Sharan Naribole

Sharan Naribole

Wireless Systems Engineer, Rice University Ph.D.

AI Trading Strategies Intro

4 minute read

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Blog Series Overview

This post kicks off a blog series where I document personal projects on machine learning based trading strategies. I’ll cover:

  • The problem statement and objectives of each project
  • What I learned technically and financially
  • Data analysis and visualization outputs

I hope this series proves useful to others interested in AI-based trading. You can follow along on GitHub or via my portfolio site.

Background

For many years, I have been a passive investor following recommendations from books like The Index Card and The Simple Path to Wealth. These books advocate investing in low-cost index funds using dollar-cost averaging, which worked extremely well as I transitioned from a low-salaried Ph.D. student into a better-paying role in the tech industry. Riding the post-COVID bull market, my portfolio saw strong growth by staying invested in S&P 500 index funds. Outside of index funds and ETFs, I hadn’t cultivated interest and knowledge in short-term trading and perceived such approaches to gambling in a casino.

In 2022, a friend introduced me to the book $25K Options Trading Challenge by Nishant Pant. This led me down the path of short-term options strategies, including mean reversion setups with all kinds of technical indicators such as Bollinger Bands, RSI, and ADX. Though exciting at first, I found many trades too unpredictable and moved on.

In summer 2022, I collaborated on a machine learning project at work, which inspired me to enroll in the Machine Learning Specialization on Coursera by OpenAI. This coursework helped refresh my fundamentals which was a bit rusty since I took statistical learning courses in grad school. While this learning was rewarding, it wasn’t a key component of my active work responsibilities and left a feeling of void.

In 2024, my interest in options trading returned. This time, I became comfortable with Credit Spreads, Butterfly strategies, Delta, Gamma, Theta decay, and particularly SPX indexs trades. Through months of paper and live trading, I gained more hands-on experience.

In early 2025, I had renewed enthusiam to apply machine learning in personal projects especially in my hobby of options trading. This is how I stumbled upon the world of quantitative trading using machine learning. While researching, I found the AI Trading Strategies Nanodegree by Udacity — a structured, project-based program that blends my interests in ML and trading.

Program Summary

This Nanodegree teaches you to apply AI and machine learning to trading strategies. Topics include:

  • Data acquisition and financial modeling
  • Feature engineering
  • Backtesting strategies
  • Building Machine Learning based Trading models

Each course features a hands-on project with real financial data — perfect for someone who wants to build, validate, and utilize trading models in their personal projects.

So far, I’ve completed projects for:

Working through this Nanodegree has also inspired side ideas for example:

  • I learnt about the Kelly Letter 9-Sig Plan in personal finance forums and its potential benefits in Roth accounts. I am interested to backtest variations of this strategy using concepts learned in Course 4.
  • I am interested to backtest popular SPX 0 DTE strategies including Opening Range Breakouts and VWAP-based Credit Spreads. My goal is to acquire historical SPX options data and build custom backtest pipelines.

Trading Psychology

One book that profoundly influenced my approach to trading is The Tao of Trading by Simon Ree. This book underscores the importance of discipline, psychological resilience, and having a well-defined strategy over random trade trials. Ree emphasizes that successful trading isn’t just about technical setups but also about mindset and risk management. His “Bounce 2.0” strategy, which combines mean reversion principles with trend identification, offers a structured approach to trading. The book’s emphasis …

Another pivotal read in my trading journey has been Trading in the Zone by Mark Douglas. This book delves deep into the psychological aspects of trading, highlighting the importance of mindset in achieving consistent success. Douglas emphasizes that trading is not about predicting market movements but about understanding probabilities and managing risk. He introduces the concept of “thinking in probabilities,” encouraging traders to accept the inherent uncertainties of the market.

Thanks for reading!

Next Post → Backtesting a Dynamic Investment Strategy with Python

References

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