Stock Market Prediction using Neural Network

This repository contains scripts for training a neural network to predict stock prices and using the trained model to make future forecasts.

---

Table of Contents

• Features
• Requirements
• Installation
• Usage
• Example Results

---

Features

• Downloads stock market data (Open, Close, High, Low, Volume)
• Computes technical indicators:
  • Moving Average
  • Relative Strength Index (RSI)
  • Moving Average Convergence Divergence (MACD)
• Scales and prepares data for training
• Trains a neural network and saves the model
• Forecasts future stock prices using the trained model

---

Requirements

The project requires the following:

• Python 3.10
• Libraries: yfinance, pandas, numpy, matplotlib.pyplot, tensorflow, sklearn

---

Installation

git clone https://github.com/pzimnota/finance_NN.git

---

Usage

1. Training the Model

• Run python train_model.py script to train a neural network on stock market data:

  • Downloads historical stock data
  • Computes technical indicators
  • Scales the data and creates sequences
  • Trains the neural network
  • Saves the trained model
  • Generates a plot comparing actual vs. predicted prices

2. Forecasting Future Prices

• Run python forecast.py script to predict the next day's closing price:

  • Downloads the latest stock data
  • Scales the data and creates sequences
  • Loads the saved model
  • Predicts the next day's closing price
  • Generates a plot comparing actual vs. predicted prices

---

Example Results

IN PROGRESS

---