Python tushare.get_today_all方法代碼示例

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def _getStockCodesFromTuShare(self):
        self._info.print("開始從TuShare獲取股票代碼表...")

        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("從TuShare獲取股票代碼表異常: {}".format(ex), DyLogData.error)
            return None

        if df is None or df.empty:
            self._info.print("從TuShare獲取股票代碼表為空", 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("從TuShare獲取股票代碼表成功")
        return codes 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def download_realtime_stock_price():
    """
    # 下載股票的實時行情
    :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  ##
####################### 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
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 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def QA_fetch_get_stock_realtime():
    data = ts.get_today_all()
    data_json = QA_util_to_json_from_pandas(data)
    return data_json 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
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()
    # 處理重複數據，保存最新一條數據。最後一步處理，否則concat有問題。
    if not data is None and len(data) > 0:
        # 插入數據庫。
        # del data["reason"]
        data["date"] = datetime_int  # 修改時間成為int類型。
        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()
    # 處理重複數據，保存最新一條數據。最後一步處理，否則concat有問題。
    if not data is None and len(data) > 0:
        # 插入數據庫。
        # del data["reason"]
        data["date"] = datetime_int  # 修改時間成為int類型。
        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函數入口 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def filter_by_roe(min):  # 篩選出最近5年ROE都高於min的公司
    path = os.path.join(current_folder, '3年平均利潤及其他財務指標%s.csv' % today)
    if not os.path.exists(path):  # 沒有就生成3年平均利潤列表
        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['當年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]

    # 獲取當前股票價格
    price_path = os.path.join(current_folder, today + '股票價格.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 = ['價格']
    gplb = gplb[[
        '名字', '行業', '地區', '流通股本', '總股本', '總資產(萬)', '流動資產', '固定資產', '每股淨資',
        '市淨率', '上市日期', '平均利潤', '當年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)
    # 因為這裏的平均利潤單位是萬元，而總股本單位是億，價格單位是元
    data['平均市盈率'] = data['總股本'] * data['價格'] * 10000 / data['平均利潤']
    data['平均市盈率'] = data['平均市盈率'].round(1)
    data['市淨率'] = data['市淨率'].round(1)

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

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def k_today():

    return ts.get_today_all() 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
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')
        
        # 正則表達式說明
        # 搜索 “ ”雙引號內的字符串，包含換行符，將匹配的字符串分為三組：用（）表示
        # group(2)：取第二組數據
        tempData = re.search('''(")(.+)(")''', stdoutInfo).group(2)
        stockInfo = tempData.split(",")
        
        #bb[0]:股票名  bb[1]:今日開盤價    bb[2]：昨日收盤價    bb[3]:當前價格   bb[4]:今日最高價    bb[5]:今日最低價
        #bb[6]:買一報價 bb[7]:賣一報價     bb[8]:成交股票數/100 bb[9]:成交金額/w bb[10]:買一申請股數 bb[11]:買一報價
        #bb[12]:買二股數 bb[13]:買二報價   bb[14]:買三股數      bb[15]:買三報價  bb[16]:買四申請股數 bb[17]:買四報價
        #bb[18]:買五股數 bb[19]:買五報價   bb[20]:賣一股數      bb[21]:賣一報價  bb[22]:賣二申請股數 bb[23]:賣二報價
        #bb[24]:賣三股數 bb[25]:賣三報價   bb[26]:賣四股數      bb[27]:賣四報價  bb[28]:賣五股數     bb[29]:賣五報價
        #bb[30]:日期     bb[31]:時間     bb[8]:不知道
        
        return st.Stock(stockInfo)
        
    except Exception as e:
        print(">>>>>> Exception: " + str(e))
    finally:
        None    

# 獲取A股所有股票的實時股價
# 通過 ts.get_today_all 獲取 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def get_real_price_dataframe():
    df = ts.get_today_all()
    return df 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def get_stock_price(code, include_realtime_price):
    """
    獲取個股股價
    :param code: 股票代碼
    :param include_realtime_price: 是否含實時股價
    :return:
    """

    # 獲取曆史股價
    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 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
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) 

# 需要導入模塊: import tushare [as 別名]
# 或者: from tushare import get_today_all [as 別名]
def filter_stock_by_average_pe(min, max):
    path = os.path.join(current_folder, '3年平均利潤及其他財務指標%s.csv' % today)
    if not os.path.exists(path):  # 沒有就生成3年平均利潤列表
        calcu_all_stocks_3year_roe_and_average_profit(
            calcu_average_profit_end_year)

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

    # 獲取當前股票價格
    price_path = os.path.join(current_folder, today + '股票價格.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 = ['價格']
    gplb = gplb[[
        '名字', '行業', '地區', '流通股本', '總股本', '總資產(萬)', '流動資產', '固定資產', '每股淨資',
        '市淨率', '上市日期', '平均利潤'
    ]]

    data = pd.merge(gplb, current_price, left_index=True, right_index=True)
    # 因為這裏的平均利潤單位是萬元，而總股本單位是億，價格單位是元
    data['平均市盈率'] = data['總股本'] * data['價格'] * 10000 / data['平均利潤']
    print('\n%s:' % today)
    print()
    print('%d個公司' % data.shape[0])
    print('3年市盈率中位數%.1f' % round(data['平均市盈率'].median(), 1))
    print('市淨率中位數%.1f' % round(data['市淨率'].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['平均利潤'] = data['平均利潤'].round()
    data['市淨率'] = data['市淨率'].round(1)
    data['固定資產'] = data['固定資產'].round()
    data['流動資產'] = data['流動資產'].round()
    data['總股本'] = data['總股本'].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之間的公司.xlsx' % (min, max))
    data.to_excel(average_pe_file, encoding='utf-8')