import logging from pathlib import Path import joblib import pandas as pd from tabulate import tabulate from freqtrade.configuration import TimeRange from freqtrade.constants import Config from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data, load_backtest_stats) from freqtrade.exceptions import OperationalException logger = logging.getLogger(__name__) def _load_signal_candles(backtest_dir: Path): if backtest_dir.is_dir(): scpf = Path(backtest_dir, Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl" ) else: scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl") try: scp = open(scpf, "rb") signal_candles = joblib.load(scp) logger.info(f"Loaded signal candles: {str(scpf)}") except Exception as e: logger.error("Cannot load signal candles from pickled results: ", e) return signal_candles def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles): analysed_trades_dict = {} analysed_trades_dict[strategy_name] = {} try: logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs") for pair in pairlist: if pair in signal_candles[strategy_name]: analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators( pair, trades, signal_candles[strategy_name][pair]) except Exception as e: print(f"Cannot process entry/exit reasons for {strategy_name}: ", e) return analysed_trades_dict def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame): buyf = signal_candles if len(buyf) > 0: buyf = buyf.set_index('date', drop=False) trades_red = trades.loc[trades['pair'] == pair].copy() trades_inds = pd.DataFrame() if trades_red.shape[0] > 0 and buyf.shape[0] > 0: for t, v in trades_red.open_date.items(): allinds = buyf.loc[(buyf['date'] < v)] if allinds.shape[0] > 0: tmp_inds = allinds.iloc[[-1]] trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0] trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag'] tmp_inds.index.rename('signal_date', inplace=True) trades_inds = pd.concat([trades_inds, tmp_inds]) if 'signal_date' in trades_red: trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True) trades_red.set_index('signal_date', inplace=True) try: trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer') except Exception as e: raise e return trades_red else: return pd.DataFrame() def _do_group_table_output(bigdf, glist): for g in glist: # 0: summary wins/losses grouped by enter tag if g == "0": group_mask = ['enter_reason'] wins = bigdf.loc[bigdf['profit_abs'] >= 0] \ .groupby(group_mask) \ .agg({'profit_abs': ['sum']}) wins.columns = ['profit_abs_wins'] loss = bigdf.loc[bigdf['profit_abs'] < 0] \ .groupby(group_mask) \ .agg({'profit_abs': ['sum']}) loss.columns = ['profit_abs_loss'] new = bigdf.groupby(group_mask).agg({'profit_abs': [ 'count', lambda x: sum(x > 0), lambda x: sum(x <= 0)]}) new = pd.concat([new, wins, loss], axis=1).fillna(0) new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss']) new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0) new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0) new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0) new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss', 'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss'] sortcols = ['total_num_buys'] _print_table(new, sortcols, show_index=True) else: agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'], 'profit_ratio': ['median', 'mean', 'sum']} agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median', 'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct', 'total_profit_pct'] sortcols = ['profit_abs_sum', 'enter_reason'] # 1: profit summaries grouped by enter_tag if g == "1": group_mask = ['enter_reason'] # 2: profit summaries grouped by enter_tag and exit_tag if g == "2": group_mask = ['enter_reason', 'exit_reason'] # 3: profit summaries grouped by pair and enter_tag if g == "3": group_mask = ['pair', 'enter_reason'] # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large) if g == "4": group_mask = ['pair', 'enter_reason', 'exit_reason'] # 5: profit summaries grouped by exit_tag if g == "5": group_mask = ['exit_reason'] sortcols = ['exit_reason'] if group_mask: new = bigdf.groupby(group_mask).agg(agg_mask).reset_index() new.columns = group_mask + agg_cols new['median_profit_pct'] = new['median_profit_pct'] * 100 new['mean_profit_pct'] = new['mean_profit_pct'] * 100 new['total_profit_pct'] = new['total_profit_pct'] * 100 _print_table(new, sortcols) else: logger.warning("Invalid group mask specified.") def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'): if timerange: if timerange.starttype == 'date': df = df.loc[(df[df_date_col] >= timerange.startdt)] if timerange.stoptype == 'date': df = df.loc[(df[df_date_col] < timerange.stopdt)] return df def _select_rows_by_tags(df, enter_reason_list, exit_reason_list): if enter_reason_list and "all" not in enter_reason_list: df = df.loc[(df['enter_reason'].isin(enter_reason_list))] if exit_reason_list and "all" not in exit_reason_list: df = df.loc[(df['exit_reason'].isin(exit_reason_list))] return df def prepare_results(analysed_trades, stratname, enter_reason_list, exit_reason_list, timerange=None): res_df = pd.DataFrame() for pair, trades in analysed_trades[stratname].items(): res_df = pd.concat([res_df, trades], ignore_index=True) res_df = _select_rows_within_dates(res_df, timerange) if res_df is not None and res_df.shape[0] > 0 and ('enter_reason' in res_df.columns): res_df = _select_rows_by_tags(res_df, enter_reason_list, exit_reason_list) return res_df def print_results(res_df, analysis_groups, indicator_list): if res_df.shape[0] > 0: if analysis_groups: _do_group_table_output(res_df, analysis_groups) if "all" in indicator_list: print(res_df) elif indicator_list is not None: available_inds = [] for ind in indicator_list: if ind in res_df: available_inds.append(ind) ilist = ["pair", "enter_reason", "exit_reason"] + available_inds _print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False) else: print("\\No trades to show") def _print_table(df, sortcols=None, show_index=False): if (sortcols is not None): data = df.sort_values(sortcols) else: data = df print( tabulate( data, headers='keys', tablefmt='psql', showindex=show_index ) ) def process_entry_exit_reasons(config: Config): try: analysis_groups = config.get('analysis_groups', []) enter_reason_list = config.get('enter_reason_list', ["all"]) exit_reason_list = config.get('exit_reason_list', ["all"]) indicator_list = config.get('indicator_list', []) timerange = TimeRange.parse_timerange(None if config.get( 'timerange') is None else str(config.get('timerange'))) backtest_stats = load_backtest_stats(config['exportfilename']) for strategy_name, results in backtest_stats['strategy'].items(): trades = load_backtest_data(config['exportfilename'], strategy_name) if not trades.empty: signal_candles = _load_signal_candles(config['exportfilename']) analysed_trades_dict = _process_candles_and_indicators( config['exchange']['pair_whitelist'], strategy_name, trades, signal_candles) res_df = prepare_results(analysed_trades_dict, strategy_name, enter_reason_list, exit_reason_list, timerange=timerange) print_results(res_df, analysis_groups, indicator_list) except ValueError as e: raise OperationalException(e) from e