Building a DeFi Yield Scanner with Python and AI

DeFi yield farming is a high-stakes game where volatility and liquidity are critical factors. While manual tracking is feasible for a few pools, scaling to hundreds of protocols requires automation. This article outlines how to build a robust DeFi Yield Scanner using Python, enhanced with AI for semantic analysis and risk assessment.

The Architecture

A basic scanner fetches APY data from aggregators like DeFiLlama or The Graph. However, a smart scanner needs context. It must distinguish between a 500% APY from a sustainable protocol and a 500% APY from a rug-pull bait. By integrating an AI API, we can analyze protocol documentation, recent social sentiment, and historical volatility to score the quality of the yield.

Core Implementation

First, we establish the data ingestion layer. We use requests to pull live APY data and pandas for data manipulation.

import requests
import pandas as pd

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

    # Filter for top chains and significant TVL to reduce noise
    df = pd.DataFrame(data)
    df = df[df['chain'].isin(['Ethereum', 'Arbitrum', 'Optimism'])]
    df = df[df['tvlUsd'] > 1_000_000]

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

Integrating AI for Risk Scoring

This is where the AI API shines. We send a summary of the top yielding projects to an LLM to evaluate their sustainability. We prompt the model to act as a DeFi risk analyst, asking it to assign a "Safety Score" (1-10) based on the project's reputation, tokenomics, and potential for impermanent loss.

python
import openai

def analyze_risk(project_details):
    prompt = f"""
    You are a DeFi risk analyst. Analyze the following yield opportunity:
    {project_details}

    Consider:
    1. Protocol maturity and audit status.
    2. Tokenomics of the reward asset.
    3. Recent community sentiment.