基于期权PCR指数的择时策略
最后更新于:2022-04-01 21:59:15
# 基于期权PCR指数的择时策略
> 来源:https://uqer.io/community/share/55bedc1af9f06c91f818c62d
## 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。
策略思路
比较交易日之前两日的PCR(Put Call Ratio)指数:
+ PCR上升时,市场恐慌情绪蔓延,卖出
+ PCR下降时,恐慌情绪有所舒缓,买入
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PCR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
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。
dateStr = date.toISO().replace('-', '')
optionsMkt = DataAPI.MktOptdGet(tradeDate = dateStr, 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 = 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):
# 计算历史一段时间内的PCR指数并返回
optionVarSecID = u"510050.XSHG"
return calDayTurnoverValuePCR(optionVarSecID, date)
```
```py
secID = '510050.XSHG'
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 |
## 2. PCR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, 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')
```
```py
font.set_size(12)
pylab.figure(figsize = (16,8))
ax1 = histPCR.plot(x=histPCR.index, y='PCR', style='r')
ax1.set_xlabel(u'日期', fontproperties=font)
ax1.set_ylabel(u'PCR(%)', 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_579cbdc247b6d.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
## 3. 基于PCR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, 31) # 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)
longest_history = 1
histPCR = getHistPCR(start, end)
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) #计算出倒数第一个交易日
lastLastTDay = cal.advanceDate(lastTDay,'-1B',BizDayConvention.Preceding) #计算出倒数第二个交易日
last_day_str = lastTDay.strftime("%Y-%m-%d")
last_last_day_str = lastLastTDay.strftime("%Y-%m-%d")
# 计算买入卖出信号
try:
pcr_last = histPCR['PCR'].loc[last_day_str] # 计算短均线值
pcr_last_last = histPCR['PCR'].loc[last_last_day_str] # 计算长均线值
long_flag = True if (pcr_last - pcr_last_last) < 0 else False
except:
return
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)
else:
# 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_579cbdc267299.jpg)
基于PCR指数上升时空仓、下降时进场的策略来买卖标的,可以比较有效地降低标的大跌的风险
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