This is final project repository of ML for trading class on large cap crypto trading based on macroeconomical variables

I explore risk parity strategy (risk budget) and optimize it using macroeconomics factors (like inflation, GDP, Rates, VIX,...) through ML techniques

A full-stack, AI-powered stock intelligence terminal built entirely on free and open data sources. No paid APIs required. Features a dark trading-terminal UI, real-time market data, ML-based directional predictions, news sentiment analysis, macroeconomic context, and continuous model evaluation.

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

An ML-based trading strategy using macroeconomic indicators.

Automated ETL + ML pipeline for forecasting UK macroeconomic indicators using ONS and Bank of England data

ML model for forecasting directional price movement of U.S. Gold Futures using macroeconomic and technical indicators

Comparative study of ML and statistical models for predicting Sri Lanka Consumer Price Index and developing Macroeconomic Condition Index

Python/R, Advanced ML and econometrics methods for forecasting main russian macroeconomic variables, sush as GDP, household consumption, investmetns etc

End-to-end FX currency pair forecasting pipeline using PyCaret, ArcticDB, and Airflow — integrates real-time market data, macroeconomic features (BTC correlation), and automated ML for directional predictions.

Market forecasting project using ML (Linear Regression, Random Forest) to predict market price and direction. Features include technical and macroeconomic factors. Backtesting showed the strategy outperformed Buy and Hold ($421\%$ vs $161\%$).

AI-powered liquidity risk prediction system using financial and macroeconomic indicators. Compared 8 ML models including a Hybrid RF–MLP, achieving **99.78% accuracy**. Supports class imbalance handling, 13-metric evaluation and dashboard-ready regulatory monitoring.

Predicting the future direction of Apple, gold, and Nasdaq. Imported data from yfinance, added technical and macroeconomic indicators, applied feature selection, and used ML models (Random Forest, XGBoost, etc.) and LSTM. Time Series Cross-Validation was applied.

This application is an interactive tool designed to provide a user-friendly environment for analyzing and forecasting macroeconomic indicators using ML models. By extracting economic data for various countries from the World Bank API, it allows users to visualize historical trends and generate 5-year forecasts for critical indicators

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ML with Macroeconomic Data (Quandl API)

Decomposing ML Forecast Gains in Macroeconomic Forecasting

Macroeconomic forecasting with ML vs professional forecasters

Predict BAC stock price using ML and Macroeconomic variables

ML task to backcast government corruption levels using macroeconomic indicator

Using ML to predict oil prices based on different macroeconomic factors.

EDA and differing ML models for forecasting and classifying macroeconomic regimes

Data science portfolio spanning finance, construction, telecommunications, and macroeconomics — featuring regression modelling, time series forecasting, ML classification, and interactive dashboards.

ML-Powered Portfolio Stress Testing Tool with Macroeconomic Scenario Analysis and Market Risk Modelling

Analysis of political violence and macroeconomic indicators in Sudan (2000–2024) with ML models.

AI-powered system to predict sovereign financial crisis risk using ML, SHAP, and macroeconomic data

ML-powered Kenyan Tea Price Prediction & Commodity Risk Dashboard using XGBoost, Flask, and macroeconomic features

Built predictive analytics pipeline combining SARIMA and ML models to forecast exchange rates from macroeconomic data.

Modeling loan prepayment, default risk, and portfolio cash flows using survival analysis, ML, and macroeconomic time series.

ML-driven macroeconomic forecasting: Random Forest models uncover non-linear debt–inflation dynamics that outperform traditional econometric baselines.