Alpha Forge — an agentic AI operating system for systematic trading.
500+ algorithmic-trading skills for AI coding agents in the agentskills.io format. Every skill ships a working Python reference implementation and its own tests - 20,291 in CI. Broker APIs, backtesting, risk, execution, ML, compliance and 10 more domains. Apache-2.0
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.
Deep RL agent for financial market signal generation — PPO/A2C/SAC/TD3, 99 indicators, ensemble signals, 4-level quality gate
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.
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.
Graph + temporal deep learning for cross-sectional S&P 500 ranking. 9-variant ablation, 224 tests, val IC 0.0284 on yfinance data.
End-to-end Machine Learning pipeline for forecasting XAU/USD hourly price direction using feature engineering, Logistic Regression, FastAPI, and automated API testing.
History-conditioned flow matching for level-2 limit-order-book generation.
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
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.
Predict S&P 500 stock performance using a graph neural network that models market correlations and sector relationships to generate long-short portfolio signals.
Open-source framework providing gradient-based XAI and feature interaction mapping for VAE in tabular synthetic data generation
ML research project using PyTorch and FRED Treasury data to study one-day-ahead yield-curve forecasting, with time-series leakage controls, baseline/LSTM ablations, uncertainty simulation, and an experimental DQN RL extension.
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In-progress AI-assisted systematic alpha research platform for factors, signals, portfolio construction, backtesting, and research automation.
Benchmarking generative models on financial time series: data pipeline, model zoo, eval harness
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