mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-15 12:43:56 +00:00
184 lines
7.2 KiB
Python
184 lines
7.2 KiB
Python
import logging
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import shutil
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from copy import deepcopy
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from datetime import timedelta
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from pathlib import Path
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from typing import Any, Dict, List
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from pandas import DataFrame
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from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.loggers.set_log_levels import (
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reduce_verbosity_for_bias_tester,
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restore_verbosity_for_bias_tester,
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)
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from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.optimize.base_analysis import BaseAnalysis, VarHolder
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logger = logging.getLogger(__name__)
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class RecursiveAnalysis(BaseAnalysis):
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def __init__(self, config: Dict[str, Any], strategy_obj: Dict):
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self._startup_candle = config.get("startup_candle", [199, 399, 499, 999, 1999])
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super().__init__(config, strategy_obj)
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self.partial_varHolder_array: List[VarHolder] = []
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self.partial_varHolder_lookahead_array: List[VarHolder] = []
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self.dict_recursive: Dict[str, Any] = dict()
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# For recursive bias check
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# analyzes two data frames with processed indicators and shows differences between them.
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def analyze_indicators(self):
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pair_to_check = self.local_config["pairs"][0]
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logger.info("Start checking for recursive bias")
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# check and report signals
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base_last_row = self.full_varHolder.indicators[pair_to_check].iloc[-1]
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for part in self.partial_varHolder_array:
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part_last_row = part.indicators[pair_to_check].iloc[-1]
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compare_df = base_last_row.compare(part_last_row)
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if compare_df.shape[0] > 0:
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# print(compare_df)
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for col_name, values in compare_df.items():
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# print(col_name)
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if "other" == col_name:
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continue
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indicators = values.index
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for indicator in indicators:
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if indicator not in self.dict_recursive:
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self.dict_recursive[indicator] = {}
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values_diff = compare_df.loc[indicator]
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values_diff_self = values_diff.loc["self"]
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values_diff_other = values_diff.loc["other"]
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diff = (values_diff_other - values_diff_self) / values_diff_self * 100
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self.dict_recursive[indicator][part.startup_candle] = f"{diff:.3f}%"
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else:
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logger.info("No variance on indicator(s) found due to recursive formula.")
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break
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# For lookahead bias check
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# analyzes two data frames with processed indicators and shows differences between them.
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def analyze_indicators_lookahead(self):
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pair_to_check = self.local_config["pairs"][0]
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logger.info("Start checking for lookahead bias on indicators only")
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part = self.partial_varHolder_lookahead_array[0]
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part_last_row = part.indicators[pair_to_check].iloc[-1]
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date_to_check = part_last_row["date"]
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index_to_get = self.full_varHolder.indicators[pair_to_check]["date"] == date_to_check
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base_row_check = self.full_varHolder.indicators[pair_to_check].loc[index_to_get].iloc[-1]
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check_time = part.to_dt.strftime("%Y-%m-%dT%H:%M:%S")
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logger.info(f"Check indicators at {check_time}")
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# logger.info(f"vs {part_timerange} with {part.startup_candle} startup candle")
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compare_df = base_row_check.compare(part_last_row)
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if compare_df.shape[0] > 0:
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# print(compare_df)
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for col_name, values in compare_df.items():
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# print(col_name)
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if "other" == col_name:
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continue
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indicators = values.index
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for indicator in indicators:
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logger.info(f"=> found lookahead in indicator {indicator}")
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# logger.info("base value {:.5f}".format(values_diff_self))
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# logger.info("part value {:.5f}".format(values_diff_other))
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else:
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logger.info("No lookahead bias on indicators found.")
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def prepare_data(self, varholder: VarHolder, pairs_to_load: List[DataFrame]):
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if "freqai" in self.local_config and "identifier" in self.local_config["freqai"]:
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# purge previous data if the freqai model is defined
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# (to be sure nothing is carried over from older backtests)
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path_to_current_identifier = Path(
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f"{self.local_config['user_data_dir']}/models/"
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f"{self.local_config['freqai']['identifier']}"
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).resolve()
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# remove folder and its contents
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if Path.exists(path_to_current_identifier):
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shutil.rmtree(path_to_current_identifier)
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prepare_data_config = deepcopy(self.local_config)
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prepare_data_config["timerange"] = (
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str(self.dt_to_timestamp(varholder.from_dt))
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+ "-"
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+ str(self.dt_to_timestamp(varholder.to_dt))
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)
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prepare_data_config["exchange"]["pair_whitelist"] = pairs_to_load
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backtesting = Backtesting(prepare_data_config, self.exchange)
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self.exchange = backtesting.exchange
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backtesting._set_strategy(backtesting.strategylist[0])
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varholder.data, varholder.timerange = backtesting.load_bt_data()
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backtesting.load_bt_data_detail()
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varholder.timeframe = backtesting.timeframe
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varholder.indicators = backtesting.strategy.advise_all_indicators(varholder.data)
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def fill_partial_varholder(self, start_date, startup_candle):
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logger.info(f"Calculating indicators using startup candle of {startup_candle}.")
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partial_varHolder = VarHolder()
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partial_varHolder.from_dt = start_date
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partial_varHolder.to_dt = self.full_varHolder.to_dt
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partial_varHolder.startup_candle = startup_candle
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self.local_config["startup_candle_count"] = startup_candle
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self.prepare_data(partial_varHolder, self.local_config["pairs"])
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self.partial_varHolder_array.append(partial_varHolder)
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def fill_partial_varholder_lookahead(self, end_date):
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logger.info("Calculating indicators to test lookahead on indicators.")
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partial_varHolder = VarHolder()
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partial_varHolder.from_dt = self.full_varHolder.from_dt
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partial_varHolder.to_dt = end_date
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self.prepare_data(partial_varHolder, self.local_config["pairs"])
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self.partial_varHolder_lookahead_array.append(partial_varHolder)
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def start(self) -> None:
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super().start()
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reduce_verbosity_for_bias_tester()
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start_date_full = self.full_varHolder.from_dt
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end_date_full = self.full_varHolder.to_dt
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timeframe_minutes = timeframe_to_minutes(self.full_varHolder.timeframe)
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end_date_partial = start_date_full + timedelta(minutes=int(timeframe_minutes * 10))
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self.fill_partial_varholder_lookahead(end_date_partial)
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# restore_verbosity_for_bias_tester()
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start_date_partial = end_date_full - timedelta(minutes=int(timeframe_minutes))
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for startup_candle in self._startup_candle:
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self.fill_partial_varholder(start_date_partial, int(startup_candle))
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# Restore verbosity, so it's not too quiet for the next strategy
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restore_verbosity_for_bias_tester()
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self.analyze_indicators()
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self.analyze_indicators_lookahead()
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