[ 50ETF 期权] 1. 历史成交持仓和 PCR 数据

发布于 2022-02-20 22:26:20 字数 34065 浏览 968 评论 0 收藏 0

在本文中，我们将通过量化实验室提供的数据，计算上证50ETF期权的历史成交持仓和PCR数据，并在最后利用PCR建立一个简单的择时策略

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

1. 期权数据接口

有关上证50ETF期权数据，量化实验室有三个接口，分别对应于不同的功能

DataAPI.OptGet：可以获取已退市和上市的所有期权的基本信息
DataAPI.MktOptdGet：拿到历史上某一天或某段时间的期权成交行情信息
DataAPI.MktTickRTSnapshotGet：此为高频数据，获取期权最新市场信息快照

在接下来对于期权的数据分析中，我们将使用这三个API提供的数据，以下为API使用示例，具体API的详情可以查看帮助文档

# 使用DataAPI.OptGet，拿到已退市和上市的所有期权的基本信息
opt_info = DataAPI.OptGet(optID='', contractStatus=[u"DE", u"L"], field='', pandas="1") 
opt_info.head(3)

	secID	optID	secShortName	tickerSymbol	exchangeCD	currencyCD	varSecID	varShortName	varTicker	varExchangeCD	...	contMultNum	contractStatus	listDate	expYear	expMonth	expDate	lastTradeDate	exerDate	deliDate	delistDate
0	510050C1503M02200.XSHG	10000001	50ETF购3月2200	510050C1503M02200	XSHG	CNY	510050.XSHG	华夏上证50ETF	510050	XSHG	...	10000	DE	2015-02-09	2015	3	2015-03-25	2015-03-25	2015-03-25	2015-03-26	2015-03-25
1	510050C1503M02250.XSHG	10000002	50ETF购3月2250	510050C1503M02250	XSHG	CNY	510050.XSHG	华夏上证50ETF	510050	XSHG	...	10000	DE	2015-02-09	2015	3	2015-03-25	2015-03-25	2015-03-25	2015-03-26	2015-03-25
2	510050C1503M02300.XSHG	10000003	50ETF购3月2300	510050C1503M02300	XSHG	CNY	510050.XSHG	华夏上证50ETF	510050	XSHG	...	10000	DE	2015-02-09	2015	3	2015-03-25	2015-03-25	2015-03-25	2015-03-26	2015-03-25

3 rows × 23 columns

#使用DataAPI.MktOptdGet，拿到历史上某一天的期权成交信息
opt_mkt = DataAPI.MktOptdGet(tradeDate='20150921', field='', pandas="1")
opt_mkt.head(2)

	secID	optID	ticker	secShortName	exchangeCD	tradeDate	preSettlePrice	preClosePrice	openPrice	highestPrice	lowestPrice	closePrice	settlPrice	turnoverVol	turnoverValue	openInt
0	510050C1512M02100.XSHG	10000368	510050C1512M02100	50ETF购12月2100	XSHG	2015-09-21	0.2069	0.1994	0.1955	0.2087	0.1955	0.2062	0.2062	21	43115	457
1	510050P1512M01950.XSHG	10000369	510050P1512M01950	50ETF沽12月1950	XSHG	2015-09-21	0.1037	0.0999	0.1000	0.1073	0.0905	0.0905	0.0927	272	261112	868

# 获取期权最新市场信息快照
opt_mkt_snapshot = DataAPI.MktOptionTickRTSnapshotGet(optionId=u"",field=u"",pandas="1")
opt_mkt_snapshot[opt_mkt_snapshot.dataDate=='2015-09-22'].head(2)

	optionId	timestamp	auctionPrice	auctionQty	dataDate	dataTime	highPrice	instrumentID	lastPrice	lowPrice	...	askBook_price1	askBook_volume1	askBook_price2	askBook_volume2	askBook_price3	askBook_volume3	askBook_price4	askBook_volume4	askBook_price5	askBook_volume5

0 rows × 37 columns

2. 期权历史成交持仓数据图

# 华夏上证50ETF收盘价数据
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

统计50ETF期权历史成交量和持仓量信息

# 计算历史一段时间内的50ETF期权持仓量交易量数据
def getOptHistVol(beginDate, endDate):
    optionVarSecID = u"510050.XSHG"
    cal = Calendar('China.SSE')
    cal.addHoliday(Date(2015,9,3))
    cal.addHoliday(Date(2015,9,4))

    dates = cal.bizDatesList(beginDate, endDate)
    dates = map(Date.toDateTime, dates)
    columns = ['callVol', 'putVol', 'callValue',   
               'putValue', 'callOpenInt', 'putOpenInt',
               'nearCallVol', 'nearPutVol', 'nearCallValue', 
               'nearPutValue', 'nearCallOpenInt', 'nearPutOpenInt',
               'netVol', 'netValue', 'netOpenInt',
               'volPCR', 'valuePCR', 'openIntPCR', 
               'nearVolPCR', 'nearValuePCR', 'nearOpenIntPCR']
    hist_opt = pd.DataFrame(0.0, index=dates, columns=columns)
    hist_opt.index.name = 'date'
    # 每一个交易日数据单独计算
    for date in hist_opt.index:
        date_str = Date.fromDateTime(date).toISO().replace('-', '')
        try:
            opt_data = DataAPI.MktOptdGet(secID=u"", tradeDate=date_str, field=u"", pandas="1")
        except:
            hist_opt = hist_opt.drop(date)
            continue

        opt_type = []
        exp_date = []
        for ticker in opt_data.secID.values:
            opt_type.append(ticker[6])
            exp_date.append(ticker[7:11])
        opt_data['optType'] = opt_type
        opt_data['expDate'] = exp_date
        near_exp = np.sort(opt_data.expDate.unique())[0]

        data = opt_data.groupby('optType')
        # 计算所有上市期权：看涨看跌交易量、看涨看跌交易额、看涨看跌持仓量
        hist_opt['callVol'][date] = data.turnoverVol.sum()['C']
        hist_opt['putVol'][date] = data.turnoverVol.sum()['P']
        hist_opt['callValue'][date] = data.turnoverValue.sum()['C']
        hist_opt['putValue'][date] = data.turnoverValue.sum()['P']
        hist_opt['callOpenInt'][date] = data.openInt.sum()['C']
        hist_opt['putOpenInt'][date] = data.openInt.sum()['P']

        near_data = opt_data[opt_data.expDate == near_exp]
        near_data = near_data.groupby('optType')
        # 计算近月期权(主力合约)： 看涨看跌交易量、看涨看跌交易额、看涨看跌持仓量
        hist_opt['nearCallVol'][date] = near_data.turnoverVol.sum()['C']
        hist_opt['nearPutVol'][date] = near_data.turnoverVol.sum()['P']
        hist_opt['nearCallValue'][date] = near_data.turnoverValue.sum()['C']
        hist_opt['nearPutValue'][date] = near_data.turnoverValue.sum()['P']
        hist_opt['nearCallOpenInt'][date] = near_data.openInt.sum()['C']
        hist_opt['nearPutOpenInt'][date] = near_data.openInt.sum()['P']

        # 计算所有上市期权： 总交易量、总交易额、总持仓量
        hist_opt['netVol'][date] = hist_opt['callVol'][date] + hist_opt['putVol'][date]
        hist_opt['netValue'][date] = hist_opt['callValue'][date] + hist_opt['putValue'][date]
        hist_opt['netOpenInt'][date] = hist_opt['callOpenInt'][date] + hist_opt['putOpenInt'][date]

        # 计算期权看跌看涨期权交易量(持仓量)的比率：
        # 交易量看跌看涨比率，交易额看跌看涨比率, 持仓量看跌看涨比率
        # 近月期权交易量看跌看涨比率，近月期权交易额看跌看涨比率, 近月期权持仓量看跌看涨比率
        # PCR = Put Call Ratio
        hist_opt['volPCR'][date] = round(hist_opt['putVol'][date]*1.0/hist_opt['callVol'][date], 4)
        hist_opt['valuePCR'][date] = round(hist_opt['putValue'][date]*1.0/hist_opt['callValue'][date], 4)
        hist_opt['openIntPCR'][date] = round(hist_opt['putOpenInt'][date]*1.0/hist_opt['callOpenInt'][date], 4)
        hist_opt['nearVolPCR'][date] = round(hist_opt['nearPutVol'][date]*1.0/hist_opt['nearCallVol'][date], 4)
        hist_opt['nearValuePCR'][date] = round(hist_opt['nearPutValue'][date]*1.0/hist_opt['nearCallValue'][date], 4)
        hist_opt['nearOpenIntPCR'][date] = round(hist_opt['nearPutOpenInt'][date]*1.0/hist_opt['nearCallOpenInt'][date], 4)
    return hist_opt

begin = Date(2015, 2, 9)
end = Date.todaysDate()

opt_hist = getOptHistVol(begin, end)
opt_hist.tail(2)

	callVol	putVol	callValue	putValue	callOpenInt	putOpenInt	nearCallVol	nearPutVol	nearCallValue	nearPutValue	...	nearPutOpenInt	netVol	netValue	netOpenInt	volPCR	valuePCR	openIntPCR	nearVolPCR	nearValuePCR	nearOpenIntPCR
date
2015-09-23	50093	42910	37809117	41517121	269395	144256	16603	11494	6217923	10409963	...	50576	93003	79326238	413651	0.8566	1.0981	0.5355	0.6923	1.6742	0.3738
2015-09-24	29352	23474	21696859	22161955	146224	98350	19785	19339	15693989	14549046	...	55217	52826	43858814	244574	0.7997	1.0214	0.6726	0.9775	0.9270	0.8012

2 rows × 21 columns

## ----- 50ETF期权成交持仓数据图 -----
fig = plt.figure(figsize=(10,5))
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
font.set_size(16)

lns1 = ax.plot(opt_hist.index, opt_hist.netOpenInt, 'grey', label = u'OpenInt')
lns2 = ax.plot(opt_hist.index, opt_hist.netVol, '-r', label = 'TurnoverVolume')
ax2 = ax.twinx()
lns3 = ax2.plot(etf.index, etf.closePrice, '-', label = 'ETF closePrice')

lns = lns1+lns2+lns3
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"TurnoverVolume / OpenInt")
ax2.set_ylabel(r"ETF closePrice")
plt.title('50ETF Option TurnoverVolume / OpenInt')
plt.show()

从上图可以看出：

期权的交易量基本上是50ETF的反向指标
五月之前的疯牛中，期权日交易量处于低位
六月中下旬之后的暴跌时间段，期权日交易量高位运行，是不是创个新高
8月17日开始的这一周中，大盘风雨飘摇，50ETF探底时，期权交易量创了新高
目前来看，期权交易仍然活跃，但是交易量较之前数据有所回落，应该是大盘企稳的节奏

3. 期权的PCR比例

期权分看跌和看涨两种，买入两种不同的期权，代表着对于后市的不同看法，因此可以引进一个量化指标，来表示对后市看衰与看涨的力量的强弱：

PCR = Put Call Ratio
PCR可以是关于成交量的PCR，可以是持仓量的PCR，也可以是成交额的PCR

begin = Date(2015, 2, 9)
end = Date.todaysDate()

opt_hist = getOptHistVol(begin, end)
opt_hist.tail(2)

	callVol	putVol	callValue	putValue	callOpenInt	putOpenInt	nearCallVol	nearPutVol	nearCallValue	nearPutValue	...	nearPutOpenInt	netVol	netValue	netOpenInt	volPCR	valuePCR	openIntPCR	nearVolPCR	nearValuePCR	nearOpenIntPCR
date
2015-09-23	50093	42910	37809117	41517121	269395	144256	16603	11494	6217923	10409963	...	50576	93003	79326238	413651	0.8566	1.0981	0.5355	0.6923	1.6742	0.3738
2015-09-24	29352	23474	21696859	22161955	146224	98350	19785	19339	15693989	14549046	...	55217	52826	43858814	244574	0.7997	1.0214	0.6726	0.9775	0.9270	0.8012

2 rows × 21 columns

首先，我们来看看成交量PCR和ETF价格走势的关系

## ----------------------------------------------
## 50ETF期权PC比例数据图
fig = plt.figure(figsize=(10,8))
fig.set_tight_layout(True)

# ------ 成交量PC比例 ------
ax = fig.add_subplot(211)
lns1 = ax.plot(opt_hist.index, opt_hist.volPCR, color='r', label = u'volPCR')
ax2 = ax.twinx()
lns2 = ax2.plot(etf.index, etf.closePrice, '-', label = 'closePrice')
lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=3)
ax.set_ylim(0, 2)

hfmt = dates.DateFormatter('%m')
ax.xaxis.set_major_formatter(hfmt)
ax.grid()
ax.set_xlabel(u"tradeDate(Month)")
ax.set_ylabel(r"PCR")
ax2.set_ylabel(r"ETF ClosePrice")
plt.title('Volume PCR')

# ------ 近月主力期权成交量PC比例 ------
ax = fig.add_subplot(212)
lns1 = ax.plot(opt_hist.index, opt_hist.nearVolPCR, color='r', label = u'nearVolPCR')
ax2 = ax.twinx()
lns2 = ax2.plot(etf.index, etf.closePrice, '-', label = 'closePrice')
lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=3)
ax.set_ylim(0, 2)

hfmt = dates.DateFormatter('%m')
ax.xaxis.set_major_formatter(hfmt)
ax.grid()
ax.set_xlabel(u"tradeDate(Month)")
ax.set_ylabel(r"PCR")
ax2.set_ylabel(r"ETF ClosePrice")
plt.title('Dominant Contract Volume PCR')

<matplotlib.text.Text at 0x6470990>

成交量数据图中，上图为全体期权的成交量PCR，下图为近月期权的成交量PCR：

上下两图中，PCR的曲线走势基本相似，因为期权交易中，近月期权最为活跃
ETF价格走势，和PCR走势有比较明显的负相关性

其次，我们来看看持仓量PCR和ETF价格走势的关系

## ----------------------------------------------
## 50ETF期权PC比例数据图
fig = plt.figure(figsize=(10,8))
fig.set_tight_layout(True)

# ------ 持仓量PC比例 ------
ax = fig.add_subplot(211)
lns1 = ax.plot(opt_hist.index, opt_hist.openIntPCR, color='r', label = u'volPCR')
ax2 = ax.twinx()
lns2 = ax2.plot(etf.index, etf.closePrice, '-', label = 'closePrice')
lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=3)
ax.set_ylim(0, 2)

hfmt = dates.DateFormatter('%m')
ax.xaxis.set_major_formatter(hfmt)
ax.grid()
ax.set_xlabel(u"tradeDate(Month)")
ax.set_ylabel(r"PCR")
ax2.set_ylabel(r"ETF ClosePrice")
plt.title('OpenInt PCR')

# ------ 近月主力期权持仓量PC比例 ------
ax = fig.add_subplot(212)
lns1 = ax.plot(opt_hist.index, opt_hist.nearOpenIntPCR, color='r', label = u'nearVolPCR')
ax2 = ax.twinx()
lns2 = ax2.plot(etf.index, etf.closePrice, '-', label = 'closePrice')
lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=3)
ax.set_ylim(0, 2)

hfmt = dates.DateFormatter('%m')
ax.xaxis.set_major_formatter(hfmt)
ax.grid()
ax.set_xlabel(u"tradeDate(Month)")
ax.set_ylabel(r"PCR")
ax2.set_ylabel(r"ETF ClosePrice")
plt.title('Dominant Contract OpenInt PCR')

<matplotlib.text.Text at 0x69e5990>

持仓量数据图中，上图为全体期权的持仓量PCR，下图为近月期权的持仓量PCR：

上下两图中，PCR的曲线走势基本相似，因为期权交易中，近月期权最为活跃
实际上，近月期权十分活跃，使得近月期权的PCR系数变动往往比整体期权PCR变化更剧烈
ETF价格走势，和PCR走势并无明显的负相关性
相反，ETF价格的低点，往往PCR也处于低点，这其实说明：股价大跌之后大家会选择平仓看跌期权

最后，我们来看看成交额PCR和ETF价格走势的关系

## ----------------------------------------------
## 50ETF期权PC比例数据图
fig = plt.figure(figsize=(10,8))
fig.set_tight_layout(True)

# ------ 成交额PC比例 ------
ax = fig.add_subplot(211)
lns1 = ax.plot(opt_hist.index, opt_hist.valuePCR, color='r', label = u'turnoverValuePCR')
ax2 = ax.twinx()
lns2 = ax2.plot(etf.index, etf.closePrice, '-', label = 'closePrice')
lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=3)
#ax.set_ylim(0, 2)
ax.set_yscale('log')

hfmt = dates.DateFormatter('%m')
ax.xaxis.set_major_formatter(hfmt)
ax.grid()
ax.set_xlabel(u"tradeDate(Month)")
ax.set_ylabel(r"PCR")
ax2.set_ylabel(r"ETF ClosePrice")
plt.title('Turnover Value PCR')

# ------ 近月主力期权成交额PC比例 ------
ax = fig.add_subplot(212)
lns1 = ax.plot(opt_hist.index, opt_hist.nearValuePCR, color='r', label = u'turnoverValuePCR')
ax2 = ax.twinx()
lns2 = ax2.plot(etf.index, etf.closePrice, '-', label = 'closePrice')
lns = lns1+lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=3)
#ax.set_ylim(0, 2)
ax.set_yscale('log')

hfmt = dates.DateFormatter('%m')
ax.xaxis.set_major_formatter(hfmt)
ax.grid()
ax.set_xlabel(u"tradeDate(Month)")
ax.set_ylabel(r"PCR")
ax2.set_ylabel(r"ETF ClosePrice")
plt.title('Dominant Contract Turnover Value PCR')

<matplotlib.text.Text at 0x70ce890>

成交额数据图中，上图为全体期权的成交额PCR，下图为近月期权的成交额PCR：

上下两图中，PCR的曲线走势基本相似，因为期权交易中，近月期权最为活跃
实际上，近月期权PCR指数十分活跃，使得近月期权的PCR系数变动往往比整体期权PCR变化更剧烈
相对于成交量和持仓量PCR指标，此处的成交额PCR指标峰值往往很高，上图中近月期权的成交额PCR最大值甚至接近30，这是由于市场恐慌时候，看跌期权成交量本身就大，而交易量大往往将看跌期权的价格大幅抬高
ETF价格走势，和PCR走势具有明显的负相关性
基于期权成交额PCR的择时策略

根据成交额PCR和ETF价格走势明显的负相关性，我们建立一个非常简单的择时策略：

PCR下降时，市场情绪趋稳定，全仓买入50ETF
PCR上升时，恐慌情绪蔓延，清仓观望

start = datetime(2015, 2, 9)                # 回测起始时间
end  = datetime(2015, 9, 21)                # 回测结束时间

hist_pcr = getOptHistVol(start, end)

start = datetime(2015, 2, 9)                # 回测起始时间
end  = datetime(2015, 9, 21)                # 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)
refresh_rate = 1

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
    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')
    cal.addHoliday(Date(2015,9,3))
    cal.addHoliday(Date(2015,9,4))

    last_day = cal.advanceDate(dt,'-1B',BizDayConvention.Preceding)            #计算出倒数第一个交易日
    last_last_day = cal.advanceDate(last_day,'-1B',BizDayConvention.Preceding)  #计算出倒数第二个交易日
    last_day_str = last_day.strftime("%Y-%m-%d")
    last_last_day_str = last_last_day.strftime("%Y-%m-%d")

    # 计算买入卖出信号
    try:
        # 拿取PCR数据
        pcr_last = hist_pcr['valuePCR'].loc[last_day_str]    
        pcr_last_last = hist_pcr['valuePCR'].loc[last_last_day_str]   
        long_flag = True if (pcr_last - pcr_last_last) < 0 else False 
    except:
        long_flag = True

    if long_flag:
        approximationAmount = int(account.cash / account.referencePrice[fund] / 100.0) * 100
        order(fund, approximationAmount)
    else:
        # 卖出时，全仓清空
        order_to(fund, 0)