Skip to main content
Glama

RL-MCP

by rlefko
use-cases.mdโ€ข15.7 kB
# ๐Ÿ’ก Stock API Use Cases & Applications > ๐Ÿš€ **Discover how to leverage our stock market intelligence in real-world applications** Our stock market API opens up endless possibilities for building intelligent financial applications. From AI trading assistants to market research platforms, here are proven use cases with practical implementation examples. ๐Ÿ“ˆโœจ ## ๐Ÿค– AI Trading & Investment Assistants ### ๐ŸŽฏ **Portfolio Intelligence Assistant** Build an AI assistant that provides intelligent insights about user portfolios using semantic search and sentiment analysis. ```python class PortfolioAssistant: def __init__(self, api_client): self.api = api_client async def analyze_portfolio(self, holdings): """Analyze user's portfolio with AI-powered insights""" insights = {} for symbol, shares in holdings.items(): # Get current price and analysis price_data = await self.api.get_price(symbol) analysis = await self.api.get_analysis(symbol) # Search for recent relevant news news_search = await self.api.search({ "query": f"{symbol} earnings revenue growth prospects", "symbols": [symbol], "include_news": True, "similarity_threshold": 0.8, "limit": 5 }) # Calculate position value and sentiment position_value = price_data['price'] * shares avg_sentiment = sum(r.get('sentiment_score', 0) for r in news_search) / len(news_search) insights[symbol] = { "position_value": position_value, "current_price": price_data['price'], "change_percent": price_data['change_percent'], "recommendation": analysis['recommendation'], "confidence": analysis['confidence_score'], "sentiment": avg_sentiment, "key_news": [r['content'][:100] for r in news_search[:3]] } return insights ``` ### ๐Ÿ“Š **Smart Trading Signals** Create intelligent trading signals based on sentiment analysis and market trends. ```python class TradingSignalGenerator: def __init__(self, api_client): self.api = api_client async def generate_signals(self, watchlist): """Generate buy/sell signals based on AI analysis""" signals = [] for symbol in watchlist: # Get comprehensive analysis analysis = await self.api.get_analysis(symbol) price_data = await self.api.get_price(symbol) # Search for momentum indicators momentum_search = await self.api.search({ "query": f"{symbol} breakthrough innovation product launch", "symbols": [symbol], "similarity_threshold": 0.8, "limit": 10 }) # Calculate signal strength signal_strength = ( analysis['confidence_score'] * 0.4 + (len(momentum_search) / 10) * 0.3 + abs(price_data['change_percent']) / 100 * 0.3 ) if analysis['recommendation'] == 'Buy' and signal_strength > 0.7: signals.append({ "symbol": symbol, "action": "BUY", "strength": signal_strength, "reasoning": analysis['insights'], "target_price": analysis.get('target_price'), "catalysts": [r['content'][:100] for r in momentum_search[:3]] }) return sorted(signals, key=lambda x: x['strength'], reverse=True) ``` ## ๐Ÿ“Š Financial Research & Analytics ### ๐Ÿ” **Market Research Platform** Build a comprehensive market research tool that analyzes trends, competitors, and opportunities. ```python class MarketResearcher: def __init__(self, api_client): self.api = api_client async def research_sector(self, sector_symbols, theme): """Research a specific sector or theme""" sector_analysis = { "theme": theme, "symbols_analyzed": len(sector_symbols), "companies": {}, "trends": [], "sentiment_overview": {} } # Analyze each company in the sector for symbol in sector_symbols: company_data = await self._analyze_company(symbol, theme) sector_analysis["companies"][symbol] = company_data # Find sector-wide trends trend_search = await self.api.search({ "query": f"{theme} industry trends market share competition", "symbols": sector_symbols, "similarity_threshold": 0.75, "limit": 50 }) # Extract trending topics sector_analysis["trends"] = self._extract_trends(trend_search) return sector_analysis ``` ### ๐Ÿ“ˆ **Competitive Intelligence** Monitor competitors and identify market opportunities. ```python class CompetitiveIntelligence: def __init__(self, api_client): self.api = api_client async def monitor_competitors(self, company_symbol, competitors): """Monitor competitive landscape""" competitive_analysis = { "focus_company": company_symbol, "competitors": {}, "innovation_comparison": {}, "market_insights": [] } all_symbols = [company_symbol] + competitors # Analyze innovation and product development innovation_search = await self.api.search({ "query": "product launch innovation breakthrough technology", "symbols": all_symbols, "similarity_threshold": 0.8, "limit": 100 }) # Group innovations by company for result in innovation_search: symbol = result['symbol'] if symbol not in competitive_analysis["innovation_comparison"]: competitive_analysis["innovation_comparison"][symbol] = [] competitive_analysis["innovation_comparison"][symbol].append({ "content": result['content'][:200], "relevance": result['similarity_score'] }) return competitive_analysis ``` ## ๐Ÿ“ฑ Investment Apps & Fintech ### ๐Ÿ”” **Smart Notification System** Build intelligent notifications that alert users to relevant market changes. ```python class SmartNotificationSystem: def __init__(self, api_client): self.api = api_client async def generate_personalized_alerts(self, user_id, portfolio, interests): """Generate personalized alerts based on user portfolio and interests""" alerts = [] # Portfolio-based alerts for symbol in portfolio.keys(): # Check for significant price movements price_data = await self.api.get_price(symbol) if abs(price_data['change_percent']) > 5: alerts.append({ "type": "price_movement", "symbol": symbol, "message": f"{symbol} moved {price_data['change_percent']:.1f}%", "urgency": "high" if abs(price_data['change_percent']) > 10 else "medium" }) # Check for breaking news news_search = await self.api.search({ "query": f"{symbol} breaking news earnings announcement", "symbols": [symbol], "similarity_threshold": 0.9, "limit": 5 }) for news in news_search: alerts.append({ "type": "news", "symbol": symbol, "message": news['content'][:100], "urgency": "high" }) return alerts ``` ### ๐Ÿ“š **Educational Content Generator** Create personalized educational content based on market events. ```python class EducationalContentGenerator: def __init__(self, api_client): self.api = api_client async def generate_market_explainer(self, topic, user_level="beginner"): """Generate educational content about market topics""" # Search for relevant content content_search = await self.api.search({ "query": f"{topic} explanation analysis market impact", "similarity_threshold": 0.8, "limit": 20 }) # Get market summary for context market_summary = await self.api.get_market_summary() explainer = { "topic": topic, "difficulty": user_level, "key_concepts": self._extract_key_concepts(content_search), "market_context": market_summary, "examples": content_search[:5], "further_reading": [r['content'][:100] for r in content_search[:5]] } return explainer ``` ## ๐Ÿข Enterprise Financial Systems ### ๐Ÿ›ก๏ธ **Risk Management Dashboard** Build comprehensive risk monitoring for institutional portfolios. ```python class RiskManagementSystem: def __init__(self, api_client): self.api = api_client async def assess_portfolio_risk(self, portfolio): """Comprehensive portfolio risk assessment""" risk_assessment = { "overall_risk_score": 0, "risk_factors": [], "recommendations": [] } # Analyze each position for symbol, position in portfolio.items(): # Get current analysis and price data analysis = await self.api.get_analysis(symbol) price_data = await self.api.get_price(symbol) # Check for high volatility if abs(price_data['change_percent']) > 5: risk_assessment["risk_factors"].append({ "type": "volatility", "symbol": symbol, "severity": "high", "description": f"High volatility: {price_data['change_percent']:.1f}%" }) # Search for risk-related news risk_search = await self.api.search({ "query": f"{symbol} risk regulatory investigation lawsuit", "symbols": [symbol], "similarity_threshold": 0.8, "limit": 10 }) for risk_item in risk_search: risk_assessment["risk_factors"].append({ "type": "news_risk", "symbol": symbol, "severity": "high" if risk_item['similarity_score'] > 0.9 else "medium", "description": risk_item['content'][:150] }) return risk_assessment ``` ### ๐Ÿ“Š **Client Reporting Automation** Automate intelligent client reports with market insights. ```python class ClientReportGenerator: def __init__(self, api_client): self.api = api_client async def generate_client_report(self, client_portfolio, report_period="monthly"): """Generate comprehensive client report""" report = { "client_id": client_portfolio["client_id"], "period": report_period, "portfolio_summary": {}, "market_insights": {}, "recommendations": [] } # Portfolio performance summary holdings = client_portfolio["holdings"] total_value = 0 for symbol, shares in holdings.items(): price_data = await self.api.get_price(symbol) total_value += price_data['price'] * shares report["portfolio_summary"]["total_value"] = total_value # Market insights relevant to portfolio symbols = list(holdings.keys()) insight_search = await self.api.search({ "query": "market outlook economic trends sector analysis", "symbols": symbols, "similarity_threshold": 0.75, "limit": 10 }) report["market_insights"] = [ { "content": insight['content'][:300], "relevance": insight['similarity_score'] } for insight in insight_search ] return report ``` ## ๐ŸŽฏ Specialized Applications ### ๐Ÿ“Š **ESG Investment Screening** Screen investments based on Environmental, Social, and Governance criteria. ```python class ESGScreener: def __init__(self, api_client): self.api = api_client async def screen_esg_compliance(self, symbols): """Screen stocks for ESG compliance""" esg_analysis = {} for symbol in symbols: esg_score = await self._calculate_esg_score(symbol) esg_analysis[symbol] = esg_score return esg_analysis async def _calculate_esg_score(self, symbol): """Calculate ESG score for a symbol""" # Environmental factors env_search = await self.api.search({ "query": f"{symbol} environmental sustainability carbon emissions", "symbols": [symbol], "similarity_threshold": 0.7, "limit": 20 }) # Social factors social_search = await self.api.search({ "query": f"{symbol} social responsibility diversity workplace", "symbols": [symbol], "similarity_threshold": 0.7, "limit": 20 }) # Governance factors governance_search = await self.api.search({ "query": f"{symbol} corporate governance board diversity transparency", "symbols": [symbol], "similarity_threshold": 0.7, "limit": 20 }) # Calculate scores env_score = min(len(env_search) / 10, 1.0) social_score = min(len(social_search) / 10, 1.0) governance_score = min(len(governance_search) / 10, 1.0) return { "overall_score": (env_score + social_score + governance_score) / 3, "environmental": env_score, "social": social_score, "governance": governance_score } ``` ## ๐Ÿš€ Getting Started ### ๐Ÿ› ๏ธ **Implementation Tips** 1. **๐ŸŽฏ Start Simple**: Begin with basic use cases and gradually add complexity 2. **๐Ÿ“Š Leverage Caching**: Use our intelligent caching for better performance 3. **๐Ÿ” Optimize Queries**: Fine-tune similarity thresholds for your specific needs 4. **๐Ÿ“… Use Date Filters**: Filter by date ranges for time-sensitive applications 5. **๐Ÿข Symbol Filtering**: Use symbol filters to focus on relevant stocks ### ๐Ÿ“š **Next Steps** Ready to implement these use cases? Check out our detailed guides: - ๐Ÿ” **[Vector Search Guide](vector-search.md)** - Master semantic search - ๐Ÿ“ฐ **[News & Analysis](news-analysis.md)** - Understand market intelligence - ๐Ÿ’น **[Market Data](market-data.md)** - Real-time pricing and metrics - ๐Ÿ”— **[API Reference](api-reference.md)** - Complete technical documentation --- <div align="center"> **๐Ÿ’ก Ready to Build the Future of Finance?** *These use cases are just the beginning. Our stock market API provides the foundation for innovative financial applications that can transform how people interact with markets. Start building today!* ๐Ÿš€โœจ </div>

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rlefko/rl-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server