{"id":22202438,"url":"https://github.com/smaddanki/pattern-pursuit-challenge","last_synced_at":"2026-05-05T12:33:48.216Z","repository":{"id":265567692,"uuid":"896262891","full_name":"smaddanki/pattern-pursuit-challenge","owner":"smaddanki","description":"A personal challenge to build a production-ready trading signal system for S\u0026P 500 stocks using deep learning. 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The code and implementations are for educational purposes and should not be considered as financial advice.*\n\n## Overview\nA systematic exploration of deep learning applications in quantitative trading, focusing on predicting 5-day forward returns for S\u0026P 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.\n- [Problem Statement](docs/overview/problem_statement.md)\n- [Project Prerequisites](docs/setup/prerequisites.md)\n\n## Project Scope\n- **Primary Objective**: Build a robust system for predicting 5-day forward returns and generating actionable trading signals\n- **Focus Area**: S\u0026P 500 stocks\n- **Duration**: 30 days of incremental development\n- **End Goal**: Production-ready trading signal system with comprehensive risk management\n\n## Essential Documentation\n1. Review the [Prerequisites](docs/setup/prerequisites.md)\n2. Follow the [Installation Guide](docs/setup/installation.md)\n3. Explore the [Project Structure](docs/overview/structure.md)\n4. Check [Development Guidelines](docs/setup/code_quality.md)\n\n## Daily Progress\nTrack the development journey:\n- Each day's implementation is self-contained in `challenges/day_XX/`\n- Comprehensive documentation available in `docs/`\n- Shared resources and data in `data/`\n\n| \u003cdiv style=\"width:100px\"\u003eDay\u003c/div\u003e| \u003cdiv style=\"width:250px\"\u003eChallenge\u003c/div\u003e | Code      | Reports   | status  |\n| :---     | :---       | :---          | :---                | :---                | \n| **Foundation Building**\n| [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) \u003cbr\u003e[Technical Report](pattern_pursuit/challenges/day_00/technical_report.md)|Not Yet Started|\n| [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) \u003cbr\u003e[Technical Report](pattern_pursuit/challenges/day_01/technical_report.md)|Not Yet Started|\n| [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) \u003cbr\u003e [Technical Report](pattern_pursuit/challenges/day_02/technical_report.md)|Not Yet Started|\n| [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) \u003cbr\u003e[Technical Report](pattern_pursuit/challenges/day_03/technical_report.md)|Not Yet Started|\n| [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) \u003cbr\u003e[Technical Report](pattern_pursuit/challenges/day_04/technical_report.md)|Not Yet Started|\n| [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) \u003cbr\u003e[Technical Report](pattern_pursuit/challenges/day_05/technical_report.md)|Not Yet Started|\n|**Pattern Enhancement**\n|**Market Regime \u0026 Adaptation**\n|**Risk \u0026 Portfolio Management**\n|**Strategy Development**\n|**Production Development**\n\n\n## QLens Package\nThe `qlens` package contains the evolving trading system components:\n- Models and algorithms\n- Data processing pipelines\n- Evaluation frameworks\n- Utility functions\n\n\u003c!-- [Package Documentation](core/README.md) --\u003e\n\n## Technology Stack\n- Python 3.9+\n- PyTorch/TensorFlow\n- Pandas, NumPy, Scikit-learn\n- Jupyter Lab\n- Docker\n- FastAPI\n\n[Full Stack Details](docs/setup/tech_stack.md)\n\n## Disclaimer\nThis 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.\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmaddanki%2Fpattern-pursuit-challenge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmaddanki%2Fpattern-pursuit-challenge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmaddanki%2Fpattern-pursuit-challenge/lists"}