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
• Project Prerequisites

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
2. Follow the Installation Guide
3. Explore the Project Structure
4. Check Development Guidelines

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	ML Baseline	notebook	Summary Report Technical Report	Not Yet Started
Day 1	Neural Network Foundation	notebook	Summary Report Technical Report	Not Yet Started
Day 2	Sequence Learning	notebook	Summary Report Technical Report	Not Yet Started
Day 3	Technical Analysis Integration	notebook	Summary Report Technical Report	Not Yet Started
Day 4	Volume-Price Dynamics	notebook	Summary Report Technical Report	Not Yet Started
Day 5	Multi-timeframe Analysis	notebook	Summary Report Technical Report	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

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 file for details.