4e68362d46
Bugs Calculating % prof ok, but abs wrong BAT/BTC DF is very broken all OHLC are the same - but exposes a buy after stop on last row "oddness" to be investigated / handled
840 lines
34 KiB
Python
840 lines
34 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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from profilehooks import profile
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from collections import OrderedDict
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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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self.stop_loss_value = self.analyze.strategy.stoploss
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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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@profile
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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', 'high', 'low']
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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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########################### Call out BSlap instead of using FT
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bslap_results: list = []
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last_bslap_results: list = []
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for pair, pair_data in processed.items():
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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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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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# #dump same DFs to disk for offline testing in scratch
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# f_pair:str = pair
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# csv = f_pair.replace("/", "_")
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# csv="/Users/creslin/PycharmProjects/freqtrade_new/frames/" + csv
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# ticker_data.to_csv(csv, sep='\t', encoding='utf-8')
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#call bslap - results are a list of dicts
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bslap_pair_results = self.backslap_pair(ticker_data, pair)
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last_bslap_results = bslap_results
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bslap_results = last_bslap_results + bslap_pair_results
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bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
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return bslap_results_df
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########################### Original BT loop
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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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#
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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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#
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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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#
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# ticker_data.drop(ticker_data.head(1).index, inplace=True)
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#
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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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# 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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#
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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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#
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# trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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#
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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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#
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#
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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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#
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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 np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int):
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import utils_find_1st as utf1st
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"""
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The purpose of this def is to return the next "buy" = 1
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after t_exit_ind.
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t_exit_ind is the index the last trade exited on
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or 0 if first time around this loop.
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"""
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t_open_ind: int
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# Create a view on our buy index starting after last trade exit
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# Search for next buy
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np_buy_arr_v = np_buy_arr[t_exit_ind:]
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t_open_ind = utf1st.find_1st(np_buy_arr_v, 1, utf1st.cmp_equal)
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t_open_ind = t_open_ind + t_exit_ind # Align numpy index
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return t_open_ind
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def backslap_pair(self, ticker_data, pair):
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import pandas as pd
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import numpy as np
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import timeit
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import utils_find_1st as utf1st
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from datetime import datetime
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### backslap debug wrap
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debug_2loops = False # only loop twice, for faster debug
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debug_timing = False # print timing for each step
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debug = False # print values, to check accuracy
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# Read Stop Loss Values and Stake
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stop = self.stop_loss_value
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p_stop = (stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
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stake = self.config.get('stake_amount')
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# Set fees
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# TODO grab these from the environment, do not hard set
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# Fees
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open_fee = 0.05
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close_fee = 0.05
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if debug:
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print("Stop is ", stop, "value from stragey file")
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print("p_stop is", p_stop, "value used to multiply to entry price")
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print("Stake is,", stake, "the sum of currency to spend per trade")
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print("The open fee is", open_fee, "The close fee is", close_fee)
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if debug:
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from pandas import set_option
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set_option('display.max_rows', 5000)
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set_option('display.max_columns', 8)
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pd.set_option('display.width', 1000)
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pd.set_option('max_colwidth', 40)
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pd.set_option('precision', 12)
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def s():
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st = timeit.default_timer()
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return st
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def f(st):
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return (timeit.default_timer() - st)
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#### backslap config
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"""
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A couple legacy Pandas vars still used for pretty debug output.
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If have debug enabled the code uses these fields for dataframe output
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Ensure bto, sto, sco are aligned with Numpy values next
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to align debug and actual. Options are:
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buy - open - close - sell - high - low - np_stop_pri
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"""
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bto = buys_triggered_on = "close"
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# sto = stops_triggered_on = "low" ## Should be low, FT uses close
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# sco = stops_calculated_on = "np_stop_pri" ## should use np_stop_pri, FT uses close
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sto = stops_triggered_on = "close" ## Should be low, FT uses close
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sco = stops_calculated_on = "close" ## should use np_stop_pri, FT uses close
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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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np_buy: int = 0
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np_open: int = 1
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np_close: int = 2
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np_sell: int = 3
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np_high: int = 4
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np_low: int = 5
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np_stop: int = 6
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np_bto: int = np_close # buys_triggered_on - should be close
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np_bco: int = np_open # buys calculated on - open of the next candle.
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#np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
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#np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
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np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
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np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
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#
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### End Config
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pair: str = pair
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loop: int = 1
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#ticker_data: DataFrame = ticker_dfs[t_file]
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bslap: DataFrame = ticker_data
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# Build a single dimension numpy array from "buy" index for faster search
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# (500x faster than pandas)
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np_buy_arr = bslap['buy'].values
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np_buy_arr_len: int = len(np_buy_arr)
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# use numpy array for faster searches in loop, 20x faster than pandas
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
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np_bslap = np.array(bslap[['buy', 'open', 'close', 'sell', 'high', 'low']])
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loop: int = 0 # how many time around the loop
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t_exit_ind = 0 # Start loop from first index
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t_exit_last = 0 # To test for exit
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st = s() # Start timer for processing dataframe
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if debug:
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print('Processing:', pair)
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# Results will be stored in a list of dicts
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bslap_pair_results: list = []
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bslap_result: dict = {}
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while t_exit_ind < np_buy_arr_len:
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loop = loop + 1
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if debug or debug_timing:
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print("-- T_exit_Ind - Numpy Index is", t_exit_ind, " ----------------------- Loop", loop, pair)
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if debug_2loops:
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if loop == 3:
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print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Loop debug max met - breaking")
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break
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'''
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Dev phases
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Phase 1
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1) Manage buy, sell, stop enter/exit
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a) Find first buy index
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b) Discover first stop and sell hit after buy index
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c) Chose first intance as trade exit
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Phase 2
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2) Manage dynamic Stop and ROI Exit
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a) Create trade slice from 1
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b) search within trade slice for dynamice stop hit
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c) search within trade slice for ROI hit
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'''
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'''
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Finds index for first buy = 1 flag, use .values numpy array for speed
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Create a slice, from first buy index onwards.
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Slice will be used to find exit conditions after trade open
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'''
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if debug_timing:
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st = s()
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t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind)
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if debug_timing:
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t_t = f(st)
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print("0-numpy", str.format('{0:.17f}', t_t))
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st = s()
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'''
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Calculate np_t_stop_pri (Trade Stop Price) based on the buy price
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As stop in based on buy price we are interested in buy
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- Buys are Triggered On np_bto, typically the CLOSE of candle
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- Buys are Calculated On np_bco, default is OPEN of the next candle.
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as we only see the CLOSE after it has happened.
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The assumption is we have bought at first available price, the OPEN
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'''
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np_t_stop_pri = (np_bslap[t_open_ind + 1, np_bco] * p_stop)
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if debug_timing:
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t_t = f(st)
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print("1-numpy", str.format('{0:.17f}', t_t))
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st = s()
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"""
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1)Create a View from our open trade forward
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The view is our search space for the next Stop or Sell
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We use a numpy view:
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Using a numpy for speed on views, 1,000 faster than pandas
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Pandas cannot assure it will always return a view, copies are
|
|
3 orders of magnitude slower
|
|
|
|
The view contains columns:
|
|
buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
|
|
"""
|
|
np_t_open_v = np_bslap[t_open_ind:]
|
|
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("2-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
Find first stop index after Trade Open:
|
|
|
|
First index in np_t_open_v (numpy view of bslap dataframe)
|
|
Using a numpy view a orders of magnitude faster
|
|
|
|
where [np_sto] (stop tiggered on variable: "close", "low" etc) < np_t_stop_pri
|
|
'''
|
|
np_t_stop_ind = utf1st.find_1st(np_t_open_v[:, np_sto],
|
|
np_t_stop_pri,
|
|
utf1st.cmp_smaller) \
|
|
+ t_open_ind
|
|
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("3-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
Find first sell index after trade open
|
|
|
|
First index in t_open_slice where ['sell'] = 1
|
|
'''
|
|
# Use numpy array for faster search for sell
|
|
# Sell uses column 3.
|
|
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
|
|
# Numpy searches 25-35x quicker than pandas on this data
|
|
|
|
np_t_sell_ind = utf1st.find_1st(np_t_open_v[:, np_sell],
|
|
1, utf1st.cmp_equal) \
|
|
+ t_open_ind
|
|
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("4-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
Determine which was hit first stop or sell, use as exit
|
|
|
|
STOP takes priority over SELL as would be 'in candle' from tick data
|
|
Sell would use Open from Next candle.
|
|
So in a draw Stop would be hit first on ticker data in live
|
|
'''
|
|
if np_t_stop_ind <= np_t_sell_ind:
|
|
t_exit_ind = np_t_stop_ind # Set Exit row index
|
|
t_exit_type = 'stop' # Set Exit type (sell|stop)
|
|
np_t_exit_pri = np_sco # The price field our STOP exit will use
|
|
else:
|
|
# move sell onto next candle, we only look back on sell
|
|
# will use the open price later.
|
|
t_exit_ind = np_t_sell_ind # Set Exit row index
|
|
t_exit_type = 'sell' # Set Exit type (sell|stop)
|
|
np_t_exit_pri = np_open # The price field our SELL exit will use
|
|
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("5-logic", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
if debug:
|
|
'''
|
|
Print out the buys, stops, sells
|
|
Include Line before and after to for easy
|
|
Human verification
|
|
'''
|
|
# Combine the np_t_stop_pri value to bslap dataframe to make debug
|
|
# life easy. This is the currenct stop price based on buy price_
|
|
# Don't care about performance in debug
|
|
# (add an extra column if printing as df has date in col1 not in npy)
|
|
bslap['np_stop_pri'] = np_t_stop_pri
|
|
|
|
# Buy
|
|
print("=================== BUY ", pair)
|
|
print("Numpy Array BUY Index is:", t_open_ind)
|
|
print("DataFrame BUY Index is:", t_open_ind + 1, "displaying DF \n")
|
|
print("HINT, BUY trade should use OPEN price from next candle, i.e ", t_open_ind + 2, "\n")
|
|
op_is = t_open_ind - 1 # Print open index start, line before
|
|
op_if = t_open_ind + 3 # Print open index finish, line after
|
|
print(bslap.iloc[op_is:op_if], "\n")
|
|
print(bslap.iloc[t_open_ind + 1]['date'])
|
|
|
|
# Stop - Stops trigger price sto, and price received sco. (Stop Trigger|Calculated On)
|
|
print("=================== STOP ", pair)
|
|
print("Numpy Array STOP Index is:", np_t_stop_ind)
|
|
print("DataFrame STOP Index is:", np_t_stop_ind + 1, "displaying DF \n")
|
|
print("First Stop after Trade open in candle", t_open_ind + 1, "is ", np_t_stop_ind + 1,": \n",
|
|
str.format('{0:.17f}', bslap.iloc[np_t_stop_ind][sto]),
|
|
"is less than", str.format('{0:.17f}', np_t_stop_pri))
|
|
print("If stop is first exit match sell rate is :", str.format('{0:.17f}', bslap.iloc[np_t_stop_ind][sco]))
|
|
print("HINT, STOPs should close in-candle, i.e", np_t_stop_ind + 1,
|
|
": As live STOPs are not linked to O-C times")
|
|
|
|
st_is = np_t_stop_ind - 1 # Print stop index start, line before
|
|
st_if = np_t_stop_ind + 2 # Print stop index finish, line after
|
|
print(bslap.iloc[st_is:st_if], "\n")
|
|
|
|
# Sell
|
|
print("=================== SELL ", pair)
|
|
print("Numpy Array SELL Index is:", np_t_sell_ind)
|
|
print("DataFrame SELL Index is:", np_t_sell_ind + 1, "displaying DF \n")
|
|
print("First Sell Index after Trade open in in candle", np_t_sell_ind + 1)
|
|
print("HINT, if exit is SELL (not stop) trade should use OPEN price from next candle",
|
|
np_t_sell_ind + 2, "\n")
|
|
sl_is = np_t_sell_ind - 1 # Print sell index start, line before
|
|
sl_if = np_t_sell_ind + 3 # Print sell index finish, line after
|
|
print(bslap.iloc[sl_is:sl_if], "\n")
|
|
|
|
# Chosen Exit (stop or sell)
|
|
print("=================== EXIT ", pair)
|
|
print("Exit type is :", t_exit_type)
|
|
# print((bslap.iloc[t_exit_ind], "\n"))
|
|
print("trade exit price field is", np_t_exit_pri, "\n")
|
|
|
|
'''
|
|
Trade entry is always the next candles "open" price
|
|
We trigger on close, so cannot see that till after
|
|
its closed.
|
|
|
|
The exception to this is a STOP which is calculated in candle
|
|
'''
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("6-depra", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
## use numpy view "np_t_open_v" for speed. Columns are
|
|
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
|
|
# exception is 6 which is use the stop value.
|
|
|
|
np_trade_enter_price = np_bslap[t_open_ind + 1, np_open]
|
|
if t_exit_type == 'stop':
|
|
if np_t_exit_pri == 6:
|
|
np_trade_exit_price = np_t_stop_pri
|
|
else:
|
|
np_trade_exit_price = np_bslap[t_exit_ind, np_t_exit_pri]
|
|
if t_exit_type == 'sell':
|
|
np_trade_exit_price = np_bslap[t_exit_ind + 1, np_t_exit_pri]
|
|
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("7-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
if debug:
|
|
print("//////////////////////////////////////////////")
|
|
print("+++++++++++++++++++++++++++++++++ Trade Enter ")
|
|
print("np_trade Enterprice is ", str.format('{0:.17f}', np_trade_enter_price))
|
|
print("--------------------------------- Trade Exit ")
|
|
print("Trade Exit Type is ", t_exit_type)
|
|
print("np_trade Exit Price is", str.format('{0:.17f}', np_trade_exit_price))
|
|
print("//////////////////////////////////////////////")
|
|
|
|
# Loop control - catch no closed trades.
|
|
if debug:
|
|
print("---------------------------------------- end of loop", loop,
|
|
" Dataframe Exit Index is: ", t_exit_ind)
|
|
print("Exit Index Last, Exit Index Now Are: ", t_exit_last, t_exit_ind)
|
|
|
|
if t_exit_last >= t_exit_ind:
|
|
"""
|
|
When last trade exit equals index of last exit, there is no
|
|
opportunity to close any more trades.
|
|
|
|
Break loop and go on to next pair.
|
|
|
|
TODO
|
|
add handing here to record none closed open trades
|
|
"""
|
|
|
|
if debug:
|
|
print(bslap_pair_results)
|
|
|
|
break
|
|
else:
|
|
"""
|
|
Add trade to backtest looking results list of dicts
|
|
Loop back to look for more trades.
|
|
"""
|
|
# Index will change if incandle stop or look back as close Open and Sell
|
|
if t_exit_type == 'stop':
|
|
close_index: int = t_exit_ind + 1
|
|
elif t_exit_type == 'sell':
|
|
close_index: int = t_exit_ind + 2
|
|
else:
|
|
close_index: int = t_exit_ind + 1
|
|
|
|
# Munge the date / delta (bt already date formats...just subract)
|
|
trade_start = bslap.iloc[t_open_ind + 1]['date']
|
|
trade_end = bslap.iloc[close_index]['date']
|
|
# def __datetime(date_str):
|
|
# return datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S+00:00')
|
|
trade_mins = (trade_end - trade_start).total_seconds() / 60
|
|
|
|
# Profit ABS.
|
|
# sumrecieved((rate * numTokens) * fee) - sumpaid ((rate * numTokens) * fee)
|
|
sumpaid: float = (np_trade_enter_price * stake)
|
|
sumpaid_fee: float = sumpaid * open_fee
|
|
sumrecieved: float = (np_trade_exit_price * stake)
|
|
sumrecieved_fee: float = sumrecieved * close_fee
|
|
profit_abs: float = sumrecieved - sumpaid - sumpaid_fee - sumrecieved_fee
|
|
|
|
# build trade dictionary
|
|
bslap_result["pair"] = pair
|
|
bslap_result["profit_percent"] = (np_trade_exit_price - np_trade_enter_price) / np_trade_enter_price
|
|
bslap_result["profit_abs"] = round(profit_abs, 15)
|
|
bslap_result["open_time"] = trade_start
|
|
bslap_result["close_time"] = trade_end
|
|
bslap_result["open_index"] = t_open_ind + 2 # +1 between np and df, +1 as we buy on next.
|
|
bslap_result["close_index"] = close_index
|
|
bslap_result["trade_duration"] = trade_mins
|
|
bslap_result["open_at_end"] = False
|
|
bslap_result["open_rate"] = round(np_trade_enter_price, 15)
|
|
bslap_result["close_rate"] = round(np_trade_exit_price, 15)
|
|
#bslap_result["exit_type"] = t_exit_type
|
|
# Add trade dictionary to list
|
|
bslap_pair_results.append(bslap_result)
|
|
if debug:
|
|
print(bslap_pair_results)
|
|
|
|
"""
|
|
Loop back to start. t_exit_last becomes where loop
|
|
will seek to open new trades from.
|
|
Push index on 1 to not open on close
|
|
"""
|
|
t_exit_last = t_exit_ind + 1
|
|
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("8+trade", str.format('{0:.17f}', t_t))
|
|
|
|
# Send back List of trade dicts
|
|
return bslap_pair_results
|
|
|
|
|
|
|
|
|
|
def start(self) -> None:
|
|
"""
|
|
Run a backtesting end-to-end
|
|
:return: None
|
|
"""
|
|
data = {}
|
|
pairs = self.config['exchange']['pair_whitelist']
|
|
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
|
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
|
|
|
|
if self.config.get('live'):
|
|
logger.info('Downloading data for all pairs in whitelist ...')
|
|
for pair in pairs:
|
|
data[pair] = self.exchange.get_ticker_history(pair, self.ticker_interval)
|
|
else:
|
|
logger.info('Using local backtesting data (using whitelist in given config) ...')
|
|
|
|
timerange = Arguments.parse_timerange(None if self.config.get(
|
|
'timerange') is None else str(self.config.get('timerange')))
|
|
data = optimize.load_data(
|
|
self.config['datadir'],
|
|
pairs=pairs,
|
|
ticker_interval=self.ticker_interval,
|
|
refresh_pairs=self.config.get('refresh_pairs', False),
|
|
exchange=self.exchange,
|
|
timerange=timerange
|
|
)
|
|
|
|
if not data:
|
|
logger.critical("No data found. Terminating.")
|
|
return
|
|
# Ignore max_open_trades in backtesting, except realistic flag was passed
|
|
if self.config.get('realistic_simulation', False):
|
|
max_open_trades = self.config['max_open_trades']
|
|
else:
|
|
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
|
|
max_open_trades = 0
|
|
|
|
preprocessed = self.tickerdata_to_dataframe(data)
|
|
|
|
# Print timeframe
|
|
min_date, max_date = self.get_timeframe(preprocessed)
|
|
logger.info(
|
|
'Measuring data from %s up to %s (%s days)..',
|
|
min_date.isoformat(),
|
|
max_date.isoformat(),
|
|
(max_date - min_date).days
|
|
)
|
|
|
|
# Execute backtest and print results
|
|
results = self.backtest(
|
|
{
|
|
'stake_amount': self.config.get('stake_amount'),
|
|
'processed': preprocessed,
|
|
'max_open_trades': max_open_trades,
|
|
'realistic': self.config.get('realistic_simulation', False),
|
|
}
|
|
)
|
|
|
|
if self.config.get('export', False):
|
|
self._store_backtest_result(self.config.get('exportfilename'), results)
|
|
|
|
logger.info(
|
|
'\n================================================= '
|
|
'BACKTESTING REPORT'
|
|
' ==================================================\n'
|
|
'%s',
|
|
self._generate_text_table(
|
|
data,
|
|
results
|
|
)
|
|
)
|
|
|
|
## TODO. Catch open trades for this report.
|
|
# logger.info(
|
|
# '\n=============================================== '
|
|
# 'LEFT OPEN TRADES REPORT'
|
|
# ' ===============================================\n'
|
|
# '%s',
|
|
# 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'] = ''
|
|
|
|
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()
|