Stock Trend Prediction Application

Overview

This is a Streamlit-based web application that predicts stock trends using historical data and a pre-trained LSTM (Long Short-Term Memory) model. The application allows users to visualize historical stock prices, compare them with moving averages, and predict future trends.

Features

1. Stock Data Retrieval:

   • Retrieves historical stock data from Yahoo Finance using the provided stock ticker symbol.
   • Default stock ticker: AAPL (Apple Inc.).

2. Visualization:

   • Displays historical closing prices from 2010 to 2023.
   • Visualizes the closing price with:
     • 100-day Moving Average.
     • Both 100-day and 200-day Moving Averages.

3. Prediction:

   • Predicts stock prices based on the pre-trained LSTM model.
   • Compares predicted prices with the original prices.

Requirements

To run this application, ensure you have the following dependencies installed:

• Python 3.x
• Streamlit
• Numpy
• Pandas
• Matplotlib
• Pandas DataReader
• yfinance
• Keras
• scikit-learn

Installation

1. Clone this repository:

   git clone <repository_url>

2. Navigate to the project directory:

   cd <project_directory>

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your browser and navigate to the URL provided by Streamlit (e.g., http://localhost:8501).

3. Enter a stock ticker symbol (e.g., GOOGL, MSFT) in the input field and view the results.

How It Works

1. Data Retrieval:

   • The app fetches historical stock data for the specified ticker symbol from Yahoo Finance.

2. Visualization:

   • The app creates various charts to show the stock's historical performance and trends.

3. Prediction:

   • The pre-trained LSTM model (stock_lstm.h5) is used to predict stock prices.
   • The model is trained on 70% of the historical data, and predictions are made on the remaining 30%.

4. Comparison:

   • The predicted prices are compared with the original prices to visualize the model's performance.

File Structure

• app.py: Main application file containing the Streamlit code.
• stock_lstm.h5: Pre-trained LSTM model file.

Visualisation

Future Improvements

• Add support for multiple stock models.
• Include additional technical indicators (e.g., RSI, Bollinger Bands).
• Enable real-time stock predictions.

License

This project is open-source and available under the MIT License.

Contributors

• Mayank Choudhary
• Shambhavi Gunda

Code Deployed

• Link:https://mayank-stock-prediction.streamlit.app/