323 lines
12 KiB
Python
323 lines
12 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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import operator
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from abc import ABC, abstractmethod
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from argparse import Namespace
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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, Tuple
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import arrow
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from pandas import DataFrame
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from tabulate import tabulate
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from freqtrade import DependencyException, constants
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.exchange import Exchange
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from freqtrade.misc import file_dump_json
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import freqtrade.optimize as optimize
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from freqtrade.strategy.interface import SellType
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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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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 IOptimize(ABC):
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"""
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Backtesting Abstract 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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if self.config.get('strategy_list', None):
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# Force one interval
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self.ticker_interval = str(self.config.get('ticker_interval'))
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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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# only one strategy
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strat = StrategyResolver(self.config).strategy
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self.strategylist.append(StrategyResolver(self.config).strategy)
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# Load one strategy
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self._set_strategy(self.strategylist[0])
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self.exchange = Exchange(self.config)
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self.fee = self.exchange.get_fee()
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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.ticker_interval = self.config.get('ticker_interval')
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self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
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self.advise_buy = strategy.advise_buy
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self.advise_sell = strategy.advise_sell
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def _get_timeframe(self, data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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Get the maximum timeframe for the given backtest data
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:param data: dictionary with preprocessed backtesting data
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:return: tuple containing min_date, max_date
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"""
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timeframe = [
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(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
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for frame in data.values()
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]
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return min(timeframe, key=operator.itemgetter(0))[0], \
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max(timeframe, key=operator.itemgetter(1))[1]
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def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> 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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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '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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'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
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for pair in data:
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result = results[results.pair == pair]
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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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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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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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return tabulate(tabular_data, headers=headers, 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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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '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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'total profit ' + stake_currency, 'avg duration', '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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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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return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
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def _store_backtest_result(self, recordfilename: str, 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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recname = Path(recordfilename)
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recordfilename = str(Path.joinpath(
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recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix))
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logger.info('Dumping backtest results to %s', recordfilename)
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file_dump_json(recordfilename, records)
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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 = {}
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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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if self.config.get('live'):
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logger.info('Downloading data for all pairs in whitelist ...')
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for pair in pairs:
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data[pair] = self.exchange.get_candle_history(pair, self.ticker_interval)
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else:
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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timerange = Arguments.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 = optimize.load_data(
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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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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.tickerdata_to_dataframe(data)
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# Print timeframe
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min_date, max_date = self._get_timeframe(preprocessed)
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logger.info(
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'Measuring 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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# Execute backtest and print results
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all_results[self.strategy.get_strategy_name()] = self.run(
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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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}
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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(self.config['exportfilename'], results,
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strategy if len(self.strategylist) > 1 else None)
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print(f"Result for strategy {strategy}")
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print(' BACKTESTING REPORT '.center(119, '='))
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print(self._generate_text_table(data, results))
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print(' SELL REASON STATS '.center(119, '='))
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print(self._generate_text_table_sell_reason(data, results))
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print(' LEFT OPEN TRADES REPORT '.center(119, '='))
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print(self._generate_text_table(data, results.loc[results.open_at_end]))
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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(119, '='))
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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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@abstractmethod
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def run(self, args: Dict) -> DataFrame:
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"""
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Runs 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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def setup_configuration(args: Namespace) -> Dict[str, Any]:
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"""
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Prepare the configuration for the backtesting
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:param args: Cli args from Arguments()
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:return: Configuration
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"""
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configuration = Configuration(args)
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config = configuration.get_config()
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# Ensure we do not use Exchange credentials
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config['exchange']['key'] = ''
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config['exchange']['secret'] = ''
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if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
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raise DependencyException('stake amount could not be "%s" for backtesting' %
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constants.UNLIMITED_STAKE_AMOUNT)
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return config
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