Quantitative Finance Research
Comprehensive Research Portfolio Across 6 Major Domains
"Institutional-grade research with formal mathematical derivations and production-validated implementations."
Research at Scale
Comprehensive quantitative research with academic rigor.
Six Research Domains
Spanning market microstructure to reinforcement learning, with production-validated implementations.
ML Algorithms
Core machine learning implementations for signal generation and predictive modeling, grounded in statistical learning theory.
Advanced Algorithms
Cutting-edge architectures including Transformers and State Space Models applied to time-series forecasting.
Market Structure
Regime detection and volatility clustering analysis using MS-GARCH models for adaptive risk budgeting.
RL Position Sizing
Kelly-convergent SAC framework for optimal position sizing with curriculum learning.
Options Strategies
RL-optimized options trading strategies with arbitrage detection and risk management.
Market Making
Inventory management and optimal quoting strategies derived from stochastic control theory.
Theoretical Foundations
Rigorous mathematical treatment with formal proofs and derivations.
Market Microstructure
- Glosten-Milgrom Model - Adverse selection
- Kyle Model - Strategic informed trading
- Ho-Stoll - Inventory risk management
- Avellaneda-Stoikov - Optimal market making
Options Pricing Models
- Heston - Stochastic volatility
- SABR - Smile calibration (10x faster)
- Bates - Combined SV + jumps (1.8% RMSE)
- Variance Gamma - Levy processes
Machine Learning
- Temporal Fusion Transformer (TFT)
- PatchTST / iTransformer
- CatBoost / NGBoost ensembles
- Boruta feature selection
Reinforcement Learning
- Soft Actor-Critic (SAC)
- Kelly-convergent rewards
- Curriculum learning
- Deep hedging (30-42% cost reduction)
Validated Trading Strategies
Production-validated strategies with documented performance metrics.
Put-Call Parity Arbitrage
Funding Rate Arbitrage
Volatility Arbitrage
Box Spread Arbitrage
Research Highlights
Key methodologies and innovations from the research portfolio.
Market Microstructure Theory
Comprehensive coverage of traditional and DeFi market making, from Glosten-Milgrom to Uniswap v3.
- Adverse selection and endogenous spreads
- Strategic informed trading (Kyle model)
- Avellaneda-Stoikov optimal market making
- AMM design: CPMM, StableSwap, Balancer
Academic Rigor
Publication-quality research with extensive peer-reviewed sourcing.
Source Distribution
- 40% Academic Papers (NeurIPS, ICML, ICLR)
- 30% Industry Research
- 20% Practitioner Content
- 10% Reference Books
Classic Foundations
- Black-Scholes (1973)
- Heston (1993)
- Merton Jump-Diffusion (1976)
- Kelly Criterion (1956)
Recent Research
- 60% from 2024-2025
- 25% from 2020-2023
- Haarnoja et al. (2018) SAC
- Lopez de Prado (2018)
Multi-Agent Research Methodology
Collaborative intelligence enabling 15-20x speedup in research synthesis.
Research Technology Stack
Tools and frameworks powering institutional-grade quantitative research.
LaTeX
40K+ lines of academic documentation
DocumentationStochastic Calculus
HJB equations, Ito calculus
MathematicsOptions Models
Heston, SABR, Bates implementations
QuantDeep Learning
Transformers, LSTM, TFT architectures
MLReinforcement Learning
SAC, DDPG, PPO algorithms
MLPython 3.12
Production-ready implementations
LanguageMarket Microstructure
Kyle, Glosten-Milgrom models
TheoryBayesian Methods
BNN, conformal prediction
MLQuality Standards
Institutional-grade documentation with full mathematical rigor.
See Research Applied in Production
This research directly powers the Trade-Matrix trading system.