Building a DeFi Yield Scanner with Python and AI

DeFi yields are dynamic, volatile, and often hidden behind complex contract logic. Traditional static data feeds fail to capture the real-time risk-adjusted returns that sophisticated traders seek. By combining Python’s data processing power with AI-driven pattern recognition, you can build a yield scanner that doesn’t just report APRs but predicts sustainable returns. This guide outlines the architecture for building such a system, focusing on data ingestion, normalization, and AI-enhanced risk scoring.

Data Ingestion and Normalization

The foundation of any reliable scanner is robust data ingestion. You need to pull data from multiple sources: DEX aggregators (like 1inch or UniV3), lending protocols (Aave, Compound), and oracle feeds. Python’s asyncio library is crucial here to handle concurrent API requests without blocking.

import asyncio
import aiohttp

async def fetch_pool_data(session, pool_address):
    url = f"https://api.dune.com/v1/executions/{pool_address}"
    async with session.get(url) as response:
        if response.status == 200:
            return await response.json()
        return None

async def scan_pools(pools):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_pool_data(session, pool) for pool in pools]
        results = await asyncio.gather(*tasks)
        return [r for r in results if r is not None]

Once data is collected, normalize it. Different protocols report yields differently—some include inflation rewards, others only trading fees. Create a unified data schema that separates base yield, incentive yield, and total APR.

AI-Enhanced Risk Scoring

Raw APR is misleading. A 500% yield on a new, unverified token is a red flag, not an opportunity. This is where AI services come in. Instead of building a complex local model, leverage external AI APIs to analyze historical volatility, liquidity depth, and token contract health.

Use an LLM-based API to generate a "risk narrative" based on recent on-chain events. For example, send a prompt with the last 24 hours of transaction data to an AI service. Ask it to identify anomalies like sudden liquidity withdrawals or large single-wallet deposits.

python
import requests

def get_ai_risk_score(token_data):
    prompt = f"Analyze this De