import logging from copy import deepcopy from datetime import datetime, timedelta, timezone from pathlib import Path from typing import Any, Dict, List, Union from numpy import int64 from pandas import DataFrame, to_datetime from tabulate import tabulate from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change, calculate_max_drawdown) from freqtrade.misc import (decimals_per_coin, file_dump_json, get_backtest_metadata_filename, round_coin_value) logger = logging.getLogger(__name__) def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> 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 """ if recordfilename.is_dir(): filename = (recordfilename / f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json') else: filename = Path.joinpath( recordfilename.parent, f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}' ).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 _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 = 'Buys') -> 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().iteritems(): 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_sell_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['sell_reason'].value_counts().iteritems(): result = results.loc[results['sell_reason'] == reason] profit_mean = result['profit_ratio'].mean() profit_sum = result['profit_ratio'].sum() profit_total = profit_sum / max_open_trades tabular_data.append( { 'sell_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") # type: ignore 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.iteritems()] 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)) starting_balance = config['dry_run_wallet'] stake_currency = config['stake_currency'] pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency, starting_balance=starting_balance, results=results, skip_nan=False) buy_tag_results = generate_tag_metrics("buy_tag", starting_balance=starting_balance, results=results, skip_nan=False) sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades, results=results) left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency, starting_balance=starting_balance, results=results.loc[results['is_open']], 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 if not results.empty: results['open_timestamp'] = results['open_date'].view(int64) // 1e6 results['close_timestamp'] = results['close_date'].view(int64) // 1e6 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_buy_tag': buy_tag_results, 'sell_reason_summary': sell_reason_stats, 'left_open_trades': left_open_results, # 'days_breakdown_stats': days_breakdown_stats, 'total_trades': len(results), '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() / starting_balance, 'profit_total_abs': results['profit_abs'].sum(), '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': starting_balance, 'dry_run_wallet': starting_balance, 'final_balance': content['final_balance'], 'rejected_signals': content['rejected_signals'], '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_sell_signal': config['use_sell_signal'], 'sell_profit_only': config['sell_profit_only'], 'sell_profit_offset': config['sell_profit_offset'], 'ignore_roi_if_buy_signal': config['ignore_roi_if_buy_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=starting_balance) strat_stats.update({ 'max_drawdown': max_drawdown_legacy, # Deprecated - do not use 'max_drawdown_account': max_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, starting_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_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_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str: """ Generate small table outlining Backtest results :param sell_reason_stats: Sell reason metrics :param stake_currency: Stakecurrency used :return: pretty printed table with tabulate as string """ headers = [ 'Sell Reason', 'Sells', 'Win Draws Loss Win%', 'Avg Profit %', 'Cum Profit %', f'Tot Profit {stake_currency}', 'Tot Profit %', ] output = [[ t['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 sell_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 == "buy_tag"): headers = _get_line_header("TAG", stake_currency) else: headers = _get_line_header("TAG", stake_currency, 'Sells') 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']) # 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%}"), ('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'])), ('', ''), # 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 Buy signals', strat_results.get('rejected_signals', 'N/A')), ('', ''), # 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'])), # Compatibility to show old hyperopt results ('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}") if 'max_drawdown_account' in strat_results else ( 'Drawdown', f"{strat_results['max_drawdown']:.2%}"), ('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']), ('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_buy_tag') is not None: table = text_table_tags( "buy_tag", results['results_per_buy_tag'], stake_currency=stake_currency) if isinstance(table, str) and len(table) > 0: print(' BUY TAG STATS '.center(len(table.splitlines()[0]), '=')) print(table) table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'], stake_currency=stake_currency) if isinstance(table, str) and len(table) > 0: print(' SELL 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: Dict, 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: Dict, 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("]")