Blog Series: Machine Learning for Options Trading

Introduction to the Blog Series

Options trading is a sophisticated financial endeavor that requires a deep understanding of market movements, risk management, and statistical probabilities. In this blog series, we explore how machine learning can be leveraged to analyze options data, identify profitable trading patterns, and optimize trading strategies. Using a structured pipeline, we conduct an exploratory data analysis, apply hypothesis testing, and use statistical modeling to uncover insights that can drive better decision-making.

This series is broken down into five parts:

1. Setup & Data Collection – How we structure and collect options data for analysis.
2. Exploratory Data Analysis (EDA) & Preprocessing – Cleaning and preparing data for hypothesis testing.
3. Hypothesis Testing for Profitable Exits – Evaluating whether options reach predefined profit targets.
4. Statistical Modeling & Machine Learning – Applying statistical and machine learning techniques to model options performance.
5. Summarization & Trade Optimization – Summarizing key takeaways and identifying the best trade setups.

A copy of these notebooks can be found on GitHub.

Here’s a YouTube demo of the notebooks: YouTube