1136 lines
50 KiB
Python
1136 lines
50 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, 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.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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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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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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#### 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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self.use_backslap = True # Enable backslap - if false Orginal code is executed.
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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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@staticmethod
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def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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Get the maximum timeframe for the given backtest data
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:param data: dictionary with preprocessed backtesting data
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:return: tuple containing min_date, max_date
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"""
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timeframe = [
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(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
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for frame in data.values()
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]
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return min(timeframe, key=operator.itemgetter(0))[0], \
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max(timeframe, key=operator.itemgetter(1))[1]
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def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:return: pretty printed table with tabulate as str
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"""
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stake_currency = str(self.config.get('stake_currency'))
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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.1f')
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tabular_data = []
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headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
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'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
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for pair in data:
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result = results[results.pair == pair]
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tabular_data.append([
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pair,
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len(result.index),
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result.profit_percent.mean() * 100.0,
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result.profit_percent.sum() * 100.0,
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result.profit_abs.sum(),
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result.trade_duration.mean(),
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len(result[result.profit_abs > 0]),
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len(result[result.profit_abs < 0])
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])
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# Append Total
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tabular_data.append([
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'TOTAL',
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len(results.index),
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results.profit_percent.mean() * 100.0,
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results.profit_percent.sum() * 100.0,
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results.profit_abs.sum(),
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results.trade_duration.mean(),
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len(results[results.profit_abs > 0]),
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len(results[results.profit_abs < 0])
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])
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return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
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def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None:
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records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
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t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
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t.open_rate, t.close_rate, t.open_at_end)
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for index, t in results.iterrows()]
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if records:
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logger.info('Dumping backtest results to %s', recordfilename)
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file_dump_json(recordfilename, records)
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def _get_sell_trade_entry(
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self, pair: str, buy_row: DataFrame,
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partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
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stake_amount = args['stake_amount']
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max_open_trades = args.get('max_open_trades', 0)
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trade = Trade(
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open_rate=buy_row.open,
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open_date=buy_row.date,
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stake_amount=stake_amount,
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amount=stake_amount / buy_row.open,
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fee_open=self.fee,
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fee_close=self.fee
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)
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# calculate win/lose forwards from buy point
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for sell_row in partial_ticker:
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if max_open_trades > 0:
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# Increase trade_count_lock for every iteration
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trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
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buy_signal = sell_row.buy
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if self.analyze.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
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sell_row.sell):
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return BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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open_time=buy_row.date,
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close_time=sell_row.date,
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trade_duration=(sell_row.date - buy_row.date).seconds // 60,
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open_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=False,
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open_rate=buy_row.open,
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close_rate=sell_row.open
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)
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if partial_ticker:
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# no sell condition found - trade stil open at end of backtest period
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sell_row = partial_ticker[-1]
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btr = BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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open_time=buy_row.date,
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close_time=sell_row.date,
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trade_duration=(sell_row.date - buy_row.date).seconds // 60,
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open_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=True,
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open_rate=buy_row.open,
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close_rate=sell_row.open
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)
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logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
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btr.profit_percent, btr.profit_abs)
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return btr
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return None
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def 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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realistic: do we try to simulate realistic trades? (default: True)
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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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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 Loop instead of Original BT code
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bslap_results: list = []
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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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ticker_data = self.populate_sell_trend(
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self.populate_buy_trend(pair_data))[headers].copy()
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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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# #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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if debug_timing: # print time taken
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tt = self.f(st)
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print("Time to BackSlap :", pair, round(tt,10))
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print("-----------------------")
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# Switch List of Trade Dicts (bslap_results) to Dataframe
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# Fill missing, calculable columns, profit, duration , abs etc.
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bslap_results_df = DataFrame(bslap_results)
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bslap_results_df['open_time'] = to_datetime(bslap_results_df['open_time'])
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bslap_results_df['close_time'] = to_datetime(bslap_results_df['close_time'])
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### don't use this, itll drop exit type field
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# bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
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bslap_results_df = self.vector_fill_results_table(bslap_results_df)
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return bslap_results_df
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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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realistic = args.get('realistic', False)
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trades = []
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trade_count_lock: Dict = {}
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for pair, pair_data in processed.items():
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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.populate_sell_trend(
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self.populate_buy_trend(pair_data))[headers].copy()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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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 realistic:
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if lock_pair_until is not None and row.date <= lock_pair_until:
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continue
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if max_open_trades > 0:
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# Check if max_open_trades has already been reached for the given date
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if not trade_count_lock.get(row.date, 0) < max_open_trades:
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continue
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trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
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trade_count_lock, args)
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if trade_entry:
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lock_pair_until = trade_entry.close_time
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trades.append(trade_entry)
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else:
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# Set lock_pair_until to end of testing period if trade could not be closed
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# This happens only if the buy-signal was with the last candle
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lock_pair_until = ticker_data.iloc[-1].date
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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 vector_fill_results_table(self, bslap_results_df: DataFrame):
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"""
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The Results frame contains a number of columns that are calculable
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from othe columns. These are left blank till all rows are added,
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to be populated in single vector calls.
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Columns to be populated are:
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- Profit
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- trade duration
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- profit abs
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:param bslap_results Dataframe
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:return: bslap_results Dataframe
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"""
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import pandas as pd
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debug = self.debug_vector
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# stake and fees
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# stake = 0.015
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# 0.05% is 0.0005
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#fee = 0.001
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stake = self.config.get('stake_amount')
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fee = self.fee
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open_fee = fee / 2
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close_fee = fee / 2
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if debug:
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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', 10)
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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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# Populate duration
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bslap_results_df['trade_duration'] = bslap_results_df['close_time'] - bslap_results_df['open_time']
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# if debug:
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# print(bslap_results_df[['open_time', 'close_time', 'trade_duration']])
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## Spends, Takes, Profit, Absolute Profit
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# Buy Price
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bslap_results_df['buy_sum'] = stake * bslap_results_df['open_rate']
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bslap_results_df['buy_fee'] = bslap_results_df['buy_sum'] * open_fee
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bslap_results_df['buy_spend'] = bslap_results_df['buy_sum'] + bslap_results_df['buy_fee']
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# Sell price
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bslap_results_df['sell_sum'] = stake * bslap_results_df['close_rate']
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bslap_results_df['sell_fee'] = bslap_results_df['sell_sum'] * close_fee
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bslap_results_df['sell_take'] = bslap_results_df['sell_sum'] - bslap_results_df['sell_fee']
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# profit_percent
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bslap_results_df['profit_percent'] = bslap_results_df['sell_take'] / bslap_results_df['buy_spend'] - 1
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# Absolute profit
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bslap_results_df['profit_abs'] = bslap_results_df['sell_take'] - bslap_results_df['buy_spend']
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if debug:
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print("\n")
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print(bslap_results_df[
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['buy_sum', 'buy_fee', 'buy_spend', 'sell_sum','sell_fee', 'sell_take', 'profit_percent', 'profit_abs', 'exit_type']])
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return bslap_results_df
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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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# Timers, to be called if in debug
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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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st = s()
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t_open_ind: int
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"""
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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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"""
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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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'''
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If -1 is returned no buy has been found, preserve the value
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'''
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if t_open_ind != -1: # send back the -1 if no buys found. otherwise update index
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|
t_open_ind = t_open_ind + t_exit_ind # Align numpy index
|
|
|
|
return t_open_ind
|
|
|
|
def backslap_pair(self, ticker_data, pair):
|
|
import pandas as pd
|
|
import numpy as np
|
|
import timeit
|
|
import utils_find_1st as utf1st
|
|
from datetime import datetime
|
|
|
|
### backslap debug wrap
|
|
# debug_2loops = False # only loop twice, for faster debug
|
|
# debug_timing = False # print timing for each step
|
|
# debug = False # print values, to check accuracy
|
|
debug_2loops = self.debug_2loops # only loop twice, for faster debug
|
|
debug_timing = self.debug_timing # print timing for each step
|
|
debug = self.debug # print values, to check accuracy
|
|
|
|
# Read Stop Loss Values and Stake
|
|
stop = self.stop_loss_value
|
|
p_stop = (stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
|
|
|
|
if debug:
|
|
print("Stop is ", stop, "value from stragey file")
|
|
print("p_stop is", p_stop, "value used to multiply to entry price")
|
|
|
|
if debug:
|
|
from pandas import set_option
|
|
set_option('display.max_rows', 5000)
|
|
set_option('display.max_columns', 8)
|
|
pd.set_option('display.width', 1000)
|
|
pd.set_option('max_colwidth', 40)
|
|
pd.set_option('precision', 12)
|
|
def s():
|
|
st = timeit.default_timer()
|
|
return st
|
|
def f(st):
|
|
return (timeit.default_timer() - st)
|
|
#### backslap config
|
|
'''
|
|
Numpy arrays are used for 100x speed up
|
|
We requires setting Int values for
|
|
buy stop triggers and stop calculated on
|
|
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
|
|
'''
|
|
# np_buy: int = 0
|
|
# np_open: int = 1
|
|
# np_close: int = 2
|
|
# np_sell: int = 3
|
|
# np_high: int = 4
|
|
# np_low: int = 5
|
|
# np_stop: int = 6
|
|
# np_bto: int = np_close # buys_triggered_on - should be close
|
|
# np_bco: int = np_open # buys calculated on - open of the next candle.
|
|
# #np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
|
|
# #np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
|
|
# np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
|
|
# np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
|
|
|
|
#######
|
|
# Use vars set at top of backtest
|
|
np_buy: int = self.np_buy
|
|
np_open: int = self.np_open
|
|
np_close: int = self.np_close
|
|
np_sell: int = self.np_sell
|
|
np_high: int = self.np_high
|
|
np_low: int = self.np_low
|
|
np_stop: int = self.np_stop
|
|
np_bto: int = self.np_bto # buys_triggered_on - should be close
|
|
np_bco: int = self.np_bco # buys calculated on - open of the next candle.
|
|
np_sto: int = self.np_sto # stops_triggered_on - Should be low, FT uses close
|
|
np_sco: int = self.np_sco # stops_calculated_on - Should be stop, FT uses close
|
|
|
|
### End Config
|
|
|
|
pair: str = pair
|
|
|
|
#ticker_data: DataFrame = ticker_dfs[t_file]
|
|
bslap: DataFrame = ticker_data
|
|
|
|
# Build a single dimension numpy array from "buy" index for faster search
|
|
# (500x faster than pandas)
|
|
np_buy_arr = bslap['buy'].values
|
|
np_buy_arr_len: int = len(np_buy_arr)
|
|
|
|
# use numpy array for faster searches in loop, 20x faster than pandas
|
|
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
|
|
np_bslap = np.array(bslap[['buy', 'open', 'close', 'sell', 'high', 'low']])
|
|
|
|
# Build a numpy list of date-times.
|
|
# We use these when building the trade
|
|
# The rationale is to address a value from a pandas cell is thousands of
|
|
# times more expensive. Processing time went X25 when trying to use any data from pandas
|
|
np_bslap_dates = bslap['date'].values
|
|
|
|
loop: int = 0 # how many time around the loop
|
|
t_exit_ind = 0 # Start loop from first index
|
|
t_exit_last = 0 # To test for exit
|
|
|
|
st = s() # Start timer for processing dataframe
|
|
if debug:
|
|
print('Processing:', pair)
|
|
|
|
# Results will be stored in a list of dicts
|
|
bslap_pair_results: list = []
|
|
bslap_result: dict = {}
|
|
|
|
while t_exit_ind < np_buy_arr_len:
|
|
loop = loop + 1
|
|
if debug or debug_timing:
|
|
print("-- T_exit_Ind - Numpy Index is", t_exit_ind, " ----------------------- Loop", loop, pair)
|
|
if debug_2loops:
|
|
if loop == 3:
|
|
print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Loop debug max met - breaking")
|
|
break
|
|
'''
|
|
Dev phases
|
|
Phase 1
|
|
1) Manage buy, sell, stop enter/exit
|
|
a) Find first buy index
|
|
b) Discover first stop and sell hit after buy index
|
|
c) Chose first instance as trade exit
|
|
|
|
Phase 2
|
|
2) Manage dynamic Stop and ROI Exit
|
|
a) Create trade slice from 1
|
|
b) search within trade slice for dynamice stop hit
|
|
c) search within trade slice for ROI hit
|
|
'''
|
|
|
|
if debug_timing:
|
|
st = s()
|
|
'''
|
|
0 - Find next buy entry
|
|
Finds index for first (buy = 1) flag
|
|
|
|
Requires: np_buy_arr - a 1D array of the 'buy' column. To find next "1"
|
|
Required: t_exit_ind - Either 0, first loop. Or The index we last exited on
|
|
Provides: The next "buy" index after t_exit_ind
|
|
|
|
If -1 is returned no buy has been found in remainder of array, skip to exit loop
|
|
'''
|
|
t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind)
|
|
|
|
if debug:
|
|
print("\n(0) numpy debug \nnp_get_t_open, has returned the next valid buy index as", t_open_ind)
|
|
print("If -1 there are no valid buys in the remainder of ticker data. Skipping to end of loop")
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("0-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
if t_open_ind != -1:
|
|
|
|
"""
|
|
1 - Create view to search within for our open trade
|
|
|
|
The view is our search space for the next Stop or Sell
|
|
Numpy view is employed as:
|
|
1,000 faster than pandas searches
|
|
Pandas cannot assure it will always return a view, it may make a slow copy.
|
|
|
|
The view contains columns:
|
|
buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
|
|
|
|
Requires: np_bslap is our numpy array of the ticker DataFrame
|
|
Requires: t_open_ind is the index row with the buy.
|
|
Provided: np_t_open_v View of array after trade.
|
|
"""
|
|
np_t_open_v = np_bslap[t_open_ind:]
|
|
|
|
if debug:
|
|
print("\n(1) numpy debug \nNumpy view row 0 is now Ticker_Data Index", t_open_ind)
|
|
print("Numpy View: Buy - Open - Close - Sell - High - Low")
|
|
print("Row 0", np_t_open_v[0])
|
|
print("Row 1", np_t_open_v[1], )
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("2-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
2 - Calculate our stop-loss price
|
|
|
|
As stop is based on buy price of our trade
|
|
- (BTO)Buys are Triggered On np_bto, typically the CLOSE of candle
|
|
- (BCO)Buys are Calculated On np_bco, default is OPEN of the next candle.
|
|
This is as we only see the CLOSE after it has happened.
|
|
The back test assumption is we have bought at first available price, the OPEN
|
|
|
|
Requires: np_bslap - is our numpy array of the ticker DataFrame
|
|
Requires: t_open_ind - is the index row with the first buy.
|
|
Requires: p_stop - is the stop rate, ie. 0.99 is -1%
|
|
Provides: np_t_stop_pri - The value stop-loss will be triggered on
|
|
'''
|
|
np_t_stop_pri = (np_bslap[t_open_ind + 1, np_bco] * p_stop)
|
|
|
|
if debug:
|
|
print("\n(2) numpy debug\nStop-Loss has been calculated at:", np_t_stop_pri)
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("2-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
3 - Find candle STO is under Stop-Loss After Trade opened.
|
|
|
|
where [np_sto] (stop tiggered on variable: "close", "low" etc) < np_t_stop_pri
|
|
|
|
Requires: np_t_open_v Numpy view of ticker_data after trade open
|
|
Requires: np_sto User Var(STO)StopTriggeredOn. Typically set to "low" or "close"
|
|
Requires: np_t_stop_pri The stop-loss price STO must fall under to trigger stop
|
|
Provides: np_t_stop_ind The first candle after trade open where STO is under stop-loss
|
|
'''
|
|
np_t_stop_ind = utf1st.find_1st(np_t_open_v[:, np_sto],
|
|
np_t_stop_pri,
|
|
utf1st.cmp_smaller)
|
|
|
|
if debug:
|
|
print("\n(3) numpy debug\nNext view index with STO (stop trigger on) under Stop-Loss is", np_t_stop_ind,
|
|
". STO is using field", np_sto,
|
|
"\nFrom key: buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5\n")
|
|
|
|
print("If -1 returned there is no stop found to end of view, then next two array lines are garbage")
|
|
print("Row", np_t_stop_ind, np_t_open_v[np_t_stop_ind])
|
|
print("Row", np_t_stop_ind + 1, np_t_open_v[np_t_stop_ind + 1])
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("3-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
4 - Find first sell index after trade open
|
|
|
|
First index in the view np_t_open_v where ['sell'] = 1
|
|
|
|
Requires: np_t_open_v - view of ticker_data from buy onwards
|
|
Requires: no_sell - integer '3', the buy column in the array
|
|
Provides: np_t_sell_ind index of view where first sell=1 after buy
|
|
'''
|
|
# 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)
|
|
if debug:
|
|
print("\n(4) numpy debug\nNext view index with sell = 1 is ", np_t_sell_ind)
|
|
print("If 0 or less is returned there is no sell found to end of view, then next lines garbage")
|
|
print("Row", np_t_sell_ind, np_t_open_v[np_t_sell_ind])
|
|
print("Row", np_t_sell_ind + 1, np_t_open_v[np_t_sell_ind + 1])
|
|
if debug_timing:
|
|
t_t = f(st)
|
|
print("4-numpy", str.format('{0:.17f}', t_t))
|
|
st = s()
|
|
|
|
'''
|
|
5 - Determine which was hit first a stop or sell
|
|
To then use as exit index price-field (sell on buy, stop on stop)
|
|
|
|
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
|
|
|
|
Validity of when types of trades may be executed can be summarised as:
|
|
|
|
Tick View
|
|
index index Buy Sell open low close high Stop price
|
|
open 2am 94 -1 0 0 ----- ------ ------ ----- -----
|
|
open 3am 95 0 1 0 ----- ------ trg buy ----- -----
|
|
open 4am 96 1 0 1 Enter trgstop trg sel ROI out Stop out
|
|
open 5am 97 2 0 0 Exit ------ ------- ----- -----
|
|
open 6am 98 3 0 0 ----- ------ ------- ----- -----
|
|
|
|
-1 means not found till end of view i.e no valid Stop found. Exclude from match.
|
|
Stop tiggering in 1, candle we bought at OPEN is valid.
|
|
|
|
Buys and sells are triggered at candle close
|
|
Both with action their postions at the open of the next candle Index + 1
|
|
|
|
Stop and buy Indexes are on the view. To map to the ticker dataframe
|
|
the t_open_ind index should be summed.
|
|
|
|
np_t_stop_ind: Stop Found index in view
|
|
t_exit_ind : Sell found in view
|
|
t_open_ind : Where view was started on ticker_data
|
|
|
|
TODO: fix this frig for logig test,, case/switch/dictionary would be better...
|
|
more so when later testing many options, dynamic stop / roi etc
|
|
cludge - Im setting np_t_sell_ind as 9999999999 when -1 (not found)
|
|
cludge - Im setting np_t_stop_ind as 9999999999 when -1 (not found)
|
|
|
|
'''
|
|
if debug:
|
|
print("\n(5) numpy debug\nStop or Sell Logic Processing")
|
|
|
|
# cludge for logic test (-1) means it was not found, set crazy high to lose < test
|
|
np_t_sell_ind = 99999999 if np_t_sell_ind <= 0 else np_t_sell_ind
|
|
np_t_stop_ind = 99999999 if np_t_stop_ind == -1 else np_t_stop_ind
|
|
|
|
# Stoploss trigger found before a sell =1
|
|
if np_t_stop_ind < 99999999 and np_t_stop_ind <= np_t_sell_ind:
|
|
t_exit_ind = t_open_ind + np_t_stop_ind # Set Exit row index
|
|
t_exit_type = 'stop' # Set Exit type (stop)
|
|
np_t_exit_pri = np_sco # The price field our STOP exit will use
|
|
if debug:
|
|
print("Type STOP is first exit condition. "
|
|
"At view index:", np_t_stop_ind, ". Ticker data exit index is", t_exit_ind)
|
|
|
|
# Buy = 1 found before a stoploss triggered
|
|
elif np_t_sell_ind < 99999999 and np_t_sell_ind < np_t_stop_ind:
|
|
# move sell onto next candle, we only look back on sell
|
|
# will use the open price later.
|
|
t_exit_ind = t_open_ind + np_t_sell_ind + 1 # Set Exit row index
|
|
t_exit_type = 'sell' # Set Exit type (sell)
|
|
np_t_exit_pri = np_open # The price field our SELL exit will use
|
|
if debug:
|
|
print("Type SELL is first exit condition. "
|
|
"At view index", np_t_sell_ind, ". Ticker data exit index is", t_exit_ind)
|
|
|
|
# No stop or buy left in view - set t_exit_last -1 to handle gracefully
|
|
else:
|
|
t_exit_last: int = -1 # Signal loop to exit, no buys or sells found.
|
|
t_exit_type = "No Exit"
|
|
np_t_exit_pri = 999 # field price should be calculated on. 999 a non-existent column
|
|
if debug:
|
|
print("No valid STOP or SELL found. Signalling t_exit_last to gracefully exit")
|
|
|
|
# TODO: fix having to cludge/uncludge this ..
|
|
# Undo cludge
|
|
np_t_sell_ind = -1 if np_t_sell_ind == 99999999 else np_t_sell_ind
|
|
np_t_stop_ind = -1 if np_t_stop_ind == 99999999 else np_t_stop_ind
|
|
|
|
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 current stop price based on buy price_
|
|
# This is slow but don't care about performance in debug
|
|
#
|
|
# When referencing equiv np_column, as example np_sto, its 5 in numpy and 6 in df, so +1
|
|
# as there is no data column in the numpy array.
|
|
bslap['np_stop_pri'] = np_t_stop_pri
|
|
|
|
# Buy
|
|
print("\n\nDATAFRAME DEBUG =================== BUY ", pair)
|
|
print("Numpy Array BUY Index is:", 0)
|
|
print("DataFrame BUY Index is:", t_open_ind, "displaying DF \n")
|
|
print("HINT, BUY trade should use OPEN price from next candle, i.e ", t_open_ind + 1)
|
|
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")
|
|
|
|
# Stop - Stops trigger price np_sto (+1 for pandas column), and price received np_sco +1. (Stop Trigger|Calculated On)
|
|
if np_t_stop_ind < 0:
|
|
print("DATAFRAME DEBUG =================== STOP ", pair)
|
|
print("No STOPS were found until the end of ticker data file\n")
|
|
else:
|
|
print("DATAFRAME DEBUG =================== STOP ", pair)
|
|
print("Numpy Array STOP Index is:", np_t_stop_ind, "View starts at index", t_open_ind)
|
|
df_stop_index = (t_open_ind + np_t_stop_ind)
|
|
|
|
print("DataFrame STOP Index is:", df_stop_index, "displaying DF \n")
|
|
print("First Stoploss trigger after Trade entered at OPEN in candle", t_open_ind + 1, "is ",
|
|
df_stop_index, ": \n",
|
|
str.format('{0:.17f}', bslap.iloc[df_stop_index][np_sto + 1]),
|
|
"is less than", str.format('{0:.17f}', np_t_stop_pri))
|
|
|
|
print("A stoploss exit will be calculated at rate:",
|
|
str.format('{0:.17f}', bslap.iloc[df_stop_index][np_sco + 1]))
|
|
|
|
print("\nHINT, STOPs should exit in-candle, i.e", df_stop_index,
|
|
": As live STOPs are not linked to O-C times")
|
|
|
|
st_is = df_stop_index - 1 # Print stop index start, line before
|
|
st_if = df_stop_index + 2 # Print stop index finish, line after
|
|
print(bslap.iloc[st_is:st_if], "\n")
|
|
|
|
# Sell
|
|
if np_t_sell_ind < 0:
|
|
print("DATAFRAME DEBUG =================== SELL ", pair)
|
|
print("No SELLS were found till the end of ticker data file\n")
|
|
else:
|
|
print("DATAFRAME DEBUG =================== SELL ", pair)
|
|
print("Numpy View SELL Index is:", np_t_sell_ind, "View starts at index", t_open_ind)
|
|
df_sell_index = (t_open_ind + np_t_sell_ind)
|
|
|
|
print("DataFrame SELL Index is:", df_sell_index, "displaying DF \n")
|
|
print("First Sell Index after Trade open is in candle", df_sell_index)
|
|
print("HINT, if exit is SELL (not stop) trade should use OPEN price from next candle",
|
|
df_sell_index + 1)
|
|
sl_is = df_sell_index - 1 # Print sell index start, line before
|
|
sl_if = df_sell_index + 3 # Print sell index finish, line after
|
|
print(bslap.iloc[sl_is:sl_if], "\n")
|
|
|
|
# Chosen Exit (stop or sell)
|
|
|
|
print("DATAFRAME DEBUG =================== EXIT ", pair)
|
|
print("Exit type is :", t_exit_type)
|
|
print("trade exit price field is", np_t_exit_pri, "\n")
|
|
|
|
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.
|
|
|
|
# TODO no! this is hard coded bleh fix this open
|
|
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, np_t_exit_pri]
|
|
|
|
# Catch no exit found
|
|
if t_exit_type == "No Exit":
|
|
np_trade_exit_price = 0
|
|
|
|
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 Enter Price 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("//////////////////////////////////////////////")
|
|
|
|
else: # no buys were found, step 0 returned -1
|
|
# Gracefully exit the loop
|
|
t_exit_last == -1
|
|
if debug:
|
|
print("\n(E) No buys were found in remaining ticker file. Exiting", pair)
|
|
|
|
# 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 or t_exit_last == -1:
|
|
"""
|
|
Break loop and go on to next pair.
|
|
|
|
When last trade exit equals index of last exit, there is no
|
|
opportunity to close any more trades.
|
|
"""
|
|
# 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.
|
|
"""
|
|
# Build trade dictionary
|
|
## In general if a field can be calculated later from other fields leave blank here
|
|
## Its X(number of trades faster) to calc all in a single vector than 1 trade at a time
|
|
|
|
# create a new dict
|
|
close_index: int = t_exit_ind
|
|
bslap_result = {} # Must have at start or we end up with a list of multiple same last result
|
|
bslap_result["pair"] = pair
|
|
bslap_result["profit_percent"] = "" # To be 1 vector calc across trades when loop complete
|
|
bslap_result["profit_abs"] = "" # To be 1 vector calc across trades when loop complete
|
|
bslap_result["open_time"] = np_bslap_dates[t_open_ind + 1] # use numpy array, pandas 20x slower
|
|
bslap_result["close_time"] = np_bslap_dates[close_index] # use numpy array, pandas 20x slower
|
|
bslap_result["open_index"] = t_open_ind + 1 # +1 as we buy on next.
|
|
bslap_result["close_index"] = close_index
|
|
bslap_result["trade_duration"] = "" # To be 1 vector calc across trades when loop complete
|
|
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
|
|
# append the dict to the list and print list
|
|
bslap_pair_results.append(bslap_result)
|
|
|
|
if debug:
|
|
print("The trade dict is: \n", bslap_result)
|
|
print("Trades dicts in list after append are: \n ", 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)
|
|
|
|
if self.use_backslap:
|
|
logger.info(
|
|
'\n====================================================== '
|
|
'BackSLAP REPORT'
|
|
' =======================================================\n'
|
|
'%s',
|
|
self._generate_text_table(
|
|
data,
|
|
results
|
|
)
|
|
)
|
|
#optional print trades
|
|
if self.backslap_show_trades:
|
|
TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs',
|
|
'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1)
|
|
def to_fwf(df, fname):
|
|
content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql')
|
|
print(content)
|
|
|
|
DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt")
|
|
|
|
#optional save trades
|
|
if self.backslap_save_trades:
|
|
TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs',
|
|
'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1)
|
|
def to_fwf(df, fname):
|
|
content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql')
|
|
open(fname, "w").write(content)
|
|
DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt")
|
|
|
|
else:
|
|
logger.info(
|
|
'\n================================================= '
|
|
'BACKTEST 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()
|