def testPlotAssignedMergedPrice(self):
start = datetime(2019, 2, 18)
end = datetime(2019, 2, 22)
call = 2550
put = 2550
close = self.obj.getMergedPrice(start,
end,
self.obj.getAssignedContract,
callStrikePrice=call,
putStrikePrice=put)
self.obj.plotMergedPrice(
'{}-to-{}-straddle'.format(dateToStr(start), dateToStr(end)),
close)
call = 2750
put = 2750
start = datetime(2019, 2, 25)
end = datetime(2019, 3, 1)
close = self.obj.getMergedPrice(start,
end,
self.obj.getAssignedContract,
callStrikePrice=call,
putStrikePrice=put)
# close.to_csv(getTestPath('bug.csv'))
self.obj.plotMergedPrice(
'{}-to-{}-straddle'.format(dateToStr(start), dateToStr(end)),
close)
def testDateConvert(self):
# self.setTestData()
print('Remove Dash. {} to {}'.format(self.dateWithDash,
fn.rmDateDash(self.dateWithDash)))
print('Add Dash. {} to {}'.format(self.dateWithoutDash,
fn.addDateDash(
self.dateWithoutDash)))
print('TodayStr: {}'.format(fn.getTodayStr()))
print('strToDate 1: {}'.format(fn.strToDate(self.dateWithDash)))
print('strToDate 2: {}'.format(fn.strToDate(self.dateWithoutDash)))
print(fn.strToDate('2018-04-20'))
print(fn.dateToStr(self.dt))
print(fn.dateToDtStr(self.dt))
def analyze_ohlc_relationship(data, a, b, direction):
"""
研究OHLC的关系
:param data: DataFrame
:param a:
:param b:
:param direction:
:return:
"""
df = data
# print(df.index)
start_date = pd.to_datetime(df.index.values[0])
end_date = pd.to_datetime(df.index.values[-1])
print(start_date)
all_trade_days = len(df)
abs_change_name = '{}_{}_abs'.format(a, b)
relative_change_name = '{}_{}_ratio'.format(a, b)
df[abs_change_name] = df[b] - df[a]
df[relative_change_name] = (df[abs_change_name] / df[a]) * 100
if direction == 'up':
df2 = df[df[abs_change_name] > 0]
zh_cn = u'大于'
elif direction == 'down':
df2 = df[df[abs_change_name] < 0]
zh_cn = u'小于'
else:
print('Wrong Direction')
return
df2 = copy(df2)
select_trade_days = len(df2)
select_ratio = float(select_trade_days) / float(all_trade_days) * 100
for i in [5, 10, 20]:
abs_ma_name = '{}_{}_ma{}'.format(a[0], b[0], i)
df2[abs_ma_name] = df2[abs_change_name].rolling(i).mean()
ratio_ma_name = '{}_{}_ratio_ma{}'.format(a[0], b[0], i)
df2[ratio_ma_name] = df2[relative_change_name].rolling(i).mean()
new_file = 'vix_bar_{}_{}_ma.csv'.format(a, b)
fp2 = os.path.join(getDataDir(), RESEARCH, OPTION, 'qvix', new_file)
df2.to_csv(fp2)
print('-' * 30)
print(u'自{}_{}，期权论坛波值{}{}{}的交易统计：'.format(dateToStr(start_date), dateToStr(end_date), b, zh_cn, a))
print('-' * 30)
print(u'总交易日：%s' % all_trade_days)
print(u'%s%s%s的交易日：%s， 占比：%.3f %%' % (b, zh_cn, a, select_trade_days, select_ratio))
# for n in range(0, 15):
# ratio_array = df2[relative_change_name].values()
# count = len(ratio_array)
# df_n = df[df['h-o-ratio'] > n]
# c_n = len(df_n)
# print(u'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(figsize=(16, 12))
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
ax1.set_title(u'Ratio({} to {}) Distribution'.format(a, b))
ax2.set_title(u'Ratio({} to {}) Cumulative Distribution'.format(a, b))
plt.subplots_adjust(hspace=0.2)
data = df[relative_change_name].values
sns.distplot(data, bins=100, color='g', ax=ax1)
sns.kdeplot(data, color='r', cumulative=True, ax=ax2)
# ax1.set_xticks(ax1.get_xticks() / 2)
# ax2.set_xticks(ax2.get_xticks() / 2)
# ax1.set_yticks(ax1.get_yticks() / 2)
# ax2.set_yticks(ax2.get_yticks() / 2)
ax1.xaxis.set_major_locator(ticker.MultipleLocator(5))
ax2.xaxis.set_major_locator(ticker.MultipleLocator(5))
ax2.yaxis.set_major_locator(ticker.MultipleLocator(0.1))
filename = '{}_to_{}_{}_{}_relationship.png'.format(dateToStr(start_date), dateToStr(end_date), a, b)
fig.savefig(getTestPath(filename))