# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging import operator from argparse import Namespace from typing import Dict, Tuple, Any, List, Optional import arrow from pandas import DataFrame from tabulate import tabulate import freqtrade.optimize as optimize from freqtrade import exchange from freqtrade.analyze import Analyze from freqtrade.arguments import Arguments from freqtrade.configuration import Configuration from freqtrade.misc import file_dump_json from freqtrade.persistence import Trade logger = logging.getLogger(__name__) 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 self.analyze = Analyze(self.config) self.ticker_interval = self.analyze.strategy.ticker_interval self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe self.populate_buy_trend = self.analyze.populate_buy_trend self.populate_sell_trend = self.analyze.populate_sell_trend # 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 exchange.init(self.config) @staticmethod def get_timeframe(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(min(frame.date)), arrow.get(max(frame.date))) 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', '.8f', '.1f') tabular_data = [] headers = ['pair', 'buy count', 'avg profit %', 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'] for pair in data: result = results[results.currency == pair] tabular_data.append([ pair, len(result.index), result.profit_percent.mean() * 100.0, result.profit_BTC.sum(), result.duration.mean(), len(result[result.profit_BTC > 0]), len(result[result.profit_BTC < 0]) ]) # Append Total tabular_data.append([ 'TOTAL', len(results.index), results.profit_percent.mean() * 100.0, results.profit_BTC.sum(), results.duration.mean(), len(results[results.profit_BTC > 0]), len(results[results.profit_BTC < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") def _get_sell_trade_entry( self, pair: str, buy_row: DataFrame, partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]: stake_amount = args['stake_amount'] max_open_trades = args.get('max_open_trades', 0) fee = exchange.get_fee() trade = Trade( open_rate=buy_row.close, open_date=buy_row.date, stake_amount=stake_amount, amount=stake_amount / buy_row.open, fee_open=fee, fee_close=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 if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal, sell_row.sell): return \ sell_row, \ ( pair, trade.calc_profit_percent(rate=sell_row.close), trade.calc_profit(rate=sell_row.close), (sell_row.date - buy_row.date).seconds // 60 ), \ sell_row.date if partial_ticker: # no sell condition found - trade stil open at end of backtest period sell_row = partial_ticker[-1] logger.info('Force_selling still open trade %s with %s perc - %s', pair, trade.calc_profit_percent(rate=sell_row.close), trade.calc_profit(rate=sell_row.close)) return \ sell_row, \ ( pair, trade.calc_profit_percent(rate=sell_row.close), trade.calc_profit(rate=sell_row.close), (sell_row.date - buy_row.date).seconds // 60 ), \ sell_row.date 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) realistic: do we try to simulate realistic trades? (default: True) sell_profit_only: sell if profit only use_sell_signal: act on sell-signal :return: DataFrame """ headers = ['date', 'buy', 'open', 'close', 'sell'] processed = args['processed'] max_open_trades = args.get('max_open_trades', 0) realistic = args.get('realistic', False) record = args.get('record', None) recordfilename = args.get('recordfn', 'backtest-result.json') records = [] trades = [] trade_count_lock: Dict = {} for pair, pair_data in processed.items(): pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run ticker_data = self.populate_sell_trend( self.populate_buy_trend(pair_data))[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) # TODO: why convert from Pandas to list?? ticker = [x for x in ticker_data.itertuples()] lock_pair_until = None for index, row in enumerate(ticker): if row.buy == 0 or row.sell == 1: continue # skip rows where no buy signal or that would immediately sell off if realistic: if lock_pair_until is not None and row.date <= lock_pair_until: 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 ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:], trade_count_lock, args) if ret: row2, trade_entry, next_date = ret lock_pair_until = next_date trades.append(trade_entry) if record: # Note, need to be json.dump friendly # record a tuple of pair, current_profit_percent, # entry-date, duration records.append((pair, trade_entry[1], row.date.strftime('%s'), row2.date.strftime('%s'), index, trade_entry[3])) else: # Set lock_pair_until to end of testing period if trade could not be closed # This happens only if the buy-signal was with the last candle lock_pair_until = ticker_data.iloc[-1].date # For now export inside backtest(), maybe change so that backtest() # returns a tuple like: (dataframe, records, logs, etc) if record and record.find('trades') >= 0: logger.info('Dumping backtest results to %s', recordfilename) file_dump_json(recordfilename, records) labels = ['currency', 'profit_percent', 'profit_BTC', 'duration'] return DataFrame.from_records(trades, columns=labels) 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] = exchange.get_ticker_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), timerange=timerange ) # Ignore max_open_trades in backtesting, except realistic flag was passed if self.config.get('realistic_simulation', False): max_open_trades = self.config['max_open_trades'] else: logger.info('Ignoring max_open_trades (realistic_simulation not set) ...') max_open_trades = 0 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 sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False) use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False) results = self.backtest( { 'stake_amount': self.config.get('stake_amount'), 'processed': preprocessed, 'max_open_trades': max_open_trades, 'realistic': self.config.get('realistic_simulation', False), 'sell_profit_only': sell_profit_only, 'use_sell_signal': use_sell_signal, 'record': self.config.get('export'), 'recordfn': self.config.get('exportfilename'), } ) logger.info( '\n==================================== ' 'BACKTESTING REPORT' ' ====================================\n' '%s', self._generate_text_table( data, results ) ) 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'] = '' 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()