docs: update memory files after PR #13 (Industry Deep Dive quality fix)

- CURRENT_STATE.md: remove Industry Deep Dive blocker (resolved), update
  test count 38 → 53, add PR #13 to Recent Progress, update milestone focus
- decisions/009-industry-deep-dive-quality.md: new ADR documenting the
  three-pronged fix (enriched data, explicit sector routing, tool-call nudge)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docs/agent/CURRENT_STATE.md

@@ -1,17 +1,17 @@
 # Current Milestone
 
-Scanner pipeline is feature-complete and running end-to-end. Focus shifts to quality improvements and pipeline command implementation.
+Scanner pipeline is feature-complete and quality-improved. Focus shifts to Macro Synthesis JSON robustness and the `pipeline` CLI command.
 
 # Recent Progress
 
 - End-to-end scanner pipeline operational (`python -m cli.main scan --date YYYY-MM-DD`)
-- All 38 tests passing (14 original + 9 scanner fallback + 15 env override)
+- All 53 tests passing (14 original + 9 scanner fallback + 15 env override + 15 industry deep dive)
 - Environment variable config overrides merged (PR #9)
 - Thread-safe rate limiter for Alpha Vantage implemented
 - Vendor fallback (AV -> yfinance) broadened to catch `AlphaVantageError`, `ConnectionError`, `TimeoutError`
+- **PR #13 merged**: Industry Deep Dive quality fixed — enriched industry data (price returns), explicit sector routing via `_extract_top_sectors()`, tool-call nudge in `run_tool_loop`
 
 # Active Blockers
 
-- Industry Deep Dive (Phase 2) report quality is sparse — LLM may not be calling tools effectively
-- Macro Synthesis JSON parsing fragile — DeepSeek R1 sometimes wraps output in markdown code blocks
+- Macro Synthesis JSON parsing fragile — DeepSeek R1 sometimes wraps output in markdown code blocks; `json.loads()` in CLI may fail
 - `pipeline` CLI command (scan -> filter -> per-ticker deep dive) not yet implemented

docs/agent/decisions/009-industry-deep-dive-quality.md

@@ -0,0 +1,87 @@
+---
+type: decision
+status: active
+date: 2026-03-17
+agent_author: "copilot+claude"
+tags: [scanner, industry-deep-dive, tool-execution, prompt-engineering, yfinance]
+related_files:
+  - tradingagents/agents/scanners/industry_deep_dive.py
+  - tradingagents/agents/utils/tool_runner.py
+  - tradingagents/agents/utils/scanner_tools.py
+  - tradingagents/dataflows/yfinance_scanner.py
+pr: "13"
+---
+
+## Context
+
+Phase 2 (Industry Deep Dive) produced sparse reports despite receiving ~21K chars of Phase 1
+context. Three root causes were identified:
+
+1. **LLM guessing sector keys** — the LLM had to infer valid `sector_key` strings (e.g., `"financial-services"` vs `"financials"`) with no guidance, leading to failed tool calls.
+2. **Thin industry data** — `get_industry_performance_yfinance` returned only static metadata (name, rating, market weight). No performance signal for the LLM to act on.
+3. **Tool-call skipping under long context** — weaker local LLMs (Ollama/qwen) sometimes produce a short prose response instead of calling tools when the prompt is long.
+
+## The Decision
+
+Three-pronged fix (PR #13):
+
+### 1. Enriched Industry Performance Data
+
+`get_industry_performance_yfinance` now batch-downloads 1-month price history for the top 10
+tickers in each industry and computes 1-day, 1-week, and 1-month percentage returns.
+Output table expands from 4 to 7 columns:
+
+```
+| Company | Symbol | Rating | Market Weight | 1-Day % | 1-Week % | 1-Month % |
+```
+
+Both download and display use `head(10)` for consistency (avoids N/A rows for positions 11-20).
+
+### 2. Explicit Sector Routing via `_extract_top_sectors()`
+
+`industry_deep_dive.py` defines:
+- `VALID_SECTOR_KEYS` — the 11 canonical yfinance sector key strings
+- `_DISPLAY_TO_KEY` — maps display names (e.g., `"Financial Services"`) to keys (e.g., `"financial-services"`)
+- `_extract_top_sectors(sector_report, n)` — parses the Phase 1 sector performance table, ranks sectors by absolute 1-month move, returns top-N valid keys
+
+The prompt now injects the pre-extracted keys directly:
+
+```
+Call get_industry_performance for EACH of these top sectors: 'energy', 'communication-services', 'technology'
+Valid sector_key values: 'technology', 'healthcare', 'financial-services', ...
+```
+
+This eliminates LLM guesswork entirely.
+
+### 3. Tool-Call Nudge in `run_tool_loop`
+
+If the LLM's first response has no `tool_calls` and is under 500 characters, a
+`HumanMessage` nudge is appended before re-invoking. Fires **once only** to avoid loops.
+Prevents short-circuit prose responses from weak LLMs under heavy context.
+
+### 4. Tool Description Update
+
+`get_industry_performance` docstring now enumerates all 11 valid sector keys so they appear
+in the tool schema visible to the LLM.
+
+## Constraints
+
+- `_extract_top_sectors()` must degrade gracefully: if parsing fails (malformed Phase 1 report),
+  it falls back to the top 3 default sectors `["technology", "financial-services", "energy"]`.
+- The tool-call nudge fires **at most once** per agent invocation — do not loop on nudge.
+- `get_industry_performance_yfinance` must use `head(10)` for **both** download and display
+  to prevent N/A rows (Mistake #11: was displaying 20 rows but only downloading data for 10).
+
+## Actionable Rules
+
+- Always inject pre-extracted sector keys into Industry Deep Dive prompt — never rely on the LLM to guess valid `sector_key` values.
+- When enriching `get_industry_performance_yfinance`, keep download count and display count in sync.
+- Tool-call nudge threshold is 500 chars — do not raise it; the intent is to catch short non-tool responses, not legitimate brief answers.
+- All 11 VALID_SECTOR_KEYS must be listed in the `get_industry_performance` tool docstring.
+
+## Tests Added
+
+15 new tests in `tests/test_industry_deep_dive.py`:
+- 8 tests for `_extract_top_sectors()` parsing and edge cases
+- 4 tests for nudge mechanism (mock chain)
+- 3 tests for enriched output format (network-dependent, auto-skip if offline)