financial-ml

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.

Adapting a language aware decision model for banking through fine tuning, evaluation and comparison with classical machine learning.

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

Benchmarking generative models on financial time series: data pipeline, model zoo, eval harness

7 pip-installable Python libraries for WorldQuant BRAIN alpha discovery, orthogonal decorrelation, and tri-factor meta-synthesis

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

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.

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.

History-conditioned flow matching for level-2 limit-order-book generation.

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