def greater(value1, value2):
if isinstance(value1, dict):
try:
if at.date_encoding(value1['date']) > at.date_encoding(
value2['date']):
return True
except:
return False
else:
if at.date_encoding(value1[0]) > at.date_encoding(value2[0]):
return True
def get_series_hist_data(code, days, start_date = '', multi_threads = 20):
'''
Gather the stock data for a range of days
:param code: str, stock index
:param days: int, the number of days
:param start_date: str, the start date
:param multi_threads: int, the number of threads, default 20
:return: a list for the data of a series of dates
'''
if days < 10:
days_n = 10
else:
days_n = days
if start_date != '':
start_date = at.date_encoding(start_date)
else:
start_date = datetime.date.today()
def record_thread_result(code, date, q, q_n):
result = get_stock_hist_data(code, date)
if result == None:
q_n.put(result)
else:
q.put(result)
q = queue.Queue()
q_n = queue.Queue()
list = []
while q.qsize() < days and q_n.qsize() < days_n * 2:
threads = []
for days_back in range(0, multi_threads):
day_mark = start_date - datetime.timedelta(days_back)
threads.append(threading.Thread(target=record_thread_result, args=(code, at.date_decoding(day_mark), q, q_n)))
for t in threads:
t.start()
for n in threads:
n.join()
start_date -= datetime.timedelta(multi_threads)
while not q.empty():
list.append(q.get())
list = at.sort_list_by_date(list)
list = list[-days:]
return list
def get_index(date, idx, type, duration=0):
'''
Return the index of specific type in a specific day
:param date: str, date
:param idx: str, stock index
:param type: str, type
:return: the index
'''
dict = {'sh': '000001', \
'sz': '399001'}
end = at.date_decoding(at.date_encoding(date) + datetime.timedelta(duration))
matrix = ts.get_k_data(dict[idx], index=True, start=date, end=end)
content = []
for i in range(len(matrix)):
line = {}
line['date'] = matrix.iloc[i]['date']
line['index'] = matrix.iloc[i][type]
content.append(line)
if len(content) == 1:
index = content[0]['index']
content = index
return content
def shift_one_day(date):
result = at.date_decoding(
at.date_encoding(date) + datetime.timedelta(shifter))
return result
def plot_difference_multi_smoothing(self,
code,
smooth,
date='',
duration=default_range,
leading_smooth=leading_smooth,
threads=threads_of_catch,
period_length=0,
type='show',
height=graph_height):
'''
Plot the difference with multiple smoothing method
:param code: str, stock index
:param date: str, date
:param duration: int, the analysis duration
:param smooth: list, the smooth method
:param leading_smooth: int, the leading smooth period
:param threads: int, the number of threads used
:param type: show or save the figure
:return: None
'''
if period_length == 0:
period_length = duration
periods_list = at.split_period(date, duration, period_length)
for i in periods_list:
date = i['start_date']
duration = i['days']
#(date, code, open, close, actual, theoretical, diff, smoothed)
list = self.__diff_lists_multi_smoothing__(code, smooth, date,
duration,
leading_smooth, threads)
if list != None:
x_date = [date2num(at.date_encoding(i['date'])) for i in list]
max_volume = max([i['volume'] for i in list])
y_price = [(date2num(at.date_encoding(i['date'])), i['open'],
i['close'], i['high'], i['low']) for i in list]
y_deviation_list = {}
for idv in smooth:
y_deviation_list['smoothed difference %s' % idv] = [
i['smoothed difference %s' % idv] for i in list
]
# y_deviation = [i['smoothed difference'] for i in list]
# y_theo = [i['smoothed theoretical'] for i in list]
# y_theo = [i for i in y_theo]
y_volume = [
(date2num(at.date_encoding(i['date'])), i['open'],
i['high'], i['low'], i['close'], i['volume'] / max_volume)
for i in list
]
support_line = []
for i in range(len(figure_deviation_line)):
support_line.append(
[figure_deviation_line[i] for k in list])
# fig, price = plt.subplots()
fig = plt.figure()
sub_plots = gridspec.GridSpec(2, 1, height_ratios=[5, 1])
sub_plots.update(wspace=0.001, hspace=0.001)
price = plt.subplot(sub_plots[0])
price.xaxis_date()
mondays = WeekdayLocator(MONDAY)
alldays = DayLocator()
weekFormatter = DateFormatter('%b %d')
price.xaxis.set_major_locator(mondays)
price.xaxis.set_minor_locator(alldays)
price.xaxis.set_major_formatter(weekFormatter)
price.xaxis_date()
deviation = price.twinx()
volume = plt.subplot(sub_plots[1], sharex=price)
mpf.candlestick_ochl(price,
y_price,
width=0.8,
colorup='r',
colordown='g',
alpha=0.8)
def pick_clor(color_list, idx):
length = len(color_list)
try:
if idx == 0:
return color_list[0]
else:
return color_list[idx % length]
except:
return color_list[0]
color_list = ['g', 'y', 'b', 'r']
for idvc in range(len(smooth)):
deviation.plot(
x_date, y_deviation_list['smoothed difference %s' %
smooth[idvc]],
pick_clor(color_list, idvc))
for j in support_line:
deviation.plot(x_date, j, 'y:')
price.set_ylabel('price')
price.get_xaxis().set_visible(False)
deviation.set_ylabel('deviation')
mpf.volume_overlay3(volume,
y_volume,
width=10,
colorup='r',
colordown='g',
alpha=0.8)
volume.get_yaxis().set_visible(False)
smooth_tag = '-'.join([str(i) for i in smooth])
volume.set_xlabel("%s %s %s" %
(code, list[-1]['date'], smooth_tag))
volume.set_ylim(0, 1)
if type == "show":
plt.show()
if type == "save":
try:
os.mkdir(os.path.join(os.getcwd(), 'graph'))
except:
pass
path = os.path.join(
os.getcwd(), 'graph/%s-%s-%i-%s.png' %
(code, list[-1]['date'], duration, smooth_tag))
fig.set_size_inches(
math.sqrt(int(duration)) * height / 3, height)
plt.savefig(path)
else:
print(the_warning)
import messenger as ms
import analyst as al
import numpy as np
import assistant as at
import matplotlib.pyplot as plt
h = al.PriceDeviation()
h.price_diff_list_save('002415', date='2017-06-28', duration=600, smooth=3)
origin_data = h.price_diff_list_load('002415',
date='2017-06-28',
duration=600,
smooth=3)
origin_data = origin_data[-200:]
result = []
date_list = []
for i in range(30, len(origin_data)):
date = origin_data[i]['date']
sub_list = origin_data[i - 30:i]
diff_list = [i['smoothed difference'] for i in sub_list]
theo_list = [i['smoothed theoretical'] for i in sub_list]
corr = np.correlate(diff_list, theo_list)
print(date, corr)
result.append(corr)
date_list.append(at.date_encoding(date))
plt.plot(date_list, result)
plt.show()