Grant Cromwell

B.S. in Computer Science

Quantitative Physicist

Applying quantitative solutions to current world problems

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Statistical ArbBayesianCopula

Multi-Factor Intraday Statistical Arbitrage

Bayesian confluence framework combining recency bias, geopolitical risk, and market microstructure for intraday equities. Student-t copula models heavy-tailed dependencies between factors. NY open entry with lambda risk aversion and volume filters. Forward-tested with regime-aware adaptation and dynamic position sizing.

Type
Statistical Arb
Timeframe
Intraday (1-4h)
Asset Class
US Equities
Entry Trigger
NY Open + Volume/Lambda
Dependency Model
Student-t Copula
Risk Management
Kelly + Stop Loss
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Signal SystemRandom ForestGaussian Copula

Financial Forecasting Signal System

Alpha signal system combining Random Forests and Gaussian Copulas across 31 assets (equities, crypto, forex). Delivers 62% 1-month hit rate with tail-aware dependency modeling via 10K Monte Carlo simulations. Institutional Trading Concepts (ICT) integration for precise entry/exit levels—risk-reward 1:1.3 to 1:2.5. Manifold-constrained clustering for sector coherence. Generates signals for systematic execution.

Type
Signal
Statistical Models
Gaussian Copula
Style
Mixture of Experts
Assets
31 (Equities, Crypto, Forex)
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Quantitative TradingMachine LearningPython

Proprietary Custom Models

Quantitative research library: factor models, mean reversion systems, VaR/CVaR risk analytics, and portfolio construction. LoRA-fine-tuned LLMs for calibrated probability estimates; VL-JEPA for temporal pattern recognition. Multi-asset regime detection, 95% confidence intervals, audit trails—built for deployment where explainability and compliance matter.

Type
Research Library
Focus Areas
Trading, Risk, Optimization
Asset Classes
Equities, Futures, Crypto, ETFs
ML Techniques
Ensemble, Neural Networks, Statistical
Language
Python
Key Libraries
PyTorch, Scikit-learn, Statsmodels
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Technical Familiarities

Languages

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Frameworks

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