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🪲 BugFix startup_candlle_count not being applied to informative dataframe

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Rikj000
2021-08-14 10:05:55 +00:00
committed by GitHub
parent 55c9b75e82
commit 1bd3e62717
1 changed files with 8 additions and 4 deletions
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12
user_data/strategies/MasterMoniGoManiHyperStrategy.py
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@@ -16,6 +16,7 @@ from numpy import timedelta64
from pandas import DataFrame
from scipy.interpolate import interp1d
from freqtrade.data.history import load_pair_history
from freqtrade.enums import RunMode
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.misc import deep_merge_dicts, round_dict
@@ -343,8 +344,7 @@ class MasterMoniGoManiHyperStrategy(IStrategy, ABC):
self.mgm_logger('info', initialization, f'Current run mode detected as: HyperOpting/BackTesting. '
f'Auto updated is_dry_live_run_detected to: False')
self.mgm_logger('info', initialization,
f'Calculating and storing "timeframe_multiplier"')
self.mgm_logger('info', initialization, f'Calculating and storing "timeframe_multiplier"')
self.timeframe_multiplier = \
int(timeframe_to_minutes(self.informative_timeframe) / timeframe_to_minutes(self.timeframe))
if self.timeframe_multiplier < 1:
@@ -468,9 +468,13 @@ class MasterMoniGoManiHyperStrategy(IStrategy, ABC):
f'informative_timeframe ({self.informative_timeframe} candles) and '
f'a zoomed backtest_timeframe ({self.backtest_timeframe} candles)')
# Warning! This method gets ALL downloaded data that you have (when in backtesting mode).
# Warning! This method gets ALL downloaded data for the given timeframe (when in BackTesting mode).
# If you have many months or years downloaded for this pair, this will take a long time!
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = load_pair_history(pair=metadata['pair'],
datadir=self.config['datadir'],
timeframe=self.informative_timeframe,
startup_candles=self.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'))
# Throw away older data that isn't needed.
first_informative = dataframe['date'].min().floor('H')