Python tushare.get_today_all() Examples

def _getStockCodesFromTuShare(self):
        self._info.print("开始从TuShare获取股票代码表...")

        try:
            df = ts.get_today_all() # it's slow because TuShare will get one page by one page
        except Exception as ex:
            self._info.print("从TuShare获取股票代码表异常: {}".format(ex), DyLogData.error)
            return None

        if df is None or df.empty:
            self._info.print("从TuShare获取股票代码表为空", DyLogData.error)
            return None

        codes = {}
        data = df[['code', 'name']].values.tolist()
        for code, name in data:
            if code[0] == '6':
                codes[code + '.SH'] = name
            else:
                codes[code + '.SZ'] = name

        self._info.print("从TuShare获取股票代码表成功")
        return codes 

def download_realtime_stock_price():
    """
    # 下载股票的实时行情
    :return:
    """
    try:
        engine = db.get_w_engine()

        df_price = ts.get_today_all()

        stock_time = GetNowTime()
        if stock_time[11:] > "15:00:00":
            stock_time = stock_time[:11] + "15:00:00"
        df_price['date'] = stock_time

        # df_price.to_sql(STOCK_REALTIME_TABLE, engine, if_exists='append', index=False)
        to_sql(STOCK_REALTIME_TABLE, engine, df_price, type='replace')

    except Exception as e:
        print(e)

#######################
##  private methods  ##
####################### 

def _getStockCodesFromTuShare(self):
        try:
            df = ts.get_today_all() # it's slow because TuShare will get one page by one page
        except Exception as ex:
            return None

        if df is None or df.empty:
            return None

        codes = {}
        data = df[['code', 'name']].values.tolist()
        for code, name in data:
            if code[0] == '6':
                codes[code + '.SH'] = name
            else:
                codes[code + '.SZ'] = name

        return codes 

def QA_fetch_get_stock_realtime():
    data = ts.get_today_all()
    data_json = QA_util_to_json_from_pandas(data)
    return data_json 

def stat_today_all(tmp_datetime):
    datetime_str = (tmp_datetime).strftime("%Y-%m-%d")
    datetime_int = (tmp_datetime).strftime("%Y%m%d")
    print("datetime_str:", datetime_str)
    print("datetime_int:", datetime_int)
    data = ts.get_today_all()
    # 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
    if not data is None and len(data) > 0:
        # 插入数据库。
        # del data["reason"]
        data["date"] = datetime_int  # 修改时间成为int类型。
        data = data.drop_duplicates(subset="code", keep="last")
        data.head(n=1)
        common.insert_db(data, "ts_today_all", False, "`date`,`code`")
    else:
        print("no data .")

    time.sleep(5)  # 停止5秒

    data = ts.get_index()
    # 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
    if not data is None and len(data) > 0:
        # 插入数据库。
        # del data["reason"]
        data["date"] = datetime_int  # 修改时间成为int类型。
        data = data.drop_duplicates(subset="code", keep="last")
        data.head(n=1)
        common.insert_db(data, "ts_index_all", False, "`date`,`code`")
    else:
        print("no data .")

    print(datetime_str)


# main函数入口 

def filter_by_roe(min):  # 筛选出最近5年ROE都高于min的公司
    path = os.path.join(current_folder, '3年平均利润及其他财务指标%s.csv' % today)
    if not os.path.exists(path):  # 没有就生成3年平均利润列表
        calcu_all_stocks_3year_roe_and_average_profit(
            calcu_average_profit_end_year)

    gplb = pd.read_csv(path, index_col=0, encoding='utf-8')
    gplb = gplb[gplb['当年roe'] > min]
    gplb = gplb[gplb['上1年roe'] > min]
    gplb = gplb[gplb['上2年roe'] > min]
    gplb = gplb[gplb['上3年roe'] > min]
    gplb = gplb[gplb['上4年roe'] > min]

    # 获取当前股票价格
    price_path = os.path.join(current_folder, today + '股票价格.csv')
    if not os.path.exists(price_path):
        ts.get_today_all().set_index('code').to_csv(
            price_path, encoding="utf-8")

    current_price = pd.read_csv(price_path, encoding="utf-8", index_col=0)
    current_price = current_price[['trade']]
    current_price.columns = ['价格']
    gplb = gplb[[
        '名字', '行业', '地区', '流通股本', '总股本', '总资产(万)', '流动资产', '固定资产', '每股净资',
        '市净率', '上市日期', '平均利润', '当年roe', '上1年roe', '上2年roe', '上3年roe', '上4年roe'
    ]]

    data = pd.merge(gplb, current_price, left_index=True, right_index=True)
    # 因为这里的平均利润单位是万元，而总股本单位是亿，价格单位是元
    data['平均市盈率'] = data['总股本'] * data['价格'] * 10000 / data['平均利润']
    data['平均市盈率'] = data['平均市盈率'].round(1)
    data['市净率'] = data['市净率'].round(1)

    high_roe_file = os.path.join(current_folder,
                                 today + f'-最近5年ROE都高于{min}%的公司.xlsx')
    data.to_excel(high_roe_file, encoding='utf-8') 

def k_today():

    return ts.get_today_all() 

def getLiveChinaStockPrice(stockCode):
    
    try:
        exchange = "sh" if (int(stockCode) // 100000 == 6) else "sz"
        dataUrl = "http://hq.sinajs.cn/list=" + exchange + stockCode
        stdout = urllib2.urlopen(dataUrl)
        stdoutInfo = stdout.read().decode('gb2312').encode('utf-8')
        
        # 正则表达式说明
        # 搜索 “ ”双引号内的字符串，包含换行符，将匹配的字符串分为三组：用（）表示
        # group(2)：取第二组数据
        tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2)
        stockInfo = tempData.split(",")
        
        #bb[0]:股票名  bb[1]:今日开盘价    bb[2]：昨日收盘价    bb[3]:当前价格   bb[4]:今日最高价    bb[5]:今日最低价
        #bb[6]:买一报价 bb[7]:卖一报价     bb[8]:成交股票数/100 bb[9]:成交金额/w bb[10]:买一申请股数 bb[11]:买一报价
        #bb[12]:买二股数 bb[13]:买二报价   bb[14]:买三股数      bb[15]:买三报价  bb[16]:买四申请股数 bb[17]:买四报价
        #bb[18]:买五股数 bb[19]:买五报价   bb[20]:卖一股数      bb[21]:卖一报价  bb[22]:卖二申请股数 bb[23]:卖二报价
        #bb[24]:卖三股数 bb[25]:卖三报价   bb[26]:卖四股数      bb[27]:卖四报价  bb[28]:卖五股数     bb[29]:卖五报价
        #bb[30]:日期     bb[31]:时间     bb[8]:不知道
        
        return st.Stock(stockInfo)
        
    except Exception as e:
        print(">>>>>> Exception: " + str(e))
    finally:
        None    

# 获取A股所有股票的实时股价
# 通过 ts.get_today_all 获取 

def get_real_price_dataframe():
    df = ts.get_today_all()
    return df 

def get_stock_price(code, include_realtime_price):
    """
    获取个股股价
    :param code: 股票代码
    :param include_realtime_price: 是否含实时股价
    :return:
    """

    # 获取历史股价
    df = ts.get_hist_data(code)
    df = df[['close']]
    df['date'] = df.index

    if include_realtime_price:
        df_today = ts.get_today_all()
        df_code = df_today[df_today['code']==code]
        df_code = df_code[['trade']]
        df_code['date'] = GetNowDate()
        df_code.rename(columns={'trade': 'close'}, inplace=True)
        df = pd.concat([df, df_code], ignore_index=True)

    df.sort(columns='date', inplace=True)
    df = df.drop_duplicates(['date'])
    df.index = range(len(df))
    print '\n'
    # print df.head()
    print df.tail()
    return df 

def plot_days():
    if request.method == 'GET' :
        today = ts.get_today_all()
        code_info = ts.get_industry_classified()

        today['code'] = today['code'].astype(unicode)
        one_day = gd.get_data_real_time(code_info, today)
        body = heatmap.get_heatmap('Today', one_day)
        return render_template('heatmap.html', body=body) 

def filter_stock_by_average_pe(min, max):
    path = os.path.join(current_folder, '3年平均利润及其他财务指标%s.csv' % today)
    if not os.path.exists(path):  # 没有就生成3年平均利润列表
        calcu_all_stocks_3year_roe_and_average_profit(
            calcu_average_profit_end_year)

    gplb = pd.read_csv(path, index_col=0, encoding='utf-8')

    # 获取当前股票价格
    price_path = os.path.join(current_folder, today + '股票价格.csv')
    if not os.path.exists(price_path):
        ts.get_today_all().set_index('code').to_csv(
            price_path, encoding="utf-8")

    current_price = pd.read_csv(price_path, encoding="utf-8", index_col=0)
    current_price = current_price[['trade']]
    current_price.columns = ['价格']
    gplb = gplb[[
        '名字', '行业', '地区', '流通股本', '总股本', '总资产(万)', '流动资产', '固定资产', '每股净资',
        '市净率', '上市日期', '平均利润'
    ]]

    data = pd.merge(gplb, current_price, left_index=True, right_index=True)
    # 因为这里的平均利润单位是万元，而总股本单位是亿，价格单位是元
    data['平均市盈率'] = data['总股本'] * data['价格'] * 10000 / data['平均利润']
    print('\n%s:' % today)
    print()
    print('%d个公司' % data.shape[0])
    print('3年市盈率中位数%.1f' % round(data['平均市盈率'].median(), 1))
    print('市净率中位数%.1f' % round(data['市净率'].median(), 1))
    data = data[data['平均市盈率'] < max]
    data = data[data['平均市盈率'] > min]
    data['平均市盈率'] = data['平均市盈率'].round(1)
    data['平均利润'] = data['平均利润'].round()
    data['市净率'] = data['市净率'].round(1)
    data['固定资产'] = data['固定资产'].round()
    data['流动资产'] = data['流动资产'].round()
    data['总股本'] = data['总股本'].round()
    data['流通股本'] = data['流通股本'].round()
    average_pe_file = os.path.join(
        current_folder, today + '-3年平均市盈率在%s和%s之间的公司.xlsx' % (min, max))
    data.to_excel(average_pe_file, encoding='utf-8')