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

Feature engineering, labeling, alternative bars, and leakage-safe datasets for financial ML.

ML-based buy signal detector for Tehran Stock Exchange using XGBoost & Random Forest

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

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.

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

End-to-end Machine Learning pipeline for forecasting XAU/USD hourly price direction using feature engineering, Logistic Regression, FastAPI, and automated API testing.

Heterophily-aware GNN pipeline for anti-money laundering detection. H2GCN + XGBoost cascade achieves PR-AUC 0.595 on IBM AML dataset, 119× improvement over standard GAT.

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

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

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.

AI-powered loan approval prediction system using XGBoost with 96.3% accuracy. Predicts credit eligibility based on income, credit score, DTI ratio & 20+ financial features. Built with Python, Scikit-learn & Streamlit

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

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

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

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

Open-source framework providing gradient-based XAI and feature interaction mapping for VAE in tabular synthetic data generation

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

A production-style PyTorch framework for financial time-series forecasting using LSTM, GRU, and Transformer models with end-to-end training, evaluation, benchmarking, and next day stock price prediction.