期权每日成交额PC比例计算

最后更新于:2022-04-01 21:59:17

# 期权每日成交额PC比例计算 > 来源:https://uqer.io/community/share/55bed777f9f06c915418c62f ## P/C作为市场情绪指标 计算方式 P/C比例作为一种反向情绪指标,是看跌期权的成交量(成交额,持仓量等)与看涨期权的成交量(持仓量)的比值。 指标含义 + 看跌期权的成交量可以作为市场看空力量多寡的衡量; + 看涨期权的成交量可以描述市场看多力量。 指标应用 + 当P/C比例过小达到一个极端时,被视为市场过度乐观,此时市场将遏制原来的上涨趋势; + 当P/C比例过大到达另一个极端时,被视为市场过度悲观,此时市场可能出现反弹。 ```py from matplotlib import pylab import numpy as np import pandas as pd import DataAPI import seaborn as sns sns.set_style('white') ``` ## 1. 定义计算PCR的函数 此处计算看跌看涨期权每日成交额的比值 ```py 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 turnoverValue # 以optID为index。 vixDateStr = date.toISO().replace('-', '') optionsMkt = DataAPI.MktOptdGet(tradeDate = vixDateStr, field = [u"optID", "tradeDate", "closePrice", "turnoverValue"], 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 = pd.concat([optionsInfo, optionsMkt], axis=1, join='inner').sort_index() return options[options.varSecID==var] def calDayTurnoverValuePCR(optionVarSecID, date): # 计算历史每日的看跌看涨期权交易额的比值 # PCR: put call ratio options = getHistDayOptions(optionVarSecID, date) call = options[options.contractType==u"CO"] put = options[options.contractType==u"PO"] callTurnoverValue = call.turnoverValue.sum() putTurnoverValue = put.turnoverValue.sum() return 1.0 * putTurnoverValue / callTurnoverValue def getHistPCR(beginDate, endDate): # 计算历史一段时间内的PCR指数并返回 optionVarSecID = u"510050.XSHG" cal = Calendar('China.SSE') dates = cal.bizDatesList(beginDate, endDate) dates = map(Date.toDateTime, dates) histPCR = pd.DataFrame(0.0, index=dates, columns=['PCR']) histPCR.index.name = 'date' for date in histPCR.index: histPCR['PCR'][date] = calDayTurnoverValuePCR(optionVarSecID, Date.fromDateTime(date)) return histPCR def getDayPCR(date): # 计算历史某一天的PCR指数并返回 optionVarSecID = u"510050.XSHG" return calDayTurnoverValuePCR(optionVarSecID, date) ``` ## 2. 计算PCR指标 ```py begin = Date(2015, 2, 9) end = Date(2015, 7, 30) getHistPCR(begin, end).tail() ``` | | PCR | | --- | --- | | date | | | 2015-07-24 | 1.032107 | | 2015-07-27 | 2.097952 | | 2015-07-28 | 2.288790 | | 2015-07-29 | 1.971831 | | 2015-07-30 | 1.527717 | ```py date = Date(2015, 7, 30) getDayPCR(date) 1.5277173819619587 ``` ## 3. PC指标历史走势 ```py secID = '510050.XSHG' begin = Date(2015, 2, 9) end = Date(2015, 7, 30) # 历史PCR histPCR = getHistPCR(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') font.set_size(12) pylab.figure(figsize = (12,6)) ax1 = histPCR.plot(x=histPCR.index, y='PCR', 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_579cbdc281366.png) 从上图可以看出,每次PC指标的上升都对应着标的价格的下挫
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