def main(offset=0):
daily001 = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == '000001.SZ').order_by(
models.DailyPro.trade_date.desc()).all()
LAST_MARKET_DATE = daily001[offset].trade_date
data_frame = DataFrame()
for i, stock_basic in enumerate(
main_session.query(models.StockBasicPro).all()):
try:
for key in models.StockBasicPro.keys:
data_frame.loc[i, key] = getattr(stock_basic, key)
data_frame.loc[i, COL_IS_MEDICAL] = api.is_medical(
stock_basic.industry)
daily = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == stock_basic.ts_code,
models.DailyPro.trade_date <= LAST_MARKET_DATE).order_by(
models.DailyPro.trade_date.desc()).limit(
sampling_count).all()
data_frame.loc[i, COL_LASTPRICE] = daily[0].close
data_frame.loc[i, COL_DAILY_PIT] = api.daily_pit(daily,
local_scale=30)
holders = main_session.query(models.FloatHolderPro).filter(
models.FloatHolderPro.ts_code == stock_basic.ts_code).all()
h_list = []
for item in holders:
h_list.append(item.holder_name)
data_frame.loc[i, COL_FLOAT_HOLDERS] = '\n'.join(h_list)
except Exception as e:
print('exception in index:{index} {code} {name}'.format(
index=i, code=stock_basic.ts_code, name=stock_basic.name))
continue
print('##### medical pit {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_DAILY_PIT] == True)
& (data_frame[COL_IS_MEDICAL] == True)]
data_frame = data_frame.sort_values(by=COL_LASTPRICE,
ascending=True).reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{logs_path}/{date}@Medical_Pit.csv'.format(
date=LAST_MARKET_DATE, logs_path=env.logs_path)
with open(file_name, 'w', encoding='utf8') as file:
data_frame.to_csv(file)
shutil.rmtree('../../buffer/medical_pit/medical_pit')
os.mkdir('../../buffer/medical_pit/medical_pit')
for i in range(len(data_frame)):
ts_code = data_frame.loc[i, 'ts_code']
name = data_frame.loc[i, 'name']
plot(ts_code=ts_code,
name=name,
last_date=LAST_MARKET_DATE,
doc='medical_pit',
file_prefix=i)
def main(offset=0):
daily001 = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == '000001.SZ').order_by(
models.DailyPro.trade_date.desc()).all()
LAST_MARKET_DATE = daily001[offset].trade_date
data_frame = DataFrame()
for i, stock_basic in enumerate(
main_session.query(models.StockBasicPro).all()):
try:
for key in models.StockBasicPro.keys:
data_frame.loc[i, key] = getattr(stock_basic, key)
data_frame.loc[i, COL_IS_MEDICAL] = api.is_medical(
stock_basic.industry)
daily = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == stock_basic.ts_code,
models.DailyPro.trade_date <= LAST_MARKET_DATE).order_by(
models.DailyPro.trade_date.desc()).limit(
sampling_count).all()
data_frame.loc[i, COL_LASTPRICE] = daily[0].close
data_frame.loc[i,
COL_DAILY_BREAK] = api.daily_break(daily,
local_scale=30)
data_frame.loc[i, COL_RECENT_AGGRESSIVE] = api.recent_limit(daily)
daily_local_min = api.daily_local_min(sequence=daily,
local_scale=30)
data_frame.loc[i, COL_UP_RANGE] = round(
(daily[0].close / daily_local_min) - 1, 2)
holders = main_session.query(models.FloatHolderPro).filter(
models.FloatHolderPro.ts_code == stock_basic.ts_code).all()
h_set = set()
for item in holders:
h_set.add(item.holder_name)
data_frame.loc[i, COL_FLOAT_HOLDERS] = '\n'.join(h_set)
data_frame.loc[i, COL_HOLDERS_COUNT] = len(h_set)
except Exception as e:
print('exception in index:{index} {code} {name}'.format(
index=i, code=stock_basic.ts_code, name=stock_basic.name))
continue
print('##### medical aggressive {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_DAILY_BREAK] == True)
# & (data_frame[COL_RECENT_AGGRESSIVE] == True)
& (data_frame[COL_IS_MEDICAL] == True)]
data_frame = data_frame.sort_values(by=COL_UP_RANGE,
ascending=True).reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{logs_path}/{date}@Medical_Aggressive.csv'.format(
date=LAST_MARKET_DATE, logs_path=env.logs_path)
with open(file_name, 'w', encoding='utf8') as file:
data_frame.to_csv(file)
plot_candle_gather(data_frame=data_frame, last_date=LAST_MARKET_DATE)
def main(offset=0):
daily001 = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == '000001.SZ').order_by(
models.DailyPro.trade_date.desc()).all()
LAST_MARKET_DATE = daily001[offset].trade_date
data_frame = DataFrame()
for i, stock_basic in enumerate(
main_session.query(models.StockBasicPro).all()):
try:
for key in models.StockBasicPro.keys:
data_frame.loc[i, key] = getattr(stock_basic, key)
data_frame.loc[i, COL_IS_MEDICAL] = api.is_medical(
stock_basic.industry)
daily = main_session.query(models.DailyPro).filter(
models.DailyPro.ts_code == stock_basic.ts_code,
models.DailyPro.trade_date <= LAST_MARKET_DATE).order_by(
models.DailyPro.trade_date.desc()).limit(
sampling_count).all()
data_frame.loc[i, COL_LASTPRICE] = daily[0].close
data_frame.loc[i, COL_DAILY_BREAK_INDEX] = api.daily_break_index(
daily, local_scale=60)
data_frame.loc[i, COL_LOCAL_LIMIT_COUNT] = api.local_limit_count(
daily, local_scale=3)
daily_basic = main_session.query(models.DailyBasic).filter(
models.DailyBasic.ts_code == stock_basic.ts_code).one()
data_frame.loc[i, COL_CIRC_MV] = daily_basic.circ_mv
holders = main_session.query(models.FloatHolderPro).filter(
models.FloatHolderPro.ts_code == stock_basic.ts_code).all()
h_set = set()
for item in holders:
h_set.add(item.holder_name)
data_frame.loc[i, COL_FLOAT_HOLDERS] = '\n'.join(h_set)
data_frame.loc[i, COL_HOLDERS_COUNT] = len(h_set)
except Exception as e:
print('exception in index:{index} {code} {name}'.format(
index=i, code=stock_basic.ts_code, name=stock_basic.name))
continue
print('##### medical_ambush {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_LOCAL_LIMIT_COUNT] > 0)
& (data_frame[COL_DAILY_BREAK_INDEX] < 5)
& (data_frame[COL_IS_MEDICAL] == True)]
data_frame = data_frame.sort_values(by=COL_LASTPRICE,
ascending=True).reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{logs_path}/{date}@Medical_Ambush.csv'.format(
date=LAST_MARKET_DATE, logs_path=env.logs_path)
with open(file_name, 'w', encoding='utf8') as file:
data_frame.to_csv(file)
batch_size = 500
sub = 0
for i in range(0, len(data_frame), batch_size):
sub_df = data_frame.iloc[i:i + batch_size, :]
sub_df = sub_df.reset_index(drop=True)
plot_candle_gather(data_frame=sub_df,
last_date=LAST_MARKET_DATE,
sub=sub)
sub += 1