Market data review#34
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stevehome wants to merge 9 commits intoed-donner:market-data-reviewfrom
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Add market data backend code review document
- Add [tool.hatch.build.targets.wheel] packages to pyproject.toml so uv sync and Docker builds work - Fix Massive tests to work without the massive package installed: set _client on source before calling _poll_once, use create=True on RESTClient patches, add conftest with stub massive modules - Fix _generate_events return type (None -> AsyncGenerator[str, None]) - Add public get_tickers() to GBMSimulator; use it in SimulatorDataSource instead of reaching into private _tickers - Remove unused DEFAULT_CORR constant (consolidated into CROSS_GROUP_CORR) - Remove unused imports in test files (pytest, math, asyncio) All 73 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
massive is a core dependency in pyproject.toml and will always be installed. Move imports to top level in massive_client.py and factory.py, remove the sys.modules stub conftest, and drop create=True from test patches. Tests now work naturally with the installed package. All 73 tests pass. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fix/market data review items
Interactive dashboard showing all 10 tickers with live GBM-simulated prices, sparklines, color-coded direction arrows, and an event log for notable moves. Runs 60 seconds or until Ctrl+C, then prints a session summary comparing final prices to seeds. Usage: cd backend && uv run market_data_demo.py Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- planning/MARKET_DATA_SUMMARY.md: comprehensive summary of everything built, tested, and reviewed in the market data backend - backend/CLAUDE.md: developer guide with key APIs, setup, and usage - planning/archive/: moved 5 superseded market data planning documents Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Market data demo, summary docs, and backend CLAUDE.md
Comprehensive review of backend/app/market/ covering test results (73/73 passing), coverage analysis (91% overall), and identified issues with recommendations. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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