Merge pull request #4215 from freqtrade/refactor/backtest
Small backtest refactor, introduce calling `bot_loop_start` in backtesting
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3fefb6f1c8
@ -49,8 +49,9 @@ This loop will be repeated again and again until the bot is stopped.
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[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
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* Load historic data for configured pairlist.
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* Calculate indicators (calls `populate_indicators()`).
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* Calls `populate_buy_trend()` and `populate_sell_trend()`
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* Calls `bot_loop_start()` once.
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* Calculate indicators (calls `populate_indicators()` once per pair).
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* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair)
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* Loops per candle simulating entry and exit points.
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* Generate backtest report output
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@ -6,7 +6,7 @@ This module contains the backtesting logic
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import logging
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from collections import defaultdict
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from copy import deepcopy
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from datetime import datetime, timedelta
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from datetime import datetime, timedelta, timezone
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple
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from pandas import DataFrame
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@ -26,6 +26,7 @@ from freqtrade.plugins.pairlistmanager import PairListManager
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from freqtrade.plugins.protectionmanager import ProtectionManager
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
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from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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logger = logging.getLogger(__name__)
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@ -76,6 +77,8 @@ class Backtesting:
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# Reset keys for backtesting
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remove_credentials(self.config)
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self.strategylist: List[IStrategy] = []
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self.all_results: Dict[str, Dict] = {}
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self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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dataprovider = DataProvider(self.config, self.exchange)
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@ -150,6 +153,10 @@ class Backtesting:
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self.strategy.order_types['stoploss_on_exchange'] = False
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def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
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"""
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Loads backtest data and returns the data combined with the timerange
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as tuple.
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"""
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timerange = TimeRange.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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@ -424,6 +431,53 @@ class Backtesting:
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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backtest_start_time = datetime.now(timezone.utc)
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self._set_strategy(strat)
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strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
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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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# Must come from strategy config, as the strategy may modify this setting.
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max_open_trades = self.strategy.config['max_open_trades']
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else:
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logger.info(
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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# need to reprocess data every time to populate signals
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preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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preprocessed[pair] = trim_dataframe(df, timerange)
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min_date, max_date = history.get_timerange(preprocessed)
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logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(max_date - min_date).days} days)..')
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# Execute backtest and store results
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results = self.backtest(
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processed=preprocessed,
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stake_amount=self.config['stake_amount'],
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start_date=min_date.datetime,
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end_date=max_date.datetime,
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max_open_trades=max_open_trades,
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position_stacking=self.config.get('position_stacking', False),
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enable_protections=self.config.get('enable_protections', False),
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)
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backtest_end_time = datetime.now(timezone.utc)
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self.all_results[self.strategy.get_strategy_name()] = {
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'results': results,
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'config': self.strategy.config,
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'locks': PairLocks.locks,
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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}
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return min_date, max_date
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def start(self) -> None:
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"""
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Run backtesting end-to-end
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@ -431,55 +485,15 @@ class Backtesting:
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"""
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data: Dict[str, Any] = {}
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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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position_stacking = self.config.get('position_stacking', False)
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data, timerange = self.load_bt_data()
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all_results = {}
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min_date = None
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max_date = None
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for strat in self.strategylist:
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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self._set_strategy(strat)
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min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
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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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# Must come from strategy config, as the strategy may modify this setting.
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max_open_trades = self.strategy.config['max_open_trades']
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else:
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logger.info(
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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max_open_trades = 0
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# need to reprocess data every time to populate signals
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preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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preprocessed[pair] = trim_dataframe(df, timerange)
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min_date, max_date = history.get_timerange(preprocessed)
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logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(max_date - min_date).days} days)..')
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# Execute backtest and print results
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results = self.backtest(
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processed=preprocessed,
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stake_amount=self.config['stake_amount'],
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start_date=min_date.datetime,
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end_date=max_date.datetime,
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max_open_trades=max_open_trades,
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position_stacking=position_stacking,
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enable_protections=self.config.get('enable_protections', False),
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)
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all_results[self.strategy.get_strategy_name()] = {
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'results': results,
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'config': self.strategy.config,
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'locks': PairLocks.locks,
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}
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stats = generate_backtest_stats(data, all_results, min_date=min_date, max_date=max_date)
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stats = generate_backtest_stats(data, self.all_results,
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min_date=min_date, max_date=max_date)
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if self.config.get('export', False):
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store_backtest_stats(self.config['exportfilename'], stats)
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@ -650,7 +650,7 @@ class Hyperopt:
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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preprocessed[pair] = trim_dataframe(df, timerange)
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min_date, max_date = get_timerange(data)
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min_date, max_date = get_timerange(preprocessed)
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logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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@ -282,6 +282,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
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'backtest_end_ts': max_date.int_timestamp * 1000,
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'backtest_days': backtest_days,
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'backtest_run_start_ts': content['backtest_start_time'],
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'backtest_run_end_ts': content['backtest_end_time'],
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'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
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'market_change': market_change,
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'pairlist': list(btdata.keys()),
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@ -290,6 +293,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
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'max_open_trades': (config['max_open_trades']
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if config['max_open_trades'] != float('inf') else -1),
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'timeframe': config['timeframe'],
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'timerange': config.get('timerange', ''),
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'enable_protections': config.get('enable_protections', False),
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'strategy_name': strategy,
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# Parameters relevant for backtesting
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'stoploss': config['stoploss'],
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'trailing_stop': config.get('trailing_stop', False),
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@ -350,17 +350,17 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
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default_conf['timerange'] = '-1510694220'
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backtesting = Backtesting(default_conf)
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backtesting.strategy.bot_loop_start = MagicMock()
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backtesting.start()
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# check the logs, that will contain the backtest result
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exists = [
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'Using stake_currency: BTC ...',
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'Using stake_amount: 0.001 ...',
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'Backtesting with data from 2017-11-14 21:17:00 '
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'up to 2017-11-14 22:59:00 (0 days)..'
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]
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for line in exists:
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assert log_has(line, caplog)
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assert backtesting.strategy.dp._pairlists is not None
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assert backtesting.strategy.bot_loop_start.call_count == 1
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def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) -> None:
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@ -722,8 +722,6 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
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'Parameter --timerange detected: 1510694220-1510700340 ...',
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f'Using data directory: {testdatadir} ...',
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'Using stake_currency: BTC ...',
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'Using stake_amount: 0.001 ...',
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'Loading data from 2017-11-14 20:57:00 '
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'up to 2017-11-14 22:58:00 (0 days)..',
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'Backtesting with data from 2017-11-14 21:17:00 '
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@ -786,8 +784,6 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
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'Parameter --timerange detected: 1510694220-1510700340 ...',
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f'Using data directory: {testdatadir} ...',
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'Using stake_currency: BTC ...',
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'Using stake_amount: 0.001 ...',
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'Loading data from 2017-11-14 20:57:00 '
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'up to 2017-11-14 22:58:00 (0 days)..',
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'Backtesting with data from 2017-11-14 21:17:00 '
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@ -865,8 +861,6 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
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'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
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'Parameter --timerange detected: 1510694220-1510700340 ...',
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f'Using data directory: {testdatadir} ...',
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'Using stake_currency: BTC ...',
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'Using stake_amount: 0.001 ...',
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'Loading data from 2017-11-14 20:57:00 '
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'up to 2017-11-14 22:58:00 (0 days)..',
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'Backtesting with data from 2017-11-14 21:17:00 '
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@ -77,7 +77,10 @@ def test_generate_backtest_stats(default_conf, testdatadir):
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SellType.ROI, SellType.FORCE_SELL]
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}),
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'config': default_conf,
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'locks': []}
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'locks': [],
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'backtest_start_time': Arrow.utcnow().int_timestamp,
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'backtest_end_time': Arrow.utcnow().int_timestamp,
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}
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}
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timerange = TimeRange.parse_timerange('1510688220-1510700340')
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min_date = Arrow.fromtimestamp(1510688220)
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