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

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.

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

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.

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

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

Binary classification neural network using Keras to predict loan approval decisions based on applicant financial and demographic data

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.

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

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

Real time fraud detection pipeline

Advanced gold price forecasting system beating academic benchmarks with 9+ ML models. Features rolling window predictions, real-time analytics dashboard, and extensible architecture. Built with uv, FastAPI, and Next.js for cross-platform performance.

Bitcoin trading agent using Deep Q-Learning and synthetic market scenarios.

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.

✅ app.py — your full Stock Market Storyteller app with: Stock charts TA-Lib indicators (SMA, RSI, MACD) Gemini-powered natural language summaries CSV export

Credit default prediction using dynamic feature importance reweighting that adapts during training. Combines gradient-based feature attribution with temporal curriculum learning to progressively emphasize the most predictive features for different risk segments. The novel contribution is an adaptive loss weighting mechanism that rebalances feature

🔬 Research Project: An automated framework to generate, configure, and evaluate multi-agent AI crews for financial modeling using a Meta-Agent pipeline. This study evaluates the performance of dynamically synthesized MAS (Multi-Agent Systems) against manual expert-defined benchmarks in financial risk contexts.