import logging from copy import deepcopy from datetime import datetime, timedelta, timezone from pathlib import Path from typing import Any, Dict, List, Union from pandas import DataFrame, to_datetime from tabulate import tabulate from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT, Config) from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change, calculate_max_drawdown) from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename logger = logging.getLogger(__name__) def store_backtest_stats( recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None: """ Stores backtest results :param recordfilename: Path object, which can either be a filename or a directory. Filenames will be appended with a timestamp right before the suffix while for directories, /backtest-result-.json will be used as filename :param stats: Dataframe containing the backtesting statistics :param dtappendix: Datetime to use for the filename """ if recordfilename.is_dir(): filename = (recordfilename / f'backtest-result-{dtappendix}.json') else: filename = Path.joinpath( recordfilename.parent, f'{recordfilename.stem}-{dtappendix}' ).with_suffix(recordfilename.suffix) # Store metadata separately. file_dump_json(get_backtest_metadata_filename(filename), stats['metadata']) del stats['metadata'] file_dump_json(filename, stats) latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN) file_dump_json(latest_filename, {'latest_backtest': str(filename.name)}) def _store_backtest_analysis_data( recordfilename: Path, data: Dict[str, Dict], dtappendix: str, name: str) -> Path: """ Stores backtest trade candles for analysis :param recordfilename: Path object, which can either be a filename or a directory. Filenames will be appended with a timestamp right before the suffix while for directories, /backtest-result-_.pkl will be used as filename :param candles: Dict containing the backtesting data for analysis :param dtappendix: Datetime to use for the filename :param name: Name to use for the file, e.g. signals, rejected """ if recordfilename.is_dir(): filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl') else: filename = Path.joinpath( recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl' ) file_dump_joblib(filename, data) return filename def store_backtest_signal_candles( recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path: return _store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals") def store_backtest_rejected_trades( recordfilename: Path, trades: Dict[str, Dict], dtappendix: str) -> Path: return _store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected") def _get_line_floatfmt(stake_currency: str) -> List[str]: """ Generate floatformat (goes in line with _generate_result_line()) """ return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f', '.2f', 'd', 's', 's'] def _get_line_header(first_column: str, stake_currency: str, direction: str = 'Entries') -> List[str]: """ Generate header lines (goes in line with _generate_result_line()) """ return [first_column, direction, 'Avg Profit %', 'Cum Profit %', f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration', 'Win Draw Loss Win%'] def generate_wins_draws_losses(wins, draws, losses): if wins > 0 and losses == 0: wl_ratio = '100' elif wins == 0: wl_ratio = '0' else: wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100' return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}' def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict: """ Generate one result dict, with "first_column" as key. """ profit_sum = result['profit_ratio'].sum() # (end-capital - starting capital) / starting capital profit_total = result['profit_abs'].sum() / starting_balance return { 'key': first_column, 'trades': len(result), 'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0, 'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0, 'profit_sum': profit_sum, 'profit_sum_pct': round(profit_sum * 100.0, 2), 'profit_total_abs': result['profit_abs'].sum(), 'profit_total': profit_total, 'profit_total_pct': round(profit_total * 100.0, 2), 'duration_avg': str(timedelta( minutes=round(result['trade_duration'].mean())) ) if not result.empty else '0:00', # 'duration_max': str(timedelta( # minutes=round(result['trade_duration'].max())) # ) if not result.empty else '0:00', # 'duration_min': str(timedelta( # minutes=round(result['trade_duration'].min())) # ) if not result.empty else '0:00', 'wins': len(result[result['profit_abs'] > 0]), 'draws': len(result[result['profit_abs'] == 0]), 'losses': len(result[result['profit_abs'] < 0]), } def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int, results: DataFrame, skip_nan: bool = False) -> List[Dict]: """ Generates and returns a list for the given backtest data and the results dataframe :param pairlist: Pairlist used :param stake_currency: stake-currency - used to correctly name headers :param starting_balance: Starting balance :param results: Dataframe containing the backtest results :param skip_nan: Print "left open" open trades :return: List of Dicts containing the metrics per pair """ tabular_data = [] for pair in pairlist: result = results[results['pair'] == pair] if skip_nan and result['profit_abs'].isnull().all(): continue tabular_data.append(_generate_result_line(result, starting_balance, pair)) # Sort by total profit %: tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True) # Append Total tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL')) return tabular_data def generate_tag_metrics(tag_type: str, starting_balance: int, results: DataFrame, skip_nan: bool = False) -> List[Dict]: """ Generates and returns a list of metrics for the given tag trades and the results dataframe :param starting_balance: Starting balance :param results: Dataframe containing the backtest results :param skip_nan: Print "left open" open trades :return: List of Dicts containing the metrics per pair """ tabular_data = [] if tag_type in results.columns: for tag, count in results[tag_type].value_counts().items(): result = results[results[tag_type] == tag] if skip_nan and result['profit_abs'].isnull().all(): continue tabular_data.append(_generate_result_line(result, starting_balance, tag)) # Sort by total profit %: tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True) # Append Total tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL')) return tabular_data else: return [] def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]: """ Generate small table outlining Backtest results :param max_open_trades: Max_open_trades parameter :param results: Dataframe containing the backtest result for one strategy :return: List of Dicts containing the metrics per Sell reason """ tabular_data = [] for reason, count in results['exit_reason'].value_counts().items(): result = results.loc[results['exit_reason'] == reason] profit_mean = result['profit_ratio'].mean() profit_sum = result['profit_ratio'].sum() profit_total = profit_sum / max_open_trades tabular_data.append( { 'exit_reason': reason, 'trades': count, 'wins': len(result[result['profit_abs'] > 0]), 'draws': len(result[result['profit_abs'] == 0]), 'losses': len(result[result['profit_abs'] < 0]), 'profit_mean': profit_mean, 'profit_mean_pct': round(profit_mean * 100, 2), 'profit_sum': profit_sum, 'profit_sum_pct': round(profit_sum * 100, 2), 'profit_total_abs': result['profit_abs'].sum(), 'profit_total': profit_total, 'profit_total_pct': round(profit_total * 100, 2), } ) return tabular_data def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]: """ Generate summary per strategy :param bt_stats: Dict of containing results for all strategies :return: List of Dicts containing the metrics per Strategy """ tabular_data = [] for strategy, result in bt_stats.items(): tabular_data.append(deepcopy(result['results_per_pair'][-1])) # Update "key" to strategy (results_per_pair has it as "Total"). tabular_data[-1]['key'] = strategy tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account'] tabular_data[-1]['max_drawdown_abs'] = round_coin_value( result['max_drawdown_abs'], result['stake_currency'], False) return tabular_data def generate_edge_table(results: dict) -> str: floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd') tabular_data = [] headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio', 'Required Risk Reward', 'Expectancy', 'Total Number of Trades', 'Average Duration (min)'] for result in results.items(): if result[1].nb_trades > 0: tabular_data.append([ result[0], result[1].stoploss, result[1].winrate, result[1].risk_reward_ratio, result[1].required_risk_reward, result[1].expectancy, result[1].nb_trades, round(result[1].avg_trade_duration) ]) # Ignore type as floatfmt does allow tuples but mypy does not know that return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") def _get_resample_from_period(period: str) -> str: if period == 'day': return '1d' if period == 'week': return '1w' if period == 'month': return '1M' raise ValueError(f"Period {period} is not supported.") def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dict[str, Any]]: results = DataFrame.from_records(trade_list) if len(results) == 0: return [] results['close_date'] = to_datetime(results['close_date'], utc=True) resample_period = _get_resample_from_period(period) resampled = results.resample(resample_period, on='close_date') stats = [] for name, day in resampled: profit_abs = day['profit_abs'].sum().round(10) wins = sum(day['profit_abs'] > 0) draws = sum(day['profit_abs'] == 0) loses = sum(day['profit_abs'] < 0) stats.append( { 'date': name.strftime('%d/%m/%Y'), 'profit_abs': profit_abs, 'wins': wins, 'draws': draws, 'loses': loses } ) return stats def generate_trading_stats(results: DataFrame) -> Dict[str, Any]: """ Generate overall trade statistics """ if len(results) == 0: return { 'wins': 0, 'losses': 0, 'draws': 0, 'holding_avg': timedelta(), 'winner_holding_avg': timedelta(), 'loser_holding_avg': timedelta(), } winning_trades = results.loc[results['profit_ratio'] > 0] draw_trades = results.loc[results['profit_ratio'] == 0] losing_trades = results.loc[results['profit_ratio'] < 0] holding_avg = (timedelta(minutes=round(results['trade_duration'].mean())) if not results.empty else timedelta()) winner_holding_avg = (timedelta(minutes=round(winning_trades['trade_duration'].mean())) if not winning_trades.empty else timedelta()) loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean())) if not losing_trades.empty else timedelta()) return { 'wins': len(winning_trades), 'losses': len(losing_trades), 'draws': len(draw_trades), 'holding_avg': holding_avg, 'holding_avg_s': holding_avg.total_seconds(), 'winner_holding_avg': winner_holding_avg, 'winner_holding_avg_s': winner_holding_avg.total_seconds(), 'loser_holding_avg': loser_holding_avg, 'loser_holding_avg_s': loser_holding_avg.total_seconds(), } def generate_daily_stats(results: DataFrame) -> Dict[str, Any]: """ Generate daily statistics """ if len(results) == 0: return { 'backtest_best_day': 0, 'backtest_worst_day': 0, 'backtest_best_day_abs': 0, 'backtest_worst_day_abs': 0, 'winning_days': 0, 'draw_days': 0, 'losing_days': 0, 'daily_profit_list': [], } daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum() daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10) worst_rel = min(daily_profit_rel) best_rel = max(daily_profit_rel) worst = min(daily_profit) best = max(daily_profit) winning_days = sum(daily_profit > 0) draw_days = sum(daily_profit == 0) losing_days = sum(daily_profit < 0) daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()] return { 'backtest_best_day': best_rel, 'backtest_worst_day': worst_rel, 'backtest_best_day_abs': best, 'backtest_worst_day_abs': worst, 'winning_days': winning_days, 'draw_days': draw_days, 'losing_days': losing_days, 'daily_profit': daily_profit_list, } def generate_strategy_stats(pairlist: List[str], strategy: str, content: Dict[str, Any], min_date: datetime, max_date: datetime, market_change: float ) -> Dict[str, Any]: """ :param pairlist: List of pairs to backtest :param strategy: Strategy name :param content: Backtest result data in the format: {'results: results, 'config: config}}. :param min_date: Backtest start date :param max_date: Backtest end date :param market_change: float indicating the market change :return: Dictionary containing results per strategy and a strategy summary. """ results: Dict[str, DataFrame] = content['results'] if not isinstance(results, DataFrame): return {} config = content['config'] max_open_trades = min(config['max_open_trades'], len(pairlist)) start_balance = config['dry_run_wallet'] stake_currency = config['stake_currency'] pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency, starting_balance=start_balance, results=results, skip_nan=False) enter_tag_results = generate_tag_metrics("enter_tag", starting_balance=start_balance, results=results, skip_nan=False) exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades, results=results) left_open_results = generate_pair_metrics( pairlist, stake_currency=stake_currency, starting_balance=start_balance, results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True) daily_stats = generate_daily_stats(results) trade_stats = generate_trading_stats(results) best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'], key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'], key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None winning_profit = results.loc[results['profit_abs'] > 0, 'profit_abs'].sum() losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum() profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0 backtest_days = (max_date - min_date).days or 1 strat_stats = { 'trades': results.to_dict(orient='records'), 'locks': [lock.to_json() for lock in content['locks']], 'best_pair': best_pair, 'worst_pair': worst_pair, 'results_per_pair': pair_results, 'results_per_enter_tag': enter_tag_results, 'exit_reason_summary': exit_reason_stats, 'left_open_trades': left_open_results, # 'days_breakdown_stats': days_breakdown_stats, 'total_trades': len(results), 'trade_count_long': len(results.loc[~results['is_short']]), 'trade_count_short': len(results.loc[results['is_short']]), 'total_volume': float(results['stake_amount'].sum()), 'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0, 'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0, 'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0, 'profit_total': results['profit_abs'].sum() / start_balance, 'profit_total_long': results.loc[~results['is_short'], 'profit_abs'].sum() / start_balance, 'profit_total_short': results.loc[results['is_short'], 'profit_abs'].sum() / start_balance, 'profit_total_abs': results['profit_abs'].sum(), 'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(), 'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(), 'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']), 'profit_factor': profit_factor, 'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT), 'backtest_start_ts': int(min_date.timestamp() * 1000), 'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT), 'backtest_end_ts': int(max_date.timestamp() * 1000), 'backtest_days': backtest_days, 'backtest_run_start_ts': content['backtest_start_time'], 'backtest_run_end_ts': content['backtest_end_time'], 'trades_per_day': round(len(results) / backtest_days, 2), 'market_change': market_change, 'pairlist': pairlist, 'stake_amount': config['stake_amount'], 'stake_currency': config['stake_currency'], 'stake_currency_decimals': decimals_per_coin(config['stake_currency']), 'starting_balance': start_balance, 'dry_run_wallet': start_balance, 'final_balance': content['final_balance'], 'rejected_signals': content['rejected_signals'], 'timedout_entry_orders': content['timedout_entry_orders'], 'timedout_exit_orders': content['timedout_exit_orders'], 'canceled_trade_entries': content['canceled_trade_entries'], 'canceled_entry_orders': content['canceled_entry_orders'], 'replaced_entry_orders': content['replaced_entry_orders'], 'max_open_trades': max_open_trades, 'max_open_trades_setting': (config['max_open_trades'] if config['max_open_trades'] != float('inf') else -1), 'timeframe': config['timeframe'], 'timeframe_detail': config.get('timeframe_detail', ''), 'timerange': config.get('timerange', ''), 'enable_protections': config.get('enable_protections', False), 'strategy_name': strategy, # Parameters relevant for backtesting 'stoploss': config['stoploss'], 'trailing_stop': config.get('trailing_stop', False), 'trailing_stop_positive': config.get('trailing_stop_positive'), 'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0), 'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False), 'use_custom_stoploss': config.get('use_custom_stoploss', False), 'minimal_roi': config['minimal_roi'], 'use_exit_signal': config['use_exit_signal'], 'exit_profit_only': config['exit_profit_only'], 'exit_profit_offset': config['exit_profit_offset'], 'ignore_roi_if_entry_signal': config['ignore_roi_if_entry_signal'], **daily_stats, **trade_stats } try: max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown( results, value_col='profit_ratio') (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val, max_drawdown) = calculate_max_drawdown( results, value_col='profit_abs', starting_balance=start_balance) # max_relative_drawdown = Underwater (_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown( results, value_col='profit_abs', starting_balance=start_balance, relative=True) strat_stats.update({ 'max_drawdown': max_drawdown_legacy, # Deprecated - do not use 'max_drawdown_account': max_drawdown, 'max_relative_drawdown': max_relative_drawdown, 'max_drawdown_abs': drawdown_abs, 'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT), 'drawdown_start_ts': drawdown_start.timestamp() * 1000, 'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT), 'drawdown_end_ts': drawdown_end.timestamp() * 1000, 'max_drawdown_low': low_val, 'max_drawdown_high': high_val, }) csum_min, csum_max = calculate_csum(results, start_balance) strat_stats.update({ 'csum_min': csum_min, 'csum_max': csum_max }) except ValueError: strat_stats.update({ 'max_drawdown': 0.0, 'max_drawdown_account': 0.0, 'max_relative_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'max_drawdown_low': 0.0, 'max_drawdown_high': 0.0, 'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc), 'drawdown_start_ts': 0, 'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc), 'drawdown_end_ts': 0, 'csum_min': 0, 'csum_max': 0 }) return strat_stats def generate_backtest_stats(btdata: Dict[str, DataFrame], all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]], min_date: datetime, max_date: datetime ) -> Dict[str, Any]: """ :param btdata: Backtest data :param all_results: backtest result - dictionary in the form: { Strategy: {'results: results, 'config: config}}. :param min_date: Backtest start date :param max_date: Backtest end date :return: Dictionary containing results per strategy and a strategy summary. """ result: Dict[str, Any] = { 'metadata': {}, 'strategy': {}, 'strategy_comparison': [], } market_change = calculate_market_change(btdata, 'close') metadata = {} pairlist = list(btdata.keys()) for strategy, content in all_results.items(): strat_stats = generate_strategy_stats(pairlist, strategy, content, min_date, max_date, market_change=market_change) metadata[strategy] = { 'run_id': content['run_id'], 'backtest_start_time': content['backtest_start_time'], } result['strategy'][strategy] = strat_stats strategy_results = generate_strategy_comparison(bt_stats=result['strategy']) result['metadata'] = metadata result['strategy_comparison'] = strategy_results return result ### # Start output section ### def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row :param stake_currency: stake-currency - used to correctly name headers :return: pretty printed table with tabulate as string """ headers = _get_line_header('Pair', stake_currency) floatfmt = _get_line_floatfmt(stake_currency) output = [[ t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'], t['duration_avg'], generate_wins_draws_losses(t['wins'], t['draws'], t['losses']) ] for t in pair_results] # Ignore type as floatfmt does allow tuples but mypy does not know that return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str: """ Generate small table outlining Backtest results :param sell_reason_stats: Exit reason metrics :param stake_currency: Stakecurrency used :return: pretty printed table with tabulate as string """ headers = [ 'Exit Reason', 'Exits', 'Win Draws Loss Win%', 'Avg Profit %', 'Cum Profit %', f'Tot Profit {stake_currency}', 'Tot Profit %', ] output = [[ t.get('exit_reason', t.get('sell_reason')), t['trades'], generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), t['profit_mean_pct'], t['profit_sum_pct'], round_coin_value(t['profit_total_abs'], stake_currency, False), t['profit_total_pct'], ] for t in exit_reason_stats] return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right") def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row :param stake_currency: stake-currency - used to correctly name headers :return: pretty printed table with tabulate as string """ if (tag_type == "enter_tag"): headers = _get_line_header("TAG", stake_currency) else: headers = _get_line_header("TAG", stake_currency, 'Exits') floatfmt = _get_line_floatfmt(stake_currency) output = [ [ t['key'] if t['key'] is not None and len( t['key']) > 0 else "OTHER", t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'], t['duration_avg'], generate_wins_draws_losses( t['wins'], t['draws'], t['losses'])] for t in tag_results] # Ignore type as floatfmt does allow tuples but mypy does not know that return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]], stake_currency: str, period: str) -> str: """ Generate small table with Backtest results by days :param days_breakdown_stats: Days breakdown metrics :param stake_currency: Stakecurrency used :return: pretty printed table with tabulate as string """ headers = [ period.capitalize(), f'Tot Profit {stake_currency}', 'Wins', 'Draws', 'Losses', ] output = [[ d['date'], round_coin_value(d['profit_abs'], stake_currency, False), d['wins'], d['draws'], d['loses'], ] for d in days_breakdown_stats] return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right") def text_table_strategy(strategy_results, stake_currency: str) -> str: """ Generate summary table per strategy :param strategy_results: Dict of containing results for all strategies :param stake_currency: stake-currency - used to correctly name headers :return: pretty printed table with tabulate as string """ floatfmt = _get_line_floatfmt(stake_currency) headers = _get_line_header('Strategy', stake_currency) # _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless # therefore we slip this column in only for strategy summary here. headers.append('Drawdown') # Align drawdown string on the center two space separator. if 'max_drawdown_account' in strategy_results[0]: drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results] else: # Support for prior backtest results drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results] dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results]) dd_pad_per = max([len(dd) for dd in drawdown]) drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%' for t, dd in zip(strategy_results, drawdown)] output = [[ t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'], t['duration_avg'], generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown] for t, drawdown in zip(strategy_results, drawdown)] # Ignore type as floatfmt does allow tuples but mypy does not know that return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") def text_table_add_metrics(strat_results: Dict) -> str: if len(strat_results['trades']) > 0: best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio']) worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio']) short_metrics = [ ('', ''), # Empty line to improve readability ('Long / Short', f"{strat_results.get('trade_count_long', 'total_trades')} / " f"{strat_results.get('trade_count_short', 0)}"), ('Total profit Long %', f"{strat_results['profit_total_long']:.2%}"), ('Total profit Short %', f"{strat_results['profit_total_short']:.2%}"), ('Absolute profit Long', round_coin_value(strat_results['profit_total_long_abs'], strat_results['stake_currency'])), ('Absolute profit Short', round_coin_value(strat_results['profit_total_short_abs'], strat_results['stake_currency'])), ] if strat_results.get('trade_count_short', 0) > 0 else [] drawdown_metrics = [] if 'max_relative_drawdown' in strat_results: # Compatibility to show old hyperopt results drawdown_metrics.append( ('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}") ) drawdown_metrics.extend([ ('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}") if 'max_drawdown_account' in strat_results else ( 'Drawdown', f"{strat_results['max_drawdown']:.2%}"), ('Absolute Drawdown', round_coin_value(strat_results['max_drawdown_abs'], strat_results['stake_currency'])), ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'], strat_results['stake_currency'])), ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'], strat_results['stake_currency'])), ('Drawdown Start', strat_results['drawdown_start']), ('Drawdown End', strat_results['drawdown_end']), ]) entry_adjustment_metrics = [ ('Canceled Trade Entries', strat_results.get('canceled_trade_entries', 'N/A')), ('Canceled Entry Orders', strat_results.get('canceled_entry_orders', 'N/A')), ('Replaced Entry Orders', strat_results.get('replaced_entry_orders', 'N/A')), ] if strat_results.get('canceled_entry_orders', 0) > 0 else [] # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show # command stores these results and newer version of freqtrade must be able to handle old # results with missing new fields. metrics = [ ('Backtesting from', strat_results['backtest_start']), ('Backtesting to', strat_results['backtest_end']), ('Max open trades', strat_results['max_open_trades']), ('', ''), # Empty line to improve readability ('Total/Daily Avg Trades', f"{strat_results['total_trades']} / {strat_results['trades_per_day']}"), ('Starting balance', round_coin_value(strat_results['starting_balance'], strat_results['stake_currency'])), ('Final balance', round_coin_value(strat_results['final_balance'], strat_results['stake_currency'])), ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'], strat_results['stake_currency'])), ('Total profit %', f"{strat_results['profit_total']:.2%}"), ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'), ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor' in strat_results else 'N/A'), ('Trades per day', strat_results['trades_per_day']), ('Avg. daily profit %', f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"), ('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'], strat_results['stake_currency'])), ('Total trade volume', round_coin_value(strat_results['total_volume'], strat_results['stake_currency'])), *short_metrics, ('', ''), # Empty line to improve readability ('Best Pair', f"{strat_results['best_pair']['key']} " f"{strat_results['best_pair']['profit_sum']:.2%}"), ('Worst Pair', f"{strat_results['worst_pair']['key']} " f"{strat_results['worst_pair']['profit_sum']:.2%}"), ('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"), ('Worst trade', f"{worst_trade['pair']} " f"{worst_trade['profit_ratio']:.2%}"), ('Best day', round_coin_value(strat_results['backtest_best_day_abs'], strat_results['stake_currency'])), ('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'], strat_results['stake_currency'])), ('Days win/draw/lose', f"{strat_results['winning_days']} / " f"{strat_results['draw_days']} / {strat_results['losing_days']}"), ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"), ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"), ('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')), ('Entry/Exit Timeouts', f"{strat_results.get('timedout_entry_orders', 'N/A')} / " f"{strat_results.get('timedout_exit_orders', 'N/A')}"), *entry_adjustment_metrics, ('', ''), # Empty line to improve readability ('Min balance', round_coin_value(strat_results['csum_min'], strat_results['stake_currency'])), ('Max balance', round_coin_value(strat_results['csum_max'], strat_results['stake_currency'])), *drawdown_metrics, ('Market change', f"{strat_results['market_change']:.2%}"), ] return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl") else: start_balance = round_coin_value(strat_results['starting_balance'], strat_results['stake_currency']) stake_amount = round_coin_value( strat_results['stake_amount'], strat_results['stake_currency'] ) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited' message = ("No trades made. " f"Your starting balance was {start_balance}, " f"and your stake was {stake_amount}." ) return message def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str, backtest_breakdown=[]): """ Print results for one strategy """ # Print results print(f"Result for strategy {strategy}") table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency) if isinstance(table, str): print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '=')) print(table) if (results.get('results_per_enter_tag') is not None or results.get('results_per_buy_tag') is not None): # results_per_buy_tag is deprecated and should be removed 2 versions after short golive. table = text_table_tags( "enter_tag", results.get('results_per_enter_tag', results.get('results_per_buy_tag')), stake_currency=stake_currency) if isinstance(table, str) and len(table) > 0: print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '=')) print(table) exit_reasons = results.get('exit_reason_summary', results.get('sell_reason_summary')) table = text_table_exit_reason(exit_reason_stats=exit_reasons, stake_currency=stake_currency) if isinstance(table, str) and len(table) > 0: print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '=')) print(table) table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency) if isinstance(table, str) and len(table) > 0: print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '=')) print(table) for period in backtest_breakdown: days_breakdown_stats = generate_periodic_breakdown_stats( trade_list=results['trades'], period=period) table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats, stake_currency=stake_currency, period=period) if isinstance(table, str) and len(table) > 0: print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '=')) print(table) table = text_table_add_metrics(results) if isinstance(table, str) and len(table) > 0: print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '=')) print(table) if isinstance(table, str) and len(table) > 0: print('=' * len(table.splitlines()[0])) print() def show_backtest_results(config: Config, backtest_stats: Dict): stake_currency = config['stake_currency'] for strategy, results in backtest_stats['strategy'].items(): show_backtest_result( strategy, results, stake_currency, config.get('backtest_breakdown', [])) if len(backtest_stats['strategy']) > 1: # Print Strategy summary table table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency) print(f"{results['backtest_start']} -> {results['backtest_end']} |" f" Max open trades : {results['max_open_trades']}") print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '=')) print(table) print('=' * len(table.splitlines()[0])) print('\nFor more details, please look at the detail tables above') def show_sorted_pairlist(config: Config, backtest_stats: Dict): if config.get('backtest_show_pair_list', False): for strategy, results in backtest_stats['strategy'].items(): print(f"Pairs for Strategy {strategy}: \n[") for result in results['results_per_pair']: if result["key"] != 'TOTAL': print(f'"{result["key"]}", // {result["profit_mean"]:.2%}') print("]")