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The inversion seems to be local, so that I could make a quick fix by replacing spreads_in_period = raw_spreads[start_date:end_date]
with raw_spreads[(raw_spreads.index>=start_date)&(raw_spreads.index<=date_time)]
without affecting the result.
But I'd like to know if you have had this problem before and what you recommend doing.
Should data be filtered during data ingestion?
The text was updated successfully, but these errors were encountered:
I have non-monotonically increasing timestamps in a spread data series which causes the spread report to fail.
https://github.com/robcarver17/pysystemtrade/blob/develop/sysproduction/reporting/data/costs.py#L273
EUROSTX
2024-04-16 23:58:51.804114 ┆ 1.0
2024-04-17 10:10:24.303295 ┆ 1.0
2024-04-16 23:58:51.804114 ┆ 1.0
2024-04-17 10:10:24.303295 ┆ 1.0
2024-04-16 23:58:51.804114 ┆ 1.0
The inversion seems to be local, so that I could make a quick fix by replacing
spreads_in_period = raw_spreads[start_date:end_date]
with
raw_spreads[(raw_spreads.index>=start_date)&(raw_spreads.index<=date_time)]
without affecting the result.
But I'd like to know if you have had this problem before and what you recommend doing.
Should data be filtered during data ingestion?
The text was updated successfully, but these errors were encountered: