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refactor: align naming for clean_ohlcv_dataframe

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Matthias
2026-03-19 07:04:49 +01:00
parent 257e26f2c0
commit 7fc6463900
1 changed files with 6 additions and 6 deletions
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freqtrade/data/converter/converter.py
+6 -6
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@@ -59,14 +59,14 @@ def ohlcv_to_dataframe(
def clean_ohlcv_dataframe( def clean_ohlcv_dataframe(
data: DataFrame, timeframe: str, pair: str, *, fill_missing: bool, drop_incomplete: bool dataframe: DataFrame, timeframe: str, pair: str, *, fill_missing: bool, drop_incomplete: bool
) -> DataFrame: ) -> DataFrame:
""" """
Cleanse a OHLCV dataframe by Cleanse a OHLCV dataframe by
* Grouping it by date (removes duplicate tics) * Grouping it by date (removes duplicate tics)
* dropping last candles if requested * dropping last candles if requested
* Filling up missing data (if requested) * Filling up missing data (if requested)
:param data: DataFrame containing candle (OHLCV) data. :param dataframe: DataFrame containing candle (OHLCV) data.
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary) :param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles :param fill_missing: fill up missing candles with 0 candles
@@ -75,7 +75,7 @@ def clean_ohlcv_dataframe(
:return: DataFrame :return: DataFrame
""" """
# group by index and aggregate results to eliminate duplicate ticks # group by index and aggregate results to eliminate duplicate ticks
data = data.groupby(by="date", as_index=False, sort=True).agg( dataframe = dataframe.groupby(by="date", as_index=False, sort=True).agg(
{ {
"open": "first", "open": "first",
"high": "max", "high": "max",
@@ -86,13 +86,13 @@ def clean_ohlcv_dataframe(
) )
# eliminate partial candle # eliminate partial candle
if drop_incomplete: if drop_incomplete:
data.drop(data.tail(1).index, inplace=True) dataframe.drop(dataframe.tail(1).index, inplace=True)
logger.debug("Dropping last candle") logger.debug("Dropping last candle")
if fill_missing: if fill_missing:
return ohlcv_fill_up_missing_data(data, timeframe, pair) return ohlcv_fill_up_missing_data(dataframe, timeframe, pair)
else: else:
return data return dataframe
def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame: def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame: