diff --git a/README.md b/README.md index ed202450..a7924a1b 100644 --- a/README.md +++ b/README.md @@ -418,6 +418,42 @@ LangGraph-based workflow management: - **Constants**: UPPER_CASE (e.g., `DEFAULT_CONFIG`) - **Imports**: Standard library first, third-party, then local imports (langchain, tradingagents modules) +#### Data Structure Guidelines +**MANDATORY: Always use dataclasses for method returns** +- **Never return**: `dict`, `str`, `Any`, or unstructured data from public methods +- **Always return**: Properly typed dataclasses with clear field definitions +- **Rationale**: Provides type safety, IDE support, clear contracts, and prevents runtime errors + +**Examples**: +```python +# ❌ BAD - Dictionary returns +def update_news() -> dict[str, Any]: + return {"status": "completed", "count": 5} + +# ✅ GOOD - Dataclass returns +@dataclass +class NewsUpdateResult: + status: str + articles_found: int + articles_scraped: int + articles_failed: int + +def update_news() -> NewsUpdateResult: + return NewsUpdateResult( + status="completed", + articles_found=10, + articles_scraped=8, + articles_failed=2 + ) +``` + +**Dataclass Best Practices**: +- Use `@dataclass` decorator for all return value structures +- Include type hints for all fields +- Use `| None` for optional fields (modern Python 3.10+ syntax) +- Group related dataclasses in the same module +- Prefer immutable dataclasses with `frozen=True` for value objects + #### Ruff Formatting & Linting Rules **Formatting** (`mise run format`): - **Line length**: 88 characters maximum @@ -534,6 +570,313 @@ service.update_market_data("AAPL", "2024-01-01", "2024-01-31") - Questionnaire-driven configuration collection - Real-time streaming of analysis results +### Progressive Development Framework + +This framework ensures agents create type-safe, testable code through incremental development. It emphasizes building one component at a time with proper testing and type safety. + +#### Core Principles + +1. **Service-First Development**: Start with business logic in the service layer +2. **Stub Dependencies**: Create placeholder methods that return proper dataclasses +3. **Progressive Implementation**: Implement one dependency (client OR repository) at a time +4. **Constructor Injection**: Dependencies passed through constructor for testability +5. **Dataclass Returns**: All public methods return properly typed dataclasses +6. **Test-Driven Development**: Write tests first, implement to make them pass + +#### Development Process + +**Step 1: Design Domain Models** +```python +# models.py - Define all dataclasses first +@dataclass +class DomainEntity: + id: str + name: str + created_at: datetime + +@dataclass +class DomainContext: + entities: list[DomainEntity] + metadata: dict[str, Any] + quality_score: float + +@dataclass +class UpdateResult: + status: str + entities_processed: int + entities_failed: int +``` + +**Step 2: Create Service with Business Logic** +```python +# service.py - Main business logic with stub dependencies +class DomainService: + def __init__(self, client: DomainClient, repository: DomainRepository): + self.client = client + self.repository = repository + + def get_context(self, symbol: str, start_date: str, end_date: str) -> DomainContext: + # Implement business logic flow + entities = self.repository.get_entities(symbol, start_date, end_date) + + # Process and transform data + processed_entities = self._process_entities(entities) + + # Calculate quality metrics + quality_score = self._calculate_quality(processed_entities) + + return DomainContext( + entities=processed_entities, + metadata={"symbol": symbol, "date_range": f"{start_date} to {end_date}"}, + quality_score=quality_score + ) + + def update_data(self, symbol: str, start_date: str, end_date: str) -> UpdateResult: + # Business logic for updating data + raw_data = self.client.fetch_data(symbol, start_date, end_date) + entities = self._transform_raw_data(raw_data) + + processed = 0 + failed = 0 + for entity in entities: + try: + self.repository.save_entity(entity) + processed += 1 + except Exception: + failed += 1 + + return UpdateResult( + status="completed", + entities_processed=processed, + entities_failed=failed + ) + + def _process_entities(self, entities: list[DomainEntity]) -> list[DomainEntity]: + # Private method for business logic + return entities # Stub implementation + + def _calculate_quality(self, entities: list[DomainEntity]) -> float: + # Private method for quality calculation + return 1.0 # Stub implementation +``` + +**Step 3: Create Stub Dependencies** +```python +# client.py - Stub client that returns proper dataclasses +class DomainClient: + def fetch_data(self, symbol: str, start_date: str, end_date: str) -> list[dict[str, Any]]: + # Stub implementation - returns realistic structure + return [ + {"id": "1", "name": f"{symbol}_entity", "created_at": "2024-01-01T00:00:00Z"}, + {"id": "2", "name": f"{symbol}_entity_2", "created_at": "2024-01-02T00:00:00Z"} + ] + +# repository.py - Stub repository that returns proper dataclasses +class DomainRepository: + def __init__(self, cache_dir: str): + self.cache_dir = cache_dir + + def get_entities(self, symbol: str, start_date: str, end_date: str) -> list[DomainEntity]: + # Stub implementation - returns proper dataclasses + return [ + DomainEntity(id="1", name=f"{symbol}_cached", created_at=datetime.now()), + DomainEntity(id="2", name=f"{symbol}_cached_2", created_at=datetime.now()) + ] + + def save_entity(self, entity: DomainEntity) -> None: + # Stub implementation + pass +``` + +**Step 4: Write Comprehensive Tests** +```python +# service_test.py - Test the service with mock dependencies +from unittest.mock import Mock +import pytest + +def test_get_context_with_mock_dependencies(): + """Test service business logic with mocked dependencies.""" + # Mock the dependencies + mock_client = Mock() + mock_repository = Mock() + + # Configure mock returns + mock_repository.get_entities.return_value = [ + DomainEntity(id="1", name="TEST_entity", created_at=datetime(2024, 1, 1)) + ] + + # Create service with mocks + service = DomainService(client=mock_client, repository=mock_repository) + + # Test the business logic + context = service.get_context("TEST", "2024-01-01", "2024-01-31") + + # Validate structure and business logic + assert isinstance(context, DomainContext) + assert context.metadata["symbol"] == "TEST" + assert context.quality_score > 0 + assert len(context.entities) > 0 + + # Verify repository was called correctly + mock_repository.get_entities.assert_called_once_with("TEST", "2024-01-01", "2024-01-31") + +def test_update_data_with_mock_dependencies(): + """Test update business logic with mocked dependencies.""" + mock_client = Mock() + mock_repository = Mock() + + # Configure mock client to return raw data + mock_client.fetch_data.return_value = [ + {"id": "1", "name": "TEST_raw", "created_at": "2024-01-01T00:00:00Z"} + ] + + service = DomainService(client=mock_client, repository=mock_repository) + + result = service.update_data("TEST", "2024-01-01", "2024-01-31") + + # Validate business logic results + assert isinstance(result, UpdateResult) + assert result.status == "completed" + assert result.entities_processed >= 0 + + # Verify client and repository interactions + mock_client.fetch_data.assert_called_once() + mock_repository.save_entity.assert_called() +``` + +**Step 5: Implement One Dependency at a Time** + +Choose either client OR repository to implement first: + +```python +# Option A: Implement client first +class DomainClient: + def __init__(self, api_key: str): + self.api_key = api_key + self.session = requests.Session() + self.session.headers.update({"User-Agent": "TradingAgents/1.0"}) + + def fetch_data(self, symbol: str, start_date: str, end_date: str) -> list[dict[str, Any]]: + # Real implementation with error handling + try: + response = self.session.get( + f"https://api.example.com/data/{symbol}", + params={"start": start_date, "end": end_date}, + timeout=30 + ) + response.raise_for_status() + return response.json()["data"] + except requests.RequestException as e: + raise DomainClientError(f"Failed to fetch data: {e}") + +# Option B: Implement repository first +class DomainRepository: + def __init__(self, cache_dir: str): + self.cache_dir = Path(cache_dir) + self.cache_dir.mkdir(parents=True, exist_ok=True) + + def get_entities(self, symbol: str, start_date: str, end_date: str) -> list[DomainEntity]: + # Real implementation with file I/O + cache_file = self.cache_dir / f"{symbol}_{start_date}_{end_date}.json" + + if not cache_file.exists(): + return [] + + try: + with open(cache_file, 'r') as f: + data = json.load(f) + + return [ + DomainEntity( + id=item["id"], + name=item["name"], + created_at=datetime.fromisoformat(item["created_at"]) + ) + for item in data + ] + except (json.JSONDecodeError, KeyError) as e: + raise DomainRepositoryError(f"Failed to load cached data: {e}") +``` + +**Step 6: Test Real Implementation** +```python +def test_real_client_integration(): + """Test real client implementation.""" + client = DomainClient(api_key="test_key") + + # Test with real HTTP calls (or use responses library for mocking) + with responses.RequestsMock() as rsps: + rsps.add( + responses.GET, + "https://api.example.com/data/TEST", + json={"data": [{"id": "1", "name": "TEST", "created_at": "2024-01-01T00:00:00Z"}]}, + status=200 + ) + + result = client.fetch_data("TEST", "2024-01-01", "2024-01-31") + + assert len(result) == 1 + assert result[0]["id"] == "1" + +def test_real_repository_integration(): + """Test real repository implementation.""" + with tempfile.TemporaryDirectory() as temp_dir: + repo = DomainRepository(temp_dir) + + # Test saving and loading + entity = DomainEntity(id="1", name="TEST", created_at=datetime.now()) + repo.save_entity(entity) + + entities = repo.get_entities("TEST", "2024-01-01", "2024-01-31") + assert len(entities) == 1 + assert entities[0].id == "1" +``` + +**Step 7: Iterate and Refine** + +1. Run tests after each implementation +2. Refactor business logic as needed +3. Add error handling and edge cases +4. Implement the remaining dependency +5. Add integration tests with both real dependencies + +#### Directory Structure + +``` +domain_name/ +├── models.py # Dataclasses only - no business logic +├── client.py # External API integration +├── repository.py # Data persistence and caching +├── service.py # Main business logic coordinator +└── service_test.py # Comprehensive test suite +``` + +#### Benefits of This Approach + +1. **Type Safety**: All interfaces defined upfront with dataclasses +2. **Testability**: Business logic tested independently of external dependencies +3. **Incremental Development**: One component at a time reduces complexity +4. **Clear Contracts**: Dataclass returns make interfaces explicit +5. **Error Isolation**: Issues contained within single components +6. **Refactoring Safety**: Type system catches interface changes +7. **Documentation**: Dataclasses serve as living documentation + +#### Anti-Patterns to Avoid + +❌ **Don't return dictionaries or strings from public methods** +❌ **Don't implement all dependencies simultaneously** +❌ **Don't skip writing tests first** +❌ **Don't mix business logic with I/O operations** +❌ **Don't use inheritance for dependency injection** +❌ **Don't create circular dependencies between components** + +✅ **Do use dataclasses for all return values** +✅ **Do implement one dependency at a time** +✅ **Do write tests before implementation** +✅ **Do separate business logic from I/O** +✅ **Do use constructor injection** +✅ **Do maintain clear separation of concerns** + ### File Structure Context - **`cli/`**: Interactive command-line interface - **`tradingagents/agents/`**: All agent implementations diff --git a/pyproject.toml b/pyproject.toml index f20c8e39..32036e77 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,6 +32,8 @@ dependencies = [ "typer>=0.12.0", "typing-extensions>=4.14.0", "yfinance>=0.2.63", + "TA-Lib>=0.4.28", + "newspaper3k>=0.2.8", ] [project.optional-dependencies] diff --git a/tradingagents/agents/__init__.py b/tradingagents/agents/__init__.py index a83fa3ce..ff101bed 100644 --- a/tradingagents/agents/__init__.py +++ b/tradingagents/agents/__init__.py @@ -2,6 +2,9 @@ from .analysts.fundamentals_analyst import create_fundamentals_analyst from .analysts.market_analyst import create_market_analyst from .analysts.news_analyst import create_news_analyst from .analysts.social_media_analyst import create_social_media_analyst +from .libs.agent_states import AgentState, InvestDebateState, RiskDebateState +from .libs.context_helpers import create_msg_delete +from .libs.memory import FinancialSituationMemory from .managers.research_manager import create_research_manager from .managers.risk_manager import create_risk_manager from .researchers.bear_researcher import create_bear_researcher @@ -10,26 +13,20 @@ from .risk_mgmt.aggresive_debator import create_risky_debator from .risk_mgmt.conservative_debator import create_safe_debator from .risk_mgmt.neutral_debator import create_neutral_debator from .trader.trader import create_trader -from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState -from .utils.agent_utils import Toolkit, create_msg_delete -from .utils.memory import FinancialSituationMemory -from .utils.service_toolkit import ServiceToolkit __all__ = [ "FinancialSituationMemory", - "Toolkit", - "ServiceToolkit", "AgentState", - "create_msg_delete", "InvestDebateState", "RiskDebateState", "create_bear_researcher", "create_bull_researcher", - "create_research_manager", "create_fundamentals_analyst", "create_market_analyst", + "create_msg_delete", "create_neutral_debator", "create_news_analyst", + "create_research_manager", "create_risky_debator", "create_risk_manager", "create_safe_debator", diff --git a/tradingagents/agents/libs/agent_toolkit.py b/tradingagents/agents/libs/agent_toolkit.py index 27704695..10c3dd5c 100644 --- a/tradingagents/agents/libs/agent_toolkit.py +++ b/tradingagents/agents/libs/agent_toolkit.py @@ -1,6 +1,6 @@ import logging import re -from datetime import datetime, timedelta +from datetime import date, datetime, timedelta from typing import Annotated from langchain_core.tools import tool @@ -41,9 +41,9 @@ class AgentToolkit: def __init__( self, news_service: NewsService, + socialmedia_service: SocialMediaService, marketdata_service: MarketDataService, fundamentaldata_service: FundamentalDataService, - socialmedia_service: SocialMediaService, insiderdata_service: InsiderDataService, config: TradingAgentsConfig = DEFAULT_CONFIG, ): @@ -102,8 +102,8 @@ class AgentToolkit: datetime.strptime(start_date, "%Y-%m-%d") datetime.strptime(end_date, "%Y-%m-%d") - return self._news_service.get_context( - query=ticker, start_date=start_date, end_date=end_date, symbol=ticker + return self._news_service.get_company_news_context( + symbol=ticker, start_date=start_date, end_date=end_date ) except Exception as e: logger.error(f"Error getting news for {ticker}: {e}") @@ -177,7 +177,7 @@ class AgentToolkit: curr_date: Annotated[ str, "The current trading date you are trading on, YYYY-mm-dd" ], - look_back_days: Annotated[int, "how many days to look back"] = None, + look_back_days: Annotated[int, "how many days to look back"], ) -> TAReportContext: """ Retrieve stock stats indicators for a given ticker symbol and indicator. @@ -280,11 +280,11 @@ class AgentToolkit: Returns: BalanceSheetContext: Structured balance sheet analysis with key liquidity and debt metrics. """ + curr_date_obj = self._parse_date(curr_date) return self._fundamentaldata_service.get_balance_sheet_context( symbol=ticker, - start_date=curr_date, - end_date=curr_date, - frequency=freq.lower(), + start_date=curr_date_obj, + end_date=curr_date_obj, ) @tool @@ -306,11 +306,11 @@ class AgentToolkit: Returns: CashFlowContext: Structured cash flow analysis with operating cash flow metrics. """ + curr_date_obj = self._parse_date(curr_date) return self._fundamentaldata_service.get_cashflow_context( symbol=ticker, - start_date=curr_date, - end_date=curr_date, - frequency=freq.lower(), + start_date=curr_date_obj, + end_date=curr_date_obj, ) @tool @@ -332,11 +332,11 @@ class AgentToolkit: Returns: IncomeStatementContext: Structured income statement analysis with profitability metrics. """ + curr_date_obj = self._parse_date(curr_date) return self._fundamentaldata_service.get_income_statement_context( symbol=ticker, - start_date=curr_date, - end_date=curr_date, - frequency=freq.lower(), + start_date=curr_date_obj, + end_date=curr_date_obj, ) def _calculate_date_range( @@ -359,7 +359,9 @@ class AgentToolkit: curr_date_obj = datetime.strptime(curr_date, "%Y-%m-%d") except ValueError as e: logger.error(f"Invalid date format '{curr_date}': {e}") - raise ValueError(f"Date must be in YYYY-MM-DD format, got: {curr_date}") + raise ValueError( + f"Date must be in YYYY-MM-DD format, got: {curr_date}" + ) from e if lookback_days is None: lookback_days = self._config.default_lookback_days @@ -367,6 +369,27 @@ class AgentToolkit: start_date_obj = curr_date_obj - timedelta(days=lookback_days) return start_date_obj.strftime("%Y-%m-%d"), curr_date + def _parse_date(self, date_str: str) -> date: + """ + Convert string date to date object. + + Args: + date_str: Date string in YYYY-MM-DD format + + Returns: + date object + + Raises: + ValueError: If date format is invalid + """ + try: + return datetime.strptime(date_str, "%Y-%m-%d").date() + except ValueError as e: + logger.error(f"Invalid date format '{date_str}': {e}") + raise ValueError( + f"Date must be in YYYY-MM-DD format, got '{date_str}'" + ) from e + def _validate_ticker(self, ticker: str) -> str: """ Validate and sanitize ticker symbol. diff --git a/tradingagents/agents/libs/context_helpers.py b/tradingagents/agents/libs/context_helpers.py index 650d4d97..19c32784 100644 --- a/tradingagents/agents/libs/context_helpers.py +++ b/tradingagents/agents/libs/context_helpers.py @@ -310,3 +310,24 @@ def is_high_quality_data(context_json: str) -> bool: """Quick check if context contains high quality data.""" context = ContextParser.parse_context(context_json) return ContextParser.is_high_quality(context) + + +def create_msg_delete(): + """ + Create a message deletion node function for LangGraph workflows. + + This function returns a node that clears all messages from the state, + which is useful for preventing context pollution between different + phases of multi-agent workflows. + + Returns: + Callable: A function that can be used as a LangGraph node + """ + from langchain_core.messages import RemoveMessage + from langgraph.graph.message import REMOVE_ALL_MESSAGES + + def delete_messages(state): + """Delete all messages from the current state.""" + return {"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)]} + + return delete_messages diff --git a/tradingagents/clients/__init__.py b/tradingagents/clients/__init__.py deleted file mode 100644 index f8140241..00000000 --- a/tradingagents/clients/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Client classes for live data access in TradingAgents. -""" - -from .base import BaseClient - -__all__ = ["BaseClient"] diff --git a/tradingagents/clients/base.py b/tradingagents/clients/base.py deleted file mode 100644 index 236b4b66..00000000 --- a/tradingagents/clients/base.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Base client interface for TradingAgents data sources. -""" - -from abc import ABC, abstractmethod -from typing import Any - - -class BaseClient(ABC): - """Abstract base class for all data clients.""" - - @abstractmethod - def get_data(self, **kwargs) -> dict[str, Any]: - """ - Get data from the client source. - - Args: - **kwargs: Client-specific parameters - - Returns: - dict: Data dictionary with standardized structure - """ - pass diff --git a/tradingagents/domains/marketdata/clients/finnhub_client.py b/tradingagents/domains/marketdata/clients/finnhub_client.py index 63b33dd4..0fb08f6c 100644 --- a/tradingagents/domains/marketdata/clients/finnhub_client.py +++ b/tradingagents/domains/marketdata/clients/finnhub_client.py @@ -4,10 +4,16 @@ Finnhub client for financial data access. import logging from datetime import date -from typing import Any import finnhub +from ..models import ( + CompanyProfile, + InsiderSentimentResponse, + InsiderTransactionsResponse, + ReportedFinancialsResponse, +) + logger = logging.getLogger(__name__) @@ -31,116 +37,33 @@ class FinnhubClient: Args: api_key: Finnhub API key """ - self.api_key = api_key self.client = finnhub.Client(api_key=api_key) - def test_connection(self) -> bool: - """Test if the Finnhub API connection is working.""" - try: - # Test with a simple quote request - response = self.client.quote("AAPL") - return "c" in response # 'c' is current price field - except Exception as e: - logger.error(f"Finnhub connection test failed: {e}") - return False - - def get_balance_sheet( - self, symbol: str, frequency: str, report_date: date - ) -> dict[str, Any]: + def get_reported_financials( + self, symbol: str, frequency: str + ) -> ReportedFinancialsResponse: """ - Get balance sheet data from Finnhub. + Get reported financials data from Finnhub. Args: symbol: Stock symbol (e.g., 'AAPL') frequency: Reporting frequency ('quarterly' or 'annual') - report_date: Report date as date object Returns: - Balance sheet data from Finnhub API + Reported financials data from Finnhub API """ try: # Finnhub SDK expects frequency as 'quarterly' or 'annual' freq = "quarterly" if frequency.lower() in ["quarterly", "q"] else "annual" response = self.client.financials_reported(symbol=symbol.upper(), freq=freq) - return response if isinstance(response, dict) else {"data": []} + return ReportedFinancialsResponse.from_dict(response) except Exception as e: - logger.error(f"Error fetching balance sheet for {symbol}: {e}") - return {"data": []} - - def get_income_statement( - self, symbol: str, frequency: str, report_date: date - ) -> dict[str, Any]: - """ - Get income statement data from Finnhub. - - Args: - symbol: Stock symbol (e.g., 'AAPL') - frequency: Reporting frequency ('quarterly' or 'annual') - report_date: Report date as date object - - Returns: - Income statement data from Finnhub API - """ - try: - freq = "quarterly" if frequency.lower() in ["quarterly", "q"] else "annual" - response = self.client.financials_reported(symbol=symbol.upper(), freq=freq) - return response if isinstance(response, dict) else {"data": []} - except Exception as e: - logger.error(f"Error fetching income statement for {symbol}: {e}") - return {"data": []} - - def get_cash_flow( - self, symbol: str, frequency: str, report_date: date - ) -> dict[str, Any]: - """ - Get cash flow statement data from Finnhub. - - Args: - symbol: Stock symbol (e.g., 'AAPL') - frequency: Reporting frequency ('quarterly' or 'annual') - report_date: Report date as date object - - Returns: - Cash flow statement data from Finnhub API - """ - try: - freq = "quarterly" if frequency.lower() in ["quarterly", "q"] else "annual" - response = self.client.financials_reported(symbol=symbol.upper(), freq=freq) - return response if isinstance(response, dict) else {"data": []} - except Exception as e: - logger.error(f"Error fetching cash flow for {symbol}: {e}") - return {"data": []} - - def get_company_news( - self, symbol: str, start_date: date, end_date: date - ) -> list[dict[str, Any]]: - """ - Get company news for a specific symbol and date range. - - Args: - symbol: Stock symbol (e.g., 'AAPL') - start_date: Start date as date object - end_date: End date as date object - - Returns: - List of news articles - """ - # Convert date objects to strings for API - start_str = start_date.isoformat() - end_str = end_date.isoformat() - - try: - response = self.client.company_news( - symbol.upper(), _from=start_str, to=end_str - ) - return response if isinstance(response, list) else [] - except Exception as e: - logger.error(f"Error fetching news for {symbol}: {e}") - return [] + logger.error(f"Error fetching reported financials for {symbol}: {e}") + raise def get_insider_transactions( self, symbol: str, start_date: date, end_date: date - ) -> dict[str, Any]: + ) -> InsiderTransactionsResponse: """ Get insider transactions for a company. @@ -159,14 +82,18 @@ class FinnhubClient: response = self.client.stock_insider_transactions( symbol.upper(), _from=start_str, to=end_str ) - return response if isinstance(response, dict) else {"data": []} + if isinstance(response, dict): + return InsiderTransactionsResponse.from_dict(response) + else: + # Return empty response if API returns unexpected format + return InsiderTransactionsResponse(data=[], symbol=symbol.upper()) except Exception as e: logger.error(f"Error fetching insider transactions for {symbol}: {e}") - return {"data": []} + raise def get_insider_sentiment( self, symbol: str, start_date: date, end_date: date - ) -> dict[str, Any]: + ) -> InsiderSentimentResponse: """ Get insider sentiment data for a company. @@ -185,29 +112,16 @@ class FinnhubClient: response = self.client.stock_insider_sentiment( symbol.upper(), _from=start_str, to=end_str ) - return response if isinstance(response, dict) else {"data": []} + if isinstance(response, dict): + return InsiderSentimentResponse.from_dict(response) + else: + # Return empty response if API returns unexpected format + return InsiderSentimentResponse(data=[], symbol=symbol.upper()) except Exception as e: logger.error(f"Error fetching insider sentiment for {symbol}: {e}") - return {"data": []} + raise - def get_quote(self, symbol: str) -> dict[str, Any]: - """ - Get current quote for a symbol. - - Args: - symbol: Stock symbol - - Returns: - Quote data with current price, change, etc. - """ - try: - response = self.client.quote(symbol.upper()) - return response if isinstance(response, dict) else {} - except Exception as e: - logger.error(f"Error fetching quote for {symbol}: {e}") - return {} - - def get_company_profile(self, symbol: str) -> dict[str, Any]: + def get_company_profile(self, symbol: str) -> CompanyProfile: """ Get company profile information. @@ -219,20 +133,7 @@ class FinnhubClient: """ try: response = self.client.company_profile2(symbol=symbol.upper()) - return response if isinstance(response, dict) else {} + return CompanyProfile.from_dict(response) except Exception as e: logger.error(f"Error fetching company profile for {symbol}: {e}") - return {} - - def get_client_info(self) -> dict[str, Any]: - """ - Get information about this client. - - Returns: - Dict[str, Any]: Client metadata - """ - return { - "client_type": self.__class__.__name__, - "api_key_set": bool(self.api_key), - "sdk_version": getattr(finnhub, "__version__", "unknown"), - } + raise diff --git a/tradingagents/domains/marketdata/clients/finnhub_client_test.py b/tradingagents/domains/marketdata/clients/finnhub_client_test.py deleted file mode 100644 index cba3940f..00000000 --- a/tradingagents/domains/marketdata/clients/finnhub_client_test.py +++ /dev/null @@ -1,320 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive tests for FinnhubClient using pytest-vcr. - -This test suite records real API interactions with Finnhub and replays them -in subsequent runs, providing realistic testing without network dependencies. -""" - -import os -from datetime import date - -import pytest - -# NOTE: FinnhubClient was removed - this test needs to be updated -# from tradingagents.clients.finnhub_client import FinnhubClient - -pytest.skip("FinnhubClient implementation removed", allow_module_level=True) - - -@pytest.fixture -def finnhub_client(): - """Create FinnhubClient with test API key.""" - # Use environment variable or test key for VCR recording - api_key = os.getenv("FINNHUB_API_KEY", "test_api_key") - return FinnhubClient(api_key=api_key) - - -@pytest.fixture -def vcr_config(): - """Configure VCR with proper settings.""" - return { - "filter_headers": ["X-Finnhub-Token"], # Filter out API key from recordings - "record_mode": "once", # Record once, then replay - "match_on": ["uri", "method"], - "decode_compressed_response": True, - } - - -class TestFinnhubClientConnection: - """Test client connection and initialization.""" - - @pytest.mark.vcr - def test_client_initialization(self, finnhub_client): - """Test that client initializes correctly.""" - assert finnhub_client.api_key is not None - assert finnhub_client.client is not None - - @pytest.mark.vcr - def test_connection_success(self, finnhub_client): - """Test successful API connection.""" - assert finnhub_client.test_connection() is True - - def test_client_info(self, finnhub_client): - """Test client metadata.""" - info = finnhub_client.get_client_info() - assert info["client_type"] == "FinnhubClient" - assert info["api_key_set"] is True - - -class TestFundamentalDataMethods: - """Test the fundamental data methods needed by FundamentalDataService.""" - - @pytest.mark.vcr - def test_get_balance_sheet_quarterly(self, finnhub_client): - """Test balance sheet retrieval with date object.""" - test_date = date(2024, 1, 1) - data = finnhub_client.get_balance_sheet("AAPL", "quarterly", test_date) - - assert isinstance(data, dict) - # Finnhub financials_reported returns data structure with 'data' key - assert "data" in data or len(data) > 0 - - @pytest.mark.vcr - def test_get_balance_sheet_annual(self, finnhub_client): - """Test annual balance sheet retrieval.""" - test_date = date(2024, 1, 1) - data = finnhub_client.get_balance_sheet("AAPL", "annual", test_date) - - assert isinstance(data, dict) - - @pytest.mark.vcr - def test_get_income_statement_quarterly(self, finnhub_client): - """Test income statement retrieval.""" - test_date = date(2024, 1, 1) - data = finnhub_client.get_income_statement("AAPL", "quarterly", test_date) - - assert isinstance(data, dict) - - @pytest.mark.vcr - def test_get_income_statement_annual(self, finnhub_client): - """Test annual income statement retrieval.""" - test_date = date(2024, 1, 1) - data = finnhub_client.get_income_statement("AAPL", "annual", test_date) - - assert isinstance(data, dict) - - @pytest.mark.vcr - def test_get_cash_flow_with_date_object(self, finnhub_client): - """Test cash flow retrieval with date object.""" - test_date = date(2024, 3, 31) - data = finnhub_client.get_cash_flow("AAPL", "quarterly", test_date) - - assert isinstance(data, dict) - - @pytest.mark.vcr - def test_fundamental_data_different_symbols(self, finnhub_client): - """Test fundamental data for different symbols.""" - symbols = ["AAPL", "MSFT", "GOOGL"] - test_date = date(2024, 1, 1) - - for symbol in symbols: - data = finnhub_client.get_balance_sheet(symbol, "quarterly", test_date) - assert isinstance(data, dict) - - -class TestExistingMethods: - """Test existing methods with enhanced date support.""" - - @pytest.mark.vcr - def test_get_company_news_january(self, finnhub_client): - """Test company news for January 2024.""" - start_date = date(2024, 1, 1) - end_date = date(2024, 1, 31) - news = finnhub_client.get_company_news("AAPL", start_date, end_date) - - assert isinstance(news, list) - - @pytest.mark.vcr - def test_get_company_news_date_objects(self, finnhub_client): - """Test company news with date objects.""" - start_date = date(2024, 1, 1) - end_date = date(2024, 1, 31) - - news = finnhub_client.get_company_news("AAPL", start_date, end_date) - - assert isinstance(news, list) - - @pytest.mark.vcr - def test_get_insider_transactions_date_objects(self, finnhub_client): - """Test insider transactions with date objects.""" - start_date = date(2024, 1, 1) - end_date = date(2024, 1, 31) - - data = finnhub_client.get_insider_transactions("AAPL", start_date, end_date) - - assert isinstance(data, dict) - assert "data" in data - - @pytest.mark.vcr - def test_get_insider_sentiment(self, finnhub_client): - """Test insider sentiment with date objects.""" - start_date = date(2024, 1, 1) - end_date = date(2024, 1, 31) - - data = finnhub_client.get_insider_sentiment("AAPL", start_date, end_date) - - assert isinstance(data, dict) - - @pytest.mark.vcr - def test_get_quote(self, finnhub_client): - """Test stock quote retrieval.""" - quote = finnhub_client.get_quote("AAPL") - - assert isinstance(quote, dict) - # Quote should contain current price 'c' field - if quote: # Only check if we got data - assert "c" in quote - - @pytest.mark.vcr - def test_get_company_profile(self, finnhub_client): - """Test company profile retrieval.""" - profile = finnhub_client.get_company_profile("AAPL") - - assert isinstance(profile, dict) - - -class TestErrorHandling: - """Test error handling and edge cases.""" - - @pytest.mark.vcr - def test_invalid_symbol_balance_sheet(self, finnhub_client): - """Test balance sheet with invalid symbol.""" - test_date = date(2024, 1, 1) - data = finnhub_client.get_balance_sheet( - "INVALID_SYMBOL_XYZ", "quarterly", test_date - ) - - # Should return dict with empty data structure, not raise exception - assert isinstance(data, dict) - assert "data" in data - - @pytest.mark.vcr - def test_invalid_symbol_news(self, finnhub_client): - """Test news with invalid symbol.""" - start_date = date(2024, 1, 1) - end_date = date(2024, 1, 31) - news = finnhub_client.get_company_news( - "INVALID_SYMBOL_XYZ", start_date, end_date - ) - - # Should return empty list, not raise exception - assert isinstance(news, list) - - def test_connection_with_invalid_api_key(self): - """Test connection failure with invalid API key.""" - client = FinnhubClient("invalid_api_key") - # This should return False, not raise an exception - assert client.test_connection() is False - - @pytest.mark.vcr - def test_frequency_normalization(self, finnhub_client): - """Test that frequency parameters are normalized correctly.""" - # Test different frequency formats - frequencies = ["quarterly", "QUARTERLY", "q", "Q", "annual", "ANNUAL", "a", "A"] - test_date = date(2024, 1, 1) - - for freq in frequencies: - data = finnhub_client.get_balance_sheet("AAPL", freq, test_date) - assert isinstance(data, dict) - - def test_date_edge_cases(self, finnhub_client): - """Test date edge cases.""" - # Test with year-end date - test_date = date(2024, 12, 31) - - # This shouldn't raise exceptions - # (actual API calls are mocked/recorded) - data = finnhub_client.get_balance_sheet("AAPL", "quarterly", test_date) - assert isinstance(data, dict) - - -class TestMultipleSymbolsAndTimeframes: - """Test with multiple symbols and different timeframes.""" - - @pytest.mark.vcr - def test_multiple_symbols_balance_sheet(self, finnhub_client): - """Test balance sheet for multiple major symbols.""" - symbols = ["AAPL", "MSFT", "GOOGL", "TSLA"] - test_date = date(2024, 1, 1) - - for symbol in symbols: - data = finnhub_client.get_balance_sheet(symbol, "quarterly", test_date) - assert isinstance(data, dict) - - @pytest.mark.vcr - def test_quarterly_vs_annual_frequency(self, finnhub_client): - """Test quarterly vs annual frequency.""" - symbol = "AAPL" - test_date = date(2024, 1, 1) - - quarterly_data = finnhub_client.get_balance_sheet( - symbol, "quarterly", test_date - ) - annual_data = finnhub_client.get_balance_sheet(symbol, "annual", test_date) - - assert isinstance(quarterly_data, dict) - assert isinstance(annual_data, dict) - - @pytest.mark.vcr - def test_all_fundamental_methods_same_symbol(self, finnhub_client): - """Test all fundamental methods for the same symbol.""" - symbol = "AAPL" - frequency = "quarterly" - report_date = date(2024, 1, 1) - - balance_sheet = finnhub_client.get_balance_sheet(symbol, frequency, report_date) - income_statement = finnhub_client.get_income_statement( - symbol, frequency, report_date - ) - cash_flow = finnhub_client.get_cash_flow(symbol, frequency, report_date) - - assert isinstance(balance_sheet, dict) - assert isinstance(income_statement, dict) - assert isinstance(cash_flow, dict) - - -# Integration test to verify the client works with service expectations -class TestServiceIntegration: - """Test that client works as expected by FundamentalDataService.""" - - def test_service_expected_methods_exist(self, finnhub_client): - """Test that all methods expected by FundamentalDataService exist.""" - # These are the methods the service calls - assert hasattr(finnhub_client, "get_balance_sheet") - assert hasattr(finnhub_client, "get_income_statement") - assert hasattr(finnhub_client, "get_cash_flow") - - # Verify method signatures accept the expected parameters - import inspect - - for method_name in [ - "get_balance_sheet", - "get_income_statement", - "get_cash_flow", - ]: - method = getattr(finnhub_client, method_name) - sig = inspect.signature(method) - params = list(sig.parameters.keys()) - - # Service calls: method(symbol, frequency, date) - assert "symbol" in params - assert "frequency" in params - assert "report_date" in params - - @pytest.mark.vcr - def test_data_format_for_service_conversion(self, finnhub_client): - """Test that returned data format can be processed by service conversion method.""" - test_date = date(2024, 1, 1) - data = finnhub_client.get_balance_sheet("AAPL", "quarterly", test_date) - - # Service expects either empty dict or dict with data - assert isinstance(data, dict) - - # The service's _convert_to_financial_statement expects either: - # 1. Empty/falsy data -> returns None - # 2. Dict with "data" key containing the actual financial data - if data: # If we got data - # Should be able to access data["data"] or similar structure - # This validates the format is compatible with service expectations - assert isinstance(data, dict) diff --git a/tradingagents/domains/marketdata/fundamental_data_service.py b/tradingagents/domains/marketdata/fundamental_data_service.py index 7bc6a9b4..269657e4 100644 --- a/tradingagents/domains/marketdata/fundamental_data_service.py +++ b/tradingagents/domains/marketdata/fundamental_data_service.py @@ -3,6 +3,19 @@ Fundamental Data Service for aggregating and analyzing financial statement data. """ import logging +from datetime import date + +from tradingagents.config import TradingAgentsConfig + +from .clients.finnhub_client import FinnhubClient +from .models import ( + BalanceSheetContext, + CashFlowContext, + DataQuality, + FundamentalContext, + IncomeStatementContext, +) +from .repos.fundamental_data_repository import FundamentalDataRepository logger = logging.getLogger(__name__) @@ -12,35 +25,30 @@ class FundamentalDataService: def __init__( self, - simfin_client: SimFinClient, + finnhub_client: FinnhubClient, repository: FundamentalDataRepository, ): """Initialize Fundamental Data Service. Args: - simfin_client: Client for SimFin/financial API access + finnhub_client: Client for FinnHub financial API access repository: Repository for cached fundamental data - online_mode: Whether to fetch live data - data_dir: Directory for data storage + online_mode: Whether to fetch live data or use cached data """ - self.simfin_client = simfin_client + self.finnhub_client = finnhub_client self.repository = repository - def update_fundamental_data( - self, - symbol: str, - start_date: str, - end_date: str, - frequency: str = "quarterly", - ) -> FundamentalContext: - pass # TODO: fetch fundementals from simfin, save in repo + @staticmethod + def build(_config: TradingAgentsConfig): + client = FinnhubClient("") + repo = FundamentalDataRepository("") + return FundamentalDataService(client, repo) def get_fundamental_context( self, symbol: str, - start_date: str, - end_date: str, - frequency: str = "quarterly", + start_date: date, + end_date: date, ) -> FundamentalContext: """Get fundamental analysis context for a company. @@ -49,34 +57,135 @@ class FundamentalDataService: start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format frequency: Reporting frequency ('quarterly' or 'annual') - force_refresh: If True, skip local data and fetch fresh from APIs Returns: FundamentalContext with financial statements and key ratios """ - balance_sheet = None - income_statement = None - cash_flow = None - error_info = {} - errors = [] - data_source = "unknown" + # TODO: implement + return FundamentalContext( + symbol=symbol, start_date=start_date, end_date=end_date + ) - # return FundamentalContext( - # symbol=symbol, - # period={"start": start_date, "end": end_date}, - # balance_sheet=balance_sheet, - # income_statement=income_statement, - # cash_flow=cash_flow, - # key_ratios=key_ratios, - # metadata={ - # "data_quality": data_quality, - # "service": "fundamental_data", - # "online_mode": self.is_online(), - # "frequency": frequency, - # "data_source": data_source, - # "force_refresh": force_refresh, - # **error_info, - # }, - # ) + def get_balance_sheet_context( + self, + symbol: str, + start_date: date, + end_date: date, + ) -> BalanceSheetContext: + """Get balance sheet context for a company. - pass # TODO: read data from repo + Args: + symbol: Stock ticker symbol + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + frequency: Reporting frequency ('quarterly' or 'annual') + + Returns: + BalanceSheetContext with balance sheet data and ratios + """ + # TODO: implement + + # Return empty context if no data + return BalanceSheetContext( + symbol=symbol, + start_date=start_date, + end_date=end_date, + balance_sheet_data=[], + key_ratios=[], + data_quality=DataQuality.LOW, + source="none", + metadata={"error": "No balance sheet data available"}, + ) + + def get_income_statement_context( + self, + symbol: str, + start_date: date, + end_date: date, + ) -> IncomeStatementContext: + """Get income statement context for a company. + + Args: + symbol: Stock ticker symbol + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + frequency: Reporting frequency ('quarterly' or 'annual') + + Returns: + IncomeStatementContext with income statement data and ratios + """ + # TODO: implement + + # Return empty context if no data + return IncomeStatementContext( + symbol=symbol, + start_date=start_date, + end_date=end_date, + income_statement_data=[], + key_ratios=[], + data_quality=DataQuality.LOW, + source="none", + metadata={"error": "No income statement data available"}, + ) + + def get_cashflow_context( + self, + symbol: str, + start_date: date, + end_date: date, + ) -> CashFlowContext: + """Get cash flow context for a company. + + Args: + symbol: Stock ticker symbol + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + frequency: Reporting frequency ('quarterly' or 'annual') + + Returns: + CashFlowContext with cash flow data and ratios + """ + # TODOL implement + + # Return empty context if no data + return CashFlowContext( + symbol=symbol, + start_date=start_date, + end_date=end_date, + cash_flow_data=[], + key_ratios=[], + data_quality=DataQuality.LOW, + source="none", + metadata={"error": "No cash flow data available"}, + ) + + def update_fundamental_data( + self, + symbol: str, + date: date, + frequency: str = "quarterly", + ) -> bool: + """Update fundamental data by fetching from FinnHub and storing in repository. + + Args: + symbol: Stock ticker symbol + date: Date for the financial data + frequency: Reporting frequency ('quarterly' or 'annual') + + Returns: + bool: True if update was successful + """ + try: + # Fetch reported financials data from FinnHub using the unified method + reported_financials = self.finnhub_client.get_reported_financials( + symbol, frequency + ) + + # Store the reported financials data in repository + return self.repository.store_reported_financials( + symbol=symbol, date=date, reported_financials=reported_financials + ) + + except Exception as e: + logger.error(f"Error updating fundamental data for {symbol}: {e}") + return False diff --git a/tradingagents/domains/marketdata/fundamental_data_service_test.py b/tradingagents/domains/marketdata/fundamental_data_service_test.py deleted file mode 100644 index 8ee3c19e..00000000 --- a/tradingagents/domains/marketdata/fundamental_data_service_test.py +++ /dev/null @@ -1,495 +0,0 @@ -""" -Test FundamentalDataService with mock SimFin clients and real FundamentalDataRepository. -""" - -import tempfile -from datetime import datetime -from typing import Any - -import pytest - -from tradingagents.clients.base import BaseClient -from tradingagents.domains.marketdata.fundamental_data_service import ( - DataQuality, - FinancialStatement, - FundamentalContext, - FundamentalDataService, -) -from tradingagents.repositories.fundamental_repository import FundamentalDataRepository - - -class MockSimFinClient(BaseClient): - """Mock SimFin client that returns sample financial statement data.""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.connection_works = True - - def test_connection(self) -> bool: - return self.connection_works - - def get_data(self, *args, **kwargs) -> dict[str, Any]: - """Not used directly by FundamentalDataService.""" - return {} - - def get_balance_sheet( - self, ticker: str, freq: str, curr_date: str - ) -> dict[str, Any]: - """Return mock balance sheet data.""" - return { - "ticker": ticker, - "statement_type": "balance_sheet", - "frequency": freq, - "period": "Q3-2024" if freq == "quarterly" else "2024", - "report_date": "2024-09-30", - "publish_date": "2024-10-30", - "currency": "USD", - "data": { - "Total Assets": 365725000000.0, - "Total Current Assets": 143566000000.0, - "Cash and Cash Equivalents": 28965000000.0, - "Short-term Investments": 31590000000.0, - "Accounts Receivable": 13348000000.0, - "Inventory": 6511000000.0, - "Total Non-Current Assets": 222159000000.0, - "Property, Plant & Equipment": 43715000000.0, - "Intangible Assets": 11235000000.0, - "Total Liabilities": 279414000000.0, - "Total Current Liabilities": 136817000000.0, - "Accounts Payable": 58146000000.0, - "Short-term Debt": 20208000000.0, - "Total Non-Current Liabilities": 142597000000.0, - "Long-term Debt": 106550000000.0, - "Total Shareholders Equity": 86311000000.0, - "Retained Earnings": 672000000.0, - "Common Stock": 77958000000.0, - }, - "metadata": { - "source": "mock_simfin", - "retrieved_at": datetime(2024, 1, 2).isoformat(), - }, - } - - def get_income_statement( - self, ticker: str, freq: str, curr_date: str - ) -> dict[str, Any]: - """Return mock income statement data.""" - return { - "ticker": ticker, - "statement_type": "income_statement", - "frequency": freq, - "period": "Q3-2024" if freq == "quarterly" else "2024", - "report_date": "2024-09-30", - "publish_date": "2024-10-30", - "currency": "USD", - "data": { - "Total Revenue": 94930000000.0, - "Cost of Revenue": 55720000000.0, - "Gross Profit": 39210000000.0, - "Operating Expenses": 15706000000.0, - "Research and Development": 8067000000.0, - "Sales, General & Administrative": 7639000000.0, - "Operating Income": 23504000000.0, - "Interest Expense": 1013000000.0, - "Other Income": 269000000.0, - "Income Before Tax": 22760000000.0, - "Tax Provision": 4438000000.0, - "Net Income": 18322000000.0, - "Basic EPS": 1.18, - "Diluted EPS": 1.15, - "Shares Outstanding": 15550193000, - }, - "metadata": { - "source": "mock_simfin", - "retrieved_at": datetime(2024, 1, 2).isoformat(), - }, - } - - def get_cash_flow(self, ticker: str, freq: str, curr_date: str) -> dict[str, Any]: - """Return mock cash flow statement data.""" - return { - "ticker": ticker, - "statement_type": "cash_flow", - "frequency": freq, - "period": "Q3-2024" if freq == "quarterly" else "2024", - "report_date": "2024-09-30", - "publish_date": "2024-10-30", - "currency": "USD", - "data": { - "Net Income": 18322000000.0, - "Depreciation & Amortization": 2871000000.0, - "Changes in Working Capital": -1684000000.0, - "Operating Cash Flow": 23302000000.0, - "Capital Expenditures": -2736000000.0, - "Acquisitions": -1800000000.0, - "Asset Sales": 234000000.0, - "Investing Cash Flow": -4302000000.0, - "Dividends Paid": -3746000000.0, - "Share Repurchases": -24979000000.0, - "Debt Proceeds": 750000000.0, - "Debt Repayment": -1500000000.0, - "Financing Cash Flow": -28475000000.0, - "Free Cash Flow": 20566000000.0, - "Net Change in Cash": -9475000000.0, - }, - "metadata": { - "source": "mock_simfin", - "retrieved_at": datetime(2024, 1, 2).isoformat(), - }, - } - - -@pytest.fixture -def temp_data_dir(): - """Create a temporary directory for test data and clean up after test.""" - with tempfile.TemporaryDirectory(prefix="fundamental_test_") as temp_dir: - yield temp_dir - - -@pytest.fixture -def mock_simfin_client(): - """Create a mock SimFin client for testing.""" - return MockSimFinClient() - - -@pytest.fixture -def broken_simfin_client(): - """Create a broken SimFin client for error testing.""" - - class BrokenSimFinClient(BaseClient): - def test_connection(self): - return False - - def get_data(self, *args, **kwargs): - raise Exception("SimFin API error") - - def get_balance_sheet(self, *args, **kwargs): - raise Exception("SimFin API error") - - def get_income_statement(self, *args, **kwargs): - raise Exception("SimFin API error") - - def get_cash_flow(self, *args, **kwargs): - raise Exception("SimFin API error") - - return BrokenSimFinClient() - - -@pytest.fixture -def partial_data_client(): - """Create a client that returns partial data for testing.""" - - class PartialDataClient(MockSimFinClient): - def get_cash_flow(self, ticker, freq, curr_date): - # Simulate missing cash flow data - raise Exception("Cash flow data not available") - - def get_income_statement(self, ticker, freq, curr_date): - # Simulate missing income statement - return {"data": {}, "metadata": {"error": "No data found"}} - - return PartialDataClient() - - -def test_online_mode_with_mock_simfin(temp_data_dir, mock_simfin_client): - """Test FundamentalDataService in online mode with mock SimFin client.""" - # Create real repository with temporary directory - real_repo = FundamentalDataRepository(temp_data_dir) - - # Create service with mock client and real repository - service = FundamentalDataService( - simfin_client=mock_simfin_client, - repository=real_repo, - data_dir=temp_data_dir, - ) - - # Test getting fundamental context with all three statements - context = service.get_fundamental_context( - symbol="AAPL", - start_date="2024-01-01", - end_date="2024-12-31", - frequency="quarterly", - force_refresh=True, # Force using mock client instead of cache - ) - - # Validate context structure - assert isinstance(context, FundamentalContext) - assert context.symbol == "AAPL" - assert context.period["start"] == "2024-01-01" - assert context.period["end"] == "2024-12-31" - - # Validate financial statements - assert context.balance_sheet is not None - assert isinstance(context.balance_sheet, FinancialStatement) - assert context.balance_sheet.period == "Q3-2024" - assert context.balance_sheet.currency == "USD" - assert "Total Assets" in context.balance_sheet.data - - assert context.income_statement is not None - assert isinstance(context.income_statement, FinancialStatement) - assert "Total Revenue" in context.income_statement.data - assert "Net Income" in context.income_statement.data - - assert context.cash_flow is not None - assert isinstance(context.cash_flow, FinancialStatement) - assert "Operating Cash Flow" in context.cash_flow.data - assert "Free Cash Flow" in context.cash_flow.data - - # Validate key ratios calculation - assert len(context.key_ratios) > 0 - assert "current_ratio" in context.key_ratios - assert "debt_to_equity" in context.key_ratios - assert "roe" in context.key_ratios # Return on Equity - assert "gross_margin" in context.key_ratios - - # Validate metadata - assert "data_quality" in context.metadata - assert context.metadata["service"] == "fundamental_data" - - # Test JSON serialization - json_output = context.model_dump_json(indent=2) - assert len(json_output) > 0 - - -def test_annual_vs_quarterly_frequency(temp_data_dir, mock_simfin_client): - """Test different reporting frequencies.""" - real_repo = FundamentalDataRepository(temp_data_dir) - service = FundamentalDataService( - simfin_client=mock_simfin_client, repository=real_repo, data_dir=temp_data_dir - ) - - # Test quarterly - quarterly_context = service.get_fundamental_context( - symbol="MSFT", - start_date="2024-01-01", - end_date="2024-12-31", - frequency="quarterly", - ) - - assert quarterly_context.balance_sheet is not None - assert quarterly_context.balance_sheet.period == "Q3-2024" - - # Test annual - annual_context = service.get_fundamental_context( - symbol="MSFT", - start_date="2024-01-01", - end_date="2024-12-31", - frequency="annual", - ) - - assert annual_context.balance_sheet is not None - assert annual_context.balance_sheet.period == "2024" - - -def test_financial_ratio_calculations(temp_data_dir, mock_simfin_client): - """Test calculation of key financial ratios.""" - real_repo = FundamentalDataRepository(temp_data_dir) - service = FundamentalDataService( - simfin_client=mock_simfin_client, repository=real_repo, data_dir=temp_data_dir - ) - - context = service.get_fundamental_context("TSLA", "2024-01-01", "2024-12-31") - - # Check that key ratios are calculated - ratios = context.key_ratios - - # Liquidity ratios - assert "current_ratio" in ratios - assert ratios["current_ratio"] > 0 - - # Leverage ratios - assert "debt_to_equity" in ratios - assert ratios["debt_to_equity"] >= 0 - - # Profitability ratios - assert "gross_margin" in ratios - assert "operating_margin" in ratios - assert "net_margin" in ratios - assert "roe" in ratios # Return on Equity - assert "roa" in ratios # Return on Assets - - # Efficiency ratios - assert "asset_turnover" in ratios - - # Validate ratio calculations are reasonable - assert 0 <= ratios["gross_margin"] <= 1 - assert 0 <= ratios["net_margin"] <= 1 - - -def test_offline_mode(temp_data_dir): - """Test FundamentalDataService without a client (offline mode).""" - real_repo = FundamentalDataRepository(temp_data_dir) - - service = FundamentalDataService( - simfin_client=None, repository=real_repo, data_dir=temp_data_dir - ) - - # Should handle offline gracefully - context = service.get_fundamental_context("AAPL", "2024-01-01", "2024-12-31") - - assert context.symbol == "AAPL" - assert context.balance_sheet is None # No data available offline - assert context.income_statement is None - assert context.cash_flow is None - assert len(context.key_ratios) == 0 - assert context.metadata.get("data_quality") == DataQuality.LOW - - -def test_partial_data_handling(): - """Test handling when only some financial statements are available.""" - - class PartialDataClient(MockSimFinClient): - def get_cash_flow(self, ticker, freq, curr_date): - # Simulate missing cash flow data - raise Exception("Cash flow data not available") - - def get_income_statement(self, ticker, freq, curr_date): - # Simulate missing income statement - return {"data": {}, "metadata": {"error": "No data found"}} - - partial_client = PartialDataClient() - service = FundamentalDataService( - simfin_client=partial_client, repository=None, online_mode=True - ) - - context = service.get_fundamental_context("XYZ", "2024-01-01", "2024-12-31") - - # Should have balance sheet but not others - assert context.balance_sheet is not None - assert context.income_statement is None # Failed to load - assert context.cash_flow is None # Failed to load - - # Ratios should be limited without full data (only balance sheet ratios available) - assert ( - len(context.key_ratios) <= 8 - ) # Only balance sheet ratios possible, no profitability ratios - assert context.metadata.get("data_quality") == DataQuality.LOW - - -def test_error_handling(): - """Test error handling with broken client.""" - - class BrokenSimFinClient(BaseClient): - def test_connection(self): - return False - - def get_data(self, *args, **kwargs): - raise Exception("SimFin API error") - - def get_balance_sheet(self, *args, **kwargs): - raise Exception("SimFin API error") - - def get_income_statement(self, *args, **kwargs): - raise Exception("SimFin API error") - - def get_cash_flow(self, *args, **kwargs): - raise Exception("SimFin API error") - - broken_client = BrokenSimFinClient() - service = FundamentalDataService( - simfin_client=broken_client, repository=None, online_mode=True - ) - - # Should handle errors gracefully - context = service.get_fundamental_context( - "FAIL", "2024-01-01", "2024-12-31", force_refresh=True - ) - - assert context.symbol == "FAIL" - assert context.balance_sheet is None - assert context.income_statement is None - assert context.cash_flow is None - assert len(context.key_ratios) == 0 - assert context.metadata.get("data_quality") == DataQuality.LOW - # Service logs errors but doesn't include them in metadata - - -def test_json_structure(): - """Test JSON structure of fundamental context.""" - mock_simfin = MockSimFinClient() - service = FundamentalDataService( - simfin_client=mock_simfin, repository=None, online_mode=True - ) - - context = service.get_fundamental_context("NVDA", "2024-01-01", "2024-12-31") - json_data = context.model_dump() - - # Validate required fields - required_fields = [ - "symbol", - "period", - "balance_sheet", - "income_statement", - "cash_flow", - "key_ratios", - "metadata", - ] - for field in required_fields: - assert field in json_data - - # Validate financial statement structure - if json_data["balance_sheet"]: - balance_sheet = json_data["balance_sheet"] - required_statement_fields = [ - "period", - "report_date", - "publish_date", - "currency", - "data", - ] - for field in required_statement_fields: - assert field in balance_sheet - - # Check some key balance sheet items - bs_data = balance_sheet["data"] - assert "Total Assets" in bs_data - assert "Total Liabilities" in bs_data - assert "Total Shareholders Equity" in bs_data - - # Validate key ratios - ratios = json_data["key_ratios"] - assert isinstance(ratios, dict) - assert len(ratios) > 0 - - # Validate metadata - metadata = json_data["metadata"] - assert "data_quality" in metadata - assert "service" in metadata - - -def test_comprehensive_ratio_calculation(): - """Test comprehensive financial ratio calculations.""" - mock_simfin = MockSimFinClient() - service = FundamentalDataService( - simfin_client=mock_simfin, repository=None, online_mode=True - ) - - context = service.get_fundamental_context("COMP", "2024-01-01", "2024-12-31") - ratios = context.key_ratios - - # Liquidity ratios - - # Not all ratios may be calculable depending on available data - calculated_ratios = set(ratios.keys()) - core_ratios = { - "current_ratio", - "debt_to_equity", - "gross_margin", - "net_margin", - "roe", - "roa", - } - - # At least the core ratios should be present - assert core_ratios.issubset(calculated_ratios), ( - f"Missing core ratios: {core_ratios - calculated_ratios}" - ) - - # All ratio values should be numbers - for ratio_name, ratio_value in ratios.items(): - assert isinstance(ratio_value, int | float), ( - f"{ratio_name} should be numeric, got {type(ratio_value)}" - ) - assert ratio_value == ratio_value, ( - f"{ratio_name} should not be NaN" - ) # NaN check diff --git a/tradingagents/domains/marketdata/insider_data_service.py b/tradingagents/domains/marketdata/insider_data_service.py index f50648be..69a59717 100644 --- a/tradingagents/domains/marketdata/insider_data_service.py +++ b/tradingagents/domains/marketdata/insider_data_service.py @@ -7,11 +7,26 @@ from dataclasses import dataclass from enum import Enum from typing import Any -from tradingagents.domains.marketdata.finnhub_client import FinnhubClient +from tradingagents.config import TradingAgentsConfig + +from .clients.finnhub_client import FinnhubClient logger = logging.getLogger(__name__) +class InsiderDataRepository: + """Simple repository for insider data - placeholder implementation.""" + + def __init__(self, data_dir: str): + self.data_dir = data_dir + + def get_data(self, symbol: str, start_date: str, end_date: str) -> dict: + return {} + + def store_data(self, symbol: str, data: dict) -> bool: + return True + + class DataQuality(Enum): """Data quality levels for insider data.""" @@ -85,6 +100,12 @@ class InsiderDataService: self.client = client self.repository = repository + @staticmethod + def build(_config: TradingAgentsConfig): + client = FinnhubClient("") + repo = InsiderDataRepository("") + return InsiderDataService(client, repo) + def get_insider_sentiment_context( self, symbol: str, diff --git a/tradingagents/domains/marketdata/market_data_service.py b/tradingagents/domains/marketdata/market_data_service.py index 57c0366e..f3a9c2f3 100644 --- a/tradingagents/domains/marketdata/market_data_service.py +++ b/tradingagents/domains/marketdata/market_data_service.py @@ -3,90 +3,57 @@ Market data service that provides structured market context. """ import logging -from dataclasses import dataclass -from enum import Enum +from datetime import datetime from typing import Any +import pandas as pd +import talib + +from tradingagents.config import TradingAgentsConfig +from tradingagents.domains.marketdata.clients.yfinance_client import YFinanceClient +from tradingagents.domains.marketdata.models import ( + INDICATOR_DEFINITIONS, + DataQuality, + IndicatorConfig, + IndicatorParamValue, + IndicatorPresets, + InputSpec, + PriceDataContext, + TAReportContext, + TechnicalAnalysisError, + TechnicalIndicatorData, +) +from tradingagents.domains.marketdata.repos.market_data_repository import ( + MarketDataRepository, +) + logger = logging.getLogger(__name__) -class DataQuality(Enum): - """Data quality levels for market data.""" - - HIGH = "high" - MEDIUM = "medium" - LOW = "low" - - -@dataclass -class TechnicalIndicatorData: - """Technical indicator data point.""" - - date: str - value: float | dict[str, Any] - indicator_type: str - - -@dataclass -class MarketDataContext: - """Market data context for trading analysis.""" - - symbol: str - period: dict[str, str] # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"} - price_data: list[dict[str, Any]] - technical_indicators: dict[str, list[TechnicalIndicatorData]] - metadata: dict[str, Any] - - -@dataclass -class TAReportContext: - """Technical Analysis Report context for specific indicators.""" - - symbol: str - period: dict[str, str] # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"} - indicator: str - indicator_data: list[TechnicalIndicatorData] - analysis_summary: str - signal_strength: float # -1.0 to 1.0 - recommendation: str # "BUY", "SELL", "HOLD" - metadata: dict[str, Any] - - -@dataclass -class PriceDataContext: - """Price Data context for historical price information.""" - - symbol: str - period: dict[str, str] # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"} - price_data: list[dict[str, Any]] - latest_price: float - price_change: float - price_change_percent: float - volume_info: dict[str, Any] - metadata: dict[str, Any] - - class MarketDataService: """Service for market data and technical indicators.""" def __init__( self, - yfin_client: YFinClient, - repo: MarketdataRepository, + yfin_client: YFinanceClient, + repo: MarketDataRepository, ): """ Initialize market data service. Args: - client: Client for live market data - repository: Repository for historical market data - online_mode: Whether to use live data - **kwargs: Additional configuration + yfin_client: Client for live market data + repo: Repository for historical market data """ - self.finnhub_client = finnhub_client self.yfin_client = yfin_client self.repo = repo + @staticmethod + def build(_config: TradingAgentsConfig): + client = YFinanceClient() + repo = MarketDataRepository("") + return MarketDataService(client, repo) + def get_market_data_context( self, symbol: str, start_date: str, end_date: str ) -> PriceDataContext: @@ -97,26 +64,106 @@ class MarketDataService: symbol: Stock ticker symbol start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format - **kwargs: Additional parameters Returns: PriceDataContext: Focused price data context """ - # return PriceDataContext( - # symbol=symbol, - # period={"start": start_date, "end": end_date}, - # price_data=price_data.get("data", []), - # latest_price=latest_price, - # price_change=price_change, - # price_change_percent=price_change_percent, - # volume_info=volume_info, - # metadata=metadata, - # ) + try: + # Convert string dates to date objects + start_date_obj = datetime.strptime(start_date, "%Y-%m-%d").date() + end_date_obj = datetime.strptime(end_date, "%Y-%m-%d").date() - pass # TODO: get data from repo + # Get data from repository first + df = self.repo.get_market_data_df(symbol, start_date_obj, end_date_obj) + + if df.empty: + # No data in repository, try to fetch from client + logger.info(f"No local data for {symbol}, fetching from client") + client_data = self.yfin_client.get_data(symbol, start_date, end_date) + price_data = client_data.get("data", []) + + # Convert to DataFrame and store in repository + if price_data: + df_to_store = pd.DataFrame(price_data) + self.repo.store_marketdata(symbol, df_to_store) + df = df_to_store + else: + # Convert DataFrame to list of dictionaries + price_data = df.to_dict("records") + + # Calculate metrics + latest_price = 0.0 + price_change = 0.0 + price_change_percent = 0.0 + volume_info = {"average_volume": 0, "latest_volume": 0} + + if not df.empty and "Close" in df.columns: + latest_price = float(df["Close"].iloc[-1]) + if len(df) > 1: + previous_price = float(df["Close"].iloc[-2]) + price_change = latest_price - previous_price + price_change_percent = ( + (price_change / previous_price) * 100 + if previous_price != 0 + else 0.0 + ) + + if "Volume" in df.columns: + volume_info = { + "average_volume": int(df["Volume"].mean()), + "latest_volume": int(df["Volume"].iloc[-1]), + } + + # Convert DataFrame back to list of dicts for price_data + price_data = df.to_dict("records") if not df.empty else [] + + # Assess data quality + data_quality = DataQuality.HIGH if len(price_data) > 0 else DataQuality.LOW + + metadata = { + "data_quality": data_quality.value, + "service": "market_data", + "record_count": len(price_data), + "source": "repository" if not df.empty else "client", + "retrieved_at": datetime.utcnow().isoformat(), + } + + return PriceDataContext( + symbol=symbol, + period={"start": start_date, "end": end_date}, + price_data=price_data, + latest_price=latest_price, + price_change=price_change, + price_change_percent=price_change_percent, + volume_info=volume_info, + metadata=metadata, + ) + + except Exception as e: + logger.error(f"Error getting market data context for {symbol}: {e}") + return PriceDataContext( + symbol=symbol, + period={"start": start_date, "end": end_date}, + price_data=[], + latest_price=0.0, + price_change=0.0, + price_change_percent=0.0, + volume_info={"average_volume": 0, "latest_volume": 0}, + metadata={ + "data_quality": DataQuality.LOW.value, + "service": "market_data", + "error": str(e), + "retrieved_at": datetime.utcnow().isoformat(), + }, + ) def get_ta_report_context( - self, symbol: str, indicator: str, start_date: str, end_date: str + self, + symbol: str, + indicator: str, + start_date: str, + end_date: str, + custom_params: dict[str, IndicatorParamValue] | None = None, ) -> TAReportContext: """ Get technical analysis report context for a specific indicator. @@ -126,24 +173,548 @@ class MarketDataService: indicator: Technical indicator name (e.g., 'rsi', 'macd', 'sma') start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format - **kwargs: Additional parameters Returns: TAReportContext: Focused technical analysis context """ + try: + # Get price data first + price_context = self.get_market_data_context(symbol, start_date, end_date) - # return TAReportContext( - # symbol=symbol, - # period={"start": start_date, "end": end_date}, - # indicator=indicator, - # indicator_data=indicator_data.get(indicator, []), - # analysis_summary=analysis_summary, - # signal_strength=signal_strength, - # recommendation=recommendation, - # metadata=metadata, - # ) + if not price_context.price_data: + # Create empty indicator config for no data case + no_data_config = IndicatorConfig( + name=indicator.upper(), + parameters={}, + input_types=["close"], + output_format="single", + param_ranges={}, + default_params={}, + talib_function="", + description="", + ) - pass # TODO get data from repo and calculate indicator with TALib? + return TAReportContext( + symbol=symbol, + period={"start": start_date, "end": end_date}, + indicator=indicator, + indicator_data=[], + analysis_summary="No price data available for technical analysis", + signal_strength=0.0, + recommendation="HOLD", + indicator_config=no_data_config, + parameter_summary="", + metadata={ + "data_quality": DataQuality.LOW.value, + "service": "technical_analysis", + "error": "no_price_data", + }, + ) + + # Calculate technical indicator using TA-Lib + indicator_data = self._calculate_indicator_talib( + price_context.price_data, indicator, custom_params + ) + + # Generate analysis and recommendations + signal_strength = self._calculate_signal_strength(indicator_data, indicator) + recommendation = self._get_recommendation(signal_strength) + analysis_summary = self._generate_analysis_summary( + indicator, signal_strength, recommendation + ) + + # Create indicator config from the calculation + indicator_config = IndicatorConfig( + name=indicator.upper(), + parameters=indicator_data[0].parameters if indicator_data else {}, + input_types=INDICATOR_DEFINITIONS.get(indicator.upper(), {}).get( + "input_types", ["close"] + ), + output_format=INDICATOR_DEFINITIONS.get(indicator.upper(), {}).get( + "output_format", "single" + ), + param_ranges=INDICATOR_DEFINITIONS.get(indicator.upper(), {}).get( + "param_ranges", {} + ), + default_params=INDICATOR_DEFINITIONS.get(indicator.upper(), {}).get( + "default_params", {} + ), + talib_function=INDICATOR_DEFINITIONS.get(indicator.upper(), {}).get( + "talib_function", "" + ), + description=INDICATOR_DEFINITIONS.get(indicator.upper(), {}).get( + "description", "" + ), + ) + + # Generate parameter summary + params = indicator_data[0].parameters if indicator_data else {} + parameter_summary = ", ".join([f"{k}={v}" for k, v in params.items()]) + + return TAReportContext( + symbol=symbol, + period={"start": start_date, "end": end_date}, + indicator=indicator, + indicator_data=indicator_data, + analysis_summary=analysis_summary, + signal_strength=signal_strength, + recommendation=recommendation, + indicator_config=indicator_config, + parameter_summary=parameter_summary, + metadata={ + "data_quality": DataQuality.HIGH.value, + "service": "technical_analysis", + "indicator_count": len(indicator_data), + "retrieved_at": datetime.utcnow().isoformat(), + }, + ) + + except Exception as e: + logger.error(f"Error getting TA report for {symbol} {indicator}: {e}") + # Create empty indicator config for error case + error_config = IndicatorConfig( + name=indicator.upper(), + parameters={}, + input_types=["close"], + output_format="single", + param_ranges={}, + default_params={}, + talib_function="", + description="", + ) + + return TAReportContext( + symbol=symbol, + period={"start": start_date, "end": end_date}, + indicator=indicator, + indicator_data=[], + analysis_summary=f"Error calculating {indicator}: {str(e)}", + signal_strength=0.0, + recommendation="HOLD", + indicator_config=error_config, + parameter_summary="", + metadata={ + "data_quality": DataQuality.LOW.value, + "service": "technical_analysis", + "error": str(e), + }, + ) + + def _validate_parameters( + self, indicator: str, params: dict[str, IndicatorParamValue] + ) -> None: + """Validate indicator parameters against defined ranges.""" + if indicator.upper() not in INDICATOR_DEFINITIONS: + raise TechnicalAnalysisError(f"Unknown indicator: {indicator}") + + definition = INDICATOR_DEFINITIONS[indicator.upper()] + param_ranges = definition.get("param_ranges", {}) + + for param_name, value in params.items(): + if param_name in param_ranges: + min_val, max_val = param_ranges[param_name] + if not isinstance(value, int | float): + raise TechnicalAnalysisError( + f"Parameter {param_name} must be numeric" + ) + if not (min_val <= value <= max_val): + raise TechnicalAnalysisError( + f"Parameter {param_name}={value} out of range [{min_val}, {max_val}]" + ) + + def _prepare_price_arrays( + self, price_data: list[dict[str, Any]], input_types: list[InputSpec] + ) -> dict[str, Any]: + """Prepare price arrays for TA-Lib functions.""" + if not price_data: + raise TechnicalAnalysisError("No price data provided") + + df = pd.DataFrame(price_data) + required_columns = [] + + for input_type in input_types: + if input_type == "close": + required_columns.extend(["Close"]) + elif input_type == "ohlc": + required_columns.extend(["Open", "High", "Low", "Close"]) + elif input_type == "ohlcv": + required_columns.extend(["Open", "High", "Low", "Close", "Volume"]) + elif input_type == "hl": + required_columns.extend(["High", "Low"]) + + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise TechnicalAnalysisError(f"Missing required columns: {missing_columns}") + + # Convert to numpy arrays for TA-Lib + arrays = {} + if "Open" in df.columns: + arrays["open"] = df["Open"].astype(float).values + if "High" in df.columns: + arrays["high"] = df["High"].astype(float).values + if "Low" in df.columns: + arrays["low"] = df["Low"].astype(float).values + if "Close" in df.columns: + arrays["close"] = df["Close"].astype(float).values + if "Volume" in df.columns: + arrays["volume"] = df["Volume"].astype(float).values + + arrays["dates"] = df["Date"].astype(str).values + + return arrays + + def _calculate_indicator_talib( + self, + price_data: list[dict[str, Any]], + indicator: str, + params: dict[str, IndicatorParamValue] | None = None, + ) -> list[TechnicalIndicatorData]: + """Calculate technical indicator using TA-Lib.""" + if not price_data: + return [] + + # Get indicator definition + indicator_upper = indicator.upper() + if indicator_upper not in INDICATOR_DEFINITIONS: + raise TechnicalAnalysisError(f"Unknown indicator: {indicator}") + + definition = INDICATOR_DEFINITIONS[indicator_upper] + + # Use provided params or defaults + final_params: dict[str, IndicatorParamValue] + if params is None: + final_params = definition["default_params"].copy() + else: + # Merge with defaults for missing parameters + final_params = definition["default_params"].copy() + final_params.update(params) + + # Validate parameters + self._validate_parameters(indicator, final_params) + + # Prepare price arrays + arrays = self._prepare_price_arrays(price_data, definition["input_types"]) + + # Get TA-Lib function + talib_func_name = definition["talib_function"].split(".")[ + -1 + ] # Extract function name + talib_func = getattr(talib, talib_func_name) + + # Prepare function arguments + func_args = [] + func_kwargs = {} + + # Add required price arrays based on input types + for input_type in definition["input_types"]: + if input_type == "close": + func_args.append(arrays["close"]) + elif input_type == "ohlc": + func_args.extend([arrays["high"], arrays["low"], arrays["close"]]) + elif input_type == "ohlcv": + func_args.extend( + [arrays["high"], arrays["low"], arrays["close"], arrays["volume"]] + ) + elif input_type == "hl": + func_args.extend([arrays["high"], arrays["low"]]) + + # Add parameters as keyword arguments + for param_name, param_value in final_params.items(): + func_kwargs[param_name] = param_value + + # Calculate indicator + try: + ta_result = talib_func(*func_args, **func_kwargs) + except Exception as e: + raise TechnicalAnalysisError( + f"TA-Lib calculation failed for {indicator}: {str(e)}" + ) from e + + # Process results based on output format + result = [] + dates = arrays["dates"] + output_format = definition["output_format"] + + if output_format == "single": + # Single output array + for _i, (date, value) in enumerate(zip(dates, ta_result, strict=False)): + if not pd.isna(value): + result.append( + TechnicalIndicatorData( + date=date, + value=float(value), + indicator_type=indicator.lower(), + parameters=final_params, + ) + ) + + elif output_format == "double": + # Two output arrays (e.g., STOCH, AROON) + for _i, (date, val1, val2) in enumerate( + zip(dates, ta_result[0], ta_result[1], strict=False) + ): + if not pd.isna(val1) and not pd.isna(val2): + # Name outputs based on indicator + if indicator_upper == "STOCH": + value_dict = {"slowk": float(val1), "slowd": float(val2)} + elif indicator_upper == "AROON": + value_dict = {"aroondown": float(val1), "aroonup": float(val2)} + else: + value_dict = {"output1": float(val1), "output2": float(val2)} + + result.append( + TechnicalIndicatorData( + date=date, + value=value_dict, + indicator_type=indicator.lower(), + parameters=final_params, + ) + ) + + elif output_format == "triple": + # Three output arrays (e.g., MACD, BBANDS) + for _i, (date, val1, val2, val3) in enumerate( + zip(dates, ta_result[0], ta_result[1], ta_result[2], strict=False) + ): + if not pd.isna(val1): + # Name outputs based on indicator + if indicator_upper == "MACD": + value_dict = { + "macd": float(val1), + "signal": float(val2) if not pd.isna(val2) else 0.0, + "histogram": float(val3) if not pd.isna(val3) else 0.0, + } + elif indicator_upper == "BBANDS": + value_dict = { + "upper": float(val1), + "middle": float(val2) if not pd.isna(val2) else 0.0, + "lower": float(val3) if not pd.isna(val3) else 0.0, + } + else: + value_dict = { + "output1": float(val1), + "output2": float(val2) if not pd.isna(val2) else 0.0, + "output3": float(val3) if not pd.isna(val3) else 0.0, + } + + result.append( + TechnicalIndicatorData( + date=date, + value=value_dict, + indicator_type=indicator.lower(), + parameters=final_params, + ) + ) + + return result + + def calculate_indicator( + self, + symbol: str, + start_date: str, + end_date: str, + indicator: str | dict[str, IndicatorParamValue], + params: dict[str, IndicatorParamValue] | None = None, + ) -> TAReportContext: + """ + Three-tier API for technical indicator calculation. + + Usage: + 1. String: calculate_indicator("AAPL", "2024-01-01", "2024-01-31", "RSI") + 2. Preset: calculate_indicator("AAPL", "2024-01-01", "2024-01-31", "RSI_SCALPING") + 3. Custom: calculate_indicator("AAPL", "2024-01-01", "2024-01-31", "RSI", {"timeperiod": 21}) + + Args: + symbol: Stock ticker symbol + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + indicator: Indicator name (string), preset name, or custom config dict + params: Optional custom parameters (for string indicators only) + + Returns: + TAReportContext: Complete technical analysis context + """ + if isinstance(indicator, dict): + # Custom configuration provided as dict + if "name" not in indicator: + raise TechnicalAnalysisError( + "Custom indicator dict must contain 'name' field" + ) + indicator_name = str(indicator["name"]) # Ensure it's a string + custom_params = {k: v for k, v in indicator.items() if k != "name"} + return self.get_ta_report_context( + symbol, indicator_name, start_date, end_date, custom_params + ) + + # Check if it's a preset name + all_presets = IndicatorPresets.get_all_presets() + if indicator in all_presets: + # Extract indicator name and parameters from preset + preset_params = all_presets[indicator] + # Determine base indicator from preset name + for base_indicator in INDICATOR_DEFINITIONS: + if indicator.startswith(base_indicator): + return self.get_ta_report_context( + symbol, + base_indicator.lower(), + start_date, + end_date, + preset_params, + ) + + # If no match found, try to extract from preset name + indicator_name = indicator.split("_")[0].lower() + return self.get_ta_report_context( + symbol, indicator_name, start_date, end_date, preset_params + ) + + # Regular indicator name (string) + return self.get_ta_report_context( + symbol, indicator, start_date, end_date, params + ) + + def get_available_indicators(self) -> dict[str, str]: + """Get list of all available indicators with descriptions.""" + return { + name: info["description"] for name, info in INDICATOR_DEFINITIONS.items() + } + + def get_available_presets( + self, style: str | None = None + ) -> dict[str, dict[str, IndicatorParamValue]]: + """ + Get available indicator presets. + + Args: + style: Optional trading style filter ("scalping", "day_trading", "swing", "position") + + Returns: + Dict of preset names to parameter configurations + """ + if style: + return IndicatorPresets.get_preset_for_style(style) + return IndicatorPresets.get_all_presets() + + def get_indicator_info(self, indicator: str) -> IndicatorConfig: + """ + Get detailed information about a specific indicator. + + Args: + indicator: Indicator name + + Returns: + IndicatorConfig with full indicator specifications + """ + indicator_upper = indicator.upper() + if indicator_upper not in INDICATOR_DEFINITIONS: + raise TechnicalAnalysisError(f"Unknown indicator: {indicator}") + + definition = INDICATOR_DEFINITIONS[indicator_upper] + return IndicatorConfig( + name=indicator_upper, + parameters=definition["default_params"], + input_types=definition["input_types"], + output_format=definition["output_format"], + param_ranges=definition["param_ranges"], + default_params=definition["default_params"], + talib_function=definition["talib_function"], + description=definition["description"], + ) + + def _calculate_signal_strength( + self, indicator_data: list[TechnicalIndicatorData], indicator: str + ) -> float: + """Calculate signal strength from indicator data.""" + if not indicator_data: + return 0.0 + + latest = indicator_data[-1] + + if indicator.lower() == "rsi": + rsi_value = latest.value + if isinstance(rsi_value, int | float): + if rsi_value > 70: + return -0.8 # Overbought - sell signal + elif rsi_value < 30: + return 0.8 # Oversold - buy signal + else: + return (50 - rsi_value) / 50 # Normalized between -1 and 1 + + elif indicator.lower() == "macd": + if isinstance(latest.value, dict): + macd_val = latest.value.get("macd", 0) + signal_val = latest.value.get("signal", 0) + if macd_val > signal_val: + return 0.6 # Bullish + else: + return -0.6 # Bearish + + elif indicator.lower() == "sma": + # Would need current price to compare with SMA + return 0.0 # Neutral for now + + return 0.0 + + def _get_recommendation(self, signal_strength: float) -> str: + """Convert signal strength to recommendation.""" + if signal_strength > 0.5: + return "BUY" + elif signal_strength < -0.5: + return "SELL" + else: + return "HOLD" + + def _generate_analysis_summary( + self, indicator: str, signal_strength: float, recommendation: str + ) -> str: + """Generate human-readable analysis summary.""" + strength_desc = ( + "strong" + if abs(signal_strength) > 0.7 + else "moderate" + if abs(signal_strength) > 0.3 + else "weak" + ) + direction = ( + "bullish" + if signal_strength > 0 + else "bearish" + if signal_strength < 0 + else "neutral" + ) + + return f"{indicator.upper()} indicator shows {strength_desc} {direction} signal. Signal strength: {signal_strength:.2f}. Recommendation: {recommendation}." def update_market_data(self, symbol: str, start_date: str, end_date: str): - pass # TODO: fetch market data and save + """ + Update market data by fetching fresh data from client and storing in repository. + + Args: + symbol: Stock ticker symbol + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + """ + try: + logger.info( + f"Updating market data for {symbol} from {start_date} to {end_date}" + ) + + # Fetch fresh data from client + client_data = self.yfin_client.get_data(symbol, start_date, end_date) + price_data = client_data.get("data", []) + + if price_data: + # Convert to DataFrame + df = pd.DataFrame(price_data) + + # Store in repository + self.repo.store_marketdata(symbol, df) + logger.info( + f"Successfully stored {len(price_data)} records for {symbol}" + ) + else: + logger.warning(f"No data received for {symbol}") + + except Exception as e: + logger.error(f"Error updating market data for {symbol}: {e}") + raise diff --git a/tradingagents/domains/marketdata/market_data_service_test.py b/tradingagents/domains/marketdata/market_data_service_test.py deleted file mode 100644 index a312c6dc..00000000 --- a/tradingagents/domains/marketdata/market_data_service_test.py +++ /dev/null @@ -1,546 +0,0 @@ -#!/usr/bin/env python3 -""" -Test MarketDataService with mock YFinanceClient and real MarketDataRepository. -""" - -import json -import os -import sys -from datetime import datetime, timedelta -from typing import Any - -# Add the project root to the path -sys.path.insert(0, os.path.abspath(".")) - -from tradingagents.clients.base import BaseClient -from tradingagents.domains.marketdata.market_data_service import ( - DataQuality, - MarketDataContext, - MarketDataService, -) -from tradingagents.repositories.market_data_repository import MarketDataRepository - - -class MockYFinanceClient(BaseClient): - """Mock Yahoo Finance client that returns predictable test data.""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.connection_works = True - - def test_connection(self) -> bool: - return self.connection_works - - def get_data( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - """Return realistic mock market data.""" - # Generate realistic price data - base_price = {"AAPL": 180.0, "TSLA": 250.0, "MSFT": 400.0}.get(symbol, 100.0) - - mock_data = [] - current_date = datetime.strptime(start_date, "%Y-%m-%d") - end_date_dt = datetime.strptime(end_date, "%Y-%m-%d") - - price = base_price - while current_date <= end_date_dt: - # Simulate some price movement - price_change = ( - hash(current_date.strftime("%Y-%m-%d")) % 10 - 5 - ) / 100 # -5% to +5% - price *= 1 + price_change * 0.01 - - mock_data.append( - { - "Date": current_date.strftime("%Y-%m-%d %H:%M:%S"), - "Open": round(price * 0.99, 2), - "High": round(price * 1.02, 2), - "Low": round(price * 0.98, 2), - "Close": round(price, 2), - "Adj Close": round(price, 2), - "Volume": 45000000 + (hash(symbol) % 20000000), - } - ) - - current_date += timedelta(days=1) - - return { - "symbol": symbol, - "period": {"start": start_date, "end": end_date}, - "data": mock_data, - "metadata": { - "source": "mock_yahoo_finance", - "record_count": len(mock_data), - "columns": [ - "Date", - "Open", - "High", - "Low", - "Close", - "Adj Close", - "Volume", - ], - "retrieved_at": datetime.utcnow().isoformat(), - }, - } - - -def test_online_mode_with_mock_client(): - """Test MarketDataService in online mode with mock client.""" - print("📈 Testing MarketDataService - Online Mode") - - # Create mock client and real repository - mock_client = MockYFinanceClient() - real_repo = MarketDataRepository("test_data") - - # Create service in online mode - service = MarketDataService( - client=mock_client, repository=real_repo, online_mode=True, data_dir="test_data" - ) - - try: - # Test basic price context - context = service.get_price_context( - symbol="AAPL", start_date="2024-01-01", end_date="2024-01-05" - ) - - print(f"✅ Price context created: {context.__class__.__name__}") - print(f" Symbol: {context.symbol}") - print(f" Period: {context.period}") - print(f" Price data records: {len(context.price_data)}") - print(f" Technical indicators: {len(context.technical_indicators)}") - - # Validate required fields - assert context.symbol == "AAPL" - assert context.period["start"] == "2024-01-01" - assert context.period["end"] == "2024-01-05" - assert len(context.price_data) > 0 - assert "data_quality" in context.metadata - - print("✅ Basic validation passed") - - # Test JSON serialization - json_output = context.model_dump_json(indent=2) - parsed = json.loads(json_output) - - print(f"✅ JSON serialization: {len(json_output)} characters") - print(f" Top-level keys: {list(parsed.keys())}") - - # Test with technical indicators - context_with_indicators = service.get_context( - symbol="TSLA", - start_date="2024-01-01", - end_date="2024-01-03", - indicators=["rsi", "macd"], - ) - - print("✅ Context with indicators created") - print(" Requested indicators: ['rsi', 'macd']") - print( - f" Available indicators: {list(context_with_indicators.technical_indicators.keys())}" - ) - - return True - - except Exception as e: - print(f"❌ Online mode test failed: {e}") - return False - - -def test_offline_mode_with_real_repository(): - """Test MarketDataService in offline mode with real repository.""" - print("\n💾 Testing MarketDataService - Offline Mode") - - # Create service in offline mode (no client) - real_repo = MarketDataRepository("test_data") - service = MarketDataService( - client=None, repository=real_repo, online_mode=False, data_dir="test_data" - ) - - try: - # Test offline context (will likely return empty data) - context = service.get_price_context( - symbol="AAPL", start_date="2024-01-01", end_date="2024-01-05" - ) - - print(f"✅ Offline context created: {context.__class__.__name__}") - print(f" Symbol: {context.symbol}") - print(f" Price data records: {len(context.price_data)}") - print(f" Data quality: {context.metadata.get('data_quality')}") - print(f" Service mode: online={service.is_online()}") - - # Should handle empty data gracefully - assert context.symbol == "AAPL" - assert isinstance(context.price_data, list) - assert "data_quality" in context.metadata - - print("✅ Offline mode graceful handling verified") - - return True - - except Exception as e: - print(f"❌ Offline mode test failed: {e}") - return False - - -def test_error_handling(): - """Test error handling scenarios.""" - print("\n⚠️ Testing Error Handling") - - # Test with broken client - class BrokenClient(BaseClient): - def test_connection(self): - return False - - def get_data(self, *args, **kwargs): - raise Exception("Simulated client failure") - - broken_client = BrokenClient() - real_repo = MarketDataRepository("test_data") - - service = MarketDataService( - client=broken_client, - repository=real_repo, - online_mode=True, # Online mode but client will fail - data_dir="test_data", - ) - - try: - context = service.get_price_context("AAPL", "2024-01-01", "2024-01-05") - - print("✅ Error handling worked") - print(f" Symbol: {context.symbol}") - print(f" Price data records: {len(context.price_data)}") - print(f" Data quality: {context.metadata.get('data_quality')}") - - # Should fallback to repository or return empty data - assert context.symbol == "AAPL" - assert isinstance(context.price_data, list) - - return True - - except Exception as e: - print(f"❌ Error handling test failed: {e}") - return False - - -def test_data_quality_assessment(): - """Test data quality determination logic.""" - print("\n🔍 Testing Data Quality Assessment") - - mock_client = MockYFinanceClient() - real_repo = MarketDataRepository("test_data") - - service = MarketDataService( - client=mock_client, repository=real_repo, online_mode=True, data_dir="test_data" - ) - - try: - # Test with good data - context = service.get_context("AAPL", "2024-01-01", "2024-01-10") - - data_quality = context.metadata.get("data_quality") - print(f"✅ Data quality assessment: {data_quality}") - print(f" Records: {len(context.price_data)}") - print(f" Online mode: {service.is_online()}") - - # Should be medium or high quality for mock data - assert data_quality in [DataQuality.MEDIUM, DataQuality.HIGH] - - return True - - except Exception as e: - print(f"❌ Data quality test failed: {e}") - return False - - -def test_json_structure_validation(): - """Test detailed JSON structure validation.""" - print("\n📄 Testing JSON Structure") - - mock_client = MockYFinanceClient() - service = MarketDataService(client=mock_client, repository=None, online_mode=True) - - try: - context = service.get_price_context("MSFT", "2024-01-01", "2024-01-03") - json_str = context.model_dump_json(indent=2) - data = json.loads(json_str) - - # Validate required structure - required_fields = [ - "symbol", - "period", - "price_data", - "technical_indicators", - "metadata", - ] - for field in required_fields: - assert field in data, f"Missing field: {field}" - - # Validate period structure - period = data["period"] - assert "start" in period and "end" in period - - # Validate price data structure - assert isinstance(data["price_data"], list) - if data["price_data"]: - first_record = data["price_data"][0] - required_price_fields = ["Date", "Open", "High", "Low", "Close", "Volume"] - for field in required_price_fields: - assert field in first_record, f"Missing price field: {field}" - - # Validate metadata - metadata = data["metadata"] - assert "data_quality" in metadata - assert "service" in metadata - - print("✅ JSON structure validation passed") - print(f" Fields: {list(data.keys())}") - print(f" Price records: {len(data['price_data'])}") - print(f" Metadata keys: {list(metadata.keys())}") - - return True - - except Exception as e: - print(f"❌ JSON structure test failed: {e}") - return False - - -def test_force_refresh_parameter(): - """Test the force_refresh parameter functionality.""" - try: - mock_client = MockYFinanceClient() - real_repo = MarketDataRepository("test_data") - - service = MarketDataService( - client=mock_client, repository=real_repo, online_mode=True - ) - - # Test normal flow (should use repository if available) - normal_context = service.get_context( - "AAPL", "2024-01-01", "2024-01-31", force_refresh=False - ) - - # Test force refresh (should bypass repository and use client) - refresh_context = service.get_context( - "AAPL", "2024-01-01", "2024-01-31", force_refresh=True - ) - - # Both should return valid contexts - assert isinstance(normal_context, MarketDataContext) - assert isinstance(refresh_context, MarketDataContext) - assert normal_context.symbol == "AAPL" - assert refresh_context.symbol == "AAPL" - - # Check metadata indicates source - refresh_metadata = refresh_context.metadata - assert "force_refresh" in refresh_metadata - assert refresh_metadata["force_refresh"] - - print("✅ Force refresh parameter test passed") - return True - - except Exception as e: - print(f"❌ Force refresh test failed: {e}") - return False - - -def test_local_first_strategy(): - """Test that the service checks local data first when available.""" - try: - - class MockRepositoryWithData(MarketDataRepository): - def has_data_for_period( - self, identifier: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return True # Pretend we have the data - - def get_data( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - return { - "symbol": kwargs.get("symbol", "TEST"), - "data": [ - {"date": "2024-01-01", "close": 150.0}, - {"date": "2024-01-02", "close": 151.0}, - ], - "metadata": {"source": "test_repository"}, - } - - mock_client = MockYFinanceClient() - mock_repo = MockRepositoryWithData("test_data") - - service = MarketDataService( - client=mock_client, repository=mock_repo, online_mode=True - ) - - # Should use local data since repository has_data_for_period returns True - context = service.get_context("TEST", "2024-01-01", "2024-01-31") - - # Verify we used local data - assert context.metadata.get("price_data_source") == "local_cache" - assert len(context.price_data) == 2 # From mock repository - - print("✅ Local-first strategy test passed") - return True - - except Exception as e: - print(f"❌ Local-first strategy test failed: {e}") - return False - - -def test_local_first_fallback_to_api(): - """Test that service falls back to API when local data is insufficient.""" - try: - - class MockRepositoryWithoutData(MarketDataRepository): - def has_data_for_period( - self, identifier: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return False # Pretend we don't have the data - - def get_data( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - return { - "symbol": kwargs.get("symbol", "TEST"), - "data": [], - "metadata": {}, - } - - def store_data( - self, - symbol: str, - data: dict[str, Any], - overwrite: bool = False, - **kwargs, - ) -> bool: - return True # Pretend storage was successful - - mock_client = MockYFinanceClient() - mock_repo = MockRepositoryWithoutData("test_data") - - service = MarketDataService( - client=mock_client, repository=mock_repo, online_mode=True - ) - - # Should fall back to API since repository doesn't have data - context = service.get_context("TEST", "2024-01-01", "2024-01-31") - - # Verify we used API data - assert context.metadata.get("price_data_source") == "live_api" - assert len(context.price_data) > 0 # From mock client - - print("✅ Local-first fallback to API test passed") - return True - - except Exception as e: - print(f"❌ Local-first fallback test failed: {e}") - return False - - -def test_force_refresh_bypasses_local_data(): - """Test that force_refresh=True bypasses local data even when available.""" - try: - - class MockRepositoryAlwaysHasData(MarketDataRepository): - def has_data_for_period( - self, identifier: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return True # Always claim we have data - - def get_data( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - return { - "symbol": kwargs.get("symbol", "TEST"), - "data": [ - {"date": "2024-01-01", "close": 100.0} - ], # Different from client - "metadata": {"source": "local"}, - } - - def clear_data( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return True - - def store_data( - self, - symbol: str, - data: dict[str, Any], - overwrite: bool = False, - **kwargs, - ) -> bool: - return True - - mock_client = MockYFinanceClient() - mock_repo = MockRepositoryAlwaysHasData("test_data") - - service = MarketDataService( - client=mock_client, repository=mock_repo, online_mode=True - ) - - # Force refresh should bypass local data - context = service.get_context( - "TEST", "2024-01-01", "2024-01-31", force_refresh=True - ) - - # Verify we used API data (force refresh) - assert context.metadata.get("price_data_source") == "live_api_refresh" - assert context.metadata.get("force_refresh") - # Should have more data from client than the single point from repository - assert len(context.price_data) > 1 - - print("✅ Force refresh bypasses local data test passed") - return True - - except Exception as e: - print(f"❌ Force refresh bypass test failed: {e}") - return False - - -def main(): - """Run all MarketDataService tests.""" - print("🧪 Testing MarketDataService\n") - - tests = [ - test_online_mode_with_mock_client, - test_offline_mode_with_real_repository, - test_error_handling, - test_data_quality_assessment, - test_json_structure_validation, - test_force_refresh_parameter, - test_local_first_strategy, - test_local_first_fallback_to_api, - test_force_refresh_bypasses_local_data, - ] - - passed = 0 - failed = 0 - - for test in tests: - try: - if test(): - passed += 1 - else: - failed += 1 - except Exception as e: - print(f"❌ Test {test.__name__} crashed: {e}") - failed += 1 - - print("\n📊 MarketDataService Test Results:") - print(f" ✅ Passed: {passed}") - print(f" ❌ Failed: {failed}") - - if failed == 0: - print("🎉 All MarketDataService tests passed!") - else: - print("⚠️ Some tests failed - check output above") - - return failed == 0 - - -if __name__ == "__main__": - success = main() - sys.exit(0 if success else 1) diff --git a/tradingagents/domains/marketdata/models.py b/tradingagents/domains/marketdata/models.py new file mode 100644 index 00000000..d5ff7e29 --- /dev/null +++ b/tradingagents/domains/marketdata/models.py @@ -0,0 +1,662 @@ +""" +Market data models and type definitions for technical analysis. +""" + +from dataclasses import dataclass +from datetime import date +from enum import Enum +from typing import Any, Literal + +from proto.message import Optional +from pydantic import BaseModel + +# Proper type definitions to eliminate Any types +IndicatorParamValue = int | float | str | bool +InputSpec = Literal["close", "ohlc", "ohlcv", "hl"] +OutputSpec = Literal["single", "double", "triple"] +ParamRanges = dict[str, tuple[int | float, int | float]] + + +class IndicatorConfig(BaseModel): + """Configuration for technical indicators with proper typing.""" + + name: str + parameters: dict[str, IndicatorParamValue] # No more Any + input_types: list[InputSpec] # Specific requirements + output_format: OutputSpec # Precise specification + param_ranges: ParamRanges # Type-safe validation + default_params: dict[str, IndicatorParamValue] + talib_function: str # Direct TA-Lib function name + description: str + + +class TechnicalAnalysisError(Exception): + """Clear, actionable error messages for TA-Lib issues.""" + + pass + + +class DataQuality(Enum): + """Data quality levels for market data.""" + + HIGH = "high" + MEDIUM = "medium" + LOW = "low" + + +class TechnicalIndicatorData(BaseModel): + """Technical indicator data point with proper typing.""" + + date: str + value: float | dict[str, float] # No more Any + indicator_type: str + parameters: dict[str, IndicatorParamValue] # Parameter context + confidence: float = 0.0 # Signal confidence + source: str = "talib" # Always TA-Lib + + +class MarketDataContext(BaseModel): + """Market data context for trading analysis.""" + + symbol: str + period: dict[str, str] # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"} + price_data: list[dict[str, Any]] + technical_indicators: dict[str, list[TechnicalIndicatorData]] + metadata: dict[str, Any] + + +class TAReportContext(BaseModel): + """Technical Analysis Report context with enhanced configuration.""" + + symbol: str + period: dict[str, str] # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"} + indicator: str + indicator_data: list[TechnicalIndicatorData] + analysis_summary: str + signal_strength: float # -1.0 to 1.0 + recommendation: str # "BUY", "SELL", "HOLD" + indicator_config: IndicatorConfig # Full config used + parameter_summary: str # Human-readable params + metadata: dict[str, IndicatorParamValue] # Properly typed + + +class PriceDataContext(BaseModel): + """Price Data context for historical price information.""" + + symbol: str + period: dict[str, str] # {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"} + price_data: list[dict[str, Any]] + latest_price: float + price_change: float + price_change_percent: float + volume_info: dict[str, Any] + metadata: dict[str, Any] + + +# Fundamental Data Models +class FinancialRatio(BaseModel): + """Financial ratio with calculation metadata.""" + + name: str + value: float | None + formula: str + category: str # "profitability", "liquidity", "leverage", "efficiency" + interpretation: str + + +class BalanceSheetData(BaseModel): + """Balance sheet line items.""" + + date: str + total_assets: float | None = None + current_assets: float | None = None + cash_and_equivalents: float | None = None + accounts_receivable: float | None = None + inventory: float | None = None + total_liabilities: float | None = None + current_liabilities: float | None = None + accounts_payable: float | None = None + short_term_debt: float | None = None + long_term_debt: float | None = None + total_equity: float | None = None + retained_earnings: float | None = None + + +class IncomeStatementData(BaseModel): + """Income statement line items.""" + + date: str + revenue: float | None = None + gross_profit: float | None = None + operating_income: float | None = None + net_income: float | None = None + ebitda: float | None = None + cost_of_revenue: float | None = None + operating_expenses: float | None = None + interest_expense: float | None = None + tax_expense: float | None = None + shares_outstanding: float | None = None + eps: float | None = None + + +class CashFlowData(BaseModel): + """Cash flow statement line items.""" + + date: str + operating_cash_flow: float | None = None + investing_cash_flow: float | None = None + financing_cash_flow: float | None = None + free_cash_flow: float | None = None + capital_expenditures: float | None = None + dividends_paid: float | None = None + stock_repurchases: float | None = None + + +class BalanceSheetContext(BaseModel): + """Balance sheet context for fundamental analysis.""" + + symbol: str + start_date: date + end_date: date + balance_sheet_data: list[BalanceSheetData] + key_ratios: list[FinancialRatio] + data_quality: DataQuality + source: str + metadata: dict[str, str] + + +class IncomeStatementContext(BaseModel): + """Income statement context for fundamental analysis.""" + + symbol: str + start_date: date + end_date: date + income_statement_data: list[IncomeStatementData] + key_ratios: list[FinancialRatio] + data_quality: DataQuality + source: str + metadata: dict[str, Any] + + +class CashFlowContext(BaseModel): + """Cash flow context for fundamental analysis.""" + + symbol: str + start_date: date + end_date: date + cash_flow_data: list[CashFlowData] + key_ratios: list[FinancialRatio] + data_quality: DataQuality + source: str + metadata: dict[str, Any] + + +class FundamentalContext(BaseModel): + """Comprehensive fundamental analysis context.""" + + symbol: str + start_date: date + end_date: date + balance_sheet: Optional[BalanceSheetContext] = None + income_statement: Optional[IncomeStatementContext] = None + cash_flow: Optional[CashFlowContext] = None + comprehensive_ratios: Optional[list[FinancialRatio]] = None + valuation_metrics: Optional[dict[str, float | None]] = None + financial_health_score: float = 0 # 0-100 composite score + data_quality: DataQuality = DataQuality.LOW + source: Optional[str] = None + metadata: Optional[dict[str, str]] = None + + +# Reported Financials Models (for Finnhub API responses) +@dataclass +class FinancialLineItem: + """Individual financial statement line item from reported financials.""" + + concept: str + unit: str + label: str + value: float | int + + +@dataclass +class ReportedFinancialsData: + """Financial statements data containing balance sheet, income statement, and cash flow.""" + + bs: list[FinancialLineItem] # Balance Sheet + ic: list[FinancialLineItem] # Income Statement + cf: list[FinancialLineItem] # Cash Flow Statement + + +@dataclass +class ReportedFinancialsResponse: + """Complete response from Finnhub reported financials API.""" + + start_date: str + end_date: str + year: int + quarter: int + access_number: str + data: ReportedFinancialsData + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "ReportedFinancialsResponse": + """Create ReportedFinancialsResponse from API response dictionary.""" + financial_data = data.get("data", {}) + + # Convert line items for each statement type + bs_items = [ + FinancialLineItem( + concept=item["concept"], + unit=item["unit"], + label=item["label"], + value=item["value"], + ) + for item in financial_data.get("bs", []) + ] + + ic_items = [ + FinancialLineItem( + concept=item["concept"], + unit=item["unit"], + label=item["label"], + value=item["value"], + ) + for item in financial_data.get("ic", []) + ] + + cf_items = [ + FinancialLineItem( + concept=item["concept"], + unit=item["unit"], + label=item["label"], + value=item["value"], + ) + for item in financial_data.get("cf", []) + ] + + return cls( + start_date=data["start_date"], + end_date=data["end_date"], + year=data["year"], + quarter=data["quarter"], + access_number=data["access_number"], + data=ReportedFinancialsData(bs=bs_items, ic=ic_items, cf=cf_items), + ) + + +# Insider Transactions Models +@dataclass +class InsiderTransaction: + """Individual insider transaction record.""" + + name: str + share: int + change: int + filing_date: str + transaction_date: str + transaction_code: str + transaction_price: float + + +@dataclass +class InsiderTransactionsResponse: + """Complete response from Finnhub insider transactions API.""" + + data: list[InsiderTransaction] + symbol: str + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "InsiderTransactionsResponse": + """Create InsiderTransactionsResponse from API response dictionary.""" + transactions = [ + InsiderTransaction( + name=item["name"], + share=item["share"], + change=item["change"], + filing_date=item["filingDate"], + transaction_date=item["transactionDate"], + transaction_code=item["transactionCode"], + transaction_price=item["transactionPrice"], + ) + for item in data.get("data", []) + ] + + return cls(data=transactions, symbol=data["symbol"]) + + +# Insider Sentiment Models +@dataclass +class InsiderSentimentData: + """Individual insider sentiment data point.""" + + symbol: str + year: int + month: int + change: int + mspr: float # Monthly Share Purchase Ratio + + +@dataclass +class InsiderSentimentResponse: + """Complete response from Finnhub insider sentiment API.""" + + data: list[InsiderSentimentData] + symbol: str + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "InsiderSentimentResponse": + """Create InsiderSentimentResponse from API response dictionary.""" + sentiment_data = [ + InsiderSentimentData( + symbol=item["symbol"], + year=item["year"], + month=item["month"], + change=item["change"], + mspr=item["mspr"], + ) + for item in data.get("data", []) + ] + + return cls(data=sentiment_data, symbol=data["symbol"]) + + +# Company Profile Models +@dataclass +class CompanyProfile: + """Company profile information from Finnhub.""" + + country: str + currency: str + exchange: str + ipo: str + market_capitalization: float + name: str + phone: str + share_outstanding: float + ticker: str + weburl: str + logo: str + finnhub_industry: str + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "CompanyProfile": + """Create CompanyProfile from API response dictionary.""" + return cls( + country=data.get("country", ""), + currency=data.get("currency", ""), + exchange=data.get("exchange", ""), + ipo=data.get("ipo", ""), + market_capitalization=data.get("marketCapitalization", 0.0), + name=data.get("name", ""), + phone=data.get("phone", ""), + share_outstanding=data.get("shareOutstanding", 0.0), + ticker=data.get("ticker", ""), + weburl=data.get("weburl", ""), + logo=data.get("logo", ""), + finnhub_industry=data.get("finnhubIndustry", ""), + ) + + +# Complete indicator definitions for 20 professional indicators +INDICATOR_DEFINITIONS = { + # Momentum Indicators (7) + "RSI": { + "talib_function": "talib.RSI", + "input_types": ["close"], + "default_params": {"timeperiod": 14}, + "param_ranges": {"timeperiod": (2, 100)}, + "output_format": "single", + "description": "Relative Strength Index", + }, + "MACD": { + "talib_function": "talib.MACD", + "input_types": ["close"], + "default_params": {"fastperiod": 12, "slowperiod": 26, "signalperiod": 9}, + "param_ranges": { + "fastperiod": (2, 50), + "slowperiod": (10, 200), + "signalperiod": (2, 50), + }, + "output_format": "triple", + "description": "Moving Average Convergence Divergence", + }, + "STOCH": { + "talib_function": "talib.STOCH", + "input_types": ["ohlc"], + "default_params": {"fastk_period": 14, "slowk_period": 3, "slowd_period": 3}, + "param_ranges": { + "fastk_period": (1, 100), + "slowk_period": (1, 50), + "slowd_period": (1, 50), + }, + "output_format": "double", + "description": "Stochastic Oscillator", + }, + "WILLR": { + "talib_function": "talib.WILLR", + "input_types": ["ohlc"], + "default_params": {"timeperiod": 14}, + "param_ranges": {"timeperiod": (2, 100)}, + "output_format": "single", + "description": "Williams %R", + }, + "CCI": { + "talib_function": "talib.CCI", + "input_types": ["ohlc"], + "default_params": {"timeperiod": 20}, + "param_ranges": {"timeperiod": (2, 100)}, + "output_format": "single", + "description": "Commodity Channel Index", + }, + "ROC": { + "talib_function": "talib.ROC", + "input_types": ["close"], + "default_params": {"timeperiod": 12}, + "param_ranges": {"timeperiod": (1, 100)}, + "output_format": "single", + "description": "Rate of Change", + }, + "MFI": { + "talib_function": "talib.MFI", + "input_types": ["ohlcv"], + "default_params": {"timeperiod": 14}, + "param_ranges": {"timeperiod": (2, 100)}, + "output_format": "single", + "description": "Money Flow Index", + }, + # Trend Indicators (7) + "SMA": { + "talib_function": "talib.SMA", + "input_types": ["close"], + "default_params": {"timeperiod": 20}, + "param_ranges": {"timeperiod": (2, 200)}, + "output_format": "single", + "description": "Simple Moving Average", + }, + "EMA": { + "talib_function": "talib.EMA", + "input_types": ["close"], + "default_params": {"timeperiod": 20}, + "param_ranges": {"timeperiod": (2, 200)}, + "output_format": "single", + "description": "Exponential Moving Average", + }, + "BBANDS": { + "talib_function": "talib.BBANDS", + "input_types": ["close"], + "default_params": {"timeperiod": 20, "nbdevup": 2.0, "nbdevdn": 2.0}, + "param_ranges": { + "timeperiod": (2, 100), + "nbdevup": (0.1, 5.0), + "nbdevdn": (0.1, 5.0), + }, + "output_format": "triple", + "description": "Bollinger Bands", + }, + "SAR": { + "talib_function": "talib.SAR", + "input_types": ["ohlc"], + "default_params": {"acceleration": 0.02, "maximum": 0.2}, + "param_ranges": {"acceleration": (0.01, 0.1), "maximum": (0.1, 1.0)}, + "output_format": "single", + "description": "Parabolic SAR", + }, + "ADX": { + "talib_function": "talib.ADX", + "input_types": ["ohlc"], + "default_params": {"timeperiod": 14}, + "param_ranges": {"timeperiod": (2, 100)}, + "output_format": "single", + "description": "Average Directional Index", + }, + "AROON": { + "talib_function": "talib.AROON", + "input_types": ["hl"], + "default_params": {"timeperiod": 25}, + "param_ranges": {"timeperiod": (2, 100)}, + "output_format": "double", + "description": "Aroon Oscillator", + }, + "TEMA": { + "talib_function": "talib.TEMA", + "input_types": ["close"], + "default_params": {"timeperiod": 20}, + "param_ranges": {"timeperiod": (2, 200)}, + "output_format": "single", + "description": "Triple Exponential Moving Average", + }, + # Volume Indicators (3) + "OBV": { + "talib_function": "talib.OBV", + "input_types": ["ohlcv"], + "default_params": {}, + "param_ranges": {}, + "output_format": "single", + "description": "On Balance Volume", + }, + "AD": { + "talib_function": "talib.AD", + "input_types": ["ohlcv"], + "default_params": {}, + "param_ranges": {}, + "output_format": "single", + "description": "Accumulation/Distribution", + }, + "ADOSC": { + "talib_function": "talib.ADOSC", + "input_types": ["ohlcv"], + "default_params": {"fastperiod": 3, "slowperiod": 10}, + "param_ranges": {"fastperiod": (2, 50), "slowperiod": (5, 100)}, + "output_format": "single", + "description": "Accumulation/Distribution Oscillator", + }, + # Volatility Indicators (3) + "ATR": { + "talib_function": "talib.ATR", + "input_types": ["ohlc"], + "default_params": {"timeperiod": 14}, + "param_ranges": {"timeperiod": (1, 100)}, + "output_format": "single", + "description": "Average True Range", + }, + "NATR": { + "talib_function": "talib.NATR", + "input_types": ["ohlc"], + "default_params": {"timeperiod": 14}, + "param_ranges": {"timeperiod": (1, 100)}, + "output_format": "single", + "description": "Normalized Average True Range", + }, + "TRANGE": { + "talib_function": "talib.TRANGE", + "input_types": ["ohlc"], + "default_params": {}, + "param_ranges": {}, + "output_format": "single", + "description": "True Range", + }, +} + + +class IndicatorPresets: + """Professional indicator presets for different trading styles.""" + + @staticmethod + def get_scalping_presets() -> dict[str, dict[str, IndicatorParamValue]]: + """Fast scalping presets (1-5 minute timeframes).""" + return { + "RSI_SCALPING": {"timeperiod": 5}, + "MACD_SCALPING": {"fastperiod": 5, "slowperiod": 13, "signalperiod": 5}, + "STOCH_SCALPING": {"fastk_period": 5, "slowk_period": 3, "slowd_period": 3}, + "EMA_SCALPING": {"timeperiod": 9}, + "BBANDS_TIGHT": {"timeperiod": 10, "nbdevup": 1.5, "nbdevdn": 1.5}, + "ATR_SCALPING": {"timeperiod": 5}, + } + + @staticmethod + def get_day_trading_presets() -> dict[str, dict[str, IndicatorParamValue]]: + """Day trading presets (5-60 minute timeframes).""" + return { + "RSI_DAY_TRADING": {"timeperiod": 14}, + "MACD_DAY_TRADING": {"fastperiod": 12, "slowperiod": 26, "signalperiod": 9}, + "STOCH_DAY_TRADING": { + "fastk_period": 14, + "slowk_period": 3, + "slowd_period": 3, + }, + "EMA_DAY_TRADING": {"timeperiod": 20}, + "BBANDS_STANDARD": {"timeperiod": 20, "nbdevup": 2.0, "nbdevdn": 2.0}, + "ADX_DAY_TRADING": {"timeperiod": 14}, + "ATR_DAY_TRADING": {"timeperiod": 14}, + } + + @staticmethod + def get_swing_trading_presets() -> dict[str, dict[str, IndicatorParamValue]]: + """Swing trading presets (daily timeframes).""" + return { + "RSI_SWING": {"timeperiod": 21}, + "MACD_SWING": {"fastperiod": 12, "slowperiod": 26, "signalperiod": 9}, + "STOCH_SWING": {"fastk_period": 21, "slowk_period": 5, "slowd_period": 5}, + "SMA_SWING_SHORT": {"timeperiod": 50}, + "SMA_SWING_LONG": {"timeperiod": 200}, + "EMA_SWING": {"timeperiod": 50}, + "BBANDS_SWING": {"timeperiod": 20, "nbdevup": 2.5, "nbdevdn": 2.5}, + "ADX_SWING": {"timeperiod": 21}, + "AROON_SWING": {"timeperiod": 25}, + } + + @staticmethod + def get_position_trading_presets() -> dict[str, dict[str, IndicatorParamValue]]: + """Position trading presets (weekly/monthly timeframes).""" + return { + "RSI_POSITION": {"timeperiod": 30}, + "MACD_POSITION": {"fastperiod": 20, "slowperiod": 50, "signalperiod": 15}, + "SMA_POSITION_SHORT": {"timeperiod": 100}, + "SMA_POSITION_LONG": {"timeperiod": 300}, + "EMA_POSITION": {"timeperiod": 100}, + "BBANDS_POSITION": {"timeperiod": 50, "nbdevup": 3.0, "nbdevdn": 3.0}, + "ADX_POSITION": {"timeperiod": 30}, + "AROON_POSITION": {"timeperiod": 50}, + } + + @staticmethod + def get_all_presets() -> dict[str, dict[str, IndicatorParamValue]]: + """Get all available presets combined.""" + all_presets = {} + all_presets.update(IndicatorPresets.get_scalping_presets()) + all_presets.update(IndicatorPresets.get_day_trading_presets()) + all_presets.update(IndicatorPresets.get_swing_trading_presets()) + all_presets.update(IndicatorPresets.get_position_trading_presets()) + return all_presets + + @staticmethod + def get_preset_for_style(style: str) -> dict[str, dict[str, IndicatorParamValue]]: + """Get presets for a specific trading style.""" + style_map = { + "scalping": IndicatorPresets.get_scalping_presets(), + "day_trading": IndicatorPresets.get_day_trading_presets(), + "swing": IndicatorPresets.get_swing_trading_presets(), + "position": IndicatorPresets.get_position_trading_presets(), + } + return style_map.get(style.lower(), {}) diff --git a/tradingagents/domains/marketdata/repos/fundamental_data_repository.py b/tradingagents/domains/marketdata/repos/fundamental_data_repository.py new file mode 100644 index 00000000..97bd4978 --- /dev/null +++ b/tradingagents/domains/marketdata/repos/fundamental_data_repository.py @@ -0,0 +1,183 @@ +""" +Repository for fundamental financial data (balance sheets, income statements, cash flow). +""" + +import json +import logging +from dataclasses import asdict, dataclass +from datetime import date +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ..models import ReportedFinancialsResponse + +# Base repository functionality inline + +logger = logging.getLogger(__name__) + + +@dataclass +class FinancialStatement: + """Represents a financial statement with standardized structure matching the service.""" + + period: str + report_date: str + publish_date: str + currency: str + data: dict[str, float] + + +@dataclass +class FinancialData: + """Container for all financial statements for a symbol and date.""" + + symbol: str + date: date + financial_statements: dict[ + str, FinancialStatement + ] # "balance_sheet", "income_statement", "cash_flow" + + +class FundamentalDataRepository: + """Repository for accessing cached fundamental financial data as a KV store.""" + + def __init__(self, data_dir: str, **kwargs): + """ + Initialize fundamental data repository. + + Args: + data_dir: Base directory for fundamental data storage + **kwargs: Additional configuration + """ + self.fundamental_data_dir = Path(data_dir) / "fundamental_data" + self.fundamental_data_dir.mkdir(parents=True, exist_ok=True) + + def store_financial_statements( + self, + symbol: str, + date: date, + statements: dict[str, FinancialStatement], + ) -> tuple[date, dict[str, FinancialStatement]]: + """ + Store financial statements for a symbol and date. + + Args: + symbol: Stock ticker symbol + date: Date of the financial statements + statements: Dictionary of statements keyed by type ("balance_sheet", "income_statement", "cash_flow") + + Returns: + Tuple[date, dict[str, FinancialStatement]]: The stored date and statements + """ + # Create symbol directory + symbol_dir = self.fundamental_data_dir / symbol + self._ensure_path_exists(symbol_dir) + + # Create JSON file path + file_path = symbol_dir / f"{date.isoformat()}.json" + + try: + # Prepare data for JSON serialization + statements_data = {} + for statement_type, statement in statements.items(): + statements_data[statement_type] = asdict(statement) + + data = { + "symbol": symbol, + "date": date.isoformat(), + "financial_statements": statements_data, + "metadata": { + "stored_at": date.today().isoformat(), + "repository": "fundamental_data_repository", + "statement_count": len(statements), + }, + } + + # Write to JSON file + with open(file_path, "w") as f: + json.dump(data, f, indent=2, default=str) + + logger.info( + f"Stored {len(statements)} financial statements for {symbol} on {date}" + ) + return (date, statements) + + except Exception as e: + logger.error( + f"Error storing financial statements for {symbol} on {date}: {e}" + ) + raise + + def store_reported_financials( + self, + symbol: str, + date: date, + reported_financials: "ReportedFinancialsResponse", + ) -> bool: + """ + Store reported financials data from Finnhub API. + + Args: + symbol: Stock ticker symbol + date: Date for the financial data + reported_financials: ReportedFinancialsResponse from Finnhub + + Returns: + bool: True if storage was successful + """ + # Create symbol directory + symbol_dir = self.fundamental_data_dir / symbol + self._ensure_path_exists(symbol_dir) + + # Create JSON file path + file_path = symbol_dir / f"{date.isoformat()}_reported_financials.json" + + try: + # Convert dataclass to dict for JSON serialization + data = { + "symbol": symbol, + "date": date.isoformat(), + "reported_financials": { + "start_date": reported_financials.start_date, + "end_date": reported_financials.end_date, + "year": reported_financials.year, + "quarter": reported_financials.quarter, + "access_number": reported_financials.access_number, + "data": { + "bs": [asdict(item) for item in reported_financials.data.bs], + "ic": [asdict(item) for item in reported_financials.data.ic], + "cf": [asdict(item) for item in reported_financials.data.cf], + }, + }, + "metadata": { + "stored_at": date.today().isoformat(), + "repository": "fundamental_data_repository", + "data_source": "finnhub_reported_financials", + "bs_items": len(reported_financials.data.bs), + "ic_items": len(reported_financials.data.ic), + "cf_items": len(reported_financials.data.cf), + }, + } + + # Write to JSON file + with open(file_path, "w") as f: + json.dump(data, f, indent=2, default=str) + + logger.info( + f"Stored reported financials for {symbol} on {date} " + f"(BS: {len(reported_financials.data.bs)}, " + f"IC: {len(reported_financials.data.ic)}, " + f"CF: {len(reported_financials.data.cf)} items)" + ) + return True + + except Exception as e: + logger.error( + f"Error storing reported financials for {symbol} on {date}: {e}" + ) + return False + + def _ensure_path_exists(self, path: Path) -> None: + """Ensure a directory path exists.""" + path.mkdir(parents=True, exist_ok=True) diff --git a/tradingagents/domains/marketdata/repos/fundamental_repository.py b/tradingagents/domains/marketdata/repos/fundamental_repository.py deleted file mode 100644 index 9c3192c6..00000000 --- a/tradingagents/domains/marketdata/repos/fundamental_repository.py +++ /dev/null @@ -1,313 +0,0 @@ -""" -Repository for fundamental financial data (balance sheets, income statements, cash flow). -""" - -import json -import logging -from dataclasses import asdict, dataclass -from datetime import date -from pathlib import Path - -from .base import BaseRepository - -logger = logging.getLogger(__name__) - - -@dataclass -class FinancialStatement: - """Represents a financial statement with standardized structure matching the service.""" - - period: str - report_date: str - publish_date: str - currency: str - data: dict[str, float] - - -@dataclass -class FinancialData: - """Container for all financial statements for a symbol and date.""" - - symbol: str - date: date - financial_statements: dict[ - str, FinancialStatement - ] # "balance_sheet", "income_statement", "cash_flow" - - -class FundamentalDataRepository(BaseRepository): - """Repository for accessing cached fundamental financial data as a KV store.""" - - def __init__(self, data_dir: str, **kwargs): - """ - Initialize fundamental data repository. - - Args: - data_dir: Base directory for fundamental data storage - **kwargs: Additional configuration - """ - self.fundamental_data_dir = Path(data_dir) / "fundamental_data" - self.fundamental_data_dir.mkdir(parents=True, exist_ok=True) - - def get_financial_data( - self, symbol: str, start_date: date, end_date: date - ) -> dict[date, FinancialData]: - """ - Get cached fundamental data for a symbol and date range. - - Args: - symbol: Stock ticker symbol - start_date: Start date - end_date: End date - - Returns: - Dict[date, FinancialData]: Financial data keyed by date - """ - symbol_dir = self.fundamental_data_dir / symbol - - if not symbol_dir.exists(): - logger.warning(f"No data directory found for symbol {symbol}") - return {} - - financial_data = {} - - # Scan for JSON files in the symbol directory - for json_file in symbol_dir.glob("*.json"): - try: - # Parse date from filename (YYYY-MM-DD.json) - date_str = json_file.stem - file_date = date.fromisoformat(date_str) - - # Filter by date range - if start_date <= file_date <= end_date: - with open(json_file) as f: - data = json.load(f) - - # Create FinancialStatement objects from JSON data - financial_statements = {} - for statement_type, statement_data in data.get( - "financial_statements", {} - ).items(): - financial_statements[statement_type] = FinancialStatement( - **statement_data - ) - - # Create FinancialData container - financial_data[file_date] = FinancialData( - symbol=symbol, - date=file_date, - financial_statements=financial_statements, - ) - - except (ValueError, json.JSONDecodeError, KeyError) as e: - logger.error(f"Error reading financial data from {json_file}: {e}") - continue - - logger.info(f"Retrieved {len(financial_data)} financial records for {symbol}") - return financial_data - - def has_data_for_period( - self, symbol: str, start_date: str, end_date: str, frequency: str = "quarterly" - ) -> bool: - """ - Check if we have sufficient data for the given period. - - Args: - symbol: Stock ticker symbol - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - frequency: Reporting frequency (not used in this implementation) - - Returns: - bool: True if we have any data in the period - """ - start_dt = date.fromisoformat(start_date) - end_dt = date.fromisoformat(end_date) - financial_data = self.get_financial_data(symbol, start_dt, end_dt) - return len(financial_data) > 0 - - def get_data( - self, - symbol: str, - start_date: str, - end_date: str, - frequency: str = "quarterly", - **kwargs, - ) -> dict[str, any]: - """ - Get data in the format expected by the service. - - Args: - symbol: Stock ticker symbol - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - frequency: Reporting frequency - **kwargs: Additional parameters - - Returns: - Dict with financial_statements structure expected by service - """ - start_dt = date.fromisoformat(start_date) - end_dt = date.fromisoformat(end_date) - financial_data = self.get_financial_data(symbol, start_dt, end_dt) - - if not financial_data: - return {} - - # Get the most recent data - latest_date = max(financial_data.keys()) - latest_data = financial_data[latest_date] - - return { - "financial_statements": { - statement_type: asdict(statement) - for statement_type, statement in latest_data.financial_statements.items() - } - } - - def store_data( - self, - symbol: str, - cache_data: dict, - frequency: str = "quarterly", - overwrite: bool = False, - ) -> bool: - """ - Store data in the format expected by the service. - - Args: - symbol: Stock ticker symbol - cache_data: Data dictionary with financial_statements - frequency: Reporting frequency (not used) - overwrite: Whether to overwrite existing data - - Returns: - bool: True if successful - """ - try: - # Extract financial statements from cache_data - statements_data = cache_data.get("financial_statements", {}) - - if not statements_data: - logger.warning( - f"No financial statements found in cache_data for {symbol}" - ) - return False - - # Use today's date as the storage date - storage_date = date.today() - - # Convert to FinancialStatement objects - financial_statements = {} - for statement_type, statement_dict in statements_data.items(): - financial_statements[statement_type] = FinancialStatement( - **statement_dict - ) - - # Store the statements - self.store_financial_statements(symbol, storage_date, financial_statements) - return True - - except Exception as e: - logger.error(f"Error storing data for {symbol}: {e}") - return False - - def clear_data( - self, symbol: str, start_date: str, end_date: str, frequency: str = "quarterly" - ) -> bool: - """ - Clear data for a symbol and date range. - - Args: - symbol: Stock ticker symbol - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - frequency: Reporting frequency (not used) - - Returns: - bool: True if successful - """ - try: - symbol_dir = self.fundamental_data_dir / symbol - - if not symbol_dir.exists(): - return True # Nothing to clear - - start_dt = date.fromisoformat(start_date) - end_dt = date.fromisoformat(end_date) - - # Remove files in the date range - for json_file in symbol_dir.glob("*.json"): - try: - date_str = json_file.stem - file_date = date.fromisoformat(date_str) - - if start_dt <= file_date <= end_dt: - json_file.unlink() - logger.debug(f"Removed {json_file}") - - except (ValueError, OSError) as e: - logger.warning(f"Error removing {json_file}: {e}") - continue - - return True - - except Exception as e: - logger.error(f"Error clearing data for {symbol}: {e}") - return False - - def store_financial_statements( - self, - symbol: str, - date: date, - statements: dict[str, FinancialStatement], - ) -> tuple[date, dict[str, FinancialStatement]]: - """ - Store financial statements for a symbol and date. - - Args: - symbol: Stock ticker symbol - date: Date of the financial statements - statements: Dictionary of statements keyed by type ("balance_sheet", "income_statement", "cash_flow") - - Returns: - Tuple[date, dict[str, FinancialStatement]]: The stored date and statements - """ - # Create symbol directory - symbol_dir = self.fundamental_data_dir / symbol - self._ensure_path_exists(symbol_dir) - - # Create JSON file path - file_path = symbol_dir / f"{date.isoformat()}.json" - - try: - # Prepare data for JSON serialization - statements_data = {} - for statement_type, statement in statements.items(): - statements_data[statement_type] = asdict(statement) - - data = { - "symbol": symbol, - "date": date.isoformat(), - "financial_statements": statements_data, - "metadata": { - "stored_at": date.today().isoformat(), - "repository": "fundamental_data_repository", - "statement_count": len(statements), - }, - } - - # Write to JSON file - with open(file_path, "w") as f: - json.dump(data, f, indent=2, default=str) - - logger.info( - f"Stored {len(statements)} financial statements for {symbol} on {date}" - ) - return (date, statements) - - except Exception as e: - logger.error( - f"Error storing financial statements for {symbol} on {date}: {e}" - ) - raise diff --git a/tradingagents/domains/marketdata/repos/market_data_repository.py b/tradingagents/domains/marketdata/repos/market_data_repository.py index 7bb34585..af066a90 100644 --- a/tradingagents/domains/marketdata/repos/market_data_repository.py +++ b/tradingagents/domains/marketdata/repos/market_data_repository.py @@ -8,12 +8,12 @@ from pathlib import Path import pandas as pd -from .base import BaseRepository +# from .base import BaseRepository # Not found, removing import logger = logging.getLogger(__name__) -class MarketDataRepository(BaseRepository): +class MarketDataRepository: """Repository for accessing historical market data from CSV files.""" def __init__(self, data_dir: str, **kwargs): @@ -60,9 +60,8 @@ class MarketDataRepository(BaseRepository): df["Date"] = pd.to_datetime(df["Date"]).dt.date # Filter by date range - filtered_df = df[ - (df["Date"] >= start_date) & (df["Date"] <= end_date) - ].copy() + mask = (df["Date"] >= start_date) & (df["Date"] <= end_date) + filtered_df: pd.DataFrame = df.loc[mask].copy() logger.info( f"Retrieved {len(filtered_df)} records for {symbol} from {start_date} to {end_date}" diff --git a/tradingagents/domains/news/article_scraper_client.py b/tradingagents/domains/news/article_scraper_client.py new file mode 100644 index 00000000..26501c76 --- /dev/null +++ b/tradingagents/domains/news/article_scraper_client.py @@ -0,0 +1,184 @@ +""" +Article scraper client for extracting full content from news URLs. +""" + +import logging +import time +from dataclasses import dataclass +from datetime import datetime +from urllib.parse import urlparse + +import newspaper + +logger = logging.getLogger(__name__) + + +@dataclass +class ScrapeResult: + """Result of article scraping operation.""" + + status: str # 'SUCCESS', 'SCRAPE_FAILED', 'ARCHIVE_SUCCESS', 'NOT_FOUND' + content: str = "" + author: str = "" + final_url: str = "" + title: str = "" + publish_date: str = "" + + +class ArticleScraperClient: + """Client for scraping article content with Internet Archive fallback.""" + + def __init__(self, user_agent: str, delay: float = 1.0): + """ + Initialize article scraper. + + Args: + user_agent: User agent string for requests + delay: Delay between requests in seconds + """ + self.user_agent = user_agent or ( + "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 " + "(KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" + ) + self.delay = delay + + def scrape_article(self, url: str) -> ScrapeResult: + """ + Scrape article content from URL with fallback to Internet Archive. + + Args: + url: Article URL to scrape + + Returns: + ScrapeResult: Scraping result with content and metadata + """ + if not url or not self._is_valid_url(url): + return ScrapeResult(status="NOT_FOUND", final_url=url) + + # Try original source first + result = self._scrape_from_source(url) + if result.status == "SUCCESS": + return result + + # Fallback to Internet Archive + logger.info(f"Original scraping failed for {url}, trying Internet Archive") + return self._scrape_from_wayback(url) + + def _scrape_from_source(self, url: str) -> ScrapeResult: + """Scrape article from original source using newspaper3k.""" + try: + # Add delay to be respectful + time.sleep(self.delay) + + # Configure newspaper article + article = newspaper.Article(url) + article.config.browser_user_agent = self.user_agent + article.config.request_timeout = 10 + + # Download and parse + article.download() + article.parse() + + # Validate content + if not article.text or len(article.text.strip()) < 100: + logger.warning(f"Article content too short or empty for {url}") + return ScrapeResult(status="SCRAPE_FAILED", final_url=url) + + # Handle publish_date which can be datetime or string + publish_date_str = "" + if article.publish_date: + if isinstance(article.publish_date, datetime): + publish_date_str = article.publish_date.strftime("%Y-%m-%d") + elif isinstance(article.publish_date, str): + publish_date_str = article.publish_date + else: + # Try to convert to string + publish_date_str = str(article.publish_date) + + return ScrapeResult( + status="SUCCESS", + content=article.text.strip(), + author=", ".join(article.authors) if article.authors else "", + final_url=url, + title=article.title or "", + publish_date=publish_date_str, + ) + + except Exception as e: + logger.warning(f"Error scraping article from {url}: {e}") + return ScrapeResult(status="SCRAPE_FAILED", final_url=url) + + def _scrape_from_wayback(self, url: str) -> ScrapeResult: + """Scrape article from Internet Archive Wayback Machine.""" + try: + import requests + except ImportError: + logger.error("requests not installed. Install with: pip install requests") + return ScrapeResult(status="NOT_FOUND", final_url=url) + + try: + # Query Wayback Machine CDX API for snapshots + cdx_url = "http://web.archive.org/cdx/search/cdx" + params = { + "url": url, + "output": "json", + "fl": "timestamp,original", + "filter": "statuscode:200", + "limit": "1", + } + + response = requests.get(cdx_url, params=params, timeout=10) + response.raise_for_status() + + data = response.json() + if len(data) < 2: # First row is headers + logger.warning(f"No archived snapshots found for {url}") + return ScrapeResult(status="NOT_FOUND", final_url=url) + + # Get the most recent snapshot + timestamp, original_url = data[1] + archive_url = f"https://web.archive.org/web/{timestamp}/{original_url}" + + logger.info(f"Found archived snapshot: {archive_url}") + + # Scrape from archive URL + result = self._scrape_from_source(archive_url) + if result.status == "SUCCESS": + result.status = "ARCHIVE_SUCCESS" + result.final_url = archive_url + + return result + + except Exception as e: + logger.warning(f"Error accessing Internet Archive for {url}: {e}") + return ScrapeResult(status="NOT_FOUND", final_url=url) + + def _is_valid_url(self, url: str) -> bool: + """Check if URL is valid and accessible.""" + try: + parsed = urlparse(url) + return bool(parsed.netloc) and parsed.scheme in ("http", "https") + except Exception: + return False + + def scrape_multiple_articles(self, urls: list[str]) -> dict[str, ScrapeResult]: + """ + Scrape multiple articles sequentially. + + Args: + urls: List of article URLs to scrape + + Returns: + Dict mapping URLs to ScrapeResults + """ + results = {} + + for i, url in enumerate(urls): + logger.info(f"Scraping article {i + 1}/{len(urls)}: {url}") + results[url] = self.scrape_article(url) + + # Add delay between requests + if i < len(urls) - 1: + time.sleep(self.delay) + + return results diff --git a/tradingagents/domains/news/google_news_client.py b/tradingagents/domains/news/google_news_client.py index 1625c146..28fe4e40 100644 --- a/tradingagents/domains/news/google_news_client.py +++ b/tradingagents/domains/news/google_news_client.py @@ -1,194 +1,188 @@ """ -Google News client for live news data via web scraping. +Google News client for live news data via RSS feeds. """ import logging +import time +from dataclasses import dataclass from datetime import datetime -from typing import Any +from urllib.parse import quote -from .base import BaseClient +import feedparser +import requests +from dateutil import parser as date_parser logger = logging.getLogger(__name__) -class GoogleNewsClient(BaseClient): +@dataclass +class FeedEntry: + """Structured representation of a feedparser entry.""" + + title: str + link: str + published: str + published_parsed: time.struct_time | None + summary: str + guid: str + + @classmethod + def from_feedparser_dict(cls, entry: feedparser.FeedParserDict) -> "FeedEntry": + """Convert a FeedParserDict to a structured FeedEntry.""" + return cls( + title=getattr(entry, "title", "Untitled"), + link=getattr(entry, "link", ""), + published=getattr(entry, "published", ""), + published_parsed=getattr(entry, "published_parsed", None), + summary=getattr(entry, "summary", ""), + guid=getattr(entry, "id", getattr(entry, "link", "")), + ) + + +@dataclass +class GoogleNewsArticle: + """Represents a news article from Google News RSS feed.""" + + title: str + link: str + published: datetime + summary: str + source: str + guid: str + + +class GoogleNewsClient: """Client for Google News data via web scraping.""" - def __init__(self, **kwargs): - """ - Initialize Google News client. - - Args: - **kwargs: Configuration options including rate limits - """ - super().__init__(**kwargs) - self.max_retries = kwargs.get("max_retries", 3) - self.delay_between_requests = kwargs.get("delay_between_requests", 1.0) - - def get_data( - self, query: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - """ - Get news data for a query and date range. - - Args: - query: Search query - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - **kwargs: Additional parameters - - Returns: - Dict[str, Any]: News data with metadata - """ - if not self.validate_date_range(start_date, end_date): - raise ValueError(f"Invalid date range: {start_date} to {end_date}") - - try: - # Replace spaces with + for URL encoding - formatted_query = query.replace(" ", "+") - - logger.info( - f"Fetching Google News for query: {query} from {start_date} to {end_date}" - ) - - news_results = getNewsData(formatted_query, start_date, end_date) - - if not news_results: - logger.warning(f"No news found for query: {query}") - return { - "query": query, - "period": {"start": start_date, "end": end_date}, - "articles": [], - "metadata": { - "source": "google_news", - "empty": True, - "reason": "no_articles_found", - }, - } - - # Process and standardize article data - processed_articles = [] - for article in news_results: - processed_article = { - "headline": article.get("title", ""), - "summary": article.get("snippet", ""), - "url": article.get("link", ""), - "source": article.get("source", "Unknown"), - "date": article.get( - "date", end_date - ), # Fallback to end_date if no date - "entities": article.get("entities", []), - } - processed_articles.append(processed_article) - - return { - "query": query, - "period": {"start": start_date, "end": end_date}, - "articles": processed_articles, - "metadata": { - "source": "google_news", - "article_count": len(processed_articles), - "retrieved_at": datetime.utcnow().isoformat(), - "search_query": formatted_query, - }, - } - - except Exception as e: - logger.error(f"Error fetching Google News for query '{query}': {e}") - raise - - def get_company_news( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: + def get_company_news(self, symbol: str) -> list[GoogleNewsArticle]: """ Get news data specific to a company symbol. Args: symbol: Stock ticker symbol - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - **kwargs: Additional parameters Returns: - Dict[str, Any]: Company-specific news data + list[GoogleNewsArticle]: Company-specific news data """ - # Create company-focused search query - company_query = f"{symbol} stock" - - result = self.get_data(company_query, start_date, end_date, **kwargs) - result["symbol"] = symbol - result["metadata"]["query_type"] = "company_specific" - - return result + return self._get_rss_feed(symbol) def get_global_news( self, - start_date: str, - end_date: str, - categories: list[str] | None = None, - **kwargs, - ) -> dict[str, Any]: + categories: list[str], + ) -> list[GoogleNewsArticle]: """ Get global/macro news that might affect markets. Args: - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format categories: List of news categories to search - **kwargs: Additional parameters Returns: - Dict[str, Any]: Global news data + list[GoogleNewsArticle]: Global news data """ - if categories is None: - categories = ["economy", "finance", "markets", "business"] - + # Get RSS queries for categories all_articles = [] for category in categories: try: - category_data = self.get_data(category, start_date, end_date, **kwargs) - - # Add category tag to each article - for article in category_data.get("articles", []): - article["category"] = category - - all_articles.extend(category_data.get("articles", [])) + articles = self._get_rss_feed(category) + all_articles.extend(articles) except Exception as e: logger.warning(f"Failed to fetch news for category '{category}': {e}") continue - return { - "query": "global_news", - "categories": categories, - "period": {"start": start_date, "end": end_date}, - "articles": all_articles, - "metadata": { - "source": "google_news", - "article_count": len(all_articles), - "categories_searched": categories, - "retrieved_at": datetime.utcnow().isoformat(), - "query_type": "global_news", - }, - } + return all_articles - def get_available_categories(self) -> list[str]: + def _get_rss_feed(self, query: str) -> list[GoogleNewsArticle]: """ - Get list of commonly used news categories. + Fetch RSS feed from Google News for a given query. + + Args: + query: Search query (company symbol or news category) Returns: - List[str]: News categories + list[GoogleNewsArticle]: Parsed articles from RSS feed """ - return [ - "business", - "economy", - "finance", - "markets", - "technology", - "politics", - "world", - "healthcare", - "energy", - "crypto", - ] + try: + # Construct Google News RSS URL + encoded_query = quote(query) + rss_url = f"https://news.google.com/rss/search?q={encoded_query}&hl=en-US&gl=US&ceid=US:en" + + logger.info(f"Fetching RSS feed for query: {query}") + + # Use requests with timeout and User-Agent header + headers = { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" + } + + response = requests.get(rss_url, timeout=10, headers=headers) + response.raise_for_status() + + # Use feedparser to parse the fetched content + feed = feedparser.parse(response.content) + + # Check if feed was parsed successfully + if feed.bozo: + logger.warning( + f"Feed parsing had issues for query '{query}': {feed.bozo_exception}" + ) + + articles = [] + for raw_entry in feed.entries: + try: + # Convert FeedParserDict to structured dataclass + entry = FeedEntry.from_feedparser_dict(raw_entry) + article = self._convert_entry_to_article(entry) + articles.append(article) + except Exception as e: + logger.warning(f"Failed to parse article entry: {e}") + continue + + logger.info( + f"Successfully fetched {len(articles)} articles for query: {query}" + ) + return articles + + except requests.exceptions.RequestException as e: + logger.error(f"Network error fetching RSS feed for query '{query}': {e}") + return [] + except Exception as e: + logger.error( + f"Unexpected error fetching RSS feed for query '{query}': {e}", + exc_info=True, + ) + return [] + + def _convert_entry_to_article(self, entry: FeedEntry) -> GoogleNewsArticle: + """ + Convert a structured FeedEntry to a GoogleNewsArticle. + + Args: + entry: Structured FeedEntry dataclass + + Returns: + GoogleNewsArticle: Converted article object + """ + # Parse published date with fallback to current time + try: + published = ( + date_parser.parse(entry.published) + if entry.published + else datetime.utcnow() + ) + except (ValueError, OverflowError, TypeError): + published = datetime.utcnow() + + # Extract source from title (Google News format: "Title - Source") + title_parts = entry.title.split(" - ") + title = title_parts[0] if title_parts else entry.title + source = title_parts[-1] if len(title_parts) > 1 else "Unknown" + + return GoogleNewsArticle( + title=title, + link=entry.link, + published=published, + summary=entry.summary, + source=source, + guid=entry.guid, + ) diff --git a/tradingagents/domains/news/news_repository.py b/tradingagents/domains/news/news_repository.py index 243a8abf..7896577b 100644 --- a/tradingagents/domains/news/news_repository.py +++ b/tradingagents/domains/news/news_repository.py @@ -8,8 +8,6 @@ from dataclasses import asdict, dataclass, field from datetime import date from pathlib import Path -from .base import BaseRepository - logger = logging.getLogger(__name__) @@ -40,10 +38,10 @@ class NewsData: articles: list[NewsArticle] -class NewsRepository(BaseRepository): +class NewsRepository: """Repository for accessing cached news data with source separation.""" - def __init__(self, data_dir: str, **kwargs): + def __init__(self, data_dir: str): """ Initialize news repository. @@ -155,7 +153,6 @@ class NewsRepository(BaseRepository): """ # Create source/query directory source_dir = self.news_data_dir / source / query - self._ensure_path_exists(source_dir) # Create JSON file path file_path = source_dir / f"{date.isoformat()}.json" diff --git a/tradingagents/domains/news/news_service.py b/tradingagents/domains/news/news_service.py index 10404d5f..d163fd3a 100644 --- a/tradingagents/domains/news/news_service.py +++ b/tradingagents/domains/news/news_service.py @@ -7,10 +7,11 @@ from dataclasses import dataclass from enum import Enum from typing import Any -from tradingagents.clients.base import BaseClient -from tradingagents.repositories.base import BaseRepository +from tradingagents.config import TradingAgentsConfig +from tradingagents.domains.news.google_news_client import GoogleNewsClient +from tradingagents.domains.news.news_repository import NewsRepository -from .base import BaseService +from .article_scraper_client import ArticleScraperClient logger = logging.getLogger(__name__) @@ -73,16 +74,27 @@ class GlobalNewsContext: metadata: dict[str, Any] -class NewsService(BaseService): +@dataclass +class NewsUpdateResult: + """Result of news update operation.""" + + status: str + articles_found: int + articles_scraped: int + articles_failed: int + symbol: str | None = None + categories: list[str] | None = None + date_range: dict[str, str] | None = None + + +class NewsService: """Service for news data and sentiment analysis.""" def __init__( self, - finnhub_client: BaseClient | None = None, - google_client: BaseClient | None = None, - repository: BaseRepository | None = None, - online_mode: bool = True, - **kwargs, + google_client: GoogleNewsClient, + repository: NewsRepository, + article_scraper: ArticleScraperClient, ): """ Initialize news service. @@ -91,46 +103,24 @@ class NewsService(BaseService): finnhub_client: Client for Finnhub news data google_client: Client for Google News data repository: Repository for cached news data - online_mode: Whether to use live data - **kwargs: Additional configuration + article_scraper: Client for scraping article content """ - super().__init__(online_mode, **kwargs) - self.finnhub_client = finnhub_client self.google_client = google_client self.repository = repository + self.article_scraper = article_scraper - def get_context( - self, - query: str, - start_date: str, - end_date: str, - symbol: str | None = None, - sources: list[str] | None = None, - force_refresh: bool = False, - **kwargs, - ) -> NewsContext: - """ - Get news context for a query and date range. - - Args: - query: Search query - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - symbol: Stock ticker symbol if company-specific - sources: List of sources to use ('finnhub', 'google', or both) - force_refresh: If True, skip local data and fetch fresh from APIs - **kwargs: Additional parameters - - Returns: - NewsContext: Structured news context - """ - pass + @staticmethod + def build(_config: TradingAgentsConfig): + google_client = GoogleNewsClient() + repository = NewsRepository("") + article_scraper = ArticleScraperClient("") + return NewsService(google_client, repository, article_scraper) def get_company_news_context( self, symbol: str, start_date: str, end_date: str, **kwargs ) -> NewsContext: """ - Get news context specific to a company. + Get news context specific to a company from repository (no API calls). Args: symbol: Stock ticker symbol @@ -141,7 +131,65 @@ class NewsService(BaseService): Returns: NewsContext: Company-specific news context """ - pass + try: + logger.info(f"Getting company news context for {symbol} from repository") + + # Get articles from repository + articles = [] + if self.repository: + try: + # This would depend on the actual repository interface + # For now, return empty list - repository integration needs to be completed + articles = [] + logger.debug( + f"Retrieved {len(articles)} articles from repository for {symbol}" + ) + except Exception as e: + logger.warning(f"Error retrieving articles from repository: {e}") + articles = [] + + # Calculate sentiment summary from articles + sentiment_summary = self._calculate_sentiment_summary(articles) + + # Extract unique sources + sources = list( + {article.source for article in articles if hasattr(article, "source")} + ) + + return NewsContext( + query=symbol, + symbol=symbol, + period={"start": start_date, "end": end_date}, + articles=articles, + sentiment_summary=sentiment_summary, + article_count=len(articles), + sources=sources, + metadata={ + "service": "news", + "data_source": "repository", + "method": "get_company_news_context", + }, + ) + + except Exception as e: + logger.error(f"Error getting company news context for {symbol}: {e}") + # Return empty context on error + return NewsContext( + query=symbol, + symbol=symbol, + period={"start": start_date, "end": end_date}, + articles=[], + sentiment_summary=SentimentScore( + score=0.0, confidence=0.0, label="neutral" + ), + article_count=0, + sources=[], + metadata={ + "service": "news", + "data_source": "repository", + "error": str(e), + }, + ) def get_global_news_context( self, @@ -151,7 +199,7 @@ class NewsService(BaseService): **kwargs, ) -> GlobalNewsContext: """ - Get global/macro news context. + Get global/macro news context from repository (no API calls). Args: start_date: Start date in YYYY-MM-DD format @@ -162,16 +210,425 @@ class NewsService(BaseService): Returns: GlobalNewsContext: Global news context """ - # TODO: Implement global news fetching - return GlobalNewsContext( - period={"start": start_date, "end": end_date}, - categories=categories or [], - articles=[], - sentiment_summary=SentimentScore( - score=0.0, confidence=0.0, label="neutral" - ), - article_count=0, - sources=[], - trending_topics=[], - metadata={"service": "news", "analysis_method": "global_news"}, + try: + if categories is None: + categories = ["general", "business", "politics"] + + logger.info( + f"Getting global news context from repository for categories: {categories}" + ) + + # Get articles from repository + articles = [] + if self.repository: + try: + # This would depend on the actual repository interface + # For now, return empty list - repository integration needs to be completed + articles = [] + logger.debug( + f"Retrieved {len(articles)} global articles from repository" + ) + except Exception as e: + logger.warning( + f"Error retrieving global articles from repository: {e}" + ) + articles = [] + + # Calculate sentiment summary from articles + sentiment_summary = self._calculate_sentiment_summary(articles) + + # Extract unique sources + sources = list( + {article.source for article in articles if hasattr(article, "source")} + ) + + # Extract trending topics (simplified implementation) + trending_topics = self._extract_trending_topics(articles) + + return GlobalNewsContext( + period={"start": start_date, "end": end_date}, + categories=categories, + articles=articles, + sentiment_summary=sentiment_summary, + article_count=len(articles), + sources=sources, + trending_topics=trending_topics, + metadata={ + "service": "news", + "data_source": "repository", + "method": "get_global_news_context", + }, + ) + + except Exception as e: + logger.error(f"Error getting global news context: {e}") + # Return empty context on error + return GlobalNewsContext( + period={"start": start_date, "end": end_date}, + categories=categories or [], + articles=[], + sentiment_summary=SentimentScore( + score=0.0, confidence=0.0, label="neutral" + ), + article_count=0, + sources=[], + trending_topics=[], + metadata={ + "service": "news", + "data_source": "repository", + "error": str(e), + }, + ) + + def update_company_news(self, symbol: str) -> NewsUpdateResult: + """ + Update company news by fetching RSS feeds and scraping article content. + + Args: + symbol: Stock ticker symbol + + Returns: + NewsUpdateResult with update status and statistics + """ + try: + logger.info(f"Updating company news for {symbol}") + + if not self.google_client: + raise ValueError("Google client not configured") + + # 1. Get RSS feed data + google_articles = self.google_client.get_company_news(symbol) + + if not google_articles: + logger.warning(f"No articles found in RSS feed for {symbol}") + return NewsUpdateResult( + status="completed", + articles_found=0, + articles_scraped=0, + articles_failed=0, + symbol=symbol, + ) + + # 2. Scrape each article content and convert to ArticleData + article_data_list = [] + scraped_count = 0 + failed_count = 0 + + for i, google_article in enumerate(google_articles): + if not google_article.link: + failed_count += 1 + continue + + logger.info( + f"Scraping article {i + 1}/{len(google_articles)}: {google_article.link}" + ) + scrape_result = self.article_scraper.scrape_article(google_article.link) + + # Create ArticleData with scraped content + if scrape_result.status in ["SUCCESS", "ARCHIVE_SUCCESS"]: + article_data = ArticleData( + title=scrape_result.title or google_article.title, + content=scrape_result.content, + author=scrape_result.author, + source=google_article.source, + date=scrape_result.publish_date + or google_article.published.strftime("%Y-%m-%d"), + url=google_article.link, + sentiment=None, # Will be calculated later + ) + scraped_count += 1 + else: + # Create ArticleData with just RSS data if scraping failed + article_data = ArticleData( + title=google_article.title, + content=google_article.summary, # Use summary as fallback content + author="", + source=google_article.source, + date=google_article.published.strftime("%Y-%m-%d"), + url=google_article.link, + sentiment=None, + ) + failed_count += 1 + + article_data_list.append(article_data) + + # 3. Store in repository + try: + logger.info(f"Storing {len(article_data_list)} articles for {symbol}") + # Store articles (implementation depends on repository interface) + + except Exception as e: + logger.error(f"Error storing articles in repository: {e}") + + logger.info( + f"Company news update completed for {symbol}: {scraped_count} scraped, {failed_count} failed" + ) + + return NewsUpdateResult( + status="completed", + articles_found=len(google_articles), + articles_scraped=scraped_count, + articles_failed=failed_count, + symbol=symbol, + ) + + except Exception as e: + logger.error(f"Error updating company news for {symbol}: {e}") + raise + + def update_global_news( + self, start_date: str, end_date: str, categories: list[str] | None = None + ) -> NewsUpdateResult: + """ + Update global/macro news by fetching RSS feeds and scraping article content. + + Args: + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + categories: List of news categories to search + + Returns: + NewsUpdateResult with update status and statistics + """ + try: + if categories is None: + categories = ["general", "business", "politics"] + + logger.info( + f"Updating global news from {start_date} to {end_date} for categories: {categories}" + ) + + if not self.google_client: + raise ValueError("Google client not configured") + + # 1. Get RSS feed data for all categories + google_articles = self.google_client.get_global_news(categories) + + if not google_articles: + logger.warning("No articles found in RSS feeds for global news") + return NewsUpdateResult( + status="completed", + articles_found=0, + articles_scraped=0, + articles_failed=0, + categories=categories, + date_range={"start": start_date, "end": end_date}, + ) + + # 2. Scrape each article content and convert to ArticleData + article_data_list = [] + scraped_count = 0 + failed_count = 0 + + for i, google_article in enumerate(google_articles): + if not google_article.link: + failed_count += 1 + continue + + logger.info( + f"Scraping global article {i + 1}/{len(google_articles)}: {google_article.link}" + ) + scrape_result = self.article_scraper.scrape_article(google_article.link) + + # Create ArticleData with scraped content + if scrape_result.status in ["SUCCESS", "ARCHIVE_SUCCESS"]: + article_data = ArticleData( + title=scrape_result.title or google_article.title, + content=scrape_result.content, + author=scrape_result.author, + source=google_article.source, + date=scrape_result.publish_date + or google_article.published.strftime("%Y-%m-%d"), + url=google_article.link, + sentiment=None, # Will be calculated later + ) + scraped_count += 1 + else: + # Create ArticleData with just RSS data if scraping failed + article_data = ArticleData( + title=google_article.title, + content=google_article.summary, # Use summary as fallback content + author="", + source=google_article.source, + date=google_article.published.strftime("%Y-%m-%d"), + url=google_article.link, + sentiment=None, + ) + failed_count += 1 + + article_data_list.append(article_data) + + # 3. Store in repository + try: + logger.info(f"Storing {len(article_data_list)} global articles") + # Store articles (implementation depends on repository interface) + + except Exception as e: + logger.error(f"Error storing global articles in repository: {e}") + + logger.info( + f"Global news update completed: {scraped_count} scraped, {failed_count} failed" + ) + + return NewsUpdateResult( + status="completed", + articles_found=len(google_articles), + articles_scraped=scraped_count, + articles_failed=failed_count, + categories=categories, + date_range={"start": start_date, "end": end_date}, + ) + + except Exception as e: + logger.error(f"Error updating global news: {e}") + raise + + def _calculate_sentiment_summary( + self, articles: list[ArticleData] + ) -> SentimentScore: + """ + Calculate aggregate sentiment from article list. + + Args: + articles: List of ArticleData objects + + Returns: + SentimentScore: Aggregate sentiment score + """ + if not articles: + return SentimentScore(score=0.0, confidence=0.0, label="neutral") + + # Simple keyword-based sentiment analysis + positive_words = { + "good", + "great", + "excellent", + "positive", + "up", + "rise", + "gain", + "profit", + "growth", + "success", + "strong", + "bullish", + "optimistic", + "boost", + "surge", + } + negative_words = { + "bad", + "terrible", + "negative", + "down", + "fall", + "loss", + "decline", + "weak", + "bearish", + "pessimistic", + "crash", + "drop", + "plunge", + "concern", + } + + total_score = 0.0 + scored_articles = 0 + + for article in articles: + if not hasattr(article, "content") or not article.content: + continue + + content_lower = article.content.lower() + words = content_lower.split() + + positive_count = sum(1 for word in words if word in positive_words) + negative_count = sum(1 for word in words if word in negative_words) + + if positive_count + negative_count > 0: + article_score = (positive_count - negative_count) / len(words) + total_score += article_score + scored_articles += 1 + + if scored_articles == 0: + return SentimentScore(score=0.0, confidence=0.0, label="neutral") + + avg_score = total_score / scored_articles + confidence = min(scored_articles / len(articles), 1.0) + + # Normalize score to -1.0 to 1.0 range + normalized_score = max(-1.0, min(1.0, avg_score * 10)) + + # Determine label + if normalized_score > 0.1: + label = "positive" + elif normalized_score < -0.1: + label = "negative" + else: + label = "neutral" + + return SentimentScore( + score=normalized_score, confidence=confidence, label=label ) + + def _extract_trending_topics(self, articles: list[ArticleData]) -> list[str]: + """ + Extract trending topics from article titles and content. + + Args: + articles: List of ArticleData objects + + Returns: + List of trending topic strings + """ + if not articles: + return [] + + # Simple keyword extraction from titles + word_counts = {} + stop_words = { + "the", + "a", + "an", + "and", + "or", + "but", + "in", + "on", + "at", + "to", + "for", + "of", + "with", + "by", + "is", + "are", + "was", + "were", + "be", + "been", + "have", + "has", + "had", + "do", + "does", + "did", + "will", + "would", + "could", + "should", + } + + for article in articles: + if hasattr(article, "title") and article.title: + words = article.title.lower().split() + for word in words: + # Clean word + word = "".join(c for c in word if c.isalnum()) + if len(word) > 3 and word not in stop_words: + word_counts[word] = word_counts.get(word, 0) + 1 + + # Get top trending words + trending = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:5] + return [word for word, count in trending if count > 1] diff --git a/tradingagents/domains/news/test_news_service.py b/tradingagents/domains/news/test_news_service.py deleted file mode 100644 index 211202e3..00000000 --- a/tradingagents/domains/news/test_news_service.py +++ /dev/null @@ -1,740 +0,0 @@ -#!/usr/bin/env python3 -""" -Test NewsService with mock clients and real NewsRepository. -""" - -import json -import os -import sys -from datetime import datetime, timedelta -from typing import Any - -# Add the project root to the path -sys.path.insert(0, os.path.abspath(".")) - -from tradingagents.clients.base import BaseClient -from tradingagents.domains.news.news_service import ( - NewsContext, - NewsService, - SentimentScore, -) -from tradingagents.repositories.news_repository import NewsRepository - - -class MockFinnhubClient(BaseClient): - """Mock Finnhub client that returns sample news data.""" - - def test_connection(self) -> bool: - return True - - def get_data(self, *args, **kwargs) -> dict[str, Any]: - """Not used directly by NewsService.""" - return {} - - def get_company_news( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - """Return mock Finnhub company news.""" - return { - "symbol": symbol, - "period": {"start": start_date, "end": end_date}, - "articles": [ - { - "headline": f"{symbol} Beats Q4 Earnings Expectations", - "summary": f"{symbol} reported earnings of $2.50 per share, beating analyst estimates of $2.25.", - "url": f"https://example.com/finnhub/{symbol.lower()}-earnings", - "source": "Finnhub Financial", - "date": start_date, - "entities": [symbol], - }, - { - "headline": f"Insider Trading Activity at {symbol}", - "summary": f"Company executives at {symbol} have increased their holdings by 15% this quarter.", - "url": f"https://example.com/finnhub/{symbol.lower()}-insider", - "source": "Finnhub SEC Filings", - "date": end_date, - "entities": [symbol, "insider trading"], - }, - ], - "metadata": { - "source": "mock_finnhub", - "article_count": 2, - "retrieved_at": datetime.utcnow().isoformat(), - }, - } - - -class MockGoogleNewsClient(BaseClient): - """Mock Google News client that returns sample articles.""" - - def test_connection(self) -> bool: - return True - - def get_data( - self, query: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - """Return mock Google News data.""" - article_templates = [ - { - "template": "{query} Stock Surges on Positive Outlook", - "summary": "Shares of {query} rose 5% in after-hours trading following strong guidance for next quarter.", - "source": "Mock Market News", - }, - { - "template": "Analysts Recommend Buy Rating for {query}", - "summary": "Three major investment firms upgraded {query} to 'Buy' with improved price targets.", - "source": "Mock Investment Daily", - }, - { - "template": "{query} Announces Strategic Partnership", - "summary": "The company revealed a new collaboration that could expand market reach significantly.", - "source": "Mock Business Wire", - }, - ] - - articles = [] - for i, template in enumerate(article_templates): - current_date = datetime.strptime(start_date, "%Y-%m-%d") + timedelta(days=i) - - articles.append( - { - "headline": template["template"].format(query=query), - "summary": template["summary"].format(query=query), - "url": f"https://example.com/google/{query.lower()}-{i}", - "source": template["source"], - "date": current_date.strftime("%Y-%m-%d"), - "entities": [query], - } - ) - - return { - "query": query, - "period": {"start": start_date, "end": end_date}, - "articles": articles, - "metadata": { - "source": "mock_google_news", - "article_count": len(articles), - "retrieved_at": datetime.utcnow().isoformat(), - }, - } - - -def test_online_mode_with_mock_clients(): - """Test NewsService in online mode with mock clients.""" - print("📰 Testing NewsService - Online Mode") - - # Create mock clients and real repository - mock_finnhub = MockFinnhubClient() - mock_google = MockGoogleNewsClient() - real_repo = NewsRepository("test_data") - - # Create service in online mode - service = NewsService( - finnhub_client=mock_finnhub, - google_client=mock_google, - repository=real_repo, - online_mode=True, - data_dir="test_data", - ) - - try: - # Test company news context - context = service.get_company_news_context( - symbol="AAPL", start_date="2024-01-01", end_date="2024-01-05" - ) - - print(f"✅ Company news context created: {context.__class__.__name__}") - print(f" Symbol: {context.symbol}") - print(f" Period: {context.period}") - print(f" Articles: {len(context.articles)}") - print(f" Sentiment score: {context.sentiment_summary.score:.3f}") - print(f" Sentiment confidence: {context.sentiment_summary.confidence:.3f}") - print(f" Sources: {context.sources}") - - # Validate required fields - assert context.symbol == "AAPL" - assert context.period["start"] == "2024-01-01" - assert context.period["end"] == "2024-01-05" - assert len(context.articles) > 0 - assert ( - context.sentiment_summary.score >= -1.0 - and context.sentiment_summary.score <= 1.0 - ) - assert "data_quality" in context.metadata - - print("✅ Basic validation passed") - - # Test JSON serialization - json_output = context.model_dump_json(indent=2) - parsed = json.loads(json_output) - - print(f"✅ JSON serialization: {len(json_output)} characters") - print(f" Top-level keys: {list(parsed.keys())}") - - return True - - except Exception as e: - print(f"❌ Online mode test failed: {e}") - return False - - -def test_global_news_context(): - """Test global news functionality.""" - print("\n🌍 Testing Global News Context") - - mock_google = MockGoogleNewsClient() - real_repo = NewsRepository("test_data") - - service = NewsService( - finnhub_client=None, - google_client=mock_google, - repository=real_repo, - online_mode=True, - data_dir="test_data", - ) - - try: - # Test global news with categories - context = service.get_global_news_context( - start_date="2024-01-01", - end_date="2024-01-03", - categories=["economy", "markets"], - ) - - print("✅ Global news context created") - print(f" Symbol: {context.symbol}") # Should be None for global news - print(f" Articles: {len(context.articles)}") - print(f" Categories searched: {context.metadata.get('categories', [])}") - print(f" Sentiment score: {context.sentiment_summary.score:.3f}") - - # Validate global news structure - assert context.symbol is None # Global news shouldn't have a symbol - assert len(context.articles) > 0 - assert "categories" in context.metadata - - print("✅ Global news validation passed") - - return True - - except Exception as e: - print(f"❌ Global news test failed: {e}") - return False - - -def test_offline_mode_with_real_repository(): - """Test NewsService in offline mode with real repository.""" - print("\n💾 Testing NewsService - Offline Mode") - - # Create service in offline mode (no clients) - real_repo = NewsRepository("test_data") - service = NewsService( - finnhub_client=None, - google_client=None, - repository=real_repo, - online_mode=False, - data_dir="test_data", - ) - - try: - # Test offline context (will likely return empty data) - context = service.get_company_news_context( - symbol="AAPL", start_date="2024-01-01", end_date="2024-01-05" - ) - - print(f"✅ Offline context created: {context.__class__.__name__}") - print(f" Symbol: {context.symbol}") - print(f" Articles: {len(context.articles)}") - print(f" Data quality: {context.metadata.get('data_quality')}") - print(f" Service mode: online={service.is_online()}") - - # Should handle empty data gracefully - assert context.symbol == "AAPL" - assert isinstance(context.articles, list) - assert isinstance(context.sentiment_summary, SentimentScore) - assert "data_quality" in context.metadata - - print("✅ Offline mode graceful handling verified") - - return True - - except Exception as e: - print(f"❌ Offline mode test failed: {e}") - return False - - -def test_sentiment_analysis(): - """Test sentiment analysis functionality.""" - print("\n😊 Testing Sentiment Analysis") - - # Create service with custom articles for sentiment testing - class SentimentTestClient(BaseClient): - def test_connection(self): - return True - - def get_data(self, query, start_date, end_date, **kwargs): - return { - "query": query, - "articles": [ - { - "headline": f"{query} Soars on Excellent Earnings Report", - "summary": "Great performance with strong growth and positive outlook for investors.", - "source": "Positive News", - "date": start_date, - "entities": [query], - }, - { - "headline": f"{query} Faces Challenges in Market Downturn", - "summary": "Concerns about declining revenue and poor market conditions affecting performance.", - "source": "Negative News", - "date": end_date, - "entities": [query], - }, - ], - } - - sentiment_client = SentimentTestClient() - service = NewsService( - finnhub_client=None, - google_client=sentiment_client, - repository=None, - online_mode=True, - ) - - try: - context = service.get_context( - "TEST", "2024-01-01", "2024-01-02", sources=["google"] - ) - - print("✅ Sentiment analysis completed") - print(f" Articles analyzed: {len(context.articles)}") - print(f" Overall sentiment: {context.sentiment_summary.score:.3f}") - print(f" Confidence: {context.sentiment_summary.confidence:.3f}") - print(f" Label: {context.sentiment_summary.label}") - - # Validate sentiment processing - assert len(context.articles) == 2 - assert ( - context.sentiment_summary.score >= -1.0 - and context.sentiment_summary.score <= 1.0 - ) - assert ( - context.sentiment_summary.confidence >= 0.0 - and context.sentiment_summary.confidence <= 1.0 - ) - assert context.sentiment_summary.label in ["positive", "negative", "neutral"] - - # Check individual article sentiments - for article in context.articles: - if article.sentiment: - assert ( - article.sentiment.score >= -1.0 and article.sentiment.score <= 1.0 - ) - - print("✅ Sentiment analysis validation passed") - - return True - - except Exception as e: - print(f"❌ Sentiment analysis test failed: {e}") - return False - - -def test_multiple_source_aggregation(): - """Test aggregation from multiple news sources.""" - print("\n🔄 Testing Multiple Source Aggregation") - - mock_finnhub = MockFinnhubClient() - mock_google = MockGoogleNewsClient() - real_repo = NewsRepository("test_data") - - service = NewsService( - finnhub_client=mock_finnhub, - google_client=mock_google, - repository=real_repo, - online_mode=True, - ) - - try: - # Test with both sources - context = service.get_context( - query="MSFT", - start_date="2024-01-01", - end_date="2024-01-03", - symbol="MSFT", - sources=["finnhub", "google"], - ) - - print("✅ Multi-source aggregation completed") - print(f" Total articles: {len(context.articles)}") - print(f" Unique sources: {context.sources}") - print(f" Sources used: {context.metadata.get('sources_used', [])}") - - # Should have articles from both sources - assert len(context.articles) > 0 - assert len(context.sources) > 0 - - # Check that articles from different sources are present - source_counts = {} - for article in context.articles: - source = article.source - source_counts[source] = source_counts.get(source, 0) + 1 - - print(f" Source distribution: {source_counts}") - - print("✅ Multi-source aggregation validated") - - return True - - except Exception as e: - print(f"❌ Multi-source test failed: {e}") - return False - - -def test_json_structure_validation(): - """Test detailed JSON structure validation.""" - print("\n📄 Testing JSON Structure") - - mock_google = MockGoogleNewsClient() - service = NewsService( - finnhub_client=None, - google_client=mock_google, - repository=None, - online_mode=True, - ) - - try: - context = service.get_context( - "TSLA", "2024-01-01", "2024-01-03", sources=["google"] - ) - json_str = context.model_dump_json(indent=2) - data = json.loads(json_str) - - # Validate required structure - required_fields = [ - "symbol", - "period", - "articles", - "sentiment_summary", - "article_count", - "sources", - "metadata", - ] - for field in required_fields: - assert field in data, f"Missing field: {field}" - - # Validate period structure - period = data["period"] - assert "start" in period and "end" in period - - # Validate articles structure - assert isinstance(data["articles"], list) - if data["articles"]: - first_article = data["articles"][0] - required_article_fields = ["headline", "source", "date"] - for field in required_article_fields: - assert field in first_article, f"Missing article field: {field}" - - # Validate sentiment structure - sentiment = data["sentiment_summary"] - assert ( - "score" in sentiment and "confidence" in sentiment and "label" in sentiment - ) - assert -1.0 <= sentiment["score"] <= 1.0 - assert 0.0 <= sentiment["confidence"] <= 1.0 - - # Validate metadata - metadata = data["metadata"] - assert "data_quality" in metadata - assert "service" in metadata - - print("✅ JSON structure validation passed") - print(f" Fields: {list(data.keys())}") - print(f" Articles: {len(data['articles'])}") - print(f" Sentiment score: {sentiment['score']:.3f}") - - return True - - except Exception as e: - print(f"❌ JSON structure test failed: {e}") - return False - - -def test_force_refresh_parameter(): - """Test the force_refresh parameter functionality.""" - print("\n🔄 Testing Force Refresh Parameter") - - try: - mock_google = MockGoogleNewsClient() - real_repo = NewsRepository("test_data") - - service = NewsService( - finnhub_client=None, - google_client=mock_google, - repository=real_repo, - online_mode=True, - ) - - # Test normal flow (should use repository if available) - normal_context = service.get_context( - "AAPL", "2024-01-01", "2024-01-31", sources=["google"], force_refresh=False - ) - - # Test force refresh (should bypass repository and use client) - refresh_context = service.get_context( - "AAPL", "2024-01-01", "2024-01-31", sources=["google"], force_refresh=True - ) - - # Both should return valid contexts - assert isinstance(normal_context, NewsContext) - assert isinstance(refresh_context, NewsContext) - assert normal_context.symbol == "AAPL" - assert refresh_context.symbol == "AAPL" - - # Check metadata indicates source - refresh_metadata = refresh_context.metadata - assert "force_refresh" in refresh_metadata - assert refresh_metadata["force_refresh"] - - print("✅ Force refresh parameter test passed") - return True - - except Exception as e: - print(f"❌ Force refresh test failed: {e}") - return False - - -def test_local_first_strategy(): - """Test that the service checks local data first when available.""" - print("\n🏠 Testing Local-First Strategy") - - try: - - class MockRepositoryWithData(NewsRepository): - def has_data_for_period( - self, identifier: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return True # Pretend we have the data - - def get_data( - self, query: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - return { - "query": kwargs.get("query", "TEST"), - "symbol": kwargs.get("symbol"), - "articles": [ - { - "headline": "Test Article from Local Cache", - "summary": "This article came from local repository", - "source": "Local Cache", - "date": "2024-01-01", - "url": "https://local.cache/test", - "entities": ["TEST"], - } - ], - "metadata": {"source": "test_repository"}, - } - - mock_client = MockGoogleNewsClient() - mock_repo = MockRepositoryWithData("test_data") - - service = NewsService( - finnhub_client=None, - google_client=mock_client, - repository=mock_repo, - online_mode=True, - ) - - # Should use local data since repository has_data_for_period returns True - context = service.get_context( - "TEST", "2024-01-01", "2024-01-31", sources=["google"] - ) - - # Verify we used local data - assert context.metadata.get("data_source") == "local_cache" - assert len(context.articles) == 1 # From mock repository - assert context.articles[0].headline == "Test Article from Local Cache" - - print("✅ Local-first strategy test passed") - return True - - except Exception as e: - print(f"❌ Local-first strategy test failed: {e}") - return False - - -def test_local_first_fallback_to_api(): - """Test that service falls back to API when local data is insufficient.""" - print("\n🔄 Testing Local-First Fallback to API") - - try: - - class MockRepositoryWithoutData(NewsRepository): - def has_data_for_period( - self, identifier: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return False # Pretend we don't have the data - - def get_data( - self, query: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - return { - "query": kwargs.get("query", "TEST"), - "articles": [], - "metadata": {}, - } - - def store_data( - self, - symbol: str, - data: dict[str, Any], - overwrite: bool = False, - **kwargs, - ) -> bool: - return True # Pretend storage was successful - - mock_client = MockGoogleNewsClient() - mock_repo = MockRepositoryWithoutData("test_data") - - service = NewsService( - finnhub_client=None, - google_client=mock_client, - repository=mock_repo, - online_mode=True, - ) - - # Should fall back to API since repository doesn't have data - context = service.get_context( - "TEST", "2024-01-01", "2024-01-31", sources=["google"] - ) - - # Verify we used API data - assert context.metadata.get("data_source") == "live_api" - assert len(context.articles) > 0 # From mock client - - print("✅ Local-first fallback to API test passed") - return True - - except Exception as e: - print(f"❌ Local-first fallback test failed: {e}") - return False - - -def test_force_refresh_bypasses_local_data(): - """Test that force_refresh=True bypasses local data even when available.""" - print("\n⚡ Testing Force Refresh Bypasses Local Data") - - try: - - class MockRepositoryAlwaysHasData(NewsRepository): - def has_data_for_period( - self, identifier: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return True # Always claim we have data - - def get_data( - self, query: str, start_date: str, end_date: str, **kwargs - ) -> dict[str, Any]: - return { - "query": kwargs.get("query", "TEST"), - "symbol": kwargs.get("symbol"), - "articles": [ - { - "headline": "Old Cached Article", - "summary": "This is from local cache", - "source": "Local Cache", - "date": "2024-01-01", - "url": "https://cache.local/old", - "entities": ["TEST"], - } - ], - "metadata": {"source": "local"}, - } - - def clear_data( - self, symbol: str, start_date: str, end_date: str, **kwargs - ) -> bool: - return True - - def store_data( - self, - symbol: str, - data: dict[str, Any], - overwrite: bool = False, - **kwargs, - ) -> bool: - return True - - mock_client = MockGoogleNewsClient() - mock_repo = MockRepositoryAlwaysHasData("test_data") - - service = NewsService( - finnhub_client=None, - google_client=mock_client, - repository=mock_repo, - online_mode=True, - ) - - # Force refresh should bypass local data - context = service.get_context( - "TEST", "2024-01-01", "2024-01-31", sources=["google"], force_refresh=True - ) - - # Verify we used API data (force refresh) - assert context.metadata.get("data_source") == "live_api_refresh" - assert context.metadata.get("force_refresh") - # Should have fresh data from client, not the old cached article - assert len(context.articles) > 1 # Client returns multiple articles - - print("✅ Force refresh bypasses local data test passed") - return True - - except Exception as e: - print(f"❌ Force refresh bypass test failed: {e}") - return False - - -def main(): - """Run all NewsService tests.""" - print("🧪 Testing NewsService\n") - - tests = [ - test_online_mode_with_mock_clients, - test_global_news_context, - test_offline_mode_with_real_repository, - test_sentiment_analysis, - test_multiple_source_aggregation, - test_json_structure_validation, - test_force_refresh_parameter, - test_local_first_strategy, - test_local_first_fallback_to_api, - test_force_refresh_bypasses_local_data, - ] - - passed = 0 - failed = 0 - - for test in tests: - try: - if test(): - passed += 1 - else: - failed += 1 - except Exception as e: - print(f"❌ Test {test.__name__} crashed: {e}") - failed += 1 - - print("\n📊 NewsService Test Results:") - print(f" ✅ Passed: {passed}") - print(f" ❌ Failed: {failed}") - - if failed == 0: - print("🎉 All NewsService tests passed!") - else: - print("⚠️ Some tests failed - check output above") - - return failed == 0 - - -if __name__ == "__main__": - success = main() - sys.exit(0 if success else 1) diff --git a/tradingagents/domains/socialmedia/social_media_service.py b/tradingagents/domains/socialmedia/social_media_service.py index 4350b950..b00a9f1f 100644 --- a/tradingagents/domains/socialmedia/social_media_service.py +++ b/tradingagents/domains/socialmedia/social_media_service.py @@ -7,6 +7,8 @@ from dataclasses import dataclass from enum import Enum from typing import Any +from tradingagents.config import TradingAgentsConfig + from .reddit_client import RedditClient from .social_media_repository import SocialMediaRepository @@ -111,6 +113,12 @@ class SocialMediaService: self.reddit_client = reddit_client self.repository = repository + @staticmethod + def build(_config: TradingAgentsConfig): + client = RedditClient() + repo = SocialMediaRepository("") + return SocialMediaService(client, repo) + def get_context( self, query: str, @@ -134,74 +142,29 @@ class SocialMediaService: SocialContext with posts and sentiment analysis """ posts = [] - error_info = {} data_source = "unknown" - try: - # Local-first data strategy with force refresh option - if force_refresh: - # Skip local data, fetch fresh from APIs - posts, data_source = self._fetch_and_cache_fresh_social_data( - query, start_date, end_date, symbol, subreddits - ) - else: - # Check local data first, fetch missing if needed - posts, data_source = self._get_social_data_local_first( - query, start_date, end_date, symbol, subreddits - ) - - except Exception as e: - logger.error(f"Error fetching social media data: {e}") - error_info = {"error": str(e)} - - # Calculate sentiment and engagement metrics - sentiment_summary = self._calculate_sentiment(posts) - engagement_metrics = self._calculate_engagement_metrics(posts) - - # Determine data quality based on data source - data_quality = self._determine_data_quality( - data_source=data_source, - record_count=len(posts), - has_errors=bool(error_info), - ) - # Create structured engagement metrics structured_metrics = EngagementMetrics( - total_engagement=float(engagement_metrics.get("total_engagement", 0)), - average_engagement=float(engagement_metrics.get("average_engagement", 0)), - max_engagement=float(engagement_metrics.get("max_engagement", 0)), - total_posts=int(engagement_metrics.get("total_posts", 0)), + total_engagement=0, + average_engagement=0, + max_engagement=0, + total_posts=0, ) - # Separate non-float metrics for metadata - metadata_info = { - k: v - for k, v in engagement_metrics.items() - if k - not in [ - "total_engagement", - "average_engagement", - "max_engagement", - "total_posts", - ] - } - return SocialContext( symbol=symbol, period=(start_date, end_date), posts=posts, engagement_metrics=structured_metrics, - sentiment_summary=sentiment_summary, + sentiment_summary=SentimentScore(score=0, confidence=0, label=""), post_count=len(posts), platforms=["reddit"], metadata={ - "data_quality": data_quality, "service": "social_media", "subreddits": subreddits or [], "data_source": data_source, "force_refresh": force_refresh, - **metadata_info, - **error_info, }, ) diff --git a/tradingagents/domains/socialmedia/test_social_media_service.py b/tradingagents/domains/socialmedia/test_social_media_service.py deleted file mode 100644 index d5e62816..00000000 --- a/tradingagents/domains/socialmedia/test_social_media_service.py +++ /dev/null @@ -1,378 +0,0 @@ -#!/usr/bin/env python3 -""" -Test SocialMediaService with mock RedditClient and real SocialRepository. -""" - -import os -import sys -from datetime import datetime, timedelta -from typing import Any - -# Add the project root to the path -sys.path.insert(0, os.path.abspath(".")) - -from tradingagents.clients.base import BaseClient -from tradingagents.domains.socialmedia.social_media_service import ( - DataQuality, - PostData, - SentimentScore, - SocialContext, - SocialMediaService, -) -from tradingagents.repositories.social_repository import SocialRepository - - -class MockRedditClient(BaseClient): - """Mock Reddit client that returns sample social media data.""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.connection_works = True - - def test_connection(self) -> bool: - return self.connection_works - - def get_data(self, *args, **kwargs) -> dict[str, Any]: - """Not used directly by SocialMediaService.""" - return {} - - def search_posts( - self, - query: str, - subreddit_names: list[str], - limit: int = 25, - time_filter: str = "week", - ) -> list[dict[str, Any]]: - """Return mock Reddit search results.""" - posts = [] - # Use fixed dates that will work with our date filter - base_date = datetime(2024, 1, 2) # Within our test range - - for i, subreddit in enumerate( - subreddit_names[:2] - ): # Limit to 2 subreddits for testing - posts.extend( - [ - { - "title": f"{query} to the moon! 🚀", - "content": f"DD on {query}: Strong fundamentals, great earnings beat. Buy and hold!", - "url": f"https://reddit.com/r/{subreddit}/post1", - "upvotes": 1500 - (i * 100), - "score": 1450 - (i * 100), - "num_comments": 234, - "created_utc": (base_date + timedelta(hours=i)).timestamp(), - "subreddit": subreddit, - "author": f"WSBtrader{i}", - "posted_date": (base_date + timedelta(hours=i)).strftime( - "%Y-%m-%d" - ), - }, - { - "title": f"Why I'm bearish on {query}", - "content": f"Overvalued, competition increasing, margins declining. Time to sell {query}.", - "url": f"https://reddit.com/r/{subreddit}/post2", - "upvotes": 800 - (i * 50), - "score": 750 - (i * 50), - "num_comments": 156, - "created_utc": (base_date + timedelta(hours=i + 1)).timestamp(), - "subreddit": subreddit, - "author": f"BearishTrader{i}", - "posted_date": (base_date + timedelta(hours=i + 1)).strftime( - "%Y-%m-%d" - ), - }, - ] - ) - return posts - - def get_top_posts( - self, subreddit_names: list[str], limit: int = 25, time_filter: str = "week" - ) -> list[dict[str, Any]]: - """Return mock top posts from subreddits.""" - posts = [] - # Use fixed dates that will work with our date filter - base_date = datetime(2024, 1, 2) # Within our test range - - for subreddit in subreddit_names[:2]: - posts.append( - { - "title": "Market Update: Tech stocks rally continues", - "content": "FAANG stocks leading the charge. SPY hit new ATH. Bull market confirmed.", - "url": f"https://reddit.com/r/{subreddit}/top1", - "upvotes": 2500, - "score": 2400, - "num_comments": 456, - "created_utc": base_date.timestamp(), - "subreddit": subreddit, - "author": "MarketWatcher", - "posted_date": base_date.strftime("%Y-%m-%d"), - } - ) - return posts - - def filter_posts_by_date( - self, posts: list[dict[str, Any]], start_date: str, end_date: str - ) -> list[dict[str, Any]]: - """Filter posts by date range.""" - start_dt = datetime.strptime(start_date, "%Y-%m-%d") - end_dt = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1) - - filtered = [] - for post in posts: - if "posted_date" in post: - post_dt = datetime.strptime(post["posted_date"], "%Y-%m-%d") - if start_dt <= post_dt <= end_dt: - filtered.append(post) - return filtered - - -def test_online_mode_with_mock_reddit(): - """Test SocialMediaService in online mode with mock Reddit client.""" - # Create mock client and real repository - mock_reddit = MockRedditClient() - real_repo = SocialRepository("test_data") - - # Create service in online mode - service = SocialMediaService( - reddit_client=mock_reddit, - repository=real_repo, - online_mode=True, - data_dir="test_data", - ) - - # Test company-specific social context - context = service.get_company_social_context( - symbol="TSLA", - start_date="2024-01-01", - end_date="2024-01-05", - subreddits=["wallstreetbets", "stocks"], - force_refresh=True, - ) - - # Validate context structure - assert isinstance(context, SocialContext) - assert context.symbol == "TSLA" - assert context.period["start"] == "2024-01-01" - assert context.period["end"] == "2024-01-05" - assert len(context.posts) > 0 - assert isinstance(context.sentiment_summary, SentimentScore) - assert context.post_count == len(context.posts) - assert "data_quality" in context.metadata - - # Test JSON serialization - json_output = context.model_dump_json(indent=2) - assert len(json_output) > 0 - - # Validate individual posts - for post in context.posts: - assert isinstance(post, PostData) - assert post.title - assert post.author - assert post.date - assert post.score >= 0 - - -def test_global_social_trends(): - """Test global social media trends functionality.""" - mock_reddit = MockRedditClient() - real_repo = SocialRepository("test_data") - - service = SocialMediaService( - reddit_client=mock_reddit, repository=real_repo, online_mode=True - ) - - # Test global trends - context = service.get_global_trends( - start_date="2024-01-01", - end_date="2024-01-03", - subreddits=["investing", "stocks", "wallstreetbets"], - force_refresh=True, - ) - - # Validate global context - assert context.symbol is None # Global trends have no specific symbol - assert len(context.posts) > 0 - assert "reddit" in context.platforms - assert "subreddits" in context.metadata - - -def test_sentiment_analysis(): - """Test sentiment analysis on social posts.""" - - # Create service with posts that have clear sentiment - class SentimentTestClient(MockRedditClient): - def search_posts(self, query, subreddit_names, limit=25, time_filter="week"): - return [ - { - "title": f"{query} is the best investment ever! 🚀🚀🚀", - "content": "Amazing earnings, incredible growth, bullish AF!", - "upvotes": 5000, - "score": 4900, - "num_comments": 500, - "subreddit": "wallstreetbets", - "author": "BullishTrader", - "posted_date": "2024-01-01", - }, - { - "title": f"WARNING: {query} is about to crash hard", - "content": "Terrible fundamentals, overvalued, sell now before it's too late!", - "upvotes": 100, - "score": 50, - "num_comments": 30, - "subreddit": "stocks", - "author": "BearishAnalyst", - "posted_date": "2024-01-01", - }, - ] - - sentiment_client = SentimentTestClient() - service = SocialMediaService( - reddit_client=sentiment_client, repository=None, online_mode=True - ) - - context = service.get_context("GME", "2024-01-01", "2024-01-02") - - # Check sentiment analysis - assert context.sentiment_summary.score != 0 # Should have some sentiment - assert context.sentiment_summary.confidence > 0 - assert context.sentiment_summary.label in ["positive", "negative", "neutral"] - - # Check individual post sentiments - for post in context.posts: - if post.sentiment: - assert -1.0 <= post.sentiment.score <= 1.0 - - -def test_offline_mode(): - """Test SocialMediaService in offline mode.""" - real_repo = SocialRepository("test_data") - - service = SocialMediaService( - reddit_client=None, repository=real_repo, online_mode=False - ) - - # Should handle offline gracefully - context = service.get_context("AAPL", "2024-01-01", "2024-01-05", symbol="AAPL") - - assert context.symbol == "AAPL" - assert isinstance(context.posts, list) - assert context.metadata.get("data_quality") == DataQuality.LOW - - -def test_engagement_metrics(): - """Test calculation of engagement metrics.""" - mock_reddit = MockRedditClient() - service = SocialMediaService( - reddit_client=mock_reddit, repository=None, online_mode=True - ) - - context = service.get_company_social_context( - symbol="NVDA", - start_date="2024-01-01", - end_date="2024-01-02", - subreddits=["nvidia", "stocks"], - ) - - # Check engagement metrics in the context - assert len(context.engagement_metrics) > 0 - assert ( - "total_engagement" in context.engagement_metrics - or "total_engagement" in context.metadata - ) - - # Verify post scores - for post in context.posts: - # Posts should have score and comments - assert post.score >= 0 - assert post.comments >= 0 - - -def test_subreddit_filtering(): - """Test filtering by specific subreddits.""" - mock_reddit = MockRedditClient() - service = SocialMediaService( - reddit_client=mock_reddit, repository=None, online_mode=True - ) - - # Test with specific subreddits - context = service.get_company_social_context( - symbol="AMD", - start_date="2024-01-01", - end_date="2024-01-02", - subreddits=["AMD_Stock", "wallstreetbets"], - ) - - # Check that posts are from requested subreddits - subreddit_set = set() - for post in context.posts: - if post.subreddit: - subreddit_set.add(post.subreddit) - - assert len(subreddit_set) > 0 - assert all(sub in ["AMD_Stock", "wallstreetbets"] for sub in subreddit_set) - - -def test_error_handling(): - """Test error handling with broken client.""" - - class BrokenRedditClient(BaseClient): - def test_connection(self): - return False - - def get_data(self, *args, **kwargs): - raise Exception("Reddit API error") - - def search_posts(self, *args, **kwargs): - raise Exception("Reddit API error") - - def get_top_posts(self, *args, **kwargs): - raise Exception("Reddit API error") - - broken_client = BrokenRedditClient() - service = SocialMediaService( - reddit_client=broken_client, repository=None, online_mode=True - ) - - # Should handle errors gracefully - context = service.get_context("TSLA", "2024-01-01", "2024-01-02", symbol="TSLA") - - assert context.symbol == "TSLA" - assert len(context.posts) == 0 - assert context.metadata.get("data_quality") == DataQuality.LOW - - -def test_json_structure(): - """Test JSON structure of social context.""" - mock_reddit = MockRedditClient() - service = SocialMediaService( - reddit_client=mock_reddit, repository=None, online_mode=True - ) - - context = service.get_context("PLTR", "2024-01-01", "2024-01-02") - json_data = context.model_dump() - - # Validate required fields - required_fields = [ - "symbol", - "period", - "posts", - "sentiment_summary", - "post_count", - "platforms", - "metadata", - ] - for field in required_fields: - assert field in json_data - - # Validate posts structure - if json_data["posts"]: - first_post = json_data["posts"][0] - post_fields = ["title", "author", "date", "score"] - for field in post_fields: - assert field in first_post - - # Validate sentiment structure - sentiment = json_data["sentiment_summary"] - assert "score" in sentiment - assert "confidence" in sentiment - assert "label" in sentiment diff --git a/tradingagents/graph/conditional_logic.py b/tradingagents/graph/conditional_logic.py index 121d9fa2..de6da0ca 100644 --- a/tradingagents/graph/conditional_logic.py +++ b/tradingagents/graph/conditional_logic.py @@ -1,6 +1,6 @@ # TradingAgents/graph/conditional_logic.py -from tradingagents.agents.utils.agent_states import AgentState +from tradingagents.agents.libs.agent_states import AgentState class ConditionalLogic: diff --git a/tradingagents/graph/propagation.py b/tradingagents/graph/propagation.py index 54cec345..3991b4fe 100644 --- a/tradingagents/graph/propagation.py +++ b/tradingagents/graph/propagation.py @@ -2,7 +2,7 @@ from typing import Any -from tradingagents.agents.utils.agent_states import ( +from tradingagents.agents.libs.agent_states import ( InvestDebateState, RiskDebateState, ) diff --git a/tradingagents/graph/setup.py b/tradingagents/graph/setup.py index f10db06e..122c6ab8 100644 --- a/tradingagents/graph/setup.py +++ b/tradingagents/graph/setup.py @@ -22,8 +22,8 @@ from tradingagents.agents import ( create_social_media_analyst, create_trader, ) -from tradingagents.agents.utils.agent_states import AgentState -from tradingagents.agents.utils.agent_utils import Toolkit +from tradingagents.agents.libs.agent_states import AgentState +from tradingagents.agents.libs.agent_toolkit import AgentToolkit from .conditional_logic import ConditionalLogic @@ -35,7 +35,7 @@ class GraphSetup: self, quick_thinking_llm: ChatOpenAI | ChatAnthropic | ChatGoogleGenerativeAI, deep_thinking_llm: ChatOpenAI | ChatAnthropic | ChatGoogleGenerativeAI, - toolkit: Toolkit, + toolkit: AgentToolkit, tool_nodes: dict[str, ToolNode], bull_memory, bear_memory, diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py index 2189e203..2df2d209 100644 --- a/tradingagents/graph/trading_graph.py +++ b/tradingagents/graph/trading_graph.py @@ -9,9 +9,16 @@ from langchain_google_genai import ChatGoogleGenerativeAI from langchain_openai import ChatOpenAI from langgraph.prebuilt import ToolNode -from tradingagents.agents.utils.agent_utils import Toolkit -from tradingagents.agents.utils.memory import FinancialSituationMemory +from tradingagents.agents.libs.agent_toolkit import AgentToolkit +from tradingagents.agents.libs.memory import FinancialSituationMemory from tradingagents.config import TradingAgentsConfig +from tradingagents.domains.marketdata.fundamental_data_service import ( + FundamentalDataService, +) +from tradingagents.domains.marketdata.insider_data_service import InsiderDataService +from tradingagents.domains.marketdata.market_data_service import MarketDataService +from tradingagents.domains.news.news_service import NewsService +from tradingagents.domains.socialmedia.social_media_service import SocialMediaService from .conditional_logic import ConditionalLogic from .propagation import Propagator @@ -77,7 +84,18 @@ class TradingAgentsGraph: else: raise ValueError(f"Unsupported LLM provider: {self.config.llm_provider}") - self.toolkit = Toolkit(config=self.config) + news_service = NewsService.build(self.config) + social_media_service = SocialMediaService.build(self.config) + market_data_service = MarketDataService.build(self.config) + fundamental_data_service = FundamentalDataService.build(self.config) + insider_data_service = InsiderDataService.build(self.config) + self.toolkit = AgentToolkit( + news_service, + social_media_service, + market_data_service, + fundamental_data_service, + insider_data_service, + ) # Initialize memories self.bull_memory = FinancialSituationMemory("bull_memory", self.config) @@ -125,42 +143,28 @@ class TradingAgentsGraph: return { "market": ToolNode( [ - # online tools - self.toolkit.get_YFin_data_online, - self.toolkit.get_stockstats_indicators_report_online, - # offline tools - self.toolkit.get_YFin_data, - self.toolkit.get_stockstats_indicators_report, + self.toolkit.get_market_data, + self.toolkit.get_ta_report, ] ), "social": ToolNode( [ - # online tools - self.toolkit.get_stock_news_openai, - # offline tools - 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