金融リサーチエージェント

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"
    }
  }'

{
  "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
}

# 重要なイベントを監視
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()

    # タスク完了を待つ
    task_id = response["task_id"]
    # ... 結果をポーリング ...

    if result["has_material_events"]:
        print(f"⚠️  {ticker}の重要なイベント:")
        for filing in result["filings"]:
            if filing["is_material"]:
                print(f"  - {filing['filed_date']}: {filing['description']}")

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
    }
  }'

{
  "ticker": "NVDA",
  "analysis": "NVIDIAの最近のTwitter感情は圧倒的に強気です。主要なインフルエンサーは強力なQ4収益とAIチップの需要について議論しています。注目すべき言及には以下が含まれます:\n- @analyst123: 'NVIDAが期待を上回る'\n- @investor_daily: 'AIの需要は衰える兆候なし'",
  "sentiment": "bullish",
  "citations": [
    "https://x.com/analyst123/status/123456789",
    "https://x.com/investor_daily/status/987654321"
  ],
  "cost_usd": 0.30
}

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
    }
  }'

{
  "symbols": "NVDA,AMD",
  "news": [
    {
      "headline": "NVIDIAが新しいAIチップのブレークスルーを発表",
      "summary": "NVIDIA Corporationは次世代のAIアクセラレータを発表...",
      "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がデータセンターGPUで市場シェアを獲得",
      "summary": "Advanced Micro Devicesはデータセンター収益の増加を報告...",
      "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
}

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
    }
  }'

{
  "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が新しいAIチップのブレークスルーを発表",
        "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": "最近のTwitter感情は圧倒的に強気です...",
    "sentiment": "bullish",
    "citations": ["https://x.com/..."]
  }
}

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"]
    # ... 結果をポーリング ...

    if result.get("has_material_events"):
        print(f"⚠️  {ticker}: {len(result['filings'])} 新しい8-K提出書類")

# 集約されたビューを取得
aggregated = client.post("/api/v1/agents/news-aggregator", json={
    "input": {"ticker": "NVDA"}
}).json()

task_id = aggregated["task_id"]
# ... 結果をポーリング ...

print(f"全体的な感情: {result['overall_sentiment']['label']}")
print(f"ソース: {', '.join(result['sources_used'])}")
print(f"ニュース記事: {result['alpaca']['total_count']}")
print(f"重要なイベント: {result['sec']['has_material_events']}")

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"]
    # ... 結果をポーリング ...

    sentiments[ticker] = result["sentiment"]

print("ピア感情比較:")
for ticker, sentiment in sentiments.items():
    print(f"  {ticker}: {sentiment}")

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

task_id = response["task_id"]
# ... 結果をポーリング ...

keywords = ["ブレークスルー", "パートナーシップ", "買収", "訴訟"]

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

# 設定不要
docker compose up -d

# .env
ALPACA_API_KEY=your_api_key_here
ALPACA_SECRET_KEY=your_secret_key_here

docker compose restart llm-service

# .env
XAI_API_KEY=your_xai_api_key_here

docker compose restart llm-service