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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