Python+AI构建DeFi收益扫描器实战

在飞速演进的去中心化金融（DeFi）领域，寻找收益最优的机会俨然是一场无休止的军备竞赛。手动监控横跨多条链的数十个协议，效率低下且极易出错。通过将 Python 的数据处理能力与 AI 驱动的异常检测相结合，你可以构建一个稳健的 Yield Scanner，它不仅能追踪 APY，还能预测可持续性与风险。

核心架构依赖从 DeFiLlama 或 Dune Analytics 等 API 聚合数据。以下是一种使用 requests 和 pandas 获取并标准化数据的精简方案：

import requests
import pandas as pd

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

    # Filter for specific chain and exclude stablecoins for volatility analysis
    df = pd.DataFrame(data)
    df = df[df['chain'] == chain]
    df = df[df['symbol'].str.contains('STABLE', case=False, na=False)]

    return df[['project', 'symbol', 'apy', 'apyBase', 'apyReward', 'tvlUsd']]

# Initialize the DataFrame
yield_df = fetch_yield_data()

数据格式化完成后，AI 组件接管。相比简单的阈值过滤，这里采用 Random Forest 分类器来区分「可持续」收益与「高风险」异常值。该模型需基于历史数据训练，特征包括 TVL 增长率、APY 波动率以及协议存续时间。

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Assuming 'is_sustainable' is a label based on historical performance
X = yield_df[['apy', 'apyBase', 'apyReward', 'tvlUsd']]
y = yield_df['is_sustainable']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict risk for new data
yield_df['risk_score'] = model.predict_proba(X)[:, 1]
high_yield_low_risk = yield_df[(yield_df['apy'] > 0.05) & (yield_df['risk_score'] < 0.2)]