29fdba7743
* on 2 weeks 5minute data Performs bitwise filter on buy/sell rows in the df before passing to the slower itertuples loop the larger the dataset the greater the performance gain
386 lines
15 KiB
Python
386 lines
15 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
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"""
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This module contains the backtesting logic
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"""
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import logging
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import operator
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from argparse import Namespace
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from datetime import datetime
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple
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import arrow
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from pandas import DataFrame
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from tabulate import tabulate
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import freqtrade.optimize as optimize
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from freqtrade import DependencyException, constants
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from freqtrade.analyze import Analyze
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.exchange import Exchange
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from freqtrade.misc import file_dump_json
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from freqtrade.persistence import Trade
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logger = logging.getLogger(__name__)
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class BacktestResult(NamedTuple):
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"""
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NamedTuple Defining BacktestResults inputs.
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"""
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pair: str
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profit_percent: float
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profit_abs: float
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open_time: datetime
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close_time: datetime
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open_index: int
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close_index: int
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trade_duration: float
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open_at_end: bool
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open_rate: float
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close_rate: float
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class Backtesting(object):
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"""
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Backtesting class, this class contains all the logic to run a backtest
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To run a backtest:
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backtesting = Backtesting(config)
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backtesting.start()
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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self.config = config
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self.analyze = Analyze(self.config)
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self.ticker_interval = self.analyze.strategy.ticker_interval
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self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
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self.populate_buy_trend = self.analyze.populate_buy_trend
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self.populate_sell_trend = self.analyze.populate_sell_trend
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# Reset keys for backtesting
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self.config['exchange']['key'] = ''
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self.config['exchange']['secret'] = ''
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self.config['exchange']['password'] = ''
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self.config['exchange']['uid'] = ''
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self.config['dry_run'] = True
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self.exchange = Exchange(self.config)
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self.fee = self.exchange.get_fee()
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@staticmethod
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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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timeframe = [
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(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
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for frame in data.values()
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]
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return min(timeframe, key=operator.itemgetter(0))[0], \
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max(timeframe, key=operator.itemgetter(1))[1]
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def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> 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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stake_currency = str(self.config.get('stake_currency'))
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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.1f')
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tabular_data = []
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headers = ['pair', 'buy count', 'avg profit %', 'cum 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.pair == 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_percent.sum() * 100.0,
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result.profit_abs.sum(),
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result.trade_duration.mean(),
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len(result[result.profit_abs > 0]),
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len(result[result.profit_abs < 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_percent.sum() * 100.0,
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results.profit_abs.sum(),
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results.trade_duration.mean(),
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len(results[results.profit_abs > 0]),
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len(results[results.profit_abs < 0])
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])
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return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
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def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None:
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records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
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t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
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t.open_rate, t.close_rate, t.open_at_end)
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for index, t in results.iterrows()]
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if records:
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logger.info('Dumping backtest results to %s', recordfilename)
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file_dump_json(recordfilename, records)
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def _get_sell_trade_entry(
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self, pair: str, buy_row: DataFrame,
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partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
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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(
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open_rate=buy_row.open,
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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_open=self.fee,
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fee_close=self.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 self.analyze.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
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sell_row.sell):
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return BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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open_time=buy_row.date,
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close_time=sell_row.date,
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trade_duration=(sell_row.date - buy_row.date).seconds // 60,
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open_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=False,
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open_rate=buy_row.open,
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close_rate=sell_row.open
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)
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if partial_ticker:
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# no sell condition found - trade stil open at end of backtest period
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sell_row = partial_ticker[-1]
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btr = BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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open_time=buy_row.date,
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close_time=sell_row.date,
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trade_duration=(sell_row.date - buy_row.date).seconds // 60,
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open_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=True,
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open_rate=buy_row.open,
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close_rate=sell_row.open
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)
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logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
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btr.profit_percent, btr.profit_abs)
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return btr
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return None
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def backtest(self, args: Dict) -> DataFrame:
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"""
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Implements backtesting functionality
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NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
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Of course try to not have ugly code. By some accessor are sometime slower than functions.
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Avoid, logging on this method
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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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: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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trades = []
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trade_count_lock: Dict = {}
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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 = self.populate_sell_trend(
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self.populate_buy_trend(pair_data))[headers].copy()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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#ticker = [x for x in ticker_data.itertuples()]
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"""
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Use faster numpy to mask out rows we will not use in slower itertuples loop
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Filter rows that are buy or sell to be processed
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Also keep the last row from in any state for open trades
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"""
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mask = (ticker_data['buy'].values != 0) | (ticker_data['sell'].values != 0)
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ticker_loop_data=ticker_data[mask]
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end_row = DataFrame(ticker_data[-1:].values, columns=ticker_data.columns)
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ticker_loop_data = ticker_loop_data.append(end_row)
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ticker = [x for x in ticker_loop_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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trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
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trade_count_lock, args)
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if trade_entry:
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lock_pair_until = trade_entry.close_time
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trades.append(trade_entry)
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else:
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# Set lock_pair_until to end of testing period if trade could not be closed
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# This happens only if the buy-signal was with the last candle
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lock_pair_until = ticker_data.iloc[-1].date
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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def start(self) -> None:
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"""
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Run a backtesting end-to-end
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:return: None
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"""
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data = {}
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pairs = self.config['exchange']['pair_whitelist']
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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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if self.config.get('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] = self.exchange.get_ticker_history(pair, self.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 = Arguments.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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data = optimize.load_data(
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self.config['datadir'],
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pairs=pairs,
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ticker_interval=self.ticker_interval,
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refresh_pairs=self.config.get('refresh_pairs', False),
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exchange=self.exchange,
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timerange=timerange
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)
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if not data:
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logger.critical("No data found. Terminating.")
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return
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# Ignore max_open_trades in backtesting, except realistic flag was passed
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if self.config.get('realistic_simulation', False):
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max_open_trades = self.config['max_open_trades']
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else:
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logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
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max_open_trades = 0
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preprocessed = self.tickerdata_to_dataframe(data)
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# Print timeframe
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min_date, max_date = self.get_timeframe(preprocessed)
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logger.info(
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'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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)
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# Execute backtest and print results
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results = self.backtest(
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{
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'stake_amount': self.config.get('stake_amount'),
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'processed': preprocessed,
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'max_open_trades': max_open_trades,
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'realistic': self.config.get('realistic_simulation', False),
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}
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)
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if self.config.get('export', False):
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self._store_backtest_result(self.config.get('exportfilename'), results)
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logger.info(
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'\n================================================= '
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'BACKTESTING REPORT'
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' ==================================================\n'
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'%s',
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self._generate_text_table(
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data,
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results
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)
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)
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logger.info(
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'\n=============================================== '
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'LEFT OPEN TRADES REPORT'
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' ===============================================\n'
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'%s',
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self._generate_text_table(
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data,
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results.loc[results.open_at_end]
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)
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)
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def setup_configuration(args: Namespace) -> Dict[str, Any]:
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"""
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Prepare the configuration for the backtesting
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:param args: Cli args from Arguments()
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:return: Configuration
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"""
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configuration = Configuration(args)
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config = configuration.get_config()
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# Ensure we do not use Exchange credentials
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config['exchange']['key'] = ''
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config['exchange']['secret'] = ''
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if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
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raise DependencyException('stake amount could not be "%s" for backtesting' %
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constants.UNLIMITED_STAKE_AMOUNT)
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return config
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def start(args: Namespace) -> None:
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"""
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Start Backtesting script
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:param args: Cli args from Arguments()
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:return: None
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"""
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# Initialize configuration
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config = setup_configuration(args)
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logger.info('Starting freqtrade in Backtesting mode')
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# Initialize backtesting object
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backtesting = Backtesting(config)
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backtesting.start()
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