Merge pull request #7612 from freqtrade/reduce-indicator-population
avoid redundant indicator population for corr_pair list
This commit is contained in:
Matthias
GitHub
commit
b82fc3fabd
@ -61,7 +61,7 @@ The FreqAI strategy requires including the following lines of code in the standa
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"""
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Function designed to automatically generate, name and merge features
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from user indicated timeframes in the configuration file. User controls the indicators
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passed to the training/prediction by prepending indicators with `'%-' + coin `
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passed to the training/prediction by prepending indicators with `'%-' + pair `
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(see convention below). I.e. user should not prepend any supporting metrics
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(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
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model.
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@ -69,20 +69,17 @@ The FreqAI strategy requires including the following lines of code in the standa
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:param df: strategy dataframe which will receive merges from informatives
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:param tf: timeframe of the dataframe which will modify the feature names
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:param informative: the dataframe associated with the informative pair
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:param coin: the name of the coin which will modify the feature names.
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"""
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coin = pair.split('/')[0]
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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@ -134,7 +131,7 @@ Notice also the location of the labels under `if set_generalized_indicators:` at
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(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
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```python
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def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
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def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
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...
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@ -2,7 +2,10 @@
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## Defining the features
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Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`.
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Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
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!!! Note
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Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
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Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
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@ -15,7 +18,7 @@ It is advisable to start from the template `populate_any_indicators()` in the so
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"""
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Function designed to automatically generate, name, and merge features
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from user-indicated timeframes in the configuration file. The user controls the indicators
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passed to the training/prediction by prepending indicators with `'%-' + coin `
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passed to the training/prediction by prepending indicators with `'%-' + pair `
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(see convention below). I.e., the user should not prepend any supporting metrics
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(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
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model.
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@ -23,37 +26,34 @@ It is advisable to start from the template `populate_any_indicators()` in the so
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:param df: strategy dataframe which will receive merges from informatives
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:param tf: timeframe of the dataframe which will modify the feature names
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:param informative: the dataframe associated with the informative pair
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:param coin: the name of the coin which will modify the feature names.
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"""
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coin = pair.split('/')[0]
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(informative), window=t, stds=2.2
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)
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informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
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informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
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informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
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informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
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informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
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informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
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informative[f"%-{coin}bb_width-period_{t}"] = (
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informative[f"{coin}bb_upperband-period_{t}"]
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- informative[f"{coin}bb_lowerband-period_{t}"]
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) / informative[f"{coin}bb_middleband-period_{t}"]
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informative[f"%-{coin}close-bb_lower-period_{t}"] = (
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informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
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informative[f"%-{pair}bb_width-period_{t}"] = (
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informative[f"{pair}bb_upperband-period_{t}"]
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- informative[f"{pair}bb_lowerband-period_{t}"]
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) / informative[f"{pair}bb_middleband-period_{t}"]
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informative[f"%-{pair}close-bb_lower-period_{t}"] = (
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informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
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)
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informative[f"%-{coin}relative_volume-period_{t}"] = (
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informative[f"%-{pair}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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@ -1137,6 +1137,51 @@ class FreqaiDataKitchen:
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if pair not in self.all_pairs:
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self.all_pairs.append(pair)
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def extract_corr_pair_columns_from_populated_indicators(
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self,
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dataframe: DataFrame
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) -> Dict[str, DataFrame]:
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"""
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Find the columns of the dataframe corresponding to the corr_pairlist, save them
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in a dictionary to be reused and attached to other pairs.
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:param dataframe: fully populated dataframe (current pair + corr_pairs)
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:return: corr_dataframes, dictionary of dataframes to be attached
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to other pairs in same candle.
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"""
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corr_dataframes: Dict[str, DataFrame] = {}
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pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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for pair in pairs:
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valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
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pair_cols = [col for col in dataframe.columns if
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any(substr in col for substr in valid_strs)]
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pair_cols.insert(0, 'date')
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corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
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return corr_dataframes
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def attach_corr_pair_columns(self, dataframe: DataFrame,
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corr_dataframes: Dict[str, DataFrame],
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current_pair: str) -> DataFrame:
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"""
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Attach the existing corr_pair dataframes to the current pair dataframe before training
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:param dataframe: current pair strategy dataframe, indicators populated already
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:param corr_dataframes: dictionary of saved dataframes from earlier in the same candle
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:param current_pair: current pair to which we will attach corr pair dataframe
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:return:
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:dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs
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ready for training
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"""
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pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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for pair in pairs:
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if current_pair != pair:
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dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
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return dataframe
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def use_strategy_to_populate_indicators(
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self,
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strategy: IStrategy,
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@ -1144,6 +1189,7 @@ class FreqaiDataKitchen:
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base_dataframes: dict = {},
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pair: str = "",
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prediction_dataframe: DataFrame = pd.DataFrame(),
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do_corr_pairs: bool = True,
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) -> DataFrame:
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"""
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Use the user defined strategy for populating indicators during retrain
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@ -1153,15 +1199,15 @@ class FreqaiDataKitchen:
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:param base_dataframes: dict = dict containing the current pair dataframes
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(for user defined timeframes)
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:param metadata: dict = strategy furnished pair metadata
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:returns:
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:return:
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dataframe: DataFrame = dataframe containing populated indicators
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"""
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# for prediction dataframe creation, we let dataprovider handle everything in the strategy
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# so we create empty dictionaries, which allows us to pass None to
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# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
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tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
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pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
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pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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if not prediction_dataframe.empty:
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dataframe = prediction_dataframe.copy()
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for tf in tfs:
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@ -1184,15 +1230,18 @@ class FreqaiDataKitchen:
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informative=base_dataframes[tf],
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set_generalized_indicators=sgi
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)
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if pairs:
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for i in pairs:
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if pair in i:
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# ensure corr pairs are always last
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for corr_pair in pairs:
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if pair == corr_pair:
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continue # dont repeat anything from whitelist
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for tf in tfs:
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if pairs and do_corr_pairs:
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dataframe = strategy.populate_any_indicators(
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i,
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corr_pair,
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dataframe.copy(),
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tf,
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informative=corr_dataframes[i][tf]
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informative=corr_dataframes[corr_pair][tf]
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)
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self.get_unique_classes_from_labels(dataframe)
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@ -5,7 +5,6 @@ from abc import ABC, abstractmethod
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from collections import deque
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from datetime import datetime, timezone
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from pathlib import Path
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from threading import Lock
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from typing import Any, Dict, List, Literal, Tuple
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import numpy as np
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@ -71,6 +70,7 @@ class IFreqaiModel(ABC):
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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self.scanning = False
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self.ft_params = self.freqai_info["feature_parameters"]
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self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
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self.keras: bool = self.freqai_info.get("keras", False)
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if self.keras and self.ft_params.get("DI_threshold", 0):
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self.ft_params["DI_threshold"] = 0
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@ -82,9 +82,6 @@ class IFreqaiModel(ABC):
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self.pair_it_train = 0
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self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
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self.train_queue = self._set_train_queue()
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self.last_trade_database_summary: DataFrame = {}
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self.current_trade_database_summary: DataFrame = {}
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self.analysis_lock = Lock()
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self.inference_time: float = 0
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self.train_time: float = 0
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self.begin_time: float = 0
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@ -92,6 +89,10 @@ 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.corr_dataframes: Dict[str, DataFrame] = {}
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# get_corr_dataframes is controlling the caching of corr_dataframes
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# for improved performance. Careful with this boolean.
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self.get_corr_dataframes: bool = True
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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@ -364,9 +365,9 @@ class IFreqaiModel(ABC):
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# load the model and associated data into the data kitchen
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self.model = self.dd.load_data(metadata["pair"], dk)
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with self.analysis_lock:
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dataframe = self.dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
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dataframe = dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"],
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do_corr_pairs=self.get_corr_dataframes
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)
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if not self.model:
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@ -376,6 +377,9 @@ class IFreqaiModel(ABC):
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self.dd.return_null_values_to_strategy(dataframe, dk)
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return dk
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if self.corr_pairlist:
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dataframe = self.cache_corr_pairlist_dfs(dataframe, dk)
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dk.find_labels(dataframe)
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self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
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@ -559,7 +563,6 @@ class IFreqaiModel(ABC):
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data_load_timerange, pair, dk
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)
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with self.analysis_lock:
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unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, pair
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)
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@ -680,6 +683,8 @@ class IFreqaiModel(ABC):
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" avoid blinding open trades and degrading performance.")
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self.pair_it = 0
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self.inference_time = 0
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if self.corr_pairlist:
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self.get_corr_dataframes = True
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return
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def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
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@ -738,6 +743,29 @@ 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 cache_corr_pairlist_dfs(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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Cache the corr_pairlist dfs to speed up performance for subsequent pairs during the
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current candle.
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:param dataframe: strategy fed dataframe
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:param dk: datakitchen object for current asset
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:return: dataframe to attach/extract cached corr_pair dfs to/from.
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"""
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if self.get_corr_dataframes:
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self.corr_dataframes = dk.extract_corr_pair_columns_from_populated_indicators(dataframe)
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if not self.corr_dataframes:
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logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
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"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
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"is included in the column names when you are creating features "
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"in `populate_any_indicators()`.")
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self.get_corr_dataframes = not bool(self.corr_dataframes)
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else:
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dataframe = dk.attach_corr_pair_columns(
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dataframe, self.corr_dataframes, dk.pair)
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return dataframe
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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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@ -110,8 +110,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
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:param informative: the dataframe associated with the informative pair
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"""
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coin = pair.split('/')[0]
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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@ -119,13 +117,13 @@ class FreqaiExampleHybridStrategy(IStrategy):
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
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informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
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informative[f"%-{coin}relative_volume-period_{t}"] = (
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informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
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informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
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informative[f"%-{pair}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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@ -53,7 +53,7 @@ class FreqaiExampleStrategy(IStrategy):
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"""
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Function designed to automatically generate, name and merge features
|
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from user indicated timeframes in the configuration file. User controls the indicators
|
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passed to the training/prediction by prepending indicators with `'%-' + coin `
|
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passed to the training/prediction by prepending indicators with `f'%-{pair}`
|
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(see convention below). I.e. user should not prepend any supporting metrics
|
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(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
|
||||
model.
|
||||
@ -63,8 +63,6 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
:param informative: the dataframe associated with the informative pair
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
@ -72,36 +70,36 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
|
||||
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
|
||||
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
|
||||
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
|
||||
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
|
||||
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(informative), window=t, stds=2.2
|
||||
)
|
||||
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
|
||||
informative[f"%-{coin}bb_width-period_{t}"] = (
|
||||
informative[f"{coin}bb_upperband-period_{t}"]
|
||||
- informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{coin}bb_middleband-period_{t}"]
|
||||
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
informative[f"%-{pair}bb_width-period_{t}"] = (
|
||||
informative[f"{pair}bb_upperband-period_{t}"]
|
||||
- informative[f"{pair}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{pair}bb_middleband-period_{t}"]
|
||||
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
|
||||
)
|
||||
|
||||
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
||||
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative[f"%-{pair}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{pair}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{pair}raw_price"] = informative["close"]
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
|
||||
Loading…
Reference in New Issue
Block a user