update code to use historic_predictions for freqai_backtest_live_models
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@@ -81,6 +81,7 @@ class FreqaiDataDrawer:
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self.historic_predictions_bkp_path = Path(
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self.full_path / "historic_predictions.backup.pkl")
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self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
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self.global_metadata_path = Path(self.full_path / "global_metadata.json")
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self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
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self.follow_mode = follow_mode
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if follow_mode:
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@@ -125,6 +126,17 @@ class FreqaiDataDrawer:
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self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
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self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
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def load_global_metadata_from_disk(self):
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"""
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Locate and load a previously saved global metadata in present model folder.
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"""
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exists = self.global_metadata_path.is_file()
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if exists:
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with open(self.global_metadata_path, "r") as fp:
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metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
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return metatada_dict
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return {}
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def load_drawer_from_disk(self):
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"""
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Locate and load a previously saved data drawer full of all pair model metadata in
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@@ -225,6 +237,15 @@ class FreqaiDataDrawer:
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rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
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number_mode=rapidjson.NM_NATIVE)
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def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
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"""
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Save global metadata json to disk
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"""
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with self.save_lock:
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with open(self.global_metadata_path, 'w') as fp:
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rapidjson.dump(metadata, fp, default=self.np_encoder,
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number_mode=rapidjson.NM_NATIVE)
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def create_follower_dict(self):
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"""
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Create or dictionary for each follower to maintain unique persistent prediction targets
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