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Mixed Availability:
  • ✅ Open Source: sec-filings (free SEC EDGAR API)
  • ✅ Shannon Cloud: All agents with managed API access
  • ✅* OSS with your API keys: alpaca-news (Alpaca free tier), twitter-sentiment (xAI API)

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Overview

Shannon provides 4 specialized agents for financial research and equity analysis, combining free public data (SEC filings) with premium sentiment analysis (Twitter/xAI) and news aggregation (Alpaca, Benzinga). Use cases:
  • Event monitoring (8-K material events, insider trading)
  • Social sentiment tracking (Twitter mentions, influencer analysis)
  • News aggregation (multi-source news with sentiment scoring)
  • Equity research workflows

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Available Agents

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sec-filings

Open Source Compatible: Free SEC EDGAR API. Works in both OSS and Cloud with no API key required.
Get recent SEC filings (8-K, 10-K, 10-Q, etc.) for a stock ticker. Agent ID: sec-filings Input Schema:
FieldTypeRequiredDefaultDescription
tickerstringYes-Stock ticker symbol (e.g., ‘NVDA’, ‘TSLA’)
days_backintegerNo30Days of history to search (1-365)
formsstringNo"8-K,10-K,10-Q,4,SC 13D"Comma-separated form types
Supported Form Types:
  • 8-K: Material events (acquisitions, executive changes, etc.)
  • 10-K: Annual report
  • 10-Q: Quarterly report
  • 4: Insider trading (officers, directors)
  • SC 13D: Major shareholder filings (>5% ownership)
  • DEF 14A: Proxy statement
  • S-1: IPO registration
Example Request: 
curl -X POST http://localhost:8080/api/v1/agents/sec-filings \
  -H "X-API-Key: sk_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "ticker": "NVDA",
      "days_back": 90,
      "forms": "8-K,10-K,10-Q"
    }
  }'
Output Structure: 
{
  "source": "sec_edgar",
  "ticker": "NVDA",
  "company_name": "NVIDIA CORP",
  "cik": "0001045810",
  "filings": [
    {
      "form_type": "8-K",
      "filed_date": "2026-01-15",
      "description": "Current Report",
      "filing_url": "https://www.sec.gov/Archives/edgar/data/1045810/...",
      "is_material": true
    },
    {
      "form_type": "10-Q",
      "filed_date": "2025-11-20",
      "description": "Quarterly Report",
      "filing_url": "https://www.sec.gov/Archives/edgar/data/1045810/...",
      "is_material": false
    }
  ],
  "has_material_events": true,
  "total_count": 5
}
Use Case Example: 
# Monitor for material events
import httpx

def check_material_events(ticker):
    response = httpx.post(
        "http://localhost:8080/api/v1/agents/sec-filings",
        headers={"X-API-Key": "sk_your_api_key"},
        json={"input": {"ticker": ticker, "forms": "8-K"}}
    ).json()

    # Wait for task completion
    task_id = response["task_id"]
    # ... poll for result ...

    if result["has_material_events"]:
        print(f"⚠️  Material events for {ticker}:")
        for filing in result["filings"]:
            if filing["is_material"]:
                print(f"  - {filing['filed_date']}: {filing['description']}")

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twitter-sentiment

Shannon Cloud Only: Uses xAI API for Twitter/X sentiment analysis.
Analyze X/Twitter sentiment for a stock using xAI’s Grok model. Agent ID: twitter-sentiment Input Schema:
FieldTypeRequiredDefaultDescription
tickerstringYes-Stock ticker symbol (e.g., ‘NVDA’, ‘TSLA’)
days_backintegerNo1Days of history to search (1-7)
Example Request: 
curl -X POST http://localhost:8080/api/v1/agents/twitter-sentiment \
  -H "X-API-Key: sk_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "ticker": "NVDA",
      "days_back": 3
    }
  }'
Output Structure: 
{
  "ticker": "NVDA",
  "analysis": "Recent Twitter sentiment for NVIDIA is predominantly bullish. Key influencers are discussing strong Q4 earnings and AI chip demand. Notable mentions include:\n- @analyst123: 'NVDA crushing expectations'\n- @investor_daily: 'AI demand shows no signs of slowing'",
  "sentiment": "bullish",
  "citations": [
    "https://x.com/analyst123/status/123456789",
    "https://x.com/investor_daily/status/987654321"
  ],
  "cost_usd": 0.30
}
Sentiment Values:
  • bullish - Positive sentiment, buying interest
  • bearish - Negative sentiment, selling pressure
  • mixed - Conflicting signals
  • neutral - No clear directional sentiment
API Cost: Twitter sentiment uses xAI’s Grok model, which costs approximately $0.30 per analysis. Monitor usage to control costs.

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alpaca-news

Open Source Compatible: Alpaca Markets offers a free tier. Works in OSS with your Alpaca API key.
Get recent stock news from Alpaca Markets API (powered by Benzinga). Agent ID: alpaca-news Input Schema:
FieldTypeRequiredDefaultDescription
symbolsstringYes-Comma-separated stock symbols (e.g., ‘NVDA’ or ‘NVDA,AAPL’)
hours_backintegerNo24Hours of history to search (1-168)
limitintegerNo10Maximum number of news items to return (1-50)
Example Request: 
curl -X POST http://localhost:8080/api/v1/agents/alpaca-news \
  -H "X-API-Key: sk_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "symbols": "NVDA,AMD",
      "hours_back": 48,
      "limit": 20
    }
  }'
Output Structure: 
{
  "symbols": "NVDA,AMD",
  "news": [
    {
      "headline": "NVIDIA announces new AI chip breakthrough",
      "summary": "NVIDIA Corporation unveiled its next-generation AI accelerator...",
      "author": "Tech News Daily",
      "created_at": "2026-02-15T09:30:00Z",
      "updated_at": "2026-02-15T09:30:00Z",
      "url": "https://example.com/nvidia-ai-chip",
      "symbols": ["NVDA"],
      "source": "benzinga"
    },
    {
      "headline": "AMD gains market share in datacenter GPUs",
      "summary": "Advanced Micro Devices reported increased datacenter revenue...",
      "author": "Market Watch",
      "created_at": "2026-02-15T08:15:00Z",
      "updated_at": "2026-02-15T08:15:00Z",
      "url": "https://example.com/amd-datacenter",
      "symbols": ["AMD"],
      "source": "benzinga"
    }
  ],
  "total_count": 18
}
Alpaca Free Tier:
  • 200 news requests per month
  • Real-time news feed
  • Benzinga news source

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news-aggregator

Shannon Cloud Only: Combines multiple premium sources (Alpaca, SEC, xAI).
Fetch comprehensive news and sentiment from multiple sources (Alpaca, SEC, Twitter) for a stock. Agent ID: news-aggregator Input Schema:
FieldTypeRequiredDefaultDescription
tickerstringYes-Stock ticker symbol (e.g., ‘NVDA’)
include_filingsbooleanNotrueInclude SEC filings in results
include_twitterbooleanNotrueInclude Twitter sentiment in results
Example Request: 
curl -X POST http://localhost:8080/api/v1/agents/news-aggregator \
  -H "X-API-Key: sk_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "ticker": "NVDA",
      "include_filings": true,
      "include_twitter": true
    }
  }'
Output Structure: 
{
  "ticker": "NVDA",
  "as_of": "2026-02-15T10:30:00Z",
  "sources_used": ["alpaca", "sec", "twitter"],
  "overall_sentiment": {
    "score": 0.72,
    "label": "bullish"
  },
  "alpaca": {
    "articles": [
      {
        "headline": "NVIDIA announces new AI chip breakthrough",
        "summary": "...",
        "created_at": "2026-02-15T09:30:00Z",
        "url": "https://..."
      }
    ],
    "total_count": 18,
    "heuristic_sentiment_score": 0.65
  },
  "sec": {
    "filings": [
      {
        "form_type": "8-K",
        "filed_date": "2026-01-15",
        "description": "Current Report",
        "filing_url": "https://www.sec.gov/...",
        "is_material": true
      }
    ],
    "has_material_events": true,
    "total_count": 3
  },
  "twitter": {
    "analysis": "Recent Twitter sentiment is predominantly bullish...",
    "sentiment": "bullish",
    "citations": ["https://x.com/..."]
  }
}
Overall Sentiment Calculation: The aggregator calculates a weighted sentiment score:
  • Alpaca news: 40% weight (heuristic keyword analysis)
  • Twitter: 40% weight (xAI sentiment)
  • SEC filings: 20% weight (material events indicator)
Score Ranges:
  • 0.6 - 1.0: Bullish
  • 0.4 - 0.6: Neutral
  • 0.0 - 0.4: Bearish

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Common Workflows

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Workflow 1: Daily Event Monitoring

Monitor multiple tickers for material SEC events: 
import httpx

tickers = ["NVDA", "AAPL", "MSFT", "GOOGL", "AMZN"]
client = httpx.Client(
    base_url="http://localhost:8080",
    headers={"X-API-Key": "sk_your_api_key"}
)

for ticker in tickers:
    response = client.post("/api/v1/agents/sec-filings", json={
        "input": {
            "ticker": ticker,
            "days_back": 1,
            "forms": "8-K"
        }
    }).json()

    task_id = response["task_id"]
    # ... poll for result ...

    if result.get("has_material_events"):
        print(f"⚠️  {ticker}: {len(result['filings'])} new 8-K filings")

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Workflow 2: Comprehensive Stock Analysis

Combine all sources for a single ticker: 
# Get aggregated view
aggregated = client.post("/api/v1/agents/news-aggregator", json={
    "input": {"ticker": "NVDA"}
}).json()

task_id = aggregated["task_id"]
# ... poll for result ...

print(f"Overall sentiment: {result['overall_sentiment']['label']}")
print(f"Sources: {', '.join(result['sources_used'])}")
print(f"News articles: {result['alpaca']['total_count']}")
print(f"Material events: {result['sec']['has_material_events']}")

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Workflow 3: Sentiment Comparison Across Peers

Compare sentiment for multiple tickers: 
peer_group = ["NVDA", "AMD", "INTC"]
sentiments = {}

for ticker in peer_group:
    response = client.post("/api/v1/agents/twitter-sentiment", json={
        "input": {"ticker": ticker, "days_back": 1}
    }).json()

    task_id = response["task_id"]
    # ... poll for result ...

    sentiments[ticker] = result["sentiment"]

print("Peer sentiment comparison:")
for ticker, sentiment in sentiments.items():
    print(f"  {ticker}: {sentiment}")

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Workflow 4: News-Driven Trading Alerts

Monitor news for specific keywords: 
response = client.post("/api/v1/agents/alpaca-news", json={
    "input": {
        "symbols": "NVDA",
        "hours_back": 1,
        "limit": 50
    }
}).json()

task_id = response["task_id"]
# ... poll for result ...

keywords = ["breakthrough", "partnership", "acquisition", "lawsuit"]

for article in result["news"]:
    headline_lower = article["headline"].lower()
    if any(kw in headline_lower for kw in keywords):
        print(f"🚨 Alert: {article['headline']}")
        print(f"   URL: {article['url']}")

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Self-Hosted OSS Setup

To use financial agents in Shannon OSS:

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sec-filings (No API Key Required)

Works out of the box - SEC EDGAR is a free public API. 
# No configuration needed
docker compose up -d

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alpaca-news (Free Tier Available)

  1. Sign up for Alpaca (free tier): https://alpaca.markets
  2. Get API keys from dashboard
  3. Add to .env:
# .env
ALPACA_API_KEY=your_api_key_here
ALPACA_SECRET_KEY=your_secret_key_here
  1. Restart services:
docker compose restart llm-service

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twitter-sentiment (Paid API)

  1. Sign up for xAI (requires payment): https://x.ai
  2. Get API key
  3. Add to .env:
# .env
XAI_API_KEY=your_xai_api_key_here
  1. Restart services:
docker compose restart llm-service
xAI Costs: Twitter sentiment analysis costs approximately $0.30 per request. Monitor your usage carefully.

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Cost Comparison

AgentOSS (Self-Hosted)Shannon Cloud
sec-filingsFree (SEC EDGAR)Free (included)
alpaca-newsFree tier (200/month)Managed (included)
twitter-sentiment~$0.30/call (your xAI key)~$0.30/call (managed)
news-aggregatorFree + $0.30 (combined)~$0.35/call (managed)
Shannon Cloud Benefits:
  • ✅ Managed API keys (no signup needed)
  • ✅ Cost accounting per tenant
  • ✅ Rate limiting and quotas
  • ✅ Infrastructure (no server maintenance)

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Agents Overview

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