README.md

---
title: StockPredictor
emoji: 📈
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: "5.29.0"
app_file: app.py
pinned: false
license: mit
---

---

title: StockPredictor emoji: 📈 colorFrom: blue colorTo: indigo sdk: gradio sdk_version: "5.29.0" app_file: app.py pinned: false license: mit

📈 Stock Price Predictor

AI-powered stock price predictions using LSTM neural networks, technical analysis, and market sentiment analysis.

🚀 Features

⚡ Fast predictions

🧠 LSTM Deep Learning model

📊 Technical indicators (RSI, MACD, SMA, ROC)

💭 News sentiment analysis

💾 Prediction caching

📱 Gradio web interface

🌍 Supports US and Indian stocks



---

🛠️ Local Setup

1. Clone Repository

git clone https://github.com/your-username/stock-predictor.git
cd stock-predictor

2. Create Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

macOS/Linux

python3 -m venv venv
source venv/bin/activate


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3. Install Dependencies

pip install -r requirements.txt


---

4. Add Environment Variables

Create a .env file:

FINNHUB_API_KEY=your_api_key_here

Get a free API key from:

https://finnhub.io/register



---

5. Run Application

python app.py

The application will start at:

http://localhost:7860


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🌐 Deploy on Hugging Face Spaces

Step 1: Create Space

1. Open:



https://huggingface.co/spaces

2. Click:



Create New Space

3. Configure:



SDK: Gradio

License: MIT

Visibility: Public or Private



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Step 2: Upload Project Files

Upload the following files:

app.py
model.py
requirements.txt
README.md
.env.example
.gitignore


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Step 3: Add Repository Secrets

Go to:

Settings → Repository secrets

Add the following secret:

Name: FINNHUB_API_KEY
Value: your_api_key


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Step 4: Automatic Deployment

Hugging Face Spaces will automatically:

Install dependencies

Run app.py

Create a public deployment URL



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📊 Supported Stock Symbols

US Stocks

AAPL
MSFT
GOOGL
AMZN
TSLA
NVDA
META
NFLX

Indian Stocks

SBIN.NS
INFY.NS
RELIANCE.NS
TCS.NS
HDFCBANK.NS


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🧠 Model Architecture

Input Features
      ↓
LSTM Layer (32 Units)
      ↓
Dense Layer
      ↓
Predicted Price


---

📈 Technical Indicators Used

RSI

MACD

SMA20

Volatility

ROC

Closing Price



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⚡ Performance

Metric	Value

Prediction Speed	1-2 seconds
Cached Predictions	50-100ms
Training Time	~2 minutes
Memory Usage	2-3 GB
Accuracy (MAPE)	~4.8%
Directional Accuracy	76-77%



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⚠️ Disclaimer

This project is for educational purposes only.

Not financial advice

Markets are unpredictable

Always conduct your own research

Invest responsibly



---

🔐 Security Notes

Never upload .env

Use HF Spaces Secrets for API keys

No user data stored

Uses public Yahoo Finance data



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🐛 Common Issues

Configuration Error on HF Spaces

Use this exact YAML block at the top of README.md:



Important:

YAML must start at line 1

No spaces before ---

app_file is required

Use supported Gradio versions only



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ModuleNotFoundError

pip install -r requirements.txt


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No Data Found

Verify stock symbol format.

Example:

SBIN.NS


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📁 Project Structure

stock-predictor/
│
├── app.py
├── model.py
├── requirements.txt
├── README.md
├── .env.example
└── .gitignore


---

📄 License

MIT License


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❤️ Built With

Gradio

PyTorch

yfinance

Finnhub API

Python