# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging 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 OperationalException from freqtrade.configuration import TimeRange, remove_credentials from freqtrade.data import history from freqtrade.data.dataprovider import DataProvider from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds from freqtrade.misc import file_dump_json from freqtrade.persistence import Trade from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.state import RunMode from freqtrade.strategy.interface import IStrategy, SellType 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: """ 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 remove_credentials(self.config) self.strategylist: List[IStrategy] = [] self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange if config.get('fee'): self.fee = config['fee'] else: 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): for strat in list(self.config['strategy_list']): stratconf = deepcopy(self.config) stratconf['strategy'] = strat self.strategylist.append(StrategyResolver(stratconf).strategy) else: # No strategy list specified, only one strategy self.strategylist.append(StrategyResolver(self.config).strategy) if "ticker_interval" not in self.config: raise OperationalException("Ticker-interval needs to be set in either configuration " "or as cli argument `--ticker-interval 5m`") self.timeframe = str(self.config.get('ticker_interval')) self.timeframe_mins = timeframe_to_minutes(self.timeframe) # Get maximum required startup period self.required_startup = max([strat.startup_candle_count for strat in self.strategylist]) # Load one (first) strategy self._set_strategy(self.strategylist[0]) def _set_strategy(self, strategy): """ Load strategy into backtesting """ self.strategy = strategy # 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 load_bt_data(self): timerange = TimeRange.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']), pairs=self.config['exchange']['pair_whitelist'], timeframe=self.timeframe, timerange=timerange, startup_candles=self.required_startup, fail_without_data=True, ) min_date, max_date = history.get_timeframe(data) logger.info( 'Loading data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days ) # Adjust startts forward if not enough data is available timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe), self.required_startup, min_date) return data, timerange 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, floatfmt=floatfmt, tablefmt="pipe") # type: ignore 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, floatfmt=floatfmt, tablefmt="pipe") # type: ignore def _store_backtest_result(self, recordfilename: Path, 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 recordfilename = Path.joinpath( recordfilename.parent, f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix) logger.info(f'Dumping backtest results to {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.loc[:, 'buy'] = 0 # cleanup from previous run pair_data.loc[:, 'sell'] = 0 # cleanup from previous run ticker_data = self.strategy.advise_sell( self.strategy.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, stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]: trade = Trade( pair=pair, 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, is_open=True, ) logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.") # 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 sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, sell_row.buy, 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): roi = self.strategy.min_roi_reached_entry(trade_dur) if roi is not None: # - (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) # Use the maximum between closerate and low as we # cannot sell outside of a candle. # Applies when using {"xx": -1} as roi to force sells after xx minutes closerate = max(closerate, sell_row.low) else: # This should not be reached... closerate = sell_row.open 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] bt_res = 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(f"{pair} - Force selling still open trade, " f"profit percent: {bt_res.profit_percent}, " f"profit abs: {bt_res.profit_abs}") return bt_res 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 """ # Arguments are long and noisy, so this is commented out. # Uncomment if you need to debug the backtest() method. # logger.debug(f"Start backtest, args: {args}") processed = args['processed'] stake_amount = args['stake_amount'] 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.timeframe_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 during data loading 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 # since indexes has been incremented before, we need to go one step back to # also check the buying candle for sell conditions. trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:], trade_count_lock, stake_amount, max_open_trades) if trade_entry: logger.debug(f"{pair} - Locking pair till " f"close_time={trade_entry.close_time}") 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.timeframe_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] = {} logger.info('Using stake_currency: %s ...', self.config['stake_currency']) logger.info('Using stake_amount: %s ...', self.config['stake_amount']) # 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 data, timerange = self.load_bt_data() 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.strategy.tickerdata_to_dataframe(data) # Trim startup period from analyzed dataframe for pair, df in preprocessed.items(): preprocessed[pair] = history.trim_dataframe(df, timerange) min_date, max_date = history.get_timeframe(preprocessed) logger.info( 'Backtesting with 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.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(Path(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')