match config and strats to upstream freqai
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@ -55,6 +55,7 @@
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}
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}
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],
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],
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"freqai": {
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"freqai": {
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"enabled": true,
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"model_save_type": "stable_baselines_ppo",
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"model_save_type": "stable_baselines_ppo",
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"conv_width": 10,
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"conv_width": 10,
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"follow_mode": false,
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"follow_mode": false,
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@ -62,7 +62,7 @@ class ReinforcementLearningExample3ac(IStrategy):
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coin = pair.split('/')[0]
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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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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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informative = self.dp.get_pair_dataframe(pair, tf)
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@ -111,8 +111,6 @@ class ReinforcementLearningExample3ac(IStrategy):
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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self.freqai_info = self.config["freqai"]
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dataframe = self.freqai.start(dataframe, metadata, self)
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dataframe = self.freqai.start(dataframe, metadata, self)
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return dataframe
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return dataframe
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@ -62,7 +62,7 @@ class ReinforcementLearningExample5ac(IStrategy):
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coin = pair.split('/')[0]
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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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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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informative = self.dp.get_pair_dataframe(pair, tf)
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@ -111,8 +111,6 @@ class ReinforcementLearningExample5ac(IStrategy):
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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self.freqai_info = self.config["freqai"]
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dataframe = self.freqai.start(dataframe, metadata, self)
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dataframe = self.freqai.start(dataframe, metadata, self)
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return dataframe
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return dataframe
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