hilothefunnydog123-coder/quant-research

Martingale is a monthly quantitative finance research project by Neil Gilani. Instead of trying to find trading strategies that beat the market, this project tests whether claimed trading advantages actually hold up, and reports the honest answer even when the answer is that something does not work. The repository follows a strict methodology for every experiment. Each study must state its hypothesis before running, use only past data for decisions, reserve data that was never touched during analysis, count how many strategies were tried, charge realistic trading costs, publish failed results, and be fully reproducible from a single script. The goal is rigor over hype. A standout feature is the Out-of-Sample Registry, a public scoreboard where anyone can submit a trading strategy locked at today's date. The system then evaluates that strategy only on future data, feeding it prices one day at a time so it can never peek ahead. This is designed to be a more honest test than a backtest, because the only thing you cannot optimize against is time itself. The repository contains four completed research notes so far. Note 001 shows how a single line of lookahead code can inflate performance numbers, and how testing 337 strategies on random data can fake results that collapse later. Note 002 finds that daily volatility does not meaningfully predict next-day returns. Note 003 shows that neither momentum nor mean reversion beats simply buying and holding after costs, though each works better in different market conditions. Note 004 finds no statistical edge in popular liquidity-grab trading patterns. A fifth note on overnight versus intraday returns is planned. The project is open to contributors. Anyone can submit a research note, which gets published under their name after peer review against the methodology standard. The full README is longer than what was shown.