Building a DeFi Yield Scanner with Python and AI

DeFi yields are volatile, fragmented, and often opaque. Relying on static aggregators is no longer sufficient for sophisticated traders. To stay ahead, you need a dynamic, AI-augmented yield scanner that not only fetches real-time APYs but also predicts risk and identifies anomalies. Here is how to build one using Python.

The foundation of your scanner is data ingestion. While you can scrape Dune or The Graph, using a robust API like DeFiLlama or CoinGecko provides structured, reliable data. Start by creating a lightweight fetching mechanism that pulls current TVL (Total Value Locked) and APY metrics for major protocols.

import requests
import pandas as pd

def fetch_yield_data():
    url = "https://yields.llama.fi/pools"
    try:
        response = requests.get(url, timeout=10)
        data = response.json()
        df = pd.DataFrame(data['data'])
        # Filter for Ethereum mainnet and stablecoins for clarity
        df = df[df['chain'] == 'Ethereum']
        df = df[df['symbol'].str.contains('USDT|USDC|DAI', case=False, na=False)]
        return df
    except Exception as e:
        print(f"Error fetching data: {e}")
        return pd.DataFrame()

df = fetch_yield_data()

Raw APY is a dangerous metric on its own. A 50% APY on a protocol with $50k TVL is high-risk, while a 5% APY on a protocol with $1B TVL is safer. To add intelligence, we introduce an AI layer. Instead of building a complex model from scratch, integrate an LLM API to analyze protocol metadata and recent news sentiment.

The key is prompting the AI to act as a risk auditor. Send a subset of the data, including protocol name, TVL, and APY, to an LLM API. Ask it to score the risk based on known vulnerabilities, audit history, and liquidity depth.

python
from openai import OpenAI

client = OpenAI(api_key="your_api_key")

def analyze_risk(protocol_name, tvl, apy):
    prompt = f"""
    Act as a DeFi risk analyst. Analyze the following protocol:
    Protocol: {protocol_name}