【50ETF期權】 3. 中國波指 iVIX · Python 量化交易教程

# 【50ETF期權】 3. 中國波指 iVIX > 來源：https://uqer.io/community/share/560493f7f9f06c590c65ef21 在本文中，我們將通過量化實驗室提供的數據，計算基于50ETF期權的中國波指 iVIX 波動率VIX指數是跟蹤市場波動性的指數，一般通過標的期權的隱含波動率計算得來。當VIX越高，表示市場參與者預期后市波動程度會更加激烈，同時也反映其不安的心理狀態；相反，VIX越低時，則反映市場參與者預期后市波動程度會趨于緩和。因此，VIX又被稱為投資人恐慌指標（The Investor Fear Gauge）。中國波指由上交所發布，用于衡量上證50ETF未來30日的預期波動。按照上交所網頁描述：該指數是根據方差互換的原理，結合50ETF期權的實際運作特點，并通過對上證所交易的50ETF期權價格的計算編制而得。網址為： http://www.sse.com.cn/assortment/derivatives/options/volatility/ ，該網頁中發布歷史 iVIX 和當日日內 iVIX 數據 ```py from CAL.PyCAL import * import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib import rc rc('mathtext', default='regular') import seaborn as sns sns.set_style('white') from matplotlib import dates from pandas import Series, DataFrame, concat from scipy import interpolate import math import time ``` 上證50ETF收盤價，用來和iVIX對比走勢 ```py # 華夏上證50ETF secID = '510050.XSHG' begin = Date(2015, 2, 9) end = Date.todaysDate() fields = ['tradeDate', 'closePrice'] etf = DataAPI.MktFunddGet(secID, beginDate=begin.toISO().replace('-', ''), endDate=end.toISO().replace('-', ''), field=fields) etf['tradeDate'] = pd.to_datetime(etf['tradeDate']) etf = etf.set_index('tradeDate') etf.tail(2) ``` | | closePrice | | --- | --- | | tradeDate | | | 2015-09-23 | 2.180 | | 2015-09-24 | 2.187 | 上海銀行間同業拆借利率 SHIBOR，用來作為無風險利率參考 ```py ## 銀行間質押式回購利率 def getHistDayInterestRateInterbankRepo(date): cal = Calendar('China.SSE') period = Period('-10B') begin = cal.advanceDate(date, period) begin_str = begin.toISO().replace('-', '') date_str = date.toISO().replace('-', '') # 以下的indicID分別對應的銀行間質押式回購利率周期為： # 1D, 7D, 14D, 21D, 1M, 3M, 4M, 6M, 9M, 1Y indicID = [u"M120000067", u"M120000068", u"M120000069", u"M120000070", u"M120000071", u"M120000072", u"M120000073", u"M120000074", u"M120000075", u"M120000076"] period = np.asarray([1.0, 7.0, 14.0, 21.0, 30.0, 90.0, 120.0, 180.0, 270.0, 360.0]) / 360.0 period_matrix = pd.DataFrame(index=indicID, data=period, columns=['period']) field = u"indicID,indicName,publishTime,periodDate,dataValue,unit" interbank_repo = DataAPI.ChinaDataInterestRateInterbankRepoGet(indicID=indicID,beginDate=begin_str,endDate=date_str,field=field,pandas="1") interbank_repo = interbank_repo.groupby('indicID').first() interbank_repo = concat([interbank_repo, period_matrix], axis=1, join='inner').sort_index() return interbank_repo ## 銀行間同業拆借利率 def getHistDaySHIBOR(date): date_str = date.toISO().replace('-', '') # 以下的indicID分別對應的SHIBOR周期為： # 1D, 7D, 14D, 1M, 3M, 6M, 9M, 1Y indicID = [u"M120000057", u"M120000058", u"M120000059", u"M120000060", u"M120000061", u"M120000062", u"M120000063", u"M120000064"] period = np.asarray([1.0, 7.0, 14.0, 30.0, 90.0, 180.0, 270.0, 360.0]) / 360.0 period_matrix = pd.DataFrame(index=indicID, data=period, columns=['period']) field = u"indicID,indicName,publishTime,periodDate,dataValue,unit" interest_shibor = DataAPI.ChinaDataInterestRateSHIBORGet(indicID=indicID,beginDate=date_str,endDate=date_str,field=field,pandas="1") interest_shibor = interest_shibor.set_index('indicID') interest_shibor = concat([interest_shibor, period_matrix], axis=1, join='inner').sort_index() return interest_shibor ## 插值得到給定的周期的無風險利率 def periodsSplineRiskFreeInterestRate(date, periods): # 此處使用SHIBOR來插值 init_shibor = getHistDaySHIBOR(date) shibor = {} min_period = min(init_shibor.period.values) max_period = max(init_shibor.period.values) for p in periods.keys(): tmp = periods[p] if periods[p] > max_period: tmp = max_period * 0.99999 elif periods[p] < min_period: tmp = min_period * 1.00001 sh = interpolate.spline(init_shibor.period.values, init_shibor.dataValue.values, [tmp], order=3) shibor[p] = sh[0]/100.0 return shibor ``` 50ETF歷史波動率，用來和iVIX走勢作對比 ```py ## 計算一段時間標的的歷史波動率，返回值包括以下不同周期的波動率： # 一周，半月，一個月，兩個月，三個月，四個月，五個月，半年，九個月，一年，兩年 def getHistVolatilityEWMA(secID, beginDate, endDate): cal = Calendar('China.SSE') spotBeginDate = cal.advanceDate(beginDate,'-520B',BizDayConvention.Preceding) spotBeginDate = Date(2006, 1, 1) begin = spotBeginDate.toISO().replace('-', '') end = endDate.toISO().replace('-', '') fields = ['tradeDate', 'preClosePrice', 'closePrice', 'settlePrice', 'preSettlePrice'] security = DataAPI.MktFunddGet(secID, beginDate=begin, endDate=end, field=fields) security['dailyReturn'] = security['closePrice']/security['preClosePrice'] # 日回報率 security['u2'] = (np.log(security['dailyReturn']))**2 # u2為復利形式的日回報率平方 # security['u2'] = (security['dailyReturn'] - 1.0)**2 # u2為日價格變化百分比的平方 security['tradeDate'] = pd.to_datetime(security['tradeDate']) periods = {'hv1W': 5, 'hv2W': 10, 'hv1M': 21, 'hv2M': 41, 'hv3M': 62, 'hv4M': 83, 'hv5M': 104, 'hv6M': 124, 'hv9M': 186, 'hv1Y': 249, 'hv2Y': 497} # 利用pandas中的ewma模型計算波動率 for prd in periods.keys(): # 此處的span實際上就是上面計算波動率公式中lambda表達式中的N security[prd] = np.round(np.sqrt(pd.ewma(security['u2'], span=periods[prd], adjust=False)), 5)*math.sqrt(252.0) security = security[security.tradeDate >= beginDate.toISO()] security = security.set_index('tradeDate') return security ``` ## 1. 計算歷史每日 iVIX 計算方法參考CBOE的手冊：http://www.cboe.com/micro/vix/part2.aspx ```py # 計算歷史某一天的iVIX def calDayVIX(date, opt_info): var_sec = u"510050.XSHG" # 使用DataAPI.MktOptdGet，拿到歷史上某一天的期權行情信息 date_str = date.toISO().replace('-', '') fields_mkt = [u"optID", "tradeDate", "closePrice", 'settlPrice'] opt_mkt = DataAPI.MktOptdGet(tradeDate=date_str, field=fields_mkt, pandas="1") opt_mkt = opt_mkt.set_index(u"optID") opt_mkt[u"price"] = opt_mkt['closePrice'] # concat某一日行情和期權基本信息，得到所需數據 opt = concat([opt_info, opt_mkt], axis=1, join='inner').sort_index() opt = opt[opt.varSecID==var_sec] exp_dates = map(Date.parseISO, np.sort(opt.expDate.unique())) trade_date = date exp_periods = {} for epd in exp_dates: exp_periods[epd] = (epd - date)*1.0/365.0 risk_free = periodsSplineRiskFreeInterestRate(trade_date, exp_periods) sigma_square = {} for date in exp_dates: # 計算某一日的vix opt_date = opt[opt.expDate==date.toISO()] rf = risk_free[date] #rf = 0.05 opt_call = opt_date[opt_date.contractType == 'CO'].set_index('strikePrice') opt_put = opt_date[opt_date.contractType == 'PO'].set_index('strikePrice') opt_call_price = opt_call[[u'price']].sort_index() opt_put_price = opt_put[[u'price']].sort_index() opt_call_price.columns = [u'callPrice'] opt_put_price.columns = [u'putPrice'] opt_call_put_price = concat([opt_call_price, opt_put_price], axis=1, join='inner').sort_index() opt_call_put_price['diffCallPut'] = opt_call_put_price.callPrice - opt_call_put_price.putPrice strike = abs(opt_call_put_price['diffCallPut']).idxmin() price_diff = opt_call_put_price['diffCallPut'][strike] ttm = exp_periods[date] fw = strike + np.exp(ttm*rf) * price_diff strikes = np.sort(opt_call_put_price.index.values) delta_K_tmp = np.concatenate((strikes, strikes[-1:], strikes[-1:])) delta_K_tmp = delta_K_tmp - np.concatenate((strikes[0:1], strikes[0:1], strikes)) delta_K = np.concatenate((delta_K_tmp[1:2], delta_K_tmp[2:-2]/2, delta_K_tmp[-2:-1])) delta_K = pd.DataFrame(delta_K, index=strikes, columns=['deltaStrike']) # opt_otm = opt_out_of_money opt_otm = concat([opt_call[opt_call.index>fw], opt_put[opt_put.index<fw]], axis=0, join='inner') opt_otm = concat([opt_otm, delta_K], axis=1, join='inner').sort_index() # 計算VIX時，比forward price低的第一個行權價被設置為參考行權價，參考值以上 # 的call和以下的put均為虛值期權，所有的虛值期權被用來計算VIX，然而計算中發 # 現，有時候沒有比forward price更低的行權價，例如2015-07-08，故有以下關于 # 參考行權價的設置 strike_ref = fw if len((strikes[strikes < fw])) > 0: strike_ref = max([k for k in strikes[strikes < fw]]) opt_otm['price'][strike_ref] = (opt_call['price'][strike_ref] + opt_call['price'][strike_ref])/2.0 exp_rt = np.exp(rf*ttm) opt_otm['sigmaTerm'] = opt_otm.deltaStrike*opt_otm.price/(opt_otm.index)**2 sigma = opt_otm.sigmaTerm.sum() sigma = (sigma*2.0*exp_rt - (fw*1.0/strike_ref - 1.0)**2)/ttm sigma_square[date] = sigma # d_one, d_two 將被用來計算VIX(30): if exp_periods[exp_dates[0]] >= 1.0/365.0: d_one = exp_dates[0] d_two = exp_dates[1] else: d_one = exp_dates[1] d_two = exp_dates[2] w = (exp_periods[d_two] - 30.0/365.0)/(exp_periods[d_two] - exp_periods[d_one]) vix30 = exp_periods[d_one]*w*sigma_square[d_one] + exp_periods[d_two]*(1 - w)*sigma_square[d_two] vix30 = 100*np.sqrt(vix30*365.0/30.0) # d_one, d_two 將被用來計算VIX(60): d_one = exp_dates[1] d_two = exp_dates[2] w = (exp_periods[d_two] - 60.0/365.0)/(exp_periods[d_two] - exp_periods[d_one]) vix60 = exp_periods[d_one]*w*sigma_square[d_one] + exp_periods[d_two]*(1 - w)*sigma_square[d_two] vix60 = 100*np.sqrt(vix60*365.0/60.0) return vix30, vix60 def getHistDailyVIX(beginDate, endDate): # 計算歷史一段時間內的VIX指數并返回 optionVarSecID = u"510050.XSHG" # 使用DataAPI.OptGet，一次拿取所有存在過的期權信息，以備后用 fields_info = ["optID", u"varSecID", u'contractType', u'strikePrice', u'expDate'] opt_info = DataAPI.OptGet(optID='', contractStatus=[u"DE", u"L"], field=fields_info, pandas="1") opt_info = opt_info.set_index(u"optID") cal = Calendar('China.SSE') cal.addHoliday(Date(2015,9,3)) cal.addHoliday(Date(2015,9,4)) dates = cal.bizDatesList(beginDate, endDate) histVIX = pd.DataFrame(0.0, index=map(Date.toDateTime, dates), columns=['VIX30','VIX60']) histVIX.index.name = 'tradeDate' for date in histVIX.index: try: vix30, vix60 = calDayVIX(Date.fromDateTime(date), opt_info) except: histVIX = histVIX.drop(date) continue histVIX['VIX30'][date] = vix30 histVIX['VIX60'][date] = vix60 return histVIX def getHistOneDayVIX(date): # 計算歷史某天的VIX指數并返回 optionVarSecID = u"510050.XSHG" # 使用DataAPI.OptGet，一次拿取所有存在過的期權信息，以備后用 fields_info = ["optID", u"varSecID", u'contractType', u'strikePrice', u'expDate'] opt_info = DataAPI.OptGet(optID='', contractStatus=[u"DE", u"L"], field=fields_info, pandas="1") opt_info = opt_info.set_index(u"optID") cal = Calendar('China.SSE') cal.addHoliday(Date(2015,9,3)) cal.addHoliday(Date(2015,9,4)) if cal.isBizDay(date): vix30, vix60 = 0.0, 0.0 vix30, vix60 = calDayVIX(date, opt_info) return vix30, vix60 else: print date, "不是工作日" ``` 歷史每日iVIX 數據 ```py begin = Date(2015, 2, 9) # 起始日 end = Date.todaysDate() # 截至今天 hist_VIX = getHistDailyVIX(begin, end) hist_VIX.tail() ``` | | VIX30 | VIX60 | | --- | --- | | tradeDate | | | | 2015-09-18 | 38.057648 | 39.074643 | | 2015-09-21 | 37.610259 | 38.559095 | | 2015-09-22 | 34.507456 | 36.788384 | | 2015-09-23 | 36.413426 | 37.837454 | | 2015-09-24 | 37.114348 | 24.346747 | iVIX、50ETF收盤價、50ETF波動率比較 ```py start = Date(2007, 1, 1) end = Date.todaysDate() secID = '510050.XSHG' hist_HV = getHistVolatilityEWMA(secID, start, end) ## ----- 50ETF VIX指數和歷史波動率比較 ----- fig = plt.figure(figsize=(10,6)) ax = fig.add_subplot(111) font.set_size(16) hist_HV_plot = hist_HV[hist_HV.index >= Date(2015,2,9).toISO()] etf_plot = etf[etf.index >= Date(2015,2,9).toISO()] lns1 = ax.plot(hist_HV_plot.index, hist_HV_plot.hv1M, '-', label = u'HV(30)') lns2 = ax.plot(hist_VIX.index, hist_VIX.VIX30/100.0, '-r', label = u'VIX(30)') #lns3 = ax.plot(hist_VIX.index, hist_VIX.VIX60/100.0, '-g', label = u'VIX(60)') ax2 = ax.twinx() lns4 = ax2.plot(etf_plot.index, etf_plot.closePrice, 'grey', label = '50ETF closePrice') lns = lns1+lns2+lns4 labs = [l.get_label() for l in lns] ax.legend(lns, labs, loc=2) ax.grid() ax.set_xlabel(u"tradeDate") ax.set_ylabel(r"VIX") ax2.set_ylabel(r"closePrice") #ax.set_ylim(0, 0.80) ax2.set_ylim(1.5, 4) plt.title('50ETF VIX') <matplotlib.text.Text at 0x5acec90> ``` ![](https://box.kancloud.cn/2016-07-30_579cbdbae930e.png) ## 2. 基于iVIX的擇時策略策略思路： + 計算 VIX 三日均線 + 前一日 VIX 向上穿過三日均線一定比例，則賣出 + 前一日 VIX 向下穿過三日均線一定比例，則買入 + 只買賣50ETF ```py start = datetime(2015, 2, 9) # 回測起始時間 end = datetime(2015, 9, 24) # 回測結束時間 hist_VIX = getHistDailyVIX(start, end) hist_VIX.tail(2) ``` | | VIX30 | VIX60 | | --- | --- | | tradeDate | | | | 2015-09-23 | 36.413426 | 37.837454 | | 2015-09-24 | 37.114348 | 24.346747 | ```py start = datetime(2015, 2, 9) # 回測起始時間 end = datetime(2015, 9, 24) # 回測結束時間 benchmark = '510050.XSHG' # 策略參考標準 universe = ['510050.XSHG'] # 股票池 capital_base = 100000 # 起始資金 commission = Commission(0.0,0.0) window_short = 1 window_long = 3 SD = 0.1 hist_VIX['short_window'] = pd.rolling_mean(hist_VIX['VIX30'], window=window_short) hist_VIX['long_window'] = pd.rolling_mean(hist_VIX['VIX30'], window=window_long) def initialize(account): # 初始化虛擬賬戶狀態 account.fund = universe[0] def handle_data(account): # 每個交易日的買入賣出指令 fund = account.fund # 獲取回測當日的前一天日期 dt = Date.fromDateTime(account.current_date) cal = Calendar('China.IB') cal.addHoliday(Date(2015,9,3)) cal.addHoliday(Date(2015,9,4)) last_day = cal.advanceDate(dt,'-1B',BizDayConvention.Preceding) #計算出倒數第一個交易日 last_last_day = cal.advanceDate(last_day,'-1B',BizDayConvention.Preceding) #計算出倒數第二個交易日 last_day_str = last_day.strftime("%Y-%m-%d") last_last_day_str = last_last_day.strftime("%Y-%m-%d") # 計算買入賣出信號 try: short_mean = hist_VIX['short_window'].loc[last_day_str] # 短均線值 long_mean = hist_VIX['long_window'].loc[last_day_str] # 長均線值 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 = True short_flag = True if long_flag: approximationAmount = int(account.cash / account.referencePrice[fund] / 100.0) * 100 order(fund, approximationAmount) elif short_flag: # 賣出時，全倉清空 order_to(fund, 0) ``` ![](https://box.kancloud.cn/2016-07-30_579cbdbb137ec.jpg) ## 3. 日內跟蹤計算 iVIX 計算方法和日間iVIX類似 ```py def calSnapshotVIX(date, opt_info): var_sec = u"510050.XSHG" # 使用DataAPI.MktOptdGet，拿到歷史上某一天的期權行情信息 date_str = date.toISO().replace('-', '') fields_mkt = [u'optionId', u'dataDate', u'highPrice', u'lastPrice', u'lowPrice', u'openPrice', u'preSettlePrice', u'bidBook_price1', u'bidBook_volume1', u'askBook_price1', u'askBook_volume1'] # opt_mkt = DataAPI.MktOptdGet(tradeDate=date_str, field=fields_mkt, pandas="1") opt_mkt = DataAPI.MktOptionTickRTSnapshotGet(optionId=u"", field='', pandas="1") opt_mkt = opt_mkt[opt_mkt.dataDate == date.toISO()] opt_mkt['optID'] = map(int, opt_mkt['optionId']) opt_mkt = opt_mkt.set_index(u"optID") opt_mkt[u"price"] = (opt_mkt['bidBook_price1'] + opt_mkt['askBook_price1'])/2.0 # concat某一日行情和期權基本信息，得到所需數據 opt = concat([opt_info, opt_mkt], axis=1, join='inner').sort_index() #opt = opt[opt.varSecID==var_sec] exp_dates = map(Date.parseISO, np.sort(opt.expDate.unique())) trade_date = date exp_periods = {} for epd in exp_dates: exp_periods[epd] = (epd - date)*1.0/365.0 risk_free = periodsSplineRiskFreeInterestRate(trade_date, exp_periods) sigma_square = {} for date in exp_dates: # 計算某一日的vix opt_date = opt[opt.expDate==date.toISO()] rf = risk_free[date] #rf = 0.05 opt_call = opt_date[opt_date.contractType == 'CO'].set_index('strikePrice') opt_put = opt_date[opt_date.contractType == 'PO'].set_index('strikePrice') opt_call_price = opt_call[[u'price']].sort_index() opt_put_price = opt_put[[u'price']].sort_index() opt_call_price.columns = [u'callPrice'] opt_put_price.columns = [u'putPrice'] opt_call_put_price = concat([opt_call_price, opt_put_price], axis=1, join='inner').sort_index() opt_call_put_price['diffCallPut'] = opt_call_put_price.callPrice - opt_call_put_price.putPrice strike = abs(opt_call_put_price['diffCallPut']).idxmin() price_diff = opt_call_put_price['diffCallPut'][strike] ttm = exp_periods[date] fw = strike + np.exp(ttm*rf) * price_diff strikes = np.sort(opt_call_put_price.index.values) delta_K_tmp = np.concatenate((strikes, strikes[-1:], strikes[-1:])) delta_K_tmp = delta_K_tmp - np.concatenate((strikes[0:1], strikes[0:1], strikes)) delta_K = np.concatenate((delta_K_tmp[1:2], delta_K_tmp[2:-2]/2, delta_K_tmp[-2:-1])) delta_K = pd.DataFrame(delta_K, index=strikes, columns=['deltaStrike']) # opt_otm = opt_out_of_money opt_otm = concat([opt_call[opt_call.index>fw], opt_put[opt_put.index<fw]], axis=0, join='inner') opt_otm = concat([opt_otm, delta_K], axis=1, join='inner').sort_index() # 計算VIX時，比forward price低的第一個行權價被設置為參考行權價，參考值以上 # 的call和以下的put均為虛值期權，所有的虛值期權被用來計算VIX，然而計算中發 # 現，有時候沒有比forward price更低的行權價，例如2015-07-08，故有以下關于 # 參考行權價的設置 strike_ref = fw if len((strikes[strikes < fw])) > 0: strike_ref = max([k for k in strikes[strikes < fw]]) opt_otm['price'][strike_ref] = (opt_call['price'][strike_ref] + opt_call['price'][strike_ref])/2.0 exp_rt = np.exp(rf*ttm) opt_otm['sigmaTerm'] = opt_otm.deltaStrike*opt_otm.price/(opt_otm.index)**2 sigma = opt_otm.sigmaTerm.sum() sigma = (sigma*2.0*exp_rt - (fw*1.0/strike_ref - 1.0)**2)/ttm sigma_square[date] = sigma # d_one, d_two 將被用來計算VIX(30): if exp_periods[exp_dates[0]] >= 1.0/365.0: d_one = exp_dates[0] d_two = exp_dates[1] else: d_one = exp_dates[1] d_two = exp_dates[2] w = (exp_periods[d_two] - 30.0/365.0)/(exp_periods[d_two] - exp_periods[d_one]) vix30 = exp_periods[d_one]*w*sigma_square[d_one] + exp_periods[d_two]*(1 - w)*sigma_square[d_two] vix30 = 100*np.sqrt(vix30*365.0/30.0) # d_one, d_two 將被用來計算VIX(60): d_one = exp_dates[1] d_two = exp_dates[2] w = (exp_periods[d_two] - 60.0/365.0)/(exp_periods[d_two] - exp_periods[d_one]) vix60 = exp_periods[d_one]*w*sigma_square[d_one] + exp_periods[d_two]*(1 - w)*sigma_square[d_two] vix60 = 100*np.sqrt(vix60*365.0/60.0) return vix30, vix60 def getTodaySnapshotVIX(): # 計算歷史某天的VIX指數并返回 optionVarSecID = u"510050.XSHG" date = Date.todaysDate() # 使用DataAPI.OptGet，一次拿取所有存在過的期權信息，以備后用 fields_info = ["optID", u"varSecID", u'contractType', u'strikePrice', u'expDate'] opt_info = DataAPI.OptGet(optID='', contractStatus=[u"DE", u"L"], field=fields_info, pandas="1") opt_info = opt_info.set_index(u"optID") cal = Calendar('China.SSE') cal.addHoliday(Date(2015,9,3)) cal.addHoliday(Date(2015,9,4)) if cal.isBizDay(date): now_long = datetime.now() now = now_long.time().isoformat() if (now > '09:25:00' and now < '11:30:00') or (now > '13:00:00' and now < '15:00:00'): vix30, vix60 = calSnapshotVIX(date, opt_info) vix = pd.DataFrame([[date, vix30, vix60]], index=[now_long], columns=['dataDate', 'VIX30', 'VIX60']) vix.index.name = 'time' else: vix = pd.DataFrame(0.0, index=[], columns=['dataDate', 'VIX30', 'VIX60']) vix.index.name = 'time' return vix else: print "今天： ", date, " 不是工作日" ``` 計算即時的VIX 如果在工作日非交易時間運行計算函數，則得到一個空的`dataframe` ```py getTodaySnapshotVIX() ``` | | dataDate | VIX30 | VIX60 | | --- | --- | | time | | | | 跟蹤計算當日日內 VIX 走勢 ```py ## 此函數跟蹤計算并記錄當日日內VIX走勢，數據記錄在： # 文件 'VIX_intraday_' + Date.todaysDate().toISO() + '.csv' 中 # 該文件保存在登錄uqer賬號的 Data 空間中 # seconds 為跟蹤計算間隔秒數 def trackTodayIntradayVIX(seconds): vix_file_str = 'VIX_intraday_' + Date.todaysDate().toISO() + '.csv' vix = pd.DataFrame(0.0, index=[], columns=['dataDate', 'VIX30', 'VIX60']) vix.index.name = 'time' vix.to_csv(vix_file_str) now = datetime.now().time() while now.isoformat() < '15:00:00': vix = pd.read_csv(vix_file_str).set_index('time') vix_now = getTodaySnapshotVIX() if vix_now.shape[0] > 0: vix = vix.append(vix_now) vix.to_csv(vix_file_str) # print vix_now.index[0], '\t', vix_now.VIX30[0], '\t', vix_now.VIX60[0] time.sleep(seconds) now = datetime.now().time() ``` 注意： `trackTodayIntradayVIX` 函數一經運行，便持續到當日收盤時，除非手動終止運行 ```py # 追蹤當前iVIX走勢，每隔60秒計算一次即時iVIX time_interval = 60 trackTodayIntradayVIX(time_interval) --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <mercury-input-20-3f8b5a5070f8> in <module>() 1 # 追蹤當前iVIX走勢，每隔60秒計算一次即時iVIX 2 time_interval = 60 ----> 3 trackTodayIntradayVIX(time_interval) <mercury-input-19-d53f12cb0e4a> in trackTodayIntradayVIX(seconds) 17 vix.to_csv(vix_file_str) 18 # print vix_now.index[0], '\t', vix_now.VIX30[0], '\t', vix_now.VIX60[0] ---> 19 time.sleep(seconds) 20 now = datetime.now().time() KeyboardInterrupt: ``` 將當日追蹤到的iVIX日內走勢作圖，注意讀取數據文件名和 trackTodayIntradayVIX 函數中的存儲文件名一致 ```py vix_file_str = 'VIX_intraday_2015-09-23-backup.csv' vix = pd.read_csv(vix_file_str) vix['time'] = [x[11:19] for x in vix.time] vix = vix.set_index('time') ax = vix.plot(figsize=(10,5)) ax.set_xlabel('time') ax.set_ylabel('VIX(%)') ax.set_ylim(35, 39) (35, 39) ``` ![](https://box.kancloud.cn/2016-07-30_579cbdbb5b87e.png)