adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

reduce code duplication, optimize auto data download per tf

Browse Source
This commit is contained in:
robcaulk
2022-08-26 13:56:44 +02:00
parent ac42c0153d
commit 4b7e640f31
4 changed files with 41 additions and 85 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
freqtrade/data/dataprovider.py
View File

@@ -91,9 +91,9 @@ class DataProvider:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
# Move informative start time respecting startup_candle_count
timerange.subtract_start(
self.get_required_startup_seconds(str(timeframe))
)
startup_candles = self.get_required_startup(str(timeframe))
tf_seconds = timeframe_to_seconds(str(timeframe))
timerange.subtract_start(tf_seconds * startup_candles)
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
pair=pair,
timeframe=timeframe or self._config['timeframe'],
@@ -105,16 +105,18 @@ class DataProvider:
)
return self.__cached_pairs_backtesting[saved_pair].copy()
def get_required_startup_seconds(self, timeframe: str) -> int:
tf_seconds = timeframe_to_seconds(timeframe)
base_seconds = tf_seconds * self._config.get('startup_candle_count', 0)
if not self._config['freqai']['enabled']:
return base_seconds
def get_required_startup(self, timeframe: str) -> int:
if not self._config.get('freqai', {}).get('enabled', False):
return self._config.get('startup_candle_count', 0)
else:
train_seconds = self._config['freqai']['train_period_days'] * 86400
# multiplied by safety factor of 2 because FreqAI users
# typically do not know the correct window.
return base_seconds * 2 + int(train_seconds)
if not self._config['startup_candle_count']:
raise OperationalException('FreqAI backtesting module requires strategy '
'set startup_candle_count.')
tf_seconds = timeframe_to_seconds(timeframe)
train_candles = self._config['freqai']['train_period_days'] * 86400 / tf_seconds
total_candles = int(self._config.get('startup_candle_count', 0) + train_candles)
logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
return total_candles
def get_pair_dataframe(
self,