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Bill
b3debc125f
Reorganize documentation into user-focused, developer-focused, and deployment-focused sections. **New structure:** - Root: README.md (streamlined), QUICK_START.md, API_REFERENCE.md - docs/user-guide/: configuration, API usage, integrations, troubleshooting - docs/developer/: contributing, development setup, testing, architecture - docs/deployment/: Docker deployment, production checklist, monitoring - docs/reference/: environment variables, MCP tools, data formats **Changes:** - Streamline README.md from 831 to 469 lines - Create QUICK_START.md for 5-minute onboarding - Create API_REFERENCE.md as single source of truth for API - Remove 9 outdated specification docs (v0.2.0 API design) - Remove DOCKER_API.md (content consolidated into new structure) - Remove docs/plans/ directory with old design documents - Update CLAUDE.md with documentation structure guide - Remove orchestration-specific references **Benefits:** - Clear entry points for different audiences - No content duplication - Better discoverability through logical hierarchy - All content reflects current v0.3.0 API
69 lines
1.4 KiB
Markdown
69 lines
1.4 KiB
Markdown
# Architecture
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System design and component overview.
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---
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## Component Diagram
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See README.md for architecture diagram.
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---
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## Key Components
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### FastAPI Server (`api/main.py`)
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- REST API endpoints
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- Request validation
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- Response formatting
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### Job Manager (`api/job_manager.py`)
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- Job lifecycle management
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- SQLite operations
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- Concurrency control
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### Simulation Worker (`api/simulation_worker.py`)
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- Background job execution
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- Date-sequential, model-parallel orchestration
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- Error handling
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### Model-Day Executor (`api/model_day_executor.py`)
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- Single model-day execution
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- Runtime config isolation
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- Agent invocation
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### Base Agent (`agent/base_agent/base_agent.py`)
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- Trading session execution
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- MCP tool integration
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- Position management
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### MCP Services (`agent_tools/`)
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- Math, Search, Trade, Price tools
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- Internal HTTP servers
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- Localhost-only access
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---
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## Data Flow
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1. API receives trigger request
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2. Job Manager validates and creates job
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3. Worker starts background execution
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4. For each date (sequential):
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- For each model (parallel):
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- Executor creates isolated runtime config
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- Agent executes trading session
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- Results stored in database
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5. Job status updated
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6. Results available via API
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---
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## Anti-Look-Ahead Controls
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- `TODAY_DATE` in runtime config limits data access
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- Price queries filter by date
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- Search results filtered by publication date
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See [CLAUDE.md](../../CLAUDE.md) for implementation details.
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