# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging import operator from argparse import Namespace from datetime import datetime, timedelta from typing import Any, Dict, List, NamedTuple, Optional, Tuple import arrow from pandas import DataFrame, to_datetime from tabulate import tabulate import freqtrade.optimize as optimize 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 from freqtrade.optimize.backslapping import Backslapping from freqtrade.persistence import Trade from freqtrade.strategy.interface import SellType from freqtrade.strategy.resolver import IStrategy, StrategyResolver from collections import OrderedDict import timeit from time import sleep import pdb 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 self.strategy: IStrategy = StrategyResolver(self.config).strategy self.ticker_interval = self.strategy.ticker_interval self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe self.advise_buy = self.strategy.advise_buy self.advise_sell = self.strategy.advise_sell # 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.exchange = Exchange(self.config) self.fee = self.exchange.get_fee() self.stop_loss_value = self.strategy.stoploss #### backslap config ''' Numpy arrays are used for 100x speed up We requires setting Int values for buy stop triggers and stop calculated on # buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6 ''' self.np_buy: int = 0 self.np_open: int = 1 self.np_close: int = 2 self.np_sell: int = 3 self.np_high: int = 4 self.np_low: int = 5 self.np_stop: int = 6 self.np_bto: int = self.np_close # buys_triggered_on - should be close self.np_bco: int = self.np_open # buys calculated on - open of the next candle. self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close # self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close # self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close if 'backslap' in config: self.use_backslap = config['backslap'] # Enable backslap - if false Orginal code is executed. else: self.use_backslap = False logger.info("using backslap: {}".format(self.use_backslap)) self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended self.debug_timing = False # Stages within Backslap self.debug_2loops = False # Limit each pair to two loops, useful when debugging self.debug_vector = False # Debug vector calcs self.debug_timing_main_loop = False # print overall timing per pair - works in Backtest and Backslap self.backslap_show_trades = False # prints trades in addition to summary report self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt self.stop_stops: int = 9999 # stop back testing any pair with this many stops, set to 999999 to not hit self.backslap = Backslapping(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(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', 'total loss ab', 'total profit ab', 'Risk Reward Ratio', 'Win Rate'] headers = ['pair', 'buy count', 'avg profit %', 'cum profit %', 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss', 'RRR', 'Win Rate %', 'Required RR'] for pair in data: result = results[results.pair == pair] win_rate = (len(result[result.profit_abs > 0]) / len(result.index)) if (len(result.index) > 0) else None 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]), # result[result.profit_abs < 0]['profit_abs'].sum(), # result[result.profit_abs > 0]['profit_abs'].sum(), abs(1 / ((result[result.profit_abs < 0]['profit_abs'].sum() / len(result[result.profit_abs < 0])) / (result[result.profit_abs > 0]['profit_abs'].sum() / len(result[result.profit_abs > 0])))), win_rate * 100 if win_rate else "nan", ((1 / win_rate) - 1) if win_rate else "nan" ]) # 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_edge_positioning(self, data: Dict[str, Dict], results: DataFrame) -> str: """ This is a temporary version of edge positioning calculation. The function will be eventually moved to a plugin called Edge in order to calculate necessary WR, RRR and other indictaors related to money management periodically (each X minutes) and keep it in a storage. The calulation will be done per pair and per strategy. """ tabular_data = [] headers = ['Number of trades', 'RRR', 'Win Rate %', 'Required RR'] ### # The algorithm should be: # 1) Removing outliers from dataframe. i.e. all profit_percent which are outside (mean -+ (2 * (standard deviation))). # 2) Removing pairs with less than X trades (X defined in config). # 3) Calculating RRR and WR. # 4) Removing pairs for which WR and RRR are not in an acceptable range (e.x. WR > 95%). # 5) Sorting the result based on the delta between required RR and RRR. # Here we assume initial data in order to calculate position size. # these values will be replaced by exchange info or config for pair in data: result = results[results.pair == pair] # WinRate is calculated as follows: (Number of profitable trades) / (Total Trades) win_rate = (len(result[result.profit_abs > 0]) / len(result.index)) if (len(result.index) > 0) else None # Risk Reward Ratio is calculated as follows: 1 / ((total loss on losing trades / number of losing trades) / (total gain on profitable trades / number of winning trades)) risk_reward_ratio = abs(1 / ((result[result.profit_abs < 0]['profit_abs'].sum() / len(result[result.profit_abs < 0])) / (result[result.profit_abs > 0]['profit_abs'].sum() / len(result[result.profit_abs > 0])))) # Required Reward Ratio is (1 / WinRate) - 1 required_risk_reward = ((1 / win_rate) - 1) if win_rate else None #pdb.set_trace() tabular_data.append([ pair, len(result.index), risk_reward_ratio, win_rate * 100 if win_rate else "nan", required_risk_reward ]) # for pair in data: # result = results[results.pair == pair] # win_rate = (len(result[result.profit_abs > 0]) / len(result.index)) if (len(result.index) > 0) else None # 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]), # # result[result.profit_abs < 0]['profit_abs'].sum(), # # result[result.profit_abs > 0]['profit_abs'].sum(), # abs(1 / ((result[result.profit_abs < 0]['profit_abs'].sum() / len(result[result.profit_abs < 0])) / (result[result.profit_abs > 0]['profit_abs'].sum() / len(result[result.profit_abs > 0])))), # win_rate * 100 if win_rate else "nan", # ((1 / win_rate) - 1) if win_rate else "nan" # ]) #return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") return tabulate(tabular_data, headers=headers, 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 _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> 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: logger.info('Dumping backtest results to %s', recordfilename) file_dump_json(recordfilename, records) 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) if sell.sell_flag: return 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=False, open_rate=buy_row.open, close_rate=sell_row.open, 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 s(self): st = timeit.default_timer() return st def f(self, st): return (timeit.default_timer() - st) 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 """ use_backslap = self.use_backslap debug_timing = self.debug_timing_main_loop if use_backslap: # Use Back Slap code return self.backslap.run(args) else: # use Original Back test code ########################## Original BT loop headers = ['date', 'buy', 'open', 'close', 'sell'] processed = args['processed'] max_open_trades = args.get('max_open_trades', 0) position_stacking = args.get('position_stacking', False) trades = [] trade_count_lock: Dict = {} for pair, pair_data in processed.items(): if debug_timing: # Start timer fl = self.s() 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) if debug_timing: # print time taken flt = self.f(fl) # print("populate_buy_trend:", pair, round(flt, 10)) st = self.s() # Convert from Pandas to list for performance reasons # (Looping Pandas is slow.) 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 not position_stacking: 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 trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:], trade_count_lock, args) if trade_entry: lock_pair_until = trade_entry.close_time trades.append(trade_entry) 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 if debug_timing: # print time taken tt = self.f(st) print("Time to BackTest :", pair, round(tt, 10)) print("-----------------------") return DataFrame.from_records(trades, columns=BacktestResult._fields) ####################### Original BT loop end 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_tickers(pairs, self.ticker_interval) data = self.exchange.klines 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 ) ld_files = self.s() 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 preprocessed = self.tickerdata_to_dataframe(data) t_t = self.f(ld_files) print("Load from json to file to df in mem took", t_t) # 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 results = 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), } ) if self.config.get('export', False): self._store_backtest_result(self.config.get('exportfilename'), results) if self.use_backslap: # logger.info( # '\n====================================================== ' # 'BackSLAP REPORT' # ' =======================================================\n' # '%s', # self._generate_text_table( # data, # results # ) # ) logger.info( '\n====================================================== ' 'Edge positionning REPORT' ' =======================================================\n' '%s', self._generate_text_table_edge_positioning( data, results ) ) # optional print trades if self.backslap_show_trades: TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs', 'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1) def to_fwf(df, fname): content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql') print(content) DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt") # optional save trades if self.backslap_save_trades: TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs', 'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1) def to_fwf(df, fname): content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql') open(fname, "w").write(content) DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt") else: logger.info( '\n================================================= ' 'BACKTEST REPORT' ' ==================================================\n' '%s', self._generate_text_table( data, results ) ) if 'sell_reason' in results.columns: logger.info( '\n' + ' SELL READON STATS '.center(119, '=') + '\n%s \n', self._generate_text_table_sell_reason(data, results) ) else: logger.info("no sell reasons available!") logger.info( '\n' + ' LEFT OPEN TRADES REPORT '.center(119, '=') + '\n%s', self._generate_text_table( data, results.loc[results.open_at_end] ) ) 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'] = '' config['backslap'] = args.backslap 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()