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Merge branch 'develop' into feat/externalsignals

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Timothy Pogue 2022-09-10 15:14:10 -06:00
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Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.10.6-slim-bullseye as base
FROM python:3.10.7-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

26
docs/backtesting.md
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@ -107,7 +107,7 @@ Strategy arguments:
## Test your strategy with Backtesting
Now you have good Buy and Sell strategies and some historic data, you want to test it against
Now you have good Entry and exit strategies and some historic data, you want to test it against
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
@ -215,7 +215,7 @@ Sometimes your account has certain fee rebates (fee reductions starting with a c
To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
For example, if the commission fee per order is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
```bash
freqtrade backtesting --fee 0.001
@ -252,9 +252,9 @@ The most important in the backtesting is to understand the result.
A backtesting result will look like that:
```
========================================================= BACKTESTING REPORT ==========================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
========================================================= BACKTESTING REPORT =========================================================
| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|--------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
@ -275,15 +275,15 @@ A backtesting result will look like that:
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
========================================================= EXIT REASON STATS ==========================================================
| Exit Reason | Sells | Wins | Draws | Losses |
| Exit Reason | Exits | Wins | Draws | Losses |
|:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 |
| stop_loss | 166 | 0 | 0 | 166 |
| exit_signal | 56 | 36 | 0 | 20 |
| force_exit | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
@ -356,7 +356,7 @@ The column `Avg Profit %` shows the average profit for all trades made while the
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
Your strategy performance is influenced by your buy strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
Your strategy performance is influenced by your entry strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
@ -515,7 +515,7 @@ You can then load the trades to perform further analysis as shown in the [data a
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Exchange [trading limits](#trading-limits-in-backtesting) are respected
- Buys happen at open-price
- Entries happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle
- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
@ -612,9 +612,9 @@ There will be an additional table comparing win/losses of the different strategi
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
```
=========================================================== STRATEGY SUMMARY =========================================================================
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
=========================================================== STRATEGY SUMMARY ===========================================================================
| Strategy | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
```

28
docs/freqai.md
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@ -98,6 +98,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> Defaults set to 0, which means models never expire. <br> **Datatype:** Positive integer.
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set. <br> **Datatype:** Positive integer.
| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. Default: `False`.
| `continual_learning` | If true, FreqAI will start training new models from the final state of the most recently trained model. <br> **Datatype:** Boolean. Default: `False`.
| | **Feature parameters**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set. <br> **Datatype:** List of timeframes (strings).
@ -281,6 +282,8 @@ The FreqAI strategy requires the user to include the following lines of code in
Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`.
*Important*: The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
### Setting the `startup_candle_count`
Users need to take care to set the `startup_candle_count` in their strategy the same way they would for any normal Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling on the `dataprovider` to avoid any NaNs at the beginning of the first training. Users can easily set this value by identifying the longest period (in candle units) that they pass to their indicator creation functions (e.g. talib functions). In the present example, the user would pass 20 to as this value (since it is the maximum value in their `indicators_periods_candles`).
@ -534,6 +537,31 @@ for each pair, for each backtesting window within the expanded `--timerange`.
---
### Hyperopt
Users can hyperopt using the same command as typical [hyperopt](hyperopt.md):
```bash
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
```
Users need to have the data pre-downloaded in the same fashion as if they were doing a FreqAI [backtest](#backtesting). In addition, users must consider some restrictions when trying to [Hyperopt](hyperopt.md) FreqAI strategies:
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The [Backtesting](#backtesting) instructions also apply to Hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers:
```python
di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
```
Which would help the user understand the appropriate Dissimilarity Index values for their particular parameter space.
### Deciding the size of the sliding training window and backtesting duration
The user defines the backtesting timerange with the typical `--timerange` parameter in the

2
freqtrade/commands/db_commands.py
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@ -4,7 +4,7 @@ from typing import Any, Dict
from sqlalchemy import func
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.enums.runmode import RunMode
from freqtrade.enums import RunMode
logger = logging.getLogger(__name__)

9
freqtrade/configuration/config_validation.py
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@ -84,6 +84,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_protections(conf)
_validate_unlimited_amount(conf)
_validate_ask_orderbook(conf)
_validate_freqai_hyperopt(conf)
validate_migrated_strategy_settings(conf)
# validate configuration before returning
@ -323,6 +324,14 @@ def _validate_pricing_rules(conf: Dict[str, Any]) -> None:
del conf['ask_strategy']
def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
freqai_enabled = conf.get('freqai', {}).get('enabled', False)
analyze_per_epoch = conf.get('analyze_per_epoch', False)
if analyze_per_epoch and freqai_enabled:
raise OperationalException(
'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
def _strategy_settings(conf: Dict[str, Any]) -> None:
process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal')

8
freqtrade/data/history/history_utils.py
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@ -228,9 +228,9 @@ def _download_pair_history(pair: str, *,
)
logger.debug("Current Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[0]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
logger.debug("Current End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[-1]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair,
@ -254,9 +254,9 @@ def _download_pair_history(pair: str, *,
fill_missing=False, drop_incomplete=False)
logger.debug("New Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[0]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
logger.debug("New End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[-1]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
data_handler.ohlcv_store(pair, timeframe, data=data, candle_type=candle_type)
return True

3
freqtrade/exchange/okx.py
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@ -4,8 +4,7 @@ from typing import Dict, List, Optional, Tuple
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums.candletype import CandleType
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange, date_minus_candles
from freqtrade.exchange.common import retrier

28
freqtrade/freqai/prediction_models/BaseClassifierModel.py → freqtrade/freqai/base_models/BaseClassifierModel.py
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@ -21,12 +21,12 @@ class BaseClassifierModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
@ -36,14 +36,14 @@ class BaseClassifierModel(IFreqaiModel):
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
# split data into train/test data.
@ -61,32 +61,32 @@ class BaseClassifierModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_dataframe)
self.data_cleaning_predict(dk, filtered_df)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

28
freqtrade/freqai/prediction_models/BaseRegressionModel.py → freqtrade/freqai/base_models/BaseRegressionModel.py
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@ -20,12 +20,12 @@ class BaseRegressionModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
@ -35,14 +35,14 @@ class BaseRegressionModel(IFreqaiModel):
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
# split data into train/test data.
@ -60,33 +60,33 @@ class BaseRegressionModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
self.data_cleaning_predict(dk, filtered_df)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

12
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py → freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@ -17,12 +17,12 @@ class BaseTensorFlowModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
@ -32,14 +32,14 @@ class BaseTensorFlowModel(IFreqaiModel):
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
# split data into train/test data.
@ -57,7 +57,7 @@ class BaseTensorFlowModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")

65
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py Normal file
View File

@ -0,0 +1,65 @@
from joblib import Parallel
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.fixes import delayed
from sklearn.utils.validation import has_fit_parameter
class FreqaiMultiOutputRegressor(MultiOutputRegressor):
def fit(self, X, y, sample_weight=None, fit_params=None):
"""Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
fit_params : A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different
eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
if not fit_params:
fit_params = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
)
for i in range(y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return

64
freqtrade/freqai/data_kitchen.py
View File

@ -184,7 +184,7 @@ class FreqaiDataKitchen:
def filter_features(
self,
unfiltered_dataframe: DataFrame,
unfiltered_df: DataFrame,
training_feature_list: List,
label_list: List = list(),
training_filter: bool = True,
@ -195,31 +195,35 @@ class FreqaiDataKitchen:
0s in the prediction dataset. However, prediction dataset do_predict will reflect any
row that had a NaN and will shield user from that prediction.
:params:
:unfiltered_dataframe: the full dataframe for the present training period
:unfiltered_df: the full dataframe for the present training period
:training_feature_list: list, the training feature list constructed by
self.build_feature_list() according to user specified parameters in the configuration file.
:labels: the labels for the dataset
:training_filter: boolean which lets the function know if it is training data or
prediction data to be filtered.
:returns:
:filtered_dataframe: dataframe cleaned of NaNs and only containing the user
:filtered_df: dataframe cleaned of NaNs and only containing the user
requested feature set.
:labels: labels cleaned of NaNs.
"""
filtered_dataframe = unfiltered_dataframe.filter(training_feature_list, axis=1)
filtered_dataframe = filtered_dataframe.replace([np.inf, -np.inf], np.nan)
filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs,
drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
logger.warning(f"Removed features {const_cols} with constant values.")
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
labels = unfiltered_dataframe.filter(label_list, axis=1)
labels = unfiltered_df.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_dataframe['date']
filtered_dataframe = filtered_dataframe[
dates = unfiltered_df['date']
filtered_df = filtered_df[
(drop_index == 0) & (drop_index_labels == 0)
] # dropping values
labels = labels[
@ -229,13 +233,13 @@ class FreqaiDataKitchen:
(drop_index == 0) & (drop_index_labels == 0)
]
logger.info(
f"dropped {len(unfiltered_dataframe) - len(filtered_dataframe)} training points"
f" due to NaNs in populated dataset {len(unfiltered_dataframe)}."
f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
f" due to NaNs in populated dataset {len(unfiltered_df)}."
)
if (1 - len(filtered_dataframe) / len(unfiltered_dataframe)) > 0.1 and self.live:
worst_indicator = str(unfiltered_dataframe.count().idxmin())
if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
worst_indicator = str(unfiltered_df.count().idxmin())
logger.warning(
f" {(1 - len(filtered_dataframe)/len(unfiltered_dataframe)) * 100:.0f} percent "
f" {(1 - len(filtered_df)/len(unfiltered_df)) * 100:.0f} percent "
" of training data dropped due to NaNs, model may perform inconsistent "
f"with expectations. Verify {worst_indicator}"
)
@ -244,9 +248,9 @@ class FreqaiDataKitchen:
else:
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_dataframe).any(1)
drop_index = pd.isnull(filtered_df).any(1)
self.data["filter_drop_index_prediction"] = drop_index
filtered_dataframe.fillna(0, inplace=True)
filtered_df.fillna(0, inplace=True)
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
# that was based on a single NaN is ultimately protected from buys with do_predict
drop_index = ~drop_index
@ -255,11 +259,11 @@ class FreqaiDataKitchen:
logger.info(
"dropped %s of %s prediction data points due to NaNs.",
len(self.do_predict) - self.do_predict.sum(),
len(filtered_dataframe),
len(filtered_df),
)
labels = []
return filtered_dataframe, labels
return filtered_df, labels
def build_data_dictionary(
self,
@ -461,6 +465,27 @@ class FreqaiDataKitchen:
return df
def remove_training_from_backtesting(
self
) -> DataFrame:
"""
Function which takes the backtesting time range and
remove training data from dataframe, keeping only the
startup_candle_count candles
"""
startup_candle_count = self.config.get('startup_candle_count', 0)
tf = self.config['timeframe']
tr = self.config["timerange"]
backtesting_timerange = TimeRange.parse_timerange(tr)
if startup_candle_count > 0 and backtesting_timerange:
backtesting_timerange.subtract_start(timeframe_to_seconds(tf) * startup_candle_count)
start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc)
df = self.return_dataframe
df = df.loc[df["date"] >= start, :]
return df
def principal_component_analysis(self) -> None:
"""
Performs Principal Component Analysis on the data for dimensionality reduction
@ -954,6 +979,7 @@ class FreqaiDataKitchen:
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
self.return_dataframe = self.remove_training_from_backtesting()
self.full_df = DataFrame()
return
@ -1200,7 +1226,6 @@ class FreqaiDataKitchen:
def save_backtesting_prediction(
self, append_df: DataFrame
) -> None:
"""
Save prediction dataframe from backtesting to h5 file format
:param append_df: dataframe for backtesting period
@ -1214,7 +1239,6 @@ class FreqaiDataKitchen:
def get_backtesting_prediction(
self
) -> DataFrame:
"""
Get prediction dataframe from h5 file format
"""

31
freqtrade/freqai/freqai_interface.py
View File

@ -14,6 +14,7 @@ from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
@ -87,10 +88,17 @@ class IFreqaiModel(ABC):
self.begin_time: float = 0
self.begin_time_train: float = 0
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
self.continual_learning = self.freqai_info.get('continual_learning', False)
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
def __getstate__(self):
"""
Return an empty state to be pickled in hyperopt
"""
return ({})
def assert_config(self, config: Dict[str, Any]) -> None:
if not config.get("freqai", {}):
@ -232,10 +240,10 @@ class IFreqaiModel(ABC):
trained_timestamp = tr_train
tr_train_startts_str = datetime.fromtimestamp(
tr_train.startts,
tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
tr_train_stopts_str = datetime.fromtimestamp(
tr_train.stopts,
tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
logger.info(
f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
@ -669,21 +677,30 @@ class IFreqaiModel(ABC):
self.train_time = 0
return
def get_init_model(self, pair: str) -> Any:
if pair not in self.dd.model_dictionary or not self.continual_learning:
init_model = None
else:
init_model = self.dd.model_dictionary[pair]
return init_model
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
def train(self, unfiltered_df: DataFrame, pair: str,
dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return: Trained model which can be used to inference (self.predict)
"""
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any]) -> Any:
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
@ -696,11 +713,11 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param first: boolean = whether this is the first prediction or not.
:return:

9
freqtrade/freqai/prediction_models/CatboostClassifier.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from catboost import CatBoostClassifier, Pool
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -16,7 +17,7 @@ class CatboostClassifier(BaseClassifierModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
@ -36,6 +37,8 @@ class CatboostClassifier(BaseClassifierModel):
**self.model_training_parameters,
)
cbr.fit(train_data)
init_model = self.get_init_model(dk.pair)
cbr.fit(train_data, init_model=init_model)
return cbr

15
freqtrade/freqai/prediction_models/CatboostRegressor.py
View File

@ -1,10 +1,10 @@
import gc
import logging
from typing import Any, Dict
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -17,7 +17,7 @@ class CatboostRegressor(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
@ -38,16 +38,13 @@ class CatboostRegressor(BaseRegressionModel):
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
model = CatBoostRegressor(
allow_writing_files=False,
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data)
# some evidence that catboost pools have memory leaks:
# https://github.com/catboost/catboost/issues/1835
del train_data, test_data
gc.collect()
model.fit(X=train_data, eval_set=test_data, init_model=init_model)
return model

44
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
View File

@ -1,10 +1,11 @@
import logging
from typing import Any, Dict
from catboost import CatBoostRegressor # , Pool
from sklearn.multioutput import MultiOutputRegressor
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -17,7 +18,7 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
@ -31,14 +32,37 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
eval_sets = [None] * data_dictionary['test_labels'].shape[1]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"].iloc[:, i],
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=cbr)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

9
freqtrade/freqai/prediction_models/LightGBMClassifier.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -16,7 +17,7 @@ class LightGBMClassifier(BaseClassifierModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
@ -35,9 +36,11 @@ class LightGBMClassifier(BaseClassifierModel):
y = data_dictionary["train_labels"].to_numpy()[:, 0]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = LGBMClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
eval_sample_weight=[test_weights])
eval_sample_weight=[test_weights], init_model=init_model)
return model

9
freqtrade/freqai/prediction_models/LightGBMRegressor.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from lightgbm import LGBMRegressor
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -16,7 +17,7 @@ class LightGBMRegressor(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
@ -35,9 +36,11 @@ class LightGBMRegressor(BaseRegressionModel):
y = data_dictionary["train_labels"]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = LGBMRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
eval_sample_weight=[eval_weights])
eval_sample_weight=[eval_weights], init_model=init_model)
return model

43
freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
View File

@ -2,9 +2,10 @@ import logging
from typing import Any, Dict
from lightgbm import LGBMRegressor
from sklearn.multioutput import MultiOutputRegressor
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -17,7 +18,7 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
@ -28,12 +29,36 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=lgb)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
eval_weights = None
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = ( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'eval_sample_weight': eval_weights,
'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=lgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

45
freqtrade/freqai/prediction_models/XGBoostRegressor.py Normal file
View File

@ -0,0 +1,45 @@
import logging
from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]
sample_weight = data_dictionary["train_weights"]
xgb_model = self.get_init_model(dk.pair)
model = XGBRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
return model

63
freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py Normal file
View File

@ -0,0 +1,63 @@
import logging
from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRegressorMultiTarget(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
xgb = XGBRegressor(**self.model_training_parameters)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
sample_weight = data_dictionary["train_weights"]
eval_weights = None
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = [( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)]
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'sample_weight_eval_set': eval_weights,
'xgb_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=xgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

5
freqtrade/optimize/optimize_reports.py
View File

@ -75,7 +75,8 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
'.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str, direction: str = 'Buys') -> List[str]:
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())
"""
@ -642,7 +643,7 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr
if (tag_type == "enter_tag"):
headers = _get_line_header("TAG", stake_currency)
else:
headers = _get_line_header("TAG", stake_currency, 'Sells')
headers = _get_line_header("TAG", stake_currency, 'Exits')
floatfmt = _get_line_floatfmt(stake_currency)
output = [
[

2
freqtrade/rpc/discord.py
View File

@ -1,7 +1,7 @@
import logging
from typing import Any, Dict
from freqtrade.enums.rpcmessagetype import RPCMessageType
from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC
from freqtrade.rpc.webhook import Webhook

5
freqtrade/strategy/interface.py
View File

@ -12,9 +12,8 @@ from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, SignalTagType,
SignalType, TradingMode)
from freqtrade.enums.runmode import RunMode
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
SignalTagType, SignalType, TradingMode)
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.misc import remove_entry_exit_signals

2
freqtrade/strategy/parameters.py
View File

@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
from contextlib import suppress
from typing import Any, Optional, Sequence, Union
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums import HyperoptState
from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer

113
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -6,9 +6,7 @@ import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
from freqtrade.strategy import CategoricalParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
@ -31,9 +29,6 @@ class FreqaiExampleStrategy(IStrategy):
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
@ -47,10 +42,10 @@ class FreqaiExampleStrategy(IStrategy):
startup_candle_count: int = 40
can_short = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
std_dev_multiplier_buy = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
std_dev_multiplier_sell = CategoricalParameter(
[0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
@ -187,21 +182,26 @@ class FreqaiExampleStrategy(IStrategy):
# `populate_any_indicators()` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
for val in self.std_dev_multiplier_buy.range:
dataframe[f'target_roi_{val}'] = dataframe["&-s_close_mean"] + \
dataframe["&-s_close_std"] * val
for val in self.std_dev_multiplier_sell.range:
dataframe[f'sell_roi_{val}'] = dataframe["&-s_close_mean"] - \
dataframe["&-s_close_std"] * val
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"]
> df[f"target_roi_{self.std_dev_multiplier_buy.value}"]]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"]
< df[f"sell_roi_{self.std_dev_multiplier_sell.value}"]]
if enter_short_conditions:
df.loc[
@ -211,11 +211,13 @@ class FreqaiExampleStrategy(IStrategy):
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] <
df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] >
df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
@ -224,83 +226,6 @@ class FreqaiExampleStrategy(IStrategy):
def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1])
def custom_exit(
self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
if trade_candle.empty:
return None
trade_candle = trade_candle.squeeze()
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.freqai.dd.follower_dict
entry_tag = trade.enter_tag
if (
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]['extras']["prediction" + entry_tag] == 0
):
pair_dict[pair]['extras']["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
if not follow_mode:
self.freqai.dd.save_drawer_to_disk()
else:
self.freqai.dd.save_follower_dict_to_disk()
roi_price = pair_dict[pair]['extras']["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (
1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
)
if roi_decay < 0:
roi_decay = self.linear_roi_offset.value
else:
roi_decay += self.linear_roi_offset.value
if current_profit > roi_decay:
return "roi_custom_win"
if current_profit < -roi_decay:
return "roi_custom_loss"
def confirm_trade_exit(
self,
pair: str,
trade: Trade,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
exit_reason: str,
current_time,
**kwargs,
) -> bool:
entry_tag = trade.enter_tag
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.freqai.dd.follower_dict
pair_dict[pair]['extras']["prediction" + entry_tag] = 0
if not follow_mode:
self.freqai.dd.save_drawer_to_disk()
else:
self.freqai.dd.save_follower_dict_to_disk()
return True
def confirm_trade_entry(
self,
pair: str,

1
requirements-freqai.txt
View File

@ -6,3 +6,4 @@ scikit-learn==1.1.2
joblib==1.1.0
catboost==1.0.6; platform_machine != 'aarch64'
lightgbm==3.3.2
xgboost==1.6.2

6
tests/data/test_history.py
View File

@ -13,7 +13,7 @@ from pandas import DataFrame
from pandas.testing import assert_frame_equal
from freqtrade.configuration import TimeRange
from freqtrade.constants import AVAILABLE_DATAHANDLERS
from freqtrade.constants import AVAILABLE_DATAHANDLERS, DATETIME_PRINT_FORMAT
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.data.history.hdf5datahandler import HDF5DataHandler
from freqtrade.data.history.history_utils import (_download_pair_history, _download_trades_history,
@ -386,7 +386,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
assert td != len(data['UNITTEST/BTC'])
start_real = data['UNITTEST/BTC'].iloc[0, 0]
assert log_has(f'UNITTEST/BTC, spot, 5m, '
f'data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
f'data starts at {start_real.strftime(DATETIME_PRINT_FORMAT)}',
caplog)
# Make sure we start fresh - test missing data at end
caplog.clear()
@ -401,7 +401,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
# Shift endtime with +5 - as last candle is dropped (partial candle)
end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
assert log_has(f'UNITTEST/BTC, spot, 5m, '
f'data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
f'data ends at {end_real.strftime(DATETIME_PRINT_FORMAT)}',
caplog)

27
tests/exchange/test_ccxt_compat.py
View File

@ -267,13 +267,8 @@ class TestCCXTExchange():
now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
def test_ccxt__async_get_candle_history(self, exchange):
exchange, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename == 'bittrex':
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe):
candle_type = CandleType.SPOT
timeframe_ms = timeframe_to_msecs(timeframe)
now = timeframe_to_prev_date(
@ -299,6 +294,24 @@ class TestCCXTExchange():
assert len(candles) >= min(candle_count, candle_count1)
assert candles[0][0] == since_ms or (since_ms + timeframe_ms)
def test_ccxt__async_get_candle_history(self, exchange):
exchange, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename in ('bittrex', 'gateio'):
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
def test_ccxt__async_get_candle_history_futures(self, exchange_futures):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
return
pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
def test_ccxt_fetch_funding_rate_history(self, exchange_futures):
exchange, exchangename = exchange_futures
if not exchange:

3
tests/exchange/test_okx.py
View File

@ -4,8 +4,7 @@ from unittest.mock import MagicMock, PropertyMock
import pytest
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums.candletype import CandleType
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exchange.exchange import timeframe_to_minutes
from tests.conftest import get_mock_coro, get_patched_exchange, log_has
from tests.exchange.test_exchange import ccxt_exceptionhandlers

98
tests/freqai/test_freqai_interface.py
View File

@ -17,8 +17,18 @@ def is_arm() -> bool:
return "arm" in machine or "aarch64" in machine
def test_extract_data_and_train_model_LightGBM(mocker, freqai_conf):
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
])
def test_extract_data_and_train_model_Regressors(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
@ -46,10 +56,18 @@ def test_extract_data_and_train_model_LightGBM(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
def test_extract_data_and_train_model_LightGBMMultiModel(mocker, freqai_conf):
@pytest.mark.parametrize('model', [
'LightGBMRegressorMultiTarget',
'XGBoostRegressorMultiTarget',
'CatboostRegressorMultiTarget',
])
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressorMultiTarget':
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"freqaimodel": "LightGBMRegressorMultiTarget"})
freqai_conf.update({"freqaimodel": model})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -78,75 +96,17 @@ def test_extract_data_and_train_model_LightGBMMultiModel(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_extract_data_and_train_model_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostRegressor"})
# freqai_conf.get('freqai', {}).update(
# {'model_training_parameters': {"n_estimators": 100, "verbose": 0}})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@pytest.mark.parametrize('model', [
'LightGBMClassifier',
'CatboostClassifier',
])
def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostClassifier':
pytest.skip("CatBoost is not supported on ARM")
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_extract_data_and_train_model_CatboostClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostClassifier"})
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"strategy": "freqai_test_classifier"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
shutil.rmtree(Path(freqai.dk.full_path))
def test_extract_data_and_train_model_LightGBMClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "LightGBMClassifier"})
freqai_conf.update({"strategy": "freqai_test_classifier"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)

20
tests/optimize/test_optimize_reports.py
View File

@ -40,14 +40,14 @@ def test_text_table_bt_results():
)
result_str = (
'| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % |'
' Avg Duration | Win Draw Loss Win% |\n'
'|---------+--------+----------------+----------------+------------------+----------------+'
'----------------+-------------------------|\n'
'| ETH/BTC | 3 | 8.33 | 25.00 | 0.50000000 | 12.50 |'
' 0:20:00 | 2 0 1 66.7 |\n'
'| TOTAL | 3 | 8.33 | 25.00 | 0.50000000 | 12.50 |'
' 0:20:00 | 2 0 1 66.7 |'
'| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | '
'Tot Profit % | Avg Duration | Win Draw Loss Win% |\n'
'|---------+-----------+----------------+----------------+------------------+'
'----------------+----------------+-------------------------|\n'
'| ETH/BTC | 3 | 8.33 | 25.00 | 0.50000000 | '
'12.50 | 0:20:00 | 2 0 1 66.7 |\n'
'| TOTAL | 3 | 8.33 | 25.00 | 0.50000000 | '
'12.50 | 0:20:00 | 2 0 1 66.7 |'
)
pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC',
@ -402,9 +402,9 @@ def test_text_table_strategy(testdatadir):
bt_res_data_comparison = bt_res_data.pop('strategy_comparison')
result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
'| Strategy | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC |'
' Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n'
'|----------------+--------+----------------+----------------+------------------+'
'|----------------+-----------+----------------+----------------+------------------+'
'----------------+----------------+-------------------------+-----------------------|\n'
'| StrategyTestV2 | 179 | 0.08 | 14.39 | 0.02608550 |'
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |\n'

3
tests/strategy/test_interface.py
View File

@ -11,8 +11,7 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data
from freqtrade.enums import ExitCheckTuple, ExitType, SignalDirection
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums import ExitCheckTuple, ExitType, HyperoptState, SignalDirection
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
from freqtrade.optimize.space import SKDecimal

10
tests/test_persistence.py
View File

@ -9,7 +9,7 @@ import arrow
import pytest
from sqlalchemy import create_engine, text
from freqtrade import constants
from freqtrade.constants import DATETIME_PRINT_FORMAT, DEFAULT_DB_PROD_URL
from freqtrade.enums import TradingMode
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.persistence import LocalTrade, Order, Trade, init_db
@ -52,7 +52,7 @@ def test_init_invalid_db_url():
def test_init_prod_db(default_conf, mocker):
default_conf.update({'dry_run': False})
default_conf.update({'db_url': constants.DEFAULT_DB_PROD_URL})
default_conf.update({'db_url': DEFAULT_DB_PROD_URL})
create_engine_mock = mocker.patch('freqtrade.persistence.models.create_engine', MagicMock())
@ -1739,7 +1739,7 @@ def test_to_json(fee):
'base_currency': 'ADA',
'quote_currency': 'USDT',
'is_open': None,
'open_date': trade.open_date.strftime("%Y-%m-%d %H:%M:%S"),
'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT),
'open_timestamp': int(trade.open_date.timestamp() * 1000),
'open_order_id': 'dry_run_buy_12345',
'close_date': None,
@ -1817,9 +1817,9 @@ def test_to_json(fee):
'pair': 'XRP/BTC',
'base_currency': 'XRP',
'quote_currency': 'BTC',
'open_date': trade.open_date.strftime("%Y-%m-%d %H:%M:%S"),
'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT),
'open_timestamp': int(trade.open_date.timestamp() * 1000),
'close_date': trade.close_date.strftime("%Y-%m-%d %H:%M:%S"),
'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
'close_timestamp': int(trade.close_date.timestamp() * 1000),
'open_rate': 0.123,
'close_rate': 0.125,