Building a DeFi Yield Scanner with Python and AI

DeFi yield farming has evolved from a simple search for the highest APY into a complex game of risk management, liquidity depth, and sustainability. Traditional static dashboards often fail to capture the dynamic nature of on-chain data. By combining Python’s data processing power with AI-driven pattern recognition, we can build a robust Yield Scanner that not only identifies opportunities but also predicts potential rug pulls or unsustainable incentives.

The foundation of this system is a reliable data pipeline. We start by fetching real-time APY data from major aggregators like DefiLlama or direct protocol APIs. Using requests and pandas, we normalize this data into a time-series format. However, raw data is noisy. To add intelligence, we integrate an AI model to classify yield sustainability.

Here is a simplified example of how to structure the data ingestion and feature engineering phase:

import pandas as pd
import requests

def fetch_yield_data(protocol_id):
    url = f"https://yields.llama.fi/pools"
    response = requests.get(url)
    data = response.json()
    df = pd.DataFrame(data['data'])
    # Filter for specific protocol and extract key metrics
    filtered_df = df[df['project'] == protocol_id]
    return filtered_df[['pool', 'apy', 'apyBase', 'apyReward', 'tvlUsd']]

def calculate_risk_score(row):
    # Simple heuristic: High APY with low TVL is risky
    if row['apy'] > 500 and row['tvlUsd'] < 100_000:
        return 1.0
    elif row['apy'] > 200:
        return 0.5
    return 0.1

# Example usage
data = fetch_yield_data('aave')
data['risk_score'] = data.apply(calculate_risk_score, axis=1)
print(data.head())

This basic heuristic is a starting point, but it lacks nuance. This is where AI API services become critical. Instead of training a massive local model, you can leverage pre-trained Large Language Models (LLMs) or specialized financial NLP models via API. You can send historical APY fluctuations and TVL data to an AI endpoint to generate a narrative risk assessment. For instance, asking the AI to analyze if a sudden 10% TV