diff --git a/freqtrade/commands/__init__.py b/freqtrade/commands/__init__.py index 66a9c995b..8add45241 100644 --- a/freqtrade/commands/__init__.py +++ b/freqtrade/commands/__init__.py @@ -22,6 +22,7 @@ from freqtrade.commands.optimize_commands import (start_backtesting, start_backt start_edge, start_hyperopt) from freqtrade.commands.pairlist_commands import start_test_pairlist from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit -from freqtrade.commands.strategy_utils_commands import start_strategy_update +from freqtrade.commands.strategy_utils_commands import (start_backtest_lookahead_bias_checker, + start_strategy_update) from freqtrade.commands.trade_commands import start_trading from freqtrade.commands.webserver_commands import start_webserver diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 47aa37fdf..d79216b21 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -116,8 +116,14 @@ NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"] -ARGS_STRATEGY_UTILS = ["strategy_list", "strategy_path", "recursive_strategy_search"] +ARGS_STRATEGY_UPDATER = ARGS_COMMON_OPTIMIZE + ["strategy_list"] +ARGS_BACKTEST_LOOKAHEAD_BIAS_CHECKER = ARGS_BACKTEST + + +# + ["target_trades", "minimum_trades", +# "target_trades", "exportfilename"] +# will be added when the base version works. class Arguments: """ @@ -192,7 +198,8 @@ class Arguments: self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot') self._build_args(optionlist=['version'], parser=self.parser) - from freqtrade.commands import (start_analysis_entries_exits, start_backtesting, + from freqtrade.commands import (start_analysis_entries_exits, + start_backtest_lookahead_bias_checker, start_backtesting, start_backtesting_show, start_convert_data, start_convert_db, start_convert_trades, start_create_userdir, start_download_data, start_edge, @@ -450,4 +457,14 @@ class Arguments: 'files to the current version', parents=[_common_parser]) strategy_updater_cmd.set_defaults(func=start_strategy_update) - self._build_args(optionlist=ARGS_STRATEGY_UTILS, parser=strategy_updater_cmd) + self._build_args(optionlist=ARGS_STRATEGY_UPDATER, parser=strategy_updater_cmd) + + # Add backtest lookahead bias checker subcommand + backtest_lookahead_bias_checker_cmd = \ + subparsers.add_parser('backtest_lookahead_bias_checker', + help="checks for potential look ahead bias", + parents=[_common_parser]) + backtest_lookahead_bias_checker_cmd.set_defaults(func=start_backtest_lookahead_bias_checker) + + self._build_args(optionlist=ARGS_BACKTEST_LOOKAHEAD_BIAS_CHECKER, + parser=backtest_lookahead_bias_checker_cmd) diff --git a/freqtrade/commands/strategy_utils_commands.py b/freqtrade/commands/strategy_utils_commands.py index e579ec475..8bce9d4f9 100644 --- a/freqtrade/commands/strategy_utils_commands.py +++ b/freqtrade/commands/strategy_utils_commands.py @@ -7,6 +7,7 @@ from typing import Any, Dict from freqtrade.configuration import setup_utils_configuration from freqtrade.enums import RunMode from freqtrade.resolvers import StrategyResolver +from freqtrade.strategy.backtest_lookahead_bias_checker import backtest_lookahead_bias_checker from freqtrade.strategy.strategyupdater import StrategyUpdater @@ -53,3 +54,47 @@ def start_conversion(strategy_obj, config): instance_strategy_updater.start(config, strategy_obj) elapsed = time.perf_counter() - start print(f"Conversion of {Path(strategy_obj['location']).name} took {elapsed:.1f} seconds.") + + # except: + # pass + + +def start_backtest_lookahead_bias_checker(args: Dict[str, Any]) -> None: + """ + Start the backtest bias tester script + :param args: Cli args from Arguments() + :return: None + """ + config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE) + + strategy_objs = StrategyResolver.search_all_objects( + config, enum_failed=False, recursive=config.get('recursive_strategy_search', False)) + + filtered_strategy_objs = [] + if 'strategy_list' in args and args['strategy_list'] is not None: + for args_strategy in args['strategy_list']: + for strategy_obj in strategy_objs: + if (strategy_obj['name'] == args_strategy + and strategy_obj not in filtered_strategy_objs): + filtered_strategy_objs.append(strategy_obj) + break + + for filtered_strategy_obj in filtered_strategy_objs: + initialize_single_lookahead_bias_checker(filtered_strategy_obj, config) + else: + processed_locations = set() + for strategy_obj in strategy_objs: + if strategy_obj['location'] not in processed_locations: + processed_locations.add(strategy_obj['location']) + initialize_single_lookahead_bias_checker(strategy_obj, config) + + +def initialize_single_lookahead_bias_checker(strategy_obj, config): + # try: + print(f"Bias test of {Path(strategy_obj['location']).name} started.") + instance_backtest_lookahead_bias_checker = backtest_lookahead_bias_checker() + start = time.perf_counter() + instance_backtest_lookahead_bias_checker.start(config, strategy_obj) + elapsed = time.perf_counter() - start + print(f"checking look ahead bias via backtests of {Path(strategy_obj['location']).name} " + f"took {elapsed:.1f} seconds.") diff --git a/freqtrade/strategy/backtest_lookahead_bias_checker.py b/freqtrade/strategy/backtest_lookahead_bias_checker.py new file mode 100644 index 000000000..288786c19 --- /dev/null +++ b/freqtrade/strategy/backtest_lookahead_bias_checker.py @@ -0,0 +1,241 @@ +# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument +from copy import deepcopy +from datetime import datetime, timedelta, timezone + +import pandas + +from freqtrade.configuration import TimeRange +from freqtrade.data.history import get_timerange +from freqtrade.exchange import timeframe_to_minutes +from freqtrade.optimize.backtesting import Backtesting + + +class backtest_lookahead_bias_checker: + class varHolder: + timerange: TimeRange + data: pandas.DataFrame + indicators: pandas.DataFrame + result: pandas.DataFrame + compared: pandas.DataFrame + from_dt: datetime + to_dt: datetime + compared_dt: datetime + + class analysis: + def __init__(self): + self.total_signals = 0 + self.false_entry_signals = 0 + self.false_exit_signals = 0 + self.false_indicators = [] + self.has_bias = False + + total_signals: int + false_entry_signals: int + false_exit_signals: int + + false_indicators: list + has_bias: bool + + def __init__(self): + self.strategy_obj + self.current_analysis + self.config + self.full_varHolder + self.entry_varholder + self.exit_varholder + self.backtesting + self.signals_to_check: int = 20 + self.current_analysis + self.full_varHolder.from_dt + self.full_varHolder.to_dt + + @staticmethod + def dt_to_timestamp(dt): + timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp()) + return timestamp + + def get_result(self, backtesting, processed): + min_date, max_date = get_timerange(processed) + + result = backtesting.backtest( + processed=deepcopy(processed), + start_date=min_date, + end_date=max_date + ) + return result + + # analyzes two data frames with processed indicators and shows differences between them. + def analyze_indicators(self, full_vars, cut_vars, current_pair): + # extract dataframes + cut_df = cut_vars.indicators[current_pair] + full_df = full_vars.indicators[current_pair] + + # cut longer dataframe to length of the shorter + full_df_cut = full_df[ + (full_df.date == cut_vars.compared_dt) + ].reset_index(drop=True) + cut_df_cut = cut_df[ + (cut_df.date == cut_vars.compared_dt) + ].reset_index(drop=True) + + # compare dataframes + if full_df_cut.shape[0] != 0: + if cut_df_cut.shape[0] != 0: + compare_df = full_df_cut.compare(cut_df_cut) + + # skippedColumns = ["date", "open", "high", "low", "close", "volume"] + for col_name, values in compare_df.items(): + col_idx = compare_df.columns.get_loc(col_name) + compare_df_row = compare_df.iloc[0] + # compare_df now is comprised of tuples with [1] having either 'self' or 'other' + if 'other' in col_name[1]: + continue + self_value = compare_df_row[col_idx] + other_value = compare_df_row[col_idx + 1] + other_value = compare_df_row[col_idx + 1] + + # output differences + if self_value != other_value: + + if not self.current_analysis.false_indicators.__contains__(col_name[0]): + self.current_analysis.false_indicators.append(col_name[0]) + print(f"=> found look ahead bias in indicator {col_name[0]}. " + + f"{str(self_value)} != {str(other_value)}") + # return compare_df + + def report_signal(self, result, column_name, checked_timestamp): + df = result['results'] + row_count = df[column_name].shape[0] + + if row_count == 0: + return False + else: + + df_cut = df[(df[column_name] == checked_timestamp)] + if df_cut[column_name].shape[0] == 0: + # print("did NOT find the same signal in column " + column_name + + # " at timestamp " + str(checked_timestamp)) + return False + else: + return True + return False + + def prepare_data(self, varholder, var_pairs): + self.config['timerange'] = \ + str(int(self.dt_to_timestamp(varholder.from_dt))) + "-" + \ + str(int(self.dt_to_timestamp(varholder.to_dt))) + self.backtesting = Backtesting(self.config) + self.backtesting._set_strategy(self.backtesting.strategylist[0]) + varholder.data, varholder.timerange = self.backtesting.load_bt_data() + varholder.indicators = self.backtesting.strategy.advise_all_indicators(varholder.data) + varholder.result = self.get_result(self.backtesting, varholder.indicators) + + def start(self, config, strategy_obj: dict) -> None: + self.strategy_obj = strategy_obj + self.config = config + self.current_analysis = backtest_lookahead_bias_checker.analysis() + + max_try_signals: int = 3 + found_signals: int = 0 + continue_with_strategy = True + + # first we need to get the necessary entry/exit signals + # so we start by 14 days and increase in 1 month steps + # until we have the desired trade amount. + for try_buysignals in range(max_try_signals): # range(3) = 0..2 + # re-initialize backtesting-variable + self.full_varHolder = backtest_lookahead_bias_checker.varHolder() + + # define datetimes in human readable format + self.full_varHolder.from_dt = datetime(2022, 9, 1) + self.full_varHolder.to_dt = datetime(2022, 9, 15) + timedelta(days=30 * try_buysignals) + + self.prepare_data(self.full_varHolder, self.config['pairs']) + + found_signals = self.full_varHolder.result['results'].shape[0] + 1 + if try_buysignals == max_try_signals - 1: + if found_signals < self.signals_to_check / 2: + print(f"... only found {str(int(found_signals / 2))} " + f"buy signals for {self.strategy_obj['name']}. " + f"Cancelling...") + continue_with_strategy = False + else: + print( + f"Found {str(found_signals)} buy signals. " + f"Going with max {str(self.signals_to_check)} " + f" buy signals in the full timerange from " + f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}") + break + elif found_signals < self.signals_to_check: + print( + f"Only found {str(found_signals)} buy signals in the full timerange from " + f"{str(self.full_varHolder.from_dt)} to " + f"{str(self.full_varHolder.to_dt)}. " + f"will increase timerange trying to get at least " + f"{str(self.signals_to_check)} signals.") + else: + print( + f"Found {str(found_signals)} buy signals, more than necessary. " + f"Reducing to {str(self.signals_to_check)} " + f"checked buy signals in the full timerange from " + f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}") + break + if not continue_with_strategy: + return + + for idx, result_row in self.full_varHolder.result['results'].iterrows(): + if self.current_analysis.total_signals == self.signals_to_check: + break + + # if force-sold, ignore this signal since here it will unconditionally exit. + if result_row.close_date == self.dt_to_timestamp(self.full_varHolder.to_dt): + continue + + self.current_analysis.total_signals += 1 + + self.entry_varholder = backtest_lookahead_bias_checker.varHolder() + self.exit_varholder = backtest_lookahead_bias_checker.varHolder() + + self.entry_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date'] + self.entry_varholder.compared_dt = result_row['open_date'] + + # to_dt needs +1 candle since it won't buy on the last candle + self.entry_varholder.to_dt = result_row['open_date'] + \ + timedelta(minutes=timeframe_to_minutes(self.config['timeframe']) * 2) + + self.prepare_data(self.entry_varholder, [result_row['pair']]) + + # --- + # print("analyzing the sell signal") + # to_dt needs +1 candle since it will always sell all trades on the last candle + self.exit_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date'] + self.exit_varholder.to_dt = \ + result_row['close_date'] + \ + timedelta(minutes=timeframe_to_minutes(self.config['timeframe'])) + self.exit_varholder.compared_dt = result_row['close_date'] + + self.prepare_data(self.exit_varholder, [result_row['pair']]) + + # register if buy signal is broken + if not self.report_signal( + self.entry_varholder.result, + "open_date", self.entry_varholder.compared_dt): + self.current_analysis.false_entry_signals += 1 + + # register if buy or sell signal is broken + if not self.report_signal(self.entry_varholder.result, + "open_date", self.entry_varholder.compared_dt) \ + or not self.report_signal(self.exit_varholder.result, + "close_date", self.exit_varholder.compared_dt): + self.current_analysis.false_exit_signals += 1 + + self.analyze_indicators(self.full_varHolder, self.entry_varholder, result_row['pair']) + self.analyze_indicators(self.full_varHolder, self.exit_varholder, result_row['pair']) + + if self.current_analysis.false_entry_signals > 0 or \ + self.current_analysis.false_exit_signals > 0 or \ + len(self.current_analysis.false_indicators) > 0: + print(" => " + self.strategy_obj['name'] + ": bias detected!") + self.current_analysis.has_bias = True + else: + print(self.strategy_obj['name'] + ": no bias detected")