Refactor loading of bt data to backtesting ...
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86624411c6
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@ -183,6 +183,7 @@ def load_data(datadir: Path,
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timerange: Optional[TimeRange] = None,
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fill_up_missing: bool = True,
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startup_candles: int = 0,
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fail_without_data: bool = False
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) -> Dict[str, DataFrame]:
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"""
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Loads ticker history data for a list of pairs
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@ -195,6 +196,7 @@ def load_data(datadir: Path,
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:param timerange: Limit data to be loaded to this timerange
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:param fill_up_missing: Fill missing values with "No action"-candles
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:param startup_candles: Additional candles to load at the start of the period
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:param fail_without_data: Raise OperationalException if no data is found.
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:return: dict(<pair>:<Dataframe>)
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TODO: refresh_pairs is still used by edge to keep the data uptodate.
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This should be replaced in the future. Instead, writing the current candles to disk
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@ -208,9 +210,13 @@ def load_data(datadir: Path,
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datadir=datadir, timerange=timerange,
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refresh_pairs=refresh_pairs,
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exchange=exchange,
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fill_up_missing=fill_up_missing)
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fill_up_missing=fill_up_missing,
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startup_candles=startup_candles)
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if hist is not None:
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result[pair] = hist
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if fail_without_data and not result:
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raise OperationalException("No data found. Terminating.")
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return result
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@ -105,6 +105,31 @@ class Backtesting:
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# And the regular "stoploss" function would not apply to that case
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self.strategy.order_types['stoploss_on_exchange'] = False
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def load_bt_data(self):
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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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data = history.load_data(
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datadir=Path(self.config['datadir']),
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pairs=self.config['exchange']['pair_whitelist'],
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ticker_interval=self.ticker_interval,
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timerange=timerange,
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startup_candles=self.required_startup,
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fail_without_data=True,
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)
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min_date, max_date = history.get_timeframe(data)
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logger.info(
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'Loading data from %s up to %s (%s days)..',
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min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
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)
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# Adjust startts forward if not enough data is available
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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.ticker_interval),
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self.required_startup, min_date)
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return data, timerange
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def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
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skip_nan: bool = False) -> str:
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"""
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@ -414,42 +439,18 @@ class Backtesting:
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:return: None
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"""
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data: Dict[str, Any] = {}
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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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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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data = history.load_data(
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datadir=Path(self.config['datadir']),
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pairs=pairs,
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ticker_interval=self.ticker_interval,
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timerange=timerange,
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startup_candles=self.required_startup
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)
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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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data, timerange = self.load_bt_data()
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all_results = {}
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min_date, max_date = history.get_timeframe(data)
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logger.info(
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'Loading backtest data from %s up to %s (%s days)..',
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min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
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)
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# Adjust startts forward if not enough data is available
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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.ticker_interval),
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self.required_startup, min_date)
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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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@ -23,7 +23,7 @@ from skopt import Optimizer
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from skopt.space import Dimension
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from freqtrade.configuration import TimeRange
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from freqtrade.data.history import load_data, get_timeframe
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from freqtrade.data.history import load_data, get_timeframe, trim_dataframe
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from freqtrade.misc import round_dict
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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@ -379,30 +379,19 @@ class Hyperopt:
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)
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def start(self) -> None:
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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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data = load_data(
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datadir=Path(self.config['datadir']),
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pairs=self.config['exchange']['pair_whitelist'],
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ticker_interval=self.backtesting.ticker_interval,
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timerange=timerange
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)
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data, timerange = self.backtesting.load_bt_data()
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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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preprocessed = self.backtesting.strategy.tickerdata_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 = get_timeframe(data)
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logger.info(
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'Hyperopting with data from %s up to %s (%s days)..',
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min_date.isoformat(),
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max_date.isoformat(),
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(max_date - min_date).days
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min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
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)
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preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
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dump(preprocessed, self.tickerdata_pickle)
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# We don't need exchange instance anymore while running hyperopt
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