mirror of
https://github.com/freqtrade/freqtrade.git
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539 lines
24 KiB
Python
539 lines
24 KiB
Python
import logging
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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 arrow import Arrow
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from numpy import int64
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from pandas import DataFrame
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from tabulate import tabulate
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from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
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from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
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calculate_max_drawdown)
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from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
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logger = logging.getLogger(__name__)
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def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> 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 diectories, <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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"""
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if recordfilename.is_dir():
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filename = (recordfilename /
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f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
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else:
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filename = Path.joinpath(
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recordfilename.parent,
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f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
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).with_suffix(recordfilename.suffix)
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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 _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', 'd', 'd', 'd']
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def _get_line_header(first_column: str, stake_currency: str) -> 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, 'Buys', 'Avg Profit %', 'Cum Profit %',
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f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
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'Wins', 'Draws', 'Losses']
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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(data: Dict[str, Dict], 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 data: Dict of <pair: dataframe> containing data that was used during backtesting.
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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 data:
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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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# 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_sell_reason_stats(max_open_trades: int, 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['sell_reason'].value_counts().iteritems():
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result = results.loc[results['sell_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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'sell_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_metrics(all_results: Dict) -> List[Dict]:
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"""
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Generate summary per strategy
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:param all_results: 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, results in all_results.items():
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tabular_data.append(_generate_result_line(
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results['results'], results['config']['dry_run_wallet'], strategy)
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)
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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") # type: ignore
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def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
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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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'winning_days': 0,
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'draw_days': 0,
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'losing_days': 0,
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'winner_holding_avg': timedelta(),
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'loser_holding_avg': timedelta(),
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}
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daily_profit = results.resample('1d', on='close_date')['profit_ratio'].sum()
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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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winning_trades = results.loc[results['profit_ratio'] > 0]
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losing_trades = results.loc[results['profit_ratio'] < 0]
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return {
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'backtest_best_day': best,
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'backtest_worst_day': 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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'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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}
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def generate_backtest_stats(btdata: Dict[str, DataFrame],
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all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
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min_date: Arrow, max_date: Arrow
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) -> Dict[str, Any]:
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"""
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:param btdata: Backtest data
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:param all_results: backtest result - dictionary in the form:
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{ Strategy: {'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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:return:
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Dictionary containing results per strategy and a stratgy summary.
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"""
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result: Dict[str, Any] = {'strategy': {}}
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market_change = calculate_market_change(btdata, 'close')
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for strategy, content in all_results.items():
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results: Dict[str, DataFrame] = content['results']
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if not isinstance(results, DataFrame):
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continue
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config = content['config']
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max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
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starting_balance = config['dry_run_wallet']
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stake_currency = config['stake_currency']
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pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
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starting_balance=starting_balance,
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results=results, skip_nan=False)
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sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
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results=results)
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left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
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starting_balance=starting_balance,
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results=results.loc[results['is_open']],
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skip_nan=True)
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daily_stats = generate_daily_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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results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
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results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
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backtest_days = (max_date - min_date).days
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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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'sell_reason_summary': sell_reason_stats,
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'left_open_trades': left_open_results,
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'total_trades': len(results),
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'total_volume': results['stake_amount'].sum(),
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'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
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'profit_total': results['profit_abs'].sum() / starting_balance,
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'profit_total_abs': results['profit_abs'].sum(),
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'backtest_start': min_date.datetime,
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'backtest_start_ts': min_date.int_timestamp * 1000,
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'backtest_end': max_date.datetime,
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'backtest_end_ts': max_date.int_timestamp * 1000,
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'backtest_days': backtest_days,
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'backtest_run_start_ts': content['backtest_start_time'],
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'backtest_run_end_ts': content['backtest_end_time'],
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'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
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'market_change': market_change,
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'pairlist': list(btdata.keys()),
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'stake_amount': config['stake_amount'],
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'stake_currency': config['stake_currency'],
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'starting_balance': starting_balance,
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'dry_run_wallet': starting_balance,
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'final_balance': content['final_balance'],
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'max_open_trades': max_open_trades,
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'max_open_trades_setting': (config['max_open_trades']
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if config['max_open_trades'] != float('inf') else -1),
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'timeframe': config['timeframe'],
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'timerange': config.get('timerange', ''),
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'enable_protections': config.get('enable_protections', False),
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'strategy_name': strategy,
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# Parameters relevant for backtesting
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'stoploss': config['stoploss'],
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'trailing_stop': config.get('trailing_stop', False),
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'trailing_stop_positive': config.get('trailing_stop_positive'),
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'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
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'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
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'use_custom_stoploss': config.get('use_custom_stoploss', False),
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'minimal_roi': config['minimal_roi'],
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'use_sell_signal': config['ask_strategy']['use_sell_signal'],
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'sell_profit_only': config['ask_strategy']['sell_profit_only'],
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'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
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'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
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**daily_stats,
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}
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result['strategy'][strategy] = strat_stats
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try:
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max_drawdown, _, _, _, _ = calculate_max_drawdown(
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results, value_col='profit_ratio')
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drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
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results, value_col='profit_abs')
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strat_stats.update({
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'max_drawdown': max_drawdown,
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'max_drawdown_abs': drawdown_abs,
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'drawdown_start': drawdown_start,
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'drawdown_start_ts': drawdown_start.timestamp() * 1000,
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'drawdown_end': drawdown_end,
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'drawdown_end_ts': drawdown_end.timestamp() * 1000,
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'max_drawdown_low': low_val,
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'max_drawdown_high': high_val,
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})
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csum_min, csum_max = calculate_csum(results)
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strat_stats.update({
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'csum_min': csum_min,
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'csum_max': csum_max
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})
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except ValueError:
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strat_stats.update({
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'max_drawdown': 0.0,
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'max_drawdown_abs': 0.0,
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'max_drawdown_low': 0.0,
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'max_drawdown_high': 0.0,
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'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
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'drawdown_start_ts': 0,
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'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
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'drawdown_end_ts': 0,
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'csum_min': 0,
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'csum_max': 0
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})
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strategy_results = generate_strategy_metrics(all_results=all_results)
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result['strategy_comparison'] = strategy_results
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return result
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###
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# Start output section
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###
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def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
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:param stake_currency: stake-currency - used to correctly name headers
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:return: pretty printed table with tabulate as string
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"""
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headers = _get_line_header('Pair', stake_currency)
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floatfmt = _get_line_floatfmt(stake_currency)
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output = [[
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t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
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t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
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] for t in pair_results]
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(output, headers=headers,
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floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
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def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
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"""
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Generate small table outlining Backtest results
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:param sell_reason_stats: Sell reason metrics
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:param stake_currency: Stakecurrency used
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:return: pretty printed table with tabulate as string
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"""
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headers = [
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'Sell Reason',
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'Sells',
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'Wins',
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'Draws',
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'Losses',
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'Avg Profit %',
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'Cum Profit %',
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f'Tot Profit {stake_currency}',
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'Tot Profit %',
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]
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output = [[
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t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
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t['profit_mean_pct'], t['profit_sum_pct'],
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round_coin_value(t['profit_total_abs'], stake_currency, False),
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t['profit_total_pct'],
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] for t in sell_reason_stats]
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return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
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def text_table_strategy(strategy_results, stake_currency: str) -> str:
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"""
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Generate summary table per strategy
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:param stake_currency: stake-currency - used to correctly name headers
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:param max_open_trades: Maximum allowed open trades used for backtest
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:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
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:return: pretty printed table with tabulate as string
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"""
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floatfmt = _get_line_floatfmt(stake_currency)
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headers = _get_line_header('Strategy', stake_currency)
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output = [[
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t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
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t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
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] for t in strategy_results]
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(output, headers=headers,
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floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
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def text_table_add_metrics(strat_results: Dict) -> str:
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if len(strat_results['trades']) > 0:
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best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
|
|
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
|
|
metrics = [
|
|
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
|
|
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
|
|
('Max open trades', strat_results['max_open_trades']),
|
|
('', ''), # Empty line to improve readability
|
|
('Total trades', strat_results['total_trades']),
|
|
('Starting capital', round_coin_value(strat_results['starting_balance'],
|
|
strat_results['stake_currency'])),
|
|
('End capital', 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"{round(strat_results['profit_total'] * 100, 2)}%"),
|
|
('Trades per day', strat_results['trades_per_day']),
|
|
('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"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
|
|
('Worst Pair', f"{strat_results['worst_pair']['key']} "
|
|
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
|
|
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"),
|
|
('Worst trade', f"{worst_trade['pair']} "
|
|
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
|
|
|
|
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
|
|
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
|
|
('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']}"),
|
|
('', ''), # Empty line to improve readability
|
|
|
|
('Abs Profit Min', round_coin_value(strat_results['csum_min'],
|
|
strat_results['stake_currency'])),
|
|
('Abs Profit Max', round_coin_value(strat_results['csum_max'],
|
|
strat_results['stake_currency'])),
|
|
|
|
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
|
|
('Max Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
|
|
strat_results['stake_currency'])),
|
|
('Max Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
|
|
strat_results['stake_currency'])),
|
|
('Max Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
|
|
strat_results['stake_currency'])),
|
|
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
|
|
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
|
|
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
|
|
]
|
|
|
|
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
|
|
else:
|
|
return ''
|
|
|
|
|
|
def show_backtest_results(config: Dict, backtest_stats: Dict):
|
|
stake_currency = config['stake_currency']
|
|
|
|
for strategy, results in backtest_stats['strategy'].items():
|
|
|
|
# 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_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)
|
|
|
|
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()
|
|
|
|
if len(backtest_stats['strategy']) > 1:
|
|
# Print Strategy summary table
|
|
|
|
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
|
|
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')
|