The alt data layer
your models are missing

import requests

# 1. Alt Data Pulse — cross-reference trending topics with market activity
resp = requests.get("https://api.gettrawl.com/api/pulse", params={"geo": "US", "max_trends": 10})
pulse = resp.json()

for item in pulse["pulse"]:
    tickers = ", ".join(item["related_tickers"][:5])
    print(f"[{item['alt_data_score']:.2f}] {item['trend']} ({item['traffic_volume']})")
    print(f"  Tickers: {tickers}")
    for signal in item["signals"]:
        print(f"  {signal['type']} ({signal['severity']}): {signal['description']}")

# 2. Deep-dive a specific topic → find exposed tickers
topic = requests.get("https://api.gettrawl.com/api/pulse/topic/artificial intelligence chips").json()
for t in topic["tickers"]:
    print(f"{t['ticker']}: score {t['alt_data_score']:.2f}, {t['mention_count']} mentions")

# 3. Anomaly detection — current vs baseline
anomalies = client.intelligence.anomalies("TSLA", current_days=7, baseline_days=90)
for a in anomalies["anomalies"]:
    print(f"  {a['source']}: {a['metric']} = {a['current']} vs baseline {a['baseline']}")
    print(f"    Z-score: {a['z_score']:+.1f} | {a['severity']}")

# 4. Batch scan your watchlist
scan = client.intelligence.scan(tickers=["AAPL", "NVDA", "TSLA", "META", "GOOG"])
for ticker in scan["results"]:
    print(f"{ticker['ticker']}: signal_score={ticker['signal_score']}")
    for signal in ticker["top_signals"]:
        print(f"  - {signal['description']}")