def query_from_mysql(self, code=None):
df = None
if (code == None):
df = pm.query(DbCreator.share_base)
else:
df = pm.query(DbCreator.share_data_day,
where='code=\'' + code + '\'')
df = df[df[Config.timeToMarket] != '0000-00-00']
df = df.sort_values(by=[Config.code])
df.index = np.arange(0, df.shape[0], 1)
return df
def _process(self, df, start):
code = df.loc[0, Config.code]
basic_df = pm.query(DbCreator.share_base, where=' code="%s"' % (code))
print(basic_df.loc[0, Config.totals])
df[Config.totals] = df[Config.close] * basic_df.loc[0, Config.totals]
return df[[Config.id, Config.totals]]
def query_test_data(self, code, tb_name=DbCreator.share_data_day):
import numpy as np
df = pm.query(tb_name, where=' code="%s"' % (code))
df.sort_values(by=[Config.db_date], inplace=True) # 按列进行排序
df.index = np.arange(0, df.shape[0], 1) # 保证索引排序
df[Config.db_date] = df[Config.db_date].astype(np.str) # 日期更改
return df
def kpi_month(code):
import numpy as np
df = pm.query(DbCreator.share_data_month,
where='code=\'' + code +
'\' and trade_date>=\'2015-01\'')
logger.info('start to month reverse kpi:' + code + ' size:' +
str(df.shape[0]))
if (df.shape[0] == 0):
logger.info('no data to kpi:')
return
df.sort_values(by=[Config.db_date], inplace=True) # 按列进行排序
df.index = np.arange(0, df.shape[0], 1) # 保证索引排序
df[Config.db_date] = df[Config.db_date].astype(np.str) # 日期更改
mk = MonthReverse()
succ = mk.run(code, df, start=0, to_mysql=True)
logger.info('end to month kpi:' + code)
return succ
def kpi_s(code, start_date, pm, dict):
df = pm.query(DbCreator.share_data_day,
where='code=\'' + code +
'\' and trade_date>=\'2011-01-01\'')
logger.info('start to kpi:' + code + ' size:' + str(df.shape[0]))
if (df.shape[0] == 0):
logger.info('no data to kpi:')
return
#流水线
succ = Pipeline.execute(PreProcess(), [
TotalMarket(),
CreateRecord(),
DeviationsRatio(),
FluctionRatio(),
IncrementRatio(),
Ma(),
TurnRatio(),
PeRank()
], code, df, start_date)
logger.info('end to kpi:' + code)
dict[code] = succ
def kpi_m(start_date, pm):
# 计算跨多只s的指标
mdf = pm.query(DbCreator.share_data_day,
where='trade_date=\'' + start_date + '\'')
mdf = mdf[[
Config.id, Config.incOfOneYear, Config.incOfHalfYear,
Config.incOf50d
]]
mdf = mdf.dropna()
rps = Rps()
return rps.run(mdf, start_date, True)
def query_month_data_and_count_reverse(self, code, dt, close):
dt = DateUtil.getLastMonthForShort(dt[0:7])
mdf = self.month_data.get('' + code + '_' + dt)
if (mdf is None):
mdf = pm.query(DbCreator.share_data_month,
where=' code="%s" and trade_date="%s"' % (code, dt))
self.month_data[code + '_' + dt] = mdf
if (not mdf.empty):
ha = mdf.loc[0, 'ha']
hb = mdf.loc[0, 'hb']
if (ha != 0 or hb != 0):
#print(ha,hb,ha*12+hb,close)
if (FactorUtil.is_reverse(ha, hb, close) == 1):
return 1
la = mdf.loc[0, 'la']
lb = mdf.loc[0, 'lb']
if (la != 0 or lb != 0):
if (FactorUtil.is_reverse(ha, hb, close, direct='l') == 1):
return 2
return 0
def __query(self, dt, type):
df = pm.query(DbCreator.share_data_finance,
where=' fin_date=\'' + dt + '\' and fin_type=\'' +
str(type) + '\' order by fin_date')
return df
for i in range(start, df.shape[0], 1):
df.loc[i, Config.incOf50d] = self.__add_increment__(i, INC_50, df)
df.loc[i, Config.incOfHalfYear] = self.__add_increment__(
i, INC_120, df)
df.loc[i, Config.incOfOneYear] = self.__add_increment__(
i, INC_250, df)
df.loc[i, Config.incOf2d] = self.__add_increment__(i, 2, df)
return df[[
Config.id, Config.incOfOneYear, Config.incOfHalfYear,
Config.incOf50d, Config.incOf2d
]]
def __add_increment__(self, i, freq, df):
freq = freq - 1
if (i >= freq):
return (
(df.loc[i, Config.close] - df.loc[i - freq, Config.close]) /
df.loc[i - freq, Config.close]) * 100
else:
return self._default_value
#main
if (__name__ == '__main__'):
df = pm.query(DbCreator.share_data_day,
where='code="000001" and trade_date>="2018-10-01"')
ir = IncrementRatio()
ir.run('000001', df, to_mysql=False)
print(df[[Config.db_date, Config.incOf2d]])
from k.algorithm.Base import Base
from k.Config import Config
import numpy as np
import pandas as pd
from k.util.PandasToMysql import pm
from k.util.DbCreator import DbCreator
class TotalMarket(Base):
def _process(self, df, start):
code = df.loc[0, Config.code]
basic_df = pm.query(DbCreator.share_base, where=' code="%s"' % (code))
print(basic_df.loc[0, Config.totals])
df[Config.totals] = df[Config.close] * basic_df.loc[0, Config.totals]
return df[[Config.id, Config.totals]]
#main
if (__name__ == '__main__'):
df = pm.query(DbCreator.share_data_day,
where='code=\'000860\' and trade_date>=\'2018-10-10\'')
print(df[[Config.id, Config.db_date, Config.totals, Config.close]])
tm = TotalMarket()
tm.run('000860', df)
print(df[[Config.id, Config.totals, Config.close]])
