Building a DeFi Yield Scanner with Python and AI

DeFi yields are volatile, fragmented, and often deceptive. A static list of APYs is useless in a market where liquidity shifts hourly. To gain a competitive edge, you need a dynamic DeFi Yield Scanner that combines real-time data ingestion with AI-driven risk assessment. In this guide, we’ll build a Python-based prototype that doesn’t just fetch numbers—it interprets them.

Step 1: Real-Time Data Ingestion

The foundation of any scanner is reliable data. We’ll use DeFiLlama’s open API, which aggregates yield data across hundreds of chains and protocols. Unlike scraping individual DEXs, this provides a normalized view of TVL (Total Value Locked) and APYs.

import requests
import pandas as pd

def fetch_yield_data():
    url = "https://yields.llama.fi/pools"
    response = requests.get(url)
    data = response.json()

    # Convert to DataFrame for easier manipulation
    df = pd.DataFrame(data["data"])

    # Clean and filter relevant columns
    df['apy'] = df['apy'].astype(float)
    df['tvlUsd'] = df['tvlUsd'].astype(float)

    # Filter out low TVL pools to reduce noise
    high_tvl_pools = df[df['tvlUsd'] > 1_000_000]

    return high_tvl_pools[['chain', 'project', 'symbol', 'apy', 'tvlUsd']]

Step 2: AI-Enhanced Risk Scoring

Raw APY is a red flag. A 500% APY usually signals high risk, unsustainable emissions, or imminent rug pulls. Here is where AI shines. Instead of hardcoding thresholds, we use an LLM to analyze the context of the yield source.

We can send a summarized snapshot of a specific pool to an AI API to generate a risk narrative and a score (1-10).

python
import json

def analyze_pool_risk(pool_info, api_key):
    prompt = f"""
    Analyze this DeFi yield pool for risk:
    Chain: {pool_info['chain']}
    Protocol: {pool_info['project']}
    Token: {pool_info['symbol']}
    APY