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How to Backtest an Options Strategy in Python (Start With the Data Nobody Cleans)
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ticker lesson
Breakdown
0:00lesson
Data cleaning is 40% of options analysis
- Cleaning data takes 40% of time
- Raw data is unstructured and unreadable
- Unix time format needs conversion
- Column names vary by provider
- Standard fields are essential
2:16lesson
Standard options data fields
- Underlying asset info
- Strike price = exercise price
- Expiry date
- Call or put type
- Bid/ask, open interest, IV
4:28lesson
Standardizing column names
- Use lowercase letters
- Remove spaces
- Add descriptive columns
- Delete unnecessary columns
- Improve code accessibility
4:29lesson
Handling missing data and outliers
- NaN values can be replaced
- Use nearest value for 1-min data
- Avoid big timestamp jumps
- Filter low-liquidity contracts
- Check and handle nulls
4:30lesson
Final data pipeline: clean, merge, filter
- Read zip files
- Extract and merge text files
- Clean columns and data
- Filter near-month contracts
- Save cleaned data locally
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