2020-03-15 14:17:35 +00:00
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import logging
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2020-06-26 18:08:45 +00:00
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from datetime import datetime, timedelta, timezone
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2020-03-15 14:17:35 +00:00
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from pathlib import Path
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2020-05-27 17:17:15 +00:00
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from typing import Any, Dict, List
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2020-01-02 06:26:43 +00:00
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2020-06-09 06:00:35 +00:00
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from arrow import Arrow
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2020-01-02 06:26:43 +00:00
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from pandas import DataFrame
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2020-07-27 05:20:40 +00:00
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from numpy import int64
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2020-01-02 06:26:43 +00:00
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from tabulate import tabulate
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2020-06-28 07:27:19 +00:00
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from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
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2020-06-25 18:39:55 +00:00
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from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
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2020-03-15 14:17:35 +00:00
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from freqtrade.misc import file_dump_json
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logger = logging.getLogger(__name__)
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2020-06-26 05:46:59 +00:00
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def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
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2020-08-18 14:15:24 +00:00
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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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2020-06-28 07:45:23 +00:00
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if recordfilename.is_dir():
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2020-07-03 18:32:04 +00:00
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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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2020-06-28 07:45:23 +00:00
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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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2020-08-20 17:51:36 +00:00
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print(stats)
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2020-06-26 05:46:59 +00:00
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file_dump_json(filename, stats)
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2020-06-28 07:45:23 +00:00
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latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
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2020-06-26 05:46:59 +00:00
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file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
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2020-05-25 17:18:53 +00:00
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def _get_line_floatfmt() -> 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', '.8f', '.2f', 'd', 'd', 'd', 'd']
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2020-05-25 04:44:51 +00:00
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def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
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2020-01-02 06:26:43 +00:00
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"""
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2020-05-25 04:44:51 +00:00
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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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2020-05-25 17:18:53 +00:00
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def _generate_result_line(result: DataFrame, max_open_trades: 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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return {
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'key': first_column,
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2020-06-07 13:30:41 +00:00
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'trades': len(result),
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2020-06-26 04:56:59 +00:00
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'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
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'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
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2020-06-07 13:30:41 +00:00
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'profit_sum': result['profit_percent'].sum(),
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'profit_sum_pct': result['profit_percent'].sum() * 100.0,
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'profit_total_abs': result['profit_abs'].sum(),
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2020-07-26 14:10:48 +00:00
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'profit_total': result['profit_percent'].sum() / max_open_trades,
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2020-06-07 13:30:41 +00:00
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'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
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2020-05-25 17:18:53 +00:00
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'duration_avg': str(timedelta(
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2020-06-07 13:30:41 +00:00
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minutes=round(result['trade_duration'].mean()))
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2020-05-25 17:18:53 +00:00
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) if not result.empty else '0:00',
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# 'duration_max': str(timedelta(
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2020-06-07 13:30:41 +00:00
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# minutes=round(result['trade_duration'].max()))
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2020-05-25 17:18:53 +00:00
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# ) if not result.empty else '0:00',
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# 'duration_min': str(timedelta(
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2020-06-07 13:30:41 +00:00
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# minutes=round(result['trade_duration'].min()))
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2020-05-25 17:18:53 +00:00
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# ) if not result.empty else '0:00',
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2020-06-07 13:30:41 +00:00
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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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2020-05-25 17:18:53 +00:00
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}
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2020-05-25 04:44:51 +00:00
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2020-05-25 18:46:31 +00:00
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def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
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2020-05-25 17:50:09 +00:00
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results: DataFrame, skip_nan: bool = False) -> List[Dict]:
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2020-05-25 04:44:51 +00:00
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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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2020-01-02 08:37:54 +00:00
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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 max_open_trades: Maximum allowed open trades
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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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2020-05-25 17:55:02 +00:00
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:return: List of Dicts containing the metrics per pair
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2020-01-02 06:26:43 +00:00
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"""
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tabular_data = []
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2020-05-25 17:18:53 +00:00
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2020-01-02 06:26:43 +00:00
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for pair in data:
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2020-06-07 13:30:41 +00:00
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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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2020-01-02 06:26:43 +00:00
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continue
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2020-05-25 04:44:51 +00:00
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tabular_data.append(_generate_result_line(result, max_open_trades, pair))
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2020-01-02 06:26:43 +00:00
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# Append Total
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2020-05-25 04:44:51 +00:00
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tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
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2020-05-25 17:18:53 +00:00
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return tabular_data
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2020-05-25 04:44:51 +00:00
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2020-05-25 17:55:02 +00:00
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def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
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2020-01-02 06:28:30 +00:00
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"""
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Generate small table outlining Backtest results
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2020-03-15 14:04:48 +00:00
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:param max_open_trades: Max_open_trades parameter
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2020-05-25 05:02:24 +00:00
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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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2020-01-02 06:28:30 +00:00
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"""
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tabular_data = []
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2020-05-25 05:02:24 +00:00
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2020-01-02 06:28:30 +00:00
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for reason, count in results['sell_reason'].value_counts().iteritems():
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2020-01-09 05:46:39 +00:00
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result = results.loc[results['sell_reason'] == reason]
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2020-05-25 05:02:24 +00:00
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profit_mean = result['profit_percent'].mean()
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profit_sum = result["profit_percent"].sum()
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2020-01-31 19:41:51 +00:00
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profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
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2020-05-25 05:02:24 +00:00
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2020-01-31 03:39:18 +00:00
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tabular_data.append(
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2020-05-25 05:02:24 +00:00
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{
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'sell_reason': reason.value,
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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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2020-07-26 12:39:00 +00:00
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'profit_total_pct': profit_percent_tot,
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2020-05-25 05:02:24 +00:00
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}
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2020-01-31 03:39:18 +00:00
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)
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2020-05-25 05:02:24 +00:00
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return tabular_data
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2020-05-25 04:44:51 +00:00
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2020-05-25 18:46:31 +00:00
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def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
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2020-05-25 17:55:02 +00:00
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all_results: Dict) -> List[Dict]:
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2020-05-25 04:44:51 +00:00
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"""
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Generate summary 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: BacktestResult> containing results for all strategies
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2020-05-25 17:55:02 +00:00
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:return: List of Dicts containing the metrics per Strategy
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2020-05-25 04:44:51 +00:00
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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(results, max_open_trades, strategy))
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2020-05-25 17:18:53 +00:00
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return tabular_data
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2020-05-25 04:44:51 +00:00
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2020-01-09 05:52:34 +00:00
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def generate_edge_table(results: dict) -> str:
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2020-05-25 04:44:51 +00:00
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floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
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2020-01-09 05:52:34 +00:00
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tabular_data = []
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2020-02-07 02:51:50 +00:00
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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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2020-01-09 05:52:34 +00:00
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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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2020-02-27 12:28:28 +00:00
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floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
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2020-03-15 14:17:53 +00:00
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2020-07-03 17:45:45 +00:00
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def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
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2020-08-20 17:51:36 +00:00
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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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2020-07-03 17:45:45 +00:00
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daily_profit = results.resample('1d', on='close_date')['profit_percent'].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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2020-07-03 17:58:02 +00:00
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winning_trades = results.loc[results['profit_percent'] > 0]
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losing_trades = results.loc[results['profit_percent'] < 0]
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2020-07-03 17:45:45 +00:00
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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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2020-07-03 17:58:02 +00:00
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'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
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2020-07-26 13:17:54 +00:00
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if not winning_trades.empty else timedelta()),
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2020-07-03 17:58:02 +00:00
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'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
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2020-07-26 13:17:54 +00:00
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if not losing_trades.empty else timedelta()),
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2020-07-03 17:45:45 +00:00
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}
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2020-06-01 07:23:24 +00:00
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def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
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2020-06-09 06:00:35 +00:00
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all_results: Dict[str, DataFrame],
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min_date: Arrow, max_date: Arrow
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) -> Dict[str, Any]:
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2020-06-07 12:32:01 +00:00
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"""
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:param config: Configuration object used for backtest
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:param btdata: Backtest data
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:param all_results: backtest result - dictionary with { Strategy: results}.
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2020-06-09 06:00:35 +00:00
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:param min_date: Backtest start date
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:param max_date: Backtest end date
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2020-06-07 12:32:01 +00:00
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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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2020-05-25 18:47:48 +00:00
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stake_currency = config['stake_currency']
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max_open_trades = config['max_open_trades']
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2020-06-01 07:24:27 +00:00
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result: Dict[str, Any] = {'strategy': {}}
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2020-06-25 18:39:55 +00:00
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market_change = calculate_market_change(btdata, 'close')
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2020-03-15 14:17:53 +00:00
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for strategy, results in all_results.items():
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2020-06-01 07:23:24 +00:00
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2020-05-25 18:47:48 +00:00
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pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
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max_open_trades=max_open_trades,
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2020-05-25 17:50:09 +00:00
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results=results, skip_nan=False)
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2020-05-25 18:47:48 +00:00
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sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
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2020-05-25 18:22:22 +00:00
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results=results)
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2020-05-25 18:47:48 +00:00
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left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
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max_open_trades=max_open_trades,
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2020-05-25 18:22:22 +00:00
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results=results.loc[results['open_at_end']],
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skip_nan=True)
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2020-07-03 17:45:45 +00:00
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daily_stats = generate_daily_stats(results)
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2020-06-08 04:37:30 +00:00
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2020-07-27 05:20:40 +00:00
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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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2020-06-09 06:00:35 +00:00
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backtest_days = (max_date - min_date).days
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2020-06-01 07:23:24 +00:00
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strat_stats = {
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2020-06-26 05:46:59 +00:00
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'trades': results.to_dict(orient='records'),
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2020-06-01 07:23:24 +00:00
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'results_per_pair': pair_results,
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'sell_reason_summary': sell_reason_stats,
|
|
|
|
'left_open_trades': left_open_results,
|
2020-06-09 06:00:35 +00:00
|
|
|
'total_trades': len(results),
|
2020-08-20 17:51:36 +00:00
|
|
|
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
|
2020-08-18 14:59:24 +00:00
|
|
|
'profit_total': results['profit_percent'].sum(),
|
|
|
|
'profit_total_abs': results['profit_abs'].sum(),
|
2020-06-09 06:00:35 +00:00
|
|
|
'backtest_start': min_date.datetime,
|
2020-07-26 13:23:21 +00:00
|
|
|
'backtest_start_ts': min_date.timestamp * 1000,
|
2020-06-09 06:00:35 +00:00
|
|
|
'backtest_end': max_date.datetime,
|
2020-07-26 13:23:21 +00:00
|
|
|
'backtest_end_ts': max_date.timestamp * 1000,
|
2020-06-09 06:00:35 +00:00
|
|
|
'backtest_days': backtest_days,
|
2020-07-03 17:30:29 +00:00
|
|
|
|
2020-08-20 17:51:36 +00:00
|
|
|
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
|
2020-06-25 18:39:55 +00:00
|
|
|
'market_change': market_change,
|
2020-06-28 07:04:19 +00:00
|
|
|
'pairlist': list(btdata.keys()),
|
2020-07-03 17:45:45 +00:00
|
|
|
'stake_amount': config['stake_amount'],
|
2020-07-26 13:55:54 +00:00
|
|
|
'stake_currency': config['stake_currency'],
|
|
|
|
'max_open_trades': config['max_open_trades'],
|
2020-08-08 18:20:58 +00:00
|
|
|
'timeframe': config['timeframe'],
|
2020-07-03 17:45:45 +00:00
|
|
|
**daily_stats,
|
2020-06-25 18:39:55 +00:00
|
|
|
}
|
2020-06-01 07:23:24 +00:00
|
|
|
result['strategy'][strategy] = strat_stats
|
|
|
|
|
2020-06-08 04:38:29 +00:00
|
|
|
try:
|
|
|
|
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
|
|
|
|
results, value_col='profit_percent')
|
|
|
|
strat_stats.update({
|
|
|
|
'max_drawdown': max_drawdown,
|
|
|
|
'drawdown_start': drawdown_start,
|
2020-07-26 13:23:21 +00:00
|
|
|
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
|
2020-06-08 04:38:29 +00:00
|
|
|
'drawdown_end': drawdown_end,
|
2020-07-26 13:23:21 +00:00
|
|
|
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
|
2020-06-08 04:38:29 +00:00
|
|
|
})
|
|
|
|
except ValueError:
|
|
|
|
strat_stats.update({
|
|
|
|
'max_drawdown': 0.0,
|
2020-06-26 18:08:45 +00:00
|
|
|
'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,
|
2020-06-08 04:38:29 +00:00
|
|
|
})
|
|
|
|
|
2020-06-01 07:23:24 +00:00
|
|
|
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
|
|
|
|
max_open_trades=max_open_trades,
|
|
|
|
all_results=all_results)
|
|
|
|
|
|
|
|
result['strategy_comparison'] = strategy_results
|
|
|
|
|
|
|
|
return result
|
|
|
|
|
|
|
|
|
2020-06-07 09:29:14 +00:00
|
|
|
###
|
|
|
|
# Start output section
|
|
|
|
###
|
|
|
|
|
2020-06-07 09:35:02 +00:00
|
|
|
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
|
2020-06-07 09:29:14 +00:00
|
|
|
"""
|
|
|
|
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()
|
|
|
|
output = [[
|
|
|
|
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
|
|
|
|
t['profit_total_pct'], t['duration_avg'], 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")
|
|
|
|
|
|
|
|
|
2020-06-07 09:31:33 +00:00
|
|
|
def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
|
2020-06-07 09:29:14 +00:00
|
|
|
"""
|
|
|
|
Generate small table outlining Backtest results
|
|
|
|
:param sell_reason_stats: Sell reason metrics
|
|
|
|
:param stake_currency: Stakecurrency used
|
|
|
|
:return: pretty printed table with tabulate as string
|
|
|
|
"""
|
|
|
|
headers = [
|
|
|
|
'Sell Reason',
|
|
|
|
'Sells',
|
|
|
|
'Wins',
|
|
|
|
'Draws',
|
|
|
|
'Losses',
|
|
|
|
'Avg Profit %',
|
|
|
|
'Cum Profit %',
|
|
|
|
f'Tot Profit {stake_currency}',
|
|
|
|
'Tot Profit %',
|
|
|
|
]
|
|
|
|
|
|
|
|
output = [[
|
|
|
|
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
|
2020-07-26 12:39:00 +00:00
|
|
|
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
|
2020-06-07 09:29:14 +00:00
|
|
|
] for t in sell_reason_stats]
|
|
|
|
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
|
|
|
|
|
|
|
|
|
2020-06-07 09:31:33 +00:00
|
|
|
def text_table_strategy(strategy_results, stake_currency: str) -> str:
|
2020-06-07 09:29:14 +00:00
|
|
|
"""
|
|
|
|
Generate summary table per strategy
|
|
|
|
:param stake_currency: stake-currency - used to correctly name headers
|
|
|
|
:param max_open_trades: Maximum allowed open trades used for backtest
|
|
|
|
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
|
|
|
|
:return: pretty printed table with tabulate as string
|
|
|
|
"""
|
|
|
|
floatfmt = _get_line_floatfmt()
|
|
|
|
headers = _get_line_header('Strategy', 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'], t['wins'], t['draws'], t['losses']
|
|
|
|
] for t in strategy_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")
|
|
|
|
|
|
|
|
|
2020-06-26 07:22:50 +00:00
|
|
|
def text_table_add_metrics(strat_results: Dict) -> str:
|
|
|
|
if len(strat_results['trades']) > 0:
|
|
|
|
min_trade = min(strat_results['trades'], key=lambda x: x['open_date'])
|
2020-06-08 04:38:29 +00:00
|
|
|
metrics = [
|
2020-07-14 17:34:01 +00:00
|
|
|
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
|
|
|
|
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
|
2020-06-26 07:22:50 +00:00
|
|
|
('Total trades', strat_results['total_trades']),
|
2020-06-26 07:19:44 +00:00
|
|
|
('First trade', min_trade['open_date'].strftime(DATETIME_PRINT_FORMAT)),
|
2020-06-26 05:46:59 +00:00
|
|
|
('First trade Pair', min_trade['pair']),
|
2020-08-18 14:59:24 +00:00
|
|
|
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
|
2020-06-26 07:22:50 +00:00
|
|
|
('Trades per day', strat_results['trades_per_day']),
|
2020-07-03 17:30:29 +00:00
|
|
|
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
|
|
|
|
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
|
2020-07-03 17:45:45 +00:00
|
|
|
('Days win/draw/lose', f"{strat_results['winning_days']} / "
|
|
|
|
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
|
2020-07-03 17:58:02 +00:00
|
|
|
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
|
|
|
|
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
|
2020-06-09 06:00:35 +00:00
|
|
|
('', ''), # Empty line to improve readability
|
2020-06-26 07:22:50 +00:00
|
|
|
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
|
|
|
|
('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)}%"),
|
2020-06-08 04:38:29 +00:00
|
|
|
]
|
|
|
|
|
|
|
|
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
|
|
|
|
else:
|
2020-06-25 18:39:55 +00:00
|
|
|
return ''
|
2020-06-08 04:37:30 +00:00
|
|
|
|
|
|
|
|
2020-06-01 07:23:24 +00:00
|
|
|
def show_backtest_results(config: Dict, backtest_stats: Dict):
|
|
|
|
stake_currency = config['stake_currency']
|
|
|
|
|
|
|
|
for strategy, results in backtest_stats['strategy'].items():
|
|
|
|
|
2020-05-25 18:22:22 +00:00
|
|
|
# Print results
|
|
|
|
print(f"Result for strategy {strategy}")
|
2020-06-07 09:35:02 +00:00
|
|
|
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
|
2020-03-15 14:17:53 +00:00
|
|
|
if isinstance(table, str):
|
|
|
|
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
|
|
|
|
print(table)
|
|
|
|
|
2020-06-07 09:31:33 +00:00
|
|
|
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
|
|
|
|
stake_currency=stake_currency)
|
2020-06-26 04:47:04 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2020-03-15 14:17:53 +00:00
|
|
|
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
|
|
|
|
print(table)
|
|
|
|
|
2020-06-07 09:35:02 +00:00
|
|
|
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
|
2020-06-26 04:47:04 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2020-03-15 14:17:53 +00:00
|
|
|
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
|
|
|
|
print(table)
|
2020-06-08 04:37:30 +00:00
|
|
|
|
|
|
|
table = text_table_add_metrics(results)
|
2020-06-26 04:47:04 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2020-06-08 04:37:30 +00:00
|
|
|
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
|
|
|
|
print(table)
|
|
|
|
|
2020-06-26 04:47:04 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2020-03-15 14:17:53 +00:00
|
|
|
print('=' * len(table.splitlines()[0]))
|
|
|
|
print()
|
2020-05-25 04:44:51 +00:00
|
|
|
|
2020-06-01 07:23:24 +00:00
|
|
|
if len(backtest_stats['strategy']) > 1:
|
2020-03-15 14:17:53 +00:00
|
|
|
# Print Strategy summary table
|
2020-05-25 17:55:02 +00:00
|
|
|
|
2020-06-07 09:31:33 +00:00
|
|
|
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
|
2020-03-15 14:17:53 +00:00
|
|
|
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')
|