# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging import operator from abc import ABC, abstractmethod from argparse import Namespace from copy import deepcopy from datetime import datetime, timedelta from pathlib import Path from typing import Any, Dict, List, NamedTuple, Optional, Tuple import arrow from pandas import DataFrame from tabulate import tabulate from freqtrade import DependencyException, constants from freqtrade.arguments import Arguments from freqtrade.configuration import Configuration from freqtrade.exchange import Exchange from freqtrade.misc import file_dump_json import freqtrade.optimize as optimize from freqtrade.strategy.interface import SellType from freqtrade.strategy.resolver import IStrategy, StrategyResolver logger = logging.getLogger(__name__) class BacktestResult(NamedTuple): """ NamedTuple Defining BacktestResults inputs. """ pair: str profit_percent: float profit_abs: float open_time: datetime close_time: datetime open_index: int close_index: int trade_duration: float open_at_end: bool open_rate: float close_rate: float sell_reason: SellType class IOptimize(ABC): """ Backtesting Abstract class, this class contains all the logic to run a backtest To run a backtest: backtesting = Backtesting(config) backtesting.start() """ def __init__(self, config: Dict[str, Any]) -> None: self.config = config # Reset keys for backtesting self.config['exchange']['key'] = '' self.config['exchange']['secret'] = '' self.config['exchange']['password'] = '' self.config['exchange']['uid'] = '' self.config['dry_run'] = True self.strategylist: List[IStrategy] = [] if self.config.get('strategy_list', None): # Force one interval self.ticker_interval = str(self.config.get('ticker_interval')) for strat in list(self.config['strategy_list']): stratconf = deepcopy(self.config) stratconf['strategy'] = strat self.strategylist.append(StrategyResolver(stratconf).strategy) else: # only one strategy strat = StrategyResolver(self.config).strategy self.strategylist.append(StrategyResolver(self.config).strategy) # Load one strategy self._set_strategy(self.strategylist[0]) self.exchange = Exchange(self.config) self.fee = self.exchange.get_fee() def _set_strategy(self, strategy): """ Load strategy into backtesting """ self.strategy = strategy self.ticker_interval = self.config.get('ticker_interval') self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe self.advise_buy = strategy.advise_buy self.advise_sell = strategy.advise_sell def _get_timeframe(self, data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]: """ Get the maximum timeframe for the given backtest data :param data: dictionary with preprocessed backtesting data :return: tuple containing min_date, max_date """ timeframe = [ (arrow.get(frame['date'].min()), arrow.get(frame['date'].max())) for frame in data.values() ] return min(timeframe, key=operator.itemgetter(0))[0], \ max(timeframe, key=operator.itemgetter(1))[1] def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :return: pretty printed table with tabulate as str """ stake_currency = str(self.config.get('stake_currency')) floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f') tabular_data = [] headers = ['pair', 'buy count', 'avg profit %', 'cum profit %', 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'] for pair in data: result = results[results.pair == pair] tabular_data.append([ pair, len(result.index), result.profit_percent.mean() * 100.0, result.profit_percent.sum() * 100.0, result.profit_abs.sum(), str(timedelta( minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00', len(result[result.profit_abs > 0]), len(result[result.profit_abs < 0]) ]) # Append Total tabular_data.append([ 'TOTAL', len(results.index), results.profit_percent.mean() * 100.0, results.profit_percent.sum() * 100.0, results.profit_abs.sum(), str(timedelta( minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00', len(results[results.profit_abs > 0]), len(results[results.profit_abs < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str: """ Generate small table outlining Backtest results """ tabular_data = [] headers = ['Sell Reason', 'Count'] for reason, count in results['sell_reason'].value_counts().iteritems(): tabular_data.append([reason.value, count]) return tabulate(tabular_data, headers=headers, tablefmt="pipe") def _generate_text_table_strategy(self, all_results: dict) -> str: """ Generate summary table per strategy """ stake_currency = str(self.config.get('stake_currency')) floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f') tabular_data = [] headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %', 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'] for strategy, results in all_results.items(): tabular_data.append([ strategy, len(results.index), results.profit_percent.mean() * 100.0, results.profit_percent.sum() * 100.0, results.profit_abs.sum(), str(timedelta( minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00', len(results[results.profit_abs > 0]), len(results[results.profit_abs < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") def _store_backtest_result(self, recordfilename: str, results: DataFrame, strategyname: Optional[str] = None) -> None: records = [(t.pair, t.profit_percent, t.open_time.timestamp(), t.close_time.timestamp(), t.open_index - 1, t.trade_duration, t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value) for index, t in results.iterrows()] if records: if strategyname: # Inject strategyname to filename recname = Path(recordfilename) recordfilename = str(Path.joinpath( recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix)) logger.info('Dumping backtest results to %s', recordfilename) file_dump_json(recordfilename, records) def start(self) -> None: """ Run a backtesting end-to-end :return: None """ data = {} pairs = self.config['exchange']['pair_whitelist'] logger.info('Using stake_currency: %s ...', self.config['stake_currency']) logger.info('Using stake_amount: %s ...', self.config['stake_amount']) if self.config.get('live'): logger.info('Downloading data for all pairs in whitelist ...') for pair in pairs: data[pair] = self.exchange.get_candle_history(pair, self.ticker_interval) else: logger.info('Using local backtesting data (using whitelist in given config) ...') timerange = Arguments.parse_timerange(None if self.config.get( 'timerange') is None else str(self.config.get('timerange'))) data = optimize.load_data( self.config['datadir'], pairs=pairs, ticker_interval=self.ticker_interval, refresh_pairs=self.config.get('refresh_pairs', False), exchange=self.exchange, timerange=timerange ) if not data: logger.critical("No data found. Terminating.") return # Use max_open_trades in backtesting, except --disable-max-market-positions is set if self.config.get('use_max_market_positions', True): max_open_trades = self.config['max_open_trades'] else: logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...') max_open_trades = 0 all_results = {} for strat in self.strategylist: logger.info("Running backtesting for Strategy %s", strat.get_strategy_name()) self._set_strategy(strat) # need to reprocess data every time to populate signals preprocessed = self.tickerdata_to_dataframe(data) # Print timeframe min_date, max_date = self._get_timeframe(preprocessed) logger.info( 'Measuring data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days ) # Execute backtest and print results all_results[self.strategy.get_strategy_name()] = self.run( { 'stake_amount': self.config.get('stake_amount'), 'processed': preprocessed, 'max_open_trades': max_open_trades, 'position_stacking': self.config.get('position_stacking', False), } ) for strategy, results in all_results.items(): if self.config.get('export', False): self._store_backtest_result(self.config['exportfilename'], results, strategy if len(self.strategylist) > 1 else None) print(f"Result for strategy {strategy}") print(' BACKTESTING REPORT '.center(119, '=')) print(self._generate_text_table(data, results)) print(' SELL REASON STATS '.center(119, '=')) print(self._generate_text_table_sell_reason(data, results)) print(' LEFT OPEN TRADES REPORT '.center(119, '=')) print(self._generate_text_table(data, results.loc[results.open_at_end])) print() if len(all_results) > 1: # Print Strategy summary table print(' Strategy Summary '.center(119, '=')) print(self._generate_text_table_strategy(all_results)) print('\nFor more details, please look at the detail tables above') @abstractmethod def run(self, args: Dict) -> DataFrame: """ Runs backtesting functionality. NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized. Of course try to not have ugly code. By some accessor are sometime slower than functions. Avoid, logging on this method :param args: a dict containing: stake_amount: btc amount to use for each trade processed: a processed dictionary with format {pair, data} max_open_trades: maximum number of concurrent trades (default: 0, disabled) position_stacking: do we allow position stacking? (default: False) :return: DataFrame """ def setup_configuration(args: Namespace) -> Dict[str, Any]: """ Prepare the configuration for the backtesting :param args: Cli args from Arguments() :return: Configuration """ configuration = Configuration(args) config = configuration.get_config() # Ensure we do not use Exchange credentials config['exchange']['key'] = '' config['exchange']['secret'] = '' if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT: raise DependencyException('stake amount could not be "%s" for backtesting' % constants.UNLIMITED_STAKE_AMOUNT) return config