StockX AI – Intelligent Stock Price Forecasting Platform

StockX AI is an advanced stock market forecasting web application that combines deep learning techniques (LSTM) with technical indicators such as RSI, MACD, and Moving Averages to generate accurate and actionable stock price predictions. Developed using Streamlit, the application offers an interactive, real-time experience tailored for data analysts, investors, and financial researchers.

Demo Previews

• Moving Averages: View Demo
• Technical Indicators (RSI, MACD): View Demo
• Price Prediction Example: View Demo
• Full Application Walkthrough: Watch Recording

Key Features

• Real-time stock data integration via Yahoo Finance

• Next-day closing price prediction using a pre-trained LSTM model

• Technical indicators visualization:

  • Moving Averages (MA50, MA100, MA200)
  • Relative Strength Index (RSI)
  • Moving Average Convergence Divergence (MACD)

• Model performance evaluation using:

  • Mean Absolute Error (MAE)
  • Root Mean Squared Error (RMSE)
  • R² Score

• Deployed with Streamlit for a smooth and responsive user interface

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Sample Output

Metric	Value
Mean Absolute Error (MAE)	$11.36
Root Mean Squared Error (RMSE)	$14.30
R² Score	0.9109 (Excellent Accuracy)

Predicted Closing Price: $508.30 Evaluation Period: Microsoft (MSFT), 2015–2025

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How It Works

1. The user provides a stock ticker and date range.
2. Historical data is retrieved using the yfinance API.
3. Technical indicators are computed using the ta library.
4. Data is preprocessed and passed into a pre-trained LSTM model.
5. The model predicts the next closing prices and visualizes actual vs. predicted results.
6. Performance metrics are calculated and displayed on-screen.

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Project Structure

StockX-AI/
│
├── app.py                         # Main Streamlit application script
├── Stock Predictions Model.keras  # Pre-trained LSTM model
├── indicators.pdf, ma.pdf         # Graphical representations
├── Screen Recording.mp4           # Application walkthrough video
├── requirements.txt               # List of dependencies
└── README.md                      # Project documentation

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Technology Stack

Layer	Technologies Used
Frontend	Streamlit
Backend	Python, TensorFlow (Keras), NumPy, Pandas
Data Source	Yahoo Finance (yfinance API)
Indicators	ta (Technical Analysis Library)
Visualization	Matplotlib

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Installation & Setup

Prerequisites

Ensure the following Python packages are installed:

• Python 3.x
• TensorFlow / Keras
• Streamlit
• yfinance
• scikit-learn
• ta
• pandas
• matplotlib
• numpy

Steps to Run Locally

1. Clone the repository:

   git clone https://github.com/your-username/StockX-AI.git
   cd StockX-AI

2. Install the required dependencies:

   pip install -r requirements.txt

3. Launch the Streamlit app:

   streamlit run app.py

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Author

Mohammad Saqlain B.Tech in Artificial Intelligence & Data Science Specialized in predictive modeling, machine learning, and financial data analysis

• 📧 Email: saqlain.engineer7@gmail.com
• 🔗 LinkedIn: linkedin.com/in/saqlain3
• 💻 GitHub: github.com/SAKKU3

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