242 lines
10 KiB
Python
242 lines
10 KiB
Python
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# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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import pandas
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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 backtest_lookahead_bias_checker:
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class varHolder:
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timerange: TimeRange
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data: pandas.DataFrame
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indicators: pandas.DataFrame
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result: pandas.DataFrame
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compared: pandas.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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def __init__(self):
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self.strategy_obj
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self.current_analysis
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self.config
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self.full_varHolder
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self.entry_varholder
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self.exit_varholder
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self.backtesting
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self.signals_to_check: int = 20
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self.current_analysis
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self.full_varHolder.from_dt
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self.full_varHolder.to_dt
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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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def get_result(self, 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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# 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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# skippedColumns = ["date", "open", "high", "low", "close", "volume"]
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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 is comprised of 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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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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# return compare_df
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def report_signal(self, 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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def prepare_data(self, varholder, var_pairs):
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self.config['timerange'] = \
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str(int(self.dt_to_timestamp(varholder.from_dt))) + "-" + \
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str(int(self.dt_to_timestamp(varholder.to_dt)))
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self.backtesting = Backtesting(self.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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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) -> None:
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self.strategy_obj = strategy_obj
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self.config = config
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self.current_analysis = backtest_lookahead_bias_checker.analysis()
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max_try_signals: int = 3
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found_signals: int = 0
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continue_with_strategy = True
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# first we need to get the necessary entry/exit signals
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# so we start by 14 days and increase in 1 month steps
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# until we have the desired trade amount.
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for try_buysignals in range(max_try_signals): # range(3) = 0..2
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# re-initialize backtesting-variable
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self.full_varHolder = backtest_lookahead_bias_checker.varHolder()
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# define datetimes in human readable format
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self.full_varHolder.from_dt = datetime(2022, 9, 1)
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self.full_varHolder.to_dt = datetime(2022, 9, 15) + timedelta(days=30 * try_buysignals)
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self.prepare_data(self.full_varHolder, self.config['pairs'])
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found_signals = self.full_varHolder.result['results'].shape[0] + 1
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if try_buysignals == max_try_signals - 1:
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if found_signals < self.signals_to_check / 2:
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print(f"... only found {str(int(found_signals / 2))} "
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f"buy signals for {self.strategy_obj['name']}. "
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f"Cancelling...")
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continue_with_strategy = False
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else:
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print(
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f"Found {str(found_signals)} buy signals. "
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f"Going with max {str(self.signals_to_check)} "
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f" buy signals in the full timerange from "
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f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}")
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break
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elif found_signals < self.signals_to_check:
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print(
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f"Only found {str(found_signals)} buy signals in the full timerange from "
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f"{str(self.full_varHolder.from_dt)} to "
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f"{str(self.full_varHolder.to_dt)}. "
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f"will increase timerange trying to get at least "
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f"{str(self.signals_to_check)} signals.")
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else:
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print(
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f"Found {str(found_signals)} buy signals, more than necessary. "
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f"Reducing to {str(self.signals_to_check)} "
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f"checked buy signals in the full timerange from "
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f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}")
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break
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if not continue_with_strategy:
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return
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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.signals_to_check:
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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 = backtest_lookahead_bias_checker.varHolder()
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self.exit_varholder = backtest_lookahead_bias_checker.varHolder()
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self.entry_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date']
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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(self.config['timeframe']) * 2)
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self.prepare_data(self.entry_varholder, [result_row['pair']])
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# ---
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# print("analyzing the sell signal")
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# to_dt needs +1 candle since it will always sell all trades on the last candle
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self.exit_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date']
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self.exit_varholder.to_dt = \
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result_row['close_date'] + \
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timedelta(minutes=timeframe_to_minutes(self.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,
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"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(self.entry_varholder.result,
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"open_date", self.entry_varholder.compared_dt) \
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or not self.report_signal(self.exit_varholder.result,
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"close_date", self.exit_varholder.compared_dt):
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self.current_analysis.false_exit_signals += 1
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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.strategy_obj['name'] + ": bias detected!")
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self.current_analysis.has_bias = True
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else:
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print(self.strategy_obj['name'] + ": no bias detected")
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