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...
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...
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...
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...
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...
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...
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...
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:...
Can AI really beat the house in a game mathematically designed for the casino to win? Over the course of 100,000 simulated bets, I set out to test that question — by building and training reinforcement learning agents to outplay American-style roulette. This is the launchpad for a 5-part blog series where I explore the math, psychology, and algorithms behind roulette strategies — from classic betting systems to...
Why I Did This I’ve been experimenting with automation and AI for a while, and I wanted to see if I could create a fully autonomous blog publishing system — one that generates content, adds SEO metadata, creates visuals, and publishes to WordPress without me lifting a finger. This post documents a real test: 11 blog posts, created and published in one day, entirely by a Python-based AI...
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