generate_backtest_stats must take config options from the strategy
config as a strategy can override certain options.
This commit is contained in:
Matthias
parent
378f03a5b1
commit
ff3e2641ae
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@ -380,12 +380,6 @@ class Backtesting:
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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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# Use max_open_trades in backtesting, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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max_open_trades = self.config['max_open_trades']
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else:
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logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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position_stacking = self.config.get('position_stacking', False)
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data, timerange = self.load_bt_data()
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@ -395,6 +389,15 @@ class Backtesting:
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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self._set_strategy(strat)
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# Use max_open_trades in backtesting, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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# Must come from strategy config, as the strategy may modify this setting.
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max_open_trades = self.strategy.config['max_open_trades']
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else:
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logger.info(
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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# need to reprocess data every time to populate signals
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preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
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@ -407,7 +410,7 @@ class Backtesting:
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f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(max_date - min_date).days} days)..')
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# Execute backtest and print results
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all_results[self.strategy.get_strategy_name()] = self.backtest(
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results = self.backtest(
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processed=preprocessed,
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stake_amount=self.config['stake_amount'],
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start_date=min_date,
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@ -415,8 +418,12 @@ class Backtesting:
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max_open_trades=max_open_trades,
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position_stacking=position_stacking,
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)
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all_results[self.strategy.get_strategy_name()] = {
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'results': results,
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'config': self.strategy.config,
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}
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stats = generate_backtest_stats(self.config, data, all_results,
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stats = generate_backtest_stats(data, all_results,
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min_date=min_date, max_date=max_date)
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if self.config.get('export', False):
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store_backtest_stats(self.config['exportfilename'], stats)
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@ -1,7 +1,7 @@
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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
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from typing import Any, Dict, List, Union
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from arrow import Arrow
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from pandas import DataFrame
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@ -143,19 +143,18 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
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return tabular_data
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def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
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all_results: Dict) -> List[Dict]:
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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 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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: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(results, max_open_trades, strategy))
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tabular_data.append(_generate_result_line(
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results['results'], results['config']['max_open_trades'], strategy)
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)
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return tabular_data
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@ -219,25 +218,29 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
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}
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def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
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all_results: Dict[str, DataFrame],
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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 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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: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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stake_currency = config['stake_currency']
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max_open_trades = config['max_open_trades']
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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, results in all_results.items():
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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 = config['max_open_trades']
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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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max_open_trades=max_open_trades,
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@ -310,9 +313,7 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
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'drawdown_end_ts': 0,
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})
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strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
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max_open_trades=max_open_trades,
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all_results=all_results)
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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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@ -60,32 +60,35 @@ def test_generate_backtest_stats(default_conf, testdatadir):
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default_conf.update({'strategy': 'DefaultStrategy'})
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StrategyResolver.load_strategy(default_conf)
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results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
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"UNITTEST/BTC", "UNITTEST/BTC"],
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"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
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"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
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"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
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Arrow(2017, 11, 14, 21, 36, 00).datetime,
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Arrow(2017, 11, 14, 22, 12, 00).datetime,
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Arrow(2017, 11, 14, 22, 44, 00).datetime],
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"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
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Arrow(2017, 11, 14, 22, 10, 00).datetime,
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Arrow(2017, 11, 14, 22, 43, 00).datetime,
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Arrow(2017, 11, 14, 22, 58, 00).datetime],
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"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
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"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
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"trade_duration": [123, 34, 31, 14],
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"open_at_end": [False, False, False, True],
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"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
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SellType.ROI, SellType.FORCE_SELL]
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})}
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results = {'DefStrat': {
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'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
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"UNITTEST/BTC", "UNITTEST/BTC"],
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"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
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"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
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"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
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Arrow(2017, 11, 14, 21, 36, 00).datetime,
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Arrow(2017, 11, 14, 22, 12, 00).datetime,
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Arrow(2017, 11, 14, 22, 44, 00).datetime],
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"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
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Arrow(2017, 11, 14, 22, 10, 00).datetime,
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Arrow(2017, 11, 14, 22, 43, 00).datetime,
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Arrow(2017, 11, 14, 22, 58, 00).datetime],
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"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
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"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
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"trade_duration": [123, 34, 31, 14],
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"open_at_end": [False, False, False, True],
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"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
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SellType.ROI, SellType.FORCE_SELL]
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}),
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'config': default_conf}
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}
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timerange = TimeRange.parse_timerange('1510688220-1510700340')
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min_date = Arrow.fromtimestamp(1510688220)
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max_date = Arrow.fromtimestamp(1510700340)
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btdata = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
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fill_up_missing=True)
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stats = generate_backtest_stats(default_conf, btdata, results, min_date, max_date)
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stats = generate_backtest_stats(btdata, results, min_date, max_date)
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assert isinstance(stats, dict)
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assert 'strategy' in stats
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assert 'DefStrat' in stats['strategy']
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@ -93,29 +96,32 @@ def test_generate_backtest_stats(default_conf, testdatadir):
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strat_stats = stats['strategy']['DefStrat']
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assert strat_stats['backtest_start'] == min_date.datetime
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assert strat_stats['backtest_end'] == max_date.datetime
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assert strat_stats['total_trades'] == len(results['DefStrat'])
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assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
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# Above sample had no loosing trade
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assert strat_stats['max_drawdown'] == 0.0
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results = {'DefStrat': pd.DataFrame(
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{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
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"profit_percent": [0.003312, 0.010801, -0.013803, 0.002780],
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"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
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"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
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Arrow(2017, 11, 14, 21, 36, 00).datetime,
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Arrow(2017, 11, 14, 22, 12, 00).datetime,
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Arrow(2017, 11, 14, 22, 44, 00).datetime],
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"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
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Arrow(2017, 11, 14, 22, 10, 00).datetime,
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Arrow(2017, 11, 14, 22, 43, 00).datetime,
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Arrow(2017, 11, 14, 22, 58, 00).datetime],
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"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
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"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
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"trade_duration": [123, 34, 31, 14],
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"open_at_end": [False, False, False, True],
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"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
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SellType.ROI, SellType.FORCE_SELL]
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})}
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results = {'DefStrat': {
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'results': pd.DataFrame(
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{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
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"profit_percent": [0.003312, 0.010801, -0.013803, 0.002780],
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"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
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"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
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Arrow(2017, 11, 14, 21, 36, 00).datetime,
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Arrow(2017, 11, 14, 22, 12, 00).datetime,
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Arrow(2017, 11, 14, 22, 44, 00).datetime],
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"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
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Arrow(2017, 11, 14, 22, 10, 00).datetime,
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Arrow(2017, 11, 14, 22, 43, 00).datetime,
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Arrow(2017, 11, 14, 22, 58, 00).datetime],
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"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
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"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
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"trade_duration": [123, 34, 31, 14],
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"open_at_end": [False, False, False, True],
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"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
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SellType.ROI, SellType.FORCE_SELL]
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}),
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'config': default_conf}
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}
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assert strat_stats['max_drawdown'] == 0.0
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assert strat_stats['drawdown_start'] == Arrow.fromtimestamp(0).datetime
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@ -283,9 +289,10 @@ def test_generate_sell_reason_stats(default_conf):
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assert stop_result['profit_mean_pct'] == round(stop_result['profit_mean'] * 100, 2)
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def test_text_table_strategy(default_conf, mocker):
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def test_text_table_strategy(default_conf):
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default_conf['max_open_trades'] = 2
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results = {}
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results['TestStrategy1'] = pd.DataFrame(
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results['TestStrategy1'] = {'results': pd.DataFrame(
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{
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'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
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'profit_percent': [0.1, 0.2, 0.3],
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@ -296,8 +303,8 @@ def test_text_table_strategy(default_conf, mocker):
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'losses': [0, 0, 1],
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'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
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}
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)
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results['TestStrategy2'] = pd.DataFrame(
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), 'config': default_conf}
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results['TestStrategy2'] = {'results': pd.DataFrame(
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{
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'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
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'profit_percent': [0.4, 0.2, 0.3],
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@ -308,7 +315,7 @@ def test_text_table_strategy(default_conf, mocker):
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'losses': [0, 0, 1],
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'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
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}
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)
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), 'config': default_conf}
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result_str = (
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'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot'
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@ -321,9 +328,7 @@ def test_text_table_strategy(default_conf, mocker):
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' 45.00 | 0:20:00 | 3 | 0 | 0 |'
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)
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strategy_results = generate_strategy_metrics(stake_currency='BTC',
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max_open_trades=2,
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all_results=results)
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strategy_results = generate_strategy_metrics(all_results=results)
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assert text_table_strategy(strategy_results, 'BTC') == result_str
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