def main():
from data.const import get_trade_calendar
from datetime import time
trade_cal = get_trade_calendar()
def transform_tick(tick):
ret = None
if tick in ['000001', '000300']:
ret = ''.join((tick, '_SH_IX'))
else:
ret = ''.join((tick, '_SZ_IX'))
return ret
today = str(datetime.now().date())
today_idx = trade_cal.index.searchsorted(today)
yesterday = str(trade_cal.index[today_idx - 1].date())
tomorrow = str(trade_cal.index[today_idx + 1].date())
from_date, to_date = None, None
if datetime.now().time() > time(15, 10, 0, 0): # 过了15：10
from_date, to_date = today, tomorrow
else:
from_date, to_date = yesterday, today
index_list = ['000001', '000300', '399001', '399005', '399006']
get_config_func = make_config(CONFIG_FILE)
ret = predict(ticks=index_list, look_back=519, index=True,
get_config_func=get_config_func)
ret = ret.sort_values(by='dt')
print(ret)
engines = [engine_company_local,
engine_company_outer]
for engine in engines:
Base.metadata.bind = engine
# Base.metadata.create_all(engine)
Session.configure(bind=engine)
session = Session()
IndexPrediction().to_sql(session, ret)
hist_rec = pd.read_sql_table('index_prediction', engine_company_outer)
win_ratio = (hist_rec.dropna()
.query('dt>"2014-01-01"')
.groupby('secu')
.apply(lambda df: np.sum(
np.isclose(df.pred, df.fact)) * 1.0 / len(df))
.to_frame()
.rename(columns={0: 'accuracy'})
.reset_index()
)
tomorrow_pred = ret.query("""dt == '{}'""".format(to_date))
to_seven_liu = pd.merge(tomorrow_pred, win_ratio, on='secu')
logger.info(u'明日预测结果:\n{}'.format(to_seven_liu))
def main():
stock_list = get_duanxianbao_b10()
get_config_func = make_config(CONFIG_FILE)
ret = predict(ticks=stock_list, look_back=519, index=False,
get_config_func=get_config_func)
ret = ret.sort_values(by='dt')
ret.loc[:, 'st'] = 'b10'
print(ret)
engines = [engine_company_local,
engine_company_outer]
for engine in engines:
Base.metadata.bind = engine
# Base.metadata.create_all(engine)
Session.configure(bind=engine)
session = Session()
Duanxianbao().to_sql(session, ret)
EquityPrediction)
import json
import os
import logging
import pickle
import numpy as np
fmt = '%(asctime)s - %(filename)s:%(lineno)s - %(name)s - %(message)s'
logging.basicConfig(formatter=fmt)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
CONFIG_FILE = 'predict_all_stock_feature5.csv'
if __name__ == '__main__':
all_stocks = pd.read_csv(CONFIG_FILE, index_col=0,
dtype={'tick':object}).tick.unique().tolist()
get_config_func = make_config(CONFIG_FILE)
ret = predict(ticks=all_stocks, look_back=519, index=False,
get_config_func=get_config_func)
ret = ret.sort_values(by='dt')
print(ret)
engines = [engine_company_local,
engine_company_outer]
for engine in engines:
Base.metadata.bind = engine
# Base.metadata.create_all(engine)
Session.configure(bind=engine)
session = Session()
EquityPrediction().to_sql(session, ret)
