Move "freqai.lock" to backend to simplify user interface
This commit is contained in:
Matthias
@@ -63,48 +63,47 @@ class freqai_test_classifier(IStrategy):
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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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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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# 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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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"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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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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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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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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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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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-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down')
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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-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down')
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return df
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@@ -62,67 +62,66 @@ class freqai_test_multimodel_strat(IStrategy):
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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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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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# 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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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"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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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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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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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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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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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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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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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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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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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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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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@@ -62,55 +62,54 @@ class freqai_test_strat(IStrategy):
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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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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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# 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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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"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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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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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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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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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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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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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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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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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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return df
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Block a user