# 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.main import should_sell from freqtrade.persistence import Trade from freqtrade.strategy.strategy import Strategy 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_data in data.values(): 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, len(result[result.profit_BTC > 0]), len(result[result.profit_BTC < 0]) ]) # Append Total tabular_data.append([ 'TOTAL', len(results.index), results.profit_percent.mean() * 100.0, results.profit_BTC.sum(), results.duration.mean() * ticker_interval, len(results[results.profit_BTC > 0]), len(results[results.profit_BTC < 0]) ]) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt) def get_sell_trade_entry(pair, row, buy_subset, ticker, trade_count_lock, args): stake_amount = args['stake_amount'] max_open_trades = args.get('max_open_trades', 0) 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[ticker.date > row.date][['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 buy_signal = buy_subset[buy_subset.date == row2.date].empty if(should_sell(trade, row2.close, row2.date, buy_signal, row2.sell)): return row2, (pair, trade.calc_profit_percent(rate=row2.close), trade.calc_profit(rate=row2.close), row2.Index - row.Index ), row2.date return None def backtest(args) -> DataFrame: """ Implements backtesting functionality :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) sell_profit_only: sell if profit only use_sell_signal: act on sell-signal stoploss: use stoploss :return: DataFrame """ processed = args['processed'] max_open_trades = args.get('max_open_trades', 0) realistic = args.get('realistic', True) record = args.get('record', None) records = [] 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 headers = ['buy', 'open', 'close', 'date', 'sell'] buy_subset = ticker[(ticker.buy == 1) & (ticker.sell == 0)][headers] for row in buy_subset.itertuples(index=True): 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 if max_open_trades > 0: # Increase lock trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1 ret = get_sell_trade_entry(pair, row, buy_subset, ticker, trade_count_lock, args) if ret: row2, trade_entry, next_date = ret lock_pair_until = next_date trades.append(trade_entry) if record: # Note, need to be json.dump friendly # record a tuple of pair, current_profit_percent, # entry-date, duration records.append((pair, trade_entry[1], row.date.strftime('%s'), row2.date.strftime('%s'), row.Index, trade_entry[3])) # For now export inside backtest(), maybe change so that backtest() # returns a tuple like: (dataframe, records, logs, etc) if record and record.find('trades') >= 0: logger.info('Dumping backtest results') misc.file_dump_json('backtest-result.json', records) labels = ['currency', 'profit_percent', 'profit_BTC', 'duration'] 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) ticker_interval = config.get('ticker_interval', args.ticker_interval) logger.info('Using ticker_interval: %s ...', 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, 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']) timerange = misc.parse_timerange(args.timerange) data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval, refresh_pairs=args.refresh_pairs, timerange=timerange) 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'] # init the strategy to use config.update({'strategy': args.strategy}) strategy = Strategy() strategy.init(config) # Monkey patch config from freqtrade import main main._CONF = config preprocessed = optimize.tickerdata_to_dataframe(data) # Print timeframe min_date, max_date = 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 sell_profit_only = config.get('experimental', {}).get('sell_profit_only', False) use_sell_signal = config.get('experimental', {}).get('use_sell_signal', False) results = backtest({'stake_amount': config['stake_amount'], 'processed': preprocessed, 'max_open_trades': max_open_trades, 'realistic': args.realistic_simulation, 'sell_profit_only': sell_profit_only, 'use_sell_signal': use_sell_signal, 'stoploss': strategy.stoploss, 'record': args.export }) logger.info( '\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa generate_text_table(data, results, config['stake_currency'], args.ticker_interval) )