AI-augmented quant research, ML-for-alpha, and execution & market-microstructure advisory for systematic funds, trading fintechs, and allocators — from a trader who has automated his entire pipeline and runs it live every day.
I'm a machine learning–driven algorithmic trader at SMB Capital (Miami), with 10+ years on the desk at one of the hardest prop firms on earth to get a seat. Before trading: five years as a Moody’s analyst and private-equity associate in New York, building financial and credit models.
I run two parallel books of 20+ fully-automated, ML-driven intraday strategies across US equities and options — owning the entire stack myself: research and machine learning, execution and market microstructure, and a proprietary market-data foundation underneath — a suite of curated, point-in-time databases I built and maintain. I design the strategies, model them, validate them, deploy them, and reconcile every fill against the backtest.
My live strategies are deliberately capacity-aware — high-Sharpe intraday models sized to the capital I trade, not billion-dollar mandates. The same research process and ML toolkit extend to developing higher-capacity, scalable strategies for clients who need to deploy at larger size.
I've also automated the entire pipeline with AI — LLMs (Claude) write, test, and ship alongside me across data collection, feature engineering, backtesting, and deployment, compressing research cycles from weeks to days. I help teams build the same AI-augmented workflow.
Salavat Advisory brings that same rigorous, full-stack process to a select number of outside engagements — for teams that want senior quant firepower without a full-time hire.
Senior, hands-on quant work — scoped to what your team actually needs.
Systematic strategy design, feature engineering, and backtesting across equities and options — from high-Sharpe intraday models to higher-capacity, scalable strategies for larger books. Ideas taken from hypothesis to validated, deployable strategy, or your existing approach pressure-tested and sharpened.
My entire research-to-deployment pipeline is automated with LLMs (Claude). I help teams build the same — AI-assisted data pipelines, feature engineering, backtesting, and code generation that compress research cycles and cut cost.
I've built and maintain a suite of curated, point-in-time market- and instrument-specific databases that feed signals across my whole book. I can build the same data-and-feature foundation for your team, rather than leaving you dependent on raw vendor feeds.
Gradient-boosted meta-models, meta-labeling, and walk-forward validation — plus overfitting audits that separate genuine edge from in-sample noise, and the judgment of when machine learning helps versus when it quietly hurts.
Order routing, order types, short-sale mechanics, and slippage forensics — a deep command of the microstructure dynamics that decide whether an edge survives real fills, plus the trading-systems architecture around it.
For allocators ($5M–$500M+): access to vetted, live intraday quant strategies through licensing or co-allocation. Custom terms, by conversation.
Calls, reviews, and audits — pay only for the time you use.
A scoped deliverable with a fixed fee and a clear outcome.
Ongoing senior quant input, a few hours a week, month to month.
Tell me what you’re building and where senior quant help would move the needle. Most engagements start with a free 15-minute consultation.
Direct is best — email or call, and I’ll get back to you quickly.