## Summary
- Adds `smart_money_scanner` as a new Phase 1b node that runs sequentially after `sector_scanner`, surfacing institutional footprints via Finviz screeners
- Introduces the **Golden Overlap** strategy in `macro_synthesis`: stocks confirmed by both top-down macro themes and bottom-up Finviz signals are labelled high-conviction
- Fixes model-name badge overflow in AgentGraph (long model IDs like OpenRouter paths were visually spilling into adjacent nodes)
- Completes all documentation: ADR-014, dataflow, architecture, components, glossary, current-state
## Key Decisions (see ADR-014)
- 3 zero-parameter tools (`get_insider_buying_stocks`, `get_unusual_volume_stocks`, `get_breakout_accumulation_stocks`) instead of 1 parameterised tool — prevents LLM hallucinations on string args
- Sequential after `sector_scanner` (not parallel fan-out) — gives access to `sector_performance_report` context and avoids `MAX_TOOL_ROUNDS=5` truncation in market_movers_scanner
- Graceful fallback: `_run_finviz_screen()` catches all exceptions and returns an error string — pipeline never hard-fails on web-scraper failure
- `breakout_accumulation` (52-wk high + 2x vol = O'Neil CAN SLIM institutional signal) replaces `oversold_bounces` (RSI<30 = retail contrarian, not smart money)
## Test Plan
- [x] 6 new mocked tests in `tests/unit/test_scanner_mocked.py` (happy path, empty DF, exception, sort order)
- [x] Fixed `tests/unit/test_scanner_graph.py` — added `smart_money_scanner` mock to compilation test
- [x] 2 pre-existing test failures excluded (verified baseline before changes)
- [x] AgentGraph badge: visually verified truncation with long OpenRouter model identifiers
🤖 Generated with [Claude Code](https://claude.com/claude-code)
* Initial plan
* Improve Industry Deep Dive quality: enrich tool data, explicit sector keys, tool-call nudge
- Enrich get_industry_performance_yfinance with 1-day/1-week/1-month price returns
via batched yf.download() for top 10 tickers (Step 1)
- Add VALID_SECTOR_KEYS, _DISPLAY_TO_KEY, _extract_top_sectors() to industry_deep_dive.py
to pre-extract top sectors from Phase 1 report and inject them into the prompt (Step 2)
- Add tool-call nudge to run_tool_loop: if first LLM response has no tool calls and is
under 500 chars, re-prompt with explicit instruction to call tools (Step 3)
- Update scanner_tools.py get_industry_performance docstring to list all valid sector keys (Step 4)
- Add 15 unit tests covering _extract_top_sectors, tool_runner nudge, and enriched output (Step 5)
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
* Address code review: move cols[3] access into try block for IndexError safety
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
* fix: align display row count with download count in get_industry_performance_yfinance
The enriched function downloads price data for top 10 tickers but displayed
20 rows, causing rows 11-20 to show N/A in all price columns. This broke
test_industry_perf_falls_back_to_yfinance which asserts N/A count < 5.
Now both download and display use head(10) for consistency.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Co-authored-by: Ahmet Guzererler <guzererler@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
- Added market-wide analysis capabilities (movers, indices, sectors, industries, topic news)
- Implemented yfinance and Alpha Vantage data fetching modules
- Added LangChain tools for scanner functions
- Created scanner state definitions and graph components
- Integrated scan command into CLI
- Added configuration for scanner_data vendor routing
- Included test files for scanner components
This implements a new feature for global macro scanning to identify market-wide trends and opportunities.
ahmet guzererler
Copilot