2018-02-09 07:35:38 +00:00
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# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
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2017-11-14 21:15:24 +00:00
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2018-02-09 07:35:38 +00:00
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"""
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This module contains the backtesting logic
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"""
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2018-03-25 19:37:14 +00:00
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import logging
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2020-10-07 18:59:05 +00:00
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from collections import defaultdict
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2018-07-27 21:01:52 +00:00
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from copy import deepcopy
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2021-01-13 06:47:03 +00:00
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from datetime import datetime, timedelta, timezone
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2021-01-23 12:02:48 +00:00
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from typing import Any, Dict, List, Optional, Tuple
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2018-03-17 21:44:47 +00:00
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2021-01-24 08:56:27 +00:00
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from pandas import DataFrame
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2017-09-28 21:26:28 +00:00
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2020-09-28 17:39:41 +00:00
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from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
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2020-06-26 05:46:59 +00:00
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from freqtrade.constants import DATETIME_PRINT_FORMAT
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2018-12-13 05:34:10 +00:00
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from freqtrade.data import history
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2021-01-24 08:56:27 +00:00
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from freqtrade.data.btanalysis import trade_list_to_dataframe
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2019-12-25 14:47:04 +00:00
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from freqtrade.data.converter import trim_dataframe
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2019-03-24 14:24:47 +00:00
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from freqtrade.data.dataprovider import DataProvider
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2021-02-10 19:37:55 +00:00
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from freqtrade.exceptions import DependencyException, OperationalException
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2019-10-20 11:56:01 +00:00
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from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
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2020-12-07 14:45:02 +00:00
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from freqtrade.mixins import LoggingMixin
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2020-09-28 17:39:41 +00:00
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
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2020-06-26 05:46:59 +00:00
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store_backtest_stats)
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2021-02-22 05:54:33 +00:00
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from freqtrade.persistence import LocalTrade, PairLocks, Trade
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2020-12-23 16:00:02 +00:00
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from freqtrade.plugins.pairlistmanager import PairListManager
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2020-11-16 19:17:47 +00:00
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from freqtrade.plugins.protectionmanager import ProtectionManager
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2019-03-06 18:55:34 +00:00
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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2020-02-02 04:00:40 +00:00
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from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
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2020-10-11 17:50:37 +00:00
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from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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2021-01-28 06:06:58 +00:00
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from freqtrade.wallets import Wallets
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2019-04-09 09:27:35 +00:00
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2018-03-25 19:37:14 +00:00
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logger = logging.getLogger(__name__)
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2020-10-18 15:16:57 +00:00
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# Indexes for backtest tuples
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DATE_IDX = 0
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BUY_IDX = 1
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OPEN_IDX = 2
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CLOSE_IDX = 3
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SELL_IDX = 4
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LOW_IDX = 5
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HIGH_IDX = 6
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2018-03-25 19:37:14 +00:00
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2019-09-12 01:39:52 +00:00
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class Backtesting:
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2017-11-14 22:14:01 +00:00
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"""
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2018-02-09 07:35:38 +00:00
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Backtesting class, this class contains all the logic to run a backtest
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To run a backtest:
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backtesting = Backtesting(config)
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backtesting.start()
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2017-11-14 22:14:01 +00:00
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"""
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2018-07-28 05:00:58 +00:00
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2018-02-09 07:35:38 +00:00
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def __init__(self, config: Dict[str, Any]) -> None:
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2020-11-19 19:05:56 +00:00
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LoggingMixin.show_output = False
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2018-02-09 07:35:38 +00:00
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self.config = config
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2018-04-06 07:57:08 +00:00
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# Reset keys for backtesting
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2019-11-05 11:39:19 +00:00
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remove_credentials(self.config)
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2018-07-28 05:41:38 +00:00
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self.strategylist: List[IStrategy] = []
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2020-09-18 05:44:11 +00:00
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self.all_results: Dict[str, Dict] = {}
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2019-12-23 09:03:18 +00:00
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self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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2019-10-05 13:29:00 +00:00
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2020-08-08 15:04:32 +00:00
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dataprovider = DataProvider(self.config, self.exchange)
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IStrategy.dp = dataprovider
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2019-03-24 14:24:47 +00:00
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2018-07-28 05:41:38 +00:00
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if self.config.get('strategy_list', None):
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2018-07-28 05:55:59 +00:00
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for strat in list(self.config['strategy_list']):
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2018-07-28 05:41:38 +00:00
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stratconf = deepcopy(self.config)
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stratconf['strategy'] = strat
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2019-12-23 09:23:48 +00:00
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self.strategylist.append(StrategyResolver.load_strategy(stratconf))
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2019-11-23 14:49:46 +00:00
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validate_config_consistency(stratconf)
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2018-07-28 05:41:38 +00:00
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else:
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2019-06-09 23:08:54 +00:00
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# No strategy list specified, only one strategy
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2019-12-23 09:23:48 +00:00
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self.strategylist.append(StrategyResolver.load_strategy(self.config))
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2019-11-23 14:49:46 +00:00
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validate_config_consistency(self.config)
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2019-06-09 23:08:54 +00:00
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2020-06-01 18:49:40 +00:00
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if "timeframe" not in self.config:
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2020-03-08 10:35:31 +00:00
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raise OperationalException("Timeframe (ticker interval) needs to be set in either "
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2020-06-01 18:49:40 +00:00
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"configuration or as cli argument `--timeframe 5m`")
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self.timeframe = str(self.config.get('timeframe'))
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2019-12-11 06:12:37 +00:00
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self.timeframe_min = timeframe_to_minutes(self.timeframe)
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2019-08-26 19:31:24 +00:00
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2020-06-07 14:06:20 +00:00
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self.pairlists = PairListManager(self.exchange, self.config)
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if 'VolumePairList' in self.pairlists.name_list:
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raise OperationalException("VolumePairList not allowed for backtesting.")
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2020-12-16 18:24:47 +00:00
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if 'PerformanceFilter' in self.pairlists.name_list:
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raise OperationalException("PerformanceFilter not allowed for backtesting.")
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2020-06-07 14:06:20 +00:00
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if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
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raise OperationalException(
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"PrecisionFilter not allowed for backtesting multiple strategies."
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)
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2020-08-30 08:07:28 +00:00
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dataprovider.add_pairlisthandler(self.pairlists)
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2020-06-07 14:06:20 +00:00
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self.pairlists.refresh_pairlist()
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if len(self.pairlists.whitelist) == 0:
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raise OperationalException("No pair in whitelist.")
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2020-07-15 17:20:07 +00:00
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if config.get('fee', None) is not None:
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2020-06-07 14:06:20 +00:00
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self.fee = config['fee']
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else:
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self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
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2020-11-16 19:17:47 +00:00
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Trade.use_db = False
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2020-11-25 08:53:13 +00:00
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Trade.reset_trades()
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2020-11-16 19:17:47 +00:00
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PairLocks.timeframe = self.config['timeframe']
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PairLocks.use_db = False
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2020-11-25 08:53:13 +00:00
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PairLocks.reset_locks()
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2020-11-24 06:38:09 +00:00
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if self.config.get('enable_protections', False):
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self.protections = ProtectionManager(self.config)
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2020-11-16 19:17:47 +00:00
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2021-02-27 09:31:21 +00:00
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self.wallets = Wallets(self.config, self.exchange, log=False)
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2021-01-28 06:06:58 +00:00
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2019-10-20 11:56:01 +00:00
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# Get maximum required startup period
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self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
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2019-06-09 23:08:54 +00:00
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# Load one (first) strategy
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2018-07-28 05:41:38 +00:00
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self._set_strategy(self.strategylist[0])
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2018-02-09 07:35:38 +00:00
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2020-11-25 08:53:13 +00:00
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def __del__(self):
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LoggingMixin.show_output = True
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PairLocks.use_db = True
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Trade.use_db = True
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2021-02-26 18:48:06 +00:00
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def _set_strategy(self, strategy: IStrategy):
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2018-07-28 04:54:33 +00:00
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"""
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Load strategy into backtesting
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"""
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2020-10-18 15:16:57 +00:00
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self.strategy: IStrategy = strategy
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2019-03-06 18:55:34 +00:00
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# Set stoploss_on_exchange to false for backtesting,
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# since a "perfect" stoploss-sell is assumed anyway
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# And the regular "stoploss" function would not apply to that case
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self.strategy.order_types['stoploss_on_exchange'] = False
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2018-07-28 04:54:33 +00:00
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2020-03-15 14:04:48 +00:00
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def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
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2020-09-18 05:45:47 +00:00
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"""
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Loads backtest data and returns the data combined with the timerange
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as tuple.
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"""
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2019-10-23 18:13:43 +00:00
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timerange = TimeRange.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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data = history.load_data(
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2019-12-23 18:32:31 +00:00
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datadir=self.config['datadir'],
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2020-04-25 13:37:13 +00:00
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pairs=self.pairlists.whitelist,
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2019-11-02 19:26:26 +00:00
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timeframe=self.timeframe,
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2019-10-23 18:13:43 +00:00
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timerange=timerange,
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startup_candles=self.required_startup,
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fail_without_data=True,
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2019-12-28 13:57:39 +00:00
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data_format=self.config.get('dataformat_ohlcv', 'json'),
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2019-10-23 18:13:43 +00:00
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)
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2019-12-17 22:06:03 +00:00
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min_date, max_date = history.get_timerange(data)
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2019-10-23 18:13:43 +00:00
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2020-06-09 06:07:34 +00:00
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logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
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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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2019-10-23 18:13:43 +00:00
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# Adjust startts forward if not enough data is available
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2019-11-02 19:34:39 +00:00
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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
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2019-10-23 18:13:43 +00:00
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self.required_startup, min_date)
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return data, timerange
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2020-11-27 16:38:15 +00:00
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def prepare_backtest(self, enable_protections):
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"""
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Backtesting setup method - called once for every call to "backtest()".
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"""
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PairLocks.use_db = False
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2021-01-14 05:53:40 +00:00
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PairLocks.timeframe = self.config['timeframe']
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2020-11-27 16:38:15 +00:00
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Trade.use_db = False
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2021-03-08 18:40:29 +00:00
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PairLocks.reset_locks()
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Trade.reset_trades()
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2020-11-27 16:38:15 +00:00
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2020-10-18 15:16:57 +00:00
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def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
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2019-03-23 14:00:07 +00:00
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"""
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2020-03-08 10:35:31 +00:00
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Helper function to convert a processed dataframes into lists for performance reasons.
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2019-03-23 14:00:07 +00:00
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Used by backtest() - so keep this optimized for performance.
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"""
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2020-10-18 15:16:57 +00:00
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# Every change to this headers list must evaluate further usages of the resulting tuple
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# and eventually change the constants for indexes at the top
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2019-03-23 14:00:07 +00:00
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headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
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2020-03-08 10:35:31 +00:00
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data: Dict = {}
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# Create dict with data
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2019-03-23 14:00:07 +00:00
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for pair, pair_data in processed.items():
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2019-11-03 09:38:21 +00:00
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pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
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pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
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2019-03-23 14:00:07 +00:00
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2020-03-13 00:54:56 +00:00
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df_analyzed = self.strategy.advise_sell(
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2019-09-18 19:57:17 +00:00
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self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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2019-03-23 14:00:07 +00:00
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2020-03-08 10:35:31 +00:00
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# To avoid using data from future, we use buy/sell signals shifted
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# from the previous candle
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2020-01-25 10:42:31 +00:00
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df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
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df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
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2019-03-23 14:00:07 +00:00
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2020-03-13 00:54:56 +00:00
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df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
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2019-03-23 14:00:07 +00:00
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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2021-03-07 10:28:54 +00:00
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data[pair] = df_analyzed.values.tolist()
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2020-03-08 10:35:31 +00:00
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return data
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2019-03-23 14:00:07 +00:00
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2021-02-20 19:22:00 +00:00
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def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
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2020-02-02 04:00:40 +00:00
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trade_dur: int) -> float:
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2019-12-07 14:28:56 +00:00
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"""
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Get close rate for backtesting result
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"""
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2019-12-07 13:30:14 +00:00
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# Special handling if high or low hit STOP_LOSS or ROI
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if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
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# Set close_rate to stoploss
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2019-12-07 14:28:56 +00:00
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return trade.stop_loss
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2019-12-07 13:30:14 +00:00
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elif sell.sell_type == (SellType.ROI):
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2019-12-07 14:18:12 +00:00
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roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
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2020-10-18 15:16:57 +00:00
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if roi is not None and roi_entry is not None:
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2019-12-14 22:10:09 +00:00
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if roi == -1 and roi_entry % self.timeframe_min == 0:
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2019-12-09 15:52:12 +00:00
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# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
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# If that entry is a multiple of the timeframe (so on candle open)
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2019-12-07 14:28:56 +00:00
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# - we'll use open instead of close
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2020-10-18 15:16:57 +00:00
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return sell_row[OPEN_IDX]
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2019-12-07 14:28:56 +00:00
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2019-12-07 13:30:14 +00:00
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# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
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2019-12-09 15:52:12 +00:00
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close_rate = - (trade.open_rate * roi + trade.open_rate *
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(1 + trade.fee_open)) / (trade.fee_close - 1)
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if (trade_dur > 0 and trade_dur == roi_entry
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2019-12-14 22:10:09 +00:00
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and roi_entry % self.timeframe_min == 0
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2020-10-18 15:16:57 +00:00
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and sell_row[OPEN_IDX] > close_rate):
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2019-12-09 15:52:12 +00:00
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# new ROI entry came into effect.
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# use Open rate if open_rate > calculated sell rate
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2020-10-18 15:16:57 +00:00
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return sell_row[OPEN_IDX]
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2019-12-07 13:30:14 +00:00
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2019-12-09 15:52:12 +00:00
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# Use the maximum between close_rate and low as we
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2019-12-07 13:30:14 +00:00
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# cannot sell outside of a candle.
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2019-12-07 14:18:12 +00:00
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# Applies when a new ROI setting comes in place and the whole candle is above that.
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2020-10-18 15:16:57 +00:00
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|
|
return max(close_rate, sell_row[LOW_IDX])
|
2019-12-07 14:18:12 +00:00
|
|
|
|
2019-12-07 13:30:14 +00:00
|
|
|
else:
|
|
|
|
# This should not be reached...
|
2020-10-18 15:16:57 +00:00
|
|
|
return sell_row[OPEN_IDX]
|
2019-12-07 13:30:14 +00:00
|
|
|
else:
|
2020-10-18 15:16:57 +00:00
|
|
|
return sell_row[OPEN_IDX]
|
2019-12-07 13:30:14 +00:00
|
|
|
|
2021-02-20 19:22:00 +00:00
|
|
|
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
|
2020-10-07 18:59:05 +00:00
|
|
|
|
2021-02-20 19:22:00 +00:00
|
|
|
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
|
|
|
|
sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
|
2020-10-18 15:16:57 +00:00
|
|
|
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
|
2020-10-07 18:59:05 +00:00
|
|
|
|
2021-03-23 08:09:41 +00:00
|
|
|
time_in_force = self.strategy.order_time_in_force['sell']
|
|
|
|
|
|
|
|
# confirm_trade_exit
|
|
|
|
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=False)(
|
|
|
|
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount, rate=sell_row[LOW_IDX],
|
|
|
|
time_in_force=time_in_force,
|
|
|
|
sell_reason=sell.sell_type.value):
|
|
|
|
return None
|
|
|
|
|
|
|
|
if sell.sell_flag:
|
2020-11-16 19:17:47 +00:00
|
|
|
trade.close_date = sell_row[DATE_IDX]
|
2021-02-06 09:30:50 +00:00
|
|
|
trade.sell_reason = sell.sell_type.value
|
|
|
|
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
|
|
|
|
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
|
2020-11-16 19:21:32 +00:00
|
|
|
trade.close(closerate, show_msg=False)
|
2021-01-23 11:50:20 +00:00
|
|
|
return trade
|
2020-11-16 19:17:47 +00:00
|
|
|
|
2020-10-08 18:10:00 +00:00
|
|
|
return None
|
|
|
|
|
2021-02-22 05:54:33 +00:00
|
|
|
def _enter_trade(self, pair: str, row: List, max_open_trades: int,
|
2021-02-20 18:29:04 +00:00
|
|
|
open_trade_count: int) -> Optional[LocalTrade]:
|
2021-02-10 19:37:55 +00:00
|
|
|
try:
|
|
|
|
stake_amount = self.wallets.get_trade_stake_amount(
|
|
|
|
pair, max_open_trades - open_trade_count, None)
|
|
|
|
except DependencyException:
|
2021-02-22 05:54:33 +00:00
|
|
|
return None
|
2021-02-20 06:20:51 +00:00
|
|
|
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
|
2021-03-23 08:09:41 +00:00
|
|
|
|
|
|
|
order_type = self.strategy.order_types['buy']
|
|
|
|
time_in_force = self.strategy.order_time_in_force['sell']
|
|
|
|
# confirm_trade_entry
|
|
|
|
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
|
|
|
|
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
|
|
|
|
time_in_force=time_in_force):
|
|
|
|
return None
|
|
|
|
|
2021-02-20 19:21:30 +00:00
|
|
|
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
|
2021-02-10 19:37:55 +00:00
|
|
|
# Enter trade
|
2021-02-20 18:29:04 +00:00
|
|
|
trade = LocalTrade(
|
2021-02-10 19:37:55 +00:00
|
|
|
pair=pair,
|
|
|
|
open_rate=row[OPEN_IDX],
|
|
|
|
open_date=row[DATE_IDX],
|
|
|
|
stake_amount=stake_amount,
|
|
|
|
amount=round(stake_amount / row[OPEN_IDX], 8),
|
|
|
|
fee_open=self.fee,
|
|
|
|
fee_close=self.fee,
|
|
|
|
is_open=True,
|
|
|
|
exchange='backtesting',
|
|
|
|
)
|
|
|
|
return trade
|
|
|
|
return None
|
|
|
|
|
2021-02-20 18:29:04 +00:00
|
|
|
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
|
|
|
|
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
|
2020-10-08 18:10:00 +00:00
|
|
|
"""
|
|
|
|
Handling of left open trades at the end of backtesting
|
|
|
|
"""
|
|
|
|
trades = []
|
|
|
|
for pair in open_trades.keys():
|
|
|
|
if len(open_trades[pair]) > 0:
|
|
|
|
for trade in open_trades[pair]:
|
|
|
|
sell_row = data[pair][-1]
|
2020-11-16 19:17:47 +00:00
|
|
|
|
2021-01-23 11:50:20 +00:00
|
|
|
trade.close_date = sell_row[DATE_IDX]
|
2021-02-06 09:30:50 +00:00
|
|
|
trade.sell_reason = SellType.FORCE_SELL.value
|
2021-01-23 11:50:20 +00:00
|
|
|
trade.close(sell_row[OPEN_IDX], show_msg=False)
|
2021-03-14 08:48:40 +00:00
|
|
|
LocalTrade.close_bt_trade(trade)
|
2021-01-28 06:06:58 +00:00
|
|
|
# Deepcopy object to have wallets update correctly
|
|
|
|
trade1 = deepcopy(trade)
|
|
|
|
trade1.is_open = True
|
|
|
|
trades.append(trade1)
|
2020-10-08 18:10:00 +00:00
|
|
|
return trades
|
2018-02-09 07:35:38 +00:00
|
|
|
|
2021-02-10 19:37:55 +00:00
|
|
|
def backtest(self, processed: Dict,
|
2020-10-18 14:18:52 +00:00
|
|
|
start_date: datetime, end_date: datetime,
|
2020-11-23 19:29:29 +00:00
|
|
|
max_open_trades: int = 0, position_stacking: bool = False,
|
|
|
|
enable_protections: bool = False) -> DataFrame:
|
2018-02-09 07:35:38 +00:00
|
|
|
"""
|
2019-12-13 23:12:16 +00:00
|
|
|
Implement backtesting functionality
|
2018-02-09 07:35:38 +00:00
|
|
|
|
|
|
|
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
|
|
|
|
Of course try to not have ugly code. By some accessor are sometime slower than functions.
|
2019-12-13 23:12:16 +00:00
|
|
|
Avoid extensive logging in this method and functions it calls.
|
|
|
|
|
|
|
|
:param processed: a processed dictionary with format {pair, data}
|
|
|
|
:param start_date: backtesting timerange start datetime
|
|
|
|
:param end_date: backtesting timerange end datetime
|
|
|
|
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
|
|
|
|
:param position_stacking: do we allow position stacking?
|
2020-11-23 19:29:29 +00:00
|
|
|
:param enable_protections: Should protections be enabled?
|
2019-12-13 23:12:16 +00:00
|
|
|
:return: DataFrame with trades (results of backtesting)
|
2018-02-09 07:35:38 +00:00
|
|
|
"""
|
2021-02-20 19:22:00 +00:00
|
|
|
trades: List[LocalTrade] = []
|
2020-11-27 16:38:15 +00:00
|
|
|
self.prepare_backtest(enable_protections)
|
2018-06-07 07:21:07 +00:00
|
|
|
|
2020-03-08 10:35:31 +00:00
|
|
|
# Use dict of lists with data for performance
|
|
|
|
# (looping lists is a lot faster than pandas DataFrames)
|
|
|
|
data: Dict = self._get_ohlcv_as_lists(processed)
|
2018-10-15 20:02:23 +00:00
|
|
|
|
2019-04-04 18:23:10 +00:00
|
|
|
# Indexes per pair, so some pairs are allowed to have a missing start.
|
2019-03-20 17:38:10 +00:00
|
|
|
indexes: Dict = {}
|
2019-12-11 06:12:37 +00:00
|
|
|
tmp = start_date + timedelta(minutes=self.timeframe_min)
|
2019-03-20 17:38:10 +00:00
|
|
|
|
2021-02-20 19:22:00 +00:00
|
|
|
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
|
2020-10-07 18:59:05 +00:00
|
|
|
open_trade_count = 0
|
|
|
|
|
2019-04-04 18:23:10 +00:00
|
|
|
# Loop timerange and get candle for each pair at that point in time
|
2020-10-07 18:59:05 +00:00
|
|
|
while tmp <= end_date:
|
|
|
|
open_trade_count_start = open_trade_count
|
2019-03-20 17:38:10 +00:00
|
|
|
|
2020-03-08 10:35:31 +00:00
|
|
|
for i, pair in enumerate(data):
|
2019-03-20 17:38:10 +00:00
|
|
|
if pair not in indexes:
|
|
|
|
indexes[pair] = 0
|
|
|
|
|
2018-10-15 20:02:23 +00:00
|
|
|
try:
|
2020-03-08 10:35:31 +00:00
|
|
|
row = data[pair][indexes[pair]]
|
2018-10-15 20:02:23 +00:00
|
|
|
except IndexError:
|
2019-04-04 17:44:03 +00:00
|
|
|
# missing Data for one pair at the end.
|
2019-06-15 11:45:50 +00:00
|
|
|
# Warnings for this are shown during data loading
|
2018-10-15 20:02:23 +00:00
|
|
|
continue
|
2018-03-04 00:42:37 +00:00
|
|
|
|
2019-04-04 17:44:03 +00:00
|
|
|
# Waits until the time-counter reaches the start of the data for this pair.
|
2020-10-18 15:16:57 +00:00
|
|
|
if row[DATE_IDX] > tmp:
|
2019-03-20 17:38:10 +00:00
|
|
|
continue
|
|
|
|
indexes[pair] += 1
|
|
|
|
|
2020-10-07 18:59:05 +00:00
|
|
|
# without positionstacking, we can only have one open trade per pair.
|
|
|
|
# max_open_trades must be respected
|
|
|
|
# don't open on the last row
|
|
|
|
if ((position_stacking or len(open_trades[pair]) == 0)
|
2020-11-01 09:51:07 +00:00
|
|
|
and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
|
2020-10-07 18:59:05 +00:00
|
|
|
and tmp != end_date
|
2020-11-16 19:17:47 +00:00
|
|
|
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
|
|
|
|
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
|
2021-02-10 19:37:55 +00:00
|
|
|
trade = self._enter_trade(pair, row, max_open_trades, open_trade_count_start)
|
|
|
|
if trade:
|
|
|
|
# TODO: hacky workaround to avoid opening > max_open_trades
|
|
|
|
# This emulates previous behaviour - not sure if this is correct
|
|
|
|
# Prevents buying if the trade-slot was freed in this candle
|
|
|
|
open_trade_count_start += 1
|
|
|
|
open_trade_count += 1
|
2021-02-16 06:56:35 +00:00
|
|
|
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
|
2021-02-10 19:37:55 +00:00
|
|
|
open_trades[pair].append(trade)
|
2021-03-13 09:16:32 +00:00
|
|
|
LocalTrade.add_bt_trade(trade)
|
2020-10-07 18:59:05 +00:00
|
|
|
|
|
|
|
for trade in open_trades[pair]:
|
|
|
|
# also check the buying candle for sell conditions.
|
|
|
|
trade_entry = self._get_sell_trade_entry(trade, row)
|
|
|
|
# Sell occured
|
|
|
|
if trade_entry:
|
2020-10-18 14:38:16 +00:00
|
|
|
# logger.debug(f"{pair} - Backtesting sell {trade}")
|
2020-10-07 18:59:05 +00:00
|
|
|
open_trade_count -= 1
|
|
|
|
open_trades[pair].remove(trade)
|
2021-03-13 09:16:32 +00:00
|
|
|
|
|
|
|
LocalTrade.close_bt_trade(trade)
|
2020-10-07 18:59:05 +00:00
|
|
|
trades.append(trade_entry)
|
2020-11-23 19:29:29 +00:00
|
|
|
if enable_protections:
|
|
|
|
self.protections.stop_per_pair(pair, row[DATE_IDX])
|
2020-11-27 16:38:15 +00:00
|
|
|
self.protections.global_stop(tmp)
|
2018-03-04 00:42:37 +00:00
|
|
|
|
2020-10-07 18:59:05 +00:00
|
|
|
# Move time one configured time_interval ahead.
|
|
|
|
tmp += timedelta(minutes=self.timeframe_min)
|
2019-04-04 17:44:03 +00:00
|
|
|
|
2020-10-08 18:10:00 +00:00
|
|
|
trades += self.handle_left_open(open_trades, data=data)
|
2021-02-17 19:07:27 +00:00
|
|
|
self.wallets.update()
|
2018-06-09 19:44:20 +00:00
|
|
|
|
2021-01-23 19:49:49 +00:00
|
|
|
return trade_list_to_dataframe(trades)
|
2018-02-09 07:35:38 +00:00
|
|
|
|
2020-09-18 05:44:11 +00:00
|
|
|
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
|
|
|
|
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
|
2021-01-13 06:47:03 +00:00
|
|
|
backtest_start_time = datetime.now(timezone.utc)
|
2020-09-18 05:44:11 +00:00
|
|
|
self._set_strategy(strat)
|
|
|
|
|
2020-10-11 17:50:37 +00:00
|
|
|
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
|
|
|
|
|
2020-09-18 05:44:11 +00:00
|
|
|
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
|
|
|
|
if self.config.get('use_max_market_positions', True):
|
|
|
|
# Must come from strategy config, as the strategy may modify this setting.
|
|
|
|
max_open_trades = self.strategy.config['max_open_trades']
|
|
|
|
else:
|
|
|
|
logger.info(
|
|
|
|
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
|
|
|
|
max_open_trades = 0
|
|
|
|
|
|
|
|
# need to reprocess data every time to populate signals
|
|
|
|
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
|
|
|
|
|
|
|
|
# Trim startup period from analyzed dataframe
|
|
|
|
for pair, df in preprocessed.items():
|
|
|
|
preprocessed[pair] = trim_dataframe(df, timerange)
|
|
|
|
min_date, max_date = history.get_timerange(preprocessed)
|
|
|
|
|
|
|
|
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
|
|
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
|
|
f'({(max_date - min_date).days} days)..')
|
|
|
|
# Execute backtest and store results
|
|
|
|
results = self.backtest(
|
|
|
|
processed=preprocessed,
|
|
|
|
start_date=min_date.datetime,
|
|
|
|
end_date=max_date.datetime,
|
|
|
|
max_open_trades=max_open_trades,
|
|
|
|
position_stacking=self.config.get('position_stacking', False),
|
|
|
|
enable_protections=self.config.get('enable_protections', False),
|
|
|
|
)
|
2021-01-13 06:47:03 +00:00
|
|
|
backtest_end_time = datetime.now(timezone.utc)
|
2020-09-18 05:44:11 +00:00
|
|
|
self.all_results[self.strategy.get_strategy_name()] = {
|
|
|
|
'results': results,
|
|
|
|
'config': self.strategy.config,
|
2021-02-07 09:49:47 +00:00
|
|
|
'locks': PairLocks.get_all_locks(),
|
2021-02-12 19:20:32 +00:00
|
|
|
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
|
2021-01-13 06:47:03 +00:00
|
|
|
'backtest_start_time': int(backtest_start_time.timestamp()),
|
|
|
|
'backtest_end_time': int(backtest_end_time.timestamp()),
|
2020-09-18 05:44:11 +00:00
|
|
|
}
|
|
|
|
return min_date, max_date
|
|
|
|
|
2018-02-09 07:35:38 +00:00
|
|
|
def start(self) -> None:
|
|
|
|
"""
|
2019-12-13 23:12:16 +00:00
|
|
|
Run backtesting end-to-end
|
2018-02-09 07:35:38 +00:00
|
|
|
:return: None
|
|
|
|
"""
|
2018-08-19 17:39:22 +00:00
|
|
|
data: Dict[str, Any] = {}
|
2019-12-13 23:12:16 +00:00
|
|
|
|
2019-10-23 18:13:43 +00:00
|
|
|
data, timerange = self.load_bt_data()
|
2019-06-14 17:37:54 +00:00
|
|
|
|
2018-07-28 05:41:38 +00:00
|
|
|
for strat in self.strategylist:
|
2020-09-18 05:44:11 +00:00
|
|
|
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
|
2021-03-01 07:57:57 +00:00
|
|
|
if len(self.strategylist) > 0:
|
|
|
|
stats = generate_backtest_stats(data, self.all_results,
|
|
|
|
min_date=min_date, max_date=max_date)
|
2020-03-15 14:36:23 +00:00
|
|
|
|
2021-03-01 07:57:57 +00:00
|
|
|
if self.config.get('export', False):
|
|
|
|
store_backtest_stats(self.config['exportfilename'], stats)
|
2020-09-26 12:55:12 +00:00
|
|
|
|
2021-03-01 07:57:57 +00:00
|
|
|
# Show backtest results
|
|
|
|
show_backtest_results(self.config, stats)
|