Add utility to convert XAUUSD time column format - #17
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| # Parse dates and drop timezone info if present, then format like 'YYYY-MM-DD HH:MM:SS' | ||
| times = pd.to_datetime(df['time']) | ||
| times = times.dt.tz_localize(None) # Remove timezone if it exists | ||
| df['time'] = times.dt.strftime('%Y-%m-%d %H:%M:%S') |
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[P1] Guard tz-localization for timezone-naive inputs
The conversion unconditionally applies times.dt.tz_localize(None). Pandas raises TypeError: Already tz-naive, use tz_localize to localize when the series has no timezone information, so the tool fails for CSVs that are already in the desired YYYY-MM-DD HH:MM:SS format. Handling naive timestamps (e.g. by using tz_convert(None) or wrapping the call in a try/except) would let the script safely normalize both timezone-aware and timezone-free files.
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Summary
convert_time_format.pyto strip timezone information and format times likeXAUUSD_data.csvREADME.mdTesting
python convert_time_format.py XAUUSD_data_10min.csvhttps://chatgpt.com/codex/tasks/task_e_68c0a472c3f4832895ed4afde5c03765