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Stock Oracle API Documentation 🔮

Comprehensive Investment Data Analysis API

Stock Oracle provides comprehensive financial, price, news, and social media data analysis through a unified REST API. Built for investors, analysts, and developers who need reliable access to SEC filings, market data, and sentiment analysis.

🚀 Quick Start

Base URL

http://localhost:18001/api/v1

Authentication

Currently no authentication required. API key support coming soon.

Rate Limits

  • 100 requests per minute per IP
  • 10,000 requests per day per IP

📊 Core Data Sources

  • SEC EDGAR: Official company filings (10-K, 10-Q, N-PORT)
  • Yahoo Finance: Real-time price data and news
  • NewsAPI: Professional news aggregation
  • Reddit API: Social sentiment analysis

🎯 API Endpoints

Health & System

GET /health

Basic health check with system status.

Response:

{
  "status": "healthy",
  "version": "1.0.0",
  "database": "healthy",
  "cache": "healthy",
  "sec_data_available": true,
  "timestamp": "2025-08-10T17:59:27.790041"
}

GET /health/detailed

Detailed system health with component status.

Financial Data

POST /financial/data

Get comprehensive financial data for a ticker.

Request Body:

{
  "ticker": "AAPL",
  "period": "1y",
  "period_type": "quarterly",
  "include_metrics": true,
  "force_refresh": false
}

Alternative Time Specifications:

// Date Range
{
  "ticker": "AAPL",
  "start_date": "2023-01-01",
  "end_date": "2023-12-31"
}

// Specific Quarters
{
  "ticker": "AAPL", 
  "quarters": ["2024Q1", "2024Q2", "2024Q3"]
}

Response:

{
  "company": {
    "ticker": "AAPL",
    "name": "Apple Inc.",
    "cik": "320193",
    "sector": "Technology",
    "industry": "Consumer Electronics"
  },
  "financial_data": [
    {
      "period_date": "2024-06-30",
      "period_type": "quarterly",
      "revenue": 85777000000,
      "gross_profit": 35398000000,
      "operating_income": 24261000000,
      "net_income": 21448000000,
      "eps": 1.40,
      "pe_ratio": 28.5,
      "roe": 0.63,
      "debt_to_equity": 1.97,
      "market_cap": 3200000000000
    }
  ],
  "metadata": {
    "data_points": 8,
    "period_type": "quarterly",
    "last_updated": "2025-08-10T12:00:00"
  }
}

GET /financial/data/{ticker}

Simplified financial data endpoint with query parameters.

Parameters:

  • period: Time period (1d, 7d, 30d, 1m, 3m, 6m, 1y, 2y, 5y, 10y)
  • period_type: quarterly, annual, all
  • include_metrics: true/false
  • force_refresh: true/false

POST /financial/data/bulk

Get financial data for multiple tickers in a single request.

Request:

{
  "tickers": ["AAPL", "MSFT", "GOOGL"],
  "period": "1y",
  "include_metrics": true
}

Price Data

POST /price/data

Get historical price data (OHLCV) for a ticker.

Request:

{
  "ticker": "AAPL",
  "period": "30d",
  "interval": "1d",
  "force_refresh": false
}

Supported Intervals:

  • 1m, 2m, 5m, 15m, 30m, 60m, 90m (minutes)
  • 1h (hour)
  • 1d, 5d (days)
  • 1wk (week)
  • 1mo, 3mo (months)

Response:

{
  "ticker": "AAPL",
  "price_data": [
    {
      "date": "2024-08-10",
      "open": 220.05,
      "high": 225.30,
      "low": 218.75,
      "close": 224.72,
      "volume": 45234567,
      "adj_close": 224.72
    }
  ],
  "interval": "1d"
}

POST /price/data/bulk

Bulk price data for multiple tickers.

Stock Market Data 🆕

GET /stocks/most-active

Most actively traded stocks from Yahoo Finance.

Parameters:

  • limit: Number of stocks to return (1-500). If omitted, returns all available (~170)
  • force_refresh: true/false (default: false). When true, bypasses cache and fetches fresh data

Caching:

  • Server-side cache TTL: 1 hour
  • Cache key: stocks:most-active:limit=<N|all>
  • Response headers:
    • X-Cache: HIT | MISS | BYPASS
    • Cache-Control: public, max-age=3600
    • ETag: Strong hash of the response
    • X-Data-Source: redis-cache | scraper

Examples:

# Default (cached up to 1h)
GET /stocks/most-active

# Limit results (cached per limit)
GET /stocks/most-active?limit=100

# Force fresh fetch (bypass cache)
GET /stocks/most-active?force_refresh=true

# Limit + fresh
GET /stocks/most-active?limit=50&force_refresh=true

News & Social Media 🆕

GET /news/{ticker}

Complete news and social media data for a ticker.

Parameters:

  • days_back: Days to look back (1-30, default: 7)
  • max_articles: Max news articles (5-100, default: 20)
  • max_social_posts: Max social posts (0-100, default: 15)
  • include_social: Include social media (true/false, default: true)

Example:

GET /news/AAPL?days_back=7&max_articles=20&include_social=true

Response:

{
  "ticker": "AAPL",
  "retrieved_at": "2025-08-10T17:40:04.781906",
  "news": {
    "total_articles": 12,
    "sources": {
      "yahoo_finance": 6,
      "newsapi": 6
    },
    "articles": [
      {
        "title": "Apple Reports Strong Q3 Results",
        "summary": "Apple exceeded expectations with record iPhone sales...",
        "url": "https://finance.yahoo.com/news/apple-q3-2024",
        "source": "Yahoo Finance", 
        "published_at": "2025-08-10T14:30:00",
        "author": "John Smith",
        "tags": ["earnings", "iphone", "revenue"]
      }
    ]
  },
  "social_media": {
    "total_posts": 8,
    "platforms": {"reddit": 8},
    "posts": [
      {
        "title": "$AAPL breakout incoming? Technical analysis",
        "content": "Looking at the charts, AAPL seems to be forming...",
        "url": "https://reddit.com/r/stocks/comments/xyz",
        "platform": "Reddit",
        "author": "trader123",
        "published_at": "2025-08-10T16:20:00",
        "score": 245,
        "comments_count": 67,
        "subreddit": "stocks"
      }
    ]
  },
  "summary": {
    "total_items": 20,
    "time_range_days": 7,
    "newest_item": "2025-08-10T16:20:00",
    "oldest_item": "2025-08-03T09:15:00"
  }
}

GET /news/{ticker}/news-only

News articles only (faster response, no social media).

GET /news/{ticker}/social-only

Social media posts only.

ETF Holdings

Temporarily unavailable. The ETF API is being redesigned. Previous endpoints under /etf/* have been removed and will return 404. See docs/ETF_API.md for historical reference only.

Database & Metadata

GET /database/stats

Database statistics and data coverage information.

GET /metadata/catalog

Complete data field catalog with descriptions and types.

Admin & Monitoring

GET /admin/errors/logs

Error log retrieval (admin access).

Parameters:

  • limit: Number of logs (default: 100)
  • offset: Pagination offset (default: 0)
  • min_level: Minimum log level (ERROR, WARNING, INFO)

GET /admin/errors/stats

Error statistics and trends.

POST /admin/migrate

Database migration from another Stock Oracle instance.

🔧 Python Client Usage

Installation

# Download the client from the repository
wget https://raw.githubusercontent.com/your-repo/stock-oracle/main/stock_oracle_client.py

Basic Usage

from stock_oracle_client import StockOracleClient

# Initialize client
client = StockOracleClient("http://localhost:18001")

# Check API health
health = client.get_health()
print("API Status:", health["status"])

# Get financial data
data = client.get_financial_data("AAPL", period="1y")
print(f"Found {len(data['financial_data'])} quarters of data")

# Get news and social media data
news = client.get_news_social_data("AAPL", days_back=7, max_articles=20)
print(f"Found {news['summary']['total_items']} news/social items")

# Get ETF holdings
etf = client.get_etf_holdings("QQQ", include_holdings=False)
print(f"QQQ has {etf['data']['holdings_count']} holdings")

# Bulk operations
bulk_data = client.get_bulk_financial_data(
    tickers=["AAPL", "MSFT", "GOOGL"],
    period="2y"
)

Error Handling

from stock_oracle_client import StockOracleAPIError, ETFDataNotAvailableError

try:
    data = client.get_financial_data("INVALID_TICKER")
except StockOracleAPIError as e:
    print(f"API Error: {e}")
    print(f"Status Code: {e.status_code}")

try:
    etf = client.get_etf_holdings("QQQ", as_of_date="1990-01-01")
except ETFDataNotAvailableError as e:
    print(f"ETF data not available: {e}")
    if e.availability_info:
        print(f"Available from: {e.availability_info['available_date_range']['start']}")

📈 Investment Analysis

Stock Oracle Analyzer

The included analyzer provides comprehensive investment analysis combining all data sources:

from stock_oracle_analyzer import StockOracleAnalyzer

# Initialize analyzer
analyzer = StockOracleAnalyzer("http://localhost:18001")

# Analyze single company
data = analyzer.get_company_data("AAPL", period="2y")
analysis = data['analysis_summary']

print(f"Investment Score: {analysis['investment_score']}/100")
print(f"Financial Grade: {analysis['financial_health']['grade']}")
print(f"Price Trend: {analysis['price_trends']['trend']}")
print(f"Sentiment: {analysis['sentiment_analysis']['sentiment_label']}")

# Compare multiple companies
tickers = ["AAPL", "MSFT", "GOOGL", "TSLA", "NVDA"]
results = analyzer.analyze_multiple_companies(tickers, period="1y")

# Generate comparison report
summary_df = analyzer.create_summary_report(results)
print(summary_df[['Ticker', 'Investment Score', 'Financial Grade', 'Sentiment']])

⚠️ Important Notes

Data Availability

  • Period parameters automatically use yesterday as end date to ensure data availability
  • SEC filings may have delays - latest data is typically 1-3 months behind
  • ETF holdings are updated quarterly via N-PORT filings
  • News data is real-time but may have API rate limits

Performance Tips

  • Use bulk endpoints for multiple tickers to reduce latency
  • Enable caching by avoiding force_refresh=true unless necessary
  • Use news-only endpoints for faster sentiment analysis
  • Implement client-side caching for frequently accessed data

Error Codes

  • 400: Bad Request (invalid parameters)
  • 404: Data not found (ticker not found, no filings available)
  • 429: Rate limit exceeded
  • 500: Internal server error
  • 503: Service temporarily unavailable

🔗 Links

📞 Support

  • Issues: Report bugs and feature requests on GitHub
  • Documentation: This page is auto-generated from the latest API specification
  • Updates: Check the changelog for latest features and improvements

Stock Oracle - Empowering investment decisions with comprehensive data analysis 🔮📈