mirror of
https://github.com/freqtrade/freqtrade.git
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313 lines
12 KiB
Python
313 lines
12 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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from argparse import Namespace
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from typing import Dict, Tuple, Any, List, Optional
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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.optimize as optimize
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from freqtrade import exchange
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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 Bittrex
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from freqtrade.logger import Logger
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from freqtrade.misc import file_dump_json
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from freqtrade.persistence import Trade
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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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# Init the logger
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self.logging = Logger(name=__name__, level=config['loglevel'])
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self.logger = self.logging.get_logger()
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self.config = config
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self.analyze = None
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self.ticker_interval = None
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self.tickerdata_to_dataframe = None
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self.populate_buy_trend = None
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self.populate_sell_trend = None
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self._init()
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def _init(self) -> None:
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"""
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Init objects required for backtesting
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:return: None
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"""
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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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exchange._API = Bittrex({'key': '', 'secret': ''})
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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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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(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 = self.config.get('stake_currency')
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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(
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self, pair: str, buy_row: DataFrame,
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partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]:
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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.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 self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
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sell_row.sell):
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return \
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sell_row, \
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(
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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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), \
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sell_row.date
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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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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 = {}
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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(self.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 = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
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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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self.logger.info('Dumping backtest results')
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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(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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self.logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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self.logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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if self.config.get('live'):
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self.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, self.ticker_interval)
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else:
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self.logger.info('Using local backtesting data (using whitelist in given config) ...')
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timerange = Arguments.parse_timerange(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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timerange=timerange
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)
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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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self.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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self.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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sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False)
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use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False)
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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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'sell_profit_only': sell_profit_only,
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'use_sell_signal': use_sell_signal,
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'record': self.config.get('export')
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}
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)
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self.logging.set_format('%(message)s')
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self.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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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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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 logger
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logger = Logger(name=__name__).get_logger()
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logger.info('Starting freqtrade in Backtesting mode')
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# Initialize configuration
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config = setup_configuration(args)
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# Initialize backtesting object
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backtesting = Backtesting(config)
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backtesting.start()
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