FOREX FORECASTING MODEL
We created an AI system that analyzes years of currency data to forecast market trends. It helps make data-driven financial decisions.
Year :
2024
Industry :
Tech
Problem :
The problem was, Raw MT5 forex data was noisy and inconsistent across symbols and timeframes, making signals unreliable and hard to compare in real time; model runs weren’t easily reproducible, and there was no unified interface to view predictions alongside actual market prices for quick decision‑making.
Solution :
To address this, I created a full‑stack app was built with a Django REST API that serves versioned LSTM predictions from preprocessed data, and a React frontend that visualizes predicted vs actual prices with automatic periodic refresh—resulting in cleaner inputs, reproducible models, and a single place to review live insights.
Challenge :
The main challenge was transforming noisy, uneven MT5 time‑series into consistent, model‑ready data while keeping training and live serving aligned and presenting results in a single, fast UI that updates without manual refreshes.
Summary :
It is a practical, end‑to‑end forex predictor: standardized data, versioned models, and a live dashboard that turns signals into action.
This project provides a web application for predicting forex market trends using machine learning models, displaying predictions and actual market data in interactive charts. The backend is built with Django, and the frontend is created using React.
More Projects
FOREX FORECASTING MODEL
We created an AI system that analyzes years of currency data to forecast market trends. It helps make data-driven financial decisions.
Year :
2024
Industry :
Tech
Problem :
The problem was, Raw MT5 forex data was noisy and inconsistent across symbols and timeframes, making signals unreliable and hard to compare in real time; model runs weren’t easily reproducible, and there was no unified interface to view predictions alongside actual market prices for quick decision‑making.
Solution :
To address this, I created a full‑stack app was built with a Django REST API that serves versioned LSTM predictions from preprocessed data, and a React frontend that visualizes predicted vs actual prices with automatic periodic refresh—resulting in cleaner inputs, reproducible models, and a single place to review live insights.
Challenge :
The main challenge was transforming noisy, uneven MT5 time‑series into consistent, model‑ready data while keeping training and live serving aligned and presenting results in a single, fast UI that updates without manual refreshes.
Summary :
It is a practical, end‑to‑end forex predictor: standardized data, versioned models, and a live dashboard that turns signals into action.
This project provides a web application for predicting forex market trends using machine learning models, displaying predictions and actual market data in interactive charts. The backend is built with Django, and the frontend is created using React.
More Projects
FOREX FORECASTING MODEL
We created an AI system that analyzes years of currency data to forecast market trends. It helps make data-driven financial decisions.
Year :
2024
Industry :
Tech
Problem :
The problem was, Raw MT5 forex data was noisy and inconsistent across symbols and timeframes, making signals unreliable and hard to compare in real time; model runs weren’t easily reproducible, and there was no unified interface to view predictions alongside actual market prices for quick decision‑making.
Solution :
To address this, I created a full‑stack app was built with a Django REST API that serves versioned LSTM predictions from preprocessed data, and a React frontend that visualizes predicted vs actual prices with automatic periodic refresh—resulting in cleaner inputs, reproducible models, and a single place to review live insights.
Challenge :
The main challenge was transforming noisy, uneven MT5 time‑series into consistent, model‑ready data while keeping training and live serving aligned and presenting results in a single, fast UI that updates without manual refreshes.
Summary :
It is a practical, end‑to‑end forex predictor: standardized data, versioned models, and a live dashboard that turns signals into action.
This project provides a web application for predicting forex market trends using machine learning models, displaying predictions and actual market data in interactive charts. The backend is built with Django, and the frontend is created using React.