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Quantitative TradingMachine LearningPythonPortfolio OptimizationRisk AnalysisStatistical Arbitrage
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
# Methodology
• Fine-Tuned Financial Forecasting: Language models specialized for quantitative analysis via LoRA adaptation on lightweight base architectures. Outputs structured metrics including 95% confidence intervals, Sharpe/Sortino ratios, Value-at-Risk (VaR), and Conditional VaR. Trained on formatted financial data with supervised fine-tuning to generate calibrated probability estimates.
• Vision-Language Embeddings: VL-JEPA integration for temporal pattern recognition. Processes 60-bar price sequences to predict 5-bar forward movements, learning joint embeddings across price action and market microstructure. Captures non-linear relationships without explicit feature engineering.
• Multi-Asset Support: Unified framework across equities (NVDA, AAPL, MSFT, TSLA), ETFs, cryptocurrencies (BTC, ETH), commodities, and forex. Entity extraction automatically parses tickers and time horizons. Regime detection adjusts confidence bounds during market transitions.
• Production Safety: Confidence thresholding (>60%) prevents low-confidence outputs. Risk disclaimers embedded in responses. Audit trail logging for compliance. Designed for institutional deployment with explainable predictions and uncertainty quantification.
• Vision-Language Embeddings: VL-JEPA integration for temporal pattern recognition. Processes 60-bar price sequences to predict 5-bar forward movements, learning joint embeddings across price action and market microstructure. Captures non-linear relationships without explicit feature engineering.
• Multi-Asset Support: Unified framework across equities (NVDA, AAPL, MSFT, TSLA), ETFs, cryptocurrencies (BTC, ETH), commodities, and forex. Entity extraction automatically parses tickers and time horizons. Regime detection adjusts confidence bounds during market transitions.
• Production Safety: Confidence thresholding (>60%) prevents low-confidence outputs. Risk disclaimers embedded in responses. Audit trail logging for compliance. Designed for institutional deployment with explainable predictions and uncertainty quantification.
Core Model
Ensemble Machine Learning + Statistical Optimization
# Data Source
Financial Market Data (Yahoo Finance, Crypto Exchanges, Institutional Data)