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Hyperparameter Optimization Pipeline Project Overview

1. Project Information

Project Name

Trading Strategy Hyperparameter Optimization Pipeline (HyperOpt-Trade)

Version

1.0.0

Last Updated

December 3, 2024

Status

Planning Phase

Team/Owner

  • Project Owner: Marwin Steiner
  • Technical Lead: Marwin Steiner
  • Implementation: Marwin Steiner

2. Business Context

Problem Statement

Manual optimization of trading strategy parameters is time-consuming, inconsistent, and potentially suboptimal. A systematic approach to hyperparameter optimization is needed to improve trading strategy performance and efficiency.

Business Objectives

  1. Automate the hyperparameter optimization process for trading strategies
  2. Reduce time spent on manual parameter tuning by 90%
  3. Improve trading strategy performance by finding optimal parameter combinations
  4. Create a standardized, repeatable process for strategy optimization

Success Criteria

  • Successful optimization of at least 3 different trading strategies
  • Reduction in parameter tuning time from days to hours
  • Improved strategy performance metrics by at least 15%
  • Integration with existing CI/CD pipeline

Key Stakeholders

  • Trading Strategy Developers
  • Quantitative Analysts
  • DevOps Team
  • Risk Management Team

3. Project Scope

Core Features

  1. Data Interface

    • CSV file input handling
    • Data validation and preprocessing

  2. Configuration Management

    • JSON configuration file parser
    • Parameter range definition
    • Constraint handling

  3. Optimization Engine

    • Multiple optimization algorithms support
    • Parallel processing capability
    • Progress tracking and logging

  4. Output Generation

    • JSON output file generation
    • Performance metrics reporting
    • Visualization of results

Feature Priority Matrix

Feature Priority Complexity
CSV Data Handler High Medium
JSON Config Parser High Low
Optimization Engine High High
Results Generator High Medium
Visualization Medium Low
Logging System Medium Low

Out of Scope Items

  • Real-time optimization
  • GUI development
  • Strategy development
  • Data collection/aggregation
  • Production deployment

Dependencies

  • Python 3.8+
  • Optimization libraries (e.g., Optuna, Hyperopt)
  • Data processing libraries (pandas, numpy)
  • Testing framework (pytest)
  • CI/CD pipeline integration tools

4. Timeline & Milestones

Project Phases

  1. Planning & Design (2 weeks)
  2. Core Development (6 weeks)
  3. Testing & Validation (2 weeks)
  4. Documentation & Integration (2 weeks)

Key Deliverables

  1. Week 2: Design documentation and architecture
  2. Week 4: Data handling and configuration modules
  3. Week 8: Optimization engine and initial results
  4. Week 10: Testing completion and validation
  5. Week 12: Final documentation and integration

Critical Deadlines

  • Architecture Review: End of Week 2
  • First Working Prototype: End of Week 6
  • Testing Completion: End of Week 10
  • Project Completion: End of Week 12

Release Schedule

  • Alpha Release: Week 6
  • Beta Release: Week 10
  • Production Release: Week 12

5. Resources & Constraints

Team Resources

  • 1 Technical Lead
  • 1 Developer
  • Part-time QA support

Technical Requirements

  • Python environment
  • Version control system (Git)
  • CI/CD pipeline access
  • Testing infrastructure
  • Development and staging environments

Budget Constraints

  • Open-source tools preferred
  • Minimal external dependencies
  • Limited cloud resource usage

Time Constraints

  • 12-week development timeline
  • Part-time resource allocation
  • Regular business hours only

6. Risk Assessment

Technical Risks

  1. Performance bottlenecks in optimization process
  2. Data format incompatibility
  3. Integration challenges with existing pipeline
  4. Scalability issues with large parameter spaces

Business Risks

  1. Missed deadlines affecting strategy deployment
  2. Suboptimal parameter selection
  3. Resource allocation conflicts
  4. Maintenance complexity

Mitigation Strategies

  1. Regular performance testing and optimization
  2. Robust error handling and validation
  3. Modular design for easy maintenance
  4. Comprehensive documentation
  5. Regular stakeholder updates

Contingency Plans

  1. Fallback to manual optimization if needed
  2. Alternative optimization algorithms ready
  3. Scaled-down version for quick deployment
  4. External expertise consultation if required

7. Success Metrics

KPIs

  1. Optimization Runtime

    • Target: < 4 hours for standard strategies
    • Threshold: < 8 hours for complex strategies

  2. Resource Utilization

    • CPU usage < 80%
    • Memory usage < 16GB

  3. Accuracy Improvement

    • Minimum 15% improvement in strategy performance
    • Maximum 5% deviation from theoretical optimum

Performance Metrics

  1. Technical Metrics

    • Optimization convergence time
    • Parameter space coverage
    • Processing speed (parameters/second)
    • Memory efficiency

  2. Business Metrics

    • Time saved vs. manual optimization
    • Strategy performance improvement
    • Resource utilization efficiency

Quality Metrics

  1. Code Quality

    • 90% test coverage
    • Zero critical bugs
    • < 5 minor bugs per release

  2. Documentation Quality

    • Complete API documentation
    • User guide with examples
    • Maintenance documentation

Acceptance Criteria

  1. Technical Criteria

    • Successful optimization of test strategies
    • All core features implemented
    • Integration tests passing
    • Performance targets met

  2. Business Criteria

    • Stakeholder sign-off
    • Documentation approval
    • Training completion
    • Successful pilot run