Neural Network Architectures for Stock Market Prediction

A Comprehensive Implementation of Various Neural Network Models

Overview

This repository contains old implementations of different neural network architectures (mainly perceptrons) for stock market prediction, ranging from simple perceptrons to complex stacked models. The project demonstrates the evolution and effectiveness of various neural network structures in financial prediction tasks.

Models Implemented

1. Simple Perceptron
2. Multilayer Perceptron (MLP)
3. Deep Neural Networks
4. Ensemble Models
5. Stacked Architectures
6. Hybrid Models

Features

• Multiple neural network architectures
• Customizable layer structures
• Advanced preprocessing techniques
• Real-time market data integration
• Performance comparison tools
• Visualization utilities
• Ensemble method implementation

Model Comparison Metrics

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Mean Absolute Error (MAE)
• Directional Accuracy
• Sharpe Ratio
• Maximum Drawdown

Data Preprocessing

• Feature scaling
• Time series normalization
• Technical indicator calculation
• Missing data handling
• Outlier detection

Visualization Tools

• Learning curves
• Prediction vs Actual plots
• Feature importance analysis
• Error distribution plots
• Network architecture visualization

Trading Strategies

• Simple moving average crossover
• Neural network signal generation
• Ensemble voting system
• Risk management integration
• Position sizing optimization

Performance Optimization

• Hyperparameter tuning
• Cross-validation techniques
• Early stopping implementation
• Learning rate scheduling
• Batch size optimization