def analyze_qvix():
filename = 'vixBar.csv'
fp = os.path.join(getDataDir(), RESEARCH, OPTION, 'qvix', filename)
df = pd.read_csv(fp)
df.datetime = df.datetime.map(lambda timestamp: datetime.fromtimestamp(timestamp / 1000))
df['pre_close'] = df['close'].shift(1)
df['open_change'] = (df['open'] - df['pre_close']) / df['pre_close']
df['open_change'].hist(bins=100)
df['open_change'].hist(bins=100, cumulative=True, normed=True)
def get_neutral_continuous_bar(group='atm', level=1):
"""
获取中性组合的分钟bar
:param group: str, 'atm' or 'straddle'
:param level:
:return:
"""
if group == 'atm':
filename = 'atm_continuous_bar.csv'
elif group == 'straddle':
filename = 'straddle_continuous_bar_{}.csv'.format(level)
else:
return
fp = os.path.join(getDataDir(), 'research', 'option', 'dailytask', filename)
df = pd.read_csv(fp, index_col=0, parse_dates=True)
return df
def load_qvix_data(start=None, end=None):
start = datetime(2016, 6, 13) if start is None else strToDate(start)
# end = datetime.now() if end is None else strToDate(end)
filename = 'vixBar.csv'
fp = os.path.join(getDataDir(), RESEARCH, OPTION, 'qvix', filename)
df = pd.read_csv(fp, index_col=0, parse_dates=True)
df['pre_close'] = df['close'].shift(1)
df.dropna(inplace=True)
if end is None:
df = df[start:]
else:
end = strToDate(end)
df = df[start: end]
df = copy(df)
return df
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 analyze_qvix():
filename = 'qvix_daily.csv'
fp = os.path.join(getDataDir(), RESEARCH, OPTION, 'qvix', filename)
df = pd.read_csv(fp, index_col=0, parse_dates=True)
df['prev_close'] = df.close.shift(1)
df['vix_open_change'] = (df['open'] - df['prev_close']) / df['prev_close']
df['vix_close_change'] = (df['close'] -
df['prev_close']) / df['prev_close']
column_name_dict = {
'open': VIX_OPEN,
'high': VIX_HIGH,
'low': VIX_LOW,
'close': VIX_CLOSE,
'vix_open_change': VIX_OPEN_CHANGE,
'vix_close_change': VIX_CLOSE_CHANGE
}
df.rename(columns=column_name_dict, inplace=True)
df.index.name = DATETIME
# df.to_csv('vix.csv', encoding='utf-8-sig')
return df
def testGetDirs(self):
print(fn.getCurrentDir())
print(fn.getParentDir())
print(fn.getDataDir())
print(fn.getTestDataDir())
print(fn.getTestPath('test.csv'))
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))
