Quantitative finance/trading. Help to develop and backtest options (premium-selling) trading strategy

Hi, I'm looking for a quant developer/data analyst with experience in the backtesting of trading strategies. The strategy involves options premium selling and options parameters solving. It's more about options, nothing about technical analysis, so before applying make sure you are an expert in options backtesting and have access to REAL HISTORICAL OPTIONS data and can backtest against that data. The goal of this project is to both backtest a strategy and an educational one, to show/explain to me how to perform similarly backtests on my own in the future. Be my quant guru, be my tutor :) I'm a software developer by myself (business web apps, database backends), but with no prior experience in professional options strategy backtesting. 1. Create a general backtesting environment including real historical options data (like a form of Virtual Machine image or similar), that I could replicate and use on my own. Should be based on one of the existing open-source solutions, preferably python based. 2. Implement the strategy described below 3. Perform backtests and optimizations including entry parameters details, optimum strikes, spread width, expiration, greeks selection, optimum exit criteria 4. Generate reports/results/charts illustrating the influence of different optimized parameters on a strategy performance and returns distributions 5. Introduce a process to assure that optimizations do not overfit historical data. Explain how to avoid overfitting while optimizing this strategy and what steps did you especially took to avoid it in this project 6. Suggest possible further improvements that I could work on my own and answer my questions. 7. Provide final materials (fully working, open source-based backtesting environment), results with tables, charts, graphs etc, source code, reasonably priced historical options and market data source Very basic strategy description: 1. Sell SPX put spread with around 30 (parameter to optimize) days expiration date and given width (list of widths). 2. Entry on a daily "oversold" condition based on market breadth indicator described (with source code) under the following URL: [login to view URL] 3. Make sure you can replicate/re-implement the above indicator in your backtesting environment, including sourcing the data and choosing/optimizing the indicator signal level (let's start with the value of 300) 4. Exit: Let the spread expire worthless or find the optimal exit parameters (ie close after 80% decay) A lot of writing, but if you have done similar tasks before you see that's the basic stuff and probably 80% of this is covered by choosing of a proper options strategy backtesting solution. I'm a tech savvy guy, including coding in Java/C#,Go, linux server administration and so on, so you know you are not working with some crazy forex easy profit seeker but a tech guy that wants to enter quant world.