# 期权市场一周纵览 > 来源：https://uqer.io/community/share/55027679f9f06c7a9ae9a53a 本文档依赖的数据 `option_data.csv` 可以通过运行 期权高频数据准备 notebook而获取。 ```py from matplotlib import pylab import pandas as pd import seaborn as sns sns.set(style="white", context="talk") import pandas as pd pd.options.display.float_format = '{:,>.4f}'.format ``` ```py res = pd.read_csv('option_data.csv', parse_dates=['pdDateTime']) res['timeStamp'] = res['dataDate'] + ' ' + res['dataTime'] res['timeStamp'] = pd.to_datetime(res['timeStamp']) res.optionId = res.optionId.astype('str') res = res.drop('Unnamed: 0', axis=1) res.pdDateTime = res.pdDateTime.apply(lambda x:Date(x.year,x.month,x.day)) print('开始日期： ' + res['dataDate'].iloc[0]) print('结束日期： ' + res['dataDate'].iloc[-1]) print('Market Sample: ') res[['dataDate', 'dataTime', 'optionId', 'lastPrice', 'bidPrice1', 'askPrice1', 'lastPrice(vol)']].head() 开始日期： 2015-03-05 结束日期： 2015-03-09 Market Sample: ``` | | dataDate | dataTime | optionId | lastPrice | bidPrice1 | askPrice1 | lastPrice(vol) | | --- | --- | | 0 | 2015-03-05 | 09:30:00 | 10000001 | 0.1677 | 0.1717 | 0.1765 | 0.3468 | | 1 | 2015-03-05 | 09:30:14 | 10000001 | 0.1717 | 0.1717 | 0.1765 | 0.3768 | | 2 | 2015-03-05 | 09:30:15 | 10000001 | 0.1717 | 0.1610 | 0.1798 | 0.3768 | | 3 | 2015-03-05 | 09:30:16 | 10000001 | 0.1678 | 0.1610 | 0.1798 | 0.3525 | | 4 | 2015-03-05 | 09:30:18 | 10000001 | 0.1798 | 0.1641 | 0.1798 | 0.4205 | ## 1. 买卖价差分析 ### 1.1 买卖价差（到期时间） ```py bidAskSample = res[[u'optionId', 'pdDateTime', 'dataDate', 'contractType', 'strikePrice', 'bidAskSpread(bps)']] bidAskSample.columns = ['optionId', 'maturity', 'tradingDate', 'contractType', 'strikePrice', 'bidAskSpread(bps)'] tmp = bidAskSample.groupby(['maturity'])[['bidAskSpread(bps)']] ax = tmp.mean().plot(kind = 'bar', figsize = (12,6), rot = 45) ax.set_title(u'买卖价差（按照期权到期时间）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x7798290> ```  ### 1.2 买卖价差（行权价） ```py tmp = bidAskSample.groupby(['maturity', 'strikePrice'])[['bidAskSpread(bps)']].mean().unstack() ax = tmp.plot(kind = 'bar', figsize = (12,6), legend = True, rot = 45) patches, labels = ax.get_legend_handles_labels() labels = ['Strike/' + l.strip('()').split()[1] for l in labels] ax.legend(patches, labels, loc='best', prop = font) ax.set_title(u'买卖价差（按照期权行权价）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x5bc08d0> ```  ### 1.3 买卖价差（期权类型） ```py tmp = bidAskSample.groupby(['maturity', 'contractType'])[['bidAskSpread(bps)']].mean().unstack() ax = tmp.plot(kind = 'bar', figsize = (12,6), rot = 45) patches, labels = ax.get_legend_handles_labels() labels = [l.strip('()').split()[1] for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'买卖价差（按照期权类型）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x7a8d7d0> ```  ## 2. 日交易量分析 ```py volumeSample = res[[u'optionId', 'pdDateTime', 'dataDate', 'contractType', 'strikePrice', 'volume']] volumeSample.columns = ['optionId', 'maturity', 'tradingDate', 'contractType', 'strikePrice', 'volume'] tmp = volumeSample.groupby(['tradingDate'])[['volume']].sum() ax = tmp.plot(kind = 'bar', figsize = (12,6), rot = 45) ax.set_title(u'日交易量（按交易日期）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'交易日期', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x7a72d90> ```  ### 2.1 日交易量（到期时间） ```py tmp = volumeSample.groupby(['maturity', 'tradingDate'])[['volume']].sum().unstack() ax = tmp.plot(kind = 'bar', figsize = (12,6), rot = 45) patches, labels = ax.get_legend_handles_labels() labels = [l.strip('()').split()[1] for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'日交易量（按照期权到期时间）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) ```  每个交易日不同到期期限期权的交易量： ```py tmp ``` | | volume | | --- | --- | | tradingDate | 2015-03-05 | 2015-03-06 | 2015-03-09 | 2015-03-10 | 2015-03-11 | | maturity | | | | | | | March 25th, 2015 | 18767.0000 | 16704.0000 | 31115.0000 | 11888.0000 | 11562.0000 | | April 22nd, 2015 | 7791.0000 | 4468.0000 | 13355.0000 | 6909.0000 | 5632.0000 | | June 24th, 2015 | 965.0000 | 326.0000 | 3091.0000 | 619.0000 | 604.0000 | | September 23rd, 2015 | 635.0000 | 101.0000 | 2426.0000 | 240.0000 | 178.0000 | ### 2.2 日交易量（行权价） ```py tmp = volumeSample.groupby(['tradingDate','strikePrice'])[['volume']].sum().unstack() ax = tmp.plot(kind = 'bar', figsize = (16,8), rot = 45) patches, labels = ax.get_legend_handles_labels() labels = ['Strike/' + l.strip('()').split()[1] for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'日交易量（按照期权行权价）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'交易日期', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x7fa5610> ```  每个交易日不同行权价期权的交易量： ```py tmp ``` | | volume | | --- | --- | | strikePrice | 2.2000 | 2.2500 | 2.3000 | 2.3500 | 2.4000 | 2.4500 | 2.5000 | 2.5500 | | tradingDate | | | | | | | | | | 2015-03-05 | 2597.0000 | 1725.0000 | 3077.0000 | 5351.0000 | 5430.0000 | 4231.0000 | 3148.0000 | 2599.0000 | | 2015-03-06 | 1352.0000 | 750.0000 | 1435.0000 | 5219.0000 | 4395.0000 | 3301.0000 | 3143.0000 | 2004.0000 | | 2015-03-09 | 4576.0000 | 3407.0000 | 3599.0000 | 8954.0000 | 9564.0000 | 9015.0000 | 5969.0000 | 4903.0000 | | 2015-03-10 | 2225.0000 | 1649.0000 | 1532.0000 | 3237.0000 | 3588.0000 | 2832.0000 | 2343.0000 | 2250.0000 | | 2015-03-11 | 2021.0000 | 1286.0000 | 1299.0000 | 2959.0000 | 3121.0000 | 2648.0000 | 2565.0000 | 2077.0000 | ### 2.3 日交易量（期权类型） ```py tmp = volumeSample.groupby(['tradingDate','contractType'])[['volume']].sum().unstack() ax = tmp.plot(kind = 'bar', y = ['volume'], figsize = (12,6), rot = 45) patches, labels = ax.get_legend_handles_labels() labels = [l.strip('()').split()[1] for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'日交易量（按照期权类型）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'交易日期', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x8813e10> ```  ## 3. 波动率价差分析 ```py bidAskVolSample = res[[u'optionId', 'pdDateTime', 'dataDate', 'contractType', 'strikePrice', 'bidAskSpread(vol bps)']] bidAskVolSample.columns = ['optionId', 'maturity', 'tradingDate', 'contractType', 'strikePrice', 'bidAskSpread(vol bps)'] ``` ### 3.1 波动率价差（到期时间） ```py tmp = bidAskVolSample.groupby(['maturity'])[['bidAskSpread(vol bps)']] ax = tmp.mean().plot(kind = 'bar', figsize = (12,6), rot = 45) ax.set_title(u'波动率价差（按照期权到期时间）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x8c0b7d0> ```  ### 3.2 波动率价差（行权价） ```py tmp = bidAskVolSample.groupby(['maturity', 'strikePrice'])[['bidAskSpread(vol bps)']].mean().unstack() ax = tmp.plot(kind = 'bar', figsize = (14,6), legend = True, rot = 45) patches, labels = ax.get_legend_handles_labels() labels = ['strike/' + l.strip('()').split()[-1] for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'波动率价差（按照期权行权价）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) ```  ### 3.3 波动率价差（期权类型） ```py tmp = bidAskVolSample.groupby(['maturity', 'contractType'])[['bidAskSpread(vol bps)']].mean().unstack() ax = tmp.plot(kind = 'bar', figsize = (12,6), rot = 45) patches, labels = ax.get_legend_handles_labels() labels = [l.split()[-1].strip('()') for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'波动率价差（按照期权类型）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'到期时间', fontproperties = font, fontsize = 15) ```  ### 3.4 波动率价差（交易时间） ```py tmp = bidAskVolSample.groupby(['tradingDate', 'maturity'])[['bidAskSpread(vol bps)']].mean().unstack() ax = tmp.plot(kind = 'bar', figsize = (12,6), rot = 45) patches, labels = ax.get_legend_handles_labels() labels = [l.split(',')[1].strip('()') for l in labels] ax.legend(patches, labels, loc='best') ax.set_title(u'波动率价差（按照交易时间）', fontproperties = font, fontsize = 25) ax.set_xlabel(u'交易日期', fontproperties = font, fontsize = 15) <matplotlib.text.Text at 0x8d1fc50> ```  ## 4. 个券分析 ### 4.1 交易量 ```py tmp = volumeSample.groupby(['tradingDate','optionId'])[['volume']].sum().unstack() fig, axs = pylab.subplots(len(tmp)/2 + len(tmp)%2, 2, figsize = (16,8 * len(tmp)/2)) for i in range(len(tmp)): sample = pd.DataFrame(tmp.iloc[i]['volume']) sample.columns = ['volume'] sample = sample.sort('volume', ascending = False) sample = sample[:10] row = i / 2 col = i % 2 sample.plot(kind = 'PIE',y = 'volume', sharex= False, ax = axs[row][col], legend = False, rot = 45) axs[row][col].set_title(u'交易日: ' + str(tmp.index[i]), fontproperties = font, fontsize = 18) ```  ### 4.2 买卖价差 ```py tmp = bidAskSample.groupby(['tradingDate','optionId'])[['bidAskSpread(bps)']].mean().unstack() fig, axs = pylab.subplots(len(tmp)/2 + len(tmp)%2, 2, figsize = (16,8*len(tmp)/2)) for i in range(len(tmp)): sample = pd.DataFrame(tmp.iloc[i]['bidAskSpread(bps)']) sample.columns = ['bidAskSpread(bps)'] sample = sample.sort('bidAskSpread(bps)') sample = sample[:10] row = i / 2 col = i % 2 sample.plot(kind = 'bar',y = 'bidAskSpread(bps)', sharex= False, ax = axs[row][col], legend = False, rot = 20) axs[row][col].set_title(u'交易日: ' + str(tmp.index[i]), fontproperties = font, fontsize = 18) ```  ### 4.3 波动率价差 ```py tmp = bidAskVolSample.groupby(['tradingDate','optionId'])[['bidAskSpread(vol bps)']].mean().unstack() fig, axs = pylab.subplots(len(tmp)/2 + len(tmp)%2, 2, figsize = (16,8*len(tmp)/2)) for i in range(len(tmp)): sample = pd.DataFrame(tmp.iloc[i]['bidAskSpread(vol bps)']) sample.columns = ['bidAskSpread(vol bps)'] sample = sample.sort('bidAskSpread(vol bps)') sample = sample[:10] row = i / 2 col = i % 2 sample.plot(kind = 'bar',y = 'bidAskSpread(vol bps)', sharex= False, ax = axs[row][col], legend = False, rot = 20) axs[row][col].set_title(u'交易日: ' + str(tmp.index[i]), fontproperties = font, fontsize = 18) ```  ### 4.4 时间序列分析 ```py tmp = volumeSample.groupby(['tradingDate','optionId'])[['volume']].sum().unstack() for i, d in enumerate(tmp.index): fig, axs = pylab.subplots(2, 1, figsize = (16,5)) sample = tmp.loc(d) sample = sample[d] sample.sort('volume', ascending = False) base = res[res['dataDate'] == d] base = base[base.optionId == sample.index[0][1]] base.index = range(len(base)) base['calTimeStamp'] = base.timeStamp.apply(lambda s: DateTime(s.year, s.month, s.day, s.hour, s.minute, s.second)) ax = base.plot(x = 'calTimeStamp', y = ['volume'], kind = 'bar', sharex=True, xticks = [], color = 'r', ax = axs[0]) ax.set_title(u'交易日: ' + unicode(d) + u' 最活跃期权：'+ unicode(sample.index[0][1]), fontproperties = font, fontsize = 18) ax = base.plot(x= 'calTimeStamp', y = ['lastPrice(vol)'], sharex=True, legend = True,ax = axs[1], rot = 45) ax.set_xlabel(u'交易时间', fontproperties = font, fontsize = 15) ```