def _main():
basics = ts.get_stock_basics()
frame = DataFrame()
frame['name'] = basics['name']
frame[COL_P_CHANGE_01] = np.nan
frame[COL_P_CHANGE_12] = np.nan
frame[COL_PASTAVERAGETURNOVER] = np.nan
frame[COL_NORMALIZING_STD] = np.nan
frame[COL_NEXTDAYTOMA5] = np.nan
frame[COL_CLOSETOMA5MARK] = ''
frame[COL_STOPMARK] = ''
for i, code in enumerate(basics.index):
hist_data = ts.get_hist_data(code)
try:
frame.loc[code, COL_P_CHANGE_01] = api.hist_p_change(hist_data,
begin=DAY_0,
end=DAY_1)
frame.loc[code, COL_P_CHANGE_12] = api.hist_p_change(hist_data,
begin=DAY_1,
end=DAY_2)
frame.loc[code,
COL_PASTAVERAGETURNOVER] = api.past_average_turnover(
hist_data, PAST_AVERAGE_TURNOVER_PERIOD)
frame.loc[code, COL_NORMALIZING_STD] = api.normalizing_std_close(
hist_data, begin=DAY_0, end=DAY_1)
frame.loc[code, COL_NEXTDAYTOMA5] = api.next_close_to_ma(hist_data,
n=5)
if close_to_target(hist_data['close'][0],
frame.loc[code, COL_NEXTDAYTOMA5]):
frame.loc[code, COL_CLOSETOMA5MARK] = '★'
if hist_data.index[0] != LAST_MARKET_DATE:
frame.loc[code, COL_STOPMARK] = 'stop'
except Exception:
print('excetion in {}'.format(i))
continue
print('#####', i, '#####')
filtered_frame = frame[(frame[COL_P_CHANGE_01] > 0)
& (frame[COL_P_CHANGE_12] < 0)
# & (frame[COL_NORMALIZING_STD] < 0.03)
& (frame[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_12)
print(sorted_frame)
file_name = '../logs/{date}@tnt.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def _main():
basics = ts.get_stock_basics()
frame = DataFrame()
frame['name'] = basics['name']
frame[COL_P_CHANGE_RANGE0] = np.nan
frame[COL_AVERAGE_TURNOVER] = np.nan
frame[COL_STOPMARK] = ''
for i, code in enumerate(basics.index):
hist_data = ts.get_hist_data(code)
try:
frame.loc[code, COL_P_CHANGE_RANGE0] = api.hist_p_change(
hist_data, begin=DAY_RANGE0[0], end=DAY_RANGE0[1])
frame.loc[code, COL_AVERAGE_TURNOVER] = api.hist_average_turnover(
hist_data, begin=TURNOVER_RANGE[0], end=TURNOVER_RANGE[1])
if hist_data.index[0] != LAST_MARKET_DATE:
frame.loc[code, COL_STOPMARK] = 'stop'
except Exception:
print('excetion in {}'.format(i))
continue
print('#####', i, '#####')
filtered_frame = frame[(frame[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_AVERAGE_TURNOVER,
ascending=False)
print(sorted_frame)
file_name = '../logs/{date}@vaporization.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def _main():
basics = ts.get_stock_basics()
frame = DataFrame()
frame['name'] = basics['name']
frame[COL_OUTSTANDING] = basics[COL_OUTSTANDING]
frame[COL_MARKET_VALUE] = np.nan
frame[COL_P_CHANGE_RANGE0] = np.nan
frame[COL_P_CHANGE_RANGE1] = np.nan
frame[COL_NORMALIZING_STD1] = np.nan
frame[COL_STOPMARK] = ''
for i, code in enumerate(basics.index):
if code.startswith('300'):
continue
hist_data = ts.get_hist_data(code)
try:
frame.loc[code, COL_MARKET_VALUE] = round(frame.loc[code, COL_OUTSTANDING] * hist_data['close'][0], 1)
frame.loc[code, COL_P_CHANGE_RANGE0] = api.hist_p_change(hist_data, begin=DAY_RANGE0[0], end=DAY_RANGE0[1])
frame.loc[code, COL_P_CHANGE_RANGE1] = api.hist_p_change(hist_data, begin=DAY_RANGE1[0], end=DAY_RANGE1[1])
frame.loc[code, COL_NORMALIZING_STD1] = api.normalizing_std_close(hist_data, begin=DAY_RANGE1[0], end=DAY_RANGE1[1])
if hist_data.index[0] != LAST_MARKET_DATE:
frame.loc[code, COL_STOPMARK] = 'stop'
except Exception:
print('excetion in {}'.format(i))
continue
print('#####', i, '#####')
# observe
filtered_frame = frame[
# (frame[COL_P_CHANGE_RANGE0] < 0)
# & (frame[COL_P_CHANGE_RANGE1] > 0)
# & (frame[COL_NORMALIZING_STD] < 0.03)
(frame[COL_MARKET_VALUE] < 100)
& (frame[COL_STOPMARK] != 'stop')
]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_RANGE1, ascending=False)
print(sorted_frame)
file_name = '../logs/{date}@observer.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def _main():
basics = ts.get_stock_basics()
frame = DataFrame()
frame['name'] = basics['name']
frame['outstanding'] = basics['outstanding']
frame[COL_P_CHANGE_RANGE] = np.nan
frame[COL_STOPMARK] = ''
for i, code in enumerate(basics.index):
if code.startswith('300'):
continue
hist_data = ts.get_hist_data(code)
try:
frame.loc[code, COL_P_CHANGE_RANGE] = api.hist_p_change(
hist_data, begin=DAY_RANGE[0], end=DAY_RANGE[1])
if hist_data.index[0] != LAST_MARKET_DATE:
frame.loc[code, COL_STOPMARK] = 'stop'
except Exception:
print('excetion in {}'.format(i))
continue
print('#####', i, '#####')
# observe
filtered_frame = frame[(frame[COL_P_CHANGE_RANGE] > 9.8)
& (frame[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_RANGE,
ascending=False)
print(sorted_frame)
file_name = '../logs/{date}@ladder.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def _main():
basics = ts.get_stock_basics()
frames = list()
for i in range(0, N):
frame = DataFrame()
frame['name'] = basics['name']
frame['outstanding'] = basics['outstanding']
frame[COL_P_CHANGE_RANGE] = np.nan
frame[COL_NORMALIZING_STD] = np.nan
frame[COL_P_CHANGE_TILL_NOW] = np.nan
frame[COL_NORMALIZING_STD_TILL_NOW] = np.nan
frame[COL_STOPMARK] = ''
frames.append(frame)
for i, code in enumerate(basics.index):
if code.startswith('300'):
continue
hist_data = ts.get_hist_data(code)
try:
for j in range(0, N):
frame = frames[j]
frame.loc[code, COL_P_CHANGE_RANGE] = api.hist_p_change(
hist_data,
begin=PERIOD * j + DELTA_RANGE[0],
end=PERIOD * j + DELTA_RANGE[1])
frame.loc[code,
COL_NORMALIZING_STD] = api.normalizing_std_close(
hist_data,
begin=PERIOD * j + DELTA_RANGE[0],
end=PERIOD * j + DELTA_RANGE[1])
frame.loc[code, COL_P_CHANGE_TILL_NOW] = api.hist_p_change(
hist_data, begin=0, end=PERIOD * j + DELTA_RANGE[0])
frame.loc[
code,
COL_NORMALIZING_STD_TILL_NOW] = api.normalizing_std_close(
hist_data, begin=0, end=PERIOD * j + DELTA_RANGE[0])
if hist_data.index[0] != LAST_MARKET_DATE:
frame.loc[code, COL_STOPMARK] = 'stop'
except Exception:
print('excetion in {}'.format(i))
continue
print('#####', i, '#####')
for i in range(0, N):
frame = frames[i]
# observe
filtered_frame = frame[(frame[COL_P_CHANGE_RANGE] < -15)
& (frame['outstanding'] < 10)
& (frame[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_RANGE)
print(sorted_frame)
file_name = '../logs/{date}@segment_period_{i}.csv'.format(
date=LAST_MARKET_DATE, i=i)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def _main():
basics = ts.get_stock_basics()
frame = DataFrame()
frame['name'] = basics['name']
frame['outstanding'] = basics['outstanding']
frame[COL_P_CHANGE_RANGE0] = np.nan
frame[COL_P_CHANGE_RANGE1] = np.nan
frame[COL_NORMALIZING_STD1] = np.nan
frame[COL_STOPMARK] = ''
frame_bud = DataFrame()
frame_bud['name'] = basics['name']
frame_bud['outstanding'] = basics['outstanding']
frame_bud[COL_P_CHANGE_RANGE2] = np.nan
frame_bud[COL_NORMALIZING_STD2] = np.nan
frame_bud[COL_STOPMARK] = ''
for i, code in enumerate(basics.index):
if code.startswith('300'):
continue
hist_data = ts.get_hist_data(code)
try:
frame.loc[code, COL_P_CHANGE_RANGE0] = api.hist_p_change(
hist_data, begin=DAY_RANGE0[0], end=DAY_RANGE0[1])
frame.loc[code, COL_P_CHANGE_RANGE1] = api.hist_p_change(
hist_data, begin=DAY_RANGE1[0], end=DAY_RANGE1[1])
frame.loc[code, COL_NORMALIZING_STD1] = api.normalizing_std_close(
hist_data, begin=DAY_RANGE1[0], end=DAY_RANGE1[1])
frame_bud.loc[code, COL_P_CHANGE_RANGE2] = api.hist_p_change(
hist_data, begin=DAY_RANGE2[0], end=DAY_RANGE2[1])
frame_bud.loc[code,
COL_NORMALIZING_STD2] = api.normalizing_std_close(
hist_data,
begin=DAY_RANGE2[0],
end=DAY_RANGE2[1])
if hist_data.index[0] != LAST_MARKET_DATE:
frame.loc[code, COL_STOPMARK] = 'stop'
frame_bud.loc[code, COL_STOPMARK] = 'stop'
except Exception:
print('excetion in {}'.format(i))
continue
print('#####', i, '#####')
# observe
filtered_frame = frame[
# (frame[COL_P_CHANGE_RANGE0] < 0)
# & (frame[COL_P_CHANGE_RANGE1] > 0)
# & (frame[COL_NORMALIZING_STD] < 0.03)
(frame[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_RANGE1,
ascending=False)
print(sorted_frame)
file_name = '../logs/{date}@reborn_observe.csv'.format(
date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
# classified
filtered_frame = frame[ #(frame[COL_P_CHANGE_RANGE0] > 0)
(frame[COL_P_CHANGE_RANGE1] < -10)
# & (frame[COL_P_CHANGE_RANGE1] > -25)
# & (frame[COL_NORMALIZING_STD] < 0.15)
& (frame['outstanding'] < 10)
& (frame[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_RANGE1)
print(sorted_frame)
file_name = '../logs/{date}@reborn_classified.csv'.format(
date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
# bud
filtered_frame = frame_bud[ #(frame[COL_P_CHANGE_RANGE0] > 0)
(frame_bud[COL_P_CHANGE_RANGE2] < -15)
# & (frame[COL_P_CHANGE_RANGE1] > -25)
& (frame_bud[COL_NORMALIZING_STD2] < 0.15)
& (frame_bud['outstanding'] < 10)
& (frame_bud[COL_STOPMARK] != 'stop')]
sorted_frame = filtered_frame.sort_values(by=COL_P_CHANGE_RANGE2)
print(sorted_frame)
file_name = '../logs/{date}@reborn_bud.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)