LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna

A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks. still work under progress

📈 LSTM + Random Forest + XGBoost Stock Predictor

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🚀 About the Project

This project integrates:

• 🔁 Recurrent Neural Networks (LSTM) for sequential financial patterns
• 🌲 Random Forest for ensemble-based classification
• ⚡ XGBoost for gradient boosting decision trees
• 🎯 Optuna for automatic hyperparameter tuning (optional mode)
• 📊 Backtesting Module to simulate trading performance

⚙️ Built by a Computer Engineering student to demonstrate real-world ML/AI skills in finance and time series prediction.

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📌 Features

• ✔️ Ensemble of 3 models: LSTM + RF + XGBoost
• ✔️ Flag-based retraining (no need to retrain every time)
• ✔️ Real stock data from Yahoo Finance
• ✔️ Feature-rich engineering: RSI, Moving Averages, Volatility, Volume
• ✔️ Backtesting for historical performance validation
• ✔️ Soft voting for final trade signal (BUY / SELL)
• ✔️ CLI-based output, no GUI bloat
• ✔️ Saved model reuse (models/ folder)

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🧠 Technologies Used

Category	Stack
Language	Python 3.10
ML Models	RandomForestClassifier, XGBClassifier
Deep Learning	TensorFlow / Keras LSTM
Optimization	Optuna
Data Source	yfinance
Indicators	RSI, MA5/10/20, Volatility, Volume

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📂 Project Structure

LSTM-RandomForest-XGBoost-Stock-Predictor/ ├── LSTM+Random Forest+XGboost Stock Predictor.py ├── LSTM+Random Forest+XGboost Stock Predictor with optuna.py ├── backtest.py ├── models/ # Contains saved models ├── README.md ├── LICENSE # MIT License

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🧪 How to Run

✅ 1. Install Dependencies

pip install -r requirements.txt If no requirements.txt, install manually pip install yfinance numpy pandas scikit-learn xgboost tensorflow optuna

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✅ 2. Run Prediction Scripts

Without Optuna (default hyperparameters):

python "LSTM+Random Forest+XGboost Stock Predictor.py"

With Optuna (automatic tuning):

python "LSTM+Random Forest+XGboost Stock Predictor with optuna.py"

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✅ 3. Backtest the System

python backtest.py This will simulate trades and give statistics based on past 3 years of stock data.

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📊 Output Example

📈 Processing RELIANCE... ✅ Trained RF | Accuracy: 0.72 | F1: 0.75
✅ Trained XGB | Accuracy: 0.74 | F1: 0.77
✅ Trained LSTM | Accuracy: 0.76 | F1: 0.79
🔎 RF Signal: BUY
🔎 XGB Signal: BUY
🔎 LSTM Signal: SELL
🟢 Final Signal: BUY

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🧠 Why This Project Matters

✅ Real-world applicability
✅ Combines traditional ML and DL
✅ Good F1/accuracy across multiple models
✅ Shows model saving, loading, and production-like behavior
✅ Demonstrates ability to tune, backtest, and validate system performance

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author

Aarav Vinayak Mehta B.Tech Integrated Computer Engineering
MPSTME, NMIMS Mumbai Campus

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🙋‍♂️ Want to Hire Me?

If you're a company looking for a student with real-world skills in AI, ML, and applied engineering — this is just the start. Let's connect on LinkedIn or drop me a message!