mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-09-21 18:11:11 +00:00
2b416d3b62
- optimized pairs for entry_varholder and exit_varholder to only check a single pair instead of all pairs. - bias-check of freqai strategies now possible - added condition to not crash when compared_df is empty (meaning no differences have been found)
253 lines
10 KiB
Python
Executable File
253 lines
10 KiB
Python
Executable File
import copy
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import pathlib
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import shutil
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.data.history import get_timerange
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from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.optimize.backtesting import Backtesting
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class VarHolder:
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timerange: TimeRange
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data: DataFrame
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indicators: DataFrame
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result: DataFrame
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compared: DataFrame
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from_dt: datetime
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to_dt: datetime
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compared_dt: datetime
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class Analysis:
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def __init__(self):
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self.total_signals = 0
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self.false_entry_signals = 0
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self.false_exit_signals = 0
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self.false_indicators = []
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self.has_bias = False
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total_signals: int
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false_entry_signals: int
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false_exit_signals: int
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false_indicators: list
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has_bias: bool
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class BacktestLookaheadBiasChecker:
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def __init__(self):
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self.exportfilename = None
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self.strategy_obj = None
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self.current_analysis = None
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self.local_config = None
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self.full_varHolder = None
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self.entry_varHolder = None
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self.exit_varHolder = None
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self.entry_varHolders = []
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self.exit_varHolders = []
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self.backtesting = None
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self.minimum_trade_amount = None
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self.targeted_trade_amount = None
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self.failed_bias_check = True
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@staticmethod
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def dt_to_timestamp(dt):
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timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp())
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return timestamp
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@staticmethod
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def get_result(backtesting, processed):
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min_date, max_date = get_timerange(processed)
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result = backtesting.backtest(
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processed=deepcopy(processed),
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start_date=min_date,
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end_date=max_date
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)
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return result
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@staticmethod
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def report_signal(result, column_name, checked_timestamp):
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df = result['results']
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row_count = df[column_name].shape[0]
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if row_count == 0:
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return False
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else:
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df_cut = df[(df[column_name] == checked_timestamp)]
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if df_cut[column_name].shape[0] == 0:
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# print("did NOT find the same signal in column " + column_name +
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# " at timestamp " + str(checked_timestamp))
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return False
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else:
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return True
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return False
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# analyzes two data frames with processed indicators and shows differences between them.
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def analyze_indicators(self, full_vars, cut_vars, current_pair):
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# extract dataframes
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cut_df = cut_vars.indicators[current_pair]
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full_df = full_vars.indicators[current_pair]
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# cut longer dataframe to length of the shorter
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full_df_cut = full_df[
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(full_df.date == cut_vars.compared_dt)
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].reset_index(drop=True)
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cut_df_cut = cut_df[
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(cut_df.date == cut_vars.compared_dt)
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].reset_index(drop=True)
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# compare dataframes
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if full_df_cut.shape[0] != 0:
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if cut_df_cut.shape[0] != 0:
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compare_df = full_df_cut.compare(cut_df_cut)
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if compare_df.shape[0] > 0:
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for col_name, values in compare_df.items():
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col_idx = compare_df.columns.get_loc(col_name)
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compare_df_row = compare_df.iloc[0]
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# compare_df now comprises tuples with [1] having either 'self' or 'other'
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if 'other' in col_name[1]:
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continue
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self_value = compare_df_row[col_idx]
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other_value = compare_df_row[col_idx + 1]
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# output differences
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if self_value != other_value:
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if not self.current_analysis.false_indicators.__contains__(col_name[0]):
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self.current_analysis.false_indicators.append(col_name[0])
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print(f"=> found look ahead bias in indicator {col_name[0]}. " +
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f"{str(self_value)} != {str(other_value)}")
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def prepare_data(self, varHolder, pairs_to_load):
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# purge previous data
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abs_folder_path = pathlib.Path("user_data/models/uniqe-id").resolve()
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# remove folder and its contents
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if pathlib.Path.exists(abs_folder_path):
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shutil.rmtree(abs_folder_path)
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prepare_data_config = copy.deepcopy(self.local_config)
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prepare_data_config['timerange'] = (str(self.dt_to_timestamp(varHolder.from_dt)) + "-" +
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str(self.dt_to_timestamp(varHolder.to_dt)))
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prepare_data_config['exchange']['pair_whitelist'] = pairs_to_load
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self.backtesting = Backtesting(prepare_data_config)
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self.backtesting._set_strategy(self.backtesting.strategylist[0])
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varHolder.data, varHolder.timerange = self.backtesting.load_bt_data()
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self.backtesting.load_bt_data_detail()
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varHolder.indicators = self.backtesting.strategy.advise_all_indicators(varHolder.data)
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varHolder.result = self.get_result(self.backtesting, varHolder.indicators)
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def start(self, config, strategy_obj: dict, args) -> None:
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# deepcopy so we can change the pairs for the 2ndary runs
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# and not worry about another strategy to check after.
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self.local_config = deepcopy(config)
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self.local_config['strategy_list'] = [strategy_obj['name']]
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self.current_analysis = Analysis()
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self.minimum_trade_amount = args['minimum_trade_amount']
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self.targeted_trade_amount = args['targeted_trade_amount']
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self.exportfilename = args['exportfilename']
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self.strategy_obj = strategy_obj
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# first make a single backtest
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self.full_varHolder = VarHolder()
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# define datetime in human-readable format
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parsed_timerange = TimeRange.parse_timerange(config['timerange'])
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if parsed_timerange.startdt is None:
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self.full_varHolder.from_dt = datetime.utcfromtimestamp(0)
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else:
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self.full_varHolder.from_dt = parsed_timerange.startdt
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if parsed_timerange.stopdt is None:
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self.full_varHolder.to_dt = datetime.now()
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else:
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self.full_varHolder.to_dt = parsed_timerange.stopdt
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self.prepare_data(self.full_varHolder, self.local_config['pairs'])
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found_signals: int = self.full_varHolder.result['results'].shape[0] + 1
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if found_signals >= self.targeted_trade_amount:
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print(f"Found {found_signals} trades, calculating {self.targeted_trade_amount} trades.")
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elif self.targeted_trade_amount >= found_signals >= self.minimum_trade_amount:
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print(f"Only found {found_signals} trades. Calculating all available trades.")
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else:
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print(f"found {found_signals} trades "
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f"which is less than minimum_trade_amount {self.minimum_trade_amount}. "
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f"Cancelling this backtest lookahead bias test.")
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return
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# now we loop through all entry signals
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# starting from the same datetime to avoid miss-reports of bias
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for idx, result_row in self.full_varHolder.result['results'].iterrows():
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if self.current_analysis.total_signals == self.targeted_trade_amount:
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break
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# if force-sold, ignore this signal since here it will unconditionally exit.
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if result_row.close_date == self.dt_to_timestamp(self.full_varHolder.to_dt):
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continue
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self.current_analysis.total_signals += 1
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self.entry_varHolder = VarHolder()
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self.exit_varHolder = VarHolder()
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self.entry_varHolders.append(self.entry_varHolder)
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self.exit_varHolders.append(self.exit_varHolder)
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self.entry_varHolder.from_dt = self.full_varHolder.from_dt
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self.entry_varHolder.compared_dt = result_row['open_date']
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# to_dt needs +1 candle since it won't buy on the last candle
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self.entry_varHolder.to_dt = (result_row['open_date'] +
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timedelta(minutes=timeframe_to_minutes(
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self.local_config['timeframe'])))
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self.prepare_data(self.entry_varHolder, [result_row['pair']])
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# to_dt needs +1 candle since it will always exit/force-exit trades on the last candle
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self.exit_varHolder.from_dt = self.full_varHolder.from_dt
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self.exit_varHolder.to_dt = (result_row['close_date'] +
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timedelta(minutes=timeframe_to_minutes(
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self.local_config['timeframe'])))
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self.exit_varHolder.compared_dt = result_row['close_date']
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self.prepare_data(self.exit_varHolder, [result_row['pair']])
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# register if buy signal is broken
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if not self.report_signal(
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self.entry_varHolder.result, "open_date", self.entry_varHolder.compared_dt):
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self.current_analysis.false_entry_signals += 1
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# register if buy or sell signal is broken
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if not self.report_signal(
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self.exit_varHolder.result, "close_date", self.exit_varHolder.compared_dt):
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self.current_analysis.false_exit_signals += 1
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if len(self.entry_varHolders) >= 10:
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pass
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# check if the indicators themselves contain biased data
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self.analyze_indicators(self.full_varHolder, self.entry_varHolder, result_row['pair'])
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self.analyze_indicators(self.full_varHolder, self.exit_varHolder, result_row['pair'])
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if (self.current_analysis.false_entry_signals > 0 or
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self.current_analysis.false_exit_signals > 0 or
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len(self.current_analysis.false_indicators) > 0):
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print(" => " + self.local_config['strategy_list'][0] + ": bias detected!")
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self.current_analysis.has_bias = True
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else:
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print(self.local_config['strategy_list'][0] + ": no bias detected")
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self.failed_bias_check = False
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