mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-14 12:13:57 +00:00
1145 lines
51 KiB
Python
1145 lines
51 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 numpy import nan
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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, LongShort
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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, CandleType, ExitCheckTuple, ExitType, TradingMode
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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
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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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OPEN_IDX = 1
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HIGH_IDX = 2
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LOW_IDX = 3
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CLOSE_IDX = 4
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LONG_IDX = 5
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ELONG_IDX = 6 # Exit long
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SHORT_IDX = 7
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ESHORT_IDX = 8 # Exit short
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ENTER_TAG_IDX = 9
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EXIT_TAG_IDX = 10
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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_name = self.config['exchange']['name']
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self.exchange = ExchangeResolver.load_exchange(self._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.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
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# strategies which define "can_short=True" will fail to load in Spot mode.
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self._can_short = self.trading_mode != TradingMode.SPOT
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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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self.futures_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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candle_type=self.config.get('candle_type_def', CandleType.SPOT)
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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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candle_type=self.config.get('candle_type_def', CandleType.SPOT)
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)
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else:
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self.detail_data = {}
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if self.trading_mode == TradingMode.FUTURES:
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# Load additional futures data.
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funding_rates_dict = 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.exchange._ft_has['mark_ohlcv_timeframe'],
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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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candle_type=CandleType.FUNDING_RATE
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)
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# For simplicity, assign to CandleType.Mark (might contian index candles!)
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mark_rates_dict = 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.exchange._ft_has['mark_ohlcv_timeframe'],
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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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candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"])
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)
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# Combine data to avoid combining the data per trade.
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for pair in self.pairlists.whitelist:
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self.futures_data[pair] = funding_rates_dict[pair].merge(
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mark_rates_dict[pair], on='date', how="inner", suffixes=["_fund", "_mark"])
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else:
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self.futures_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', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
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'enter_short', 'exit_short', 'enter_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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# Cleanup from prior runs
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pair_data.drop(headers[5:] + ['buy', 'sell'], axis=1, errors='ignore')
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df_analyzed = self.strategy.advise_exit(
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self.strategy.advise_entry(pair_data, {'pair': pair}),
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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 = 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(
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pair, self.timeframe, df_analyzed, self.config['candle_type_def'])
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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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for col in headers[5:]:
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tag_col = col in ('enter_tag', 'exit_tag')
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if col in df_analyzed.columns:
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df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace(
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[nan], [0 if not tag_col else None]).shift(1)
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elif not df_analyzed.empty:
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df_analyzed.loc[:, col] = 0 if not tag_col else None
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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() if not df_analyzed.empty else []
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return data
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def _get_close_rate(self, row: Tuple, trade: LocalTrade, sell: ExitCheckTuple,
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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.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS):
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return self._get_close_rate_for_stoploss(row, trade, sell, trade_dur)
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elif sell.exit_type == (ExitType.ROI):
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return self._get_close_rate_for_roi(row, trade, sell, trade_dur)
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else:
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return row[OPEN_IDX]
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def _get_close_rate_for_stoploss(self, row: Tuple, trade: LocalTrade, sell: ExitCheckTuple,
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trade_dur: int) -> float:
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# our stoploss was already lower 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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is_short = trade.is_short or False
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leverage = trade.leverage or 1.0
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side_1 = -1 if is_short else 1
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if is_short:
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if trade.stop_loss < row[LOW_IDX]:
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return row[OPEN_IDX]
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else:
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if trade.stop_loss > row[HIGH_IDX]:
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return 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.exit_type == ExitType.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 = (row[OPEN_IDX] *
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(1 + side_1 * abs(self.strategy.trailing_stop_positive_offset) -
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side_1 * abs(self.strategy.trailing_stop_positive / leverage)))
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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 = row[OPEN_IDX] * (1 - side_1 * abs(trade.stop_loss_pct / leverage))
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if is_short:
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assert stop_rate > row[LOW_IDX]
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else:
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assert stop_rate < 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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if is_short:
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return min(row[HIGH_IDX], stop_rate)
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else:
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return max(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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def _get_close_rate_for_roi(self, row: Tuple, trade: LocalTrade, sell: ExitCheckTuple,
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trade_dur: int) -> float:
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is_short = trade.is_short or False
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leverage = trade.leverage or 1.0
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side_1 = -1 if is_short else 1
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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 row[OPEN_IDX]
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# - (Expected abs profit - open_rate - open_fee) / (fee_close -1)
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roi_rate = trade.open_rate * roi / leverage
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open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open)
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close_rate = -(roi_rate + open_fee_rate) / (trade.fee_close - side_1 * 1)
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if is_short:
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is_new_roi = row[OPEN_IDX] < close_rate
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else:
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is_new_roi = row[OPEN_IDX] > close_rate
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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 is_new_roi):
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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 row[OPEN_IDX]
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if (trade_dur == 0 and (
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(
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is_short
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# Red candle (for longs)
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and row[OPEN_IDX] < row[CLOSE_IDX] # Red candle
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and trade.open_rate > row[OPEN_IDX] # trade-open above open_rate
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and close_rate < row[CLOSE_IDX] # closes below close
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)
|
|
or
|
|
(
|
|
not is_short
|
|
# green candle (for shorts)
|
|
and row[OPEN_IDX] > row[CLOSE_IDX] # green candle
|
|
and trade.open_rate < row[OPEN_IDX] # trade-open below open_rate
|
|
and close_rate > row[CLOSE_IDX] # closes above close
|
|
)
|
|
)):
|
|
# ROI on opening candles with custom pricing can only
|
|
# trigger if the entry was at Open or lower wick.
|
|
# details: https: // github.com/freqtrade/freqtrade/issues/6261
|
|
# If open_rate is < open, only allow sells below the close on red candles.
|
|
raise ValueError("Opening candle ROI on red candles.")
|
|
|
|
# Use the maximum between close_rate and low as we
|
|
# cannot sell outside of a candle.
|
|
# Applies when a new ROI setting comes in place and the whole candle is above that.
|
|
return min(max(close_rate, row[LOW_IDX]), row[HIGH_IDX])
|
|
|
|
else:
|
|
# This should not be reached...
|
|
return row[OPEN_IDX]
|
|
|
|
def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
|
|
) -> LocalTrade:
|
|
current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
|
|
min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
|
|
max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, row[OPEN_IDX])
|
|
stake_available = self.wallets.get_available_stake_amount()
|
|
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
|
|
default_retval=None)(
|
|
trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
|
|
current_profit=current_profit, min_stake=min_stake,
|
|
max_stake=min(max_stake, stake_available))
|
|
|
|
# Check if we should increase our position
|
|
if stake_amount is not None and stake_amount > 0.0:
|
|
|
|
pos_trade = self._enter_trade(
|
|
trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
|
|
if pos_trade is not None:
|
|
self.wallets.update()
|
|
return pos_trade
|
|
|
|
return trade
|
|
|
|
def _get_order_filled(self, rate: float, row: Tuple) -> bool:
|
|
""" Rate is within candle, therefore filled"""
|
|
return row[LOW_IDX] <= rate <= row[HIGH_IDX]
|
|
|
|
def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
|
|
row: Tuple) -> Optional[LocalTrade]:
|
|
|
|
# Check if we need to adjust our current positions
|
|
if self.strategy.position_adjustment_enable:
|
|
check_adjust_entry = True
|
|
if self.strategy.max_entry_position_adjustment > -1:
|
|
entry_count = trade.nr_of_successful_entries
|
|
check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
|
|
if check_adjust_entry:
|
|
trade = self._get_adjust_trade_entry_for_candle(trade, row)
|
|
|
|
sell_candle_time: datetime = row[DATE_IDX].to_pydatetime()
|
|
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
|
|
exit_ = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
|
|
sell = self.strategy.should_exit(
|
|
trade, row[OPEN_IDX], sell_candle_time, # type: ignore
|
|
enter=enter, exit_=exit_,
|
|
low=row[LOW_IDX], high=row[HIGH_IDX]
|
|
)
|
|
|
|
if sell.exit_flag:
|
|
trade.close_date = sell_candle_time
|
|
|
|
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
|
|
try:
|
|
closerate = self._get_close_rate(row, trade, sell, trade_dur)
|
|
except ValueError:
|
|
return None
|
|
# call the custom exit price,with default value as previous closerate
|
|
current_profit = trade.calc_profit_ratio(closerate)
|
|
order_type = self.strategy.order_types['exit']
|
|
if sell.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT):
|
|
# Custom exit pricing only for sell-signals
|
|
if order_type == 'limit':
|
|
closerate = strategy_safe_wrapper(self.strategy.custom_exit_price,
|
|
default_retval=closerate)(
|
|
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
|
|
if trade.is_short:
|
|
closerate = min(closerate, row[HIGH_IDX])
|
|
else:
|
|
closerate = max(closerate, row[LOW_IDX])
|
|
# Confirm trade exit:
|
|
time_in_force = self.strategy.order_time_in_force['exit']
|
|
|
|
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.exit_reason, # deprecated
|
|
exit_reason=sell.exit_reason,
|
|
current_time=sell_candle_time):
|
|
return None
|
|
|
|
trade.exit_reason = sell.exit_reason
|
|
|
|
# Checks and adds an exit tag, after checking that the length of the
|
|
# row has the length for an exit tag column
|
|
if(
|
|
len(row) > EXIT_TAG_IDX
|
|
and row[EXIT_TAG_IDX] is not None
|
|
and len(row[EXIT_TAG_IDX]) > 0
|
|
):
|
|
trade.exit_reason = 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=trade.exit_side,
|
|
side=trade.exit_side,
|
|
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, row: Tuple) -> Optional[LocalTrade]:
|
|
sell_candle_time: datetime = row[DATE_IDX].to_pydatetime()
|
|
|
|
if self.trading_mode == TradingMode.FUTURES:
|
|
trade.funding_fees = self.exchange.calculate_funding_fees(
|
|
self.futures_data[trade.pair],
|
|
amount=trade.amount,
|
|
is_short=trade.is_short,
|
|
open_date=trade.open_date_utc,
|
|
close_date=sell_candle_time,
|
|
)
|
|
|
|
if self.timeframe_detail and trade.pair in self.detail_data:
|
|
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, row)
|
|
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
|
|
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
|
|
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
|
|
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
|
|
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
|
|
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
|
|
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
|
|
'enter_short', 'exit_short', 'enter_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, row)
|
|
|
|
def get_valid_price_and_stake(
|
|
self, pair: str, row: Tuple, propose_rate: float, stake_amount: Optional[float],
|
|
direction: LongShort, current_time: datetime, entry_tag: Optional[str],
|
|
trade: Optional[LocalTrade], order_type: str
|
|
) -> Tuple[float, float, float, float]:
|
|
|
|
if order_type == 'limit':
|
|
propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
|
|
default_retval=propose_rate)(
|
|
pair=pair, current_time=current_time,
|
|
proposed_rate=propose_rate, entry_tag=entry_tag,
|
|
side=direction,
|
|
) # 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.
|
|
if direction == "short":
|
|
propose_rate = max(propose_rate, row[LOW_IDX])
|
|
else:
|
|
propose_rate = min(propose_rate, row[HIGH_IDX])
|
|
|
|
pos_adjust = trade is not None
|
|
leverage = trade.leverage if trade else 1.0
|
|
if not pos_adjust:
|
|
try:
|
|
stake_amount = self.wallets.get_trade_stake_amount(pair, None, update=False)
|
|
except DependencyException:
|
|
return 0, 0, 0, 0
|
|
|
|
max_leverage = self.exchange.get_max_leverage(pair, stake_amount)
|
|
leverage = strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)(
|
|
pair=pair,
|
|
current_time=current_time,
|
|
current_rate=row[OPEN_IDX],
|
|
proposed_leverage=1.0,
|
|
max_leverage=max_leverage,
|
|
side=direction,
|
|
) if self._can_short else 1.0
|
|
# Cap leverage between 1.0 and max_leverage.
|
|
leverage = min(max(leverage, 1.0), max_leverage)
|
|
|
|
min_stake_amount = self.exchange.get_min_pair_stake_amount(
|
|
pair, propose_rate, -0.05, leverage=leverage) or 0
|
|
max_stake_amount = self.exchange.get_max_pair_stake_amount(
|
|
pair, propose_rate, leverage=leverage)
|
|
stake_available = self.wallets.get_available_stake_amount()
|
|
|
|
if not pos_adjust:
|
|
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=min(stake_available, max_stake_amount),
|
|
entry_tag=entry_tag, side=direction)
|
|
|
|
stake_amount_val = self.wallets.validate_stake_amount(
|
|
pair=pair,
|
|
stake_amount=stake_amount,
|
|
min_stake_amount=min_stake_amount,
|
|
max_stake_amount=max_stake_amount,
|
|
)
|
|
|
|
return propose_rate, stake_amount_val, leverage, min_stake_amount
|
|
|
|
def _enter_trade(self, pair: str, row: Tuple, direction: LongShort,
|
|
stake_amount: Optional[float] = None,
|
|
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
|
|
|
|
current_time = row[DATE_IDX].to_pydatetime()
|
|
entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_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['entry']
|
|
pos_adjust = trade is not None
|
|
|
|
propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
|
|
pair, row, row[OPEN_IDX], stake_amount, direction, current_time, entry_tag, trade,
|
|
order_type
|
|
)
|
|
|
|
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['entry']
|
|
|
|
if not pos_adjust:
|
|
# Confirm trade entry:
|
|
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
|
|
pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
|
|
time_in_force=time_in_force, current_time=current_time,
|
|
entry_tag=entry_tag, side=direction):
|
|
return trade
|
|
|
|
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) * leverage, 8)
|
|
is_short = (direction == 'short')
|
|
# Necessary for Margin trading. Disabled until support is enabled.
|
|
# interest_rate = self.exchange.get_interest_rate()
|
|
|
|
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,
|
|
enter_tag=entry_tag,
|
|
exchange=self._exchange_name,
|
|
is_short=is_short,
|
|
trading_mode=self.trading_mode,
|
|
leverage=leverage,
|
|
# interest_rate=interest_rate,
|
|
orders=[],
|
|
)
|
|
|
|
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
|
|
|
|
trade.set_isolated_liq(self.exchange.get_liquidation_price(
|
|
pair=pair,
|
|
open_rate=propose_rate,
|
|
amount=amount,
|
|
leverage=leverage,
|
|
is_short=is_short,
|
|
))
|
|
|
|
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=trade.entry_side,
|
|
side=trade.entry_side,
|
|
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_entries == 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.exit_reason = ExitType.FORCE_EXIT.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 check_for_trade_entry(self, row) -> Optional[LongShort]:
|
|
enter_long = row[LONG_IDX] == 1
|
|
exit_long = row[ELONG_IDX] == 1
|
|
enter_short = self._can_short and row[SHORT_IDX] == 1
|
|
exit_short = self._can_short and row[ESHORT_IDX] == 1
|
|
|
|
if enter_long == 1 and not any([exit_long, enter_short]):
|
|
# Long
|
|
return 'long'
|
|
if enter_short == 1 and not any([exit_short, enter_long]):
|
|
# Short
|
|
return 'short'
|
|
return None
|
|
|
|
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(trade, order, current_time)
|
|
if timedout:
|
|
if order.side == trade.entry_side:
|
|
self.timedout_entry_orders += 1
|
|
if trade.nr_of_successful_entries == 0:
|
|
# Remove trade due to entry timeout expiration.
|
|
return True
|
|
else:
|
|
# Close additional buy order
|
|
del trade.orders[trade.orders.index(order)]
|
|
if order.side == trade.exit_side:
|
|
self.timedout_exit_orders += 1
|
|
# Close exit order and retry exiting 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]:
|
|
"""
|
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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}, which gets cleared to
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optimize memory usage!
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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] = []
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self.prepare_backtest(enable_protections)
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# Ensure wallets are uptodate (important for --strategy-list)
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self.wallets.update()
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# Use dict of lists with data for performance
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# (looping lists is a lot faster than pandas DataFrames)
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data: Dict = self._get_ohlcv_as_lists(processed)
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = defaultdict(int)
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current_time = start_date + timedelta(minutes=self.timeframe_min)
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open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
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open_trade_count = 0
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self.progress.init_step(BacktestState.BACKTEST, int(
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(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
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# Loop timerange and get candle for each pair at that point in time
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while current_time <= end_date:
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open_trade_count_start = open_trade_count
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self.check_abort()
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for i, pair in enumerate(data):
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row_index = indexes[pair]
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row = self.validate_row(data, pair, row_index, current_time)
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if not row:
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continue
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row_index += 1
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indexes[pair] = row_index
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self.dataprovider._set_dataframe_max_index(row_index)
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for t in list(open_trades[pair]):
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# 1. Cancel expired buy/sell orders.
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if self.check_order_cancel(t, current_time):
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# Close trade due to buy timeout expiration.
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open_trade_count -= 1
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open_trades[pair].remove(t)
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self.wallets.update()
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# 2. Process buys.
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# without positionstacking, we can only have one open trade per pair.
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# max_open_trades must be respected
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# don't open on the last row
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trade_dir = self.check_for_trade_entry(row)
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if (
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(position_stacking or len(open_trades[pair]) == 0)
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and self.trade_slot_available(max_open_trades, open_trade_count_start)
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and current_time != end_date
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and trade_dir is not None
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and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
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):
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trade = self._enter_trade(pair, row, trade_dir)
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if trade:
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# TODO: hacky workaround to avoid opening > max_open_trades
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# This emulates previous behavior - not sure if this is correct
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# Prevents buying if the trade-slot was freed in this candle
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open_trade_count_start += 1
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open_trade_count += 1
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# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
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open_trades[pair].append(trade)
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for trade in list(open_trades[pair]):
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# 3. Process entry orders.
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order = trade.select_order(trade.entry_side, is_open=True)
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if order and self._get_order_filled(order.price, row):
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order.close_bt_order(current_time)
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trade.open_order_id = None
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LocalTrade.add_bt_trade(trade)
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self.wallets.update()
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# 4. Create sell orders (if any)
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if not trade.open_order_id:
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self._get_sell_trade_entry(trade, row) # Place sell order if necessary
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# 5. Process sell orders.
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order = trade.select_order(trade.exit_side, is_open=True)
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if order and self._get_order_filled(order.price, row):
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trade.open_order_id = None
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trade.close_date = current_time
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trade.close(order.price, show_msg=False)
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# logger.debug(f"{pair} - Backtesting sell {trade}")
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open_trade_count -= 1
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open_trades[pair].remove(trade)
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LocalTrade.close_bt_trade(trade)
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trades.append(trade)
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self.wallets.update()
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self.run_protections(enable_protections, pair, current_time)
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# Move time one configured time_interval ahead.
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self.progress.increment()
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current_time += timedelta(minutes=self.timeframe_min)
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trades += self.handle_left_open(open_trades, data=data)
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self.wallets.update()
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results = trade_list_to_dataframe(trades)
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return {
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'results': results,
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'config': self.strategy.config,
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'locks': PairLocks.get_all_locks(),
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'rejected_signals': self.rejected_trades,
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'timedout_entry_orders': self.timedout_entry_orders,
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'timedout_exit_orders': self.timedout_exit_orders,
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'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
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}
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def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
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timerange: TimeRange):
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self.progress.init_step(BacktestState.ANALYZE, 0)
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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backtest_start_time = datetime.now(timezone.utc)
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self._set_strategy(strat)
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strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
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# Use max_open_trades in backtesting, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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# Must come from strategy config, as the strategy may modify this setting.
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max_open_trades = self.strategy.config['max_open_trades']
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else:
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logger.info(
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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# need to reprocess data every time to populate signals
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preprocessed = self.strategy.advise_all_indicators(data)
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# Trim startup period from analyzed dataframe
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preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
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if not preprocessed_tmp:
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raise OperationalException(
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"No data left after adjusting for startup candles.")
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# Use preprocessed_tmp for date generation (the trimmed dataframe).
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# Backtesting will re-trim the dataframes after buy/sell signal generation.
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min_date, max_date = history.get_timerange(preprocessed_tmp)
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logger.info(f'Backtesting with 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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# Execute backtest and store results
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results = self.backtest(
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processed=preprocessed,
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start_date=min_date,
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end_date=max_date,
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max_open_trades=max_open_trades,
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position_stacking=self.config.get('position_stacking', False),
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enable_protections=self.config.get('enable_protections', False),
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)
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backtest_end_time = datetime.now(timezone.utc)
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results.update({
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'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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})
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self.all_results[self.strategy.get_strategy_name()] = results
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return min_date, max_date
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def _get_min_cached_backtest_date(self):
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min_backtest_date = None
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backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
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if self.timerange.stopts == 0 or datetime.fromtimestamp(
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self.timerange.stopts, tz=timezone.utc) > datetime.now(tz=timezone.utc):
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logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
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elif backtest_cache_age == 'day':
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min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)
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elif backtest_cache_age == 'week':
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min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1)
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elif backtest_cache_age == 'month':
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min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4)
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return min_backtest_date
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def load_prior_backtest(self):
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self.run_ids = {
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strategy.get_strategy_name(): get_strategy_run_id(strategy)
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for strategy in self.strategylist
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}
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# Load previous result that will be updated incrementally.
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# This can be circumvented in certain instances in combination with downloading more data
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min_backtest_date = self._get_min_cached_backtest_date()
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if min_backtest_date is not None:
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self.results = find_existing_backtest_stats(
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self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date)
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def start(self) -> None:
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"""
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Run backtesting end-to-end
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:return: None
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"""
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data: Dict[str, Any] = {}
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data, timerange = self.load_bt_data()
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self.load_bt_data_detail()
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logger.info("Dataload complete. Calculating indicators")
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self.load_prior_backtest()
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for strat in self.strategylist:
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if self.results and strat.get_strategy_name() in self.results['strategy']:
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# When previous result hash matches - reuse that result and skip backtesting.
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logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
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continue
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min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
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# Update old results with new ones.
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if len(self.all_results) > 0:
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results = generate_backtest_stats(
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data, self.all_results, min_date=min_date, max_date=max_date)
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if self.results:
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self.results['metadata'].update(results['metadata'])
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self.results['strategy'].update(results['strategy'])
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self.results['strategy_comparison'].extend(results['strategy_comparison'])
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else:
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self.results = results
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if self.config.get('export', 'none') == 'trades':
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store_backtest_stats(self.config['exportfilename'], self.results)
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# Results may be mixed up now. Sort them so they follow --strategy-list order.
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if 'strategy_list' in self.config and len(self.results) > 0:
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self.results['strategy_comparison'] = sorted(
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self.results['strategy_comparison'],
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key=lambda c: self.config['strategy_list'].index(c['key']))
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self.results['strategy'] = dict(
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sorted(self.results['strategy'].items(),
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key=lambda kv: self.config['strategy_list'].index(kv[0])))
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if len(self.strategylist) > 0:
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# Show backtest results
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show_backtest_results(self.config, self.results)
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