📈 Real-Time Stock Movement Predictor using SVM + Streamlit

[MyrinNew]

🚀 Try the Live Demo

👉 Live App

🧠 GitHub Repo


📸 Project in Action

UI:





Model Evaluation:





💡 What Does This App Do?

It predicts whether a stock’s next-day movement will be:


📈 UP or 📉 DOWN


You simply:


Choose a stock (AAPL, MSFT, TSLA, etc.)

Click “Predict”

See the next-day prediction powered by an SVM model


🔧 How It Works

✅ Features Used:

Past 3-day returns

Short & Long moving averages

Momentum = short − long

Volatility (rolling std dev)

Ticker encoded as a feature


📊 Model:

SVM classifier trained on 3 years of daily data (from Yahoo Finance)

Achieved ~99% test accuracy 🎯


🛠️ Tech Stack

Model	Scikit-learn (SVM)
Data Source	yfinance (Yahoo Finance API)
Frontend	Streamlit
Feature Engg	pandas, NumPy
Visualization	seaborn, matplotlib

📁 Project Structure

stock-movement-svm/

├── app.py # Streamlit frontend + logic

├── model.pkl # Trained SVM model

├── scaler.pkl # Feature scaler

├── generate_features.py # Feature generation logic

├── requirements.txt

├── screenshots/

│ ├── screenshot-ui.png

│ └── confusion-matrix.png

└── README.md


🔌 Run Locally

git clone https://github.com/snoorbasha50/stock-movement-svm.git

cd stock-movement-svm

pip install -r requirements.txt

streamlit run app.py


🔍 Future Enhancements

Add more stocks dynamically

Include candlestick chart visualizations

Fine-tune SVM hyperparameters

Try LSTM or deep learning for sequence modeling


📬 Let’s Connect

💼 LinkedIn

🌐 GitHub




More...