426 lines
19 KiB
Python
426 lines
19 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 copy import deepcopy
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple
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import arrow
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from pandas import DataFrame
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from freqtrade.configuration import (TimeRange, remove_credentials,
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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.converter import trim_dataframe
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
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show_backtest_results,
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store_backtest_stats)
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from freqtrade.pairlist.pairlistmanager import PairListManager
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from freqtrade.persistence import Trade
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
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logger = logging.getLogger(__name__)
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class BacktestResult(NamedTuple):
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"""
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NamedTuple Defining BacktestResults inputs.
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"""
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pair: str
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profit_percent: float
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profit_abs: float
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open_date: datetime
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open_rate: float
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open_fee: float
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close_date: datetime
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close_rate: float
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close_fee: float
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amount: float
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trade_duration: float
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open_at_end: bool
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sell_reason: SellType
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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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self.config = config
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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.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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dataprovider = DataProvider(self.config, self.exchange)
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IStrategy.dp = dataprovider
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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 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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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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# 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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# Load one (first) strategy
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self._set_strategy(self.strategylist[0])
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def _set_strategy(self, strategy):
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"""
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Load strategy into backtesting
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"""
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self.strategy = strategy
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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_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
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timerange = TimeRange.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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data = history.load_data(
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datadir=self.config['datadir'],
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pairs=self.pairlists.whitelist,
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timeframe=self.timeframe,
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timerange=timerange,
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startup_candles=self.required_startup,
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fail_without_data=True,
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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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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
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self.required_startup, min_date)
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return data, timerange
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def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
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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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headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
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data: Dict = {}
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# Create dict with data
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for pair, pair_data in processed.items():
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pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
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pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
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df_analyzed = self.strategy.advise_sell(
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self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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# To avoid using data from future, we use buy/sell signals shifted
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# from the previous candle
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df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
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df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
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df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
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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] = [x for x in df_analyzed.itertuples()]
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return data
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def _get_close_rate(self, sell_row, trade: Trade, 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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# 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:
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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
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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 > 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
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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 max(close_rate, sell_row.low)
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else:
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# This should not be reached...
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return sell_row.open
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else:
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return sell_row.open
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def _get_sell_trade_entry(
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self, pair: str, buy_row: DataFrame,
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partial_ohlcv: List, trade_count_lock: Dict,
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stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
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trade = Trade(
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pair=pair,
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open_rate=buy_row.open,
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open_date=buy_row.date,
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stake_amount=stake_amount,
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amount=round(stake_amount / buy_row.open, 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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)
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logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
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# calculate win/lose forwards from buy point
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for sell_row in partial_ohlcv:
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if max_open_trades > 0:
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# Increase trade_count_lock for every iteration
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trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
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sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, sell_row.buy,
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sell_row.sell, low=sell_row.low, high=sell_row.high)
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if sell.sell_flag:
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trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
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closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
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return BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_ratio(rate=closerate),
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profit_abs=trade.calc_profit(rate=closerate),
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open_date=buy_row.date,
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open_rate=buy_row.open,
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open_fee=self.fee,
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close_date=sell_row.date,
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close_rate=closerate,
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close_fee=self.fee,
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amount=trade.amount,
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trade_duration=trade_dur,
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open_at_end=False,
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sell_reason=sell.sell_type
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)
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if partial_ohlcv:
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# no sell condition found - trade stil open at end of backtest period
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sell_row = partial_ohlcv[-1]
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bt_res = BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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open_date=buy_row.date,
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open_rate=buy_row.open,
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open_fee=self.fee,
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close_date=sell_row.date,
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close_rate=sell_row.open,
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close_fee=self.fee,
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amount=trade.amount,
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trade_duration=int((
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sell_row.date - buy_row.date).total_seconds() // 60),
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open_at_end=True,
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sell_reason=SellType.FORCE_SELL
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)
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logger.debug(f"{pair} - Force selling still open trade, "
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f"profit percent: {bt_res.profit_percent}, "
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f"profit abs: {bt_res.profit_abs}")
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return bt_res
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return None
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def backtest(self, processed: Dict, stake_amount: float,
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start_date: arrow.Arrow, end_date: arrow.Arrow,
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max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
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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 stake_amount: amount to use for each trade
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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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:return: DataFrame with trades (results of backtesting)
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"""
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logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
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f"start_date: {start_date}, end_date: {end_date}, "
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f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
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)
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trades = []
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trade_count_lock: Dict = {}
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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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lock_pair_until: Dict = {}
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = {}
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tmp = 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 tmp < end_date:
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for i, pair in enumerate(data):
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if pair not in indexes:
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indexes[pair] = 0
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try:
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row = data[pair][indexes[pair]]
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except IndexError:
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# missing Data for one pair at the end.
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# Warnings for this are shown during data loading
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continue
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# Waits until the time-counter reaches the start of the data for this pair.
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if row.date > tmp.datetime:
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continue
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indexes[pair] += 1
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if row.buy == 0 or row.sell == 1:
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continue # skip rows where no buy signal or that would immediately sell off
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if (not position_stacking and pair in lock_pair_until
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and row.date <= lock_pair_until[pair]):
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# without positionstacking, we can only have one open trade per pair.
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continue
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if max_open_trades > 0:
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# Check if max_open_trades has already been reached for the given date
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if not trade_count_lock.get(row.date, 0) < max_open_trades:
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continue
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trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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# since indexes has been incremented before, we need to go one step back to
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# also check the buying candle for sell conditions.
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trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
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trade_count_lock, stake_amount,
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max_open_trades)
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if trade_entry:
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logger.debug(f"{pair} - Locking pair till "
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f"close_date={trade_entry.close_date}")
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lock_pair_until[pair] = trade_entry.close_date
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trades.append(trade_entry)
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else:
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# Set lock_pair_until to end of testing period if trade could not be closed
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lock_pair_until[pair] = end_date.datetime
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# Move time one configured time_interval ahead.
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tmp += timedelta(minutes=self.timeframe_min)
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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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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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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# Use max_open_trades in backtesting, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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max_open_trades = self.config['max_open_trades']
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else:
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logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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position_stacking = self.config.get('position_stacking', False)
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data, timerange = self.load_bt_data()
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all_results = {}
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for strat in self.strategylist:
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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self._set_strategy(strat)
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# need to reprocess data every time to populate signals
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preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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preprocessed[pair] = trim_dataframe(df, timerange)
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min_date, max_date = history.get_timerange(preprocessed)
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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 print results
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all_results[self.strategy.get_strategy_name()] = self.backtest(
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processed=preprocessed,
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stake_amount=self.config['stake_amount'],
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start_date=min_date,
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end_date=max_date,
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max_open_trades=max_open_trades,
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position_stacking=position_stacking,
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)
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stats = generate_backtest_stats(self.config, data, all_results,
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min_date=min_date, max_date=max_date)
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if self.config.get('export', False):
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store_backtest_stats(self.config['exportfilename'], stats)
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# Show backtest results
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show_backtest_results(self.config, stats)
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