940 lines
42 KiB
Python
940 lines
42 KiB
Python
import logging
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Union
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from pandas import DataFrame, to_datetime
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from tabulate import tabulate
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from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
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Config, IntOrInf)
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from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
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calculate_expectancy, calculate_market_change,
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calculate_max_drawdown, calculate_sharpe, calculate_sortino)
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from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
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from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
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logger = logging.getLogger(__name__)
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def store_backtest_stats(
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recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None:
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"""
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Stores backtest results
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:param recordfilename: Path object, which can either be a filename or a directory.
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Filenames will be appended with a timestamp right before the suffix
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while for directories, <directory>/backtest-result-<datetime>.json will be used as filename
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:param stats: Dataframe containing the backtesting statistics
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:param dtappendix: Datetime to use for the filename
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"""
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if recordfilename.is_dir():
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filename = (recordfilename / f'backtest-result-{dtappendix}.json')
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else:
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filename = Path.joinpath(
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recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
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).with_suffix(recordfilename.suffix)
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# Store metadata separately.
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file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
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del stats['metadata']
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file_dump_json(filename, stats)
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latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
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file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
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def store_backtest_signal_candles(
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recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
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"""
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Stores backtest trade signal candles
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:param recordfilename: Path object, which can either be a filename or a directory.
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Filenames will be appended with a timestamp right before the suffix
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while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
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as filename
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:param stats: Dict containing the backtesting signal candles
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:param dtappendix: Datetime to use for the filename
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"""
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if recordfilename.is_dir():
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filename = (recordfilename / f'backtest-result-{dtappendix}_signals.pkl')
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else:
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filename = Path.joinpath(
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recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_signals.pkl'
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)
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file_dump_joblib(filename, candles)
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return filename
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def _get_line_floatfmt(stake_currency: str) -> List[str]:
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"""
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Generate floatformat (goes in line with _generate_result_line())
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"""
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return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
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'.2f', 'd', 's', 's']
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def _get_line_header(first_column: str, stake_currency: str,
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direction: str = 'Entries') -> List[str]:
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"""
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Generate header lines (goes in line with _generate_result_line())
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"""
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return [first_column, direction, 'Avg Profit %', 'Cum Profit %',
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f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
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'Win Draw Loss Win%']
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def generate_wins_draws_losses(wins, draws, losses):
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if wins > 0 and losses == 0:
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wl_ratio = '100'
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elif wins == 0:
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wl_ratio = '0'
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else:
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wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
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return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
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def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
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"""
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Generate one result dict, with "first_column" as key.
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"""
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profit_sum = result['profit_ratio'].sum()
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# (end-capital - starting capital) / starting capital
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profit_total = result['profit_abs'].sum() / starting_balance
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return {
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'key': first_column,
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'trades': len(result),
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'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
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'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
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'profit_sum': profit_sum,
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'profit_sum_pct': round(profit_sum * 100.0, 2),
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'profit_total_abs': result['profit_abs'].sum(),
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'profit_total': profit_total,
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'profit_total_pct': round(profit_total * 100.0, 2),
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'duration_avg': str(timedelta(
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minutes=round(result['trade_duration'].mean()))
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) if not result.empty else '0:00',
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# 'duration_max': str(timedelta(
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# minutes=round(result['trade_duration'].max()))
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# ) if not result.empty else '0:00',
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# 'duration_min': str(timedelta(
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# minutes=round(result['trade_duration'].min()))
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# ) if not result.empty else '0:00',
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'wins': len(result[result['profit_abs'] > 0]),
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'draws': len(result[result['profit_abs'] == 0]),
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'losses': len(result[result['profit_abs'] < 0]),
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}
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def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int,
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results: DataFrame, skip_nan: bool = False) -> List[Dict]:
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"""
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Generates and returns a list for the given backtest data and the results dataframe
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:param pairlist: Pairlist used
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:param stake_currency: stake-currency - used to correctly name headers
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:param starting_balance: Starting balance
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:param results: Dataframe containing the backtest results
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:param skip_nan: Print "left open" open trades
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:return: List of Dicts containing the metrics per pair
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"""
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tabular_data = []
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for pair in pairlist:
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result = results[results['pair'] == pair]
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if skip_nan and result['profit_abs'].isnull().all():
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continue
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tabular_data.append(_generate_result_line(result, starting_balance, pair))
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# Sort by total profit %:
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tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
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# Append Total
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tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
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return tabular_data
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def generate_tag_metrics(tag_type: str,
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starting_balance: int,
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results: DataFrame,
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skip_nan: bool = False) -> List[Dict]:
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"""
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Generates and returns a list of metrics for the given tag trades and the results dataframe
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:param starting_balance: Starting balance
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:param results: Dataframe containing the backtest results
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:param skip_nan: Print "left open" open trades
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:return: List of Dicts containing the metrics per pair
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"""
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tabular_data = []
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if tag_type in results.columns:
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for tag, count in results[tag_type].value_counts().items():
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result = results[results[tag_type] == tag]
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if skip_nan and result['profit_abs'].isnull().all():
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continue
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tabular_data.append(_generate_result_line(result, starting_balance, tag))
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# Sort by total profit %:
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tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
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# Append Total
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tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
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return tabular_data
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else:
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return []
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def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) -> List[Dict]:
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"""
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Generate small table outlining Backtest results
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:param max_open_trades: Max_open_trades parameter
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:param results: Dataframe containing the backtest result for one strategy
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:return: List of Dicts containing the metrics per Sell reason
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"""
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tabular_data = []
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for reason, count in results['exit_reason'].value_counts().items():
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result = results.loc[results['exit_reason'] == reason]
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profit_mean = result['profit_ratio'].mean()
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profit_sum = result['profit_ratio'].sum()
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profit_total = profit_sum / max_open_trades
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tabular_data.append(
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{
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'exit_reason': reason,
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'trades': count,
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'wins': len(result[result['profit_abs'] > 0]),
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'draws': len(result[result['profit_abs'] == 0]),
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'losses': len(result[result['profit_abs'] < 0]),
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'profit_mean': profit_mean,
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'profit_mean_pct': round(profit_mean * 100, 2),
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'profit_sum': profit_sum,
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'profit_sum_pct': round(profit_sum * 100, 2),
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'profit_total_abs': result['profit_abs'].sum(),
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'profit_total': profit_total,
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'profit_total_pct': round(profit_total * 100, 2),
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}
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)
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return tabular_data
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def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
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"""
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Generate summary per strategy
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:param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
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:return: List of Dicts containing the metrics per Strategy
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"""
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tabular_data = []
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for strategy, result in bt_stats.items():
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tabular_data.append(deepcopy(result['results_per_pair'][-1]))
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# Update "key" to strategy (results_per_pair has it as "Total").
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tabular_data[-1]['key'] = strategy
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tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
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tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
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result['max_drawdown_abs'], result['stake_currency'], False)
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return tabular_data
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def generate_edge_table(results: dict) -> str:
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floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
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tabular_data = []
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headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
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'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
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'Average Duration (min)']
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for result in results.items():
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if result[1].nb_trades > 0:
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tabular_data.append([
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result[0],
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result[1].stoploss,
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result[1].winrate,
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result[1].risk_reward_ratio,
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result[1].required_risk_reward,
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result[1].expectancy,
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result[1].nb_trades,
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round(result[1].avg_trade_duration)
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])
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(tabular_data, headers=headers,
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floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
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def _get_resample_from_period(period: str) -> str:
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if period == 'day':
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return '1d'
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if period == 'week':
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return '1w'
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if period == 'month':
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return '1M'
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raise ValueError(f"Period {period} is not supported.")
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def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dict[str, Any]]:
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results = DataFrame.from_records(trade_list)
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if len(results) == 0:
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return []
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results['close_date'] = to_datetime(results['close_date'], utc=True)
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resample_period = _get_resample_from_period(period)
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resampled = results.resample(resample_period, on='close_date')
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stats = []
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for name, day in resampled:
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profit_abs = day['profit_abs'].sum().round(10)
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wins = sum(day['profit_abs'] > 0)
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draws = sum(day['profit_abs'] == 0)
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loses = sum(day['profit_abs'] < 0)
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stats.append(
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{
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'date': name.strftime('%d/%m/%Y'),
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'profit_abs': profit_abs,
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'wins': wins,
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'draws': draws,
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'loses': loses
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}
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)
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return stats
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def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
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""" Generate overall trade statistics """
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if len(results) == 0:
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return {
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'wins': 0,
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'losses': 0,
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'draws': 0,
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'holding_avg': timedelta(),
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'winner_holding_avg': timedelta(),
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'loser_holding_avg': timedelta(),
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}
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winning_trades = results.loc[results['profit_ratio'] > 0]
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draw_trades = results.loc[results['profit_ratio'] == 0]
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losing_trades = results.loc[results['profit_ratio'] < 0]
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holding_avg = (timedelta(minutes=round(results['trade_duration'].mean()))
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if not results.empty else timedelta())
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winner_holding_avg = (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
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if not winning_trades.empty else timedelta())
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loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
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if not losing_trades.empty else timedelta())
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return {
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'wins': len(winning_trades),
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'losses': len(losing_trades),
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'draws': len(draw_trades),
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'holding_avg': holding_avg,
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'holding_avg_s': holding_avg.total_seconds(),
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'winner_holding_avg': winner_holding_avg,
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'winner_holding_avg_s': winner_holding_avg.total_seconds(),
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'loser_holding_avg': loser_holding_avg,
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'loser_holding_avg_s': loser_holding_avg.total_seconds(),
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}
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def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
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""" Generate daily statistics """
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if len(results) == 0:
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return {
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'backtest_best_day': 0,
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'backtest_worst_day': 0,
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'backtest_best_day_abs': 0,
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'backtest_worst_day_abs': 0,
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'winning_days': 0,
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'draw_days': 0,
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'losing_days': 0,
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'daily_profit_list': [],
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}
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daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
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daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
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worst_rel = min(daily_profit_rel)
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best_rel = max(daily_profit_rel)
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worst = min(daily_profit)
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best = max(daily_profit)
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winning_days = sum(daily_profit > 0)
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draw_days = sum(daily_profit == 0)
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losing_days = sum(daily_profit < 0)
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daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
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return {
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'backtest_best_day': best_rel,
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'backtest_worst_day': worst_rel,
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'backtest_best_day_abs': best,
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'backtest_worst_day_abs': worst,
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'winning_days': winning_days,
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'draw_days': draw_days,
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'losing_days': losing_days,
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'daily_profit': daily_profit_list,
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}
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def generate_strategy_stats(pairlist: List[str],
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strategy: str,
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content: Dict[str, Any],
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min_date: datetime, max_date: datetime,
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market_change: float
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) -> Dict[str, Any]:
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"""
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:param pairlist: List of pairs to backtest
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:param strategy: Strategy name
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:param content: Backtest result data in the format:
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{'results: results, 'config: config}}.
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:param min_date: Backtest start date
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:param max_date: Backtest end date
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:param market_change: float indicating the market change
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:return: Dictionary containing results per strategy and a strategy summary.
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"""
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results: Dict[str, DataFrame] = content['results']
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if not isinstance(results, DataFrame):
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return {}
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config = content['config']
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max_open_trades = min(config['max_open_trades'], len(pairlist))
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start_balance = config['dry_run_wallet']
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stake_currency = config['stake_currency']
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pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
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starting_balance=start_balance,
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results=results, skip_nan=False)
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enter_tag_results = generate_tag_metrics("enter_tag", starting_balance=start_balance,
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results=results, skip_nan=False)
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exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
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results=results)
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left_open_results = generate_pair_metrics(
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pairlist, stake_currency=stake_currency, starting_balance=start_balance,
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results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True)
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daily_stats = generate_daily_stats(results)
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trade_stats = generate_trading_stats(results)
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best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
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key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
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worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
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key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
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winning_profit = results.loc[results['profit_abs'] > 0, 'profit_abs'].sum()
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losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
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profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
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backtest_days = (max_date - min_date).days or 1
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strat_stats = {
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'trades': results.to_dict(orient='records'),
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'locks': [lock.to_json() for lock in content['locks']],
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'best_pair': best_pair,
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'worst_pair': worst_pair,
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'results_per_pair': pair_results,
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'results_per_enter_tag': enter_tag_results,
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'exit_reason_summary': exit_reason_stats,
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'left_open_trades': left_open_results,
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# 'days_breakdown_stats': days_breakdown_stats,
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'total_trades': len(results),
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'trade_count_long': len(results.loc[~results['is_short']]),
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'trade_count_short': len(results.loc[results['is_short']]),
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'total_volume': float(results['stake_amount'].sum()),
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'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
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'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
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'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
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'profit_total': results['profit_abs'].sum() / start_balance,
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'profit_total_long': results.loc[~results['is_short'], 'profit_abs'].sum() / start_balance,
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'profit_total_short': results.loc[results['is_short'], 'profit_abs'].sum() / start_balance,
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'profit_total_abs': results['profit_abs'].sum(),
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'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
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'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
|
|
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
|
|
'expectancy': calculate_expectancy(results),
|
|
'sortino': calculate_sortino(results, min_date, max_date, start_balance),
|
|
'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
|
|
'calmar': calculate_calmar(results, min_date, max_date, start_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 <Strategyname: DataFrame> 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'),
|
|
('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
|
|
('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
|
|
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
|
|
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
|
|
in strat_results else 'N/A'),
|
|
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
|
|
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']
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|
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
|
|
|
|
message = ("No trades made. "
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|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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("]")
|