# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging 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 from pandas import DataFrame from tabulate import tabulate from freqtrade import optimize from freqtrade import DependencyException, constants from freqtrade.arguments import Arguments from freqtrade.configuration import Configuration from freqtrade.data import history from freqtrade.data.dataprovider import DataProvider from freqtrade.misc import file_dump_json, timeframe_to_minutes from freqtrade.persistence import Trade from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.state import RunMode from freqtrade.strategy.interface import SellType, IStrategy 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 Backtesting(object): """ Backtesting 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] = [] exchange_name = self.config.get('exchange', {}).get('name').title() self.exchange = ExchangeResolver(exchange_name, self.config).exchange self.fee = self.exchange.get_fee() if self.config.get('runmode') != RunMode.HYPEROPT: self.dataprovider = DataProvider(self.config, self.exchange) IStrategy.dp = self.dataprovider if self.config.get('strategy_list', None): # Force one interval self.ticker_interval = str(self.config.get('ticker_interval')) self.ticker_interval_mins = timeframe_to_minutes(self.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 self.strategylist.append(StrategyResolver(self.config).strategy) # Load one strategy self._set_strategy(self.strategylist[0]) def _set_strategy(self, strategy): """ Load strategy into backtesting """ self.strategy = strategy self.ticker_interval = self.config.get('ticker_interval') self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval) self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe self.advise_buy = strategy.advise_buy self.advise_sell = strategy.advise_sell # Set stoploss_on_exchange to false for backtesting, # since a "perfect" stoploss-sell is assumed anyway # And the regular "stoploss" function would not apply to that case self.strategy.order_types['stoploss_on_exchange'] = False def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame, skip_nan: bool = False) -> 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')) max_open_trades = self.config.get('max_open_trades') floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f') tabular_data = [] headers = ['pair', 'buy count', 'avg profit %', 'cum profit %', 'tot profit ' + stake_currency, 'tot profit %', 'avg duration', 'profit', 'loss'] for pair in data: result = results[results.pair == pair] if skip_nan and result.profit_abs.isnull().all(): continue tabular_data.append([ pair, len(result.index), result.profit_percent.mean() * 100.0, result.profit_percent.sum() * 100.0, result.profit_abs.sum(), result.profit_percent.sum() * 100.0 / max_open_trades, 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(), results.profit_percent.sum() * 100.0 / max_open_trades, 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]) ]) # Ignore type as floatfmt does allow tuples but mypy does not know that return tabulate(tabular_data, headers=headers, # type: ignore 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')) max_open_trades = self.config.get('max_open_trades') floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f') tabular_data = [] headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %', 'tot profit ' + stake_currency, 'tot profit %', '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(), results.profit_percent.sum() * 100.0 / max_open_trades, 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]) ]) # Ignore type as floatfmt does allow tuples but mypy does not know that return tabulate(tabular_data, headers=headers, # type: ignore 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 _get_ticker_list(self, processed) -> Dict[str, DataFrame]: """ Helper function to convert a processed tickerlist into a list for performance reasons. Used by backtest() - so keep this optimized for performance. """ headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high'] ticker: Dict = {} # Create ticker dict for pair, pair_data in processed.items(): pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run ticker_data = self.advise_sell( self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy() # to avoid using data from future, we buy/sell with signal from previous candle ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1) ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1) ticker_data.drop(ticker_data.head(1).index, inplace=True) # Convert from Pandas to list for performance reasons # (Looping Pandas is slow.) ticker[pair] = [x for x in ticker_data.itertuples()] return ticker def _get_sell_trade_entry( self, pair: str, buy_row: DataFrame, partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]: stake_amount = args['stake_amount'] max_open_trades = args.get('max_open_trades', 0) trade = Trade( open_rate=buy_row.open, open_date=buy_row.date, stake_amount=stake_amount, amount=stake_amount / buy_row.open, fee_open=self.fee, fee_close=self.fee ) # calculate win/lose forwards from buy point for sell_row in partial_ticker: if max_open_trades > 0: # Increase trade_count_lock for every iteration trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1 buy_signal = sell_row.buy sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal, sell_row.sell, low=sell_row.low, high=sell_row.high) if sell.sell_flag: trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60) # Special handling if high or low hit STOP_LOSS or ROI if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS): # Set close_rate to stoploss closerate = trade.stop_loss elif sell.sell_type == (SellType.ROI): # get next entry in min_roi > to trade duration # Interface.py skips on trade_duration <= duration roi_entry = max(list(filter(lambda x: trade_dur >= x, self.strategy.minimal_roi.keys()))) roi = self.strategy.minimal_roi[roi_entry] # - (Expected abs profit + open_rate + open_fee) / (fee_close -1) closerate = - (trade.open_rate * roi + trade.open_rate * (1 + trade.fee_open)) / (trade.fee_close - 1) else: closerate = sell_row.open return BacktestResult(pair=pair, profit_percent=trade.calc_profit_percent(rate=closerate), profit_abs=trade.calc_profit(rate=closerate), open_time=buy_row.date, close_time=sell_row.date, trade_duration=trade_dur, open_index=buy_row.Index, close_index=sell_row.Index, open_at_end=False, open_rate=buy_row.open, close_rate=closerate, sell_reason=sell.sell_type ) if partial_ticker: # no sell condition found - trade stil open at end of backtest period sell_row = partial_ticker[-1] btr = BacktestResult(pair=pair, profit_percent=trade.calc_profit_percent(rate=sell_row.open), profit_abs=trade.calc_profit(rate=sell_row.open), open_time=buy_row.date, close_time=sell_row.date, trade_duration=int(( sell_row.date - buy_row.date).total_seconds() // 60), open_index=buy_row.Index, close_index=sell_row.Index, open_at_end=True, open_rate=buy_row.open, close_rate=sell_row.open, sell_reason=SellType.FORCE_SELL ) logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair, btr.profit_percent, btr.profit_abs) return btr return None def backtest(self, args: Dict) -> DataFrame: """ Implements 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 """ processed = args['processed'] max_open_trades = args.get('max_open_trades', 0) position_stacking = args.get('position_stacking', False) start_date = args['start_date'] end_date = args['end_date'] trades = [] trade_count_lock: Dict = {} # Dict of ticker-lists for performance (looping lists is a lot faster than dataframes) ticker: Dict = self._get_ticker_list(processed) lock_pair_until: Dict = {} # Indexes per pair, so some pairs are allowed to have a missing start. indexes: Dict = {} tmp = start_date + timedelta(minutes=self.ticker_interval_mins) # Loop timerange and get candle for each pair at that point in time while tmp < end_date: for i, pair in enumerate(ticker): if pair not in indexes: indexes[pair] = 0 try: row = ticker[pair][indexes[pair]] except IndexError: # missing Data for one pair at the end. # Warnings for this are shown by `validate_backtest_data` continue # Waits until the time-counter reaches the start of the data for this pair. if row.date > tmp.datetime: continue indexes[pair] += 1 if row.buy == 0 or row.sell == 1: continue # skip rows where no buy signal or that would immediately sell off if (not position_stacking and pair in lock_pair_until and row.date <= lock_pair_until[pair]): # without positionstacking, we can only have one open trade per pair. continue if max_open_trades > 0: # Check if max_open_trades has already been reached for the given date if not trade_count_lock.get(row.date, 0) < max_open_trades: continue trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1 trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]:], trade_count_lock, args) if trade_entry: lock_pair_until[pair] = trade_entry.close_time trades.append(trade_entry) else: # Set lock_pair_until to end of testing period if trade could not be closed lock_pair_until[pair] = end_date.datetime # Move time one configured time_interval ahead. tmp += timedelta(minutes=self.ticker_interval_mins) return DataFrame.from_records(trades, columns=BacktestResult._fields) def start(self) -> None: """ Run a backtesting end-to-end :return: None """ data: Dict[str, Any] = {} 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 ...') self.exchange.refresh_latest_ohlcv([(pair, self.ticker_interval) for pair in pairs]) data = {key[0]: value for key, value in self.exchange._klines.items()} 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 = history.load_data( datadir=Path(self.config['datadir']) if self.config.get('datadir') else None, 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) min_date, max_date = optimize.get_timeframe(data) # Validate dataframe for missing values (mainly at start and end, as fillup is called) optimize.validate_backtest_data(data, min_date, max_date, timeframe_to_minutes(self.ticker_interval)) logger.info( 'Measuring data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days ) # need to reprocess data every time to populate signals preprocessed = self.strategy.tickerdata_to_dataframe(data) # Execute backtest and print results all_results[self.strategy.get_strategy_name()] = self.backtest( { 'stake_amount': self.config.get('stake_amount'), 'processed': preprocessed, 'max_open_trades': max_open_trades, 'position_stacking': self.config.get('position_stacking', False), 'start_date': min_date, 'end_date': max_date, } ) 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(133, '=')) print(self._generate_text_table(data, results)) print(' SELL REASON STATS '.center(133, '=')) print(self._generate_text_table_sell_reason(data, results)) print(' LEFT OPEN TRADES REPORT '.center(133, '=')) print(self._generate_text_table(data, results.loc[results.open_at_end], True)) print() if len(all_results) > 1: # Print Strategy summary table print(' Strategy Summary '.center(133, '=')) print(self._generate_text_table_strategy(all_results)) print('\nFor more details, please look at the detail tables above') 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, RunMode.BACKTEST) 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 def start(args: Namespace) -> None: """ Start Backtesting script :param args: Cli args from Arguments() :return: None """ # Initialize configuration config = setup_configuration(args) logger.info('Starting freqtrade in Backtesting mode') # Initialize backtesting object backtesting = Backtesting(config) backtesting.start()