mirror of
https://github.com/freqtrade/freqtrade.git
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262 lines
11 KiB
Python
Executable File
262 lines
11 KiB
Python
Executable File
import copy
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import logging
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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 typing import Any, Dict, List
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import pandas as pd
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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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logger = logging.getLogger(__name__)
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class VarHolder:
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timerange: TimeRange
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data: pd.DataFrame
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indicators: pd.DataFrame
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result: pd.DataFrame
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compared: pd.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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timeframe: str
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class Analysis:
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def __init__(self) -> None:
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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: List[str] = []
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self.has_bias = False
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class LookaheadAnalysis:
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def __init__(self, config: Dict[str, Any], strategy_obj: Dict):
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self.failed_bias_check = True
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self.full_varHolder = VarHolder
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self.entry_varHolders: List[VarHolder] = []
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self.exit_varHolders: List[VarHolder] = []
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# pull variables the scope of the lookahead_analysis-instance
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self.local_config = deepcopy(config)
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self.local_config['strategy'] = strategy_obj['name']
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self.current_analysis = Analysis()
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self.minimum_trade_amount = config['minimum_trade_amount']
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self.targeted_trade_amount = config['targeted_trade_amount']
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self.strategy_obj = strategy_obj
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@staticmethod
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def dt_to_timestamp(dt: datetime):
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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: pd.DataFrame):
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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: dict, column_name: str, checked_timestamp: datetime):
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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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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: VarHolder, cut_vars: VarHolder, 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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logging.info(f"=> found look ahead bias in indicator "
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f"{col_name[0]}. "
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f"{str(self_value)} != {str(other_value)}")
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def prepare_data(self, varholder: VarHolder, pairs_to_load: List[pd.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 = (
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pathlib.Path(f"{self.local_config['user_data_dir']}"
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"/models/"
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f"{self.local_config['freqai']['identifier']}").resolve())
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# remove folder and its contents
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if pathlib.Path.exists(path_to_current_identifier):
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shutil.rmtree(path_to_current_identifier)
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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.timeframe = self.backtesting.timeframe
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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 fill_full_varholder(self):
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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(self.local_config['timerange'])
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if parsed_timerange.startdt is None:
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self.full_varHolder.from_dt = datetime.fromtimestamp(0, tz=timezone.utc)
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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.utcnow()
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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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def fill_entry_and_exit_varHolders(self, result_row):
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# entry_varHolder
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entry_varHolder = VarHolder()
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self.entry_varHolders.append(entry_varHolder)
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entry_varHolder.from_dt = self.full_varHolder.from_dt
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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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entry_varHolder.to_dt = (
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result_row['open_date'] +
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timedelta(minutes=timeframe_to_minutes(self.full_varHolder.timeframe)))
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self.prepare_data(entry_varHolder, [result_row['pair']])
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# exit_varHolder
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exit_varHolder = VarHolder()
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self.exit_varHolders.append(exit_varHolder)
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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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exit_varHolder.from_dt = self.full_varHolder.from_dt
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exit_varHolder.to_dt = (
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result_row['close_date'] +
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timedelta(minutes=timeframe_to_minutes(self.full_varHolder.timeframe)))
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exit_varHolder.compared_dt = result_row['close_date']
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self.prepare_data(exit_varHolder, [result_row['pair']])
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# now we analyze a full trade of full_varholder and look for analyze its bias
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def analyze_row(self, idx, result_row):
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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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return
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# keep track of how many signals are processed at total
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self.current_analysis.total_signals += 1
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# fill entry_varHolder and exit_varHolder
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self.fill_entry_and_exit_varHolders(result_row)
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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_varHolders[idx].result,
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"open_date",
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self.entry_varHolders[idx].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_varHolders[idx].result,
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"close_date",
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self.exit_varHolders[idx].compared_dt):
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self.current_analysis.false_exit_signals += 1
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# check if the indicators themselves contain biased data
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self.analyze_indicators(self.full_varHolder, self.entry_varHolders[idx], result_row['pair'])
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self.analyze_indicators(self.full_varHolder, self.exit_varHolders[idx], result_row['pair'])
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def start(self) -> None:
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# first make a single backtest
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self.fill_full_varholder()
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# check if requirements have been met of full_varholder
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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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logging.info(f"Found {found_signals} trades, "
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f"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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logging.info(f"Only found {found_signals} trades. Calculating all available trades.")
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else:
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logging.info(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 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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self.analyze_row(idx, result_row)
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# check and report signals
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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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logging.info(f" => {self.local_config['strategy']} + : bias detected!")
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self.current_analysis.has_bias = True
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else:
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logging.info(self.local_config['strategy'] + ": no bias detected")
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self.failed_bias_check = False
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