filter_stock.py

# -*-coding=utf-8-*-
__author__ = 'Rocky'
'''
email: weigesysu@qq.com
'''
import datetime
import tushare as ts
import os
from setting import get_engine, get_mysql_conn
import pandas as pd
import numpy as np
from collections import OrderedDict
import matplotlib

matplotlib.use("Pdf")
pd.set_option('display.max_rows', None)
import matplotlib.pyplot as plt
 

# daily_engine = get_engine('daily')
# history_engine = get_engine('history')

# 过滤器，剔除不想要的个股

class Filter_Stock():
    def __init__(self):
        current = os.getcwd()
        work_space = os.path.join(current, 'data')
        if os.path.exists(work_space) == False:
            os.mkdir(work_space)
        os.chdir(work_space)
        self.today = datetime.datetime.now().strftime("%Y-%m-%d")

    def get_location(self):
        df = ts.get_area_classified()
        print(df)
        # df.to_excel('location.xls')
        self.save_to_excel(df, 'location.xls')

    def get_ST(self):
        # 暂停上市
        zt = ts.get_suspended()
        print(zt)
        # 终止上市
        zz = ts.get_terminated()
        print(zz)

    def get_achievement(self):
        fc = ts.forecast_data(2016, 4)
        print(fc)

    def daily_market(self):
        '''
        保存每天收盘后的市场行情
        :return:
        '''
        df = ts.get_today_all()
        print(df)
        try:
            df.to_sql(self.today, daily_engine, if_exists='replace')
        except Exception as e:
            print(e)
        print("Save {} data to MySQL".format(self.today))

    def break_low(self, date):
        '''
        筛选出一年内创新低的股票
        :param date: 某一天的日期 ‘'2017-11-11
        :return:
        '''
        # cmd = 'select * from `{}`'.format(date)
        df = pd.read_sql_table(date, daily_engine, index_col='index')
        # **** 这里的index需要删除一个
        low_db = get_mysql_conn('db_selection')
        low_cursor = low_db.cursor()
        for i in range(len(df)):
            code = df.loc[i]['code']
            cur_low = df.loc[i]['low']

            mins_date, mins = self.get_lowest(code, '2017', date)
            if not mins_date:
                continue
            if mins and float(cur_low) <= float(mins) and float(cur_low) != 0.0:
                print(code, )
                print(df.loc[i]['name'])
                print('year mins {} at {}'.format(mins, mins_date))
                print('curent mins ', cur_low)
                create_cmd = 'create table if not exists break_low' \
                             '(`index` int primary key auto_increment,datetime datetime,code text,name text,low_price float,last_price float, last_price_date datetime);'
                low_cursor.execute(create_cmd)
                insert_cmd = 'insert into break_low (datetime,code,name,low_price,last_price,last_price_date) values (%s,%s,%s,%s,%s,%s);'
                insert_data = (date, code, df.loc[i]['name'], cur_low, mins, mins_date)
                low_cursor.execute(insert_cmd, insert_data)
                low_db.commit()

    def get_lowest(self, code, date, current_date):
        '''
        返回个股某一年最低价
        :param code: 股票代码
        :param date: 年份
        :return:
        '''
        date = date + '-01-01'
        cmd = 'select * from `{}` where datetime > \'{}\' and datetime <\'{}\''.format(code, date, current_date)

        try:
            df = pd.read_sql(cmd, history_engine, index_col='index')
        except Exception as e:
            print(e)
            return None, None
        # print(df.dtypes)
        # 不知道为啥，这里的类型发生改变
        if len(df) < 1:
            return None, None
        df['low'] = df['low'].astype('float64')
        idx = df['low'].idxmin()
        min_date = df.loc[idx]
        return min_date['datetime'], min_date['low']

    def get_highest(self, code, date):
        '''
        返回个股某一年最高价
        :param code: 股票代码
        :param date: 年份
        :return:
        '''
        date = date + '-01-01'
        cmd = 'select high from `{}` where datetime > \'{}\''.format(code, date)
        df = pd.read_sql(cmd, history_engine)
        return df['high'].max()

    def save_to_excel(self, df, filename, encoding='gbk'):
        try:
            df.to_csv('temp.csv', encoding=encoding, index=False)
            df = pd.read_csv('temp.csv', encoding=encoding, dtype={'code': str})
            df.to_excel(filename, encoding=encoding)
            return True
        except Exception as e:
            print("Save to excel faile")
            print(e)
            return None

    # 专门用来存储数据，数据保存为excel，不必每次都要从网络读取
    def store_data(self):
        # 预测
        # year_2016=ts.forecast_data(2016, 4)
        # self.save_to_excel(year_2016,'2016-profit.xls')

        # year_2017=ts.forecast_data(2017, 4)
        # self.save_to_excel(year_2017,'2017-profit.xls')
        # 盈利能力
        # profit_2016=ts.get_profit_data(2016,4)
        # profit_2017=ts.get_profit_data(2017,3)
        # self.save_to_excel(profit_2016, '2016-profit.xls')
        # self.save_to_excel(profit_2017, '2017-3rdprofit.xls')
        # 股票基本信息
        # basic=ts.get_stock_basics()
        # basic.to_csv('temp.xls',encoding='gbk')
        # df=pd.read_csv('temp.xls',encoding='gbk',dtype={'code':str})
        # # print(df)
        # self.save_to_excel(df,'Markets.xls')

        # 基本面 每股净资产<1
        df = ts.get_report_data(2017, 3)
        self.save_to_excel(df, '2017-3rd-report.xls')

    def to_be_ST(self):
        '''
        df_2016=pd.read_excel('2016-profit.xls',dtype={'code':str})
        df_2017=pd.read_excel('2017-3rdprofit.xls',dtype={'code':str})
        loss_2016=set(df_2016[df_2016['net_profits']<0]['code'])
        loss_2017=set(df_2017[df_2017['net_profits']<0]['code'])
        st= list(loss_2016 & loss_2017)
        basic=pd.read_excel('Markets.xls',dtype={'code':str})
        # print(basic.head(5))
        # for x in st:
        #     print(x)
        # print(basic[basic['code']==st])
        for i in st:
            print(basic[basic['code']==i][['code','name']])
        '''

        # 每股净资产小于0
        df_bpvs = pd.read_excel('2017-3rd-report.xls', dtype={'code': str})
        # print(df_bpvs.head())
        print(df_bpvs[df_bpvs['bvps'] < 0][['code', 'name']])

    # 返回新股信息
    def get_new_stock(self, start='2010', end='2011'):
        '''

        :param start: 开始年份 如 '2010'
        :param end:  结束年份 如 '2011'
        :return:
        '''
        df = pd.read_sql('tb_basic_info', get_engine('db_stock'), index_col='index')
        df = df[df['timeToMarket'] != 0]
        df['timeToMarket'] = pd.to_datetime(df['timeToMarket'], format='%Y%m%d')
        df = df.set_index('timeToMarket', drop=True)
        # print(len(df['2010']))
        years = OrderedDict()
        values = []
        # for year in range(1994, 2019):
        #     years[year] = len(df[str(year)])
        #     values.append(len(df[str(year)]))
        # x = np.arange(1994, 2019)
        # plt.figure(figsize=(10, 9))
        # rect = plt.bar(x, values)
        # self.rect_show(rect)
        # plt.xticks(x[::2])
        # plt.show()

        new_stock = df[start:end]
        return new_stock

    def rect_show(self, rects):
        for rect in rects:
            height = rect.get_height()
            plt.text(rect.get_x(), 1.05 * height, '%s' % int(height))

    def show(self):
        df = self.get_new_stock('2017', '2018')


def main():
    obj = Filter_Stock()
    # obj.get_ST()
    # obj.get_achievement()
    # obj.get_location(u'深圳')
    # obj.break_low()
    # obj.break_low('2017-11-17')
    # print(type(obj.get_lowest('300333','2017')))
    # print(obj.get_lowest('300333', '2017'))
    # print(obj.get_highest('300333', '2017'))
    # obj.break_low('2017-11-17')

    # obj.store_data()
    # obj.to_be_ST()
    # obj.get_location()
    # print(obj.get_new_stock())
    obj.get_location()

if __name__ == '__main__':
    main()