def main():
pro = ts.pro_api()
trade_dates = pro.daily(ts_code='000001.SZ').trade_date
LAST_MARKET_DATE = trade_dates[0]
stock_basic = pro.stock_basic(list_status='L', fields='ts_code,symbol,name,industry,fullname')
data_frame = DataFrame()
for key in ['ts_code', 'name', 'industry']:
data_frame[key] = stock_basic[key]
for key in [COL_CONTINUOUSLY_UP, COL_AVERAGE_TURNOVER, COL_LAST_5DAY_WEIGHT_P_CHANGE, COL_LAST_DAY_P_CHANGE]:
data_frame[key] = np.nan
for i, ts_code in enumerate(data_frame.ts_code):
try:
weekly = pro.weekly(ts_code=ts_code, #trade_date=LAST_MARKET_DATE
start_date=trade_dates[sampling_count], end_date=LAST_MARKET_DATE)
continuous_up = api.weekly_continuously_weight_up_count(weekly=weekly)
data_frame.loc[i, COL_CONTINUOUSLY_UP] = continuous_up
daily_basic = pro.daily_basic(ts_code=ts_code, # trade_date=LAST_MARKET_DATE
start_date=trade_dates[10], end_date=LAST_MARKET_DATE)
data_frame.loc[i, COL_AVERAGE_TURNOVER] = api.daily_basic_average_turnover_rate(daily_basic=daily_basic, begin=0, end=2)
daily = pro.daily(ts_code=ts_code, start_date=trade_dates[sampling_count], end_date=LAST_MARKET_DATE)
weight_0 = api.daily_period_weight(daily=daily, begin=0, end=period)
weight_1 = api.daily_period_weight(daily=daily, begin=period, end=2 * period)
data_frame.loc[i, COL_LAST_5DAY_WEIGHT_P_CHANGE] = round(((weight_0 / weight_1) - 1) * 100, 2)
data_frame.loc[i, COL_LAST_DAY_P_CHANGE] = api.daily_accumulate_p_change(daily=daily, begin=0, end=1)
except Exception as e:
print('excetion in {}'.format(i))
continue
print('##### {i} #####'.format(i=i))
time.sleep(0.1)
data_frame = data_frame[
# (data_frame['circ_mv'] < 1000000)
# & (data_frame[COL_CONTINUOUSLY_UP] > 1)
(data_frame[COL_LAST_5DAY_WEIGHT_P_CHANGE] > 10)
| (data_frame[COL_LAST_DAY_P_CHANGE] > 9.8)
]
sorted_frame = data_frame.sort_values(by=COL_LAST_5DAY_WEIGHT_P_CHANGE, ascending=False)
file_name = '../logs/{date}@WeeklyTrend.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def main():
pro = ts.pro_api()
trade_dates = pro.daily(ts_code='000001.SZ').trade_date
LAST_MARKET_DATE = trade_dates[0]
stock_basic = pro.stock_basic(
list_status='L', fields='ts_code,symbol,name,industry,fullname')
data_frame = DataFrame()
for key in ['ts_code', 'name', 'industry']:
data_frame[key] = stock_basic[key]
for key in [
COL_CONTINUOUS_FALL, COL_AVERAGE_P_CHANGE, COL_ACCUMULATE_P_CHANGE,
'circ_mv'
]:
data_frame[key] = np.nan
for i, ts_code in enumerate(data_frame.ts_code):
try:
daily_basic = pro.daily_basic(
ts_code=ts_code, #trade_date=LAST_MARKET_DATE
start_date=trade_dates[sampling_count],
end_date=LAST_MARKET_DATE)
for key in [
'circ_mv',
]:
data_frame.loc[i, key] = daily_basic.loc[0, key]
daily = pro.daily(ts_code=ts_code,
start_date=trade_dates[sampling_count],
end_date=LAST_MARKET_DATE)
continuous_fall = api.daily_converge_continuously_weight_fall_count(
daily=daily)
# continuous_up = api.daily_continuously_low_up_count(daily=daily)
data_frame.loc[i, COL_CONTINUOUS_FALL] = continuous_fall
data_frame.loc[i,
COL_AVERAGE_P_CHANGE] = api.daily_average_p_change(
daily=daily, begin=0, end=1 + continuous_fall)
data_frame.loc[
i, COL_ACCUMULATE_P_CHANGE] = api.daily_accumulate_p_change(
daily=daily, begin=0, end=1 + continuous_fall)
except Exception as e:
print('excetion in {}'.format(i))
continue
print('##### {i} #####'.format(i=i))
time.sleep(0.1)
data_frame = data_frame[
(data_frame[COL_CONTINUOUS_FALL] > 0)
# & (data_frame[COL_AVERAGE_TURNOVER] < 5)
# & (data_frame[COL_ACCUMULATE_P_CHANGE] > 5)
# & (data_frame[COL_PRE_ACCUMULATE_P_CHANGE] < data_frame[COL_ACCUMULATE_P_CHANGE])
]
sorted_frame = data_frame.sort_values(by=COL_CONTINUOUS_FALL,
ascending=False)
file_name = '../logs/{date}@FallConverge.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
def main():
pro = ts.pro_api()
trade_dates = pro.daily(ts_code='000001.SZ').trade_date
LAST_MARKET_DATE = trade_dates[0]
stock_basic = pro.stock_basic(
list_status='L', fields='ts_code,symbol,name,industry,fullname')
data_frame = DataFrame()
for key in ['ts_code', 'name', 'industry']:
data_frame[key] = stock_basic[key]
for key in [
COL_CONTINUOUSLY_UP, COL_AVERAGE_P_CHANGE, COL_ACCUMULATE_P_CHANGE,
COL_PRE_ACCUMULATE_P_CHANGE, COL_LAST_MA20_GAP, 'circ_mv'
]:
data_frame[key] = np.nan
for i, ts_code in enumerate(data_frame.ts_code):
# if ts_code.startswith('300'):
# continue
try:
daily_basic = pro.daily_basic(
ts_code=ts_code, #trade_date=LAST_MARKET_DATE
start_date=trade_dates[sampling_count],
end_date=LAST_MARKET_DATE)
for key in [
'circ_mv',
]:
data_frame.loc[i, key] = daily_basic.loc[0, key]
daily = pro.daily(ts_code=ts_code,
start_date=trade_dates[sampling_count],
end_date=LAST_MARKET_DATE)
continuous_up = api.daily_continuously_low_up_count(daily=daily)
data_frame.loc[i, COL_CONTINUOUSLY_UP] = continuous_up
# data_frame.loc[i, COL_AVERAGE_TURNOVER] = api.daily_basic_average_turnover_rate(daily_basic=daily_basic,
# begin=0, end=continuous_up)
data_frame.loc[i,
COL_AVERAGE_P_CHANGE] = api.daily_average_p_change(
daily=daily, begin=0, end=continuous_up)
data_frame.loc[
i, COL_ACCUMULATE_P_CHANGE] = api.daily_accumulate_p_change(
daily=daily, begin=0, end=continuous_up)
data_frame.loc[
i,
COL_PRE_ACCUMULATE_P_CHANGE] = api.daily_accumulate_p_change(
daily=daily, begin=continuous_up, end=continuous_up * 2)
data_frame.loc[i, COL_LAST_MA20_GAP] = round(
daily.close[0] -
api.daily_ma(daily=daily, begin=0, end=1, step=20)[0], 2)
except Exception as e:
print('excetion in {}'.format(i))
continue
print('##### {i} #####'.format(i=i))
time.sleep(0.1)
data_frame = data_frame[(data_frame['circ_mv'] < 1000000)
& (data_frame[COL_CONTINUOUSLY_UP] > 1)
& (data_frame[COL_AVERAGE_P_CHANGE] > 5)
# & (data_frame[COL_ACCUMULATE_P_CHANGE] > 5)
& (data_frame[COL_PRE_ACCUMULATE_P_CHANGE] <
data_frame[COL_ACCUMULATE_P_CHANGE])
& (data_frame[COL_LAST_MA20_GAP] > 0)]
# industrys = dict()
# for industry in data_frame.industry:
# if industry in industrys:
# industrys[industry] = industrys[industry] + 1
# else:
# industrys[industry] = 1
#
# industry_frame = DataFrame()
# for i, industry in enumerate(industrys):
# industry_frame.loc[i, 'industry'] = industry
# industry_frame.loc[i, 'count'] = industrys[industry]
sorted_frame = data_frame.sort_values(by=COL_CONTINUOUSLY_UP,
ascending=False)
file_name = '../logs/{date}@ContinuouslyUp.csv'.format(
date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)