Advanced Stock Price Prediction Model

Welcome to the Advanced Stock Price Prediction Model repository! This project is designed to predict stock prices with high accuracy using a combination of machine learning and deep learning techniques. The model integrates technical indicators, tree-based models, and advanced neural networks to deliver state-of-the-art predictions.

Features

• Data Fetching and Preprocessing: Automatically fetches historical stock data, computes essential technical indicators, and preprocesses the data for model training.
• Advanced Model Architecture: Utilizes a hybrid approach combining Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and Transformer layers for robust feature extraction and prediction.
• Tree-based Model Integration: Includes Random Forest, Gradient Boosting, and XGBoost models to enhance prediction accuracy.
• Comprehensive Evaluation: Offers detailed performance metrics and visualization tools for model evaluation and future performance assessment.

Project Structure

• data_fetching.py: Handles data fetching from Yahoo Finance, computes technical indicators like RSI, Bollinger Bands, and more, and preprocesses data for model consumption.
• model_building.py: Contains functions to build and train tree-based models and the advanced CNN-BiLSTM-Transformer model. Also includes feature selection and model saving/loading utilities.
• training.py: The main script for training the models. Fetches and preprocesses data, trains the models, and saves the trained models and preprocessing artifacts.
• prediction.py: Facilitates predicting future stock prices using the trained models. It preprocesses input data, makes predictions, and inversely transforms the predictions to their original scale.
• evaluation.py: Provides functions for evaluating model performance using various metrics and visualizing actual vs. predicted prices.

Installation

To get started, clone the repository and install the required dependencies:

git clone https://github.com/yourusername/stock-price-prediction.git
cd stock-price-prediction
pip install -r requirements.txt

Usage

Training the Model

To train the model on a specific stock ticker, use the main.py script. This will fetch the data, train the models, and save the necessary artifacts.

from training import main_train
from data_fetching import fetch_stock_data, add_technical_indicators, preprocess_data

ticker = 'NVDA'
main_train(ticker, fetch_stock_data, add_technical_indicators, preprocess_data)

Predicting Stock Prices

After training the model, you can predict the next week's stock prices using the main_predict function.

from prediction import main_predict
from data_fetching import fetch_stock_data, add_technical_indicators

ticker = 'NVDA'
predictions = main_predict(ticker, fetch_stock_data, add_technical_indicators)
print(f"Predicted stock prices for the next week for {ticker}:")
for i, price in enumerate(predictions, 1):
    print(f"Day {i}: {price:.2f}")

Evaluating Model Performance

You can evaluate the model's performance on actual stock prices (if available) using the evaluate_performance function.

from prediction import evaluate_performance

# Replace with actual stock prices for evaluation
actual_prices = [...]
mae, mse, rmse, r2 = evaluate_performance(predictions, actual_prices)
print(f"Future Performance Evaluation:
MAE: {mae:.2f}
MSE: {mse:.2f}
RMSE: {rmse:.2f}
R2: {r2:.2f}")

Contribution

We welcome contributions to enhance the functionality and performance of this project. Feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License.

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