chore: ensure date-column is in ms range
This commit is contained in:
@@ -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(
|
||||
|
||||
@@ -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(
|
||||
|
||||
@@ -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(
|
||||
|
||||
+6
-6
@@ -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,
|
||||
|
||||
Reference in New Issue
Block a user