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add-spice-
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71
docs/freqai-spice-rack.md
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71
docs/freqai-spice-rack.md
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@ -0,0 +1,71 @@
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# Using the `spice_rack`
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!!! Note:
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`spice_rack` indicators should not be used exclusively for entries and exits, the following example is just a demonstration of syntax. `spice_rack` indicators should **always** be used to support existing strategies.
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The `spice_rack` is aimed at users who do not wish to deal with setting up `FreqAI` confgs, but instead prefer to interact with `FreqAI` similar to a `talib` indicator. In this case, the user can instead simply add two keys to their config:
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```json
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"freqai_spice_rack": true,
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"freqai_identifier": "spicey-id",
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```
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Which tells `FreqAI` to set up a pre-set `FreqAI` instance automatically under the hood with preset parameters. Now the user can access a suite of custom `FreqAI` supercharged indicators inside their strategy by placing the following code into `populate_indicators`:
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```python
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dataframe['dissimilarity_index'] = self.freqai.spice_rack(
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'DI_values', dataframe, metadata, self)
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dataframe['extrema'] = self.freqai.spice_rack(
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'&s-extrema', dataframe, metadata, self)
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self.freqai.close_spice_rack() # user must close the spicerack
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```
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Users can then use these columns in concert with all their own additional indicators added to `populate_indicators` in their entry/exit criteria and strategy callback methods the same way as any typical indicator. For example:
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```python
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] < -0.1)
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),
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'enter_long'] = 1
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] > 0.1)
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),
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'enter_short'] = 1
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] > 0.1)
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),
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'exit_long'] = 1
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df.loc[
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(
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(df['dissimilarity_index'] < 1) &
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(df['extrema'] < -0.1)
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),
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'exit_short'] = 1
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return df
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```
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## Available indicators
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| Parameter | Description |
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|------------|-------------|
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| `DI_values` | **Required.** <br> The dissimilarity index of the current candle to the recent candles. More information available [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) <br> **Datatype:** Floats.
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| `extrema` | **Required.** <br> A continuous prediction from FreqAI which aims to help predict if the current candle is a maxima or a minma. FreqAI aims for 1 to be a maxima and -1 to be a minima - but the values should typically hover between -0.2 and 0.2. <br> **Datatype:** Floats.
|
@ -11,7 +11,8 @@ from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_oh
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refresh_backtest_trades_data)
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from freqtrade.enums import CandleType, RunMode, TradingMode
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import market_is_active, timeframe_to_minutes
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from freqtrade.exchange import Exchange, market_is_active, timeframe_to_minutes
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from freqtrade.freqai.utils import setup_freqai_spice_rack
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from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
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from freqtrade.resolvers import ExchangeResolver
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@ -48,6 +49,10 @@ def start_download_data(args: Dict[str, Any]) -> None:
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# Init exchange
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exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
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if config.get('freqai_spice_rack', False):
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config = setup_freqai_spice_rack(config, exchange)
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markets = [p for p, m in exchange.markets.items() if market_is_active(m)
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or config.get('include_inactive')]
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@ -63,37 +68,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
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exchange.validate_timeframes(timeframe)
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try:
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if config.get('download_trades'):
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if config.get('trading_mode') == 'futures':
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raise OperationalException("Trade download not supported for futures.")
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pairs_not_available = refresh_backtest_trades_data(
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exchange, pairs=expanded_pairs, datadir=config['datadir'],
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timerange=timerange, new_pairs_days=config['new_pairs_days'],
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erase=bool(config.get('erase')), data_format=config['dataformat_trades'])
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# Convert downloaded trade data to different timeframes
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convert_trades_to_ohlcv(
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pairs=expanded_pairs, timeframes=config['timeframes'],
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datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
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data_format_ohlcv=config['dataformat_ohlcv'],
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data_format_trades=config['dataformat_trades'],
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)
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else:
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if not exchange.get_option('ohlcv_has_history', True):
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raise OperationalException(
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f"Historic klines not available for {exchange.name}. "
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"Please use `--dl-trades` instead for this exchange "
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"(will unfortunately take a long time)."
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)
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pairs_not_available = refresh_backtest_ohlcv_data(
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exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
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datadir=config['datadir'], timerange=timerange,
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new_pairs_days=config['new_pairs_days'],
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erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'],
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trading_mode=config.get('trading_mode', 'spot'),
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prepend=config.get('prepend_data', False)
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)
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pairs_not_available = download_trades(exchange, expanded_pairs, config, timerange)
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except KeyboardInterrupt:
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sys.exit("SIGINT received, aborting ...")
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@ -104,6 +79,42 @@ def start_download_data(args: Dict[str, Any]) -> None:
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f"on exchange {exchange.name}.")
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def download_trades(exchange: Exchange, expanded_pairs: list,
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config: Dict[str, Any], timerange: TimeRange) -> list:
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if config.get('download_trades'):
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if config.get('trading_mode') == 'futures':
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raise OperationalException("Trade download not supported for futures.")
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pairs_not_available = refresh_backtest_trades_data(
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exchange, pairs=expanded_pairs, datadir=config['datadir'],
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timerange=timerange, new_pairs_days=config['new_pairs_days'],
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erase=bool(config.get('erase')), data_format=config['dataformat_trades'])
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# Convert downloaded trade data to different timeframes
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convert_trades_to_ohlcv(
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pairs=expanded_pairs, timeframes=config['timeframes'],
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datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
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data_format_ohlcv=config['dataformat_ohlcv'],
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data_format_trades=config['dataformat_trades'],
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)
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else:
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if not exchange.get_option('ohlcv_has_history', True):
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raise OperationalException(
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f"Historic klines not available for {exchange.name}. "
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"Please use `--dl-trades` instead for this exchange "
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"(will unfortunately take a long time)."
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)
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pairs_not_available = refresh_backtest_ohlcv_data(
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exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
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datadir=config['datadir'], timerange=timerange,
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new_pairs_days=config['new_pairs_days'],
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erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'],
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trading_mode=config.get('trading_mode', 'spot'),
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prepend=config.get('prepend_data', False)
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)
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return pairs_not_available
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def start_convert_trades(args: Dict[str, Any]) -> None:
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config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
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|
@ -520,7 +520,7 @@ class FreqaiDataDrawer:
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f"Unable to load model, ensure model exists at " f"{dk.data_path} "
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)
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if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
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if self.config["freqai"]["feature_parameters"].get("principal_component_analysis", False):
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dk.pca = cloudpickle.load(
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open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
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)
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@ -616,7 +616,6 @@ class FreqaiDataDrawer:
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pairs = self.freqai_info["feature_parameters"].get(
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"include_corr_pairlist", []
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)
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for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
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base_dataframes[tf] = dk.slice_dataframe(
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timerange, historic_data[pair][tf]).reset_index(drop=True)
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|
@ -99,6 +99,7 @@ class FreqaiDataKitchen:
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self.train_dates: DataFrame = pd.DataFrame()
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self.unique_classes: Dict[str, list] = {}
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self.unique_class_list: list = []
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self.spice_dataframe: DataFrame = None
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def set_paths(
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self,
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@ -1259,3 +1260,11 @@ class FreqaiDataKitchen:
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f"Could not find backtesting prediction file at {path_to_predictionfile}"
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)
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return file_exists
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def spice_extractor(self, indicator: str, dataframe: DataFrame) -> npt.NDArray:
|
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if indicator in dataframe.columns:
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return np.array(dataframe[indicator])
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else:
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logger.warning(f'User asked spice_rack for {indicator}, '
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f'but it is not available. Returning 0s')
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return np.zeros(len(dataframe.index))
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|
@ -93,7 +93,7 @@ class IFreqaiModel(ABC):
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self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
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self.continual_learning = self.freqai_info.get('continual_learning', False)
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self.plot_features = self.ft_params.get("plot_feature_importances", 0)
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self.spice_rack_open: bool = False
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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@ -142,7 +142,7 @@ class IFreqaiModel(ABC):
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dk = self.start_backtesting(dataframe, metadata, self.dk)
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dataframe = dk.remove_features_from_df(dk.return_dataframe)
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self.clean_up()
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# self.clean_up()
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if self.live:
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self.inference_timer('stop')
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return dataframe
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@ -732,6 +732,18 @@ class IFreqaiModel(ABC):
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f'Best approximation queue: {best_queue}')
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return best_queue
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def spice_rack(self, indicator: str, dataframe: DataFrame,
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metadata: dict, strategy: IStrategy) -> NDArray:
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if not self.spice_rack_open:
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dataframe = self.start(dataframe, metadata, strategy)
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self.dk.spice_dataframe = dataframe
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self.spice_rack_open = True
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return self.dk.spice_extractor(indicator, dataframe)
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else:
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return self.dk.spice_extractor(indicator, self.dk.spice_dataframe)
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def close_spice_rack(self):
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self.spice_rack_open = False
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# Following methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModel.py for an example.
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|
37
freqtrade/freqai/spice_rack/lightgbm_config.json
Normal file
37
freqtrade/freqai/spice_rack/lightgbm_config.json
Normal file
@ -0,0 +1,37 @@
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{
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||||
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"freqai": {
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"enabled": true,
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"purge_old_models": true,
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"train_period_days": 4,
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"backtest_period_days": 1,
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"identifier": "spicy-id",
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"feature_parameters": {
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"include_timeframes": [
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"30m",
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"1h",
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"4h"
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],
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"include_corr_pairlist": [
|
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"BTC/USD",
|
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"ETH/USD"
|
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],
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"label_period_candles": 20,
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"include_shifted_candles": 2,
|
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": true,
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"indicator_periods_candles": [
|
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10,
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20
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]
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},
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"data_split_parameters": {
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"test_size": 0,
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"random_state": 1
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},
|
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"model_training_parameters": {
|
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"n_estimators": 800
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}
|
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}
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||||
}
|
@ -1,19 +1,24 @@
|
||||
import logging
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||||
from datetime import datetime, timezone
|
||||
from typing import Any
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import numpy as np
|
||||
# for spice rack
|
||||
import pandas as pd
|
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import talib.abstract as ta
|
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from scipy.signal import argrelextrema
|
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from technical import qtpylib
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_seconds
|
||||
from freqtrade.exchange import Exchange, timeframe_to_seconds
|
||||
from freqtrade.exchange.exchange import market_is_active
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
|
||||
from freqtrade.strategy import merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -89,6 +94,136 @@ def get_required_data_timerange(config: Config) -> TimeRange:
|
||||
return data_load_timerange
|
||||
|
||||
|
||||
def auto_populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
This is a premade `populate_any_indicators()` function which is set in
|
||||
the user strategy is they enable `freqai_spice_rack: true` in their
|
||||
configuration file.
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
|
||||
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
|
||||
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(informative), window=t, stds=2.2
|
||||
)
|
||||
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
|
||||
informative[f"%-{coin}bb_width-period_{t}"] = (
|
||||
informative[f"{coin}bb_upperband-period_{t}"]
|
||||
- informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{coin}bb_middleband-period_{t}"]
|
||||
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
)
|
||||
|
||||
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
df["&s-extrema"] = 0
|
||||
min_peaks = argrelextrema(df["close"].values, np.less, order=80)
|
||||
max_peaks = argrelextrema(df["close"].values, np.greater, order=80)
|
||||
for mp in min_peaks[0]:
|
||||
df.at[mp, "&s-extrema"] = -1
|
||||
for mp in max_peaks[0]:
|
||||
df.at[mp, "&s-extrema"] = 1
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def setup_freqai_spice_rack(config: dict, exchange: Optional[Exchange]) -> Dict[str, Any]:
|
||||
import difflib
|
||||
import json
|
||||
from pathlib import Path
|
||||
auto_config = config.get('freqai_config', 'lightgbm_config.json')
|
||||
with open(Path(__file__).parent / Path('spice_rack') / auto_config) as json_file:
|
||||
freqai_config = json.load(json_file)
|
||||
config['freqai'] = freqai_config['freqai']
|
||||
config['freqai']['identifier'] = config['freqai_identifier']
|
||||
corr_pairs = config['freqai']['feature_parameters']['include_corr_pairlist']
|
||||
timeframes = config['freqai']['feature_parameters']['include_timeframes']
|
||||
new_corr_pairs = []
|
||||
new_tfs = []
|
||||
|
||||
if not exchange:
|
||||
logger.warning('No dataprovider available.')
|
||||
config['freqai']['enabled'] = False
|
||||
return config
|
||||
# find the closest pairs to what the default config wants
|
||||
for pair in corr_pairs:
|
||||
closest_pair = difflib.get_close_matches(
|
||||
pair,
|
||||
exchange.markets
|
||||
)
|
||||
if not closest_pair:
|
||||
logger.warning(f'Could not find {pair} in markets, removing from '
|
||||
f'corr_pairlist.')
|
||||
else:
|
||||
closest_pair = closest_pair[0]
|
||||
|
||||
new_corr_pairs.append(closest_pair)
|
||||
logger.info(f'Spice rack will use {closest_pair} as informative in FreqAI model.')
|
||||
|
||||
# find the closest matching timeframes to what the default config wants
|
||||
if timeframe_to_seconds(config['timeframe']) > timeframe_to_seconds('15m'):
|
||||
logger.warning('Default spice rack is designed for lower base timeframes (e.g. > '
|
||||
f'15m). But user passed {config["timeframe"]}.')
|
||||
new_tfs.append(config['timeframe'])
|
||||
|
||||
list_tfs = [timeframe_to_seconds(tf) for tf
|
||||
in exchange.timeframes]
|
||||
for tf in timeframes:
|
||||
tf_secs = timeframe_to_seconds(tf)
|
||||
closest_index = min(range(len(list_tfs)), key=lambda i: abs(list_tfs[i] - tf_secs))
|
||||
closest_tf = exchange.timeframes[closest_index]
|
||||
logger.info(f'Spice rack will use {closest_tf} as informative tf in FreqAI model.')
|
||||
new_tfs.append(closest_tf)
|
||||
|
||||
config['freqai']['feature_parameters'].update({'include_timeframes': new_tfs})
|
||||
config['freqai']['feature_parameters'].update({'include_corr_pairlist': new_corr_pairs})
|
||||
config.update({"freqaimodel": 'LightGBMRegressor'})
|
||||
return config
|
||||
|
||||
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
|
||||
# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
|
||||
# """
|
||||
|
@ -89,6 +89,10 @@ class Backtesting:
|
||||
self._exchange_name, self.config, load_leverage_tiers=True)
|
||||
self.dataprovider = DataProvider(self.config, self.exchange)
|
||||
|
||||
if config.get('freqai_spice_rack', False):
|
||||
from freqtrade.freqai.utils import setup_freqai_spice_rack
|
||||
self.config = setup_freqai_spice_rack(self.config, self.exchange)
|
||||
|
||||
if self.config.get('strategy_list'):
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
|
||||
|
@ -146,12 +146,28 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
self._ft_informative.append((informative_data, cls_method))
|
||||
|
||||
def load_freqAI_model(self) -> None:
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
spice_rack = self.config.get('freqai_spice_rack', False)
|
||||
if self.config.get('freqai', {}).get('enabled', False) or spice_rack:
|
||||
if spice_rack:
|
||||
from freqtrade.freqai.utils import setup_freqai_spice_rack
|
||||
self.config = setup_freqai_spice_rack(self.config, self.dp._exchange)
|
||||
# Import here to avoid importing this if freqAI is disabled
|
||||
from freqtrade.freqai.utils import download_all_data_for_training
|
||||
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
|
||||
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
|
||||
self.freqai_info = self.config["freqai"]
|
||||
if not self.process_only_new_candles:
|
||||
logger.warning('User set process_only_new_candles to false, '
|
||||
'FreqAI requires true. Changing to true.')
|
||||
self.process_only_new_candles = True
|
||||
|
||||
if spice_rack:
|
||||
import types
|
||||
|
||||
from freqtrade.freqai.utils import auto_populate_any_indicators
|
||||
self.populate_any_indicators = types.MethodType( # type: ignore
|
||||
auto_populate_any_indicators, self)
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
# download the desired data in dry/live
|
||||
if self.config.get('runmode') in (RunMode.DRY_RUN, RunMode.LIVE):
|
||||
@ -161,6 +177,7 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
"already on disk."
|
||||
)
|
||||
download_all_data_for_training(self.dp, self.config)
|
||||
|
||||
else:
|
||||
# Gracious failures if freqAI is disabled but "start" is called.
|
||||
class DummyClass():
|
||||
|
@ -29,6 +29,7 @@ nav:
|
||||
- Parameter table: freqai-parameter-table.md
|
||||
- Feature engineering: freqai-feature-engineering.md
|
||||
- Running FreqAI: freqai-running.md
|
||||
- Spice Rack: freqai-spice-rack.md
|
||||
- Developer guide: freqai-developers.md
|
||||
- Short / Leverage: leverage.md
|
||||
- Utility Sub-commands: utils.md
|
||||
|
@ -158,3 +158,28 @@ def test_make_train_test_datasets(mocker, freqai_conf):
|
||||
assert data_dictionary
|
||||
assert len(data_dictionary) == 7
|
||||
assert len(data_dictionary['train_features'].index) == 1916
|
||||
|
||||
|
||||
@pytest.mark.parametrize('indicator', [
|
||||
'%-ADArsi-period_10_5m',
|
||||
'doesnt_exist',
|
||||
])
|
||||
def test_spice_extractor(mocker, freqai_conf, indicator, caplog):
|
||||
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
|
||||
freqai.dk.find_features(unfiltered_dataframe)
|
||||
|
||||
features_filtered, labels_filtered = freqai.dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
freqai.dk.training_features_list,
|
||||
freqai.dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
vec = freqai.dk.spice_extractor(indicator, features_filtered)
|
||||
if 'doesnt_exist' in indicator:
|
||||
assert log_has_re(
|
||||
"User asked spice_rack for",
|
||||
caplog,
|
||||
)
|
||||
else:
|
||||
assert len(vec) == 2860
|
||||
|
@ -1,3 +1,4 @@
|
||||
import copy
|
||||
import platform
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
@ -382,6 +383,31 @@ def test_plot_feature_importance(mocker, freqai_conf):
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_spice_rack(mocker, default_conf, tmpdir, caplog):
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, default_conf)
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
strategy.dp = DataProvider(default_conf, exchange)
|
||||
|
||||
default_conf.update({"freqai_spice_rack": "true"})
|
||||
default_conf.update({"freqai_identifier": "spicy-id"})
|
||||
default_conf["config_files"] = [Path('config_examples', 'config_freqai.example.json')]
|
||||
default_conf["timerange"] = "20180110-20180115"
|
||||
default_conf["datadir"] = Path(default_conf["datadir"])
|
||||
default_conf['exchange'].update({'pair_whitelist':
|
||||
['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
|
||||
default_conf["user_data_dir"] = Path(tmpdir)
|
||||
freqai_conf = copy.deepcopy(default_conf)
|
||||
|
||||
strategy.config = freqai_conf
|
||||
strategy.load_freqAI_model()
|
||||
|
||||
assert log_has_re("Spice rack will use LTC/USD", caplog)
|
||||
assert log_has_re("Spice rack will use 15m", caplog)
|
||||
assert 'freqai' in freqai_conf
|
||||
assert strategy.freqai
|
||||
|
||||
|
||||
@pytest.mark.parametrize('timeframes,corr_pairs', [
|
||||
(['5m'], ['ADA/BTC', 'DASH/BTC']),
|
||||
(['5m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
|
||||
|
Loading…
Reference in New Issue
Block a user