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https://github.com/smaddanki/pattern-pursuit-challenge

A personal challenge to build a production-ready trading signal system for S&P 500 stocks using deep learning. This project progresses from basic ML models to a complete trading infrastructure, focusing on 5-day forward return prediction and signal generation.
https://github.com/smaddanki/pattern-pursuit-challenge

deep-learning machine-learning pytorch quantative-trading quantitative-finance quantitative-research scikit-learn

Last synced: about 1 month ago
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Host: GitHub
URL: https://github.com/smaddanki/pattern-pursuit-challenge
Owner: smaddanki
License: mit
Created: 2024-11-29T22:54:03.000Z (about 2 months ago)
Default Branch: main
Last Pushed: 2024-11-29T23:12:10.000Z (about 2 months ago)
Last Synced: 2024-11-30T00:19:36.271Z (about 2 months ago)
Topics: deep-learning, machine-learning, pytorch, quantative-trading, quantitative-finance, quantitative-research, scikit-learn
Language: Python
Homepage:
Size: 9.77 KB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Pattern Pursuit Challenge: 30-Day Deep Learning Journey in Quantitative Trading

> **_Note:_** This is a personal learning project documenting my journey in applying deep learning to quantitative trading. The code and implementations are for educational purposes and should not be considered as financial advice.*

## Overview

A systematic exploration of deep learning applications in quantitative trading, focusing on predicting 5-day forward returns for S&P 500 stocks. This personal challenge documents the development of a production-ready trading signal system, progressing from basic ML models to a comprehensive trading infrastructure.

- [Problem Statement](docs/overview/problem_statement.md)

- [Project Prerequisites](docs/setup/prerequisites.md)

## Project Scope

- **Primary Objective**: Build a robust system for predicting 5-day forward returns and generating actionable trading signals

- **Focus Area**: S&P 500 stocks

- **Duration**: 30 days of incremental development

- **End Goal**: Production-ready trading signal system with comprehensive risk management

## Essential Documentation

1. Review the [Prerequisites](docs/setup/prerequisites.md)

2. Follow the [Installation Guide](docs/setup/installation.md)

3. Explore the [Project Structure](docs/overview/structure.md)

4. Check [Development Guidelines](docs/setup/code_quality.md)

## Daily Progress

Track the development journey:

- Each day's implementation is self-contained in `challenges/day_XX/`

- Comprehensive documentation available in `docs/`

- Shared resources and data in `data/`

| 
Day
| Challenge | Code      | Reports   | status  |

| :---     | :---       | :---          | :---                | :---                | 

| **Foundation Building**

| [Day 0](pattern_pursuit/challenges/day_00/)|[ML Baseline](pattern_pursuit/challenges/day_00/challenge.md)|[notebook](pattern_pursuit/challenges/day_00/main.ipynb)|[Summary Report](pattern_pursuit/challenges/day_00/summary_report.md) 
[Technical Report](pattern_pursuit/challenges/day_00/technical_report.md)|Not Yet Started|

| [Day 1](pattern_pursuit/challenges/day_01/)|[Neural Network Foundation](pattern_pursuit/challenges/day_01/challenge.md)|[notebook](pattern_pursuit/challenges/day_01/main.ipynb)|[Summary Report](pattern_pursuit/challenges/day_01/summary_report.md) 
[Technical Report](pattern_pursuit/challenges/day_01/technical_report.md)|Not Yet Started|

| [Day 2](pattern_pursuit/challenges/day_02/)|[Sequence Learning](pattern_pursuit/challenges/day_02/challenge.md)|[notebook](pattern_pursuit/challenges/day_02/main.ipynb)|[Summary Report](pattern_pursuit/challenges/day_02/summary_report.md) 
 [Technical Report](pattern_pursuit/challenges/day_02/technical_report.md)|Not Yet Started|

| [Day 3](pattern_pursuit/challenges/day_03/)|[Technical Analysis Integration](pattern_pursuit/challenges/day_03/challenge.md)|[notebook](pattern_pursuit/challenges/day_03/main.ipynb)|[Summary Report](pattern_pursuit/challenges/day_03/summary_report.md) 
[Technical Report](pattern_pursuit/challenges/day_03/technical_report.md)|Not Yet Started|

| [Day 4](pattern_pursuit/challenges/day_04/)|[Volume-Price Dynamics](pattern_pursuit/challenges/day_04/challenge.md)|[notebook](pattern_pursuit/challenges/day_04/main.ipynb)|[Summary Report](pattern_pursuit/challenges/day_04/summary_report.md) 
[Technical Report](pattern_pursuit/challenges/day_04/technical_report.md)|Not Yet Started|

| [Day 5](pattern_pursuit/challenges/day_05/)|[Multi-timeframe Analysis](pattern_pursuit/challenges/day_05/challenge.md)|[notebook](pattern_pursuit/challenges/day_05/main.ipynb)|[Summary Report](pattern_pursuit/challenges/day_05/summary_report.md) 
[Technical Report](pattern_pursuit/challenges/day_05/technical_report.md)|Not Yet Started|

|**Pattern Enhancement**

|**Market Regime & Adaptation**

|**Risk & Portfolio Management**

|**Strategy Development**

|**Production Development**

## QLens Package

The `qlens` package contains the evolving trading system components:

- Models and algorithms

- Data processing pipelines

- Evaluation frameworks

- Utility functions

## Technology Stack

- Python 3.9+

- PyTorch/TensorFlow

- Pandas, NumPy, Scikit-learn

- Jupyter Lab

- Docker

- FastAPI

[Full Stack Details](docs/setup/tech_stack.md)

## Disclaimer

This is a personal learning project documenting my journey in applying deep learning to quantitative trading. The code and implementations are for educational purposes and should not be considered financial advice.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.