DeFi liquidity is fragmented across hundreds of protocols, making manual yield hunting inefficient and error-prone. Building an automated scanner requires more than just fetching APY data; it demands context-aware analysis to distinguish between sustainable yields and high-risk, unsustainable incentives. By combining Python’s data processing power with AI-driven sentiment and risk assessment, you can build a robust tool that filters noise to surface high-quality opportunities.
The foundation of your scanner is a robust data ingestion layer. While public APIs exist for major chains like Ethereum and Solana, data quality varies. Start by normalizing data from sources like DeFiLlama or specific protocol APIs. Use requests to fetch current TVL (Total Value Locked) and APY metrics. However, raw APY is a misleading metric. A 500% APY on a low-liquidity pool is often a red flag for rug pulls or unsustainable emission schedules. This is where AI integration becomes critical.
Instead of relying on static thresholds, implement a heuristic engine that scores each yield opportunity. Calculate the "Yield Sustainability Ratio" by dividing current APY by the protocol’s historical average. High deviations trigger an AI review. Here is a simplified Python structure for this logic:
python
import requests
import json
def fetch_yield_data(protocol_id):
url = f"https://yields.llama.fi/pools"
response = requests.get(url)
pools = response.json().get('data', [])
# Filter for specific protocol or chain
filtered = [p for p in pools if p['project'] == protocol_id]
return filtered
def analyze_risk(pool_data):
# Calculate volatility and volume stability
apy = pool_data.get('apy', 0)
tvl = pool_data.get('tvlUsd', 0)
# Basic heuristic: High APY + Low TVL = High Risk
risk_score = 0
if apy > 100 and tvl < 1_000_000:
risk_score += 50
if apy > 500:
risk_score += 30
# Send context to AI for qualitative analysis
prompt = f"Analyze risk for a DeFi pool with APY {apy}% and TVL ${tvl}. Is this sustainable?"
ai_insight
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