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Merge branch 'develop' into backtest_live_models

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Wagner Costa Santos 2022-11-03 13:29:25 -03:00
commit 17798b3397
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8
.github/workflows/ci.yml vendored
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@ -258,7 +258,7 @@ jobs:
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
mypy_version_check:
runs-on: ubuntu-20.04
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v3
@ -283,7 +283,7 @@ jobs:
- uses: pre-commit/action@v3.0.0
docs_check:
runs-on: ubuntu-20.04
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v3
@ -313,7 +313,7 @@ jobs:
# Notify only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
runs-on: ubuntu-20.04
runs-on: ubuntu-22.04
# Discord notification can't handle schedule events
if: (github.event_name != 'schedule')
permissions:
@ -338,7 +338,7 @@ jobs:
deploy:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
runs-on: ubuntu-20.04
runs-on: ubuntu-22.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'

2
.pre-commit-config.yaml
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@ -17,7 +17,7 @@ repos:
- types-filelock==3.2.7
- types-requests==2.28.11.2
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.1
- types-python-dateutil==2.8.19.2
# stages: [push]
- repo: https://github.com/pycqa/isort

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build_helpers/pyarrow-10.0.0-cp39-cp39-linux_armv7l.whl Normal file
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2
config_examples/config_binance.example.json
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@ -53,7 +53,7 @@
"XTZ/BTC"
],
"pair_blacklist": [
"BNB/BTC"
"BNB/.*"
]
},
"pairlists": [

9
config_examples/config_freqai.example.json
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@ -18,13 +18,8 @@
"name": "binance",
"key": "",
"secret": "",
"ccxt_config": {
"enableRateLimit": true
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
"1INCH/USDT",
"ALGO/USDT"

3
docker/Dockerfile.armhf
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@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker"
# Prepare environment
RUN mkdir /freqtrade \
&& apt-get update \
&& apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev \
&& apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev libutf8proc-dev libsnappy-dev \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m ftuser \
&& chown ftuser:ftuser /freqtrade \
@ -37,6 +37,7 @@ ENV LD_LIBRARY_PATH /usr/local/lib
COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
USER ftuser
RUN pip install --user --no-cache-dir numpy \
&& pip install --user /tmp/pyarrow-*.whl \
&& pip install --user --no-cache-dir -r requirements.txt
# Copy dependencies to runtime-image

29
docs/advanced-hyperopt.md
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@ -78,6 +78,8 @@ This function needs to return a floating point number (`float`). Smaller numbers
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
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
@ -94,6 +96,33 @@ class MyAwesomeStrategy(IStrategy):
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
def generate_roi_table(params: Dict) -> Dict[int, float]:
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
def trailing_space() -> List[Dimension]:
# All parameters here are mandatory, you can only modify their type or the range.
return [
# Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
Categorical([True], name='trailing_stop'),
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.
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
```
!!! Note

8
docs/backtesting.md
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@ -522,13 +522,13 @@ Since backtesting lacks some detailed information about what happens within a ca
- ROI
- exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
- exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
- Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
- Low happens before high for stoploss, protecting capital first
- Trailing stoploss
- Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point. This rule is NOT applicable to custom-stoploss scenarios, since there's no information about the stoploss logic available.
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
- ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
@ -546,8 +546,8 @@ In addition to the above assumptions, strategy authors should carefully read the
### Trading limits in backtesting
Exchanges have certain trading limits, like minimum base currency, or minimum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar.
Exchanges have certain trading limits, like minimum (and maximum) base currency, or minimum/maximum stake (quote) currency.
These limits are usually listed in the exchange documentation as "trading rules" or similar and can be quite different between different pairs.
Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
Freqtrade has however no information about historic limits.

6
docs/faq.md
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@ -102,6 +102,12 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
### I'm getting "Price jump between 2 candles detected"
This message is a warning that the candles had a price jump of > 30%.
This might be a sign that the pair stopped trading, and some token exchange took place (e.g. COCOS in 2021 - where price jumped from 0.0000154 to 0.01621).
This message is often accompanied by ["Missing data fillup"](#im-getting-missing-data-fillup-messages-in-the-log) - as trading on such pairs is often stopped for some time.
### I'm getting "Outdated history for pair xxx" in the log
The bot is trying to tell you that it got an outdated last candle (not the last complete candle).

17
docs/freqai-configuration.md
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@ -61,7 +61,7 @@ The FreqAI strategy requires including the following lines of code in the standa
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
@ -69,20 +69,17 @@ The FreqAI strategy requires including the following lines of code in the standa
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
@ -134,7 +131,7 @@ Notice also the location of the labels under `if set_generalized_indicators:` at
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
```python
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
...
@ -192,11 +189,11 @@ dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
```
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
```json
"freqai": {
"fit_live_prediction_candles": 300,
"fit_live_predictions_candles": 300,
}
```

36
docs/freqai-feature-engineering.md
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@ -2,7 +2,10 @@
## Defining the features
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`.
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
!!! Note
Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
@ -15,7 +18,7 @@ It is advisable to start from the template `populate_any_indicators()` in the so
"""
Function designed to automatically generate, name, and merge features
from user-indicated timeframes in the configuration file. The user controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e., the user should not prepend any supporting metrics
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
@ -23,37 +26,34 @@ It is advisable to start from the template `populate_any_indicators()` in the so
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)

8
docs/freqai-running.md
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@ -173,9 +173,13 @@ You can indicate to the bot that it should not train models, but instead should
```json
"freqai": {
"enabled": true,
"follow_mode": true,
"identifier": "example"
"identifier": "example",
"feature_parameters": {
// leader bots feature_parameters inserted here
},
}
```
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models.
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models. The user will also need to duplicate the `feature_parameters` parameters from from the leaders freqai configuration file into the freqai section of the followers config.

7
docs/freqai.md
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@ -4,7 +4,7 @@
## Introduction
FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.
FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, the FreqAI aims to be a sand-box for easily deploying robust machine-learning libraries on real-time data ([details])(#freqai-position-in-open-source-machine-learning-landscape).
Features include:
@ -72,6 +72,11 @@ pip install -r requirements-freqai.txt
If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
### FreqAI position in open-source machine learning landscape
Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data-exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data-collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data.
## Common pitfalls
FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).

12
docs/includes/pairlists.md
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@ -286,6 +286,18 @@ Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 -
Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
By default, ShuffleFilter will shuffle pairs once per candle.
To shuffle on every iteration, set `"shuffle_frequency"` to `"iteration"` instead of the default of `"candle"`.
``` json
{
"method": "ShuffleFilter",
"shuffle_frequency": "candle",
"seed": 42
}
```
!!! Tip
You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order. ShuffleFilter will automatically detect runmodes and apply the `seed` only for backtesting modes - if a `seed` value is set.

4
docs/requirements-docs.txt
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@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.1
mkdocs-material==8.5.6
mkdocs-material==8.5.7
mdx_truly_sane_lists==1.3
pymdown-extensions==9.6
pymdown-extensions==9.7
jinja2==3.1.2

1
docs/strategy-callbacks.md
View File

@ -159,6 +159,7 @@ The stoploss price can only ever move upwards - if the stoploss value returned f
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.
During backtesting, `current_rate` (and `current_profit`) are provided against the candle's high (or low for short trades) - while the resulting stoploss is evaluated against the candle's low (or high for short trades).
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.

9
docs/windows_installation.md
View File

@ -3,15 +3,16 @@
We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
Otherwise, try the instructions below.
Otherwise, please follow the instructions below.
## Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Note "64bit Python version"
Please make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
32bit python versions are no longer supported under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#Anaconda) in this document for more information.
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#installation-with-conda) in the documentation for more information.
### 1. Clone the git repository

4
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.10.dev'
__version__ = '2022.11.dev'
if 'dev' in __version__:
try:
@ -16,6 +16,6 @@ if 'dev' in __version__:
from pathlib import Path
versionfile = Path('./freqtrade_commit')
if versionfile.is_file():
__version__ = f"docker-{versionfile.read_text()[:8]}"
__version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
except Exception:
pass

2
freqtrade/commands/cli_options.py
View File

@ -49,7 +49,7 @@ AVAILABLE_CLI_OPTIONS = {
default=0,
),
"logfile": Arg(
'--logfile',
'--logfile', '--log-file',
help="Log to the file specified. Special values are: 'syslog', 'journald'. "
"See the documentation for more details.",
metavar='FILE',

21
freqtrade/data/history/idatahandler.py
View File

@ -303,7 +303,7 @@ class IDataHandler(ABC):
timerange=timerange_startup,
candle_type=candle_type
)
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@ -323,8 +323,9 @@ class IDataHandler(ABC):
self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data)
return pairdf
def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str,
candle_type: CandleType, warn_no_data: bool):
def _check_empty_df(
self, pairdf: DataFrame, pair: str, timeframe: str, candle_type: CandleType,
warn_no_data: bool, warn_price: bool = False) -> bool:
"""
Warn on empty dataframe
"""
@ -335,6 +336,20 @@ class IDataHandler(ABC):
"Use `freqtrade download-data` to download the data"
)
return True
elif warn_price:
candle_price_gap = 0
if (candle_type in (CandleType.SPOT, CandleType.FUTURES) and
not pairdf.empty
and 'close' in pairdf.columns and 'open' in pairdf.columns):
# Detect gaps between prior close and open
gaps = ((pairdf['open'] - pairdf['close'].shift(1)) / pairdf['close'].shift(1))
gaps = gaps.dropna()
if len(gaps):
candle_price_gap = max(abs(gaps))
if candle_price_gap > 0.1:
logger.info(f"Price jump in {pair}, {timeframe}, {candle_type} between two candles "
f"of {candle_price_gap:.2%} detected.")
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str,

17
freqtrade/exchange/__init__.py
View File

@ -9,14 +9,15 @@ from freqtrade.exchange.bitpanda import Bitpanda
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.coinbasepro import Coinbasepro
from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts,
amount_to_precision, available_exchanges, ccxt_exchanges,
contracts_to_amount, date_minus_candles,
is_exchange_known_ccxt, market_is_active,
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds,
validate_exchange, validate_exchanges)
from freqtrade.exchange.exchange_utils import (amount_to_contract_precision, amount_to_contracts,
amount_to_precision, available_exchanges,
ccxt_exchanges, contracts_to_amount,
date_minus_candles, is_exchange_known_ccxt,
market_is_active, price_to_precision,
timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.hitbtc import Hitbtc

18
freqtrade/exchange/binance.py
View File

@ -42,24 +42,6 @@ class Binance(Exchange):
(TradingMode.FUTURES, MarginMode.ISOLATED)
]
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
:param side: "buy" or "sell"
"""
order_types = ('stop_loss_limit', 'stop', 'stop_market')
return (
order.get('stopPrice', None) is None
or (
order['type'] in order_types
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)
))
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
tickers = super().get_tickers(symbols=symbols, cached=cached)
if self.trading_mode == TradingMode.FUTURES:

277
freqtrade/exchange/exchange.py
View File

@ -8,7 +8,6 @@ import inspect
import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from math import ceil
from threading import Lock
from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
@ -16,7 +15,7 @@ import arrow
import ccxt
import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
from ccxt import TICK_SIZE
from dateutil import parser
from pandas import DataFrame, concat
@ -28,17 +27,19 @@ from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
remove_credentials, retrier, retrier_async)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_credentials, retrier,
retrier_async)
from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contract_precision,
amount_to_contracts, amount_to_precision,
contracts_to_amount, date_minus_candles,
is_exchange_known_ccxt, market_is_active,
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.exchange.types import Ticker, Tickers
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.util import FtPrecise
CcxtModuleType = Any
logger = logging.getLogger(__name__)
@ -1076,7 +1077,14 @@ class Exchange:
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
raise OperationalException(f"stoploss is not implemented for {self.name}.")
if not self._ft_has.get('stoploss_on_exchange'):
raise OperationalException(f"stoploss is not implemented for {self.name}.")
return (
order.get('stopPrice', None) is None
or ((side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice'])))
)
def _get_stop_order_type(self, user_order_type) -> Tuple[str, str]:
@ -1106,7 +1114,7 @@ class Exchange:
'In stoploss limit order, stop price should be more than limit price')
return limit_rate
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
# Verify if stopPrice works for your exchange!
params.update({'stopPrice': stop_price})
@ -1155,7 +1163,8 @@ class Exchange:
return dry_order
try:
params = self._get_stop_params(ordertype=ordertype, stop_price=stop_price_norm)
params = self._get_stop_params(side=side, ordertype=ordertype,
stop_price=stop_price_norm)
if self.trading_mode == TradingMode.FUTURES:
params['reduceOnly'] = True
@ -1995,11 +2004,8 @@ class Exchange:
def _now_is_time_to_refresh(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
return not (
(self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0)
+ interval_in_sec) >= arrow.utcnow().int_timestamp
)
plr = self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0) + interval_in_sec
return plr < arrow.utcnow().int_timestamp
@retrier_async
async def _async_get_candle_history(
@ -2802,240 +2808,3 @@ class Exchange:
# describes the min amt for a tier, and the lowest tier will always go down to 0
else:
raise OperationalException(f"Cannot get maintenance ratio using {self.name}")
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
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 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:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(timeframe)
def timeframe_to_minutes(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(timeframe) // 60
def timeframe_to_msecs(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of previous candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_DOWN) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of next candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_UP) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def date_minus_candles(
timeframe: str, candle_count: int, date: Optional[datetime] = None) -> datetime:
"""
subtract X candles from a date.
:param timeframe: timeframe in string format (e.g. "5m")
:param candle_count: Amount of candles to subtract.
:param date: date to use. Defaults to now(utc)
"""
if not date:
date = datetime.now(timezone.utc)
tf_min = timeframe_to_minutes(timeframe)
new_date = timeframe_to_prev_date(timeframe, date) - timedelta(minutes=tf_min * candle_count)
return new_date
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.
"""
# "It's active, if the active flag isn't explicitly set to false. If it's missing or
# true then it's true. If it's undefined, then it's most likely true, but not 100% )"
# See https://github.com/ccxt/ccxt/issues/4874,
# https://github.com/ccxt/ccxt/issues/4075#issuecomment-434760520
return market.get('active', True) is not False
def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
"""
Convert amount to contracts.
:param amount: amount to convert
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: num-contracts
"""
if contract_size and contract_size != 1:
return float(FtPrecise(amount) / FtPrecise(contract_size))
else:
return amount
def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float:
"""
Takes num-contracts and converts it to contract size
:param num_contracts: number of contracts
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: Amount
"""
if contract_size and contract_size != 1:
return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
else:
return num_contracts
def amount_to_precision(amount: float, amount_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
precision = int(amount_precision) if precisionMode != TICK_SIZE else amount_precision
# precision must be an int for non-ticksize inputs.
amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE,
precision=precision,
counting_mode=precisionMode,
))
return amount
def amount_to_contract_precision(
amount, amount_precision: Optional[float], precisionMode: Optional[int],
contract_size: Optional[float]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
including calculation to and from contracts.
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
contracts = amount_to_contracts(amount, contract_size)
amount_p = amount_to_precision(contracts, amount_precision, precisionMode)
return contracts_to_amount(amount_p, contract_size)
return amount
def price_to_precision(price: float, price_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the price rounded up to the precision the Exchange accepts.
Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
!!! Rounds up
:param price: price to convert
:param price_precision: price precision to use. Used from markets[pair]['precision']['price']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: price rounded up to the precision the Exchange accepts
"""
if price_precision is not None and precisionMode is not None:
# price = float(decimal_to_precision(price, rounding_mode=ROUND,
# precision=price_precision,
# counting_mode=self.precisionMode,
# ))
if precisionMode == TICK_SIZE:
precision = FtPrecise(price_precision)
price_str = FtPrecise(price)
missing = price_str % precision
if not missing == FtPrecise("0"):
price = round(float(str(price_str - missing + precision)), 14)
else:
symbol_prec = price_precision
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price

252
freqtrade/exchange/exchange_utils.py Normal file
View File

@ -0,0 +1,252 @@
"""
Exchange support utils
"""
from datetime import datetime, timedelta, timezone
from math import ceil
from typing import Any, Dict, List, Optional, Tuple
import ccxt
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
from freqtrade.exchange.common import BAD_EXCHANGES, EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED
from freqtrade.util import FtPrecise
CcxtModuleType = Any
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
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 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:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(timeframe)
def timeframe_to_minutes(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(timeframe) // 60
def timeframe_to_msecs(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of previous candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_DOWN) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of next candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_UP) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def date_minus_candles(
timeframe: str, candle_count: int, date: Optional[datetime] = None) -> datetime:
"""
subtract X candles from a date.
:param timeframe: timeframe in string format (e.g. "5m")
:param candle_count: Amount of candles to subtract.
:param date: date to use. Defaults to now(utc)
"""
if not date:
date = datetime.now(timezone.utc)
tf_min = timeframe_to_minutes(timeframe)
new_date = timeframe_to_prev_date(timeframe, date) - timedelta(minutes=tf_min * candle_count)
return new_date
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.
"""
# "It's active, if the active flag isn't explicitly set to false. If it's missing or
# true then it's true. If it's undefined, then it's most likely true, but not 100% )"
# See https://github.com/ccxt/ccxt/issues/4874,
# https://github.com/ccxt/ccxt/issues/4075#issuecomment-434760520
return market.get('active', True) is not False
def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
"""
Convert amount to contracts.
:param amount: amount to convert
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: num-contracts
"""
if contract_size and contract_size != 1:
return float(FtPrecise(amount) / FtPrecise(contract_size))
else:
return amount
def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float:
"""
Takes num-contracts and converts it to contract size
:param num_contracts: number of contracts
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: Amount
"""
if contract_size and contract_size != 1:
return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
else:
return num_contracts
def amount_to_precision(amount: float, amount_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
precision = int(amount_precision) if precisionMode != TICK_SIZE else amount_precision
# precision must be an int for non-ticksize inputs.
amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE,
precision=precision,
counting_mode=precisionMode,
))
return amount
def amount_to_contract_precision(
amount, amount_precision: Optional[float], precisionMode: Optional[int],
contract_size: Optional[float]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
including calculation to and from contracts.
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
contracts = amount_to_contracts(amount, contract_size)
amount_p = amount_to_precision(contracts, amount_precision, precisionMode)
return contracts_to_amount(amount_p, contract_size)
return amount
def price_to_precision(price: float, price_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the price rounded up to the precision the Exchange accepts.
Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
!!! Rounds up
:param price: price to convert
:param price_precision: price precision to use. Used from markets[pair]['precision']['price']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: price rounded up to the precision the Exchange accepts
"""
if price_precision is not None and precisionMode is not None:
# price = float(decimal_to_precision(price, rounding_mode=ROUND,
# precision=price_precision,
# counting_mode=self.precisionMode,
# ))
if precisionMode == TICK_SIZE:
precision = FtPrecise(price_precision)
price_str = FtPrecise(price)
missing = price_str % precision
if not missing == FtPrecise("0"):
price = round(float(str(price_str - missing + precision)), 14)
else:
symbol_prec = price_precision
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price

10
freqtrade/exchange/gateio.py
View File

@ -126,13 +126,3 @@ class Gateio(Exchange):
pair=pair,
params={'stop': True}
)
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return (order.get('stopPrice', None) is None or (
side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)

16
freqtrade/exchange/huobi.py
View File

@ -2,6 +2,7 @@
import logging
from typing import Dict
from freqtrade.constants import BuySell
from freqtrade.exchange import Exchange
@ -22,20 +23,7 @@ class Huobi(Exchange):
"l2_limit_range_required": False,
}
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return (
order.get('stopPrice', None) is None
or (
order['type'] == 'stop'
and stop_loss > float(order['stopPrice'])
)
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
params.update({

13
freqtrade/exchange/kucoin.py
View File

@ -2,6 +2,7 @@
import logging
from typing import Dict
from freqtrade.constants import BuySell
from freqtrade.exchange import Exchange
@ -27,17 +28,7 @@ class Kucoin(Exchange):
"ohlcv_candle_limit": 1500,
}
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return (
order.get('stopPrice', None) is None
or stop_loss > float(order['stopPrice'])
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
params.update({

2
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@ -51,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)

2
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@ -50,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)

2
freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@ -47,7 +47,7 @@ class BaseTensorFlowModel(IFreqaiModel):
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)

116
freqtrade/freqai/data_kitchen.py
View File

@ -214,7 +214,10 @@ class FreqaiDataKitchen:
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
self.data['constant_features_list'] = const_cols
logger.warning(f"Removed features {const_cols} with constant values.")
else:
self.data['constant_features_list'] = []
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
@ -245,7 +248,8 @@ class FreqaiDataKitchen:
self.data["filter_drop_index_training"] = drop_index
else:
filtered_df = self.check_pred_labels(filtered_df)
if len(self.data['constant_features_list']):
filtered_df = self.check_pred_labels(filtered_df)
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_df).any(axis=1)
@ -354,13 +358,19 @@ class FreqaiDataKitchen:
:param df: Dataframe to be standardized
"""
for item in df.keys():
df[item] = (
2
* (df[item] - self.data[f"{item}_min"])
/ (self.data[f"{item}_max"] - self.data[f"{item}_min"])
- 1
)
train_max = [None] * len(df.keys())
train_min = [None] * len(df.keys())
for i, item in enumerate(df.keys()):
train_max[i] = self.data[f"{item}_max"]
train_min[i] = self.data[f"{item}_min"]
train_max_series = pd.Series(train_max, index=df.keys())
train_min_series = pd.Series(train_min, index=df.keys())
df = (
2 * (df - train_min_series) / (train_max_series - train_min_series) - 1
)
return df
@ -491,18 +501,16 @@ class FreqaiDataKitchen:
def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
"""
Check that prediction feature labels match training feature labels.
:params:
:df_predictions: incoming predictions
:param df_predictions: incoming predictions
"""
train_labels = self.data_dictionary["train_features"].columns
pred_labels = df_predictions.columns
num_diffs = len(pred_labels.difference(train_labels))
if num_diffs != 0:
df_predictions = df_predictions[train_labels]
logger.warning(
f"Removed {num_diffs} features from prediction features, "
f"these were likely considered constant values during most recent training."
)
constant_labels = self.data['constant_features_list']
df_predictions = df_predictions.filter(
df_predictions.columns.difference(constant_labels)
)
logger.warning(
f"Removed {len(constant_labels)} features from prediction features, "
f"these were considered constant values during most recent training."
)
return df_predictions
@ -986,6 +994,9 @@ class FreqaiDataKitchen:
if "labels_std" in self.data:
append_df[f"{label}_std"] = self.data["labels_std"][label]
for extra_col in self.data["extra_returns_per_train"]:
append_df[f"{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
append_df["do_predict"] = do_predict
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
@ -1150,6 +1161,51 @@ class FreqaiDataKitchen:
if pair not in self.all_pairs:
self.all_pairs.append(pair)
def extract_corr_pair_columns_from_populated_indicators(
self,
dataframe: DataFrame
) -> Dict[str, DataFrame]:
"""
Find the columns of the dataframe corresponding to the corr_pairlist, save them
in a dictionary to be reused and attached to other pairs.
:param dataframe: fully populated dataframe (current pair + corr_pairs)
:return: corr_dataframes, dictionary of dataframes to be attached
to other pairs in same candle.
"""
corr_dataframes: Dict[str, DataFrame] = {}
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)]
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
return corr_dataframes
def attach_corr_pair_columns(self, dataframe: DataFrame,
corr_dataframes: Dict[str, DataFrame],
current_pair: str) -> DataFrame:
"""
Attach the existing corr_pair dataframes to the current pair dataframe before training
:param dataframe: current pair strategy dataframe, indicators populated already
:param corr_dataframes: dictionary of saved dataframes from earlier in the same candle
:param current_pair: current pair to which we will attach corr pair dataframe
:return:
:dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs
ready for training
"""
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
if current_pair != pair:
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
return dataframe
def use_strategy_to_populate_indicators(
self,
strategy: IStrategy,
@ -1157,6 +1213,7 @@ class FreqaiDataKitchen:
base_dataframes: dict = {},
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
@ -1166,15 +1223,15 @@ class FreqaiDataKitchen:
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param metadata: dict = strategy furnished pair metadata
:returns:
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs:
@ -1197,15 +1254,18 @@ class FreqaiDataKitchen:
informative=base_dataframes[tf],
set_generalized_indicators=sgi
)
if pairs:
for i in pairs:
if pair in i:
continue # dont repeat anything from whitelist
# ensure corr pairs are always last
for corr_pair in pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
for tf in tfs:
if pairs and do_corr_pairs:
dataframe = strategy.populate_any_indicators(
i,
corr_pair,
dataframe.copy(),
tf,
informative=corr_dataframes[i][tf]
informative=corr_dataframes[corr_pair][tf]
)
self.get_unique_classes_from_labels(dataframe)

68
freqtrade/freqai/freqai_interface.py
View File

@ -1,12 +1,10 @@
import logging
import shutil
import threading
import time
from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from threading import Lock
from typing import Any, Dict, List, Literal, Tuple
import numpy as np
@ -21,7 +19,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import plot_feature_importance
from freqtrade.freqai.utils import plot_feature_importance, record_params
from freqtrade.strategy.interface import IStrategy
@ -61,6 +59,7 @@ class IFreqaiModel(ABC):
"data_split_parameters", {})
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
"model_training_parameters", {})
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.retrain = False
self.first = True
self.set_full_path()
@ -69,9 +68,9 @@ class IFreqaiModel(ABC):
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
@ -83,9 +82,6 @@ class IFreqaiModel(ABC):
self.pair_it_train = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.train_queue = self._set_train_queue()
self.last_trade_database_summary: DataFrame = {}
self.current_trade_database_summary: DataFrame = {}
self.analysis_lock = Lock()
self.inference_time: float = 0
self.train_time: float = 0
self.begin_time: float = 0
@ -93,10 +89,16 @@ class IFreqaiModel(ABC):
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
self.continual_learning = self.freqai_info.get('continual_learning', False)
self.plot_features = self.ft_params.get("plot_feature_importances", 0)
self.corr_dataframes: Dict[str, DataFrame] = {}
# get_corr_dataframes is controlling the caching of corr_dataframes
# for improved performance. Careful with this boolean.
self.get_corr_dataframes: bool = True
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
record_params(config, self.full_path)
def __getstate__(self):
"""
Return an empty state to be pickled in hyperopt
@ -385,10 +387,10 @@ class IFreqaiModel(ABC):
# load the model and associated data into the data kitchen
self.model = self.dd.load_data(metadata["pair"], dk)
with self.analysis_lock:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
dataframe = dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"],
do_corr_pairs=self.get_corr_dataframes
)
if not self.model:
logger.warning(
@ -397,6 +399,9 @@ class IFreqaiModel(ABC):
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
if self.corr_pairlist:
dataframe = self.cache_corr_pairlist_dfs(dataframe, dk)
dk.find_labels(dataframe)
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
@ -548,14 +553,13 @@ class IFreqaiModel(ABC):
return file_exists
def set_full_path(self) -> None:
"""
Creates and sets the full path for the identifier
"""
self.full_path = Path(
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
self.config["user_data_dir"] / "models" / f"{self.identifier}"
)
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
self.config["config_files"][0],
Path(self.full_path, Path(self.config["config_files"][0]).name),
)
def extract_data_and_train_model(
self,
@ -581,10 +585,9 @@ class IFreqaiModel(ABC):
data_load_timerange, pair, dk
)
with self.analysis_lock:
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
strategy, corr_dataframes, base_dataframes, pair
)
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
strategy, corr_dataframes, base_dataframes, pair
)
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
@ -702,6 +705,8 @@ class IFreqaiModel(ABC):
" avoid blinding open trades and degrading performance.")
self.pair_it = 0
self.inference_time = 0
if self.corr_pairlist:
self.get_corr_dataframes = True
return
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
@ -760,6 +765,29 @@ class IFreqaiModel(ABC):
f'Best approximation queue: {best_queue}')
return best_queue
def cache_corr_pairlist_dfs(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
Cache the corr_pairlist dfs to speed up performance for subsequent pairs during the
current candle.
:param dataframe: strategy fed dataframe
:param dk: datakitchen object for current asset
:return: dataframe to attach/extract cached corr_pair dfs to/from.
"""
if self.get_corr_dataframes:
self.corr_dataframes = dk.extract_corr_pair_columns_from_populated_indicators(dataframe)
if not self.corr_dataframes:
logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
"is included in the column names when you are creating features "
"in `populate_any_indicators()`.")
self.get_corr_dataframes = not bool(self.corr_dataframes)
else:
dataframe = dk.attach_corr_pair_columns(
dataframe, self.corr_dataframes, dk.pair)
return dataframe
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

7
freqtrade/freqai/prediction_models/XGBoostRFClassifier.py
View File

@ -26,9 +26,8 @@ class XGBoostRFClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
@ -65,7 +64,7 @@ class XGBoostRFClassifier(BaseClassifierModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

1
freqtrade/freqai/prediction_models/XGBoostRFRegressor.py
View File

@ -29,6 +29,7 @@ class XGBoostRFRegressor(BaseRegressionModel):
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]

1
freqtrade/freqai/prediction_models/XGBoostRegressor.py
View File

@ -29,6 +29,7 @@ class XGBoostRegressor(BaseRegressionModel):
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]

29
freqtrade/freqai/utils.py
View File

@ -1,9 +1,11 @@
import logging
from datetime import datetime, timezone
from typing import Any
from pathlib import Path
from typing import Any, Dict
import numpy as np
import pandas as pd
import rapidjson
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
@ -193,6 +195,31 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
def record_params(config: Dict[str, Any], full_path: Path) -> None:
"""
Records run params in the full path for reproducibility
"""
params_record_path = full_path / "run_params.json"
run_params = {
"freqai": config.get('freqai', {}),
"timeframe": config.get('timeframe'),
"stake_amount": config.get('stake_amount'),
"stake_currency": config.get('stake_currency'),
"max_open_trades": config.get('max_open_trades'),
"pairs": config.get('exchange', {}).get('pair_whitelist')
}
with open(params_record_path, "w") as handle:
rapidjson.dump(
run_params,
handle,
indent=4,
default=str,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
)
def get_timerange_backtest_live_models(config: Config):
"""
Returns a formated timerange for backtest live/ready models

8
freqtrade/freqtradebot.py
View File

@ -1471,12 +1471,13 @@ class FreqtradeBot(LoggingMixin):
)
return cancelled
def _safe_exit_amount(self, pair: str, amount: float) -> float:
def _safe_exit_amount(self, trade: Trade, pair: str, amount: float) -> float:
"""
Get sellable amount.
Should be trade.amount - but will fall back to the available amount if necessary.
This should cover cases where get_real_amount() was not able to update the amount
for whatever reason.
:param trade: Trade we're working with
:param pair: Pair we're trying to sell
:param amount: amount we expect to be available
:return: amount to sell
@ -1495,6 +1496,7 @@ class FreqtradeBot(LoggingMixin):
return amount
elif wallet_amount > amount * 0.98:
logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
trade.amount = wallet_amount
return wallet_amount
else:
raise DependencyException(
@ -1553,7 +1555,7 @@ class FreqtradeBot(LoggingMixin):
# Emergency sells (default to market!)
order_type = self.strategy.order_types.get("emergency_exit", "market")
amount = self._safe_exit_amount(trade.pair, sub_trade_amt or trade.amount)
amount = self._safe_exit_amount(trade, trade.pair, sub_trade_amt or trade.amount)
time_in_force = self.strategy.order_time_in_force['exit']
if (exit_check.exit_type != ExitType.LIQUIDATION
@ -1828,7 +1830,7 @@ class FreqtradeBot(LoggingMixin):
never in base currency.
"""
self.wallets.update()
amount_ = amount
amount_ = trade.amount
if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == 'stoploss':
# check against remaining amount!
amount_ = trade.amount - amount

84
freqtrade/persistence/trade_model.py
View File

@ -1520,3 +1520,87 @@ class Trade(_DECL_BASE, LocalTrade):
Order.status == 'closed'
).scalar()
return trading_volume
@staticmethod
def from_json(json_str: str) -> 'Trade':
"""
Create a Trade instance from a json string.
Used for debugging purposes - please keep.
:param json_str: json string to parse
:return: Trade instance
"""
import rapidjson
data = rapidjson.loads(json_str)
trade = Trade(
id=data["trade_id"],
pair=data["pair"],
base_currency=data["base_currency"],
stake_currency=data["quote_currency"],
is_open=data["is_open"],
exchange=data["exchange"],
amount=data["amount"],
amount_requested=data["amount_requested"],
stake_amount=data["stake_amount"],
strategy=data["strategy"],
enter_tag=data["enter_tag"],
timeframe=data["timeframe"],
fee_open=data["fee_open"],
fee_open_cost=data["fee_open_cost"],
fee_open_currency=data["fee_open_currency"],
fee_close=data["fee_close"],
fee_close_cost=data["fee_close_cost"],
fee_close_currency=data["fee_close_currency"],
open_date=datetime.fromtimestamp(data["open_timestamp"] // 1000, tz=timezone.utc),
open_rate=data["open_rate"],
open_rate_requested=data["open_rate_requested"],
open_trade_value=data["open_trade_value"],
close_date=(datetime.fromtimestamp(data["close_timestamp"] // 1000, tz=timezone.utc)
if data["close_timestamp"] else None),
realized_profit=data["realized_profit"],
close_rate=data["close_rate"],
close_rate_requested=data["close_rate_requested"],
close_profit=data["close_profit"],
close_profit_abs=data["close_profit_abs"],
exit_reason=data["exit_reason"],
exit_order_status=data["exit_order_status"],
stop_loss=data["stop_loss_abs"],
stop_loss_pct=data["stop_loss_ratio"],
stoploss_order_id=data["stoploss_order_id"],
stoploss_last_update=(datetime.fromtimestamp(data["stoploss_last_update"] // 1000,
tz=timezone.utc) if data["stoploss_last_update"] else None),
initial_stop_loss=data["initial_stop_loss_abs"],
initial_stop_loss_pct=data["initial_stop_loss_ratio"],
min_rate=data["min_rate"],
max_rate=data["max_rate"],
leverage=data["leverage"],
interest_rate=data["interest_rate"],
liquidation_price=data["liquidation_price"],
is_short=data["is_short"],
trading_mode=data["trading_mode"],
funding_fees=data["funding_fees"],
open_order_id=data["open_order_id"],
)
for order in data["orders"]:
order_obj = Order(
amount=order["amount"],
ft_order_side=order["ft_order_side"],
ft_pair=order["pair"],
ft_is_open=order["is_open"],
order_id=order["order_id"],
status=order["status"],
average=order["average"],
cost=order["cost"],
filled=order["filled"],
order_date=datetime.strptime(order["order_date"], DATETIME_PRINT_FORMAT),
order_filled_date=(datetime.fromtimestamp(
order["order_filled_timestamp"] // 1000, tz=timezone.utc)
if order["order_filled_timestamp"] else None),
order_type=order["order_type"],
price=order["price"],
remaining=order["remaining"],
)
trade.orders.append(order_obj)
return trade

1
freqtrade/plugins/pairlist/IPairList.py
View File

@ -36,7 +36,6 @@ class IPairList(LoggingMixin, ABC):
self._pairlistconfig = pairlistconfig
self._pairlist_pos = pairlist_pos
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._last_refresh = 0
LoggingMixin.__init__(self, logger, self.refresh_period)
@property

17
freqtrade/plugins/pairlist/ShuffleFilter.py
View File

@ -3,16 +3,20 @@ Shuffle pair list filter
"""
import logging
import random
from typing import Any, Dict, List
from typing import Any, Dict, List, Literal
from freqtrade.constants import Config
from freqtrade.enums import RunMode
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.types import Tickers
from freqtrade.plugins.pairlist.IPairList import IPairList
from freqtrade.util.periodic_cache import PeriodicCache
logger = logging.getLogger(__name__)
ShuffleValues = Literal['candle', 'iteration']
class ShuffleFilter(IPairList):
@ -31,6 +35,9 @@ class ShuffleFilter(IPairList):
logger.info(f"Backtesting mode detected, applying seed value: {self._seed}")
self._random = random.Random(self._seed)
self._shuffle_freq: ShuffleValues = pairlistconfig.get('shuffle_frequency', 'candle')
self.__pairlist_cache = PeriodicCache(
maxsize=1000, ttl=timeframe_to_seconds(self._config['timeframe']))
@property
def needstickers(self) -> bool:
@ -45,7 +52,7 @@ class ShuffleFilter(IPairList):
"""
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Shuffling pairs" +
return (f"{self.name} - Shuffling pairs every {self._shuffle_freq}" +
(f", seed = {self._seed}." if self._seed is not None else "."))
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
@ -56,7 +63,13 @@ class ShuffleFilter(IPairList):
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
pairlist_bef = tuple(pairlist)
pairlist_new = self.__pairlist_cache.get(pairlist_bef)
if pairlist_new and self._shuffle_freq == 'candle':
# Use cached pairlist.
return pairlist_new
# Shuffle is done inplace
self._random.shuffle(pairlist)
self.__pairlist_cache[pairlist_bef] = pairlist
return pairlist

23
freqtrade/rpc/api_server/api_ws.py
View File

@ -1,4 +1,3 @@
import asyncio
import logging
from typing import Any, Dict
@ -11,6 +10,7 @@ from freqtrade.enums import RPCMessageType, RPCRequestType
from freqtrade.rpc.api_server.api_auth import validate_ws_token
from freqtrade.rpc.api_server.deps import get_channel_manager, get_rpc
from freqtrade.rpc.api_server.ws import WebSocketChannel
from freqtrade.rpc.api_server.ws.channel import ChannelManager
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
WSRequestSchema, WSWhitelistMessage)
from freqtrade.rpc.rpc import RPC
@ -37,7 +37,8 @@ async def is_websocket_alive(ws: WebSocket) -> bool:
async def _process_consumer_request(
request: Dict[str, Any],
channel: WebSocketChannel,
rpc: RPC
rpc: RPC,
channel_manager: ChannelManager
):
"""
Validate and handle a request from a websocket consumer
@ -74,7 +75,7 @@ async def _process_consumer_request(
# Format response
response = WSWhitelistMessage(data=whitelist)
# Send it back
await channel.send(response.dict(exclude_none=True))
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
elif type == RPCRequestType.ANALYZED_DF:
limit = None
@ -89,9 +90,7 @@ async def _process_consumer_request(
# For every dataframe, send as a separate message
for _, message in analyzed_df.items():
response = WSAnalyzedDFMessage(data=message)
await channel.send(response.dict(exclude_none=True))
# Throttle the messages to 50/s
await asyncio.sleep(0.02)
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
@router.websocket("/message/ws")
@ -116,7 +115,7 @@ async def message_endpoint(
request = await channel.recv()
# Process the request here
await _process_consumer_request(request, channel, rpc)
await _process_consumer_request(request, channel, rpc, channel_manager)
except (WebSocketDisconnect, WebSocketException):
# Handle client disconnects
@ -128,13 +127,6 @@ async def message_endpoint(
except Exception as e:
logger.info(f"Consumer connection failed - {channel}: {e}")
logger.debug(e, exc_info=e)
finally:
await channel_manager.on_disconnect(ws)
else:
if channel:
await channel_manager.on_disconnect(ws)
await ws.close()
except RuntimeError:
# WebSocket was closed
@ -145,4 +137,5 @@ async def message_endpoint(
# Log tracebacks to keep track of what errors are happening
logger.exception(e)
finally:
await channel_manager.on_disconnect(ws)
if channel:
await channel_manager.on_disconnect(ws)

13
freqtrade/rpc/api_server/webserver.py
View File

@ -16,6 +16,7 @@ from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
from freqtrade.rpc.api_server.ws import ChannelManager
from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
@ -127,7 +128,7 @@ class ApiServer(RPCHandler):
cls._has_rpc = False
cls._rpc = None
def send_msg(self, msg: Dict[str, str]) -> None:
def send_msg(self, msg: Dict[str, Any]) -> None:
if self._ws_queue:
sync_q = self._ws_queue.sync_q
sync_q.put(msg)
@ -194,14 +195,11 @@ class ApiServer(RPCHandler):
while True:
logger.debug("Getting queue messages...")
# Get data from queue
message = await async_queue.get()
message: WSMessageSchemaType = await async_queue.get()
logger.debug(f"Found message of type: {message.get('type')}")
async_queue.task_done()
# Broadcast it
await self._ws_channel_manager.broadcast(message)
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
await asyncio.sleep(0.001)
except asyncio.CancelledError:
pass
@ -213,6 +211,9 @@ class ApiServer(RPCHandler):
# Disconnect channels and stop the loop on cancel
await self._ws_channel_manager.disconnect_all()
self._ws_loop.stop()
# Avoid adding more items to the queue if they aren't
# going to get broadcasted.
self._ws_queue = None
def start_api(self):
"""

93
freqtrade/rpc/api_server/ws/channel.py
View File

@ -1,7 +1,8 @@
import asyncio
import logging
import time
from threading import RLock
from typing import Any, Dict, List, Optional, Type
from typing import Any, Dict, List, Optional, Type, Union
from uuid import uuid4
from fastapi import WebSocket as FastAPIWebSocket
@ -10,6 +11,7 @@ from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
WebSocketSerializer)
from freqtrade.rpc.api_server.ws.types import WebSocketType
from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
logger = logging.getLogger(__name__)
@ -24,6 +26,8 @@ class WebSocketChannel:
self,
websocket: WebSocketType,
channel_id: Optional[str] = None,
drain_timeout: int = 3,
throttle: float = 0.01,
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
):
@ -34,12 +38,16 @@ class WebSocketChannel:
# The Serializing class for the WebSocket object
self._serializer_cls = serializer_cls
self.drain_timeout = drain_timeout
self.throttle = throttle
self._subscriptions: List[str] = []
# 32 is the size of the receiving queue in websockets package
self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
self._relay_task = asyncio.create_task(self.relay())
# Internal event to signify a closed websocket
self._closed = False
self._closed = asyncio.Event()
# Wrap the WebSocket in the Serializing class
self._wrapped_ws = self._serializer_cls(self._websocket)
@ -47,6 +55,10 @@ class WebSocketChannel:
def __repr__(self):
return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
@property
def raw_websocket(self):
return self._websocket.raw_websocket
@property
def remote_addr(self):
return self._websocket.remote_addr
@ -57,11 +69,30 @@ class WebSocketChannel:
"""
await self._wrapped_ws.send(data)
async def send(self, data):
async def send(self, data) -> bool:
"""
Add the data to the queue to be sent
Add the data to the queue to be sent.
:returns: True if data added to queue, False otherwise
"""
self.queue.put_nowait(data)
# This block only runs if the queue is full, it will wait
# until self.drain_timeout for the relay to drain the outgoing queue
# We can't use asyncio.wait_for here because the queue may have been created with a
# different eventloop
start = time.time()
while self.queue.full():
await asyncio.sleep(1)
if (time.time() - start) > self.drain_timeout:
return False
# If for some reason the queue is still full, just return False
try:
self.queue.put_nowait(data)
except asyncio.QueueFull:
return False
# If we got here everything is ok
return True
async def recv(self):
"""
@ -80,14 +111,19 @@ class WebSocketChannel:
Close the WebSocketChannel
"""
self._closed = True
try:
await self.raw_websocket.close()
except Exception:
pass
self._closed.set()
self._relay_task.cancel()
def is_closed(self) -> bool:
"""
Closed flag
"""
return self._closed
return self._closed.is_set()
def set_subscriptions(self, subscriptions: List[str] = []) -> None:
"""
@ -110,7 +146,7 @@ class WebSocketChannel:
Relay messages from the channel's queue and send them out. This is started
as a task.
"""
while True:
while not self._closed.is_set():
message = await self.queue.get()
try:
await self._send(message)
@ -119,8 +155,8 @@ class WebSocketChannel:
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
# 0.001 = 1000/s
await asyncio.sleep(0.001)
# 0.01 = 100/s
await asyncio.sleep(self.throttle)
except RuntimeError:
# The connection was closed, just exit the task
return
@ -160,6 +196,7 @@ class ChannelManager:
with self._lock:
channel = self.channels.get(websocket)
if channel:
logger.info(f"Disconnecting channel {channel}")
if not channel.is_closed():
await channel.close()
@ -170,36 +207,30 @@ class ChannelManager:
Disconnect all Channels
"""
with self._lock:
for websocket, channel in self.channels.copy().items():
if not channel.is_closed():
await channel.close()
for websocket in self.channels.copy().keys():
await self.on_disconnect(websocket)
self.channels = dict()
async def broadcast(self, data):
async def broadcast(self, message: WSMessageSchemaType):
"""
Broadcast data on all Channels
Broadcast a message on all Channels
:param data: The data to send
:param message: The message to send
"""
with self._lock:
message_type = data.get('type')
for websocket, channel in self.channels.copy().items():
if channel.subscribed_to(message_type):
if not channel.queue.full():
await channel.send(data)
else:
logger.info(f"Channel {channel} is too far behind, disconnecting")
await self.on_disconnect(websocket)
for channel in self.channels.copy().values():
if channel.subscribed_to(message.get('type')):
await self.send_direct(channel, message)
async def send_direct(self, channel, data):
async def send_direct(
self, channel: WebSocketChannel, message: Union[WSMessageSchemaType, Dict[str, Any]]):
"""
Send data directly through direct_channel only
Send a message directly through direct_channel only
:param direct_channel: The WebSocketChannel object to send data through
:param data: The data to send
:param direct_channel: The WebSocketChannel object to send the message through
:param message: The message to send
"""
await channel.send(data)
if not await channel.send(message):
await self.on_disconnect(channel.raw_websocket)
def has_channels(self):
"""

4
freqtrade/rpc/api_server/ws/proxy.py
View File

@ -15,6 +15,10 @@ class WebSocketProxy:
def __init__(self, websocket: WebSocketType):
self._websocket: Union[FastAPIWebSocket, WebSocket] = websocket
@property
def raw_websocket(self):
return self._websocket
@property
def remote_addr(self) -> Tuple[Any, ...]:
if isinstance(self._websocket, WebSocket):

8
freqtrade/rpc/api_server/ws_schemas.py
View File

@ -1,5 +1,5 @@
from datetime import datetime
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, TypedDict
from pandas import DataFrame
from pydantic import BaseModel
@ -18,6 +18,12 @@ class WSRequestSchema(BaseArbitraryModel):
data: Optional[Any] = None
class WSMessageSchemaType(TypedDict):
# Type for typing to avoid doing pydantic typechecks.
type: RPCMessageType
data: Optional[Dict[str, Any]]
class WSMessageSchema(BaseArbitraryModel):
type: RPCMessageType
data: Optional[Any] = None

13
freqtrade/rpc/external_message_consumer.py
View File

@ -264,14 +264,19 @@ class ExternalMessageConsumer:
# We haven't received data yet. Check the connection and continue.
try:
# ping
ping = await channel.ping()
pong = await channel.ping()
latency = (await asyncio.wait_for(pong, timeout=self.ping_timeout) * 1000)
await asyncio.wait_for(ping, timeout=self.ping_timeout)
logger.debug(f"Connection to {channel} still alive...")
logger.info(f"Connection to {channel} still alive, latency: {latency}ms")
continue
except (websockets.exceptions.ConnectionClosed):
# Just eat the error and continue reconnecting
logger.warning(f"Disconnection in {channel} - retrying in {self.sleep_time}s")
await asyncio.sleep(self.sleep_time)
break
except Exception as e:
logger.warning(f"Ping error {channel} - retrying in {self.sleep_time}s")
logger.warning(f"Ping error {channel} - {e} - retrying in {self.sleep_time}s")
logger.debug(e, exc_info=e)
await asyncio.sleep(self.sleep_time)

20
freqtrade/strategy/interface.py
View File

@ -1072,28 +1072,26 @@ class IStrategy(ABC, HyperStrategyMixin):
trade.stop_loss > (high or current_rate)
)
# Make sure current_profit is calculated using high for backtesting.
bound = (low if trade.is_short else high)
bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
if self.use_custom_stoploss and dir_correct:
stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
)(pair=trade.pair, trade=trade,
current_time=current_time,
current_rate=current_rate,
current_profit=current_profit)
current_rate=(bound or current_rate),
current_profit=bound_profit)
# Sanity check - error cases will return None
if stop_loss_value:
# logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}")
trade.adjust_stop_loss(current_rate, stop_loss_value)
# logger.info(f"{trade.pair} {stop_loss_value=} {bound_profit=}")
trade.adjust_stop_loss(bound or current_rate, stop_loss_value)
else:
logger.warning("CustomStoploss function did not return valid stoploss")
sl_lower_long = (trade.stop_loss < (low or current_rate) and not trade.is_short)
sl_higher_short = (trade.stop_loss > (high or current_rate) and trade.is_short)
if self.trailing_stop and (sl_lower_long or sl_higher_short):
if self.trailing_stop and dir_correct:
# trailing stoploss handling
sl_offset = self.trailing_stop_positive_offset
# Make sure current_profit is calculated using high for backtesting.
bound = low if trade.is_short else high
bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (self.trailing_only_offset_is_reached and bound_profit < sl_offset):
@ -1101,7 +1099,7 @@ class IStrategy(ABC, HyperStrategyMixin):
if self.trailing_stop_positive is not None and bound_profit > sl_offset:
stop_loss_value = self.trailing_stop_positive
logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
f"offset: {sl_offset:.4g} profit: {current_profit:.2%}")
f"offset: {sl_offset:.4g} profit: {bound_profit:.2%}")
trade.adjust_stop_loss(bound or current_rate, stop_loss_value)

16
freqtrade/templates/FreqaiHybridExampleStrategy.py → freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@ -110,8 +110,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
:param informative: the dataframe associated with the informative pair
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -119,13 +117,13 @@ class FreqaiExampleHybridStrategy(IStrategy):
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)

42
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -53,7 +53,7 @@ class FreqaiExampleStrategy(IStrategy):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
passed to the training/prediction by prepending indicators with `f'%-{pair}`
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
@ -63,8 +63,6 @@ class FreqaiExampleStrategy(IStrategy):
:param informative: the dataframe associated with the informative pair
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -72,36 +70,36 @@ class FreqaiExampleStrategy(IStrategy):
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data

35
freqtrade/worker.py
View File

@ -14,6 +14,7 @@ from freqtrade.configuration import Configuration
from freqtrade.constants import PROCESS_THROTTLE_SECS, RETRY_TIMEOUT, Config
from freqtrade.enums import State
from freqtrade.exceptions import OperationalException, TemporaryError
from freqtrade.exchange import timeframe_to_next_date
from freqtrade.freqtradebot import FreqtradeBot
@ -35,7 +36,6 @@ class Worker:
self._config = config
self._init(False)
self.last_throttle_start_time: float = 0
self._heartbeat_msg: float = 0
# Tell systemd that we completed initialization phase
@ -112,7 +112,10 @@ class Worker:
# Ping systemd watchdog before throttling
self._notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
# Use an offset of 1s to ensure a new candle has been issued
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs,
timeframe=self._config['timeframe'] if self._config else None,
timeframe_offset=1)
if self._heartbeat_interval:
now = time.time()
@ -127,24 +130,42 @@ class Worker:
return state
def _throttle(self, func: Callable[..., Any], throttle_secs: float, *args, **kwargs) -> Any:
def _throttle(self, func: Callable[..., Any], throttle_secs: float,
timeframe: Optional[str] = None, timeframe_offset: float = 1.0,
*args, **kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param throttle_secs: throttling interation execution time limit in seconds
:param timeframe: ensure iteration is executed at the beginning of the next candle.
:param timeframe_offset: offset in seconds to apply to the next candle time.
:return: Any (result of execution of func)
"""
self.last_throttle_start_time = time.time()
last_throttle_start_time = time.time()
logger.debug("========================================")
result = func(*args, **kwargs)
time_passed = time.time() - self.last_throttle_start_time
sleep_duration = max(throttle_secs - time_passed, 0.0)
time_passed = time.time() - last_throttle_start_time
sleep_duration = throttle_secs - time_passed
if timeframe:
next_tf = timeframe_to_next_date(timeframe)
# Maximum throttling should be until new candle arrives
# Offset of 0.2s is added to ensure a new candle has been issued.
next_tf_with_offset = next_tf.timestamp() - time.time() + timeframe_offset
sleep_duration = min(sleep_duration, next_tf_with_offset)
sleep_duration = max(sleep_duration, 0.0)
# next_iter = datetime.now(timezone.utc) + timedelta(seconds=sleep_duration)
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
f"last iteration took {time_passed:.2f} s.")
time.sleep(sleep_duration)
self._sleep(sleep_duration)
return result
@staticmethod
def _sleep(sleep_duration: float) -> None:
"""Local sleep method - to improve testability"""
time.sleep(sleep_duration)
def _process_stopped(self) -> None:
self.freqtrade.process_stopped()

6
requirements-dev.txt
View File

@ -11,7 +11,7 @@ flake8-tidy-imports==4.8.0
mypy==0.982
pre-commit==2.20.0
pytest==7.1.3
pytest-asyncio==0.19.0
pytest-asyncio==0.20.1
pytest-cov==4.0.0
pytest-mock==3.10.0
pytest-random-order==1.0.4
@ -20,11 +20,11 @@ isort==5.10.1
time-machine==2.8.2
# Convert jupyter notebooks to markdown documents
nbconvert==7.2.1
nbconvert==7.2.3
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.11.2
types-tabulate==0.9.0.0
types-python-dateutil==2.8.19.1
types-python-dateutil==2.8.19.2

2
requirements-freqai.txt
View File

@ -2,7 +2,7 @@
-r requirements.txt
# Required for freqai
scikit-learn==1.1.2
scikit-learn==1.1.3
joblib==1.2.0
catboost==1.1; platform_machine != 'aarch64'
lightgbm==3.3.3

6
requirements-hyperopt.txt
View File

@ -2,8 +2,8 @@
-r requirements.txt
# Required for hyperopt
scipy==1.9.2
scikit-learn==1.1.2
scipy==1.9.3
scikit-learn==1.1.3
scikit-optimize==0.9.0
filelock==3.8.0
progressbar2==4.0.0
progressbar2==4.1.1

2
requirements-plot.txt
View File

@ -1,4 +1,4 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==5.10.0
plotly==5.11.0

24
requirements.txt
View File

@ -1,10 +1,8 @@
numpy==1.23.4
pandas==1.5.0; platform_machine != 'armv7l'
# Piwheels doesn't have 1.5.0 yet.
pandas==1.4.3; platform_machine == 'armv7l'
pandas==1.5.1
pandas-ta==0.3.14b
ccxt==2.0.25
ccxt==2.0.96
# Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1
aiohttp==3.8.3
@ -18,18 +16,18 @@ jsonschema==4.16.0
TA-Lib==0.4.25
technical==1.3.0
tabulate==0.9.0
pycoingecko==3.0.0
pycoingecko==3.1.0
jinja2==3.1.2
tables==3.7.0
blosc==1.10.6
joblib==1.2.0
pyarrow==9.0.0; platform_machine != 'armv7l'
pyarrow==10.0.0; platform_machine != 'armv7l'
# find first, C search in arrays
py_find_1st==1.1.5
# Load ticker files 30% faster
python-rapidjson==1.8
python-rapidjson==1.9
# Properly format api responses
orjson==3.8.0
@ -38,14 +36,14 @@ sdnotify==0.3.2
# API Server
fastapi==0.85.1
pydantic>=1.8.0
uvicorn==0.18.3
pyjwt==2.5.0
pydantic==1.10.2
uvicorn==0.19.0
pyjwt==2.6.0
aiofiles==22.1.0
psutil==5.9.2
psutil==5.9.3
# Support for colorized terminal output
colorama==0.4.5
colorama==0.4.6
# Building config files interactively
questionary==1.10.0
prompt-toolkit==3.0.31
@ -56,5 +54,5 @@ python-dateutil==2.8.2
schedule==1.1.0
#WS Messages
websockets==10.3
websockets==10.4
janus==1.0.0

31
scripts/ws_client.py
View File

@ -18,7 +18,6 @@ import orjson
import pandas
import rapidjson
import websockets
from dateutil.relativedelta import relativedelta
logger = logging.getLogger("WebSocketClient")
@ -28,7 +27,7 @@ logger = logging.getLogger("WebSocketClient")
def setup_logging(filename: str):
logging.basicConfig(
level=logging.INFO,
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(filename),
@ -75,16 +74,15 @@ def load_config(configfile):
def readable_timedelta(delta):
"""
Convert a dateutil.relativedelta to a readable format
Convert a millisecond delta to a readable format
:param delta: A dateutil.relativedelta
:param delta: A delta between two timestamps in milliseconds
:returns: The readable time difference string
"""
attrs = ['years', 'months', 'days', 'hours', 'minutes', 'seconds', 'microseconds']
return ", ".join([
'%d %s' % (getattr(delta, attr), attr if getattr(delta, attr) > 0 else attr[:-1])
for attr in attrs if getattr(delta, attr)
])
seconds, milliseconds = divmod(delta, 1000)
minutes, seconds = divmod(seconds, 60)
return f"{int(minutes)}:{int(seconds)}.{int(milliseconds)}"
# ----------------------------------------------------------------------------
@ -170,8 +168,8 @@ class ClientProtocol:
def _calculate_time_difference(self):
old_last_received_at = self._LAST_RECEIVED_AT
self._LAST_RECEIVED_AT = time.time() * 1e6
time_delta = relativedelta(microseconds=(self._LAST_RECEIVED_AT - old_last_received_at))
self._LAST_RECEIVED_AT = time.time() * 1e3
time_delta = self._LAST_RECEIVED_AT - old_last_received_at
return readable_timedelta(time_delta)
@ -242,12 +240,10 @@ async def create_client(
):
# Try pinging
try:
pong = ws.ping()
await asyncio.wait_for(
pong,
timeout=ping_timeout
)
logger.info("Connection still alive...")
pong = await ws.ping()
latency = (await asyncio.wait_for(pong, timeout=ping_timeout) * 1000)
logger.info(f"Connection still alive, latency: {latency}ms")
continue
@ -272,6 +268,7 @@ async def create_client(
websockets.exceptions.ConnectionClosedError,
websockets.exceptions.ConnectionClosedOK
):
logger.info("Connection was closed")
# Just keep trying to connect again indefinitely
await asyncio.sleep(sleep_time)

81
tests/data/test_datahandler.py
View File

@ -15,7 +15,7 @@ from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler, g
from freqtrade.data.history.jsondatahandler import JsonDataHandler, JsonGzDataHandler
from freqtrade.data.history.parquetdatahandler import ParquetDataHandler
from freqtrade.enums import CandleType, TradingMode
from tests.conftest import log_has
from tests.conftest import log_has, log_has_re
def test_datahandler_ohlcv_get_pairs(testdatadir):
@ -154,6 +154,85 @@ def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
assert df.columns.equals(df1.columns)
def test_datahandler__check_empty_df(testdatadir, caplog):
dh = JsonDataHandler(testdatadir)
expected_text = r"Price jump in UNITTEST/USDT, 1h, spot between"
df = DataFrame([
[
1511686200000, # 8:50:00
8.794, # open
8.948, # high
8.794, # low
8.88, # close
2255, # volume (in quote currency)
],
[
1511686500000, # 8:55:00
8.88,
8.942,
8.88,
8.893,
9911,
],
[
1511687100000, # 9:05:00
8.891,
8.893,
8.875,
8.877,
2251
],
[
1511687400000, # 9:10:00
8.877,
8.883,
8.895,
8.817,
123551
]
], columns=['date', 'open', 'high', 'low', 'close', 'volume'])
dh._check_empty_df(df, 'UNITTEST/USDT', '1h', CandleType.SPOT, True, True)
assert not log_has_re(expected_text, caplog)
df = DataFrame([
[
1511686200000, # 8:50:00
8.794, # open
8.948, # high
8.794, # low
8.88, # close
2255, # volume (in quote currency)
],
[
1511686500000, # 8:55:00
8.88,
8.942,
8.88,
8.893,
9911,
],
[
1511687100000, # 9:05:00
889.1, # Price jump by several decimals
889.3,
887.5,
887.7,
2251
],
[
1511687400000, # 9:10:00
8.877,
8.883,
8.895,
8.817,
123551
]
], columns=['date', 'open', 'high', 'low', 'close', 'volume'])
dh._check_empty_df(df, 'UNITTEST/USDT', '1h', CandleType.SPOT, True, True)
assert log_has_re(expected_text, caplog)
@pytest.mark.parametrize('datahandler', ['feather', 'parquet'])
def test_datahandler_trades_not_supported(datahandler, testdatadir, ):
dh = get_datahandler(testdatadir, datahandler)

3
tests/exchange/test_binance.py
View File

@ -162,9 +162,6 @@ def test_stoploss_adjust_binance(mocker, default_conf, sl1, sl2, sl3, side):
}
assert exchange.stoploss_adjust(sl1, order, side=side)
assert not exchange.stoploss_adjust(sl2, order, side=side)
# Test with invalid order case
order['type'] = 'stop_loss'
assert not exchange.stoploss_adjust(sl3, order, side=side)
def test_fill_leverage_tiers_binance(default_conf, mocker):

3
tests/exchange/test_huobi.py
View File

@ -113,5 +113,4 @@ def test_stoploss_adjust_huobi(mocker, default_conf):
assert exchange.stoploss_adjust(1501, order, 'sell')
assert not exchange.stoploss_adjust(1499, order, 'sell')
# Test with invalid order case
order['type'] = 'stop_loss'
assert not exchange.stoploss_adjust(1501, order, 'sell')
assert exchange.stoploss_adjust(1501, order, 'sell')

3
tests/freqai/test_freqai_datakitchen.py
View File

@ -130,7 +130,8 @@ def test_normalize_data(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
data_dict = freqai.dk.data_dictionary
freqai.dk.normalize_data(data_dict)
assert len(freqai.dk.data) == 32
assert any('_max' in entry for entry in freqai.dk.data.keys())
assert any('_min' in entry for entry in freqai.dk.data.keys())
def test_filter_features(mocker, freqai_conf):

15
tests/freqai/test_freqai_interface.py
View File

@ -27,13 +27,13 @@ def is_mac() -> bool:
return "Darwin" in machine
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'XGBoostRFRegressor',
'CatboostRegressor',
@pytest.mark.parametrize('model, pca, dbscan', [
('LightGBMRegressor', True, False),
('XGBoostRegressor', False, True),
('XGBoostRFRegressor', False, False),
('CatboostRegressor', False, False),
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan):
if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM")
@ -41,6 +41,8 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
freqai_conf['freqai']['feature_parameters'].update({"principal_component_analysis": pca})
freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
@ -234,6 +236,7 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 9
shutil.rmtree(Path(freqai.dk.full_path))

0
tests/persistence/__init__.py Normal file
View File

2
tests/test_persistence.py → tests/persistence/test_persistence.py
View File

@ -2404,7 +2404,7 @@ def test_Trade_object_idem():
'get_enter_tag_performance',
'get_mix_tag_performance',
'get_trading_volume',
'from_json',
)
EXCLUDES2 = ('trades', 'trades_open', 'bt_trades_open_pp', 'bt_open_open_trade_count',
'total_profit')

181
tests/persistence/test_trade_fromjson.py Normal file
View File

@ -0,0 +1,181 @@
from datetime import datetime, timezone
from freqtrade.persistence.trade_model import Trade
def test_trade_fromjson():
"""Test the Trade.from_json() method."""
trade_string = """{
"trade_id": 25,
"pair": "ETH/USDT",
"base_currency": "ETH",
"quote_currency": "USDT",
"is_open": false,
"exchange": "binance",
"amount": 407.0,
"amount_requested": 102.92547026,
"stake_amount": 102.7494348,
"strategy": "SampleStrategy55",
"buy_tag": "Strategy2",
"enter_tag": "Strategy2",
"timeframe": 5,
"fee_open": 0.001,
"fee_open_cost": 0.1027494,
"fee_open_currency": "ETH",
"fee_close": 0.001,
"fee_close_cost": 0.1054944,
"fee_close_currency": "USDT",
"open_date": "2022-10-18 09:12:42",
"open_timestamp": 1666084362912,
"open_rate": 0.2518998249562391,
"open_rate_requested": 0.2516,
"open_trade_value": 102.62575199,
"close_date": "2022-10-18 09:45:22",
"close_timestamp": 1666086322208,
"realized_profit": 2.76315361,
"close_rate": 0.2592,
"close_rate_requested": 0.2592,
"close_profit": 0.026865,
"close_profit_pct": 2.69,
"close_profit_abs": 2.76315361,
"trade_duration_s": 1959,
"trade_duration": 32,
"profit_ratio": 0.02686,
"profit_pct": 2.69,
"profit_abs": 2.76315361,
"sell_reason": "no longer good",
"exit_reason": "no longer good",
"exit_order_status": "closed",
"stop_loss_abs": 0.1981,
"stop_loss_ratio": -0.216,
"stop_loss_pct": -21.6,
"stoploss_order_id": null,
"stoploss_last_update": null,
"stoploss_last_update_timestamp": null,
"initial_stop_loss_abs": 0.1981,
"initial_stop_loss_ratio": -0.216,
"initial_stop_loss_pct": -21.6,
"min_rate": 0.2495,
"max_rate": 0.2592,
"leverage": 1.0,
"interest_rate": 0.0,
"liquidation_price": null,
"is_short": false,
"trading_mode": "spot",
"funding_fees": 0.0,
"open_order_id": null,
"orders": [
{
"amount": 102.0,
"safe_price": 0.2526,
"ft_order_side": "buy",
"order_filled_timestamp": 1666084370887,
"ft_is_entry": true,
"pair": "ETH/USDT",
"order_id": "78404228",
"status": "closed",
"average": 0.2526,
"cost": 25.7652,
"filled": 102.0,
"is_open": false,
"order_date": "2022-10-18 09:12:42",
"order_timestamp": 1666084362684,
"order_filled_date": "2022-10-18 09:12:50",
"order_type": "limit",
"price": 0.2526,
"remaining": 0.0
},
{
"amount": 102.0,
"safe_price": 0.2517,
"ft_order_side": "buy",
"order_filled_timestamp": 1666084379056,
"ft_is_entry": true,
"pair": "ETH/USDT",
"order_id": "78405139",
"status": "closed",
"average": 0.2517,
"cost": 25.6734,
"filled": 102.0,
"is_open": false,
"order_date": "2022-10-18 09:12:57",
"order_timestamp": 1666084377681,
"order_filled_date": "2022-10-18 09:12:59",
"order_type": "limit",
"price": 0.2517,
"remaining": 0.0
},
{
"amount": 102.0,
"safe_price": 0.2517,
"ft_order_side": "buy",
"order_filled_timestamp": 1666084389644,
"ft_is_entry": true,
"pair": "ETH/USDT",
"order_id": "78405265",
"status": "closed",
"average": 0.2517,
"cost": 25.6734,
"filled": 102.0,
"is_open": false,
"order_date": "2022-10-18 09:13:03",
"order_timestamp": 1666084383295,
"order_filled_date": "2022-10-18 09:13:09",
"order_type": "limit",
"price": 0.2517,
"remaining": 0.0
},
{
"amount": 102.0,
"safe_price": 0.2516,
"ft_order_side": "buy",
"order_filled_timestamp": 1666084723521,
"ft_is_entry": true,
"pair": "ETH/USDT",
"order_id": "78405395",
"status": "closed",
"average": 0.2516,
"cost": 25.6632,
"filled": 102.0,
"is_open": false,
"order_date": "2022-10-18 09:13:13",
"order_timestamp": 1666084393920,
"order_filled_date": "2022-10-18 09:18:43",
"order_type": "limit",
"price": 0.2516,
"remaining": 0.0
},
{
"amount": 407.0,
"safe_price": 0.2592,
"ft_order_side": "sell",
"order_filled_timestamp": 1666086322198,
"ft_is_entry": false,
"pair": "ETH/USDT",
"order_id": "78432649",
"status": "closed",
"average": 0.2592,
"cost": 105.4944,
"filled": 407.0,
"is_open": false,
"order_date": "2022-10-18 09:45:21",
"order_timestamp": 1666086321435,
"order_filled_date": "2022-10-18 09:45:22",
"order_type": "market",
"price": 0.2592,
"remaining": 0.0
}
]
}"""
trade = Trade.from_json(trade_string)
assert trade.id == 25
assert trade.pair == 'ETH/USDT'
assert trade.open_date == datetime(2022, 10, 18, 9, 12, 42, tzinfo=timezone.utc)
assert isinstance(trade.open_date, datetime)
assert trade.exit_reason == 'no longer good'
assert len(trade.orders) == 5
last_o = trade.orders[-1]
assert last_o.order_filled_date == datetime(2022, 10, 18, 9, 45, 22, tzinfo=timezone.utc)
assert isinstance(last_o.order_date, datetime)

21
tests/plugins/test_pairlist.py
View File

@ -2,6 +2,8 @@
import logging
import time
from copy import deepcopy
from datetime import timedelta
from unittest.mock import MagicMock, PropertyMock
import pandas as pd
@ -719,15 +721,26 @@ def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
def test_ShuffleFilter_init(mocker, whitelist_conf, caplog) -> None:
whitelist_conf['pairlists'] = [
{"method": "StaticPairList"},
{"method": "ShuffleFilter", "seed": 42}
{"method": "ShuffleFilter", "seed": 43}
]
exchange = get_patched_exchange(mocker, whitelist_conf)
PairListManager(exchange, whitelist_conf)
assert log_has("Backtesting mode detected, applying seed value: 42", caplog)
plm = PairListManager(exchange, whitelist_conf)
assert log_has("Backtesting mode detected, applying seed value: 43", caplog)
with time_machine.travel("2021-09-01 05:01:00 +00:00") as t:
plm.refresh_pairlist()
pl1 = deepcopy(plm.whitelist)
plm.refresh_pairlist()
assert plm.whitelist == pl1
t.shift(timedelta(minutes=10))
plm.refresh_pairlist()
assert plm.whitelist != pl1
caplog.clear()
whitelist_conf['runmode'] = RunMode.DRY_RUN
PairListManager(exchange, whitelist_conf)
plm = PairListManager(exchange, whitelist_conf)
assert not log_has("Backtesting mode detected, applying seed value: 42", caplog)
assert log_has("Live mode detected, not applying seed.", caplog)

8
tests/test_freqtradebot.py
View File

@ -3969,15 +3969,17 @@ def test__safe_exit_amount(default_conf_usdt, fee, caplog, mocker, amount_wallet
patch_get_signal(freqtrade)
if has_err:
with pytest.raises(DependencyException, match=r"Not enough amount to exit trade."):
assert freqtrade._safe_exit_amount(trade.pair, trade.amount)
assert freqtrade._safe_exit_amount(trade, trade.pair, trade.amount)
else:
wallet_update.reset_mock()
assert freqtrade._safe_exit_amount(trade.pair, trade.amount) == amount_wallet
assert trade.amount != amount_wallet
assert freqtrade._safe_exit_amount(trade, trade.pair, trade.amount) == amount_wallet
assert log_has_re(r'.*Falling back to wallet-amount.', caplog)
assert trade.amount == amount_wallet
assert wallet_update.call_count == 1
caplog.clear()
wallet_update.reset_mock()
assert freqtrade._safe_exit_amount(trade.pair, amount_wallet) == amount_wallet
assert freqtrade._safe_exit_amount(trade, trade.pair, amount_wallet) == amount_wallet
assert not log_has_re(r'.*Falling back to wallet-amount.', caplog)
assert wallet_update.call_count == 1

4
tests/test_integration.py
View File

@ -420,7 +420,7 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
assert trade.open_order_id is None
# Open rate is not adjusted yet
assert trade.open_rate == 1.99
assert trade.stake_amount == 60
assert pytest.approx(trade.stake_amount) == 60
assert trade.stop_loss_pct == -0.1
assert pytest.approx(trade.stop_loss) == 1.99 * (1 - 0.1 / leverage)
assert pytest.approx(trade.initial_stop_loss) == 1.99 * (1 - 0.1 / leverage)
@ -446,7 +446,7 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
assert len(trade.orders) == 4
assert trade.open_order_id is not None
assert trade.open_rate == 1.99
assert trade.stake_amount == 60
assert pytest.approx(trade.stake_amount) == 60
assert trade.orders[-1].price == 1.95
assert pytest.approx(trade.orders[-1].cost) == 120 * leverage

50
tests/test_worker.py
View File

@ -1,7 +1,10 @@
import logging
import time
from datetime import timedelta
from unittest.mock import MagicMock, PropertyMock
import time_machine
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import State
from freqtrade.worker import Worker
@ -59,13 +62,58 @@ def test_throttle(mocker, default_conf, caplog) -> None:
end = time.time()
assert result == 42
assert end - start > 0.1
assert 0.3 > end - start > 0.1
assert log_has_re(r"Throttling with 'throttled_func\(\)': sleep for \d\.\d{2} s.*", caplog)
result = worker._throttle(throttled_func, throttle_secs=-1)
assert result == 42
def test_throttle_sleep_time(mocker, default_conf, caplog) -> None:
caplog.set_level(logging.DEBUG)
worker = get_patched_worker(mocker, default_conf)
sleep_mock = mocker.patch("freqtrade.worker.Worker._sleep")
with time_machine.travel("2022-09-01 05:00:00 +00:00") as t:
def throttled_func(x=1):
t.shift(timedelta(seconds=x))
return 42
assert worker._throttle(throttled_func, throttle_secs=5) == 42
# This moves the clock by 1 second
assert sleep_mock.call_count == 1
assert 3.8 < sleep_mock.call_args[0][0] < 4.1
sleep_mock.reset_mock()
# This moves the clock by 1 second
assert worker._throttle(throttled_func, throttle_secs=10) == 42
assert sleep_mock.call_count == 1
assert 8.8 < sleep_mock.call_args[0][0] < 9.1
sleep_mock.reset_mock()
# This moves the clock by 5 second, so we only throttle by 5s
assert worker._throttle(throttled_func, throttle_secs=10, x=5) == 42
assert sleep_mock.call_count == 1
assert 4.8 < sleep_mock.call_args[0][0] < 5.1
t.move_to("2022-09-01 05:01:00 +00:00")
sleep_mock.reset_mock()
# Throttle for more than 5m (1 timeframe)
assert worker._throttle(throttled_func, throttle_secs=400, x=5) == 42
assert sleep_mock.call_count == 1
assert 394.8 < sleep_mock.call_args[0][0] < 395.1
t.move_to("2022-09-01 05:01:00 +00:00")
sleep_mock.reset_mock()
# Throttle for more than 5m (1 timeframe)
assert worker._throttle(throttled_func, throttle_secs=400, timeframe='5m',
timeframe_offset=0.4, x=5) == 42
assert sleep_mock.call_count == 1
# 300 (5m) - 60 (1m - see set time above) - 5 (duration of throttled_func) = 235
assert 235.2 < sleep_mock.call_args[0][0] < 235.6
def test_throttle_with_assets(mocker, default_conf) -> None:
def throttled_func(nb_assets=-1):
return nb_assets