Simon/Cycles
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fixed depricated parameters

Browse Source
This commit is contained in:
Ajasra 2025-05-22 17:24:16 +08:00
parent 8e220b564c
commit 934c807246
1 changed files with 40 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
cycles/utils/data_utils.py
View File

@ -150,12 +150,50 @@ def aggregate_to_hourly(data_df: pd.DataFrame, hours: int = 1) -> pd.DataFrame:
return pd.DataFrame(index=pd.to_datetime([]))
# Resample to hourly frequency and apply aggregation rules
hourly_data = data_df.resample(f'{hours}H').agg(agg_rules)
hourly_data = data_df.resample(f'{hours}h').agg(agg_rules)
hourly_data.dropna(how='all', inplace=True)
# Adjust timestamps to the start of the hour
if not hourly_data.empty and isinstance(hourly_data.index, pd.DatetimeIndex):
hourly_data.index = hourly_data.index.floor('H')
hourly_data.index = hourly_data.index.floor('h')
return hourly_data
def aggregate_to_minutes(data_df: pd.DataFrame, minutes: int) -> pd.DataFrame:
"""
Aggregates time-series financial data to N-minute OHLCV format.
The input DataFrame is expected to have a DatetimeIndex.
'open' will be the first 'open' price of the N-minute interval.
'close' will be the last 'close' price of the N-minute interval.
'high' will be the maximum 'high' price of the N-minute interval.
'low' will be the minimum 'low' price of the N-minute interval.
'volume' (if present) will be the sum of volumes for the N-minute interval.
Args:
data_df (pd.DataFrame): DataFrame with a DatetimeIndex and columns
like 'open', 'high', 'low', 'close', and optionally 'volume'.
minutes (int): The number of minutes to aggregate to.
Returns:
pd.DataFrame: DataFrame aggregated to N-minute OHLCV data.
The index will be a DatetimeIndex.
Returns an empty DataFrame if no relevant OHLCV columns are found or
if the input DataFrame does not have a DatetimeIndex.
"""
agg_rules_obj = check_data(data_df) # check_data returns rules or False
if not agg_rules_obj:
# check_data already prints a warning if index is not DatetimeIndex or no OHLCV columns
# Ensure an empty DataFrame with a DatetimeIndex is returned for consistency
return pd.DataFrame(index=pd.to_datetime([]))
# Resample to N-minute frequency and apply aggregation rules
# Using .agg(agg_rules_obj) where agg_rules_obj is the dict from check_data
resampled_data = data_df.resample(f'{minutes}min').agg(agg_rules_obj)
resampled_data.dropna(how='all', inplace=True)
return resampled_data