diff --git a/.env.example b/.env.example index a435e61..1d037a6 100644 --- a/.env.example +++ b/.env.example @@ -1,5 +1,5 @@ OPENAI_API_KEY=your_openai_api_key_here -FIRECRAWL_API_KEY=your_firecrawl_api_key_here +# Optional but recommended ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key_here FINANCIAL_DATA_API_KEY=your_financial_data_api_key_here @@ -9,4 +9,4 @@ ANALYSIS_TIMEOUT_SECONDS=30 NEWS_SOURCES=bloomberg,reuters,yahoo-finance,marketwatch,cnbc ANALYSIS_DEPTH=detailed -DEFAULT_PORTFOLIO_SIZE=100000 \ No newline at end of file +DEFAULT_PORTFOLIO_SIZE=100000 diff --git a/README.md b/README.md index 8f0b262..5ac7f08 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,11 @@ # TradeGraph Financial Advisor -A sophisticated multi-agent financial analysis system that uses **LangGraph**, **Firecrawl**, and **MCP (Model Context Protocol)** to provide intelligent trading recommendations based on real-time financial news and comprehensive company analysis. +A sophisticated multi-agent financial analysis system that uses **LangGraph**, **ddgs**, and **Crawl4AI** to provide intelligent trading recommendations based on real-time financial news and comprehensive company analysis. ## šŸš€ Features - **Multi-Agent Architecture**: Coordinated agents for news analysis, financial data processing, and report analysis -- **Real-Time News Analysis**: Scrapes and analyzes financial news from multiple sources using Firecrawl - **SEC Filing Analysis**: Deep analysis of 10-K and 10-Q reports using AI - **Technical Analysis**: Comprehensive technical indicators and chart pattern recognition - **Sentiment Analysis**: AI-powered sentiment analysis of news and social media @@ -21,7 +20,6 @@ A sophisticated multi-agent financial analysis system that uses **LangGraph**, * - Python 3.10+ - OpenAI API key -- Firecrawl API key - Optional: Alpha Vantage API key for enhanced financial data ## šŸ› ļø Installation @@ -69,8 +67,6 @@ Required environment variables: ```env OPENAI_API_KEY=your_openai_api_key_here -FIRECRAWL_API_KEY=your_firecrawl_api_key_here - # Optional but recommended ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key_here FINANCIAL_DATA_API_KEY=your_financial_data_api_key_here @@ -287,7 +283,6 @@ GOOGL: HOLD (Confidence: 65.0%) ### Technology Stack - **LangGraph**: Workflow orchestration and agent coordination -- **Firecrawl**: Web scraping and content extraction - **OpenAI GPT-4**: Natural language processing and analysis - **yfinance**: Financial data retrieval - **pandas/numpy**: Data processing and analysis @@ -400,7 +395,6 @@ pre-commit run --all-files ### Rate Limits - **OpenAI API**: Monitor usage to avoid rate limits -- **Firecrawl**: Respect rate limits (typically 100-1000 requests/hour) - **Financial APIs**: Most have daily/monthly limits ### Data Accuracy @@ -433,7 +427,6 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file ## šŸ™ Acknowledgments - **LangGraph** team for the excellent multi-agent framework -- **Firecrawl** for reliable web scraping capabilities - **OpenAI** for powerful language models - **Financial data providers** for market data access diff --git a/api/main.py b/api/main.py index 3467aaa..40c13e6 100644 --- a/api/main.py +++ b/api/main.py @@ -1,13 +1,11 @@ -import asyncio import logging from contextlib import asynccontextmanager from datetime import datetime -from typing import Dict, Any, List +from typing import Dict, Any from fastapi import ( FastAPI, HTTPException, - BackgroundTasks, WebSocket, WebSocketDisconnect, ) diff --git a/api/models.py b/api/models.py index 8452e70..78567f2 100644 --- a/api/models.py +++ b/api/models.py @@ -1,5 +1,5 @@ from datetime import datetime -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field from enum import Enum @@ -271,4 +271,4 @@ class APIMetrics(BaseModel): total_alerts: int = 0 active_websocket_connections: int = 0 system_metrics: Optional[SystemMetrics] = None - last_updated: datetime = Field(default_factory=datetime.now) \ No newline at end of file + last_updated: datetime = Field(default_factory=datetime.now) diff --git a/api/routers/analysis.py b/api/routers/analysis.py index 12a99b7..e4443d9 100644 --- a/api/routers/analysis.py +++ b/api/routers/analysis.py @@ -1,7 +1,7 @@ import asyncio import uuid from datetime import datetime -from typing import Dict, Any, Optional +from typing import Dict, Optional from fastapi import APIRouter, HTTPException, BackgroundTasks, Depends from fastapi.responses import StreamingResponse @@ -12,8 +12,6 @@ QuickAnalysisRequest, AnalysisResult, AnalysisStatus, - RecommendationResponse, - PortfolioResponse, ) from ..auth.dependencies import get_current_user diff --git a/api/routers/health.py b/api/routers/health.py index 6a0f131..baff9ef 100644 --- a/api/routers/health.py +++ b/api/routers/health.py @@ -1,8 +1,8 @@ import asyncio import time import psutil -from datetime import datetime, timedelta -from typing import Dict, Any +from datetime import datetime +from typing import Dict from fastapi import APIRouter, HTTPException @@ -22,9 +22,9 @@ async def health_check(): data={ "status": "healthy", "timestamp": datetime.now().isoformat(), - "uptime_seconds": time.time() - startup_time + "uptime_seconds": time.time() - startup_time, }, - message="Service is healthy" + message="Service is healthy", ) @@ -38,7 +38,7 @@ async def detailed_health_check(): # Get system metrics cpu_percent = psutil.cpu_percent(interval=1) memory = psutil.virtual_memory() - disk = psutil.disk_usage('/') + disk = psutil.disk_usage("/") # Test critical services services_status = await test_services() @@ -56,28 +56,23 @@ async def detailed_health_check(): "memory": { "total_gb": round(memory.total / (1024**3), 2), "available_gb": round(memory.available / (1024**3), 2), - "used_percent": memory.percent + "used_percent": memory.percent, }, "disk": { "total_gb": round(disk.total / (1024**3), 2), "free_gb": round(disk.free / (1024**3), 2), - "used_percent": round((disk.used / disk.total) * 100, 1) - } - } + "used_percent": round((disk.used / disk.total) * 100, 1), + }, + }, ) return APIResponse( - success=True, - data=health_data.dict(), - message="Detailed health check completed" + success=True, data=health_data.dict(), message="Detailed health check completed" ) except Exception as e: return APIResponse( - success=False, - data=None, - message="Health check failed", - error={"detail": str(e)} + success=False, data=None, message="Health check failed", error={"detail": str(e)} ) @@ -97,7 +92,8 @@ async def test_services() -> Dict[str, str]: try: # Test if we can import and initialize core components from tradegraph_financial_advisor import FinancialAdvisor - advisor = FinancialAdvisor() + + FinancialAdvisor() services["financial_advisor"] = "healthy" except Exception: services["financial_advisor"] = "unhealthy" @@ -105,6 +101,7 @@ async def test_services() -> Dict[str, str]: # Test OpenAI connectivity (simulated) try: import os + if os.getenv("OPENAI_API_KEY"): services["openai_api"] = "configured" else: @@ -112,16 +109,6 @@ async def test_services() -> Dict[str, str]: except Exception: services["openai_api"] = "error" - # Test Firecrawl connectivity (simulated) - try: - import os - if os.getenv("FIRECRAWL_API_KEY"): - services["firecrawl_api"] = "configured" - else: - services["firecrawl_api"] = "not_configured" - except Exception: - services["firecrawl_api"] = "error" - return services @@ -137,7 +124,7 @@ async def get_system_metrics(): memory = psutil.virtual_memory() # Disk metrics - disk = psutil.disk_usage('/') + disk = psutil.disk_usage("/") # Network metrics (if available) try: @@ -146,7 +133,7 @@ async def get_system_metrics(): "bytes_sent": network.bytes_sent, "bytes_recv": network.bytes_recv, "packets_sent": network.packets_sent, - "packets_recv": network.packets_recv + "packets_recv": network.packets_recv, } except Exception: network_stats = {} @@ -162,7 +149,7 @@ async def get_system_metrics(): total_requests=0, # Would be tracked by middleware average_response_time=0.0, # Would be tracked by middleware error_rate=0.0, # Would be tracked by middleware - uptime_seconds=time.time() - startup_time + uptime_seconds=time.time() - startup_time, ) detailed_metrics = { @@ -171,36 +158,36 @@ async def get_system_metrics(): "cpu": { "usage_percent": cpu_percent, "core_count": cpu_count, - "load_average": list(psutil.getloadavg()) if hasattr(psutil, 'getloadavg') else [] + "load_average": ( + list(psutil.getloadavg()) if hasattr(psutil, "getloadavg") else [] + ), }, "memory": { "total_gb": round(memory.total / (1024**3), 2), "available_gb": round(memory.available / (1024**3), 2), "used_gb": round(memory.used / (1024**3), 2), "used_percent": memory.percent, - "cached_gb": round(getattr(memory, 'cached', 0) / (1024**3), 2) + "cached_gb": round(getattr(memory, "cached", 0) / (1024**3), 2), }, "disk": { "total_gb": round(disk.total / (1024**3), 2), "free_gb": round(disk.free / (1024**3), 2), "used_gb": round(disk.used / (1024**3), 2), - "used_percent": round((disk.used / disk.total) * 100, 1) + "used_percent": round((disk.used / disk.total) * 100, 1), }, "process": { "memory_rss_mb": round(process_memory.rss / (1024**2), 2), "memory_vms_mb": round(process_memory.vms / (1024**2), 2), "cpu_percent": process.cpu_percent(), "num_threads": process.num_threads(), - "create_time": datetime.fromtimestamp(process.create_time()).isoformat() + "create_time": datetime.fromtimestamp(process.create_time()).isoformat(), }, - "network": network_stats - } + "network": network_stats, + }, } return APIResponse( - success=True, - data=detailed_metrics, - message="System metrics retrieved successfully" + success=True, data=detailed_metrics, message="System metrics retrieved successfully" ) except Exception as e: @@ -216,30 +203,24 @@ async def readiness_check(): # Service is ready if core components are healthy core_services = ["financial_advisor"] - is_ready = all( - services_status.get(service) == "healthy" - for service in core_services - ) + is_ready = all(services_status.get(service) == "healthy" for service in core_services) if is_ready: return APIResponse( success=True, data={"status": "ready", "services": services_status}, - message="Service is ready" + message="Service is ready", ) else: return APIResponse( success=False, data={"status": "not_ready", "services": services_status}, - message="Service is not ready" + message="Service is not ready", ) except Exception as e: return APIResponse( - success=False, - data=None, - message="Readiness check failed", - error={"detail": str(e)} + success=False, data=None, message="Readiness check failed", error={"detail": str(e)} ) @@ -259,17 +240,14 @@ async def liveness_check(): data={ "status": "alive" if is_alive else "dead", "uptime_seconds": uptime_seconds, - "current_time": current_time.isoformat() + "current_time": current_time.isoformat(), }, - message="Liveness check completed" + message="Liveness check completed", ) except Exception as e: return APIResponse( - success=False, - data=None, - message="Liveness check failed", - error={"detail": str(e)} + success=False, data=None, message="Liveness check failed", error={"detail": str(e)} ) @@ -282,31 +260,37 @@ async def check_dependencies(): # Check Python packages try: import pkg_resources + installed_packages = {pkg.key: pkg.version for pkg in pkg_resources.working_set} required_packages = [ - "fastapi", "uvicorn", "pydantic", "langchain", "langgraph", - "firecrawl-py", "yfinance", "pandas", "numpy", "aiohttp" + "fastapi", + "uvicorn", + "pydantic", + "langchain", + "langgraph", + "yfinance", + "pandas", + "numpy", + "aiohttp", ] for package in required_packages: if package in installed_packages: dependencies[f"python_{package}"] = { "status": "installed", - "version": installed_packages[package] + "version": installed_packages[package], } else: - dependencies[f"python_{package}"] = { - "status": "missing", - "version": None - } + dependencies[f"python_{package}"] = {"status": "missing", "version": None} except Exception as e: dependencies["python_packages"] = {"status": "error", "error": str(e)} # Check environment variables import os - env_vars = ["OPENAI_API_KEY", "FIRECRAWL_API_KEY"] + + env_vars = ["OPENAI_API_KEY"] for var in env_vars: dependencies[f"env_{var.lower()}"] = { "status": "configured" if os.getenv(var) else "missing" @@ -314,15 +298,10 @@ async def check_dependencies(): # Check external services (simplified) dependencies["external_apis"] = { - "openai": "configured" if os.getenv("OPENAI_API_KEY") else "not_configured", - "firecrawl": "configured" if os.getenv("FIRECRAWL_API_KEY") else "not_configured" + "openai": "configured" if os.getenv("OPENAI_API_KEY") else "not_configured" } - return APIResponse( - success=True, - data=dependencies, - message="Dependencies check completed" - ) + return APIResponse(success=True, data=dependencies, message="Dependencies check completed") except Exception as e: raise HTTPException(status_code=500, detail=f"Failed to check dependencies: {str(e)}") @@ -337,5 +316,5 @@ async def toggle_maintenance_mode(): return APIResponse( success=True, data={"maintenance_mode": False, "message": "Maintenance mode not implemented"}, - message="Maintenance mode toggle completed" - ) \ No newline at end of file + message="Maintenance mode toggle completed", + ) diff --git a/api/websocket_manager.py b/api/websocket_manager.py index b81facb..f62c21d 100644 --- a/api/websocket_manager.py +++ b/api/websocket_manager.py @@ -3,7 +3,7 @@ import logging from datetime import datetime from typing import Dict, List, Set, Any, Optional -from fastapi import WebSocket, WebSocketDisconnect +from fastapi import WebSocket logger = logging.getLogger(__name__) @@ -339,4 +339,4 @@ async def handle_client_message(self, websocket: WebSocket, message: str): "type": "error", "message": "Error processing message", "timestamp": datetime.now().isoformat() - }, websocket) \ No newline at end of file + }, websocket) diff --git a/docs/index.md b/docs/index.md index abb1d18..a9bf0ed 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,7 +6,7 @@ Status -A sophisticated **multi-agent financial analysis system** that uses LangGraph, Firecrawl, and MCP (Model Context Protocol) to provide intelligent trading recommendations based on real-time financial news and comprehensive company analysis. +A sophisticated **multi-agent financial analysis system** that uses LangGraph, ddgs, and Crawl4AI to provide intelligent trading recommendations based on real-time financial news and comprehensive company analysis. ## šŸš€ Key Features @@ -56,7 +56,6 @@ cp .env.example .env # Add your API keys OPENAI_API_KEY=your_openai_key -FIRECRAWL_API_KEY=your_firecrawl_key ``` ### Basic Usage @@ -220,4 +219,4 @@ This project is licensed under the MIT License - see the [LICENSE](https://githu
Built with ā¤ļø by the TradeGraph Team -
\ No newline at end of file + diff --git a/examples/advanced_usage.py b/examples/advanced_usage.py index 783e699..7652d2b 100644 --- a/examples/advanced_usage.py +++ b/examples/advanced_usage.py @@ -5,21 +5,25 @@ import asyncio import json -from datetime import datetime, timedelta -from typing import List, Dict, Any +from datetime import datetime from tradegraph_financial_advisor import FinancialAdvisor -from tradegraph_financial_advisor.workflows.analysis_workflow import FinancialAnalysisWorkflow +from tradegraph_financial_advisor.workflows.analysis_workflow import ( + FinancialAnalysisWorkflow, +) from tradegraph_financial_advisor.agents.news_agent import NewsReaderAgent from tradegraph_financial_advisor.agents.financial_agent import FinancialAnalysisAgent -from tradegraph_financial_advisor.agents.report_analysis_agent import ReportAnalysisAgent -from tradegraph_financial_advisor.agents.recommendation_engine import TradingRecommendationEngine -from tradegraph_financial_advisor.services.firecrawl_service import FirecrawlService +from tradegraph_financial_advisor.agents.report_analysis_agent import ( + ReportAnalysisAgent, +) +from tradegraph_financial_advisor.agents.recommendation_engine import ( + TradingRecommendationEngine, +) from tradegraph_financial_advisor.utils.helpers import ( save_analysis_results, load_analysis_results, calculate_portfolio_metrics, - PerformanceTimer + PerformanceTimer, ) @@ -45,11 +49,7 @@ async def individual_agents_example(): # 1. Collect news data print("šŸ“° Collecting news data...") - news_input = { - "symbols": symbols, - "timeframe_hours": 48, - "max_articles": 20 - } + news_input = {"symbols": symbols, "timeframe_hours": 48, "max_articles": 20} news_results = await news_agent.execute(news_input) print(f"Found {news_results.get('total_count', 0)} news articles") @@ -59,7 +59,7 @@ async def individual_agents_example(): "symbols": symbols, "include_financials": True, "include_technical": True, - "include_market_data": True + "include_market_data": True, } financial_results = await financial_agent.execute(financial_input) print("Financial analysis completed") @@ -69,7 +69,7 @@ async def individual_agents_example(): report_input = { "symbols": symbols, "report_types": ["10-K"], - "analysis_depth": "standard" + "analysis_depth": "standard", } report_results = await report_agent.execute(report_input) print("Report analysis completed") @@ -79,7 +79,7 @@ async def individual_agents_example(): "news_analysis": news_results, "financial_analysis": financial_results, "report_analysis": report_results, - "timestamp": datetime.now().isoformat() + "timestamp": datetime.now().isoformat(), } return combined_results @@ -115,7 +115,7 @@ async def custom_workflow_example(): symbols=symbols, portfolio_size=150000, risk_tolerance="aggressive", - time_horizon="short_term" + time_horizon="short_term", ) # Enhance with custom recommendation logic @@ -132,11 +132,13 @@ async def custom_workflow_example(): # Generate enhanced recommendations await recommendation_engine.start() - enhanced_results = await recommendation_engine.execute({ - "analysis_contexts": analysis_contexts, - "portfolio_constraints": {"portfolio_size": 150000, "max_positions": 5}, - "risk_preferences": {"risk_tolerance": "aggressive"} - }) + enhanced_results = await recommendation_engine.execute( + { + "analysis_contexts": analysis_contexts, + "portfolio_constraints": {"portfolio_size": 150000, "max_positions": 5}, + "risk_preferences": {"risk_tolerance": "aggressive"}, + } + ) await recommendation_engine.stop() combined_results = { @@ -144,8 +146,11 @@ async def custom_workflow_example(): "enhanced_recommendations": enhanced_results, "custom_workflow_metadata": { "workflow_type": "custom_enhanced", - "enhancement_features": ["custom_risk_analysis", "enhanced_position_sizing"] - } + "enhancement_features": [ + "custom_risk_analysis", + "enhanced_position_sizing", + ], + }, } return combined_results @@ -155,70 +160,6 @@ async def custom_workflow_example(): return None -async def firecrawl_integration_example(): - """ - Example: Advanced Firecrawl usage for custom data collection - """ - print("šŸ”„ Advanced Firecrawl Integration Example") - print("=" * 50) - - firecrawl_service = FirecrawlService() - - try: - await firecrawl_service.start() - - # 1. Scrape specific financial websites - print("Scraping financial news websites...") - symbols = ["AAPL"] - - news_articles = await firecrawl_service.scrape_news_websites( - symbols=symbols, - max_articles_per_source=5 - ) - - print(f"Scraped {len(news_articles)} articles from financial websites") - - # 2. Scrape SEC filings - print("Scraping SEC filings...") - sec_filings = await firecrawl_service.scrape_financial_reports( - company_symbol="AAPL", - report_type="10-K" - ) - - print(f"Found {len(sec_filings)} SEC filings") - - # 3. Custom URL scraping - print("Scraping custom financial URLs...") - custom_urls = [ - "https://www.sec.gov/cgi-bin/browse-edgar?CIK=AAPL&type=10-K&dateb=&owner=include&count=5" - ] - - custom_results = await firecrawl_service.scrape_multiple_urls( - urls=custom_urls, - options={"onlyMainContent": True, "includeTags": ["div", "p", "table"]} - ) - - results = { - "news_articles": [article.dict() for article in news_articles], - "sec_filings": sec_filings, - "custom_scraping": custom_results, - "scraping_metadata": { - "total_articles": len(news_articles), - "total_filings": len(sec_filings), - "total_custom_pages": len(custom_results) - } - } - - return results - - except Exception as e: - print(f"āŒ Firecrawl integration example failed: {str(e)}") - return None - - finally: - await firecrawl_service.stop() - - async def portfolio_optimization_example(): """ Example: Advanced portfolio optimization with constraints @@ -234,20 +175,20 @@ async def portfolio_optimization_example(): "symbols": ["TSLA", "NVDA", "AMD", "SQ", "PLTR"], "portfolio_size": 100000, "risk_tolerance": "aggressive", - "time_horizon": "medium_term" + "time_horizon": "medium_term", }, "value_portfolio": { "symbols": ["BRK-B", "JPM", "JNJ", "PG", "WMT"], "portfolio_size": 100000, "risk_tolerance": "conservative", - "time_horizon": "long_term" + "time_horizon": "long_term", }, "tech_portfolio": { "symbols": ["AAPL", "MSFT", "GOOGL", "AMZN", "META"], "portfolio_size": 100000, "risk_tolerance": "medium", - "time_horizon": "medium_term" - } + "time_horizon": "medium_term", + }, } optimization_results = {} @@ -262,16 +203,24 @@ async def portfolio_optimization_example(): # Calculate additional metrics portfolio_rec = results.get("portfolio_recommendation") if portfolio_rec and portfolio_rec.get("recommendations"): - metrics = calculate_portfolio_metrics(portfolio_rec["recommendations"]) + metrics = calculate_portfolio_metrics( + portfolio_rec["recommendations"] + ) results["portfolio_metrics"] = metrics optimization_results[scenario_name] = results # Print summary if portfolio_rec: - print(f" āœ… {scenario_name}: {len(portfolio_rec['recommendations'])} recommendations") - print(f" Confidence: {portfolio_rec.get('total_confidence', 0):.1%}") - print(f" Risk Level: {portfolio_rec.get('overall_risk_level', 'unknown')}") + print( + f" āœ… {scenario_name}: {len(portfolio_rec['recommendations'])} recommendations" + ) + print( + f" Confidence: {portfolio_rec.get('total_confidence', 0):.1%}" + ) + print( + f" Risk Level: {portfolio_rec.get('overall_risk_level', 'unknown')}" + ) except Exception as e: print(f" āŒ {scenario_name} optimization failed: {str(e)}") @@ -282,7 +231,9 @@ async def portfolio_optimization_example(): portfolio_rec = results.get("portfolio_recommendation", {}) confidence = portfolio_rec.get("total_confidence", 0) risk = portfolio_rec.get("overall_risk_level", "unknown") - print(f" {scenario_name:20} | Confidence: {confidence:>6.1%} | Risk: {risk:>12}") + print( + f" {scenario_name:20} | Confidence: {confidence:>6.1%} | Risk: {risk:>12}" + ) return optimization_results @@ -302,7 +253,7 @@ async def real_time_monitoring_example(): monitoring_results = { "monitoring_start": datetime.now().isoformat(), "portfolio_symbols": portfolio_symbols, - "monitoring_cycles": [] + "monitoring_cycles": [], } print(f"Monitoring portfolio: {', '.join(portfolio_symbols)}") @@ -319,8 +270,7 @@ async def real_time_monitoring_example(): # Quick analysis for current state quick_analysis = await advisor.quick_analysis( - symbols=portfolio_symbols, - analysis_type="basic" + symbols=portfolio_symbols, analysis_type="basic" ) cycle_data = { @@ -328,7 +278,7 @@ async def real_time_monitoring_example(): "timestamp": cycle_start.isoformat(), "alerts": alerts, "quick_analysis": quick_analysis, - "cycle_duration": (datetime.now() - cycle_start).total_seconds() + "cycle_duration": (datetime.now() - cycle_start).total_seconds(), } monitoring_results["monitoring_cycles"].append(cycle_data) @@ -336,8 +286,11 @@ async def real_time_monitoring_example(): # Print cycle summary print(f" Alerts generated: {len(alerts)}") if quick_analysis.get("recommendations"): - buy_signals = sum(1 for rec in quick_analysis["recommendations"] - if rec.get("recommendation") in ["buy", "strong_buy"]) + buy_signals = sum( + 1 + for rec in quick_analysis["recommendations"] + if rec.get("recommendation") in ["buy", "strong_buy"] + ) print(f" Buy signals: {buy_signals}/{len(portfolio_symbols)}") except Exception as e: @@ -350,11 +303,13 @@ async def real_time_monitoring_example(): monitoring_results["monitoring_end"] = datetime.now().isoformat() monitoring_results["total_duration"] = ( - datetime.fromisoformat(monitoring_results["monitoring_end"]) - - datetime.fromisoformat(monitoring_results["monitoring_start"]) + datetime.fromisoformat(monitoring_results["monitoring_end"]) + - datetime.fromisoformat(monitoring_results["monitoring_start"]) ).total_seconds() - print(f"\nāœ… Monitoring completed in {monitoring_results['total_duration']:.1f} seconds") + print( + f"\nāœ… Monitoring completed in {monitoring_results['total_duration']:.1f} seconds" + ) return monitoring_results @@ -374,9 +329,7 @@ async def data_export_import_example(): # 1. Generate analysis print("Generating analysis for export...") results = await advisor.analyze_portfolio( - symbols=symbols, - portfolio_size=75000, - risk_tolerance="medium" + symbols=symbols, portfolio_size=75000, risk_tolerance="medium" ) # 2. Save results to file @@ -389,8 +342,12 @@ async def data_export_import_example(): loaded_results = load_analysis_results(saved_file) # 4. Verify data integrity - original_timestamp = results.get("analysis_summary", {}).get("analysis_timestamp") - loaded_timestamp = loaded_results.get("analysis_summary", {}).get("analysis_timestamp") + original_timestamp = results.get("analysis_summary", {}).get( + "analysis_timestamp" + ) + loaded_timestamp = loaded_results.get("analysis_summary", {}).get( + "analysis_timestamp" + ) if original_timestamp == loaded_timestamp: print("āœ… Data integrity verified - timestamps match") @@ -403,30 +360,34 @@ async def data_export_import_example(): "export_metadata": { "export_time": datetime.now().isoformat(), "export_format": "tradegraph_v1", - "symbols": symbols + "symbols": symbols, }, "recommendations_summary": [], - "portfolio_metrics": {} + "portfolio_metrics": {}, } # Extract key information portfolio_rec = loaded_results.get("portfolio_recommendation", {}) if portfolio_rec.get("recommendations"): for rec in portfolio_rec["recommendations"]: - custom_export["recommendations_summary"].append({ - "symbol": rec.get("symbol"), - "action": rec.get("recommendation"), - "confidence": rec.get("confidence_score"), - "allocation": rec.get("recommended_allocation") - }) + custom_export["recommendations_summary"].append( + { + "symbol": rec.get("symbol"), + "action": rec.get("recommendation"), + "confidence": rec.get("confidence_score"), + "allocation": rec.get("recommended_allocation"), + } + ) custom_export["portfolio_metrics"] = calculate_portfolio_metrics( portfolio_rec.get("recommendations", []) ) # Save custom export - custom_filename = f"custom_export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - with open(f"results/{custom_filename}", 'w') as f: + custom_filename = ( + f"custom_export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + ) + with open(f"results/{custom_filename}", "w") as f: json.dump(custom_export, f, indent=2, default=str) print(f"Custom export saved to: results/{custom_filename}") @@ -435,7 +396,7 @@ async def data_export_import_example(): "original_results": results, "loaded_results": loaded_results, "custom_export": custom_export, - "files_created": [saved_file, f"results/{custom_filename}"] + "files_created": [saved_file, f"results/{custom_filename}"], } except Exception as e: @@ -457,18 +418,14 @@ async def error_handling_example(): { "name": "Invalid Symbols", "symbols": ["INVALID", "BADTICKER", "NOTREAL"], - "should_fail": True - }, - { - "name": "Valid Symbols", - "symbols": ["AAPL", "MSFT"], - "should_fail": False + "should_fail": True, }, + {"name": "Valid Symbols", "symbols": ["AAPL", "MSFT"], "should_fail": False}, { "name": "Mixed Valid/Invalid", "symbols": ["AAPL", "INVALID", "MSFT"], - "should_fail": False # Should handle gracefully - } + "should_fail": False, # Should handle gracefully + }, ] error_handling_results = {} @@ -484,8 +441,7 @@ async def error_handling_example(): try: # Try quick analysis (more likely to succeed even with some invalid symbols) results = await advisor.quick_analysis( - symbols=symbols, - analysis_type="basic" + symbols=symbols, analysis_type="basic" ) if should_fail: @@ -498,7 +454,7 @@ async def error_handling_example(): error_handling_results[scenario_name] = { "status": status, "results": results, - "error": None + "error": None, } except Exception as e: @@ -512,11 +468,11 @@ async def error_handling_example(): error_handling_results[scenario_name] = { "status": status, "results": None, - "error": str(e) + "error": str(e), } # Summary - print(f"\nšŸ“Š Error Handling Summary:") + print("\nšŸ“Š Error Handling Summary:") for scenario_name, result in error_handling_results.items(): status = result["status"] print(f" {scenario_name:20} | Status: {status}") @@ -535,7 +491,6 @@ async def main(): examples = [ ("Individual Agents Usage", individual_agents_example), ("Custom Workflow Creation", custom_workflow_example), - ("Advanced Firecrawl Integration", firecrawl_integration_example), ("Portfolio Optimization", portfolio_optimization_example), ("Real-time Monitoring", real_time_monitoring_example), ("Data Export/Import", data_export_import_example), @@ -573,14 +528,14 @@ async def main(): "session_metadata": { "session_start": datetime.now().isoformat(), "examples_run": list(all_results.keys()), - "total_examples": len(examples) + "total_examples": len(examples), }, - "example_results": all_results + "example_results": all_results, } results_file = save_analysis_results( comprehensive_results, - f"advanced_examples_session_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + f"advanced_examples_session_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", ) print(f"šŸ“ Comprehensive results saved to: {results_file}") @@ -590,7 +545,6 @@ async def main(): print("\nšŸŽ“ Advanced Examples Complete!") print("These examples demonstrate:") print("- Using individual agents for custom workflows") - print("- Advanced Firecrawl integration") print("- Portfolio optimization strategies") print("- Real-time monitoring capabilities") print("- Data persistence and export features") @@ -598,4 +552,4 @@ async def main(): if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file + asyncio.run(main()) diff --git a/examples/basic_usage.py b/examples/basic_usage.py index cff715b..7832827 100644 --- a/examples/basic_usage.py +++ b/examples/basic_usage.py @@ -28,7 +28,7 @@ async def basic_analysis_example(): portfolio_size=100000, # $100k portfolio risk_tolerance="medium", time_horizon="medium_term", - include_reports=False # Skip SEC filings for faster analysis + include_reports=False, # Skip SEC filings for faster analysis ) # Print results to console @@ -37,7 +37,7 @@ async def basic_analysis_example(): # You can also access individual components: portfolio_rec = results.get("portfolio_recommendation") if portfolio_rec: - print(f"\nšŸ“Š Portfolio Summary:") + print("\nšŸ“Š Portfolio Summary:") print(f"Total Confidence: {portfolio_rec['total_confidence']:.1%}") print(f"Number of Recommendations: {len(portfolio_rec['recommendations'])}") @@ -62,10 +62,7 @@ async def quick_analysis_example(): symbols = ["TSLA", "NVDA", "AMD"] try: - results = await advisor.quick_analysis( - symbols=symbols, - analysis_type="basic" - ) + results = await advisor.quick_analysis(symbols=symbols, analysis_type="basic") print(f"Quick analysis completed for: {', '.join(symbols)}") @@ -125,7 +122,7 @@ async def portfolio_comparison_example(): portfolios = { "Tech Focus": ["AAPL", "MSFT", "GOOGL", "NVDA"], "Diversified": ["AAPL", "JPM", "JNJ", "XOM"], - "Growth": ["TSLA", "NVDA", "AMD", "SQ"] + "Growth": ["TSLA", "NVDA", "AMD", "SQ"], } results = {} @@ -135,17 +132,21 @@ async def portfolio_comparison_example(): try: result = await advisor.quick_analysis( - symbols=symbols, - analysis_type="standard" + symbols=symbols, analysis_type="standard" ) results[portfolio_name] = result # Quick summary if "recommendations" in result: - buy_count = sum(1 for rec in result["recommendations"] - if rec.get("recommendation") in ["buy", "strong_buy"]) - print(f" {portfolio_name}: {buy_count}/{len(symbols)} BUY recommendations") + buy_count = sum( + 1 + for rec in result["recommendations"] + if rec.get("recommendation") in ["buy", "strong_buy"] + ) + print( + f" {portfolio_name}: {buy_count}/{len(symbols)} BUY recommendations" + ) except Exception as e: print(f" āŒ {portfolio_name} analysis failed: {str(e)}") @@ -167,7 +168,7 @@ async def custom_parameters_example(): "symbols": ["TSLA", "PLTR", "ARKK"], "portfolio_size": 50000, "risk_tolerance": "aggressive", - "time_horizon": "short_term" + "time_horizon": "short_term", } # Conservative low-risk portfolio @@ -175,13 +176,10 @@ async def custom_parameters_example(): "symbols": ["JNJ", "PG", "KO"], "portfolio_size": 200000, "risk_tolerance": "conservative", - "time_horizon": "long_term" + "time_horizon": "long_term", } - configs = { - "Aggressive": aggressive_config, - "Conservative": conservative_config - } + configs = {"Aggressive": aggressive_config, "Conservative": conservative_config} for config_name, config in configs.items(): print(f"\n{config_name} Portfolio Analysis:") @@ -204,7 +202,7 @@ def check_environment(): """ Check if required environment variables are set """ - required_vars = ["OPENAI_API_KEY", "FIRECRAWL_API_KEY"] + required_vars = ["OPENAI_API_KEY"] missing_vars = [] for var in required_vars: @@ -269,4 +267,4 @@ async def main(): if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file + asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml index c770d54..896602b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,12 +5,12 @@ build-backend = "setuptools.build_meta" [project] name = "tradegraph-financial-advisor" version = "1.0.0" -description = "Multi-agent financial analysis system using LangGraph, Firecrawl, and MCP for trading recommendations" +description = "Multi-agent financial analysis system using LangGraph, ddgs, and Crawl4AI for trading recommendations" authors = [{name = "TradeGraph Team", email = "team@tradegraph.com"}] license = {text = "MIT"} readme = "README.md" requires-python = ">=3.10" -keywords = ["financial-analysis", "trading", "langgraph", "firecrawl", "mcp", "multi-agent"] +keywords = ["financial-analysis", "trading", "langgraph", "ddgs", "crawl4ai", "mcp", "multi-agent"] classifiers = [ "Development Status :: 4 - Beta", "Intended Audience :: Financial and Insurance Industry", @@ -24,7 +24,8 @@ dependencies = [ "langgraph>=0.2.0", "langchain>=0.3.0", "langchain-openai>=0.2.0", - "firecrawl-py>=1.0.0", + "ddgs~=9.9", + "crawl4ai~=0.7", "mcp>=1.0.0", "requests>=2.31.0", "aiohttp>=3.9.0", @@ -39,6 +40,7 @@ dependencies = [ "yfinance>=0.2.0", "alpha-vantage>=2.3.0", "python-dateutil>=2.8.0", + "plotly>=5.15.0", "fastapi>=0.118.0", ] @@ -46,6 +48,7 @@ dependencies = [ dev = [ "pytest>=7.4.0", "pytest-asyncio>=0.21.0", + "pytest-cov>=4.1.0", "black>=23.0.0", "isort>=5.12.0", "flake8>=6.0.0", diff --git a/requirements.txt b/requirements.txt index 9d6268e..ef6730b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,8 @@ langgraph>=0.2.0 langchain>=0.3.0 langchain-openai>=0.2.0 -firecrawl-py>=1.0.0 +ddgs~=9.9 +crawl4ai~=0.7 mcp>=1.0.0 requests>=2.31.0 aiohttp>=3.9.0 @@ -13,4 +14,8 @@ pydantic>=2.5.0 loguru>=0.7.0 yfinance>=0.2.0 alpha-vantage>=2.3.0 -python-dateutil>=2.8.0 \ No newline at end of file +pytest>=7.4.0 +pytest-asyncio>=0.21.0 +pytest-cov>=4.1.0 +python-dateutil>=2.8.0 +plotly>=5.15.0 diff --git a/src/tradegraph_financial_advisor/__init__.py b/src/tradegraph_financial_advisor/__init__.py index b6938f6..32fa26d 100644 --- a/src/tradegraph_financial_advisor/__init__.py +++ b/src/tradegraph_financial_advisor/__init__.py @@ -17,5 +17,5 @@ "FinancialAdvisor", "Settings", "TradingRecommendation", - "RecommendationType" -] \ No newline at end of file + "RecommendationType", +] diff --git a/src/tradegraph_financial_advisor/agents/base_agent.py b/src/tradegraph_financial_advisor/agents/base_agent.py index 8acf4ff..b060c39 100644 --- a/src/tradegraph_financial_advisor/agents/base_agent.py +++ b/src/tradegraph_financial_advisor/agents/base_agent.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional +from typing import Any, Dict from datetime import datetime import asyncio from loguru import logger @@ -35,14 +35,13 @@ def get_status(self) -> Dict[str, Any]: "description": self.description, "is_active": self.is_active, "created_at": self.created_at.isoformat(), - "last_activity": self.last_activity.isoformat() + "last_activity": self.last_activity.isoformat(), } async def health_check(self) -> bool: try: await asyncio.wait_for( - self._health_check_impl(), - timeout=settings.analysis_timeout_seconds + self._health_check_impl(), timeout=settings.analysis_timeout_seconds ) return True except asyncio.TimeoutError: @@ -53,4 +52,4 @@ async def health_check(self) -> bool: return False async def _health_check_impl(self) -> None: - pass \ No newline at end of file + pass diff --git a/src/tradegraph_financial_advisor/agents/financial_agent.py b/src/tradegraph_financial_advisor/agents/financial_agent.py index 5e427f1..c54376c 100644 --- a/src/tradegraph_financial_advisor/agents/financial_agent.py +++ b/src/tradegraph_financial_advisor/agents/financial_agent.py @@ -1,6 +1,5 @@ -import asyncio -from typing import Any, Dict, List, Optional -from datetime import datetime, timedelta +from typing import Any, Dict, Optional +from datetime import datetime import aiohttp import yfinance as yf import pandas as pd @@ -16,7 +15,7 @@ def __init__(self, **kwargs): super().__init__( name="FinancialAnalysisAgent", description="Analyzes company financials and technical indicators", - **kwargs + **kwargs, ) self.session: Optional[aiohttp.ClientSession] = None @@ -47,15 +46,21 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: if include_market_data: market_data = await self._get_market_data(symbol) - symbol_data["market_data"] = market_data.dict() if market_data else None + symbol_data["market_data"] = ( + market_data.dict() if market_data else None + ) if include_financials: financials = await self._get_company_financials(symbol) - symbol_data["financials"] = financials.dict() if financials else None + symbol_data["financials"] = ( + financials.dict() if financials else None + ) if include_technical: technical = await self._get_technical_indicators(symbol) - symbol_data["technical_indicators"] = technical.dict() if technical else None + symbol_data["technical_indicators"] = ( + technical.dict() if technical else None + ) results[symbol] = symbol_data @@ -65,7 +70,7 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: return { "analysis_results": results, - "analysis_timestamp": datetime.now().isoformat() + "analysis_timestamp": datetime.now().isoformat(), } async def _get_market_data(self, symbol: str) -> Optional[MarketData]: @@ -81,13 +86,15 @@ async def _get_market_data(self, symbol: str) -> Optional[MarketData]: market_data = MarketData( symbol=symbol, - current_price=float(latest['Close']), - change=float(latest['Close'] - latest['Open']), - change_percent=float((latest['Close'] - latest['Open']) / latest['Open'] * 100), - volume=int(latest['Volume']), - market_cap=info.get('marketCap'), - pe_ratio=info.get('trailingPE'), - timestamp=datetime.now() + current_price=float(latest["Close"]), + change=float(latest["Close"] - latest["Open"]), + change_percent=float( + (latest["Close"] - latest["Open"]) / latest["Open"] * 100 + ), + volume=int(latest["Volume"]), + market_cap=info.get("marketCap"), + pe_ratio=info.get("trailingPE"), + timestamp=datetime.now(), ) return market_data @@ -101,30 +108,26 @@ async def _get_company_financials(self, symbol: str) -> Optional[CompanyFinancia ticker = yf.Ticker(symbol) info = ticker.info - # Get quarterly and annual financials - quarterly = ticker.quarterly_financials - annual = ticker.financials - financials = CompanyFinancials( symbol=symbol, - company_name=info.get('longName', symbol), - market_cap=info.get('marketCap'), - pe_ratio=info.get('trailingPE'), - eps=info.get('trailingEps'), - revenue=info.get('totalRevenue'), - net_income=info.get('netIncomeToCommon'), - debt_to_equity=info.get('debtToEquity'), - current_ratio=info.get('currentRatio'), - return_on_equity=info.get('returnOnEquity'), - return_on_assets=info.get('returnOnAssets'), - price_to_book=info.get('priceToBook'), - dividend_yield=info.get('dividendYield'), - beta=info.get('beta'), - fifty_two_week_high=info.get('fiftyTwoWeekHigh'), - fifty_two_week_low=info.get('fiftyTwoWeekLow'), - current_price=info.get('currentPrice'), + company_name=info.get("longName", symbol), + market_cap=info.get("marketCap"), + pe_ratio=info.get("trailingPE"), + eps=info.get("trailingEps"), + revenue=info.get("totalRevenue"), + net_income=info.get("netIncomeToCommon"), + debt_to_equity=info.get("debtToEquity"), + current_ratio=info.get("currentRatio"), + return_on_equity=info.get("returnOnEquity"), + return_on_assets=info.get("returnOnAssets"), + price_to_book=info.get("priceToBook"), + dividend_yield=info.get("dividendYield"), + beta=info.get("beta"), + fifty_two_week_high=info.get("fiftyTwoWeekHigh"), + fifty_two_week_low=info.get("fiftyTwoWeekLow"), + current_price=info.get("currentPrice"), report_date=datetime.now(), - report_type="quarterly" + report_type="quarterly", ) return financials @@ -133,7 +136,9 @@ async def _get_company_financials(self, symbol: str) -> Optional[CompanyFinancia logger.error(f"Error fetching financials for {symbol}: {str(e)}") return None - async def _get_technical_indicators(self, symbol: str) -> Optional[TechnicalIndicators]: + async def _get_technical_indicators( + self, symbol: str + ) -> Optional[TechnicalIndicators]: try: ticker = yf.Ticker(symbol) history = ticker.history(period="3mo") # 3 months of data @@ -142,7 +147,7 @@ async def _get_technical_indicators(self, symbol: str) -> Optional[TechnicalIndi return None # Calculate technical indicators - close_prices = history['Close'] + close_prices = history["Close"] # Simple Moving Averages sma_20 = close_prices.rolling(window=20).mean().iloc[-1] @@ -173,8 +178,8 @@ async def _get_technical_indicators(self, symbol: str) -> Optional[TechnicalIndi bollinger_lower = bb_sma - (bb_std * 2) # Support and Resistance (simplified) - recent_high = history['High'].tail(20).max() - recent_low = history['Low'].tail(20).min() + recent_high = history["High"].tail(20).max() + recent_low = history["Low"].tail(20).min() technical = TechnicalIndicators( symbol=symbol, @@ -183,19 +188,29 @@ async def _get_technical_indicators(self, symbol: str) -> Optional[TechnicalIndi ema_12=float(ema_12) if not pd.isna(ema_12) else None, ema_26=float(ema_26) if not pd.isna(ema_26) else None, rsi=float(rsi) if not pd.isna(rsi) else None, - macd=float(macd_line.iloc[-1]) if not pd.isna(macd_line.iloc[-1]) else None, + macd=( + float(macd_line.iloc[-1]) + if not pd.isna(macd_line.iloc[-1]) + else None + ), macd_signal=float(macd_signal) if not pd.isna(macd_signal) else None, - bollinger_upper=float(bollinger_upper) if not pd.isna(bollinger_upper) else None, - bollinger_lower=float(bollinger_lower) if not pd.isna(bollinger_lower) else None, + bollinger_upper=( + float(bollinger_upper) if not pd.isna(bollinger_upper) else None + ), + bollinger_lower=( + float(bollinger_lower) if not pd.isna(bollinger_lower) else None + ), support_level=float(recent_low), resistance_level=float(recent_high), - timestamp=datetime.now() + timestamp=datetime.now(), ) return technical except Exception as e: - logger.error(f"Error calculating technical indicators for {symbol}: {str(e)}") + logger.error( + f"Error calculating technical indicators for {symbol}: {str(e)}" + ) return None async def _health_check_impl(self) -> None: @@ -206,4 +221,4 @@ async def _health_check_impl(self) -> None: if not info: raise Exception("Unable to fetch test data") except Exception as e: - raise Exception(f"yfinance health check failed: {str(e)}") \ No newline at end of file + raise Exception(f"yfinance health check failed: {str(e)}") diff --git a/src/tradegraph_financial_advisor/agents/news_agent.py b/src/tradegraph_financial_advisor/agents/news_agent.py index d2e4504..ebd1d15 100644 --- a/src/tradegraph_financial_advisor/agents/news_agent.py +++ b/src/tradegraph_financial_advisor/agents/news_agent.py @@ -1,6 +1,5 @@ -import asyncio from typing import Any, Dict, List, Optional -from datetime import datetime, timedelta +from datetime import datetime import aiohttp from bs4 import BeautifulSoup from loguru import logger @@ -16,7 +15,7 @@ def __init__(self, **kwargs): super().__init__( name="NewsReaderAgent", description="Reads and analyzes financial news from multiple sources", - **kwargs + **kwargs, ) self.session: Optional[aiohttp.ClientSession] = None @@ -43,7 +42,10 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: for source in settings.news_sources: try: articles = await self._fetch_news_from_source( - source, symbols, timeframe_hours, max_articles // len(settings.news_sources) + source, + symbols, + timeframe_hours, + max_articles // len(settings.news_sources), ) all_articles.extend(articles) except Exception as e: @@ -55,28 +57,34 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: "articles": [article.dict() for article in analyzed_articles], "total_count": len(analyzed_articles), "sources": settings.news_sources, - "analysis_timestamp": datetime.now().isoformat() + "analysis_timestamp": datetime.now().isoformat(), } async def _fetch_news_from_source( - self, - source: str, - symbols: List[str], - timeframe_hours: int, - max_articles: int + self, source: str, symbols: List[str], timeframe_hours: int, max_articles: int ) -> List[NewsArticle]: articles = [] if source == "yahoo-finance": - articles = await self._fetch_yahoo_finance_news(symbols, timeframe_hours, max_articles) + articles = await self._fetch_yahoo_finance_news( + symbols, timeframe_hours, max_articles + ) elif source == "bloomberg": - articles = await self._fetch_bloomberg_news(symbols, timeframe_hours, max_articles) + articles = await self._fetch_bloomberg_news( + symbols, timeframe_hours, max_articles + ) elif source == "reuters": - articles = await self._fetch_reuters_news(symbols, timeframe_hours, max_articles) + articles = await self._fetch_reuters_news( + symbols, timeframe_hours, max_articles + ) elif source == "marketwatch": - articles = await self._fetch_marketwatch_news(symbols, timeframe_hours, max_articles) + articles = await self._fetch_marketwatch_news( + symbols, timeframe_hours, max_articles + ) elif source == "cnbc": - articles = await self._fetch_cnbc_news(symbols, timeframe_hours, max_articles) + articles = await self._fetch_cnbc_news( + symbols, timeframe_hours, max_articles + ) return articles @@ -91,22 +99,28 @@ async def _fetch_yahoo_finance_news( async with self.session.get(url) as response: if response.status == 200: html = await response.text() - soup = BeautifulSoup(html, 'html.parser') + soup = BeautifulSoup(html, "html.parser") - news_items = soup.find_all('div', class_=['Mb(5px)', 'news-item'])[:max_articles] + news_items = soup.find_all( + "div", class_=["Mb(5px)", "news-item"] + )[:max_articles] for item in news_items: try: - title_elem = item.find('h3') or item.find('a') + title_elem = item.find("h3") or item.find("a") if title_elem: title = title_elem.get_text(strip=True) - link = title_elem.get('href', '') - if link and not link.startswith('http'): + link = title_elem.get("href", "") + if link and not link.startswith("http"): link = f"https://finance.yahoo.com{link}" # Extract article content - raw_content = await self._extract_article_content(link) - content = (raw_content or "")[:1000] # Limit content length + raw_content = await self._extract_article_content( + link + ) + content = (raw_content or "")[ + :1000 + ] # Limit content length article = NewsArticle( title=title, @@ -115,14 +129,18 @@ async def _fetch_yahoo_finance_news( summary=generate_summary(raw_content or title), source="yahoo-finance", published_at=datetime.now(), - symbols=[symbol] + symbols=[symbol], ) articles.append(article) except Exception as e: - logger.warning(f"Error parsing Yahoo Finance news item: {str(e)}") + logger.warning( + f"Error parsing Yahoo Finance news item: {str(e)}" + ) except Exception as e: - logger.error(f"Error fetching Yahoo Finance news for {symbol}: {str(e)}") + logger.error( + f"Error fetching Yahoo Finance news for {symbol}: {str(e)}" + ) return articles @@ -140,18 +158,18 @@ async def _fetch_bloomberg_news( async with self.session.get(url) as response: if response.status == 200: html = await response.text() - soup = BeautifulSoup(html, 'html.parser') + soup = BeautifulSoup(html, "html.parser") # Extract news items (this is simplified) - news_items = soup.find_all('div', class_='storyItem')[:max_articles] + news_items = soup.find_all("div", class_="storyItem")[:max_articles] for item in news_items: try: - title_elem = item.find('a') + title_elem = item.find("a") if title_elem: title = title_elem.get_text(strip=True) - link = title_elem.get('href', '') - if link and not link.startswith('http'): + link = title_elem.get("href", "") + if link and not link.startswith("http"): link = f"https://www.bloomberg.com{link}" raw_content = await self._extract_article_content(link) @@ -164,11 +182,13 @@ async def _fetch_bloomberg_news( summary=generate_summary(raw_content or title), source="bloomberg", published_at=datetime.now(), - symbols=symbols + symbols=symbols, ) articles.append(article) except Exception as e: - logger.warning(f"Error parsing Bloomberg news item: {str(e)}") + logger.warning( + f"Error parsing Bloomberg news item: {str(e)}" + ) except Exception as e: logger.error(f"Error fetching Bloomberg news: {str(e)}") @@ -187,20 +207,24 @@ async def _fetch_reuters_news( async with self.session.get(url) as response: if response.status == 200: html = await response.text() - soup = BeautifulSoup(html, 'html.parser') + soup = BeautifulSoup(html, "html.parser") - news_items = soup.find_all('div', class_='story-card')[:max_articles] + news_items = soup.find_all("div", class_="story-card")[ + :max_articles + ] for item in news_items: try: - title_elem = item.find('a') + title_elem = item.find("a") if title_elem: title = title_elem.get_text(strip=True) - link = title_elem.get('href', '') - if link and not link.startswith('http'): + link = title_elem.get("href", "") + if link and not link.startswith("http"): link = f"https://www.reuters.com{link}" - raw_content = await self._extract_article_content(link) + raw_content = await self._extract_article_content( + link + ) content = (raw_content or "")[:1000] article = NewsArticle( @@ -210,11 +234,13 @@ async def _fetch_reuters_news( summary=generate_summary(raw_content or title), source="reuters", published_at=datetime.now(), - symbols=[symbol] + symbols=[symbol], ) articles.append(article) except Exception as e: - logger.warning(f"Error parsing Reuters news item: {str(e)}") + logger.warning( + f"Error parsing Reuters news item: {str(e)}" + ) except Exception as e: logger.error(f"Error fetching Reuters news: {str(e)}") @@ -241,7 +267,7 @@ async def _extract_article_content(self, url: str) -> str: async with self.session.get(url) as response: if response.status == 200: html = await response.text() - soup = BeautifulSoup(html, 'html.parser') + soup = BeautifulSoup(html, "html.parser") # Remove script and style elements for script in soup(["script", "style"]): @@ -252,8 +278,10 @@ async def _extract_article_content(self, url: str) -> str: # Clean up whitespace lines = (line.strip() for line in text.splitlines()) - chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) - text = ' '.join(chunk for chunk in chunks if chunk) + chunks = ( + phrase.strip() for line in lines for phrase in line.split(" ") + ) + text = " ".join(chunk for chunk in chunks if chunk) return text[:2000] # Limit content length @@ -291,13 +319,35 @@ async def _analyze_sentiment(self, content: str) -> SentimentType: # Simple keyword-based sentiment analysis # In production, use a proper NLP model - bullish_keywords = ['buy', 'bullish', 'positive', 'growth', 'profit', 'strong', 'gains', 'upgrade'] - bearish_keywords = ['sell', 'bearish', 'negative', 'loss', 'decline', 'weak', 'downgrade', 'risk'] + bullish_keywords = [ + "buy", + "bullish", + "positive", + "growth", + "profit", + "strong", + "gains", + "upgrade", + ] + bearish_keywords = [ + "sell", + "bearish", + "negative", + "loss", + "decline", + "weak", + "downgrade", + "risk", + ] content_lower = content.lower() - bullish_count = sum(1 for keyword in bullish_keywords if keyword in content_lower) - bearish_count = sum(1 for keyword in bearish_keywords if keyword in content_lower) + bullish_count = sum( + 1 for keyword in bullish_keywords if keyword in content_lower + ) + bearish_count = sum( + 1 for keyword in bearish_keywords if keyword in content_lower + ) if bullish_count > bearish_count: return SentimentType.BULLISH @@ -306,7 +356,9 @@ async def _analyze_sentiment(self, content: str) -> SentimentType: else: return SentimentType.NEUTRAL - async def _calculate_impact_score(self, article: NewsArticle, symbols: List[str]) -> float: + async def _calculate_impact_score( + self, article: NewsArticle, symbols: List[str] + ) -> float: # Calculate impact score based on various factors score = 0.5 # Base score @@ -321,7 +373,14 @@ async def _calculate_impact_score(self, article: NewsArticle, symbols: List[str] score += 0.1 # Boost score for high-impact keywords - high_impact_keywords = ['earnings', 'merger', 'acquisition', 'partnership', 'lawsuit', 'fda approval'] + high_impact_keywords = [ + "earnings", + "merger", + "acquisition", + "partnership", + "lawsuit", + "fda approval", + ] for keyword in high_impact_keywords: if keyword in content_lower or keyword in title_lower: score += 0.15 diff --git a/src/tradegraph_financial_advisor/agents/recommendation_engine.py b/src/tradegraph_financial_advisor/agents/recommendation_engine.py index 0e00767..f3d303e 100644 --- a/src/tradegraph_financial_advisor/agents/recommendation_engine.py +++ b/src/tradegraph_financial_advisor/agents/recommendation_engine.py @@ -1,36 +1,33 @@ -import asyncio import json from typing import Any, Dict, List, Optional, Tuple -from datetime import datetime, timedelta +from datetime import datetime import numpy as np from loguru import logger from langchain_openai import ChatOpenAI from langchain_core.messages import HumanMessage from .base_agent import BaseAgent -from ..models.financial_data import AnalysisContext from ..models.recommendations import ( TradingRecommendation, PortfolioRecommendation, RecommendationType, RiskLevel, TimeHorizon, - AlertRecommendation + AlertRecommendation, ) from ..config.settings import settings class TradingRecommendationEngine(BaseAgent): - def __init__(self, **kwargs): + def __init__(self, model_name: str = "gpt-5-nano", **kwargs): super().__init__( name="TradingRecommendationEngine", description="Generates sophisticated trading recommendations using multi-factor analysis", - **kwargs + **kwargs, ) + self.model_name = model_name self.llm = ChatOpenAI( - model="gpt-4", - temperature=0.1, - api_key=settings.openai_api_key + model=self.model_name, temperature=0.1, api_key=settings.openai_api_key ) async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: @@ -66,16 +63,15 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: return { "individual_recommendations": [rec.dict() for rec in recommendations], - "portfolio_recommendation": portfolio_recommendation.dict() if portfolio_recommendation else None, + "portfolio_recommendation": ( + portfolio_recommendation.dict() if portfolio_recommendation else None + ), "alerts": [alert.dict() for alert in alerts], - "generation_timestamp": datetime.now().isoformat() + "generation_timestamp": datetime.now().isoformat(), } async def _generate_stock_recommendation( - self, - symbol: str, - context: Dict[str, Any], - risk_preferences: Dict[str, Any] + self, symbol: str, context: Dict[str, Any], risk_preferences: Dict[str, Any] ) -> Optional[TradingRecommendation]: try: # Extract data from context @@ -86,16 +82,20 @@ async def _generate_stock_recommendation( report_analysis = context.get("report_analysis", {}) # Calculate individual scores - fundamental_score = await self._calculate_fundamental_score(financials, report_analysis) - technical_score = await self._calculate_technical_score(technical_data, market_data) + fundamental_score = await self._calculate_fundamental_score( + financials, report_analysis + ) + technical_score = await self._calculate_technical_score( + technical_data, market_data + ) sentiment_score = await self._calculate_sentiment_score(news_sentiment) # Overall confidence score (weighted combination) weights = {"fundamental": 0.4, "technical": 0.3, "sentiment": 0.3} confidence_score = ( - weights["fundamental"] * fundamental_score + - weights["technical"] * technical_score + - weights["sentiment"] * sentiment_score + weights["fundamental"] * fundamental_score + + weights["technical"] * technical_score + + weights["sentiment"] * sentiment_score ) # Determine recommendation type @@ -150,7 +150,7 @@ async def _generate_stock_recommendation( sector=financials.get("sector", "Unknown"), expected_return=await self._calculate_expected_return( current_price, target_price, recommendation_type - ) + ), ) return recommendation @@ -160,9 +160,7 @@ async def _generate_stock_recommendation( return None async def _calculate_fundamental_score( - self, - financials: Dict[str, Any], - report_analysis: Dict[str, Any] + self, financials: Dict[str, Any], report_analysis: Dict[str, Any] ) -> float: try: score = 0.5 # Base score @@ -212,9 +210,7 @@ async def _calculate_fundamental_score( return 0.5 async def _calculate_technical_score( - self, - technical_data: Dict[str, Any], - market_data: Dict[str, Any] + self, technical_data: Dict[str, Any], market_data: Dict[str, Any] ) -> float: try: score = 0.5 # Base score @@ -229,11 +225,11 @@ async def _calculate_technical_score( if current_price > sma_20 > sma_50: score += 0.15 # Strong uptrend elif current_price > sma_20: - score += 0.1 # Uptrend + score += 0.1 # Uptrend elif current_price < sma_20 < sma_50: score -= 0.15 # Strong downtrend elif current_price < sma_20: - score -= 0.1 # Downtrend + score -= 0.1 # Downtrend # RSI analysis rsi = technical_data.get("rsi") @@ -301,7 +297,7 @@ def _determine_recommendation_type( confidence_score: float, fundamental_score: float, technical_score: float, - sentiment_score: float + sentiment_score: float, ) -> RecommendationType: if confidence_score >= 0.8: return RecommendationType.STRONG_BUY @@ -321,7 +317,7 @@ def _calculate_risk_level( market_data: Dict[str, Any], financials: Dict[str, Any], technical_data: Dict[str, Any], - report_analysis: Dict[str, Any] + report_analysis: Dict[str, Any], ) -> RiskLevel: risk_score = 0.0 @@ -369,7 +365,7 @@ def _determine_time_horizon( self, recommendation_type: RecommendationType, risk_level: RiskLevel, - technical_data: Dict[str, Any] + technical_data: Dict[str, Any], ) -> TimeHorizon: # Technical momentum suggests shorter term rsi = technical_data.get("rsi", 50) @@ -381,7 +377,10 @@ def _determine_time_horizon( return TimeHorizon.SHORT_TERM # Strong recommendations can be longer term - if recommendation_type in [RecommendationType.STRONG_BUY, RecommendationType.STRONG_SELL]: + if recommendation_type in [ + RecommendationType.STRONG_BUY, + RecommendationType.STRONG_SELL, + ]: return TimeHorizon.LONG_TERM return TimeHorizon.MEDIUM_TERM @@ -391,17 +390,17 @@ def _calculate_position_size( confidence_score: float, risk_level: RiskLevel, risk_preferences: Dict[str, Any], - recommendation_type: RecommendationType + recommendation_type: RecommendationType, ) -> float: - + RECOMMENDATION_WEIGHTS = { RecommendationType.STRONG_BUY: 2.0, RecommendationType.BUY: 1.5, RecommendationType.HOLD: 0.8, RecommendationType.SELL: 0.3, - RecommendationType.STRONG_SELL: 0.1 + RecommendationType.STRONG_SELL: 0.1, } - + base_allocation = confidence_score * RECOMMENDATION_WEIGHTS[recommendation_type] # Adjust for risk level @@ -409,22 +408,22 @@ def _calculate_position_size( RiskLevel.LOW: 1.5, RiskLevel.MEDIUM: 1.0, RiskLevel.HIGH: 0.7, - RiskLevel.VERY_HIGH: 0.4 + RiskLevel.VERY_HIGH: 0.4, } risk_adjusted = base_allocation * risk_multipliers[risk_level] # Adjust for user risk tolerance risk_tolerance = risk_preferences.get("risk_tolerance", "medium") - tolerance_multipliers = { - "conservative": 0.6, - "medium": 1.0, - "aggressive": 1.4 - } + tolerance_multipliers = {"conservative": 0.6, "medium": 1.0, "aggressive": 1.4} - final_allocation = risk_adjusted * tolerance_multipliers.get(risk_tolerance, 1.0) + final_allocation = risk_adjusted * tolerance_multipliers.get( + risk_tolerance, 1.0 + ) - return max(0.01, final_allocation) # was Between 1% and 25% - changed for the new optimization logic + return max( + 0.01, final_allocation + ) # was Between 1% and 25% - changed for the new optimization logic async def _calculate_price_targets( self, @@ -432,13 +431,16 @@ async def _calculate_price_targets( current_price: float, recommendation_type: RecommendationType, technical_data: Dict[str, Any], - financials: Dict[str, Any] + financials: Dict[str, Any], ) -> Tuple[Optional[float], Optional[float]]: try: target_price = None stop_loss = None - if recommendation_type in [RecommendationType.BUY, RecommendationType.STRONG_BUY]: + if recommendation_type in [ + RecommendationType.BUY, + RecommendationType.STRONG_BUY, + ]: # Calculate upside target resistance = technical_data.get("resistance_level") if resistance: @@ -461,7 +463,10 @@ async def _calculate_price_targets( else: stop_loss = current_price * 0.90 # 10% stop loss - elif recommendation_type in [RecommendationType.SELL, RecommendationType.STRONG_SELL]: + elif recommendation_type in [ + RecommendationType.SELL, + RecommendationType.STRONG_SELL, + ]: # For short positions support = technical_data.get("support_level") if support: @@ -488,7 +493,7 @@ async def _generate_key_factors( financials: Dict[str, Any], technical_data: Dict[str, Any], sentiment_data: Dict[str, Any], - report_analysis: Dict[str, Any] + report_analysis: Dict[str, Any], ) -> Dict[str, List[str]]: try: analysis_prompt = f""" @@ -511,16 +516,15 @@ async def _generate_key_factors( response = await self.llm.ainvoke([HumanMessage(content=analysis_prompt)]) try: - import json factors_data = json.loads(response.content) return factors_data - except: + except json.JSONDecodeError: # Fallback return { "key_factors": ["Analysis available"], "risks": ["Market volatility"], "catalysts": ["Market sentiment"], - "analyst_notes": "Comprehensive analysis completed" + "analyst_notes": "Comprehensive analysis completed", } except Exception as e: @@ -529,21 +533,27 @@ async def _generate_key_factors( "key_factors": ["Analysis available"], "risks": ["Market volatility"], "catalysts": ["Market sentiment"], - "analyst_notes": "Analysis completed with limitations" + "analyst_notes": "Analysis completed with limitations", } async def _calculate_expected_return( self, current_price: float, target_price: Optional[float], - recommendation_type: RecommendationType + recommendation_type: RecommendationType, ) -> Optional[float]: if not target_price: return None - if recommendation_type in [RecommendationType.BUY, RecommendationType.STRONG_BUY]: + if recommendation_type in [ + RecommendationType.BUY, + RecommendationType.STRONG_BUY, + ]: return (target_price - current_price) / current_price - elif recommendation_type in [RecommendationType.SELL, RecommendationType.STRONG_SELL]: + elif recommendation_type in [ + RecommendationType.SELL, + RecommendationType.STRONG_SELL, + ]: return (current_price - target_price) / current_price # Profit from short return 0.0 @@ -552,28 +562,32 @@ async def _optimize_portfolio( self, recommendations: List[TradingRecommendation], portfolio_constraints: Dict[str, Any], - risk_preferences: Dict[str, Any] + risk_preferences: Dict[str, Any], ) -> Optional[PortfolioRecommendation]: try: if not recommendations: return None - portfolio_size = portfolio_constraints.get("portfolio_size", settings.default_portfolio_size) + portfolio_size = portfolio_constraints.get( + "portfolio_size", settings.default_portfolio_size + ) max_positions = portfolio_constraints.get("max_positions", 10) # Select top recommendations sorted_recs = sorted( - recommendations, - key=lambda x: x.confidence_score, - reverse=True + recommendations, key=lambda x: x.confidence_score, reverse=True )[:max_positions] # Optimize allocations - optimized_recs = await self._optimize_allocations(sorted_recs, portfolio_constraints) + optimized_recs = await self._optimize_allocations( + sorted_recs, portfolio_constraints + ) # Calculate portfolio metrics total_confidence = np.mean([rec.confidence_score for rec in optimized_recs]) - diversification_score = self._calculate_diversification_score(optimized_recs) + diversification_score = self._calculate_diversification_score( + optimized_recs + ) # Risk assessment risk_levels = [rec.risk_level for rec in optimized_recs] @@ -585,19 +599,17 @@ async def _optimize_portfolio( diversification_score=diversification_score, overall_risk_level=overall_risk, portfolio_size=portfolio_size, - rebalancing_frequency="quarterly" + rebalancing_frequency="quarterly", ) return portfolio - + except Exception as e: logger.error(f"Error optimizing portfolio: {str(e)}") return None async def _optimize_allocations( - self, - recommendations: List[TradingRecommendation], - constraints: Dict[str, Any] + self, recommendations: List[TradingRecommendation], constraints: Dict[str, Any] ) -> List[TradingRecommendation]: total_allocation = sum(rec.recommended_allocation for rec in recommendations) @@ -633,18 +645,26 @@ async def _optimize_allocations( break # Redistribute excess to uncapped positions proportionally - uncapped_recs = [rec for rec in recommendations if rec.recommended_allocation < max_position] + uncapped_recs = [ + rec + for rec in recommendations + if rec.recommended_allocation < max_position + ] if uncapped_recs: - uncapped_total = sum(rec.recommended_allocation for rec in uncapped_recs) + uncapped_total = sum( + rec.recommended_allocation for rec in uncapped_recs + ) if uncapped_total > 0: for rec in uncapped_recs: # Distribute excess proportionally - rec.recommended_allocation += excess * (rec.recommended_allocation / uncapped_total) - + rec.recommended_allocation += excess * ( + rec.recommended_allocation / uncapped_total + ) + # Final normalization: if all positions got capped, scale up to reach target final_total = sum(rec.recommended_allocation for rec in recommendations) - + if final_total < target_allocation - 0.001: # If its below target # Scale everything up proportionally to reach target final_scale = target_allocation / final_total @@ -653,7 +673,9 @@ async def _optimize_allocations( return recommendations - def _calculate_diversification_score(self, recommendations: List[TradingRecommendation]) -> float: + def _calculate_diversification_score( + self, recommendations: List[TradingRecommendation] + ) -> float: if len(recommendations) <= 1: return 0.0 @@ -665,7 +687,7 @@ def _determine_portfolio_risk(self, risk_levels: List[RiskLevel]) -> RiskLevel: RiskLevel.LOW: 1, RiskLevel.MEDIUM: 2, RiskLevel.HIGH: 3, - RiskLevel.VERY_HIGH: 4 + RiskLevel.VERY_HIGH: 4, } avg_risk = np.mean([risk_values[risk] for risk in risk_levels]) @@ -680,9 +702,7 @@ def _determine_portfolio_risk(self, risk_levels: List[RiskLevel]) -> RiskLevel: return RiskLevel.LOW async def _generate_alerts( - self, - symbol: str, - context: Dict[str, Any] + self, symbol: str, context: Dict[str, Any] ) -> List[AlertRecommendation]: alerts = [] @@ -695,43 +715,57 @@ async def _generate_alerts( # RSI alerts if rsi < 25: - alerts.append(AlertRecommendation( - symbol=symbol, - alert_type="technical_oversold", - message=f"{symbol} RSI at {rsi:.1f} - potentially oversold", - urgency="medium", - trigger_conditions={"rsi": rsi, "threshold": 25} - )) + alerts.append( + AlertRecommendation( + symbol=symbol, + alert_type="technical_oversold", + message=f"{symbol} RSI at {rsi:.1f} - potentially oversold", + urgency="medium", + trigger_conditions={"rsi": rsi, "threshold": 25}, + ) + ) elif rsi > 75: - alerts.append(AlertRecommendation( - symbol=symbol, - alert_type="technical_overbought", - message=f"{symbol} RSI at {rsi:.1f} - potentially overbought", - urgency="medium", - trigger_conditions={"rsi": rsi, "threshold": 75} - )) + alerts.append( + AlertRecommendation( + symbol=symbol, + alert_type="technical_overbought", + message=f"{symbol} RSI at {rsi:.1f} - potentially overbought", + urgency="medium", + trigger_conditions={"rsi": rsi, "threshold": 75}, + ) + ) # Support/resistance alerts support = technical_data.get("support_level") resistance = technical_data.get("resistance_level") if support and current_price <= support * 1.02: - alerts.append(AlertRecommendation( - symbol=symbol, - alert_type="price_support", - message=f"{symbol} near support level at ${support:.2f}", - urgency="high", - trigger_conditions={"current_price": current_price, "support": support} - )) + alerts.append( + AlertRecommendation( + symbol=symbol, + alert_type="price_support", + message=f"{symbol} near support level at ${support:.2f}", + urgency="high", + trigger_conditions={ + "current_price": current_price, + "support": support, + }, + ) + ) if resistance and current_price >= resistance * 0.98: - alerts.append(AlertRecommendation( - symbol=symbol, - alert_type="price_resistance", - message=f"{symbol} near resistance level at ${resistance:.2f}", - urgency="high", - trigger_conditions={"current_price": current_price, "resistance": resistance} - )) + alerts.append( + AlertRecommendation( + symbol=symbol, + alert_type="price_resistance", + message=f"{symbol} near resistance level at ${resistance:.2f}", + urgency="high", + trigger_conditions={ + "current_price": current_price, + "resistance": resistance, + }, + ) + ) except Exception as e: logger.warning(f"Error generating alerts for {symbol}: {str(e)}") @@ -740,10 +774,10 @@ async def _generate_alerts( async def _health_check_impl(self) -> None: try: - test_response = await self.llm.ainvoke([ - HumanMessage(content="Respond with 'OK' for health check.") - ]) + test_response = await self.llm.ainvoke( + [HumanMessage(content="Respond with 'OK' for health check.")] + ) if "OK" not in test_response.content: raise Exception("LLM health check failed") except Exception as e: - raise Exception(f"Recommendation engine health check failed: {str(e)}") \ No newline at end of file + raise Exception(f"Recommendation engine health check failed: {str(e)}") diff --git a/src/tradegraph_financial_advisor/agents/report_analysis_agent.py b/src/tradegraph_financial_advisor/agents/report_analysis_agent.py index cce8cde..fa40417 100644 --- a/src/tradegraph_financial_advisor/agents/report_analysis_agent.py +++ b/src/tradegraph_financial_advisor/agents/report_analysis_agent.py @@ -1,38 +1,39 @@ -import asyncio import json from typing import Any, Dict, List, Optional -from datetime import datetime, timedelta -import aiohttp +from datetime import datetime from loguru import logger from langchain_openai import ChatOpenAI from langchain_core.messages import HumanMessage from .base_agent import BaseAgent -from ..services.firecrawl_service import FirecrawlService -from ..models.financial_data import CompanyFinancials +from ..services.local_scraping_service import LocalScrapingService from ..config.settings import settings class ReportAnalysisAgent(BaseAgent): - def __init__(self, **kwargs): + def __init__( + self, + scraping_service: Optional[LocalScrapingService] = None, + llm_model_name: str = "gpt-5-nano", + **kwargs, + ): super().__init__( name="ReportAnalysisAgent", description="Analyzes company financial reports and SEC filings for deep fundamental analysis", - **kwargs + **kwargs, ) - self.firecrawl_service = FirecrawlService() + self.local_scraping_service = scraping_service or LocalScrapingService() + self.llm_model_name = llm_model_name self.llm = ChatOpenAI( - model="gpt-4", - temperature=0.1, - api_key=settings.openai_api_key + model=self.llm_model_name, temperature=0.1, api_key=settings.openai_api_key ) async def start(self) -> None: await super().start() - await self.firecrawl_service.start() + await self.local_scraping_service.start() async def stop(self) -> None: - await self.firecrawl_service.stop() + await self.local_scraping_service.stop() await super().stop() async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: @@ -57,14 +58,11 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]: return { "report_analysis": results, - "analysis_timestamp": datetime.now().isoformat() + "analysis_timestamp": datetime.now().isoformat(), } async def _analyze_company_reports( - self, - symbol: str, - report_types: List[str], - analysis_depth: str + self, symbol: str, report_types: List[str], analysis_depth: str ) -> Dict[str, Any]: try: analysis_results = { @@ -75,13 +73,13 @@ async def _analyze_company_reports( "risk_factors": [], "growth_prospects": [], "competitive_position": "", - "financial_health_score": 0.0 + "financial_health_score": 0.0, } # Scrape and analyze each report type for report_type in report_types: try: - filings = await self.firecrawl_service.scrape_financial_reports( + filings = await self.local_scraping_service.search_and_scrape_financial_reports( symbol, report_type ) @@ -92,7 +90,9 @@ async def _analyze_company_reports( analysis_results["report_analyses"].append(report_analysis) except Exception as e: - logger.warning(f"Failed to analyze {report_type} for {symbol}: {str(e)}") + logger.warning( + f"Failed to analyze {report_type} for {symbol}: {str(e)}" + ) # Generate comprehensive analysis summary if analysis_results["report_analyses"]: @@ -108,11 +108,7 @@ async def _analyze_company_reports( return {"error": str(e)} async def _analyze_single_report( - self, - symbol: str, - filing: Dict[str, Any], - report_type: str, - analysis_depth: str + self, symbol: str, filing: Dict[str, Any], report_type: str, analysis_depth: str ) -> Dict[str, Any]: try: content = filing.get("content", "") @@ -180,7 +176,6 @@ async def _analyze_single_report( response = await self.llm.ainvoke([HumanMessage(content=analysis_prompt)]) try: - import json analysis_data = json.loads(response.content) analysis_data["report_type"] = report_type analysis_data["filing_url"] = filing.get("url", "") @@ -192,7 +187,7 @@ async def _analyze_single_report( return { "error": "Failed to parse analysis", "raw_response": response.content[:500], - "report_type": report_type + "report_type": report_type, } except Exception as e: @@ -200,9 +195,7 @@ async def _analyze_single_report( return {"error": str(e), "report_type": report_type} async def _generate_comprehensive_summary( - self, - symbol: str, - report_analyses: List[Dict[str, Any]] + self, symbol: str, report_analyses: List[Dict[str, Any]] ) -> Dict[str, Any]: try: # Combine all analyses for comprehensive view @@ -242,35 +235,38 @@ async def _generate_comprehensive_summary( }} """ - response = await self.llm.ainvoke([HumanMessage(content=combined_analysis_prompt)]) + response = await self.llm.ainvoke( + [HumanMessage(content=combined_analysis_prompt)] + ) try: - import json summary_data = json.loads(response.content) return summary_data except json.JSONDecodeError as e: - logger.warning(f"Failed to parse comprehensive summary for {symbol}: {str(e)}") + logger.warning( + f"Failed to parse comprehensive summary for {symbol}: {str(e)}" + ) return { "executive_summary": "Analysis completed but summary parsing failed", "financial_health_score": 5.0, "key_metrics": {}, "risk_factors": [], - "growth_prospects": [] + "growth_prospects": [], } except Exception as e: - logger.error(f"Error generating comprehensive summary for {symbol}: {str(e)}") + logger.error( + f"Error generating comprehensive summary for {symbol}: {str(e)}" + ) return { "error": str(e), "executive_summary": "Failed to generate summary", - "financial_health_score": 0.0 + "financial_health_score": 0.0, } async def analyze_earnings_calls( - self, - symbol: str, - quarters: int = 4 + self, symbol: str, quarters: int = 4 ) -> Dict[str, Any]: try: # Search for earnings call transcripts @@ -278,7 +274,7 @@ async def analyze_earnings_calls( f"{symbol} earnings call transcript Q1 2024", f"{symbol} earnings call transcript Q2 2024", f"{symbol} earnings call transcript Q3 2024", - f"{symbol} earnings call transcript Q4 2024" + f"{symbol} earnings call transcript Q4 2024", ] earnings_analyses = [] @@ -286,31 +282,33 @@ async def analyze_earnings_calls( for query in search_queries[:quarters]: try: # Use web search or specific financial sites - search_url = f"https://www.google.com/search?q={query.replace(' ', '+')}" - # This would typically use a more sophisticated search # For demo purposes, showing the structure earnings_analysis = { - "quarter": query.split("Q")[1][:1] if "Q" in query else "Unknown", + "quarter": ( + query.split("Q")[1][:1] if "Q" in query else "Unknown" + ), "year": "2024", "management_tone": "confident/cautious/concerned", "key_themes": [], "guidance_changes": [], - "analyst_sentiment": "positive/neutral/negative" + "analyst_sentiment": "positive/neutral/negative", } earnings_analyses.append(earnings_analysis) except Exception as e: - logger.warning(f"Failed to analyze earnings call for query {query}: {str(e)}") + logger.warning( + f"Failed to analyze earnings call for query {query}: {str(e)}" + ) return { "symbol": symbol, "earnings_call_analyses": earnings_analyses, "overall_management_sentiment": "positive", # Would be calculated "guidance_trends": [], - "key_investor_concerns": [] + "key_investor_concerns": [], } except Exception as e: @@ -318,17 +316,21 @@ async def analyze_earnings_calls( return {"error": str(e)} async def _health_check_impl(self) -> None: - # Test Firecrawl service - health_ok = await self.firecrawl_service.health_check() + # Test LocalScrapingService + health_ok = await self.local_scraping_service.health_check() if not health_ok: - raise Exception("Firecrawl service health check failed") + raise Exception("Local scraping service health check failed") # Test LLM try: - test_response = await self.llm.ainvoke([ - HumanMessage(content="Respond with 'OK' if you can process this message.") - ]) + test_response = await self.llm.ainvoke( + [ + HumanMessage( + content="Respond with 'OK' if you can process this message." + ) + ] + ) if "OK" not in test_response.content: raise Exception("LLM health check failed") except Exception as e: - raise Exception(f"LLM health check failed: {str(e)}") \ No newline at end of file + raise Exception(f"LLM health check failed: {str(e)}") diff --git a/src/tradegraph_financial_advisor/config/settings.py b/src/tradegraph_financial_advisor/config/settings.py index 7d212e2..f509ca9 100644 --- a/src/tradegraph_financial_advisor/config/settings.py +++ b/src/tradegraph_financial_advisor/config/settings.py @@ -1,4 +1,3 @@ -import os from typing import List, Optional from pydantic import Field from pydantic_settings import BaseSettings @@ -9,7 +8,6 @@ class Settings(BaseSettings): openai_api_key: str = Field("", env="OPENAI_API_KEY") - firecrawl_api_key: str = Field("", env="FIRECRAWL_API_KEY") alpha_vantage_api_key: Optional[str] = Field(None, env="ALPHA_VANTAGE_API_KEY") financial_data_api_key: Optional[str] = Field(None, env="FINANCIAL_DATA_API_KEY") @@ -18,16 +16,19 @@ class Settings(BaseSettings): analysis_timeout_seconds: int = Field(30, env="ANALYSIS_TIMEOUT_SECONDS") news_sources: List[str] = Field( - default_factory=lambda: ["bloomberg", "reuters", "yahoo-finance", "marketwatch", "cnbc"], - env="NEWS_SOURCES" + default_factory=lambda: [ + "bloomberg", + "reuters", + "yahoo-finance", + "marketwatch", + "cnbc", + ], + env="NEWS_SOURCES", ) analysis_depth: str = Field("detailed", env="ANALYSIS_DEPTH") default_portfolio_size: float = Field(100000.0, env="DEFAULT_PORTFOLIO_SIZE") - model_config = { - "env_file": ".env", - "case_sensitive": False - } + model_config = {"env_file": ".env", "case_sensitive": False} @classmethod def get_news_sources_list(cls, v: str) -> List[str]: @@ -36,4 +37,4 @@ def get_news_sources_list(cls, v: str) -> List[str]: return v -settings = Settings() \ No newline at end of file +settings = Settings() diff --git a/src/tradegraph_financial_advisor/main.py b/src/tradegraph_financial_advisor/main.py index 19d5199..b244c6b 100644 --- a/src/tradegraph_financial_advisor/main.py +++ b/src/tradegraph_financial_advisor/main.py @@ -1,6 +1,6 @@ import asyncio import sys -from typing import List, Optional, Dict, Any +from typing import List, Dict, Any from datetime import datetime import argparse from loguru import logger @@ -9,16 +9,18 @@ from .agents.recommendation_engine import TradingRecommendationEngine from .agents.report_analysis_agent import ReportAnalysisAgent from .config.settings import settings -from .models.recommendations import PortfolioRecommendation from .utils.helpers import save_analysis_results from .visualization import charts class FinancialAdvisor: - def __init__(self): - self.workflow = FinancialAnalysisWorkflow() - self.recommendation_engine = TradingRecommendationEngine() - self.report_analyzer = ReportAnalysisAgent() + def __init__(self, llm_model_name: str = "gpt-5-nano"): + self.llm_model_name = llm_model_name + self.workflow = FinancialAnalysisWorkflow(llm_model_name=self.llm_model_name) + self.recommendation_engine = TradingRecommendationEngine( + model_name=self.llm_model_name + ) + self.report_analyzer = ReportAnalysisAgent(llm_model_name=self.llm_model_name) async def analyze_portfolio( self, @@ -26,7 +28,7 @@ async def analyze_portfolio( portfolio_size: float = None, risk_tolerance: str = "medium", time_horizon: str = "medium_term", - include_reports: bool = True + include_reports: bool = True, ) -> Dict[str, Any]: """ Perform comprehensive portfolio analysis and generate recommendations. @@ -52,7 +54,7 @@ async def analyze_portfolio( symbols=symbols, portfolio_size=portfolio_size, risk_tolerance=risk_tolerance, - time_horizon=time_horizon + time_horizon=time_horizon, ) portfolio_recommendation = workflow_results.get("portfolio_recommendation") @@ -67,7 +69,7 @@ async def analyze_portfolio( report_input = { "symbols": symbols, "report_types": ["10-K", "10-Q"], - "analysis_depth": "detailed" + "analysis_depth": "detailed", } report_result = await self.report_analyzer.execute(report_input) @@ -87,8 +89,8 @@ async def analyze_portfolio( "portfolio_context": { "portfolio_size": portfolio_size, "risk_tolerance": risk_tolerance, - "time_horizon": time_horizon - } + "time_horizon": time_horizon, + }, } # Combine all results @@ -98,17 +100,23 @@ async def analyze_portfolio( "portfolio_size": portfolio_size, "risk_tolerance": risk_tolerance, "time_horizon": time_horizon, - "analysis_timestamp": datetime.now().isoformat() + "analysis_timestamp": datetime.now().isoformat(), }, - "portfolio_recommendation": portfolio_recommendation if portfolio_recommendation else None, + "portfolio_recommendation": ( + portfolio_recommendation if portfolio_recommendation else None + ), "sentiment_analysis": sentiment_analysis, "detailed_reports": report_analyses, "analysis_metadata": { "workflow_version": "1.0.0", - "agents_used": ["NewsReaderAgent", "FinancialAnalysisAgent", "ReportAnalysisAgent"], + "agents_used": [ + "NewsReaderAgent", + "FinancialAnalysisAgent", + "ReportAnalysisAgent", + ], "data_sources": settings.news_sources, - "total_analysis_time": "calculated_at_runtime" - } + "total_analysis_time": "calculated_at_runtime", + }, } logger.info("Comprehensive analysis completed successfully") @@ -119,9 +127,7 @@ async def analyze_portfolio( raise async def quick_analysis( - self, - symbols: List[str], - analysis_type: str = "standard" + self, symbols: List[str], analysis_type: str = "standard" ) -> Dict[str, Any]: """ Perform quick analysis without full report scraping. @@ -141,26 +147,28 @@ async def quick_analysis( portfolio_rec = await self.workflow.analyze_portfolio( symbols=symbols, portfolio_size=50000, # Default smaller size for quick analysis - risk_tolerance="medium" + risk_tolerance="medium", ) return { "analysis_type": "basic", "symbols": symbols, - "recommendations": [rec.dict() for rec in portfolio_rec.recommendations] if portfolio_rec else [], - "analysis_timestamp": datetime.now().isoformat() + "recommendations": ( + [rec.dict() for rec in portfolio_rec.recommendations] + if portfolio_rec + else [] + ), + "analysis_timestamp": datetime.now().isoformat(), } elif analysis_type == "standard": return await self.analyze_portfolio( - symbols=symbols, - include_reports=False + symbols=symbols, include_reports=False ) else: # detailed return await self.analyze_portfolio( - symbols=symbols, - include_reports=True + symbols=symbols, include_reports=True ) except Exception as e: @@ -182,18 +190,20 @@ async def get_stock_alerts(self, symbols: List[str]) -> List[Dict[str, Any]]: # This would typically connect to a real-time data feed # For demo purposes, using current analysis - results = await self.quick_analysis(symbols, "basic") + await self.quick_analysis(symbols, "basic") alerts = [] # Extract alerts from analysis (simplified) for symbol in symbols: - alerts.append({ - "symbol": symbol, - "alert_type": "analysis_available", - "message": f"Analysis completed for {symbol}", - "timestamp": datetime.now().isoformat(), - "urgency": "low" - }) + alerts.append( + { + "symbol": symbol, + "alert_type": "analysis_available", + "message": f"Analysis completed for {symbol}", + "timestamp": datetime.now().isoformat(), + "urgency": "low", + } + ) return alerts @@ -205,9 +215,9 @@ def print_recommendations(self, results: Dict[str, Any]) -> None: """ Pretty print analysis results to console. """ - print("\n" + "="*80) + print("\n" + "=" * 80) print("TRADEGRAPH FINANCIAL ADVISOR - ANALYSIS RESULTS") - print("="*80) + print("=" * 80) # Analysis Summary summary = results.get("analysis_summary", {}) @@ -219,14 +229,18 @@ def print_recommendations(self, results: Dict[str, Any]) -> None: # Portfolio Recommendation portfolio = results.get("portfolio_recommendation") if portfolio: - print(f"\nšŸ“Š PORTFOLIO RECOMMENDATION") + print("\nšŸ“Š PORTFOLIO RECOMMENDATION") print(f"Overall Confidence: {portfolio.get('total_confidence', 0):.1%}") - print(f"Diversification Score: {portfolio.get('diversification_score', 0):.1%}") + print( + f"Diversification Score: {portfolio.get('diversification_score', 0):.1%}" + ) print(f"Risk Level: {portfolio.get('overall_risk_level', 'Unknown')}") recommendations = portfolio.get("recommendations", []) if recommendations: - print(f"\nšŸ“ˆ INDIVIDUAL RECOMMENDATIONS ({len(recommendations)} stocks):") + print( + f"\nšŸ“ˆ INDIVIDUAL RECOMMENDATIONS ({len(recommendations)} stocks):" + ) print("-" * 60) for rec in recommendations: @@ -237,9 +251,17 @@ def print_recommendations(self, results: Dict[str, Any]) -> None: target = rec.get("target_price") current = rec.get("current_price", 0) - print(f"\n{symbol}: {recommendation} (Confidence: {confidence:.1%})") - print(f" Current: ${current:.2f} | Target: ${target:.2f}" if target else f" Current: ${current:.2f}") - print(f" Allocation: {allocation:.1%} | Risk: {rec.get('risk_level', 'Unknown')}") + print( + f"\n{symbol}: {recommendation} (Confidence: {confidence:.1%})" + ) + print( + f" Current: ${current:.2f} | Target: ${target:.2f}" + if target + else f" Current: ${current:.2f}" + ) + print( + f" Allocation: {allocation:.1%} | Risk: {rec.get('risk_level', 'Unknown')}" + ) factors = rec.get("key_factors", []) if factors: @@ -248,7 +270,7 @@ def print_recommendations(self, results: Dict[str, Any]) -> None: # Detailed Reports Summary reports = results.get("detailed_reports", {}) if reports: - print(f"\nšŸ“‹ DETAILED REPORT ANALYSIS") + print("\nšŸ“‹ DETAILED REPORT ANALYSIS") print("-" * 40) for symbol, report in reports.items(): @@ -260,7 +282,7 @@ def print_recommendations(self, results: Dict[str, Any]) -> None: if summary_text: print(f" Summary: {summary_text[:100]}...") - print("\n" + "="*80) + print("\n" + "=" * 80) async def main(): @@ -271,44 +293,40 @@ async def main(): description="TradeGraph Financial Advisor - AI-powered investment analysis" ) parser.add_argument( - "symbols", - nargs="+", - help="Stock symbols to analyze (e.g., AAPL MSFT GOOGL)" + "symbols", nargs="+", help="Stock symbols to analyze (e.g., AAPL MSFT GOOGL)" ) parser.add_argument( "--portfolio-size", type=float, default=None, - help="Portfolio size in dollars (default: from config)" + help="Portfolio size in dollars (default: from config)", ) parser.add_argument( "--risk-tolerance", choices=["conservative", "medium", "aggressive"], default="medium", - help="Risk tolerance level" + help="Risk tolerance level", ) parser.add_argument( "--time-horizon", choices=["short_term", "medium_term", "long_term"], default="medium_term", - help="Investment time horizon" + help="Investment time horizon", ) parser.add_argument( "--analysis-type", choices=["quick", "standard", "comprehensive"], default="standard", - help="Analysis depth" + help="Analysis depth", ) parser.add_argument( "--output-format", choices=["console", "json"], default="console", - help="Output format" + help="Output format", ) parser.add_argument( - "--alerts-only", - action="store_true", - help="Generate alerts only" + "--alerts-only", action="store_true", help="Generate alerts only" ) args = parser.parse_args() @@ -318,7 +336,7 @@ async def main(): logger.add( sys.stderr, level=settings.log_level, - format="{time} | {level: <8} | {name}:{function}:{line} - {message}" + format="{time} | {level: <8} | {name}:{function}:{line} - {message}", ) try: @@ -335,9 +353,9 @@ async def main(): "analysis_summary": { "symbols_analyzed": args.symbols, "analysis_type": "alerts_only", - "analysis_timestamp": datetime.now().isoformat() + "analysis_timestamp": datetime.now().isoformat(), }, - "alerts": alerts + "alerts": alerts, } filepath = save_analysis_results(alerts_results) logger.info(f"Alerts results automatically saved to: {filepath}") @@ -346,6 +364,7 @@ async def main(): if args.output_format == "json": import json + print(json.dumps(alerts, indent=2)) else: print("\nšŸšØ CURRENT ALERTS:") @@ -362,7 +381,7 @@ async def main(): portfolio_size=args.portfolio_size, risk_tolerance=args.risk_tolerance, time_horizon=args.time_horizon, - include_reports=True + include_reports=True, ) else: # standard results = await advisor.analyze_portfolio( @@ -370,7 +389,7 @@ async def main(): portfolio_size=args.portfolio_size, risk_tolerance=args.risk_tolerance, time_horizon=args.time_horizon, - include_reports=False + include_reports=False, ) # Always save results to JSON file with timestamp @@ -379,29 +398,34 @@ async def main(): logger.info(f"Analysis results automatically saved to: {filepath}") except Exception as e: logger.warning(f"Failed to save results file: {str(e)}") - + # Generate portfolio allocation chart try: - portfolio_rec = results.get('portfolio_recommendation') - - if portfolio_rec and portfolio_rec.get('recommendations'): - recommendations = portfolio_rec.get('recommendations', []) - + portfolio_rec = results.get("portfolio_recommendation") + + if portfolio_rec and portfolio_rec.get("recommendations"): + recommendations = portfolio_rec.get("recommendations", []) + chart_path = charts.create_portfolio_allocation_chart( recommendations=recommendations, - output_path="results/portfolio_allocation.html" + output_path="results/portfolio_allocation.html", ) - + logger.info(f"Portfolio allocation chart saved to: {chart_path}") else: - logger.warning("No recommendations found to create allocation chart") - + logger.warning( + "No recommendations found to create allocation chart" + ) + except Exception as e: - logger.warning(f"Failed to generate portfolio allocation chart: {str(e)}") + logger.warning( + f"Failed to generate portfolio allocation chart: {str(e)}" + ) # Display results based on output format if args.output_format == "json": import json + print(json.dumps(results, indent=2, default=str)) else: advisor.print_recommendations(results) @@ -422,4 +446,4 @@ def cli_main(): if __name__ == "__main__": - cli_main() \ No newline at end of file + cli_main() diff --git a/src/tradegraph_financial_advisor/models/recommendations.py b/src/tradegraph_financial_advisor/models/recommendations.py index 90efd44..fdb6371 100644 --- a/src/tradegraph_financial_advisor/models/recommendations.py +++ b/src/tradegraph_financial_advisor/models/recommendations.py @@ -38,7 +38,9 @@ class TradingRecommendation(BaseModel): time_horizon: TimeHorizon # Position sizing - recommended_allocation: float = Field(..., ge=0.0, le=1.0) # Percentage of portfolio + recommended_allocation: float = Field( + ..., ge=0.0, le=1.0 + ) # Percentage of portfolio max_position_size: Optional[float] = None # Dollar amount # Analysis breakdown @@ -95,4 +97,4 @@ class AlertRecommendation(BaseModel): urgency: str # "low", "medium", "high", "critical" trigger_conditions: Dict[str, Any] = Field(default_factory=dict) expiry_date: Optional[datetime] = None - created_at: datetime = Field(default_factory=datetime.now) \ No newline at end of file + created_at: datetime = Field(default_factory=datetime.now) diff --git a/src/tradegraph_financial_advisor/services/__init__.py b/src/tradegraph_financial_advisor/services/__init__.py index e69de29..36f14fc 100644 --- a/src/tradegraph_financial_advisor/services/__init__.py +++ b/src/tradegraph_financial_advisor/services/__init__.py @@ -0,0 +1,3 @@ +from .local_scraping_service import LocalScrapingService + +__all__ = ["LocalScrapingService"] diff --git a/src/tradegraph_financial_advisor/services/firecrawl_service.py b/src/tradegraph_financial_advisor/services/firecrawl_service.py deleted file mode 100644 index 11947f6..0000000 --- a/src/tradegraph_financial_advisor/services/firecrawl_service.py +++ /dev/null @@ -1,555 +0,0 @@ -import asyncio -from typing import Any, Dict, List, Optional, Union -from datetime import datetime, timedelta -import aiohttp -from loguru import logger -import re -from dateutil import parser as date_parser - -from ..config.settings import settings -from ..models.financial_data import NewsArticle -from ..utils.helpers import generate_summary - - -class FirecrawlService: - def __init__(self): - self.api_key = settings.firecrawl_api_key - self.base_url = "https://api.firecrawl.dev" - self.session: Optional[aiohttp.ClientSession] = None - - async def __aenter__(self): - await self.start() - return self - - async def __aexit__(self, exc_type, exc_val, exc_tb): - await self.stop() - - async def start(self) -> None: - self.session = aiohttp.ClientSession( - headers={ - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - }, - timeout=aiohttp.ClientTimeout(total=settings.analysis_timeout_seconds) - ) - - async def stop(self) -> None: - if self.session: - await self.session.close() - - async def scrape_url(self, url: str, options: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: - if not self.session: - raise RuntimeError("FirecrawlService not started") - - payload = { - "url": url, - "formats": ["markdown", "html"], - "onlyMainContent": True, - "includeTags": ["article", "main", "div"], - "excludeTags": ["nav", "footer", "aside", "script", "style"], - "waitFor": 2000, - "timeout": 30000 - } - - if options: - payload.update(options) - - try: - async with self.session.post(f"{self.base_url}/v1/scrape", json=payload) as response: - if response.status == 200: - data = await response.json() - return data - else: - error_text = await response.text() - logger.error(f"Firecrawl scrape failed for {url}: {response.status} - {error_text}") - raise Exception(f"Firecrawl API error: {response.status}") - - except Exception as e: - logger.error(f"Error scraping {url} with Firecrawl: {str(e)}") - raise - - async def scrape_multiple_urls( - self, - urls: List[str], - options: Optional[Dict[str, Any]] = None - ) -> List[Dict[str, Any]]: - if not urls: - return [] - - # Limit concurrent requests to avoid overwhelming the API - semaphore = asyncio.Semaphore(3) - - async def scrape_with_semaphore(url: str) -> Dict[str, Any]: - async with semaphore: - try: - return await self.scrape_url(url, options) - except Exception as e: - logger.warning(f"Failed to scrape {url}: {str(e)}") - return {"url": url, "error": str(e)} - - tasks = [scrape_with_semaphore(url) for url in urls] - results = await asyncio.gather(*tasks, return_exceptions=True) - - # Filter out exceptions and return valid results - valid_results = [] - for i, result in enumerate(results): - if isinstance(result, Exception): - logger.error(f"Exception scraping {urls[i]}: {str(result)}") - elif isinstance(result, dict) and "error" not in result: - valid_results.append(result) - - return valid_results - - async def scrape_financial_reports( - self, - company_symbol: str, - report_type: str = "10-K" - ) -> List[Dict[str, Any]]: - try: - # SEC EDGAR search URL - edgar_search_url = f"https://www.sec.gov/cgi-bin/browse-edgar?CIK={company_symbol}&type={report_type}&dateb=&owner=include&count=10" - - # First, scrape the search results page - search_results = await self.scrape_url(edgar_search_url, { - "includeTags": ["table", "tr", "td", "a"], - "waitFor": 3000 - }) - - if "data" not in search_results: - return [] - - # Extract filing URLs from the search results - filing_urls = self._extract_filing_urls(search_results["data"]) - - # Scrape the actual filing documents - filings = [] - if isinstance(filing_urls, list) and len(filing_urls) > 0: - urls_to_process = filing_urls[:3] # Limit to 3 most recent filings - else: - urls_to_process = [] - - for url in urls_to_process: - try: - filing_data = await self.scrape_url(url, { - "onlyMainContent": True, - "includeTags": ["div", "p", "table", "span"], - "excludeTags": ["script", "style", "nav", "header", "footer"] - }) - - if "data" in filing_data: - markdown_content = filing_data["data"].get("markdown", "") - if isinstance(markdown_content, str): - content = markdown_content[:10000] - else: - content = str(markdown_content)[:10000] - - filings.append({ - "url": url, - "content": content, - "scraped_at": datetime.now().isoformat(), - "report_type": report_type - }) - - except Exception as e: - logger.warning(f"Failed to scrape filing {url}: {str(e)}") - - return filings - - except Exception as e: - logger.error(f"Error scraping financial reports for {company_symbol}: {str(e)}") - return [] - - def _extract_filing_urls(self, html_content: str) -> List[str]: - """Extract SEC filing URLs, prioritizing the most recent filings.""" - filing_data = [] - - try: - from bs4 import BeautifulSoup - - soup = BeautifulSoup(html_content, 'html.parser') - - # Look for filing tables (SEC EDGAR format) - tables = soup.find_all('table') - - for table in tables: - rows = table.find_all('tr')[1:] # Skip header row - - for row in rows: - cells = row.find_all(['td', 'th']) - if len(cells) >= 4: # Typical SEC filing table has multiple columns - try: - # Extract filing date (usually in first few columns) - filing_date_text = None - for i in range(min(3, len(cells))): - cell_text = cells[i].get_text(strip=True) - if re.match(r'\d{4}-\d{2}-\d{2}', cell_text): - filing_date_text = cell_text - break - - # Extract filing URL - link = row.find('a', href=True) - if link and '/Archives/edgar/data/' in link.get('href', ''): - href = link['href'] - if not href.startswith('http'): - href = f"https://www.sec.gov{href}" - - # Parse filing date - filing_date = datetime.now() - if filing_date_text: - try: - filing_date = date_parser.parse(filing_date_text) - except: - pass - - filing_data.append({ - 'url': href, - 'date': filing_date, - 'date_text': filing_date_text - }) - - except Exception as e: - continue - - # If table parsing failed, fall back to simple link extraction - if not filing_data: - links = soup.find_all('a', href=True) - for link in links: - href = link['href'] - if '/Archives/edgar/data/' in href and href.endswith('.htm'): - if not href.startswith('http'): - href = f"https://www.sec.gov{href}" - - # Try to extract date from link text or nearby elements - link_text = link.get_text(strip=True) - parent_text = link.parent.get_text(strip=True) if link.parent else "" - - filing_date = datetime.now() - for text in [link_text, parent_text]: - date_match = re.search(r'(\d{4}-\d{2}-\d{2})', text) - if date_match: - try: - filing_date = date_parser.parse(date_match.group(1)) - break - except: - pass - - filing_data.append({ - 'url': href, - 'date': filing_date, - 'date_text': None - }) - - # Sort by date (newest first) and remove duplicates - filing_data.sort(key=lambda x: x['date'], reverse=True) - - # Remove duplicate URLs, keeping the newest - seen_urls = set() - unique_filings = [] - - for filing in filing_data: - url = filing['url'] - if url not in seen_urls: - seen_urls.add(url) - unique_filings.append(filing) - - # Only keep filings from the last 2 years for relevance - cutoff_date = datetime.now() - timedelta(days=730) - recent_filings = [f for f in unique_filings if f['date'] >= cutoff_date] - - # Return only URLs of recent, unique filings (up to 5 most recent) - return [filing['url'] for filing in recent_filings[:5]] - - except Exception as e: - logger.warning(f"Error extracting filing URLs: {str(e)}") - return [] - - async def scrape_news_websites( - self, - symbols: List[str], - max_articles_per_source: int = 10 - ) -> List[NewsArticle]: - news_sources = { - "marketwatch": f"https://www.marketwatch.com/search?q={'+'.join(symbols)}", - "yahoo_finance": f"https://finance.yahoo.com/lookup?s={symbols[0]}" if symbols else "", - "seeking_alpha": f"https://seekingalpha.com/search?q={'+'.join(symbols)}", - "bloomberg": f"https://www.bloomberg.com/search?query={'+'.join(symbols)}" - } - - all_articles = [] - - for source, search_url in news_sources.items(): - try: - if not search_url: - continue - - logger.info(f"Scraping {source} for symbols: {symbols}") - - search_results = await self.scrape_url(search_url, { - "includeTags": ["article", "div", "h1", "h2", "h3", "p", "a", "time"], - "excludeTags": ["script", "style", "nav", "footer", "aside", "advertisement"], - "waitFor": 3000 - }) - - if "data" in search_results: - articles = self._extract_articles_from_html( - search_results["data"], - source, - symbols, - max_articles_per_source - ) - all_articles.extend(articles) - - except Exception as e: - logger.warning(f"Failed to scrape {source}: {str(e)}") - - return all_articles - - def _extract_articles_from_html( - self, - scraped_data: Dict[str, Any], - source: str, - symbols: List[str], - max_articles: int - ) -> List[NewsArticle]: - articles = [] - - try: - markdown_content = scraped_data.get("markdown", "") - html_content = scraped_data.get("html", "") - - from bs4 import BeautifulSoup - - if html_content: - soup = BeautifulSoup(html_content, 'html.parser') - else: - # Fallback to markdown parsing - soup = BeautifulSoup(markdown_content, 'html.parser') - - # Extract article elements based on common patterns - article_selectors = [ - 'article', - '.story-card', - '.news-item', - '.article-card', - '[data-testid*="article"]', - '.headline' - ] - - found_articles = [] - for selector in article_selectors: - elements = soup.select(selector) - found_articles.extend(elements) - - # Remove duplicates and limit results - unique_articles = list({str(elem): elem for elem in found_articles}.values()) - - # Sort by extractable publication dates first - article_data = [] - - for elem in unique_articles: - try: - # Extract title - title_elem = ( - elem.find(['h1', 'h2', 'h3']) or - elem.find('a') or - elem.find(class_=['title', 'headline']) - ) - - if not title_elem: - continue - - title = title_elem.get_text(strip=True) - - # Extract URL - link_elem = elem.find('a') - url = link_elem.get('href', '') if link_elem else '' - - if url and not url.startswith('http'): - if source == "marketwatch": - url = f"https://www.marketwatch.com{url}" - elif source == "yahoo_finance": - url = f"https://finance.yahoo.com{url}" - elif source == "seeking_alpha": - url = f"https://seekingalpha.com{url}" - elif source == "bloomberg": - url = f"https://www.bloomberg.com{url}" - - # Extract publication date - published_at = self._extract_publication_date(elem, source) - - # Extract content preview - content_elem = elem.find(['p', 'div']) - content = content_elem.get_text(strip=True)[:500] if content_elem else "" - - # Check if article is relevant to symbols - is_relevant = any( - symbol.lower() in title.lower() or symbol.lower() in content.lower() - for symbol in symbols - ) - - if title and (is_relevant or not symbols): - article_data.append({ - 'title': title, - 'url': url, - 'content': content, - 'published_at': published_at, - 'source': source, - 'symbols': symbols - }) - - except Exception as e: - logger.warning(f"Error parsing article element: {str(e)}") - continue - - # Sort by publication date (newest first) and filter recent articles - cutoff_date = datetime.now() - timedelta(days=90) # Extend to 90 days for better coverage - article_data.sort(key=lambda x: x['published_at'], reverse=True) - - logger.info(f"Found {len(article_data)} articles before filtering for {source}") - - # Remove duplicates based on title similarity and keep newest - filtered_articles = self._remove_duplicate_articles(article_data) - - logger.info(f"After duplicate removal: {len(filtered_articles)} articles for {source}") - - # Take recent articles (be more lenient with date filtering) - added_count = 0 - for article_info in filtered_articles[:max_articles * 2]: # Check more candidates - try: - # Be more lenient with date filtering - include articles even if slightly older - if article_info['published_at'] >= cutoff_date or added_count < 3: - article = NewsArticle( - title=article_info['title'], - url=article_info['url'], - content=article_info['content'], - summary=generate_summary(article_info['content'] or article_info['title']), - source=article_info['source'], - published_at=article_info['published_at'], - symbols=article_info['symbols'] - ) - articles.append(article) - added_count += 1 - - if added_count >= max_articles: - break - - except Exception as e: - logger.warning(f"Error creating article object: {str(e)}") - continue - - logger.info(f"Final article count for {source}: {len(articles)}") - - except Exception as e: - logger.error(f"Error extracting articles from {source}: {str(e)}") - - return articles - - def _extract_publication_date(self, elem, source: str) -> datetime: - """Extract publication date from article element.""" - try: - # Try various date element selectors - date_selectors = [ - 'time', - '.timestamp', '.date', '.published-date', - '[datetime]', '[data-timestamp]', - '.article-date', '.post-date' - ] - - date_text = None - - for selector in date_selectors: - date_elem = elem.select_one(selector) - if date_elem: - # Try datetime attribute first - if date_elem.has_attr('datetime'): - date_text = date_elem['datetime'] - elif date_elem.has_attr('data-timestamp'): - date_text = date_elem['data-timestamp'] - else: - date_text = date_elem.get_text(strip=True) - break - - # If no dedicated date element, look for date patterns in text - if not date_text: - text_content = elem.get_text() - # Common date patterns - date_patterns = [ - r'(\d{1,2}/\d{1,2}/\d{4})', # MM/DD/YYYY - r'(\d{4}-\d{1,2}-\d{1,2})', # YYYY-MM-DD - r'(\w+ \d{1,2}, \d{4})', # Month DD, YYYY - r'(\d{1,2} \w+ \d{4})', # DD Month YYYY - r'(\d+ hours? ago)', # X hours ago - r'(\d+ days? ago)', # X days ago - ] - - for pattern in date_patterns: - match = re.search(pattern, text_content, re.IGNORECASE) - if match: - date_text = match.group(1) - break - - if date_text: - # Handle relative dates - if 'ago' in date_text.lower(): - if 'hour' in date_text: - hours = int(re.search(r'(\d+)', date_text).group(1)) - return datetime.now() - timedelta(hours=hours) - elif 'day' in date_text: - days = int(re.search(r'(\d+)', date_text).group(1)) - return datetime.now() - timedelta(days=days) - - # Try to parse the date - return date_parser.parse(date_text, fuzzy=True) - - except Exception as e: - logger.debug(f"Could not extract date from {source} article: {str(e)}") - - # Fallback: return current time minus a small random offset to maintain some ordering - import random - return datetime.now() - timedelta(minutes=random.randint(0, 60)) - - def _remove_duplicate_articles(self, article_data: List[Dict]) -> List[Dict]: - """Remove duplicate articles based on title similarity, keeping the newest.""" - from difflib import SequenceMatcher - - def title_similarity(title1: str, title2: str) -> float: - """Calculate similarity between two titles.""" - return SequenceMatcher(None, title1.lower(), title2.lower()).ratio() - - unique_articles = [] - similarity_threshold = 0.85 # 85% similarity threshold - - for article in article_data: - is_duplicate = False - - for existing in unique_articles: - if title_similarity(article['title'], existing['title']) > similarity_threshold: - # Found a duplicate - keep the newer one - if article['published_at'] > existing['published_at']: - unique_articles.remove(existing) - break - else: - is_duplicate = True - break - - if not is_duplicate: - unique_articles.append(article) - - return unique_articles - - async def health_check(self) -> bool: - try: - if not self.session: - await self.start() - - # Test with a simple URL - test_result = await self.scrape_url( - "https://httpbin.org/json", - {"formats": ["markdown"]} - ) - - return "data" in test_result - - except Exception as e: - logger.error(f"Firecrawl health check failed: {str(e)}") - return False diff --git a/src/tradegraph_financial_advisor/services/local_scraping_service.py b/src/tradegraph_financial_advisor/services/local_scraping_service.py new file mode 100644 index 0000000..864338f --- /dev/null +++ b/src/tradegraph_financial_advisor/services/local_scraping_service.py @@ -0,0 +1,96 @@ +from typing import Any, Dict, List +from loguru import logger +from ddgs import DDGS +from crawl4ai import AsyncWebCrawler +from ..models.financial_data import NewsArticle +from ..utils.helpers import generate_summary + + +class LocalScrapingService: + def __init__(self): + self.crawler = AsyncWebCrawler() + + async def search_and_scrape_news( + self, symbols: List[str], max_articles_per_symbol: int = 5 + ) -> List[NewsArticle]: + all_articles = [] + async with DDGS() as ddgs: + for symbol in symbols: + query = f"{symbol} stock news" + try: + results = await ddgs.news( + keywords=query, + region="us-en", + safesearch="off", + timelimit="d", + max_results=max_articles_per_symbol, + ) + if results: + for result in results: + try: + scraped_data = await self.crawler.arun(result["url"]) + if scraped_data and scraped_data.markdown: + article = NewsArticle( + title=result.get("title", ""), + url=result.get("url", ""), + content=scraped_data.markdown, + summary=generate_summary(scraped_data.markdown), + source=result.get("source", ""), + published_at=result.get("date", ""), + symbols=[symbol], + ) + all_articles.append(article) + except Exception as e: + logger.warning( + f"Failed to scrape article {result['url']}: {e}" + ) + except Exception as e: + logger.error(f"Failed to search for news for symbol {symbol}: {e}") + return all_articles + + async def search_and_scrape_financial_reports( + self, company_symbol: str, report_type: str = "10-K" + ) -> List[Dict[str, Any]]: + query = f"{company_symbol} {report_type} site:sec.gov" + filings = [] + async with DDGS() as ddgs: + try: + results = await ddgs.text( + keywords=query, + region="us-en", + safesearch="off", + max_results=5, + ) + if results: + for result in results: + try: + scraped_data = await self.crawler.arun(result["href"]) + if scraped_data and scraped_data.markdown: + filings.append( + { + "url": result["href"], + "content": scraped_data.markdown, + "report_type": report_type, + } + ) + except Exception as e: + logger.warning( + f"Failed to scrape report {result['href']}: {e}" + ) + except Exception as e: + logger.error( + f"Failed to search for financial reports for symbol {company_symbol}: {e}" + ) + return filings + + async def start(self): + logger.info("LocalScrapingService started.") + await self.crawler.start() + + async def stop(self): + logger.info("LocalScrapingService stopped.") + await self.crawler.close() + + async def health_check(self) -> bool: + # For now, we assume the service is healthy if it can be instantiated. + return True diff --git a/src/tradegraph_financial_advisor/utils/helpers.py b/src/tradegraph_financial_advisor/utils/helpers.py index e16238a..1080da6 100644 --- a/src/tradegraph_financial_advisor/utils/helpers.py +++ b/src/tradegraph_financial_advisor/utils/helpers.py @@ -1,6 +1,6 @@ import asyncio from typing import Any, Dict, List, Optional, Callable -from datetime import datetime, timedelta +from datetime import datetime import json import os import re @@ -70,7 +70,9 @@ def generate_summary(text: str, max_sentences: int = 2, max_length: int = 320) - return summary -def calculate_portfolio_metrics(recommendations: List[Dict[str, Any]]) -> Dict[str, float]: +def calculate_portfolio_metrics( + recommendations: List[Dict[str, Any]], +) -> Dict[str, float]: """ Calculate basic portfolio metrics from recommendations. @@ -83,8 +85,12 @@ def calculate_portfolio_metrics(recommendations: List[Dict[str, Any]]) -> Dict[s if not recommendations: return {} - total_allocation = sum(rec.get("recommended_allocation", 0) for rec in recommendations) - avg_confidence = sum(rec.get("confidence_score", 0) for rec in recommendations) / len(recommendations) + total_allocation = sum( + rec.get("recommended_allocation", 0) for rec in recommendations + ) + avg_confidence = sum( + rec.get("confidence_score", 0) for rec in recommendations + ) / len(recommendations) # Risk distribution risk_counts = {} @@ -93,19 +99,27 @@ def calculate_portfolio_metrics(recommendations: List[Dict[str, Any]]) -> Dict[s risk_counts[risk] = risk_counts.get(risk, 0) + 1 # Expected returns - expected_returns = [rec.get("expected_return", 0) for rec in recommendations if rec.get("expected_return")] - avg_expected_return = sum(expected_returns) / len(expected_returns) if expected_returns else 0 + expected_returns = [ + rec.get("expected_return", 0) + for rec in recommendations + if rec.get("expected_return") + ] + avg_expected_return = ( + sum(expected_returns) / len(expected_returns) if expected_returns else 0 + ) return { "total_allocation": total_allocation, "average_confidence": avg_confidence, "number_of_positions": len(recommendations), "risk_distribution": risk_counts, - "average_expected_return": avg_expected_return + "average_expected_return": avg_expected_return, } -def save_analysis_results(results: Dict[str, Any], filename: Optional[str] = None) -> str: +def save_analysis_results( + results: Dict[str, Any], filename: Optional[str] = None +) -> str: """ Save analysis results to a JSON file. @@ -118,7 +132,9 @@ def save_analysis_results(results: Dict[str, Any], filename: Optional[str] = Non """ if not filename: timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - symbols = results.get("analysis_summary", {}).get("symbols_analyzed", ["unknown"]) + symbols = results.get("analysis_summary", {}).get( + "symbols_analyzed", ["unknown"] + ) symbols_str = "_".join(symbols[:3]) # First 3 symbols filename = f"tradegraph_analysis_{symbols_str}_{timestamp}.json" @@ -127,7 +143,7 @@ def save_analysis_results(results: Dict[str, Any], filename: Optional[str] = Non filepath = os.path.join("results", filename) try: - with open(filepath, 'w') as f: + with open(filepath, "w") as f: json.dump(results, f, indent=2, default=str) logger.info(f"Analysis results saved to {filepath}") @@ -149,7 +165,7 @@ def load_analysis_results(filepath: str) -> Dict[str, Any]: Analysis results dictionary """ try: - with open(filepath, 'r') as f: + with open(filepath, "r") as f: results = json.load(f) logger.info(f"Analysis results loaded from {filepath}") @@ -164,7 +180,7 @@ async def retry_async_operation( operation: Callable, max_retries: int = 3, delay: float = 1.0, - exponential_backoff: bool = True + exponential_backoff: bool = True, ) -> Any: """ Retry an async operation with configurable backoff. @@ -191,8 +207,10 @@ async def retry_async_operation( logger.error(f"Operation failed after {max_retries} retries: {str(e)}") raise e - wait_time = delay * (2 ** attempt) if exponential_backoff else delay - logger.warning(f"Operation failed (attempt {attempt + 1}/{max_retries + 1}), retrying in {wait_time}s: {str(e)}") + wait_time = delay * (2**attempt) if exponential_backoff else delay + logger.warning( + f"Operation failed (attempt {attempt + 1}/{max_retries + 1}), retrying in {wait_time}s: {str(e)}" + ) await asyncio.sleep(wait_time) raise last_exception @@ -268,8 +286,7 @@ def get_market_hours_status() -> Dict[str, Any]: current_time = hour + minute / 60.0 is_market_hours = ( - not is_weekend and - market_open_time <= current_time <= market_close_time + not is_weekend and market_open_time <= current_time <= market_close_time ) if is_weekend: @@ -286,7 +303,7 @@ def get_market_hours_status() -> Dict[str, Any]: "status": status, "current_time": now.isoformat(), "next_open": "calculated_separately", # Would need more complex logic - "timezone": "US/Eastern" # Simplified + "timezone": "US/Eastern", # Simplified } diff --git a/src/tradegraph_financial_advisor/visualization/charts.py b/src/tradegraph_financial_advisor/visualization/charts.py index d35238c..304d01a 100644 --- a/src/tradegraph_financial_advisor/visualization/charts.py +++ b/src/tradegraph_financial_advisor/visualization/charts.py @@ -1,36 +1,35 @@ import plotly.graph_objects as go -from plotly.subplots import make_subplots -from typing import List, Dict, Any -from datetime import datetime -def create_portfolio_allocation_chart(recommendations, output_path="portfolio_allocation.html"): - + +def create_portfolio_allocation_chart( + recommendations, output_path="portfolio_allocation.html" +): """ Creates a pie chart showing portfolio allocation - + Args: recommendations: List of recommendation dicts from analysis results output_path: Where to save the HTML file """ - symbols = [rec['symbol'] for rec in recommendations] - allocations = [rec['allocation_percentage'] * 100 for rec in recommendations] - - fig = go.Figure(data=[go.Pie( - labels=symbols, - values=allocations, - hole=.3, - textinfo='label+percent', - textposition='outside' - )]) - - fig.update_layout( - title="Portfolio Allocation Recommendation", - showlegend=True + symbols = [rec["symbol"] for rec in recommendations] + allocations = [rec["allocation_percentage"] * 100 for rec in recommendations] + + fig = go.Figure( + data=[ + go.Pie( + labels=symbols, + values=allocations, + hole=0.3, + textinfo="label+percent", + textposition="outside", + ) + ] ) - + + fig.update_layout(title="Portfolio Allocation Recommendation", showlegend=True) + # Save to HTML file fig.write_html(output_path) print(f"Chart saved to: {output_path}") - + return output_path - \ No newline at end of file diff --git a/src/tradegraph_financial_advisor/workflows/analysis_workflow.py b/src/tradegraph_financial_advisor/workflows/analysis_workflow.py index c08f2ac..215b4c6 100644 --- a/src/tradegraph_financial_advisor/workflows/analysis_workflow.py +++ b/src/tradegraph_financial_advisor/workflows/analysis_workflow.py @@ -1,6 +1,5 @@ -import asyncio import json -from typing import Any, Dict, List, Optional, TypedDict, Annotated +from typing import Any, Dict, List, Optional, TypedDict from datetime import datetime from langchain_core.messages import HumanMessage, AIMessage from langchain_openai import ChatOpenAI @@ -10,14 +9,12 @@ from ..agents.news_agent import NewsReaderAgent from ..agents.financial_agent import FinancialAnalysisAgent from ..agents.recommendation_engine import TradingRecommendationEngine -from ..services.firecrawl_service import FirecrawlService -from ..models.financial_data import AnalysisContext +from ..services.local_scraping_service import LocalScrapingService from ..models.recommendations import ( TradingRecommendation, - PortfolioRecommendation, RecommendationType, RiskLevel, - TimeHorizon + TimeHorizon, ) from ..config.settings import settings @@ -37,16 +34,21 @@ class AnalysisState(TypedDict): class FinancialAnalysisWorkflow: - def __init__(self): + def __init__( + self, + scraping_service: Optional[LocalScrapingService] = None, + llm_model_name: str = "gpt-5-nano", + ): + self.llm_model_name = llm_model_name self.llm = ChatOpenAI( - model="gpt-4", - temperature=0.1, - api_key=settings.openai_api_key + model=self.llm_model_name, temperature=0.1, api_key=settings.openai_api_key ) self.news_agent = NewsReaderAgent() self.financial_agent = FinancialAnalysisAgent() - self.recommendation_engine = TradingRecommendationEngine() - self.firecrawl_service = FirecrawlService() + self.recommendation_engine = TradingRecommendationEngine( + model_name=self.llm_model_name + ) + self.local_scraping_service = scraping_service or LocalScrapingService() self.workflow = None self._build_workflow() @@ -77,7 +79,7 @@ async def analyze_portfolio( symbols: List[str], portfolio_size: float = None, risk_tolerance: str = "medium", - time_horizon: str = "medium_term" + time_horizon: str = "medium_term", ) -> Dict[str, Any]: if portfolio_size is None: @@ -89,7 +91,7 @@ async def analyze_portfolio( "portfolio_size": portfolio_size, "risk_tolerance": risk_tolerance, "time_horizon": time_horizon, - "analysis_timestamp": datetime.now().isoformat() + "analysis_timestamp": datetime.now().isoformat(), }, news_data={}, financial_data={}, @@ -99,14 +101,14 @@ async def analyze_portfolio( portfolio_recommendation=None, messages=[], next_step="collect_news", - error_messages=[] + error_messages=[], ) try: # Start agents await self.news_agent.start() await self.financial_agent.start() - await self.firecrawl_service.start() + await self.local_scraping_service.start() # Execute workflow result = await self.workflow.ainvoke(initial_state) @@ -118,7 +120,7 @@ async def analyze_portfolio( "news_data": result.get("news_data", {}), "financial_data": result.get("financial_data", {}), "recommendations": result.get("recommendations", []), - "analysis_context": result.get("analysis_context", {}) + "analysis_context": result.get("analysis_context", {}), } return analysis_result @@ -130,7 +132,7 @@ async def analyze_portfolio( # Cleanup await self.news_agent.stop() await self.financial_agent.stop() - await self.firecrawl_service.stop() + await self.local_scraping_service.stop() async def _collect_news(self, state: AnalysisState) -> AnalysisState: try: @@ -140,35 +142,45 @@ async def _collect_news(self, state: AnalysisState) -> AnalysisState: news_input = { "symbols": state["symbols"], "timeframe_hours": 48, - "max_articles": 100 + "max_articles": 100, } news_result = await self.news_agent.execute(news_input) + if not isinstance(news_result, dict): + news_result = {} - # Also use Firecrawl for additional news scraping - firecrawl_articles = await self.firecrawl_service.scrape_news_websites( - state["symbols"], - max_articles_per_source=20 + local_articles = await self.local_scraping_service.search_and_scrape_news( + state["symbols"] + ) + if not isinstance(local_articles, list): + local_articles = [] + + news_articles = news_result.get("articles", []) + if not isinstance(news_articles, list): + news_articles = [] + + combined_news = [ + self._article_to_dict(article) for article in news_articles + ] + combined_news.extend( + self._article_to_dict(article) for article in local_articles ) - - # Combine results - all_articles = news_result.get("articles", []) - all_articles.extend([article.dict() for article in firecrawl_articles]) state["news_data"] = { - "articles": all_articles, - "total_count": len(all_articles), - "collection_timestamp": datetime.now().isoformat() + "articles": combined_news, + "total_count": len(combined_news), + "collection_timestamp": datetime.now().isoformat(), } state["messages"].append( - AIMessage(content=f"Collected {len(all_articles)} news articles") + AIMessage(content=f"Collected {len(combined_news)} news articles") ) except Exception as e: error_msg = f"News collection failed: {str(e)}" logger.error(error_msg) state["error_messages"].append(error_msg) + raise return state @@ -180,7 +192,7 @@ async def _analyze_financials(self, state: AnalysisState) -> AnalysisState: "symbols": state["symbols"], "include_financials": True, "include_technical": True, - "include_market_data": True + "include_market_data": True, } financial_result = await self.financial_agent.execute(financial_input) @@ -190,17 +202,19 @@ async def _analyze_financials(self, state: AnalysisState) -> AnalysisState: # Scrape SEC filings for additional fundamental analysis for symbol in state["symbols"]: try: - filings = await self.firecrawl_service.scrape_financial_reports(symbol) + filings = await self.local_scraping_service.search_and_scrape_financial_reports( + symbol + ) if filings: if "sec_filings" not in state["financial_data"]: state["financial_data"]["sec_filings"] = {} state["financial_data"]["sec_filings"][symbol] = filings except Exception as e: - logger.warning(f"Failed to scrape SEC filings for {symbol}: {str(e)}") + logger.warning( + f"Failed to scrape SEC filings for {symbol}: {str(e)}" + ) - state["messages"].append( - AIMessage(content="Completed financial analysis") - ) + state["messages"].append(AIMessage(content="Completed financial analysis")) except Exception as e: error_msg = f"Financial analysis failed: {str(e)}" @@ -219,15 +233,22 @@ async def _analyze_sentiment(self, state: AnalysisState) -> AnalysisState: for symbol in state["symbols"]: symbol_articles = [ - article for article in articles - if symbol in getattr(article, 'symbols', []) + article + for article in articles + if symbol in self._get_article_symbols(article) ] if not symbol_articles: continue # Use LLM for advanced sentiment analysis with news summary - articles_text = chr(10).join([f"- {article.title}: {article.content[:300]}..." for article in symbol_articles[:10]]) + articles_text = chr(10).join( + [ + f"- {self._get_article_value(article, 'title', 'Article')}: " + f"{(self._get_article_value(article, 'content', '') or '')[:300]}..." + for article in symbol_articles[:10] + ] + ) sentiment_prompt = f""" Analyze the sentiment of the following news articles about {symbol}. @@ -248,15 +269,25 @@ async def _analyze_sentiment(self, state: AnalysisState) -> AnalysisState: - articles: list of objects with title, url, sentiment_contribution (positive/negative/neutral) """ - response = await self.llm.ainvoke([HumanMessage(content=sentiment_prompt)]) + response = await self.llm.ainvoke( + [HumanMessage(content=sentiment_prompt)] + ) try: import json + sentiment_data = json.loads(response.content) sentiment_analysis[symbol] = sentiment_data - except: + except Exception: # Fallback simple sentiment with news data - article_summaries = [{"title": getattr(article, 'title', ''), "url": getattr(article, 'url', ''), "sentiment_contribution": "neutral"} for article in symbol_articles[:5]] + article_summaries = [ + { + "title": self._get_article_value(article, "title", ""), + "url": self._get_article_value(article, "url", ""), + "sentiment_contribution": "neutral", + } + for article in symbol_articles[:5] + ] sentiment_analysis[symbol] = { "sentiment_score": 0.0, @@ -266,14 +297,12 @@ async def _analyze_sentiment(self, state: AnalysisState) -> AnalysisState: "sentiment_drivers": [], "news_summary": f"Analysis of {len(symbol_articles)} news articles about {symbol}. Detailed AI analysis temporarily unavailable.", "article_count": len(symbol_articles), - "articles": article_summaries + "articles": article_summaries, } state["sentiment_analysis"] = sentiment_analysis - state["messages"].append( - AIMessage(content="Completed sentiment analysis") - ) + state["messages"].append(AIMessage(content="Completed sentiment analysis")) except Exception as e: error_msg = f"Sentiment analysis failed: {str(e)}" @@ -286,15 +315,19 @@ async def _generate_recommendations(self, state: AnalysisState) -> AnalysisState try: logger.info("Generating trading recommendations") - recommendations = [] + recommendations: List[TradingRecommendation] = [] for symbol in state["symbols"]: try: - # Get data for this symbol - financial_data = state["financial_data"].get("analysis_results", {}).get(symbol, {}) + # Get analysis inputs for this symbol + financial_data = ( + state["financial_data"] + .get("analysis_results", {}) + .get(symbol, {}) + ) sentiment_data = state["sentiment_analysis"].get(symbol, {}) - # Generate recommendation using LLM + # Generate recommendation using LLM for additional context recommendation_prompt = f""" Based on the following financial and sentiment analysis for {symbol}, generate a trading recommendation. @@ -325,60 +358,54 @@ async def _generate_recommendations(self, state: AnalysisState) -> AnalysisState }} """ - response = await self.llm.ainvoke([HumanMessage(content=recommendation_prompt)]) - + parsed_response: Optional[Dict[str, Any]] = None try: - import json - rec_data = json.loads(response.content) - - # Add required fields - current_price = financial_data.get("market_data", {}).get("current_price", 100.0) - rec_data["current_price"] = current_price - rec_data["company_name"] = symbol # Fallback - - # Handle missing sentiment_score - if rec_data.get("sentiment_score") is None: - rec_data["sentiment_score"] = 0.5 - - # Calculate recommended allocation using new logic - confidence_score = rec_data.get("confidence_score", 0.5) - risk_level = RiskLevel(rec_data.get("risk_level", "medium")) - recommendation_type = RecommendationType(rec_data.get("recommendation", "hold")) - risk_preferences = {"risk_tolerance": state["analysis_context"].get("risk_tolerance", "medium")} - - # Use position sizing logic - rec_data["recommended_allocation"] = self.recommendation_engine._calculate_position_size( - confidence_score=confidence_score, - risk_level=risk_level, - risk_preferences=risk_preferences, - recommendation_type=recommendation_type + response = await self.llm.ainvoke( + [HumanMessage(content=recommendation_prompt)] + ) + if response and getattr(response, "content", None): + potential_payload = json.loads(response.content) + if isinstance(potential_payload, dict): + parsed_response = potential_payload + except Exception as llm_error: + logger.warning( + f"Failed to obtain structured recommendation for {symbol}: {str(llm_error)}" ) - recommendation = TradingRecommendation(**rec_data) - recommendations.append(recommendation) - - except Exception as e: - logger.warning(f"Failed to parse recommendation for {symbol}: {str(e)}") + recommendation = self._build_recommendation_model( + symbol=symbol, + financial_data=financial_data, + sentiment_data=sentiment_data, + state=state, + model_response=parsed_response, + ) + recommendations.append(recommendation) except Exception as e: - logger.warning(f"Failed to generate recommendation for {symbol}: {str(e)}") + logger.warning( + f"Failed to generate recommendation for {symbol}: {str(e)}" + ) # Optimize portfolio allocations after all recommendations are collected if recommendations: portfolio_constraints = { - "portfolio_size": state["analysis_context"].get("portfolio_size", 100000), - "max_positions": 10 + "portfolio_size": state["analysis_context"].get( + "portfolio_size", 100000 + ), + "max_positions": 10, } optimized_recs = await self.recommendation_engine._optimize_allocations( recommendations, - portfolio_constraints + portfolio_constraints, ) state["recommendations"] = [rec.dict() for rec in optimized_recs] else: state["recommendations"] = [] state["messages"].append( - AIMessage(content=f"Generated {len(recommendations)} trading recommendations") + AIMessage( + content=f"Generated {len(recommendations)} trading recommendations" + ) ) except Exception as e: @@ -431,8 +458,11 @@ async def _create_portfolio(self, state: AnalysisState) -> AnalysisState: try: import json + portfolio_data = json.loads(response.content) - portfolio_data["recommendations"] = recommendations # Ensure recommendations are included + portfolio_data["recommendations"] = ( + recommendations # Ensure recommendations are included + ) state["portfolio_recommendation"] = portfolio_data except Exception as e: @@ -443,7 +473,7 @@ async def _create_portfolio(self, state: AnalysisState) -> AnalysisState: "conservative": "low", "moderate": "medium", "aggressive": "high", - "very_aggressive": "very_high" + "very_aggressive": "very_high", } mapped_risk_level = risk_level_mapping.get(risk_tolerance, "medium") @@ -452,7 +482,7 @@ async def _create_portfolio(self, state: AnalysisState) -> AnalysisState: "total_confidence": 0.7, "diversification_score": 0.6, "overall_risk_level": mapped_risk_level, - "portfolio_size": portfolio_size + "portfolio_size": portfolio_size, } state["messages"].append( @@ -481,26 +511,34 @@ async def _validate_recommendations(self, state: AnalysisState) -> AnalysisState ) if abs(total_allocation - 1.0) > 0.1: # Allow 10% tolerance - validation_results.append(f"Warning: Total allocation is {total_allocation:.2%}") + validation_results.append( + f"Warning: Total allocation is {total_allocation:.2%}" + ) # Check for obvious conflicts buy_count = sum( - 1 for rec in state["recommendations"] + 1 + for rec in state["recommendations"] if rec.get("recommendation") in ["buy", "strong_buy"] ) sell_count = sum( - 1 for rec in state["recommendations"] + 1 + for rec in state["recommendations"] if rec.get("recommendation") in ["sell", "strong_sell"] ) if buy_count == 0 and sell_count == 0: - validation_results.append("Warning: No buy or sell recommendations generated") + validation_results.append( + "Warning: No buy or sell recommendations generated" + ) state["analysis_context"]["validation_results"] = validation_results state["messages"].append( - AIMessage(content=f"Validation completed with {len(validation_results)} notes") + AIMessage( + content=f"Validation completed with {len(validation_results)} notes" + ) ) except Exception as e: @@ -508,4 +546,408 @@ async def _validate_recommendations(self, state: AnalysisState) -> AnalysisState logger.error(error_msg) state["error_messages"].append(error_msg) - return state \ No newline at end of file + return state + + def _build_recommendation_model( + self, + symbol: str, + financial_data: Dict[str, Any], + sentiment_data: Dict[str, Any], + state: AnalysisState, + model_response: Optional[Dict[str, Any]] = None, + ) -> TradingRecommendation: + response_payload = model_response if isinstance(model_response, dict) else {} + analysis_context = ( + state.get("analysis_context", {}) if isinstance(state, dict) else {} + ) + + market_data = financial_data.get("market_data") or {} + fundamentals = financial_data.get("financials") or {} + technicals = financial_data.get("technical_indicators") or {} + + portfolio_size = ( + analysis_context.get("portfolio_size") or settings.default_portfolio_size + ) + current_price = self._extract_current_price( + market_data, fundamentals, technicals + ) + company_name = ( + response_payload.get("company_name") + or fundamentals.get("company_name") + or market_data.get("symbol") + or symbol + ) + + raw_sentiment = response_payload.get("sentiment_score") + if raw_sentiment is None: + raw_sentiment = sentiment_data.get("sentiment_score") + sentiment_score = self._normalize_sentiment_score(raw_sentiment) + + raw_confidence = response_payload.get("confidence_score") + if raw_confidence is None: + raw_confidence = sentiment_data.get("confidence") + if raw_confidence is None: + raw_confidence = sentiment_score + confidence_score = float( + min(0.95, max(0.3, raw_confidence if raw_confidence is not None else 0.5)) + ) + + risk_level = self._normalize_risk_level( + response_payload.get("risk_level") + or analysis_context.get("risk_tolerance") + or "medium" + ) + time_horizon = self._normalize_time_horizon( + response_payload.get("time_horizon") + or analysis_context.get("time_horizon") + or "medium_term" + ) + recommendation_type = self._normalize_recommendation_type( + response_payload.get("recommendation"), sentiment_score + ) + + fundamental_score = self._clamp_score( + response_payload.get("fundamental_score"), + self._compute_fundamental_score(fundamentals), + ) + technical_score = self._clamp_score( + response_payload.get("technical_score"), + self._compute_technical_score(technicals), + ) + + sentiment_score = round( + sentiment_score if sentiment_score is not None else 0.5, 3 + ) + key_factors = self._ensure_list( + response_payload.get("key_factors") + or sentiment_data.get("key_themes") + or [f"Market outlook for {symbol}"] + ) + risks = self._ensure_list( + response_payload.get("risks") + or sentiment_data.get("risks") + or [f"Potential volatility in {symbol}"] + ) + catalysts = self._ensure_list( + response_payload.get("catalysts") + or sentiment_data.get("sentiment_drivers") + or [] + ) + + analyst_notes = ( + response_payload.get("analyst_notes") + or sentiment_data.get("news_summary") + or f"Auto-generated analysis for {symbol}." + ) + + target_price = response_payload.get("target_price") + if target_price in (None, 0): + target_price = self._estimate_target_price( + current_price, recommendation_type, sentiment_score + ) + + stop_loss = response_payload.get("stop_loss") + if stop_loss in (None, 0): + stop_loss = self._estimate_stop_loss( + current_price, risk_level, recommendation_type + ) + + risk_preferences = { + "risk_tolerance": analysis_context.get("risk_tolerance", "medium") + } + recommended_allocation = self.recommendation_engine._calculate_position_size( + confidence_score=confidence_score, + risk_level=risk_level, + risk_preferences=risk_preferences, + recommendation_type=recommendation_type, + ) + recommended_allocation = float(min(1.0, max(0.01, recommended_allocation))) + + max_position_size = round(float(portfolio_size) * recommended_allocation, 2) + expected_return = round( + ((target_price - current_price) / current_price) if current_price else 0.0, + 3, + ) + expected_volatility = self._map_expected_volatility(risk_level) + sharpe_ratio = None + if expected_volatility: + sharpe_ratio = round((expected_return - 0.02) / expected_volatility, 2) + + market_cap_value = ( + response_payload.get("market_cap") + or fundamentals.get("market_cap") + or market_data.get("market_cap") + ) + + recommendation_data = { + "symbol": symbol, + "company_name": company_name, + "recommendation": recommendation_type, + "confidence_score": round(confidence_score, 3), + "target_price": round(target_price, 2), + "stop_loss": round(stop_loss, 2) if stop_loss is not None else None, + "current_price": round(current_price, 2), + "risk_level": risk_level, + "time_horizon": time_horizon, + "recommended_allocation": recommended_allocation, + "max_position_size": max_position_size, + "fundamental_score": round(fundamental_score, 3), + "technical_score": round(technical_score, 3), + "sentiment_score": sentiment_score, + "key_factors": key_factors, + "risks": risks, + "catalysts": catalysts, + "analyst_notes": analyst_notes, + "sector": fundamentals.get("sector") + or response_payload.get("sector") + or "Unknown", + "market_cap_category": self._categorize_market_cap(market_cap_value), + "expected_return": expected_return, + "expected_volatility": expected_volatility, + "sharpe_ratio": sharpe_ratio, + } + + return TradingRecommendation(**recommendation_data) + + def _normalize_sentiment_score(self, value: Optional[float]) -> float: + if value is None: + return 0.5 + try: + numeric_value = float(value) + except (TypeError, ValueError): + return 0.5 + + if -1.0 <= numeric_value <= 1.0: + normalized = (numeric_value + 1.0) / 2.0 + else: + normalized = numeric_value + return float(min(1.0, max(0.0, normalized))) + + def _normalize_risk_level(self, value: Optional[str]) -> RiskLevel: + if isinstance(value, RiskLevel): + return value + try: + return RiskLevel(str(value)) + except Exception: + return RiskLevel.MEDIUM + + def _normalize_time_horizon(self, value: Optional[str]) -> TimeHorizon: + if isinstance(value, TimeHorizon): + return value + try: + return TimeHorizon(str(value)) + except Exception: + return TimeHorizon.MEDIUM_TERM + + def _normalize_recommendation_type( + self, value: Optional[str], sentiment_score: Optional[float] + ) -> RecommendationType: + if isinstance(value, RecommendationType): + return value + if value: + try: + return RecommendationType(str(value)) + except Exception: + pass + + score = sentiment_score if sentiment_score is not None else 0.5 + if score >= 0.75: + return RecommendationType.STRONG_BUY + if score >= 0.6: + return RecommendationType.BUY + if score <= 0.25: + return RecommendationType.STRONG_SELL + if score <= 0.4: + return RecommendationType.SELL + return RecommendationType.HOLD + + def _compute_fundamental_score(self, financials: Dict[str, Any]) -> float: + if not financials: + return 0.55 + + score = 0.55 + pe_ratio = financials.get("pe_ratio") + if pe_ratio and pe_ratio > 0: + if pe_ratio < 15: + score += 0.1 + elif pe_ratio < 30: + score += 0.05 + else: + score -= 0.05 + + roe = financials.get("return_on_equity") + if roe is not None: + score += min(max(roe, 0.0), 0.3) + + debt_to_equity = financials.get("debt_to_equity") + if debt_to_equity: + score -= min(max(debt_to_equity - 1.0, 0.0) * 0.05, 0.15) + + revenue = financials.get("revenue") + net_income = financials.get("net_income") + if revenue and net_income: + margin = net_income / revenue + score += min(max(margin, 0.0), 0.2) + + return float(min(0.95, max(0.3, score))) + + def _compute_technical_score(self, technicals: Dict[str, Any]) -> float: + if not technicals: + return 0.55 + + score = 0.5 + + rsi = technicals.get("rsi") + if rsi is not None: + if 40 <= rsi <= 70: + score += 0.1 + elif rsi < 30 or rsi > 80: + score -= 0.1 + + macd = technicals.get("macd") + macd_signal = technicals.get("macd_signal") + if macd is not None and macd_signal is not None: + score += 0.05 if macd > macd_signal else -0.05 + + sma_20 = technicals.get("sma_20") + sma_50 = technicals.get("sma_50") + if sma_20 is not None and sma_50 is not None: + score += 0.05 if sma_20 >= sma_50 else -0.05 + + return float(min(0.9, max(0.3, score))) + + def _clamp_score(self, value: Optional[float], fallback: float) -> float: + base = value if value is not None else fallback + try: + base = float(base) + except (TypeError, ValueError): + base = fallback if fallback is not None else 0.5 + return float(min(0.95, max(0.0, base))) + + def _ensure_list(self, value: Optional[Any]) -> List[str]: + if value is None: + return [] + if isinstance(value, list): + return [str(item) for item in value if item is not None] + return [str(value)] + + def _extract_current_price( + self, + market_data: Dict[str, Any], + fundamentals: Dict[str, Any], + technicals: Dict[str, Any], + ) -> float: + for source in (market_data, fundamentals, technicals): + price = source.get("current_price") + if price is not None: + try: + return float(price) + except (TypeError, ValueError): + continue + return 100.0 + + def _estimate_target_price( + self, + current_price: float, + recommendation_type: RecommendationType, + sentiment_score: float, + ) -> float: + delta = self._estimate_target_delta(recommendation_type) + adjustment = (sentiment_score - 0.5) * 0.1 + target = current_price * (1 + delta + adjustment) + return max(target, 1.0) + + def _estimate_target_delta(self, recommendation_type: RecommendationType) -> float: + deltas = { + RecommendationType.STRONG_BUY: 0.2, + RecommendationType.BUY: 0.12, + RecommendationType.HOLD: 0.04, + RecommendationType.SELL: -0.05, + RecommendationType.STRONG_SELL: -0.1, + } + return deltas.get(recommendation_type, 0.05) + + def _estimate_stop_loss( + self, + current_price: float, + risk_level: RiskLevel, + recommendation_type: RecommendationType, + ) -> Optional[float]: + risk_percents = { + RiskLevel.LOW: 0.04, + RiskLevel.MEDIUM: 0.07, + RiskLevel.HIGH: 0.1, + RiskLevel.VERY_HIGH: 0.15, + } + pct = risk_percents.get(risk_level, 0.07) + + if recommendation_type in [ + RecommendationType.SELL, + RecommendationType.STRONG_SELL, + ]: + return current_price * (1 + pct) + return current_price * (1 - pct) + + def _map_expected_volatility(self, risk_level: RiskLevel) -> Optional[float]: + mapping = { + RiskLevel.LOW: 0.08, + RiskLevel.MEDIUM: 0.12, + RiskLevel.HIGH: 0.18, + RiskLevel.VERY_HIGH: 0.25, + } + return mapping.get(risk_level) + + def _categorize_market_cap(self, value: Optional[float]) -> Optional[str]: + if value is None: + return None + try: + market_cap = float(value) + except (TypeError, ValueError): + return None + + if market_cap >= 200_000_000_000: + return "mega_cap" + if market_cap >= 10_000_000_000: + return "large_cap" + if market_cap >= 2_000_000_000: + return "mid_cap" + if market_cap >= 300_000_000: + return "small_cap" + return "micro_cap" + + def _get_article_symbols(self, article: Any) -> List[str]: + if isinstance(article, dict): + symbols = article.get("symbols", []) + else: + symbols = getattr(article, "symbols", []) + + if isinstance(symbols, list): + return [str(symbol) for symbol in symbols if symbol is not None] + if symbols is None: + return [] + return [str(symbols)] + + def _get_article_value( + self, article: Any, key: str, default: Optional[str] = None + ) -> Any: + if isinstance(article, dict): + return article.get(key, default) + return getattr(article, key, default) + + def _article_to_dict(self, article: Any) -> Dict[str, Any]: + if isinstance(article, dict): + return article + if hasattr(article, "model_dump"): + return article.model_dump() + if hasattr(article, "dict"): + return article.dict() + return { + "title": self._get_article_value(article, "title", ""), + "url": self._get_article_value(article, "url", ""), + "content": self._get_article_value(article, "content", ""), + "source": self._get_article_value(article, "source", ""), + "published_at": self._get_article_value( + article, "published_at", datetime.now().isoformat() + ), + "symbols": self._get_article_symbols(article), + } diff --git a/tests/conftest.py b/tests/conftest.py index 63afee6..014b136 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3,11 +3,11 @@ import os from unittest.mock import Mock, AsyncMock from datetime import datetime, timedelta -from typing import Dict, Any, List + +from tradegraph_financial_advisor.models.financial_data import NewsArticle # Test configuration os.environ["OPENAI_API_KEY"] = "test-openai-key" -os.environ["FIRECRAWL_API_KEY"] = "test-firecrawl-key" os.environ["ALPHA_VANTAGE_API_KEY"] = "test-alpha-vantage-key" os.environ["LOG_LEVEL"] = "DEBUG" @@ -23,7 +23,7 @@ def event_loop(): @pytest.fixture def mock_openai_response(): """Mock OpenAI API response.""" - mock_response = Mock() + mock_response = AsyncMock() mock_response.content = '{"sentiment_score": 0.2, "sentiment_label": "bullish", "confidence": 0.8, "key_themes": ["growth", "innovation"], "sentiment_drivers": ["strong earnings", "new product launch"]}' return mock_response @@ -40,7 +40,7 @@ def sample_news_articles(): "published_at": datetime.now().isoformat(), "symbols": ["AAPL"], "sentiment": "bullish", - "impact_score": 0.8 + "impact_score": 0.8, }, { "title": "Microsoft Azure Growth Continues", @@ -50,8 +50,8 @@ def sample_news_articles(): "published_at": (datetime.now() - timedelta(hours=2)).isoformat(), "symbols": ["MSFT"], "sentiment": "bullish", - "impact_score": 0.7 - } + "impact_score": 0.7, + }, ] @@ -67,7 +67,7 @@ def sample_market_data(): "volume": 45234567, "market_cap": 3000000000000, "pe_ratio": 28.5, - "timestamp": datetime.now().isoformat() + "timestamp": datetime.now().isoformat(), }, "MSFT": { "symbol": "MSFT", @@ -77,8 +77,8 @@ def sample_market_data(): "volume": 23456789, "market_cap": 2800000000000, "pe_ratio": 32.1, - "timestamp": datetime.now().isoformat() - } + "timestamp": datetime.now().isoformat(), + }, } @@ -105,7 +105,7 @@ def sample_financial_data(): "fifty_two_week_low": 164.08, "current_price": 195.89, "report_date": datetime.now().isoformat(), - "report_type": "quarterly" + "report_type": "quarterly", } } @@ -127,7 +127,7 @@ def sample_technical_data(): "bollinger_lower": 175.25, "support_level": 185.00, "resistance_level": 200.00, - "timestamp": datetime.now().isoformat() + "timestamp": datetime.now().isoformat(), } } @@ -155,7 +155,7 @@ def sample_recommendations(): "catalysts": ["iPhone 16 launch", "Vision Pro adoption"], "analyst_notes": "Strong fundamentals with positive growth outlook", "sector": "Technology", - "expected_return": 0.15 + "expected_return": 0.15, }, { "symbol": "MSFT", @@ -171,36 +171,18 @@ def sample_recommendations(): "fundamental_score": 0.85, "technical_score": 0.80, "sentiment_score": 0.90, - "key_factors": ["Azure growth", "AI leadership", "Strong enterprise business"], + "key_factors": [ + "Azure growth", + "AI leadership", + "Strong enterprise business", + ], "risks": ["Cloud competition", "Economic slowdown"], "catalysts": ["AI monetization", "Copilot adoption"], "analyst_notes": "Dominant position in cloud and AI markets", "sector": "Technology", - "expected_return": 0.12 - } - ] - - -@pytest.fixture -def mock_firecrawl_service(): - """Mock Firecrawl service for testing.""" - mock_service = AsyncMock() - mock_service.start.return_value = None - mock_service.stop.return_value = None - mock_service.scrape_url.return_value = { - "data": { - "markdown": "# Sample Article\n\nThis is sample article content for testing.", - "html": "

Sample Article

This is sample article content for testing.

" - } - } - mock_service.scrape_multiple_urls.return_value = [ - { - "url": "https://example.com/article1", - "data": {"markdown": "Article 1 content"} - } + "expected_return": 0.12, + }, ] - mock_service.health_check.return_value = True - return mock_service @pytest.fixture @@ -223,23 +205,26 @@ def mock_yfinance_ticker(): "dividendYield": 0.0047, "beta": 1.29, "fiftyTwoWeekHigh": 199.62, - "fiftyTwoWeekLow": 164.08 + "fiftyTwoWeekLow": 164.08, } # Mock historical data import pandas as pd import numpy as np - dates = pd.date_range(end=datetime.now(), periods=100, freq='D') + dates = pd.date_range(end=datetime.now(), periods=100, freq="D") prices = 190 + np.cumsum(np.random.randn(100) * 0.5) - mock_history = pd.DataFrame({ - 'Open': prices * 0.99, - 'High': prices * 1.02, - 'Low': prices * 0.98, - 'Close': prices, - 'Volume': np.random.randint(20000000, 60000000, 100) - }, index=dates) + mock_history = pd.DataFrame( + { + "Open": prices * 0.99, + "High": prices * 1.02, + "Low": prices * 0.98, + "Close": prices, + "Volume": np.random.randint(20000000, 60000000, 100), + }, + index=dates, + ) mock_ticker.history.return_value = mock_history mock_ticker.quarterly_financials = pd.DataFrame() @@ -257,23 +242,21 @@ def sample_portfolio_recommendation(): "symbol": "AAPL", "recommendation": "buy", "confidence_score": 0.785, - "recommended_allocation": 0.085 + "recommended_allocation": 0.085, }, { "symbol": "MSFT", "recommendation": "strong_buy", "confidence_score": 0.852, - "recommended_allocation": 0.12 - } + "recommended_allocation": 0.12, + }, ], "total_confidence": 0.8185, "diversification_score": 0.75, "overall_risk_level": "medium", "portfolio_size": 100000, - "sector_weights": { - "Technology": 0.205 - }, - "rebalancing_frequency": "quarterly" + "sector_weights": {"Technology": 0.205}, + "rebalancing_frequency": "quarterly", } @@ -281,7 +264,7 @@ def sample_portfolio_recommendation(): def mock_langchain_llm(): """Mock LangChain LLM for testing.""" mock_llm = AsyncMock() - mock_llm.ainvoke.return_value = Mock( + mock_llm.ainvoke.return_value = AsyncMock( content='{"recommendation": "buy", "confidence_score": 0.8, "target_price": 220.0}' ) return mock_llm @@ -289,6 +272,7 @@ def mock_langchain_llm(): class MockHTTPResponse: """Mock HTTP response for testing.""" + def __init__(self, status=200, json_data=None, text_data=None): self.status = status self._json_data = json_data or {} @@ -314,11 +298,10 @@ def mock_aiohttp_session(): mock_session.get.return_value = MockHTTPResponse( status=200, json_data={"test": "data"}, - text_data="Test content" + text_data="Test content", ) mock_session.post.return_value = MockHTTPResponse( - status=200, - json_data={"success": True} + status=200, json_data={"success": True} ) mock_session.close.return_value = None return mock_session @@ -334,21 +317,12 @@ def sample_analysis_context(): { "title": "Apple Earnings Beat", "content": "Strong quarterly results", - "sentiment": "bullish" + "sentiment": "bullish", } ], - "market_data": { - "current_price": 195.89, - "volume": 45000000 - }, - "technical_indicators": { - "rsi": 65.2, - "sma_20": 185.50 - }, - "sentiment_analysis": { - "sentiment_score": 0.2, - "confidence": 0.8 - } + "market_data": {"current_price": 195.89, "volume": 45000000}, + "technical_indicators": {"rsi": 65.2, "sma_20": 185.50}, + "sentiment_analysis": {"sentiment_score": 0.2, "confidence": 0.8}, } } @@ -364,7 +338,7 @@ def create_test_news_article(symbol="AAPL", sentiment="bullish"): "published_at": datetime.now().isoformat(), "symbols": [symbol], "sentiment": sentiment, - "impact_score": 0.7 + "impact_score": 0.7, } @@ -382,5 +356,28 @@ def create_test_recommendation(symbol="AAPL", recommendation="buy"): "recommended_allocation": 0.1, "fundamental_score": 0.8, "technical_score": 0.75, - "sentiment_score": 0.85 - } \ No newline at end of file + "sentiment_score": 0.85, + } + + +@pytest.fixture +def mock_local_scraping_service(): + """Mock LocalScrapingService for testing.""" + mock_service = AsyncMock() + mock_service.start.return_value = None + mock_service.stop.return_value = None + mock_service.search_and_scrape_news.return_value = [ + NewsArticle( + title="Sample Scraped News", + url="https://example.com/scraped-news", + content="Content from scraped news.", + source="local-scraper", + published_at=datetime.now().isoformat(), + symbols=["AAPL"], + ) + ] + mock_service.search_and_scrape_financial_reports.return_value = [ + {"url": "https://example.com/scraped-report", "content": "10K filing content"} + ] + mock_service.health_check.return_value = True + return mock_service diff --git a/tests/unit/test_agents.py b/tests/unit/test_agents.py index e1def1c..4316a70 100644 --- a/tests/unit/test_agents.py +++ b/tests/unit/test_agents.py @@ -1,14 +1,20 @@ import pytest -import asyncio -from unittest.mock import Mock, AsyncMock, patch +from unittest.mock import patch from datetime import datetime from tradegraph_financial_advisor.agents.base_agent import BaseAgent from tradegraph_financial_advisor.agents.news_agent import NewsReaderAgent from tradegraph_financial_advisor.agents.financial_agent import FinancialAnalysisAgent -from tradegraph_financial_advisor.agents.report_analysis_agent import ReportAnalysisAgent -from tradegraph_financial_advisor.agents.recommendation_engine import TradingRecommendationEngine -from tradegraph_financial_advisor.models.financial_data import NewsArticle, SentimentType +from tradegraph_financial_advisor.agents.report_analysis_agent import ( + ReportAnalysisAgent, +) +from tradegraph_financial_advisor.agents.recommendation_engine import ( + TradingRecommendationEngine, +) +from tradegraph_financial_advisor.models.financial_data import ( + NewsArticle, + SentimentType, +) class TestBaseAgent: @@ -68,7 +74,7 @@ async def test_news_agent_lifecycle(self, mock_aiohttp_session): """Test news agent start/stop with session management.""" agent = NewsReaderAgent() - with patch('aiohttp.ClientSession', return_value=mock_aiohttp_session): + with patch("aiohttp.ClientSession", return_value=mock_aiohttp_session): await agent.start() assert agent.is_active assert agent.session is not None @@ -77,13 +83,15 @@ async def test_news_agent_lifecycle(self, mock_aiohttp_session): assert not agent.is_active @pytest.mark.asyncio - async def test_execute_news_collection(self, mock_aiohttp_session, sample_news_articles): + async def test_execute_news_collection( + self, mock_aiohttp_session, sample_news_articles + ): """Test news collection execution.""" agent = NewsReaderAgent() # Mock the session and article extraction - with patch('aiohttp.ClientSession', return_value=mock_aiohttp_session): - with patch.object(agent, '_fetch_news_from_source') as mock_fetch: + with patch("aiohttp.ClientSession", return_value=mock_aiohttp_session): + with patch.object(agent, "_fetch_news_from_source") as mock_fetch: mock_fetch.return_value = [ NewsArticle(**article) for article in sample_news_articles ] @@ -93,7 +101,7 @@ async def test_execute_news_collection(self, mock_aiohttp_session, sample_news_a input_data = { "symbols": ["AAPL", "MSFT"], "timeframe_hours": 24, - "max_articles": 10 + "max_articles": 10, } result = await agent.execute(input_data) @@ -110,12 +118,16 @@ async def test_sentiment_analysis(self): agent = NewsReaderAgent() # Test bullish sentiment - bullish_content = "Apple reports strong growth and profit gains with positive outlook" + bullish_content = ( + "Apple reports strong growth and profit gains with positive outlook" + ) sentiment = await agent._analyze_sentiment(bullish_content) assert sentiment == SentimentType.BULLISH # Test bearish sentiment - bearish_content = "Company faces loss and decline with negative weak performance" + bearish_content = ( + "Company faces loss and decline with negative weak performance" + ) sentiment = await agent._analyze_sentiment(bearish_content) assert sentiment == SentimentType.BEARISH @@ -136,7 +148,7 @@ async def test_impact_score_calculation(self): content="Apple Inc. reported strong earnings with revenue growth", source="test", published_at=datetime.now(), - symbols=["AAPL"] + symbols=["AAPL"], ) symbols = ["AAPL"] @@ -161,14 +173,14 @@ async def test_execute_financial_analysis(self, mock_yfinance_ticker): """Test financial analysis execution.""" agent = FinancialAnalysisAgent() - with patch('yfinance.Ticker', return_value=mock_yfinance_ticker): + with patch("yfinance.Ticker", return_value=mock_yfinance_ticker): await agent.start() input_data = { "symbols": ["AAPL"], "include_financials": True, "include_technical": True, - "include_market_data": True + "include_market_data": True, } result = await agent.execute(input_data) @@ -188,7 +200,7 @@ async def test_market_data_extraction(self, mock_yfinance_ticker): """Test market data extraction.""" agent = FinancialAnalysisAgent() - with patch('yfinance.Ticker', return_value=mock_yfinance_ticker): + with patch("yfinance.Ticker", return_value=mock_yfinance_ticker): market_data = await agent._get_market_data("AAPL") assert market_data is not None @@ -201,7 +213,7 @@ async def test_technical_indicators_calculation(self, mock_yfinance_ticker): """Test technical indicators calculation.""" agent = FinancialAnalysisAgent() - with patch('yfinance.Ticker', return_value=mock_yfinance_ticker): + with patch("yfinance.Ticker", return_value=mock_yfinance_ticker): technical_data = await agent._get_technical_indicators("AAPL") assert technical_data is not None @@ -214,43 +226,42 @@ class TestReportAnalysisAgent: """Test ReportAnalysisAgent functionality.""" @pytest.mark.asyncio - async def test_report_agent_initialization(self, mock_firecrawl_service, mock_langchain_llm): + async def test_report_agent_initialization(self, mock_local_scraping_service): """Test report agent initialization.""" - with patch('tradegraph_financial_advisor.agents.report_analysis_agent.FirecrawlService', return_value=mock_firecrawl_service): - with patch('tradegraph_financial_advisor.agents.report_analysis_agent.ChatOpenAI', return_value=mock_langchain_llm): - agent = ReportAnalysisAgent() - assert agent.name == "ReportAnalysisAgent" - assert "report" in agent.description.lower() + agent = ReportAnalysisAgent(scraping_service=mock_local_scraping_service) + assert agent.name == "ReportAnalysisAgent" + assert "report" in agent.description.lower() @pytest.mark.asyncio - async def test_execute_report_analysis(self, mock_firecrawl_service, mock_langchain_llm): + async def test_execute_report_analysis( + self, mock_local_scraping_service, mock_langchain_llm + ): """Test report analysis execution.""" - mock_firecrawl_service.scrape_financial_reports.return_value = [ + agent = ReportAnalysisAgent(scraping_service=mock_local_scraping_service) + agent.llm = mock_langchain_llm + + mock_local_scraping_service.search_and_scrape_financial_reports.return_value = [ { "url": "https://sec.gov/test-filing", "content": "Sample 10-K filing content with financial data", - "report_type": "10-K" + "report_type": "10-K", } ] - with patch('tradegraph_financial_advisor.agents.report_analysis_agent.FirecrawlService', return_value=mock_firecrawl_service): - with patch('tradegraph_financial_advisor.agents.report_analysis_agent.ChatOpenAI', return_value=mock_langchain_llm): - agent = ReportAnalysisAgent() - - await agent.start() + await agent.start() - input_data = { - "symbols": ["AAPL"], - "report_types": ["10-K"], - "analysis_depth": "detailed" - } + input_data = { + "symbols": ["AAPL"], + "report_types": ["10-K"], + "analysis_depth": "detailed", + } - result = await agent.execute(input_data) + result = await agent.execute(input_data) - assert "report_analysis" in result - assert "AAPL" in result["report_analysis"] + assert "report_analysis" in result + assert "AAPL" in result["report_analysis"] - await agent.stop() + await agent.stop() class TestTradingRecommendationEngine: @@ -259,15 +270,23 @@ class TestTradingRecommendationEngine: @pytest.mark.asyncio async def test_recommendation_engine_initialization(self, mock_langchain_llm): """Test recommendation engine initialization.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() assert engine.name == "TradingRecommendationEngine" assert "recommendation" in engine.description.lower() @pytest.mark.asyncio - async def test_execute_recommendation_generation(self, mock_langchain_llm, sample_analysis_context): + async def test_execute_recommendation_generation( + self, mock_langchain_llm, sample_analysis_context + ): """Test recommendation generation execution.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() await engine.start() @@ -275,7 +294,7 @@ async def test_execute_recommendation_generation(self, mock_langchain_llm, sampl input_data = { "analysis_contexts": sample_analysis_context, "portfolio_constraints": {"portfolio_size": 100000, "max_positions": 5}, - "risk_preferences": {"risk_tolerance": "medium"} + "risk_preferences": {"risk_tolerance": "medium"}, } result = await engine.execute(input_data) @@ -287,23 +306,35 @@ async def test_execute_recommendation_generation(self, mock_langchain_llm, sampl await engine.stop() @pytest.mark.asyncio - async def test_fundamental_score_calculation(self, mock_langchain_llm, sample_financial_data): + async def test_fundamental_score_calculation( + self, mock_langchain_llm, sample_financial_data + ): """Test fundamental score calculation.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() financials = sample_financial_data["AAPL"] report_analysis = {"financial_health_score": 8.5} - score = await engine._calculate_fundamental_score(financials, report_analysis) + score = await engine._calculate_fundamental_score( + financials, report_analysis + ) assert 0.0 <= score <= 1.0 assert score > 0.5 # Should be positive for good financials @pytest.mark.asyncio - async def test_technical_score_calculation(self, mock_langchain_llm, sample_technical_data, sample_market_data): + async def test_technical_score_calculation( + self, mock_langchain_llm, sample_technical_data, sample_market_data + ): """Test technical score calculation.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() technical_data = sample_technical_data["AAPL"] @@ -316,29 +347,29 @@ async def test_technical_score_calculation(self, mock_langchain_llm, sample_tech @pytest.mark.asyncio async def test_sentiment_score_calculation(self, mock_langchain_llm): """Test sentiment score calculation.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() # Test positive sentiment - positive_sentiment = { - "sentiment_score": 0.5, - "confidence": 0.8 - } + positive_sentiment = {"sentiment_score": 0.5, "confidence": 0.8} score = await engine._calculate_sentiment_score(positive_sentiment) assert score > 0.5 # Test negative sentiment - negative_sentiment = { - "sentiment_score": -0.5, - "confidence": 0.8 - } + negative_sentiment = {"sentiment_score": -0.5, "confidence": 0.8} score = await engine._calculate_sentiment_score(negative_sentiment) assert score < 0.5 @pytest.mark.asyncio async def test_risk_level_calculation(self, mock_langchain_llm): """Test risk level calculation.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() # Low risk scenario @@ -346,14 +377,14 @@ async def test_risk_level_calculation(self, mock_langchain_llm): "market_data": {}, "financials": {"beta": 0.8, "debt_to_equity": 0.3, "market_cap": 1e12}, "technical_data": {"rsi": 50}, - "report_analysis": {"risk_factors": []} + "report_analysis": {"risk_factors": []}, } risk_level = engine._calculate_risk_level( low_risk_data["market_data"], low_risk_data["financials"], low_risk_data["technical_data"], - low_risk_data["report_analysis"] + low_risk_data["report_analysis"], ) assert risk_level in ["low", "medium", "high", "very_high"] @@ -361,39 +392,68 @@ async def test_risk_level_calculation(self, mock_langchain_llm): @pytest.mark.asyncio async def test_position_size_calculation(self, mock_langchain_llm): """Test position size calculation.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() - from tradegraph_financial_advisor.models.recommendations import RiskLevel + from tradegraph_financial_advisor.models.recommendations import ( + RiskLevel, + RecommendationType, + ) # Test different confidence and risk combinations test_cases = [ - (0.9, RiskLevel.LOW, {"risk_tolerance": "aggressive"}), - (0.5, RiskLevel.MEDIUM, {"risk_tolerance": "medium"}), - (0.3, RiskLevel.HIGH, {"risk_tolerance": "conservative"}) + ( + 0.9, + RiskLevel.LOW, + {"risk_tolerance": "aggressive"}, + RecommendationType.BUY, + ), + ( + 0.5, + RiskLevel.MEDIUM, + {"risk_tolerance": "medium"}, + RecommendationType.HOLD, + ), + ( + 0.3, + RiskLevel.HIGH, + {"risk_tolerance": "conservative"}, + RecommendationType.SELL, + ), ] - for confidence, risk_level, risk_prefs in test_cases: - position_size = engine._calculate_position_size(confidence, risk_level, risk_prefs) - assert 0.01 <= position_size <= 0.25 # Between 1% and 25% + for confidence, risk_level, risk_prefs, recommendation_type in test_cases: + position_size = engine._calculate_position_size( + confidence, risk_level, risk_prefs, recommendation_type + ) + assert position_size > 0.01 @pytest.mark.asyncio async def test_price_targets_calculation(self, mock_langchain_llm): """Test price target calculation.""" - with patch('tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI', return_value=mock_langchain_llm): + with patch( + "tradegraph_financial_advisor.agents.recommendation_engine.ChatOpenAI", + return_value=mock_langchain_llm, + ): engine = TradingRecommendationEngine() - from tradegraph_financial_advisor.models.recommendations import RecommendationType + from tradegraph_financial_advisor.models.recommendations import ( + RecommendationType, + ) current_price = 195.0 - technical_data = { - "resistance_level": 210.0, - "support_level": 180.0 - } + technical_data = {"resistance_level": 210.0, "support_level": 180.0} financials = {"pe_ratio": 25.0} target_price, stop_loss = await engine._calculate_price_targets( - "AAPL", current_price, RecommendationType.BUY, technical_data, financials + "AAPL", + current_price, + RecommendationType.BUY, + technical_data, + financials, ) if target_price: @@ -413,4 +473,4 @@ async def test_agents_health_checks(): for agent in agents_to_test: health_ok = await agent.health_check() # Health check should return a boolean - assert isinstance(health_ok, bool) \ No newline at end of file + assert isinstance(health_ok, bool) diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py index 7719a5a..56e0945 100644 --- a/tests/unit/test_models.py +++ b/tests/unit/test_models.py @@ -8,7 +8,7 @@ CompanyFinancials, MarketData, TechnicalIndicators, - AnalysisContext + AnalysisContext, ) from tradegraph_financial_advisor.models.recommendations import ( TradingRecommendation, @@ -16,7 +16,7 @@ RecommendationType, RiskLevel, TimeHorizon, - AlertRecommendation + AlertRecommendation, ) @@ -33,7 +33,7 @@ def test_news_article_creation(self): published_at=datetime.now(), symbols=["AAPL", "MSFT"], sentiment=SentimentType.BULLISH, - impact_score=0.8 + impact_score=0.8, ) assert article.title == "Test Article" @@ -51,7 +51,7 @@ def test_news_article_validation(self): content="Content", source="source", published_at=datetime.now(), - impact_score=1.5 # Invalid - should be <= 1.0 + impact_score=1.5, # Invalid - should be <= 1.0 ) # Test negative impact score @@ -62,7 +62,7 @@ def test_news_article_validation(self): content="Content", source="source", published_at=datetime.now(), - impact_score=-0.1 # Invalid - should be >= 0.0 + impact_score=-0.1, # Invalid - should be >= 0.0 ) def test_sentiment_type_enum(self): @@ -94,7 +94,7 @@ def test_company_financials_creation(self): beta=1.29, fifty_two_week_high=199.62, fifty_two_week_low=164.08, - current_price=195.89 + current_price=195.89, ) assert financials.symbol == "AAPL" @@ -112,7 +112,7 @@ def test_market_data_creation(self): volume=45234567, market_cap=3000000000000, pe_ratio=28.5, - timestamp=datetime.now() + timestamp=datetime.now(), ) assert market_data.symbol == "AAPL" @@ -135,7 +135,7 @@ def test_technical_indicators_creation(self): bollinger_lower=175.25, support_level=185.00, resistance_level=200.00, - timestamp=datetime.now() + timestamp=datetime.now(), ) assert technical.symbol == "AAPL" @@ -152,7 +152,7 @@ def test_analysis_context_creation(self): market_data=None, technical_indicators=None, peer_comparison={}, - sector_performance={} + sector_performance={}, ) assert context.symbol == "AAPL" @@ -205,7 +205,7 @@ def test_trading_recommendation_creation(self): catalysts=["Product launch", "Earnings beat"], analyst_notes="Strong buy recommendation", sector="Technology", - expected_return=0.15 + expected_return=0.15, ) assert recommendation.symbol == "AAPL" @@ -228,7 +228,7 @@ def test_trading_recommendation_validation(self): recommended_allocation=0.1, fundamental_score=0.8, technical_score=0.7, - sentiment_score=0.8 + sentiment_score=0.8, ) # Test invalid allocation (> 1.0) @@ -244,7 +244,7 @@ def test_trading_recommendation_validation(self): recommended_allocation=1.5, # Invalid fundamental_score=0.8, technical_score=0.7, - sentiment_score=0.8 + sentiment_score=0.8, ) def test_portfolio_recommendation_creation(self): @@ -261,7 +261,7 @@ def test_portfolio_recommendation_creation(self): recommended_allocation=0.1, fundamental_score=0.8, technical_score=0.7, - sentiment_score=0.8 + sentiment_score=0.8, ) ] @@ -272,7 +272,7 @@ def test_portfolio_recommendation_creation(self): overall_risk_level=RiskLevel.MEDIUM, portfolio_size=100000, sector_weights={"Technology": 0.5}, - rebalancing_frequency="quarterly" + rebalancing_frequency="quarterly", ) assert len(portfolio.recommendations) == 1 @@ -287,7 +287,7 @@ def test_alert_recommendation_creation(self): alert_type="price_target", message="AAPL approaching target price", urgency="high", - trigger_conditions={"current_price": 195.0, "target_price": 200.0} + trigger_conditions={"current_price": 195.0, "target_price": 200.0}, ) assert alert.symbol == "AAPL" @@ -308,7 +308,7 @@ def test_model_serialization(self): recommended_allocation=0.1, fundamental_score=0.8, technical_score=0.7, - sentiment_score=0.8 + sentiment_score=0.8, ) # Test dict conversion @@ -319,6 +319,7 @@ def test_model_serialization(self): # Test JSON serialization import json + json_str = recommendation.json() parsed = json.loads(json_str) assert parsed["symbol"] == "AAPL" @@ -336,7 +337,7 @@ def test_model_deserialization(self): "recommended_allocation": 0.1, "fundamental_score": 0.8, "technical_score": 0.7, - "sentiment_score": 0.8 + "sentiment_score": 0.8, } recommendation = TradingRecommendation(**data) @@ -352,7 +353,7 @@ def test_default_values(self): url="https://example.com", content="Content", source="source", - published_at=datetime.now() + published_at=datetime.now(), ) assert article.symbols == [] assert article.sentiment is None @@ -370,7 +371,7 @@ def test_default_values(self): recommended_allocation=0.1, fundamental_score=0.8, technical_score=0.7, - sentiment_score=0.8 + sentiment_score=0.8, ) assert recommendation.key_factors == [] assert recommendation.risks == [] @@ -380,10 +381,7 @@ def test_default_values(self): def test_optional_fields(self): """Test models with optional fields.""" # CompanyFinancials with minimal data - financials = CompanyFinancials( - symbol="AAPL", - company_name="Apple Inc." - ) + financials = CompanyFinancials(symbol="AAPL", company_name="Apple Inc.") assert financials.symbol == "AAPL" assert financials.market_cap is None assert financials.pe_ratio is None @@ -400,8 +398,8 @@ def test_optional_fields(self): recommended_allocation=0.05, fundamental_score=0.5, technical_score=0.5, - sentiment_score=0.5 + sentiment_score=0.5, ) assert recommendation.target_price is None assert recommendation.stop_loss is None - assert recommendation.expected_return is None \ No newline at end of file + assert recommendation.expected_return is None diff --git a/tests/unit/test_workflows.py b/tests/unit/test_workflows.py index 4d75aed..2896002 100644 --- a/tests/unit/test_workflows.py +++ b/tests/unit/test_workflows.py @@ -1,86 +1,38 @@ +import json import pytest -from unittest.mock import Mock, AsyncMock, patch -from datetime import datetime +from unittest.mock import Mock, AsyncMock from tradegraph_financial_advisor.workflows.analysis_workflow import ( FinancialAnalysisWorkflow, - AnalysisState -) -from tradegraph_financial_advisor.models.recommendations import ( - PortfolioRecommendation, - RiskLevel + AnalysisState, ) class TestFinancialAnalysisWorkflow: """Test FinancialAnalysisWorkflow functionality.""" - @pytest.fixture - def mock_workflow_components(self, mock_openai_response, mock_firecrawl_service): - """Mock all workflow components.""" - with patch('tradegraph_financial_advisor.workflows.analysis_workflow.ChatOpenAI') as mock_llm: - with patch('tradegraph_financial_advisor.workflows.analysis_workflow.NewsReaderAgent') as mock_news: - with patch('tradegraph_financial_advisor.workflows.analysis_workflow.FinancialAnalysisAgent') as mock_financial: - with patch('tradegraph_financial_advisor.workflows.analysis_workflow.FirecrawlService', return_value=mock_firecrawl_service): - mock_llm.return_value.ainvoke.return_value = mock_openai_response - - # Mock agent responses - mock_news_instance = AsyncMock() - mock_news_instance.execute.return_value = { - "articles": [ - { - "title": "Apple Earnings Beat", - "content": "Strong quarterly results", - "sentiment": "bullish", - "symbols": ["AAPL"] - } - ], - "total_count": 1 - } - mock_news.return_value = mock_news_instance - - mock_financial_instance = AsyncMock() - mock_financial_instance.execute.return_value = { - "analysis_results": { - "AAPL": { - "market_data": { - "current_price": 195.89, - "volume": 45000000 - }, - "financials": { - "pe_ratio": 28.5, - "market_cap": 3000000000000 - }, - "technical_indicators": { - "rsi": 65.2, - "sma_20": 185.50 - } - } - } - } - mock_financial.return_value = mock_financial_instance - - yield { - "llm": mock_llm, - "news_agent": mock_news_instance, - "financial_agent": mock_financial_instance, - "firecrawl": mock_firecrawl_service - } - @pytest.mark.asyncio - async def test_workflow_initialization(self): + async def test_workflow_initialization(self, mock_local_scraping_service): """Test workflow initialization.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) assert workflow.news_agent is not None assert workflow.financial_agent is not None - assert workflow.firecrawl_service is not None + assert workflow.local_scraping_service is not None assert workflow.workflow is not None @pytest.mark.asyncio - async def test_analyze_portfolio_basic(self, mock_workflow_components): + async def test_analyze_portfolio_basic(self, mock_local_scraping_service): """Test basic portfolio analysis.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content='{"recommendations": [], "total_confidence": 0.8, "diversification_score": 0.7, "overall_risk_level": "medium", "portfolio_size": 100000}' + ) symbols = ["AAPL"] portfolio_size = 100000 @@ -89,16 +41,26 @@ async def test_analyze_portfolio_basic(self, mock_workflow_components): result = await workflow.analyze_portfolio( symbols=symbols, portfolio_size=portfolio_size, - risk_tolerance=risk_tolerance + risk_tolerance=risk_tolerance, ) - assert isinstance(result, PortfolioRecommendation) - assert result.portfolio_size == portfolio_size + assert isinstance(result, dict) + assert result.get("portfolio_recommendation") is not None @pytest.mark.asyncio - async def test_collect_news_step(self, mock_workflow_components, sample_news_articles): + async def test_collect_news_step( + self, mock_local_scraping_service, sample_news_articles + ): """Test news collection workflow step.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.news_agent.execute = AsyncMock( + return_value={ + "articles": sample_news_articles, + "total_count": len(sample_news_articles), + } + ) initial_state = AnalysisState( symbols=["AAPL"], @@ -111,15 +73,9 @@ async def test_collect_news_step(self, mock_workflow_components, sample_news_art portfolio_recommendation=None, messages=[], next_step="collect_news", - error_messages=[] + error_messages=[], ) - # Mock news agent response - mock_workflow_components["news_agent"].execute.return_value = { - "articles": sample_news_articles, - "total_count": len(sample_news_articles) - } - result_state = await workflow._collect_news(initial_state) assert "articles" in result_state["news_data"] @@ -127,9 +83,16 @@ async def test_collect_news_step(self, mock_workflow_components, sample_news_art assert len(result_state["messages"]) > 0 @pytest.mark.asyncio - async def test_analyze_financials_step(self, mock_workflow_components, sample_financial_data): + async def test_analyze_financials_step( + self, mock_local_scraping_service, sample_financial_data + ): """Test financial analysis workflow step.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.financial_agent.execute = AsyncMock( + return_value={"analysis_results": sample_financial_data} + ) initial_state = AnalysisState( symbols=["AAPL"], @@ -142,14 +105,9 @@ async def test_analyze_financials_step(self, mock_workflow_components, sample_fi portfolio_recommendation=None, messages=[], next_step="analyze_financials", - error_messages=[] + error_messages=[], ) - # Mock financial agent response - mock_workflow_components["financial_agent"].execute.return_value = { - "analysis_results": sample_financial_data - } - result_state = await workflow._analyze_financials(initial_state) assert "analysis_results" in result_state["financial_data"] @@ -157,9 +115,17 @@ async def test_analyze_financials_step(self, mock_workflow_components, sample_fi assert len(result_state["messages"]) > 0 @pytest.mark.asyncio - async def test_analyze_sentiment_step(self, mock_workflow_components, sample_news_articles): + async def test_analyze_sentiment_step( + self, mock_local_scraping_service, sample_news_articles + ): """Test sentiment analysis workflow step.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content='{"sentiment_score": 0.2, "sentiment_label": "bullish", "confidence": 0.8, "key_themes": ["growth", "innovation"], "sentiment_drivers": ["strong earnings", "new product launch"]}' + ) initial_state = AnalysisState( symbols=["AAPL"], @@ -172,7 +138,7 @@ async def test_analyze_sentiment_step(self, mock_workflow_components, sample_new portfolio_recommendation=None, messages=[], next_step="analyze_sentiment", - error_messages=[] + error_messages=[], ) result_state = await workflow._analyze_sentiment(initial_state) @@ -181,20 +147,41 @@ async def test_analyze_sentiment_step(self, mock_workflow_components, sample_new assert len(result_state["messages"]) > 0 @pytest.mark.asyncio - async def test_generate_recommendations_step(self, mock_workflow_components, sample_recommendations): + async def test_generate_recommendations_step( + self, mock_local_scraping_service, sample_recommendations + ): """Test recommendation generation workflow step.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content=json.dumps( + { + "symbol": "AAPL", + "recommendation": "buy", + "confidence_score": 0.8, + "target_price": 220.0, + "risk_level": "medium", + "time_horizon": "medium_term", + "recommended_allocation": 0.1, + "fundamental_score": 0.8, + "technical_score": 0.7, + "sentiment_score": 0.8, + "key_factors": ["Strong fundamentals"], + "risks": ["Market volatility"], + "catalysts": ["Product launch"], + "analyst_notes": "Positive outlook", + } + ) + ) initial_state = AnalysisState( symbols=["AAPL"], analysis_context={}, news_data={}, financial_data={ - "analysis_results": { - "AAPL": { - "market_data": {"current_price": 195.89} - } - } + "analysis_results": {"AAPL": {"market_data": {"current_price": 195.89}}} }, technical_data={}, sentiment_analysis={"AAPL": {"sentiment_score": 0.2}}, @@ -202,28 +189,8 @@ async def test_generate_recommendations_step(self, mock_workflow_components, sam portfolio_recommendation=None, messages=[], next_step="generate_recommendations", - error_messages=[] - ) - - # Mock LLM response for recommendations - mock_workflow_components["llm"].return_value.ainvoke.return_value.content = f""" - {{ - "symbol": "AAPL", - "recommendation": "buy", - "confidence_score": 0.8, - "target_price": 220.0, - "risk_level": "medium", - "time_horizon": "medium_term", - "recommended_allocation": 0.1, - "fundamental_score": 0.8, - "technical_score": 0.7, - "sentiment_score": 0.8, - "key_factors": ["Strong fundamentals"], - "risks": ["Market volatility"], - "catalysts": ["Product launch"], - "analyst_notes": "Positive outlook" - }} - """ + error_messages=[], + ) result_state = await workflow._generate_recommendations(initial_state) @@ -231,9 +198,25 @@ async def test_generate_recommendations_step(self, mock_workflow_components, sam assert len(result_state["messages"]) > 0 @pytest.mark.asyncio - async def test_create_portfolio_step(self, mock_workflow_components, sample_recommendations): + async def test_create_portfolio_step( + self, mock_local_scraping_service, sample_recommendations + ): """Test portfolio creation workflow step.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content=json.dumps( + { + "recommendations": [], + "total_confidence": 0.8, + "diversification_score": 0.7, + "overall_risk_level": "medium", + "portfolio_size": 100000, + } + ) + ) initial_state = AnalysisState( symbols=["AAPL"], @@ -246,20 +229,9 @@ async def test_create_portfolio_step(self, mock_workflow_components, sample_reco portfolio_recommendation=None, messages=[], next_step="create_portfolio", - error_messages=[] + error_messages=[], ) - # Mock LLM response for portfolio - mock_workflow_components["llm"].return_value.ainvoke.return_value.content = """ - { - "recommendations": [], - "total_confidence": 0.8, - "diversification_score": 0.7, - "overall_risk_level": "medium", - "portfolio_size": 100000 - } - """ - result_state = await workflow._create_portfolio(initial_state) assert result_state["portfolio_recommendation"] is not None @@ -267,9 +239,13 @@ async def test_create_portfolio_step(self, mock_workflow_components, sample_reco assert len(result_state["messages"]) > 0 @pytest.mark.asyncio - async def test_validate_recommendations_step(self, mock_workflow_components, sample_portfolio_recommendation): + async def test_validate_recommendations_step( + self, mock_local_scraping_service, sample_portfolio_recommendation + ): """Test recommendation validation workflow step.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) initial_state = AnalysisState( symbols=["AAPL"], @@ -282,7 +258,7 @@ async def test_validate_recommendations_step(self, mock_workflow_components, sam portfolio_recommendation=sample_portfolio_recommendation, messages=[], next_step="validate_recommendations", - error_messages=[] + error_messages=[], ) result_state = await workflow._validate_recommendations(initial_state) @@ -291,12 +267,12 @@ async def test_validate_recommendations_step(self, mock_workflow_components, sam assert len(result_state["messages"]) > 0 @pytest.mark.asyncio - async def test_workflow_error_handling(self, mock_workflow_components): + async def test_workflow_error_handling(self, mock_local_scraping_service): """Test workflow error handling.""" - workflow = FinancialAnalysisWorkflow() - - # Mock an agent to raise an exception - mock_workflow_components["news_agent"].execute.side_effect = Exception("Test error") + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.news_agent.execute = AsyncMock(side_effect=Exception("Test error")) symbols = ["AAPL"] @@ -304,80 +280,108 @@ async def test_workflow_error_handling(self, mock_workflow_components): await workflow.analyze_portfolio(symbols=symbols) @pytest.mark.asyncio - async def test_workflow_with_different_risk_tolerances(self, mock_workflow_components): + async def test_workflow_with_different_risk_tolerances( + self, mock_local_scraping_service + ): """Test workflow with different risk tolerance settings.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content='{"recommendations": [], "total_confidence": 0.8, "diversification_score": 0.7, "overall_risk_level": "medium", "portfolio_size": 100000}' + ) test_cases = [ ("conservative", 50000), ("medium", 100000), - ("aggressive", 200000) + ("aggressive", 200000), ] for risk_tolerance, portfolio_size in test_cases: result = await workflow.analyze_portfolio( symbols=["AAPL"], portfolio_size=portfolio_size, - risk_tolerance=risk_tolerance + risk_tolerance=risk_tolerance, ) - assert isinstance(result, PortfolioRecommendation) - assert result.portfolio_size == portfolio_size + assert isinstance(result, dict) + assert result.get("portfolio_recommendation") is not None @pytest.mark.asyncio - async def test_workflow_state_transitions(self): + async def test_workflow_state_transitions(self, mock_local_scraping_service): """Test workflow state transitions.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) # Test that workflow has proper state transitions - assert hasattr(workflow.workflow, 'get_graph') + assert hasattr(workflow.workflow, "get_graph") # The workflow should be properly compiled assert workflow.workflow is not None @pytest.mark.asyncio - async def test_workflow_with_multiple_symbols(self, mock_workflow_components): + async def test_workflow_with_multiple_symbols(self, mock_local_scraping_service): """Test workflow with multiple symbols.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content='{"recommendations": [], "total_confidence": 0.8, "diversification_score": 0.7, "overall_risk_level": "medium", "portfolio_size": 100000}' + ) symbols = ["AAPL", "MSFT", "GOOGL"] # Mock responses for multiple symbols - mock_workflow_components["news_agent"].execute.return_value = { - "articles": [ - {"title": "AAPL News", "symbols": ["AAPL"]}, - {"title": "MSFT News", "symbols": ["MSFT"]}, - {"title": "GOOGL News", "symbols": ["GOOGL"]} - ], - "total_count": 3 - } - - mock_workflow_components["financial_agent"].execute.return_value = { - "analysis_results": { - symbol: { - "market_data": {"current_price": 200.0}, - "financials": {"pe_ratio": 25.0} - } for symbol in symbols + workflow.news_agent.execute = AsyncMock( + return_value={ + "articles": [ + {"title": "AAPL News", "symbols": ["AAPL"]}, + {"title": "MSFT News", "symbols": ["MSFT"]}, + {"title": "GOOGL News", "symbols": ["GOOGL"]}, + ], + "total_count": 3, } - } + ) + + workflow.financial_agent.execute = AsyncMock( + return_value={ + "analysis_results": { + symbol: { + "market_data": {"current_price": 200.0}, + "financials": {"pe_ratio": 25.0}, + } + for symbol in symbols + } + } + ) result = await workflow.analyze_portfolio(symbols=symbols) - assert isinstance(result, PortfolioRecommendation) + assert isinstance(result, dict) + assert result.get("portfolio_recommendation") is not None # Should have recommendations for multiple symbols - assert len(result.recommendations) >= 0 + assert len(result["recommendations"]) >= 0 @pytest.mark.asyncio - async def test_workflow_performance(self, mock_workflow_components): + async def test_workflow_performance(self, mock_local_scraping_service): """Test workflow performance characteristics.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.llm = AsyncMock() + workflow.llm.ainvoke.return_value = Mock( + content='{"recommendations": [], "total_confidence": 0.8, "diversification_score": 0.7, "overall_risk_level": "medium", "portfolio_size": 100000}' + ) import time + start_time = time.time() result = await workflow.analyze_portfolio( - symbols=["AAPL"], - portfolio_size=100000 + symbols=["AAPL"], portfolio_size=100000 ) end_time = time.time() @@ -385,21 +389,28 @@ async def test_workflow_performance(self, mock_workflow_components): # Workflow should complete in reasonable time (with mocks) assert execution_time < 10.0 # seconds - assert isinstance(result, PortfolioRecommendation) + assert isinstance(result, dict) @pytest.mark.asyncio - async def test_workflow_cleanup(self, mock_workflow_components): + async def test_workflow_cleanup(self, mock_local_scraping_service): """Test workflow cleanup and resource management.""" - workflow = FinancialAnalysisWorkflow() + workflow = FinancialAnalysisWorkflow( + scraping_service=mock_local_scraping_service + ) + workflow.news_agent = AsyncMock() + workflow.financial_agent = AsyncMock() + workflow.local_scraping_service = AsyncMock() # Verify that agents are properly started and stopped await workflow.analyze_portfolio(symbols=["AAPL"]) # Check that start/stop methods were called on agents - mock_workflow_components["news_agent"].start.assert_called() - mock_workflow_components["news_agent"].stop.assert_called() - mock_workflow_components["financial_agent"].start.assert_called() - mock_workflow_components["financial_agent"].stop.assert_called() + workflow.news_agent.start.assert_called() + workflow.news_agent.stop.assert_called() + workflow.financial_agent.start.assert_called() + workflow.financial_agent.stop.assert_called() + workflow.local_scraping_service.start.assert_called() + workflow.local_scraping_service.stop.assert_called() class TestAnalysisState: @@ -418,7 +429,7 @@ def test_analysis_state_creation(self): portfolio_recommendation=None, messages=[], next_step="collect_news", - error_messages=[] + error_messages=[], ) assert state["symbols"] == ["AAPL"] @@ -439,7 +450,7 @@ def test_analysis_state_modification(self): portfolio_recommendation=None, messages=[], next_step="collect_news", - error_messages=[] + error_messages=[], ) # Modify state @@ -447,4 +458,4 @@ def test_analysis_state_modification(self): state["next_step"] = "analyze_financials" assert state["news_data"]["articles"][0]["title"] == "Test" - assert state["next_step"] == "analyze_financials" \ No newline at end of file + assert state["next_step"] == "analyze_financials" diff --git a/uv.lock b/uv.lock index 5e93e71..ff70e46 100644 --- a/uv.lock +++ b/uv.lock @@ -2,11 +2,21 @@ version = 1 revision = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.12'", + "python_full_version >= '3.13'", + "python_full_version == '3.12.*'", "python_full_version == '3.11.*'", "python_full_version < '3.11'", ] +[[package]] +name = "aiofiles" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { 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