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Remove candle_count from dataframe before backtesting

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closes #3754
This commit is contained in:
Matthias
2021-03-29 20:22:52 +02:00
parent 00d8f3a479
commit 89bbfd2324
4 changed files with 25 additions and 7 deletions
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16
freqtrade/data/converter.py
View File

@@ -115,17 +115,23 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
return df
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
startup_candles: int = 0) -> DataFrame:
"""
Trim dataframe based on given timerange
:param df: Dataframe to trim
:param timerange: timerange (use start and end date if available)
:param: df_date_col: Column in the dataframe to use as Date column
:param df_date_col: Column in the dataframe to use as Date column
:param startup_candles: When not 0, is used instead the timerange start date
:return: trimmed dataframe
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if startup_candles:
# Trim candles instead of timeframe in case of given startup_candle count
df = df.iloc[startup_candles:, :]
else:
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :]

3
freqtrade/optimize/backtesting.py
View File

@@ -443,7 +443,8 @@ class Backtesting:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.required_startup)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '

3
freqtrade/optimize/hyperopt.py
View File

@@ -379,7 +379,8 @@ class Hyperopt:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
preprocessed[pair] = trim_dataframe(df, timerange,
startup_candles=self.backtesting.required_startup)
min_date, max_date = get_timerange(preprocessed)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '