*BREAKING CHANGE* remove unnecessary arguments from populate_any_indicators(), accommodate tests
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@ -892,29 +892,26 @@ class FreqaiDataKitchen:
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else:
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dataframe = base_dataframes[self.config["timeframe"]].copy()
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sgi = True
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sgi = False
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for tf in tfs:
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if tf == tfs[-1]:
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sgi = True # doing this last allows user to use all tf raw prices in labels
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dataframe = strategy.populate_any_indicators(
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pair,
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pair,
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dataframe.copy(),
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tf,
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informative=base_dataframes[tf],
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coin=pair.split("/")[0] + "-",
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set_generalized_indicators=sgi,
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set_generalized_indicators=sgi
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)
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sgi = False
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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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continue # dont repeat anything from whitelist
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dataframe = strategy.populate_any_indicators(
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pair,
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i,
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dataframe.copy(),
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tf,
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informative=corr_dataframes[i][tf],
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coin=i.split("/")[0] + "-",
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informative=corr_dataframes[i][tf]
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)
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return dataframe
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@ -557,8 +557,8 @@ class IStrategy(ABC, HyperStrategyMixin):
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"""
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return None
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def populate_any_indicators(self, basepair: str, pair: str, df: DataFrame, tf: str,
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informative: DataFrame = None, coin: str = "",
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def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
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informative: DataFrame = None,
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set_generalized_indicators: bool = False) -> DataFrame:
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"""
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Function designed to automatically generate, name and merge features
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@ -570,7 +570,6 @@ class IStrategy(ABC, HyperStrategyMixin):
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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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return df
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@ -65,7 +65,7 @@ class FreqaiExampleStrategy(IStrategy):
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return informative_pairs
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def populate_any_indicators(
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self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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"""
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Function designed to automatically generate, name and merge features
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@ -78,9 +78,10 @@ class FreqaiExampleStrategy(IStrategy):
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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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with self.freqai.lock:
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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@ -92,11 +93,8 @@ class FreqaiExampleStrategy(IStrategy):
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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"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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informative[f"%-{coin}close_over_20sma-period_{t}"] = (
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informative["close"] / informative[f"{coin}20sma-period_{t}"]
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)
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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}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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@ -148,8 +146,6 @@ class FreqaiExampleStrategy(IStrategy):
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# user adds targets here by prepending them with &- (see convention below)
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# If user wishes to use multiple targets, a multioutput prediction model
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# needs to be used such as templates/CatboostPredictionMultiModel.py
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df["&-s_close"] = (
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df["close"]
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.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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@ -159,6 +155,23 @@ class FreqaiExampleStrategy(IStrategy):
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- 1
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)
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# If user wishes to use multiple targets, they can add more by
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# appending more columns with '&'. User should keep in mind that multi targets
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# requires a multioutput prediction model such as
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# templates/CatboostPredictionMultiModel.py,
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# df["&-s_range"] = (
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .max()
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# -
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .min()
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# )
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@ -62,7 +62,7 @@ class freqai_test_multimodel_strat(IStrategy):
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return informative_pairs
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def populate_any_indicators(
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self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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"""
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Function designed to automatically generate, name and merge features
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@ -79,6 +79,8 @@ class freqai_test_multimodel_strat(IStrategy):
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: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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with self.freqai.lock:
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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@ -62,7 +62,7 @@ class freqai_test_strat(IStrategy):
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return informative_pairs
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def populate_any_indicators(
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self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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"""
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Function designed to automatically generate, name and merge features
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@ -79,6 +79,8 @@ class freqai_test_strat(IStrategy):
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: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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with self.freqai.lock:
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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