612 lines
27 KiB
Python
612 lines
27 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.configuration import TimeRange, remove_credentials, 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 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.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, 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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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: Optional[Dict[str, Any]] = None
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# Reset keys for backtesting
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remove_credentials(self.config)
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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, None)
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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 (ticker interval) 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.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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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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Trade.use_db = False
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Trade.reset_trades()
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PairLocks.timeframe = self.config['timeframe']
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PairLocks.use_db = False
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PairLocks.reset_locks()
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self.wallets = Wallets(self.config, self.exchange, log=False)
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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.progress = BTProgress()
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self.abort = False
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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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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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IStrategy.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 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.dataprovider.clear_cache()
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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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"""
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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']
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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, pair_data in processed.items():
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self.check_abort()
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self.progress.increment()
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has_buy_tag = 'buy_tag' in pair_data
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headers = headers + ['buy_tag'] if has_buy_tag else headers
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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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if has_buy_tag:
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pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
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df_analyzed = self.strategy.advise_sell(
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self.strategy.advise_buy(
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pair_data,
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{'pair': pair}
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),
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{'pair': pair}
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).copy()
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# Trim startup period from analyzed dataframe
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df_analyzed = trim_dataframe(df_analyzed, self.timerange,
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startup_candles=self.required_startup)
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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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if has_buy_tag:
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df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
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df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
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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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# 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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return 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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# 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_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
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sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
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sell_row[DATE_IDX].to_pydatetime(), 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_row[DATE_IDX].to_pydatetime()
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trade.sell_reason = sell.sell_reason
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trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
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closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
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# Confirm trade exit:
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time_in_force = self.strategy.order_time_in_force['sell']
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if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
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pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
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rate=closerate,
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time_in_force=time_in_force,
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sell_reason=sell.sell_reason,
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current_time=sell_row[DATE_IDX].to_pydatetime()):
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return None
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trade.close(closerate, show_msg=False)
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return trade
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return None
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def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
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try:
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stake_amount = self.wallets.get_trade_stake_amount(pair, None)
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except DependencyException:
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return None
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min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05) or 0
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max_stake_amount = self.wallets.get_available_stake_amount()
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stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
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default_retval=stake_amount)(
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pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
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proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
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stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
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if not stake_amount:
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return None
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order_type = self.strategy.order_types['buy']
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time_in_force = self.strategy.order_time_in_force['sell']
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# Confirm trade entry:
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if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
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pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
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time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
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return None
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if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
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# Enter trade
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has_buy_tag = len(row) >= BUY_TAG_IDX + 1
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trade = LocalTrade(
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pair=pair,
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open_rate=row[OPEN_IDX],
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open_date=row[DATE_IDX].to_pydatetime(),
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stake_amount=stake_amount,
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amount=round(stake_amount / row[OPEN_IDX], 8),
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fee_open=self.fee,
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fee_close=self.fee,
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is_open=True,
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buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
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exchange='backtesting',
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)
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return trade
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return None
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def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
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data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
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"""
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Handling of left open trades at the end of backtesting
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"""
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trades = []
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for pair in open_trades.keys():
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if len(open_trades[pair]) > 0:
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for trade in open_trades[pair]:
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sell_row = data[pair][-1]
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trade.close_date = sell_row[DATE_IDX].to_pydatetime()
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trade.sell_reason = SellType.FORCE_SELL.value
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trade.close(sell_row[OPEN_IDX], show_msg=False)
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LocalTrade.close_bt_trade(trade)
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# Deepcopy object to have wallets update correctly
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trade1 = deepcopy(trade)
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trade1.is_open = True
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trades.append(trade1)
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return trades
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def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
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# Always allow trades when max_open_trades is enabled.
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if max_open_trades <= 0 or open_trade_count < max_open_trades:
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return True
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# Rejected trade
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self.rejected_trades += 1
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return False
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def backtest(self, processed: Dict,
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start_date: datetime, end_date: datetime,
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max_open_trades: int = 0, position_stacking: bool = False,
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enable_protections: bool = False) -> Dict[str, Any]:
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"""
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Implement backtesting functionality
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NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
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Of course try to not have ugly code. By some accessor are sometime slower than functions.
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Avoid extensive logging in this method and functions it calls.
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:param processed: a processed dictionary with format {pair, data}
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:param start_date: backtesting timerange start datetime
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:param end_date: backtesting timerange end datetime
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:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
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:param position_stacking: do we allow position stacking?
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:param enable_protections: Should protections be enabled?
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:return: DataFrame with trades (results of backtesting)
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"""
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trades: List[LocalTrade] = []
|
|
self.prepare_backtest(enable_protections)
|
|
|
|
# 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)
|
|
tmp = 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 tmp <= end_date:
|
|
open_trade_count_start = open_trade_count
|
|
self.check_abort()
|
|
for i, pair in enumerate(data):
|
|
row_index = indexes[pair]
|
|
try:
|
|
row = data[pair][row_index]
|
|
except IndexError:
|
|
# missing Data for one pair at the end.
|
|
# Warnings for this are shown during data loading
|
|
continue
|
|
|
|
# Waits until the time-counter reaches the start of the data for this pair.
|
|
if row[DATE_IDX] > tmp:
|
|
continue
|
|
|
|
row_index += 1
|
|
self.dataprovider._set_dataframe_max_index(row_index)
|
|
indexes[pair] = row_index
|
|
|
|
# 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 tmp != 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 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
|
|
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
|
|
open_trades[pair].append(trade)
|
|
LocalTrade.add_bt_trade(trade)
|
|
|
|
for trade in open_trades[pair]:
|
|
# also check the buying candle for sell conditions.
|
|
trade_entry = self._get_sell_trade_entry(trade, row)
|
|
# Sell occurred
|
|
if trade_entry:
|
|
# logger.debug(f"{pair} - Backtesting sell {trade}")
|
|
open_trade_count -= 1
|
|
open_trades[pair].remove(trade)
|
|
|
|
LocalTrade.close_bt_trade(trade)
|
|
trades.append(trade_entry)
|
|
if enable_protections:
|
|
self.protections.stop_per_pair(pair, row[DATE_IDX])
|
|
self.protections.global_stop(tmp)
|
|
|
|
# Move time one configured time_interval ahead.
|
|
self.progress.increment()
|
|
tmp += 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,
|
|
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
|
|
}
|
|
|
|
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], 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.ohlcvdata_to_dataframe(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({
|
|
'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 start(self) -> None:
|
|
"""
|
|
Run backtesting end-to-end
|
|
:return: None
|
|
"""
|
|
data: Dict[str, Any] = {}
|
|
|
|
data, timerange = self.load_bt_data()
|
|
logger.info("Dataload complete. Calculating indicators")
|
|
|
|
for strat in self.strategylist:
|
|
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
|
|
if len(self.strategylist) > 0:
|
|
|
|
self.results = generate_backtest_stats(data, self.all_results,
|
|
min_date=min_date, max_date=max_date)
|
|
|
|
if self.config.get('export', 'none') == 'trades':
|
|
store_backtest_stats(self.config['exportfilename'], self.results)
|
|
|
|
# Show backtest results
|
|
show_backtest_results(self.config, self.results)
|