Grant Cromwell

B.S. in Computer Science

Quantitative Researcher

A computer scientist who found a passion for statstics and financial markets. With a strong foundation in programming, I specialize in uncovering new correlations in complex data.

Featured Work

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Random ForestGaussian CopulaMHC

Financial Forecasting System

A comprehensive quantitative trading system implementing Random Forests and Gaussian Copulas for alpha generation across multiple asset classes.

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

Proprietary Custom Models

A collection of quantitative models, backtesting frameworks, and trading strategies. Includes mean reversion systems, factor models, risk analytics, and portfolio construction tools built in Python.

Quantitative Models
Repository Type
Trading, Risk, Optimization
Focus Areas
Equities, Futures, Crypto, ETFs
Asset Classes
Ensemble, Neural Networks, Statistical
ML Techniques
Python
Language
PyTorch, Scikit-learn, Statsmodels
Key Libraries
LangGraphConvNeXtGAF

Hybrid-Adaptive Quant Trading System

Combines GAF pattern recognition with ConvNeXt-Tiny for market regime and direction prediction. LLM confidence calibration filters low-confidence signals. Vector memory stores historical patterns for adaptive strategy refinement.

Phi-3.5-Mini & ConvNeXt-Tiny
Core Technology
Gramian Angular Fields
Pattern Recognition
78% BSC Model
Confidence Threshold
31K Tokens
Context Window
pgvector with HNSW
Vector Database
Python, LangGraph
Backend

Technical Familiarities

Languages

PythonTypeScriptJavaScriptC/C++RustGoSQLRMATLABShell/BashSolidity

Frameworks

PyTorchTensorFlowKerasLangGraph
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Tools

GitDockerKubernetesJupyter
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