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:
if 'ST' in stock_basic.name \
or stock_basic.symbol.startswith('688'):
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_VOL] = daily[0].vol
aggressive_rate = get_aggressive_rate(stock_basic.symbol)
limit_rate = get_limit_rate(stock_basic.symbol)
data_frame.loc[
i,
COL_ADJACENT_AGGRESSIVE_INDEX] = get_adjacent_aggressive_index(
daily,
local_scale=5,
aggressive_rate=aggressive_rate,
limit_rate=limit_rate)
# data_frame.loc[i, COL_DAILY_LOCAL_MAX] = api.daily_local_max(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('##### relimit ones {i} #####'.format(i=i))
data_frame = data_frame[
(data_frame[COL_ADJACENT_AGGRESSIVE_INDEX] > 0)
# & (data_frame[COL_DAILY_LOCAL_MAX] < data_frame[COL_LASTPRICE] * 1.1)
]
data_frame = data_frame.reset_index(drop=True)
# data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, COL_FLOAT_HOLDERS]]
file_name = '{data_path}/relimit_ones.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)
file_name = '{logs_path}/{date}@Relimit_Ones.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)
def main(offset=0):
limit_up = 0
limit_down = 0
positive = 0
negative = 0
more_than_5 = 0
less_than_minus_5 = 0
total = 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:
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).first()
if daily:
if daily.pct_chg > 9.9:
limit_up = limit_up + 1
if daily.pct_chg < -9.9:
limit_down = limit_down + 1
if daily.pct_chg >= 0:
positive = positive + 1
if daily.pct_chg < 0:
negative = negative + 1
if daily.pct_chg > 5:
more_than_5 = more_than_5 + 1
if daily.pct_chg < -5:
less_than_minus_5 = less_than_minus_5 + 1
total = total + 1
except Exception as e:
print('excetion in index:{index} {code} {name}'.format(
index=i, code=stock_basic.ts_code, name=stock_basic.name))
continue
print('##### {i} #####'.format(i=i))
data_frame.loc[0, '涨停'] = limit_up
data_frame.loc[0, '跌停'] = limit_down
data_frame.loc[0, '涨'] = round(positive / total, 2)
data_frame.loc[0, '跌'] = round(negative / total, 2)
data_frame.loc[0, '> 5%'] = round(more_than_5 / total, 2)
data_frame.loc[0, '< -5%'] = round(less_than_minus_5 / total, 2)
file_name = '../../logs/{date}@Atmosphere.csv'.format(
date=LAST_MARKET_DATE)
# print(fileName)
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_WEIGHT_RISE_EFFICIENCY],
data_frame.loc[i, COL_WEIGHT_MIN_INDEX],
data_frame.loc[i, COL_WEIGHT_MAX_INDEX]
) = api.daily_weight_rise_efficiency(daily=daily, begin=0, end=10)
data_frame.loc[i, COL_LASTPRICE] = daily[0].close
cons = main_session.query(models.ConceptPro).join(
models.ConceptDetailPro,
models.ConceptPro.code == models.ConceptDetailPro.code).filter(
models.ConceptDetailPro.ts_code ==
stock_basic.ts_code).all()
concept_value = ''
for con in cons:
concept_value = concept_value + '{c}, '.format(c=con.name)
data_frame.loc[i, 'concept'] = concept_value
except Exception as e:
print('excetion in index:{index} {code} {name}'.format(
index=i, code=stock_basic.ts_code, name=stock_basic.name))
continue
print('##### {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_WEIGHT_RISE_EFFICIENCY] > 2)
& (data_frame[COL_WEIGHT_MAX_INDEX] < 2)]
sorted_frame = data_frame.sort_values(
by=COL_WEIGHT_RISE_EFFICIENCY, ascending=False).reset_index(drop=True)
file_name = '../../logs/{date}@RiseTempo.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
sorted_frame.to_csv(file)
return sorted_frame
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
outputs = []
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(
'688'):
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()
aggressive_rate = get_aggressive_rate(stock_basic.symbol)
res = parse(daily, aggressive_rate)
if res:
daily_basic = main_session.query(models.DailyBasic).filter(
models.DailyBasic.ts_code == stock_basic.ts_code).one()
outputs.append('{name} {symbol} {cir_mv}亿\n{res}'.format(
name=stock_basic.name,
symbol=stock_basic.ts_code,
cir_mv=int(daily_basic.circ_mv),
res=res))
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('##### continuous_2_limits {i} #####'.format(i=i))
file_name = '{logs_path}/{date}@9_continuous_2_limits.txt'.format(
date=LAST_MARKET_DATE, logs_path=env.logs_path)
with open(file_name, 'w', encoding='utf8') as file:
for item in outputs:
file.write(item)
file.write('\n\n')
