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')
class FreqtradeBot(object): """ Freqtrade is the main class of the bot. This is from here the bot start its logic. """ def __init__(self, config: Dict[str, Any]) -> None: """ Init all variables and objects the bot needs to work :param config: configuration dict, you can use Configuration.get_config() to get the config dict. """ logger.info('Starting freqtrade %s', __version__) # Init bot state self.state = State.STOPPED # Init objects self.config = config self.strategy: IStrategy = StrategyResolver(self.config).strategy self.rpc: RPCManager = RPCManager(self) self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange self.wallets = Wallets(self.config, self.exchange) self.dataprovider = DataProvider(self.config, self.exchange) # Attach Dataprovider to Strategy baseclass IStrategy.dp = self.dataprovider # Attach Wallets to Strategy baseclass IStrategy.wallets = self.wallets pairlistname = self.config.get('pairlist', {}).get('method', 'StaticPairList') self.pairlists = PairListResolver(pairlistname, self, self.config).pairlist # Initializing Edge only if enabled self.edge = Edge(self.config, self.exchange, self.strategy) if \ self.config.get('edge', {}).get('enabled', False) else None self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist'] persistence.init(self.config.get('db_url', None), clean_open_orders=self.config.get('dry_run', False)) # Set initial bot state from config initial_state = self.config.get('initial_state') self.state = State[initial_state.upper()] if initial_state else State.STOPPED def cleanup(self) -> None: """ Cleanup pending resources on an already stopped bot :return: None """ logger.info('Cleaning up modules ...') self.rpc.cleanup() persistence.cleanup() def startup(self) -> None: """ Called on startup and after reloading the bot - triggers notifications and performs startup tasks """ self.rpc.startup_messages(self.config, self.pairlists) if not self.edge: # Adjust stoploss if it was changed Trade.stoploss_reinitialization(self.strategy.stoploss) def process(self) -> bool: """ Queries the persistence layer for open trades and handles them, otherwise a new trade is created. :return: True if one or more trades has been created or closed, False otherwise """ state_changed = False # Check whether markets have to be reloaded self.exchange._reload_markets() # Refresh whitelist self.pairlists.refresh_pairlist() self.active_pair_whitelist = self.pairlists.whitelist # Calculating Edge positioning if self.edge: self.edge.calculate() self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist) # Query trades from persistence layer trades = Trade.get_open_trades() # Extend active-pair whitelist with pairs from open trades # It ensures that tickers are downloaded for open trades self._extend_whitelist_with_trades(self.active_pair_whitelist, trades) # Refreshing candles self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist), self.strategy.informative_pairs()) # First process current opened trades for trade in trades: state_changed |= self.process_maybe_execute_sell(trade) # Then looking for buy opportunities if len(trades) < self.config['max_open_trades']: state_changed = self.process_maybe_execute_buy() if 'unfilledtimeout' in self.config: # Check and handle any timed out open orders self.check_handle_timedout() Trade.session.flush() return state_changed def _extend_whitelist_with_trades(self, whitelist: List[str], trades: List[Any]): """ Extend whitelist with pairs from open trades """ whitelist.extend([trade.pair for trade in trades if trade.pair not in whitelist]) def _create_pair_whitelist(self, pairs: List[str]) -> List[Tuple[str, str]]: """ Create pair-whitelist tuple with (pair, ticker_interval) """ return [(pair, self.config['ticker_interval']) for pair in pairs] def get_target_bid(self, pair: str, tick: Dict = None) -> float: """ Calculates bid target between current ask price and last price :return: float: Price """ config_bid_strategy = self.config.get('bid_strategy', {}) if 'use_order_book' in config_bid_strategy and\ config_bid_strategy.get('use_order_book', False): logger.info('Getting price from order book') order_book_top = config_bid_strategy.get('order_book_top', 1) order_book = self.exchange.get_order_book(pair, order_book_top) logger.debug('order_book %s', order_book) # top 1 = index 0 order_book_rate = order_book['bids'][order_book_top - 1][0] logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate) used_rate = order_book_rate else: if not tick: logger.info('Using Last Ask / Last Price') ticker = self.exchange.get_ticker(pair) else: ticker = tick if ticker['ask'] < ticker['last']: ticker_rate = ticker['ask'] else: balance = self.config['bid_strategy']['ask_last_balance'] ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask']) used_rate = ticker_rate return used_rate def _get_trade_stake_amount(self, pair) -> Optional[float]: """ Check if stake amount can be fulfilled with the available balance for the stake currency :return: float: Stake Amount """ if self.edge: return self.edge.stake_amount( pair, self.wallets.get_free(self.config['stake_currency']), self.wallets.get_total(self.config['stake_currency']), Trade.total_open_trades_stakes() ) else: stake_amount = self.config['stake_amount'] available_amount = self.wallets.get_free(self.config['stake_currency']) if stake_amount == constants.UNLIMITED_STAKE_AMOUNT: open_trades = len(Trade.get_open_trades()) if open_trades >= self.config['max_open_trades']: logger.warning('Can\'t open a new trade: max number of trades is reached') return None return available_amount / (self.config['max_open_trades'] - open_trades) # Check if stake_amount is fulfilled if available_amount < stake_amount: raise DependencyException( f"Available balance({available_amount} {self.config['stake_currency']}) is " f"lower than stake amount({stake_amount} {self.config['stake_currency']})" ) return stake_amount def _get_min_pair_stake_amount(self, pair: str, price: float) -> Optional[float]: try: market = self.exchange.markets[pair] except KeyError: raise ValueError(f"Can't get market information for symbol {pair}") if 'limits' not in market: return None min_stake_amounts = [] limits = market['limits'] if ('cost' in limits and 'min' in limits['cost'] and limits['cost']['min'] is not None): min_stake_amounts.append(limits['cost']['min']) if ('amount' in limits and 'min' in limits['amount'] and limits['amount']['min'] is not None): min_stake_amounts.append(limits['amount']['min'] * price) if not min_stake_amounts: return None # reserve some percent defined in config (5% default) + stoploss amount_reserve_percent = 1.0 - self.config.get('amount_reserve_percent', constants.DEFAULT_AMOUNT_RESERVE_PERCENT) if self.strategy.stoploss is not None: amount_reserve_percent += self.strategy.stoploss # it should not be more than 50% amount_reserve_percent = max(amount_reserve_percent, 0.5) return min(min_stake_amounts) / amount_reserve_percent def create_trade(self) -> bool: """ Checks the implemented trading indicator(s) for a randomly picked pair, if one pair triggers the buy_signal a new trade record gets created :return: True if a trade object has been created and persisted, False otherwise """ interval = self.strategy.ticker_interval whitelist = copy.deepcopy(self.active_pair_whitelist) if not whitelist: logger.warning("Whitelist is empty.") return False # Remove currently opened and latest pairs from whitelist for trade in Trade.get_open_trades(): if trade.pair in whitelist: whitelist.remove(trade.pair) logger.debug('Ignoring %s in pair whitelist', trade.pair) if not whitelist: logger.info("No currency pair in whitelist, but checking to sell open trades.") return False # running get_signal on historical data fetched for _pair in whitelist: (buy, sell) = self.strategy.get_signal( _pair, interval, self.dataprovider.ohlcv(_pair, self.strategy.ticker_interval)) if buy and not sell: stake_amount = self._get_trade_stake_amount(_pair) if not stake_amount: return False logger.info(f"Buy signal found: about create a new trade with stake_amount: " f"{stake_amount} ...") bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\ get('check_depth_of_market', {}) if (bidstrat_check_depth_of_market.get('enabled', False)) and\ (bidstrat_check_depth_of_market.get('bids_to_ask_delta', 0) > 0): if self._check_depth_of_market_buy(_pair, bidstrat_check_depth_of_market): return self.execute_buy(_pair, stake_amount) else: return False return self.execute_buy(_pair, stake_amount) return False def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool: """ Checks depth of market before executing a buy """ conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0) logger.info('checking depth of market for %s', pair) order_book = self.exchange.get_order_book(pair, 1000) order_book_data_frame = order_book_to_dataframe(order_book['bids'], order_book['asks']) order_book_bids = order_book_data_frame['b_size'].sum() order_book_asks = order_book_data_frame['a_size'].sum() bids_ask_delta = order_book_bids / order_book_asks logger.info('bids: %s, asks: %s, delta: %s', order_book_bids, order_book_asks, bids_ask_delta) if bids_ask_delta >= conf_bids_to_ask_delta: return True return False def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None) -> bool: """ Executes a limit buy for the given pair :param pair: pair for which we want to create a LIMIT_BUY :return: None """ pair_s = pair.replace('_', '/') stake_currency = self.config['stake_currency'] fiat_currency = self.config.get('fiat_display_currency', None) time_in_force = self.strategy.order_time_in_force['buy'] if price: buy_limit_requested = price else: # Calculate amount buy_limit_requested = self.get_target_bid(pair) min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit_requested) if min_stake_amount is not None and min_stake_amount > stake_amount: logger.warning( f'Can\'t open a new trade for {pair_s}: stake amount ' f'is too small ({stake_amount} < {min_stake_amount})' ) return False amount = stake_amount / buy_limit_requested order_type = self.strategy.order_types['buy'] order = self.exchange.buy(pair=pair, ordertype=order_type, amount=amount, rate=buy_limit_requested, time_in_force=time_in_force) order_id = order['id'] order_status = order.get('status', None) # we assume the order is executed at the price requested buy_limit_filled_price = buy_limit_requested if order_status == 'expired' or order_status == 'rejected': order_tif = self.strategy.order_time_in_force['buy'] # return false if the order is not filled if float(order['filled']) == 0: logger.warning('Buy %s order with time in force %s for %s is %s by %s.' ' zero amount is fulfilled.', order_tif, order_type, pair_s, order_status, self.exchange.name) return False else: # the order is partially fulfilled # in case of IOC orders we can check immediately # if the order is fulfilled fully or partially logger.warning('Buy %s order with time in force %s for %s is %s by %s.' ' %s amount fulfilled out of %s (%s remaining which is canceled).', order_tif, order_type, pair_s, order_status, self.exchange.name, order['filled'], order['amount'], order['remaining'] ) stake_amount = order['cost'] amount = order['amount'] buy_limit_filled_price = order['price'] order_id = None # in case of FOK the order may be filled immediately and fully elif order_status == 'closed': stake_amount = order['cost'] amount = order['amount'] buy_limit_filled_price = order['price'] self.rpc.send_msg({ 'type': RPCMessageType.BUY_NOTIFICATION, 'exchange': self.exchange.name.capitalize(), 'pair': pair_s, 'limit': buy_limit_filled_price, 'order_type': order_type, 'stake_amount': stake_amount, 'stake_currency': stake_currency, 'fiat_currency': fiat_currency }) # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker') trade = Trade( pair=pair, stake_amount=stake_amount, amount=amount, fee_open=fee, fee_close=fee, open_rate=buy_limit_filled_price, open_rate_requested=buy_limit_requested, open_date=datetime.utcnow(), exchange=self.exchange.id, open_order_id=order_id, strategy=self.strategy.get_strategy_name(), ticker_interval=timeframe_to_minutes(self.config['ticker_interval']) ) # Update fees if order is closed if order_status == 'closed': self.update_trade_state(trade, order) Trade.session.add(trade) Trade.session.flush() # Updating wallets self.wallets.update() return True def process_maybe_execute_buy(self) -> bool: """ Tries to execute a buy trade in a safe way :return: True if executed """ try: # Create entity and execute trade if self.create_trade(): return True logger.info('Found no buy signals for whitelisted currencies. Trying again..') return False except DependencyException as exception: logger.warning('Unable to create trade: %s', exception) return False def process_maybe_execute_sell(self, trade: Trade) -> bool: """ Tries to execute a sell trade :return: True if executed """ try: self.update_trade_state(trade) if self.strategy.order_types.get('stoploss_on_exchange') and trade.is_open: result = self.handle_stoploss_on_exchange(trade) if result: self.wallets.update() return result if trade.is_open and trade.open_order_id is None: # Check if we can sell our current pair result = self.handle_trade(trade) # Updating wallets if any trade occured if result: self.wallets.update() return result except DependencyException as exception: logger.warning('Unable to sell trade: %s', exception) return False def get_real_amount(self, trade: Trade, order: Dict) -> float: """ Get real amount for the trade Necessary for exchanges which charge fees in base currency (e.g. binance) """ order_amount = order['amount'] # Only run for closed orders if trade.fee_open == 0 or order['status'] == 'open': return order_amount # use fee from order-dict if possible if 'fee' in order and order['fee'] and (order['fee'].keys() >= {'currency', 'cost'}): if trade.pair.startswith(order['fee']['currency']): new_amount = order_amount - order['fee']['cost'] logger.info("Applying fee on amount for %s (from %s to %s) from Order", trade, order['amount'], new_amount) return new_amount # Fallback to Trades trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair, trade.open_date) if len(trades) == 0: logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade) return order_amount amount = 0 fee_abs = 0 for exectrade in trades: amount += exectrade['amount'] if "fee" in exectrade and (exectrade['fee'].keys() >= {'currency', 'cost'}): # only applies if fee is in quote currency! if trade.pair.startswith(exectrade['fee']['currency']): fee_abs += exectrade['fee']['cost'] if amount != order_amount: logger.warning(f"Amount {amount} does not match amount {trade.amount}") raise OperationalException("Half bought? Amounts don't match") real_amount = amount - fee_abs if fee_abs != 0: logger.info(f"Applying fee on amount for {trade} " f"(from {order_amount} to {real_amount}) from Trades") return real_amount def update_trade_state(self, trade, action_order: dict = None): """ Checks trades with open orders and updates the amount if necessary """ # Get order details for actual price per unit if trade.open_order_id: # Update trade with order values logger.info('Found open order for %s', trade) order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair) # Try update amount (binance-fix) try: new_amount = self.get_real_amount(trade, order) if order['amount'] != new_amount: order['amount'] = new_amount # Fee was applied, so set to 0 trade.fee_open = 0 except OperationalException as exception: logger.warning("Could not update trade amount: %s", exception) trade.update(order) # Updating wallets when order is closed if not trade.is_open: self.wallets.update() def get_sell_rate(self, pair: str, refresh: bool) -> float: """ Get sell rate - either using get-ticker bid or first bid based on orderbook The orderbook portion is only used for rpc messaging, which would otherwise fail for BitMex (has no bid/ask in get_ticker) or remain static in any other case since it's not updating. :return: Bid rate """ config_ask_strategy = self.config.get('ask_strategy', {}) if config_ask_strategy.get('use_order_book', False): logger.debug('Using order book to get sell rate') order_book = self.exchange.get_order_book(pair, 1) rate = order_book['bids'][0][0] else: rate = self.exchange.get_ticker(pair, refresh)['bid'] return rate def handle_trade(self, trade: Trade) -> bool: """ Sells the current pair if the threshold is reached and updates the trade record. :return: True if trade has been sold, False otherwise """ if not trade.is_open: raise ValueError(f'Attempt to handle closed trade: {trade}') logger.debug('Handling %s ...', trade) (buy, sell) = (False, False) experimental = self.config.get('experimental', {}) if experimental.get('use_sell_signal') or experimental.get('ignore_roi_if_buy_signal'): (buy, sell) = self.strategy.get_signal( trade.pair, self.strategy.ticker_interval, self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval)) config_ask_strategy = self.config.get('ask_strategy', {}) if config_ask_strategy.get('use_order_book', False): logger.info('Using order book for selling...') # logger.debug('Order book %s',orderBook) order_book_min = config_ask_strategy.get('order_book_min', 1) order_book_max = config_ask_strategy.get('order_book_max', 1) order_book = self.exchange.get_order_book(trade.pair, order_book_max) for i in range(order_book_min, order_book_max + 1): order_book_rate = order_book['asks'][i - 1][0] logger.info(' order book asks top %s: %0.8f', i, order_book_rate) sell_rate = order_book_rate if self.check_sell(trade, sell_rate, buy, sell): return True else: logger.debug('checking sell') sell_rate = self.get_sell_rate(trade.pair, True) if self.check_sell(trade, sell_rate, buy, sell): return True logger.debug('Found no sell signal for %s.', trade) return False def handle_stoploss_on_exchange(self, trade: Trade) -> bool: """ Check if trade is fulfilled in which case the stoploss on exchange should be added immediately if stoploss on exchange is enabled. """ logger.debug('Handling stoploss on exchange %s ...', trade) stoploss_order = None try: # First we check if there is already a stoploss on exchange stoploss_order = self.exchange.get_order(trade.stoploss_order_id, trade.pair) \ if trade.stoploss_order_id else None except InvalidOrderException as exception: logger.warning('Unable to fetch stoploss order: %s', exception) # If trade open order id does not exist: buy order is fulfilled buy_order_fulfilled = not trade.open_order_id # Limit price threshold: As limit price should always be below price limit_price_pct = 0.99 # If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange if (buy_order_fulfilled and not stoploss_order): if self.edge: stoploss = self.edge.stoploss(pair=trade.pair) else: stoploss = self.strategy.stoploss stop_price = trade.open_rate * (1 + stoploss) # limit price should be less than stop price. limit_price = stop_price * limit_price_pct try: stoploss_order_id = self.exchange.stoploss_limit( pair=trade.pair, amount=trade.amount, stop_price=stop_price, rate=limit_price )['id'] trade.stoploss_order_id = str(stoploss_order_id) trade.stoploss_last_update = datetime.now() return False except DependencyException as exception: logger.warning('Unable to place a stoploss order on exchange: %s', exception) # If stoploss order is canceled for some reason we add it if stoploss_order and stoploss_order['status'] == 'canceled': try: stoploss_order_id = self.exchange.stoploss_limit( pair=trade.pair, amount=trade.amount, stop_price=trade.stop_loss, rate=trade.stop_loss * limit_price_pct )['id'] trade.stoploss_order_id = str(stoploss_order_id) return False except DependencyException as exception: logger.warning('Stoploss order was cancelled, ' 'but unable to recreate one: %s', exception) # We check if stoploss order is fulfilled if stoploss_order and stoploss_order['status'] == 'closed': trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value trade.update(stoploss_order) self.notify_sell(trade) return True # Finally we check if stoploss on exchange should be moved up because of trailing. if stoploss_order and self.config.get('trailing_stop', False): # if trailing stoploss is enabled we check if stoploss value has changed # in which case we cancel stoploss order and put another one with new # value immediately self.handle_trailing_stoploss_on_exchange(trade, stoploss_order) return False def handle_trailing_stoploss_on_exchange(self, trade: Trade, order): """ Check to see if stoploss on exchange should be updated in case of trailing stoploss on exchange :param Trade: Corresponding Trade :param order: Current on exchange stoploss order :return: None """ if trade.stop_loss > float(order['info']['stopPrice']): # we check if the update is neccesary update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60) if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() > update_beat: # cancelling the current stoploss on exchange first logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s})' 'in order to add another one ...', order['id']) try: self.exchange.cancel_order(order['id'], trade.pair) except InvalidOrderException: logger.exception(f"Could not cancel stoploss order {order['id']} " f"for pair {trade.pair}") try: # creating the new one stoploss_order_id = self.exchange.stoploss_limit( pair=trade.pair, amount=trade.amount, stop_price=trade.stop_loss, rate=trade.stop_loss * 0.99 )['id'] trade.stoploss_order_id = str(stoploss_order_id) except DependencyException: logger.exception(f"Could create trailing stoploss order " f"for pair {trade.pair}.") def check_sell(self, trade: Trade, sell_rate: float, buy: bool, sell: bool) -> bool: if self.edge: stoploss = self.edge.stoploss(trade.pair) should_sell = self.strategy.should_sell( trade, sell_rate, datetime.utcnow(), buy, sell, force_stoploss=stoploss) else: should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell) if should_sell.sell_flag: self.execute_sell(trade, sell_rate, should_sell.sell_type) logger.info('executed sell, reason: %s', should_sell.sell_type) return True return False def check_handle_timedout(self) -> None: """ Check if any orders are timed out and cancel if neccessary :param timeoutvalue: Number of minutes until order is considered timed out :return: None """ buy_timeout = self.config['unfilledtimeout']['buy'] sell_timeout = self.config['unfilledtimeout']['sell'] buy_timeoutthreashold = arrow.utcnow().shift(minutes=-buy_timeout).datetime sell_timeoutthreashold = arrow.utcnow().shift(minutes=-sell_timeout).datetime for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all(): try: # FIXME: Somehow the query above returns results # where the open_order_id is in fact None. # This is probably because the record got # updated via /forcesell in a different thread. if not trade.open_order_id: continue order = self.exchange.get_order(trade.open_order_id, trade.pair) except (RequestException, DependencyException): logger.info( 'Cannot query order for %s due to %s', trade, traceback.format_exc()) continue ordertime = arrow.get(order['datetime']).datetime # Check if trade is still actually open if float(order['remaining']) == 0.0: self.wallets.update() continue # Handle cancelled on exchange if order['status'] == 'canceled': if order['side'] == 'buy': self.handle_buy_order_full_cancel(trade, "canceled on Exchange") elif order['side'] == 'sell': self.handle_timedout_limit_sell(trade, order) self.wallets.update() # Check if order is still actually open elif order['status'] == 'open': if order['side'] == 'buy' and ordertime < buy_timeoutthreashold: self.handle_timedout_limit_buy(trade, order) self.wallets.update() elif order['side'] == 'sell' and ordertime < sell_timeoutthreashold: self.handle_timedout_limit_sell(trade, order) self.wallets.update() def handle_buy_order_full_cancel(self, trade: Trade, reason: str) -> None: """Close trade in database and send message""" Trade.session.delete(trade) Trade.session.flush() logger.info('Buy order %s for %s.', reason, trade) self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'Unfilled buy order for {trade.pair} {reason}' }) def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool: """Buy timeout - cancel order :return: True if order was fully cancelled """ self.exchange.cancel_order(trade.open_order_id, trade.pair) if order['remaining'] == order['amount']: # if trade is not partially completed, just delete the trade self.handle_buy_order_full_cancel(trade, "cancelled due to timeout") return True # if trade is partially complete, edit the stake details for the trade # and close the order trade.amount = order['amount'] - order['remaining'] trade.stake_amount = trade.amount * trade.open_rate trade.open_order_id = None logger.info('Partial buy order timeout for %s.', trade) self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'Remaining buy order for {trade.pair} cancelled due to timeout' }) return False def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool: """ Sell timeout - cancel order and update trade :return: True if order was fully cancelled """ if order['remaining'] == order['amount']: # if trade is not partially completed, just cancel the trade if order["status"] != "canceled": reason = "due to timeout" self.exchange.cancel_order(trade.open_order_id, trade.pair) logger.info('Sell order timeout for %s.', trade) else: reason = "on exchange" logger.info('Sell order canceled on exchange for %s.', trade) trade.close_rate = None trade.close_profit = None trade.close_date = None trade.is_open = True trade.open_order_id = None self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'Unfilled sell order for {trade.pair} cancelled {reason}' }) return True # TODO: figure out how to handle partially complete sell orders return False def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None: """ Executes a limit sell for the given trade and limit :param trade: Trade instance :param limit: limit rate for the sell order :param sellreason: Reason the sell was triggered :return: None """ sell_type = 'sell' if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS): sell_type = 'stoploss' # if stoploss is on exchange and we are on dry_run mode, # we consider the sell price stop price if self.config.get('dry_run', False) and sell_type == 'stoploss' \ and self.strategy.order_types['stoploss_on_exchange']: limit = trade.stop_loss # First cancelling stoploss on exchange ... if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id: try: self.exchange.cancel_order(trade.stoploss_order_id, trade.pair) except InvalidOrderException: logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}") # Execute sell and update trade record order_id = self.exchange.sell(pair=str(trade.pair), ordertype=self.strategy.order_types[sell_type], amount=trade.amount, rate=limit, time_in_force=self.strategy.order_time_in_force['sell'] )['id'] trade.open_order_id = order_id trade.close_rate_requested = limit trade.sell_reason = sell_reason.value Trade.session.flush() self.notify_sell(trade) def notify_sell(self, trade: Trade): """ Sends rpc notification when a sell occured. """ profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested profit_trade = trade.calc_profit(rate=profit_rate) # Use cached ticker here - it was updated seconds ago. current_rate = self.get_sell_rate(trade.pair, False) profit_percent = trade.calc_profit_percent(profit_rate) gain = "profit" if profit_percent > 0 else "loss" msg = { 'type': RPCMessageType.SELL_NOTIFICATION, 'exchange': trade.exchange.capitalize(), 'pair': trade.pair, 'gain': gain, 'limit': trade.close_rate_requested, 'order_type': self.strategy.order_types['sell'], 'amount': trade.amount, 'open_rate': trade.open_rate, 'current_rate': current_rate, 'profit_amount': profit_trade, 'profit_percent': profit_percent, 'sell_reason': trade.sell_reason } # For regular case, when the configuration exists if 'stake_currency' in self.config and 'fiat_display_currency' in self.config: stake_currency = self.config['stake_currency'] fiat_currency = self.config['fiat_display_currency'] msg.update({ 'stake_currency': stake_currency, 'fiat_currency': fiat_currency, }) # Send the message self.rpc.send_msg(msg)
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 # 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.strategylist: List[IStrategy] = [] exchange_name = self.config.get('exchange', {}).get('name', 'bittrex').title() self.exchange = ExchangeResolver(exchange_name, self.config).exchange 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): # Force one interval self.ticker_interval = str(self.config.get('ticker_interval')) self.ticker_interval_mins = timeframe_to_minutes( self.ticker_interval) for strat in list(self.config['strategy_list']): stratconf = deepcopy(self.config) stratconf['strategy'] = strat self.strategylist.append(StrategyResolver(stratconf).strategy) else: # only one strategy self.strategylist.append(StrategyResolver(self.config).strategy) # Load one strategy self._set_strategy(self.strategylist[0]) def _set_strategy(self, strategy): """ Load strategy into backtesting """ self.strategy = strategy self.ticker_interval = self.config.get('ticker_interval') self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval) self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe self.advise_buy = strategy.advise_buy self.advise_sell = strategy.advise_sell # 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 _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, # type: ignore floatfmt=floatfmt, 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 _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, # type: ignore floatfmt=floatfmt, tablefmt="pipe") def _store_backtest_result(self, recordfilename: str, 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 recname = Path(recordfilename) recordfilename = str( Path.joinpath( recname.parent, f'{recname.stem}-{strategyname}').with_suffix( recname.suffix)) logger.info('Dumping backtest results to %s', 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['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[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, 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, 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): # get next entry in min_roi > to trade duration # Interface.py skips on trade_duration <= duration roi_entry = max( list( filter(lambda x: trade_dur >= x, self.strategy.minimal_roi.keys()))) roi = self.strategy.minimal_roi[roi_entry] # - (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) 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] 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 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 """ processed = args['processed'] 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.ticker_interval_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 by `validate_backtest_data` 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 trade_entry = self._get_sell_trade_entry( pair, row, ticker[pair][indexes[pair]:], trade_count_lock, args) if trade_entry: 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.ticker_interval_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] = {} 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_latest_ohlcv([(pair, self.ticker_interval) for pair in pairs]) data = { key[0]: value for key, value in self.exchange._klines.items() } 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 = history.load_data(datadir=Path(self.config['datadir']) if self.config.get('datadir') else None, pairs=pairs, ticker_interval=self.ticker_interval, refresh_pairs=self.config.get( 'refresh_pairs', False), exchange=self.exchange, timerange=timerange) 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 all_results = {} for strat in self.strategylist: logger.info("Running backtesting for Strategy %s", strat.get_strategy_name()) self._set_strategy(strat) min_date, max_date = optimize.get_timeframe(data) # Validate dataframe for missing values (mainly at start and end, as fillup is called) optimize.validate_backtest_data( data, min_date, max_date, timeframe_to_minutes(self.ticker_interval)) logger.info('Measuring data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days) # need to reprocess data every time to populate signals preprocessed = self.strategy.tickerdata_to_dataframe(data) # 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( 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')