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# Directory Structure
```
├── .dockerignore
├── .env.example
├── .github
│ ├── dependabot.yml
│ ├── FUNDING.yml
│ ├── ISSUE_TEMPLATE
│ │ ├── bug_report.md
│ │ ├── config.yml
│ │ ├── feature_request.md
│ │ ├── question.md
│ │ └── security_report.md
│ ├── pull_request_template.md
│ └── workflows
│ ├── claude-code-review.yml
│ └── claude.yml
├── .gitignore
├── .python-version
├── .vscode
│ ├── launch.json
│ └── settings.json
├── alembic
│ ├── env.py
│ ├── script.py.mako
│ └── versions
│ ├── 001_initial_schema.py
│ ├── 003_add_performance_indexes.py
│ ├── 006_rename_metadata_columns.py
│ ├── 008_performance_optimization_indexes.py
│ ├── 009_rename_to_supply_demand.py
│ ├── 010_self_contained_schema.py
│ ├── 011_remove_proprietary_terms.py
│ ├── 013_add_backtest_persistence_models.py
│ ├── 014_add_portfolio_models.py
│ ├── 08e3945a0c93_merge_heads.py
│ ├── 9374a5c9b679_merge_heads_for_testing.py
│ ├── abf9b9afb134_merge_multiple_heads.py
│ ├── adda6d3fd84b_merge_proprietary_terms_removal_with_.py
│ ├── e0c75b0bdadb_fix_financial_data_precision_only.py
│ ├── f0696e2cac15_add_essential_performance_indexes.py
│ └── fix_database_integrity_issues.py
├── alembic.ini
├── CLAUDE.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── DATABASE_SETUP.md
├── docker-compose.override.yml.example
├── docker-compose.yml
├── Dockerfile
├── docs
│ ├── api
│ │ └── backtesting.md
│ ├── BACKTESTING.md
│ ├── COST_BASIS_SPECIFICATION.md
│ ├── deep_research_agent.md
│ ├── exa_research_testing_strategy.md
│ ├── PORTFOLIO_PERSONALIZATION_PLAN.md
│ ├── PORTFOLIO.md
│ ├── SETUP_SELF_CONTAINED.md
│ └── speed_testing_framework.md
├── examples
│ ├── complete_speed_validation.py
│ ├── deep_research_integration.py
│ ├── llm_optimization_example.py
│ ├── llm_speed_demo.py
│ ├── monitoring_example.py
│ ├── parallel_research_example.py
│ ├── speed_optimization_demo.py
│ └── timeout_fix_demonstration.py
├── LICENSE
├── Makefile
├── MANIFEST.in
├── maverick_mcp
│ ├── __init__.py
│ ├── agents
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── circuit_breaker.py
│ │ ├── deep_research.py
│ │ ├── market_analysis.py
│ │ ├── optimized_research.py
│ │ ├── supervisor.py
│ │ └── technical_analysis.py
│ ├── api
│ │ ├── __init__.py
│ │ ├── api_server.py
│ │ ├── connection_manager.py
│ │ ├── dependencies
│ │ │ ├── __init__.py
│ │ │ ├── stock_analysis.py
│ │ │ └── technical_analysis.py
│ │ ├── error_handling.py
│ │ ├── inspector_compatible_sse.py
│ │ ├── inspector_sse.py
│ │ ├── middleware
│ │ │ ├── error_handling.py
│ │ │ ├── mcp_logging.py
│ │ │ ├── rate_limiting_enhanced.py
│ │ │ └── security.py
│ │ ├── openapi_config.py
│ │ ├── routers
│ │ │ ├── __init__.py
│ │ │ ├── agents.py
│ │ │ ├── backtesting.py
│ │ │ ├── data_enhanced.py
│ │ │ ├── data.py
│ │ │ ├── health_enhanced.py
│ │ │ ├── health_tools.py
│ │ │ ├── health.py
│ │ │ ├── intelligent_backtesting.py
│ │ │ ├── introspection.py
│ │ │ ├── mcp_prompts.py
│ │ │ ├── monitoring.py
│ │ │ ├── news_sentiment_enhanced.py
│ │ │ ├── performance.py
│ │ │ ├── portfolio.py
│ │ │ ├── research.py
│ │ │ ├── screening_ddd.py
│ │ │ ├── screening_parallel.py
│ │ │ ├── screening.py
│ │ │ ├── technical_ddd.py
│ │ │ ├── technical_enhanced.py
│ │ │ ├── technical.py
│ │ │ └── tool_registry.py
│ │ ├── server.py
│ │ ├── services
│ │ │ ├── __init__.py
│ │ │ ├── base_service.py
│ │ │ ├── market_service.py
│ │ │ ├── portfolio_service.py
│ │ │ ├── prompt_service.py
│ │ │ └── resource_service.py
│ │ ├── simple_sse.py
│ │ └── utils
│ │ ├── __init__.py
│ │ ├── insomnia_export.py
│ │ └── postman_export.py
│ ├── application
│ │ ├── __init__.py
│ │ ├── commands
│ │ │ └── __init__.py
│ │ ├── dto
│ │ │ ├── __init__.py
│ │ │ └── technical_analysis_dto.py
│ │ ├── queries
│ │ │ ├── __init__.py
│ │ │ └── get_technical_analysis.py
│ │ └── screening
│ │ ├── __init__.py
│ │ ├── dtos.py
│ │ └── queries.py
│ ├── backtesting
│ │ ├── __init__.py
│ │ ├── ab_testing.py
│ │ ├── analysis.py
│ │ ├── batch_processing_stub.py
│ │ ├── batch_processing.py
│ │ ├── model_manager.py
│ │ ├── optimization.py
│ │ ├── persistence.py
│ │ ├── retraining_pipeline.py
│ │ ├── strategies
│ │ │ ├── __init__.py
│ │ │ ├── base.py
│ │ │ ├── ml
│ │ │ │ ├── __init__.py
│ │ │ │ ├── adaptive.py
│ │ │ │ ├── ensemble.py
│ │ │ │ ├── feature_engineering.py
│ │ │ │ └── regime_aware.py
│ │ │ ├── ml_strategies.py
│ │ │ ├── parser.py
│ │ │ └── templates.py
│ │ ├── strategy_executor.py
│ │ ├── vectorbt_engine.py
│ │ └── visualization.py
│ ├── config
│ │ ├── __init__.py
│ │ ├── constants.py
│ │ ├── database_self_contained.py
│ │ ├── database.py
│ │ ├── llm_optimization_config.py
│ │ ├── logging_settings.py
│ │ ├── plotly_config.py
│ │ ├── security_utils.py
│ │ ├── security.py
│ │ ├── settings.py
│ │ ├── technical_constants.py
│ │ ├── tool_estimation.py
│ │ └── validation.py
│ ├── core
│ │ ├── __init__.py
│ │ ├── technical_analysis.py
│ │ └── visualization.py
│ ├── data
│ │ ├── __init__.py
│ │ ├── cache_manager.py
│ │ ├── cache.py
│ │ ├── django_adapter.py
│ │ ├── health.py
│ │ ├── models.py
│ │ ├── performance.py
│ │ ├── session_management.py
│ │ └── validation.py
│ ├── database
│ │ ├── __init__.py
│ │ ├── base.py
│ │ └── optimization.py
│ ├── dependencies.py
│ ├── domain
│ │ ├── __init__.py
│ │ ├── entities
│ │ │ ├── __init__.py
│ │ │ └── stock_analysis.py
│ │ ├── events
│ │ │ └── __init__.py
│ │ ├── portfolio.py
│ │ ├── screening
│ │ │ ├── __init__.py
│ │ │ ├── entities.py
│ │ │ ├── services.py
│ │ │ └── value_objects.py
│ │ ├── services
│ │ │ ├── __init__.py
│ │ │ └── technical_analysis_service.py
│ │ ├── stock_analysis
│ │ │ ├── __init__.py
│ │ │ └── stock_analysis_service.py
│ │ └── value_objects
│ │ ├── __init__.py
│ │ └── technical_indicators.py
│ ├── exceptions.py
│ ├── infrastructure
│ │ ├── __init__.py
│ │ ├── cache
│ │ │ └── __init__.py
│ │ ├── caching
│ │ │ ├── __init__.py
│ │ │ └── cache_management_service.py
│ │ ├── connection_manager.py
│ │ ├── data_fetching
│ │ │ ├── __init__.py
│ │ │ └── stock_data_service.py
│ │ ├── health
│ │ │ ├── __init__.py
│ │ │ └── health_checker.py
│ │ ├── persistence
│ │ │ ├── __init__.py
│ │ │ └── stock_repository.py
│ │ ├── providers
│ │ │ └── __init__.py
│ │ ├── screening
│ │ │ ├── __init__.py
│ │ │ └── repositories.py
│ │ └── sse_optimizer.py
│ ├── langchain_tools
│ │ ├── __init__.py
│ │ ├── adapters.py
│ │ └── registry.py
│ ├── logging_config.py
│ ├── memory
│ │ ├── __init__.py
│ │ └── stores.py
│ ├── monitoring
│ │ ├── __init__.py
│ │ ├── health_check.py
│ │ ├── health_monitor.py
│ │ ├── integration_example.py
│ │ ├── metrics.py
│ │ ├── middleware.py
│ │ └── status_dashboard.py
│ ├── providers
│ │ ├── __init__.py
│ │ ├── dependencies.py
│ │ ├── factories
│ │ │ ├── __init__.py
│ │ │ ├── config_factory.py
│ │ │ └── provider_factory.py
│ │ ├── implementations
│ │ │ ├── __init__.py
│ │ │ ├── cache_adapter.py
│ │ │ ├── macro_data_adapter.py
│ │ │ ├── market_data_adapter.py
│ │ │ ├── persistence_adapter.py
│ │ │ └── stock_data_adapter.py
│ │ ├── interfaces
│ │ │ ├── __init__.py
│ │ │ ├── cache.py
│ │ │ ├── config.py
│ │ │ ├── macro_data.py
│ │ │ ├── market_data.py
│ │ │ ├── persistence.py
│ │ │ └── stock_data.py
│ │ ├── llm_factory.py
│ │ ├── macro_data.py
│ │ ├── market_data.py
│ │ ├── mocks
│ │ │ ├── __init__.py
│ │ │ ├── mock_cache.py
│ │ │ ├── mock_config.py
│ │ │ ├── mock_macro_data.py
│ │ │ ├── mock_market_data.py
│ │ │ ├── mock_persistence.py
│ │ │ └── mock_stock_data.py
│ │ ├── openrouter_provider.py
│ │ ├── optimized_screening.py
│ │ ├── optimized_stock_data.py
│ │ └── stock_data.py
│ ├── README.md
│ ├── tests
│ │ ├── __init__.py
│ │ ├── README_INMEMORY_TESTS.md
│ │ ├── test_cache_debug.py
│ │ ├── test_fixes_validation.py
│ │ ├── test_in_memory_routers.py
│ │ ├── test_in_memory_server.py
│ │ ├── test_macro_data_provider.py
│ │ ├── test_mailgun_email.py
│ │ ├── test_market_calendar_caching.py
│ │ ├── test_mcp_tool_fixes_pytest.py
│ │ ├── test_mcp_tool_fixes.py
│ │ ├── test_mcp_tools.py
│ │ ├── test_models_functional.py
│ │ ├── test_server.py
│ │ ├── test_stock_data_enhanced.py
│ │ ├── test_stock_data_provider.py
│ │ └── test_technical_analysis.py
│ ├── tools
│ │ ├── __init__.py
│ │ ├── performance_monitoring.py
│ │ ├── portfolio_manager.py
│ │ ├── risk_management.py
│ │ └── sentiment_analysis.py
│ ├── utils
│ │ ├── __init__.py
│ │ ├── agent_errors.py
│ │ ├── batch_processing.py
│ │ ├── cache_warmer.py
│ │ ├── circuit_breaker_decorators.py
│ │ ├── circuit_breaker_services.py
│ │ ├── circuit_breaker.py
│ │ ├── data_chunking.py
│ │ ├── database_monitoring.py
│ │ ├── debug_utils.py
│ │ ├── fallback_strategies.py
│ │ ├── llm_optimization.py
│ │ ├── logging_example.py
│ │ ├── logging_init.py
│ │ ├── logging.py
│ │ ├── mcp_logging.py
│ │ ├── memory_profiler.py
│ │ ├── monitoring_middleware.py
│ │ ├── monitoring.py
│ │ ├── orchestration_logging.py
│ │ ├── parallel_research.py
│ │ ├── parallel_screening.py
│ │ ├── quick_cache.py
│ │ ├── resource_manager.py
│ │ ├── shutdown.py
│ │ ├── stock_helpers.py
│ │ ├── structured_logger.py
│ │ ├── tool_monitoring.py
│ │ ├── tracing.py
│ │ └── yfinance_pool.py
│ ├── validation
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── data.py
│ │ ├── middleware.py
│ │ ├── portfolio.py
│ │ ├── responses.py
│ │ ├── screening.py
│ │ └── technical.py
│ └── workflows
│ ├── __init__.py
│ ├── agents
│ │ ├── __init__.py
│ │ ├── market_analyzer.py
│ │ ├── optimizer_agent.py
│ │ ├── strategy_selector.py
│ │ └── validator_agent.py
│ ├── backtesting_workflow.py
│ └── state.py
├── PLANS.md
├── pyproject.toml
├── pyrightconfig.json
├── README.md
├── scripts
│ ├── dev.sh
│ ├── INSTALLATION_GUIDE.md
│ ├── load_example.py
│ ├── load_market_data.py
│ ├── load_tiingo_data.py
│ ├── migrate_db.py
│ ├── README_TIINGO_LOADER.md
│ ├── requirements_tiingo.txt
│ ├── run_stock_screening.py
│ ├── run-migrations.sh
│ ├── seed_db.py
│ ├── seed_sp500.py
│ ├── setup_database.sh
│ ├── setup_self_contained.py
│ ├── setup_sp500_database.sh
│ ├── test_seeded_data.py
│ ├── test_tiingo_loader.py
│ ├── tiingo_config.py
│ └── validate_setup.py
├── SECURITY.md
├── server.json
├── setup.py
├── tests
│ ├── __init__.py
│ ├── conftest.py
│ ├── core
│ │ └── test_technical_analysis.py
│ ├── data
│ │ └── test_portfolio_models.py
│ ├── domain
│ │ ├── conftest.py
│ │ ├── test_portfolio_entities.py
│ │ └── test_technical_analysis_service.py
│ ├── fixtures
│ │ └── orchestration_fixtures.py
│ ├── integration
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── README.md
│ │ ├── run_integration_tests.sh
│ │ ├── test_api_technical.py
│ │ ├── test_chaos_engineering.py
│ │ ├── test_config_management.py
│ │ ├── test_full_backtest_workflow_advanced.py
│ │ ├── test_full_backtest_workflow.py
│ │ ├── test_high_volume.py
│ │ ├── test_mcp_tools.py
│ │ ├── test_orchestration_complete.py
│ │ ├── test_portfolio_persistence.py
│ │ ├── test_redis_cache.py
│ │ ├── test_security_integration.py.disabled
│ │ └── vcr_setup.py
│ ├── performance
│ │ ├── __init__.py
│ │ ├── test_benchmarks.py
│ │ ├── test_load.py
│ │ ├── test_profiling.py
│ │ └── test_stress.py
│ ├── providers
│ │ └── test_stock_data_simple.py
│ ├── README.md
│ ├── test_agents_router_mcp.py
│ ├── test_backtest_persistence.py
│ ├── test_cache_management_service.py
│ ├── test_cache_serialization.py
│ ├── test_circuit_breaker.py
│ ├── test_database_pool_config_simple.py
│ ├── test_database_pool_config.py
│ ├── test_deep_research_functional.py
│ ├── test_deep_research_integration.py
│ ├── test_deep_research_parallel_execution.py
│ ├── test_error_handling.py
│ ├── test_event_loop_integrity.py
│ ├── test_exa_research_integration.py
│ ├── test_exception_hierarchy.py
│ ├── test_financial_search.py
│ ├── test_graceful_shutdown.py
│ ├── test_integration_simple.py
│ ├── test_langgraph_workflow.py
│ ├── test_market_data_async.py
│ ├── test_market_data_simple.py
│ ├── test_mcp_orchestration_functional.py
│ ├── test_ml_strategies.py
│ ├── test_optimized_research_agent.py
│ ├── test_orchestration_integration.py
│ ├── test_orchestration_logging.py
│ ├── test_orchestration_tools_simple.py
│ ├── test_parallel_research_integration.py
│ ├── test_parallel_research_orchestrator.py
│ ├── test_parallel_research_performance.py
│ ├── test_performance_optimizations.py
│ ├── test_production_validation.py
│ ├── test_provider_architecture.py
│ ├── test_rate_limiting_enhanced.py
│ ├── test_runner_validation.py
│ ├── test_security_comprehensive.py.disabled
│ ├── test_security_cors.py
│ ├── test_security_enhancements.py.disabled
│ ├── test_security_headers.py
│ ├── test_security_penetration.py
│ ├── test_session_management.py
│ ├── test_speed_optimization_validation.py
│ ├── test_stock_analysis_dependencies.py
│ ├── test_stock_analysis_service.py
│ ├── test_stock_data_fetching_service.py
│ ├── test_supervisor_agent.py
│ ├── test_supervisor_functional.py
│ ├── test_tool_estimation_config.py
│ ├── test_visualization.py
│ └── utils
│ ├── test_agent_errors.py
│ ├── test_logging.py
│ ├── test_parallel_screening.py
│ └── test_quick_cache.py
├── tools
│ ├── check_orchestration_config.py
│ ├── experiments
│ │ ├── validation_examples.py
│ │ └── validation_fixed.py
│ ├── fast_dev.sh
│ ├── hot_reload.py
│ ├── quick_test.py
│ └── templates
│ ├── new_router_template.py
│ ├── new_tool_template.py
│ ├── screening_strategy_template.py
│ └── test_template.py
└── uv.lock
```
# Files
--------------------------------------------------------------------------------
/examples/speed_optimization_demo.py:
--------------------------------------------------------------------------------
```python
1 | #!/usr/bin/env python3
2 | """
3 | Live Speed Optimization Demonstration for MaverickMCP Research Agent
4 |
5 | This script validates the speed improvements through live API testing across
6 | different research scenarios with actual performance metrics.
7 |
8 | Demonstrates:
9 | - Emergency research (<30s timeout)
10 | - Simple research queries
11 | - Model selection efficiency (Gemini 2.5 Flash for speed)
12 | - Search provider performance
13 | - Token generation speeds
14 | - 2-3x speed improvement validation
15 | """
16 |
17 | import asyncio
18 | import os
19 | import sys
20 | import time
21 | from datetime import datetime
22 | from typing import Any
23 |
24 | # Add the project root to Python path
25 | sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
26 |
27 | from maverick_mcp.agents.optimized_research import OptimizedDeepResearchAgent
28 | from maverick_mcp.providers.openrouter_provider import OpenRouterProvider, TaskType
29 | from maverick_mcp.utils.llm_optimization import AdaptiveModelSelector
30 |
31 |
32 | class SpeedDemonstrationSuite:
33 | """Comprehensive speed optimization demonstration and validation."""
34 |
35 | def __init__(self):
36 | """Initialize the demonstration suite."""
37 | api_key = os.getenv("OPENROUTER_API_KEY")
38 | if not api_key:
39 | raise ValueError(
40 | "OPENROUTER_API_KEY environment variable is required. "
41 | "Please set it with your OpenRouter API key."
42 | )
43 | self.openrouter_provider = OpenRouterProvider(api_key=api_key)
44 | self.model_selector = AdaptiveModelSelector(self.openrouter_provider)
45 | self.results: list[dict[str, Any]] = []
46 |
47 | # Test scenarios with expected performance targets
48 | self.test_scenarios = [
49 | {
50 | "name": "Emergency Research - AI Earnings",
51 | "topic": "NVIDIA Q4 2024 earnings impact on AI market",
52 | "time_budget": 25.0, # Emergency mode
53 | "target_time": 25.0,
54 | "description": "Emergency research under extreme time pressure",
55 | },
56 | {
57 | "name": "Simple Stock Analysis",
58 | "topic": "Apple stock technical analysis today",
59 | "time_budget": 40.0, # Simple query
60 | "target_time": 35.0,
61 | "description": "Basic stock analysis query",
62 | },
63 | {
64 | "name": "Market Trend Research",
65 | "topic": "Federal Reserve interest rate impact on technology stocks",
66 | "time_budget": 60.0, # Moderate complexity
67 | "target_time": 50.0,
68 | "description": "Moderate complexity market research",
69 | },
70 | {
71 | "name": "Sector Analysis",
72 | "topic": "Renewable energy sector outlook 2025 investment opportunities",
73 | "time_budget": 90.0, # Standard research
74 | "target_time": 75.0,
75 | "description": "Standard sector analysis research",
76 | },
77 | ]
78 |
79 | def print_header(self, title: str):
80 | """Print formatted section header."""
81 | print("\n" + "=" * 80)
82 | print(f" {title}")
83 | print("=" * 80)
84 |
85 | def print_subheader(self, title: str):
86 | """Print formatted subsection header."""
87 | print(f"\n--- {title} ---")
88 |
89 | async def validate_api_connections(self) -> bool:
90 | """Validate that all required APIs are accessible."""
91 | self.print_header("🔧 API CONNECTION VALIDATION")
92 |
93 | connection_results = {}
94 |
95 | # Test OpenRouter connection
96 | try:
97 | test_llm = self.openrouter_provider.get_llm(TaskType.GENERAL)
98 | await asyncio.wait_for(
99 | test_llm.ainvoke([{"role": "user", "content": "test"}]), timeout=10.0
100 | )
101 | connection_results["OpenRouter"] = "✅ Connected"
102 | print("✅ OpenRouter API: Connected successfully")
103 | except Exception as e:
104 | connection_results["OpenRouter"] = f"❌ Failed: {e}"
105 | print(f"❌ OpenRouter API: Failed - {e}")
106 | return False
107 |
108 | # Test search providers using the actual deep_research imports
109 | try:
110 | from maverick_mcp.agents.deep_research import get_cached_search_provider
111 |
112 | search_provider = await get_cached_search_provider(
113 | exa_api_key=os.getenv("EXA_API_KEY")
114 | )
115 |
116 | if search_provider:
117 | # Test provider with a simple search
118 | await asyncio.wait_for(
119 | search_provider.search("test query", num_results=1), timeout=15.0
120 | )
121 | connection_results["Search Providers"] = "✅ Connected (Exa provider)"
122 | print("✅ Search Providers: Connected (Exa provider)")
123 | else:
124 | connection_results["Search Providers"] = "⚠️ No providers configured"
125 | print("⚠️ Search Providers: No API keys configured, will use mock mode")
126 |
127 | except Exception as e:
128 | connection_results["Search Providers"] = f"❌ Failed: {e}"
129 | print(f"❌ Search Providers: Failed - {e}")
130 | print(" 🔧 Will continue with mock search data for demonstration")
131 |
132 | print("\n🎉 API Validation Complete - Core systems ready")
133 | return True
134 |
135 | async def demonstrate_model_selection(self):
136 | """Demonstrate intelligent model selection for speed."""
137 | self.print_header("🧠 INTELLIGENT MODEL SELECTION DEMO")
138 |
139 | # Test different scenarios for model selection
140 | test_cases = [
141 | {
142 | "scenario": "Emergency Research (Time Critical)",
143 | "time_budget": 20.0,
144 | "task_type": TaskType.DEEP_RESEARCH,
145 | "content_size": 1000,
146 | "expected_model": "gemini-2.5-flash-199",
147 | },
148 | {
149 | "scenario": "Simple Query (Speed Focus)",
150 | "time_budget": 30.0,
151 | "task_type": TaskType.SENTIMENT_ANALYSIS,
152 | "content_size": 500,
153 | "expected_model": "gemini-2.5-flash-199",
154 | },
155 | {
156 | "scenario": "Complex Analysis (Balanced)",
157 | "time_budget": 60.0,
158 | "task_type": TaskType.RESULT_SYNTHESIS,
159 | "content_size": 2000,
160 | "expected_model": "claude-3.5-haiku-20241022",
161 | },
162 | ]
163 |
164 | for test_case in test_cases:
165 | print(f"\nTest: {test_case['scenario']}")
166 | print(f" Time Budget: {test_case['time_budget']}s")
167 | print(f" Task Type: {test_case['task_type'].value}")
168 | print(f" Content Size: {test_case['content_size']} tokens")
169 |
170 | # Calculate task complexity
171 | complexity = self.model_selector.calculate_task_complexity(
172 | content="x" * test_case["content_size"],
173 | task_type=test_case["task_type"],
174 | focus_areas=["analysis"],
175 | )
176 |
177 | # Get model recommendation
178 | model_config = self.model_selector.select_model_for_time_budget(
179 | task_type=test_case["task_type"],
180 | time_remaining_seconds=test_case["time_budget"],
181 | complexity_score=complexity,
182 | content_size_tokens=test_case["content_size"],
183 | )
184 |
185 | print(f" 📊 Complexity Score: {complexity:.2f}")
186 | print(f" 🎯 Selected Model: {model_config.model_id}")
187 | print(f" ⏱️ Timeout: {model_config.timeout_seconds}s")
188 | print(f" 🎛️ Temperature: {model_config.temperature}")
189 | print(f" 📝 Max Tokens: {model_config.max_tokens}")
190 |
191 | # Validate speed-optimized selection
192 | is_speed_optimized = (
193 | "gemini-2.5-flash" in model_config.model_id
194 | or "claude-3.5-haiku" in model_config.model_id
195 | )
196 | print(f" 🚀 Speed Optimized: {'✅' if is_speed_optimized else '❌'}")
197 |
198 | async def run_research_scenario(self, scenario: dict[str, Any]) -> dict[str, Any]:
199 | """Execute a single research scenario and collect metrics."""
200 |
201 | print(f"\n🔍 Running: {scenario['name']}")
202 | print(f" Topic: {scenario['topic']}")
203 | print(f" Time Budget: {scenario['time_budget']}s")
204 | print(f" Target: <{scenario['target_time']}s")
205 |
206 | # Create optimized research agent
207 | agent = OptimizedDeepResearchAgent(
208 | openrouter_provider=self.openrouter_provider,
209 | persona="moderate",
210 | exa_api_key=os.getenv("EXA_API_KEY"),
211 | optimization_enabled=True,
212 | )
213 |
214 | # Execute research with timing
215 | start_time = time.time()
216 | session_id = f"demo_{int(start_time)}"
217 |
218 | try:
219 | result = await agent.research_comprehensive(
220 | topic=scenario["topic"],
221 | session_id=session_id,
222 | depth="standard",
223 | focus_areas=["fundamental", "technical"],
224 | time_budget_seconds=scenario["time_budget"],
225 | target_confidence=0.75,
226 | )
227 |
228 | execution_time = time.time() - start_time
229 |
230 | # Extract key metrics
231 | metrics = {
232 | "scenario_name": scenario["name"],
233 | "topic": scenario["topic"],
234 | "execution_time": execution_time,
235 | "time_budget": scenario["time_budget"],
236 | "target_time": scenario["target_time"],
237 | "budget_utilization": (execution_time / scenario["time_budget"]) * 100,
238 | "target_achieved": execution_time <= scenario["target_time"],
239 | "status": result.get("status", "unknown"),
240 | "sources_processed": result.get("sources_analyzed", 0),
241 | "final_confidence": result.get("findings", {}).get(
242 | "confidence_score", 0.0
243 | ),
244 | "optimization_metrics": result.get("optimization_metrics", {}),
245 | "emergency_mode": result.get("emergency_mode", False),
246 | "early_terminated": result.get("findings", {}).get(
247 | "early_terminated", False
248 | ),
249 | "synthesis_length": len(
250 | result.get("findings", {}).get("synthesis", "")
251 | ),
252 | }
253 |
254 | # Print immediate results
255 | self.print_results_summary(metrics, result)
256 |
257 | return metrics
258 |
259 | except Exception as e:
260 | execution_time = time.time() - start_time
261 | print(f" ❌ Failed: {str(e)}")
262 |
263 | # If search providers are unavailable, run LLM optimization demo instead
264 | if "search providers" in str(e).lower() or "no module" in str(e).lower():
265 | print(" 🔧 Running LLM-only optimization demo instead...")
266 | return await self.run_llm_only_optimization_demo(scenario)
267 |
268 | return {
269 | "scenario_name": scenario["name"],
270 | "execution_time": execution_time,
271 | "status": "error",
272 | "error": str(e),
273 | "target_achieved": False,
274 | }
275 |
276 | async def run_llm_only_optimization_demo(
277 | self, scenario: dict[str, Any]
278 | ) -> dict[str, Any]:
279 | """Run an LLM-only demonstration of optimization features when search is unavailable."""
280 |
281 | start_time = time.time()
282 |
283 | try:
284 | # Demonstrate model selection for the scenario
285 | complexity = self.model_selector.calculate_task_complexity(
286 | content=scenario["topic"],
287 | task_type=TaskType.DEEP_RESEARCH,
288 | focus_areas=["analysis"],
289 | )
290 |
291 | model_config = self.model_selector.select_model_for_time_budget(
292 | task_type=TaskType.DEEP_RESEARCH,
293 | time_remaining_seconds=scenario["time_budget"],
294 | complexity_score=complexity,
295 | content_size_tokens=len(scenario["topic"]) // 4,
296 | )
297 |
298 | print(f" 🎯 Selected Model: {model_config.model_id}")
299 | print(f" ⏱️ Timeout: {model_config.timeout_seconds}s")
300 |
301 | # Simulate optimized LLM processing
302 | llm = self.openrouter_provider.get_llm(
303 | model_override=model_config.model_id,
304 | temperature=model_config.temperature,
305 | max_tokens=model_config.max_tokens,
306 | )
307 |
308 | # Create a research-style query to demonstrate speed
309 | research_query = f"""Provide a brief analysis of {scenario["topic"]} covering:
310 | 1. Key market factors
311 | 2. Current sentiment
312 | 3. Risk assessment
313 | 4. Investment outlook
314 |
315 | Keep response concise but comprehensive."""
316 |
317 | llm_start = time.time()
318 | response = await asyncio.wait_for(
319 | llm.ainvoke([{"role": "user", "content": research_query}]),
320 | timeout=model_config.timeout_seconds,
321 | )
322 | llm_time = time.time() - llm_start
323 | execution_time = time.time() - start_time
324 |
325 | # Calculate token generation metrics
326 | response_length = len(response.content)
327 | estimated_tokens = response_length // 4
328 | tokens_per_second = estimated_tokens / llm_time if llm_time > 0 else 0
329 |
330 | print(
331 | f" 🚀 LLM Execution: {llm_time:.2f}s (~{tokens_per_second:.0f} tok/s)"
332 | )
333 | print(f" 📝 Response Length: {response_length} chars")
334 |
335 | return {
336 | "scenario_name": scenario["name"],
337 | "topic": scenario["topic"],
338 | "execution_time": execution_time,
339 | "llm_execution_time": llm_time,
340 | "tokens_per_second": tokens_per_second,
341 | "time_budget": scenario["time_budget"],
342 | "target_time": scenario["target_time"],
343 | "budget_utilization": (execution_time / scenario["time_budget"]) * 100,
344 | "target_achieved": execution_time <= scenario["target_time"],
345 | "status": "llm_demo_success",
346 | "model_used": model_config.model_id,
347 | "response_length": response_length,
348 | "optimization_applied": True,
349 | "sources_processed": 0, # No search performed
350 | "final_confidence": 0.8, # Simulated high confidence for LLM analysis
351 | }
352 |
353 | except Exception as e:
354 | execution_time = time.time() - start_time
355 | print(f" ❌ LLM Demo Failed: {str(e)}")
356 |
357 | return {
358 | "scenario_name": scenario["name"],
359 | "execution_time": execution_time,
360 | "status": "error",
361 | "error": str(e),
362 | "target_achieved": False,
363 | }
364 |
365 | def print_results_summary(
366 | self, metrics: dict[str, Any], full_result: dict[str, Any] | None = None
367 | ):
368 | """Print immediate results summary."""
369 |
370 | status_icon = "✅" if metrics.get("target_achieved") else "⚠️"
371 | emergency_icon = "🚨" if metrics.get("emergency_mode") else ""
372 | llm_demo_icon = "🧠" if metrics.get("status") == "llm_demo_success" else ""
373 |
374 | print(
375 | f" {status_icon} {emergency_icon} {llm_demo_icon} Complete: {metrics['execution_time']:.2f}s"
376 | )
377 | print(f" Budget Used: {metrics['budget_utilization']:.1f}%")
378 |
379 | if metrics.get("status") == "llm_demo_success":
380 | # LLM-only demo results
381 | print(f" Model: {metrics.get('model_used', 'unknown')}")
382 | print(f" LLM Speed: {metrics.get('tokens_per_second', 0):.0f} tok/s")
383 | print(f" LLM Time: {metrics.get('llm_execution_time', 0):.2f}s")
384 | else:
385 | # Full research results
386 | print(f" Sources: {metrics['sources_processed']}")
387 | print(f" Confidence: {metrics['final_confidence']:.2f}")
388 |
389 | if metrics.get("early_terminated") and full_result:
390 | print(
391 | f" Early Exit: {full_result.get('findings', {}).get('termination_reason', 'unknown')}"
392 | )
393 |
394 | # Show optimization features used
395 | opt_metrics = metrics.get("optimization_metrics", {})
396 | if opt_metrics:
397 | features_used = opt_metrics.get("optimization_features_used", [])
398 | if features_used:
399 | print(f" Optimizations: {', '.join(features_used[:3])}")
400 |
401 | # Show a brief excerpt of findings
402 | if full_result:
403 | synthesis = full_result.get("findings", {}).get("synthesis", "")
404 | if synthesis and len(synthesis) > 100:
405 | excerpt = synthesis[:200] + "..."
406 | print(f" Preview: {excerpt}")
407 |
408 | async def run_performance_comparison(self):
409 | """Run all scenarios and compare against previous baseline."""
410 | self.print_header("🚀 PERFORMANCE VALIDATION SUITE")
411 |
412 | print("Running comprehensive speed tests with live API calls...")
413 | print(
414 | "This validates our 2-3x speed improvements against 138s/129s timeout failures"
415 | )
416 |
417 | results = []
418 | total_start_time = time.time()
419 |
420 | # Run all test scenarios
421 | for scenario in self.test_scenarios:
422 | try:
423 | result = await self.run_research_scenario(scenario)
424 | results.append(result)
425 |
426 | # Brief pause between tests
427 | await asyncio.sleep(2)
428 |
429 | except Exception as e:
430 | print(f"❌ Scenario '{scenario['name']}' failed: {e}")
431 | results.append(
432 | {
433 | "scenario_name": scenario["name"],
434 | "status": "error",
435 | "error": str(e),
436 | "target_achieved": False,
437 | }
438 | )
439 |
440 | total_execution_time = time.time() - total_start_time
441 |
442 | # Analyze results
443 | self.analyze_performance_results(results, total_execution_time)
444 |
445 | return results
446 |
447 | def analyze_performance_results(
448 | self, results: list[dict[str, Any]], total_time: float
449 | ):
450 | """Analyze and report performance results."""
451 | self.print_header("📊 PERFORMANCE ANALYSIS REPORT")
452 |
453 | successful_tests = [
454 | r for r in results if r.get("status") in ["success", "llm_demo_success"]
455 | ]
456 | failed_tests = [
457 | r for r in results if r.get("status") not in ["success", "llm_demo_success"]
458 | ]
459 | targets_achieved = [r for r in results if r.get("target_achieved")]
460 | llm_demo_tests = [r for r in results if r.get("status") == "llm_demo_success"]
461 |
462 | print("📈 Overall Results:")
463 | print(f" Total Tests: {len(results)}")
464 | print(
465 | f" Successful: {len(successful_tests)} (Full Research: {len(successful_tests) - len(llm_demo_tests)}, LLM Demos: {len(llm_demo_tests)})"
466 | )
467 | print(f" Failed: {len(failed_tests)}")
468 | print(f" Targets Achieved: {len(targets_achieved)}/{len(results)}")
469 | print(f" Success Rate: {(len(targets_achieved) / len(results) * 100):.1f}%")
470 | print(f" Total Suite Time: {total_time:.2f}s")
471 |
472 | if successful_tests:
473 | avg_execution_time = sum(
474 | r["execution_time"] for r in successful_tests
475 | ) / len(successful_tests)
476 | avg_budget_utilization = sum(
477 | r["budget_utilization"] for r in successful_tests
478 | ) / len(successful_tests)
479 | avg_sources = sum(r["sources_processed"] for r in successful_tests) / len(
480 | successful_tests
481 | )
482 | avg_confidence = sum(r["final_confidence"] for r in successful_tests) / len(
483 | successful_tests
484 | )
485 |
486 | print("\n📊 Performance Metrics (Successful Tests):")
487 | print(f" Average Execution Time: {avg_execution_time:.2f}s")
488 | print(f" Average Budget Utilization: {avg_budget_utilization:.1f}%")
489 | print(f" Average Sources Processed: {avg_sources:.1f}")
490 | print(f" Average Confidence Score: {avg_confidence:.2f}")
491 |
492 | # Speed improvement validation
493 | self.print_subheader("🎯 SPEED OPTIMIZATION VALIDATION")
494 |
495 | # Historical baseline (previous timeout issues: 138s, 129s)
496 | historical_baseline = 130 # Average of timeout failures
497 |
498 | if successful_tests:
499 | max_execution_time = max(r["execution_time"] for r in successful_tests)
500 | speed_improvement = (
501 | historical_baseline / max_execution_time
502 | if max_execution_time > 0
503 | else 0
504 | )
505 |
506 | print(f" Historical Baseline (Timeout Issues): {historical_baseline}s")
507 | print(f" Current Max Execution Time: {max_execution_time:.2f}s")
508 | print(f" Speed Improvement Factor: {speed_improvement:.1f}x")
509 |
510 | if speed_improvement >= 2.0:
511 | print(
512 | f" 🎉 SUCCESS: Achieved {speed_improvement:.1f}x speed improvement!"
513 | )
514 | elif speed_improvement >= 1.5:
515 | print(
516 | f" ✅ GOOD: Achieved {speed_improvement:.1f}x improvement (target: 2x)"
517 | )
518 | else:
519 | print(f" ⚠️ NEEDS WORK: Only {speed_improvement:.1f}x improvement")
520 |
521 | # Emergency mode validation
522 | emergency_tests = [r for r in results if r.get("emergency_mode")]
523 | if emergency_tests:
524 | print("\n🚨 Emergency Mode Performance:")
525 | for test in emergency_tests:
526 | print(f" {test['scenario_name']}: {test['execution_time']:.2f}s")
527 |
528 | # Feature utilization analysis
529 | self.print_subheader("🔧 OPTIMIZATION FEATURE UTILIZATION")
530 |
531 | feature_usage = {}
532 | for result in successful_tests:
533 | opt_metrics = result.get("optimization_metrics", {})
534 | features = opt_metrics.get("optimization_features_used", [])
535 | for feature in features:
536 | feature_usage[feature] = feature_usage.get(feature, 0) + 1
537 |
538 | if feature_usage:
539 | print(" Optimization Features Used:")
540 | for feature, count in sorted(
541 | feature_usage.items(), key=lambda x: x[1], reverse=True
542 | ):
543 | percentage = (count / len(successful_tests)) * 100
544 | print(
545 | f" {feature}: {count}/{len(successful_tests)} tests ({percentage:.0f}%)"
546 | )
547 |
548 | async def demonstrate_token_generation_speed(self):
549 | """Demonstrate token generation speeds with different models."""
550 | self.print_header("⚡ TOKEN GENERATION SPEED DEMO")
551 |
552 | models_to_test = [
553 | ("gemini-2.5-flash-199", "Ultra-fast model (199 tok/s)"),
554 | ("claude-3.5-haiku-20241022", "Balanced speed model"),
555 | ("gpt-4o-mini", "OpenAI speed model"),
556 | ]
557 |
558 | test_prompt = (
559 | "Analyze the current market sentiment for technology stocks in 200 words."
560 | )
561 |
562 | for model_id, description in models_to_test:
563 | print(f"\n🧠 Testing: {model_id}")
564 | print(f" Description: {description}")
565 |
566 | try:
567 | llm = self.openrouter_provider.get_llm(
568 | model_override=model_id,
569 | temperature=0.7,
570 | max_tokens=300,
571 | )
572 |
573 | start_time = time.time()
574 | response = await asyncio.wait_for(
575 | llm.ainvoke([{"role": "user", "content": test_prompt}]),
576 | timeout=30.0,
577 | )
578 | execution_time = time.time() - start_time
579 |
580 | # Calculate approximate token generation speed
581 | response_length = len(response.content)
582 | estimated_tokens = response_length // 4 # Rough estimate
583 | tokens_per_second = (
584 | estimated_tokens / execution_time if execution_time > 0 else 0
585 | )
586 |
587 | print(f" ⏱️ Execution Time: {execution_time:.2f}s")
588 | print(
589 | f" 📝 Response Length: {response_length} chars (~{estimated_tokens} tokens)"
590 | )
591 | print(f" 🚀 Speed: ~{tokens_per_second:.0f} tokens/second")
592 |
593 | # Show brief response preview
594 | preview = (
595 | response.content[:150] + "..."
596 | if len(response.content) > 150
597 | else response.content
598 | )
599 | print(f" 💬 Preview: {preview}")
600 |
601 | except Exception as e:
602 | print(f" ❌ Failed: {str(e)}")
603 |
604 | async def run_comprehensive_demo(self):
605 | """Run the complete speed optimization demonstration."""
606 | print("🚀 MaverickMCP Speed Optimization Live Demonstration")
607 | print(f"⏰ Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
608 | print("🎯 Goal: Validate 2-3x speed improvements with live API calls")
609 |
610 | # Step 1: Validate API connections
611 | if not await self.validate_api_connections():
612 | print("\n❌ Cannot proceed - API connections failed")
613 | return False
614 |
615 | # Step 2: Demonstrate model selection intelligence
616 | await self.demonstrate_model_selection()
617 |
618 | # Step 3: Demonstrate token generation speeds
619 | await self.demonstrate_token_generation_speed()
620 |
621 | # Step 4: Run comprehensive performance tests
622 | results = await self.run_performance_comparison()
623 |
624 | # Final summary
625 | self.print_header("🎉 DEMONSTRATION COMPLETE")
626 |
627 | successful_results = [r for r in results if r.get("status") == "success"]
628 | targets_achieved = [r for r in results if r.get("target_achieved")]
629 |
630 | print("✅ Speed Optimization Demonstration Results:")
631 | print(f" Tests Run: {len(results)}")
632 | print(f" Successful: {len(successful_results)}")
633 | print(f" Targets Achieved: {len(targets_achieved)}")
634 | print(f" Success Rate: {(len(targets_achieved) / len(results) * 100):.1f}%")
635 |
636 | if successful_results:
637 | max_time = max(r["execution_time"] for r in successful_results)
638 | avg_time = sum(r["execution_time"] for r in successful_results) / len(
639 | successful_results
640 | )
641 | print(f" Max Execution Time: {max_time:.2f}s")
642 | print(f" Avg Execution Time: {avg_time:.2f}s")
643 | print(" Historical Baseline: 130s (timeout failures)")
644 | print(f" Speed Improvement: {130 / max_time:.1f}x faster")
645 |
646 | print("\n📊 Key Optimizations Validated:")
647 | print(" ✅ Adaptive Model Selection (Gemini 2.5 Flash for speed)")
648 | print(" ✅ Progressive Token Budgeting")
649 | print(" ✅ Parallel Processing")
650 | print(" ✅ Early Termination Based on Confidence")
651 | print(" ✅ Intelligent Content Filtering")
652 | print(" ✅ Optimized Prompt Engineering")
653 |
654 | return len(targets_achieved) >= len(results) * 0.7 # 70% success threshold
655 |
656 |
657 | async def main():
658 | """Main demonstration entry point."""
659 | demo = SpeedDemonstrationSuite()
660 |
661 | try:
662 | success = await demo.run_comprehensive_demo()
663 |
664 | if success:
665 | print("\n🎉 Demonstration PASSED - Speed optimizations validated!")
666 | return 0
667 | else:
668 | print("\n⚠️ Demonstration had issues - review results above")
669 | return 1
670 |
671 | except KeyboardInterrupt:
672 | print("\n\n⏹️ Demonstration interrupted by user")
673 | return 130
674 | except Exception as e:
675 | print(f"\n💥 Demonstration failed with error: {e}")
676 | import traceback
677 |
678 | traceback.print_exc()
679 | return 1
680 |
681 |
682 | if __name__ == "__main__":
683 | # Ensure we have the required environment variables
684 | required_vars = ["OPENROUTER_API_KEY"]
685 | missing_vars = [var for var in required_vars if not os.getenv(var)]
686 |
687 | if missing_vars:
688 | print(f"❌ Missing required environment variables: {missing_vars}")
689 | print("Please check your .env file")
690 | sys.exit(1)
691 |
692 | # Run the demonstration
693 | exit_code = asyncio.run(main())
694 | sys.exit(exit_code)
695 |
```
--------------------------------------------------------------------------------
/maverick_mcp/backtesting/strategies/ml/feature_engineering.py:
--------------------------------------------------------------------------------
```python
1 | """Feature engineering for ML trading strategies."""
2 |
3 | import logging
4 | from typing import Any
5 |
6 | import numpy as np
7 | import pandas as pd
8 | import pandas_ta as ta
9 | from pandas import DataFrame, Series
10 | from sklearn.ensemble import RandomForestClassifier
11 | from sklearn.preprocessing import StandardScaler
12 |
13 | logger = logging.getLogger(__name__)
14 |
15 |
16 | class FeatureExtractor:
17 | """Extract technical and statistical features for ML models."""
18 |
19 | def __init__(self, lookback_periods: list[int] = None):
20 | """Initialize feature extractor.
21 |
22 | Args:
23 | lookback_periods: Lookback periods for rolling features
24 | """
25 | self.lookback_periods = lookback_periods or [5, 10, 20, 50]
26 | self.scaler = StandardScaler()
27 |
28 | def extract_price_features(self, data: DataFrame) -> DataFrame:
29 | """Extract price-based features.
30 |
31 | Args:
32 | data: OHLCV price data
33 |
34 | Returns:
35 | DataFrame with price features
36 | """
37 | features = pd.DataFrame(index=data.index)
38 |
39 | # Normalize column names to handle both cases
40 | high = data.get("high", data.get("High"))
41 | low = data.get("low", data.get("Low"))
42 | close = data.get("close", data.get("Close"))
43 | open_ = data.get("open", data.get("Open"))
44 |
45 | # Safe division helper function
46 | def safe_divide(numerator, denominator, default=0.0):
47 | """Safely divide two values, handling None, NaN, and zero cases."""
48 | if numerator is None or denominator is None:
49 | return default
50 | # Convert to numpy arrays to handle pandas Series
51 | num = np.asarray(numerator)
52 | den = np.asarray(denominator)
53 | # Use numpy divide with where condition for safety
54 | return np.divide(
55 | num, den, out=np.full_like(num, default, dtype=float), where=(den != 0)
56 | )
57 |
58 | # Price ratios and spreads with safe division
59 | features["high_low_ratio"] = safe_divide(high, low, 1.0)
60 | features["close_open_ratio"] = safe_divide(close, open_, 1.0)
61 | features["hl_spread"] = (
62 | safe_divide(high - low, close, 0.0)
63 | if high is not None and low is not None and close is not None
64 | else 0.0
65 | )
66 | features["co_spread"] = (
67 | safe_divide(close - open_, open_, 0.0)
68 | if close is not None and open_ is not None
69 | else 0.0
70 | )
71 |
72 | # Returns with safe calculation
73 | if close is not None:
74 | features["returns"] = close.pct_change().fillna(0)
75 | # Safe log returns calculation
76 | price_ratio = safe_divide(close, close.shift(1), 1.0)
77 | features["log_returns"] = np.log(
78 | np.maximum(price_ratio, 1e-8)
79 | ) # Prevent log(0)
80 | else:
81 | features["returns"] = 0
82 | features["log_returns"] = 0
83 |
84 | # Volume features with safe calculations
85 | volume = data.get("volume", data.get("Volume"))
86 | if volume is not None and close is not None:
87 | volume_ma = volume.rolling(20).mean()
88 | features["volume_ma_ratio"] = safe_divide(volume, volume_ma, 1.0)
89 | features["price_volume"] = close * volume
90 | features["volume_returns"] = volume.pct_change().fillna(0)
91 | else:
92 | features["volume_ma_ratio"] = 1.0
93 | features["price_volume"] = 0.0
94 | features["volume_returns"] = 0.0
95 |
96 | return features
97 |
98 | def extract_technical_features(self, data: DataFrame) -> DataFrame:
99 | """Extract technical indicator features.
100 |
101 | Args:
102 | data: OHLCV price data
103 |
104 | Returns:
105 | DataFrame with technical features
106 | """
107 | features = pd.DataFrame(index=data.index)
108 |
109 | # Normalize column names
110 | close = data.get("close", data.get("Close"))
111 | high = data.get("high", data.get("High"))
112 | low = data.get("low", data.get("Low"))
113 |
114 | # Safe division helper (reused from price features)
115 | def safe_divide(numerator, denominator, default=0.0):
116 | """Safely divide two values, handling None, NaN, and zero cases."""
117 | if numerator is None or denominator is None:
118 | return default
119 | # Convert to numpy arrays to handle pandas Series
120 | num = np.asarray(numerator)
121 | den = np.asarray(denominator)
122 | # Use numpy divide with where condition for safety
123 | return np.divide(
124 | num, den, out=np.full_like(num, default, dtype=float), where=(den != 0)
125 | )
126 |
127 | # Moving averages with safe calculations
128 | for period in self.lookback_periods:
129 | if close is not None:
130 | sma = ta.sma(close, length=period)
131 | ema = ta.ema(close, length=period)
132 |
133 | features[f"sma_{period}_ratio"] = safe_divide(close, sma, 1.0)
134 | features[f"ema_{period}_ratio"] = safe_divide(close, ema, 1.0)
135 | features[f"sma_ema_diff_{period}"] = (
136 | safe_divide(sma - ema, close, 0.0)
137 | if sma is not None and ema is not None
138 | else 0.0
139 | )
140 | else:
141 | features[f"sma_{period}_ratio"] = 1.0
142 | features[f"ema_{period}_ratio"] = 1.0
143 | features[f"sma_ema_diff_{period}"] = 0.0
144 |
145 | # RSI
146 | rsi = ta.rsi(close, length=14)
147 | features["rsi"] = rsi
148 | features["rsi_oversold"] = (rsi < 30).astype(int)
149 | features["rsi_overbought"] = (rsi > 70).astype(int)
150 |
151 | # MACD
152 | macd = ta.macd(close)
153 | if macd is not None and not macd.empty:
154 | macd_cols = macd.columns
155 | macd_col = [
156 | col
157 | for col in macd_cols
158 | if "MACD" in col and "h" not in col and "s" not in col.lower()
159 | ]
160 | signal_col = [
161 | col for col in macd_cols if "signal" in col.lower() or "MACDs" in col
162 | ]
163 | hist_col = [
164 | col for col in macd_cols if "hist" in col.lower() or "MACDh" in col
165 | ]
166 |
167 | if macd_col:
168 | features["macd"] = macd[macd_col[0]]
169 | else:
170 | features["macd"] = 0
171 |
172 | if signal_col:
173 | features["macd_signal"] = macd[signal_col[0]]
174 | else:
175 | features["macd_signal"] = 0
176 |
177 | if hist_col:
178 | features["macd_histogram"] = macd[hist_col[0]]
179 | else:
180 | features["macd_histogram"] = 0
181 |
182 | features["macd_bullish"] = (
183 | features["macd"] > features["macd_signal"]
184 | ).astype(int)
185 | else:
186 | features["macd"] = 0
187 | features["macd_signal"] = 0
188 | features["macd_histogram"] = 0
189 | features["macd_bullish"] = 0
190 |
191 | # Bollinger Bands
192 | bb = ta.bbands(close, length=20)
193 | if bb is not None and not bb.empty:
194 | # Handle different pandas_ta versions that may have different column names
195 | bb_cols = bb.columns
196 | upper_col = [
197 | col for col in bb_cols if "BBU" in col or "upper" in col.lower()
198 | ]
199 | middle_col = [
200 | col for col in bb_cols if "BBM" in col or "middle" in col.lower()
201 | ]
202 | lower_col = [
203 | col for col in bb_cols if "BBL" in col or "lower" in col.lower()
204 | ]
205 |
206 | if upper_col and middle_col and lower_col:
207 | features["bb_upper"] = bb[upper_col[0]]
208 | features["bb_middle"] = bb[middle_col[0]]
209 | features["bb_lower"] = bb[lower_col[0]]
210 |
211 | # Safe BB position calculation
212 | bb_width = features["bb_upper"] - features["bb_lower"]
213 | features["bb_position"] = safe_divide(
214 | close - features["bb_lower"], bb_width, 0.5
215 | )
216 | features["bb_squeeze"] = safe_divide(
217 | bb_width, features["bb_middle"], 0.1
218 | )
219 | else:
220 | # Fallback to manual calculation with safe operations
221 | if close is not None:
222 | sma_20 = close.rolling(20).mean()
223 | std_20 = close.rolling(20).std()
224 | features["bb_upper"] = sma_20 + (std_20 * 2)
225 | features["bb_middle"] = sma_20
226 | features["bb_lower"] = sma_20 - (std_20 * 2)
227 |
228 | # Safe BB calculations
229 | bb_width = features["bb_upper"] - features["bb_lower"]
230 | features["bb_position"] = safe_divide(
231 | close - features["bb_lower"], bb_width, 0.5
232 | )
233 | features["bb_squeeze"] = safe_divide(
234 | bb_width, features["bb_middle"], 0.1
235 | )
236 | else:
237 | features["bb_upper"] = 0
238 | features["bb_middle"] = 0
239 | features["bb_lower"] = 0
240 | features["bb_position"] = 0.5
241 | features["bb_squeeze"] = 0.1
242 | else:
243 | # Manual calculation fallback with safe operations
244 | if close is not None:
245 | sma_20 = close.rolling(20).mean()
246 | std_20 = close.rolling(20).std()
247 | features["bb_upper"] = sma_20 + (std_20 * 2)
248 | features["bb_middle"] = sma_20
249 | features["bb_lower"] = sma_20 - (std_20 * 2)
250 |
251 | # Safe BB calculations
252 | bb_width = features["bb_upper"] - features["bb_lower"]
253 | features["bb_position"] = safe_divide(
254 | close - features["bb_lower"], bb_width, 0.5
255 | )
256 | features["bb_squeeze"] = safe_divide(
257 | bb_width, features["bb_middle"], 0.1
258 | )
259 | else:
260 | features["bb_upper"] = 0
261 | features["bb_middle"] = 0
262 | features["bb_lower"] = 0
263 | features["bb_position"] = 0.5
264 | features["bb_squeeze"] = 0.1
265 |
266 | # Stochastic
267 | stoch = ta.stoch(high, low, close)
268 | if stoch is not None and not stoch.empty:
269 | stoch_cols = stoch.columns
270 | k_col = [col for col in stoch_cols if "k" in col.lower()]
271 | d_col = [col for col in stoch_cols if "d" in col.lower()]
272 |
273 | if k_col:
274 | features["stoch_k"] = stoch[k_col[0]]
275 | else:
276 | features["stoch_k"] = 50
277 |
278 | if d_col:
279 | features["stoch_d"] = stoch[d_col[0]]
280 | else:
281 | features["stoch_d"] = 50
282 | else:
283 | features["stoch_k"] = 50
284 | features["stoch_d"] = 50
285 |
286 | # ATR (Average True Range) with safe calculation
287 | if high is not None and low is not None and close is not None:
288 | features["atr"] = ta.atr(high, low, close)
289 | features["atr_ratio"] = safe_divide(
290 | features["atr"], close, 0.02
291 | ) # Default 2% ATR ratio
292 | else:
293 | features["atr"] = 0
294 | features["atr_ratio"] = 0.02
295 |
296 | return features
297 |
298 | def extract_statistical_features(self, data: DataFrame) -> DataFrame:
299 | """Extract statistical features.
300 |
301 | Args:
302 | data: OHLCV price data
303 |
304 | Returns:
305 | DataFrame with statistical features
306 | """
307 | features = pd.DataFrame(index=data.index)
308 |
309 | # Safe division helper function
310 | def safe_divide(numerator, denominator, default=0.0):
311 | """Safely divide two values, handling None, NaN, and zero cases."""
312 | if numerator is None or denominator is None:
313 | return default
314 | # Convert to numpy arrays to handle pandas Series
315 | num = np.asarray(numerator)
316 | den = np.asarray(denominator)
317 | # Use numpy divide with where condition for safety
318 | return np.divide(
319 | num, den, out=np.full_like(num, default, dtype=float), where=(den != 0)
320 | )
321 |
322 | # Rolling statistics
323 | for period in self.lookback_periods:
324 | returns = data["close"].pct_change()
325 |
326 | # Volatility with safe calculations
327 | vol_short = returns.rolling(period).std()
328 | vol_long = returns.rolling(period * 2).std()
329 | features[f"volatility_{period}"] = vol_short
330 | features[f"volatility_ratio_{period}"] = safe_divide(
331 | vol_short, vol_long, 1.0
332 | )
333 |
334 | # Skewness and Kurtosis
335 | features[f"skewness_{period}"] = returns.rolling(period).skew()
336 | features[f"kurtosis_{period}"] = returns.rolling(period).kurt()
337 |
338 | # Min/Max ratios with safe division
339 | if "high" in data.columns and "low" in data.columns:
340 | rolling_high = data["high"].rolling(period).max()
341 | rolling_low = data["low"].rolling(period).min()
342 | features[f"high_ratio_{period}"] = safe_divide(
343 | data["close"], rolling_high, 1.0
344 | )
345 | features[f"low_ratio_{period}"] = safe_divide(
346 | data["close"], rolling_low, 1.0
347 | )
348 | else:
349 | features[f"high_ratio_{period}"] = 1.0
350 | features[f"low_ratio_{period}"] = 1.0
351 |
352 | # Momentum features with safe division
353 | features[f"momentum_{period}"] = safe_divide(
354 | data["close"], data["close"].shift(period), 1.0
355 | )
356 | features[f"roc_{period}"] = data["close"].pct_change(periods=period)
357 |
358 | return features
359 |
360 | def extract_microstructure_features(self, data: DataFrame) -> DataFrame:
361 | """Extract market microstructure features.
362 |
363 | Args:
364 | data: OHLCV price data
365 |
366 | Returns:
367 | DataFrame with microstructure features
368 | """
369 | features = pd.DataFrame(index=data.index)
370 |
371 | # Safe division helper function
372 | def safe_divide(numerator, denominator, default=0.0):
373 | """Safely divide two values, handling None, NaN, and zero cases."""
374 | if numerator is None or denominator is None:
375 | return default
376 | # Convert to numpy arrays to handle pandas Series
377 | num = np.asarray(numerator)
378 | den = np.asarray(denominator)
379 | # Use numpy divide with where condition for safety
380 | return np.divide(
381 | num, den, out=np.full_like(num, default, dtype=float), where=(den != 0)
382 | )
383 |
384 | # Bid-ask spread proxy (high-low spread) with safe calculation
385 | if "high" in data.columns and "low" in data.columns:
386 | mid_price = (data["high"] + data["low"]) / 2
387 | features["spread_proxy"] = safe_divide(
388 | data["high"] - data["low"], mid_price, 0.02
389 | )
390 | else:
391 | features["spread_proxy"] = 0.02
392 |
393 | # Price impact measures with safe calculations
394 | if "volume" in data.columns:
395 | returns_abs = abs(data["close"].pct_change())
396 | features["amihud_illiquidity"] = safe_divide(
397 | returns_abs, data["volume"], 0.0
398 | )
399 |
400 | if "high" in data.columns and "low" in data.columns:
401 | features["volume_weighted_price"] = (
402 | data["high"] + data["low"] + data["close"]
403 | ) / 3
404 | else:
405 | features["volume_weighted_price"] = data["close"]
406 | else:
407 | features["amihud_illiquidity"] = 0.0
408 | features["volume_weighted_price"] = data.get("close", 0.0)
409 |
410 | # Intraday patterns with safe calculations
411 | if "open" in data.columns and "close" in data.columns:
412 | prev_close = data["close"].shift(1)
413 | features["open_gap"] = safe_divide(
414 | data["open"] - prev_close, prev_close, 0.0
415 | )
416 | else:
417 | features["open_gap"] = 0.0
418 |
419 | if "high" in data.columns and "low" in data.columns and "close" in data.columns:
420 | features["close_to_high"] = safe_divide(
421 | data["high"] - data["close"], data["close"], 0.0
422 | )
423 | features["close_to_low"] = safe_divide(
424 | data["close"] - data["low"], data["close"], 0.0
425 | )
426 | else:
427 | features["close_to_high"] = 0.0
428 | features["close_to_low"] = 0.0
429 |
430 | return features
431 |
432 | def create_target_variable(
433 | self, data: DataFrame, forward_periods: int = 5, threshold: float = 0.02
434 | ) -> Series:
435 | """Create target variable for classification.
436 |
437 | Args:
438 | data: Price data
439 | forward_periods: Number of periods to look forward
440 | threshold: Return threshold for classification
441 |
442 | Returns:
443 | Target variable (0: sell, 1: hold, 2: buy)
444 | """
445 | close = data.get("close", data.get("Close"))
446 | forward_returns = close.pct_change(periods=forward_periods).shift(
447 | -forward_periods
448 | )
449 |
450 | target = pd.Series(1, index=data.index) # Default to hold
451 | target[forward_returns > threshold] = 2 # Buy
452 | target[forward_returns < -threshold] = 0 # Sell
453 |
454 | return target
455 |
456 | def extract_all_features(self, data: DataFrame) -> DataFrame:
457 | """Extract all features for ML model.
458 |
459 | Args:
460 | data: OHLCV price data
461 |
462 | Returns:
463 | DataFrame with all features
464 | """
465 | try:
466 | # Validate input data
467 | if data is None or data.empty:
468 | logger.warning("Empty or None data provided to extract_all_features")
469 | return pd.DataFrame()
470 |
471 | # Extract all feature types with individual error handling
472 | feature_dfs = []
473 |
474 | try:
475 | price_features = self.extract_price_features(data)
476 | if not price_features.empty:
477 | feature_dfs.append(price_features)
478 | except Exception as e:
479 | logger.warning(f"Failed to extract price features: {e}")
480 | # Create empty DataFrame with same index as fallback
481 | price_features = pd.DataFrame(index=data.index)
482 |
483 | try:
484 | technical_features = self.extract_technical_features(data)
485 | if not technical_features.empty:
486 | feature_dfs.append(technical_features)
487 | except Exception as e:
488 | logger.warning(f"Failed to extract technical features: {e}")
489 |
490 | try:
491 | statistical_features = self.extract_statistical_features(data)
492 | if not statistical_features.empty:
493 | feature_dfs.append(statistical_features)
494 | except Exception as e:
495 | logger.warning(f"Failed to extract statistical features: {e}")
496 |
497 | try:
498 | microstructure_features = self.extract_microstructure_features(data)
499 | if not microstructure_features.empty:
500 | feature_dfs.append(microstructure_features)
501 | except Exception as e:
502 | logger.warning(f"Failed to extract microstructure features: {e}")
503 |
504 | # Combine all successfully extracted features
505 | if feature_dfs:
506 | all_features = pd.concat(feature_dfs, axis=1)
507 | else:
508 | # Fallback: create minimal feature set
509 | logger.warning(
510 | "No features extracted successfully, creating minimal fallback features"
511 | )
512 | all_features = pd.DataFrame(
513 | {
514 | "returns": data.get("close", pd.Series(0, index=data.index))
515 | .pct_change()
516 | .fillna(0),
517 | "close": data.get("close", pd.Series(0, index=data.index)),
518 | },
519 | index=data.index,
520 | )
521 |
522 | # Handle missing values with robust method
523 | if not all_features.empty:
524 | # Forward fill, then backward fill, then zero fill
525 | all_features = all_features.ffill().bfill().fillna(0)
526 |
527 | # Replace any infinite values
528 | all_features = all_features.replace([np.inf, -np.inf], 0)
529 |
530 | logger.info(
531 | f"Extracted {len(all_features.columns)} features for {len(all_features)} data points"
532 | )
533 | else:
534 | logger.warning("No features could be extracted")
535 |
536 | return all_features
537 |
538 | except Exception as e:
539 | logger.error(f"Critical error extracting features: {e}")
540 | # Return minimal fallback instead of raising
541 | return pd.DataFrame(
542 | {
543 | "returns": pd.Series(
544 | 0, index=data.index if data is not None else [0]
545 | ),
546 | "close": pd.Series(
547 | 0, index=data.index if data is not None else [0]
548 | ),
549 | }
550 | )
551 |
552 |
553 | class MLPredictor:
554 | """Machine learning predictor for trading signals."""
555 |
556 | def __init__(self, model_type: str = "random_forest", **model_params):
557 | """Initialize ML predictor.
558 |
559 | Args:
560 | model_type: Type of ML model to use
561 | **model_params: Model parameters
562 | """
563 | self.model_type = model_type
564 | self.model_params = model_params
565 | self.model = None
566 | self.scaler = StandardScaler()
567 | self.feature_extractor = FeatureExtractor()
568 | self.is_trained = False
569 |
570 | def _create_model(self):
571 | """Create ML model based on type."""
572 | if self.model_type == "random_forest":
573 | self.model = RandomForestClassifier(
574 | n_estimators=self.model_params.get("n_estimators", 100),
575 | max_depth=self.model_params.get("max_depth", 10),
576 | random_state=self.model_params.get("random_state", 42),
577 | **{
578 | k: v
579 | for k, v in self.model_params.items()
580 | if k not in ["n_estimators", "max_depth", "random_state"]
581 | },
582 | )
583 | else:
584 | raise ValueError(f"Unsupported model type: {self.model_type}")
585 |
586 | def prepare_data(
587 | self, data: DataFrame, target_periods: int = 5, return_threshold: float = 0.02
588 | ) -> tuple[DataFrame, Series]:
589 | """Prepare features and target for training.
590 |
591 | Args:
592 | data: OHLCV price data
593 | target_periods: Periods to look forward for target
594 | return_threshold: Return threshold for classification
595 |
596 | Returns:
597 | Tuple of (features, target)
598 | """
599 | # Extract features
600 | features = self.feature_extractor.extract_all_features(data)
601 |
602 | # Create target variable
603 | target = self.feature_extractor.create_target_variable(
604 | data, target_periods, return_threshold
605 | )
606 |
607 | # Align features and target (remove NaN values)
608 | valid_idx = features.dropna().index.intersection(target.dropna().index)
609 | features = features.loc[valid_idx]
610 | target = target.loc[valid_idx]
611 |
612 | return features, target
613 |
614 | def train(
615 | self, data: DataFrame, target_periods: int = 5, return_threshold: float = 0.02
616 | ) -> dict[str, Any]:
617 | """Train the ML model.
618 |
619 | Args:
620 | data: OHLCV price data
621 | target_periods: Periods to look forward for target
622 | return_threshold: Return threshold for classification
623 |
624 | Returns:
625 | Training metrics
626 | """
627 | try:
628 | # Prepare data
629 | features, target = self.prepare_data(data, target_periods, return_threshold)
630 |
631 | if len(features) == 0:
632 | raise ValueError("No valid training data available")
633 |
634 | # Create and train model
635 | self._create_model()
636 |
637 | # Scale features
638 | features_scaled = self.scaler.fit_transform(features)
639 |
640 | # Train model
641 | self.model.fit(features_scaled, target)
642 | self.is_trained = True
643 |
644 | # Calculate training metrics
645 | train_score = self.model.score(features_scaled, target)
646 |
647 | # Convert numpy int64 to Python int for JSON serialization
648 | target_dist = target.value_counts().to_dict()
649 | target_dist = {int(k): int(v) for k, v in target_dist.items()}
650 |
651 | metrics = {
652 | "train_accuracy": float(
653 | train_score
654 | ), # Convert numpy float to Python float
655 | "n_samples": int(len(features)),
656 | "n_features": int(len(features.columns)),
657 | "target_distribution": target_dist,
658 | }
659 |
660 | # Feature importance (if available)
661 | if hasattr(self.model, "feature_importances_"):
662 | # Convert numpy floats to Python floats
663 | feature_importance = {
664 | str(col): float(imp)
665 | for col, imp in zip(
666 | features.columns, self.model.feature_importances_, strict=False
667 | )
668 | }
669 | metrics["feature_importance"] = feature_importance
670 |
671 | logger.info(f"Model trained successfully: {metrics}")
672 | return metrics
673 |
674 | except Exception as e:
675 | logger.error(f"Error training model: {e}")
676 | raise
677 |
678 | def generate_signals(self, data: DataFrame) -> tuple[Series, Series]:
679 | """Generate trading signals using the trained model.
680 |
681 | Alias for predict() to match the expected interface.
682 |
683 | Args:
684 | data: OHLCV price data
685 |
686 | Returns:
687 | Tuple of (entry_signals, exit_signals)
688 | """
689 | return self.predict(data)
690 |
691 | def predict(self, data: DataFrame) -> tuple[Series, Series]:
692 | """Generate trading signals using the trained model.
693 |
694 | Args:
695 | data: OHLCV price data
696 |
697 | Returns:
698 | Tuple of (entry_signals, exit_signals)
699 | """
700 | if not self.is_trained:
701 | raise ValueError("Model must be trained before making predictions")
702 |
703 | try:
704 | # Extract features
705 | features = self.feature_extractor.extract_all_features(data)
706 |
707 | # Handle missing values
708 | features = features.ffill().fillna(0)
709 |
710 | # Scale features
711 | features_scaled = self.scaler.transform(features)
712 |
713 | # Make predictions
714 | predictions = self.model.predict(features_scaled)
715 | prediction_proba = self.model.predict_proba(features_scaled)
716 |
717 | # Convert to signals
718 | predictions_series = pd.Series(predictions, index=features.index)
719 |
720 | # Entry signals (buy predictions with high confidence)
721 | entry_signals = (predictions_series == 2) & (
722 | pd.Series(prediction_proba[:, 2], index=features.index) > 0.6
723 | )
724 |
725 | # Exit signals (sell predictions or low confidence holds)
726 | exit_signals = (predictions_series == 0) | (
727 | (predictions_series == 1)
728 | & (pd.Series(prediction_proba[:, 1], index=features.index) < 0.4)
729 | )
730 |
731 | return entry_signals, exit_signals
732 |
733 | except Exception as e:
734 | logger.error(f"Error making predictions: {e}")
735 | raise
736 |
737 | def get_feature_importance(self) -> dict[str, float]:
738 | """Get feature importance from trained model.
739 |
740 | Returns:
741 | Dictionary of feature importance scores
742 | """
743 | if not self.is_trained or not hasattr(self.model, "feature_importances_"):
744 | return {}
745 |
746 | feature_names = self.feature_extractor.extract_all_features(
747 | pd.DataFrame() # Empty DataFrame to get column names
748 | ).columns
749 |
750 | return dict(zip(feature_names, self.model.feature_importances_, strict=False))
751 |
752 | def update_model(
753 | self, data: DataFrame, target_periods: int = 5, return_threshold: float = 0.02
754 | ) -> dict[str, Any]:
755 | """Update model with new data (online learning simulation).
756 |
757 | Args:
758 | data: New OHLCV price data
759 | target_periods: Periods to look forward for target
760 | return_threshold: Return threshold for classification
761 |
762 | Returns:
763 | Update metrics
764 | """
765 | try:
766 | # For now, retrain the model with all data
767 | # In production, this could use partial_fit for online learning
768 | return self.train(data, target_periods, return_threshold)
769 |
770 | except Exception as e:
771 | logger.error(f"Error updating model: {e}")
772 | raise
773 |
```
--------------------------------------------------------------------------------
/maverick_mcp/backtesting/persistence.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | Backtesting persistence layer for saving and retrieving backtest results.
3 |
4 | This module provides comprehensive database operations for backtest results,
5 | including saving VectorBT results, querying historical tests, and comparing
6 | multiple backtests with proper error handling.
7 | """
8 |
9 | import logging
10 | from datetime import datetime, timedelta
11 | from decimal import Decimal, InvalidOperation
12 | from typing import Any
13 | from uuid import UUID, uuid4
14 |
15 | import pandas as pd
16 | from sqlalchemy import desc
17 | from sqlalchemy.exc import SQLAlchemyError
18 | from sqlalchemy.orm import Session
19 |
20 | from maverick_mcp.data.models import (
21 | BacktestResult,
22 | BacktestTrade,
23 | OptimizationResult,
24 | SessionLocal,
25 | WalkForwardTest,
26 | )
27 |
28 | logger = logging.getLogger(__name__)
29 |
30 |
31 | class BacktestPersistenceError(Exception):
32 | """Custom exception for backtest persistence operations."""
33 |
34 | pass
35 |
36 |
37 | class BacktestPersistenceManager:
38 | """Manages persistence of backtesting results with comprehensive error handling."""
39 |
40 | def __init__(self, session: Session | None = None):
41 | """Initialize persistence manager.
42 |
43 | Args:
44 | session: Optional SQLAlchemy session. If None, creates a new one.
45 | """
46 | self.session = session
47 | self._owns_session = session is None
48 |
49 | def __enter__(self):
50 | """Context manager entry."""
51 | if self._owns_session:
52 | self.session = SessionLocal()
53 | return self
54 |
55 | def __exit__(self, exc_type, exc_val, exc_tb):
56 | """Context manager exit with proper cleanup."""
57 | if self._owns_session and self.session:
58 | if exc_type is None:
59 | self.session.commit()
60 | else:
61 | self.session.rollback()
62 | self.session.close()
63 |
64 | def save_backtest_result(
65 | self,
66 | vectorbt_results: dict[str, Any],
67 | execution_time: float | None = None,
68 | notes: str | None = None,
69 | ) -> str:
70 | """
71 | Save VectorBT backtest results to database.
72 |
73 | Args:
74 | vectorbt_results: Results dictionary from VectorBTEngine
75 | execution_time: Time taken to run the backtest in seconds
76 | notes: Optional user notes
77 |
78 | Returns:
79 | UUID string of the saved backtest
80 |
81 | Raises:
82 | BacktestPersistenceError: If saving fails
83 | """
84 | try:
85 | # Extract basic metadata
86 | symbol = vectorbt_results.get("symbol", "").upper()
87 | strategy_type = vectorbt_results.get("strategy", "")
88 | parameters = vectorbt_results.get("parameters", {})
89 | metrics = vectorbt_results.get("metrics", {})
90 |
91 | if not symbol or not strategy_type:
92 | raise BacktestPersistenceError("Symbol and strategy type are required")
93 |
94 | # Create backtest result record
95 | backtest_result = BacktestResult(
96 | backtest_id=uuid4(),
97 | symbol=symbol,
98 | strategy_type=strategy_type,
99 | backtest_date=datetime.utcnow(),
100 | # Date range
101 | start_date=pd.to_datetime(vectorbt_results.get("start_date")).date(),
102 | end_date=pd.to_datetime(vectorbt_results.get("end_date")).date(),
103 | initial_capital=Decimal(
104 | str(vectorbt_results.get("initial_capital", 10000))
105 | ),
106 | # Strategy parameters
107 | parameters=parameters,
108 | # Performance metrics
109 | total_return=self._safe_decimal(metrics.get("total_return")),
110 | annualized_return=self._safe_decimal(metrics.get("annualized_return")),
111 | sharpe_ratio=self._safe_decimal(metrics.get("sharpe_ratio")),
112 | sortino_ratio=self._safe_decimal(metrics.get("sortino_ratio")),
113 | calmar_ratio=self._safe_decimal(metrics.get("calmar_ratio")),
114 | # Risk metrics
115 | max_drawdown=self._safe_decimal(metrics.get("max_drawdown")),
116 | max_drawdown_duration=metrics.get("max_drawdown_duration"),
117 | volatility=self._safe_decimal(metrics.get("volatility")),
118 | downside_volatility=self._safe_decimal(
119 | metrics.get("downside_volatility")
120 | ),
121 | # Trade statistics
122 | total_trades=metrics.get("total_trades", 0),
123 | winning_trades=metrics.get("winning_trades", 0),
124 | losing_trades=metrics.get("losing_trades", 0),
125 | win_rate=self._safe_decimal(metrics.get("win_rate")),
126 | # P&L statistics
127 | profit_factor=self._safe_decimal(metrics.get("profit_factor")),
128 | average_win=self._safe_decimal(metrics.get("average_win")),
129 | average_loss=self._safe_decimal(metrics.get("average_loss")),
130 | largest_win=self._safe_decimal(metrics.get("largest_win")),
131 | largest_loss=self._safe_decimal(metrics.get("largest_loss")),
132 | # Portfolio values
133 | final_portfolio_value=self._safe_decimal(metrics.get("final_value")),
134 | peak_portfolio_value=self._safe_decimal(metrics.get("peak_value")),
135 | # Market analysis
136 | beta=self._safe_decimal(metrics.get("beta")),
137 | alpha=self._safe_decimal(metrics.get("alpha")),
138 | # Time series data
139 | equity_curve=vectorbt_results.get("equity_curve"),
140 | drawdown_series=vectorbt_results.get("drawdown_series"),
141 | # Execution metadata
142 | execution_time_seconds=Decimal(str(execution_time))
143 | if execution_time
144 | else None,
145 | data_points=len(vectorbt_results.get("equity_curve", [])),
146 | # Status
147 | status="completed",
148 | notes=notes,
149 | )
150 |
151 | self.session.add(backtest_result)
152 | self.session.flush() # Get the ID without committing
153 |
154 | # Save individual trades if available
155 | trades_data = vectorbt_results.get("trades", [])
156 | if trades_data:
157 | self._save_trades(backtest_result.backtest_id, trades_data)
158 |
159 | self.session.commit()
160 | logger.info(f"Saved backtest result: {backtest_result.backtest_id}")
161 |
162 | return str(backtest_result.backtest_id)
163 |
164 | except SQLAlchemyError as e:
165 | self.session.rollback()
166 | logger.error(f"Database error saving backtest: {e}")
167 | raise BacktestPersistenceError(f"Failed to save backtest: {e}")
168 | except Exception as e:
169 | self.session.rollback()
170 | logger.error(f"Unexpected error saving backtest: {e}")
171 | raise BacktestPersistenceError(f"Unexpected error: {e}")
172 |
173 | def _save_trades(
174 | self, backtest_id: UUID, trades_data: list[dict[str, Any]]
175 | ) -> None:
176 | """Save individual trade records."""
177 | try:
178 | trades = []
179 | for i, trade in enumerate(trades_data, 1):
180 | trade_record = BacktestTrade(
181 | trade_id=uuid4(),
182 | backtest_id=backtest_id,
183 | trade_number=i,
184 | # Entry details
185 | entry_date=pd.to_datetime(trade.get("entry_date")).date(),
186 | entry_price=self._safe_decimal(trade.get("entry_price")),
187 | entry_time=pd.to_datetime(trade.get("entry_time"))
188 | if trade.get("entry_time")
189 | else None,
190 | # Exit details
191 | exit_date=pd.to_datetime(trade.get("exit_date")).date()
192 | if trade.get("exit_date")
193 | else None,
194 | exit_price=self._safe_decimal(trade.get("exit_price")),
195 | exit_time=pd.to_datetime(trade.get("exit_time"))
196 | if trade.get("exit_time")
197 | else None,
198 | # Position details
199 | position_size=self._safe_decimal(trade.get("position_size")),
200 | direction=trade.get("direction", "long"),
201 | # P&L
202 | pnl=self._safe_decimal(trade.get("pnl")),
203 | pnl_percent=self._safe_decimal(trade.get("pnl_percent")),
204 | # Risk metrics
205 | mae=self._safe_decimal(trade.get("mae")),
206 | mfe=self._safe_decimal(trade.get("mfe")),
207 | # Duration
208 | duration_days=trade.get("duration_days"),
209 | duration_hours=self._safe_decimal(trade.get("duration_hours")),
210 | # Exit details
211 | exit_reason=trade.get("exit_reason"),
212 | fees_paid=self._safe_decimal(trade.get("fees_paid")),
213 | slippage_cost=self._safe_decimal(trade.get("slippage_cost")),
214 | )
215 | trades.append(trade_record)
216 |
217 | self.session.add_all(trades)
218 | logger.info(f"Saved {len(trades)} trades for backtest {backtest_id}")
219 |
220 | except Exception as e:
221 | logger.error(f"Error saving trades: {e}")
222 | raise
223 |
224 | def get_backtest_by_id(self, backtest_id: str) -> BacktestResult | None:
225 | """
226 | Retrieve a backtest by ID.
227 |
228 | Args:
229 | backtest_id: UUID string of the backtest
230 |
231 | Returns:
232 | BacktestResult or None if not found
233 | """
234 | try:
235 | # Convert string to UUID for database query
236 | if isinstance(backtest_id, str):
237 | backtest_uuid = UUID(backtest_id)
238 | else:
239 | backtest_uuid = backtest_id
240 |
241 | return (
242 | self.session.query(BacktestResult)
243 | .filter(BacktestResult.backtest_id == backtest_uuid)
244 | .first()
245 | )
246 | except SQLAlchemyError as e:
247 | logger.error(f"Error retrieving backtest {backtest_id}: {e}")
248 | return None
249 | except ValueError as e:
250 | logger.error(f"Invalid UUID format {backtest_id}: {e}")
251 | return None
252 |
253 | def get_backtests_by_symbol(
254 | self, symbol: str, strategy_type: str | None = None, limit: int = 10
255 | ) -> list[BacktestResult]:
256 | """
257 | Get backtests for a specific symbol.
258 |
259 | Args:
260 | symbol: Stock symbol
261 | strategy_type: Optional strategy filter
262 | limit: Maximum number of results
263 |
264 | Returns:
265 | List of BacktestResult objects
266 | """
267 | try:
268 | query = self.session.query(BacktestResult).filter(
269 | BacktestResult.symbol == symbol.upper()
270 | )
271 |
272 | if strategy_type:
273 | query = query.filter(BacktestResult.strategy_type == strategy_type)
274 |
275 | return query.order_by(desc(BacktestResult.backtest_date)).limit(limit).all()
276 |
277 | except SQLAlchemyError as e:
278 | logger.error(f"Error retrieving backtests for {symbol}: {e}")
279 | return []
280 |
281 | def get_best_performing_strategies(
282 | self, metric: str = "sharpe_ratio", min_trades: int = 10, limit: int = 20
283 | ) -> list[BacktestResult]:
284 | """
285 | Get best performing backtests by specified metric.
286 |
287 | Args:
288 | metric: Performance metric (sharpe_ratio, total_return, profit_factor)
289 | min_trades: Minimum number of trades required
290 | limit: Maximum number of results
291 |
292 | Returns:
293 | List of top performing BacktestResult objects
294 | """
295 | try:
296 | metric_column = getattr(BacktestResult, metric, BacktestResult.sharpe_ratio)
297 |
298 | return (
299 | self.session.query(BacktestResult)
300 | .filter(
301 | BacktestResult.status == "completed",
302 | BacktestResult.total_trades >= min_trades,
303 | metric_column.isnot(None),
304 | )
305 | .order_by(desc(metric_column))
306 | .limit(limit)
307 | .all()
308 | )
309 |
310 | except SQLAlchemyError as e:
311 | logger.error(f"Error retrieving best performing strategies: {e}")
312 | return []
313 |
314 | def compare_strategies(
315 | self, backtest_ids: list[str], metrics: list[str] | None = None
316 | ) -> dict[str, Any]:
317 | """
318 | Compare multiple backtests across specified metrics.
319 |
320 | Args:
321 | backtest_ids: List of backtest UUID strings
322 | metrics: List of metrics to compare (default: common metrics)
323 |
324 | Returns:
325 | Dictionary with comparison results
326 | """
327 | if not metrics:
328 | metrics = [
329 | "total_return",
330 | "sharpe_ratio",
331 | "max_drawdown",
332 | "win_rate",
333 | "profit_factor",
334 | "total_trades",
335 | ]
336 |
337 | try:
338 | # Convert string UUIDs to UUID objects
339 | uuid_list = []
340 | for bt_id in backtest_ids:
341 | if isinstance(bt_id, str):
342 | uuid_list.append(UUID(bt_id))
343 | else:
344 | uuid_list.append(bt_id)
345 |
346 | backtests = (
347 | self.session.query(BacktestResult)
348 | .filter(BacktestResult.backtest_id.in_(uuid_list))
349 | .all()
350 | )
351 |
352 | if not backtests:
353 | return {"error": "No backtests found"}
354 |
355 | comparison = {"backtests": [], "summary": {}, "rankings": {}}
356 |
357 | # Extract data for each backtest
358 | for bt in backtests:
359 | bt_data = {
360 | "backtest_id": str(bt.backtest_id),
361 | "symbol": bt.symbol,
362 | "strategy": bt.strategy_type,
363 | "date": bt.backtest_date.isoformat(),
364 | "metrics": {},
365 | }
366 |
367 | for metric in metrics:
368 | value = getattr(bt, metric, None)
369 | bt_data["metrics"][metric] = float(value) if value else None
370 |
371 | comparison["backtests"].append(bt_data)
372 |
373 | # Calculate rankings for each metric
374 | for metric in metrics:
375 | metric_values = [
376 | (bt["backtest_id"], bt["metrics"].get(metric))
377 | for bt in comparison["backtests"]
378 | if bt["metrics"].get(metric) is not None
379 | ]
380 |
381 | if metric_values:
382 | # Sort by metric value (descending for most metrics)
383 | reverse_sort = metric != "max_drawdown" # Lower drawdown is better
384 | sorted_values = sorted(
385 | metric_values, key=lambda x: x[1], reverse=reverse_sort
386 | )
387 |
388 | comparison["rankings"][metric] = [
389 | {"backtest_id": bt_id, "value": value, "rank": i + 1}
390 | for i, (bt_id, value) in enumerate(sorted_values)
391 | ]
392 |
393 | # Summary statistics
394 | comparison["summary"] = {
395 | "total_backtests": len(backtests),
396 | "date_range": {
397 | "earliest": min(bt.backtest_date for bt in backtests).isoformat(),
398 | "latest": max(bt.backtest_date for bt in backtests).isoformat(),
399 | },
400 | }
401 |
402 | return comparison
403 |
404 | except SQLAlchemyError as e:
405 | logger.error(f"Error comparing strategies: {e}")
406 | return {"error": f"Database error: {e}"}
407 |
408 | def save_optimization_results(
409 | self,
410 | backtest_id: str,
411 | optimization_results: list[dict[str, Any]],
412 | objective_function: str = "sharpe_ratio",
413 | ) -> int:
414 | """
415 | Save parameter optimization results.
416 |
417 | Args:
418 | backtest_id: Parent backtest UUID
419 | optimization_results: List of optimization result dictionaries
420 | objective_function: Optimization objective (sharpe_ratio, total_return, etc.)
421 |
422 | Returns:
423 | Number of optimization results saved
424 | """
425 | try:
426 | # Convert string UUID to UUID object
427 | if isinstance(backtest_id, str):
428 | backtest_uuid = UUID(backtest_id)
429 | else:
430 | backtest_uuid = backtest_id
431 |
432 | optimization_records = []
433 |
434 | for i, result in enumerate(optimization_results, 1):
435 | record = OptimizationResult(
436 | optimization_id=uuid4(),
437 | backtest_id=backtest_uuid,
438 | parameter_set=i,
439 | parameters=result.get("parameters", {}),
440 | objective_function=objective_function,
441 | objective_value=self._safe_decimal(result.get("objective_value")),
442 | total_return=self._safe_decimal(result.get("total_return")),
443 | sharpe_ratio=self._safe_decimal(result.get("sharpe_ratio")),
444 | max_drawdown=self._safe_decimal(result.get("max_drawdown")),
445 | win_rate=self._safe_decimal(result.get("win_rate")),
446 | profit_factor=self._safe_decimal(result.get("profit_factor")),
447 | total_trades=result.get("total_trades"),
448 | rank=result.get("rank", i),
449 | is_statistically_significant=result.get(
450 | "is_statistically_significant", False
451 | ),
452 | p_value=self._safe_decimal(result.get("p_value")),
453 | )
454 | optimization_records.append(record)
455 |
456 | self.session.add_all(optimization_records)
457 | self.session.commit()
458 |
459 | logger.info(f"Saved {len(optimization_records)} optimization results")
460 | return len(optimization_records)
461 |
462 | except SQLAlchemyError as e:
463 | self.session.rollback()
464 | logger.error(f"Error saving optimization results: {e}")
465 | raise BacktestPersistenceError(f"Failed to save optimization results: {e}")
466 |
467 | def save_walk_forward_test(
468 | self, parent_backtest_id: str, walk_forward_data: dict[str, Any]
469 | ) -> str:
470 | """
471 | Save walk-forward validation test results.
472 |
473 | Args:
474 | parent_backtest_id: Parent backtest UUID
475 | walk_forward_data: Walk-forward test data
476 |
477 | Returns:
478 | UUID string of saved walk-forward test
479 | """
480 | try:
481 | # Convert string UUID to UUID object
482 | if isinstance(parent_backtest_id, str):
483 | parent_uuid = UUID(parent_backtest_id)
484 | else:
485 | parent_uuid = parent_backtest_id
486 |
487 | wf_test = WalkForwardTest(
488 | walk_forward_id=uuid4(),
489 | parent_backtest_id=parent_uuid,
490 | window_size_months=walk_forward_data.get("window_size_months"),
491 | step_size_months=walk_forward_data.get("step_size_months"),
492 | # Time periods
493 | training_start=pd.to_datetime(
494 | walk_forward_data.get("training_start")
495 | ).date(),
496 | training_end=pd.to_datetime(
497 | walk_forward_data.get("training_end")
498 | ).date(),
499 | test_period_start=pd.to_datetime(
500 | walk_forward_data.get("test_period_start")
501 | ).date(),
502 | test_period_end=pd.to_datetime(
503 | walk_forward_data.get("test_period_end")
504 | ).date(),
505 | # Results
506 | optimal_parameters=walk_forward_data.get("optimal_parameters"),
507 | training_performance=self._safe_decimal(
508 | walk_forward_data.get("training_performance")
509 | ),
510 | out_of_sample_return=self._safe_decimal(
511 | walk_forward_data.get("out_of_sample_return")
512 | ),
513 | out_of_sample_sharpe=self._safe_decimal(
514 | walk_forward_data.get("out_of_sample_sharpe")
515 | ),
516 | out_of_sample_drawdown=self._safe_decimal(
517 | walk_forward_data.get("out_of_sample_drawdown")
518 | ),
519 | out_of_sample_trades=walk_forward_data.get("out_of_sample_trades"),
520 | # Performance analysis
521 | performance_ratio=self._safe_decimal(
522 | walk_forward_data.get("performance_ratio")
523 | ),
524 | degradation_factor=self._safe_decimal(
525 | walk_forward_data.get("degradation_factor")
526 | ),
527 | is_profitable=walk_forward_data.get("is_profitable"),
528 | is_statistically_significant=walk_forward_data.get(
529 | "is_statistically_significant", False
530 | ),
531 | )
532 |
533 | self.session.add(wf_test)
534 | self.session.commit()
535 |
536 | logger.info(f"Saved walk-forward test: {wf_test.walk_forward_id}")
537 | return str(wf_test.walk_forward_id)
538 |
539 | except SQLAlchemyError as e:
540 | self.session.rollback()
541 | logger.error(f"Error saving walk-forward test: {e}")
542 | raise BacktestPersistenceError(f"Failed to save walk-forward test: {e}")
543 |
544 | def get_backtest_performance_summary(
545 | self,
546 | symbol: str | None = None,
547 | strategy_type: str | None = None,
548 | days_back: int = 30,
549 | ) -> dict[str, Any]:
550 | """
551 | Get performance summary of recent backtests.
552 |
553 | Args:
554 | symbol: Optional symbol filter
555 | strategy_type: Optional strategy filter
556 | days_back: Days to look back
557 |
558 | Returns:
559 | Dictionary with performance summary
560 | """
561 | try:
562 | cutoff_date = datetime.utcnow() - timedelta(days=days_back)
563 |
564 | query = self.session.query(BacktestResult).filter(
565 | BacktestResult.backtest_date >= cutoff_date,
566 | BacktestResult.status == "completed",
567 | )
568 |
569 | if symbol:
570 | query = query.filter(BacktestResult.symbol == symbol.upper())
571 | if strategy_type:
572 | query = query.filter(BacktestResult.strategy_type == strategy_type)
573 |
574 | backtests = query.all()
575 |
576 | if not backtests:
577 | return {"message": "No backtests found in the specified period"}
578 |
579 | # Calculate summary statistics
580 | returns = [float(bt.total_return) for bt in backtests if bt.total_return]
581 | sharpe_ratios = [
582 | float(bt.sharpe_ratio) for bt in backtests if bt.sharpe_ratio
583 | ]
584 | win_rates = [float(bt.win_rate) for bt in backtests if bt.win_rate]
585 |
586 | summary = {
587 | "period": f"Last {days_back} days",
588 | "total_backtests": len(backtests),
589 | "performance_metrics": {
590 | "average_return": sum(returns) / len(returns) if returns else 0,
591 | "best_return": max(returns) if returns else 0,
592 | "worst_return": min(returns) if returns else 0,
593 | "average_sharpe": sum(sharpe_ratios) / len(sharpe_ratios)
594 | if sharpe_ratios
595 | else 0,
596 | "average_win_rate": sum(win_rates) / len(win_rates)
597 | if win_rates
598 | else 0,
599 | },
600 | "strategy_breakdown": {},
601 | "symbol_breakdown": {},
602 | }
603 |
604 | # Group by strategy
605 | strategy_groups = {}
606 | for bt in backtests:
607 | strategy = bt.strategy_type
608 | if strategy not in strategy_groups:
609 | strategy_groups[strategy] = []
610 | strategy_groups[strategy].append(bt)
611 |
612 | for strategy, strategy_backtests in strategy_groups.items():
613 | strategy_returns = [
614 | float(bt.total_return)
615 | for bt in strategy_backtests
616 | if bt.total_return
617 | ]
618 | summary["strategy_breakdown"][strategy] = {
619 | "count": len(strategy_backtests),
620 | "average_return": sum(strategy_returns) / len(strategy_returns)
621 | if strategy_returns
622 | else 0,
623 | }
624 |
625 | # Group by symbol
626 | symbol_groups = {}
627 | for bt in backtests:
628 | symbol = bt.symbol
629 | if symbol not in symbol_groups:
630 | symbol_groups[symbol] = []
631 | symbol_groups[symbol].append(bt)
632 |
633 | for symbol, symbol_backtests in symbol_groups.items():
634 | symbol_returns = [
635 | float(bt.total_return) for bt in symbol_backtests if bt.total_return
636 | ]
637 | summary["symbol_breakdown"][symbol] = {
638 | "count": len(symbol_backtests),
639 | "average_return": sum(symbol_returns) / len(symbol_returns)
640 | if symbol_returns
641 | else 0,
642 | }
643 |
644 | return summary
645 |
646 | except SQLAlchemyError as e:
647 | logger.error(f"Error generating performance summary: {e}")
648 | return {"error": f"Database error: {e}"}
649 |
650 | def delete_backtest(self, backtest_id: str) -> bool:
651 | """
652 | Delete a backtest and all associated data.
653 |
654 | Args:
655 | backtest_id: UUID string of backtest to delete
656 |
657 | Returns:
658 | True if deleted successfully, False otherwise
659 | """
660 | try:
661 | # Convert string UUID to UUID object
662 | if isinstance(backtest_id, str):
663 | backtest_uuid = UUID(backtest_id)
664 | else:
665 | backtest_uuid = backtest_id
666 |
667 | backtest = (
668 | self.session.query(BacktestResult)
669 | .filter(BacktestResult.backtest_id == backtest_uuid)
670 | .first()
671 | )
672 |
673 | if not backtest:
674 | logger.warning(f"Backtest {backtest_id} not found")
675 | return False
676 |
677 | # Delete associated records (cascading should handle this)
678 | self.session.delete(backtest)
679 | self.session.commit()
680 |
681 | logger.info(f"Deleted backtest {backtest_id}")
682 | return True
683 |
684 | except SQLAlchemyError as e:
685 | self.session.rollback()
686 | logger.error(f"Error deleting backtest {backtest_id}: {e}")
687 | return False
688 |
689 | @staticmethod
690 | def _safe_decimal(value: Any) -> Decimal | None:
691 | """Safely convert value to Decimal, handling None and invalid values."""
692 | if value is None:
693 | return None
694 | try:
695 | if isinstance(value, int | float):
696 | return Decimal(str(value))
697 | elif isinstance(value, Decimal):
698 | return value
699 | else:
700 | return Decimal(str(float(value)))
701 | except (ValueError, TypeError, InvalidOperation):
702 | return None
703 |
704 |
705 | def get_persistence_manager(
706 | session: Session | None = None,
707 | ) -> BacktestPersistenceManager:
708 | """
709 | Factory function to create a persistence manager.
710 |
711 | Args:
712 | session: Optional SQLAlchemy session
713 |
714 | Returns:
715 | BacktestPersistenceManager instance
716 | """
717 | return BacktestPersistenceManager(session)
718 |
719 |
720 | # Convenience functions for common operations
721 |
722 |
723 | def save_vectorbt_results(
724 | vectorbt_results: dict[str, Any],
725 | execution_time: float | None = None,
726 | notes: str | None = None,
727 | ) -> str:
728 | """
729 | Convenience function to save VectorBT results.
730 |
731 | Args:
732 | vectorbt_results: Results from VectorBTEngine
733 | execution_time: Execution time in seconds
734 | notes: Optional notes
735 |
736 | Returns:
737 | Backtest UUID string
738 | """
739 | with get_persistence_manager() as manager:
740 | return manager.save_backtest_result(vectorbt_results, execution_time, notes)
741 |
742 |
743 | def get_recent_backtests(symbol: str, days: int = 7) -> list[BacktestResult]:
744 | """
745 | Get recent backtests for a symbol.
746 |
747 | Args:
748 | symbol: Stock symbol
749 | days: Number of days to look back
750 |
751 | Returns:
752 | List of recent BacktestResult objects
753 | """
754 | with get_persistence_manager() as manager:
755 | cutoff_date = datetime.utcnow() - timedelta(days=days)
756 | return (
757 | manager.session.query(BacktestResult)
758 | .filter(
759 | BacktestResult.symbol == symbol.upper(),
760 | BacktestResult.backtest_date >= cutoff_date,
761 | )
762 | .order_by(desc(BacktestResult.backtest_date))
763 | .all()
764 | )
765 |
766 |
767 | def find_best_strategy_for_symbol(
768 | symbol: str, metric: str = "sharpe_ratio"
769 | ) -> BacktestResult | None:
770 | """
771 | Find the best performing strategy for a symbol.
772 |
773 | Args:
774 | symbol: Stock symbol
775 | metric: Performance metric to optimize
776 |
777 | Returns:
778 | Best BacktestResult or None
779 | """
780 | with get_persistence_manager() as manager:
781 | return (
782 | manager.get_best_performing_strategies(metric=metric, limit=1)[0]
783 | if manager.get_backtests_by_symbol(symbol, limit=1000)
784 | else None
785 | )
786 |
```
--------------------------------------------------------------------------------
/tests/performance/test_profiling.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | Profiling Tests for Bottleneck Identification.
3 |
4 | This test suite covers:
5 | - Profile critical code paths with cProfile
6 | - Identify slow database queries with timing
7 | - Find memory allocation hotspots
8 | - Document optimization opportunities
9 | - Line-by-line profiling of key functions
10 | - Call graph analysis for performance
11 | - I/O bottleneck identification
12 | - CPU-bound vs I/O-bound analysis
13 | """
14 |
15 | import cProfile
16 | import io
17 | import logging
18 | import pstats
19 | import time
20 | import tracemalloc
21 | from collections.abc import Callable
22 | from contextlib import contextmanager
23 | from typing import Any
24 | from unittest.mock import Mock
25 |
26 | import numpy as np
27 | import pandas as pd
28 | import pytest
29 |
30 | from maverick_mcp.backtesting import VectorBTEngine
31 | from maverick_mcp.backtesting.persistence import BacktestPersistenceManager
32 | from maverick_mcp.backtesting.strategies import STRATEGY_TEMPLATES
33 |
34 | logger = logging.getLogger(__name__)
35 |
36 |
37 | class PerformanceProfiler:
38 | """Comprehensive performance profiler for backtesting operations."""
39 |
40 | def __init__(self):
41 | self.profiling_data = {}
42 | self.memory_snapshots = []
43 |
44 | @contextmanager
45 | def profile_cpu(self, operation_name: str):
46 | """Profile CPU usage of an operation."""
47 | profiler = cProfile.Profile()
48 | start_time = time.time()
49 |
50 | profiler.enable()
51 | try:
52 | yield
53 | finally:
54 | profiler.disable()
55 | execution_time = time.time() - start_time
56 |
57 | # Capture profiling stats
58 | stats_stream = io.StringIO()
59 | stats = pstats.Stats(profiler, stream=stats_stream)
60 | stats.sort_stats("cumulative")
61 | stats.print_stats(20) # Top 20 functions
62 |
63 | self.profiling_data[operation_name] = {
64 | "execution_time": execution_time,
65 | "cpu_profile": stats_stream.getvalue(),
66 | "stats_object": stats,
67 | }
68 |
69 | @contextmanager
70 | def profile_memory(self, operation_name: str):
71 | """Profile memory usage of an operation."""
72 | tracemalloc.start()
73 | start_memory = tracemalloc.get_traced_memory()
74 |
75 | try:
76 | yield
77 | finally:
78 | current_memory, peak_memory = tracemalloc.get_traced_memory()
79 | tracemalloc.stop()
80 |
81 | memory_data = {
82 | "start_memory_mb": start_memory[0] / 1024 / 1024,
83 | "current_memory_mb": current_memory / 1024 / 1024,
84 | "peak_memory_mb": peak_memory / 1024 / 1024,
85 | "memory_growth_mb": (current_memory - start_memory[0]) / 1024 / 1024,
86 | }
87 |
88 | if operation_name in self.profiling_data:
89 | self.profiling_data[operation_name]["memory_profile"] = memory_data
90 | else:
91 | self.profiling_data[operation_name] = {"memory_profile": memory_data}
92 |
93 | def profile_database_query(
94 | self, query_name: str, query_func: Callable
95 | ) -> dict[str, Any]:
96 | """Profile database query performance."""
97 | start_time = time.time()
98 |
99 | try:
100 | result = query_func()
101 | execution_time = time.time() - start_time
102 |
103 | return {
104 | "query_name": query_name,
105 | "execution_time_ms": execution_time * 1000,
106 | "success": True,
107 | "result_size": len(str(result)) if result else 0,
108 | }
109 | except Exception as e:
110 | execution_time = time.time() - start_time
111 | return {
112 | "query_name": query_name,
113 | "execution_time_ms": execution_time * 1000,
114 | "success": False,
115 | "error": str(e),
116 | }
117 |
118 | def analyze_hotspots(self, operation_name: str) -> dict[str, Any]:
119 | """Analyze performance hotspots from profiling data."""
120 | if operation_name not in self.profiling_data:
121 | return {"error": f"No profiling data for {operation_name}"}
122 |
123 | data = self.profiling_data[operation_name]
124 | stats = data.get("stats_object")
125 |
126 | if not stats:
127 | return {"error": "No CPU profiling stats available"}
128 |
129 | # Extract top functions by cumulative time
130 | stats.sort_stats("cumulative")
131 | top_functions = []
132 |
133 | for func_data in list(stats.stats.items())[:10]:
134 | func_name, (cc, nc, tt, ct, callers) = func_data
135 | top_functions.append(
136 | {
137 | "function": f"{func_name[0]}:{func_name[1]}({func_name[2]})",
138 | "cumulative_time": ct,
139 | "total_time": tt,
140 | "call_count": nc,
141 | "time_per_call": ct / nc if nc > 0 else 0,
142 | }
143 | )
144 |
145 | # Extract top functions by self time
146 | stats.sort_stats("tottime")
147 | self_time_functions = []
148 |
149 | for func_data in list(stats.stats.items())[:10]:
150 | func_name, (cc, nc, tt, ct, callers) = func_data
151 | self_time_functions.append(
152 | {
153 | "function": f"{func_name[0]}:{func_name[1]}({func_name[2]})",
154 | "self_time": tt,
155 | "cumulative_time": ct,
156 | "call_count": nc,
157 | }
158 | )
159 |
160 | return {
161 | "operation_name": operation_name,
162 | "total_execution_time": data.get("execution_time", 0),
163 | "top_functions_by_cumulative": top_functions,
164 | "top_functions_by_self_time": self_time_functions,
165 | "memory_profile": data.get("memory_profile", {}),
166 | }
167 |
168 | def generate_optimization_report(self) -> dict[str, Any]:
169 | """Generate comprehensive optimization report."""
170 | optimization_opportunities = []
171 | performance_summary = {}
172 |
173 | for operation_name, data in self.profiling_data.items():
174 | analysis = self.analyze_hotspots(operation_name)
175 |
176 | performance_summary[operation_name] = {
177 | "execution_time": data.get("execution_time", 0),
178 | "peak_memory_mb": data.get("memory_profile", {}).get(
179 | "peak_memory_mb", 0
180 | ),
181 | }
182 |
183 | # Identify optimization opportunities
184 | if "top_functions_by_cumulative" in analysis:
185 | for func in analysis["top_functions_by_cumulative"][
186 | :3
187 | ]: # Top 3 functions
188 | if func["cumulative_time"] > 0.1: # More than 100ms
189 | optimization_opportunities.append(
190 | {
191 | "operation": operation_name,
192 | "function": func["function"],
193 | "issue": "High cumulative time",
194 | "time": func["cumulative_time"],
195 | "priority": "High"
196 | if func["cumulative_time"] > 1.0
197 | else "Medium",
198 | }
199 | )
200 |
201 | # Memory optimization opportunities
202 | memory_profile = data.get("memory_profile", {})
203 | if memory_profile.get("peak_memory_mb", 0) > 100: # More than 100MB
204 | optimization_opportunities.append(
205 | {
206 | "operation": operation_name,
207 | "issue": "High memory usage",
208 | "memory_mb": memory_profile["peak_memory_mb"],
209 | "priority": "High"
210 | if memory_profile["peak_memory_mb"] > 500
211 | else "Medium",
212 | }
213 | )
214 |
215 | return {
216 | "performance_summary": performance_summary,
217 | "optimization_opportunities": optimization_opportunities,
218 | "total_operations_profiled": len(self.profiling_data),
219 | }
220 |
221 |
222 | class TestPerformanceProfiling:
223 | """Performance profiling test suite."""
224 |
225 | @pytest.fixture
226 | async def profiling_data_provider(self):
227 | """Create data provider for profiling tests."""
228 | provider = Mock()
229 |
230 | def generate_profiling_data(symbol: str) -> pd.DataFrame:
231 | """Generate data with known performance characteristics."""
232 | # Generate larger dataset to create measurable performance impact
233 | dates = pd.date_range(
234 | start="2020-01-01", end="2023-12-31", freq="D"
235 | ) # 4 years
236 | np.random.seed(hash(symbol) % 1000)
237 |
238 | returns = np.random.normal(0.0008, 0.02, len(dates))
239 | prices = 100 * np.cumprod(1 + returns)
240 |
241 | # Add some computationally expensive operations
242 | high_prices = prices * np.random.uniform(1.01, 1.05, len(dates))
243 | low_prices = prices * np.random.uniform(0.95, 0.99, len(dates))
244 |
245 | # Simulate expensive volume calculations
246 | base_volume = np.random.randint(1000000, 10000000, len(dates))
247 | volume_multiplier = np.exp(
248 | np.random.normal(0, 0.1, len(dates))
249 | ) # Log-normal distribution
250 | volumes = (base_volume * volume_multiplier).astype(int)
251 |
252 | return pd.DataFrame(
253 | {
254 | "Open": prices * np.random.uniform(0.995, 1.005, len(dates)),
255 | "High": high_prices,
256 | "Low": low_prices,
257 | "Close": prices,
258 | "Volume": volumes,
259 | "Adj Close": prices,
260 | },
261 | index=dates,
262 | )
263 |
264 | provider.get_stock_data.side_effect = generate_profiling_data
265 | return provider
266 |
267 | async def test_profile_backtest_execution(self, profiling_data_provider):
268 | """Profile complete backtest execution to identify bottlenecks."""
269 | profiler = PerformanceProfiler()
270 | engine = VectorBTEngine(data_provider=profiling_data_provider)
271 |
272 | strategies_to_profile = ["sma_cross", "rsi", "macd", "bollinger"]
273 |
274 | for strategy in strategies_to_profile:
275 | with profiler.profile_cpu(f"backtest_{strategy}"):
276 | with profiler.profile_memory(f"backtest_{strategy}"):
277 | await engine.run_backtest(
278 | symbol="PROFILE_TEST",
279 | strategy_type=strategy,
280 | parameters=STRATEGY_TEMPLATES[strategy]["parameters"],
281 | start_date="2022-01-01",
282 | end_date="2023-12-31",
283 | )
284 |
285 | # Analyze profiling results
286 | report = profiler.generate_optimization_report()
287 |
288 | # Log performance analysis
289 | logger.info("Backtest Execution Profiling Results:")
290 | for operation, summary in report["performance_summary"].items():
291 | logger.info(
292 | f" {operation}: {summary['execution_time']:.3f}s, "
293 | f"{summary['peak_memory_mb']:.1f}MB peak"
294 | )
295 |
296 | # Log optimization opportunities
297 | if report["optimization_opportunities"]:
298 | logger.info("Optimization Opportunities:")
299 | for opportunity in report["optimization_opportunities"]:
300 | priority_symbol = "🔴" if opportunity["priority"] == "High" else "🟡"
301 | logger.info(
302 | f" {priority_symbol} {opportunity['operation']}: {opportunity['issue']}"
303 | )
304 |
305 | # Performance assertions
306 | max_execution_time = max(
307 | summary["execution_time"]
308 | for summary in report["performance_summary"].values()
309 | )
310 | assert max_execution_time <= 5.0, (
311 | f"Slowest backtest took too long: {max_execution_time:.2f}s"
312 | )
313 |
314 | high_priority_issues = [
315 | opp
316 | for opp in report["optimization_opportunities"]
317 | if opp["priority"] == "High"
318 | ]
319 | assert len(high_priority_issues) <= 2, (
320 | f"Too many high-priority performance issues: {len(high_priority_issues)}"
321 | )
322 |
323 | return report
324 |
325 | async def test_profile_data_loading_bottlenecks(self, profiling_data_provider):
326 | """Profile data loading operations to identify I/O bottlenecks."""
327 | profiler = PerformanceProfiler()
328 | engine = VectorBTEngine(data_provider=profiling_data_provider)
329 |
330 | symbols = ["DATA_1", "DATA_2", "DATA_3", "DATA_4", "DATA_5"]
331 |
332 | # Profile data loading operations
333 | for symbol in symbols:
334 | with profiler.profile_cpu(f"data_loading_{symbol}"):
335 | with profiler.profile_memory(f"data_loading_{symbol}"):
336 | # Profile the data fetching specifically
337 | await engine.get_historical_data(
338 | symbol=symbol, start_date="2020-01-01", end_date="2023-12-31"
339 | )
340 |
341 | # Analyze data loading performance
342 | data_loading_times = []
343 | data_loading_memory = []
344 |
345 | for symbol in symbols:
346 | operation_name = f"data_loading_{symbol}"
347 | if operation_name in profiler.profiling_data:
348 | data_loading_times.append(
349 | profiler.profiling_data[operation_name]["execution_time"]
350 | )
351 | memory_profile = profiler.profiling_data[operation_name].get(
352 | "memory_profile", {}
353 | )
354 | data_loading_memory.append(memory_profile.get("peak_memory_mb", 0))
355 |
356 | avg_loading_time = np.mean(data_loading_times) if data_loading_times else 0
357 | max_loading_time = max(data_loading_times) if data_loading_times else 0
358 | avg_loading_memory = np.mean(data_loading_memory) if data_loading_memory else 0
359 |
360 | logger.info("Data Loading Performance Analysis:")
361 | logger.info(f" Average Loading Time: {avg_loading_time:.3f}s")
362 | logger.info(f" Maximum Loading Time: {max_loading_time:.3f}s")
363 | logger.info(f" Average Memory Usage: {avg_loading_memory:.1f}MB")
364 |
365 | # Performance assertions for data loading
366 | assert avg_loading_time <= 0.5, (
367 | f"Average data loading too slow: {avg_loading_time:.3f}s"
368 | )
369 | assert max_loading_time <= 1.0, (
370 | f"Slowest data loading too slow: {max_loading_time:.3f}s"
371 | )
372 | assert avg_loading_memory <= 50.0, (
373 | f"Data loading memory usage too high: {avg_loading_memory:.1f}MB"
374 | )
375 |
376 | return {
377 | "avg_loading_time": avg_loading_time,
378 | "max_loading_time": max_loading_time,
379 | "avg_loading_memory": avg_loading_memory,
380 | "individual_times": data_loading_times,
381 | }
382 |
383 | async def test_profile_database_query_performance(
384 | self, profiling_data_provider, db_session
385 | ):
386 | """Profile database queries to identify slow operations."""
387 | profiler = PerformanceProfiler()
388 | engine = VectorBTEngine(data_provider=profiling_data_provider)
389 |
390 | # Generate test data for database profiling
391 | test_results = []
392 | for i in range(10):
393 | result = await engine.run_backtest(
394 | symbol=f"DB_PROFILE_{i}",
395 | strategy_type="sma_cross",
396 | parameters=STRATEGY_TEMPLATES["sma_cross"]["parameters"],
397 | start_date="2023-01-01",
398 | end_date="2023-12-31",
399 | )
400 | test_results.append(result)
401 |
402 | # Profile database operations
403 | query_profiles = []
404 |
405 | with BacktestPersistenceManager(session=db_session) as persistence:
406 | # Profile save operations
407 | for i, result in enumerate(test_results):
408 | query_profile = profiler.profile_database_query(
409 | f"save_backtest_{i}",
410 | lambda r=result: persistence.save_backtest_result(
411 | vectorbt_results=r,
412 | execution_time=2.0,
413 | notes="Database profiling test",
414 | ),
415 | )
416 | query_profiles.append(query_profile)
417 |
418 | # Get saved IDs for retrieval profiling
419 | saved_ids = [qp.get("result") for qp in query_profiles if qp.get("success")]
420 |
421 | # Profile retrieval operations
422 | for i, backtest_id in enumerate(
423 | saved_ids[:5]
424 | ): # Profile first 5 retrievals
425 | query_profile = profiler.profile_database_query(
426 | f"retrieve_backtest_{i}",
427 | lambda bid=backtest_id: persistence.get_backtest_by_id(bid),
428 | )
429 | query_profiles.append(query_profile)
430 |
431 | # Profile bulk query operations
432 | bulk_query_profile = profiler.profile_database_query(
433 | "bulk_query_by_strategy",
434 | lambda: persistence.get_backtests_by_strategy("sma_cross"),
435 | )
436 | query_profiles.append(bulk_query_profile)
437 |
438 | # Analyze database query performance
439 | save_times = [
440 | qp["execution_time_ms"]
441 | for qp in query_profiles
442 | if "save_backtest" in qp["query_name"] and qp["success"]
443 | ]
444 | retrieve_times = [
445 | qp["execution_time_ms"]
446 | for qp in query_profiles
447 | if "retrieve_backtest" in qp["query_name"] and qp["success"]
448 | ]
449 |
450 | avg_save_time = np.mean(save_times) if save_times else 0
451 | avg_retrieve_time = np.mean(retrieve_times) if retrieve_times else 0
452 | bulk_query_time = (
453 | bulk_query_profile["execution_time_ms"]
454 | if bulk_query_profile["success"]
455 | else 0
456 | )
457 |
458 | logger.info("Database Query Performance Analysis:")
459 | logger.info(f" Average Save Time: {avg_save_time:.1f}ms")
460 | logger.info(f" Average Retrieve Time: {avg_retrieve_time:.1f}ms")
461 | logger.info(f" Bulk Query Time: {bulk_query_time:.1f}ms")
462 |
463 | # Identify slow queries
464 | slow_queries = [
465 | qp
466 | for qp in query_profiles
467 | if qp["execution_time_ms"] > 100 and qp["success"]
468 | ]
469 | logger.info(f" Slow Queries (>100ms): {len(slow_queries)}")
470 |
471 | # Performance assertions for database queries
472 | assert avg_save_time <= 50.0, (
473 | f"Average save time too slow: {avg_save_time:.1f}ms"
474 | )
475 | assert avg_retrieve_time <= 20.0, (
476 | f"Average retrieve time too slow: {avg_retrieve_time:.1f}ms"
477 | )
478 | assert bulk_query_time <= 100.0, f"Bulk query too slow: {bulk_query_time:.1f}ms"
479 | assert len(slow_queries) <= 2, f"Too many slow queries: {len(slow_queries)}"
480 |
481 | return {
482 | "avg_save_time": avg_save_time,
483 | "avg_retrieve_time": avg_retrieve_time,
484 | "bulk_query_time": bulk_query_time,
485 | "slow_queries": len(slow_queries),
486 | "query_profiles": query_profiles,
487 | }
488 |
489 | async def test_profile_memory_allocation_patterns(self, profiling_data_provider):
490 | """Profile memory allocation patterns to identify hotspots."""
491 | profiler = PerformanceProfiler()
492 | engine = VectorBTEngine(data_provider=profiling_data_provider)
493 |
494 | # Test different memory usage patterns
495 | memory_test_cases = [
496 | ("small_dataset", "2023-06-01", "2023-12-31"),
497 | ("medium_dataset", "2022-01-01", "2023-12-31"),
498 | ("large_dataset", "2020-01-01", "2023-12-31"),
499 | ]
500 |
501 | memory_profiles = []
502 |
503 | for case_name, start_date, end_date in memory_test_cases:
504 | with profiler.profile_memory(f"memory_{case_name}"):
505 | await engine.run_backtest(
506 | symbol="MEMORY_TEST",
507 | strategy_type="macd",
508 | parameters=STRATEGY_TEMPLATES["macd"]["parameters"],
509 | start_date=start_date,
510 | end_date=end_date,
511 | )
512 |
513 | memory_data = profiler.profiling_data[f"memory_{case_name}"][
514 | "memory_profile"
515 | ]
516 | memory_profiles.append(
517 | {
518 | "case": case_name,
519 | "peak_memory_mb": memory_data["peak_memory_mb"],
520 | "memory_growth_mb": memory_data["memory_growth_mb"],
521 | "data_points": len(
522 | pd.date_range(start=start_date, end=end_date, freq="D")
523 | ),
524 | }
525 | )
526 |
527 | # Analyze memory scaling
528 | data_points = [mp["data_points"] for mp in memory_profiles]
529 | peak_memories = [mp["peak_memory_mb"] for mp in memory_profiles]
530 |
531 | # Calculate memory efficiency (MB per 1000 data points)
532 | memory_efficiency = [
533 | (peak_mem / data_pts * 1000)
534 | for peak_mem, data_pts in zip(peak_memories, data_points, strict=False)
535 | ]
536 |
537 | avg_memory_efficiency = np.mean(memory_efficiency)
538 |
539 | logger.info("Memory Allocation Pattern Analysis:")
540 | for profile in memory_profiles:
541 | efficiency = profile["peak_memory_mb"] / profile["data_points"] * 1000
542 | logger.info(
543 | f" {profile['case']}: {profile['peak_memory_mb']:.1f}MB peak "
544 | f"({efficiency:.2f} MB/1k points)"
545 | )
546 |
547 | logger.info(
548 | f" Average Memory Efficiency: {avg_memory_efficiency:.2f} MB per 1000 data points"
549 | )
550 |
551 | # Memory efficiency assertions
552 | assert avg_memory_efficiency <= 5.0, (
553 | f"Memory efficiency too poor: {avg_memory_efficiency:.2f} MB/1k points"
554 | )
555 | assert max(peak_memories) <= 200.0, (
556 | f"Peak memory usage too high: {max(peak_memories):.1f}MB"
557 | )
558 |
559 | return {
560 | "memory_profiles": memory_profiles,
561 | "avg_memory_efficiency": avg_memory_efficiency,
562 | "peak_memory_usage": max(peak_memories),
563 | }
564 |
565 | async def test_profile_cpu_vs_io_bound_operations(self, profiling_data_provider):
566 | """Profile CPU-bound vs I/O-bound operations to optimize resource usage."""
567 | profiler = PerformanceProfiler()
568 | engine = VectorBTEngine(data_provider=profiling_data_provider)
569 |
570 | # Profile CPU-intensive strategy
571 | with profiler.profile_cpu("cpu_intensive_strategy"):
572 | await engine.run_backtest(
573 | symbol="CPU_TEST",
574 | strategy_type="bollinger", # More calculations
575 | parameters=STRATEGY_TEMPLATES["bollinger"]["parameters"],
576 | start_date="2022-01-01",
577 | end_date="2023-12-31",
578 | )
579 |
580 | # Profile I/O-intensive operations (multiple data fetches)
581 | with profiler.profile_cpu("io_intensive_operations"):
582 | io_symbols = ["IO_1", "IO_2", "IO_3", "IO_4", "IO_5"]
583 | io_results = []
584 |
585 | for symbol in io_symbols:
586 | result = await engine.run_backtest(
587 | symbol=symbol,
588 | strategy_type="sma_cross", # Simpler calculations
589 | parameters=STRATEGY_TEMPLATES["sma_cross"]["parameters"],
590 | start_date="2023-06-01",
591 | end_date="2023-12-31",
592 | )
593 | io_results.append(result)
594 |
595 | # Analyze CPU vs I/O characteristics
596 | cpu_analysis = profiler.analyze_hotspots("cpu_intensive_strategy")
597 | io_analysis = profiler.analyze_hotspots("io_intensive_operations")
598 |
599 | cpu_time = cpu_analysis.get("total_execution_time", 0)
600 | io_time = io_analysis.get("total_execution_time", 0)
601 |
602 | # Analyze function call patterns
603 | cpu_top_functions = cpu_analysis.get("top_functions_by_cumulative", [])
604 | io_top_functions = io_analysis.get("top_functions_by_cumulative", [])
605 |
606 | # Calculate I/O vs CPU characteristics
607 | cpu_bound_ratio = (
608 | cpu_time / (cpu_time + io_time) if (cpu_time + io_time) > 0 else 0
609 | )
610 |
611 | logger.info("CPU vs I/O Bound Analysis:")
612 | logger.info(f" CPU-Intensive Operation: {cpu_time:.3f}s")
613 | logger.info(f" I/O-Intensive Operations: {io_time:.3f}s")
614 | logger.info(f" CPU-Bound Ratio: {cpu_bound_ratio:.2%}")
615 |
616 | logger.info(" Top CPU-Intensive Functions:")
617 | for func in cpu_top_functions[:3]:
618 | logger.info(f" {func['function']}: {func['cumulative_time']:.3f}s")
619 |
620 | logger.info(" Top I/O-Intensive Functions:")
621 | for func in io_top_functions[:3]:
622 | logger.info(f" {func['function']}: {func['cumulative_time']:.3f}s")
623 |
624 | # Performance balance assertions
625 | assert cpu_time <= 3.0, f"CPU-intensive operation too slow: {cpu_time:.3f}s"
626 | assert io_time <= 5.0, f"I/O-intensive operations too slow: {io_time:.3f}s"
627 |
628 | return {
629 | "cpu_time": cpu_time,
630 | "io_time": io_time,
631 | "cpu_bound_ratio": cpu_bound_ratio,
632 | "cpu_top_functions": cpu_top_functions[:5],
633 | "io_top_functions": io_top_functions[:5],
634 | }
635 |
636 | async def test_comprehensive_profiling_suite(
637 | self, profiling_data_provider, db_session
638 | ):
639 | """Run comprehensive profiling suite and generate optimization report."""
640 | logger.info("Starting Comprehensive Performance Profiling Suite...")
641 |
642 | profiling_results = {}
643 |
644 | # Run all profiling tests
645 | profiling_results[
646 | "backtest_execution"
647 | ] = await self.test_profile_backtest_execution(profiling_data_provider)
648 | profiling_results[
649 | "data_loading"
650 | ] = await self.test_profile_data_loading_bottlenecks(profiling_data_provider)
651 | profiling_results[
652 | "database_queries"
653 | ] = await self.test_profile_database_query_performance(
654 | profiling_data_provider, db_session
655 | )
656 | profiling_results[
657 | "memory_allocation"
658 | ] = await self.test_profile_memory_allocation_patterns(profiling_data_provider)
659 | profiling_results[
660 | "cpu_vs_io"
661 | ] = await self.test_profile_cpu_vs_io_bound_operations(profiling_data_provider)
662 |
663 | # Generate comprehensive optimization report
664 | optimization_report = {
665 | "executive_summary": {
666 | "profiling_areas": len(profiling_results),
667 | "performance_bottlenecks": [],
668 | "optimization_priorities": [],
669 | },
670 | "detailed_analysis": profiling_results,
671 | }
672 |
673 | # Identify key bottlenecks and priorities
674 | bottlenecks = []
675 | priorities = []
676 |
677 | # Analyze backtest execution performance
678 | backtest_report = profiling_results["backtest_execution"]
679 | high_priority_issues = [
680 | opp
681 | for opp in backtest_report.get("optimization_opportunities", [])
682 | if opp["priority"] == "High"
683 | ]
684 | if high_priority_issues:
685 | bottlenecks.append("High-priority performance issues in backtest execution")
686 | priorities.append("Optimize hot functions in strategy calculations")
687 |
688 | # Analyze data loading performance
689 | data_loading = profiling_results["data_loading"]
690 | if data_loading["max_loading_time"] > 0.8:
691 | bottlenecks.append("Slow data loading operations")
692 | priorities.append("Implement data caching or optimize data provider")
693 |
694 | # Analyze database performance
695 | db_performance = profiling_results["database_queries"]
696 | if db_performance["slow_queries"] > 1:
697 | bottlenecks.append("Multiple slow database queries detected")
698 | priorities.append("Add database indexes or optimize query patterns")
699 |
700 | # Analyze memory efficiency
701 | memory_analysis = profiling_results["memory_allocation"]
702 | if memory_analysis["avg_memory_efficiency"] > 3.0:
703 | bottlenecks.append("High memory usage per data point")
704 | priorities.append("Optimize memory allocation patterns")
705 |
706 | optimization_report["executive_summary"]["performance_bottlenecks"] = (
707 | bottlenecks
708 | )
709 | optimization_report["executive_summary"]["optimization_priorities"] = priorities
710 |
711 | # Log comprehensive report
712 | logger.info(
713 | f"\n{'=' * 60}\n"
714 | f"COMPREHENSIVE PROFILING REPORT\n"
715 | f"{'=' * 60}\n"
716 | f"Profiling Areas Analyzed: {len(profiling_results)}\n"
717 | f"Performance Bottlenecks: {len(bottlenecks)}\n"
718 | f"{'=' * 60}\n"
719 | )
720 |
721 | if bottlenecks:
722 | logger.info("🔍 PERFORMANCE BOTTLENECKS IDENTIFIED:")
723 | for i, bottleneck in enumerate(bottlenecks, 1):
724 | logger.info(f" {i}. {bottleneck}")
725 |
726 | if priorities:
727 | logger.info("\n🎯 OPTIMIZATION PRIORITIES:")
728 | for i, priority in enumerate(priorities, 1):
729 | logger.info(f" {i}. {priority}")
730 |
731 | logger.info(f"\n{'=' * 60}")
732 |
733 | # Assert profiling success
734 | assert len(bottlenecks) <= 3, (
735 | f"Too many performance bottlenecks identified: {len(bottlenecks)}"
736 | )
737 |
738 | return optimization_report
739 |
740 |
741 | if __name__ == "__main__":
742 | # Run profiling tests
743 | pytest.main(
744 | [
745 | __file__,
746 | "-v",
747 | "--tb=short",
748 | "--asyncio-mode=auto",
749 | "--timeout=300", # 5 minute timeout for profiling tests
750 | ]
751 | )
752 |
```
--------------------------------------------------------------------------------
/tests/performance/test_benchmarks.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | Performance Benchmarks Against Target Metrics.
3 |
4 | This test suite covers:
5 | - Backtest execution < 2 seconds per backtest
6 | - Memory usage < 500MB per backtest
7 | - Cache hit rate > 80%
8 | - API failure rate < 0.1%
9 | - Database query performance < 100ms
10 | - Throughput targets (requests per second)
11 | - Response time SLA compliance
12 | - Resource utilization efficiency
13 | """
14 |
15 | import asyncio
16 | import gc
17 | import logging
18 | import os
19 | import statistics
20 | import time
21 | from dataclasses import dataclass
22 | from typing import Any
23 | from unittest.mock import Mock, patch
24 |
25 | import numpy as np
26 | import pandas as pd
27 | import psutil
28 | import pytest
29 |
30 | from maverick_mcp.backtesting import VectorBTEngine
31 | from maverick_mcp.backtesting.persistence import BacktestPersistenceManager
32 | from maverick_mcp.backtesting.strategies import STRATEGY_TEMPLATES
33 |
34 | logger = logging.getLogger(__name__)
35 |
36 |
37 | @dataclass
38 | class BenchmarkResult:
39 | """Data class for benchmark test results."""
40 |
41 | test_name: str
42 | target_value: float
43 | actual_value: float
44 | unit: str
45 | passed: bool
46 | margin: float
47 | details: dict[str, Any]
48 |
49 |
50 | class BenchmarkTracker:
51 | """Track and validate performance benchmarks."""
52 |
53 | def __init__(self):
54 | self.results = []
55 | self.process = psutil.Process(os.getpid())
56 |
57 | def add_benchmark(
58 | self,
59 | test_name: str,
60 | target_value: float,
61 | actual_value: float,
62 | unit: str,
63 | comparison: str = "<=",
64 | details: dict[str, Any] | None = None,
65 | ) -> BenchmarkResult:
66 | """Add a benchmark result."""
67 | if comparison == "<=":
68 | passed = actual_value <= target_value
69 | margin = (
70 | (actual_value - target_value) / target_value if target_value > 0 else 0
71 | )
72 | elif comparison == ">=":
73 | passed = actual_value >= target_value
74 | margin = (
75 | (target_value - actual_value) / target_value if target_value > 0 else 0
76 | )
77 | else:
78 | raise ValueError(f"Unsupported comparison: {comparison}")
79 |
80 | result = BenchmarkResult(
81 | test_name=test_name,
82 | target_value=target_value,
83 | actual_value=actual_value,
84 | unit=unit,
85 | passed=passed,
86 | margin=margin,
87 | details=details or {},
88 | )
89 |
90 | self.results.append(result)
91 |
92 | status = "✓ PASS" if passed else "✗ FAIL"
93 | logger.info(
94 | f"{status} {test_name}: {actual_value:.3f}{unit} (target: {target_value}{unit})"
95 | )
96 |
97 | return result
98 |
99 | def get_memory_usage(self) -> float:
100 | """Get current memory usage in MB."""
101 | return self.process.memory_info().rss / 1024 / 1024
102 |
103 | def get_cpu_usage(self) -> float:
104 | """Get current CPU usage percentage."""
105 | return self.process.cpu_percent()
106 |
107 | def summary(self) -> dict[str, Any]:
108 | """Generate benchmark summary."""
109 | total_tests = len(self.results)
110 | passed_tests = sum(1 for r in self.results if r.passed)
111 | failed_tests = total_tests - passed_tests
112 |
113 | return {
114 | "total_tests": total_tests,
115 | "passed_tests": passed_tests,
116 | "failed_tests": failed_tests,
117 | "pass_rate": passed_tests / total_tests if total_tests > 0 else 0,
118 | "results": self.results,
119 | }
120 |
121 |
122 | class TestPerformanceBenchmarks:
123 | """Performance benchmarks against target metrics."""
124 |
125 | @pytest.fixture
126 | async def benchmark_data_provider(self):
127 | """Create optimized data provider for benchmarks."""
128 | provider = Mock()
129 |
130 | def generate_benchmark_data(symbol: str) -> pd.DataFrame:
131 | """Generate optimized data for benchmarking."""
132 | # Use symbol hash for deterministic but varied data
133 | seed = hash(symbol) % 1000
134 | np.random.seed(seed)
135 |
136 | # Generate 1 year of data
137 | dates = pd.date_range(start="2023-01-01", end="2023-12-31", freq="D")
138 | returns = np.random.normal(0.0008, 0.02, len(dates))
139 | prices = 100 * np.cumprod(1 + returns)
140 |
141 | return pd.DataFrame(
142 | {
143 | "Open": prices * np.random.uniform(0.995, 1.005, len(dates)),
144 | "High": prices * np.random.uniform(1.005, 1.025, len(dates)),
145 | "Low": prices * np.random.uniform(0.975, 0.995, len(dates)),
146 | "Close": prices,
147 | "Volume": np.random.randint(1000000, 5000000, len(dates)),
148 | "Adj Close": prices,
149 | },
150 | index=dates,
151 | )
152 |
153 | provider.get_stock_data.side_effect = generate_benchmark_data
154 | return provider
155 |
156 | async def test_backtest_execution_time_benchmark(self, benchmark_data_provider):
157 | """Test: Backtest execution < 2 seconds per backtest."""
158 | benchmark = BenchmarkTracker()
159 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
160 |
161 | test_cases = [
162 | ("AAPL", "sma_cross"),
163 | ("GOOGL", "rsi"),
164 | ("MSFT", "macd"),
165 | ("AMZN", "bollinger"),
166 | ("TSLA", "momentum"),
167 | ]
168 |
169 | execution_times = []
170 |
171 | for symbol, strategy in test_cases:
172 | parameters = STRATEGY_TEMPLATES[strategy]["parameters"]
173 |
174 | start_time = time.time()
175 | result = await engine.run_backtest(
176 | symbol=symbol,
177 | strategy_type=strategy,
178 | parameters=parameters,
179 | start_date="2023-01-01",
180 | end_date="2023-12-31",
181 | )
182 | execution_time = time.time() - start_time
183 |
184 | execution_times.append(execution_time)
185 |
186 | # Individual backtest benchmark
187 | benchmark.add_benchmark(
188 | test_name=f"backtest_time_{symbol}_{strategy}",
189 | target_value=2.0,
190 | actual_value=execution_time,
191 | unit="s",
192 | comparison="<=",
193 | details={
194 | "symbol": symbol,
195 | "strategy": strategy,
196 | "result_size": len(str(result)),
197 | },
198 | )
199 |
200 | # Overall benchmark
201 | avg_execution_time = statistics.mean(execution_times)
202 | max_execution_time = max(execution_times)
203 |
204 | benchmark.add_benchmark(
205 | test_name="avg_backtest_execution_time",
206 | target_value=2.0,
207 | actual_value=avg_execution_time,
208 | unit="s",
209 | comparison="<=",
210 | details={"individual_times": execution_times},
211 | )
212 |
213 | benchmark.add_benchmark(
214 | test_name="max_backtest_execution_time",
215 | target_value=3.0, # Allow some variance
216 | actual_value=max_execution_time,
217 | unit="s",
218 | comparison="<=",
219 | details={
220 | "slowest_case": test_cases[execution_times.index(max_execution_time)]
221 | },
222 | )
223 |
224 | logger.info(
225 | f"Backtest Execution Time Benchmark Summary:\n"
226 | f" • Average: {avg_execution_time:.3f}s\n"
227 | f" • Maximum: {max_execution_time:.3f}s\n"
228 | f" • Minimum: {min(execution_times):.3f}s\n"
229 | f" • Standard Deviation: {statistics.stdev(execution_times):.3f}s"
230 | )
231 |
232 | return benchmark.summary()
233 |
234 | async def test_memory_usage_benchmark(self, benchmark_data_provider):
235 | """Test: Memory usage < 500MB per backtest."""
236 | benchmark = BenchmarkTracker()
237 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
238 |
239 | initial_memory = benchmark.get_memory_usage()
240 | memory_measurements = []
241 |
242 | test_symbols = [
243 | "MEM_TEST_1",
244 | "MEM_TEST_2",
245 | "MEM_TEST_3",
246 | "MEM_TEST_4",
247 | "MEM_TEST_5",
248 | ]
249 |
250 | for _i, symbol in enumerate(test_symbols):
251 | gc.collect() # Force garbage collection before measurement
252 | pre_backtest_memory = benchmark.get_memory_usage()
253 |
254 | # Run backtest
255 | result = await engine.run_backtest(
256 | symbol=symbol,
257 | strategy_type="sma_cross",
258 | parameters=STRATEGY_TEMPLATES["sma_cross"]["parameters"],
259 | start_date="2023-01-01",
260 | end_date="2023-12-31",
261 | )
262 |
263 | post_backtest_memory = benchmark.get_memory_usage()
264 | memory_delta = post_backtest_memory - pre_backtest_memory
265 |
266 | memory_measurements.append(
267 | {
268 | "symbol": symbol,
269 | "pre_memory": pre_backtest_memory,
270 | "post_memory": post_backtest_memory,
271 | "delta": memory_delta,
272 | }
273 | )
274 |
275 | # Individual memory benchmark
276 | benchmark.add_benchmark(
277 | test_name=f"memory_usage_{symbol}",
278 | target_value=500.0,
279 | actual_value=memory_delta,
280 | unit="MB",
281 | comparison="<=",
282 | details={
283 | "pre_memory": pre_backtest_memory,
284 | "post_memory": post_backtest_memory,
285 | "result_size": len(str(result)),
286 | },
287 | )
288 |
289 | # Overall memory benchmarks
290 | total_memory_growth = benchmark.get_memory_usage() - initial_memory
291 | avg_memory_per_backtest = (
292 | total_memory_growth / len(test_symbols) if test_symbols else 0
293 | )
294 | max_memory_delta = max(m["delta"] for m in memory_measurements)
295 |
296 | benchmark.add_benchmark(
297 | test_name="avg_memory_per_backtest",
298 | target_value=500.0,
299 | actual_value=avg_memory_per_backtest,
300 | unit="MB",
301 | comparison="<=",
302 | details={
303 | "total_growth": total_memory_growth,
304 | "measurements": memory_measurements,
305 | },
306 | )
307 |
308 | benchmark.add_benchmark(
309 | test_name="max_memory_per_backtest",
310 | target_value=750.0, # Allow some variance
311 | actual_value=max_memory_delta,
312 | unit="MB",
313 | comparison="<=",
314 | details={
315 | "worst_case": memory_measurements[
316 | next(
317 | i
318 | for i, m in enumerate(memory_measurements)
319 | if m["delta"] == max_memory_delta
320 | )
321 | ]
322 | },
323 | )
324 |
325 | logger.info(
326 | f"Memory Usage Benchmark Summary:\n"
327 | f" • Total Growth: {total_memory_growth:.1f}MB\n"
328 | f" • Avg per Backtest: {avg_memory_per_backtest:.1f}MB\n"
329 | f" • Max per Backtest: {max_memory_delta:.1f}MB\n"
330 | f" • Initial Memory: {initial_memory:.1f}MB"
331 | )
332 |
333 | return benchmark.summary()
334 |
335 | async def test_cache_hit_rate_benchmark(self, benchmark_data_provider):
336 | """Test: Cache hit rate > 80%."""
337 | benchmark = BenchmarkTracker()
338 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
339 |
340 | # Mock cache to track hits/misses
341 | cache_stats = {"hits": 0, "misses": 0, "total_requests": 0}
342 |
343 | def mock_cache_get(key):
344 | cache_stats["total_requests"] += 1
345 | # Simulate realistic cache behavior
346 | if cache_stats["total_requests"] <= 5: # First few are misses
347 | cache_stats["misses"] += 1
348 | return None
349 | else: # Later requests are hits
350 | cache_stats["hits"] += 1
351 | return "cached_result"
352 |
353 | with patch(
354 | "maverick_mcp.core.cache.CacheManager.get", side_effect=mock_cache_get
355 | ):
356 | # Run multiple backtests with repeated data access
357 | symbols = [
358 | "CACHE_A",
359 | "CACHE_B",
360 | "CACHE_A",
361 | "CACHE_B",
362 | "CACHE_A",
363 | "CACHE_C",
364 | "CACHE_A",
365 | ]
366 |
367 | for symbol in symbols:
368 | await engine.run_backtest(
369 | symbol=symbol,
370 | strategy_type="sma_cross",
371 | parameters=STRATEGY_TEMPLATES["sma_cross"]["parameters"],
372 | start_date="2023-01-01",
373 | end_date="2023-12-31",
374 | )
375 |
376 | # Calculate cache hit rate
377 | total_cache_requests = cache_stats["total_requests"]
378 | cache_hits = cache_stats["hits"]
379 | cache_hit_rate = (
380 | (cache_hits / total_cache_requests * 100) if total_cache_requests > 0 else 0
381 | )
382 |
383 | benchmark.add_benchmark(
384 | test_name="cache_hit_rate",
385 | target_value=80.0,
386 | actual_value=cache_hit_rate,
387 | unit="%",
388 | comparison=">=",
389 | details={
390 | "total_requests": total_cache_requests,
391 | "hits": cache_hits,
392 | "misses": cache_stats["misses"],
393 | },
394 | )
395 |
396 | logger.info(
397 | f"Cache Hit Rate Benchmark:\n"
398 | f" • Total Cache Requests: {total_cache_requests}\n"
399 | f" • Cache Hits: {cache_hits}\n"
400 | f" • Cache Misses: {cache_stats['misses']}\n"
401 | f" • Hit Rate: {cache_hit_rate:.1f}%"
402 | )
403 |
404 | return benchmark.summary()
405 |
406 | async def test_api_failure_rate_benchmark(self, benchmark_data_provider):
407 | """Test: API failure rate < 0.1%."""
408 | benchmark = BenchmarkTracker()
409 |
410 | # Mock API with occasional failures
411 | api_stats = {"total_calls": 0, "failures": 0}
412 |
413 | def mock_api_call(*args, **kwargs):
414 | api_stats["total_calls"] += 1
415 | # Simulate very low failure rate
416 | if api_stats["total_calls"] % 2000 == 0: # 0.05% failure rate
417 | api_stats["failures"] += 1
418 | raise ConnectionError("Simulated API failure")
419 | return benchmark_data_provider.get_stock_data(*args, **kwargs)
420 |
421 | # Test with many API calls
422 | with patch.object(
423 | benchmark_data_provider, "get_stock_data", side_effect=mock_api_call
424 | ):
425 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
426 |
427 | test_symbols = [
428 | f"API_TEST_{i}" for i in range(50)
429 | ] # 50 symbols to test API reliability
430 |
431 | successful_backtests = 0
432 | failed_backtests = 0
433 |
434 | for symbol in test_symbols:
435 | try:
436 | await engine.run_backtest(
437 | symbol=symbol,
438 | strategy_type="rsi",
439 | parameters=STRATEGY_TEMPLATES["rsi"]["parameters"],
440 | start_date="2023-01-01",
441 | end_date="2023-12-31",
442 | )
443 | successful_backtests += 1
444 | except Exception:
445 | failed_backtests += 1
446 |
447 | # Calculate failure rates
448 | total_api_calls = api_stats["total_calls"]
449 | api_failures = api_stats["failures"]
450 | api_failure_rate = (
451 | (api_failures / total_api_calls * 100) if total_api_calls > 0 else 0
452 | )
453 |
454 | total_backtests = successful_backtests + failed_backtests
455 | backtest_failure_rate = (
456 | (failed_backtests / total_backtests * 100) if total_backtests > 0 else 0
457 | )
458 |
459 | benchmark.add_benchmark(
460 | test_name="api_failure_rate",
461 | target_value=0.1,
462 | actual_value=api_failure_rate,
463 | unit="%",
464 | comparison="<=",
465 | details={
466 | "total_api_calls": total_api_calls,
467 | "api_failures": api_failures,
468 | "successful_backtests": successful_backtests,
469 | "failed_backtests": failed_backtests,
470 | },
471 | )
472 |
473 | benchmark.add_benchmark(
474 | test_name="backtest_success_rate",
475 | target_value=99.5,
476 | actual_value=100 - backtest_failure_rate,
477 | unit="%",
478 | comparison=">=",
479 | details={"backtest_failure_rate": backtest_failure_rate},
480 | )
481 |
482 | logger.info(
483 | f"API Reliability Benchmark:\n"
484 | f" • Total API Calls: {total_api_calls}\n"
485 | f" • API Failures: {api_failures}\n"
486 | f" • API Failure Rate: {api_failure_rate:.3f}%\n"
487 | f" • Backtest Success Rate: {100 - backtest_failure_rate:.2f}%"
488 | )
489 |
490 | return benchmark.summary()
491 |
492 | async def test_database_query_performance_benchmark(
493 | self, benchmark_data_provider, db_session
494 | ):
495 | """Test: Database query performance < 100ms."""
496 | benchmark = BenchmarkTracker()
497 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
498 |
499 | # Generate test data for database operations
500 | test_results = []
501 | for i in range(10):
502 | result = await engine.run_backtest(
503 | symbol=f"DB_PERF_{i}",
504 | strategy_type="macd",
505 | parameters=STRATEGY_TEMPLATES["macd"]["parameters"],
506 | start_date="2023-01-01",
507 | end_date="2023-12-31",
508 | )
509 | test_results.append(result)
510 |
511 | # Test database save performance
512 | save_times = []
513 | with BacktestPersistenceManager(session=db_session) as persistence:
514 | for result in test_results:
515 | start_time = time.time()
516 | backtest_id = persistence.save_backtest_result(
517 | vectorbt_results=result,
518 | execution_time=2.0,
519 | notes="DB performance test",
520 | )
521 | save_time = (time.time() - start_time) * 1000 # Convert to ms
522 | save_times.append((backtest_id, save_time))
523 |
524 | # Test database query performance
525 | query_times = []
526 | with BacktestPersistenceManager(session=db_session) as persistence:
527 | for backtest_id, _ in save_times:
528 | start_time = time.time()
529 | persistence.get_backtest_by_id(backtest_id)
530 | query_time = (time.time() - start_time) * 1000 # Convert to ms
531 | query_times.append(query_time)
532 |
533 | # Test bulk query performance
534 | start_time = time.time()
535 | bulk_results = persistence.get_backtests_by_strategy("macd")
536 | bulk_query_time = (time.time() - start_time) * 1000
537 |
538 | # Calculate benchmarks
539 | avg_save_time = statistics.mean([t for _, t in save_times])
540 | max_save_time = max([t for _, t in save_times])
541 | avg_query_time = statistics.mean(query_times)
542 | max_query_time = max(query_times)
543 |
544 | # Add benchmarks
545 | benchmark.add_benchmark(
546 | test_name="avg_db_save_time",
547 | target_value=100.0,
548 | actual_value=avg_save_time,
549 | unit="ms",
550 | comparison="<=",
551 | details={"individual_times": [t for _, t in save_times]},
552 | )
553 |
554 | benchmark.add_benchmark(
555 | test_name="max_db_save_time",
556 | target_value=200.0,
557 | actual_value=max_save_time,
558 | unit="ms",
559 | comparison="<=",
560 | )
561 |
562 | benchmark.add_benchmark(
563 | test_name="avg_db_query_time",
564 | target_value=50.0,
565 | actual_value=avg_query_time,
566 | unit="ms",
567 | comparison="<=",
568 | details={"individual_times": query_times},
569 | )
570 |
571 | benchmark.add_benchmark(
572 | test_name="max_db_query_time",
573 | target_value=100.0,
574 | actual_value=max_query_time,
575 | unit="ms",
576 | comparison="<=",
577 | )
578 |
579 | benchmark.add_benchmark(
580 | test_name="bulk_query_time",
581 | target_value=200.0,
582 | actual_value=bulk_query_time,
583 | unit="ms",
584 | comparison="<=",
585 | details={"records_returned": len(bulk_results)},
586 | )
587 |
588 | logger.info(
589 | f"Database Performance Benchmark:\n"
590 | f" • Avg Save Time: {avg_save_time:.1f}ms\n"
591 | f" • Max Save Time: {max_save_time:.1f}ms\n"
592 | f" • Avg Query Time: {avg_query_time:.1f}ms\n"
593 | f" • Max Query Time: {max_query_time:.1f}ms\n"
594 | f" • Bulk Query Time: {bulk_query_time:.1f}ms"
595 | )
596 |
597 | return benchmark.summary()
598 |
599 | async def test_throughput_benchmark(self, benchmark_data_provider):
600 | """Test: Throughput targets (requests per second)."""
601 | benchmark = BenchmarkTracker()
602 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
603 |
604 | # Test sequential throughput
605 | symbols = ["THRU_1", "THRU_2", "THRU_3", "THRU_4", "THRU_5"]
606 | start_time = time.time()
607 |
608 | for symbol in symbols:
609 | await engine.run_backtest(
610 | symbol=symbol,
611 | strategy_type="sma_cross",
612 | parameters=STRATEGY_TEMPLATES["sma_cross"]["parameters"],
613 | start_date="2023-01-01",
614 | end_date="2023-12-31",
615 | )
616 |
617 | sequential_time = time.time() - start_time
618 | sequential_throughput = len(symbols) / sequential_time
619 |
620 | # Test concurrent throughput
621 | concurrent_symbols = ["CONC_1", "CONC_2", "CONC_3", "CONC_4", "CONC_5"]
622 | start_time = time.time()
623 |
624 | concurrent_tasks = []
625 | for symbol in concurrent_symbols:
626 | task = engine.run_backtest(
627 | symbol=symbol,
628 | strategy_type="sma_cross",
629 | parameters=STRATEGY_TEMPLATES["sma_cross"]["parameters"],
630 | start_date="2023-01-01",
631 | end_date="2023-12-31",
632 | )
633 | concurrent_tasks.append(task)
634 |
635 | await asyncio.gather(*concurrent_tasks)
636 | concurrent_time = time.time() - start_time
637 | concurrent_throughput = len(concurrent_symbols) / concurrent_time
638 |
639 | # Benchmarks
640 | benchmark.add_benchmark(
641 | test_name="sequential_throughput",
642 | target_value=2.0, # 2 backtests per second
643 | actual_value=sequential_throughput,
644 | unit="req/s",
645 | comparison=">=",
646 | details={"execution_time": sequential_time, "requests": len(symbols)},
647 | )
648 |
649 | benchmark.add_benchmark(
650 | test_name="concurrent_throughput",
651 | target_value=5.0, # 5 backtests per second with concurrency
652 | actual_value=concurrent_throughput,
653 | unit="req/s",
654 | comparison=">=",
655 | details={
656 | "execution_time": concurrent_time,
657 | "requests": len(concurrent_symbols),
658 | },
659 | )
660 |
661 | # Concurrency speedup
662 | speedup = concurrent_throughput / sequential_throughput
663 | benchmark.add_benchmark(
664 | test_name="concurrency_speedup",
665 | target_value=2.0, # At least 2x speedup
666 | actual_value=speedup,
667 | unit="x",
668 | comparison=">=",
669 | details={
670 | "sequential_throughput": sequential_throughput,
671 | "concurrent_throughput": concurrent_throughput,
672 | },
673 | )
674 |
675 | logger.info(
676 | f"Throughput Benchmark:\n"
677 | f" • Sequential: {sequential_throughput:.2f} req/s\n"
678 | f" • Concurrent: {concurrent_throughput:.2f} req/s\n"
679 | f" • Speedup: {speedup:.2f}x"
680 | )
681 |
682 | return benchmark.summary()
683 |
684 | async def test_response_time_sla_benchmark(self, benchmark_data_provider):
685 | """Test: Response time SLA compliance."""
686 | benchmark = BenchmarkTracker()
687 | engine = VectorBTEngine(data_provider=benchmark_data_provider)
688 |
689 | response_times = []
690 | symbols = [f"SLA_{i}" for i in range(20)]
691 |
692 | for symbol in symbols:
693 | start_time = time.time()
694 | await engine.run_backtest(
695 | symbol=symbol,
696 | strategy_type="rsi",
697 | parameters=STRATEGY_TEMPLATES["rsi"]["parameters"],
698 | start_date="2023-01-01",
699 | end_date="2023-12-31",
700 | )
701 | response_time = (time.time() - start_time) * 1000 # Convert to ms
702 | response_times.append(response_time)
703 |
704 | # SLA percentile benchmarks
705 | p50 = np.percentile(response_times, 50)
706 | p95 = np.percentile(response_times, 95)
707 | p99 = np.percentile(response_times, 99)
708 |
709 | benchmark.add_benchmark(
710 | test_name="response_time_p50",
711 | target_value=1500.0, # 1.5 seconds for 50th percentile
712 | actual_value=p50,
713 | unit="ms",
714 | comparison="<=",
715 | details={"percentile": "50th"},
716 | )
717 |
718 | benchmark.add_benchmark(
719 | test_name="response_time_p95",
720 | target_value=3000.0, # 3 seconds for 95th percentile
721 | actual_value=p95,
722 | unit="ms",
723 | comparison="<=",
724 | details={"percentile": "95th"},
725 | )
726 |
727 | benchmark.add_benchmark(
728 | test_name="response_time_p99",
729 | target_value=5000.0, # 5 seconds for 99th percentile
730 | actual_value=p99,
731 | unit="ms",
732 | comparison="<=",
733 | details={"percentile": "99th"},
734 | )
735 |
736 | # SLA compliance rate (percentage of requests under target)
737 | sla_target = 2000.0 # 2 seconds
738 | sla_compliant = sum(1 for t in response_times if t <= sla_target)
739 | sla_compliance_rate = sla_compliant / len(response_times) * 100
740 |
741 | benchmark.add_benchmark(
742 | test_name="sla_compliance_rate",
743 | target_value=95.0, # 95% of requests should meet SLA
744 | actual_value=sla_compliance_rate,
745 | unit="%",
746 | comparison=">=",
747 | details={
748 | "sla_target_ms": sla_target,
749 | "compliant_requests": sla_compliant,
750 | "total_requests": len(response_times),
751 | },
752 | )
753 |
754 | logger.info(
755 | f"Response Time SLA Benchmark:\n"
756 | f" • 50th Percentile: {p50:.1f}ms\n"
757 | f" • 95th Percentile: {p95:.1f}ms\n"
758 | f" • 99th Percentile: {p99:.1f}ms\n"
759 | f" • SLA Compliance: {sla_compliance_rate:.1f}%"
760 | )
761 |
762 | return benchmark.summary()
763 |
764 | async def test_comprehensive_benchmark_suite(
765 | self, benchmark_data_provider, db_session
766 | ):
767 | """Run comprehensive benchmark suite and generate report."""
768 | logger.info("Running Comprehensive Benchmark Suite...")
769 |
770 | # Run all individual benchmarks
771 | benchmark_results = []
772 |
773 | benchmark_results.append(
774 | await self.test_backtest_execution_time_benchmark(benchmark_data_provider)
775 | )
776 | benchmark_results.append(
777 | await self.test_memory_usage_benchmark(benchmark_data_provider)
778 | )
779 | benchmark_results.append(
780 | await self.test_cache_hit_rate_benchmark(benchmark_data_provider)
781 | )
782 | benchmark_results.append(
783 | await self.test_api_failure_rate_benchmark(benchmark_data_provider)
784 | )
785 | benchmark_results.append(
786 | await self.test_database_query_performance_benchmark(
787 | benchmark_data_provider, db_session
788 | )
789 | )
790 | benchmark_results.append(
791 | await self.test_throughput_benchmark(benchmark_data_provider)
792 | )
793 | benchmark_results.append(
794 | await self.test_response_time_sla_benchmark(benchmark_data_provider)
795 | )
796 |
797 | # Aggregate results
798 | total_tests = sum(r["total_tests"] for r in benchmark_results)
799 | total_passed = sum(r["passed_tests"] for r in benchmark_results)
800 | total_failed = sum(r["failed_tests"] for r in benchmark_results)
801 | overall_pass_rate = total_passed / total_tests if total_tests > 0 else 0
802 |
803 | # Generate comprehensive report
804 | report = {
805 | "summary": {
806 | "total_tests": total_tests,
807 | "passed_tests": total_passed,
808 | "failed_tests": total_failed,
809 | "overall_pass_rate": overall_pass_rate,
810 | },
811 | "benchmark_suites": benchmark_results,
812 | "critical_failures": [
813 | result
814 | for suite in benchmark_results
815 | for result in suite["results"]
816 | if not result.passed
817 | and result.margin > 0.2 # More than 20% over target
818 | ],
819 | }
820 |
821 | logger.info(
822 | f"\n{'=' * 60}\n"
823 | f"COMPREHENSIVE BENCHMARK REPORT\n"
824 | f"{'=' * 60}\n"
825 | f"Total Tests: {total_tests}\n"
826 | f"Passed: {total_passed} ({overall_pass_rate:.1%})\n"
827 | f"Failed: {total_failed}\n"
828 | f"{'=' * 60}\n"
829 | )
830 |
831 | # Assert overall benchmark success
832 | assert overall_pass_rate >= 0.8, (
833 | f"Overall benchmark pass rate too low: {overall_pass_rate:.1%}"
834 | )
835 | assert len(report["critical_failures"]) == 0, (
836 | f"Critical benchmark failures detected: {len(report['critical_failures'])}"
837 | )
838 |
839 | return report
840 |
841 |
842 | if __name__ == "__main__":
843 | # Run benchmark tests
844 | pytest.main(
845 | [
846 | __file__,
847 | "-v",
848 | "--tb=short",
849 | "--asyncio-mode=auto",
850 | "--timeout=300", # 5 minute timeout for benchmarks
851 | ]
852 | )
853 |
```