NASDAQ Daily Change Prediction

This project aims to build a Machine Learning model to predict the daily percentage change of the NASDAQ index. The model will be trained using a dataset created from various data sources, including historical NASDAQ data, the VIX index, and technical indicators calculated from this data.

Project Goals

• Predict the daily percentage change of the NASDAQ.
• Use historical NASDAQ data and the VIX index.
• Calculate and add technical indicators like RSI, MACD, and moving averages.
• Explore the possibility of web scraping to gather additional relevant data.

Data Sources

The data for the model will come from several sources:

• Historical NASDAQ Data: Daily performance data of the NASDAQ index.
• VIX Index: A measure of market volatility.
• Technical Indicators: Calculated indicators like RSI, moving averages, derived from the NASDAQ historical data.
• FRED: Specifically daily frequency data from the Federal Reserve St. Louis.
• Others sources via APIs: Sites like Alpha Vantage.
• Web Scraping (optional): Collecting additional data from relevant financial websites.

Approach

1. Data Preprocessing: Clean and transform the collected data so it's ready for Machine Learning.
2. Modeling: Build and train Machine Learning models, such as KNN, decision trees, or neural networks.
3. Model Evaluation: Assess the model’s accuracy and reliability using various metrics.
4. Prediction: Use the trained model to predict the daily percentage change of the NASDAQ.

Requirements

• Libraries: pandas, numpy, scikit-learn, matplotlib, yfinance and other dependencies for data processing and modeling.

Installation

Clone this repo:

git clone https://github.com/pbuitragoa33/NASDAQ-Change-Prediction.git