Grant Cromwell
B.S. in Computer Science
Quantitative Researcher
Building Quantitative Systems
I build intelligent trading systems that adapt to market dynamics using math and machine learning. By combining rigorous statistical methods with robust engineering, I create solutions that turn complex market data into actionable insights that utilize neural networks and machine learning.
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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.
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.
Volatility Estimation Framework
A comprehensive volatility estimation tool combining traditional statistical methods with machine learning for multi-timeframe analysis.
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View the complete collection of quantitative research, trading systems, and technical projects.
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