960 lines
43 KiB
Python
960 lines
43 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
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"""
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This module contains the backtesting logic
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"""
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import logging
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from collections import defaultdict
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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from typing import Any, Dict, List, Optional, Tuple
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from pandas import DataFrame
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from freqtrade import constants
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from freqtrade.configuration import TimeRange, validate_config_consistency
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from freqtrade.constants import DATETIME_PRINT_FORMAT
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from freqtrade.data import history
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from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
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from freqtrade.data.converter import trim_dataframe, trim_dataframes
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.enums import BacktestState, SellType
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from freqtrade.exceptions import DependencyException, OperationalException
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from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
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from freqtrade.misc import get_strategy_run_id
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from freqtrade.mixins import LoggingMixin
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from freqtrade.optimize.bt_progress import BTProgress
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
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store_backtest_stats)
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from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
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from freqtrade.plugins.pairlistmanager import PairListManager
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from freqtrade.plugins.protectionmanager import ProtectionManager
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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from freqtrade.strategy.interface import IStrategy, SellCheckTuple
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from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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from freqtrade.wallets import Wallets
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logger = logging.getLogger(__name__)
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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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BUY_TAG_IDX = 7
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EXIT_TAG_IDX = 8
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class Backtesting:
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"""
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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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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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LoggingMixin.show_output = False
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self.config = config
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self.results: Dict[str, Any] = {}
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self.trade_id_counter: int = 0
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self.order_id_counter: int = 0
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config['dry_run'] = True
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self.run_ids: Dict[str, str] = {}
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self.strategylist: List[IStrategy] = []
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self.all_results: Dict[str, Dict] = {}
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self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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self.dataprovider = DataProvider(self.config, self.exchange)
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if self.config.get('strategy_list', None):
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for strat in list(self.config['strategy_list']):
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stratconf = deepcopy(self.config)
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stratconf['strategy'] = strat
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self.strategylist.append(StrategyResolver.load_strategy(stratconf))
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validate_config_consistency(stratconf)
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else:
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# No strategy list specified, only one strategy
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self.strategylist.append(StrategyResolver.load_strategy(self.config))
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validate_config_consistency(self.config)
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if "timeframe" not in self.config:
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raise OperationalException("Timeframe needs to be set in either "
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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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self.timeframe_min = timeframe_to_minutes(self.timeframe)
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self.init_backtest_detail()
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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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"Please use StaticPairlist instead.")
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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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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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self.dataprovider.add_pairlisthandler(self.pairlists)
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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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if config.get('fee', None) is not None:
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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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self.timerange = TimeRange.parse_timerange(
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None if self.config.get('timerange') is None else str(self.config.get('timerange')))
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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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# Add maximum startup candle count to configuration for informative pairs support
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self.config['startup_candle_count'] = self.required_startup
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self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
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self.init_backtest()
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def __del__(self):
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self.cleanup()
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@staticmethod
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def cleanup():
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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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def init_backtest_detail(self):
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# Load detail timeframe if specified
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self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
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if self.timeframe_detail:
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self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail)
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if self.timeframe_min <= self.timeframe_detail_min:
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raise OperationalException(
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"Detail timeframe must be smaller than strategy timeframe.")
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else:
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self.timeframe_detail_min = 0
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self.detail_data: Dict[str, DataFrame] = {}
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def init_backtest(self):
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self.prepare_backtest(False)
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self.wallets = Wallets(self.config, self.exchange, log=False)
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self.progress = BTProgress()
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self.abort = False
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def _set_strategy(self, strategy: IStrategy):
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"""
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Load strategy into backtesting
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"""
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self.strategy: IStrategy = strategy
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strategy.dp = self.dataprovider
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# Attach Wallets to Strategy baseclass
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strategy.wallets = self.wallets
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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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def _load_protections(self, strategy: IStrategy):
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if self.config.get('enable_protections', False):
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conf = self.config
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if hasattr(strategy, 'protections'):
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conf = deepcopy(conf)
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conf['protections'] = strategy.protections
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self.protections = ProtectionManager(self.config, strategy.protections)
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def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
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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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self.progress.init_step(BacktestState.DATALOAD, 1)
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data = history.load_data(
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datadir=self.config['datadir'],
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pairs=self.pairlists.whitelist,
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timeframe=self.timeframe,
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timerange=self.timerange,
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startup_candles=self.required_startup,
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fail_without_data=True,
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data_format=self.config.get('dataformat_ohlcv', 'json'),
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)
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min_date, max_date = history.get_timerange(data)
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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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# Adjust startts forward if not enough data is available
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self.timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
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self.required_startup, min_date)
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self.progress.set_new_value(1)
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return data, self.timerange
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def load_bt_data_detail(self) -> None:
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"""
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Loads backtest detail data (smaller timeframe) if necessary.
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"""
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if self.timeframe_detail:
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self.detail_data = history.load_data(
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datadir=self.config['datadir'],
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pairs=self.pairlists.whitelist,
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timeframe=self.timeframe_detail,
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timerange=self.timerange,
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startup_candles=0,
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fail_without_data=True,
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data_format=self.config.get('dataformat_ohlcv', 'json'),
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)
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else:
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self.detail_data = {}
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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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PairLocks.timeframe = self.config['timeframe']
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Trade.use_db = False
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PairLocks.reset_locks()
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Trade.reset_trades()
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self.rejected_trades = 0
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self.timedout_entry_orders = 0
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self.timedout_exit_orders = 0
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self.dataprovider.clear_cache()
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if enable_protections:
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self._load_protections(self.strategy)
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def check_abort(self):
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"""
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Check if abort was requested, raise DependencyException if that's the case
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Only applies to Interactive backtest mode (webserver mode)
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"""
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if self.abort:
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self.abort = False
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raise DependencyException("Stop requested")
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def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
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"""
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Helper function to convert a processed dataframes into lists for performance reasons.
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Used by backtest() - so keep this optimized for performance.
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:param processed: a processed dictionary with format {pair, data}, which gets cleared to
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optimize memory usage!
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"""
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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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headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
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data: Dict = {}
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self.progress.init_step(BacktestState.CONVERT, len(processed))
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# Create dict with data
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for pair in processed.keys():
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pair_data = processed[pair]
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self.check_abort()
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self.progress.increment()
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if not pair_data.empty:
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pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
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pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
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pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
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pair_data.loc[:, 'exit_tag'] = None # cleanup if exit_tag is exist
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df_analyzed = self.strategy.advise_sell(
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self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
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# Trim startup period from analyzed dataframe
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df_analyzed = processed[pair] = pair_data = trim_dataframe(
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df_analyzed, self.timerange, startup_candles=self.required_startup)
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# Update dataprovider cache
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self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
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# Create a copy of the dataframe before shifting, that way the buy signal/tag
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# remains on the correct candle for callbacks.
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df_analyzed = df_analyzed.copy()
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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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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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df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
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df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
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df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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data[pair] = df_analyzed[headers].values.tolist()
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return data
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def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
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trade_dur: int) -> float:
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"""
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Get close rate for backtesting result
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"""
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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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if trade.stop_loss > sell_row[HIGH_IDX]:
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# our stoploss was already higher than candle high,
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# possibly due to a cancelled trade exit.
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# sell at open price.
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return sell_row[OPEN_IDX]
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# Special case: trailing triggers within same candle as trade opened. Assume most
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# pessimistic price movement, which is moving just enough to arm stoploss and
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# immediately going down to stop price.
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if sell.sell_type == SellType.TRAILING_STOP_LOSS and trade_dur == 0:
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if (
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not self.strategy.use_custom_stoploss and self.strategy.trailing_stop
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and self.strategy.trailing_only_offset_is_reached
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and self.strategy.trailing_stop_positive_offset is not None
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and self.strategy.trailing_stop_positive
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):
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# Worst case: price reaches stop_positive_offset and dives down.
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stop_rate = (sell_row[OPEN_IDX] *
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(1 + abs(self.strategy.trailing_stop_positive_offset) -
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abs(self.strategy.trailing_stop_positive)))
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else:
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# Worst case: price ticks tiny bit above open and dives down.
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stop_rate = sell_row[OPEN_IDX] * (1 - abs(trade.stop_loss_pct))
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assert stop_rate < sell_row[HIGH_IDX]
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# Limit lower-end to candle low to avoid sells below the low.
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# This still remains "worst case" - but "worst realistic case".
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return max(sell_row[LOW_IDX], stop_rate)
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# Set close_rate to stoploss
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return trade.stop_loss
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elif sell.sell_type == (SellType.ROI):
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roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
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if roi is not None and roi_entry is not None:
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if roi == -1 and roi_entry % self.timeframe_min == 0:
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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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# - we'll use open instead of close
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return sell_row[OPEN_IDX]
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# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
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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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and roi_entry % self.timeframe_min == 0
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and sell_row[OPEN_IDX] > close_rate):
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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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return sell_row[OPEN_IDX]
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if (
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trade_dur == 0
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# Red candle (for longs), TODO: green candle (for shorts)
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and sell_row[OPEN_IDX] > sell_row[CLOSE_IDX] # Red candle
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and trade.open_rate < sell_row[OPEN_IDX] # trade-open below open_rate
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and close_rate > sell_row[CLOSE_IDX]
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):
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# ROI on opening candles with custom pricing can only
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# trigger if the entry was at Open or lower.
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# details: https: // github.com/freqtrade/freqtrade/issues/6261
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# If open_rate is < open, only allow sells below the close on red candles.
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raise ValueError("Opening candle ROI on red candles.")
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# Use the maximum between close_rate and low as we
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# cannot sell outside of a candle.
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# Applies when a new ROI setting comes in place and the whole candle is above that.
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return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
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else:
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# This should not be reached...
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return sell_row[OPEN_IDX]
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else:
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return sell_row[OPEN_IDX]
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def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
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) -> LocalTrade:
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current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
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min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
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max_stake = self.wallets.get_available_stake_amount()
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stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
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default_retval=None)(
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trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
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current_profit=current_profit, min_stake=min_stake, max_stake=max_stake)
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# Check if we should increase our position
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if stake_amount is not None and stake_amount > 0.0:
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pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
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if pos_trade is not None:
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self.wallets.update()
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return pos_trade
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return trade
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def _get_order_filled(self, rate: float, row: Tuple) -> bool:
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""" Rate is within candle, therefore filled"""
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return row[LOW_IDX] <= rate <= row[HIGH_IDX]
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def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
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sell_row: Tuple) -> Optional[LocalTrade]:
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# Check if we need to adjust our current positions
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if self.strategy.position_adjustment_enable:
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check_adjust_buy = True
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if self.strategy.max_entry_position_adjustment > -1:
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count_of_buys = trade.nr_of_successful_buys
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check_adjust_buy = (count_of_buys <= self.strategy.max_entry_position_adjustment)
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if check_adjust_buy:
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trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
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sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
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sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
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sell_candle_time, sell_row[BUY_IDX],
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sell_row[SELL_IDX],
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low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
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if sell.sell_flag:
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trade.close_date = sell_candle_time
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trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
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try:
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closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
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except ValueError:
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return None
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# call the custom exit price,with default value as previous closerate
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current_profit = trade.calc_profit_ratio(closerate)
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order_type = self.strategy.order_types['sell']
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if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
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# Custom exit pricing only for sell-signals
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if order_type == 'limit':
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closerate = strategy_safe_wrapper(self.strategy.custom_exit_price,
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default_retval=closerate)(
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pair=trade.pair, trade=trade,
|
|
current_time=sell_candle_time,
|
|
proposed_rate=closerate, current_profit=current_profit)
|
|
# We can't place orders lower than current low.
|
|
# freqtrade does not support this in live, and the order would fill immediately
|
|
closerate = max(closerate, sell_row[LOW_IDX])
|
|
# Confirm trade exit:
|
|
time_in_force = self.strategy.order_time_in_force['sell']
|
|
|
|
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
|
|
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
|
|
rate=closerate,
|
|
time_in_force=time_in_force,
|
|
sell_reason=sell.sell_reason,
|
|
current_time=sell_candle_time):
|
|
return None
|
|
|
|
trade.sell_reason = sell.sell_reason
|
|
|
|
# Checks and adds an exit tag, after checking that the length of the
|
|
# sell_row has the length for an exit tag column
|
|
if(
|
|
len(sell_row) > EXIT_TAG_IDX
|
|
and sell_row[EXIT_TAG_IDX] is not None
|
|
and len(sell_row[EXIT_TAG_IDX]) > 0
|
|
):
|
|
trade.sell_reason = sell_row[EXIT_TAG_IDX]
|
|
|
|
self.order_id_counter += 1
|
|
order = Order(
|
|
id=self.order_id_counter,
|
|
ft_trade_id=trade.id,
|
|
order_date=sell_candle_time,
|
|
order_update_date=sell_candle_time,
|
|
ft_is_open=True,
|
|
ft_pair=trade.pair,
|
|
order_id=str(self.order_id_counter),
|
|
symbol=trade.pair,
|
|
ft_order_side="sell",
|
|
side="sell",
|
|
order_type=order_type,
|
|
status="open",
|
|
price=closerate,
|
|
average=closerate,
|
|
amount=trade.amount,
|
|
filled=0,
|
|
remaining=trade.amount,
|
|
cost=trade.amount * closerate,
|
|
)
|
|
trade.orders.append(order)
|
|
return trade
|
|
|
|
return None
|
|
|
|
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
|
|
if self.timeframe_detail and trade.pair in self.detail_data:
|
|
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
|
|
sell_candle_end = sell_candle_time + timedelta(minutes=self.timeframe_min)
|
|
|
|
detail_data = self.detail_data[trade.pair]
|
|
detail_data = detail_data.loc[
|
|
(detail_data['date'] >= sell_candle_time) &
|
|
(detail_data['date'] < sell_candle_end)
|
|
].copy()
|
|
if len(detail_data) == 0:
|
|
# Fall back to "regular" data if no detail data was found for this candle
|
|
return self._get_sell_trade_entry_for_candle(trade, sell_row)
|
|
detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
|
|
detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
|
|
detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
|
|
detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
|
|
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
|
|
for det_row in detail_data[headers].values.tolist():
|
|
res = self._get_sell_trade_entry_for_candle(trade, det_row)
|
|
if res:
|
|
return res
|
|
|
|
return None
|
|
|
|
else:
|
|
return self._get_sell_trade_entry_for_candle(trade, sell_row)
|
|
|
|
def _enter_trade(self, pair: str, row: Tuple, stake_amount: Optional[float] = None,
|
|
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
|
|
|
|
current_time = row[DATE_IDX].to_pydatetime()
|
|
entry_tag = row[BUY_TAG_IDX] if len(row) >= BUY_TAG_IDX + 1 else None
|
|
# let's call the custom entry price, using the open price as default price
|
|
order_type = self.strategy.order_types['buy']
|
|
propose_rate = row[OPEN_IDX]
|
|
if order_type == 'limit':
|
|
propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
|
|
default_retval=row[OPEN_IDX])(
|
|
pair=pair, current_time=current_time,
|
|
proposed_rate=propose_rate, entry_tag=entry_tag) # default value is the open rate
|
|
# We can't place orders higher than current high (otherwise it'd be a stop limit buy)
|
|
# which freqtrade does not support in live.
|
|
propose_rate = min(propose_rate, row[HIGH_IDX])
|
|
|
|
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, propose_rate, -0.05) or 0
|
|
max_stake_amount = self.wallets.get_available_stake_amount()
|
|
|
|
pos_adjust = trade is not None
|
|
if not pos_adjust:
|
|
try:
|
|
stake_amount = self.wallets.get_trade_stake_amount(pair, None, update=False)
|
|
except DependencyException:
|
|
return None
|
|
|
|
stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
|
|
default_retval=stake_amount)(
|
|
pair=pair, current_time=current_time, current_rate=propose_rate,
|
|
proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount,
|
|
entry_tag=entry_tag)
|
|
|
|
stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
|
|
|
|
if not stake_amount:
|
|
# In case of pos adjust, still return the original trade
|
|
# If not pos adjust, trade is None
|
|
return trade
|
|
|
|
time_in_force = self.strategy.order_time_in_force['buy']
|
|
# Confirm trade entry:
|
|
if not pos_adjust:
|
|
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
|
|
pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
|
|
time_in_force=time_in_force, current_time=current_time,
|
|
entry_tag=entry_tag):
|
|
return None
|
|
|
|
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
|
|
self.order_id_counter += 1
|
|
amount = round(stake_amount / propose_rate, 8)
|
|
if trade is None:
|
|
# Enter trade
|
|
self.trade_id_counter += 1
|
|
trade = LocalTrade(
|
|
id=self.trade_id_counter,
|
|
open_order_id=self.order_id_counter,
|
|
pair=pair,
|
|
open_rate=propose_rate,
|
|
open_rate_requested=propose_rate,
|
|
open_date=current_time,
|
|
stake_amount=stake_amount,
|
|
amount=amount,
|
|
amount_requested=amount,
|
|
fee_open=self.fee,
|
|
fee_close=self.fee,
|
|
is_open=True,
|
|
buy_tag=entry_tag,
|
|
exchange='backtesting',
|
|
orders=[]
|
|
)
|
|
|
|
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
|
|
|
|
order = Order(
|
|
id=self.order_id_counter,
|
|
ft_trade_id=trade.id,
|
|
ft_is_open=True,
|
|
ft_pair=trade.pair,
|
|
order_id=str(self.order_id_counter),
|
|
symbol=trade.pair,
|
|
ft_order_side="buy",
|
|
side="buy",
|
|
order_type=order_type,
|
|
status="open",
|
|
order_date=current_time,
|
|
order_filled_date=current_time,
|
|
order_update_date=current_time,
|
|
price=propose_rate,
|
|
average=propose_rate,
|
|
amount=amount,
|
|
filled=0,
|
|
remaining=amount,
|
|
cost=stake_amount + trade.fee_open,
|
|
)
|
|
if pos_adjust and self._get_order_filled(order.price, row):
|
|
order.close_bt_order(current_time)
|
|
else:
|
|
trade.open_order_id = str(self.order_id_counter)
|
|
trade.orders.append(order)
|
|
trade.recalc_trade_from_orders()
|
|
|
|
return trade
|
|
|
|
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
|
|
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
|
|
"""
|
|
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]:
|
|
if trade.open_order_id and trade.nr_of_successful_buys == 0:
|
|
# Ignore trade if buy-order did not fill yet
|
|
continue
|
|
sell_row = data[pair][-1]
|
|
|
|
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
|
|
trade.sell_reason = SellType.FORCE_SELL.value
|
|
trade.close(sell_row[OPEN_IDX], show_msg=False)
|
|
LocalTrade.close_bt_trade(trade)
|
|
# Deepcopy object to have wallets update correctly
|
|
trade1 = deepcopy(trade)
|
|
trade1.is_open = True
|
|
trades.append(trade1)
|
|
return trades
|
|
|
|
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
|
|
# Always allow trades when max_open_trades is enabled.
|
|
if max_open_trades <= 0 or open_trade_count < max_open_trades:
|
|
return True
|
|
# Rejected trade
|
|
self.rejected_trades += 1
|
|
return False
|
|
|
|
def run_protections(self, enable_protections, pair: str, current_time: datetime):
|
|
if enable_protections:
|
|
self.protections.stop_per_pair(pair, current_time)
|
|
self.protections.global_stop(current_time)
|
|
|
|
def check_order_cancel(self, trade: LocalTrade, current_time) -> bool:
|
|
"""
|
|
Check if an order has been canceled.
|
|
Returns True if the trade should be Deleted (initial order was canceled).
|
|
"""
|
|
for order in [o for o in trade.orders if o.ft_is_open]:
|
|
|
|
timedout = self.strategy.ft_check_timed_out(order.side, trade, order, current_time)
|
|
if timedout:
|
|
if order.side == 'buy':
|
|
self.timedout_entry_orders += 1
|
|
if trade.nr_of_successful_buys == 0:
|
|
# Remove trade due to buy timeout expiration.
|
|
return True
|
|
else:
|
|
# Close additional buy order
|
|
del trade.orders[trade.orders.index(order)]
|
|
if order.side == 'sell':
|
|
self.timedout_exit_orders += 1
|
|
# Close sell order and retry selling on next signal.
|
|
del trade.orders[trade.orders.index(order)]
|
|
|
|
return False
|
|
|
|
def validate_row(
|
|
self, data: Dict, pair: str, row_index: int, current_time: datetime) -> Optional[Tuple]:
|
|
try:
|
|
# Row is treated as "current incomplete candle".
|
|
# Buy / sell signals are shifted by 1 to compensate for this.
|
|
row = data[pair][row_index]
|
|
except IndexError:
|
|
# missing Data for one pair at the end.
|
|
# Warnings for this are shown during data loading
|
|
return None
|
|
|
|
# Waits until the time-counter reaches the start of the data for this pair.
|
|
if row[DATE_IDX] > current_time:
|
|
return None
|
|
return row
|
|
|
|
def backtest(self, processed: Dict,
|
|
start_date: datetime, end_date: datetime,
|
|
max_open_trades: int = 0, position_stacking: bool = False,
|
|
enable_protections: bool = False) -> Dict[str, Any]:
|
|
"""
|
|
Implement backtesting functionality
|
|
|
|
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.
|
|
Avoid extensive logging in this method and functions it calls.
|
|
|
|
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
|
|
optimize memory usage!
|
|
: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?
|
|
:param enable_protections: Should protections be enabled?
|
|
:return: DataFrame with trades (results of backtesting)
|
|
"""
|
|
trades: List[LocalTrade] = []
|
|
self.prepare_backtest(enable_protections)
|
|
# Ensure wallets are uptodate (important for --strategy-list)
|
|
self.wallets.update()
|
|
# 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)
|
|
|
|
# Indexes per pair, so some pairs are allowed to have a missing start.
|
|
indexes: Dict = defaultdict(int)
|
|
current_time = start_date + timedelta(minutes=self.timeframe_min)
|
|
|
|
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
|
|
open_trade_count = 0
|
|
|
|
self.progress.init_step(BacktestState.BACKTEST, int(
|
|
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
|
|
|
|
# Loop timerange and get candle for each pair at that point in time
|
|
while current_time <= end_date:
|
|
open_trade_count_start = open_trade_count
|
|
self.check_abort()
|
|
for i, pair in enumerate(data):
|
|
row_index = indexes[pair]
|
|
row = self.validate_row(data, pair, row_index, current_time)
|
|
if not row:
|
|
continue
|
|
|
|
row_index += 1
|
|
indexes[pair] = row_index
|
|
self.dataprovider._set_dataframe_max_index(row_index)
|
|
|
|
# 1. Process buys.
|
|
# 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)
|
|
and self.trade_slot_available(max_open_trades, open_trade_count_start)
|
|
and current_time != end_date
|
|
and row[BUY_IDX] == 1
|
|
and row[SELL_IDX] != 1
|
|
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
|
|
):
|
|
trade = self._enter_trade(pair, row)
|
|
if trade:
|
|
# TODO: hacky workaround to avoid opening > max_open_trades
|
|
# This emulates previous behavior - 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
|
|
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
|
|
open_trades[pair].append(trade)
|
|
|
|
for trade in list(open_trades[pair]):
|
|
# 2. Process buy orders.
|
|
order = trade.select_order('buy', is_open=True)
|
|
if order and self._get_order_filled(order.price, row):
|
|
order.close_bt_order(current_time)
|
|
trade.open_order_id = None
|
|
LocalTrade.add_bt_trade(trade)
|
|
self.wallets.update()
|
|
|
|
# 3. Create sell orders (if any)
|
|
if not trade.open_order_id:
|
|
self._get_sell_trade_entry(trade, row) # Place sell order if necessary
|
|
|
|
# 4. Process sell orders.
|
|
order = trade.select_order('sell', is_open=True)
|
|
if order and self._get_order_filled(order.price, row):
|
|
trade.open_order_id = None
|
|
trade.close_date = current_time
|
|
trade.close(order.price, show_msg=False)
|
|
|
|
# logger.debug(f"{pair} - Backtesting sell {trade}")
|
|
open_trade_count -= 1
|
|
open_trades[pair].remove(trade)
|
|
LocalTrade.close_bt_trade(trade)
|
|
trades.append(trade)
|
|
self.wallets.update()
|
|
self.run_protections(enable_protections, pair, current_time)
|
|
|
|
# 5. Cancel expired buy/sell orders.
|
|
if self.check_order_cancel(trade, current_time):
|
|
# Close trade due to buy timeout expiration.
|
|
open_trade_count -= 1
|
|
open_trades[pair].remove(trade)
|
|
self.wallets.update()
|
|
|
|
# Move time one configured time_interval ahead.
|
|
self.progress.increment()
|
|
current_time += timedelta(minutes=self.timeframe_min)
|
|
|
|
trades += self.handle_left_open(open_trades, data=data)
|
|
self.wallets.update()
|
|
|
|
results = trade_list_to_dataframe(trades)
|
|
return {
|
|
'results': results,
|
|
'config': self.strategy.config,
|
|
'locks': PairLocks.get_all_locks(),
|
|
'rejected_signals': self.rejected_trades,
|
|
'timedout_entry_orders': self.timedout_entry_orders,
|
|
'timedout_exit_orders': self.timedout_exit_orders,
|
|
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
|
|
}
|
|
|
|
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
|
|
timerange: TimeRange):
|
|
self.progress.init_step(BacktestState.ANALYZE, 0)
|
|
|
|
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
|
|
backtest_start_time = datetime.now(timezone.utc)
|
|
self._set_strategy(strat)
|
|
|
|
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
|
|
|
|
# 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.advise_all_indicators(data)
|
|
|
|
# Trim startup period from analyzed dataframe
|
|
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
|
|
|
|
if not preprocessed_tmp:
|
|
raise OperationalException(
|
|
"No data left after adjusting for startup candles.")
|
|
|
|
# Use preprocessed_tmp for date generation (the trimmed dataframe).
|
|
# Backtesting will re-trim the dataframes after buy/sell signal generation.
|
|
min_date, max_date = history.get_timerange(preprocessed_tmp)
|
|
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,
|
|
end_date=max_date,
|
|
max_open_trades=max_open_trades,
|
|
position_stacking=self.config.get('position_stacking', False),
|
|
enable_protections=self.config.get('enable_protections', False),
|
|
)
|
|
backtest_end_time = datetime.now(timezone.utc)
|
|
results.update({
|
|
'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
|
|
'backtest_start_time': int(backtest_start_time.timestamp()),
|
|
'backtest_end_time': int(backtest_end_time.timestamp()),
|
|
})
|
|
self.all_results[self.strategy.get_strategy_name()] = results
|
|
|
|
return min_date, max_date
|
|
|
|
def _get_min_cached_backtest_date(self):
|
|
min_backtest_date = None
|
|
backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
|
|
if self.timerange.stopts == 0 or datetime.fromtimestamp(
|
|
self.timerange.stopts, tz=timezone.utc) > datetime.now(tz=timezone.utc):
|
|
logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
|
|
elif backtest_cache_age == 'day':
|
|
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)
|
|
elif backtest_cache_age == 'week':
|
|
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1)
|
|
elif backtest_cache_age == 'month':
|
|
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4)
|
|
return min_backtest_date
|
|
|
|
def load_prior_backtest(self):
|
|
self.run_ids = {
|
|
strategy.get_strategy_name(): get_strategy_run_id(strategy)
|
|
for strategy in self.strategylist
|
|
}
|
|
|
|
# Load previous result that will be updated incrementally.
|
|
# This can be circumvented in certain instances in combination with downloading more data
|
|
min_backtest_date = self._get_min_cached_backtest_date()
|
|
if min_backtest_date is not None:
|
|
self.results = find_existing_backtest_stats(
|
|
self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date)
|
|
|
|
def start(self) -> None:
|
|
"""
|
|
Run backtesting end-to-end
|
|
:return: None
|
|
"""
|
|
data: Dict[str, Any] = {}
|
|
|
|
data, timerange = self.load_bt_data()
|
|
self.load_bt_data_detail()
|
|
logger.info("Dataload complete. Calculating indicators")
|
|
|
|
self.load_prior_backtest()
|
|
|
|
for strat in self.strategylist:
|
|
if self.results and strat.get_strategy_name() in self.results['strategy']:
|
|
# When previous result hash matches - reuse that result and skip backtesting.
|
|
logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
|
|
continue
|
|
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
|
|
|
|
# Update old results with new ones.
|
|
if len(self.all_results) > 0:
|
|
results = generate_backtest_stats(
|
|
data, self.all_results, min_date=min_date, max_date=max_date)
|
|
if self.results:
|
|
self.results['metadata'].update(results['metadata'])
|
|
self.results['strategy'].update(results['strategy'])
|
|
self.results['strategy_comparison'].extend(results['strategy_comparison'])
|
|
else:
|
|
self.results = results
|
|
|
|
if self.config.get('export', 'none') == 'trades':
|
|
store_backtest_stats(self.config['exportfilename'], self.results)
|
|
|
|
# Results may be mixed up now. Sort them so they follow --strategy-list order.
|
|
if 'strategy_list' in self.config and len(self.results) > 0:
|
|
self.results['strategy_comparison'] = sorted(
|
|
self.results['strategy_comparison'],
|
|
key=lambda c: self.config['strategy_list'].index(c['key']))
|
|
self.results['strategy'] = dict(
|
|
sorted(self.results['strategy'].items(),
|
|
key=lambda kv: self.config['strategy_list'].index(kv[0])))
|
|
|
|
if len(self.strategylist) > 0:
|
|
# Show backtest results
|
|
show_backtest_results(self.config, self.results)
|