Building a DeFi Yield Scanner with Python and AI — 2026-10-07 #2

DeFi yields are volatile, opaque, and often hide behind complex smart contract logic. A static list of APYs is insufficient for serious traders; you need a dynamic scanner that contextualizes risk with real-time data. By combining Python’s data manipulation power with AI-driven analysis, you can build a tool that doesn’t just report numbers but interprets them.

The core of this system is a pipeline that fetches data from aggregators like DeFiLlama or Dune, cleans it, and passes it through an LLM for risk assessment. Below is a streamlined implementation using requests and a hypothetical AI API client.

python
import requests
import pandas as pd
from ai_client import get_ai_insight

def fetch_yield_data():
    """Fetch raw yield data from a public API."""
    url = "https://yields.llama.fi/pools"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        df = pd.DataFrame(data['data'])
        # Filter for high-risk/high-reward assets (example)
        df = df[df['apyBase'] > 10]
        return df
    return pd.DataFrame()

def analyze_pool(pool_data: dict) -> str:
    """Send specific pool metrics to AI for qualitative risk analysis."""
    prompt = f"""
    Analyze this DeFi pool for risk factors and sustainability.
    Metrics: TVL ${pool_data['tvlUsd']:.2f}, APY {pool_data['apyBase']:.2f}%, 
    Chain: {pool_data['chain']}, Project: {pool_data['project']}.
    Consider: TVL stability, fee structure, and known contract vulnerabilities.
    Return a concise risk summary (low/medium/high) and 3 key factors.
    """
    return get_ai_insight(prompt)

def main():
    df = fetch_yield_data()
    results = []
    for _, row in df.head(10).iterrows():
        insight = analyze_pool(row.to_dict())
        results.append({
            "project": row['project'],
            "apy": row['apyBase'],
            "ai_insight": insight
        })
    print(pd.DataFrame(results).to_string(index=False))

if __name__ == "__main__