Neil Gilani Claim this profile

**I build the full quantitative-finance stack — a matching engine, the pricing math, a backtester, and a language to write strategies in — then use it to test trading ideas *honestly* in a monthly research lab, publishing what the data really says (null results included).

| 🧪 Tests passing | 🔬 Research papers | 🏦 Quant repos | 🧮 Pricing engines | ⚡ Order book |
|:--:|:--:|:--:|:--:|:--:|
| 140+ | 3 · reproducible | 6 | 3 cross-validating | 165k orders/sec |

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> 🔬 Latest from Martingale: Note 004 — How effective are liquidity-grab / FVG setups, statistically?** → *Across 2,903 days of SPY, no edge: at 1 day nothing is significant; the 5-day "significant" results are just market drift, and the bearish patterns are followed by the largest positive moves — the opposite of the claim.* &nbsp;read the paper →
>
> 🧫 Currently researching: Note 005 — *Where do SPY's returns actually come from — overnight vs. intraday?*
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> A question, an experiment, an honest answer — including the null ones. Most projects claim to *find* edges; this lab rigorously tests whether claimed edges are *real*, and reports what the data actually says. Every note is a hypothesis fixed in advance, a reproducible experiment with no lookahead, and a written paper — findings that stand on their own, especially when the answer is "it doesn't work."

| # | Question | Honest finding |
|:--:|---|---|
| 001 | Do backtests overstate performance? | A 1-line lookahead bug inflates a strategy's Sharpe by +1.12; cherry-picking 337 strategies on random data fakes a 0.77 Sharpe that flips to −0.54 out-of-sample |
| 002 | Does volatility predict next-day *direction*? | Across 2,881 days of SPY, today's volatility → tomorrow's return correlation is +0.035 — no meaningful directional edge (R² ≈ 0.1%) |
| 003 | Momentum vs. mean reversion, after costs? | Over 4,916 days, neither beats buy-and-hold — but each is a regime bet: mean reversion earns a 1.07 Sharpe in bear markets, then its ~6× turnover lets 5bp costs turn it negative |

📄 Read the papers · 🔁 Reproduce any note · 📌 Cite this work · *open to contributors*

📈 Live strategy tracker

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> 🤖 Auto-updated daily by a GitHub Action — a backtest of my own quantsim engine, refreshed every morning. *(paper research, not investment advice)*

| as of | strategy | buy & hold | verdict |
|:--|:--|:--|:--|
| 2026-07-26 on sample series (offline) | +60.9% · Sharpe 0.72 · maxDD -21.3% | +14.6% · Sharpe 0.22 | ✅ beating buy & hold |
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🎛️ Engine room — everything below is animated, zero JavaScript

> Two ideas from my repos, brought to life as pure self-animating SVG (SMIL) — regenerated daily by the same Action. Left: a limit order book matching engine. Right: Monte Carlo option pricing converging to Black–Scholes.

Market microstructure from exchange-simulator · pricing math from optionslab

🚀 About me

I got curious about how markets *actually* work — so I built the whole stack to find out: a matching engine, the pricing math, a backtester, a live trading bot, and finally a language to write strategies in. Then I started a research lab to point that stack at real data and test popular trading ideas the honest way — hypothesis first, no lookahead, realistic costs, and the null results published alongside the wins. I like zero-dependency code, tests that assert real properties (not just "it runs"), and work you can reproduce in under a minute.

• 💸 Quantitative finance — backtesting, options pricing, market microstructure, honest strategy evaluation
• 🔬 Research — reproducible experiments, dual-licensed papers, methodology over hype
• 🤖 AI developer tooling — MCP servers and infrastructure for LLMs

📌 Featured projects

| Project | What it does | Built with |
|---|---|---|
| 🔬 quant-research | Martingale — monthly, peer-reviewable quant research. Reproducible experiments + written papers that report the honest answer, null results included. Open to contributors | Python |
| 📈 quantsim | Full quant stack — backtesting engine, price-time-priority order book with market-impact execution, Monte Carlo risk analytics, and a live paper-trading bot that commits its P&L to git | Python · NumPy |
| 🏛️ exchange-simulator | Agent-based market where fat tails, volatility clustering & flash crashes *emerge* from autonomous traders — statistically verified | Python |
| 📜 quantlang | A programming language for trading strategies — hand-written lexer, parser & interpreter; compiled output proven bitwise-identical to hand-written Python | Python |
| 🧮 optionslab | Options pricing with three independent engines that cross-validate to 4 decimals — Black–Scholes, binomial trees, Monte Carlo — plus Greeks & implied vol | Python |
| 🧭 pathfinding-visualizer | Watch A*, Dijkstra, BFS & Greedy race across a grid you draw — 60fps canvas, zero deps | JavaScript |

🛠️ Tech stack

!Python
!TypeScript
!JavaScript
!NumPy
!React
!Next.js
!Node.js
!Git

🐍 My contribution graph

📊 GitHub stats

The banner and strategy tracker above regenerate themselves daily via GitHub Actions — the code is in /assets.

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