Merge branch 'develop' into pr/Bloodhunter4rc/8819
This commit is contained in:
Matthias
@@ -160,7 +160,8 @@ jobs:
|
|||||||
- name: Installation - macOS
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- name: Installation - macOS
|
||||||
if: runner.os == 'macOS'
|
if: runner.os == 'macOS'
|
||||||
run: |
|
run: |
|
||||||
brew update
|
# brew update
|
||||||
|
# TODO: Should be the brew upgrade
|
||||||
# homebrew fails to update python due to unlinking failures
|
# homebrew fails to update python due to unlinking failures
|
||||||
# https://github.com/actions/runner-images/issues/6817
|
# https://github.com/actions/runner-images/issues/6817
|
||||||
rm /usr/local/bin/2to3 || true
|
rm /usr/local/bin/2to3 || true
|
||||||
@@ -460,7 +461,7 @@ jobs:
|
|||||||
python setup.py sdist bdist_wheel
|
python setup.py sdist bdist_wheel
|
||||||
|
|
||||||
- name: Publish to PyPI (Test)
|
- name: Publish to PyPI (Test)
|
||||||
uses: pypa/gh-action-pypi-publish@v1.8.6
|
uses: pypa/gh-action-pypi-publish@v1.8.7
|
||||||
if: (github.event_name == 'release')
|
if: (github.event_name == 'release')
|
||||||
with:
|
with:
|
||||||
user: __token__
|
user: __token__
|
||||||
@@ -468,7 +469,7 @@ jobs:
|
|||||||
repository_url: https://test.pypi.org/legacy/
|
repository_url: https://test.pypi.org/legacy/
|
||||||
|
|
||||||
- name: Publish to PyPI
|
- name: Publish to PyPI
|
||||||
uses: pypa/gh-action-pypi-publish@v1.8.6
|
uses: pypa/gh-action-pypi-publish@v1.8.7
|
||||||
if: (github.event_name == 'release')
|
if: (github.event_name == 'release')
|
||||||
with:
|
with:
|
||||||
user: __token__
|
user: __token__
|
||||||
|
|||||||
@@ -18,7 +18,7 @@ repos:
|
|||||||
- types-requests==2.31.0.1
|
- types-requests==2.31.0.1
|
||||||
- types-tabulate==0.9.0.2
|
- types-tabulate==0.9.0.2
|
||||||
- types-python-dateutil==2.8.19.13
|
- types-python-dateutil==2.8.19.13
|
||||||
- SQLAlchemy==2.0.16
|
- SQLAlchemy==2.0.17
|
||||||
# stages: [push]
|
# stages: [push]
|
||||||
|
|
||||||
- repo: https://github.com/pycqa/isort
|
- repo: https://github.com/pycqa/isort
|
||||||
|
|||||||
@@ -136,7 +136,7 @@ class MyAwesomeStrategy(IStrategy):
|
|||||||
|
|
||||||
### Dynamic parameters
|
### Dynamic parameters
|
||||||
|
|
||||||
Parameters can also be defined dynamically, but must be available to the instance once the * [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
|
Parameters can also be defined dynamically, but must be available to the instance once the [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
|
||||||
|
|
||||||
``` python
|
``` python
|
||||||
|
|
||||||
|
|||||||
+7
-1
@@ -453,7 +453,13 @@ Once the PR against stable is merged (best right after merging):
|
|||||||
* Use the button "Draft a new release" in the Github UI (subsection releases).
|
* Use the button "Draft a new release" in the Github UI (subsection releases).
|
||||||
* Use the version-number specified as tag.
|
* Use the version-number specified as tag.
|
||||||
* Use "stable" as reference (this step comes after the above PR is merged).
|
* Use "stable" as reference (this step comes after the above PR is merged).
|
||||||
* Use the above changelog as release comment (as codeblock)
|
* Use the above changelog as release comment (as codeblock).
|
||||||
|
* Use the below snippet for the new release
|
||||||
|
|
||||||
|
??? Tip "Release template"
|
||||||
|
````
|
||||||
|
--8<-- "includes/release_template.md"
|
||||||
|
````
|
||||||
|
|
||||||
## Releases
|
## Releases
|
||||||
|
|
||||||
|
|||||||
@@ -160,7 +160,7 @@ Below are the values you can expect to include/use inside a typical strategy dat
|
|||||||
|------------|-------------|
|
|------------|-------------|
|
||||||
| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
|
| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
|
||||||
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
|
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
|
||||||
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
|
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
|
||||||
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
|
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
|
||||||
| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
|
| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
|
||||||
|
|
||||||
|
|||||||
@@ -0,0 +1,37 @@
|
|||||||
|
## Highlighted changes
|
||||||
|
|
||||||
|
- ...
|
||||||
|
|
||||||
|
### How to update
|
||||||
|
|
||||||
|
As always, you can update your bot using one of the following commands:
|
||||||
|
|
||||||
|
#### docker-compose
|
||||||
|
|
||||||
|
```bash
|
||||||
|
docker-compose pull
|
||||||
|
docker-compose up -d
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Installation via setup script
|
||||||
|
|
||||||
|
```
|
||||||
|
# Deactivate venv and run
|
||||||
|
./setup.sh --update
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Plain native installation
|
||||||
|
|
||||||
|
```
|
||||||
|
git pull
|
||||||
|
pip install -U -r requirements.txt
|
||||||
|
```
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>Expand full changelog</summary>
|
||||||
|
|
||||||
|
```
|
||||||
|
<Paste your changelog here>
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
@@ -1,6 +1,6 @@
|
|||||||
markdown==3.3.7
|
markdown==3.3.7
|
||||||
mkdocs==1.4.3
|
mkdocs==1.4.3
|
||||||
mkdocs-material==9.1.16
|
mkdocs-material==9.1.17
|
||||||
mdx_truly_sane_lists==1.3
|
mdx_truly_sane_lists==1.3
|
||||||
pymdown-extensions==10.0.1
|
pymdown-extensions==10.0.1
|
||||||
jinja2==3.1.2
|
jinja2==3.1.2
|
||||||
|
|||||||
@@ -800,8 +800,8 @@ class MyCoolFreqaiModel(BaseRegressionModel):
|
|||||||
if self.freqai_info.get("DI_threshold", 0) > 0:
|
if self.freqai_info.get("DI_threshold", 0) > 0:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
|
|
||||||
# ... your custom code
|
# ... your custom code
|
||||||
return (pred_df, dk.do_predict)
|
return (pred_df, dk.do_predict)
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
""" Freqtrade bot """
|
""" Freqtrade bot """
|
||||||
__version__ = '2023.6.dev'
|
__version__ = '2023.7.dev'
|
||||||
|
|
||||||
if 'dev' in __version__:
|
if 'dev' in __version__:
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|||||||
@@ -33,11 +33,11 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
|
|||||||
else:
|
else:
|
||||||
headers = {
|
headers = {
|
||||||
'name': 'Exchange name',
|
'name': 'Exchange name',
|
||||||
'valid': 'Valid',
|
|
||||||
'supported': 'Supported',
|
'supported': 'Supported',
|
||||||
'trade_modes': 'Markets',
|
'trade_modes': 'Markets',
|
||||||
'comment': 'Reason',
|
'comment': 'Reason',
|
||||||
}
|
}
|
||||||
|
headers.update({'valid': 'Valid'} if args['list_exchanges_all'] else {})
|
||||||
|
|
||||||
def build_entry(exchange: ValidExchangesType, valid: bool):
|
def build_entry(exchange: ValidExchangesType, valid: bool):
|
||||||
valid_entry = {'valid': exchange['valid']} if valid else {}
|
valid_entry = {'valid': exchange['valid']} if valid else {}
|
||||||
|
|||||||
@@ -112,6 +112,8 @@ MINIMAL_CONFIG = {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
__MESSAGE_TYPE_DICT: Dict[str, Dict[str, str]] = {x: {'type': 'object'} for x in RPCMessageType}
|
||||||
|
|
||||||
# Required json-schema for user specified config
|
# Required json-schema for user specified config
|
||||||
CONF_SCHEMA = {
|
CONF_SCHEMA = {
|
||||||
'type': 'object',
|
'type': 'object',
|
||||||
@@ -354,7 +356,8 @@ CONF_SCHEMA = {
|
|||||||
'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
|
'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
|
||||||
'retries': {'type': 'integer', 'minimum': 0},
|
'retries': {'type': 'integer', 'minimum': 0},
|
||||||
'retry_delay': {'type': 'number', 'minimum': 0},
|
'retry_delay': {'type': 'number', 'minimum': 0},
|
||||||
**dict([(x, {'type': 'object'}) for x in RPCMessageType]),
|
**__MESSAGE_TYPE_DICT,
|
||||||
|
# **{x: {'type': 'object'} for x in RPCMessageType},
|
||||||
# Below -> Deprecated
|
# Below -> Deprecated
|
||||||
'webhookentry': {'type': 'object'},
|
'webhookentry': {'type': 'object'},
|
||||||
'webhookentrycancel': {'type': 'object'},
|
'webhookentrycancel': {'type': 'object'},
|
||||||
|
|||||||
@@ -14,8 +14,8 @@ from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
|
|||||||
from freqtrade.enums import CandleType
|
from freqtrade.enums import CandleType
|
||||||
from freqtrade.exceptions import OperationalException
|
from freqtrade.exceptions import OperationalException
|
||||||
from freqtrade.exchange import Exchange
|
from freqtrade.exchange import Exchange
|
||||||
from freqtrade.misc import format_ms_time
|
|
||||||
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
|
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
|
||||||
|
from freqtrade.util import format_ms_time
|
||||||
from freqtrade.util.binance_mig import migrate_binance_futures_data
|
from freqtrade.util.binance_mig import migrate_binance_futures_data
|
||||||
|
|
||||||
|
|
||||||
@@ -354,7 +354,7 @@ def _download_trades_history(exchange: Exchange,
|
|||||||
trades = []
|
trades = []
|
||||||
|
|
||||||
if not since:
|
if not since:
|
||||||
since = int((datetime.now() - timedelta(days=-new_pairs_days)).timestamp()) * 1000
|
since = int((datetime.now() - timedelta(days=new_pairs_days)).timestamp()) * 1000
|
||||||
|
|
||||||
from_id = trades[-1][1] if trades else None
|
from_id = trades[-1][1] if trades else None
|
||||||
if trades and since < trades[-1][0]:
|
if trades and since < trades[-1][0]:
|
||||||
|
|||||||
@@ -120,7 +120,7 @@ class BaseClassifierModel(IFreqaiModel):
|
|||||||
if dk.feature_pipeline["di"]:
|
if dk.feature_pipeline["di"]:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
|
|
||||||
return (pred_df, dk.do_predict)
|
return (pred_df, dk.do_predict)
|
||||||
|
|||||||
@@ -94,8 +94,8 @@ class BasePyTorchClassifier(BasePyTorchModel):
|
|||||||
if dk.feature_pipeline["di"]:
|
if dk.feature_pipeline["di"]:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
|
|
||||||
return (pred_df, dk.do_predict)
|
return (pred_df, dk.do_predict)
|
||||||
|
|
||||||
|
|||||||
@@ -55,8 +55,8 @@ class BasePyTorchRegressor(BasePyTorchModel):
|
|||||||
if dk.feature_pipeline["di"]:
|
if dk.feature_pipeline["di"]:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
return (pred_df, dk.do_predict)
|
return (pred_df, dk.do_predict)
|
||||||
|
|
||||||
def train(
|
def train(
|
||||||
|
|||||||
@@ -114,7 +114,7 @@ class BaseRegressionModel(IFreqaiModel):
|
|||||||
if dk.feature_pipeline["di"]:
|
if dk.feature_pipeline["di"]:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
|
|
||||||
return (pred_df, dk.do_predict)
|
return (pred_df, dk.do_predict)
|
||||||
|
|||||||
@@ -515,7 +515,7 @@ class IFreqaiModel(ABC):
|
|||||||
]
|
]
|
||||||
|
|
||||||
if ft_params.get("principal_component_analysis", False):
|
if ft_params.get("principal_component_analysis", False):
|
||||||
pipe_steps.append(('pca', ds.PCA()))
|
pipe_steps.append(('pca', ds.PCA(n_components=0.999)))
|
||||||
pipe_steps.append(('post-pca-scaler',
|
pipe_steps.append(('post-pca-scaler',
|
||||||
SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))))
|
SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))))
|
||||||
|
|
||||||
@@ -1012,6 +1012,6 @@ class IFreqaiModel(ABC):
|
|||||||
if self.freqai_info.get("DI_threshold", 0) > 0:
|
if self.freqai_info.get("DI_threshold", 0) > 0:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
return
|
return
|
||||||
|
|||||||
@@ -136,8 +136,8 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
|
|||||||
if self.freqai_info.get("DI_threshold", 0) > 0:
|
if self.freqai_info.get("DI_threshold", 0) > 0:
|
||||||
dk.DI_values = dk.feature_pipeline["di"].di_values
|
dk.DI_values = dk.feature_pipeline["di"].di_values
|
||||||
else:
|
else:
|
||||||
dk.DI_values = np.zeros(len(outliers.index))
|
dk.DI_values = np.zeros(outliers.shape[0])
|
||||||
dk.do_predict = outliers.to_numpy()
|
dk.do_predict = outliers
|
||||||
|
|
||||||
if x.shape[1] > 1:
|
if x.shape[1] > 1:
|
||||||
zeros_df = pd.DataFrame(np.zeros((x.shape[1] - len(pred_df), len(pred_df.columns))),
|
zeros_df = pd.DataFrame(np.zeros((x.shape[1] - len(pred_df), len(pred_df.columns))),
|
||||||
|
|||||||
@@ -3,7 +3,6 @@ Various tool function for Freqtrade and scripts
|
|||||||
"""
|
"""
|
||||||
import gzip
|
import gzip
|
||||||
import logging
|
import logging
|
||||||
from datetime import datetime
|
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from typing import Any, Dict, Iterator, List, Mapping, Optional, TextIO, Union
|
from typing import Any, Dict, Iterator, List, Mapping, Optional, TextIO, Union
|
||||||
from urllib.parse import urlparse
|
from urllib.parse import urlparse
|
||||||
@@ -123,14 +122,6 @@ def pair_to_filename(pair: str) -> str:
|
|||||||
return pair
|
return pair
|
||||||
|
|
||||||
|
|
||||||
def format_ms_time(date: int) -> str:
|
|
||||||
"""
|
|
||||||
convert MS date to readable format.
|
|
||||||
: epoch-string in ms
|
|
||||||
"""
|
|
||||||
return datetime.fromtimestamp(date / 1000.0).strftime('%Y-%m-%dT%H:%M:%S')
|
|
||||||
|
|
||||||
|
|
||||||
def deep_merge_dicts(source, destination, allow_null_overrides: bool = True):
|
def deep_merge_dicts(source, destination, allow_null_overrides: bool = True):
|
||||||
"""
|
"""
|
||||||
Values from Source override destination, destination is returned (and modified!!)
|
Values from Source override destination, destination is returned (and modified!!)
|
||||||
|
|||||||
@@ -3,7 +3,7 @@ from typing import Callable
|
|||||||
from cachetools import TTLCache, cached
|
from cachetools import TTLCache, cached
|
||||||
|
|
||||||
|
|
||||||
class LoggingMixin():
|
class LoggingMixin:
|
||||||
"""
|
"""
|
||||||
Logging Mixin
|
Logging Mixin
|
||||||
Shows similar messages only once every `refresh_period`.
|
Shows similar messages only once every `refresh_period`.
|
||||||
|
|||||||
@@ -35,7 +35,7 @@ def hyperopt_serializer(x):
|
|||||||
return str(x)
|
return str(x)
|
||||||
|
|
||||||
|
|
||||||
class HyperoptStateContainer():
|
class HyperoptStateContainer:
|
||||||
""" Singleton class to track state of hyperopt"""
|
""" Singleton class to track state of hyperopt"""
|
||||||
state: HyperoptState = HyperoptState.OPTIMIZE
|
state: HyperoptState = HyperoptState.OPTIMIZE
|
||||||
|
|
||||||
@@ -44,7 +44,7 @@ class HyperoptStateContainer():
|
|||||||
cls.state = value
|
cls.state = value
|
||||||
|
|
||||||
|
|
||||||
class HyperoptTools():
|
class HyperoptTools:
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]:
|
def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]:
|
||||||
|
|||||||
@@ -0,0 +1,18 @@
|
|||||||
|
# flake8: noqa: F401
|
||||||
|
from freqtrade.optimize.optimize_reports.bt_output import (generate_edge_table,
|
||||||
|
show_backtest_result,
|
||||||
|
show_backtest_results,
|
||||||
|
show_sorted_pairlist,
|
||||||
|
text_table_add_metrics,
|
||||||
|
text_table_bt_results,
|
||||||
|
text_table_exit_reason,
|
||||||
|
text_table_periodic_breakdown,
|
||||||
|
text_table_strategy, text_table_tags)
|
||||||
|
from freqtrade.optimize.optimize_reports.bt_storage import (store_backtest_analysis_results,
|
||||||
|
store_backtest_stats)
|
||||||
|
from freqtrade.optimize.optimize_reports.optimize_reports import (
|
||||||
|
generate_all_periodic_breakdown_stats, generate_backtest_stats, generate_daily_stats,
|
||||||
|
generate_exit_reason_stats, generate_pair_metrics, generate_periodic_breakdown_stats,
|
||||||
|
generate_rejected_signals, generate_strategy_comparison, generate_strategy_stats,
|
||||||
|
generate_tag_metrics, generate_trade_signal_candles, generate_trading_stats,
|
||||||
|
generate_wins_draws_losses)
|
||||||
@@ -0,0 +1,405 @@
|
|||||||
|
import logging
|
||||||
|
from typing import Any, Dict, List
|
||||||
|
|
||||||
|
from tabulate import tabulate
|
||||||
|
|
||||||
|
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
|
||||||
|
from freqtrade.misc import decimals_per_coin, round_coin_value
|
||||||
|
from freqtrade.optimize.optimize_reports.optimize_reports import (generate_periodic_breakdown_stats,
|
||||||
|
generate_wins_draws_losses)
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
def _get_line_floatfmt(stake_currency: str) -> List[str]:
|
||||||
|
"""
|
||||||
|
Generate floatformat (goes in line with _generate_result_line())
|
||||||
|
"""
|
||||||
|
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
|
||||||
|
'.2f', 'd', 's', 's']
|
||||||
|
|
||||||
|
|
||||||
|
def _get_line_header(first_column: str, stake_currency: str,
|
||||||
|
direction: str = 'Entries') -> List[str]:
|
||||||
|
"""
|
||||||
|
Generate header lines (goes in line with _generate_result_line())
|
||||||
|
"""
|
||||||
|
return [first_column, direction, 'Avg Profit %', 'Cum Profit %',
|
||||||
|
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
|
||||||
|
'Win Draw Loss Win%']
|
||||||
|
|
||||||
|
|
||||||
|
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
|
||||||
|
"""
|
||||||
|
Generates and returns a text table for the given backtest data and the results dataframe
|
||||||
|
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
|
||||||
|
:param stake_currency: stake-currency - used to correctly name headers
|
||||||
|
:return: pretty printed table with tabulate as string
|
||||||
|
"""
|
||||||
|
|
||||||
|
headers = _get_line_header('Pair', stake_currency)
|
||||||
|
floatfmt = _get_line_floatfmt(stake_currency)
|
||||||
|
output = [[
|
||||||
|
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
|
||||||
|
t['profit_total_pct'], t['duration_avg'],
|
||||||
|
generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
|
||||||
|
] for t in pair_results]
|
||||||
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
||||||
|
return tabulate(output, headers=headers,
|
||||||
|
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
||||||
|
|
||||||
|
|
||||||
|
def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
|
||||||
|
"""
|
||||||
|
Generate small table outlining Backtest results
|
||||||
|
:param sell_reason_stats: Exit reason metrics
|
||||||
|
:param stake_currency: Stakecurrency used
|
||||||
|
:return: pretty printed table with tabulate as string
|
||||||
|
"""
|
||||||
|
headers = [
|
||||||
|
'Exit Reason',
|
||||||
|
'Exits',
|
||||||
|
'Win Draws Loss Win%',
|
||||||
|
'Avg Profit %',
|
||||||
|
'Cum Profit %',
|
||||||
|
f'Tot Profit {stake_currency}',
|
||||||
|
'Tot Profit %',
|
||||||
|
]
|
||||||
|
|
||||||
|
output = [[
|
||||||
|
t.get('exit_reason', t.get('sell_reason')), t['trades'],
|
||||||
|
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
|
||||||
|
t['profit_mean_pct'], t['profit_sum_pct'],
|
||||||
|
round_coin_value(t['profit_total_abs'], stake_currency, False),
|
||||||
|
t['profit_total_pct'],
|
||||||
|
] for t in exit_reason_stats]
|
||||||
|
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
|
||||||
|
|
||||||
|
|
||||||
|
def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
|
||||||
|
"""
|
||||||
|
Generates and returns a text table for the given backtest data and the results dataframe
|
||||||
|
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
|
||||||
|
:param stake_currency: stake-currency - used to correctly name headers
|
||||||
|
:return: pretty printed table with tabulate as string
|
||||||
|
"""
|
||||||
|
if (tag_type == "enter_tag"):
|
||||||
|
headers = _get_line_header("TAG", stake_currency)
|
||||||
|
else:
|
||||||
|
headers = _get_line_header("TAG", stake_currency, 'Exits')
|
||||||
|
floatfmt = _get_line_floatfmt(stake_currency)
|
||||||
|
output = [
|
||||||
|
[
|
||||||
|
t['key'] if t['key'] is not None and len(
|
||||||
|
t['key']) > 0 else "OTHER",
|
||||||
|
t['trades'],
|
||||||
|
t['profit_mean_pct'],
|
||||||
|
t['profit_sum_pct'],
|
||||||
|
t['profit_total_abs'],
|
||||||
|
t['profit_total_pct'],
|
||||||
|
t['duration_avg'],
|
||||||
|
generate_wins_draws_losses(
|
||||||
|
t['wins'],
|
||||||
|
t['draws'],
|
||||||
|
t['losses'])] for t in tag_results]
|
||||||
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
||||||
|
return tabulate(output, headers=headers,
|
||||||
|
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
||||||
|
|
||||||
|
|
||||||
|
def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
|
||||||
|
stake_currency: str, period: str) -> str:
|
||||||
|
"""
|
||||||
|
Generate small table with Backtest results by days
|
||||||
|
:param days_breakdown_stats: Days breakdown metrics
|
||||||
|
:param stake_currency: Stakecurrency used
|
||||||
|
:return: pretty printed table with tabulate as string
|
||||||
|
"""
|
||||||
|
headers = [
|
||||||
|
period.capitalize(),
|
||||||
|
f'Tot Profit {stake_currency}',
|
||||||
|
'Wins',
|
||||||
|
'Draws',
|
||||||
|
'Losses',
|
||||||
|
]
|
||||||
|
output = [[
|
||||||
|
d['date'], round_coin_value(d['profit_abs'], stake_currency, False),
|
||||||
|
d['wins'], d['draws'], d['loses'],
|
||||||
|
] for d in days_breakdown_stats]
|
||||||
|
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
|
||||||
|
|
||||||
|
|
||||||
|
def text_table_strategy(strategy_results, stake_currency: str) -> str:
|
||||||
|
"""
|
||||||
|
Generate summary table per strategy
|
||||||
|
:param strategy_results: Dict of <Strategyname: DataFrame> containing results for all strategies
|
||||||
|
:param stake_currency: stake-currency - used to correctly name headers
|
||||||
|
:return: pretty printed table with tabulate as string
|
||||||
|
"""
|
||||||
|
floatfmt = _get_line_floatfmt(stake_currency)
|
||||||
|
headers = _get_line_header('Strategy', stake_currency)
|
||||||
|
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
|
||||||
|
# therefore we slip this column in only for strategy summary here.
|
||||||
|
headers.append('Drawdown')
|
||||||
|
|
||||||
|
# Align drawdown string on the center two space separator.
|
||||||
|
if 'max_drawdown_account' in strategy_results[0]:
|
||||||
|
drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
|
||||||
|
else:
|
||||||
|
# Support for prior backtest results
|
||||||
|
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
|
||||||
|
|
||||||
|
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
|
||||||
|
dd_pad_per = max([len(dd) for dd in drawdown])
|
||||||
|
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
|
||||||
|
for t, dd in zip(strategy_results, drawdown)]
|
||||||
|
|
||||||
|
output = [[
|
||||||
|
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
|
||||||
|
t['profit_total_pct'], t['duration_avg'],
|
||||||
|
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
|
||||||
|
for t, drawdown in zip(strategy_results, drawdown)]
|
||||||
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
||||||
|
return tabulate(output, headers=headers,
|
||||||
|
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
||||||
|
|
||||||
|
|
||||||
|
def text_table_add_metrics(strat_results: Dict) -> str:
|
||||||
|
if len(strat_results['trades']) > 0:
|
||||||
|
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
|
||||||
|
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
|
||||||
|
|
||||||
|
short_metrics = [
|
||||||
|
('', ''), # Empty line to improve readability
|
||||||
|
('Long / Short',
|
||||||
|
f"{strat_results.get('trade_count_long', 'total_trades')} / "
|
||||||
|
f"{strat_results.get('trade_count_short', 0)}"),
|
||||||
|
('Total profit Long %', f"{strat_results['profit_total_long']:.2%}"),
|
||||||
|
('Total profit Short %', f"{strat_results['profit_total_short']:.2%}"),
|
||||||
|
('Absolute profit Long', round_coin_value(strat_results['profit_total_long_abs'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Absolute profit Short', round_coin_value(strat_results['profit_total_short_abs'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
] if strat_results.get('trade_count_short', 0) > 0 else []
|
||||||
|
|
||||||
|
drawdown_metrics = []
|
||||||
|
if 'max_relative_drawdown' in strat_results:
|
||||||
|
# Compatibility to show old hyperopt results
|
||||||
|
drawdown_metrics.append(
|
||||||
|
('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}")
|
||||||
|
)
|
||||||
|
drawdown_metrics.extend([
|
||||||
|
('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
|
||||||
|
if 'max_drawdown_account' in strat_results else (
|
||||||
|
'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
|
||||||
|
('Absolute Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Drawdown Start', strat_results['drawdown_start']),
|
||||||
|
('Drawdown End', strat_results['drawdown_end']),
|
||||||
|
])
|
||||||
|
|
||||||
|
entry_adjustment_metrics = [
|
||||||
|
('Canceled Trade Entries', strat_results.get('canceled_trade_entries', 'N/A')),
|
||||||
|
('Canceled Entry Orders', strat_results.get('canceled_entry_orders', 'N/A')),
|
||||||
|
('Replaced Entry Orders', strat_results.get('replaced_entry_orders', 'N/A')),
|
||||||
|
] if strat_results.get('canceled_entry_orders', 0) > 0 else []
|
||||||
|
|
||||||
|
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
|
||||||
|
# command stores these results and newer version of freqtrade must be able to handle old
|
||||||
|
# results with missing new fields.
|
||||||
|
metrics = [
|
||||||
|
('Backtesting from', strat_results['backtest_start']),
|
||||||
|
('Backtesting to', strat_results['backtest_end']),
|
||||||
|
('Max open trades', strat_results['max_open_trades']),
|
||||||
|
('', ''), # Empty line to improve readability
|
||||||
|
('Total/Daily Avg Trades',
|
||||||
|
f"{strat_results['total_trades']} / {strat_results['trades_per_day']}"),
|
||||||
|
|
||||||
|
('Starting balance', round_coin_value(strat_results['starting_balance'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Final balance', round_coin_value(strat_results['final_balance'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Total profit %', f"{strat_results['profit_total']:.2%}"),
|
||||||
|
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
|
||||||
|
('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
|
||||||
|
('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
|
||||||
|
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
|
||||||
|
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
|
||||||
|
in strat_results else 'N/A'),
|
||||||
|
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
|
||||||
|
in strat_results else 'N/A'),
|
||||||
|
('Trades per day', strat_results['trades_per_day']),
|
||||||
|
('Avg. daily profit %',
|
||||||
|
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
|
||||||
|
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Total trade volume', round_coin_value(strat_results['total_volume'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
*short_metrics,
|
||||||
|
('', ''), # Empty line to improve readability
|
||||||
|
('Best Pair', f"{strat_results['best_pair']['key']} "
|
||||||
|
f"{strat_results['best_pair']['profit_sum']:.2%}"),
|
||||||
|
('Worst Pair', f"{strat_results['worst_pair']['key']} "
|
||||||
|
f"{strat_results['worst_pair']['profit_sum']:.2%}"),
|
||||||
|
('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
|
||||||
|
('Worst trade', f"{worst_trade['pair']} "
|
||||||
|
f"{worst_trade['profit_ratio']:.2%}"),
|
||||||
|
|
||||||
|
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Days win/draw/lose', f"{strat_results['winning_days']} / "
|
||||||
|
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
|
||||||
|
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
|
||||||
|
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
|
||||||
|
('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')),
|
||||||
|
('Entry/Exit Timeouts',
|
||||||
|
f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
|
||||||
|
f"{strat_results.get('timedout_exit_orders', 'N/A')}"),
|
||||||
|
*entry_adjustment_metrics,
|
||||||
|
('', ''), # Empty line to improve readability
|
||||||
|
|
||||||
|
('Min balance', round_coin_value(strat_results['csum_min'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
('Max balance', round_coin_value(strat_results['csum_max'],
|
||||||
|
strat_results['stake_currency'])),
|
||||||
|
|
||||||
|
*drawdown_metrics,
|
||||||
|
('Market change', f"{strat_results['market_change']:.2%}"),
|
||||||
|
]
|
||||||
|
|
||||||
|
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
|
||||||
|
else:
|
||||||
|
start_balance = round_coin_value(strat_results['starting_balance'],
|
||||||
|
strat_results['stake_currency'])
|
||||||
|
stake_amount = round_coin_value(
|
||||||
|
strat_results['stake_amount'], strat_results['stake_currency']
|
||||||
|
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
|
||||||
|
|
||||||
|
message = ("No trades made. "
|
||||||
|
f"Your starting balance was {start_balance}, "
|
||||||
|
f"and your stake was {stake_amount}."
|
||||||
|
)
|
||||||
|
return message
|
||||||
|
|
||||||
|
|
||||||
|
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
|
||||||
|
backtest_breakdown=[]):
|
||||||
|
"""
|
||||||
|
Print results for one strategy
|
||||||
|
"""
|
||||||
|
# Print results
|
||||||
|
print(f"Result for strategy {strategy}")
|
||||||
|
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
|
||||||
|
if isinstance(table, str):
|
||||||
|
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
|
||||||
|
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
|
||||||
|
if isinstance(table, str) and len(table) > 0:
|
||||||
|
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
|
||||||
|
if (results.get('results_per_enter_tag') is not None
|
||||||
|
or results.get('results_per_buy_tag') is not None):
|
||||||
|
# results_per_buy_tag is deprecated and should be removed 2 versions after short golive.
|
||||||
|
table = text_table_tags(
|
||||||
|
"enter_tag",
|
||||||
|
results.get('results_per_enter_tag', results.get('results_per_buy_tag')),
|
||||||
|
stake_currency=stake_currency)
|
||||||
|
|
||||||
|
if isinstance(table, str) and len(table) > 0:
|
||||||
|
print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
|
||||||
|
exit_reasons = results.get('exit_reason_summary', results.get('sell_reason_summary'))
|
||||||
|
table = text_table_exit_reason(exit_reason_stats=exit_reasons,
|
||||||
|
stake_currency=stake_currency)
|
||||||
|
if isinstance(table, str) and len(table) > 0:
|
||||||
|
print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
|
||||||
|
for period in backtest_breakdown:
|
||||||
|
if period in results.get('periodic_breakdown', {}):
|
||||||
|
days_breakdown_stats = results['periodic_breakdown'][period]
|
||||||
|
else:
|
||||||
|
days_breakdown_stats = generate_periodic_breakdown_stats(
|
||||||
|
trade_list=results['trades'], period=period)
|
||||||
|
table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats,
|
||||||
|
stake_currency=stake_currency, period=period)
|
||||||
|
if isinstance(table, str) and len(table) > 0:
|
||||||
|
print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
|
||||||
|
table = text_table_add_metrics(results)
|
||||||
|
if isinstance(table, str) and len(table) > 0:
|
||||||
|
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
|
||||||
|
if isinstance(table, str) and len(table) > 0:
|
||||||
|
print('=' * len(table.splitlines()[0]))
|
||||||
|
|
||||||
|
print()
|
||||||
|
|
||||||
|
|
||||||
|
def show_backtest_results(config: Config, backtest_stats: Dict):
|
||||||
|
stake_currency = config['stake_currency']
|
||||||
|
|
||||||
|
for strategy, results in backtest_stats['strategy'].items():
|
||||||
|
show_backtest_result(
|
||||||
|
strategy, results, stake_currency,
|
||||||
|
config.get('backtest_breakdown', []))
|
||||||
|
|
||||||
|
if len(backtest_stats['strategy']) > 0:
|
||||||
|
# Print Strategy summary table
|
||||||
|
|
||||||
|
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
|
||||||
|
print(f"Backtested {results['backtest_start']} -> {results['backtest_end']} |"
|
||||||
|
f" Max open trades : {results['max_open_trades']}")
|
||||||
|
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
|
||||||
|
print(table)
|
||||||
|
print('=' * len(table.splitlines()[0]))
|
||||||
|
print('\nFor more details, please look at the detail tables above')
|
||||||
|
|
||||||
|
|
||||||
|
def show_sorted_pairlist(config: Config, backtest_stats: Dict):
|
||||||
|
if config.get('backtest_show_pair_list', False):
|
||||||
|
for strategy, results in backtest_stats['strategy'].items():
|
||||||
|
print(f"Pairs for Strategy {strategy}: \n[")
|
||||||
|
for result in results['results_per_pair']:
|
||||||
|
if result["key"] != 'TOTAL':
|
||||||
|
print(f'"{result["key"]}", // {result["profit_mean"]:.2%}')
|
||||||
|
print("]")
|
||||||
|
|
||||||
|
|
||||||
|
def generate_edge_table(results: dict) -> str:
|
||||||
|
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
|
||||||
|
tabular_data = []
|
||||||
|
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
|
||||||
|
'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
|
||||||
|
'Average Duration (min)']
|
||||||
|
|
||||||
|
for result in results.items():
|
||||||
|
if result[1].nb_trades > 0:
|
||||||
|
tabular_data.append([
|
||||||
|
result[0],
|
||||||
|
result[1].stoploss,
|
||||||
|
result[1].winrate,
|
||||||
|
result[1].risk_reward_ratio,
|
||||||
|
result[1].required_risk_reward,
|
||||||
|
result[1].expectancy,
|
||||||
|
result[1].nb_trades,
|
||||||
|
round(result[1].avg_trade_duration)
|
||||||
|
])
|
||||||
|
|
||||||
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
||||||
|
return tabulate(tabular_data, headers=headers,
|
||||||
|
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
||||||
@@ -0,0 +1,71 @@
|
|||||||
|
import logging
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Dict
|
||||||
|
|
||||||
|
from pandas import DataFrame
|
||||||
|
|
||||||
|
from freqtrade.constants import LAST_BT_RESULT_FN
|
||||||
|
from freqtrade.misc import file_dump_joblib, file_dump_json
|
||||||
|
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
def store_backtest_stats(
|
||||||
|
recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None:
|
||||||
|
"""
|
||||||
|
Stores backtest results
|
||||||
|
:param recordfilename: Path object, which can either be a filename or a directory.
|
||||||
|
Filenames will be appended with a timestamp right before the suffix
|
||||||
|
while for directories, <directory>/backtest-result-<datetime>.json will be used as filename
|
||||||
|
:param stats: Dataframe containing the backtesting statistics
|
||||||
|
:param dtappendix: Datetime to use for the filename
|
||||||
|
"""
|
||||||
|
if recordfilename.is_dir():
|
||||||
|
filename = (recordfilename / f'backtest-result-{dtappendix}.json')
|
||||||
|
else:
|
||||||
|
filename = Path.joinpath(
|
||||||
|
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
|
||||||
|
).with_suffix(recordfilename.suffix)
|
||||||
|
|
||||||
|
# Store metadata separately.
|
||||||
|
file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
|
||||||
|
del stats['metadata']
|
||||||
|
|
||||||
|
file_dump_json(filename, stats)
|
||||||
|
|
||||||
|
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
|
||||||
|
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
|
||||||
|
|
||||||
|
|
||||||
|
def _store_backtest_analysis_data(
|
||||||
|
recordfilename: Path, data: Dict[str, Dict],
|
||||||
|
dtappendix: str, name: str) -> Path:
|
||||||
|
"""
|
||||||
|
Stores backtest trade candles for analysis
|
||||||
|
:param recordfilename: Path object, which can either be a filename or a directory.
|
||||||
|
Filenames will be appended with a timestamp right before the suffix
|
||||||
|
while for directories, <directory>/backtest-result-<datetime>_<name>.pkl will be used
|
||||||
|
as filename
|
||||||
|
:param candles: Dict containing the backtesting data for analysis
|
||||||
|
:param dtappendix: Datetime to use for the filename
|
||||||
|
:param name: Name to use for the file, e.g. signals, rejected
|
||||||
|
"""
|
||||||
|
if recordfilename.is_dir():
|
||||||
|
filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
|
||||||
|
else:
|
||||||
|
filename = Path.joinpath(
|
||||||
|
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
|
||||||
|
)
|
||||||
|
|
||||||
|
file_dump_joblib(filename, data)
|
||||||
|
|
||||||
|
return filename
|
||||||
|
|
||||||
|
|
||||||
|
def store_backtest_analysis_results(
|
||||||
|
recordfilename: Path, candles: Dict[str, Dict], trades: Dict[str, Dict],
|
||||||
|
dtappendix: str) -> None:
|
||||||
|
_store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals")
|
||||||
|
_store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
|
||||||
+2
-462
@@ -1,83 +1,20 @@
|
|||||||
import logging
|
import logging
|
||||||
from copy import deepcopy
|
from copy import deepcopy
|
||||||
from datetime import datetime, timedelta, timezone
|
from datetime import datetime, timedelta, timezone
|
||||||
from pathlib import Path
|
|
||||||
from typing import Any, Dict, List, Union
|
from typing import Any, Dict, List, Union
|
||||||
|
|
||||||
from pandas import DataFrame, concat, to_datetime
|
from pandas import DataFrame, concat, to_datetime
|
||||||
from tabulate import tabulate
|
|
||||||
|
|
||||||
from freqtrade.constants import (BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN,
|
from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, IntOrInf
|
||||||
UNLIMITED_STAKE_AMOUNT, Config, IntOrInf)
|
|
||||||
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
|
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
|
||||||
calculate_expectancy, calculate_market_change,
|
calculate_expectancy, calculate_market_change,
|
||||||
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
|
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
|
||||||
from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
|
from freqtrade.misc import decimals_per_coin, round_coin_value
|
||||||
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
|
|
||||||
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
def store_backtest_stats(
|
|
||||||
recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None:
|
|
||||||
"""
|
|
||||||
Stores backtest results
|
|
||||||
:param recordfilename: Path object, which can either be a filename or a directory.
|
|
||||||
Filenames will be appended with a timestamp right before the suffix
|
|
||||||
while for directories, <directory>/backtest-result-<datetime>.json will be used as filename
|
|
||||||
:param stats: Dataframe containing the backtesting statistics
|
|
||||||
:param dtappendix: Datetime to use for the filename
|
|
||||||
"""
|
|
||||||
if recordfilename.is_dir():
|
|
||||||
filename = (recordfilename / f'backtest-result-{dtappendix}.json')
|
|
||||||
else:
|
|
||||||
filename = Path.joinpath(
|
|
||||||
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
|
|
||||||
).with_suffix(recordfilename.suffix)
|
|
||||||
|
|
||||||
# Store metadata separately.
|
|
||||||
file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
|
|
||||||
del stats['metadata']
|
|
||||||
|
|
||||||
file_dump_json(filename, stats)
|
|
||||||
|
|
||||||
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
|
|
||||||
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
|
|
||||||
|
|
||||||
|
|
||||||
def _store_backtest_analysis_data(
|
|
||||||
recordfilename: Path, data: Dict[str, Dict],
|
|
||||||
dtappendix: str, name: str) -> Path:
|
|
||||||
"""
|
|
||||||
Stores backtest trade candles for analysis
|
|
||||||
:param recordfilename: Path object, which can either be a filename or a directory.
|
|
||||||
Filenames will be appended with a timestamp right before the suffix
|
|
||||||
while for directories, <directory>/backtest-result-<datetime>_<name>.pkl will be used
|
|
||||||
as filename
|
|
||||||
:param candles: Dict containing the backtesting data for analysis
|
|
||||||
:param dtappendix: Datetime to use for the filename
|
|
||||||
:param name: Name to use for the file, e.g. signals, rejected
|
|
||||||
"""
|
|
||||||
if recordfilename.is_dir():
|
|
||||||
filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
|
|
||||||
else:
|
|
||||||
filename = Path.joinpath(
|
|
||||||
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
|
|
||||||
)
|
|
||||||
|
|
||||||
file_dump_joblib(filename, data)
|
|
||||||
|
|
||||||
return filename
|
|
||||||
|
|
||||||
|
|
||||||
def store_backtest_analysis_results(
|
|
||||||
recordfilename: Path, candles: Dict[str, Dict], trades: Dict[str, Dict],
|
|
||||||
dtappendix: str) -> None:
|
|
||||||
_store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals")
|
|
||||||
_store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
|
|
||||||
|
|
||||||
|
|
||||||
def generate_trade_signal_candles(preprocessed_df: Dict[str, DataFrame],
|
def generate_trade_signal_candles(preprocessed_df: Dict[str, DataFrame],
|
||||||
bt_results: Dict[str, Any]) -> DataFrame:
|
bt_results: Dict[str, Any]) -> DataFrame:
|
||||||
signal_candles_only = {}
|
signal_candles_only = {}
|
||||||
@@ -120,24 +57,6 @@ def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame],
|
|||||||
return rejected_candles_only
|
return rejected_candles_only
|
||||||
|
|
||||||
|
|
||||||
def _get_line_floatfmt(stake_currency: str) -> List[str]:
|
|
||||||
"""
|
|
||||||
Generate floatformat (goes in line with _generate_result_line())
|
|
||||||
"""
|
|
||||||
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
|
|
||||||
'.2f', 'd', 's', 's']
|
|
||||||
|
|
||||||
|
|
||||||
def _get_line_header(first_column: str, stake_currency: str,
|
|
||||||
direction: str = 'Entries') -> List[str]:
|
|
||||||
"""
|
|
||||||
Generate header lines (goes in line with _generate_result_line())
|
|
||||||
"""
|
|
||||||
return [first_column, direction, 'Avg Profit %', 'Cum Profit %',
|
|
||||||
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
|
|
||||||
'Win Draw Loss Win%']
|
|
||||||
|
|
||||||
|
|
||||||
def generate_wins_draws_losses(wins, draws, losses):
|
def generate_wins_draws_losses(wins, draws, losses):
|
||||||
if wins > 0 and losses == 0:
|
if wins > 0 and losses == 0:
|
||||||
wl_ratio = '100'
|
wl_ratio = '100'
|
||||||
@@ -295,31 +214,6 @@ def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
|
|||||||
return tabular_data
|
return tabular_data
|
||||||
|
|
||||||
|
|
||||||
def generate_edge_table(results: dict) -> str:
|
|
||||||
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
|
|
||||||
tabular_data = []
|
|
||||||
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
|
|
||||||
'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
|
|
||||||
'Average Duration (min)']
|
|
||||||
|
|
||||||
for result in results.items():
|
|
||||||
if result[1].nb_trades > 0:
|
|
||||||
tabular_data.append([
|
|
||||||
result[0],
|
|
||||||
result[1].stoploss,
|
|
||||||
result[1].winrate,
|
|
||||||
result[1].risk_reward_ratio,
|
|
||||||
result[1].required_risk_reward,
|
|
||||||
result[1].expectancy,
|
|
||||||
result[1].nb_trades,
|
|
||||||
round(result[1].avg_trade_duration)
|
|
||||||
])
|
|
||||||
|
|
||||||
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
|
||||||
return tabulate(tabular_data, headers=headers,
|
|
||||||
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
|
||||||
|
|
||||||
|
|
||||||
def _get_resample_from_period(period: str) -> str:
|
def _get_resample_from_period(period: str) -> str:
|
||||||
if period == 'day':
|
if period == 'day':
|
||||||
return '1d'
|
return '1d'
|
||||||
@@ -652,357 +546,3 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
|||||||
result['strategy_comparison'] = strategy_results
|
result['strategy_comparison'] = strategy_results
|
||||||
|
|
||||||
return result
|
return result
|
||||||
|
|
||||||
|
|
||||||
###
|
|
||||||
# Start output section
|
|
||||||
###
|
|
||||||
|
|
||||||
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
|
|
||||||
"""
|
|
||||||
Generates and returns a text table for the given backtest data and the results dataframe
|
|
||||||
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
|
|
||||||
:param stake_currency: stake-currency - used to correctly name headers
|
|
||||||
:return: pretty printed table with tabulate as string
|
|
||||||
"""
|
|
||||||
|
|
||||||
headers = _get_line_header('Pair', stake_currency)
|
|
||||||
floatfmt = _get_line_floatfmt(stake_currency)
|
|
||||||
output = [[
|
|
||||||
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
|
|
||||||
t['profit_total_pct'], t['duration_avg'],
|
|
||||||
generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
|
|
||||||
] for t in pair_results]
|
|
||||||
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
|
||||||
return tabulate(output, headers=headers,
|
|
||||||
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
|
||||||
|
|
||||||
|
|
||||||
def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
|
|
||||||
"""
|
|
||||||
Generate small table outlining Backtest results
|
|
||||||
:param sell_reason_stats: Exit reason metrics
|
|
||||||
:param stake_currency: Stakecurrency used
|
|
||||||
:return: pretty printed table with tabulate as string
|
|
||||||
"""
|
|
||||||
headers = [
|
|
||||||
'Exit Reason',
|
|
||||||
'Exits',
|
|
||||||
'Win Draws Loss Win%',
|
|
||||||
'Avg Profit %',
|
|
||||||
'Cum Profit %',
|
|
||||||
f'Tot Profit {stake_currency}',
|
|
||||||
'Tot Profit %',
|
|
||||||
]
|
|
||||||
|
|
||||||
output = [[
|
|
||||||
t.get('exit_reason', t.get('sell_reason')), t['trades'],
|
|
||||||
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
|
|
||||||
t['profit_mean_pct'], t['profit_sum_pct'],
|
|
||||||
round_coin_value(t['profit_total_abs'], stake_currency, False),
|
|
||||||
t['profit_total_pct'],
|
|
||||||
] for t in exit_reason_stats]
|
|
||||||
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
|
|
||||||
|
|
||||||
|
|
||||||
def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
|
|
||||||
"""
|
|
||||||
Generates and returns a text table for the given backtest data and the results dataframe
|
|
||||||
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
|
|
||||||
:param stake_currency: stake-currency - used to correctly name headers
|
|
||||||
:return: pretty printed table with tabulate as string
|
|
||||||
"""
|
|
||||||
if (tag_type == "enter_tag"):
|
|
||||||
headers = _get_line_header("TAG", stake_currency)
|
|
||||||
else:
|
|
||||||
headers = _get_line_header("TAG", stake_currency, 'Exits')
|
|
||||||
floatfmt = _get_line_floatfmt(stake_currency)
|
|
||||||
output = [
|
|
||||||
[
|
|
||||||
t['key'] if t['key'] is not None and len(
|
|
||||||
t['key']) > 0 else "OTHER",
|
|
||||||
t['trades'],
|
|
||||||
t['profit_mean_pct'],
|
|
||||||
t['profit_sum_pct'],
|
|
||||||
t['profit_total_abs'],
|
|
||||||
t['profit_total_pct'],
|
|
||||||
t['duration_avg'],
|
|
||||||
generate_wins_draws_losses(
|
|
||||||
t['wins'],
|
|
||||||
t['draws'],
|
|
||||||
t['losses'])] for t in tag_results]
|
|
||||||
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
|
||||||
return tabulate(output, headers=headers,
|
|
||||||
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
|
||||||
|
|
||||||
|
|
||||||
def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
|
|
||||||
stake_currency: str, period: str) -> str:
|
|
||||||
"""
|
|
||||||
Generate small table with Backtest results by days
|
|
||||||
:param days_breakdown_stats: Days breakdown metrics
|
|
||||||
:param stake_currency: Stakecurrency used
|
|
||||||
:return: pretty printed table with tabulate as string
|
|
||||||
"""
|
|
||||||
headers = [
|
|
||||||
period.capitalize(),
|
|
||||||
f'Tot Profit {stake_currency}',
|
|
||||||
'Wins',
|
|
||||||
'Draws',
|
|
||||||
'Losses',
|
|
||||||
]
|
|
||||||
output = [[
|
|
||||||
d['date'], round_coin_value(d['profit_abs'], stake_currency, False),
|
|
||||||
d['wins'], d['draws'], d['loses'],
|
|
||||||
] for d in days_breakdown_stats]
|
|
||||||
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
|
|
||||||
|
|
||||||
|
|
||||||
def text_table_strategy(strategy_results, stake_currency: str) -> str:
|
|
||||||
"""
|
|
||||||
Generate summary table per strategy
|
|
||||||
:param strategy_results: Dict of <Strategyname: DataFrame> containing results for all strategies
|
|
||||||
:param stake_currency: stake-currency - used to correctly name headers
|
|
||||||
:return: pretty printed table with tabulate as string
|
|
||||||
"""
|
|
||||||
floatfmt = _get_line_floatfmt(stake_currency)
|
|
||||||
headers = _get_line_header('Strategy', stake_currency)
|
|
||||||
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
|
|
||||||
# therefore we slip this column in only for strategy summary here.
|
|
||||||
headers.append('Drawdown')
|
|
||||||
|
|
||||||
# Align drawdown string on the center two space separator.
|
|
||||||
if 'max_drawdown_account' in strategy_results[0]:
|
|
||||||
drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
|
|
||||||
else:
|
|
||||||
# Support for prior backtest results
|
|
||||||
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
|
|
||||||
|
|
||||||
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
|
|
||||||
dd_pad_per = max([len(dd) for dd in drawdown])
|
|
||||||
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
|
|
||||||
for t, dd in zip(strategy_results, drawdown)]
|
|
||||||
|
|
||||||
output = [[
|
|
||||||
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
|
|
||||||
t['profit_total_pct'], t['duration_avg'],
|
|
||||||
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
|
|
||||||
for t, drawdown in zip(strategy_results, drawdown)]
|
|
||||||
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
|
||||||
return tabulate(output, headers=headers,
|
|
||||||
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
|
|
||||||
|
|
||||||
|
|
||||||
def text_table_add_metrics(strat_results: Dict) -> str:
|
|
||||||
if len(strat_results['trades']) > 0:
|
|
||||||
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
|
|
||||||
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
|
|
||||||
|
|
||||||
short_metrics = [
|
|
||||||
('', ''), # Empty line to improve readability
|
|
||||||
('Long / Short',
|
|
||||||
f"{strat_results.get('trade_count_long', 'total_trades')} / "
|
|
||||||
f"{strat_results.get('trade_count_short', 0)}"),
|
|
||||||
('Total profit Long %', f"{strat_results['profit_total_long']:.2%}"),
|
|
||||||
('Total profit Short %', f"{strat_results['profit_total_short']:.2%}"),
|
|
||||||
('Absolute profit Long', round_coin_value(strat_results['profit_total_long_abs'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Absolute profit Short', round_coin_value(strat_results['profit_total_short_abs'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
] if strat_results.get('trade_count_short', 0) > 0 else []
|
|
||||||
|
|
||||||
drawdown_metrics = []
|
|
||||||
if 'max_relative_drawdown' in strat_results:
|
|
||||||
# Compatibility to show old hyperopt results
|
|
||||||
drawdown_metrics.append(
|
|
||||||
('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}")
|
|
||||||
)
|
|
||||||
drawdown_metrics.extend([
|
|
||||||
('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
|
|
||||||
if 'max_drawdown_account' in strat_results else (
|
|
||||||
'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
|
|
||||||
('Absolute Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Drawdown Start', strat_results['drawdown_start']),
|
|
||||||
('Drawdown End', strat_results['drawdown_end']),
|
|
||||||
])
|
|
||||||
|
|
||||||
entry_adjustment_metrics = [
|
|
||||||
('Canceled Trade Entries', strat_results.get('canceled_trade_entries', 'N/A')),
|
|
||||||
('Canceled Entry Orders', strat_results.get('canceled_entry_orders', 'N/A')),
|
|
||||||
('Replaced Entry Orders', strat_results.get('replaced_entry_orders', 'N/A')),
|
|
||||||
] if strat_results.get('canceled_entry_orders', 0) > 0 else []
|
|
||||||
|
|
||||||
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
|
|
||||||
# command stores these results and newer version of freqtrade must be able to handle old
|
|
||||||
# results with missing new fields.
|
|
||||||
metrics = [
|
|
||||||
('Backtesting from', strat_results['backtest_start']),
|
|
||||||
('Backtesting to', strat_results['backtest_end']),
|
|
||||||
('Max open trades', strat_results['max_open_trades']),
|
|
||||||
('', ''), # Empty line to improve readability
|
|
||||||
('Total/Daily Avg Trades',
|
|
||||||
f"{strat_results['total_trades']} / {strat_results['trades_per_day']}"),
|
|
||||||
|
|
||||||
('Starting balance', round_coin_value(strat_results['starting_balance'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Final balance', round_coin_value(strat_results['final_balance'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Total profit %', f"{strat_results['profit_total']:.2%}"),
|
|
||||||
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
|
|
||||||
('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
|
|
||||||
('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
|
|
||||||
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
|
|
||||||
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
|
|
||||||
in strat_results else 'N/A'),
|
|
||||||
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
|
|
||||||
in strat_results else 'N/A'),
|
|
||||||
('Trades per day', strat_results['trades_per_day']),
|
|
||||||
('Avg. daily profit %',
|
|
||||||
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
|
|
||||||
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Total trade volume', round_coin_value(strat_results['total_volume'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
*short_metrics,
|
|
||||||
('', ''), # Empty line to improve readability
|
|
||||||
('Best Pair', f"{strat_results['best_pair']['key']} "
|
|
||||||
f"{strat_results['best_pair']['profit_sum']:.2%}"),
|
|
||||||
('Worst Pair', f"{strat_results['worst_pair']['key']} "
|
|
||||||
f"{strat_results['worst_pair']['profit_sum']:.2%}"),
|
|
||||||
('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
|
|
||||||
('Worst trade', f"{worst_trade['pair']} "
|
|
||||||
f"{worst_trade['profit_ratio']:.2%}"),
|
|
||||||
|
|
||||||
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Days win/draw/lose', f"{strat_results['winning_days']} / "
|
|
||||||
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
|
|
||||||
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
|
|
||||||
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
|
|
||||||
('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')),
|
|
||||||
('Entry/Exit Timeouts',
|
|
||||||
f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
|
|
||||||
f"{strat_results.get('timedout_exit_orders', 'N/A')}"),
|
|
||||||
*entry_adjustment_metrics,
|
|
||||||
('', ''), # Empty line to improve readability
|
|
||||||
|
|
||||||
('Min balance', round_coin_value(strat_results['csum_min'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
('Max balance', round_coin_value(strat_results['csum_max'],
|
|
||||||
strat_results['stake_currency'])),
|
|
||||||
|
|
||||||
*drawdown_metrics,
|
|
||||||
('Market change', f"{strat_results['market_change']:.2%}"),
|
|
||||||
]
|
|
||||||
|
|
||||||
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
|
|
||||||
else:
|
|
||||||
start_balance = round_coin_value(strat_results['starting_balance'],
|
|
||||||
strat_results['stake_currency'])
|
|
||||||
stake_amount = round_coin_value(
|
|
||||||
strat_results['stake_amount'], strat_results['stake_currency']
|
|
||||||
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
|
|
||||||
|
|
||||||
message = ("No trades made. "
|
|
||||||
f"Your starting balance was {start_balance}, "
|
|
||||||
f"and your stake was {stake_amount}."
|
|
||||||
)
|
|
||||||
return message
|
|
||||||
|
|
||||||
|
|
||||||
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
|
|
||||||
backtest_breakdown=[]):
|
|
||||||
"""
|
|
||||||
Print results for one strategy
|
|
||||||
"""
|
|
||||||
# Print results
|
|
||||||
print(f"Result for strategy {strategy}")
|
|
||||||
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
|
|
||||||
if isinstance(table, str):
|
|
||||||
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
|
|
||||||
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
|
|
||||||
if isinstance(table, str) and len(table) > 0:
|
|
||||||
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
|
|
||||||
if (results.get('results_per_enter_tag') is not None
|
|
||||||
or results.get('results_per_buy_tag') is not None):
|
|
||||||
# results_per_buy_tag is deprecated and should be removed 2 versions after short golive.
|
|
||||||
table = text_table_tags(
|
|
||||||
"enter_tag",
|
|
||||||
results.get('results_per_enter_tag', results.get('results_per_buy_tag')),
|
|
||||||
stake_currency=stake_currency)
|
|
||||||
|
|
||||||
if isinstance(table, str) and len(table) > 0:
|
|
||||||
print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
|
|
||||||
exit_reasons = results.get('exit_reason_summary', results.get('sell_reason_summary'))
|
|
||||||
table = text_table_exit_reason(exit_reason_stats=exit_reasons,
|
|
||||||
stake_currency=stake_currency)
|
|
||||||
if isinstance(table, str) and len(table) > 0:
|
|
||||||
print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
|
|
||||||
for period in backtest_breakdown:
|
|
||||||
if period in results.get('periodic_breakdown', {}):
|
|
||||||
days_breakdown_stats = results['periodic_breakdown'][period]
|
|
||||||
else:
|
|
||||||
days_breakdown_stats = generate_periodic_breakdown_stats(
|
|
||||||
trade_list=results['trades'], period=period)
|
|
||||||
table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats,
|
|
||||||
stake_currency=stake_currency, period=period)
|
|
||||||
if isinstance(table, str) and len(table) > 0:
|
|
||||||
print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
|
|
||||||
table = text_table_add_metrics(results)
|
|
||||||
if isinstance(table, str) and len(table) > 0:
|
|
||||||
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
|
|
||||||
if isinstance(table, str) and len(table) > 0:
|
|
||||||
print('=' * len(table.splitlines()[0]))
|
|
||||||
|
|
||||||
print()
|
|
||||||
|
|
||||||
|
|
||||||
def show_backtest_results(config: Config, backtest_stats: Dict):
|
|
||||||
stake_currency = config['stake_currency']
|
|
||||||
|
|
||||||
for strategy, results in backtest_stats['strategy'].items():
|
|
||||||
show_backtest_result(
|
|
||||||
strategy, results, stake_currency,
|
|
||||||
config.get('backtest_breakdown', []))
|
|
||||||
|
|
||||||
if len(backtest_stats['strategy']) > 0:
|
|
||||||
# Print Strategy summary table
|
|
||||||
|
|
||||||
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
|
|
||||||
print(f"Backtested {results['backtest_start']} -> {results['backtest_end']} |"
|
|
||||||
f" Max open trades : {results['max_open_trades']}")
|
|
||||||
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
|
|
||||||
print(table)
|
|
||||||
print('=' * len(table.splitlines()[0]))
|
|
||||||
print('\nFor more details, please look at the detail tables above')
|
|
||||||
|
|
||||||
|
|
||||||
def show_sorted_pairlist(config: Config, backtest_stats: Dict):
|
|
||||||
if config.get('backtest_show_pair_list', False):
|
|
||||||
for strategy, results in backtest_stats['strategy'].items():
|
|
||||||
print(f"Pairs for Strategy {strategy}: \n[")
|
|
||||||
for result in results['results_per_pair']:
|
|
||||||
if result["key"] != 'TOTAL':
|
|
||||||
print(f'"{result["key"]}", // {result["profit_mean"]:.2%}')
|
|
||||||
print("]")
|
|
||||||
@@ -42,7 +42,7 @@ class _KeyValueStoreModel(ModelBase):
|
|||||||
int_value: Mapped[Optional[int]]
|
int_value: Mapped[Optional[int]]
|
||||||
|
|
||||||
|
|
||||||
class KeyValueStore():
|
class KeyValueStore:
|
||||||
"""
|
"""
|
||||||
Generic bot-wide, persistent key-value store
|
Generic bot-wide, persistent key-value store
|
||||||
Can be used to store generic values, e.g. very first bot startup time.
|
Can be used to store generic values, e.g. very first bot startup time.
|
||||||
|
|||||||
@@ -11,7 +11,7 @@ from freqtrade.persistence.models import PairLock
|
|||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
class PairLocks():
|
class PairLocks:
|
||||||
"""
|
"""
|
||||||
Pairlocks middleware class
|
Pairlocks middleware class
|
||||||
Abstracts the database layer away so it becomes optional - which will be necessary to support
|
Abstracts the database layer away so it becomes optional - which will be necessary to support
|
||||||
|
|||||||
@@ -283,7 +283,7 @@ class Order(ModelBase):
|
|||||||
return Order.session.scalars(select(Order).filter(Order.order_id == order_id)).first()
|
return Order.session.scalars(select(Order).filter(Order.order_id == order_id)).first()
|
||||||
|
|
||||||
|
|
||||||
class LocalTrade():
|
class LocalTrade:
|
||||||
"""
|
"""
|
||||||
Trade database model.
|
Trade database model.
|
||||||
Used in backtesting - must be aligned to Trade model!
|
Used in backtesting - must be aligned to Trade model!
|
||||||
|
|||||||
@@ -13,9 +13,8 @@ from freqtrade.constants import Config, ListPairsWithTimeframes
|
|||||||
from freqtrade.exceptions import OperationalException
|
from freqtrade.exceptions import OperationalException
|
||||||
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
|
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
|
||||||
from freqtrade.exchange.types import Tickers
|
from freqtrade.exchange.types import Tickers
|
||||||
from freqtrade.misc import format_ms_time
|
|
||||||
from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter
|
from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter
|
||||||
from freqtrade.util import dt_now
|
from freqtrade.util import dt_now, format_ms_time
|
||||||
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|||||||
@@ -15,7 +15,7 @@ from freqtrade.resolvers import ProtectionResolver
|
|||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
class ProtectionManager():
|
class ProtectionManager:
|
||||||
|
|
||||||
def __init__(self, config: Config, protections: List) -> None:
|
def __init__(self, config: Config, protections: List) -> None:
|
||||||
self._config = config
|
self._config = config
|
||||||
|
|||||||
@@ -7,12 +7,14 @@ from fastapi.websockets import WebSocket
|
|||||||
from pydantic import ValidationError
|
from pydantic import ValidationError
|
||||||
|
|
||||||
from freqtrade.enums import RPCMessageType, RPCRequestType
|
from freqtrade.enums import RPCMessageType, RPCRequestType
|
||||||
|
from freqtrade.exceptions import FreqtradeException
|
||||||
from freqtrade.rpc.api_server.api_auth import validate_ws_token
|
from freqtrade.rpc.api_server.api_auth import validate_ws_token
|
||||||
from freqtrade.rpc.api_server.deps import get_message_stream, get_rpc
|
from freqtrade.rpc.api_server.deps import get_message_stream, get_rpc
|
||||||
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
|
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
|
||||||
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
|
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
|
||||||
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
|
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSErrorMessage,
|
||||||
WSRequestSchema, WSWhitelistMessage)
|
WSMessageSchema, WSRequestSchema,
|
||||||
|
WSWhitelistMessage)
|
||||||
from freqtrade.rpc.rpc import RPC
|
from freqtrade.rpc.rpc import RPC
|
||||||
|
|
||||||
|
|
||||||
@@ -27,7 +29,13 @@ async def channel_reader(channel: WebSocketChannel, rpc: RPC):
|
|||||||
Iterate over the messages from the channel and process the request
|
Iterate over the messages from the channel and process the request
|
||||||
"""
|
"""
|
||||||
async for message in channel:
|
async for message in channel:
|
||||||
await _process_consumer_request(message, channel, rpc)
|
try:
|
||||||
|
await _process_consumer_request(message, channel, rpc)
|
||||||
|
except FreqtradeException:
|
||||||
|
logger.exception(f"Error processing request from {channel}")
|
||||||
|
response = WSErrorMessage(data='Error processing request')
|
||||||
|
|
||||||
|
await channel.send(response.dict(exclude_none=True))
|
||||||
|
|
||||||
|
|
||||||
async def channel_broadcaster(channel: WebSocketChannel, message_stream: MessageStream):
|
async def channel_broadcaster(channel: WebSocketChannel, message_stream: MessageStream):
|
||||||
@@ -62,13 +70,13 @@ async def _process_consumer_request(
|
|||||||
logger.error(f"Invalid request from {channel}: {e}")
|
logger.error(f"Invalid request from {channel}: {e}")
|
||||||
return
|
return
|
||||||
|
|
||||||
type, data = websocket_request.type, websocket_request.data
|
type_, data = websocket_request.type, websocket_request.data
|
||||||
response: WSMessageSchema
|
response: WSMessageSchema
|
||||||
|
|
||||||
logger.debug(f"Request of type {type} from {channel}")
|
logger.debug(f"Request of type {type_} from {channel}")
|
||||||
|
|
||||||
# If we have a request of type SUBSCRIBE, set the topics in this channel
|
# If we have a request of type SUBSCRIBE, set the topics in this channel
|
||||||
if type == RPCRequestType.SUBSCRIBE:
|
if type_ == RPCRequestType.SUBSCRIBE:
|
||||||
# If the request is empty, do nothing
|
# If the request is empty, do nothing
|
||||||
if not data:
|
if not data:
|
||||||
return
|
return
|
||||||
@@ -80,7 +88,7 @@ async def _process_consumer_request(
|
|||||||
# We don't send a response for subscriptions
|
# We don't send a response for subscriptions
|
||||||
return
|
return
|
||||||
|
|
||||||
elif type == RPCRequestType.WHITELIST:
|
elif type_ == RPCRequestType.WHITELIST:
|
||||||
# Get whitelist
|
# Get whitelist
|
||||||
whitelist = rpc._ws_request_whitelist()
|
whitelist = rpc._ws_request_whitelist()
|
||||||
|
|
||||||
@@ -88,7 +96,7 @@ async def _process_consumer_request(
|
|||||||
response = WSWhitelistMessage(data=whitelist)
|
response = WSWhitelistMessage(data=whitelist)
|
||||||
await channel.send(response.dict(exclude_none=True))
|
await channel.send(response.dict(exclude_none=True))
|
||||||
|
|
||||||
elif type == RPCRequestType.ANALYZED_DF:
|
elif type_ == RPCRequestType.ANALYZED_DF:
|
||||||
# Limit the amount of candles per dataframe to 'limit' or 1500
|
# Limit the amount of candles per dataframe to 'limit' or 1500
|
||||||
limit = int(min(data.get('limit', 1500), 1500)) if data else None
|
limit = int(min(data.get('limit', 1500), 1500)) if data else None
|
||||||
pair = data.get('pair', None) if data else None
|
pair = data.get('pair', None) if data else None
|
||||||
|
|||||||
@@ -14,7 +14,7 @@ class JobsContainer(TypedDict):
|
|||||||
error: Optional[str]
|
error: Optional[str]
|
||||||
|
|
||||||
|
|
||||||
class ApiBG():
|
class ApiBG:
|
||||||
# Backtesting type: Backtesting
|
# Backtesting type: Backtesting
|
||||||
bt: Dict[str, Any] = {
|
bt: Dict[str, Any] = {
|
||||||
'bt': None,
|
'bt': None,
|
||||||
|
|||||||
@@ -66,4 +66,9 @@ class WSAnalyzedDFMessage(WSMessageSchema):
|
|||||||
type: RPCMessageType = RPCMessageType.ANALYZED_DF
|
type: RPCMessageType = RPCMessageType.ANALYZED_DF
|
||||||
data: AnalyzedDFData
|
data: AnalyzedDFData
|
||||||
|
|
||||||
|
|
||||||
|
class WSErrorMessage(WSMessageSchema):
|
||||||
|
type: RPCMessageType = RPCMessageType.EXCEPTION
|
||||||
|
data: str
|
||||||
|
|
||||||
# --------------------------------------------------------------------------
|
# --------------------------------------------------------------------------
|
||||||
|
|||||||
@@ -168,7 +168,7 @@ class IStrategy(ABC, HyperStrategyMixin):
|
|||||||
download_all_data_for_training(self.dp, self.config)
|
download_all_data_for_training(self.dp, self.config)
|
||||||
else:
|
else:
|
||||||
# Gracious failures if freqAI is disabled but "start" is called.
|
# Gracious failures if freqAI is disabled but "start" is called.
|
||||||
class DummyClass():
|
class DummyClass:
|
||||||
def start(self, *args, **kwargs):
|
def start(self, *args, **kwargs):
|
||||||
raise OperationalException(
|
raise OperationalException(
|
||||||
'freqAI is not enabled. '
|
'freqAI is not enabled. '
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize, dt_now, dt_ts,
|
from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize, dt_now, dt_ts,
|
||||||
dt_utc, shorten_date)
|
dt_utc, format_ms_time, shorten_date)
|
||||||
from freqtrade.util.ft_precise import FtPrecise
|
from freqtrade.util.ft_precise import FtPrecise
|
||||||
from freqtrade.util.periodic_cache import PeriodicCache
|
from freqtrade.util.periodic_cache import PeriodicCache
|
||||||
|
|
||||||
@@ -7,11 +7,12 @@ from freqtrade.util.periodic_cache import PeriodicCache
|
|||||||
__all__ = [
|
__all__ = [
|
||||||
'dt_floor_day',
|
'dt_floor_day',
|
||||||
'dt_from_ts',
|
'dt_from_ts',
|
||||||
|
'dt_humanize',
|
||||||
'dt_now',
|
'dt_now',
|
||||||
'dt_ts',
|
'dt_ts',
|
||||||
'dt_utc',
|
'dt_utc',
|
||||||
'dt_humanize',
|
'format_ms_time',
|
||||||
'shorten_date',
|
|
||||||
'FtPrecise',
|
'FtPrecise',
|
||||||
'PeriodicCache',
|
'PeriodicCache',
|
||||||
|
'shorten_date',
|
||||||
]
|
]
|
||||||
|
|||||||
@@ -61,3 +61,11 @@ def dt_humanize(dt: datetime, **kwargs) -> str:
|
|||||||
:param kwargs: kwargs to pass to arrow's humanize()
|
:param kwargs: kwargs to pass to arrow's humanize()
|
||||||
"""
|
"""
|
||||||
return arrow.get(dt).humanize(**kwargs)
|
return arrow.get(dt).humanize(**kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def format_ms_time(date: int) -> str:
|
||||||
|
"""
|
||||||
|
convert MS date to readable format.
|
||||||
|
: epoch-string in ms
|
||||||
|
"""
|
||||||
|
return datetime.fromtimestamp(date / 1000.0).strftime('%Y-%m-%dT%H:%M:%S')
|
||||||
|
|||||||
@@ -7,10 +7,10 @@
|
|||||||
-r docs/requirements-docs.txt
|
-r docs/requirements-docs.txt
|
||||||
|
|
||||||
coveralls==3.3.1
|
coveralls==3.3.1
|
||||||
ruff==0.0.272
|
ruff==0.0.275
|
||||||
mypy==1.3.0
|
mypy==1.4.1
|
||||||
pre-commit==3.3.3
|
pre-commit==3.3.3
|
||||||
pytest==7.3.2
|
pytest==7.4.0
|
||||||
pytest-asyncio==0.21.0
|
pytest-asyncio==0.21.0
|
||||||
pytest-cov==4.1.0
|
pytest-cov==4.1.0
|
||||||
pytest-mock==3.11.1
|
pytest-mock==3.11.1
|
||||||
@@ -20,7 +20,7 @@ isort==5.12.0
|
|||||||
time-machine==2.10.0
|
time-machine==2.10.0
|
||||||
|
|
||||||
# Convert jupyter notebooks to markdown documents
|
# Convert jupyter notebooks to markdown documents
|
||||||
nbconvert==7.5.0
|
nbconvert==7.6.0
|
||||||
|
|
||||||
# mypy types
|
# mypy types
|
||||||
types-cachetools==5.3.0.5
|
types-cachetools==5.3.0.5
|
||||||
|
|||||||
@@ -5,7 +5,7 @@
|
|||||||
torch==2.0.1
|
torch==2.0.1
|
||||||
#until these branches will be released we can use this
|
#until these branches will be released we can use this
|
||||||
gymnasium==0.28.1
|
gymnasium==0.28.1
|
||||||
stable_baselines3==2.0.0a13
|
stable_baselines3==2.0.0
|
||||||
sb3_contrib>=2.0.0a9
|
sb3_contrib>=2.0.0a9
|
||||||
# Progress bar for stable-baselines3 and sb3-contrib
|
# Progress bar for stable-baselines3 and sb3-contrib
|
||||||
tqdm==4.65.0
|
tqdm==4.65.0
|
||||||
|
|||||||
@@ -9,4 +9,4 @@ catboost==1.2; 'arm' not in platform_machine
|
|||||||
lightgbm==3.3.5
|
lightgbm==3.3.5
|
||||||
xgboost==1.7.6
|
xgboost==1.7.6
|
||||||
tensorboard==2.13.0
|
tensorboard==2.13.0
|
||||||
datasieve==0.1.5
|
datasieve==0.1.7
|
||||||
|
|||||||
@@ -2,7 +2,8 @@
|
|||||||
-r requirements.txt
|
-r requirements.txt
|
||||||
|
|
||||||
# Required for hyperopt
|
# Required for hyperopt
|
||||||
scipy==1.10.1
|
scipy==1.11.1; python_version >= '3.9'
|
||||||
|
scipy==1.10.1; python_version < '3.9'
|
||||||
scikit-learn==1.1.3
|
scikit-learn==1.1.3
|
||||||
scikit-optimize==0.9.0
|
scikit-optimize==0.9.0
|
||||||
filelock==3.12.2
|
filelock==3.12.2
|
||||||
|
|||||||
+5
-5
@@ -1,12 +1,12 @@
|
|||||||
numpy==1.24.3
|
numpy==1.24.3
|
||||||
pandas==2.0.2
|
pandas==2.0.3
|
||||||
pandas-ta==0.3.14b
|
pandas-ta==0.3.14b
|
||||||
|
|
||||||
ccxt==3.1.44
|
ccxt==4.0.12
|
||||||
cryptography==41.0.1; platform_machine != 'armv7l'
|
cryptography==41.0.1; platform_machine != 'armv7l'
|
||||||
cryptography==40.0.1; platform_machine == 'armv7l'
|
cryptography==40.0.1; platform_machine == 'armv7l'
|
||||||
aiohttp==3.8.4
|
aiohttp==3.8.4
|
||||||
SQLAlchemy==2.0.16
|
SQLAlchemy==2.0.17
|
||||||
python-telegram-bot==20.3
|
python-telegram-bot==20.3
|
||||||
# can't be hard-pinned due to telegram-bot pinning httpx with ~
|
# can't be hard-pinned due to telegram-bot pinning httpx with ~
|
||||||
httpx>=0.24.1
|
httpx>=0.24.1
|
||||||
@@ -38,7 +38,7 @@ orjson==3.9.1
|
|||||||
sdnotify==0.3.2
|
sdnotify==0.3.2
|
||||||
|
|
||||||
# API Server
|
# API Server
|
||||||
fastapi==0.97.0
|
fastapi==0.99.1
|
||||||
pydantic==1.10.9
|
pydantic==1.10.9
|
||||||
uvicorn==0.22.0
|
uvicorn==0.22.0
|
||||||
pyjwt==2.7.0
|
pyjwt==2.7.0
|
||||||
@@ -60,5 +60,5 @@ schedule==1.2.0
|
|||||||
websockets==11.0.3
|
websockets==11.0.3
|
||||||
janus==1.0.0
|
janus==1.0.0
|
||||||
|
|
||||||
ast-comments==1.0.1
|
ast-comments==1.1.0
|
||||||
packaging==23.1
|
packaging==23.1
|
||||||
|
|||||||
@@ -29,7 +29,7 @@ logging.basicConfig(
|
|||||||
logger = logging.getLogger("ft_rest_client")
|
logger = logging.getLogger("ft_rest_client")
|
||||||
|
|
||||||
|
|
||||||
class FtRestClient():
|
class FtRestClient:
|
||||||
|
|
||||||
def __init__(self, serverurl, username=None, password=None):
|
def __init__(self, serverurl, username=None, password=None):
|
||||||
|
|
||||||
|
|||||||
@@ -43,6 +43,7 @@ EXCHANGES = {
|
|||||||
'hasQuoteVolumeFutures': True,
|
'hasQuoteVolumeFutures': True,
|
||||||
'leverage_tiers_public': False,
|
'leverage_tiers_public': False,
|
||||||
'leverage_in_spot_market': False,
|
'leverage_in_spot_market': False,
|
||||||
|
'trades_lookback_hours': 4,
|
||||||
'private_methods': [
|
'private_methods': [
|
||||||
'fapiPrivateGetPositionSideDual',
|
'fapiPrivateGetPositionSideDual',
|
||||||
'fapiPrivateGetMultiAssetsMargin'
|
'fapiPrivateGetMultiAssetsMargin'
|
||||||
@@ -98,6 +99,7 @@ EXCHANGES = {
|
|||||||
'timeframe': '1h',
|
'timeframe': '1h',
|
||||||
'leverage_tiers_public': False,
|
'leverage_tiers_public': False,
|
||||||
'leverage_in_spot_market': True,
|
'leverage_in_spot_market': True,
|
||||||
|
'trades_lookback_hours': 12,
|
||||||
},
|
},
|
||||||
'kucoin': {
|
'kucoin': {
|
||||||
'pair': 'XRP/USDT',
|
'pair': 'XRP/USDT',
|
||||||
@@ -342,7 +344,7 @@ def exchange_futures(request, exchange_conf, class_mocker):
|
|||||||
|
|
||||||
|
|
||||||
@pytest.mark.longrun
|
@pytest.mark.longrun
|
||||||
class TestCCXTExchange():
|
class TestCCXTExchange:
|
||||||
|
|
||||||
def test_load_markets(self, exchange: EXCHANGE_FIXTURE_TYPE):
|
def test_load_markets(self, exchange: EXCHANGE_FIXTURE_TYPE):
|
||||||
exch, exchangename = exchange
|
exch, exchangename = exchange
|
||||||
@@ -640,7 +642,21 @@ class TestCCXTExchange():
|
|||||||
assert isinstance(funding_fee, float)
|
assert isinstance(funding_fee, float)
|
||||||
# assert funding_fee > 0
|
# assert funding_fee > 0
|
||||||
|
|
||||||
# TODO: tests fetch_trades (?)
|
def test_ccxt__async_get_trade_history(self, exchange: EXCHANGE_FIXTURE_TYPE):
|
||||||
|
exch, exchangename = exchange
|
||||||
|
if not (lookback := EXCHANGES[exchangename].get('trades_lookback_hours')):
|
||||||
|
pytest.skip('test_fetch_trades not enabled for this exchange')
|
||||||
|
pair = EXCHANGES[exchangename]['pair']
|
||||||
|
since = int((datetime.now(timezone.utc) - timedelta(hours=lookback)).timestamp() * 1000)
|
||||||
|
res = exch.loop.run_until_complete(
|
||||||
|
exch._async_get_trade_history(pair, since, None, None)
|
||||||
|
)
|
||||||
|
assert len(res) == 2
|
||||||
|
res_pair, res_trades = res
|
||||||
|
assert res_pair == pair
|
||||||
|
assert isinstance(res_trades, list)
|
||||||
|
assert res_trades[0][0] >= since
|
||||||
|
assert len(res_trades) > 1200
|
||||||
|
|
||||||
def test_ccxt_get_fee(self, exchange: EXCHANGE_FIXTURE_TYPE):
|
def test_ccxt_get_fee(self, exchange: EXCHANGE_FIXTURE_TYPE):
|
||||||
exch, exchangename = exchange
|
exch, exchangename = exchange
|
||||||
|
|||||||
@@ -1437,9 +1437,11 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
|
|||||||
strattable_mock = MagicMock()
|
strattable_mock = MagicMock()
|
||||||
strat_summary = MagicMock()
|
strat_summary = MagicMock()
|
||||||
|
|
||||||
mocker.patch.multiple('freqtrade.optimize.optimize_reports',
|
mocker.patch.multiple('freqtrade.optimize.optimize_reports.bt_output',
|
||||||
text_table_bt_results=text_table_mock,
|
text_table_bt_results=text_table_mock,
|
||||||
text_table_strategy=strattable_mock,
|
text_table_strategy=strattable_mock,
|
||||||
|
)
|
||||||
|
mocker.patch.multiple('freqtrade.optimize.optimize_reports.optimize_reports',
|
||||||
generate_pair_metrics=MagicMock(),
|
generate_pair_metrics=MagicMock(),
|
||||||
generate_exit_reason_stats=sell_reason_mock,
|
generate_exit_reason_stats=sell_reason_mock,
|
||||||
generate_strategy_comparison=strat_summary,
|
generate_strategy_comparison=strat_summary,
|
||||||
|
|||||||
@@ -14,15 +14,16 @@ from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backte
|
|||||||
load_backtest_stats)
|
load_backtest_stats)
|
||||||
from freqtrade.edge import PairInfo
|
from freqtrade.edge import PairInfo
|
||||||
from freqtrade.enums import ExitType
|
from freqtrade.enums import ExitType
|
||||||
from freqtrade.optimize.optimize_reports import (_get_resample_from_period, generate_backtest_stats,
|
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
|
||||||
generate_daily_stats, generate_edge_table,
|
generate_edge_table, generate_exit_reason_stats,
|
||||||
generate_exit_reason_stats, generate_pair_metrics,
|
generate_pair_metrics,
|
||||||
generate_periodic_breakdown_stats,
|
generate_periodic_breakdown_stats,
|
||||||
generate_strategy_comparison,
|
generate_strategy_comparison,
|
||||||
generate_trading_stats, show_sorted_pairlist,
|
generate_trading_stats, show_sorted_pairlist,
|
||||||
store_backtest_analysis_results,
|
store_backtest_analysis_results,
|
||||||
store_backtest_stats, text_table_bt_results,
|
store_backtest_stats, text_table_bt_results,
|
||||||
text_table_exit_reason, text_table_strategy)
|
text_table_exit_reason, text_table_strategy)
|
||||||
|
from freqtrade.optimize.optimize_reports.optimize_reports import _get_resample_from_period
|
||||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||||
from freqtrade.util import dt_ts
|
from freqtrade.util import dt_ts
|
||||||
from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc
|
from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc
|
||||||
@@ -209,7 +210,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
|||||||
|
|
||||||
def test_store_backtest_stats(testdatadir, mocker):
|
def test_store_backtest_stats(testdatadir, mocker):
|
||||||
|
|
||||||
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_json')
|
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.bt_storage.file_dump_json')
|
||||||
|
|
||||||
store_backtest_stats(testdatadir, {'metadata': {}}, '2022_01_01_15_05_13')
|
store_backtest_stats(testdatadir, {'metadata': {}}, '2022_01_01_15_05_13')
|
||||||
|
|
||||||
@@ -228,7 +229,8 @@ def test_store_backtest_stats(testdatadir, mocker):
|
|||||||
|
|
||||||
def test_store_backtest_candles(testdatadir, mocker):
|
def test_store_backtest_candles(testdatadir, mocker):
|
||||||
|
|
||||||
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_joblib')
|
dump_mock = mocker.patch(
|
||||||
|
'freqtrade.optimize.optimize_reports.bt_storage.file_dump_joblib')
|
||||||
|
|
||||||
candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
|
candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
|
||||||
|
|
||||||
|
|||||||
+1
-15
@@ -1,6 +1,5 @@
|
|||||||
# pragma pylint: disable=missing-docstring,C0103
|
# pragma pylint: disable=missing-docstring,C0103
|
||||||
|
|
||||||
import datetime
|
|
||||||
from copy import deepcopy
|
from copy import deepcopy
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from unittest.mock import MagicMock
|
from unittest.mock import MagicMock
|
||||||
@@ -9,7 +8,7 @@ import pandas as pd
|
|||||||
import pytest
|
import pytest
|
||||||
|
|
||||||
from freqtrade.misc import (dataframe_to_json, decimals_per_coin, deep_merge_dicts, file_dump_json,
|
from freqtrade.misc import (dataframe_to_json, decimals_per_coin, deep_merge_dicts, file_dump_json,
|
||||||
file_load_json, format_ms_time, json_to_dataframe, pair_to_filename,
|
file_load_json, json_to_dataframe, pair_to_filename,
|
||||||
parse_db_uri_for_logging, plural, render_template,
|
parse_db_uri_for_logging, plural, render_template,
|
||||||
render_template_with_fallback, round_coin_value, safe_value_fallback,
|
render_template_with_fallback, round_coin_value, safe_value_fallback,
|
||||||
safe_value_fallback2)
|
safe_value_fallback2)
|
||||||
@@ -91,19 +90,6 @@ def test_pair_to_filename(pair, expected_result):
|
|||||||
assert pair_s == expected_result
|
assert pair_s == expected_result
|
||||||
|
|
||||||
|
|
||||||
def test_format_ms_time() -> None:
|
|
||||||
# Date 2018-04-10 18:02:01
|
|
||||||
date_in_epoch_ms = 1523383321000
|
|
||||||
date = format_ms_time(date_in_epoch_ms)
|
|
||||||
assert type(date) is str
|
|
||||||
res = datetime.datetime(2018, 4, 10, 18, 2, 1, tzinfo=datetime.timezone.utc)
|
|
||||||
assert date == res.astimezone(None).strftime('%Y-%m-%dT%H:%M:%S')
|
|
||||||
res = datetime.datetime(2017, 12, 13, 8, 2, 1, tzinfo=datetime.timezone.utc)
|
|
||||||
# Date 2017-12-13 08:02:01
|
|
||||||
date_in_epoch_ms = 1513152121000
|
|
||||||
assert format_ms_time(date_in_epoch_ms) == res.astimezone(None).strftime('%Y-%m-%dT%H:%M:%S')
|
|
||||||
|
|
||||||
|
|
||||||
def test_safe_value_fallback():
|
def test_safe_value_fallback():
|
||||||
dict1 = {'keya': None, 'keyb': 2, 'keyc': 5, 'keyd': None}
|
dict1 = {'keya': None, 'keyb': 2, 'keyc': 5, 'keyd': None}
|
||||||
assert safe_value_fallback(dict1, 'keya', 'keyb') == 2
|
assert safe_value_fallback(dict1, 'keya', 'keyb') == 2
|
||||||
|
|||||||
@@ -3,8 +3,8 @@ from datetime import datetime, timedelta, timezone
|
|||||||
import pytest
|
import pytest
|
||||||
import time_machine
|
import time_machine
|
||||||
|
|
||||||
from freqtrade.util import dt_floor_day, dt_from_ts, dt_now, dt_ts, dt_utc, shorten_date
|
from freqtrade.util import (dt_floor_day, dt_from_ts, dt_humanize, dt_now, dt_ts, dt_utc,
|
||||||
from freqtrade.util.datetime_helpers import dt_humanize
|
format_ms_time, shorten_date)
|
||||||
|
|
||||||
|
|
||||||
def test_dt_now():
|
def test_dt_now():
|
||||||
@@ -57,3 +57,16 @@ def test_dt_humanize() -> None:
|
|||||||
assert dt_humanize(dt_now()) == 'just now'
|
assert dt_humanize(dt_now()) == 'just now'
|
||||||
assert dt_humanize(dt_now(), only_distance=True) == 'instantly'
|
assert dt_humanize(dt_now(), only_distance=True) == 'instantly'
|
||||||
assert dt_humanize(dt_now() - timedelta(hours=16), only_distance=True) == '16 hours'
|
assert dt_humanize(dt_now() - timedelta(hours=16), only_distance=True) == '16 hours'
|
||||||
|
|
||||||
|
|
||||||
|
def test_format_ms_time() -> None:
|
||||||
|
# Date 2018-04-10 18:02:01
|
||||||
|
date_in_epoch_ms = 1523383321000
|
||||||
|
date = format_ms_time(date_in_epoch_ms)
|
||||||
|
assert type(date) is str
|
||||||
|
res = datetime(2018, 4, 10, 18, 2, 1, tzinfo=timezone.utc)
|
||||||
|
assert date == res.astimezone(None).strftime('%Y-%m-%dT%H:%M:%S')
|
||||||
|
res = datetime(2017, 12, 13, 8, 2, 1, tzinfo=timezone.utc)
|
||||||
|
# Date 2017-12-13 08:02:01
|
||||||
|
date_in_epoch_ms = 1513152121000
|
||||||
|
assert format_ms_time(date_in_epoch_ms) == res.astimezone(None).strftime('%Y-%m-%dT%H:%M:%S')
|
||||||
|
|||||||
Reference in New Issue
Block a user