use-cases.mdโข15.7 kB
# ๐ก Stock API Use Cases & Applications
> ๐ **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. ๐โจ
## ๐ค AI Trading & Investment Assistants
### ๐ฏ **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
```
### ๐ **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)
```
## ๐ Financial Research & Analytics
### ๐ **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
```
### ๐ **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
```
## ๐ฑ Investment Apps & Fintech
### ๐ **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
```
### ๐ **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
```
## ๐ข Enterprise Financial Systems
### ๐ก๏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
```
### ๐ **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
```
## ๐ฏ Specialized Applications
### ๐ **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
}
```
## ๐ Getting Started
### ๐ ๏ธ **Implementation Tips**
1. **๐ฏ Start Simple**: Begin with basic use cases and gradually add complexity
2. **๐ Leverage Caching**: Use our intelligent caching for better performance
3. **๐ Optimize Queries**: Fine-tune similarity thresholds for your specific needs
4. **๐
Use Date Filters**: Filter by date ranges for time-sensitive applications
5. **๐ข Symbol Filtering**: Use symbol filters to focus on relevant stocks
### ๐ **Next Steps**
Ready to implement these use cases? Check out our detailed guides:
- ๐ **[Vector Search Guide](vector-search.md)** - Master semantic search
- ๐ฐ **[News & Analysis](news-analysis.md)** - Understand market intelligence
- ๐น **[Market Data](market-data.md)** - Real-time pricing and metrics
- ๐ **[API Reference](api-reference.md)** - Complete technical documentation
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**๐ก 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!* ๐โจ
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