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] = [] if self.config.get('strategy_list', None): # Force one interval self.ticker_interval = str(self.config.get('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 strat = StrategyResolver(self.config).strategy self.strategylist.append(StrategyResolver(self.config).strategy) # Load one strategy self._set_strategy(self.strategylist[0]) self.exchange = Exchange(self.config) self.fee = self.exchange.get_fee() def _set_strategy(self, strategy): """ Load strategy into backtesting """ self.strategy = strategy self.ticker_interval = self.config.get('ticker_interval') self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe self.advise_buy = strategy.advise_buy self.advise_sell = strategy.advise_sell @staticmethod def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]: """ Get the maximum timeframe for the given backtest data :param data: dictionary with preprocessed backtesting data :return: tuple containing min_date, max_date """ timeframe = [ (arrow.get(frame['date'].min()), arrow.get(frame['date'].max())) for frame in data.values() ] return min(timeframe, key=operator.itemgetter(0))[0], \ max(timeframe, key=operator.itemgetter(1))[1] def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :return: pretty printed table with tabulate as str """ stake_currency = str(self.config.get('stake_currency')) floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f') tabular_data = [] headers = ['pair', 'buy count', 'avg profit %', 'cum profit %', 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'] for pair in data: result = results[results.pair == pair] tabular_data.append([ pair, len(result.index), result.profit_percent.mean() * 100.0, result.profit_percent.sum() * 100.0, result.profit_abs.sum(), str(timedelta( minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00', len(result[result.profit_abs > 0]), len(result[result.profit_abs < 0]) ]) # Append Total tabular_data.append([ 'TOTAL', len(results.index), results.profit_percent.mean() * 100.0, results.profit_percent.sum() * 100.0, results.profit_abs.sum(), str(timedelta( minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00', len(results[results.profit_abs > 0]), len(results[results.profit_abs < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") def _generate_text_table_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')) floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f') tabular_data = [] headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %', 'total profit ' + stake_currency, '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(), str(timedelta( minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00', len(results[results.profit_abs > 0]), len(results[results.profit_abs < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") def _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_sell_trade_entry( self, pair: str, buy_row: DataFrame, partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]: stake_amount = args['stake_amount'] max_open_trades = args.get('max_open_trades', 0) trade = Trade( open_rate=buy_row.open, open_date=buy_row.date, stake_amount=stake_amount, amount=stake_amount / buy_row.open, fee_open=self.fee, fee_close=self.fee ) # calculate win/lose forwards from buy point for sell_row in partial_ticker: if max_open_trades > 0: # Increase trade_count_lock for every iteration trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1 buy_signal = sell_row.buy sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal, sell_row.sell) if sell.sell_flag: return BacktestResult(pair=pair, profit_percent=trade.calc_profit_percent(rate=sell_row.open), profit_abs=trade.calc_profit(rate=sell_row.open), open_time=buy_row.date, close_time=sell_row.date, trade_duration=int(( sell_row.date - buy_row.date).total_seconds() // 60), open_index=buy_row.Index, close_index=sell_row.Index, open_at_end=False, open_rate=buy_row.open, close_rate=sell_row.open, sell_reason=sell.sell_type ) if partial_ticker: # no sell condition found - trade stil open at end of backtest period sell_row = partial_ticker[-1] btr = BacktestResult(pair=pair, profit_percent=trade.calc_profit_percent(rate=sell_row.open), profit_abs=trade.calc_profit(rate=sell_row.open), open_time=buy_row.date, close_time=sell_row.date, trade_duration=int(( sell_row.date - buy_row.date).total_seconds() // 60), open_index=buy_row.Index, close_index=sell_row.Index, open_at_end=True, open_rate=buy_row.open, close_rate=sell_row.open, sell_reason=SellType.FORCE_SELL ) logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair, btr.profit_percent, btr.profit_abs) return btr return None def 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 """ headers = ['date', 'buy', 'open', 'close', 'sell'] processed = args['processed'] max_open_trades = args.get('max_open_trades', 0) position_stacking = args.get('position_stacking', False) trades = [] trade_count_lock: Dict = {} for pair, pair_data in processed.items(): pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run ticker_data = self.advise_sell( self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy() # to avoid using data from future, we buy/sell with signal from previous candle ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1) ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1) ticker_data.drop(ticker_data.head(1).index, inplace=True) # Convert from Pandas to list for performance reasons # (Looping Pandas is slow.) ticker = [x for x in ticker_data.itertuples()] lock_pair_until = None for index, row in enumerate(ticker): if row.buy == 0 or row.sell == 1: continue # skip rows where no buy signal or that would immediately sell off if not position_stacking: if lock_pair_until is not None and row.date <= lock_pair_until: continue if max_open_trades > 0: # Check if max_open_trades has already been reached for the given date if not trade_count_lock.get(row.date, 0) < max_open_trades: continue trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1 trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:], trade_count_lock, args) if trade_entry: lock_pair_until = trade_entry.close_time trades.append(trade_entry) else: # Set lock_pair_until to end of testing period if trade could not be closed # This happens only if the buy-signal was with the last candle lock_pair_until = ticker_data.iloc[-1].date return DataFrame.from_records(trades, columns=BacktestResult._fields) def start(self) -> None: """ Run a backtesting end-to-end :return: None """ data = {} pairs = self.config['exchange']['pair_whitelist'] logger.info('Using stake_currency: %s ...', self.config['stake_currency']) logger.info('Using stake_amount: %s ...', self.config['stake_amount']) if self.config.get('live'): logger.info('Downloading data for all pairs in whitelist ...') for pair in pairs: data[pair] = self.exchange.get_candle_history(pair, self.ticker_interval) else: logger.info('Using local backtesting data (using whitelist in given config) ...') timerange = Arguments.parse_timerange(None if self.config.get( 'timerange') is None else str(self.config.get('timerange'))) data = optimize.load_data( self.config['datadir'], pairs=pairs, ticker_interval=self.ticker_interval, refresh_pairs=self.config.get('refresh_pairs', False), 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) # need to reprocess data every time to populate signals preprocessed = self.tickerdata_to_dataframe(data) # Print timeframe min_date, max_date = self.get_timeframe(preprocessed) logger.info( 'Measuring data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days ) # Execute backtest and print results 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), } ) 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(119, '=')) print(self._generate_text_table(data, results)) print(' SELL REASON STATS '.center(119, '=')) print(self._generate_text_table_sell_reason(data, results)) print(' LEFT OPEN TRADES REPORT '.center(119, '=')) print(self._generate_text_table(data, results.loc[results.open_at_end])) print() if len(all_results) > 1: # Print Strategy summary table print(' Strategy Summary '.center(119, '=')) 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 object the bot need to work :param config: configuration dict, you can use the Configuration.get_config() method to get the config dict. """ logger.info( 'Starting freqtrade %s', __version__, ) # Init bot states self.state = State.STOPPED # Init objects self.config = config self.strategy: IStrategy = StrategyResolver(self.config).strategy self.rpc: RPCManager = RPCManager(self) self.persistence = None self.exchange = Exchange(self.config) self._init_modules() def _init_modules(self) -> None: """ Initializes all modules and updates the config :return: None """ # Initialize all modules persistence.init(self.config) # Set initial application state initial_state = self.config.get('initial_state') if initial_state: self.state = State[initial_state.upper()] else: self.state = 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 worker(self, old_state: State = None) -> State: """ Trading routine that must be run at each loop :param old_state: the previous service state from the previous call :return: current service state """ # Log state transition state = self.state if state != old_state: self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'{state.name.lower()}' }) logger.info('Changing state to: %s', state.name) if state == State.RUNNING: self._startup_messages() if state == State.STOPPED: time.sleep(1) elif state == State.RUNNING: min_secs = self.config.get('internals', {}).get('process_throttle_secs', constants.PROCESS_THROTTLE_SECS) nb_assets = self.config.get('dynamic_whitelist', None) self._throttle(func=self._process, min_secs=min_secs, nb_assets=nb_assets) return state def _startup_messages(self) -> None: if self.config.get('dry_run', False): self.rpc.send_msg({ 'type': RPCMessageType.WARNING_NOTIFICATION, 'status': 'Dry run is enabled. All trades are simulated.' }) stake_currency = self.config['stake_currency'] stake_amount = self.config['stake_amount'] minimal_roi = self.config['minimal_roi'] ticker_interval = self.config['ticker_interval'] exchange_name = self.config['exchange']['name'] strategy_name = self.config.get('strategy', '') self.rpc.send_msg({ 'type': RPCMessageType.CUSTOM_NOTIFICATION, 'status': f'*Exchange:* `{exchange_name}`\n' f'*Stake per trade:* `{stake_amount} {stake_currency}`\n' f'*Minimum ROI:* `{minimal_roi}`\n' f'*Ticker Interval:* `{ticker_interval}`\n' f'*Strategy:* `{strategy_name}`' }) if self.config.get('dynamic_whitelist', False): top_pairs = 'top ' + str(self.config.get('dynamic_whitelist', 20)) specific_pairs = '' else: top_pairs = 'whitelisted' specific_pairs = '\n' + ', '.join(self.config['exchange'].get( 'pair_whitelist', '')) self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'Searching for {top_pairs} {stake_currency} pairs to buy and sell...' f'{specific_pairs}' }) def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any: """ Throttles the given callable that it takes at least `min_secs` to finish execution. :param func: Any callable :param min_secs: minimum execution time in seconds :return: Any """ start = time.time() result = func(*args, **kwargs) end = time.time() duration = max(min_secs - (end - start), 0.0) logger.debug('Throttling %s for %.2f seconds', func.__name__, duration) time.sleep(duration) return result def _process(self, nb_assets: Optional[int] = 0) -> bool: """ Queries the persistence layer for open trades and handles them, otherwise a new trade is created. :param: nb_assets: the maximum number of pairs to be traded at the same time :return: True if one or more trades has been created or closed, False otherwise """ state_changed = False try: # Refresh whitelist based on wallet maintenance sanitized_list = self._refresh_whitelist( self._gen_pair_whitelist(self.config['stake_currency']) if nb_assets else self.config['exchange']['pair_whitelist']) # Keep only the subsets of pairs wanted (up to nb_assets) final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list self.config['exchange']['pair_whitelist'] = final_list # Query trades from persistence layer trades = Trade.query.filter(Trade.is_open.is_(True)).all() # 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() except TemporaryError as error: logger.warning('%s, retrying in 30 seconds...', error) time.sleep(constants.RETRY_TIMEOUT) except OperationalException: tb = traceback.format_exc() hint = 'Issue `/start` if you think it is safe to restart.' self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'OperationalException:\n```\n{tb}```{hint}' }) logger.exception('OperationalException. Stopping trader ...') self.state = State.STOPPED return state_changed @cached(TTLCache(maxsize=1, ttl=1800)) def _gen_pair_whitelist(self, base_currency: str, key: str = 'quoteVolume') -> List[str]: """ Updates the whitelist with with a dynamically generated list :param base_currency: base currency as str :param key: sort key (defaults to 'quoteVolume') :return: List of pairs """ if not self.exchange.exchange_has('fetchTickers'): raise OperationalException( 'Exchange does not support dynamic whitelist.' 'Please edit your config and restart the bot') tickers = self.exchange.get_tickers() # check length so that we make sure that '/' is actually in the string tickers = [ v for k, v in tickers.items() if len(k.split('/')) == 2 and k.split('/')[1] == base_currency ] sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key]) pairs = [s['symbol'] for s in sorted_tickers] return pairs def _refresh_whitelist(self, whitelist: List[str]) -> List[str]: """ Check available markets and remove pair from whitelist if necessary :param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to trade :return: the list of pairs the user wants to trade without the one unavailable or black_listed """ sanitized_whitelist = whitelist markets = self.exchange.get_markets() markets = [ m for m in markets if m['quote'] == self.config['stake_currency'] ] known_pairs = set() for market in markets: pair = market['symbol'] # pair is not int the generated dynamic market, or in the blacklist ... ignore it if pair not in whitelist or pair in self.config['exchange'].get( 'pair_blacklist', []): continue # else the pair is valid known_pairs.add(pair) # Market is not active if not market['active']: sanitized_whitelist.remove(pair) logger.info( 'Ignoring %s from whitelist. Market is not active.', pair) # We need to remove pairs that are unknown final_list = [x for x in sanitized_whitelist if x in known_pairs] return final_list def get_target_bid(self, ticker: Dict[str, float]) -> float: """ Calculates bid target between current ask price and last price :param ticker: Ticker to use for getting Ask and Last Price :return: float: Price """ if ticker['ask'] < ticker['last']: return ticker['ask'] balance = self.config['bid_strategy']['ask_last_balance'] return ticker['ask'] + balance * (ticker['last'] - ticker['ask']) def _get_trade_stake_amount(self) -> Optional[float]: """ Check if stake amount can be fulfilled with the available balance for the stake currency :return: float: Stake Amount """ stake_amount = self.config['stake_amount'] avaliable_amount = self.exchange.get_balance( self.config['stake_currency']) if stake_amount == constants.UNLIMITED_STAKE_AMOUNT: open_trades = len( Trade.query.filter(Trade.is_open.is_(True)).all()) 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 avaliable_amount / (self.config['max_open_trades'] - open_trades) # Check if stake_amount is fulfilled if avaliable_amount < stake_amount: raise DependencyException( 'Available balance(%f %s) is lower than stake amount(%f %s)' % (avaliable_amount, self.config['stake_currency'], stake_amount, self.config['stake_currency'])) return stake_amount def _get_min_pair_stake_amount(self, pair: str, price: float) -> Optional[float]: markets = self.exchange.get_markets() markets = [m for m in markets if m['symbol'] == pair] if not markets: raise ValueError( f'Can\'t get market information for symbol {pair}') market = markets[0] 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 amount_reserve_percent = 1 - 0.05 # reserve 5% + stoploss 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 stake_amount = self._get_trade_stake_amount() if not stake_amount: return False logger.info( 'Checking buy signals to create a new trade with stake_amount: %f ...', stake_amount) whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist']) # Remove currently opened and latest pairs from whitelist for trade in Trade.query.filter(Trade.is_open.is_(True)).all(): if trade.pair in whitelist: whitelist.remove(trade.pair) logger.debug('Ignoring %s in pair whitelist', trade.pair) if not whitelist: raise DependencyException('No currency pairs in whitelist') # Pick pair based on buy signals for _pair in whitelist: logger.info('Checking buy signals: %s ', _pair) #thistory = self.exchange.get_candle_history(_pair, interval) #(buy, sell) = self.strategy.get_signal(_pair, interval, thistory) (buy, sell) = (False, False) (buy, sell) = self.exchange.get_indicators(_pair) #print(buy, sell) if buy and not sell: return self.execute_buy(_pair, stake_amount) return False def execute_buy(self, pair: str, stake_amount: float) -> bool: """ Executes a limit buy for the given pair :param pair: pair for which we want to create a LIMIT_BUY :return: None """ order_id = 0 pair_s = pair.replace('_', '/') pair_url = self.exchange.get_pair_detail_url(pair) stake_currency = self.config['stake_currency'] fiat_currency = self.config.get('fiat_display_currency', None) # Calculate amount buy_limit = self.get_target_bid(self.exchange.get_ticker(pair)) min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit) 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 order_id = self.exchange.buy(pair, buy_limit, amount)['orderId'] if order_id != 0: self.rpc.send_msg({ 'type': RPCMessageType.BUY_NOTIFICATION, 'exchange': self.exchange.name.capitalize(), 'pair': pair_s, 'market_url': pair_url, 'limit': buy_limit, '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') #!!!currency = pair[pair.find('/')+1:] #balances = self.exchange.get_balances() #amount = balances.get(currency) trade = Trade(pair=pair, stake_amount=stake_amount, amount=amount, fee_open=fee, fee_close=fee, open_rate=buy_limit, open_rate_requested=buy_limit, open_date=datetime.utcnow(), exchange=self.exchange.id, open_order_id=order_id, strategy=self.strategy.get_strategy_name(), ticker_interval=constants.TICKER_INTERVAL_MINUTES[ self.config['ticker_interval']]) Trade.session.add(trade) Trade.session.flush() 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: # 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 = 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) if trade.is_open and trade.open_order_id is None: # Check if we can sell our current pair return self.handle_trade(trade) 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 self.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} \ (from {order_amount} to {real_amount}) from Trades""") return real_amount 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 """ getcontext().prec = 8 if not trade.is_open: raise ValueError(f'attempt to handle closed trade: {trade}') logger.debug('Handling %s ...', trade) cur_rate_dec = self.exchange.get_ticker(trade.pair)['bid'] current_rate = cur_rate_dec (buy, sell) = (False, False) experimental = self.config.get('experimental', {}) if experimental.get('use_sell_signal') or experimental.get( 'ignore_roi_if_buy_signal'): #ticker = self.exchange.get_candle_history(trade.pair, self.strategy.ticker_interval) #(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.ticker_interval, ticker) (buy, sell) = (False, False) (buy, sell) = self.exchange.get_indicators(trade.pair) should_sell = self.strategy.should_sell(trade, current_rate, datetime.utcnow(), buy, sell) if should_sell.sell_flag: self.execute_sell(trade, current_rate, should_sell.sell_type) print(trade, current_rate, should_sell.sell_type) return True logger.info( '%s Found no sell signals for whitelisted currencies. Trying again..', trade.pair) 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 requests.exceptions.RequestException: 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 int(order['remaining']) == 0: continue # Check if trade is still actually open if order['status'] == 'open': if order['side'] == 'buy' and ordertime < buy_timeoutthreashold: self.handle_timedout_limit_buy(trade, order) elif order[ 'side'] == 'sell' and ordertime < sell_timeoutthreashold: self.handle_timedout_limit_sell(trade, order) # FIX: 20180110, why is cancel.order unconditionally here, whereas # it is conditionally called in the # handle_timedout_limit_sell()? def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool: """Buy timeout - cancel order :return: True if order was fully cancelled """ pair_s = trade.pair.replace('_', '/') 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 Trade.session.delete(trade) Trade.session.flush() logger.info('Buy order timeout for %s.', trade) self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'Unfilled buy order for {pair_s} 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 {pair_s} cancelled due to timeout' }) return False # FIX: 20180110, should cancel_order() be cond. or unconditionally called? 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 """ pair_s = trade.pair.replace('_', '/') if order['remaining'] == order['amount']: # if trade is not partially completed, just cancel the trade self.exchange.cancel_order(trade.open_order_id, trade.pair) 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 {pair_s} cancelled due to timeout' }) logger.info('Sell order timeout for %s.', trade) 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 """ # Execute sell and update trade record order_id = 0 amount, quaselltrade = self.exchange.symbol_amount_prec_str( trade.pair, trade.amount) rate = self.exchange.symbol_price_prec_str(trade.pair, limit) if amount > quaselltrade: amount = quaselltrade if amount > 0: order_id = self.exchange.sell(str(trade.pair), limit, amount)['orderId'] if order_id != 0: print(order_id) trade.open_order_id = order_id trade.close_rate_requested = limit trade.sell_reason = sell_reason.value profit_trade = trade.calc_profit(rate=limit) current_rate = self.exchange.get_ticker(trade.pair)['bid'] profit_percent = trade.calc_profit_percent(limit) pair_url = self.exchange.get_pair_detail_url(trade.pair) gain = "profit" if profit_percent > 0 else "loss" print('execute_sell ') print(trade.pair, limit, current_rate, trade.sell_reason, profit_trade) msg = { 'type': RPCMessageType.SELL_NOTIFICATION, 'exchange': trade.exchange.capitalize(), 'pair': trade.pair, 'gain': gain, 'market_url': pair_url, 'limit': limit, 'amount': trade.amount, 'open_rate': trade.open_rate, 'current_rate': current_rate, 'profit_amount': profit_trade, 'profit_percent': profit_percent, } # 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) Trade.session.flush()
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] = [] if self.config.get('strategy_list', None): # Force one interval self.ticker_interval = str(self.config.get('ticker_interval')) self.ticker_interval_mins = constants.TICKER_INTERVAL_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]) self.exchange = Exchange(self.config) self.fee = self.exchange.get_fee() def _set_strategy(self, strategy): """ Load strategy into backtesting """ self.strategy = strategy self.ticker_interval = self.config.get('ticker_interval') self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[ self.ticker_interval] self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe self.advise_buy = strategy.advise_buy self.advise_sell = strategy.advise_sell 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_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 """ headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high'] 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 = {} ticker: Dict = {} pairs = [] # 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()] pairs.append(pair) lock_pair_until: Dict = {} tmp = start_date + timedelta(minutes=self.ticker_interval_mins) index = 0 # Loop timerange and test per pair while tmp < end_date: # print(f"time: {tmp}") for i, pair in enumerate(ticker): try: row = ticker[pair][index] except IndexError: # missing Data for one pair ... # Warnings for this are shown by `validate_backtest_data` continue if row.buy == 0 or row.sell == 1: continue # skip rows where no buy signal or that would immediately sell off if not position_stacking: if pair in lock_pair_until and row.date <= lock_pair_until[ 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][index + 1:], 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 # This happens only if the buy-signal was with the last candle lock_pair_until[pair] = end_date tmp += timedelta(minutes=self.ticker_interval_mins) index += 1 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, constants.TICKER_INTERVAL_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')
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 states self.state = State.STOPPED # Init objects self.config = config self.strategy: IStrategy = StrategyResolver(self.config).strategy self.rpc: RPCManager = RPCManager(self) self.exchange = Exchange(self.config) self.wallets = Wallets(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'] self._init_modules() def _init_modules(self) -> None: """ Initializes all modules and updates the config :return: None """ # Initialize all modules persistence.init(self.config) # Set initial application state initial_state = self.config.get('initial_state') if initial_state: self.state = State[initial_state.upper()] else: self.state = 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 worker(self, old_state: State = None) -> State: """ Trading routine that must be run at each loop :param old_state: the previous service state from the previous call :return: current service state """ # Log state transition state = self.state if state != old_state: self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'{state.name.lower()}' }) logger.info('Changing state to: %s', state.name) if state == State.RUNNING: self.rpc.startup_messages(self.config, self.pairlists) if state == State.STOPPED: time.sleep(1) elif state == State.RUNNING: min_secs = self.config.get('internals', {}).get('process_throttle_secs', constants.PROCESS_THROTTLE_SECS) self._throttle(func=self._process, min_secs=min_secs) return state def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any: """ Throttles the given callable that it takes at least `min_secs` to finish execution. :param func: Any callable :param min_secs: minimum execution time in seconds :return: Any """ start = time.time() result = func(*args, **kwargs) end = time.time() duration = max(min_secs - (end - start), 0.0) logger.debug('Throttling %s for %.2f seconds', func.__name__, duration) time.sleep(duration) return result 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 try: # Refresh whitelist self.pairlists.refresh_pairlist() self.active_pair_whitelist = self.pairlists.whitelist # Calculating Edge positiong if self.edge: self.edge.calculate() self.active_pair_whitelist = self.edge.adjust( self.active_pair_whitelist) # Query trades from persistence layer trades = Trade.query.filter(Trade.is_open.is_(True)).all() # Extend active-pair whitelist with pairs from open trades # ensures that tickers are downloaded for open trades self.active_pair_whitelist.extend([ trade.pair for trade in trades if trade.pair not in self.active_pair_whitelist ]) # Create pair-whitelist tuple with (pair, ticker_interval) pair_whitelist_tuple = [(pair, self.config['ticker_interval']) for pair in self.active_pair_whitelist] # Refreshing candles self.dataprovider.refresh(pair_whitelist_tuple, 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() except TemporaryError as error: logger.warning( f"Error: {error}, retrying in {constants.RETRY_TIMEOUT} seconds..." ) time.sleep(constants.RETRY_TIMEOUT) except OperationalException: tb = traceback.format_exc() hint = 'Issue `/start` if you think it is safe to restart.' self.rpc.send_msg({ 'type': RPCMessageType.STATUS_NOTIFICATION, 'status': f'OperationalException:\n```\n{tb}```{hint}' }) logger.exception('OperationalException. Stopping trader ...') self.state = State.STOPPED return state_changed def get_target_bid(self, pair: str) -> 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: logger.info('Using Last Ask / Last Price') ticker = self.exchange.get_ticker(pair) 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'] avaliable_amount = self.wallets.get_free(self.config['stake_currency']) if stake_amount == constants.UNLIMITED_STAKE_AMOUNT: open_trades = len( Trade.query.filter(Trade.is_open.is_(True)).all()) 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 avaliable_amount / (self.config['max_open_trades'] - open_trades) # Check if stake_amount is fulfilled if avaliable_amount < stake_amount: raise DependencyException( f"Available balance({avaliable_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]: markets = self.exchange.get_markets() markets = [m for m in markets if m['symbol'] == pair] if not markets: raise ValueError( f'Can\'t get market information for symbol {pair}') market = markets[0] 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) # Remove currently opened and latest pairs from whitelist for trade in Trade.query.filter(Trade.is_open.is_(True)).all(): if trade.pair in whitelist: whitelist.remove(trade.pair) logger.debug('Ignoring %s in pair whitelist', trade.pair) if not whitelist: raise DependencyException('No currency pairs in whitelist') # 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('_', '/') pair_url = self.exchange.get_pair_detail_url(pair) 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 = self.exchange.buy(pair=pair, ordertype=self.strategy.order_types['buy'], 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_type = self.strategy.order_types['buy'] 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'] order_id = None self.rpc.send_msg({ 'type': RPCMessageType.BUY_NOTIFICATION, 'exchange': self.exchange.name.capitalize(), 'pair': pair_s, 'market_url': pair_url, 'limit': buy_limit_filled_price, '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=constants.TICKER_INTERVAL_MINUTES[ self.config['ticker_interval']]) 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: # 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 = 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) 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 self.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 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.exchange.get_ticker(trade.pair)['bid'] 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. """ result = False # If trade is open and the buy order is fulfilled but there is no stoploss, # then we add a stoploss on exchange if not trade.open_order_id and not trade.stoploss_order_id: 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. # 0.99 is arbitrary here. limit_price = stop_price * 0.99 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() # Or the trade open and there is already a stoploss on exchange. # so we check if it is hit ... elif trade.stoploss_order_id: logger.debug('Handling stoploss on exchange %s ...', trade) order = self.exchange.get_order(trade.stoploss_order_id, trade.pair) if order['status'] == 'closed': trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value trade.update(order) result = True elif 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, order) return result 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 ' 'in order to add another one ...') if self.exchange.cancel_order(order['id'], trade.pair): # 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) 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: self.exchange.cancel_order(trade.stoploss_order_id, trade.pair) # 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 profit_trade = trade.calc_profit(rate=limit) current_rate = self.exchange.get_ticker(trade.pair)['bid'] profit_percent = trade.calc_profit_percent(limit) pair_url = self.exchange.get_pair_detail_url(trade.pair) gain = "profit" if profit_percent > 0 else "loss" msg = { 'type': RPCMessageType.SELL_NOTIFICATION, 'exchange': trade.exchange.capitalize(), 'pair': trade.pair, 'gain': gain, 'market_url': pair_url, 'limit': limit, 'amount': trade.amount, 'open_rate': trade.open_rate, 'current_rate': current_rate, 'profit_amount': profit_trade, 'profit_percent': profit_percent, 'sell_reason': sell_reason.value } # 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) Trade.session.flush()
class Backtesting(object): """ Backtesting class, this class contains all the logic to run a backtest To run a backtest: backtesting = Backtesting(config) backtesting.start() """ def __init__(self, config: Dict[str, Any]) -> None: self.config = config self.analyze = Analyze(self.config) self.ticker_interval = self.analyze.strategy.ticker_interval self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe self.populate_buy_trend = self.analyze.populate_buy_trend self.populate_sell_trend = self.analyze.populate_sell_trend # Reset keys for backtesting self.config['exchange']['key'] = '' self.config['exchange']['secret'] = '' self.config['exchange']['password'] = '' self.config['exchange']['uid'] = '' self.config['dry_run'] = True self.exchange = Exchange(self.config) self.fee = self.exchange.get_fee() @staticmethod def get_timeframe( data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]: """ Get the maximum timeframe for the given backtest data :param data: dictionary with preprocessed backtesting data :return: tuple containing min_date, max_date """ timeframe = [(arrow.get(min(frame.date)), arrow.get(max(frame.date))) for frame in data.values()] return min(timeframe, key=operator.itemgetter(0))[0], \ max(timeframe, key=operator.itemgetter(1))[1] def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :return: pretty printed table with tabulate as str """ stake_currency = str(self.config.get('stake_currency')) floatfmt = ('s', 'd', '.2f', '.8f', '.1f') tabular_data = [] headers = [ 'pair', 'buy count', 'avg profit %', 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss' ] for pair in data: result = results[results.pair == pair] tabular_data.append([ pair, len(result.index), result.profit_percent.mean() * 100.0, result.profit_abs.sum(), result.trade_duration.mean(), 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_abs.sum(), results.trade_duration.mean(), len(results[results.profit_abs > 0]), len(results[results.profit_abs < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe") def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None: records = [(trade_entry.pair, trade_entry.profit_percent, trade_entry.open_time.timestamp(), trade_entry.close_time.timestamp(), trade_entry.open_index - 1, trade_entry.trade_duration) for index, trade_entry in results.iterrows()] if records: logger.info('Dumping backtest results to %s', recordfilename) file_dump_json(recordfilename, records) def _get_sell_trade_entry(self, pair: str, buy_row: DataFrame, partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]: stake_amount = args['stake_amount'] max_open_trades = args.get('max_open_trades', 0) trade = Trade(open_rate=buy_row.close, 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 if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal, sell_row.sell): return BacktestResult( pair=pair, profit_percent=trade.calc_profit_percent( rate=sell_row.close), profit_abs=trade.calc_profit(rate=sell_row.close), open_time=buy_row.date, close_time=sell_row.date, trade_duration=(sell_row.date - buy_row.date).seconds // 60, open_index=buy_row.Index, close_index=sell_row.Index, open_at_end=False) 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.close), profit_abs=trade.calc_profit(rate=sell_row.close), open_time=buy_row.date, close_time=sell_row.date, trade_duration=(sell_row.date - buy_row.date).seconds // 60, open_index=buy_row.Index, close_index=sell_row.Index, open_at_end=True) 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) realistic: do we try to simulate realistic trades? (default: True) :return: DataFrame """ headers = ['date', 'buy', 'open', 'close', 'sell'] processed = args['processed'] max_open_trades = args.get('max_open_trades', 0) realistic = args.get('realistic', False) trades = [] trade_count_lock: Dict = {} for pair, pair_data in processed.items(): pair_data['buy'], pair_data[ 'sell'] = 0, 0 # cleanup from previous run ticker_data = self.populate_sell_trend( self.populate_buy_trend(pair_data))[headers].copy() # to avoid using data from future, we buy/sell with signal from previous candle ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1) ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1) ticker_data.drop(ticker_data.head(1).index, inplace=True) # Convert from Pandas to list for performance reasons # (Looping Pandas is slow.) ticker = [x for x in ticker_data.itertuples()] lock_pair_until = None for index, row in enumerate(ticker): if row.buy == 0 or row.sell == 1: continue # skip rows where no buy signal or that would immediately sell off if realistic: if lock_pair_until is not None and row.date <= lock_pair_until: continue if max_open_trades > 0: # Check if max_open_trades has already been reached for the given date if not trade_count_lock.get(row.date, 0) < max_open_trades: continue trade_count_lock[row.date] = trade_count_lock.get( row.date, 0) + 1 trade_entry = self._get_sell_trade_entry( pair, row, ticker[index + 1:], trade_count_lock, args) if trade_entry: lock_pair_until = trade_entry.close_time trades.append(trade_entry) else: # Set lock_pair_until to end of testing period if trade could not be closed # This happens only if the buy-signal was with the last candle lock_pair_until = ticker_data.iloc[-1].date return DataFrame.from_records(trades, columns=BacktestResult._fields) def start(self) -> None: """ Run a backtesting end-to-end :return: None """ data = {} pairs = self.config['exchange']['pair_whitelist'] logger.info('Using stake_currency: %s ...', self.config['stake_currency']) logger.info('Using stake_amount: %s ...', self.config['stake_amount']) if self.config.get('live'): logger.info('Downloading data for all pairs in whitelist ...') for pair in pairs: data[pair] = self.exchange.get_ticker_history( pair, self.ticker_interval) else: logger.info( 'Using local backtesting data (using whitelist in given config) ...' ) timerange = Arguments.parse_timerange(None if self.config.get( 'timerange') is None else str(self.config.get('timerange'))) data = optimize.load_data(self.config['datadir'], pairs=pairs, ticker_interval=self.ticker_interval, refresh_pairs=self.config.get( 'refresh_pairs', False), exchange=self.exchange, timerange=timerange) if not data: logger.critical("No data found. Terminating.") return # Ignore max_open_trades in backtesting, except realistic flag was passed if self.config.get('realistic_simulation', False): max_open_trades = self.config['max_open_trades'] else: logger.info( 'Ignoring max_open_trades (realistic_simulation not set) ...') max_open_trades = 0 preprocessed = self.tickerdata_to_dataframe(data) # Print timeframe min_date, max_date = self.get_timeframe(preprocessed) logger.info('Measuring data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days) # Execute backtest and print results results = self.backtest({ 'stake_amount': self.config.get('stake_amount'), 'processed': preprocessed, 'max_open_trades': max_open_trades, 'realistic': self.config.get('realistic_simulation', False), }) if self.config.get('export', False): self._store_backtest_result(self.config.get('exportfilename'), results) logger.info( '\n======================================== ' 'BACKTESTING REPORT' ' =========================================\n' '%s', self._generate_text_table(data, results)) logger.info( '\n====================================== ' 'LEFT OPEN TRADES REPORT' ' ======================================\n' '%s', self._generate_text_table(data, results.loc[results.open_at_end]))