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