Deep dives into AI systems, quantitative strategies, and the future of intelligent investing.
Retrieval-Augmented Generation (RAG) is transforming how AI systems access and reason over financial documents — from SEC filings to earnings transcripts. Here's a complete technical breakdown with code examples.
A full guide to understanding the data pipeline lifecycle within GitHub Copilot.
Read MoreA practical guide to building a production-ready RAG pipeline that ingests 10-K filings, creates semantic embeddings, and enables natural language Q&A over financial documents.
Read MoreLangGraph enables stateful, multi-actor AI applications. Learn how to build a research agent that autonomously fetches data, runs analysis, and generates investment reports.
Read MoreThe full lifecycle of designing a quantitative trading strategy — from forming a research hypothesis, to rigorous backtesting, walk-forward optimization, and paper trading validation.
Read MoreHow I built an LLM-powered agent that applies Peter Lynch's Growth at a Reasonable Price methodology — including PEG ratio analysis, business categorization, and growth narrative scoring.
Read MoreMark Minervini's Volatility Contraction Pattern has a powerful edge when identified correctly. Here's how I built a Python scanner to detect VCPs automatically across the entire NSE universe.
Read MoreSeasonal-Trend decomposition using LOESS (STL) is a powerful technique for isolating price trends from seasonal and noise components. Here's how to apply it to build more robust trading signals.
Read MoreBuilding an LLM app that actually works in production is very different from a hackathon demo. Caching, rate limiting, prompt versioning, error handling, and cost management — here's what I learned.
Read MoreMost quantitative strategies confuse correlation with causation. Here's how I'm applying causal ML methods (DoWhy, CausalML) to build strategies that are more robust to regime changes.
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