This tool allows Excel to be the main interface for trading through the Alpaca broker Below are two demo videos of the tool This tool allows the following to be done via Excel Some aspects of the tool that can be customized are Known Limitations Please note: this has only been tested on windows based machines Link to tool for purchase
Most financial analysis stops at ratios — revenue growth, margins, ROE. Those are useful, but they don’t tell you how reliable or persistent a company’s performance actually is. I built a structural feature layer on top of SEC financial data to focus on three things: earnings quality, stability, and consistency over time. Instead of just asking “what are the numbers today?”, the goal is to understand how those...
secfsn_plus is a canonical financial feature engineering layer built on top of SEC fundamental data. The project takes a wide SEC financial panel indexed by company and period, then transforms it into research-ready outputs: accounting ratios, factor scores, composite signals, screening outputs, and ML-ready feature matrices. The important design choice is separation. secfsn handles the base SEC data layer. secfsn_plus handles deterministic feature engineering. Future layers can handle...
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...
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....
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...
One of the most effective uses of large language models (LLMs) is helping people get up to speed on unfamiliar code. Excel models don’t get that benefit. As spreadsheets grow, logic becomes harder to reason about: If you didn’t build the model — or if enough time has passed — understanding how it works can be surprisingly slow. A simple shift in approach Instead of asking an LLM...
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...
Introduction This Small Business Finance Template Suite was built for owners who want visibility into their business without adopting complex software or changing how they already work. The goal is simple: clear structure, realistic data flow, and decision support. Not automation for its own sake. Each template works on its own, but together they form a lightweight system for managing clients, revenue, commissions, and cash flow using Google...
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