windpyplus_test.py

# -*- coding: utf-8 -*-

import pandas as pd

from windpyplus.utils.tradedate import tradedate
from windpyplus.stockSector.StockSector import allAstock, MSCIAStock
from windpyplus.fundamental.foreCastWind import foreCastWind
from windpyplus.utils.convertToWindCode import convertBQCode, convertCode
from windpyplus.utils.dfToExcel import dftoSameWorkbook, dfToExcel
from windpyplus.fundamental.valucation import valucationWind
from windpyplus.fundamental.fundamentalWind import  financialDataWind, netProfit_filter, ROE_filter, growth_filter, cashFlow_filter, multi_filter

allastocks = list(allAstock().index.values)


def fiter_ForecastValucation(qt = '20170930', CHG_MIN= 7):
    allastocks = list(allAstock().index.values)
    df = foreCastWind(allastocks, qt)
    df = df[df['PROFITNOTICE_CHANGEMEAN'] > CHG_MIN]
    print(df.head(10))
    
    stocklists = df.index.values
    df_V = valucationWind(stocklists)
    print(df_V.head(10))
    df_f = pd.merge(df_V,df, how='left')
    
    dfToExcel(df_f, "filter_valucation_forecast_"+ str(qt)+"_"+ str(CHG_MIN)) 
    print('num of filter_forecastValucation : {}'.format(df_f)) 
    return df_f

if __name__ == '__main__':
    print(tradedate())
    df = fiter_ForecastValucation(qt= '20170930',CHG_MIN= 30)
   
    #stocks_muiltfilter = multi_filter(list(df.index.values), )
    #df = financialDataWind(stocks_muiltfilter)
    #print(df)