After 3 years trading equities derivatives at Goldman Sachs — market-making a multi-billion dollar synthetic funding book, running basis arbitrage, and pricing structured trades — I left to pursue what I’m most passionate about: developing and deploying systematic trading strategies across macro, crypto, and equities, while building technology businesses from the ground up.
Trading, building, and researching — from institutional desks to independent systematic strategies.
I spent 3 years at Goldman Sachs on the Equities Derivatives desk trading synthetics — managing delta across a multi-billion dollar TRS funding book, running speculative basis arbitrage, and pricing structured trades for institutional clients.
Now, I develop and run systematic trading strategies across macro relative value, cross-sectional momentum, trend following, and quantamental equity selection. I also build technology businesses — from algorithmic trading indicator platforms to AI-powered analytics tools — handling everything from backend infrastructure to frontend deployment.
Building AI Teams for Portfolio Managers
Designing and deploying autonomous AI research teams that replicate the structure of an institutional quant desk — operating 24/7 across the full research and trading lifecycle. Have your own team of AI agents covering the entire spectrum of systematic research, development, and execution.
A selection of the systematic strategies I run across macro, crypto, and equities. This is a high-level overview of the main strategy groups.
Focuses on price gaps between related markets and looks to capture reversion when those gaps normalize.
A diversified trend strategy that aims to participate in sustained market moves across assets.
Targets temporary mispricings in index futures basis and mean-reversion opportunities.
A long/short crypto momentum portfolio designed to capture relative strength while controlling overall market exposure.
Systematic stock selection using a blend of value, quality, and growth signals.
An AI-assisted macro process that turns market data and events into structured trade ideas.
Event-driven strategies in prediction markets focused on probability mispricings and cross-venue opportunities.
Beyond systematic trading, I build full-stack products from idea to deployment.
Algorithmic trading indicator platform with 1,000+ users. 25+ proprietary TradingView indicators covering trend models, regime detection, PCA-based residual signals, and volatility systems. Full-stack subscription platform built with React, Supabase, and Stripe.
Quantitative analytics platform for prediction markets. Features probability distribution modelling, order-flow imbalance metrics, cross-platform arbitrage scanners, and anomaly detection across binary event markets.
E-commerce analytics platform with an integrated AI chatbot. Aggregates product and market data from Shopify, TikTok, Amazon, and AliExpress using custom scraping engines, then provides conversational AI-driven insights via a Claude-powered LLM with RAG architecture, embeddings, and memory modules.
A dating app for missed connections. Users post anonymous sightings of someone they noticed but never approached. If the other person claims it, both decide whether to match and begin anonymous conversations. Features two modes — Find (missed connections) and Play (daily curated picks) — with built-in privacy and safety features.
Probability-based algorithm for Polymarket BTC binary markets. Uses diffusion models for probability calibration and order-flow microstructure analysis to identify mispriced contracts and generate trading signals.
Full backtesting and live execution platform (BT3). Multi-asset, event-driven framework with HMM regime models, PCA factor analysis, walk-forward evaluation, FX-aware sizing, and rich reporting. Connected to Interactive Brokers, Binance, and Alpaca for live deployment.
Interested in discussing systematic trading, collaborating on a project, or just want to connect?