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Stock Pattern Classifier (Deep Learning)

A deep learning-based stock pattern classification system for identifying and analyzing chart patterns in financial markets.

Overview

This project uses deep learning techniques to classify stock price patterns and predict market movements based on historical price data.

Project Structure

stock_pattern_classifier_deep_learning/
├── data/               # Raw and processed datasets
├── models/             # Trained model checkpoints
├── notebooks/          # Jupyter notebooks for experimentation
├── src/                # Source code
│   ├── data/          # Data processing and loading
│   ├── models/        # Model architectures
│   ├── training/      # Training scripts
│   └── utils/         # Utility functions
├── tests/             # Unit tests
├── scripts/           # Training and evaluation scripts
└── requirements.txt   # Python dependencies

Installation

Prerequisites

  • Python 3.8+
  • pip or conda

Setup

  1. Clone the repository:
git clone <repository-url>
cd stock_pattern_classifier_deep_learning
  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt

Usage

Data Preparation

python scripts/prepare_data.py --input data/raw --output data/processed

Training

python scripts/train.py --config configs/default.yaml

Evaluation

python scripts/evaluate.py --model models/best_model.pth --data data/test

Model Architecture

[TODO: Describe your model architecture here]

Dataset

[TODO: Describe your dataset and data sources]

Results

[TODO: Add performance metrics and results]

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

[TODO: Add license information]

Acknowledgments

[TODO: Add acknowledgments and references]