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HyperOpt-Trade System Architecture Document

1. System Overview

High-Level Architecture

graph TD
    A[Config Manager] --> B[Data Handler]
    B --> C[Optimization Engine]
    C --> D[Results Manager]
    E[Logger] --> A
    E --> B
    E --> C
    E --> D
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Core Components

  1. Configuration Manager
  2. Data Handler
  3. Optimization Engine
  4. Results Manager
  5. Logging System

System Flow

  1. Load configuration from JSON
  2. Validate and preprocess data from CSV
  3. Execute optimization process
  4. Generate and store results
  5. Log all operations

2. Component Architecture

Configuration Manager

class ConfigManager:
    - load_config()
    - validate_config()
    - get_parameter_space()
    - get_optimization_settings()

Data Handler

class DataHandler:
    - load_data()
    - validate_data()
    - preprocess_data()
    - get_training_data()
    - get_validation_data()

Optimization Engine

class OptimizationEngine:
    - initialize_optimizer()
    - define_objective()
    - run_optimization()
    - evaluate_parameters()
    - store_results()

Results Manager

class ResultsManager:
    - collect_results()
    - format_results()
    - save_results()
    - generate_reports()

3. Technical Stack

Core Technologies

  • Python 3.8+
  • pandas (data handling)
  • numpy (numerical operations)
  • optuna (optimization framework)
  • pytest (testing)
  • logging (system logging)

Development Tools

  • Git (version control)
  • Black (code formatting)
  • pylint (code analysis)
  • mypy (type checking)
  • pytest-cov (code coverage)

Dependencies

Some suggestions for dependencies:

requirements.txt:
pandas>=1.5.0
numpy>=1.21.0
optuna>=3.0.0
pytest>=7.0.0
pytest-cov>=3.0.0
black>=22.0.0
pylint>=2.15.0
mypy>=1.0.0

4. Data Architecture

Data Flow

graph LR
    A[CSV Input] --> B[Data Validator]
    B --> C[Preprocessor]
    C --> D[Feature Engineering]
    D --> E[Training Data]
    D --> F[Validation Data]
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Data Structures

# Configuration Schema
{
    "strategy_name": str,
    "parameters": {
        "param_name": {
            "type": str,
            "range": [min, max],
            "step": float
        }
    },
    "optimization": {
        "method": str,
        "trials": int,
        "timeout": int
    }
}

# Results Schema
{
    "best_parameters": dict,
    "performance_metrics": dict,
    "optimization_history": list,
    "metadata": dict
}

5. Integration Architecture

External Interfaces

  1. CSV Data Source
  2. JSON Configuration
  3. CI/CD Pipeline
  4. Logging System

Integration Points

graph TD
    A[HyperOpt-Trade] --> B[CSV Source]
    A --> C[JSON Config]
    A --> D[CI/CD Pipeline]
    A --> E[Logging System]
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6. Deployment Architecture

Deployment Flow

graph LR
    A[Development] --> B[Testing]
    B --> C[Staging]
    C --> D[Production]
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Environment Setup

environments:
  development:
    python_version: "3.8"
    dependencies: "requirements-dev.txt"
    
  testing:
    python_version: "3.8"
    dependencies: "requirements-test.txt"
    
  production:
    python_version: "3.8"
    dependencies: "requirements.txt"

7. Security Architecture

Security Measures

  1. Input Validation

    • JSON schema validation
    • CSV data validation
    • Parameter range checks

  2. Error Handling

    • Exception handling
    • Error logging
    • Graceful degradation

  3. Data Protection

    • File permissions
    • Configuration encryption
    • Secure logging

8. Performance Architecture

Optimization Strategies

  1. Parallel Processing
class ParallelOptimizer:
    - num_workers: int
    - batch_size: int
    - initialize_workers()
    - distribute_workload()
    - collect_results()
  1. Memory Management
class MemoryManager:
    - max_memory: int
    - cleanup_threshold: float
    - monitor_usage()
    - optimize_memory()

Performance Metrics

  1. Execution Time

    • Optimization runtime
    • Data loading time
    • Results generation time

  2. Resource Usage

    • CPU utilization
    • Memory consumption
    • Disk I/O

9. Development Architecture

Development Workflow

  1. Feature Development
graph LR
    A[Feature Branch] --> B[Development]
    B --> C[Testing]
    C --> D[Code Review]
    D --> E[Merge]
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  1. Testing Strategy
# Test Structure
tests/
    ├── unit/
    │   ├── test_config.py
    │   ├── test_data.py
    │   └── test_optimizer.py
    ├── integration/
    │   ├── test_pipeline.py
    │   └── test_results.py
    └── performance/
        └── test_optimization.py

Code Organization

src/
├── config/
│   ├── __init__.py
│   └── config_manager.py
├── data/
│   ├── __init__.py
│   └── data_handler.py
├── optimization/
│   ├── __init__.py
│   └── optimizer.py
├── results/
│   ├── __init__.py
│   └── results_manager.py
└── utils/
    ├── __init__.py
    ├── logger.py
    └── validators.py

Development Standards

  1. Code Style

    • PEP 8 compliance
    • Type hints
    • Documentation strings

  2. Version Control

    • Feature branches
    • Semantic versioning
    • Commit conventions

  3. Documentation

    • API documentation
    • Architecture updates
    • Change logs

This architecture provides a robust foundation for the hyperparameter optimization pipeline while maintaining modularity, scalability, and maintainability. The system is designed to handle various trading strategies efficiently while providing clear integration points with the existing CI/CD pipeline.