LLMs (especially smaller models) sometimes pass multiple indicator
names as a single comma-separated string instead of making separate
tool calls. Split and process each individually at the tool boundary.
InvestDebateState was missing bull_history, bear_history, judge_decision.
RiskDebateState was missing aggressive_history, conservative_history,
neutral_history, latest_speaker, judge_decision. This caused KeyError
in _log_state() and reflection, especially with edge-case config values.
Prevents UnicodeEncodeError on Windows where the default encoding
(cp1252/gbk) cannot handle Unicode characters in LLM output.
Closes#77, closes#114, closes#126, closes#215, closes#332
- Created new files for industry performance, market indices, market movers, sector performance, and topic news.
- Implemented end-to-end tests for scanner functionality, ensuring all tools return expected data formats and can save results to files.
- Added integration tests to verify scanner tools work seamlessly with the CLI scan command.
- Enhanced test coverage for individual scanner tools, validating output structure and content.
## Summary
The changes refactor the scanner tool invocation to use LangChain's StructuredTool `.invoke()` method consistently across the codebase. This includes updating the CLI scan command, rewriting tests to use the new invocation pattern, and correcting yfinance screener key mappings. The changes also add comprehensive end-to-end test suites for scanner functionality.
## Issues Found
| Severity | File:Line | Issue |
|----------|-----------|-------|
| WARNING | cli/main.py:1193-1218 | Inconsistent error handling - some tools check for "Error" prefix while others check for "No data" prefix, but the actual error messages from yfinance_scanner.py use different formats |
| WARNING | tradingagents/dataflows/yfinance_scanner.py:34 | The condition `if not data or 'quotes' not in data:` may not catch all error cases - yfinance screener can return empty data structures that evaluate to False but don't contain 'quotes' key |
| SUGGESTION | tests/test_scanner_tools.py:38-46 | Test could be more robust by checking for actual data content rather than just headers |
| SUGGESTION | cli/main.py:1193-1218 | Consider extracting the scanner tool invocation pattern into a helper function to reduce duplication |
## Detailed Findings
### File: cli/main.py:1193-1218
- **Confidence:** 85%
- **Problem:** The error handling checks for different prefixes ("Error" vs "No data") but the actual functions in yfinance_scanner.py return error messages with different formats (e.g., "Error fetching market movers for..."). This inconsistency could lead to improper error handling where error results are still saved to files.
- **Suggestion:** Standardize error checking by creating a helper function that checks if a result indicates an error, or modify the yfinance_scanner functions to return consistent error prefixes.
### File: tradingagents/dataflows/yfinance_scanner.py:34
- **Confidence:** 80%
- **Problem:** The condition `if not data or 'quotes' not in data:` assumes that if data exists, it will contain a 'quotes' key. However, yfinance screener might return data in different formats or empty objects that don't contain this key, leading to potential KeyError exceptions.
- **Suggestion:** Add more robust checking: `if not data or not isinstance(data, dict) or 'quotes' not in data:` to prevent attribute errors.
### File: tests/test_scanner_tools.py:38-46
- **Confidence:** 75%
- **Problem:** The test for market movers only checks that the result contains the expected header but doesn't verify that actual financial data is present in the table rows.
- **Suggestion:** Enhance the test to verify that data rows are present (e.g., check for table rows with actual data, not just headers).
### File: cli/main.py:1193-1218
- **Confidence:** 70%
- **Problem:** The scanner tool invocation pattern is repeated 5 times with only minor variations in arguments, violating the DRY principle.
- **Suggestion:** Extract this pattern into a helper function like `invoke_scanner_tool(tool, args, filename)` to reduce code duplication and improve maintainability.
## Recommendation
**APPROVE WITH SUGGESTIONS**
The changes are fundamentally sound and improve code consistency by standardizing on the StructuredTool `.invoke()` interface. The added test coverage is excellent. Addressing the minor issues noted above would further improve robustness and maintainability.
- 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.
- Add StatsCallbackHandler for tracking LLM calls, tool calls, and tokens
- Integrate callbacks into TradingAgentsGraph and all LLM clients
- Dynamic agent/report counts based on selected analysts
- Fix report completion counting (tied to agent completion)
- Replace hardcoded column indices with column name lookup
- Add mapping for all supported indicators to their expected CSV column names
- Handle missing columns gracefully with descriptive error messages
- Strip whitespace from header parsing for reliability
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Replace FinnHub with Alpha Vantage API in README documentation
- Implement comprehensive Alpha Vantage modules:
- Stock data (daily OHLCV with date filtering)
- Technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, ATR)
- Fundamental data (overview, balance sheet, cashflow, income statement)
- News and sentiment data with insider transactions
- Update news analyst tools to use ticker-based news search
- Integrate Alpha Vantage vendor methods into interface routing
- Maintain backward compatibility with existing vendor system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER
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