518 lines
21 KiB
Python
518 lines
21 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, timedelta
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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, to_datetime
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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.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.optimize.backslapping import Backslapping
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from freqtrade.persistence import Trade
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from freqtrade.strategy.interface import SellType
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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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from collections import OrderedDict
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import timeit
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from time import sleep
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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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sell_reason: SellType
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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.strategy: IStrategy = StrategyResolver(self.config).strategy
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self.ticker_interval = self.strategy.ticker_interval
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self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
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self.advise_buy = self.strategy.advise_buy
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self.advise_sell = self.strategy.advise_sell
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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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self.stop_loss_value = self.strategy.stoploss
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#### backslap config
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'''
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Numpy arrays are used for 100x speed up
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We requires setting Int values for
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buy stop triggers and stop calculated on
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
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'''
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self.np_buy: int = 0
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self.np_open: int = 1
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self.np_close: int = 2
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self.np_sell: int = 3
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self.np_high: int = 4
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self.np_low: int = 5
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self.np_stop: int = 6
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self.np_bto: int = self.np_close # buys_triggered_on - should be close
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self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
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self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
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self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
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# self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
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# self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
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if 'backslap' in config:
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self.use_backslap = config['backslap'] # Enable backslap - if false Orginal code is executed.
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else:
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self.use_backslap = False
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logger.info("using backslap: {}".format(self.use_backslap))
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self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
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self.debug_timing = False # Stages within Backslap
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self.debug_2loops = False # Limit each pair to two loops, useful when debugging
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self.debug_vector = False # Debug vector calcs
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self.debug_timing_main_loop = False # print overall timing per pair - works in Backtest and Backslap
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self.backslap_show_trades = False # prints trades in addition to summary report
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self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt
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self.stop_stops: int = 9999 # stop back testing any pair with this many stops, set to 999999 to not hit
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self.backslap = Backslapping(config)
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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(frame['date'].min()), arrow.get(frame['date'].max()))
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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', 'd', '.1f', '.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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str(timedelta(
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minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
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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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str(timedelta(
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minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
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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 _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
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"""
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Generate small table outlining Backtest results
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"""
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tabular_data = []
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headers = ['Sell Reason', 'Count']
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for reason, count in results['sell_reason'].value_counts().iteritems():
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tabular_data.append([reason.value, count])
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return tabulate(tabular_data, headers=headers, 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, t.sell_reason.value)
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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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sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
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sell_row.sell)
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if sell.sell_flag:
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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=int((
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sell_row.date - buy_row.date).total_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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sell_reason=sell.sell_type
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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=int((
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sell_row.date - buy_row.date).total_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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sell_reason=SellType.FORCE_SELL
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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 s(self):
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st = timeit.default_timer()
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return st
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def f(self, st):
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return (timeit.default_timer() - st)
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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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position_stacking: do we allow position stacking? (default: False)
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:return: DataFrame
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"""
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use_backslap = self.use_backslap
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debug_timing = self.debug_timing_main_loop
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if use_backslap: # Use Back Slap code
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return self.backslap.run(args)
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else: # use Original Back test code
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########################## Original BT loop
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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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position_stacking = args.get('position_stacking', 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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if debug_timing: # Start timer
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fl = self.s()
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.advise_sell(
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self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[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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if debug_timing: # print time taken
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flt = self.f(fl)
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# print("populate_buy_trend:", pair, round(flt, 10))
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st = self.s()
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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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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 not position_stacking:
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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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if debug_timing: # print time taken
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tt = self.f(st)
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print("Time to BackTest :", pair, round(tt, 10))
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print("-----------------------")
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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####################### Original BT loop end
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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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ld_files = self.s()
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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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# Use max_open_trades in backtesting, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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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 (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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preprocessed = self.tickerdata_to_dataframe(data)
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t_t = self.f(ld_files)
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print("Load from json to file to df in mem took", t_t)
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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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'position_stacking': self.config.get('position_stacking', 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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if self.use_backslap:
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logger.info(
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'\n====================================================== '
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'BackSLAP 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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# optional print trades
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if self.backslap_show_trades:
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TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs',
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'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1)
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def to_fwf(df, fname):
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content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql')
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print(content)
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DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt")
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# optional save trades
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if self.backslap_save_trades:
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TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs',
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'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1)
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def to_fwf(df, fname):
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content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql')
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open(fname, "w").write(content)
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DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt")
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else:
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logger.info(
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'\n================================================= '
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'BACKTEST 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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if 'sell_reason' in results.columns:
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logger.info(
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'\n' +
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' SELL READON STATS '.center(119, '=') +
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'\n%s \n',
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self._generate_text_table_sell_reason(data, results)
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)
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else:
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logger.info("no sell reasons available!")
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logger.info(
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'\n' +
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' LEFT OPEN TRADES REPORT '.center(119, '=') +
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'\n%s',
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self._generate_text_table(
|
|
data,
|
|
results.loc[results.open_at_end]
|
|
)
|
|
)
|
|
|
|
|
|
def setup_configuration(args: Namespace) -> Dict[str, Any]:
|
|
"""
|
|
Prepare the configuration for the backtesting
|
|
:param args: Cli args from Arguments()
|
|
:return: Configuration
|
|
"""
|
|
configuration = Configuration(args)
|
|
config = configuration.get_config()
|
|
|
|
# Ensure we do not use Exchange credentials
|
|
config['exchange']['key'] = ''
|
|
config['exchange']['secret'] = ''
|
|
config['backslap'] = args.backslap
|
|
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
|
|
raise DependencyException('stake amount could not be "%s" for backtesting' %
|
|
constants.UNLIMITED_STAKE_AMOUNT)
|
|
|
|
return config
|
|
|
|
|
|
def start(args: Namespace) -> None:
|
|
"""
|
|
Start Backtesting script
|
|
:param args: Cli args from Arguments()
|
|
:return: None
|
|
"""
|
|
# Initialize configuration
|
|
config = setup_configuration(args)
|
|
logger.info('Starting freqtrade in Backtesting mode')
|
|
|
|
# Initialize backtesting object
|
|
backtesting = Backtesting(config)
|
|
backtesting.start()
|