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()
ma_5 = api.daily_close_ma(daily=daily, step=5)
ma_10 = api.daily_close_ma(daily=daily, step=10)
ma_20 = api.daily_close_ma(daily=daily, step=20)
data_frame.loc[i, COL_MA_5] = ma_5[0]
data_frame.loc[i, COL_MA_10] = ma_10[0]
data_frame.loc[i, COL_MA_20] = ma_20[0]
data_frame.loc[i, COL_MA_5_SLOPE] = round((ma_5[0] / ma_5[1] - 1) * 100, 2)
data_frame.loc[i, COL_MA_10_SLOPE] = round((ma_10[0] / ma_10[1] - 1) * 100, 2)
data_frame.loc[i, COL_MA_20_SLOPE] = round((ma_20[0] / ma_20[1] - 1) * 100, 2)
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
daily_basic = main_session.query(models.DailyBasicPro).filter(models.DailyBasicPro.ts_code == stock_basic.ts_code).first()
if daily_basic:
data_frame.loc[i, 'circ_mv'] = '{}亿'.format(round(daily_basic.circ_mv / 10000, 2))
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_MA_5] > data_frame[COL_MA_10])
& (data_frame[COL_MA_10] > data_frame[COL_MA_20])
& (data_frame[COL_MA_5_SLOPE] > data_frame[COL_MA_10_SLOPE])
& (data_frame[COL_MA_10_SLOPE] > data_frame[COL_MA_20_SLOPE])
& (data_frame[COL_MA_20_SLOPE] > 0)
]
# data_frame = data_frame.sort_values(by=COL_MAXGAP, ascending=False).reset_index(drop=True)
# data_frame = data_frame.iloc[:200]
data_frame = data_frame.sort_values(by=COL_MA_5_SLOPE, ascending=False).reset_index(drop=True)
data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_LASTPRICE, 'concept', 'circ_mv']]
file_name = '../../logs/{date}@MA_diffuse.csv'.format(date=LAST_MARKET_DATE)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
data_frame.to_csv(file)
def main(ma_a=10, ma_b=20):
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[0].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_MA_A] = api.daily_close_ma(daily=daily, step=ma_a)[0]
data_frame.loc[i, COL_MA_B] = api.daily_close_ma(daily=daily, step=ma_b)[0]
data_frame.loc[i, COL_MA_GAP] = round((data_frame.loc[i, COL_MA_A] / data_frame.loc[i, COL_MA_B] - 1) * 100, 2)
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_MA_GAP] > 0)
]
# data_frame = data_frame.sort_values(by=COL_MAXGAP, ascending=False).reset_index(drop=True)
# data_frame = data_frame.iloc[:200]
data_frame = data_frame.sort_values(by=COL_MA_GAP, ascending=True).reset_index(drop=True)
file_name = '../../logs/{date}@Ma_dive_ma{ma_a}_ma{ma_b}.csv'.format(date=LAST_MARKET_DATE, ma_a=ma_a, ma_b=ma_b)
# 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)
weekly = main_session.query(models.WeeklyPro).filter(models.WeeklyPro.ts_code == stock_basic.ts_code,
models.WeeklyPro.trade_date <= LAST_MARKET_DATE).order_by(
models.WeeklyPro.trade_date.desc()).limit(sampling_count).all()
ma_20 = api.daily_close_ma(daily=weekly, step=20)
ma_20_diff_1 = api.differ(ma_20)
ma_20_diff_2 = api.differ(ma_20_diff_1)
data_frame.loc[i, COL_MA_20] = ma_20[0]
data_frame.loc[i, COL_MA_20_SLOPE] = ma_20_diff_1[0]
data_frame.loc[i, COL_MA_20_SLOPE_CHANGE] = ma_20_diff_2[0]
data_frame.loc[i, COL_WEEKLY_BREAK] = api.weekly_break(weekly[:10])
# 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_CRIMSON_RATE] = round(api.crimson_rate(daily[:15]), 2)
# data_frame.loc[i, COL_PCT_CHG_STD] = round(api.pct_chg_std(daily[:15]), 2)
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('##### tidal {i} #####'.format(i=i))
data_frame = data_frame[
(data_frame[COL_WEEKLY_BREAK] == True)
]
data_frame = data_frame[
(data_frame[COL_MA_20_SLOPE] > 3)
| (data_frame[COL_MA_20_SLOPE_CHANGE] > 100)
# | (data_frame[COL_CRIMSON_RATE] > 0.7)
]
data_frame = data_frame.sort_values(by=COL_MA_20_SLOPE_CHANGE, ascending=False).reset_index(drop=True)
data_frame = data_frame.loc[:, ['ts_code', 'name', 'industry', COL_MA_20, COL_MA_20_SLOPE, COL_MA_20_SLOPE_CHANGE, COL_FLOAT_HOLDERS]]
file_name = '{logs_path}/{date}@Tidal.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)
# -*- coding: utf-8 -*-
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_PRICE] = daily[0].close
data_frame.loc[i, COL_LIMIT_COUNT_A] = local_limit_count(
daily[2:], local_scale=10)
data_frame.loc[i, COL_LIMIT_COUNT_B] = local_limit_count(
daily[2:], local_scale=1)
# data_frame.loc[i, COL_CONTINUOUS_LIMIT_COUNT] = continuous_limit(daily, local_scale=10)
data_frame.loc[i, COL_HISTORY_BREAK_INDEX] = daily_break_index(
daily[2:], local_scale=30)
ma_20 = api.daily_close_ma(daily=daily[2:], step=20)
data_frame.loc[i, COL_MA_20_SLOPE] = round(
(ma_20[0] / ma_20[1] - 1) * 100, 2)
if data_frame.loc[i, COL_LIMIT_COUNT_A] == 1 and data_frame.loc[
i, COL_LIMIT_COUNT_B] == 1:
data_frame.loc[i, COL_PASS_1] = True
else:
data_frame.loc[i, COL_PASS_1] = False
if (daily[1].close > daily[1].pre_close) and (
daily[1].close < round(daily[1].pre_close * 1.1, 2)) and (
daily[1].close > daily[1].open):
data_frame.loc[i, COL_PASS_2] = True
else:
data_frame.loc[i, COL_PASS_2] = False
data_frame.loc[i, COL_LAST_CHG] = daily[0].pct_chg
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('##### analyze_n {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_PASS_1] == True)
& (data_frame[COL_PASS_2] == True)
& (data_frame[COL_MA_20_SLOPE] > 0)
& (data_frame[COL_HISTORY_BREAK_INDEX] == 0)]
data_frame = data_frame.sort_values(by=COL_LAST_CHG,
ascending=False).reset_index(drop=True)
# data_frame = data_frame.head(100)
file_name = '{logs_path}/{date}@Analyze_N.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 = 100
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,
offset=i)
sub += 1
for i in range(0, len(data_frame)):
print('\'{}\','.format(data_frame.loc[i, 'symbol']))
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)
weekly = main_session.query(models.WeeklyPro).filter(
models.WeeklyPro.ts_code == stock_basic.ts_code,
models.WeeklyPro.trade_date <= LAST_MARKET_DATE).order_by(
models.WeeklyPro.trade_date.desc()).limit(
sampling_count).all()
ma_20 = api.daily_close_ma(daily=weekly, step=20)
ma_20_diff_1 = api.differ(ma_20)
data_frame.loc[i, COL_MA_20] = ma_20[0]
data_frame.loc[i, COL_MA_20_SLOPE] = round(ma_20_diff_1[0], 2)
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_DAILY_VIBRATION] = round(api.avg_vibration_chg(daily[:20]), 2)
data_frame.loc[i, COL_LASTPRICE] = daily[0].close
data_frame.loc[i, COL_DAILY_AGGRESSIVE_ACCUMULATION] = round(
api.aggressive_chg_accumulation(daily[:5]), 2)
# data_frame.loc[i, COL_DAILY_NEGATIVE_ACCUMULATION] = round(api.negative_chg_accumulation(daily[:5]), 2)
# if data_frame.loc[i, COL_DAILY_NEGATIVE_ACCUMULATION] < 0:
# data_frame.loc[i, COL_DAILY_AGGRESSIVE_RATE] = round(- data_frame.loc[i, COL_DAILY_AGGRESSIVE_ACCUMULATION] / data_frame.loc[i, COL_DAILY_NEGATIVE_ACCUMULATION], 2)
# else:
# data_frame.loc[i, COL_DAILY_AGGRESSIVE_RATE] = round(data_frame.loc[i, COL_DAILY_AGGRESSIVE_ACCUMULATION], 2)
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('excetion in index:{index} {code} {name}'.format(
index=i, code=stock_basic.ts_code, name=stock_basic.name))
continue
print('##### weekly diffuse {i} #####'.format(i=i))
data_frame = data_frame[(data_frame[COL_MA_20_SLOPE] > 2)
# & (data_frame[COL_DAILY_VIBRATION] > 5)
& (data_frame[COL_DAILY_AGGRESSIVE_ACCUMULATION] >
0)]
# data_frame = data_frame.sort_values(by=COL_MAXGAP, ascending=False).reset_index(drop=True)
# data_frame = data_frame.iloc[:200]
data_frame = data_frame.sort_values(by=COL_DAILY_AGGRESSIVE_ACCUMULATION,
ascending=False).reset_index(drop=True)
data_frame = data_frame.loc[:, [
'ts_code', 'name', 'industry', COL_LASTPRICE, COL_MA_20_SLOPE,
COL_DAILY_AGGRESSIVE_ACCUMULATION, COL_FLOAT_HOLDERS
]]
file_name = '{logs_path}/{date}@Weekly_diffuse.csv'.format(
date=LAST_MARKET_DATE, logs_path=env.logs_path)
# print(fileName)
with open(file_name, 'w', encoding='utf8') as file:
data_frame.to_csv(file)