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Stock Market & Finance

Building a Buffett-Style Shareholder Letter Scoring Pipeline

June 5, 2026

I recently finished the first version of a philosophy-oriented NLP research pipeline focused on shareholder letters. The idea behind philo_nlp is simple: long-form corporate communication often reflects an underlying philosophy. In this first implementation, the reference philosophy is Berkshire Hathaway and Warren Buffett shareholder communication. The project is split into three repositories. Berkshire_Letters builds the Buffett reference corpus by downloading and normalizing Berkshire shareholder letters into a reusable...

Stock Market & Finance

Building a COT Positioning Dashboard for Faster Futures Market Review

June 5, 2026

I built a local research dashboard to make Commitments of Traders data easier to review across a focused futures market universe. The goal is simple: take raw CFTC COT data, clean it, match it to a configured set of markets, and surface where current positioning sits relative to recent history. This is not a trading signal tool. It does not predict prices, recommend trades, or replace market judgment....

Stock Market & Finance

Building a Practical Macro Regime Research Lab with FRED

January 20, 2026

Most macroeconomic analysis lives at one of two extremes: This project sits somewhere in the middle. I wanted a clear, repeatable, and modular way to explore macroeconomic regimes using publicly available FRED data — something that supports learning, experimentation, and practical monitoring without pretending to predict the economy. That’s what this repository is. Here’s a link to GitHub. What This Project Is This repo is a macro research...

General Business Automation, Stock Market & Finance

Building an NLP-Ready Corpus of Berkshire Hathaway Shareholder Letters

January 19, 2026

As part of a larger NLP project focused on analyzing shareholder communications, I needed a clean, consistent corpus of historical letters to shareholders. One obvious and valuable dataset is the collection of Chairman’s Letters from Berkshire Hathaway, which spans nearly five decades. While the letters are publicly available on Berkshire’s website, assembling them into a single, NLP-ready dataset isn’t quite as simple as it sounds. Here’s a link...

Stock Market & Finance

Building a Practical SEC Fundamentals Pipeline with Polars, Excel, and Plotly

December 29, 2025

Working with SEC fundamentals data is powerful—but rarely simple. Raw filings are large, fragmented across periods, and difficult to transform into something usable for research, screening, or visualization. To solve this, I built SEC FSN, a lightweight Python-based data engineering and analytics pipeline for working with SEC EDGAR Financial Statements & Notes (FSN) data. The goal was simple: create a transparent, reproducible fundamentals foundation that works equally well...

Stock Market & Finance

Reviving a Legacy Quant Repo: Modernizing Algorithmic Short Selling with Python for 2025

November 4, 2025

Background One of the first Python and finance books I ever read was Algorithmic Short Selling with Python.Hands down, it’s one of the best books I’ve ever read that blended Python with finance — teaching both the programming and financial concepts in a clear and practical way. I continue to come back to this book over time and recently noticed that some of the included Jupyter notebooks had...

Articles, General Business Automation, Stock Market & Finance

Teaching a Machine to Beat Blackjack: An RL Journey

September 30, 2025

Blackjack is one of the most widely studied games in both casinos and academia. The rules are simple, but the strategy is subtle, and the possibility of card counting has made it a fascinating testbed for probability, game theory, and decision-making. The question at the heart of this project: can a machine, using reinforcement learning (RL), teach itself how to play blackjack — and even rediscover professional strategies...

Stock Market & Finance

Neural Networks Part 3: Using Neural Networks to Detect Mispriced Option Spreads

September 25, 2025

In Part 2, we trained a neural network to price options using the Cox-Ross-Rubinstein (CRR) model. It learned to predict the fair value of an option using inputs like strike, time to expiry, interest rate, and volatility. Now, we’ll use that trained model to do something powerful:Scan live option chains and identify mispriced vertical call spreads — a core strategy for directional or volatility-based options trading. This is...

Stock Market & Finance

Neural Networks Part 2: Teaching a Neural Network to Price Options

September 25, 2025

In Part 1 of this series, we showed that neural networks can learn simple mathematical functions just by observing inputs and outputs — no formulas needed. But that was just warm-up. In this post, we’ll see if a neural network can learn to price options, specifically using the Cox-Ross-Rubinstein (CRR) binomial pricing model. This takes us into quantitative finance territory — and shows how machine learning can replace...

Stock Market & Finance

Neural Networks Part 1: Teaching a Neural Network to Learn Math

September 25, 2025

Can a Neural Network Learn to Think Like a Calculator? Neural networks have become one of the most powerful and flexible tools in modern machine learning. But how do they actually learn — and can they really approximate things like a calculator? In this first post of our three-part series, we introduce neural networks in a hands-on way by showing how they can approximate simple mathematical functions like:...

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