mirror of
https://github.com/freqtrade/freqtrade.git
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232 lines
9.2 KiB
Python
232 lines
9.2 KiB
Python
# pragma pylint: disable=missing-docstring,W0212
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import logging
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from typing import Dict, Tuple
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import arrow
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from pandas import DataFrame, Series
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from tabulate import tabulate
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import freqtrade.misc as misc
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import freqtrade.optimize as optimize
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from freqtrade import exchange
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from freqtrade.analyze import populate_buy_trend, populate_sell_trend
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from freqtrade.exchange import Bittrex
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from freqtrade.main import should_sell
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from freqtrade.persistence import Trade
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from freqtrade.strategy.strategy import Strategy
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logger = logging.getLogger(__name__)
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def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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Get the maximum timeframe for the given backtest data
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:param data: dictionary with preprocessed backtesting data
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:return: tuple containing min_date, max_date
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"""
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all_dates = Series([])
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for pair_data in data.values():
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all_dates = all_dates.append(pair_data['date'])
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all_dates.sort_values(inplace=True)
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return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])
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def generate_text_table(
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data: Dict[str, Dict], results: DataFrame, stake_currency) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:return: pretty printed table with tabulate as str
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"""
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floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
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tabular_data = []
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headers = ['pair', 'buy count', 'avg profit %',
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'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
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for pair in data:
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result = results[results.currency == pair]
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tabular_data.append([
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pair,
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len(result.index),
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result.profit_percent.mean() * 100.0,
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result.profit_BTC.sum(),
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result.duration.mean(),
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len(result[result.profit_BTC > 0]),
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len(result[result.profit_BTC < 0])
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])
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# Append Total
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tabular_data.append([
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'TOTAL',
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len(results.index),
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results.profit_percent.mean() * 100.0,
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results.profit_BTC.sum(),
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results.duration.mean(),
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len(results[results.profit_BTC > 0]),
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len(results[results.profit_BTC < 0])
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])
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return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
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def get_sell_trade_entry(pair, buy_row, partial_ticker, trade_count_lock, args):
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stake_amount = args['stake_amount']
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max_open_trades = args.get('max_open_trades', 0)
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trade = Trade(open_rate=buy_row.close,
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open_date=buy_row.date,
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stake_amount=stake_amount,
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amount=stake_amount / buy_row.open,
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fee=exchange.get_fee()
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)
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# calculate win/lose forwards from buy point
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for sell_row in partial_ticker:
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if max_open_trades > 0:
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# Increase trade_count_lock for every iteration
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trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
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buy_signal = sell_row.buy
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if should_sell(trade, sell_row.close, sell_row.date, buy_signal, sell_row.sell):
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return sell_row, (pair,
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trade.calc_profit_percent(rate=sell_row.close),
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trade.calc_profit(rate=sell_row.close),
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(sell_row.date - buy_row.date).seconds // 60
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), sell_row.date
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return None
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def backtest(args) -> DataFrame:
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"""
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Implements backtesting functionality
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:param args: a dict containing:
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stake_amount: btc amount to use for each trade
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processed: a processed dictionary with format {pair, data}
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max_open_trades: maximum number of concurrent trades (default: 0, disabled)
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realistic: do we try to simulate realistic trades? (default: True)
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sell_profit_only: sell if profit only
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use_sell_signal: act on sell-signal
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:return: DataFrame
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"""
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headers = ['date', 'buy', 'open', 'close', 'sell']
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processed = args['processed']
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max_open_trades = args.get('max_open_trades', 0)
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realistic = args.get('realistic', False)
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record = args.get('record', None)
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records = []
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trades = []
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trade_count_lock: dict = {}
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exchange._API = Bittrex({'key': '', 'secret': ''})
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for pair, pair_data in processed.items():
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = populate_sell_trend(populate_buy_trend(pair_data))[headers]
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ticker = [x for x in ticker_data.itertuples()]
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lock_pair_until = None
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for index, row in enumerate(ticker):
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if row.buy == 0 or row.sell == 1:
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continue # skip rows where no buy signal or that would immediately sell off
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if realistic:
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if lock_pair_until is not None and row.date <= lock_pair_until:
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continue
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if max_open_trades > 0:
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# Check if max_open_trades has already been reached for the given date
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if not trade_count_lock.get(row.date, 0) < max_open_trades:
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continue
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trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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ret = get_sell_trade_entry(pair, row, ticker[index+1:], trade_count_lock, args)
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if ret:
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row2, trade_entry, next_date = ret
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lock_pair_until = next_date
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trades.append(trade_entry)
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if record:
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# Note, need to be json.dump friendly
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# record a tuple of pair, current_profit_percent,
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# entry-date, duration
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records.append((pair, trade_entry[1],
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row.date.strftime('%s'),
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row2.date.strftime('%s'),
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row.date, trade_entry[3]))
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# For now export inside backtest(), maybe change so that backtest()
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# returns a tuple like: (dataframe, records, logs, etc)
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if record and record.find('trades') >= 0:
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logger.info('Dumping backtest results')
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misc.file_dump_json('backtest-result.json', records)
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labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
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return DataFrame.from_records(trades, columns=labels)
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def start(args):
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# Initialize logger
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logging.basicConfig(
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level=args.loglevel,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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)
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exchange._API = Bittrex({'key': '', 'secret': ''})
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logger.info('Using config: %s ...', args.config)
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config = misc.load_config(args.config)
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# If -i/--ticker-interval is use we override the configuration parameter
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# (that will override the strategy configuration)
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if args.ticker_interval:
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config.update({'ticker_interval': args.ticker_interval})
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# init the strategy to use
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config.update({'strategy': args.strategy})
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strategy = Strategy()
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strategy.init(config)
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logger.info('Using ticker_interval: %d ...', strategy.ticker_interval)
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data = {}
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pairs = config['exchange']['pair_whitelist']
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logger.info('Using stake_currency: %s ...', config['stake_currency'])
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logger.info('Using stake_amount: %s ...', config['stake_amount'])
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if args.live:
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logger.info('Downloading data for all pairs in whitelist ...')
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for pair in pairs:
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data[pair] = exchange.get_ticker_history(pair, strategy.ticker_interval)
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else:
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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timerange = misc.parse_timerange(args.timerange)
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data = optimize.load_data(args.datadir,
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pairs=pairs,
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ticker_interval=strategy.ticker_interval,
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refresh_pairs=args.refresh_pairs,
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timerange=timerange)
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max_open_trades = 0
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if args.realistic_simulation:
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logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
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max_open_trades = config['max_open_trades']
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# Monkey patch config
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from freqtrade import main
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main._CONF = config
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preprocessed = optimize.tickerdata_to_dataframe(data)
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# Print timeframe
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min_date, max_date = get_timeframe(preprocessed)
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logger.info('Measuring data from %s up to %s (%s days)..',
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min_date.isoformat(),
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max_date.isoformat(),
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(max_date-min_date).days)
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# Execute backtest and print results
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sell_profit_only = config.get('experimental', {}).get('sell_profit_only', False)
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use_sell_signal = config.get('experimental', {}).get('use_sell_signal', False)
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results = backtest({'stake_amount': config['stake_amount'],
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'processed': preprocessed,
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'max_open_trades': max_open_trades,
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'realistic': args.realistic_simulation,
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'sell_profit_only': sell_profit_only,
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'use_sell_signal': use_sell_signal,
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'record': args.export
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})
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logger.info(
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'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
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generate_text_table(data, results, config['stake_currency'])
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)
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