# pragma pylint: disable=missing-docstring,W0212 import logging from typing import Dict, Tuple import arrow from pandas import DataFrame, Series from tabulate import tabulate import freqtrade.misc as misc import freqtrade.optimize as optimize from freqtrade import exchange from freqtrade.analyze import populate_buy_trend, populate_sell_trend from freqtrade.exchange import Bittrex from freqtrade.main import min_roi_reached from freqtrade.persistence import Trade logger = logging.getLogger(__name__) 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 """ all_dates = Series([]) for pair, pair_data in data.items(): all_dates = all_dates.append(pair_data['date']) all_dates.sort_values(inplace=True) return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1]) def generate_text_table( data: Dict[str, Dict], results: DataFrame, stake_currency, ticker_interval) -> str: """ Generates and returns a text table for the given backtest data and the results dataframe :return: pretty printed table with tabulate as str """ 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.currency == pair] tabular_data.append([ pair, len(result.index), result.profit_percent.mean() * 100.0, result.profit_BTC.sum(), result.duration.mean() * ticker_interval, result.profit.sum(), result.loss.sum() ]) # Append Total tabular_data.append([ 'TOTAL', len(results.index), results.profit_percent.mean() * 100.0, results.profit_BTC.sum(), results.duration.mean() * ticker_interval, results.profit.sum(), results.loss.sum() ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt) def backtest(stake_amount: float, processed: Dict[str, DataFrame], max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False, stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame: """ Implements backtesting functionality :param stake_amount: btc amount to use for each trade :param processed: a processed dictionary with format {pair, data} :param max_open_trades: maximum number of concurrent trades (default: 0, disabled) :param realistic: do we try to simulate realistic trades? (default: True) :return: DataFrame """ trades = [] trade_count_lock: dict = {} exchange._API = Bittrex({'key': '', 'secret': ''}) for pair, pair_data in processed.items(): pair_data['buy'], pair_data['sell'] = 0, 0 ticker = populate_sell_trend(populate_buy_trend(pair_data)) # for each buy point lock_pair_until = None buy_subset = ticker[ticker.buy == 1][['buy', 'open', 'close', 'date', 'sell']] for row in buy_subset.itertuples(index=True): if realistic: if lock_pair_until is not None and row.Index <= 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 if max_open_trades > 0: # Increase lock trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1 trade = Trade( open_rate=row.close, open_date=row.date, stake_amount=stake_amount, amount=stake_amount / row.open, fee=exchange.get_fee() ) # calculate win/lose forwards from buy point sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']] for row2 in sell_subset.itertuples(index=True): if max_open_trades > 0: # Increase trade_count_lock for every iteration trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1 current_profit_percent = trade.calc_profit_percent(rate=row2.close) if (sell_profit_only and current_profit_percent < 0): continue if min_roi_reached(trade, row2.close, row2.date) or \ (row2.sell == 1 and use_sell_signal) or \ current_profit_percent <= stoploss: current_profit_btc = trade.calc_profit(rate=row2.close) lock_pair_until = row2.Index trades.append( ( pair, current_profit_percent, current_profit_btc, row2.Index - row.Index, current_profit_btc > 0, current_profit_btc < 0 ) ) break labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss'] return DataFrame.from_records(trades, columns=labels) def start(args): # Initialize logger logging.basicConfig( level=args.loglevel, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', ) exchange._API = Bittrex({'key': '', 'secret': ''}) logger.info('Using config: %s ...', args.config) config = misc.load_config(args.config) logger.info('Using ticker_interval: %s ...', args.ticker_interval) data = {} pairs = config['exchange']['pair_whitelist'] if args.live: logger.info('Downloading data for all pairs in whitelist ...') for pair in pairs: data[pair] = exchange.get_ticker_history(pair, args.ticker_interval) else: logger.info('Using local backtesting data (using whitelist in given config) ...') logger.info('Using stake_currency: %s ...', config['stake_currency']) logger.info('Using stake_amount: %s ...', config['stake_amount']) data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval, refresh_pairs=args.refresh_pairs) max_open_trades = 0 if args.realistic_simulation: logger.info('Using max_open_trades: %s ...', config['max_open_trades']) max_open_trades = config['max_open_trades'] # Monkey patch config from freqtrade import main main._CONF = config preprocessed = optimize.tickerdata_to_dataframe(data, timeperiod=args.timeperiod) # Print timeframe min_date, max_date = get_timeframe(preprocessed) logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat()) # Execute backtest and print results results = backtest( stake_amount=config['stake_amount'], processed=preprocessed, max_open_trades=max_open_trades, realistic=args.realistic_simulation, sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False), stoploss=config.get('stoploss'), use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False) ) logger.info( '\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa generate_text_table(data, results, config['stake_currency'], args.ticker_interval) )