# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging from argparse import Namespace from typing import Any, Dict, List, Optional from pandas import DataFrame import freqtrade.optimize as optimize from freqtrade.optimize.optimize import IOptimize, BacktestResult, setup_configuration from freqtrade.arguments import Arguments from freqtrade.persistence import Trade from freqtrade.strategy.interface import SellType logger = logging.getLogger(__name__) class Backtesting(IOptimize): """ 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: super().__init__(config) 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 run(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 """ 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(): 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 = [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 return DataFrame.from_records(trades, columns=BacktestResult._fields) 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()