Building a DeFi Yield Scanner with Python and AI

DeFi yield farming has evolved from a simple strategy of depositing assets into protocol pools into a complex optimization problem. With thousands of pools across Ethereum, Solana, and Layer 2s, manually tracking APYs is impossible. By combining Python’s data processing power with AI-driven analysis, you can build a scanner that not only fetches live yields but also predicts stability and risk.

The Architecture

The core of this system involves three layers: Data Ingestion, Feature Engineering, and AI Prediction.

1. Data Ingestion
Start by fetching data from APIs like DeFiLlama, The Graph, or specific protocol subgraphs. Python’s requests library handles HTTP calls, while pandas structures the raw JSON into manageable DataFrames.

import requests
import pandas as pd

def fetch_yields():
    url = "https://yields.llama.fi/pools"
    response = requests.get(url)
    data = response.json()
    df = pd.DataFrame(data['data'])
    # Filter for major chains to reduce noise
    df = df[df['chain'].isin(['Ethereum', 'Arbitrum', 'Optimism'])]
    return df

2. Feature Engineering
Raw APY is misleading. A 500% APY on a volatile token is high-risk, while a 10% APY on stablecoins is robust. Create composite features:

• Volatility Ratio: Current APY divided by the historical 7-day average.
• TVL Stability: Standard deviation of Total Value Locked over the last 30 days.
• Token Correlation: How closely the underlying asset moves with the broader market.

3. AI-Driven Scoring
Instead of simple thresholding, use a Machine Learning model to predict "Yield Sustainability." Train a Gradient Boosting Classifier (using scikit-learn or XGBoost) on historical data where the target variable is whether the yield remained above a certain threshold for the next 7 days.

python
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split

# Assume X is features, y is binary target (sustainable=1, collapsed=0)
X_train, X_test, y_train, y_test = train_test_split(X, y,