Optimize My Options Trading Strategy

I have built a rules-based equity-options strategy that has performed reasonably well, but I know it can be tighter on risk management and more consistent on month-to-month returns. I’m ready to hand the logic, transaction history and my current Python back-tester over to a fresh set of eyes so the whole approach can be stress-tested, tuned and benchmarked.

Here’s what I need from you:

• Review my existing entry, exit and position-sizing rules (all documented in code and plain English).
• Propose precise tweaks or entirely new modules—volatility filters, dynamic hedging, multi-leg adjustments, whatever the data justifies—and show why they improve expectancy.
• Implement the changes in the same Python environment (pandas, NumPy, yfinance, Zipline-like framework) or suggest a clearly superior stack.
• Back-test against at least 10 years of tick or minute data, providing clean performance metrics and equity curves.
• Supply a short report that explains the rationale, parameter sensitivity and next steps for live deployment.

Time is not an issue; robustness matters more than speed. When you respond, attach examples of past optimization or quantitative trading work so I can see the depth of your analysis. If your previous projects include options Greeks modelling, Monte Carlo simulations, or walk-forward testing, all the better.

Once we agree on the improvement plan, I’ll share the repo and data so you can get started.