Course Website on Macroeconomic Analysis with Machine Learning and Big Data

Pure Zig implementation of Jinja2 templating. Bytecode compilation, 40+ filters, template inheritance, macros, autoescaping, and sandboxing. Built for HuggingFace ML pipelines and high-performance applications.

An implementation of MacroML [Ganz, Sabry & Taha 2001]

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Modeling Macroeconomics with Deep Reinforcement Learning

A LOWL-to-LLVM map for compiling the ML/I Macro Processor.

Macro malware is a strain of malware found in VBA macros in Microsoft Office files. This project extracts, preprocesses, and engineers features from VBA then trains SVM, KNN, and RF machine learning models and evaluates how effectively they can detect malicious macros.

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Implement of Advances in Financial Machine Learning. Marco's book.

This repository explores AI/ML approaches towards automated tracking of dietary intake: estimating the ingredients & macro-nutrients in a food plate using digital images.

Advanced multi-factor market downturn prediction model using options data, volatility metrics, gamma exposure, jump risk, and macro sentiment. Features real-time data integration, regime detection, ensemble ML, risk-adjusted signals, and backtesting for precise early warning of market stress events.

This project highlights a Spark application built on Scala. It utilizes Spark Core, Spark SQL and Spark ML (Machine Learning libraries) for predicting stock prices of specific airline companies. We have used the Google trending words (searched on internet and relevant to financial domain) and also macro-economic oil prices as alternate data to predict stock prices.

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Forecasting inflation, GDP, and unemployment using ML and classical econometric models

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Event-driven ML end-to-end pipeline for small-cap biotech (news/NLP, options-implied metrics, macro/tech). Purged k-fold + embargo, regime gate, deterministic backtester.

FE800 Financial Engineering project on market regime detection and regime-aware portfolio allocation. Compares Hidden Markov, Semi-Markov, Jump Models, RHSM, and ML models using macro, volatility, and market indicators to dynamically allocate across equities, bonds, and alternatives.

MacroPredict-ML: Monitor de Riesgo Económico Regional

Final proyect on: "Inflation forecasting in perú: benchmark versus Machine Learning Models".

Collection of helper macros for machine learning in Rust

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Forecasting macroeconomic indicators such as GDP using LSTM-based time series modeling in Python.

We predict equity market vol using tweets from major news sources, hedge funds and investment banks, and notable economists

Test cases of the features of the ancient macro processor ML/I

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SAS macro "ml" implements maximum-likelihood estimation via PROC NLMIXED as described in the paper:

🥇 Analyse & aide à la décision pour métaux précieux — ML walk-forward (XGBoost, LSTM), backtesting, macro. Streamlit PWA.

A two-stage ML framework combining return prediction and macro-driven gating to improve trading decisions under noisy financial signals.