🐢 SYSTEM STATUS: This Tortoise has entered indefinite hibernation 💤. Back when the timing is right. 🐢
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Quantitative researcher and seasoned data scientist holding an M.S. in Data Science from the University of Illinois Urbana-Champaign (UIUC), complemented by a diploma in International Economics and Finance. Passionate about exploring the intersection of mathematical finance, statistical learning, and market microstructure to build robust, systematic alpha.
My work is about turning noisy market data into research that can be trusted: building workflows that carry an idea from first hypothesis to a strategy tested under realistic costs and changing market regimes. |
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My objective is not to find the most impressive back-test. It is to understand the mechanism behind a signal, and determine whether it survives costs, uncertainty, and changing market conditions.
ℹ️ Research notes, strategy write-ups and code are available on request. |
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| Statistical arbitrage: Pairs trading, co-integration, mean reversion, Kalman filtering, and vector error correction models (VECM). | Financial sequence modeling: Transformers and Temporal Convolutional Networks (TCNs) for limit order book and time-series data. | ||||||||||
| Factor research: PCA, Fama–French factor models, cross-sectional signals, and time-series momentum. | Reinforcement learning: PPO and MADDPG for execution policies, sequential decisions, and market interaction. | ||||||||||
| Derivatives & stochastic modeling: Monte Carlo simulation, Heston stochastic volatility, and Black–Derman–Toy short-rate modeling. | Graph learning: Graph Neural Networks for cross-asset relationships and structured financial representations. | ||||||||||
| Portfolio construction: Black–Litterman, Kelly criterion, mean–CVaR optimization, and constrained portfolio allocation. | Explainable AI: SHAP, LIME, feature attribution, and model diagnostics for financial ML systems. | ||||||||||
| Regime detection: Hidden Markov Models (HMM), Markov-switching models, and regime-conditioned risk analysis. | Financial NLP: Language models, topic modeling, and information extraction from financial news, filings, and earnings-call transcripts. | ||||||||||
Languages, Acceleration & HPC           
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The first principle is that you must not fool yourself, and you are the easiest person to fool. |
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Quantitative Finance & Econometrics         
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Machine Learning, Deep Learning & Reinforcement Learning        
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LLMs, GenAI & Financial NLP       
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Backtesting, Execution & Trading Systems      
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Data Engineering, Vector Search & Distributed Computing          
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DevOps, Infrastructure & MLOps         
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| Level-2 / Level-3 limit order book representations | Regime-aware factor allocation | PPO-based execution research | |||||||||
| Order-flow imbalance and liquidity dynamics | Dynamic covariance and volatility estimation | Reward design under trading constraints | |||||||||
| Short-horizon price and volatility forecasting | Tail-risk measurement and CVaR optimization | Execution benchmarks and implementation shortfall | |||||||||
| Transaction costs, slippage, and market impact | Robustness across market environments | Offline evaluation and out-of-sample validation | |||||||||
| Research loop: data → signal → portfolio → execution → risk review | |||||||||||
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shsarv
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