add async method
This commit is contained in:
Matthias
parent
721fb3e326
commit
2602cbe683
@ -161,14 +161,6 @@ class Arguments(object):
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dest='exportfilename',
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metavar='PATH',
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)
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parser.add_argument(
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'--backslap',
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help="Utilize the Backslapping approach instead of the default Backtesting. This should provide more "
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"accurate results, unless you are utilizing Min/Max function in your strategy.",
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required=False,
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dest='backslap',
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action='store_true'
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)
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@staticmethod
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def optimizer_shared_options(parser: argparse.ArgumentParser) -> None:
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@ -236,7 +228,7 @@ class Arguments(object):
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Builds and attaches all subcommands
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:return: None
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"""
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from freqtrade.optimize import backtesting, hyperopt
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from freqtrade.optimize import backtesting, backslapping, hyperopt
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subparsers = self.parser.add_subparsers(dest='subparser')
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@ -246,6 +238,12 @@ class Arguments(object):
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self.optimizer_shared_options(backtesting_cmd)
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self.backtesting_options(backtesting_cmd)
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# Add backslapping subcommand
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backslapping_cmd = subparsers.add_parser('backslapping', help='backslapping module')
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backslapping_cmd.set_defaults(func=backslapping.start)
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self.optimizer_shared_options(backslapping_cmd)
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self.backtesting_options(backslapping_cmd)
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# Add hyperopt subcommand
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hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
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hyperopt_cmd.set_defaults(func=hyperopt.start)
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@ -1,48 +1,35 @@
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import timeit
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from argparse import Namespace
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import logging
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from typing import Dict, Any
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from pandas import DataFrame
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from freqtrade.exchange import Exchange
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from freqtrade.optimize.optimize import IOptimize, BacktestResult, setup_configuration
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from freqtrade.strategy import IStrategy
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from freqtrade.strategy.interface import SellType
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from freqtrade.strategy.resolver import StrategyResolver
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logger = logging.getLogger(__name__)
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class Backslapping:
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class Backslapping(IOptimize):
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"""
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provides a quick way to evaluate strategies over a longer term of time
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"""
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def __init__(self, config: Dict[str, Any], exchange = None) -> None:
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def __init__(self, config: Dict[str, Any]) -> None:
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"""
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constructor
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"""
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self.config = config
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self.strategy: IStrategy = StrategyResolver(self.config).strategy
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self.ticker_interval = self.strategy.ticker_interval
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self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
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self.populate_buy_trend = self.strategy.populate_buy_trend
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self.populate_sell_trend = self.strategy.populate_sell_trend
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###
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#
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###
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if exchange is None:
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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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else:
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self.exchange = exchange
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super().__init__(config)
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self.fee = self.exchange.get_fee()
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self.stop_loss_value = self.strategy.stoploss
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#### backslap config
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# 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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@ -96,8 +83,8 @@ class Backslapping:
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if self.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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ticker_data = self.advise_sell(self.advise_buy(pair_data, {'pair': pair}),
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{'pair': pair})[headers].copy()
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if self.debug_timing: # print time taken
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flt = self.f(fl)
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@ -132,7 +119,7 @@ class Backslapping:
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bslap_results_df = self.vector_fill_results_table(bslap_results_df, pair)
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else:
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from freqtrade.optimize.backtesting import BacktestResult
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bslap_results_df = []
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bslap_results_df = DataFrame.from_records(bslap_results_df, columns=BacktestResult._fields)
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@ -787,3 +774,18 @@ class Backslapping:
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# Send back List of trade dicts
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return bslap_pair_results
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def start(args: Namespace) -> None:
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"""
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Start Backtesting script
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:param args: Cli args from Arguments()
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:return: None
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"""
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# Initialize configuration
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config = setup_configuration(args)
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logger.info('Starting freqtrade in Backtesting mode')
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# Initialize backtesting object
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backslapping = Backslapping(config)
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backslapping.start()
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@ -4,51 +4,21 @@
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This module contains the backtesting logic
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"""
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import logging
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import operator
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from argparse import Namespace
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple
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from typing import Any, Dict, List, Optional
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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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from pandas import DataFrame
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import freqtrade.optimize as optimize
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from freqtrade import DependencyException, constants
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from freqtrade.optimize.optimize import IOptimize, BacktestResult, setup_configuration
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.exchange import Exchange
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from freqtrade.misc import file_dump_json
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from freqtrade.optimize.backslapping import Backslapping
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from freqtrade.persistence import Trade
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from freqtrade.strategy.interface import SellType
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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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from collections import OrderedDict
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import timeit
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from time import sleep
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logger = logging.getLogger(__name__)
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class BacktestResult(NamedTuple):
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"""
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NamedTuple Defining BacktestResults inputs.
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"""
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pair: str
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profit_percent: float
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profit_abs: float
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open_time: datetime
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close_time: datetime
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open_index: int
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close_index: int
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trade_duration: float
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open_at_end: bool
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open_rate: float
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close_rate: float
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sell_reason: SellType
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class Backtesting(object):
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class Backtesting(IOptimize):
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"""
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Backtesting class, this class contains all the logic to run a backtest
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@ -58,139 +28,7 @@ class Backtesting(object):
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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self.config = config
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self.strategy: IStrategy = StrategyResolver(self.config).strategy
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self.ticker_interval = self.strategy.ticker_interval
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self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
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self.advise_buy = self.strategy.advise_buy
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self.advise_sell = self.strategy.advise_sell
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# Reset keys for backtesting
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self.config['exchange']['key'] = ''
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self.config['exchange']['secret'] = ''
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self.config['exchange']['password'] = ''
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self.config['exchange']['uid'] = ''
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self.config['dry_run'] = True
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self.exchange = Exchange(self.config)
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self.fee = self.exchange.get_fee()
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self.stop_loss_value = self.strategy.stoploss
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#### backslap config
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'''
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Numpy arrays are used for 100x speed up
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We requires setting Int values for
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buy stop triggers and stop calculated on
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
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'''
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self.np_buy: int = 0
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self.np_open: int = 1
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self.np_close: int = 2
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self.np_sell: int = 3
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self.np_high: int = 4
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self.np_low: int = 5
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self.np_stop: int = 6
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self.np_bto: int = self.np_close # buys_triggered_on - should be close
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self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
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self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
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self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
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# self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
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# self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
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if 'backslap' in config:
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self.use_backslap = config['backslap'] # Enable backslap - if false Orginal code is executed.
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else:
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self.use_backslap = False
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logger.info("using backslap: {}".format(self.use_backslap))
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self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
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self.debug_timing = False # Stages within Backslap
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self.debug_2loops = False # Limit each pair to two loops, useful when debugging
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self.debug_vector = False # Debug vector calcs
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self.debug_timing_main_loop = False # print overall timing per pair - works in Backtest and Backslap
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self.backslap_show_trades = False # prints trades in addition to summary report
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self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt
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self.stop_stops: int = 9999 # stop back testing any pair with this many stops, set to 999999 to not hit
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self.backslap = Backslapping(config)
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@staticmethod
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def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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Get the maximum timeframe for the given backtest data
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:param data: dictionary with preprocessed backtesting data
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:return: tuple containing min_date, max_date
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"""
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timeframe = [
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(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
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for frame in data.values()
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]
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return min(timeframe, key=operator.itemgetter(0))[0], \
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max(timeframe, key=operator.itemgetter(1))[1]
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def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:return: pretty printed table with tabulate as str
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"""
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stake_currency = str(self.config.get('stake_currency'))
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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
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tabular_data = []
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headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
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'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
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for pair in data:
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result = results[results.pair == pair]
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tabular_data.append([
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pair,
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len(result.index),
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result.profit_percent.mean() * 100.0,
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result.profit_percent.sum() * 100.0,
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result.profit_abs.sum(),
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str(timedelta(
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minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
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len(result[result.profit_abs > 0]),
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len(result[result.profit_abs < 0])
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])
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# Append Total
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tabular_data.append([
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'TOTAL',
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len(results.index),
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results.profit_percent.mean() * 100.0,
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results.profit_percent.sum() * 100.0,
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results.profit_abs.sum(),
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str(timedelta(
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minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
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len(results[results.profit_abs > 0]),
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len(results[results.profit_abs < 0])
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])
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return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
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def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
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"""
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Generate small table outlining Backtest results
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"""
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tabular_data = []
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headers = ['Sell Reason', 'Count']
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for reason, count in results['sell_reason'].value_counts().iteritems():
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tabular_data.append([reason.value, count])
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return tabulate(tabular_data, headers=headers, tablefmt="pipe")
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def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None:
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records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
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t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
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t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
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for index, t in results.iterrows()]
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if records:
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logger.info('Dumping backtest results to %s', recordfilename)
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file_dump_json(recordfilename, records)
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super().__init__(config)
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def _get_sell_trade_entry(
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self, pair: str, buy_row: DataFrame,
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@ -217,6 +55,7 @@ class Backtesting(object):
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sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
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sell_row.sell)
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if sell.sell_flag:
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return BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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@ -253,14 +92,7 @@ class Backtesting(object):
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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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def run(self, args: Dict) -> DataFrame:
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"""
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Implements backtesting functionality
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@ -275,26 +107,13 @@ class Backtesting(object):
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position_stacking: do we allow position stacking? (default: False)
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:return: DataFrame
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"""
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use_backslap = self.use_backslap
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debug_timing = self.debug_timing_main_loop
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if use_backslap: # Use Back Slap code
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return self.backslap.run(args)
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else: # use Original Back test code
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########################## Original BT loop
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headers = ['date', 'buy', 'open', 'close', 'sell']
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processed = args['processed']
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max_open_trades = args.get('max_open_trades', 0)
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position_stacking = args.get('position_stacking', False)
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trades = []
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trade_count_lock: Dict = {}
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for pair, pair_data in processed.items():
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if debug_timing: # Start timer
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fl = self.s()
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.advise_sell(
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@ -306,11 +125,6 @@ class Backtesting(object):
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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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@ -341,165 +155,9 @@ class Backtesting(object):
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# This happens only if the buy-signal was with the last candle
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lock_pair_until = ticker_data.iloc[-1].date
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if debug_timing: # print time taken
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tt = self.f(st)
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print("Time to BackTest :", pair, round(tt, 10))
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print("-----------------------")
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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####################### Original BT loop end
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def start(self) -> None:
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"""
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Run a backtesting end-to-end
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:return: None
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"""
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data = {}
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pairs = self.config['exchange']['pair_whitelist']
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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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if self.config.get('live'):
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logger.info('Downloading data for all pairs in whitelist ...')
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for pair in pairs:
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data[pair] = self.exchange.get_ticker_history(pair, self.ticker_interval)
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else:
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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timerange = Arguments.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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data = optimize.load_data(
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self.config['datadir'],
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pairs=pairs,
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ticker_interval=self.ticker_interval,
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refresh_pairs=self.config.get('refresh_pairs', False),
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exchange=self.exchange,
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timerange=timerange
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)
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ld_files = self.s()
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if not data:
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logger.critical("No data found. Terminating.")
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return
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# Use max_open_trades in backtesting, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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max_open_trades = self.config['max_open_trades']
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else:
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logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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preprocessed = self.tickerdata_to_dataframe(data)
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t_t = self.f(ld_files)
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print("Load from json to file to df in mem took", t_t)
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# Print timeframe
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min_date, max_date = self.get_timeframe(preprocessed)
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logger.info(
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'Measuring data from %s up to %s (%s days)..',
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min_date.isoformat(),
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max_date.isoformat(),
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||||
(max_date - min_date).days
|
||||
)
|
||||
|
||||
# Execute backtest and print results
|
||||
results = self.backtest(
|
||||
{
|
||||
'stake_amount': self.config.get('stake_amount'),
|
||||
'processed': preprocessed,
|
||||
'max_open_trades': max_open_trades,
|
||||
'position_stacking': self.config.get('position_stacking', 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
|
||||
)
|
||||
)
|
||||
|
||||
if 'sell_reason' in results.columns:
|
||||
logger.info(
|
||||
'\n' +
|
||||
' SELL READON STATS '.center(119, '=') +
|
||||
'\n%s \n',
|
||||
self._generate_text_table_sell_reason(data, results)
|
||||
|
||||
)
|
||||
else:
|
||||
logger.info("no sell reasons available!")
|
||||
|
||||
logger.info(
|
||||
'\n' +
|
||||
' LEFT OPEN TRADES REPORT '.center(119, '=') +
|
||||
'\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'] = ''
|
||||
config['backslap'] = args.backslap
|
||||
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
|
||||
raise DependencyException('stake amount could not be "%s" for backtesting' %
|
||||
constants.UNLIMITED_STAKE_AMOUNT)
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def start(args: Namespace) -> None:
|
||||
|
||||
@ -276,7 +276,7 @@ class Hyperopt(Backtesting):
|
||||
self.strategy.stoploss = params['stoploss']
|
||||
|
||||
processed = load(TICKERDATA_PICKLE)
|
||||
results = self.backtest(
|
||||
results = self.run(
|
||||
{
|
||||
'stake_amount': self.config['stake_amount'],
|
||||
'processed': processed,
|
||||
|
||||
322
freqtrade/optimize/optimize.py
Normal file
322
freqtrade/optimize/optimize.py
Normal file
@ -0,0 +1,322 @@
|
||||
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
|
||||
|
||||
"""
|
||||
This module contains the backtesting logic
|
||||
"""
|
||||
import logging
|
||||
import operator
|
||||
from abc import ABC, abstractmethod
|
||||
from argparse import Namespace
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
|
||||
|
||||
import arrow
|
||||
from pandas import DataFrame
|
||||
from tabulate import tabulate
|
||||
|
||||
from freqtrade import DependencyException, constants
|
||||
from freqtrade.arguments import Arguments
|
||||
from freqtrade.configuration import Configuration
|
||||
from freqtrade.exchange import Exchange
|
||||
from freqtrade.misc import file_dump_json
|
||||
import freqtrade.optimize as optimize
|
||||
from freqtrade.strategy.interface import SellType
|
||||
from freqtrade.strategy.resolver import IStrategy, StrategyResolver
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BacktestResult(NamedTuple):
|
||||
"""
|
||||
NamedTuple Defining BacktestResults inputs.
|
||||
"""
|
||||
pair: str
|
||||
profit_percent: float
|
||||
profit_abs: float
|
||||
open_time: datetime
|
||||
close_time: datetime
|
||||
open_index: int
|
||||
close_index: int
|
||||
trade_duration: float
|
||||
open_at_end: bool
|
||||
open_rate: float
|
||||
close_rate: float
|
||||
sell_reason: SellType
|
||||
|
||||
|
||||
class IOptimize(ABC):
|
||||
"""
|
||||
Backtesting Abstract class, this class contains all the logic to run a backtest
|
||||
|
||||
To run a backtest:
|
||||
backtesting = Backtesting(config)
|
||||
backtesting.start()
|
||||
"""
|
||||
|
||||
def __init__(self, config: Dict[str, Any]) -> None:
|
||||
self.config = config
|
||||
|
||||
# Reset keys for backtesting
|
||||
self.config['exchange']['key'] = ''
|
||||
self.config['exchange']['secret'] = ''
|
||||
self.config['exchange']['password'] = ''
|
||||
self.config['exchange']['uid'] = ''
|
||||
self.config['dry_run'] = True
|
||||
self.strategylist: List[IStrategy] = []
|
||||
if self.config.get('strategy_list', None):
|
||||
# Force one interval
|
||||
self.ticker_interval = str(self.config.get('ticker_interval'))
|
||||
for strat in list(self.config['strategy_list']):
|
||||
stratconf = deepcopy(self.config)
|
||||
stratconf['strategy'] = strat
|
||||
self.strategylist.append(StrategyResolver(stratconf).strategy)
|
||||
|
||||
else:
|
||||
# only one strategy
|
||||
strat = StrategyResolver(self.config).strategy
|
||||
|
||||
self.strategylist.append(StrategyResolver(self.config).strategy)
|
||||
# Load one strategy
|
||||
self._set_strategy(self.strategylist[0])
|
||||
|
||||
self.exchange = Exchange(self.config)
|
||||
self.fee = self.exchange.get_fee()
|
||||
|
||||
def _set_strategy(self, strategy):
|
||||
"""
|
||||
Load strategy into backtesting
|
||||
"""
|
||||
self.strategy = strategy
|
||||
self.ticker_interval = self.config.get('ticker_interval')
|
||||
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
|
||||
self.advise_buy = strategy.advise_buy
|
||||
self.advise_sell = strategy.advise_sell
|
||||
|
||||
def _get_timeframe(self, data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
|
||||
"""
|
||||
Get the maximum timeframe for the given backtest data
|
||||
:param data: dictionary with preprocessed backtesting data
|
||||
:return: tuple containing min_date, max_date
|
||||
"""
|
||||
timeframe = [
|
||||
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
|
||||
for frame in data.values()
|
||||
]
|
||||
return min(timeframe, key=operator.itemgetter(0))[0], \
|
||||
max(timeframe, key=operator.itemgetter(1))[1]
|
||||
|
||||
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
|
||||
"""
|
||||
Generates and returns a text table for the given backtest data and the results dataframe
|
||||
:return: pretty printed table with tabulate as str
|
||||
"""
|
||||
stake_currency = str(self.config.get('stake_currency'))
|
||||
|
||||
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
|
||||
tabular_data = []
|
||||
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
|
||||
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
|
||||
for pair in data:
|
||||
result = results[results.pair == pair]
|
||||
tabular_data.append([
|
||||
pair,
|
||||
len(result.index),
|
||||
result.profit_percent.mean() * 100.0,
|
||||
result.profit_percent.sum() * 100.0,
|
||||
result.profit_abs.sum(),
|
||||
str(timedelta(
|
||||
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
|
||||
len(result[result.profit_abs > 0]),
|
||||
len(result[result.profit_abs < 0])
|
||||
])
|
||||
|
||||
# Append Total
|
||||
tabular_data.append([
|
||||
'TOTAL',
|
||||
len(results.index),
|
||||
results.profit_percent.mean() * 100.0,
|
||||
results.profit_percent.sum() * 100.0,
|
||||
results.profit_abs.sum(),
|
||||
str(timedelta(
|
||||
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
|
||||
len(results[results.profit_abs > 0]),
|
||||
len(results[results.profit_abs < 0])
|
||||
])
|
||||
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
|
||||
|
||||
def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
|
||||
"""
|
||||
Generate small table outlining Backtest results
|
||||
"""
|
||||
tabular_data = []
|
||||
headers = ['Sell Reason', 'Count']
|
||||
for reason, count in results['sell_reason'].value_counts().iteritems():
|
||||
tabular_data.append([reason.value, count])
|
||||
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
|
||||
|
||||
def _generate_text_table_strategy(self, all_results: dict) -> str:
|
||||
"""
|
||||
Generate summary table per strategy
|
||||
"""
|
||||
stake_currency = str(self.config.get('stake_currency'))
|
||||
|
||||
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
|
||||
tabular_data = []
|
||||
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
|
||||
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
|
||||
for strategy, results in all_results.items():
|
||||
tabular_data.append([
|
||||
strategy,
|
||||
len(results.index),
|
||||
results.profit_percent.mean() * 100.0,
|
||||
results.profit_percent.sum() * 100.0,
|
||||
results.profit_abs.sum(),
|
||||
str(timedelta(
|
||||
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
|
||||
len(results[results.profit_abs > 0]),
|
||||
len(results[results.profit_abs < 0])
|
||||
])
|
||||
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
|
||||
|
||||
def _store_backtest_result(self, recordfilename: str, results: DataFrame,
|
||||
strategyname: Optional[str] = None) -> None:
|
||||
|
||||
records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
|
||||
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
|
||||
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
|
||||
for index, t in results.iterrows()]
|
||||
|
||||
if records:
|
||||
if strategyname:
|
||||
# Inject strategyname to filename
|
||||
recname = Path(recordfilename)
|
||||
recordfilename = str(Path.joinpath(
|
||||
recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix))
|
||||
logger.info('Dumping backtest results to %s', recordfilename)
|
||||
file_dump_json(recordfilename, records)
|
||||
|
||||
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_candle_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
|
||||
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
|
||||
if self.config.get('use_max_market_positions', True):
|
||||
max_open_trades = self.config['max_open_trades']
|
||||
else:
|
||||
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
|
||||
max_open_trades = 0
|
||||
all_results = {}
|
||||
|
||||
for strat in self.strategylist:
|
||||
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
|
||||
self._set_strategy(strat)
|
||||
|
||||
# need to reprocess data every time to populate signals
|
||||
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
|
||||
all_results[self.strategy.get_strategy_name()] = self.run(
|
||||
{
|
||||
'stake_amount': self.config.get('stake_amount'),
|
||||
'processed': preprocessed,
|
||||
'max_open_trades': max_open_trades,
|
||||
'position_stacking': self.config.get('position_stacking', False),
|
||||
}
|
||||
)
|
||||
|
||||
for strategy, results in all_results.items():
|
||||
|
||||
if self.config.get('export', False):
|
||||
self._store_backtest_result(self.config['exportfilename'], results,
|
||||
strategy if len(self.strategylist) > 1 else None)
|
||||
|
||||
print(f"Result for strategy {strategy}")
|
||||
print(' BACKTESTING REPORT '.center(119, '='))
|
||||
print(self._generate_text_table(data, results))
|
||||
|
||||
print(' SELL REASON STATS '.center(119, '='))
|
||||
print(self._generate_text_table_sell_reason(data, results))
|
||||
|
||||
print(' LEFT OPEN TRADES REPORT '.center(119, '='))
|
||||
print(self._generate_text_table(data, results.loc[results.open_at_end]))
|
||||
print()
|
||||
if len(all_results) > 1:
|
||||
# Print Strategy summary table
|
||||
print(' Strategy Summary '.center(119, '='))
|
||||
print(self._generate_text_table_strategy(all_results))
|
||||
print('\nFor more details, please look at the detail tables above')
|
||||
|
||||
@abstractmethod
|
||||
def run(self, args: Dict) -> DataFrame:
|
||||
"""
|
||||
Runs backtesting functionality.
|
||||
|
||||
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
|
||||
Of course try to not have ugly code. By some accessor are sometime slower than functions.
|
||||
Avoid, logging on this method
|
||||
|
||||
:param args: a dict containing:
|
||||
stake_amount: btc amount to use for each trade
|
||||
processed: a processed dictionary with format {pair, data}
|
||||
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
|
||||
position_stacking: do we allow position stacking? (default: False)
|
||||
:return: DataFrame
|
||||
"""
|
||||
|
||||
|
||||
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
|
||||
Loading…
Reference in New Issue
Block a user