It's great to meet you!

Your interest in integrating quantitative analysis into your derivatives trading and research is a smart move, given the increasing role of data-driven strategies in financial markets.

I'd be glad to guide you on your journey into the world of quants and statistics for backtesting and research in derivatives.

Starting Point: Fundamentals of Statistics for Finance

Since you already have basic knowledge of statistics, it's best to build upon that foundation. Here's what I recommend:

1. Refresher: Review key statistical concepts relevant to finance, such as probability distributions (normal, log-normal, t-distribution), hypothesis testing, regression analysis, and time series analysis. You can find excellent resources online or in textbooks like "Statistical Methods in Finance" by David Ruppert.
2. Financial Statistics: Explore specialized statistical techniques used in finance, such as volatility modeling (GARCH), correlation analysis, and risk measures (VaR, Expected Shortfall).
3. Derivatives-Specific Statistics: Focus on statistics that are particularly relevant to derivatives, including option pricing models (Black-Scholes), implied volatility, and the Greeks (delta, gamma, vega, etc.).

Getting Started Without Programming (Initially):

• Excel: Surprisingly powerful for basic backtesting calculations and data analysis. There are plenty of online tutorials teaching backtesting with Excel's built-in statistical functions.
• Online Backtesting Tools: Some online platforms offer basic backtesting functionalities with user-friendly interfaces. These can help you experiment with different strategies before diving into coding.

Programming Language and Software

As you rightly mentioned, comprehensive data analytics courses might not be the most efficient route for your specific needs. Here's a tailored approach:

1. Choose a Language: For financial analysis and backtesting, Python is an excellent choice due to its extensive libraries for data manipulation, statistics, and financial modeling (pandas, NumPy, SciPy, statsmodels, etc.). R is another popular option, especially for statistical analysis.
2. Software: You don't necessarily need separate statistical software initially. Python and R have powerful built-in statistical capabilities and can handle most backtesting tasks. However, you might consider exploring specialized backtesting platforms like QuantConnect or Backtrader later on.

Practical Steps

1. Online Courses: Look for online courses or tutorials specifically focused on Python for finance or statistical analysis with Python/R. Many platforms offer free or affordable options.
2. Practice Projects: Start with small projects to apply your learnings. For example, try downloading historical derivatives data, calculating basic statistics, and visualizing price trends. Gradually move to more complex tasks like implementing simple trading strategies and backtesting them.
3. Community and Resources: Engage with online communities of quant traders and researchers. Platforms like QuantConnect, Stack Overflow, and GitHub offer valuable resources, discussions, and code examples.
4. Research Literature: Stay up-to-date with academic research on quantitative finance and derivatives. Many papers are freely available online and can provide insights into advanced techniques.