📈 Real-Time AI Stock Predictor (ML + Streamlit Dashboard)

An AI-powered real-time stock analysis system built using:

• 🧠 Machine Learning (LightGBM)
• 📊 Interactive Dashboard (Streamlit + Plotly)
• 💹 Live market data feed (Yahoo Finance API)
• 🗂 PostgreSQL / Local mode fallback
• 🔁 Automated prediction pipeline

This project predicts BUY / SELL signals with confidence scores and displays:

✔ Candlestick charts
✔ Volume trends
✔ Technical indicators (RSI, MACD, SMA, EMA)
✔ Feature importance
✔ Auto-generated human insights

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🚀 Features

Feature	Status
Real-time market data fetch	✅
Model prediction (Buy/Sell + confidence)	✅
ML Models stored for each stock	✅
Interactive charts (candles, volume, RSI, MACD)	✅
Technical analysis insights	✅
Refresh + live update	✅
Deployable to Streamlit Cloud	✅

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🏗 Project Structure

stock-rt-powerbi-ml/
│
├─ dashboard/
│  └─ app.py                    # Streamlit UI
│
├─ src/
│  ├─ predict_realtime.py       # Load model + run live predictions
│  ├─ fetch_live.py             # Fetch latest price from Yahoo Finance
│  ├─ insights.py               # Technical indicators + insights generator
│  ├─ train_model.py            # Model training script (LightGBM)
│  └─ download_historical.py    # Historical data downloader
│
├─ models/                      # Saved ML models (AAPL.pkl, MSFT.pkl…)
├─ data/                        # Optional seed data
├─ requirements.txt
└─ README.md

Live Deployment:

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Deployment Link : https://stock-price-analysis-and-prediction-qsnxiuus2ysweyidepeb9c.streamlit.app/

Screenshots of Project

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⚙️ Installation

1️⃣ Clone Repo

git clone https://github.com/<your-username>/stock-rt-powerbi-ml.git
cd stock-rt-powerbi-ml

2️⃣ Create Virtual Environment

python -m venv venv

Activate:

# Windows
venv\Scripts\activate
# Mac/Linux
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

🧠 How It Works

Stage Description

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1. Data fetching Live stock data retrieved via Yahoo Finance
2. Feature engineering Volume, OHLC features, technical indicators
3. Model inference LightGBM model predicts BUY/SELL
4. Confidence scores predict_proba() returns decision confidence
5. Visualization Plotly + Streamlit render interactive analysis charts
6. Insights engine Auto-text reasoning based on RSI/MACD/Crossovers

Candlestick RSI + MACD

🔮 Future Enhancements

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📩 Telegram or Email trading alerts

🧩 Portfolio optimization / backtesting

🧠 Reinforcement learning model

⏱ Auto-refresh interval (5s / 15s / 30s toggle)

🌍 Multi-market (Crypto, Forex, Indian NSE/BSE)

🛠 Tech Stack

Layer Tools Programming Python Dashboard Streamlit + Plotly AI/ML Scikit-learn, LightGBM Data Source Yahoo Finance (yfinance) Optional DB PostgreSQL

##🤝 Contributing

PRs are welcome. For major changes, please open an issue.

⭐ Support

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If this project helped you — star the repo ⭐ and share it!

Author

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👤 Ayush 💻 AI/ML Developer 🚀 Gen-Z Engineer who automates financial decision making.