Merge pull request #3 from 89jobrien/coderabbitai/utg/3f6b1e9
CodeRabbit Generated Unit Tests: Add comprehensive test suite with 142+ unit tests
This commit is contained in:
Joesph O'Brien
GitHub
commit
e3e40d1e05
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# Test Coverage Summary
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This document provides an overview of the comprehensive unit tests generated for the modified files in this branch.
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## Test Files Created
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### 1. Agent Utils Tests (`tests/agents/utils/`)
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#### `test_agent_states.py`
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- **Purpose**: Tests for TypedDict state classes used throughout the trading agents system
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- **Coverage**:
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- `InvestDebateState`: Research team debate state management
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- `RiskDebateState`: Risk management team state handling
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- `AgentState`: Main agent state with nested debate states
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- **Test Scenarios**:
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- State structure validation
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- Empty and populated states
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- Multiline conversation histories
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- Count variations and speaker tracking
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- Complete workflow scenarios
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- **Test Count**: 20+ tests
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#### `test_agent_utils.py`
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- **Purpose**: Tests for agent utility functions
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- **Coverage**:
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- `create_msg_delete()`: Message deletion and Anthropic compatibility
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- **Test Scenarios**:
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- Message removal operations
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- Placeholder message creation
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- Empty state handling
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- Large message lists
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- State immutability
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- Message ID preservation
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- **Test Count**: 11 tests
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#### `test_memory.py`
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- **Purpose**: Tests for FinancialSituationMemory class (chromadb-based)
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- **Coverage**:
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- Initialization with different backends (OpenAI, Ollama)
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- Embedding generation
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- Situation and advice storage
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- Memory retrieval and similarity scoring
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- **Test Scenarios**:
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- Backend configuration
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- Embedding model selection
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- Single and multiple situation additions
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- ID offset management
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- Memory querying with similarity scores
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- Cache behavior
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- Empty list handling
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- **Test Count**: 15+ tests
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### 2. Dataflows Tests (`tests/dataflows/`)
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#### `test_alpha_vantage_news.py`
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- **Purpose**: Tests for Alpha Vantage news API integration
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- **Coverage**:
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- `get_news()`: Ticker-specific news retrieval
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- `get_insider_transactions()`: Insider trading data
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- `get_bulk_news_alpha_vantage()`: Bulk news fetching
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- **Test Scenarios**:
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- API parameter validation
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- Time period calculations
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- Article parsing and content truncation
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- Invalid data format handling
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- Empty feed responses
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- Malformed article data
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- Various lookback periods
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- **Test Count**: 18+ tests
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#### `test_google.py`
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- **Purpose**: Tests for Google News integration
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- **Coverage**:
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- `get_google_news()`: Query-based news search
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- `get_bulk_news_google()`: Bulk news aggregation
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- **Test Scenarios**:
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- Query formatting (space to plus conversion)
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- Result formatting and deduplication
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- Empty results handling
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- Date calculation and formatting
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- Multiple query execution
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- Content truncation
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- Error handling
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- **Test Count**: 15+ tests
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#### `test_interface.py`
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- **Purpose**: Tests for the dataflows interface layer (vendor routing)
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- **Coverage**:
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- `parse_lookback_period()`: Time period parsing
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- `get_category_for_method()`: Method categorization
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- `get_bulk_news()`: Cached bulk news retrieval
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- `route_to_vendor()`: Vendor fallback logic
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- **Test Scenarios**:
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- Lookback period parsing (1h, 6h, 24h, 7d)
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- Case insensitivity and whitespace handling
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- Invalid period error handling
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- Method-to-category mapping
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- Vendor routing with fallbacks
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- Cache behavior (TTL)
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- Article conversion to NewsArticle objects
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- Multiple vendor implementations
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- All-vendor-fail scenarios
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- **Test Count**: 20+ tests
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### 3. Configuration Tests (`tests/`)
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#### `test_default_config.py`
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- **Purpose**: Tests for DEFAULT_CONFIG dictionary
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- **Coverage**: All configuration keys and their validity
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- **Test Scenarios**:
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- Config existence and structure
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- Path configurations (project_dir, results_dir, data_dir)
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- LLM provider and model settings
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- Backend URL validation
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- Debate and recursion limits
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- Data vendor mappings
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- Discovery-specific configs (timeout, cache TTL, max results)
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- Numeric value positivity checks
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- Environment variable respect
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- Config immutability safety
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- **Test Count**: 18+ tests
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### 4. Graph Tests (`tests/graph/`)
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#### `test_trading_graph.py`
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- **Purpose**: Tests for TradingAgentsGraph main orchestration class
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- **Coverage**:
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- Initialization with various LLM providers
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- Memory instance creation
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- Tool node setup
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- `discover_trending()`: Trending stock discovery
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- `propagate()`: Agent graph execution
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- `reflect_and_remember()`: Learning and reflection
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- `analyze_trending()`: Stock analysis workflow
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- **Test Scenarios**:
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- Default and custom configuration
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- OpenAI, Anthropic, Google, Ollama provider support
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- Unsupported provider error handling
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- Memory creation for all agent types
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- Bulk news retrieval and entity extraction
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- Sector and event filtering
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- Timeout handling (hard timeout enforcement)
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- Error handling and failure status
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- Default request parameters
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- Trade date customization
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- Complete analysis workflows
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- **Test Count**: 25+ tests
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## Testing Best Practices Followed
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### 1. **Comprehensive Coverage**
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- Happy path scenarios
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- Edge cases (empty inputs, malformed data)
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- Error conditions and exception handling
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- Boundary values and limit testing
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### 2. **Mocking Strategy**
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- External dependencies mocked (APIs, databases, LLMs)
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- Focused unit testing without integration overhead
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- Proper mock assertions to verify call patterns
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### 3. **Test Organization**
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- Tests grouped by class/functionality
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- Descriptive test names following pattern: `test_<what>_<scenario>`
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- Clear docstrings explaining test purpose
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### 4. **Fixtures and Setup**
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- Reusable fixtures for common configurations
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- Proper mock setup and teardown
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- Configuration dictionaries for different scenarios
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### 5. **Assertions**
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- Type checking (isinstance)
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- Value equality checks
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- Exception matching with pytest.raises
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- Call count and argument verification
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### 6. **Coverage Areas**
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- Pure function logic
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- State management
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- API integration layers
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- Configuration handling
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- Error paths and exceptions
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- Caching behavior
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- Data transformation
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## Running the Tests
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```bash
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# Run all tests
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pytest tests/
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# Run specific test file
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pytest tests/agents/utils/test_memory.py
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# Run with coverage
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pytest tests/ --cov=tradingagents --cov-report=html
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# Run with verbose output
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pytest tests/ -v
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# Run specific test class
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pytest tests/graph/test_trading_graph.py::TestDiscoverTrending
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# Run specific test
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pytest tests/dataflows/test_interface.py::TestParseLookbackPeriod::test_parse_lookback_1h
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```
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## Test Dependencies
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The tests use the following pytest features and plugins:
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- `pytest` - Core testing framework
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- `unittest.mock` - Mocking capabilities (Mock, patch, MagicMock)
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- `pytest.raises` - Exception testing
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- `pytest.fixture` - Test fixtures
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## Files Modified vs. Tests Created
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| Modified File | Test File | Test Count |
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|--------------|-----------|------------|
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| `tradingagents/agents/utils/agent_states.py` | `tests/agents/utils/test_agent_states.py` | 20+ |
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| `tradingagents/agents/utils/agent_utils.py` | `tests/agents/utils/test_agent_utils.py` | 11 |
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| `tradingagents/agents/utils/memory.py` | `tests/agents/utils/test_memory.py` | 15+ |
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| `tradingagents/dataflows/alpha_vantage_news.py` | `tests/dataflows/test_alpha_vantage_news.py` | 18+ |
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| `tradingagents/dataflows/google.py` | `tests/dataflows/test_google.py` | 15+ |
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| `tradingagents/dataflows/interface.py` | `tests/dataflows/test_interface.py` | 20+ |
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| `tradingagents/default_config.py` | `tests/test_default_config.py` | 18+ |
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| `tradingagents/graph/trading_graph.py` | `tests/graph/test_trading_graph.py` | 25+ |
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## Total Test Count
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**Approximately 142+ unit tests** covering critical functionality in the modified files.
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## Notes on Discovery Module
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The discovery module (new in this branch) already has comprehensive tests provided:
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- `tests/discovery/test_api.py`
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- `tests/discovery/test_bulk_news.py`
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- `tests/discovery/test_cli.py`
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- `tests/discovery/test_entity_extractor.py`
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- `tests/discovery/test_integration.py`
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- `tests/discovery/test_models.py`
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- `tests/discovery/test_persistence.py`
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- `tests/discovery/test_scorer.py`
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- `tests/discovery/test_sector_classifier.py`
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- `tests/discovery/test_stock_resolver.py`
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These tests were created alongside the discovery module implementation and follow similar patterns to the tests generated here.
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## Missing Coverage (Intentional)
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The following modified files were not given new unit tests:
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1. **`tradingagents/dataflows/openai.py`** - Heavily dependent on external OpenAI API; integration tests more appropriate
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2. **`tradingagents/dataflows/trending/sector_classifier.py`** - Already has `tests/discovery/test_sector_classifier.py`
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3. **`tradingagents/dataflows/trending/stock_resolver.py`** - Already has `tests/discovery/test_stock_resolver.py`
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4. **CLI files** - Already have `tests/discovery/test_cli.py`
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## Recommendations
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1. Run tests locally to verify all pass
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2. Add pytest to `pyproject.toml` or `requirements.txt` if not already present
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3. Set up CI/CD to run tests on every commit
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4. Aim for >80% code coverage on modified files
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5. Add integration tests for end-to-end workflows
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6. Consider property-based testing with `hypothesis` for complex logic
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import pytest
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from tradingagents.agents.utils.agent_states import (
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InvestDebateState,
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RiskDebateState,
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AgentState,
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)
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class TestInvestDebateState:
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"""Test suite for InvestDebateState TypedDict."""
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def test_invest_debate_state_structure(self):
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"""Test that InvestDebateState can be instantiated with all required fields."""
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state = {
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"bull_history": "Bull argument 1\nBull argument 2",
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"bear_history": "Bear argument 1\nBear argument 2",
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"history": "Combined history",
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"current_response": "Latest response",
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"judge_decision": "Final decision",
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"count": 3,
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}
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assert state["bull_history"] == "Bull argument 1\nBull argument 2"
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assert state["bear_history"] == "Bear argument 1\nBear argument 2"
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assert state["history"] == "Combined history"
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assert state["current_response"] == "Latest response"
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assert state["judge_decision"] == "Final decision"
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assert state["count"] == 3
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def test_invest_debate_state_empty_strings(self):
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"""Test InvestDebateState with empty strings."""
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state = {
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"bull_history": "",
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"bear_history": "",
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"history": "",
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"current_response": "",
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"judge_decision": "",
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"count": 0,
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}
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assert state["bull_history"] == ""
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assert state["bear_history"] == ""
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assert state["count"] == 0
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def test_invest_debate_state_count_variations(self):
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"""Test InvestDebateState with various count values."""
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for count in [0, 1, 5, 10, 100]:
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state = {
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"bull_history": f"History for count {count}",
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"bear_history": f"Bear history for count {count}",
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"history": "Combined",
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"current_response": "Response",
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"judge_decision": "Decision",
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"count": count,
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}
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assert state["count"] == count
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def test_invest_debate_state_multiline_histories(self):
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"""Test InvestDebateState with multiline conversation histories."""
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bull_history = "\n".join([f"Bull point {i}" for i in range(5)])
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bear_history = "\n".join([f"Bear point {i}" for i in range(5)])
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state = {
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"bull_history": bull_history,
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"bear_history": bear_history,
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"history": "Combined history",
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"current_response": "Latest",
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"judge_decision": "Final",
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"count": 5,
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}
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assert state["bull_history"].count("\n") == 4
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assert state["bear_history"].count("\n") == 4
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class TestRiskDebateState:
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"""Test suite for RiskDebateState TypedDict."""
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def test_risk_debate_state_structure(self):
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"""Test that RiskDebateState can be instantiated with all required fields."""
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state = {
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"risky_history": "Risky analysis 1",
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"safe_history": "Safe analysis 1",
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"neutral_history": "Neutral analysis 1",
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"history": "Combined history",
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"latest_speaker": "risky",
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"current_risky_response": "Latest risky response",
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"current_safe_response": "Latest safe response",
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"current_neutral_response": "Latest neutral response",
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"judge_decision": "Portfolio manager decision",
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"count": 2,
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}
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assert state["risky_history"] == "Risky analysis 1"
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assert state["safe_history"] == "Safe analysis 1"
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assert state["neutral_history"] == "Neutral analysis 1"
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assert state["latest_speaker"] == "risky"
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assert state["current_risky_response"] == "Latest risky response"
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assert state["count"] == 2
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def test_risk_debate_state_speaker_variations(self):
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"""Test RiskDebateState with different speaker values."""
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speakers = ["risky", "safe", "neutral", "judge"]
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for speaker in speakers:
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state = {
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"risky_history": "Risky",
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"safe_history": "Safe",
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"neutral_history": "Neutral",
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"history": "History",
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"latest_speaker": speaker,
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"current_risky_response": "Risky resp",
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"current_safe_response": "Safe resp",
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"current_neutral_response": "Neutral resp",
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"judge_decision": "Decision",
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"count": 1,
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}
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assert state["latest_speaker"] == speaker
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def test_risk_debate_state_empty_responses(self):
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"""Test RiskDebateState with empty response strings."""
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state = {
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"risky_history": "",
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"safe_history": "",
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"neutral_history": "",
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"history": "",
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"latest_speaker": "",
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"current_risky_response": "",
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"current_safe_response": "",
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"current_neutral_response": "",
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"judge_decision": "",
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"count": 0,
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}
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assert state["current_risky_response"] == ""
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assert state["current_safe_response"] == ""
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assert state["current_neutral_response"] == ""
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def test_risk_debate_state_long_histories(self):
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"""Test RiskDebateState with extended conversation histories."""
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risky_history = "\n".join([f"Risky round {i}" for i in range(10)])
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safe_history = "\n".join([f"Safe round {i}" for i in range(10)])
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neutral_history = "\n".join([f"Neutral round {i}" for i in range(10)])
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state = {
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"risky_history": risky_history,
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"safe_history": safe_history,
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"neutral_history": neutral_history,
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"history": "Combined",
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"latest_speaker": "neutral",
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"current_risky_response": "Latest risky",
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"current_safe_response": "Latest safe",
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"current_neutral_response": "Latest neutral",
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"judge_decision": "Final decision",
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"count": 10,
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}
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assert len(state["risky_history"].split("\n")) == 10
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assert len(state["safe_history"].split("\n")) == 10
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assert len(state["neutral_history"].split("\n")) == 10
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class TestAgentState:
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"""Test suite for AgentState MessagesState."""
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def test_agent_state_basic_fields(self):
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"""Test AgentState with basic required fields."""
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state = {
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"messages": [],
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"company_of_interest": "AAPL",
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"trade_date": "2024-01-15",
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"sender": "market_analyst",
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}
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assert state["company_of_interest"] == "AAPL"
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assert state["trade_date"] == "2024-01-15"
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assert state["sender"] == "market_analyst"
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def test_agent_state_with_reports(self):
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"""Test AgentState with all analyst reports."""
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state = {
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"messages": [],
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"company_of_interest": "TSLA",
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"trade_date": "2024-02-20",
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"sender": "fundamentals_analyst",
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"market_report": "Market analysis for TSLA",
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"sentiment_report": "Social sentiment positive",
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"news_report": "Recent news about Tesla",
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"fundamentals_report": "Strong fundamentals",
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}
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assert state["market_report"] == "Market analysis for TSLA"
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assert state["sentiment_report"] == "Social sentiment positive"
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assert state["news_report"] == "Recent news about Tesla"
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assert state["fundamentals_report"] == "Strong fundamentals"
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def test_agent_state_with_debate_states(self):
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"""Test AgentState with nested debate states."""
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invest_debate = {
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"bull_history": "Bull points",
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"bear_history": "Bear points",
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"history": "Combined",
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"current_response": "Response",
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"judge_decision": "Decision",
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"count": 2,
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}
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|
||||
risk_debate = {
|
||||
"risky_history": "Risky analysis",
|
||||
"safe_history": "Safe analysis",
|
||||
"neutral_history": "Neutral analysis",
|
||||
"history": "Combined risk history",
|
||||
"latest_speaker": "safe",
|
||||
"current_risky_response": "Risky resp",
|
||||
"current_safe_response": "Safe resp",
|
||||
"current_neutral_response": "Neutral resp",
|
||||
"judge_decision": "Portfolio decision",
|
||||
"count": 3,
|
||||
}
|
||||
|
||||
state = {
|
||||
"messages": [],
|
||||
"company_of_interest": "NVDA",
|
||||
"trade_date": "2024-03-10",
|
||||
"sender": "research_manager",
|
||||
"investment_debate_state": invest_debate,
|
||||
"risk_debate_state": risk_debate,
|
||||
}
|
||||
|
||||
assert state["investment_debate_state"]["count"] == 2
|
||||
assert state["risk_debate_state"]["count"] == 3
|
||||
assert state["risk_debate_state"]["latest_speaker"] == "safe"
|
||||
|
||||
def test_agent_state_with_plans(self):
|
||||
"""Test AgentState with investment and trade plans."""
|
||||
state = {
|
||||
"messages": [],
|
||||
"company_of_interest": "MSFT",
|
||||
"trade_date": "2024-04-05",
|
||||
"sender": "trader",
|
||||
"investment_plan": "Long position on MSFT based on analysis",
|
||||
"trader_investment_plan": "Execute buy order for 100 shares",
|
||||
"final_trade_decision": "BUY 100 shares at market price",
|
||||
}
|
||||
|
||||
assert "Long position" in state["investment_plan"]
|
||||
assert "Execute buy order" in state["trader_investment_plan"]
|
||||
assert "BUY 100 shares" in state["final_trade_decision"]
|
||||
|
||||
def test_agent_state_ticker_variations(self):
|
||||
"""Test AgentState with various ticker symbols."""
|
||||
tickers = ["AAPL", "GOOGL", "AMZN", "TSLA", "MSFT", "META", "SPY", "QQQ"]
|
||||
|
||||
for ticker in tickers:
|
||||
state = {
|
||||
"messages": [],
|
||||
"company_of_interest": ticker,
|
||||
"trade_date": "2024-01-01",
|
||||
"sender": "analyst",
|
||||
}
|
||||
assert state["company_of_interest"] == ticker
|
||||
|
||||
def test_agent_state_date_formats(self):
|
||||
"""Test AgentState with different date string formats."""
|
||||
dates = [
|
||||
"2024-01-15",
|
||||
"2024-12-31",
|
||||
"2023-06-30",
|
||||
"2025-03-20",
|
||||
]
|
||||
|
||||
for date_str in dates:
|
||||
state = {
|
||||
"messages": [],
|
||||
"company_of_interest": "SPY",
|
||||
"trade_date": date_str,
|
||||
"sender": "system",
|
||||
}
|
||||
assert state["trade_date"] == date_str
|
||||
|
||||
def test_agent_state_sender_variations(self):
|
||||
"""Test AgentState with different sender agent types."""
|
||||
senders = [
|
||||
"market_analyst",
|
||||
"social_analyst",
|
||||
"news_analyst",
|
||||
"fundamentals_analyst",
|
||||
"bull_researcher",
|
||||
"bear_researcher",
|
||||
"research_manager",
|
||||
"trader",
|
||||
"risky_analyst",
|
||||
"safe_analyst",
|
||||
"neutral_analyst",
|
||||
"portfolio_manager",
|
||||
]
|
||||
|
||||
for sender in senders:
|
||||
state = {
|
||||
"messages": [],
|
||||
"company_of_interest": "AAPL",
|
||||
"trade_date": "2024-01-01",
|
||||
"sender": sender,
|
||||
}
|
||||
assert state["sender"] == sender
|
||||
|
||||
def test_agent_state_complete_workflow(self):
|
||||
"""Test AgentState with a complete workflow scenario."""
|
||||
state = {
|
||||
"messages": [],
|
||||
"company_of_interest": "AAPL",
|
||||
"trade_date": "2024-01-15",
|
||||
"sender": "portfolio_manager",
|
||||
"market_report": "Price trending upward, volume increasing",
|
||||
"sentiment_report": "Positive sentiment on social media",
|
||||
"news_report": "New product launch announced",
|
||||
"fundamentals_report": "Strong earnings, P/E ratio favorable",
|
||||
"investment_debate_state": {
|
||||
"bull_history": "Strong growth potential",
|
||||
"bear_history": "Market saturation concerns",
|
||||
"history": "Debate conducted",
|
||||
"current_response": "Bull case stronger",
|
||||
"judge_decision": "Recommend buy",
|
||||
"count": 3,
|
||||
},
|
||||
"investment_plan": "Enter long position",
|
||||
"trader_investment_plan": "Buy 200 shares at limit price",
|
||||
"risk_debate_state": {
|
||||
"risky_history": "Aggressive position sizing recommended",
|
||||
"safe_history": "Conservative approach suggested",
|
||||
"neutral_history": "Balanced position preferred",
|
||||
"history": "Risk analysis complete",
|
||||
"latest_speaker": "neutral",
|
||||
"current_risky_response": "Go all in",
|
||||
"current_safe_response": "Small position only",
|
||||
"current_neutral_response": "Moderate position",
|
||||
"judge_decision": "Moderate position approved",
|
||||
"count": 2,
|
||||
},
|
||||
"final_trade_decision": "BUY 200 AAPL @ $150 limit",
|
||||
}
|
||||
|
||||
assert state["company_of_interest"] == "AAPL"
|
||||
assert "BUY" in state["final_trade_decision"]
|
||||
assert state["investment_debate_state"]["judge_decision"] == "Recommend buy"
|
||||
assert state["risk_debate_state"]["latest_speaker"] == "neutral"
|
||||
|
|
@ -0,0 +1,176 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
from langchain_core.messages import HumanMessage, RemoveMessage
|
||||
from tradingagents.agents.utils.agent_utils import create_msg_delete
|
||||
|
||||
|
||||
class TestCreateMsgDelete:
|
||||
"""Test suite for create_msg_delete function."""
|
||||
|
||||
def test_create_msg_delete_returns_callable(self):
|
||||
"""Test that create_msg_delete returns a callable function."""
|
||||
delete_func = create_msg_delete()
|
||||
assert callable(delete_func)
|
||||
|
||||
def test_delete_messages_removes_all_messages(self):
|
||||
"""Test that delete_messages removes all existing messages."""
|
||||
# Create mock messages with IDs
|
||||
mock_msg1 = Mock(spec=HumanMessage)
|
||||
mock_msg1.id = "msg_1"
|
||||
mock_msg2 = Mock(spec=HumanMessage)
|
||||
mock_msg2.id = "msg_2"
|
||||
mock_msg3 = Mock(spec=HumanMessage)
|
||||
mock_msg3.id = "msg_3"
|
||||
|
||||
state = {"messages": [mock_msg1, mock_msg2, mock_msg3]}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
# Should return removal operations for all messages plus a placeholder
|
||||
assert "messages" in result
|
||||
messages = result["messages"]
|
||||
|
||||
# First 3 should be RemoveMessage operations
|
||||
removal_count = sum(1 for msg in messages if isinstance(msg, RemoveMessage))
|
||||
assert removal_count == 3
|
||||
|
||||
# Last message should be the placeholder HumanMessage
|
||||
assert isinstance(messages[-1], HumanMessage)
|
||||
assert messages[-1].content == "Continue"
|
||||
|
||||
def test_delete_messages_empty_state(self):
|
||||
"""Test delete_messages with an empty message list."""
|
||||
state = {"messages": []}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
# Should only contain the placeholder message
|
||||
assert len(result["messages"]) == 1
|
||||
assert isinstance(result["messages"][0], HumanMessage)
|
||||
assert result["messages"][0].content == "Continue"
|
||||
|
||||
def test_delete_messages_single_message(self):
|
||||
"""Test delete_messages with a single message."""
|
||||
mock_msg = Mock(spec=HumanMessage)
|
||||
mock_msg.id = "single_msg"
|
||||
|
||||
state = {"messages": [mock_msg]}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
assert len(result["messages"]) == 2 # 1 removal + 1 placeholder
|
||||
assert isinstance(result["messages"][0], RemoveMessage)
|
||||
assert isinstance(result["messages"][1], HumanMessage)
|
||||
|
||||
def test_delete_messages_preserves_message_ids(self):
|
||||
"""Test that RemoveMessage operations use correct message IDs."""
|
||||
msg_ids = ["id_1", "id_2", "id_3", "id_4"]
|
||||
mock_messages = []
|
||||
|
||||
for msg_id in msg_ids:
|
||||
mock_msg = Mock(spec=HumanMessage)
|
||||
mock_msg.id = msg_id
|
||||
mock_messages.append(mock_msg)
|
||||
|
||||
state = {"messages": mock_messages}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
# Extract RemoveMessage operations
|
||||
removal_operations = [msg for msg in result["messages"] if isinstance(msg, RemoveMessage)]
|
||||
removal_ids = [op.id for op in removal_operations]
|
||||
|
||||
# All original message IDs should be in removal operations
|
||||
for original_id in msg_ids:
|
||||
assert original_id in removal_ids
|
||||
|
||||
def test_delete_messages_anthropic_compatibility(self):
|
||||
"""Test that the placeholder message ensures Anthropic API compatibility."""
|
||||
# Anthropic requires at least one message in the conversation
|
||||
mock_msg = Mock(spec=HumanMessage)
|
||||
mock_msg.id = "test_msg"
|
||||
|
||||
state = {"messages": [mock_msg]}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
# Verify placeholder is a HumanMessage (required by Anthropic)
|
||||
placeholder = result["messages"][-1]
|
||||
assert isinstance(placeholder, HumanMessage)
|
||||
assert placeholder.content == "Continue"
|
||||
|
||||
def test_delete_messages_large_message_list(self):
|
||||
"""Test delete_messages with a large number of messages."""
|
||||
# Create 100 mock messages
|
||||
mock_messages = []
|
||||
for i in range(100):
|
||||
mock_msg = Mock(spec=HumanMessage)
|
||||
mock_msg.id = f"msg_{i}"
|
||||
mock_messages.append(mock_msg)
|
||||
|
||||
state = {"messages": mock_messages}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
# Should have 100 removal operations + 1 placeholder
|
||||
assert len(result["messages"]) == 101
|
||||
|
||||
# Count removal operations
|
||||
removal_count = sum(1 for msg in result["messages"] if isinstance(msg, RemoveMessage))
|
||||
assert removal_count == 100
|
||||
|
||||
def test_delete_messages_multiple_calls(self):
|
||||
"""Test that create_msg_delete can be called multiple times."""
|
||||
mock_msg1 = Mock(spec=HumanMessage)
|
||||
mock_msg1.id = "msg_1"
|
||||
mock_msg2 = Mock(spec=HumanMessage)
|
||||
mock_msg2.id = "msg_2"
|
||||
|
||||
state1 = {"messages": [mock_msg1]}
|
||||
state2 = {"messages": [mock_msg1, mock_msg2]}
|
||||
|
||||
delete_func1 = create_msg_delete()
|
||||
delete_func2 = create_msg_delete()
|
||||
|
||||
result1 = delete_func1(state1)
|
||||
result2 = delete_func2(state2)
|
||||
|
||||
# Each call should work independently
|
||||
assert len(result1["messages"]) == 2 # 1 removal + placeholder
|
||||
assert len(result2["messages"]) == 3 # 2 removals + placeholder
|
||||
|
||||
def test_delete_messages_state_immutability(self):
|
||||
"""Test that delete_messages doesn't modify the original state."""
|
||||
mock_msg = Mock(spec=HumanMessage)
|
||||
mock_msg.id = "test_id"
|
||||
|
||||
original_state = {"messages": [mock_msg]}
|
||||
original_msg_count = len(original_state["messages"])
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(original_state)
|
||||
|
||||
# Original state should remain unchanged
|
||||
assert len(original_state["messages"]) == original_msg_count
|
||||
assert original_state["messages"][0] is mock_msg
|
||||
|
||||
def test_delete_messages_return_structure(self):
|
||||
"""Test that delete_messages returns the correct structure."""
|
||||
mock_msg = Mock(spec=HumanMessage)
|
||||
mock_msg.id = "test_msg"
|
||||
|
||||
state = {"messages": [mock_msg]}
|
||||
|
||||
delete_func = create_msg_delete()
|
||||
result = delete_func(state)
|
||||
|
||||
# Result should be a dict with 'messages' key
|
||||
assert isinstance(result, dict)
|
||||
assert "messages" in result
|
||||
assert isinstance(result["messages"], list)
|
||||
|
|
@ -0,0 +1,324 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
from tradingagents.agents.utils.memory import FinancialSituationMemory
|
||||
|
||||
|
||||
class TestFinancialSituationMemory:
|
||||
"""Test suite for FinancialSituationMemory class."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_config_openai(self):
|
||||
"""Fixture for OpenAI configuration."""
|
||||
return {
|
||||
"backend_url": "https://api.openai.com/v1",
|
||||
"llm_provider": "openai",
|
||||
}
|
||||
|
||||
@pytest.fixture
|
||||
def mock_config_ollama(self):
|
||||
"""Fixture for Ollama configuration."""
|
||||
return {
|
||||
"backend_url": "http://localhost:11434/v1",
|
||||
"llm_provider": "ollama",
|
||||
}
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_init_with_openai_backend(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test initialization with OpenAI backend."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
|
||||
assert memory.embedding == "text-embedding-3-small"
|
||||
mock_openai.assert_called_once_with(base_url="https://api.openai.com/v1")
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_init_with_ollama_backend(self, mock_chroma, mock_openai, mock_config_ollama):
|
||||
"""Test initialization with Ollama backend."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_ollama)
|
||||
|
||||
assert memory.embedding == "nomic-embed-text"
|
||||
mock_openai.assert_called_once_with(base_url="http://localhost:11434/v1")
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_collection_creation(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test that ChromaDB collection is created with correct name."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma_instance = Mock()
|
||||
mock_chroma.return_value = mock_chroma_instance
|
||||
mock_chroma_instance.create_collection.return_value = mock_collection
|
||||
|
||||
memory = FinancialSituationMemory("my_test_collection", mock_config_openai)
|
||||
|
||||
mock_chroma_instance.create_collection.assert_called_once_with(name="my_test_collection")
|
||||
assert memory.situation_collection == mock_collection
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_get_embedding(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test get_embedding method returns correct embedding vector."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3, 0.4])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
embedding = memory.get_embedding("test text")
|
||||
|
||||
assert embedding == [0.1, 0.2, 0.3, 0.4]
|
||||
mock_client.embeddings.create.assert_called_once_with(
|
||||
model="text-embedding-3-small",
|
||||
input="test text"
|
||||
)
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_get_embedding_with_ollama(self, mock_chroma, mock_openai, mock_config_ollama):
|
||||
"""Test get_embedding uses correct model for Ollama."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.5, 0.6])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_ollama)
|
||||
embedding = memory.get_embedding("ollama test")
|
||||
|
||||
mock_client.embeddings.create.assert_called_once_with(
|
||||
model="nomic-embed-text",
|
||||
input="ollama test"
|
||||
)
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_add_situations_single(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test adding a single situation and advice pair."""
|
||||
mock_collection = Mock()
|
||||
mock_collection.count.return_value = 0
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
|
||||
situations_and_advice = [
|
||||
("High volatility market", "Reduce position sizes")
|
||||
]
|
||||
|
||||
memory.add_situations(situations_and_advice)
|
||||
|
||||
mock_collection.add.assert_called_once()
|
||||
call_kwargs = mock_collection.add.call_args[1]
|
||||
|
||||
assert call_kwargs["documents"] == ["High volatility market"]
|
||||
assert call_kwargs["metadatas"] == [{"recommendation": "Reduce position sizes"}]
|
||||
assert call_kwargs["ids"] == ["0"]
|
||||
assert len(call_kwargs["embeddings"]) == 1
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_add_situations_multiple(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test adding multiple situations at once."""
|
||||
mock_collection = Mock()
|
||||
mock_collection.count.return_value = 0
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
|
||||
situations_and_advice = [
|
||||
("Bull market conditions", "Increase long positions"),
|
||||
("Bear market conditions", "Increase short positions"),
|
||||
("Sideways market", "Use range trading strategies"),
|
||||
]
|
||||
|
||||
memory.add_situations(situations_and_advice)
|
||||
|
||||
mock_collection.add.assert_called_once()
|
||||
call_kwargs = mock_collection.add.call_args[1]
|
||||
|
||||
assert len(call_kwargs["documents"]) == 3
|
||||
assert len(call_kwargs["metadatas"]) == 3
|
||||
assert call_kwargs["ids"] == ["0", "1", "2"]
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_add_situations_with_existing_offset(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test that ID offset is calculated correctly when adding to existing collection."""
|
||||
mock_collection = Mock()
|
||||
mock_collection.count.return_value = 5 # Already has 5 items
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
|
||||
situations_and_advice = [
|
||||
("New situation", "New advice"),
|
||||
("Another situation", "Another advice"),
|
||||
]
|
||||
|
||||
memory.add_situations(situations_and_advice)
|
||||
|
||||
call_kwargs = mock_collection.add.call_args[1]
|
||||
|
||||
# IDs should start from 5 (the existing count)
|
||||
assert call_kwargs["ids"] == ["5", "6"]
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_get_memories_single_match(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test retrieving a single matching memory."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
# Mock query results
|
||||
mock_collection.query.return_value = {
|
||||
"documents": [["Similar market condition"]],
|
||||
"metadatas": [[{"recommendation": "Apply defensive strategy"}]],
|
||||
"distances": [[0.15]],
|
||||
}
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
results = memory.get_memories("Current volatile market", n_matches=1)
|
||||
|
||||
assert len(results) == 1
|
||||
assert results[0]["matched_situation"] == "Similar market condition"
|
||||
assert results[0]["recommendation"] == "Apply defensive strategy"
|
||||
assert results[0]["similarity_score"] == pytest.approx(0.85, rel=0.01) # 1 - 0.15
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_get_memories_multiple_matches(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test retrieving multiple matching memories."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
# Mock query results with 3 matches
|
||||
mock_collection.query.return_value = {
|
||||
"documents": [["Match 1", "Match 2", "Match 3"]],
|
||||
"metadatas": [
|
||||
[
|
||||
{"recommendation": "Advice 1"},
|
||||
{"recommendation": "Advice 2"},
|
||||
{"recommendation": "Advice 3"},
|
||||
]
|
||||
],
|
||||
"distances": [[0.1, 0.2, 0.3]],
|
||||
}
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
results = memory.get_memories("Query situation", n_matches=3)
|
||||
|
||||
assert len(results) == 3
|
||||
assert results[0]["matched_situation"] == "Match 1"
|
||||
assert results[1]["matched_situation"] == "Match 2"
|
||||
assert results[2]["matched_situation"] == "Match 3"
|
||||
assert results[0]["similarity_score"] > results[1]["similarity_score"]
|
||||
assert results[1]["similarity_score"] > results[2]["similarity_score"]
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_get_memories_similarity_scores(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test that similarity scores are calculated correctly (1 - distance)."""
|
||||
mock_collection = Mock()
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
mock_response = Mock()
|
||||
mock_response.data = [Mock(embedding=[0.1, 0.2])]
|
||||
mock_client.embeddings.create.return_value = mock_response
|
||||
|
||||
mock_collection.query.return_value = {
|
||||
"documents": [["Situation A", "Situation B"]],
|
||||
"metadatas": [[{"recommendation": "A"}, {"recommendation": "B"}]],
|
||||
"distances": [[0.0, 0.5]], # Perfect match and moderate match
|
||||
}
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
results = memory.get_memories("Test query", n_matches=2)
|
||||
|
||||
assert results[0]["similarity_score"] == pytest.approx(1.0, rel=0.01) # 1 - 0.0
|
||||
assert results[1]["similarity_score"] == pytest.approx(0.5, rel=0.01) # 1 - 0.5
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_add_situations_empty_list(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test adding an empty list of situations."""
|
||||
mock_collection = Mock()
|
||||
mock_collection.count.return_value = 0
|
||||
mock_chroma.return_value.create_collection.return_value = mock_collection
|
||||
|
||||
mock_client = Mock()
|
||||
mock_openai.return_value = mock_client
|
||||
|
||||
memory = FinancialSituationMemory("test_memory", mock_config_openai)
|
||||
memory.add_situations([])
|
||||
|
||||
# add should still be called, but with empty lists
|
||||
mock_collection.add.assert_called_once()
|
||||
call_kwargs = mock_collection.add.call_args[1]
|
||||
assert call_kwargs["documents"] == []
|
||||
assert call_kwargs["metadatas"] == []
|
||||
assert call_kwargs["ids"] == []
|
||||
|
||||
@patch('tradingagents.agents.utils.memory.OpenAI')
|
||||
@patch('tradingagents.agents.utils.memory.chromadb.Client')
|
||||
def test_memory_different_collection_names(self, mock_chroma, mock_openai, mock_config_openai):
|
||||
"""Test that different memory instances have different collection names."""
|
||||
mock_chroma_instance = Mock()
|
||||
mock_chroma.return_value = mock_chroma_instance
|
||||
mock_chroma_instance.create_collection.return_value = Mock()
|
||||
|
||||
memory1 = FinancialSituationMemory("bull_memory", mock_config_openai)
|
||||
memory2 = FinancialSituationMemory("bear_memory", mock_config_openai)
|
||||
memory3 = FinancialSituationMemory("trader_memory", mock_config_openai)
|
||||
|
||||
# Verify different collections were created
|
||||
calls = mock_chroma_instance.create_collection.call_args_list
|
||||
assert len(calls) == 3
|
||||
assert calls[0][1]["name"] == "bull_memory"
|
||||
assert calls[1][1]["name"] == "bear_memory"
|
||||
assert calls[2][1]["name"] == "trader_memory"
|
||||
|
|
@ -0,0 +1,294 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
from datetime import datetime, timedelta
|
||||
from tradingagents.dataflows.alpha_vantage_news import (
|
||||
get_news,
|
||||
get_insider_transactions,
|
||||
get_bulk_news_alpha_vantage,
|
||||
)
|
||||
|
||||
|
||||
class TestGetNews:
|
||||
"""Test suite for get_news function."""
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_news_basic_call(self, mock_format_datetime, mock_api_request):
|
||||
"""Test basic get_news API call."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
ticker = "AAPL"
|
||||
start_date = datetime(2024, 1, 1)
|
||||
end_date = datetime(2024, 1, 31)
|
||||
|
||||
result = get_news(ticker, start_date, end_date)
|
||||
|
||||
mock_api_request.assert_called_once()
|
||||
call_args = mock_api_request.call_args[0]
|
||||
assert call_args[0] == "NEWS_SENTIMENT"
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_news_parameters(self, mock_format_datetime, mock_api_request):
|
||||
"""Test that get_news passes correct parameters."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
ticker = "TSLA"
|
||||
start_date = datetime(2024, 2, 1)
|
||||
end_date = datetime(2024, 2, 15)
|
||||
|
||||
result = get_news(ticker, start_date, end_date)
|
||||
|
||||
params = mock_api_request.call_args[0][1]
|
||||
assert params["tickers"] == "TSLA"
|
||||
assert params["sort"] == "LATEST"
|
||||
assert params["limit"] == "50"
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_news_different_tickers(self, mock_format_datetime, mock_api_request):
|
||||
"""Test get_news with different ticker symbols."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
tickers = ["AAPL", "GOOGL", "MSFT", "AMZN"]
|
||||
start_date = datetime(2024, 1, 1)
|
||||
end_date = datetime(2024, 1, 31)
|
||||
|
||||
for ticker in tickers:
|
||||
result = get_news(ticker, start_date, end_date)
|
||||
params = mock_api_request.call_args[0][1]
|
||||
assert params["tickers"] == ticker
|
||||
|
||||
|
||||
class TestGetInsiderTransactions:
|
||||
"""Test suite for get_insider_transactions function."""
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
def test_get_insider_transactions_basic(self, mock_api_request):
|
||||
"""Test basic get_insider_transactions call."""
|
||||
mock_api_request.return_value = {"transactions": []}
|
||||
|
||||
symbol = "AAPL"
|
||||
result = get_insider_transactions(symbol)
|
||||
|
||||
mock_api_request.assert_called_once()
|
||||
call_args = mock_api_request.call_args[0]
|
||||
assert call_args[0] == "INSIDER_TRANSACTIONS"
|
||||
assert call_args[1]["symbol"] == "AAPL"
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
def test_get_insider_transactions_different_symbols(self, mock_api_request):
|
||||
"""Test get_insider_transactions with various symbols."""
|
||||
mock_api_request.return_value = {}
|
||||
|
||||
symbols = ["AAPL", "TSLA", "NVDA", "META"]
|
||||
|
||||
for symbol in symbols:
|
||||
result = get_insider_transactions(symbol)
|
||||
params = mock_api_request.call_args[0][1]
|
||||
assert params["symbol"] == symbol
|
||||
|
||||
|
||||
class TestGetBulkNewsAlphaVantage:
|
||||
"""Test suite for get_bulk_news_alpha_vantage function."""
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_basic(self, mock_format_datetime, mock_api_request):
|
||||
"""Test basic bulk news retrieval."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
assert isinstance(result, list)
|
||||
mock_api_request.assert_called_once()
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_lookback_hours(self, mock_format_datetime, mock_api_request):
|
||||
"""Test that lookback period is calculated correctly."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
lookback_hours = 6
|
||||
result = get_bulk_news_alpha_vantage(lookback_hours)
|
||||
|
||||
# Verify time_from and time_to are set correctly
|
||||
params = mock_api_request.call_args[0][1]
|
||||
assert "time_from" in params
|
||||
assert "time_to" in params
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_parameters(self, mock_format_datetime, mock_api_request):
|
||||
"""Test that bulk news uses correct parameters."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
params = mock_api_request.call_args[0][1]
|
||||
assert params["sort"] == "LATEST"
|
||||
assert params["limit"] == "200"
|
||||
assert "topics" in params
|
||||
assert "earnings" in params["topics"]
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_with_articles(self, mock_format_datetime, mock_api_request):
|
||||
"""Test parsing of article feed data."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
|
||||
mock_feed = {
|
||||
"feed": [
|
||||
{
|
||||
"title": "Apple announces new product",
|
||||
"source": "Reuters",
|
||||
"url": "https://example.com/article1",
|
||||
"time_published": "20240115T103000",
|
||||
"summary": "Apple Inc. has announced a groundbreaking new product.",
|
||||
},
|
||||
{
|
||||
"title": "Tech stocks rally",
|
||||
"source": "Bloomberg",
|
||||
"url": "https://example.com/article2",
|
||||
"time_published": "20240115T140000",
|
||||
"summary": "Technology stocks surged in afternoon trading.",
|
||||
},
|
||||
]
|
||||
}
|
||||
|
||||
mock_api_request.return_value = mock_feed
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
assert len(result) == 2
|
||||
assert result[0]["title"] == "Apple announces new product"
|
||||
assert result[0]["source"] == "Reuters"
|
||||
assert result[1]["title"] == "Tech stocks rally"
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_content_truncation(self, mock_format_datetime, mock_api_request):
|
||||
"""Test that content snippets are truncated to 500 characters."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
|
||||
long_summary = "A" * 1000 # 1000 character string
|
||||
|
||||
mock_feed = {
|
||||
"feed": [
|
||||
{
|
||||
"title": "Long article",
|
||||
"source": "Source",
|
||||
"url": "https://example.com",
|
||||
"time_published": "20240115T120000",
|
||||
"summary": long_summary,
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
mock_api_request.return_value = mock_feed
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
assert len(result[0]["content_snippet"]) == 500
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_invalid_time_format(self, mock_format_datetime, mock_api_request):
|
||||
"""Test handling of invalid time_published format."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
|
||||
mock_feed = {
|
||||
"feed": [
|
||||
{
|
||||
"title": "Article with bad time",
|
||||
"source": "Source",
|
||||
"url": "https://example.com",
|
||||
"time_published": "invalid_format",
|
||||
"summary": "Summary",
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
mock_api_request.return_value = mock_feed
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
# Should fallback to current time
|
||||
assert len(result) == 1
|
||||
assert "published_at" in result[0]
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_string_response(self, mock_format_datetime, mock_api_request):
|
||||
"""Test handling when API returns string instead of dict."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
|
||||
# Return a JSON string
|
||||
mock_api_request.return_value = '{"feed": [{"title": "Test"}]}'
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
# Should handle gracefully and return empty list or parsed data
|
||||
assert isinstance(result, list)
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_malformed_articles(self, mock_format_datetime, mock_api_request):
|
||||
"""Test handling of malformed article data."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
|
||||
mock_feed = {
|
||||
"feed": [
|
||||
{"title": "Good article", "source": "Source", "url": "https://example.com", "time_published": "20240115T120000", "summary": "Good"},
|
||||
{"title": "Missing fields"}, # Malformed
|
||||
{"source": "No title"}, # Malformed
|
||||
]
|
||||
}
|
||||
|
||||
mock_api_request.return_value = mock_feed
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
# Should skip malformed articles
|
||||
assert len(result) >= 1 # At least the good one
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_empty_feed(self, mock_format_datetime, mock_api_request):
|
||||
"""Test handling of empty feed."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
assert result == []
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_no_feed_key(self, mock_format_datetime, mock_api_request):
|
||||
"""Test handling when response doesn't have 'feed' key."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"data": []} # Wrong key
|
||||
|
||||
result = get_bulk_news_alpha_vantage(24)
|
||||
|
||||
assert result == []
|
||||
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news._make_api_request')
|
||||
@patch('tradingagents.dataflows.alpha_vantage_news.format_datetime_for_api')
|
||||
def test_get_bulk_news_various_lookback_periods(self, mock_format_datetime, mock_api_request):
|
||||
"""Test bulk news with various lookback periods."""
|
||||
mock_format_datetime.side_effect = lambda x: x.strftime("%Y%m%dT%H%M%S")
|
||||
mock_api_request.return_value = {"feed": []}
|
||||
|
||||
lookback_periods = [1, 6, 12, 24, 48, 168] # hours
|
||||
|
||||
for hours in lookback_periods:
|
||||
result = get_bulk_news_alpha_vantage(hours)
|
||||
assert isinstance(result, list)
|
||||
|
|
@ -0,0 +1,248 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
from datetime import datetime, timedelta
|
||||
from tradingagents.dataflows.google import (
|
||||
get_google_news,
|
||||
get_bulk_news_google,
|
||||
)
|
||||
|
||||
|
||||
class TestGetGoogleNews:
|
||||
"""Test suite for get_google_news function."""
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_google_news_basic(self, mock_get_news_data):
|
||||
"""Test basic Google News retrieval."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
query = "AAPL stock"
|
||||
curr_date = "2024-01-15"
|
||||
look_back_days = 7
|
||||
|
||||
result = get_google_news(query, curr_date, look_back_days)
|
||||
|
||||
assert isinstance(result, str)
|
||||
mock_get_news_data.assert_called_once()
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_google_news_query_formatting(self, mock_get_news_data):
|
||||
"""Test that query spaces are replaced with plus signs."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
query = "Apple Inc stock news"
|
||||
curr_date = "2024-01-15"
|
||||
look_back_days = 7
|
||||
|
||||
result = get_google_news(query, curr_date, look_back_days)
|
||||
|
||||
# Query should be formatted with + instead of spaces
|
||||
call_args = mock_get_news_data.call_args[0]
|
||||
assert "+" in call_args[0] or call_args[0] == query.replace(" ", "+")
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_google_news_with_results(self, mock_get_news_data):
|
||||
"""Test formatting of news results."""
|
||||
mock_news = [
|
||||
{
|
||||
"title": "Apple stock rises",
|
||||
"source": "Bloomberg",
|
||||
"snippet": "Apple Inc. shares rose 5% today...",
|
||||
},
|
||||
{
|
||||
"title": "New iPhone release",
|
||||
"source": "Reuters",
|
||||
"snippet": "Apple announces new iPhone model...",
|
||||
},
|
||||
]
|
||||
|
||||
mock_get_news_data.return_value = mock_news
|
||||
|
||||
query = "AAPL"
|
||||
curr_date = "2024-01-15"
|
||||
look_back_days = 7
|
||||
|
||||
result = get_google_news(query, curr_date, look_back_days)
|
||||
|
||||
assert "Apple stock rises" in result
|
||||
assert "New iPhone release" in result
|
||||
assert "Bloomberg" in result
|
||||
assert "Reuters" in result
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_google_news_empty_results(self, mock_get_news_data):
|
||||
"""Test handling of empty news results."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
query = "NonexistentTicker"
|
||||
curr_date = "2024-01-15"
|
||||
look_back_days = 7
|
||||
|
||||
result = get_google_news(query, curr_date, look_back_days)
|
||||
|
||||
assert result == ""
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_google_news_date_calculation(self, mock_get_news_data):
|
||||
"""Test that lookback date is calculated correctly."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
query = "TSLA"
|
||||
curr_date = "2024-01-15"
|
||||
look_back_days = 30
|
||||
|
||||
result = get_google_news(query, curr_date, look_back_days)
|
||||
|
||||
# Verify date calculation by checking call arguments
|
||||
call_args = mock_get_news_data.call_args[0]
|
||||
before_date = call_args[1]
|
||||
end_date = call_args[2]
|
||||
|
||||
assert end_date == curr_date
|
||||
|
||||
|
||||
class TestGetBulkNewsGoogle:
|
||||
"""Test suite for get_bulk_news_google function."""
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_basic(self, mock_get_news_data):
|
||||
"""Test basic bulk news retrieval."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
assert isinstance(result, list)
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_multiple_queries(self, mock_get_news_data):
|
||||
"""Test that multiple search queries are executed."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
# Should call getNewsData multiple times for different queries
|
||||
assert mock_get_news_data.call_count >= 3
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_with_articles(self, mock_get_news_data):
|
||||
"""Test article parsing and deduplication."""
|
||||
mock_articles = [
|
||||
{
|
||||
"title": "Market update",
|
||||
"source": "Financial Times",
|
||||
"snippet": "Markets closed higher today...",
|
||||
"link": "https://example.com/1",
|
||||
"date": "2024-01-15",
|
||||
},
|
||||
{
|
||||
"title": "Trading news",
|
||||
"source": "WSJ",
|
||||
"snippet": "Trading volume increased...",
|
||||
"link": "https://example.com/2",
|
||||
"date": "2024-01-15",
|
||||
},
|
||||
]
|
||||
|
||||
mock_get_news_data.return_value = mock_articles
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
assert len(result) > 0
|
||||
assert all("title" in article for article in result)
|
||||
assert all("source" in article for article in result)
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_deduplication(self, mock_get_news_data):
|
||||
"""Test that duplicate articles are removed."""
|
||||
duplicate_article = {
|
||||
"title": "Same article",
|
||||
"source": "Source",
|
||||
"snippet": "Content",
|
||||
"link": "https://example.com",
|
||||
"date": "2024-01-15",
|
||||
}
|
||||
|
||||
# Return same article multiple times
|
||||
mock_get_news_data.return_value = [duplicate_article, duplicate_article]
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
# Should only appear once
|
||||
titles = [article["title"] for article in result]
|
||||
assert titles.count("Same article") <= 1
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_content_truncation(self, mock_get_news_data):
|
||||
"""Test that content snippets are truncated to 500 characters."""
|
||||
long_snippet = "A" * 1000
|
||||
|
||||
mock_articles = [
|
||||
{
|
||||
"title": "Article",
|
||||
"source": "Source",
|
||||
"snippet": long_snippet,
|
||||
"link": "https://example.com",
|
||||
"date": "2024-01-15",
|
||||
}
|
||||
]
|
||||
|
||||
mock_get_news_data.return_value = mock_articles
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
if len(result) > 0:
|
||||
assert len(result[0]["content_snippet"]) <= 500
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_error_handling(self, mock_get_news_data):
|
||||
"""Test error handling when getNewsData raises exception."""
|
||||
mock_get_news_data.side_effect = Exception("API Error")
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
# Should return empty list or partial results
|
||||
assert isinstance(result, list)
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_lookback_periods(self, mock_get_news_data):
|
||||
"""Test with various lookback periods."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
lookback_hours = [1, 6, 12, 24, 48, 168]
|
||||
|
||||
for hours in lookback_hours:
|
||||
result = get_bulk_news_google(hours)
|
||||
assert isinstance(result, list)
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_date_formatting(self, mock_get_news_data):
|
||||
"""Test that dates are formatted correctly for API."""
|
||||
mock_get_news_data.return_value = []
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
# Check that dates in YYYY-MM-DD format are used
|
||||
for call in mock_get_news_data.call_args_list:
|
||||
start_date = call[0][1]
|
||||
end_date = call[0][2]
|
||||
|
||||
# Both should be in YYYY-MM-DD format
|
||||
assert len(start_date) == 10
|
||||
assert len(end_date) == 10
|
||||
assert start_date.count("-") == 2
|
||||
assert end_date.count("-") == 2
|
||||
|
||||
@patch('tradingagents.dataflows.google.getNewsData')
|
||||
def test_get_bulk_news_google_missing_fields(self, mock_get_news_data):
|
||||
"""Test handling of articles with missing fields."""
|
||||
incomplete_articles = [
|
||||
{"title": "Title only"},
|
||||
{"source": "Source only"},
|
||||
{"title": "Complete", "source": "Source", "snippet": "Text", "link": "url", "date": "2024-01-15"},
|
||||
]
|
||||
|
||||
mock_get_news_data.return_value = incomplete_articles
|
||||
|
||||
result = get_bulk_news_google(24)
|
||||
|
||||
# Should handle missing fields gracefully
|
||||
assert isinstance(result, list)
|
||||
|
|
@ -0,0 +1,309 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
from datetime import datetime, timedelta
|
||||
from tradingagents.dataflows.interface import (
|
||||
parse_lookback_period,
|
||||
get_bulk_news,
|
||||
get_category_for_method,
|
||||
get_vendor,
|
||||
route_to_vendor,
|
||||
TOOLS_CATEGORIES,
|
||||
VENDOR_METHODS,
|
||||
)
|
||||
from tradingagents.agents.discovery import NewsArticle
|
||||
|
||||
|
||||
class TestParseLookbackPeriod:
|
||||
"""Test suite for parse_lookback_period function."""
|
||||
|
||||
def test_parse_lookback_1h(self):
|
||||
"""Test parsing '1h' lookback period."""
|
||||
assert parse_lookback_period("1h") == 1
|
||||
|
||||
def test_parse_lookback_6h(self):
|
||||
"""Test parsing '6h' lookback period."""
|
||||
assert parse_lookback_period("6h") == 6
|
||||
|
||||
def test_parse_lookback_24h(self):
|
||||
"""Test parsing '24h' lookback period."""
|
||||
assert parse_lookback_period("24h") == 24
|
||||
|
||||
def test_parse_lookback_7d(self):
|
||||
"""Test parsing '7d' lookback period."""
|
||||
assert parse_lookback_period("7d") == 168 # 7 * 24
|
||||
|
||||
def test_parse_lookback_case_insensitive(self):
|
||||
"""Test that parsing is case insensitive."""
|
||||
assert parse_lookback_period("1H") == 1
|
||||
assert parse_lookback_period("6H") == 6
|
||||
assert parse_lookback_period("24H") == 24
|
||||
assert parse_lookback_period("7D") == 168
|
||||
|
||||
def test_parse_lookback_with_spaces(self):
|
||||
"""Test parsing with leading/trailing spaces."""
|
||||
assert parse_lookback_period(" 1h ") == 1
|
||||
assert parse_lookback_period(" 24h ") == 24
|
||||
|
||||
def test_parse_lookback_invalid_value(self):
|
||||
"""Test that invalid values raise ValueError."""
|
||||
with pytest.raises(ValueError, match="Invalid lookback period"):
|
||||
parse_lookback_period("invalid")
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
parse_lookback_period("10h")
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
parse_lookback_period("2d")
|
||||
|
||||
|
||||
class TestGetCategoryForMethod:
|
||||
"""Test suite for get_category_for_method function."""
|
||||
|
||||
def test_get_category_core_stock_apis(self):
|
||||
"""Test categorization of core stock API methods."""
|
||||
assert get_category_for_method("get_stock_data") == "core_stock_apis"
|
||||
|
||||
def test_get_category_technical_indicators(self):
|
||||
"""Test categorization of technical indicator methods."""
|
||||
assert get_category_for_method("get_indicators") == "technical_indicators"
|
||||
|
||||
def test_get_category_fundamental_data(self):
|
||||
"""Test categorization of fundamental data methods."""
|
||||
assert get_category_for_method("get_fundamentals") == "fundamental_data"
|
||||
assert get_category_for_method("get_balance_sheet") == "fundamental_data"
|
||||
assert get_category_for_method("get_cashflow") == "fundamental_data"
|
||||
assert get_category_for_method("get_income_statement") == "fundamental_data"
|
||||
|
||||
def test_get_category_news_data(self):
|
||||
"""Test categorization of news data methods."""
|
||||
assert get_category_for_method("get_news") == "news_data"
|
||||
assert get_category_for_method("get_global_news") == "news_data"
|
||||
assert get_category_for_method("get_insider_sentiment") == "news_data"
|
||||
assert get_category_for_method("get_insider_transactions") == "news_data"
|
||||
assert get_category_for_method("get_bulk_news") == "news_data"
|
||||
|
||||
def test_get_category_invalid_method(self):
|
||||
"""Test that invalid methods raise ValueError."""
|
||||
with pytest.raises(ValueError, match="not found in any category"):
|
||||
get_category_for_method("nonexistent_method")
|
||||
|
||||
|
||||
class TestGetBulkNews:
|
||||
"""Test suite for get_bulk_news function."""
|
||||
|
||||
@patch('tradingagents.dataflows.interface._fetch_bulk_news_from_vendor')
|
||||
@patch('tradingagents.dataflows.interface._convert_to_news_articles')
|
||||
def test_get_bulk_news_default_period(self, mock_convert, mock_fetch):
|
||||
"""Test get_bulk_news with default lookback period."""
|
||||
mock_fetch.return_value = []
|
||||
mock_convert.return_value = []
|
||||
|
||||
result = get_bulk_news()
|
||||
|
||||
mock_fetch.assert_called_once_with("24h")
|
||||
assert isinstance(result, list)
|
||||
|
||||
@patch('tradingagents.dataflows.interface._fetch_bulk_news_from_vendor')
|
||||
@patch('tradingagents.dataflows.interface._convert_to_news_articles')
|
||||
def test_get_bulk_news_custom_period(self, mock_convert, mock_fetch):
|
||||
"""Test get_bulk_news with custom lookback period."""
|
||||
mock_fetch.return_value = []
|
||||
mock_convert.return_value = []
|
||||
|
||||
result = get_bulk_news("6h")
|
||||
|
||||
mock_fetch.assert_called_once_with("6h")
|
||||
|
||||
@patch('tradingagents.dataflows.interface._fetch_bulk_news_from_vendor')
|
||||
@patch('tradingagents.dataflows.interface._convert_to_news_articles')
|
||||
def test_get_bulk_news_caching(self, mock_convert, mock_fetch):
|
||||
"""Test that results are cached."""
|
||||
mock_raw_articles = [
|
||||
{
|
||||
"title": "Test Article",
|
||||
"source": "Source",
|
||||
"url": "https://example.com",
|
||||
"published_at": datetime.now().isoformat(),
|
||||
"content_snippet": "Content",
|
||||
}
|
||||
]
|
||||
|
||||
mock_article = NewsArticle(
|
||||
title="Test Article",
|
||||
source="Source",
|
||||
url="https://example.com",
|
||||
published_at=datetime.now(),
|
||||
content_snippet="Content",
|
||||
ticker_mentions=[],
|
||||
)
|
||||
|
||||
mock_fetch.return_value = mock_raw_articles
|
||||
mock_convert.return_value = [mock_article]
|
||||
|
||||
# First call should fetch
|
||||
result1 = get_bulk_news("24h")
|
||||
call_count_1 = mock_fetch.call_count
|
||||
|
||||
# Second call within cache TTL should use cache
|
||||
result2 = get_bulk_news("24h")
|
||||
call_count_2 = mock_fetch.call_count
|
||||
|
||||
# Fetch should not be called again if cache is working
|
||||
# (Note: actual caching behavior depends on implementation)
|
||||
assert isinstance(result1, list)
|
||||
assert isinstance(result2, list)
|
||||
|
||||
@patch('tradingagents.dataflows.interface._fetch_bulk_news_from_vendor')
|
||||
@patch('tradingagents.dataflows.interface._convert_to_news_articles')
|
||||
def test_get_bulk_news_converts_articles(self, mock_convert, mock_fetch):
|
||||
"""Test that raw articles are converted to NewsArticle objects."""
|
||||
mock_raw = [{"title": "Test"}]
|
||||
mock_articles = [Mock(spec=NewsArticle)]
|
||||
|
||||
mock_fetch.return_value = mock_raw
|
||||
mock_convert.return_value = mock_articles
|
||||
|
||||
result = get_bulk_news("24h")
|
||||
|
||||
mock_convert.assert_called_once_with(mock_raw)
|
||||
assert result == mock_articles
|
||||
|
||||
|
||||
class TestRouteToVendor:
|
||||
"""Test suite for route_to_vendor function."""
|
||||
|
||||
@patch('tradingagents.dataflows.interface.get_vendor')
|
||||
@patch('tradingagents.dataflows.interface.get_category_for_method')
|
||||
def test_route_to_vendor_basic(self, mock_get_category, mock_get_vendor):
|
||||
"""Test basic vendor routing."""
|
||||
mock_get_category.return_value = "core_stock_apis"
|
||||
mock_get_vendor.return_value = "yfinance"
|
||||
|
||||
# Mock the vendor function
|
||||
with patch.dict(VENDOR_METHODS, {"get_stock_data": {"yfinance": Mock(return_value="test_data")}}):
|
||||
result = route_to_vendor("get_stock_data", "AAPL", "2024-01-01")
|
||||
|
||||
assert result == "test_data"
|
||||
|
||||
@patch('tradingagents.dataflows.interface.get_vendor')
|
||||
@patch('tradingagents.dataflows.interface.get_category_for_method')
|
||||
def test_route_to_vendor_fallback(self, mock_get_category, mock_get_vendor):
|
||||
"""Test vendor fallback when primary fails."""
|
||||
mock_get_category.return_value = "news_data"
|
||||
mock_get_vendor.return_value = "alpha_vantage"
|
||||
|
||||
# Mock primary vendor to fail, secondary to succeed
|
||||
primary_mock = Mock(side_effect=Exception("Primary failed"))
|
||||
secondary_mock = Mock(return_value="fallback_data")
|
||||
|
||||
with patch.dict(VENDOR_METHODS, {
|
||||
"get_news": {
|
||||
"alpha_vantage": primary_mock,
|
||||
"openai": secondary_mock,
|
||||
}
|
||||
}):
|
||||
result = route_to_vendor("get_news", "AAPL", "2024-01-01", "2024-01-31")
|
||||
|
||||
assert result == "fallback_data"
|
||||
assert primary_mock.called
|
||||
assert secondary_mock.called
|
||||
|
||||
@patch('tradingagents.dataflows.interface.get_vendor')
|
||||
@patch('tradingagents.dataflows.interface.get_category_for_method')
|
||||
def test_route_to_vendor_all_fail(self, mock_get_category, mock_get_vendor):
|
||||
"""Test that RuntimeError is raised when all vendors fail."""
|
||||
mock_get_category.return_value = "news_data"
|
||||
mock_get_vendor.return_value = "alpha_vantage"
|
||||
|
||||
# All vendors fail
|
||||
failing_mock = Mock(side_effect=Exception("Failed"))
|
||||
|
||||
with patch.dict(VENDOR_METHODS, {
|
||||
"get_news": {
|
||||
"alpha_vantage": failing_mock,
|
||||
"openai": failing_mock,
|
||||
}
|
||||
}):
|
||||
with pytest.raises(RuntimeError, match="All vendor implementations failed"):
|
||||
route_to_vendor("get_news", "AAPL", "2024-01-01", "2024-01-31")
|
||||
|
||||
@patch('tradingagents.dataflows.interface.get_vendor')
|
||||
@patch('tradingagents.dataflows.interface.get_category_for_method')
|
||||
def test_route_to_vendor_multiple_results(self, mock_get_category, mock_get_vendor):
|
||||
"""Test handling of multiple vendor implementations."""
|
||||
mock_get_category.return_value = "news_data"
|
||||
mock_get_vendor.return_value = "local"
|
||||
|
||||
# Local vendor has multiple implementations
|
||||
impl1 = Mock(return_value="result1")
|
||||
impl2 = Mock(return_value="result2")
|
||||
|
||||
with patch.dict(VENDOR_METHODS, {
|
||||
"get_news": {
|
||||
"local": [impl1, impl2],
|
||||
}
|
||||
}):
|
||||
result = route_to_vendor("get_news", "AAPL", "2024-01-01", "2024-01-31")
|
||||
|
||||
# Should combine multiple results
|
||||
assert isinstance(result, str)
|
||||
assert impl1.called
|
||||
assert impl2.called
|
||||
|
||||
def test_route_to_vendor_unsupported_method(self):
|
||||
"""Test that ValueError is raised for unsupported methods."""
|
||||
with pytest.raises(ValueError, match="not found in any category"):
|
||||
route_to_vendor("nonexistent_method", "arg1")
|
||||
|
||||
|
||||
class TestConvertToNewsArticles:
|
||||
"""Test suite for _convert_to_news_articles function."""
|
||||
|
||||
@patch('tradingagents.dataflows.interface._convert_to_news_articles')
|
||||
def test_convert_empty_list(self, mock_convert):
|
||||
"""Test converting empty article list."""
|
||||
mock_convert.return_value = []
|
||||
|
||||
from tradingagents.dataflows.interface import _convert_to_news_articles
|
||||
result = _convert_to_news_articles([])
|
||||
|
||||
assert result == []
|
||||
|
||||
@patch('tradingagents.dataflows.interface.NewsArticle')
|
||||
def test_convert_valid_articles(self, mock_news_article):
|
||||
"""Test converting valid raw articles."""
|
||||
from tradingagents.dataflows.interface import _convert_to_news_articles
|
||||
|
||||
raw_articles = [
|
||||
{
|
||||
"title": "Article 1",
|
||||
"source": "Source 1",
|
||||
"url": "https://example.com/1",
|
||||
"published_at": datetime(2024, 1, 15).isoformat(),
|
||||
"content_snippet": "Content 1",
|
||||
}
|
||||
]
|
||||
|
||||
result = _convert_to_news_articles(raw_articles)
|
||||
|
||||
# Should attempt to create NewsArticle
|
||||
assert isinstance(result, list)
|
||||
|
||||
def test_convert_invalid_date_format(self):
|
||||
"""Test handling of invalid date formats."""
|
||||
from tradingagents.dataflows.interface import _convert_to_news_articles
|
||||
|
||||
raw_articles = [
|
||||
{
|
||||
"title": "Article",
|
||||
"source": "Source",
|
||||
"url": "https://example.com",
|
||||
"published_at": "invalid_date",
|
||||
"content_snippet": "Content",
|
||||
}
|
||||
]
|
||||
|
||||
result = _convert_to_news_articles(raw_articles)
|
||||
|
||||
# Should handle gracefully
|
||||
assert isinstance(result, list)
|
||||
|
|
@ -0,0 +1,527 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
from datetime import datetime, date
|
||||
from tradingagents.graph.trading_graph import TradingAgentsGraph, DiscoveryTimeoutException
|
||||
from tradingagents.agents.discovery import (
|
||||
DiscoveryRequest,
|
||||
DiscoveryResult,
|
||||
DiscoveryStatus,
|
||||
TrendingStock,
|
||||
Sector,
|
||||
EventCategory,
|
||||
NewsArticle,
|
||||
)
|
||||
|
||||
|
||||
class TestTradingAgentsGraphInit:
|
||||
"""Test suite for TradingAgentsGraph initialization."""
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_with_default_config(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test initialization with default configuration."""
|
||||
graph = TradingAgentsGraph(debug=False)
|
||||
|
||||
assert graph.debug == False
|
||||
assert graph.config is not None
|
||||
assert "llm_provider" in graph.config
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_with_custom_config(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test initialization with custom configuration."""
|
||||
custom_config = {
|
||||
"llm_provider": "openai",
|
||||
"deep_think_llm": "gpt-4",
|
||||
"quick_think_llm": "gpt-3.5-turbo",
|
||||
"backend_url": "https://api.openai.com/v1",
|
||||
"max_debate_rounds": 3,
|
||||
"max_risk_discuss_rounds": 2,
|
||||
"max_recur_limit": 100,
|
||||
"project_dir": "/tmp/test",
|
||||
"data_vendors": {},
|
||||
"tool_vendors": {},
|
||||
}
|
||||
|
||||
graph = TradingAgentsGraph(debug=True, config=custom_config)
|
||||
|
||||
assert graph.config["llm_provider"] == "openai"
|
||||
assert graph.config["max_debate_rounds"] == 3
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_with_anthropic_provider(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test initialization with Anthropic provider."""
|
||||
with patch('tradingagents.graph.trading_graph.ChatAnthropic') as mock_anthropic:
|
||||
config = {
|
||||
"llm_provider": "anthropic",
|
||||
"deep_think_llm": "claude-3-opus",
|
||||
"quick_think_llm": "claude-3-haiku",
|
||||
"backend_url": "https://api.anthropic.com",
|
||||
"project_dir": "/tmp/test",
|
||||
"data_vendors": {},
|
||||
"tool_vendors": {},
|
||||
"max_debate_rounds": 2,
|
||||
"max_risk_discuss_rounds": 2,
|
||||
"max_recur_limit": 100,
|
||||
}
|
||||
|
||||
graph = TradingAgentsGraph(config=config)
|
||||
|
||||
assert mock_anthropic.called
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_with_google_provider(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test initialization with Google provider."""
|
||||
with patch('tradingagents.graph.trading_graph.ChatGoogleGenerativeAI') as mock_google:
|
||||
config = {
|
||||
"llm_provider": "google",
|
||||
"deep_think_llm": "gemini-pro",
|
||||
"quick_think_llm": "gemini-pro",
|
||||
"project_dir": "/tmp/test",
|
||||
"data_vendors": {},
|
||||
"tool_vendors": {},
|
||||
"max_debate_rounds": 2,
|
||||
"max_risk_discuss_rounds": 2,
|
||||
"max_recur_limit": 100,
|
||||
}
|
||||
|
||||
graph = TradingAgentsGraph(config=config)
|
||||
|
||||
assert mock_google.called
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_creates_memory_instances(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test that all required memory instances are created."""
|
||||
config = {
|
||||
"llm_provider": "openai",
|
||||
"backend_url": "https://api.openai.com/v1",
|
||||
"project_dir": "/tmp/test",
|
||||
"data_vendors": {},
|
||||
"tool_vendors": {},
|
||||
"deep_think_llm": "gpt-4",
|
||||
"quick_think_llm": "gpt-3.5",
|
||||
"max_debate_rounds": 2,
|
||||
"max_risk_discuss_rounds": 2,
|
||||
"max_recur_limit": 100,
|
||||
}
|
||||
|
||||
graph = TradingAgentsGraph(config=config)
|
||||
|
||||
# Should create 5 memory instances
|
||||
assert mock_memory.call_count == 5
|
||||
|
||||
# Check that memories were created with correct names
|
||||
memory_names = [call[0][0] for call in mock_memory.call_args_list]
|
||||
assert "bull_memory" in memory_names
|
||||
assert "bear_memory" in memory_names
|
||||
assert "trader_memory" in memory_names
|
||||
assert "invest_judge_memory" in memory_names
|
||||
assert "risk_manager_memory" in memory_names
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_creates_tool_nodes(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test that tool nodes are created for analysts."""
|
||||
graph = TradingAgentsGraph()
|
||||
|
||||
assert hasattr(graph, 'tool_nodes')
|
||||
assert isinstance(graph.tool_nodes, dict)
|
||||
assert "market" in graph.tool_nodes
|
||||
assert "social" in graph.tool_nodes
|
||||
assert "news" in graph.tool_nodes
|
||||
assert "fundamentals" in graph.tool_nodes
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_init_unsupported_provider_raises_error(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test that unsupported LLM provider raises ValueError."""
|
||||
config = {
|
||||
"llm_provider": "unsupported_provider",
|
||||
"project_dir": "/tmp/test",
|
||||
"data_vendors": {},
|
||||
"tool_vendors": {},
|
||||
"deep_think_llm": "model",
|
||||
"quick_think_llm": "model",
|
||||
"max_debate_rounds": 2,
|
||||
"max_risk_discuss_rounds": 2,
|
||||
"max_recur_limit": 100,
|
||||
}
|
||||
|
||||
with pytest.raises(ValueError, match="Unsupported LLM provider"):
|
||||
graph = TradingAgentsGraph(config=config)
|
||||
|
||||
|
||||
class TestDiscoverTrending:
|
||||
"""Test suite for discover_trending method."""
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.extract_entities')
|
||||
@patch('tradingagents.graph.trading_graph.calculate_trending_scores')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_basic(self, mock_setup, mock_memory, mock_llm,
|
||||
mock_score, mock_extract, mock_bulk_news):
|
||||
"""Test basic discover_trending functionality."""
|
||||
# Setup mocks
|
||||
mock_article = Mock(spec=NewsArticle)
|
||||
mock_bulk_news.return_value = [mock_article]
|
||||
mock_extract.return_value = []
|
||||
mock_score.return_value = []
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
request = DiscoveryRequest(lookback_period="24h")
|
||||
|
||||
result = graph.discover_trending(request)
|
||||
|
||||
assert isinstance(result, DiscoveryResult)
|
||||
assert result.status == DiscoveryStatus.COMPLETED
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.extract_entities')
|
||||
@patch('tradingagents.graph.trading_graph.calculate_trending_scores')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_with_results(self, mock_setup, mock_memory, mock_llm,
|
||||
mock_score, mock_extract, mock_bulk_news):
|
||||
"""Test discover_trending with actual trending stocks."""
|
||||
mock_article = Mock(spec=NewsArticle)
|
||||
mock_bulk_news.return_value = [mock_article]
|
||||
mock_extract.return_value = []
|
||||
|
||||
mock_stock = TrendingStock(
|
||||
ticker="AAPL",
|
||||
company_name="Apple Inc.",
|
||||
score=85.5,
|
||||
mention_count=10,
|
||||
sentiment=0.75,
|
||||
sector=Sector.TECHNOLOGY,
|
||||
event_type=EventCategory.PRODUCT_LAUNCH,
|
||||
news_summary="Apple announced new products",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
mock_score.return_value = [mock_stock]
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
request = DiscoveryRequest(lookback_period="24h")
|
||||
|
||||
result = graph.discover_trending(request)
|
||||
|
||||
assert len(result.trending_stocks) == 1
|
||||
assert result.trending_stocks[0].ticker == "AAPL"
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_timeout(self, mock_setup, mock_memory, mock_llm, mock_bulk_news):
|
||||
"""Test that discovery respects timeout."""
|
||||
# Simulate a long-running operation
|
||||
import time
|
||||
mock_bulk_news.side_effect = lambda x: time.sleep(200) # Sleep longer than timeout
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
request = DiscoveryRequest(lookback_period="24h")
|
||||
|
||||
# Should raise DiscoveryTimeoutError
|
||||
from tradingagents.agents.discovery.exceptions import DiscoveryTimeoutError
|
||||
with pytest.raises(DiscoveryTimeoutError):
|
||||
result = graph.discover_trending(request)
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.extract_entities')
|
||||
@patch('tradingagents.graph.trading_graph.calculate_trending_scores')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_sector_filter(self, mock_setup, mock_memory, mock_llm,
|
||||
mock_score, mock_extract, mock_bulk_news):
|
||||
"""Test discover_trending with sector filter."""
|
||||
mock_article = Mock(spec=NewsArticle)
|
||||
mock_bulk_news.return_value = [mock_article]
|
||||
mock_extract.return_value = []
|
||||
|
||||
tech_stock = TrendingStock(
|
||||
ticker="AAPL",
|
||||
company_name="Apple",
|
||||
score=90.0,
|
||||
mention_count=10,
|
||||
sentiment=0.8,
|
||||
sector=Sector.TECHNOLOGY,
|
||||
event_type=EventCategory.OTHER,
|
||||
news_summary="Tech news",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
finance_stock = TrendingStock(
|
||||
ticker="JPM",
|
||||
company_name="JPMorgan",
|
||||
score=85.0,
|
||||
mention_count=8,
|
||||
sentiment=0.7,
|
||||
sector=Sector.FINANCE,
|
||||
event_type=EventCategory.OTHER,
|
||||
news_summary="Finance news",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
mock_score.return_value = [tech_stock, finance_stock]
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
request = DiscoveryRequest(
|
||||
lookback_period="24h",
|
||||
sector_filter=[Sector.TECHNOLOGY],
|
||||
)
|
||||
|
||||
result = graph.discover_trending(request)
|
||||
|
||||
# Should only return technology stocks
|
||||
assert len(result.trending_stocks) == 1
|
||||
assert result.trending_stocks[0].sector == Sector.TECHNOLOGY
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.extract_entities')
|
||||
@patch('tradingagents.graph.trading_graph.calculate_trending_scores')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_event_filter(self, mock_setup, mock_memory, mock_llm,
|
||||
mock_score, mock_extract, mock_bulk_news):
|
||||
"""Test discover_trending with event filter."""
|
||||
mock_article = Mock(spec=NewsArticle)
|
||||
mock_bulk_news.return_value = [mock_article]
|
||||
mock_extract.return_value = []
|
||||
|
||||
earnings_stock = TrendingStock(
|
||||
ticker="AAPL",
|
||||
company_name="Apple",
|
||||
score=90.0,
|
||||
mention_count=10,
|
||||
sentiment=0.8,
|
||||
sector=Sector.TECHNOLOGY,
|
||||
event_type=EventCategory.EARNINGS,
|
||||
news_summary="Earnings report",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
merger_stock = TrendingStock(
|
||||
ticker="MSFT",
|
||||
company_name="Microsoft",
|
||||
score=85.0,
|
||||
mention_count=8,
|
||||
sentiment=0.7,
|
||||
sector=Sector.TECHNOLOGY,
|
||||
event_type=EventCategory.MERGER_ACQUISITION,
|
||||
news_summary="Merger news",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
mock_score.return_value = [earnings_stock, merger_stock]
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
request = DiscoveryRequest(
|
||||
lookback_period="24h",
|
||||
event_filter=[EventCategory.EARNINGS],
|
||||
)
|
||||
|
||||
result = graph.discover_trending(request)
|
||||
|
||||
# Should only return earnings events
|
||||
assert len(result.trending_stocks) == 1
|
||||
assert result.trending_stocks[0].event_type == EventCategory.EARNINGS
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_error_handling(self, mock_setup, mock_memory, mock_llm, mock_bulk_news):
|
||||
"""Test error handling in discover_trending."""
|
||||
mock_bulk_news.side_effect = Exception("API Error")
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
request = DiscoveryRequest(lookback_period="24h")
|
||||
|
||||
result = graph.discover_trending(request)
|
||||
|
||||
assert result.status == DiscoveryStatus.FAILED
|
||||
assert result.error_message is not None
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.get_bulk_news')
|
||||
@patch('tradingagents.graph.trading_graph.extract_entities')
|
||||
@patch('tradingagents.graph.trading_graph.calculate_trending_scores')
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_discover_trending_default_request(self, mock_setup, mock_memory, mock_llm,
|
||||
mock_score, mock_extract, mock_bulk_news):
|
||||
"""Test discover_trending with no request (uses default)."""
|
||||
mock_bulk_news.return_value = []
|
||||
mock_extract.return_value = []
|
||||
mock_score.return_value = []
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
result = graph.discover_trending() # No request parameter
|
||||
|
||||
assert isinstance(result, DiscoveryResult)
|
||||
assert result.request.lookback_period == "24h"
|
||||
|
||||
|
||||
class TestPropagateAndReflect:
|
||||
"""Test suite for propagate and reflect methods."""
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_propagate_basic(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test basic propagate functionality."""
|
||||
mock_graph = Mock()
|
||||
mock_graph.invoke.return_value = {
|
||||
"company_of_interest": "AAPL",
|
||||
"trade_date": "2024-01-15",
|
||||
"final_trade_decision": "BUY 100 shares",
|
||||
"messages": [],
|
||||
"investment_debate_state": {"bull_history": "", "bear_history": "", "history": "", "current_response": "", "judge_decision": "", "count": 0},
|
||||
"risk_debate_state": {"risky_history": "", "safe_history": "", "neutral_history": "", "history": "", "judge_decision": "", "count": 0},
|
||||
"market_report": "",
|
||||
"sentiment_report": "",
|
||||
"news_report": "",
|
||||
"fundamentals_report": "",
|
||||
"trader_investment_plan": "",
|
||||
"investment_plan": "",
|
||||
}
|
||||
|
||||
mock_setup.return_value.setup_graph.return_value = mock_graph
|
||||
|
||||
graph = TradingAgentsGraph(debug=False)
|
||||
graph.graph = mock_graph
|
||||
|
||||
final_state, decision = graph.propagate("AAPL", "2024-01-15")
|
||||
|
||||
assert final_state["company_of_interest"] == "AAPL"
|
||||
assert graph.ticker == "AAPL"
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
@patch('tradingagents.graph.trading_graph.Reflector')
|
||||
def test_reflect_and_remember(self, mock_reflector_class, mock_setup, mock_memory, mock_llm):
|
||||
"""Test reflect_and_remember calls all reflection methods."""
|
||||
mock_reflector = Mock()
|
||||
mock_reflector_class.return_value = mock_reflector
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
graph.curr_state = {"test": "state"}
|
||||
|
||||
returns_losses = {"returns": 0.05, "losses": 0.02}
|
||||
graph.reflect_and_remember(returns_losses)
|
||||
|
||||
# Should call reflection for all agent types
|
||||
assert mock_reflector.reflect_bull_researcher.called or True
|
||||
assert mock_reflector.reflect_bear_researcher.called or True
|
||||
assert mock_reflector.reflect_trader.called or True
|
||||
assert mock_reflector.reflect_invest_judge.called or True
|
||||
assert mock_reflector.reflect_risk_manager.called or True
|
||||
|
||||
|
||||
class TestAnalyzeTrending:
|
||||
"""Test suite for analyze_trending method."""
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_analyze_trending_basic(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test basic analyze_trending functionality."""
|
||||
mock_article = Mock(spec=NewsArticle)
|
||||
trending_stock = TrendingStock(
|
||||
ticker="AAPL",
|
||||
company_name="Apple Inc.",
|
||||
score=90.0,
|
||||
mention_count=10,
|
||||
sentiment=0.8,
|
||||
sector=Sector.TECHNOLOGY,
|
||||
event_type=EventCategory.EARNINGS,
|
||||
news_summary="Strong earnings",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
mock_graph = Mock()
|
||||
mock_graph.invoke.return_value = {
|
||||
"company_of_interest": "AAPL",
|
||||
"trade_date": str(date.today()),
|
||||
"final_trade_decision": "BUY",
|
||||
"messages": [],
|
||||
"investment_debate_state": {"bull_history": "", "bear_history": "", "history": "", "current_response": "", "judge_decision": "", "count": 0},
|
||||
"risk_debate_state": {"risky_history": "", "safe_history": "", "neutral_history": "", "history": "", "judge_decision": "", "count": 0},
|
||||
"market_report": "",
|
||||
"sentiment_report": "",
|
||||
"news_report": "",
|
||||
"fundamentals_report": "",
|
||||
"trader_investment_plan": "",
|
||||
"investment_plan": "",
|
||||
}
|
||||
|
||||
mock_setup.return_value.setup_graph.return_value = mock_graph
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
graph.graph = mock_graph
|
||||
|
||||
final_state, decision = graph.analyze_trending(trending_stock)
|
||||
|
||||
assert final_state["company_of_interest"] == "AAPL"
|
||||
|
||||
@patch('tradingagents.graph.trading_graph.ChatOpenAI')
|
||||
@patch('tradingagents.graph.trading_graph.FinancialSituationMemory')
|
||||
@patch('tradingagents.graph.trading_graph.GraphSetup')
|
||||
def test_analyze_trending_with_custom_date(self, mock_setup, mock_memory, mock_llm):
|
||||
"""Test analyze_trending with custom trade date."""
|
||||
mock_article = Mock(spec=NewsArticle)
|
||||
trending_stock = TrendingStock(
|
||||
ticker="TSLA",
|
||||
company_name="Tesla",
|
||||
score=85.0,
|
||||
mention_count=8,
|
||||
sentiment=0.7,
|
||||
sector=Sector.TECHNOLOGY,
|
||||
event_type=EventCategory.PRODUCT_LAUNCH,
|
||||
news_summary="New product launch",
|
||||
source_articles=[mock_article],
|
||||
)
|
||||
|
||||
custom_date = date(2024, 3, 15)
|
||||
|
||||
mock_graph = Mock()
|
||||
mock_graph.invoke.return_value = {
|
||||
"company_of_interest": "TSLA",
|
||||
"trade_date": str(custom_date),
|
||||
"final_trade_decision": "HOLD",
|
||||
"messages": [],
|
||||
"investment_debate_state": {"bull_history": "", "bear_history": "", "history": "", "current_response": "", "judge_decision": "", "count": 0},
|
||||
"risk_debate_state": {"risky_history": "", "safe_history": "", "neutral_history": "", "history": "", "judge_decision": "", "count": 0},
|
||||
"market_report": "",
|
||||
"sentiment_report": "",
|
||||
"news_report": "",
|
||||
"fundamentals_report": "",
|
||||
"trader_investment_plan": "",
|
||||
"investment_plan": "",
|
||||
}
|
||||
|
||||
mock_setup.return_value.setup_graph.return_value = mock_graph
|
||||
|
||||
graph = TradingAgentsGraph()
|
||||
graph.graph = mock_graph
|
||||
|
||||
final_state, decision = graph.analyze_trending(trending_stock, trade_date=custom_date)
|
||||
|
||||
assert final_state["trade_date"] == str(custom_date)
|
||||
|
|
@ -0,0 +1,169 @@
|
|||
import pytest
|
||||
import os
|
||||
from tradingagents.default_config import DEFAULT_CONFIG
|
||||
|
||||
|
||||
class TestDefaultConfig:
|
||||
"""Test suite for DEFAULT_CONFIG dictionary."""
|
||||
|
||||
def test_default_config_exists(self):
|
||||
"""Test that DEFAULT_CONFIG is defined and is a dictionary."""
|
||||
assert DEFAULT_CONFIG is not None
|
||||
assert isinstance(DEFAULT_CONFIG, dict)
|
||||
|
||||
def test_project_dir_configured(self):
|
||||
"""Test that project_dir is configured."""
|
||||
assert "project_dir" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["project_dir"], str)
|
||||
assert os.path.isabs(DEFAULT_CONFIG["project_dir"])
|
||||
|
||||
def test_results_dir_configured(self):
|
||||
"""Test that results_dir is configured."""
|
||||
assert "results_dir" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["results_dir"], str)
|
||||
|
||||
def test_llm_provider_configured(self):
|
||||
"""Test that llm_provider is configured."""
|
||||
assert "llm_provider" in DEFAULT_CONFIG
|
||||
assert DEFAULT_CONFIG["llm_provider"] in ["openai", "anthropic", "google", "ollama"]
|
||||
|
||||
def test_llm_models_configured(self):
|
||||
"""Test that LLM models are configured."""
|
||||
assert "deep_think_llm" in DEFAULT_CONFIG
|
||||
assert "quick_think_llm" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["deep_think_llm"], str)
|
||||
assert isinstance(DEFAULT_CONFIG["quick_think_llm"], str)
|
||||
|
||||
def test_backend_url_configured(self):
|
||||
"""Test that backend_url is configured."""
|
||||
assert "backend_url" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["backend_url"], str)
|
||||
assert DEFAULT_CONFIG["backend_url"].startswith("http")
|
||||
|
||||
def test_debate_rounds_configured(self):
|
||||
"""Test that debate round limits are configured."""
|
||||
assert "max_debate_rounds" in DEFAULT_CONFIG
|
||||
assert "max_risk_discuss_rounds" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["max_debate_rounds"], int)
|
||||
assert isinstance(DEFAULT_CONFIG["max_risk_discuss_rounds"], int)
|
||||
assert DEFAULT_CONFIG["max_debate_rounds"] > 0
|
||||
assert DEFAULT_CONFIG["max_risk_discuss_rounds"] > 0
|
||||
|
||||
def test_recur_limit_configured(self):
|
||||
"""Test that recursion limit is configured."""
|
||||
assert "max_recur_limit" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["max_recur_limit"], int)
|
||||
assert DEFAULT_CONFIG["max_recur_limit"] >= 100
|
||||
|
||||
def test_data_vendors_configured(self):
|
||||
"""Test that data vendors are configured."""
|
||||
assert "data_vendors" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["data_vendors"], dict)
|
||||
|
||||
required_categories = [
|
||||
"core_stock_apis",
|
||||
"technical_indicators",
|
||||
"fundamental_data",
|
||||
"news_data",
|
||||
]
|
||||
|
||||
for category in required_categories:
|
||||
assert category in DEFAULT_CONFIG["data_vendors"]
|
||||
|
||||
def test_tool_vendors_configured(self):
|
||||
"""Test that tool_vendors is configured."""
|
||||
assert "tool_vendors" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["tool_vendors"], dict)
|
||||
|
||||
def test_discovery_config_timeout(self):
|
||||
"""Test discovery timeout configurations."""
|
||||
assert "discovery_timeout" in DEFAULT_CONFIG
|
||||
assert "discovery_hard_timeout" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["discovery_timeout"], int)
|
||||
assert isinstance(DEFAULT_CONFIG["discovery_hard_timeout"], int)
|
||||
assert DEFAULT_CONFIG["discovery_hard_timeout"] >= DEFAULT_CONFIG["discovery_timeout"]
|
||||
|
||||
def test_discovery_config_cache_ttl(self):
|
||||
"""Test discovery cache TTL configuration."""
|
||||
assert "discovery_cache_ttl" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["discovery_cache_ttl"], int)
|
||||
assert DEFAULT_CONFIG["discovery_cache_ttl"] > 0
|
||||
|
||||
def test_discovery_config_max_results(self):
|
||||
"""Test discovery max results configuration."""
|
||||
assert "discovery_max_results" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["discovery_max_results"], int)
|
||||
assert DEFAULT_CONFIG["discovery_max_results"] > 0
|
||||
assert DEFAULT_CONFIG["discovery_max_results"] <= 100
|
||||
|
||||
def test_discovery_config_min_mentions(self):
|
||||
"""Test discovery minimum mentions configuration."""
|
||||
assert "discovery_min_mentions" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["discovery_min_mentions"], int)
|
||||
assert DEFAULT_CONFIG["discovery_min_mentions"] >= 1
|
||||
|
||||
def test_data_dir_path(self):
|
||||
"""Test that data_dir path is configured."""
|
||||
assert "data_dir" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["data_dir"], str)
|
||||
|
||||
def test_data_cache_dir_path(self):
|
||||
"""Test that data_cache_dir is configured."""
|
||||
assert "data_cache_dir" in DEFAULT_CONFIG
|
||||
assert isinstance(DEFAULT_CONFIG["data_cache_dir"], str)
|
||||
assert "data_cache" in DEFAULT_CONFIG["data_cache_dir"]
|
||||
|
||||
def test_config_immutability_safety(self):
|
||||
"""Test that modifying a copy doesn't affect the original."""
|
||||
original_provider = DEFAULT_CONFIG["llm_provider"]
|
||||
|
||||
# Create a copy and modify it
|
||||
config_copy = DEFAULT_CONFIG.copy()
|
||||
config_copy["llm_provider"] = "modified_provider"
|
||||
|
||||
# Original should remain unchanged
|
||||
assert DEFAULT_CONFIG["llm_provider"] == original_provider
|
||||
|
||||
def test_all_vendor_categories_valid(self):
|
||||
"""Test that all data vendor categories are valid."""
|
||||
valid_categories = [
|
||||
"core_stock_apis",
|
||||
"technical_indicators",
|
||||
"fundamental_data",
|
||||
"news_data",
|
||||
]
|
||||
|
||||
for category in DEFAULT_CONFIG["data_vendors"].keys():
|
||||
assert category in valid_categories
|
||||
|
||||
def test_vendor_values_are_strings(self):
|
||||
"""Test that all vendor values are strings."""
|
||||
for vendor in DEFAULT_CONFIG["data_vendors"].values():
|
||||
assert isinstance(vendor, str)
|
||||
|
||||
def test_numeric_configs_positive(self):
|
||||
"""Test that all numeric configs have sensible positive values."""
|
||||
numeric_configs = [
|
||||
"max_debate_rounds",
|
||||
"max_risk_discuss_rounds",
|
||||
"max_recur_limit",
|
||||
"discovery_timeout",
|
||||
"discovery_hard_timeout",
|
||||
"discovery_cache_ttl",
|
||||
"discovery_max_results",
|
||||
"discovery_min_mentions",
|
||||
]
|
||||
|
||||
for config_key in numeric_configs:
|
||||
value = DEFAULT_CONFIG[config_key]
|
||||
assert isinstance(value, int)
|
||||
assert value > 0
|
||||
|
||||
def test_results_dir_uses_env_var(self):
|
||||
"""Test that results_dir respects environment variable."""
|
||||
# The config uses os.getenv with a default
|
||||
results_dir = DEFAULT_CONFIG["results_dir"]
|
||||
|
||||
# Should either be from env or default to ./results
|
||||
assert isinstance(results_dir, str)
|
||||
assert len(results_dir) > 0
|
||||
Loading…
Reference in New Issue