# 简单低波动率指数 > 来源：https://uqer.io/community/share/5566a9b8f9f06c6641e97aea 金融市场的波动性加剧，为了提供更好的下行保护，低波动率的Smart Beta策略受到了广泛的欢迎 代表指数 [S&P 500 Low Volatility Index](https://us.spindices.com/indices/strategy/sp-500-low-volatility-index) 目标指数 HS300 选股 计算目标指数股票池中样本股过去100个交易日中的历史波动率，并挑选其中波动率最低的50只股票作为指数的成分股 加权 与传统指数市值加权不同，本指数根据股票波动率倒数为个股权重 ## 实现细节 通过`DataAPI.EquRetudGet`获取不考虑现金红利再投资情况下的每日收益率，波动率为调仓前100个交易日的日收益率标准差 ```py import numpy as np import pandas as pd start = '2012-01-01' # 回测起始时间 end = '2015-05-01' # 回测结束时间 benchmark = 'HS300' # 策略参考标准 universe = set_universe('HS300') # 证券池，回测支持股票和基金 capital_base = 10000000 # 起始资金 refresh_rate = 100 # 调仓频率，即每 refresh_rate 个交易日执行一次 handle_data() 函数 cal = Calendar('China.SSE') def initialize(account): # 初始化虚拟账户状态 pass def handle_data(account): # 每个交易日的买入卖出指令 volatility_res = {} cal_today = Date.fromDateTime(account.current_date) start_day = cal.advanceDate(cal_today, '-101B', BizDayConvention.Following) yesterday = cal.advanceDate(cal_today, '-1B', BizDayConvention.Following) for stk in universe: try: data = DataAPI.EquRetudGet(ticker=stk[:6], beginDate=Date.toDateTime(start_day).strftime('%Y%m%d'), endDate=Date.toDateTime(yesterday).strftime('%Y%m%d'), field=['ticker',"dailyReturnNoReinv"]) revenue = data['dailyReturnNoReinv'] volatility_res[stk] = np.std(revenue) except: universe.remove(stk) res = pd.Series(volatility_res).order()[:50] temp = np.ones(50) res = np.divide(temp, res) weight_sum = res.values.sum() order_list = dict(res/weight_sum) for stk in account.valid_secpos: order_to(stk, 0) for s, weight in order_list.iteritems(): if account.referencePrice[s] == 0: continue order(s, capital_base*weight/account.referencePrice[s]) ```  ```py print "Benchmark Volatility : ", perf['benchmark_volatility'] print "Index Volatility : ", perf['volatility'] Benchmark Volatility : 0.213927304422 Index Volatility : 0.156413355501 ``` ## 结果分析 通过以上结果我们可以看到，该策略alpha极小，beta较大，并显著减小了波动率