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Merge pull request #4628 from freqtrade/fix-startupcandles

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Remove candle_count from dataframe
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Matthias 2021-03-30 06:49:24 +02:00 committed by GitHub
commit 71dea3fb93
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4 changed files with 25 additions and 7 deletions
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16
freqtrade/data/converter.py
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@ -115,17 +115,23 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
return df 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 Trim dataframe based on given timerange
:param df: Dataframe to trim :param df: Dataframe to trim
:param timerange: timerange (use start and end date if available) :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 :return: trimmed dataframe
""" """
if timerange.starttype == 'date': if startup_candles:
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc) # Trim candles instead of timeframe in case of given startup_candle count
df = df.loc[df[df_date_col] >= start, :] 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': if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc) stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :] df = df.loc[df[df_date_col] <= stop, :]

3
freqtrade/optimize/backtesting.py
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@ -443,7 +443,8 @@ class Backtesting:
# Trim startup period from analyzed dataframe # Trim startup period from analyzed dataframe
for pair, df in preprocessed.items(): 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) min_date, max_date = history.get_timerange(preprocessed)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '

3
freqtrade/optimize/hyperopt.py
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@ -379,7 +379,8 @@ class Hyperopt:
# Trim startup period from analyzed dataframe # Trim startup period from analyzed dataframe
for pair, df in preprocessed.items(): 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) min_date, max_date = get_timerange(preprocessed)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '

10
tests/data/test_converter.py
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@ -197,6 +197,16 @@ def test_trim_dataframe(testdatadir) -> None:
assert all(data_modify.iloc[-1] == data.iloc[-1]) assert all(data_modify.iloc[-1] == data.iloc[-1])
assert all(data_modify.iloc[0] == data.iloc[30]) assert all(data_modify.iloc[0] == data.iloc[30])
data_modify = data.copy()
tr = TimeRange('date', None, min_date + 1800, 0)
# Remove first 20 candles - ignores min date
data_modify = trim_dataframe(data_modify, tr, startup_candles=20)
assert not data_modify.equals(data)
assert len(data_modify) < len(data)
assert len(data_modify) == len(data) - 20
assert all(data_modify.iloc[-1] == data.iloc[-1])
assert all(data_modify.iloc[0] == data.iloc[20])
data_modify = data.copy() data_modify = data.copy()
# Remove last 30 minutes (1800 s) # Remove last 30 minutes (1800 s)
tr = TimeRange(None, 'date', 0, max_date - 1800) tr = TimeRange(None, 'date', 0, max_date - 1800)