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Merge pull request #13048 from freqtrade/dependabot/pip/develop/pandas-3.0.2

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chore(deps): bump pandas from 2.3.3 to 3.0.2
This commit is contained in:
Matthias
2026-04-17 06:21:04 +02:00
committed by GitHub
parent ee65ffc68f 2c5dc72985
commit c1795c79f3
10 changed files with 29 additions and 21 deletions
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freqtrade/data/history/datahandlers/featherdatahandler.py
+2 -2
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@@ -1,6 +1,6 @@
import logging
from pandas import DataFrame, read_feather, to_datetime
from pandas import DataFrame, read_feather
from pyarrow import dataset
from freqtrade.configuration import TimeRange
@@ -71,7 +71,7 @@ class FeatherDataHandler(IDataHandler):
"volume": "float",
}
)
pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True)
pairdata["date"] = pairdata["date"].dt.as_unit("ms")
return pairdata
except Exception as e:
logger.exception(
freqtrade/data/history/datahandlers/jsondatahandler.py
+4 -1
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@@ -80,7 +80,7 @@ class JsonDataHandler(IDataHandler):
"volume": "float",
}
)
pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True)
pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True).dt.as_unit("ms")
return pairdata
def ohlcv_append(
@@ -104,6 +104,9 @@ class JsonDataHandler(IDataHandler):
:param trading_mode: Trading mode to use (used to determine the filename)
"""
filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
# Convert StringDtype columns to object to avoid NaN serialization issues
for col in data.select_dtypes(include="string").columns:
data[col] = data[col].astype(object).where(data[col].notna(), other=None)
trades = data.values.tolist()
misc.file_dump_json(filename, trades, is_zip=self._use_zip)
freqtrade/data/history/datahandlers/parquetdatahandler.py
+2 -2
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@@ -1,6 +1,6 @@
import logging
from pandas import DataFrame, read_parquet, to_datetime
from pandas import DataFrame, read_parquet
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
@@ -68,7 +68,7 @@ class ParquetDataHandler(IDataHandler):
"volume": "float",
}
)
pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True)
pairdata["date"] = pairdata["date"].dt.as_unit("ms")
return pairdata
except Exception as e:
logger.exception(
freqtrade/freqai/data_kitchen.py
+2 -6
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@@ -24,8 +24,6 @@ from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
pd.set_option("future.no_silent_downcasting", True)
SECONDS_IN_DAY = 86400
SECONDS_IN_HOUR = 3600
@@ -239,16 +237,14 @@ class FreqaiDataKitchen:
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_df).any(axis=1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects(copy=False)
drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects()
if training_filter:
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
labels = unfiltered_df.filter(label_list or [], axis=1)
drop_index_labels = pd.isnull(labels).any(axis=1)
drop_index_labels = (
drop_index_labels.replace(True, 1).replace(False, 0).infer_objects(copy=False)
)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0).infer_objects()
dates = unfiltered_df["date"]
filtered_df = filtered_df[
(drop_index == 0) & (drop_index_labels == 0)
pyproject.toml
+1 -1
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@@ -40,7 +40,7 @@ dependencies = [
"urllib3",
"jsonschema",
"numpy>2.0,<3.0",
"pandas>=2.2.0,<3.0",
"pandas>=2.2.0,<4.0",
"TA-Lib<0.7",
"ft-pandas-ta",
"technical",
requirements.txt
+1 -1
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@@ -1,5 +1,5 @@
numpy==2.4.4
pandas==2.3.3
pandas==3.0.2
bottleneck==1.6.0
numexpr==2.14.1
# Indicator libraries
tests/conftest.py
+6 -6
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@@ -176,20 +176,20 @@ def generate_test_data(
base = np.random.normal(base, 2, size=size)
if timeframe == "1y":
date = pd.date_range(start, periods=size, freq="1YS", tz="UTC")
date = pd.date_range(start, periods=size, freq="1YS", tz="UTC", unit="ms")
elif timeframe == "1M":
date = pd.date_range(start, periods=size, freq="1MS", tz="UTC")
date = pd.date_range(start, periods=size, freq="1MS", tz="UTC", unit="ms")
elif timeframe == "3M":
date = pd.date_range(start, periods=size, freq="3MS", tz="UTC")
date = pd.date_range(start, periods=size, freq="3MS", tz="UTC", unit="ms")
elif timeframe == "1w" or timeframe == "7d":
date = pd.date_range(start, periods=size, freq="1W-MON", tz="UTC")
date = pd.date_range(start, periods=size, freq="1W-MON", tz="UTC", unit="ms")
else:
tf_mins = timeframe_to_minutes(timeframe)
if tf_mins >= 1:
date = pd.date_range(start, periods=size, freq=f"{tf_mins}min", tz="UTC")
date = pd.date_range(start, periods=size, freq=f"{tf_mins}min", tz="UTC", unit="ms")
else:
tf_secs = timeframe_to_seconds(timeframe)
date = pd.date_range(start, periods=size, freq=f"{tf_secs}s", tz="UTC")
date = pd.date_range(start, periods=size, freq=f"{tf_secs}s", tz="UTC", unit="ms")
df = pd.DataFrame(
{
"date": date,
tests/data/test_converter.py
+5 -2
View File
@@ -207,10 +207,13 @@ def test_ohlcv_to_dataframe_multi(timeframe):
data1 = data.copy()
if timeframe in ("1M", "3M", "1y"):
data1.loc[:, "date"] = data1.loc[:, "date"] + pd.to_timedelta("1w")
data1.loc[:, "date"] = data1.loc[:, "date"] + pd.to_timedelta("1W")
else:
# Shift by half a timeframe
data1.loc[:, "date"] = data1.loc[:, "date"] + (pd.to_timedelta(timeframe) / 2)
timeframe_f = (
timeframe.upper() if timeframe.endswith("d") or timeframe.endswith("w") else timeframe
)
data1.loc[:, "date"] = data1.loc[:, "date"] + (pd.to_timedelta(timeframe_f) / 2)
df2 = ohlcv_to_dataframe(data1, timeframe, "UNITTEST/USDT")
assert len(df2) == len(data) - 1
tests/optimize/test_backtesting.py
+3
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@@ -860,6 +860,9 @@ def test_backtest_one(default_conf, mocker, testdatadir) -> None:
"funding_fees": [0.0, 0.0],
}
)
# TODO: pandas3 - create correctly above ?!?
expected["open_date"] = expected["open_date"].astype("datetime64[ms, UTC]")
expected["close_date"] = expected["close_date"].astype("datetime64[ms, UTC]")
pd.testing.assert_frame_equal(results, expected)
assert "orders" in results.columns
data_pair = processed[pair]
tests/optimize/test_backtesting_adjust_position.py
+3
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@@ -83,6 +83,9 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
"funding_fees": [0.0, 0.0],
}
)
# TODO: pandas3 - create correctly above ?!?
expected["open_date"] = expected["open_date"].astype("datetime64[ms, UTC]")
expected["close_date"] = expected["close_date"].astype("datetime64[ms, UTC]")
results_no = results.drop(columns=["orders"])
pd.testing.assert_frame_equal(results_no, expected, check_exact=True)