Building a DeFi Yield Scanner with Python and AI

DeFi yields are volatile, fragmented, and often deceptive. A static list of top APYs is useless today; you need a dynamic scanner that assesses risk-adjusted returns in real-time. By combining Python’s data processing power with AI-driven anomaly detection, you can build a robust yield scanner that filters out rug pulls and unsustainable incentives.

The foundation of this system is a robust data pipeline. Start by aggregating data from major DeFi aggregators like DefiLlama or The Graph. Use aiohttp for concurrent API requests to minimize latency.

import aiohttp
import asyncio

async def fetch_pool_data(session, pool_id):
    url = f"https://yields.llama.fi/pools/{pool_id}"
    async with session.get(url) as response:
        if response.status == 200:
            return await response.json()
        return None

async def scan_multiple_pools(pool_ids):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_pool_data(session, pid) for pid in pool_ids]
        results = await asyncio.gather(*tasks)
        return [r for r in results if r]

Once data is ingested, normalize it into a pandas DataFrame. Key metrics include apyBase, apyReward, and tvlUsd. However, raw numbers don't tell the whole story. This is where AI enters the picture. Instead of simple threshold filtering, use an AI API to analyze historical volatility patterns and sentiment.

For instance, an APY of 500% on a new protocol is a red flag. An LLM can analyze the project’s documentation, recent GitHub activity, and social media sentiment to assign a "Trust Score." You can structure your prompt to ask the AI to evaluate the sustainability of the reward emissions relative to the TVL.

python
def calculate_risk_score(apy, tvl, ai_trust_score):
    # Simple heuristic: High APY + Low TVL + Low Trust = High Risk
    if apy > 100 and tvl < 1_000_000:
        risk_penalty = 0.5
    elif ai_trust_score < 0.6:
        risk_penalty = 0.3
    else:
        risk_penalty = 0.0

Comments

[Luis Cruz]

Your approach to leveraging aiohttp for concurrent API requests is a smart move, especially given the need for real-time data in the DeFi space—minimizing latency can significantly impact user experience. I also appreciate how you integrated AI for a more nuanced risk assessment, particularly the idea of a "Trust Score" based on various data sources; this layered approach could really enhance decision-making for users. If you're looking for help optimizing the AI integration or scaling the data pipeline further, I'd be glad to explore a paid collaboration. What challenges have you faced with the AI analysis so far?