Alpha Forge — an agentic AI operating system for systematic trading.

38M-param time-series world model: FSQ tokenizer → Mamba-2 JEPA → OT-CFM → TD-MPC2 agent. 838M tokens, TPU v6e, JAX/Flax.

Graph + temporal deep learning for cross-sectional S&P 500 ranking. 9-variant ablation, 224 tests, val IC 0.0284 on yfinance data.

Deep RL agent for financial market signal generation — PPO/A2C/SAC/TD3, 99 indicators, ensemble signals, 4-level quality gate

End-to-end ML pipeline that predicts BTC/USDT price direction (4h horizon) using XGBoost + Optuna + SHAP. 9-phase architecture, Walk-Forward Validation across 15 folds, 37 technical indicators, 98 automated tests. ROC-AUC: 0.5431.

NIFTY 50 5-day trend classification using Decision Tree, Random Forest and Logistic Regression with live prediction system.

Deep learning pipeline for financial time-series forecasting using LSTM, CNN, CNN–LSTM and ResNet–LSTM with Gramian Angular Difference Field (GADF) encoding and an interactive Streamlit dashboard.

Predict S&P 500 stock performance using a graph neural network that models market correlations and sector relationships to generate long-short portfolio signals.

QuantLab alpha construction component for purified thematic signals, walk-forward weighting, IC evaluation, turnover diagnostics, and ML alpha experiments.

Transformer‑based Bull/Bear classifier for Bitcoin using long‑window trend features and pretrained inference‑only weights.

Real-time financial sentiment pipeline: 3-model FinBERT ensemble (86.7% acc, neg PR-AUC=0.908) · MC Dropout uncertainty · TimescaleDB · Kafka · FastAPI · MLflow · Prefect 2

FinFusion: S&P 500 return forecasting with Temporal Fusion Transformers - compares TFT, ARIMAX, LSTM, and regime-aware variants.

Advanced ML system combining LSTM attention networks, Transformer architectures, and gradient boosting ensembles for financial time series forecasting

Reinforcement-learning aggregator on top of the TradingAgents multi-agent LLM trading framework. PPO policy that beats buy-and-hold on the 2026 YTD test across two LLM backbones (Anthropic + OpenAI); cross-LLM transfer holds. Course project, Columbia IEORE 4733.

A Python research framework that tests whether stock factor models really explain market behavior, or just look accurate because of hidden proxy effects.

Intelligent loan approval system using Support Vector Machine (SVM) for automated credit assessment and loan status prediction

ML pipeline for bank insolvency prediction (1–5 years). 4 ensemble models + SHAP explainability. Production-ready with Docker, reproducible research.

Trabajo de Fin de Grado en Ingeniería Matemática: Sistema de predicción direccional de Bitcoin mediante modelos de machine learning (LightGBM) y análisis de sentimiento (RoBERTa). Investigación sobre integración multimodal en mercados financieros.

In-progress AI-assisted systematic alpha research platform for factors, signals, portfolio construction, backtesting, and research automation.

Production-grade ML signal intelligence engine for quantitative trading. Powers real-time XGBoost inference across 100 S&P 500 tickers, 4-agent decision governance, algorithmic drift detection with automatic exposure scaling, and geopolitical risk overlay via live news APIs.