基于 VIX 指数的择时策略

最后更新于:2022-04-01 21:57:43

# 基于 VIX 指数的择时策略 > 来源:https://uqer.io/community/share/55b6152ff9f06c91fa18c5c9 波动率VIX指数是跟踪市场波动性的指数,一般通过标的期权的隐含波动率计算得来,以芝加哥期权交易所的VIX指数为例,如标的期权的隐含波动率越高,则VIX指数相应越高,一般而言,该指数反映出投资者愿意付出多少成本去对冲投资风险。业内认为,当VIX越高时,表示市场参与者预期后市波动程度会更加激烈,同时也反映其不安的心理状态;相反,VIX越低时,则反映市场参与者预期后市波动程度会趋于缓和的心态。因此,VIX又被称为投资人恐慌指标(The Investor Fear Gauge)。 中国波指是由上证所发布,用于衡量上证50ETF未来30日的预期波动。该指数是根据方差互换的原理,结合50ETF期权的实际运作特点,并通过对上证所交易的50ETF期权价格的计算编制而得。网址为: http://www.sse.com.cn/assortment/derivatives/options/volatility/ 本文中,基于优矿平台,自己尝试计算了日间的中国波指,并将其用在了华夏上证50的择时买卖上,以验证VIX指数对未来的预测性 由于上证所未发布其iVIX计算方法,所以此处的计算基于CBOE发布的方法,具体参见: http://www.cboe.com/micro/vix/part2.aspx ## 策略思路 + 当VIX指数快速上升时,表示市场恐慌情绪蔓延,产生卖出信号 + 当VIX指数快速下降时,恐慌情绪有所舒缓,产生买入信号 + 卖出买入信号均用来买卖华夏上证50ETF基金 注:国内唯一一只期权上证50ETF期权,跟踪标的为华夏上证50ETF(510050)基金 ## 1. 计算历史VIX指数 ```py from matplotlib import pylab import numpy as np import pandas as pd import DataAPI import seaborn as sns sns.set_style('white') ``` ```py from CAL.PyCAL import * from pandas import Series, DataFrame, concat import pandas as pd import numpy as np import seaborn as sns sns.set_style('white') from matplotlib import pylab import time import math def getHistDayOptions(var, date): # 使用DataAPI.OptGet,拿到已退市和上市的所有期权的基本信息; # 同时使用DataAPI.MktOptdGet,拿到历史上某一天的期权成交信息; # 返回历史上指定日期交易的所有期权信息,包括: # optID varSecID contractType strikePrice expDate tradeDate closePrice # 以optID为index。 vixDateStr = date.toISO().replace('-', '') optionsMkt = DataAPI.MktOptdGet(tradeDate = vixDateStr, field = [u"optID", "tradeDate", "closePrice"], pandas = "1") optionsMkt = optionsMkt.set_index(u"optID") optionsMkt.closePrice.name = u"price" optionsID = map(str, optionsMkt.index.values.tolist()) fieldNeeded = ["optID", u"varSecID", u'contractType', u'strikePrice', u'expDate'] optionsInfo = DataAPI.OptGet(optID=optionsID, contractStatus = [u"DE", u"L"], field=fieldNeeded, pandas="1") optionsInfo = optionsInfo.set_index(u"optID") options = concat([optionsInfo, optionsMkt], axis=1, join='inner').sort_index() return options[options.varSecID==var] def getNearNextOptExpDate(options, vixDate): # 找到options中的当月和次月期权到期日; # 用这两个期权隐含的未来波动率来插值计算未来30隐含波动率,是为市场恐慌指数VIX; # 如果options中的最近到期期权离到期日仅剩1天以内,则抛弃这一期权,改 # 选择次月期权和次月期权之后第一个到期的期权来计算。 # 返回的near和next就是用来计算VIX的两个期权的到期日 optionsExpDate = Series(options.expDate.values.ravel()).unique().tolist() near = min(optionsExpDate) optionsExpDate.remove(near) if Date.parseISO(near) - vixDate < 1: near = min(optionsExpDate) optionsExpDate.remove(near) next = min(optionsExpDate) return near, next def getStrikeMinCallMinusPutClosePrice(options): # options 中包括计算某日VIX的call和put两种期权, # 对每个行权价,计算相应的call和put的价格差的绝对值, # 返回这一价格差的绝对值最小的那个行权价, # 并返回该行权价对应的call和put期权价格的差 call = options[options.contractType==u"CO"].set_index(u"strikePrice").sort_index() put = options[options.contractType==u"PO"].set_index(u"strikePrice").sort_index() callMinusPut = call.closePrice - put.closePrice strike = abs(callMinusPut).idxmin() priceDiff = callMinusPut[strike] return strike, priceDiff def calSigmaSquare(options, FF, R, T): # 计算某个到期日期权对于VIX的贡献sigma; # 输入为期权数据options,FF为forward index price, # R为无风险利率, T为期权剩余到期时间 callAll = options[options.contractType==u"CO"].set_index(u"strikePrice").sort_index() putAll = options[options.contractType==u"PO"].set_index(u"strikePrice").sort_index() callAll['deltaK'] = 0.05 putAll['deltaK'] = 0.05 # Interval between strike prices index = callAll.index if len(index) < 3: callAll['deltaK'] = index[-1] - index[0] else: for i in range(1,len(index)-1): callAll['deltaK'].ix[index[i]] = (index[i+1]-index[i-1])/2.0 callAll['deltaK'].ix[index[0]] = index[1]-index[0] callAll['deltaK'].ix[index[-1]] = index[-1] - index[-2] index = putAll.index if len(index) < 3: putAll['deltaK'] = index[-1] - index[0] else: for i in range(1,len(index)-1): putAll['deltaK'].ix[index[i]] = (index[i+1]-index[i-1])/2.0 putAll['deltaK'].ix[index[0]] = index[1]-index[0] putAll['deltaK'].ix[index[-1]] = index[-1] - index[-2] call = callAll[callAll.index > FF] put = putAll[putAll.index < FF] FF_idx = FF if not put.empty: FF_idx = put.index[-1] put['closePrice'].iloc[-1] = (putAll.ix[FF_idx].closePrice + callAll.ix[FF_idx].closePrice)/2.0 callComponent = call.closePrice*call.deltaK/call.index/call.index putComponent = put.closePrice*put.deltaK/put.index/put.index sigma = (sum(callComponent)+sum(putComponent))*np.exp(T*R)*2/T sigma = sigma - (FF/FF_idx - 1)**2/T return sigma def calDayVIX(optionVarSecID, vixDate): # 利用CBOE的计算方法,计算历史某一日的未来30日期权波动率指数VIX # The risk-free interest rates R_near = 0.06 R_next = 0.06 # 拿取所需期权信息 options = getHistDayOptions(optionVarSecID, vixDate) termNearNext = getNearNextOptExpDate(options, vixDate) optionsNearTerm = options[options.expDate == termNearNext[0]] optionsNextTerm = options[options.expDate == termNearNext[1]] # time to expiration T_near = (Date.parseISO(termNearNext[0]) - vixDate)/365.0 T_next = (Date.parseISO(termNearNext[1]) - vixDate)/365.0 # the forward index prices nearPriceDiff = getStrikeMinCallMinusPutClosePrice(optionsNearTerm) nextPriceDiff = getStrikeMinCallMinusPutClosePrice(optionsNextTerm) near_F = nearPriceDiff[0] + np.exp(T_near*R_near)*nearPriceDiff[1] next_F = nextPriceDiff[0] + np.exp(T_next*R_next)*nextPriceDiff[1] # 计算不同到期日期权对于VIX的贡献 near_sigma = calSigmaSquare(optionsNearTerm, near_F, R_near, T_near) next_sigma = calSigmaSquare(optionsNextTerm, next_F, R_next, T_next) # 利用两个不同到期日的期权对VIX的贡献sig1和sig2, # 已经相应的期权剩余到期时间T1和T2; # 差值得到并返回VIX指数(%) w = (T_next - 30.0/365.0)/(T_next - T_near) vix = T_near*w*near_sigma + T_next*(1 - w)*next_sigma return 100*np.sqrt(vix*365.0/30.0) def getHistVIX(beginDate, endDate): # 计算历史一段时间内的VIX指数并返回 optionVarSecID = u"510050.XSHG" cal = Calendar('China.SSE') dates = cal.bizDatesList(beginDate, endDate) dates = map(Date.toDateTime, dates) histVIX = pd.DataFrame(0.0, index=dates, columns=['VIX']) histVIX.index.name = 'date' for date in histVIX.index: histVIX['VIX'][date] = calDayVIX(optionVarSecID, Date.fromDateTime(date)) return histVIX def getDayVIX(date): optionVarSecID = u"510050.XSHG" return calDayVIX(optionVarSecID, date) ``` ## 2. VIX指数与华夏上证50ETF基金的走势对比 ```py secID = '510050.XSHG' begin = Date(2015, 2, 9) end = Date(2015, 7, 23) # 历史VIX histVIX = getHistVIX(begin, end) # 华夏上证50ETF etf = DataAPI.MktFunddGet(secID, beginDate=begin.toISO().replace('-', ''), endDate=end.toISO().replace('-', ''), field=['tradeDate', 'closePrice']) etf['tradeDate'] = pd.to_datetime(etf['tradeDate']) etf = etf.set_index('tradeDate') ``` ```py font.set_size(12) pylab.figure(figsize = (16,8)) ax1 = histVIX.plot(x=histVIX.index, y='VIX', style='r') ax1.set_xlabel(u'日期', fontproperties=font) ax1.set_ylabel(u'VIX(%)', fontproperties=font) ax2 = ax1.twinx() ax2.plot(etf.index,etf.closePrice) ax2.set_ylabel(u'ETF Price', fontproperties=font) ``` ![](https://docs.gechiui.com/gc-content/uploads/sites/kancloud/2016-07-30_579cbdb42fdd0.png) 关于VIX,比较成熟的美国市场中,标普500指数和相应的VIX之间呈负相关性。具体可以参照CBOE的数据:http://www.cboe.com/micro/vix/part3.aspx 这可以理解为: + 当VIX越高时,表示市场参与者预期后市波动程度会更加激烈,所以谨慎持仓,甚至逐渐减仓; + 相反,VIX越低时,市场参与者预期后市波动程度会趋于缓和,开始放心投资股市。 上图中的中国市场VIX指数与华夏上证50ETF走势对比中,我们不难发现以下几点: + 上证50ETF期权于2月9日上市,之后一个月VIX稳定在低位运行,同时市场也表现出稳定的态势 + 3月下旬到5月初一段时间,VIX指数显著上升,表示市场认为后期震荡会加剧,但这种恐慌淹没在牛市大潮中 + 5月到6月VIX高位运行,但似乎没有引起市场的足够重视 + 6月中的股市大跌开始后,VIX指数快速上升到接近60 + 7月时候,市场认可国家救市决心,VIX开始从高位迅速下降,股指也日趋稳定 可以看出,VIX指数在和股指的并驾齐驱中总是慢人一步,没法充分表现出股指在六月极高位时候市场的不安;实际上,国内期权市场建立不足半年,期权流动性并不够大,导致基于期权市场的VIX指数对于中国股市的预测并不如成熟市场一样流畅 ## 3. 基于VIX指数的择时策略示例 ```py start = datetime(2015, 2, 9) # 回测起始时间 end = datetime(2015, 7, 26) # 回测结束时间 benchmark = '510050.XSHG' # 策略参考标准 universe = ['510050.XSHG'] # 股票池 capital_base = 100000 # 起始资金 commission = Commission(0.0,0.0) window_short = 1 window_long = 5 longest_history = 1 SD = 0.08 histVIX['short_window'] = pd.rolling_mean(histVIX['VIX'], window=window_short) histVIX['long_window'] = pd.rolling_mean(histVIX['VIX'], window=window_long) def initialize(account): # 初始化虚拟账户状态 account.fund = universe[0] def handle_data(account): # 每个交易日的买入卖出指令 hist = account.get_history(longest_history) fund = account.fund # 获取回测当日的前一天日期 dt = Date.fromDateTime(account.current_date) cal = Calendar('China.IB') lastTDay = cal.advanceDate(dt,'-1B',BizDayConvention.Preceding) #计算出前一个交易日期 last_day_str = lastTDay.strftime("%Y-%m-%d") # 计算买入卖出信号 try: short_mean = histVIX['short_window'].loc[last_day_str] # 计算短均线值 long_mean = histVIX['long_window'].loc[last_day_str] # 计算长均线值 long_flag = True if (short_mean - long_mean) < -SD * long_mean else False short_flag = True if (short_mean - long_mean) > SD * long_mean else False except: long_flag = False short_flag = False if long_flag: if account.position.secpos.get(fund, 0) == 0: # 空仓时全仓买入,买入股数为100的整数倍 approximationAmount = int(account.cash / hist[fund]['closePrice'][-1]/100.0) * 100 order(fund, approximationAmount) elif short_flag: # 卖出时,全仓清空 if account.position.secpos.get(fund, 0) >= 0: order_to(fund, 0) ``` ![](https://docs.gechiui.com/gc-content/uploads/sites/kancloud/2016-07-30_579cbdb44a569.jpg) 可以看出: + 基于VIX指数高位时空仓、低位时进场的策略,可以比较有效地避开股指大跌的风险 + 但由于国内期权市场流动性不足,VIX指数并不能有效反应市场的情绪,导致我们也错过了很多牛市的蛋糕
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