def analyze_atm():
filename = 'strategyAtmReturnNextTradedayOpen.csv'
fp = getTestPath(filename)
df = pd.read_csv(fp, index_col=0, parse_dates=True)
# df = df.groupby('tradeDay').apply(analyze_single_atm)
df = df.groupby('tradeDay').apply(analyze_single_atm_open)
grouped = df.groupby('time_flag')
groupedDict = dict(list(grouped))
keys = groupedDict.keys()
keys.sort()
print(u'昨日平值期权今日开盘n分钟后，价和与昨日收盘对比统计情况统计结果(单位：元)：')
for key in keys:
print('' * 30)
df = groupedDict[key]
# if key != '0-minute':
if key == '1-minute':
all_count = len(df)
higer_count = len(df[df.abs_change > 0])
lower_count = len(df[df.abs_change <= 0])
min = df.abs_change.min()
max = df.abs_change.max()
mean = df.abs_change.mean()
median = df.abs_change.median()
df2 = df.iloc[-240:]
volume_mean = df2.volume.mean()
print('-' * 30)
print(u'开盘%s分钟后：' % key[0])
print('-' * 30)
print(u'总交易日：%s' % all_count)
print(u'价和比昨收盘更高交易日：%s' % higer_count)
print(u'价和比昨收盘更低交易日：%s' % lower_count)
print(u'价和跌最低：%.2f' % (min * 10000))
print(u'价和涨最高：%.2f' % (max * 10000))
print(u'价和涨跌平均值：%.2f' % (mean * 10000))
print(u'价和涨跌中位数：%.2f' % (median * 10000))
print(u'近一年成交量平均值：%.2f' % volume_mean)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title(key)
ax.set_ylabel('trade_days')
df.abs_change.hist(bins=200, ax=ax)
filename = '{}_open.png'.format(key)
fig.savefig(getTestPath(filename))
def testGetNearbyContract(self):
# filename = 'option_daily_2018-12-07.csv'
filename = 'option_daily_2018-12-25.csv'
fp = os.path.join(self.obj.jqsdk.getPricePath('option', 'daily'),
filename)
df = self.obj.getNearbyContract(fp)
df.to_csv(getTestPath('nearbyContract.csv'))
def testGetOHLC(self):
# method = 'match'
# df = self.obj.getOHLCdaily(method=method)
# df.to_csv(getTestPath('atm_ohlc_{}.csv'.format(method)))
method = 'match'
df = self.obj.getOHLCdaily(method=method, isIncludePre=True)
df.to_csv(getTestPath('atm_ohlc_{}_includePre.csv'.format(method)))
def plot_test_date_kline(filename):
fp = getTestPath(filename)
df = pd.read_csv(fp, index_col=0)
plotter = KlinePlotter(df)
plotter.plotAll('atm-ohlc-no-gap-test',
'dailytask',
'atm-ohlc-no-gap-test.html',
item=['ma'])
def testGetVolatility(self):
stats_df = []
for n in [10, 20, 30, 60, 90]:
self.obj.getHistVolatility(n)
name = 'HV_{}'.format(n)
stats_df.append(self.obj.price[name])
# print(self.obj.price[name].describe())
self.obj.price.to_csv(getTestPath('510050_hist_volatility.csv'))
def testGetPrice(self):
begin = datetime(2018, 11, 22)
end = datetime(2018, 11, 23)
# df = self.obj.get_price('RB9999.XSGE', start_date='2018-11-22', end_date='2018-12-01', frequency='1m')
df = self.obj.get_price('CU9999.XSGE',
start_date=begin,
end_date=end,
frequency='1m')
df.to_csv(fn.getTestPath('testjq.csv'), encoding='utf-8-sig')
print(df.iloc[0])
print(df.iloc[-1])
def testGetAtmContract(self):
filename = 'option_daily_2019-04-10.csv'
fp = os.path.join(self.obj.jqsdk.getPricePath('option', 'daily'),
filename)
# df = self.obj.getAtmContract(fp)
# df.to_csv(getTestPath('atmContract.csv'))
# df2 = self.obj.getStraddleContract(fp)
# df2.to_csv(getTestPath('straddle.csv'))
df3 = self.obj.getAssignedContract(fp, 3300, 2700)
df3.to_csv(getTestPath('assigned.csv'))
def analyze_atm_range(start_time, end_time):
start_l = start_time.split(':')
start_time = time(int(start_l[0]), int(start_l[1]))
end_l = end_time.split(':')
end_time = time(int(end_l[0]), int(end_l[1]))
filename = 'atm_continuous_bar.csv'
fp = os.path.join(getDataDir(), 'research', 'option', 'dailytask',
'atm_continuous_bar.csv')
df = pd.read_csv(fp, index_col=0, parse_dates=True)
df = df.groupby('tradeDay').apply(analyze_single_atm_range, start_time,
end_time)
all_count = len(df)
higer_count = len(df[df.abs_change > 0])
lower_count = len(df[df.abs_change <= 0])
min = df.abs_change.min()
max = df.abs_change.max()
mean = df.abs_change.mean()
median = df.abs_change.median()
# df2 = df.iloc[-240:]
# volume_mean = df2.volume.mean()
print('-' * 30)
print(u'昨日平值期权今日%s-%s价和对比：' % (':'.join(start_l), ':'.join(end_l)))
print('-' * 30)
print(u'总交易日：%s' % all_count)
print(u'后面的时间价和更高交易日：%s' % higer_count)
print(u'后面的时间价和更低交易日：%s' % lower_count)
print(u'价和跌最低：%.2f' % (min * 10000))
print(u'价和涨最高：%.2f' % (max * 10000))
print(u'价和涨跌平均值：%.2f' % (mean * 10000))
print(u'价和涨跌中位数：%.2f' % (median * 10000))
# print(u'近一年成交量平均值：%.2f' % volume_mean)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title(u'%s-%s' % (':'.join(start_l), ':'.join(end_l)))
ax.set_ylabel('trade_days')
df.abs_change.hist(bins=100, ax=ax)
filename = '{}_open.png'.format(
u'%s-%s' % (start_time.strftime('%H%M'), end_time.strftime('%H%M')))
fig.savefig(getTestPath(filename))
def analyze_qvix_high_close():
df = load_qvix_data()
all_trade_days = len(df)
df['h-c'] = df['high'] - df['close']
df['h-c-ratio'] = (df['h-c'] / df['close']) * 100
df2 = df[df['h-c'] > 0]
df = copy(df2)
h_gt_o_trade_days = len(df)
for i in [5, 10, 20]:
h_o_name = 'h-c-ma-{}'.format(i)
df[h_o_name] = df['h-c'].rolling(i).mean()
h_o_ratio_name = 'h-c-ratio-ma-{}'.format(i)
df[h_o_ratio_name] = df['h-c-ratio'].rolling(i).mean()
new_file = 'vix_bar_high_close_ma.csv'
fp2 = os.path.join(getDataDir(), RESEARCH, OPTION, 'qvix', new_file)
df.to_csv(fp2)
print('-' * 30)
print(u'自2016-6-13以来，期权论坛波值最高价大于收盘价的交易统计：')
print('-' * 30)
print(u'总交易日：%s' % all_trade_days)
print(u'最高价大于收盘价的交易日：%s， 占比：%.3f %%' %
(h_gt_o_trade_days,
(float(h_gt_o_trade_days) / float(all_trade_days)) * 100))
for n in range(0, 15):
df_n = df[df['h-c-ratio'] > n]
c_n = len(df_n)
print(u'最高价对比收盘价百分比超过%s交易日：%s，占比：%.3f %%' %
(n, c_n, (float(c_n) / float(h_gt_o_trade_days)) * 100))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title(u'Ratio(High To Close) Distribution')
ax.set_ylabel('trade_days')
df['h-c-ratio'].hist(bins=100, ax=ax)
filename = '{}_open.png'.format('high_gt_close')
fig.savefig(getTestPath(filename))
def testGetEtfRatio(self):
df = self.obj.getEtfMarketRatio()
df.to_csv(getTestPath('etfRatio.csv'))
def testGetRatio(self):
df = self.obj.getRatio()
df.to_csv(getTestPath('ratio.csv'), encoding='utf-8-sig')
def testStrategyAtmReturnNextTradedayOpen(self):
df = self.obj.strategyAtmReturnNextTradedayOpen(
'2017-01-01', '2019-05-10')
df.to_csv(getTestPath('strategyAtmReturnNextTradedayOpen.csv'))
def testGetHistVol(self):
self.obj.getHisVolatility(20)
df = self.obj.getVix()
df.to_csv(getTestPath('vix_hist_vol_test.csv'))
def testGetPercentile(self):
print(self.obj.getVixPercentile())
self.obj.vix.to_csv(getTestPath('vix_test.csv'))
def testGetPosition(self):
df = self.obj.getPosition()
df.to_csv(getTestPath('position.csv'), encoding='utf-8-sig')
def testGetContractInfo(self):
df = self.obj.getContractInfo()
print(self.obj.contractInfo)
df.to_csv(getTestPath('contractInfo.csv'), encoding='utf-8-sig')
def testAdd50etf(self):
df = self.obj.add50etf()
print(df)
df.to_csv(getTestPath('50etf-qvix.csv'))
def testMonteCarlo(self):
df = self.obj.monteCarloSim(100, 21)
df.to_csv(getTestPath('montecarlo.csv'))
def testGetDailyPrice(self):
contract = '10001562.XSHG'
df = self.obj.getDailyPrice(contract)
df.to_csv(getTestPath('optionPrice.csv'), encoding='utf-8-sig')
def testGetStraddleContract(self):
level = 1
df = self.obj.getStraddleContract(self.fp, method='match', level=level)
df.to_csv(getTestPath('straddle{}_contract_match.csv'.format(level)))
def testGetAtmContract(self):
df = self.obj.getAtmContract(self.fp, method='match')
df.to_csv(getTestPath('atm_contract_match.csv'))
df = self.obj.getAtmContract(self.fp, method='simple')
df.to_csv(getTestPath('atm_contract_simple.csv'))
def testGetNearbyContract(self):
df = self.obj.getNearbyContract(self.fp)
df.to_csv(getTestPath('nearbyContract_20190225.csv'))
def testGetMoneyFlowOf50EtfInPeriod(self):
d1 = datetime(2019, 2, 25)
d2 = datetime.today()
d3 = datetime(2019, 3, 1)
df = self.obj.getMoneyFlowOf50Etf(d1, d3)
df.to_csv(getTestPath('50etf_money_flow.csv'))
def testGetCompContract(self):
df = self.obj.getCompContract()
df.to_csv(getTestPath('optionDisplayContract.csv'),
encoding='utf-8-sig')
def testGetHVPercentile(self):
self.obj.getHVPercentile(20)
df = self.obj.getVix()
df.to_csv(getTestPath('HV20_percentile.csv'))
def testGetTradingContractInfo(self):
df = self.obj.getTradingContract()
df.to_csv(getTestPath('optionTradingContract.csv'),
encoding='utf-8-sig')
def testAddHisVol(self):
s = self.obj.addHistVolotility(20)
print(s)
self.obj.dailyData.to_csv(getTestPath('50etf-hisvol.csv'))
def testStrategyAtmLastTradeDays(self):
df = self.obj.strategyAtmLastTradeDays()
df.to_csv(getTestPath('strategyAtmLastTradeDays.csv'))
def testRemoveGap(self):
df = self.obj.removeOHLCgap()
df.to_csv(getTestPath('test_atm_ohlc_remove_gap.csv'))
def testGetMainContractDailyPrice(self):
price = self.obj.getAllContractDailyPrice()
for code, df in price.items():
fp = getTestPath('{}.csv'.format(code))
df.to_csv(fp, encoding='utf-8-sig')
