Trading

TL;DR I run an LLM-driven trading hypothesis engine. For a while, every result that came back looked too good — Sharpe ratios above 5, win rates above 70%, all on out-of-sample windows. They were lies. The model was reading dates, headlines, and tickers in the prompt and pattern-matching against its training data, which extends well past my “out-of-sample” cutoff. The fix was a masking layer I now call Blind Oracle: strip every leak before evaluation, run the trigger before the eval, gate promotion on out-of-sample Sharpe with the masking enforced. After it shipped, the inflated numbers collapsed back to honest reality. Some hypotheses survived; most didn’t. That’s exactly what I needed to know. ...

TL;DR I built a complete swing trading research platform from an empty scaffold to 13,674 lines of Python in a single day. Five phases: data layer and backtesting, fundamentals and sentiment, portfolio construction, ML signals and Monte Carlo, then paper trading with a terminal dashboard. 199 tests across 48 test files. The platform fetches from Yahoo Finance, FRED, SEC EDGAR, and news APIs, runs technical and fundamental analysis, backtests strategies with walk-forward validation, and presents recommendations through a Rich terminal dashboard with human-in-the-loop approval. No cloud dependencies, no subscriptions, no vendor lock-in. ...