484 lines
21 KiB
Python
484 lines
21 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 pathlib import Path
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from typing import Any, Dict, List, NamedTuple, Optional
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from pandas import DataFrame
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from freqtrade import OperationalException
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from freqtrade.configuration import TimeRange
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from freqtrade.data import history
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.misc import file_dump_json
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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.state import RunMode
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from freqtrade.strategy.interface import IStrategy, SellType
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from tabulate import tabulate
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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_time: datetime
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close_time: datetime
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open_index: int
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close_index: int
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trade_duration: float
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open_at_end: bool
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open_rate: float
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close_rate: float
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sell_reason: SellType
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class Backtesting(object):
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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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self.config['exchange']['key'] = ''
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self.config['exchange']['secret'] = ''
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self.config['exchange']['password'] = ''
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self.config['exchange']['uid'] = ''
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self.config['dry_run'] = True
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self.strategylist: List[IStrategy] = []
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self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
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self.fee = self.exchange.get_fee()
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if self.config.get('runmode') != RunMode.HYPEROPT:
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self.dataprovider = DataProvider(self.config, self.exchange)
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IStrategy.dp = self.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(stratconf).strategy)
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else:
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# No strategy list specified, only one strategy
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self.strategylist.append(StrategyResolver(self.config).strategy)
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if "ticker_interval" not in self.config:
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raise OperationalException("Ticker-interval needs to be set in either configuration "
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"or as cli argument `--ticker-interval 5m`")
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self.ticker_interval = str(self.config.get('ticker_interval'))
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self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
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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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self.advise_buy = strategy.advise_buy
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self.advise_sell = strategy.advise_sell
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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 _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
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skip_nan: bool = False) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:return: pretty printed table with tabulate as str
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"""
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stake_currency = str(self.config.get('stake_currency'))
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max_open_trades = self.config.get('max_open_trades')
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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
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tabular_data = []
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headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
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'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
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'profit', 'loss']
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for pair in data:
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result = results[results.pair == pair]
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if skip_nan and result.profit_abs.isnull().all():
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continue
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tabular_data.append([
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pair,
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len(result.index),
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result.profit_percent.mean() * 100.0,
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result.profit_percent.sum() * 100.0,
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result.profit_abs.sum(),
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result.profit_percent.sum() * 100.0 / max_open_trades,
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str(timedelta(
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minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
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len(result[result.profit_abs > 0]),
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len(result[result.profit_abs < 0])
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])
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# Append Total
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tabular_data.append([
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'TOTAL',
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len(results.index),
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results.profit_percent.mean() * 100.0,
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results.profit_percent.sum() * 100.0,
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results.profit_abs.sum(),
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results.profit_percent.sum() * 100.0 / max_open_trades,
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str(timedelta(
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minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
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len(results[results.profit_abs > 0]),
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len(results[results.profit_abs < 0])
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])
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(tabular_data, headers=headers, # type: ignore
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floatfmt=floatfmt, tablefmt="pipe")
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def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
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"""
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Generate small table outlining Backtest results
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"""
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tabular_data = []
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headers = ['Sell Reason', 'Count']
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for reason, count in results['sell_reason'].value_counts().iteritems():
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tabular_data.append([reason.value, count])
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return tabulate(tabular_data, headers=headers, tablefmt="pipe")
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def _generate_text_table_strategy(self, all_results: dict) -> str:
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"""
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Generate summary table per strategy
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"""
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stake_currency = str(self.config.get('stake_currency'))
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max_open_trades = self.config.get('max_open_trades')
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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
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tabular_data = []
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headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
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'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
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'profit', 'loss']
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for strategy, results in all_results.items():
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tabular_data.append([
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strategy,
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len(results.index),
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results.profit_percent.mean() * 100.0,
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results.profit_percent.sum() * 100.0,
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results.profit_abs.sum(),
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results.profit_percent.sum() * 100.0 / max_open_trades,
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str(timedelta(
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minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
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len(results[results.profit_abs > 0]),
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len(results[results.profit_abs < 0])
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])
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(tabular_data, headers=headers, # type: ignore
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floatfmt=floatfmt, tablefmt="pipe")
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def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
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strategyname: Optional[str] = None) -> None:
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records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
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t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
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t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
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for index, t in results.iterrows()]
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if records:
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if strategyname:
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# Inject strategyname to filename
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recordfilename = Path.joinpath(
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recordfilename.parent,
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f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
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logger.info(f'Dumping backtest results to {recordfilename}')
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file_dump_json(recordfilename, records)
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def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
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"""
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Helper function to convert a processed tickerlist into a list 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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ticker: Dict = {}
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# Create ticker dict
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for pair, pair_data in processed.items():
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.advise_sell(
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self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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ticker_data.drop(ticker_data.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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ticker[pair] = [x for x in ticker_data.itertuples()]
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return ticker
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def _get_sell_trade_entry(
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self, pair: str, buy_row: DataFrame,
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partial_ticker: 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=stake_amount / buy_row.open,
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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_ticker:
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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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# 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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closerate = trade.stop_loss
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elif sell.sell_type == (SellType.ROI):
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roi = self.strategy.min_roi_reached_entry(trade_dur)
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if roi is not None:
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# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
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closerate = - (trade.open_rate * roi + trade.open_rate *
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(1 + trade.fee_open)) / (trade.fee_close - 1)
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else:
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# This should not be reached...
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closerate = sell_row.open
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else:
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closerate = sell_row.open
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return BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=closerate),
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profit_abs=trade.calc_profit(rate=closerate),
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open_time=buy_row.date,
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close_time=sell_row.date,
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trade_duration=trade_dur,
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open_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=False,
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open_rate=buy_row.open,
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close_rate=closerate,
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sell_reason=sell.sell_type
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)
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if partial_ticker:
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# no sell condition found - trade stil open at end of backtest period
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sell_row = partial_ticker[-1]
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bt_res = BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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open_time=buy_row.date,
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close_time=sell_row.date,
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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_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=True,
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open_rate=buy_row.open,
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close_rate=sell_row.open,
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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, args: Dict) -> DataFrame:
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"""
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Implements 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, logging on this method
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:param args: a dict containing:
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stake_amount: btc amount to use for each trade
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processed: a processed dictionary with format {pair, data}
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max_open_trades: maximum number of concurrent trades (default: 0, disabled)
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position_stacking: do we allow position stacking? (default: False)
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:return: DataFrame
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"""
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# Arguments are long and noisy, so this is commented out.
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# Uncomment if you need to debug the backtest() method.
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# logger.debug(f"Start backtest, args: {args}")
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processed = args['processed']
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stake_amount = args['stake_amount']
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max_open_trades = args.get('max_open_trades', 0)
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position_stacking = args.get('position_stacking', False)
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start_date = args['start_date']
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end_date = args['end_date']
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trades = []
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trade_count_lock: Dict = {}
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# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
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ticker: Dict = self._get_ticker_list(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.ticker_interval_mins)
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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(ticker):
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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 = ticker[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, ticker[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_time={trade_entry.close_time}")
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lock_pair_until[pair] = trade_entry.close_time
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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.ticker_interval_mins)
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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 a 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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pairs = self.config['exchange']['pair_whitelist']
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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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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=Path(self.config['datadir']),
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pairs=pairs,
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ticker_interval=self.ticker_interval,
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refresh_pairs=self.config.get('refresh_pairs', False),
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exchange=self.exchange,
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timerange=timerange,
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)
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if not data:
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logger.critical("No data found. Terminating.")
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return
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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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all_results = {}
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min_date, max_date = history.get_timeframe(data)
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logger.info(
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'Backtesting with data from %s up to %s (%s days)..',
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min_date.isoformat(),
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max_date.isoformat(),
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(max_date - min_date).days
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)
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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.tickerdata_to_dataframe(data)
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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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{
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'stake_amount': self.config.get('stake_amount'),
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'processed': preprocessed,
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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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'start_date': min_date,
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'end_date': max_date,
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}
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)
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for strategy, results in all_results.items():
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if self.config.get('export', False):
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self._store_backtest_result(Path(self.config['exportfilename']), results,
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strategy if len(self.strategylist) > 1 else None)
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|
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print(f"Result for strategy {strategy}")
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print(' BACKTESTING REPORT '.center(133, '='))
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print(self._generate_text_table(data, results))
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|
|
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print(' SELL REASON STATS '.center(133, '='))
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print(self._generate_text_table_sell_reason(data, results))
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|
|
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print(' LEFT OPEN TRADES REPORT '.center(133, '='))
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print(self._generate_text_table(data, results.loc[results.open_at_end], True))
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print()
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if len(all_results) > 1:
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# Print Strategy summary table
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print(' Strategy Summary '.center(133, '='))
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print(self._generate_text_table_strategy(all_results))
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print('\nFor more details, please look at the detail tables above')
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