Building a DeFi Yield Scanner with Python and AI

Monitoring DeFi yields manually is inefficient and prone to human error. With thousands of protocols across multiple chains, identifying high-APY opportunities while filtering out unsustainable or risky ones requires automation. By combining Python’s data handling capabilities with AI-driven analysis, you can build a robust DeFi Yield Scanner that not only aggregates data but also contextualizes risk.

Here is how to construct the core engine of such a scanner.

Step 1: Aggregating Real-Time Data

First, you need to fetch live APY data. Tools like DeFiLlama provide free, comprehensive APIs. We’ll use requests to pull this data.

import requests
import pandas as pd

def fetch_apy_data():
    url = "https://yields.llama.fi/pools"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        # Convert to DataFrame for easier manipulation
        df = pd.DataFrame(data['data'])
        return df
    else:
        raise Exception("Failed to fetch data")

yield_data = fetch_apy_data()
# Basic filtering: Look for pools above 10% APY
high_yield_pools = yield_data[yield_data['apy'] > 10]

Step 2: AI-Driven Risk Contextualization

Raw APY is misleading. A 100% APY on a new, unaudited token is high-risk, while 5% on a blue-chip stablecoin protocol is safer. This is where AI shines. Instead of hardcoding rules, use an LLM to analyze the protocol’s reputation, audit status, and TVL stability.

You can feed a summary of the top candidates to an AI API to generate a "Risk Score" and a brief explanation.

python
import json

def analyze_risk_with_ai(pool_info):
    prompt = f"""
    Analyze the following DeFi pool for risk. Consider TVL stability, 
    protocol age, and token volatility.
    Pool: {pool_info['project']}, Chain: {pool_info['chain']}, 
    TVL: ${pool_info['tvlUsd']}, APY: {pool_info['apy']}%
    Return a JSON object with keys: 'risk_score' (1-10), 'reasoning'