# pragma pylint: disable=missing-docstring, W0212, too-many-arguments """ This module contains the backtesting logic """ import logging from collections import defaultdict from copy import deepcopy from datetime import datetime, timedelta, timezone from typing import Any, Dict, List, Optional, Tuple import pandas as pd from numpy import nan from pandas import DataFrame from freqtrade import constants from freqtrade.configuration import TimeRange, validate_config_consistency from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort from freqtrade.data import history from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe from freqtrade.data.converter import trim_dataframe, trim_dataframes from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode, TradingMode) from freqtrade.exceptions import DependencyException, OperationalException from freqtrade.exchange import (amount_to_contract_precision, price_to_precision, timeframe_to_minutes, timeframe_to_seconds) from freqtrade.mixins import LoggingMixin from freqtrade.optimize.backtest_caching import get_strategy_run_id from freqtrade.optimize.bt_progress import BTProgress from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results, store_backtest_rejected_trades, store_backtest_signal_candles, store_backtest_stats) from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade from freqtrade.plugins.pairlistmanager import PairListManager from freqtrade.plugins.protectionmanager import ProtectionManager from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.strategy.interface import IStrategy from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper from freqtrade.wallets import Wallets logger = logging.getLogger(__name__) # Indexes for backtest tuples DATE_IDX = 0 OPEN_IDX = 1 HIGH_IDX = 2 LOW_IDX = 3 CLOSE_IDX = 4 LONG_IDX = 5 ELONG_IDX = 6 # Exit long SHORT_IDX = 7 ESHORT_IDX = 8 # Exit short ENTER_TAG_IDX = 9 EXIT_TAG_IDX = 10 # Every change to this headers list must evaluate further usages of the resulting tuple # and eventually change the constants for indexes at the top HEADERS = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long', 'enter_short', 'exit_short', 'enter_tag', 'exit_tag'] 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: Config) -> None: LoggingMixin.show_output = False self.config = config self.results: Dict[str, Any] = {} self.trade_id_counter: int = 0 self.order_id_counter: int = 0 config['dry_run'] = True self.run_ids: Dict[str, str] = {} self.strategylist: List[IStrategy] = [] self.all_results: Dict[str, Dict] = {} self.processed_dfs: Dict[str, Dict] = {} self.rejected_dict: Dict[str, List] = {} self.rejected_df: Dict[str, Dict] = {} self._exchange_name = self.config['exchange']['name'] self.exchange = ExchangeResolver.load_exchange( self._exchange_name, self.config, load_leverage_tiers=True) self.dataprovider = DataProvider(self.config, self.exchange) if self.config.get('strategy_list'): if self.config.get('freqai', {}).get('enabled', False): logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies " "to have identical populate_any_indicators.") for strat in list(self.config['strategy_list']): stratconf = deepcopy(self.config) stratconf['strategy'] = strat self.strategylist.append(StrategyResolver.load_strategy(stratconf)) validate_config_consistency(stratconf) else: # No strategy list specified, only one strategy self.strategylist.append(StrategyResolver.load_strategy(self.config)) validate_config_consistency(self.config) if "timeframe" not in self.config: raise OperationalException("Timeframe needs to be set in either " "configuration or as cli argument `--timeframe 5m`") self.timeframe = str(self.config.get('timeframe')) self.timeframe_min = timeframe_to_minutes(self.timeframe) self.init_backtest_detail() self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider) if 'VolumePairList' in self.pairlists.name_list: raise OperationalException("VolumePairList not allowed for backtesting. " "Please use StaticPairList instead.") if 'PerformanceFilter' in self.pairlists.name_list: raise OperationalException("PerformanceFilter not allowed for backtesting.") if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list: raise OperationalException( "PrecisionFilter not allowed for backtesting multiple strategies." ) self.dataprovider.add_pairlisthandler(self.pairlists) self.pairlists.refresh_pairlist() if len(self.pairlists.whitelist) == 0: raise OperationalException("No pair in whitelist.") if config.get('fee', None) is not None: self.fee = config['fee'] else: self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0]) self.precision_mode = self.exchange.precisionMode if self.config.get('freqai_backtest_live_models', False): from freqtrade.freqai.utils import get_timerange_backtest_live_models self.config['timerange'] = get_timerange_backtest_live_models(self.config) self.timerange = TimeRange.parse_timerange( None if self.config.get('timerange') is None else str(self.config.get('timerange'))) # Get maximum required startup period self.required_startup = max([strat.startup_candle_count for strat in self.strategylist]) self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe) if self.config.get('freqai', {}).get('enabled', False): # For FreqAI, increase the required_startup to includes the training data self.required_startup = self.dataprovider.get_required_startup(self.timeframe) # Add maximum startup candle count to configuration for informative pairs support self.config['startup_candle_count'] = self.required_startup self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT) # strategies which define "can_short=True" will fail to load in Spot mode. self._can_short = self.trading_mode != TradingMode.SPOT self._position_stacking: bool = self.config.get('position_stacking', False) self.enable_protections: bool = self.config.get('enable_protections', False) self.init_backtest() @staticmethod def cleanup(): LoggingMixin.show_output = True PairLocks.use_db = True Trade.use_db = True def init_backtest_detail(self) -> None: # Load detail timeframe if specified self.timeframe_detail = str(self.config.get('timeframe_detail', '')) if self.timeframe_detail: self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail) if self.timeframe_min <= self.timeframe_detail_min: raise OperationalException( "Detail timeframe must be smaller than strategy timeframe.") else: self.timeframe_detail_min = 0 self.detail_data: Dict[str, DataFrame] = {} self.futures_data: Dict[str, DataFrame] = {} def init_backtest(self): self.prepare_backtest(False) self.wallets = Wallets(self.config, self.exchange, log=False) self.progress = BTProgress() self.abort = False def _set_strategy(self, strategy: IStrategy): """ Load strategy into backtesting """ self.strategy: IStrategy = strategy strategy.dp = self.dataprovider # Attach Wallets to Strategy baseclass strategy.wallets = self.wallets # Set stoploss_on_exchange to false for backtesting, # since a "perfect" stoploss-exit is assumed anyway # And the regular "stoploss" function would not apply to that case self.strategy.order_types['stoploss_on_exchange'] = False self.strategy.ft_bot_start() strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)() def _load_protections(self, strategy: IStrategy): if self.config.get('enable_protections', False): conf = self.config if hasattr(strategy, 'protections'): conf = deepcopy(conf) conf['protections'] = strategy.protections self.protections = ProtectionManager(self.config, strategy.protections) def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]: """ Loads backtest data and returns the data combined with the timerange as tuple. """ self.progress.init_step(BacktestState.DATALOAD, 1) data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.timeframe, timerange=self.timerange, startup_candles=self.config['startup_candle_count'], fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) min_date, max_date = history.get_timerange(data) logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} ' f'({(max_date - min_date).days} days).') # Adjust startts forward if not enough data is available self.timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe), self.required_startup, min_date) self.progress.set_new_value(1) return data, self.timerange def load_bt_data_detail(self) -> None: """ Loads backtest detail data (smaller timeframe) if necessary. """ if self.timeframe_detail: self.detail_data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.timeframe_detail, timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) else: self.detail_data = {} if self.trading_mode == TradingMode.FUTURES: # Load additional futures data. funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange.get_option('mark_ohlcv_timeframe'), timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.FUNDING_RATE ) # For simplicity, assign to CandleType.Mark (might contian index candles!) mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange.get_option('mark_ohlcv_timeframe'), timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price")) ) # Combine data to avoid combining the data per trade. unavailable_pairs = [] for pair in self.pairlists.whitelist: if pair not in self.exchange._leverage_tiers: unavailable_pairs.append(pair) continue self.futures_data[pair] = self.exchange.combine_funding_and_mark( funding_rates=funding_rates_dict[pair], mark_rates=mark_rates_dict[pair], futures_funding_rate=self.config.get('futures_funding_rate', None), ) if unavailable_pairs: raise OperationalException( f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. " "It is therefore impossible to backtest with this pair at the moment.") else: self.futures_data = {} def prepare_backtest(self, enable_protections): """ Backtesting setup method - called once for every call to "backtest()". """ PairLocks.use_db = False PairLocks.timeframe = self.config['timeframe'] Trade.use_db = False PairLocks.reset_locks() Trade.reset_trades() self.rejected_trades = 0 self.timedout_entry_orders = 0 self.timedout_exit_orders = 0 self.canceled_trade_entries = 0 self.canceled_entry_orders = 0 self.replaced_entry_orders = 0 self.dataprovider.clear_cache() if enable_protections: self._load_protections(self.strategy) def check_abort(self): """ Check if abort was requested, raise DependencyException if that's the case Only applies to Interactive backtest mode (webserver mode) """ if self.abort: self.abort = False raise DependencyException("Stop requested") def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]: """ Helper function to convert a processed dataframes into lists for performance reasons. Used by backtest() - so keep this optimized for performance. :param processed: a processed dictionary with format {pair, data}, which gets cleared to optimize memory usage! """ data: Dict = {} self.progress.init_step(BacktestState.CONVERT, len(processed)) # Create dict with data for pair in processed.keys(): pair_data = processed[pair] self.check_abort() self.progress.increment() if not pair_data.empty: # Cleanup from prior runs pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore') df_analyzed = self.strategy.advise_exit( self.strategy.advise_entry(pair_data, {'pair': pair}), {'pair': pair} ).copy() # Trim startup period from analyzed dataframe df_analyzed = processed[pair] = pair_data = trim_dataframe( df_analyzed, self.timerange, startup_candles=self.required_startup) # Update dataprovider cache self.dataprovider._set_cached_df( pair, self.timeframe, df_analyzed, self.config['candle_type_def']) # Create a copy of the dataframe before shifting, that way the entry signal/tag # remains on the correct candle for callbacks. df_analyzed = df_analyzed.copy() # To avoid using data from future, we use entry/exit signals shifted # from the previous candle for col in HEADERS[5:]: tag_col = col in ('enter_tag', 'exit_tag') if col in df_analyzed.columns: df_analyzed[col] = df_analyzed.loc[:, col].replace( [nan], [0 if not tag_col else None]).shift(1) elif not df_analyzed.empty: df_analyzed[col] = 0 if not tag_col else None df_analyzed = df_analyzed.drop(df_analyzed.head(1).index) # Convert from Pandas to list for performance reasons # (Looping Pandas is slow.) data[pair] = df_analyzed[HEADERS].values.tolist() if not df_analyzed.empty else [] return data def _get_close_rate(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple, trade_dur: int) -> float: """ Get close rate for backtesting result """ # Special handling if high or low hit STOP_LOSS or ROI if exit.exit_type in ( ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION): return self._get_close_rate_for_stoploss(row, trade, exit, trade_dur) elif exit.exit_type == (ExitType.ROI): return self._get_close_rate_for_roi(row, trade, exit, trade_dur) else: return row[OPEN_IDX] def _get_close_rate_for_stoploss(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple, trade_dur: int) -> float: # our stoploss was already lower than candle high, # possibly due to a cancelled trade exit. # exit at open price. is_short = trade.is_short or False leverage = trade.leverage or 1.0 side_1 = -1 if is_short else 1 if exit.exit_type == ExitType.LIQUIDATION and trade.liquidation_price: stoploss_value = trade.liquidation_price else: stoploss_value = trade.stop_loss if is_short: if stoploss_value < row[LOW_IDX]: return row[OPEN_IDX] else: if stoploss_value > row[HIGH_IDX]: return row[OPEN_IDX] # Special case: trailing triggers within same candle as trade opened. Assume most # pessimistic price movement, which is moving just enough to arm stoploss and # immediately going down to stop price. if exit.exit_type == ExitType.TRAILING_STOP_LOSS and trade_dur == 0: if ( not self.strategy.use_custom_stoploss and self.strategy.trailing_stop and self.strategy.trailing_only_offset_is_reached and self.strategy.trailing_stop_positive_offset is not None and self.strategy.trailing_stop_positive ): # Worst case: price reaches stop_positive_offset and dives down. stop_rate = (row[OPEN_IDX] * (1 + side_1 * abs(self.strategy.trailing_stop_positive_offset) - side_1 * abs(self.strategy.trailing_stop_positive / leverage))) else: # Worst case: price ticks tiny bit above open and dives down. stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(trade.stop_loss_pct / leverage)) if is_short: assert stop_rate > row[LOW_IDX] else: assert stop_rate < row[HIGH_IDX] # Limit lower-end to candle low to avoid exits below the low. # This still remains "worst case" - but "worst realistic case". if is_short: return min(row[HIGH_IDX], stop_rate) else: return max(row[LOW_IDX], stop_rate) # Set close_rate to stoploss return stoploss_value def _get_close_rate_for_roi(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple, trade_dur: int) -> float: is_short = trade.is_short or False leverage = trade.leverage or 1.0 side_1 = -1 if is_short else 1 roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur) if roi is not None and roi_entry is not None: if roi == -1 and roi_entry % self.timeframe_min == 0: # When force_exiting with ROI=-1, the roi time will always be equal to trade_dur. # If that entry is a multiple of the timeframe (so on candle open) # - we'll use open instead of close return row[OPEN_IDX] # - (Expected abs profit - open_rate - open_fee) / (fee_close -1) roi_rate = trade.open_rate * roi / leverage open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open) close_rate = -(roi_rate + open_fee_rate) / (trade.fee_close - side_1 * 1) if is_short: is_new_roi = row[OPEN_IDX] < close_rate else: is_new_roi = row[OPEN_IDX] > close_rate if (trade_dur > 0 and trade_dur == roi_entry and roi_entry % self.timeframe_min == 0 and is_new_roi): # new ROI entry came into effect. # use Open rate if open_rate > calculated exit rate return row[OPEN_IDX] if (trade_dur == 0 and ( ( is_short # Red candle (for longs) and row[OPEN_IDX] < row[CLOSE_IDX] # Red candle and trade.open_rate > row[OPEN_IDX] # trade-open above open_rate and close_rate < row[CLOSE_IDX] # closes below close ) or ( not is_short # green candle (for shorts) and row[OPEN_IDX] > row[CLOSE_IDX] # green candle and trade.open_rate < row[OPEN_IDX] # trade-open below open_rate and close_rate > row[CLOSE_IDX] # closes above close ) )): # ROI on opening candles with custom pricing can only # trigger if the entry was at Open or lower wick. # details: https: // github.com/freqtrade/freqtrade/issues/6261 # If open_rate is < open, only allow exits below the close on red candles. raise ValueError("Opening candle ROI on red candles.") # Use the maximum between close_rate and low as we # cannot exit outside of a candle. # Applies when a new ROI setting comes in place and the whole candle is above that. return min(max(close_rate, row[LOW_IDX]), row[HIGH_IDX]) else: # This should not be reached... return row[OPEN_IDX] def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple ) -> LocalTrade: current_rate = row[OPEN_IDX] current_date = row[DATE_IDX].to_pydatetime() current_profit = trade.calc_profit_ratio(current_rate) min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, current_rate, -0.1) max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate) stake_available = self.wallets.get_available_stake_amount() stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position, default_retval=None)( trade=trade, # type: ignore[arg-type] current_time=current_date, current_rate=current_rate, current_profit=current_profit, min_stake=min_stake, max_stake=min(max_stake, stake_available), current_entry_rate=current_rate, current_exit_rate=current_rate, current_entry_profit=current_profit, current_exit_profit=current_profit) # Check if we should increase our position if stake_amount is not None and stake_amount > 0.0: check_adjust_entry = True if self.strategy.max_entry_position_adjustment > -1: entry_count = trade.nr_of_successful_entries check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment) if check_adjust_entry: pos_trade = self._enter_trade( trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade) if pos_trade is not None: self.wallets.update() return pos_trade if stake_amount is not None and stake_amount < 0.0: amount = amount_to_contract_precision( abs(stake_amount * trade.leverage) / current_rate, trade.amount_precision, self.precision_mode, trade.contract_size) if amount == 0.0: return trade if amount > trade.amount: # This is currently ineffective as remaining would become < min tradable amount = trade.amount remaining = (trade.amount - amount) * current_rate if remaining < min_stake: # Remaining stake is too low to be sold. return trade exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT) pos_trade = self._get_exit_for_signal(trade, row, exit_, amount) if pos_trade is not None: order = pos_trade.orders[-1] if self._get_order_filled(order.price, row): order.close_bt_order(current_date, trade) trade.recalc_trade_from_orders() self.wallets.update() return pos_trade return trade def _get_order_filled(self, rate: float, row: Tuple) -> bool: """ Rate is within candle, therefore filled""" return row[LOW_IDX] <= rate <= row[HIGH_IDX] def _get_exit_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]: # Check if we need to adjust our current positions if self.strategy.position_adjustment_enable: trade = self._get_adjust_trade_entry_for_candle(trade, row) enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX] exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX] exits = self.strategy.should_exit( trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore enter=enter, exit_=exit_sig, low=row[LOW_IDX], high=row[HIGH_IDX] ) for exit_ in exits: t = self._get_exit_for_signal(trade, row, exit_) if t: return t return None def _get_exit_for_signal( self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple, amount: Optional[float] = None) -> Optional[LocalTrade]: exit_candle_time: datetime = row[DATE_IDX].to_pydatetime() if exit_.exit_flag: trade.close_date = exit_candle_time exit_reason = exit_.exit_reason amount_ = amount if amount is not None else trade.amount trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60) try: close_rate = self._get_close_rate(row, trade, exit_, trade_dur) except ValueError: return None # call the custom exit price,with default value as previous close_rate current_profit = trade.calc_profit_ratio(close_rate) order_type = self.strategy.order_types['exit'] if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT, ExitType.PARTIAL_EXIT): # Checks and adds an exit tag, after checking that the length of the # row has the length for an exit tag column if ( len(row) > EXIT_TAG_IDX and row[EXIT_TAG_IDX] is not None and len(row[EXIT_TAG_IDX]) > 0 and exit_.exit_type in (ExitType.EXIT_SIGNAL,) ): exit_reason = row[EXIT_TAG_IDX] # Custom exit pricing only for exit-signals if order_type == 'limit': rate = strategy_safe_wrapper(self.strategy.custom_exit_price, default_retval=close_rate)( pair=trade.pair, trade=trade, # type: ignore[arg-type] current_time=exit_candle_time, proposed_rate=close_rate, current_profit=current_profit, exit_tag=exit_reason) if rate != close_rate: close_rate = price_to_precision(rate, trade.price_precision, self.precision_mode) # We can't place orders lower than current low. # freqtrade does not support this in live, and the order would fill immediately if trade.is_short: close_rate = min(close_rate, row[HIGH_IDX]) else: close_rate = max(close_rate, row[LOW_IDX]) # Confirm trade exit: time_in_force = self.strategy.order_time_in_force['exit'] if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT) and not strategy_safe_wrapper( self.strategy.confirm_trade_exit, default_retval=True)( pair=trade.pair, trade=trade, # type: ignore[arg-type] order_type=order_type, amount=amount_, rate=close_rate, time_in_force=time_in_force, sell_reason=exit_reason, # deprecated exit_reason=exit_reason, current_time=exit_candle_time)): return None trade.exit_reason = exit_reason return self._exit_trade(trade, row, close_rate, amount_) return None def _exit_trade(self, trade: LocalTrade, sell_row: Tuple, close_rate: float, amount: float = None) -> Optional[LocalTrade]: self.order_id_counter += 1 exit_candle_time = sell_row[DATE_IDX].to_pydatetime() order_type = self.strategy.order_types['exit'] # amount = amount or trade.amount amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision, self.precision_mode, trade.contract_size) order = Order( id=self.order_id_counter, ft_trade_id=trade.id, order_date=exit_candle_time, order_update_date=exit_candle_time, ft_is_open=True, ft_pair=trade.pair, order_id=str(self.order_id_counter), symbol=trade.pair, ft_order_side=trade.exit_side, side=trade.exit_side, order_type=order_type, status="open", price=close_rate, average=close_rate, amount=amount, filled=0, remaining=amount, cost=amount * close_rate, ) trade.orders.append(order) return trade def _get_exit_trade_entry( self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]: exit_candle_time: datetime = row[DATE_IDX].to_pydatetime() if is_first and self.trading_mode == TradingMode.FUTURES: trade.funding_fees = self.exchange.calculate_funding_fees( self.futures_data[trade.pair], amount=trade.amount, is_short=trade.is_short, open_date=trade.date_last_filled_utc, close_date=exit_candle_time, ) return self._get_exit_trade_entry_for_candle(trade, row) def get_valid_price_and_stake( self, pair: str, row: Tuple, propose_rate: float, stake_amount: float, direction: LongShort, current_time: datetime, entry_tag: Optional[str], trade: Optional[LocalTrade], order_type: str, price_precision: Optional[float] ) -> Tuple[float, float, float, float]: if order_type == 'limit': new_rate = strategy_safe_wrapper(self.strategy.custom_entry_price, default_retval=propose_rate)( pair=pair, current_time=current_time, proposed_rate=propose_rate, entry_tag=entry_tag, side=direction, ) # default value is the open rate # We can't place orders higher than current high (otherwise it'd be a stop limit entry) # which freqtrade does not support in live. if new_rate != propose_rate: propose_rate = price_to_precision(new_rate, price_precision, self.precision_mode) if direction == "short": propose_rate = max(propose_rate, row[LOW_IDX]) else: propose_rate = min(propose_rate, row[HIGH_IDX]) pos_adjust = trade is not None leverage = trade.leverage if trade else 1.0 if not pos_adjust: try: stake_amount = self.wallets.get_trade_stake_amount(pair, None, update=False) except DependencyException: return 0, 0, 0, 0 max_leverage = self.exchange.get_max_leverage(pair, stake_amount) leverage = strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)( pair=pair, current_time=current_time, current_rate=row[OPEN_IDX], proposed_leverage=1.0, max_leverage=max_leverage, side=direction, entry_tag=entry_tag, ) if self._can_short else 1.0 # Cap leverage between 1.0 and max_leverage. leverage = min(max(leverage, 1.0), max_leverage) min_stake_amount = self.exchange.get_min_pair_stake_amount( pair, propose_rate, -0.05, leverage=leverage) or 0 max_stake_amount = self.exchange.get_max_pair_stake_amount( pair, propose_rate, leverage=leverage) stake_available = self.wallets.get_available_stake_amount() if not pos_adjust: stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount, default_retval=stake_amount)( pair=pair, current_time=current_time, current_rate=propose_rate, proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=min(stake_available, max_stake_amount), leverage=leverage, entry_tag=entry_tag, side=direction) stake_amount_val = self.wallets.validate_stake_amount( pair=pair, stake_amount=stake_amount, min_stake_amount=min_stake_amount, max_stake_amount=max_stake_amount, ) return propose_rate, stake_amount_val, leverage, min_stake_amount def _enter_trade(self, pair: str, row: Tuple, direction: LongShort, stake_amount: Optional[float] = None, trade: Optional[LocalTrade] = None, requested_rate: Optional[float] = None, requested_stake: Optional[float] = None) -> Optional[LocalTrade]: current_time = row[DATE_IDX].to_pydatetime() entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None # let's call the custom entry price, using the open price as default price order_type = self.strategy.order_types['entry'] pos_adjust = trade is not None and requested_rate is None stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0) precision_price = self.exchange.get_precision_price(pair) propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake( pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade, order_type, precision_price, ) # replace proposed rate if another rate was requested propose_rate = requested_rate if requested_rate else propose_rate stake_amount = requested_stake if requested_stake else stake_amount if not stake_amount: # In case of pos adjust, still return the original trade # If not pos adjust, trade is None return trade time_in_force = self.strategy.order_time_in_force['entry'] if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount): self.order_id_counter += 1 base_currency = self.exchange.get_pair_base_currency(pair) amount_p = (stake_amount / propose_rate) * leverage contract_size = self.exchange.get_contract_size(pair) precision_amount = self.exchange.get_precision_amount(pair) amount = amount_to_contract_precision(amount_p, precision_amount, self.precision_mode, contract_size) # Backcalculate actual stake amount. stake_amount = amount * propose_rate / leverage if not pos_adjust: # Confirm trade entry: if not strategy_safe_wrapper( self.strategy.confirm_trade_entry, default_retval=True)( pair=pair, order_type=order_type, amount=amount, rate=propose_rate, time_in_force=time_in_force, current_time=current_time, entry_tag=entry_tag, side=direction): return trade is_short = (direction == 'short') # Necessary for Margin trading. Disabled until support is enabled. # interest_rate = self.exchange.get_interest_rate() if trade is None: # Enter trade self.trade_id_counter += 1 trade = LocalTrade( id=self.trade_id_counter, open_order_id=self.order_id_counter, pair=pair, base_currency=base_currency, stake_currency=self.config['stake_currency'], open_rate=propose_rate, open_rate_requested=propose_rate, open_date=current_time, stake_amount=stake_amount, amount=amount, amount_requested=amount, fee_open=self.fee, fee_close=self.fee, is_open=True, enter_tag=entry_tag, exchange=self._exchange_name, is_short=is_short, trading_mode=self.trading_mode, leverage=leverage, # interest_rate=interest_rate, amount_precision=precision_amount, price_precision=precision_price, precision_mode=self.precision_mode, contract_size=contract_size, orders=[], ) trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True) trade.set_liquidation_price(self.exchange.get_liquidation_price( pair=pair, open_rate=propose_rate, amount=amount, stake_amount=trade.stake_amount, wallet_balance=trade.stake_amount, is_short=is_short, )) order = Order( id=self.order_id_counter, ft_trade_id=trade.id, ft_is_open=True, ft_pair=trade.pair, order_id=str(self.order_id_counter), symbol=trade.pair, ft_order_side=trade.entry_side, side=trade.entry_side, order_type=order_type, status="open", order_date=current_time, order_filled_date=current_time, order_update_date=current_time, price=propose_rate, average=propose_rate, amount=amount, filled=0, remaining=amount, cost=stake_amount + trade.fee_open, ) trade.orders.append(order) if pos_adjust and self._get_order_filled(order.price, row): order.close_bt_order(current_time, trade) else: trade.open_order_id = str(self.order_id_counter) trade.recalc_trade_from_orders() return trade def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]], data: Dict[str, List[Tuple]]) -> None: """ Handling of left open trades at the end of backtesting """ for pair in open_trades.keys(): for trade in list(open_trades[pair]): if trade.open_order_id and trade.nr_of_successful_entries == 0: # Ignore trade if entry-order did not fill yet continue exit_row = data[pair][-1] self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount) trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade) trade.close_date = exit_row[DATE_IDX].to_pydatetime() trade.exit_reason = ExitType.FORCE_EXIT.value trade.close(exit_row[OPEN_IDX], show_msg=False) LocalTrade.close_bt_trade(trade) def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool: # Always allow trades when max_open_trades is enabled. if max_open_trades <= 0 or open_trade_count < max_open_trades: return True # Rejected trade self.rejected_trades += 1 return False def check_for_trade_entry(self, row) -> Optional[LongShort]: enter_long = row[LONG_IDX] == 1 exit_long = row[ELONG_IDX] == 1 enter_short = self._can_short and row[SHORT_IDX] == 1 exit_short = self._can_short and row[ESHORT_IDX] == 1 if enter_long == 1 and not any([exit_long, enter_short]): # Long return 'long' if enter_short == 1 and not any([exit_short, enter_long]): # Short return 'short' return None def run_protections(self, pair: str, current_time: datetime, side: LongShort): if self.enable_protections: self.protections.stop_per_pair(pair, current_time, side) self.protections.global_stop(current_time, side) def manage_open_orders(self, trade: LocalTrade, current_time: datetime, row: Tuple) -> bool: """ Check if any open order needs to be cancelled or replaced. Returns True if the trade should be deleted. """ for order in [o for o in trade.orders if o.ft_is_open]: oc = self.check_order_cancel(trade, order, current_time) if oc: # delete trade due to order timeout return True elif oc is None and self.check_order_replace(trade, order, current_time, row): # delete trade due to user request self.canceled_trade_entries += 1 return True # default maintain trade return False def check_order_cancel( self, trade: LocalTrade, order: Order, current_time: datetime) -> Optional[bool]: """ Check if current analyzed order has to be canceled. Returns True if the trade should be Deleted (initial order was canceled), False if it's Canceled None if the order is still active. """ timedout = self.strategy.ft_check_timed_out( trade, # type: ignore[arg-type] order, current_time) if timedout: if order.side == trade.entry_side: self.timedout_entry_orders += 1 if trade.nr_of_successful_entries == 0: # Remove trade due to entry timeout expiration. return True else: # Close additional entry order del trade.orders[trade.orders.index(order)] trade.open_order_id = None return False if order.side == trade.exit_side: self.timedout_exit_orders += 1 # Close exit order and retry exiting on next signal. del trade.orders[trade.orders.index(order)] trade.open_order_id = None return False return None def check_order_replace(self, trade: LocalTrade, order: Order, current_time, row: Tuple) -> bool: """ Check if current analyzed entry order has to be replaced and do so. If user requested cancellation and there are no filled orders in the trade will instruct caller to delete the trade. Returns True if the trade should be deleted. """ # only check on new candles for open entry orders if order.side == trade.entry_side and current_time > order.order_date_utc: requested_rate = strategy_safe_wrapper(self.strategy.adjust_entry_price, default_retval=order.price)( trade=trade, # type: ignore[arg-type] order=order, pair=trade.pair, current_time=current_time, proposed_rate=row[OPEN_IDX], current_order_rate=order.price, entry_tag=trade.enter_tag, side=trade.trade_direction ) # default value is current order price # cancel existing order whenever a new rate is requested (or None) if requested_rate == order.price: # assumption: there can't be multiple open entry orders at any given time return False else: del trade.orders[trade.orders.index(order)] trade.open_order_id = None self.canceled_entry_orders += 1 # place new order if result was not None if requested_rate: self._enter_trade(pair=trade.pair, row=row, trade=trade, requested_rate=requested_rate, requested_stake=(order.remaining * order.price / trade.leverage), direction='short' if trade.is_short else 'long') self.replaced_entry_orders += 1 else: # assumption: there can't be multiple open entry orders at any given time return (trade.nr_of_successful_entries == 0) return False def validate_row( self, data: Dict, pair: str, row_index: int, current_time: datetime) -> Optional[Tuple]: try: # Row is treated as "current incomplete candle". # entry / exit signals are shifted by 1 to compensate for this. row = data[pair][row_index] except IndexError: # missing Data for one pair at the end. # Warnings for this are shown during data loading return None # Waits until the time-counter reaches the start of the data for this pair. if row[DATE_IDX] > current_time: return None return row def _collate_rejected(self, pair, row): """ Temporarily store rejected trade information for downstream use in backtesting_analysis """ # It could be fun to enable hyperopt mode to write # a loss function to reduce rejected signals if (self.config.get('export', 'none') == 'signals' and self.dataprovider.runmode == RunMode.BACKTEST): if pair not in self.rejected_dict: self.rejected_dict[pair] = [] self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]]) def backtest_loop( self, row: Tuple, pair: str, current_time: datetime, end_date: datetime, max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int: """ NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized. Backtesting processing for one candle/pair. """ for t in list(LocalTrade.bt_trades_open_pp[pair]): # 1. Manage currently open orders of active trades if self.manage_open_orders(t, current_time, row): # Close trade open_trade_count_start -= 1 LocalTrade.remove_bt_trade(t) self.wallets.update() # 2. Process entries. # without positionstacking, we can only have one open trade per pair. # max_open_trades must be respected # don't open on the last row # We only open trades on the main candle, not on detail candles trade_dir = self.check_for_trade_entry(row) if ( (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0) and is_first and current_time != end_date and trade_dir is not None and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir) ): if (self.trade_slot_available(max_open_trades, open_trade_count_start)): trade = self._enter_trade(pair, row, trade_dir) if trade: # TODO: hacky workaround to avoid opening > max_open_trades # This emulates previous behavior - not sure if this is correct # Prevents entering if the trade-slot was freed in this candle open_trade_count_start += 1 # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.") LocalTrade.add_bt_trade(trade) self.wallets.update() else: self._collate_rejected(pair, row) for trade in list(LocalTrade.bt_trades_open_pp[pair]): # 3. Process entry orders. order = trade.select_order(trade.entry_side, is_open=True) if order and self._get_order_filled(order.price, row): order.close_bt_order(current_time, trade) trade.open_order_id = None self.wallets.update() # 4. Create exit orders (if any) if not trade.open_order_id: self._get_exit_trade_entry(trade, row, is_first) # Place exit order if necessary # 5. Process exit orders. order = trade.select_order(trade.exit_side, is_open=True) if order and self._get_order_filled(order.price, row): order.close_bt_order(current_time, trade) trade.open_order_id = None sub_trade = order.safe_amount_after_fee != trade.amount if sub_trade: order.close_bt_order(current_time, trade) trade.recalc_trade_from_orders() else: trade.close_date = current_time trade.close(order.price, show_msg=False) # logger.debug(f"{pair} - Backtesting exit {trade}") LocalTrade.close_bt_trade(trade) self.wallets.update() self.run_protections(pair, current_time, trade.trade_direction) return open_trade_count_start def backtest(self, processed: Dict, start_date: datetime, end_date: datetime, max_open_trades: int = 0) -> Dict[str, Any]: """ Implement 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 extensive logging in this method and functions it calls. :param processed: a processed dictionary with format {pair, data}, which gets cleared to optimize memory usage! :param start_date: backtesting timerange start datetime :param end_date: backtesting timerange end datetime :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited :return: DataFrame with trades (results of backtesting) """ self.prepare_backtest(self.enable_protections) # Ensure wallets are uptodate (important for --strategy-list) self.wallets.update() # Use dict of lists with data for performance # (looping lists is a lot faster than pandas DataFrames) data: Dict = self._get_ohlcv_as_lists(processed) # Indexes per pair, so some pairs are allowed to have a missing start. indexes: Dict = defaultdict(int) current_time = start_date + timedelta(minutes=self.timeframe_min) self.progress.init_step(BacktestState.BACKTEST, int( (end_date - start_date) / timedelta(minutes=self.timeframe_min))) # Loop timerange and get candle for each pair at that point in time while current_time <= end_date: open_trade_count_start = LocalTrade.bt_open_open_trade_count self.check_abort() for i, pair in enumerate(data): row_index = indexes[pair] row = self.validate_row(data, pair, row_index, current_time) if not row: continue row_index += 1 indexes[pair] = row_index self.dataprovider._set_dataframe_max_index(row_index) current_detail_time: datetime = row[DATE_IDX].to_pydatetime() if self.timeframe_detail and pair in self.detail_data: exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min) detail_data = self.detail_data[pair] detail_data = detail_data.loc[ (detail_data['date'] >= current_detail_time) & (detail_data['date'] < exit_candle_end) ].copy() if len(detail_data) == 0: # Fall back to "regular" data if no detail data was found for this candle open_trade_count_start = self.backtest_loop( row, pair, current_time, end_date, max_open_trades, open_trade_count_start) detail_data.loc[:, 'enter_long'] = row[LONG_IDX] detail_data.loc[:, 'exit_long'] = row[ELONG_IDX] detail_data.loc[:, 'enter_short'] = row[SHORT_IDX] detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX] detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX] detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX] is_first = True current_time_det = current_time for det_row in detail_data[HEADERS].values.tolist(): open_trade_count_start = self.backtest_loop( det_row, pair, current_time_det, end_date, max_open_trades, open_trade_count_start, is_first) current_time_det += timedelta(minutes=self.timeframe_detail_min) is_first = False else: open_trade_count_start = self.backtest_loop( row, pair, current_time, end_date, max_open_trades, open_trade_count_start) # Move time one configured time_interval ahead. self.progress.increment() current_time += timedelta(minutes=self.timeframe_min) self.handle_left_open(LocalTrade.bt_trades_open_pp, data=data) self.wallets.update() results = trade_list_to_dataframe(LocalTrade.trades) return { 'results': results, 'config': self.strategy.config, 'locks': PairLocks.get_all_locks(), 'rejected_signals': self.rejected_trades, 'timedout_entry_orders': self.timedout_entry_orders, 'timedout_exit_orders': self.timedout_exit_orders, 'canceled_trade_entries': self.canceled_trade_entries, 'canceled_entry_orders': self.canceled_entry_orders, 'replaced_entry_orders': self.replaced_entry_orders, 'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']), } def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame], timerange: TimeRange): self.progress.init_step(BacktestState.ANALYZE, 0) logger.info(f"Running backtesting for Strategy {strat.get_strategy_name()}") backtest_start_time = datetime.now(timezone.utc) self._set_strategy(strat) # Use max_open_trades in backtesting, except --disable-max-market-positions is set if self.config.get('use_max_market_positions', True): # Must come from strategy config, as the strategy may modify this setting. max_open_trades = self.strategy.config['max_open_trades'] else: logger.info( 'Ignoring max_open_trades (--disable-max-market-positions was used) ...') max_open_trades = 0 # need to reprocess data every time to populate signals preprocessed = self.strategy.advise_all_indicators(data) # Trim startup period from analyzed dataframe preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup) if not preprocessed_tmp: raise OperationalException( "No data left after adjusting for startup candles.") # Use preprocessed_tmp for date generation (the trimmed dataframe). # Backtesting will re-trim the dataframes after entry/exit signal generation. min_date, max_date = history.get_timerange(preprocessed_tmp) logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} ' f'({(max_date - min_date).days} days).') # Execute backtest and store results results = self.backtest( processed=preprocessed, start_date=min_date, end_date=max_date, max_open_trades=max_open_trades, ) backtest_end_time = datetime.now(timezone.utc) results.update({ 'run_id': self.run_ids.get(strat.get_strategy_name(), ''), 'backtest_start_time': int(backtest_start_time.timestamp()), 'backtest_end_time': int(backtest_end_time.timestamp()), }) self.all_results[self.strategy.get_strategy_name()] = results if (self.config.get('export', 'none') == 'signals' and self.dataprovider.runmode == RunMode.BACKTEST): self._generate_trade_signal_candles(preprocessed_tmp, results) self._generate_rejected_trades(preprocessed_tmp, self.rejected_dict) return min_date, max_date def _generate_trade_signal_candles(self, preprocessed_df, bt_results): signal_candles_only = {} for pair in preprocessed_df.keys(): signal_candles_only_df = DataFrame() pairdf = preprocessed_df[pair] resdf = bt_results['results'] pairresults = resdf.loc[(resdf["pair"] == pair)] if pairdf.shape[0] > 0: for t, v in pairresults.open_date.items(): allinds = pairdf.loc[(pairdf['date'] < v)] signal_inds = allinds.iloc[[-1]] signal_candles_only_df = pd.concat([ signal_candles_only_df.infer_objects(), signal_inds.infer_objects()]) signal_candles_only[pair] = signal_candles_only_df self.processed_dfs[self.strategy.get_strategy_name()] = signal_candles_only def _generate_rejected_trades(self, preprocessed_df, rejected_dict): rejected_candles_only = {} for pair, trades in rejected_dict.items(): rejected_trades_only_df = DataFrame() pairdf = preprocessed_df[pair] for t in trades: data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy() data_df_row['pair'] = pair data_df_row['enter_tag'] = t[1] rejected_trades_only_df = pd.concat([ rejected_trades_only_df.infer_objects(), data_df_row.infer_objects()]) rejected_candles_only[pair] = rejected_trades_only_df self.rejected_df[self.strategy.get_strategy_name()] = rejected_candles_only def _get_min_cached_backtest_date(self): min_backtest_date = None backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT) if self.timerange.stopts == 0 or self.timerange.stopdt > datetime.now(tz=timezone.utc): logger.warning('Backtest result caching disabled due to use of open-ended timerange.') elif backtest_cache_age == 'day': min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1) elif backtest_cache_age == 'week': min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1) elif backtest_cache_age == 'month': min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4) return min_backtest_date def load_prior_backtest(self): self.run_ids = { strategy.get_strategy_name(): get_strategy_run_id(strategy) for strategy in self.strategylist } # Load previous result that will be updated incrementally. # This can be circumvented in certain instances in combination with downloading more data min_backtest_date = self._get_min_cached_backtest_date() if min_backtest_date is not None: self.results = find_existing_backtest_stats( self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date) def start(self) -> None: """ Run backtesting end-to-end :return: None """ data: Dict[str, Any] = {} data, timerange = self.load_bt_data() self.load_bt_data_detail() logger.info("Dataload complete. Calculating indicators") self.load_prior_backtest() for strat in self.strategylist: if self.results and strat.get_strategy_name() in self.results['strategy']: # When previous result hash matches - reuse that result and skip backtesting. logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}') continue min_date, max_date = self.backtest_one_strategy(strat, data, timerange) # Update old results with new ones. if len(self.all_results) > 0: results = generate_backtest_stats( data, self.all_results, min_date=min_date, max_date=max_date) if self.results: self.results['metadata'].update(results['metadata']) self.results['strategy'].update(results['strategy']) self.results['strategy_comparison'].extend(results['strategy_comparison']) else: self.results = results dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") if self.config.get('export', 'none') in ('trades', 'signals'): store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix) if (self.config.get('export', 'none') == 'signals' and self.dataprovider.runmode == RunMode.BACKTEST): store_backtest_signal_candles( self.config['exportfilename'], self.processed_dfs, dt_appendix) store_backtest_rejected_trades( self.config['exportfilename'], self.rejected_df, dt_appendix) # Results may be mixed up now. Sort them so they follow --strategy-list order. if 'strategy_list' in self.config and len(self.results) > 0: self.results['strategy_comparison'] = sorted( self.results['strategy_comparison'], key=lambda c: self.config['strategy_list'].index(c['key'])) self.results['strategy'] = dict( sorted(self.results['strategy'].items(), key=lambda kv: self.config['strategy_list'].index(kv[0]))) if len(self.strategylist) > 0: # Show backtest results show_backtest_results(self.config, self.results)