', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")

104
freqtrade/optimize/optimize_reports.py
View File

@ -8,7 +8,7 @@ from numpy import int64
from pandas import DataFrame
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
calculate_max_drawdown)
from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
@ -56,12 +56,13 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
'Wins', 'Draws', 'Losses']
def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
profit_sum = result['profit_ratio'].sum()
profit_total = profit_sum / max_open_trades
# (end-capital - starting capital) / starting capital
profit_total = result['profit_abs'].sum() / starting_balance
return {
'key': first_column,
@ -88,13 +89,13 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
}
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list for the given backtest data and the results dataframe
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param starting_balance: Starting balance
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: List of Dicts containing the metrics per pair
@ -107,10 +108,13 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_t
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append(_generate_result_line(result, max_open_trades, pair))
tabular_data.append(_generate_result_line(result, starting_balance, pair))
# Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
# Append Total
tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
@ -132,7 +136,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
tabular_data.append(
{
'sell_reason': reason.value,
'sell_reason': reason,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
@ -159,7 +163,7 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
results['results'], results['config']['dry_run_wallet'], strategy)
)
return tabular_data
@ -195,13 +199,18 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'backtest_best_day_abs': 0,
'backtest_worst_day_abs': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
worst_rel = min(daily_profit_rel)
best_rel = max(daily_profit_rel)
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
@ -212,8 +221,10 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel,
'backtest_best_day_abs': best,
'backtest_worst_day_abs': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
@ -246,15 +257,16 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
continue
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
@ -275,8 +287,10 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_ratio'].sum() / max_open_trades,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
@ -292,6 +306,10 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
@ -316,17 +334,23 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results)
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
@ -335,6 +359,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
@ -431,8 +458,19 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Starting balance', round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])),
('Final balance', round_coin_value(strat_results['final_balance'],
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@ -442,20 +480,28 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('', ''), # Empty line to improve readability
('Abs Profit Min', round_coin_value(strat_results['csum_min'],
('Min balance', round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Abs Profit Max', round_coin_value(strat_results['csum_max'],
('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
@ -463,7 +509,17 @@ def text_table_add_metrics(strat_results: Dict) -> str:
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
start_balance = round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])
stake_amount = round_coin_value(
strat_results['stake_amount'], strat_results['stake_currency']
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
message = ("No trades made. "
f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}."
)
return message
def show_backtest_results(config: Dict, backtest_stats: Dict):

4
freqtrade/optimize/space/__init__.py Normal file
View File

@ -0,0 +1,4 @@
# flake8: noqa: F401
from skopt.space import Categorical, Dimension, Integer, Real
from .decimalspace import SKDecimal

33
freqtrade/optimize/space/decimalspace.py Normal file
View File

@ -0,0 +1,33 @@
import numpy as np
from skopt.space import Integer
class SKDecimal(Integer):
def __init__(self, low, high, decimals=3, prior="uniform", base=10, transform=None,
name=None, dtype=np.int64):
self.decimals = decimals
_low = int(low * pow(10, self.decimals))
_high = int(high * pow(10, self.decimals))
# trunc to precision to avoid points out of space
self.low_orig = round(_low * pow(0.1, self.decimals), self.decimals)
self.high_orig = round(_high * pow(0.1, self.decimals), self.decimals)
super().__init__(_low, _high, prior, base, transform, name, dtype)
def __repr__(self):
return "Decimal(low={}, high={}, decimals={}, prior='{}', transform='{}')".format(
self.low_orig, self.high_orig, self.decimals, self.prior, self.transform_)
def __contains__(self, point):
if isinstance(point, list):
point = np.array(point)
return self.low_orig <= point <= self.high_orig
def transform(self, Xt):
aa = [int(x * pow(10, self.decimals)) for x in Xt]
return super().transform(aa)
def inverse_transform(self, Xt):
res = super().inverse_transform(Xt)
return [round(x * pow(0.1, self.decimals), self.decimals) for x in res]

3
freqtrade/persistence/__init__.py
View File

@ -1,4 +1,5 @@
# flake8: noqa: F401
from freqtrade.persistence.models import Order, Trade, clean_dry_run_db, cleanup_db, init_db
from freqtrade.persistence.models import (LocalTrade, Order, Trade, clean_dry_run_db, cleanup_db,
init_db)
from freqtrade.persistence.pairlock_middleware import PairLocks

2
freqtrade/persistence/migrations.py
View File

@ -141,7 +141,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
inspector = inspect(engine)
cols = inspector.get_columns('trades')
if 'orders' not in previous_tables:
if 'orders' not in previous_tables and 'trades' in previous_tables:
logger.info('Moving open orders to Orders table.')
migrate_open_orders_to_trades(engine)
else:

304
freqtrade/persistence/models.py
View File

@ -6,7 +6,6 @@ from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
import arrow
from sqlalchemy import (Boolean, Column, DateTime, Float, ForeignKey, Integer, String,
create_engine, desc, func, inspect)
from sqlalchemy.exc import NoSuchModuleError
@ -59,13 +58,10 @@ def init_db(db_url: str, clean_open_orders: bool = False) -> None:
# https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope
# Scoped sessions proxy requests to the appropriate thread-local session.
# We should use the scoped_session object - not a seperately initialized version
Trade.session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.query = Trade.session.query_property()
# Copy session attributes to order object too
Order.session = Trade.session
Order.query = Order.session.query_property()
PairLock.session = Trade.session
PairLock.query = PairLock.session.query_property()
Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.query = Trade._session.query_property()
Order.query = Trade._session.query_property()
PairLock.query = Trade._session.query_property()
previous_tables = inspect(engine).get_table_names()
_DECL_BASE.metadata.create_all(engine)
@ -81,7 +77,7 @@ def cleanup_db() -> None:
Flushes all pending operations to disk.
:return: None
"""
Trade.session.flush()
Trade.query.session.flush()
def clean_dry_run_db() -> None:
@ -163,8 +159,8 @@ class Order(_DECL_BASE):
if self.status in ('closed', 'canceled', 'cancelled'):
self.ft_is_open = False
if order.get('filled', 0) > 0:
self.order_filled_date = arrow.utcnow().datetime
self.order_update_date = arrow.utcnow().datetime
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
@staticmethod
def update_orders(orders: List['Order'], order: Dict[str, Any]):
@ -199,67 +195,69 @@ class Order(_DECL_BASE):
return Order.query.filter(Order.ft_is_open.is_(True)).all()
class Trade(_DECL_BASE):
class LocalTrade():
"""
Trade database model.
Also handles updating and querying trades
Used in backtesting - must be aligned to Trade model!
"""
__tablename__ = 'trades'
use_db: bool = True
use_db: bool = False
# Trades container for backtesting
trades: List['Trade'] = []
trades: List['LocalTrade'] = []
trades_open: List['LocalTrade'] = []
total_profit: float = 0
id = Column(Integer, primary_key=True)
id: int = 0
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
orders: List[Order] = []
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
exchange: str = ''
pair: str = ''
is_open: bool = True
fee_open: float = 0.0
fee_open_cost: Optional[float] = None
fee_open_currency: str = ''
fee_close: float = 0.0
fee_close_cost: Optional[float] = None
fee_close_currency: str = ''
open_rate: float = 0.0
open_rate_requested: Optional[float] = None
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
open_trade_value: float = 0.0
close_rate: Optional[float] = None
close_rate_requested: Optional[float] = None
close_profit: Optional[float] = None
close_profit_abs: Optional[float] = None
stake_amount: float = 0.0
amount: float = 0.0
amount_requested: Optional[float] = None
open_date: datetime
close_date: Optional[datetime] = None
open_order_id: Optional[str] = None
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
stop_loss: float = 0.0
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
stop_loss_pct: float = 0.0
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
initial_stop_loss: float = 0.0
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
initial_stop_loss_pct: float = 0.0
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
stoploss_order_id: Optional[str] = None
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
stoploss_last_update: Optional[datetime] = None
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
max_rate: float = 0.0
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
timeframe = Column(Integer, nullable=True)
min_rate: float = 0.0
sell_reason: str = ''
sell_order_status: str = ''
strategy: str = ''
timeframe: Optional[int] = None
def __init__(self, **kwargs):
super().__init__(**kwargs)
for key in kwargs:
setattr(self, key, kwargs[key])
self.recalc_open_trade_value()
def __repr__(self):
@ -268,6 +266,14 @@ class Trade(_DECL_BASE):
return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
f'open_rate={self.open_rate:.8f}, open_since={open_since})')
@property
def open_date_utc(self):
return self.open_date.replace(tzinfo=timezone.utc)
@property
def close_date_utc(self):
return self.close_date.replace(tzinfo=timezone.utc)
def to_json(self) -> Dict[str, Any]:
return {
'trade_id': self.id,
@ -287,15 +293,12 @@ class Trade(_DECL_BASE):
'fee_close_cost': self.fee_close_cost,
'fee_close_currency': self.fee_close_currency,
'open_date_hum': arrow.get(self.open_date).humanize(),
'open_date': self.open_date.strftime(DATETIME_PRINT_FORMAT),
'open_timestamp': int(self.open_date.replace(tzinfo=timezone.utc).timestamp() * 1000),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_value': round(self.open_trade_value, 8),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime(DATETIME_PRINT_FORMAT)
if self.close_date else None),
'close_timestamp': int(self.close_date.replace(
@ -306,9 +309,9 @@ class Trade(_DECL_BASE):
'close_profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
'close_profit_abs': self.close_profit_abs, # Deprecated
'trade_duration_s': (int((self.close_date - self.open_date).total_seconds())
'trade_duration_s': (int((self.close_date_utc - self.open_date_utc).total_seconds())
if self.close_date else None),
'trade_duration': (int((self.close_date - self.open_date).total_seconds() // 60)
'trade_duration': (int((self.close_date_utc - self.open_date_utc).total_seconds() // 60)
if self.close_date else None),
'profit_ratio': self.close_profit,
@ -341,8 +344,9 @@ class Trade(_DECL_BASE):
"""
Resets all trades. Only active for backtesting mode.
"""
if not Trade.use_db:
Trade.trades = []
LocalTrade.trades = []
LocalTrade.trades_open = []
LocalTrade.total_profit = 0
def adjust_min_max_rates(self, current_price: float) -> None:
"""
@ -410,8 +414,8 @@ class Trade(_DECL_BASE):
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.open_rate = float(safe_value_fallback(order, 'average', 'price'))
self.amount = float(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_value()
if self.is_open:
logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
@ -425,7 +429,7 @@ class Trade(_DECL_BASE):
self.close_rate_requested = self.stop_loss
if self.is_open:
logger.info(f'{order_type.upper()} is hit for {self}.')
self.close(order['average'])
self.close(safe_value_fallback(order, 'average', 'price'))
else:
raise ValueError(f'Unknown order type: {order_type}')
cleanup_db()
@ -435,7 +439,7 @@ class Trade(_DECL_BASE):
Sets close_rate to the given rate, calculates total profit
and marks trade as closed
"""
self.close_rate = Decimal(rate)
self.close_rate = rate
self.close_profit = self.calc_profit_ratio()
self.close_profit_abs = self.calc_profit()
self.close_date = self.close_date or datetime.utcnow()
@ -480,14 +484,6 @@ class Trade(_DECL_BASE):
def update_order(self, order: Dict) -> None:
Order.update_orders(self.orders, order)
def delete(self) -> None:
for order in self.orders:
Order.session.delete(order)
Trade.session.delete(self)
Trade.session.flush()
def _calc_open_trade_value(self) -> float:
"""
Calculate the open_rate including open_fee.
@ -517,7 +513,7 @@ class Trade(_DECL_BASE):
if rate is None and not self.close_rate:
return 0.0
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate)
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate) # type: ignore
fees = sell_trade * Decimal(fee or self.fee_close)
return float(sell_trade - fees)
@ -551,6 +547,8 @@ class Trade(_DECL_BASE):
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
if self.open_trade_value == 0.0:
return 0.0
profit_ratio = (close_trade_value / self.open_trade_value) - 1
return float(f"{profit_ratio:.8f}")
@ -589,7 +587,7 @@ class Trade(_DECL_BASE):
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['Trade']:
) -> List['LocalTrade']:
"""
Helper function to query Trades.
Returns a List of trades, filtered on the parameters given.
@ -598,20 +596,18 @@ class Trade(_DECL_BASE):
:return: unsorted List[Trade]
"""
if Trade.use_db:
trade_filter = []
if pair:
trade_filter.append(Trade.pair == pair)
if open_date:
trade_filter.append(Trade.open_date > open_date)
if close_date:
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
else:
# Offline mode - without database
sel_trades = [trade for trade in Trade.trades]
if is_open is not None:
if is_open:
sel_trades = LocalTrade.trades_open
else:
sel_trades = LocalTrade.trades
else:
# Not used during backtesting, but might be used by a strategy
sel_trades = list(LocalTrade.trades + LocalTrade.trades_open)
if pair:
sel_trades = [trade for trade in sel_trades if trade.pair == pair]
if open_date:
@ -619,10 +615,22 @@ class Trade(_DECL_BASE):
if close_date:
sel_trades = [trade for trade in sel_trades if trade.close_date
and trade.close_date > close_date]
if is_open is not None:
sel_trades = [trade for trade in sel_trades if trade.is_open == is_open]
return sel_trades
@staticmethod
def close_bt_trade(trade):
LocalTrade.trades_open.remove(trade)
LocalTrade.trades.append(trade)
LocalTrade.total_profit += trade.close_profit_abs
@staticmethod
def add_bt_trade(trade):
if trade.is_open:
LocalTrade.trades_open.append(trade)
else:
LocalTrade.trades.append(trade)
@staticmethod
def get_open_trades() -> List[Any]:
"""
@ -663,9 +671,12 @@ class Trade(_DECL_BASE):
Calculates total invested amount in open trades
in stake currency
"""
total_open_stake_amount = Trade.session.query(func.sum(Trade.stake_amount))\
.filter(Trade.is_open.is_(True))\
.scalar()
if Trade.use_db:
total_open_stake_amount = Trade.query.with_entities(
func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
else:
total_open_stake_amount = sum(
t.stake_amount for t in Trade.get_trades_proxy(is_open=True))
return total_open_stake_amount or 0
@staticmethod
@ -673,7 +684,7 @@ class Trade(_DECL_BASE):
"""
Returns List of dicts containing all Trades, including profit and trade count
"""
pair_rates = Trade.session.query(
pair_rates = Trade.query.with_entities(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.count(Trade.pair).label('count')
@ -696,7 +707,7 @@ class Trade(_DECL_BASE):
Get best pair with closed trade.
:returns: Tuple containing (pair, profit_sum)
"""
best_pair = Trade.session.query(
best_pair = Trade.query.with_entities(
Trade.pair, func.sum(Trade.close_profit).label('profit_sum')
).filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
@ -723,6 +734,108 @@ class Trade(_DECL_BASE):
logger.info(f"New stoploss: {trade.stop_loss}.")
class Trade(_DECL_BASE, LocalTrade):
"""
Trade database model.
Also handles updating and querying trades
Note: Fields must be aligned with LocalTrade class
"""
__tablename__ = 'trades'
use_db: bool = True
id = Column(Integer, primary_key=True)
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.recalc_open_trade_value()
def delete(self) -> None:
for order in self.orders:
Order.query.session.delete(order)
Trade.query.session.delete(self)
Trade.query.session.flush()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.
Returns a List of trades, filtered on the parameters given.
In live mode, converts the filter to a database query and returns all rows
In Backtest mode, uses filters on Trade.trades to get the result.
:return: unsorted List[Trade]
"""
if Trade.use_db:
trade_filter = []
if pair:
trade_filter.append(Trade.pair == pair)
if open_date:
trade_filter.append(Trade.open_date > open_date)
if close_date:
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
else:
return LocalTrade.get_trades_proxy(
pair=pair, is_open=is_open,
open_date=open_date,
close_date=close_date
)
class PairLock(_DECL_BASE):
"""
Pair Locks database model.
@ -765,6 +878,7 @@ class PairLock(_DECL_BASE):
def to_json(self) -> Dict[str, Any]:
return {
'id': self.id,
'pair': self.pair,
'lock_time': self.lock_time.strftime(DATETIME_PRINT_FORMAT),
'lock_timestamp': int(self.lock_time.replace(tzinfo=timezone.utc).timestamp() * 1000),

14
freqtrade/persistence/pairlock_middleware.py
View File

@ -48,8 +48,8 @@ class PairLocks():
active=True
)
if PairLocks.use_db:
PairLock.session.add(lock)
PairLock.session.flush()
PairLock.query.session.add(lock)
PairLock.query.session.flush()
else:
PairLocks.locks.append(lock)
@ -99,7 +99,7 @@ class PairLocks():
for lock in locks:
lock.active = False
if PairLocks.use_db:
PairLock.session.flush()
PairLock.query.session.flush()
@staticmethod
def is_global_lock(now: Optional[datetime] = None) -> bool:
@ -123,3 +123,11 @@ class PairLocks():
now = datetime.now(timezone.utc)
return len(PairLocks.get_pair_locks(pair, now)) > 0 or PairLocks.is_global_lock(now)
@staticmethod
def get_all_locks() -> List[PairLock]:
if PairLocks.use_db:
return PairLock.query.all()
else:
return PairLocks.locks

2
freqtrade/plot/plotting.py
View File

@ -145,7 +145,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
Add scatter points indicating max drawdown
"""
try:
max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
drawdown = go.Scatter(
x=[highdate, lowdate],

2
freqtrade/plugins/pairlist/IPairList.py
View File

@ -85,7 +85,7 @@ class IPairList(LoggingMixin, ABC):
position in the chain.
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
raise OperationalException("This Pairlist Handler should not be used "

7
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@ -2,7 +2,7 @@
Performance pair list filter
"""
import logging
from typing import Any, Dict, List
from typing import Dict, List
import pandas as pd
@ -15,11 +15,6 @@ logger = logging.getLogger(__name__)
class PerformanceFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
@property
def needstickers(self) -> bool:
"""

2
freqtrade/plugins/pairlist/PriceFilter.py
View File

@ -64,7 +64,7 @@ class PriceFilter(IPairList):
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
if ticker['last'] is None or ticker['last'] == 0:
if ticker.get('last', None) is None or ticker.get('last') == 0:
self.log_once(f"Removed {pair} from whitelist, because "
"ticker['last'] is empty (Usually no trade in the last 24h).",
logger.info)

2
freqtrade/plugins/pairlist/StaticPairList.py
View File

@ -46,7 +46,7 @@ class StaticPairList(IPairList):
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
if self._allow_inactive:

121
freqtrade/plugins/pairlist/VolatilityFilter.py Normal file
View File

@ -0,0 +1,121 @@
"""
Volatility pairlist filter
"""
import logging
import sys
from copy import deepcopy
from typing import Any, Dict, List, Optional
import arrow
import numpy as np
from cachetools.ttl import TTLCache
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class VolatilityFilter(IPairList):
'''
Filters pairs by volatility
'''
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._days = pairlistconfig.get('lookback_days', 10)
self._min_volatility = pairlistconfig.get('min_volatility', 0)
self._max_volatility = pairlistconfig.get('max_volatility', sys.maxsize)
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
if self._days < 1:
raise OperationalException("VolatilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("VolatilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return False
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Filtering pairs with volatility range "
f"{self._min_volatility}-{self._max_volatility} "
f" the last {self._days} {plural(self._days, 'day')}.")
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Validate trading range
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new allowlist
"""
needed_pairs = [(p, '1d') for p in pairlist if p not in self._pair_cache]
since_ms = int(arrow.utcnow()
.floor('day')
.shift(days=-self._days - 1)
.float_timestamp) * 1000
# Get all candles
candles = {}
if needed_pairs:
candles = self._exchange.refresh_latest_ohlcv(needed_pairs, since_ms=since_ms,
cache=False)
if self._enabled:
for p in deepcopy(pairlist):
daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
if not self._validate_pair_loc(p, daily_candles):
pairlist.remove(p)
return pairlist
def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
cached_res = self._pair_cache.get(pair, None)
if cached_res is not None:
return cached_res
result = False
if daily_candles is not None and not daily_candles.empty:
returns = (np.log(daily_candles.close / daily_candles.close.shift(-1)))
returns.fillna(0, inplace=True)
volatility_series = returns.rolling(window=self._days).std()*np.sqrt(self._days)
volatility_avg = volatility_series.mean()
if self._min_volatility <= volatility_avg <= self._max_volatility:
result = True
else:
self.log_once(f"Removed {pair} from whitelist, because volatility "
f"over {self._days} {plural(self._days, 'day')} "
f"is: {volatility_avg:.3f} "
f"which is not in the configured range of "
f"{self._min_volatility}-{self._max_volatility}.",
logger.info)
result = False
self._pair_cache[pair] = result
return result

2
freqtrade/plugins/pairlist/VolumePairList.py
View File

@ -67,7 +67,7 @@ class VolumePairList(IPairList):
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
# Generate dynamic whitelist

7
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@ -28,7 +28,7 @@ class RangeStabilityFilter(IPairList):
self._min_rate_of_change = pairlistconfig.get('min_rate_of_change', 0.01)
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._pair_cache: TTLCache = TTLCache(maxsize=100, ttl=self._refresh_period)
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
if self._days < 1:
raise OperationalException("RangeStabilityFilter requires lookback_days to be >= 1")
@ -87,8 +87,9 @@ class RangeStabilityFilter(IPairList):
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
if pair in self._pair_cache:
return self._pair_cache[pair]
cached_res = self._pair_cache.get(pair, None)
if cached_res is not None:
return cached_res
result = False
if daily_candles is not None and not daily_candles.empty:

7
freqtrade/plugins/protections/cooldown_period.py
View File

@ -1,7 +1,6 @@
import logging
from datetime import datetime, timedelta
from typing import Any, Dict
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
@ -15,9 +14,6 @@ class CooldownPeriod(IProtection):
has_global_stop: bool = False
has_local_stop: bool = True
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
super().__init__(config, protection_config)
def _reason(self) -> str:
"""
LockReason to use
@ -44,7 +40,8 @@ class CooldownPeriod(IProtection):
trades = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
if trades:
# Get latest trade
trade = sorted(trades, key=lambda t: t.close_date)[-1]
# Ignore type error as we know we only get closed trades.
trade = sorted(trades, key=lambda t: t.close_date)[-1] # type: ignore
self.log_once(f"Cooldown for {pair} for {self.stop_duration_str}.", logger.info)
until = self.calculate_lock_end([trade], self._stop_duration)

6
freqtrade/plugins/protections/iprotection.py
View File

@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Tuple
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import plural
from freqtrade.mixins import LoggingMixin
from freqtrade.persistence import Trade
from freqtrade.persistence import LocalTrade
logger = logging.getLogger(__name__)
@ -93,11 +93,11 @@ class IProtection(LoggingMixin, ABC):
"""
@staticmethod
def calculate_lock_end(trades: List[Trade], stop_minutes: int) -> datetime:
def calculate_lock_end(trades: List[LocalTrade], stop_minutes: int) -> datetime:
"""
Get lock end time
"""
max_date: datetime = max([trade.close_date for trade in trades])
max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
# comming from Database, tzinfo is not set.
if max_date.tzinfo is None:
max_date = max_date.replace(tzinfo=timezone.utc)

2
freqtrade/plugins/protections/low_profit_pairs.py
View File

@ -53,7 +53,7 @@ class LowProfitPairs(IProtection):
# Not enough trades in the relevant period
return False, None, None
profit = sum(trade.close_profit for trade in trades)
profit = sum(trade.close_profit for trade in trades if trade.close_profit)
if profit < self._required_profit:
self.log_once(
f"Trading for {pair} stopped due to {profit:.2f} < {self._required_profit} "

4
freqtrade/plugins/protections/max_drawdown_protection.py
View File

@ -55,13 +55,13 @@ class MaxDrawdown(IProtection):
# Drawdown is always positive
try:
drawdown, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
except ValueError:
return False, None, None
if drawdown > self._max_allowed_drawdown:
self.log_once(
f"Trading stopped due to Max Drawdown {drawdown:.2f} < {self._max_allowed_drawdown}"
f"Trading stopped due to Max Drawdown {drawdown:.2f} > {self._max_allowed_drawdown}"
f" within {self.lookback_period_str}.", logger.info)
until = self.calculate_lock_end(trades, self._stop_duration)

2
freqtrade/plugins/protections/stoploss_guard.py
View File

@ -56,7 +56,7 @@ class StoplossGuard(IProtection):
trades = [trade for trade in trades1 if (str(trade.sell_reason) in (
SellType.TRAILING_STOP_LOSS.value, SellType.STOP_LOSS.value,
SellType.STOPLOSS_ON_EXCHANGE.value)
and trade.close_profit < 0)]
and trade.close_profit and trade.close_profit < 0)]
if len(trades) < self._trade_limit:
return False, None, None

4
freqtrade/resolvers/strategy_resolver.py
View File

@ -196,9 +196,9 @@ class StrategyResolver(IResolver):
strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
if any([x == 2 for x in [strategy._populate_fun_len,
if any(x == 2 for x in [strategy._populate_fun_len,
strategy._buy_fun_len,
strategy._sell_fun_len]]):
strategy._sell_fun_len]):
strategy.INTERFACE_VERSION = 1
return strategy

12
freqtrade/rpc/api_server/api_schemas.py
View File

@ -62,14 +62,12 @@ class PerformanceEntry(BaseModel):
class Profit(BaseModel):
profit_closed_coin: float
profit_closed_percent: float
profit_closed_percent_mean: float
profit_closed_ratio_mean: float
profit_closed_percent_sum: float
profit_closed_ratio_sum: float
profit_closed_fiat: float
profit_all_coin: float
profit_all_percent: float
profit_all_percent_mean: float
profit_all_ratio_mean: float
profit_all_percent_sum: float
@ -153,13 +151,11 @@ class TradeSchema(BaseModel):
fee_close: Optional[float]
fee_close_cost: Optional[float]
fee_close_currency: Optional[str]
open_date_hum: str
open_date: str
open_timestamp: int
open_rate: float
open_rate_requested: Optional[float]
open_trade_value: float
close_date_hum: Optional[str]
close_date: Optional[str]
close_timestamp: Optional[int]
close_rate: Optional[float]
@ -170,6 +166,7 @@ class TradeSchema(BaseModel):
profit_ratio: Optional[float]
profit_pct: Optional[float]
profit_abs: Optional[float]
profit_fiat: Optional[float]
sell_reason: Optional[str]
sell_order_status: Optional[str]
stop_loss_abs: Optional[float]
@ -192,7 +189,6 @@ class OpenTradeSchema(TradeSchema):
stoploss_current_dist_ratio: Optional[float]
stoploss_entry_dist: Optional[float]
stoploss_entry_dist_ratio: Optional[float]
base_currency: str
current_profit: float
current_profit_abs: float
current_profit_pct: float
@ -210,6 +206,7 @@ class ForceBuyResponse(BaseModel):
class LockModel(BaseModel):
id: int
active: bool
lock_end_time: str
lock_end_timestamp: int
@ -224,6 +221,11 @@ class Locks(BaseModel):
locks: List[LockModel]
class DeleteLockRequest(BaseModel):
pair: Optional[str]
lockid: Optional[int]
class Logs(BaseModel):
log_count: int
logs: List[List]

38
freqtrade/rpc/api_server/api_v1.py
View File

@ -11,13 +11,13 @@ from freqtrade.data.history import get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.rpc import RPC
from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, BlacklistPayload,
BlacklistResponse, Count, Daily, DeleteTrade,
ForceBuyPayload, ForceBuyResponse,
ForceSellPayload, Locks, Logs, OpenTradeSchema,
PairHistory, PerformanceEntry, Ping, PlotConfig,
Profit, ResultMsg, ShowConfig, Stats, StatusMsg,
StrategyListResponse, StrategyResponse,
TradeResponse, Version, WhitelistResponse)
BlacklistResponse, Count, Daily,
DeleteLockRequest, DeleteTrade, ForceBuyPayload,
ForceBuyResponse, ForceSellPayload, Locks, Logs,
OpenTradeSchema, PairHistory, PerformanceEntry,
Ping, PlotConfig, Profit, ResultMsg, ShowConfig,
Stats, StatusMsg, StrategyListResponse,
StrategyResponse, Version, WhitelistResponse)
from freqtrade.rpc.api_server.deps import get_config, get_rpc, get_rpc_optional
from freqtrade.rpc.rpc import RPCException
@ -82,11 +82,21 @@ def status(rpc: RPC = Depends(get_rpc)):
return []
@router.get('/trades', response_model=TradeResponse, tags=['info', 'trading'])
# Using the responsemodel here will cause a ~100% increase in response time (from 1s to 2s)
# on big databases. Correct response model: response_model=TradeResponse,
@router.get('/trades', tags=['info', 'trading'])
def trades(limit: int = 0, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_trade_history(limit)
@router.get('/trade/{tradeid}', response_model=OpenTradeSchema, tags=['info', 'trading'])
def trade(tradeid: int = 0, rpc: RPC = Depends(get_rpc)):
try:
return rpc._rpc_trade_status([tradeid])[0]
except (RPCException, KeyError):
raise HTTPException(status_code=404, detail='Trade not found.')
@router.delete('/trades/{tradeid}', response_model=DeleteTrade, tags=['info', 'trading'])
def trades_delete(tradeid: int, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_delete(tradeid)
@ -136,11 +146,21 @@ def whitelist(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_whitelist()
@router.get('/locks', response_model=Locks, tags=['info'])
@router.get('/locks', response_model=Locks, tags=['info', 'locks'])
def locks(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_locks()
@router.delete('/locks/{lockid}', response_model=Locks, tags=['info', 'locks'])
def delete_lock(lockid: int, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_delete_lock(lockid=lockid)
@router.post('/locks/delete', response_model=Locks, tags=['info', 'locks'])
def delete_lock_pair(payload: DeleteLockRequest, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_delete_lock(lockid=payload.lockid, pair=payload.pair)
@router.get('/logs', response_model=Logs, tags=['info'])
def logs(limit: Optional[int] = None, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_get_logs(limit)

27
freqtrade/rpc/api_server/uvicorn_threaded.py
View File

@ -8,12 +8,33 @@ import uvicorn
class UvicornServer(uvicorn.Server):
"""
Multithreaded server - as found in https://github.com/encode/uvicorn/issues/742
Removed install_signal_handlers() override based on changes from this commit:
https://github.com/encode/uvicorn/commit/ce2ef45a9109df8eae038c0ec323eb63d644cbc6
Cannot rely on asyncio.get_event_loop() to create new event loop because of this check:
https://github.com/python/cpython/blob/4d7f11e05731f67fd2c07ec2972c6cb9861d52be/Lib/asyncio/events.py#L638
Fix by overriding run() and forcing creation of new event loop if uvloop is available
"""
def install_signal_handlers(self):
def run(self, sockets=None):
import asyncio
"""
In the parent implementation, this starts the thread, therefore we must patch it away here.
Parent implementation calls self.config.setup_event_loop(),
but we need to create uvloop event loop manually
"""
pass
try:
import uvloop # noqa
except ImportError: # pragma: no cover
from uvicorn.loops.asyncio import asyncio_setup
asyncio_setup()
else:
asyncio.set_event_loop(uvloop.new_event_loop())
loop = asyncio.get_event_loop()
loop.run_until_complete(self.serve(sockets=sockets))
@contextlib.contextmanager
def run_in_thread(self):

5
freqtrade/rpc/api_server/web_ui.py
View File

@ -13,6 +13,11 @@ async def favicon():
return FileResponse(str(Path(__file__).parent / 'ui/favicon.ico'))
@router_ui.get('/fallback_file.html', include_in_schema=False)
async def fallback():
return FileResponse(str(Path(__file__).parent / 'ui/fallback_file.html'))
@router_ui.get('/{rest_of_path:path}', include_in_schema=False)
async def index_html(rest_of_path: str):
"""

103
freqtrade/rpc/fiat_convert.py
View File

@ -4,9 +4,9 @@ e.g BTC to USD
"""
import logging
import time
from typing import Dict, List
from typing import Dict
from cachetools.ttl import TTLCache
from pycoingecko import CoinGeckoAPI
from freqtrade.constants import SUPPORTED_FIAT
@ -15,51 +15,6 @@ from freqtrade.constants import SUPPORTED_FIAT
logger = logging.getLogger(__name__)
class CryptoFiat:
"""
Object to describe what is the price of Crypto-currency in a FIAT
"""
# Constants
CACHE_DURATION = 6 * 60 * 60 # 6 hours
def __init__(self, crypto_symbol: str, fiat_symbol: str, price: float) -> None:
"""
Create an object that will contains the price for a crypto-currency in fiat
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:param price: Price in FIAT
"""
# Public attributes
self.crypto_symbol = None
self.fiat_symbol = None
self.price = 0.0
# Private attributes
self._expiration = 0.0
self.crypto_symbol = crypto_symbol.lower()
self.fiat_symbol = fiat_symbol.lower()
self.set_price(price=price)
def set_price(self, price: float) -> None:
"""
Set the price of the Crypto-currency in FIAT and set the expiration time
:param price: Price of the current Crypto currency in the fiat
:return: None
"""
self.price = price
self._expiration = time.time() + self.CACHE_DURATION
def is_expired(self) -> bool:
"""
Return if the current price is still valid or needs to be refreshed
:return: bool, true the price is expired and needs to be refreshed, false the price is
still valid
"""
return self._expiration - time.time() <= 0
class CryptoToFiatConverter:
"""
Main class to initiate Crypto to FIAT.
@ -84,7 +39,9 @@ class CryptoToFiatConverter:
return CryptoToFiatConverter.__instance
def __init__(self) -> None:
self._pairs: List[CryptoFiat] = []
# Timeout: 6h
self._pair_price: TTLCache = TTLCache(maxsize=500, ttl=6 * 60 * 60)
self._load_cryptomap()
def _load_cryptomap(self) -> None:
@ -118,49 +75,31 @@ class CryptoToFiatConverter:
"""
crypto_symbol = crypto_symbol.lower()
fiat_symbol = fiat_symbol.lower()
inverse = False
if crypto_symbol == 'usd':
# usd corresponds to "uniswap-state-dollar" for coingecko.
# We'll therefore need to "swap" the currencies
logger.info(f"reversing Rates {crypto_symbol}, {fiat_symbol}")
crypto_symbol = fiat_symbol
fiat_symbol = 'usd'
inverse = True
symbol = f"{crypto_symbol}/{fiat_symbol}"
# Check if the fiat convertion you want is supported
if not self._is_supported_fiat(fiat=fiat_symbol):
raise ValueError(f'The fiat {fiat_symbol} is not supported.')
# Get the pair that interest us and return the price in fiat
for pair in self._pairs:
if pair.crypto_symbol == crypto_symbol and pair.fiat_symbol == fiat_symbol:
# If the price is expired we refresh it, avoid to call the API all the time
if pair.is_expired():
pair.set_price(
price=self._find_price(
crypto_symbol=pair.crypto_symbol,
fiat_symbol=pair.fiat_symbol
)
)
price = self._pair_price.get(symbol, None)
# return the last price we have for this pair
return pair.price
# The pair does not exist, so we create it and return the price
return self._add_pair(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol,
price=self._find_price(
if not price:
price = self._find_price(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol
)
)
def _add_pair(self, crypto_symbol: str, fiat_symbol: str, price: float) -> float:
"""
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:return: price in FIAT
"""
self._pairs.append(
CryptoFiat(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol,
price=price
)
)
if inverse and price != 0.0:
price = 1 / price
self._pair_price[symbol] = price
return price

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