# 簡單低波動率指數 > 來源：https://uqer.io/community/share/5566a9b8f9f06c6641e97aea 金融市場的波動性加劇，為了提供更好的下行保護，低波動率的Smart Beta策略受到了廣泛的歡迎 代表指數 [S&P 500 Low Volatility Index](https://us.spindices.com/indices/strategy/sp-500-low-volatility-index) 目標指數 HS300 選股 計算目標指數股票池中樣本股過去100個交易日中的歷史波動率，并挑選其中波動率最低的50只股票作為指數的成分股 加權 與傳統指數市值加權不同，本指數根據股票波動率倒數為個股權重 ## 實現細節 通過`DataAPI.EquRetudGet`獲取不考慮現金紅利再投資情況下的每日收益率，波動率為調倉前100個交易日的日收益率標準差 ```py import numpy as np import pandas as pd start = '2012-01-01' # 回測起始時間 end = '2015-05-01' # 回測結束時間 benchmark = 'HS300' # 策略參考標準 universe = set_universe('HS300') # 證券池，回測支持股票和基金 capital_base = 10000000 # 起始資金 refresh_rate = 100 # 調倉頻率，即每 refresh_rate 個交易日執行一次 handle_data() 函數 cal = Calendar('China.SSE') def initialize(account): # 初始化虛擬賬戶狀態 pass def handle_data(account): # 每個交易日的買入賣出指令 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 ``` ## 結果分析 通過以上結果我們可以看到，該策略alpha極小，beta較大，并顯著減小了波動率