remove analysis_lock and realign example hybrid strat
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parent
391c3f56f7
commit
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@ -6,7 +6,6 @@ from abc import ABC, abstractmethod
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from collections import deque
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from collections import deque
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from datetime import datetime, timezone
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from datetime import datetime, timezone
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from pathlib import Path
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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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from typing import Any, Dict, List, Literal, Tuple
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import numpy as np
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import numpy as np
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@ -84,7 +83,6 @@ class IFreqaiModel(ABC):
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self.pair_it_train = 0
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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.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.train_queue = self._set_train_queue()
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self.analysis_lock = Lock()
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self.inference_time: float = 0
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self.inference_time: float = 0
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self.train_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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self.begin_time: float = 0
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@ -563,10 +561,9 @@ class IFreqaiModel(ABC):
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data_load_timerange, pair, dk
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data_load_timerange, pair, dk
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)
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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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unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, pair
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strategy, corr_dataframes, base_dataframes, pair
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)
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)
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unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
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unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
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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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:param informative: the dataframe associated with the informative pair
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"""
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"""
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coin = pair.split('/')[0]
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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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@ -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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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(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"%-{pair}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"%-{pair}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"%-{pair}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"%-{pair}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"%-{pair}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"%-{pair}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}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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)
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