Scanner Pipeline — Progress Tracker

Milestone: End-to-End Scanner ✅ COMPLETE

The 3-phase scanner pipeline runs successfully from python -m cli.main scan --date 2026-03-17.

Component	Status	Notes
Phase 1: Geopolitical Scanner	✅	Ollama/qwen3.5:27b, uses `get_topic_news`
Phase 1: Market Movers Scanner	✅	Ollama/qwen3.5:27b, uses `get_market_movers` + `get_market_indices`
Phase 1: Sector Scanner	✅	Ollama/qwen3.5:27b, uses `get_sector_performance` (SPDR ETF proxies)
Phase 2: Industry Deep Dive	✅	Ollama/qwen3.5:27b, uses `get_industry_performance` + `get_topic_news`
Phase 3: Macro Synthesis	✅	OpenRouter/DeepSeek R1, pure LLM synthesis (no tools)
Parallel fan-out (Phase 1)	✅	LangGraph with `_last_value` reducers
Tool execution loop	✅	`run_tool_loop()` in `tool_runner.py`
Data vendor fallback	✅	AV → yfinance fallback on `AlphaVantageError`
CLI `--date` flag	✅	`python -m cli.main scan --date YYYY-MM-DD`
.env loading	✅	Keys loaded from project root `.env`
Tests (23 total)	✅	14 original + 9 scanner fallback tests

Report	Size	Content
geopolitical_report	6,295 chars	Iran conflict, energy risks, central bank signals
market_movers_report	6,211 chars	Top gainers/losers, volume anomalies, index trends
sector_performance_report	8,747 chars	Sector rotation analysis with ranked table
industry_deep_dive_report	—	Ran but was sparse (Phase 1 reports were the primary context)
macro_scan_summary	10,309 chars	Full synthesis with stock picks and JSON structure

New files:

tradingagents/agents/utils/tool_runner.py — inline tool execution loop
tradingagents/agents/utils/scanner_states.py — ScannerState with reducers
tradingagents/agents/utils/scanner_tools.py — LangChain tool wrappers for scanner data
tradingagents/agents/scanners/ — all 5 scanner agent modules
tradingagents/graph/scanner_graph.py — ScannerGraph orchestrator
tradingagents/graph/scanner_setup.py — LangGraph workflow setup
tradingagents/dataflows/yfinance_scanner.py — yfinance data for scanner
tradingagents/dataflows/alpha_vantage_scanner.py — Alpha Vantage data for scanner
tests/test_scanner_fallback.py — 9 fallback tests

Modified files:

tradingagents/default_config.py — per-tier LLM provider config (hybrid setup)
tradingagents/llm_clients/openai_client.py — Ollama remote host support
tradingagents/dataflows/interface.py — broadened fallback catch to AlphaVantageError
cli/main.py — scan command with --date flag, .env loading fix
.env — real API keys

Industry Deep Dive quality: Phase 2 report was sparse in test run. The LLM receives Phase 1 reports as context but may not call tools effectively. Consider: pre-fetching industry data and injecting it directly, or tuning the prompt to be more directive about which sectors to drill into.
Macro Synthesis JSON parsing: The macro_scan_summary should be valid JSON but DeepSeek R1 sometimes wraps it in markdown code blocks or adds preamble text. The CLI tries json.loads(summary) to build a watchlist table — this may fail. Add robust JSON extraction (strip markdown fences, find first {).
pipeline command: cli/main.py has a run_pipeline() placeholder that chains scan → filter → per-ticker deep dive. Not yet implemented.

Scanner report persistence: Reports are saved to results/macro_scan/{date}/ as .md files. Verify this works and add JSON output option.
Rate limiting for parallel tool calls: Phase 1 runs 3 agents in parallel, each calling tools. If tools hit the same API (e.g., Google News), they may get rate-limited. Consider adding delays or a shared rate limiter.
Ollama model validation: Before running the pipeline, validate that the configured model exists on the Ollama server (call /api/tags endpoint). Currently a 404 error is only caught at first LLM call.
Test coverage for scanner agents: Current tests cover data layer (yfinance/AV fallback) but not the agent nodes themselves. Add integration tests that mock the LLM and verify tool loop behavior.

Configurable MAX_TOOL_ROUNDS: Currently hardcoded to 5 in tool_runner.py. Could be made configurable via DEFAULT_CONFIG.
Streaming output: Scanner currently runs with Live(Spinner(...)) — no intermediate output. Could stream phase completions to the console.
Remove top-level llm_provider references: scanner_graph.py lines 69, 78 still fall back to self.config["llm_provider"] which doesn't exist in current config. Works because per-tier providers are always set, but will crash if they're ever None.