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] = str(getattr(stock_basic, key))
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_LOCAL_MAX] = api.daily_local_max(
daily, local_scale=60)
data_frame.loc[i, COL_LOCAL_LIMIT_COUNT_1] = api.local_limit_count(
daily, local_scale=1)
data_frame.loc[i,
COL_LOCAL_LIMIT_COUNT_10] = api.local_limit_count(
daily, local_scale=10)
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
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('##### live oneplus {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_LOCAL_LIMIT_COUNT_1] ==
data_frame[COL_LOCAL_LIMIT_COUNT_10])]
data_frame = data_frame.sort_values(by=COL_DAILY_LOCAL_MAX,
ascending=True).reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{data_path}/live_oneplus.csv'.format(date=LAST_MARKET_DATE,
data_path=env.data_path)
with open(file_name, 'w', encoding='utf8') as file:
data_frame.to_csv(file)
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)
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_RECENT_LIMIT_COUNT] = api.local_limit_count(
daily, local_scale=10)
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('##### ergodic_graph {i} #####'.format(i=i))
data_frame = data_frame[
# (data_frame[COL_RECENT_LIMIT_COUNT_15] > 1)
# | ((data_frame[COL_RECENT_LIMIT_COUNT_15] == 1) & (data_frame[COL_RECENT_LIMIT_COUNT_3] > 0))
(data_frame[COL_RECENT_LIMIT_COUNT] > 0)]
data_frame = data_frame.sort_values(by=COL_RECENT_LIMIT_COUNT,
ascending=False).reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{logs_path}/{date}@Ergodic_Graph.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 = 200
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
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
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:
if 'ST' in stock_basic.name or stock_basic.symbol.startswith(
'300'):
continue
for key in models.StockBasicPro.keys:
data_frame.loc[i, key] = getattr(stock_basic, key)
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_RECENT_LIMIT_COUNT_15] = api.local_limit_count(daily, local_scale=15)
data_frame.loc[i,
COL_RECENT_LIMIT_COUNT_3] = api.local_limit_count(
daily, local_scale=3)
data_frame.loc[
i,
COL_CONTINUOUS_LIMIT_COUNT] = api.daily_continuous_limit_count(
daily)
limit_score = 100 if (
daily[0].pct_chg > 9.8
and data_frame.loc[i, COL_CONTINUOUS_LIMIT_COUNT] > 1) else 0
data_frame.loc[i, COL_RANK_SCORE] = limit_score + data_frame.loc[
i, COL_CONTINUOUS_LIMIT_COUNT]
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('##### limit_rank {i} #####'.format(i=i))
data_frame = data_frame[
# (data_frame[COL_RECENT_LIMIT_COUNT_15] > 1)
# | ((data_frame[COL_RECENT_LIMIT_COUNT_15] == 1) & (data_frame[COL_RECENT_LIMIT_COUNT_3] > 0))
(data_frame[COL_RECENT_LIMIT_COUNT_3] > 0)]
data_frame = data_frame.sort_values(by=COL_RANK_SCORE,
ascending=False).reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{logs_path}/{date}@Limit_Rank.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)