def backtest_one_strategy(self, strat: IStrategy, data: Dict[str,
DataFrame],
timerange: TimeRange):
self.progress.init_step(BacktestState.ANALYZE, 0)
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start,
supress_error=True)()
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...'
)
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange,
self.required_startup)
if not preprocessed_tmp:
raise OperationalException(
"No data left after adjusting for startup candles.")
# Use preprocessed_tmp for date generation (the trimmed dataframe).
# Backtesting will re-trim the dataframes after buy/sell signal generation.
min_date, max_date = history.get_timerange(preprocessed_tmp)
logger.info(
f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days).')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
results.update({
'backtest_start_time':
int(backtest_start_time.timestamp()),
'backtest_end_time':
int(backtest_end_time.timestamp()),
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe to get correct dates for output.
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
processed = trim_dataframes(preprocessed, timerange,
self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
logger.info(
f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
dump(processed, self.data_pickle_file)
