claudios/dypybench_functions · Datasets at Hugging Face

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_daily

(date: str = "20201018")

return temp_df

电影票房-单日票房 https://www.endata.com.cn/BoxOffice/BO/Day/index.html :param date: 只能设置当前日期的前一天的票房数据 :type date: str :return: 每日票房 :rtype: pandas.DataFrame

101

144

def movie_boxoffice_daily(date: str = "20201018") -> pd.DataFrame: """ 电影票房-单日票房 https://www.endata.com.cn/BoxOffice/BO/Day/index.html :param date: 只能设置当前日期的前一天的票房数据 :type date: str :return: 每日票房 :rtype: pandas.DataFrame """ last_date = datetime.datetime.strptime(date, "%Y%m%d") - datetime.timedelta(days=1) last_date = last_date.strftime("%Y%m%d") url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "sdate": f"{date[:4]}-{date[4:6]}-{date[6:]}", "edate": f"{last_date[:4]}-{last_date[4:6]}-{last_date[6:]}", "MethodName": "BoxOffice_GetDayBoxOffice", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影片名称", "_", "累计票房", "平均票价", "上映天数", "场均人次", "_", "_", "_", "_", "_", "单日票房", "环比变化", "_", "口碑指数", ] temp_df = temp_df[ ["排序", "影片名称", "单日票房", "环比变化", "累计票房", "平均票价", "场均人次", "口碑指数", "上映天数"] ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L101-L144

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

20.454545

[ 9, 10, 11, 12, 17, 18, 19, 20, 21, 40, 43 ]

25

false

15.037594

44

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75

6

def movie_boxoffice_daily(date: str = "20201018") -> pd.DataFrame: last_date = datetime.datetime.strptime(date, "%Y%m%d") - datetime.timedelta(days=1) last_date = last_date.strftime("%Y%m%d") url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "sdate": f"{date[:4]}-{date[4:6]}-{date[6:]}", "edate": f"{last_date[:4]}-{last_date[4:6]}-{last_date[6:]}", "MethodName": "BoxOffice_GetDayBoxOffice", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影片名称", "_", "累计票房", "平均票价", "上映天数", "场均人次", "_", "_", "_", "_", "_", "单日票房", "环比变化", "_", "口碑指数", ] temp_df = temp_df[ ["排序", "影片名称", "单日票房", "环比变化", "累计票房", "平均票价", "场均人次", "口碑指数", "上映天数"] ] return temp_df

18,741

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_weekly

(date: str = "20201018")

return temp_df

电影票房-单周票房 https://www.endata.com.cn/BoxOffice/BO/Week/oneWeek.html :param date: 只能获取指定日期所在完整周的票房数据 :type date: str :return: 单周票房 :rtype: pandas.DataFrame

147

184

def movie_boxoffice_weekly(date: str = "20201018") -> pd.DataFrame: """ 电影票房-单周票房 https://www.endata.com.cn/BoxOffice/BO/Week/oneWeek.html :param date: 只能获取指定日期所在完整周的票房数据 :type date: str :return: 单周票房 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "sdate": get_current_week(date=date).strftime("%Y-%m-%d"), "MethodName": "BoxOffice_GetWeekInfoData", } r = requests.post(url, data=payload) data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影片名称", "单周票房", "累计票房", "_", "上映天数", "平均票价", "场均人次", "环比变化", "_", "_", "_", "排名变化", "口碑指数", ] temp_df = temp_df[ ["排序", "影片名称", "排名变化", "单周票房", "环比变化", "累计票房", "平均票价", "场均人次", "口碑指数", "上映天数"] ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L147-L184

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

23.684211

[ 9, 10, 14, 15, 16, 17, 34, 37 ]

21.052632

false

15.037594

38

1

78.947368

6

def movie_boxoffice_weekly(date: str = "20201018") -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "sdate": get_current_week(date=date).strftime("%Y-%m-%d"), "MethodName": "BoxOffice_GetWeekInfoData", } r = requests.post(url, data=payload) data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影片名称", "单周票房", "累计票房", "_", "上映天数", "平均票价", "场均人次", "环比变化", "_", "_", "_", "排名变化", "口碑指数", ] temp_df = temp_df[ ["排序", "影片名称", "排名变化", "单周票房", "环比变化", "累计票房", "平均票价", "场均人次", "口碑指数", "上映天数"] ] return temp_df

18,742

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_monthly

(date: str = "20201018")

return temp_df

电影票房-单月票房 https://www.endata.com.cn/BoxOffice/BO/Month/oneMonth.html :param date: 指定日期所在月份的月度票房 :type date: str :return: 单月票房 :rtype: pandas.DataFrame

187

221

def movie_boxoffice_monthly(date: str = "20201018") -> pd.DataFrame: """ 电影票房-单月票房 https://www.endata.com.cn/BoxOffice/BO/Month/oneMonth.html :param date: 指定日期所在月份的月度票房 :type date: str :return: 单月票房 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "startTime": f"{date[:4]}-{date[4:6]}-01", "MethodName": "BoxOffice_GetMonthBox", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影片名称", "月内天数", "单月票房", "平均票价", "场均人次", "月度占比", "上映日期", "_", "口碑指数", ] temp_df = temp_df[ ["排序", "影片名称", "单月票房", "月度占比", "平均票价", "场均人次", "上映日期", "口碑指数", "月内天数"] ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L187-L221

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

25.714286

[ 9, 10, 14, 15, 16, 17, 18, 31, 34 ]

25.714286

false

15.037594

35

1

74.285714

6

def movie_boxoffice_monthly(date: str = "20201018") -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "startTime": f"{date[:4]}-{date[4:6]}-01", "MethodName": "BoxOffice_GetMonthBox", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影片名称", "月内天数", "单月票房", "平均票价", "场均人次", "月度占比", "上映日期", "_", "口碑指数", ] temp_df = temp_df[ ["排序", "影片名称", "单月票房", "月度占比", "平均票价", "场均人次", "上映日期", "口碑指数", "月内天数"] ] return temp_df

18,743

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_yearly

(date: str = "20201018")

return temp_df

电影票房-年度票房 https://www.endata.com.cn/BoxOffice/BO/Year/index.html :param date: 当前日期所在年度的票房数据 :type date: str :return: 年度票房 :rtype: pandas.DataFrame

224

257

def movie_boxoffice_yearly(date: str = "20201018") -> pd.DataFrame: """ 电影票房-年度票房 https://www.endata.com.cn/BoxOffice/BO/Year/index.html :param date: 当前日期所在年度的票房数据 :type date: str :return: 年度票房 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "year": f"{date[:4]}", "MethodName": "BoxOffice_GetYearInfoData", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.reset_index(inplace=True) temp_df.columns = [ "排序", "_", "影片名称", "类型", "总票房", "平均票价", "场均人次", "国家及地区", "上映日期", "_", ] temp_df["排序"] = range(1, len(temp_df) + 1) temp_df = temp_df[["排序", "影片名称", "类型", "总票房", "平均票价", "场均人次", "国家及地区", "上映日期"]] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L224-L257

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

26.470588

[ 9, 10, 14, 15, 16, 17, 18, 19, 31, 32, 33 ]

32.352941

false

15.037594

34

1

67.647059

6

def movie_boxoffice_yearly(date: str = "20201018") -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "year": f"{date[:4]}", "MethodName": "BoxOffice_GetYearInfoData", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.reset_index(inplace=True) temp_df.columns = [ "排序", "_", "影片名称", "类型", "总票房", "平均票价", "场均人次", "国家及地区", "上映日期", "_", ] temp_df["排序"] = range(1, len(temp_df) + 1) temp_df = temp_df[["排序", "影片名称", "类型", "总票房", "平均票价", "场均人次", "国家及地区", "上映日期"]] return temp_df

18,744

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_yearly_first_week

(date: str = "20201018")

return temp_df

电影票房-年度票房-年度首周票房 https://www.endata.com.cn/BoxOffice/BO/Year/firstWeek.html :param date: 当前日期所在年度的年度首周票房票房数据 :type date: str :return: 年度首周票房 :rtype: pandas.DataFrame

260

297

def movie_boxoffice_yearly_first_week(date: str = "20201018") -> pd.DataFrame: """ 电影票房-年度票房-年度首周票房 https://www.endata.com.cn/BoxOffice/BO/Year/firstWeek.html :param date: 当前日期所在年度的年度首周票房票房数据 :type date: str :return: 年度首周票房 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "year": f"{date[:4]}", "MethodName": "BoxOffice_getYearInfo_fData", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.reset_index(inplace=True) temp_df.columns = [ "排序", "_", "_", "影片名称", "首周票房", "场均人次", "上映日期", "首周天数", "类型", "国家及地区", "_", "占总票房比重", ] temp_df["排序"] = range(1, len(temp_df) + 1) temp_df = temp_df[ ["排序", "影片名称", "类型", "首周票房", "占总票房比重", "场均人次", "国家及地区", "上映日期", "首周天数"] ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L260-L297

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

23.684211

[ 9, 10, 14, 15, 16, 17, 18, 19, 33, 34, 37 ]

28.947368

false

15.037594

38

1

71.052632

6

def movie_boxoffice_yearly_first_week(date: str = "20201018") -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "year": f"{date[:4]}", "MethodName": "BoxOffice_getYearInfo_fData", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.reset_index(inplace=True) temp_df.columns = [ "排序", "_", "_", "影片名称", "首周票房", "场均人次", "上映日期", "首周天数", "类型", "国家及地区", "_", "占总票房比重", ] temp_df["排序"] = range(1, len(temp_df) + 1) temp_df = temp_df[ ["排序", "影片名称", "类型", "首周票房", "占总票房比重", "场均人次", "国家及地区", "上映日期", "首周天数"] ] return temp_df

18,745

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_cinema_daily

(date: str = "20201018")

return temp_df

电影票房-影院票房-日票房排行 https://www.endata.com.cn/BoxOffice/BO/Cinema/day.html :param date: 当前日期前一日的票房数据 :type date: str :return: 影票房-影院票房-日票房排行 :rtype: pandas.DataFrame

300

333

def movie_boxoffice_cinema_daily(date: str = "20201018") -> pd.DataFrame: """ 电影票房-影院票房-日票房排行 https://www.endata.com.cn/BoxOffice/BO/Cinema/day.html :param date: 当前日期前一日的票房数据 :type date: str :return: 影票房-影院票房-日票房排行 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "rowNum1": "1", "rowNum2": "100", "date": date, "MethodName": "BoxOffice_GetCinemaDayBoxOffice", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影院名称", "单日票房", "单日场次", "_", "_", "场均票价", "场均人次", "上座率", ] temp_df = temp_df[["排序", "影院名称", "单日票房", "单日场次", "场均人次", "场均票价", "上座率"]] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L300-L333

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

26.470588

[ 9, 10, 16, 17, 18, 19, 20, 32, 33 ]

26.470588

false

15.037594

34

1

73.529412

6

def movie_boxoffice_cinema_daily(date: str = "20201018") -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "rowNum1": "1", "rowNum2": "100", "date": date, "MethodName": "BoxOffice_GetCinemaDayBoxOffice", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影院名称", "单日票房", "单日场次", "_", "_", "场均票价", "场均人次", "上座率", ] temp_df = temp_df[["排序", "影院名称", "单日票房", "单日场次", "场均人次", "场均票价", "上座率"]] return temp_df

18,746

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/movie_yien.py

movie_boxoffice_cinema_weekly

(date: str = "20201018")

return temp_df

电影票房-影院票房-周票房排行 https://www.endata.com.cn/BoxOffice/BO/Cinema/week.html :param date: 当前日期前完整一周的票房数据 :type date: str :return: 影票房-影院票房-轴票房排行 :rtype: pandas.DataFrame

336

375

def movie_boxoffice_cinema_weekly(date: str = "20201018") -> pd.DataFrame: """ 电影票房-影院票房-周票房排行 https://www.endata.com.cn/BoxOffice/BO/Cinema/week.html :param date: 当前日期前完整一周的票房数据 :type date: str :return: 影票房-影院票房-轴票房排行 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "dateID": str( datetime.date.fromisoformat( f"{date[:4]}-{date[4:6]}-{date[6:]}" ).isocalendar()[1] - 1 - 41 + 1128 ), "rowNum1": "1", "rowNum2": "100", "MethodName": "BoxOffice_GetCinemaWeekBoxOffice", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影院名称", "当周票房", "_", "单银幕票房", "场均人次", "单日单厅票房", "单日单厅场次", ] temp_df = temp_df[["排序", "影院名称", "当周票房", "单银幕票房", "场均人次", "单日单厅票房", "单日单厅场次"]] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/movie_yien.py#L336-L375

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

22.5

[ 9, 10, 23, 24, 25, 26, 27, 38, 39 ]

22.5

false

15.037594

40

1

77.5

6

def movie_boxoffice_cinema_weekly(date: str = "20201018") -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = { "dateID": str( datetime.date.fromisoformat( f"{date[:4]}-{date[4:6]}-{date[6:]}" ).isocalendar()[1] - 1 - 41 + 1128 ), "rowNum1": "1", "rowNum2": "100", "MethodName": "BoxOffice_GetCinemaWeekBoxOffice", } r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) temp_df.columns = [ "排序", "_", "影院名称", "当周票房", "_", "单银幕票房", "场均人次", "单日单厅票房", "单日单厅场次", ] temp_df = temp_df[["排序", "影院名称", "当周票房", "单银幕票房", "场均人次", "单日单厅票房", "单日单厅场次"]] return temp_df

18,747

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/video_yien.py

_get_js_path

(name: str = "", module_file: str = "")

return module_json_path

get JS file path :param name: file name :type name: str :param module_file: filename :type module_file: str :return: 路径 :rtype: str

19

31

def _get_js_path(name: str = "", module_file: str = "") -> str: """ get JS file path :param name: file name :type name: str :param module_file: filename :type module_file: str :return: 路径 :rtype: str """ module_folder = os.path.abspath(os.path.dirname(os.path.dirname(module_file))) module_json_path = os.path.join(module_folder, "movie", name) return module_json_path

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/video_yien.py#L19-L31

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]

76.923077

[ 10, 11, 12 ]

23.076923

false

23.529412

13

1

76.923077

7

def _get_js_path(name: str = "", module_file: str = "") -> str: module_folder = os.path.abspath(os.path.dirname(os.path.dirname(module_file))) module_json_path = os.path.join(module_folder, "movie", name) return module_json_path

18,748

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/video_yien.py

_get_file_content

(file_name: str = "jm.js")

return file_data

read the file content :param file_name: filename :type file_name: str :return: file content :rtype: str

34

46

def _get_file_content(file_name: str = "jm.js"): """ read the file content :param file_name: filename :type file_name: str :return: file content :rtype: str """ setting_file_name = file_name setting_file_path = _get_js_path(setting_file_name, __file__) with open(setting_file_path) as f: file_data = f.read() return file_data

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/video_yien.py#L34-L46

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

61.538462

[ 8, 9, 10, 11, 12 ]

38.461538

false

23.529412

13

2

61.538462

5

def _get_file_content(file_name: str = "jm.js"): setting_file_name = file_name setting_file_path = _get_js_path(setting_file_name, __file__) with open(setting_file_path) as f: file_data = f.read() return file_data

18,749

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/video_yien.py

decrypt

(origin_data: str = "")

return data

解密艺恩的加密数据 :param origin_data: 解密前的字符串 :type origin_data: str :return: 解密后的字符串 :rtype: str

49

61

def decrypt(origin_data: str = "") -> str: """ 解密艺恩的加密数据 :param origin_data: 解密前的字符串 :type origin_data: str :return: 解密后的字符串 :rtype: str """ file_data = _get_file_content(file_name="jm.js") ctx = py_mini_racer.MiniRacer() ctx.eval(file_data) data = ctx.call("webInstace.shell", origin_data) return data

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/video_yien.py#L49-L61

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

61.538462

[ 8, 9, 10, 11, 12 ]

38.461538

false

23.529412

13

1

61.538462

5

def decrypt(origin_data: str = "") -> str: file_data = _get_file_content(file_name="jm.js") ctx = py_mini_racer.MiniRacer() ctx.eval(file_data) data = ctx.call("webInstace.shell", origin_data) return data

18,750

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/video_yien.py

video_tv

()

return temp_df

艺恩-视频放映-电视剧集 https://www.endata.com.cn/Video/index.html :return: 电视剧集 :rtype: pandas.DataFrame

64

81

def video_tv() -> pd.DataFrame: """ 艺恩-视频放映-电视剧集 https://www.endata.com.cn/Video/index.html :return: 电视剧集 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = {"tvType": 2, "MethodName": "BoxOffice_GetTvData_PlayIndexRank"} r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) report_date = data_json["Data"]["Table1"][0]["MaxDate"] temp_df.columns = ["排序", "名称", "类型", "播映指数", "用户热度", "媒体热度", "观看度", "好评度"] temp_df = temp_df[["排序", "名称", "类型", "播映指数", "媒体热度", "用户热度", "好评度", "观看度"]] temp_df["统计日期"] = report_date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/video_yien.py#L64-L81

25

[ 0, 1, 2, 3, 4, 5, 6 ]

38.888889

[ 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ]

61.111111

false

23.529412

18

1

38.888889

4

def video_tv() -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = {"tvType": 2, "MethodName": "BoxOffice_GetTvData_PlayIndexRank"} r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) report_date = data_json["Data"]["Table1"][0]["MaxDate"] temp_df.columns = ["排序", "名称", "类型", "播映指数", "用户热度", "媒体热度", "观看度", "好评度"] temp_df = temp_df[["排序", "名称", "类型", "播映指数", "媒体热度", "用户热度", "好评度", "观看度"]] temp_df["统计日期"] = report_date return temp_df

18,751

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/movie/video_yien.py

video_variety_show

()

return temp_df

艺恩-视频放映-综艺节目 https://www.endata.com.cn/Video/index.html :return: 综艺节目 :rtype: pandas.DataFrame

84

101

def video_variety_show() -> pd.DataFrame: """ 艺恩-视频放映-综艺节目 https://www.endata.com.cn/Video/index.html :return: 综艺节目 :rtype: pandas.DataFrame """ url = "https://www.endata.com.cn/API/GetData.ashx" payload = {"tvType": 8, "MethodName": "BoxOffice_GetTvData_PlayIndexRank"} r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) report_date = data_json["Data"]["Table1"][0]["MaxDate"] temp_df.columns = ["排序", "名称", "类型", "播映指数", "用户热度", "媒体热度", "观看度", "好评度"] temp_df = temp_df[["排序", "名称", "类型", "播映指数", "媒体热度", "用户热度", "好评度", "观看度"]] temp_df["统计日期"] = report_date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/movie/video_yien.py#L84-L101

25

[ 0, 1, 2, 3, 4, 5, 6 ]

38.888889

[ 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ]

61.111111

false

23.529412

18

1

38.888889

4

def video_variety_show() -> pd.DataFrame: url = "https://www.endata.com.cn/API/GetData.ashx" payload = {"tvType": 8, "MethodName": "BoxOffice_GetTvData_PlayIndexRank"} r = requests.post(url, data=payload) r.encoding = "utf8" data_json = json.loads(decrypt(r.text)) temp_df = pd.DataFrame(data_json["Data"]["Table"]) report_date = data_json["Data"]["Table1"][0]["MaxDate"] temp_df.columns = ["排序", "名称", "类型", "播映指数", "用户热度", "媒体热度", "观看度", "好评度"] temp_df = temp_df[["排序", "名称", "类型", "播映指数", "媒体热度", "用户热度", "好评度", "观看度"]] temp_df["统计日期"] = report_date return temp_df

18,752

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/tool/trade_date_hist.py

tool_trade_date_hist_sina

()

return temp_df

交易日历-历史数据 https://finance.sina.com.cn/realstock/company/klc_td_sh.txt :return: 交易日历 :rtype: pandas.DataFrame

18

39

def tool_trade_date_hist_sina() -> pd.DataFrame: """ 交易日历-历史数据 https://finance.sina.com.cn/realstock/company/klc_td_sh.txt :return: 交易日历 :rtype: pandas.DataFrame """ url = "https://finance.sina.com.cn/realstock/company/klc_td_sh.txt" r = requests.get(url) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", r.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 temp_df = pd.DataFrame(dict_list) temp_df.columns = ["trade_date"] temp_df["trade_date"] = pd.to_datetime(temp_df["trade_date"]).dt.date temp_list = temp_df["trade_date"].to_list() temp_list.append(datetime.date(1992, 5, 4)) # 是交易日但是交易日历缺失该日期 temp_list.sort() temp_df = pd.DataFrame(temp_list, columns=["trade_date"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/tool/trade_date_hist.py#L18-L39

25

[ 0, 1, 2, 3, 4, 5, 6 ]

31.818182

[ 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21 ]

59.090909

false

34.782609

22

1

40.909091

4

def tool_trade_date_hist_sina() -> pd.DataFrame: url = "https://finance.sina.com.cn/realstock/company/klc_td_sh.txt" r = requests.get(url) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", r.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 temp_df = pd.DataFrame(dict_list) temp_df.columns = ["trade_date"] temp_df["trade_date"] = pd.to_datetime(temp_df["trade_date"]).dt.date temp_list = temp_df["trade_date"].to_list() temp_list.append(datetime.date(1992, 5, 4)) # 是交易日但是交易日历缺失该日期 temp_list.sort() temp_df = pd.DataFrame(temp_list, columns=["trade_date"]) return temp_df

18,753

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/cost/cost_living.py

_get_region

()

return name_url_dict

获取主要板块, 一般不调用 :return: 主要板块 :rtype: dict

13

32

def _get_region() -> dict: """ 获取主要板块, 一般不调用 :return: 主要板块 :rtype: dict """ url = "https://www.expatistan.com/cost-of-living/index" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") half_url_list = [ item["href"] for item in soup.find("ul", attrs={"class": "regions"}).find_all("a") ] name_list = [ item["href"].split("/")[-1] for item in soup.find("ul", attrs={"class": "regions"}).find_all("a") ] name_url_dict = dict(zip(name_list, half_url_list)) name_url_dict["world"] = "/cost-of-living/index" return name_url_dict

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/cost/cost_living.py#L13-L32

25

[ 0, 1, 2, 3, 4, 5 ]

30

[ 6, 7, 8, 9, 13, 17, 18, 19 ]

40

false

56.521739

20

3

60

3

def _get_region() -> dict: url = "https://www.expatistan.com/cost-of-living/index" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") half_url_list = [ item["href"] for item in soup.find("ul", attrs={"class": "regions"}).find_all("a") ] name_list = [ item["href"].split("/")[-1] for item in soup.find("ul", attrs={"class": "regions"}).find_all("a") ] name_url_dict = dict(zip(name_list, half_url_list)) name_url_dict["world"] = "/cost-of-living/index" return name_url_dict

18,754

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/cost/cost_living.py

cost_living

(region: str = "world")

return temp_df

国家或地区生活成本数据 https://expatistan.com/cost-of-living/index :param region: choice of {"europe", "north-america", "latin-america", "asia", "middle-east", "africa", "oceania", "world"} :type region: str :return: 国家或地区生活成本数据 :rtype: pandas.DataFrame

35

58

def cost_living(region: str = "world") -> pd.DataFrame: """ 国家或地区生活成本数据 https://expatistan.com/cost-of-living/index :param region: choice of {"europe", "north-america", "latin-america", "asia", "middle-east", "africa", "oceania", "world"} :type region: str :return: 国家或地区生活成本数据 :rtype: pandas.DataFrame """ name_url_map = { "europe": "/cost-of-living/index/europe", "north-america": "/cost-of-living/index/north-america", "latin-america": "/cost-of-living/index/latin-america", "asia": "/cost-of-living/index/asia", "middle-east": "/cost-of-living/index/middle-east", "africa": "/cost-of-living/index/africa", "oceania": "/cost-of-living/index/oceania", "world": "/cost-of-living/index", } url = f"https://www.expatistan.com{name_url_map[region]}" r = requests.get(url) temp_df = pd.read_html(r.text)[0] temp_df.columns = ["rank", "city", "index"] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/cost/cost_living.py#L35-L58

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ]

100

[]

0

true

56.521739

24

1

100

6

def cost_living(region: str = "world") -> pd.DataFrame: name_url_map = { "europe": "/cost-of-living/index/europe", "north-america": "/cost-of-living/index/north-america", "latin-america": "/cost-of-living/index/latin-america", "asia": "/cost-of-living/index/asia", "middle-east": "/cost-of-living/index/middle-east", "africa": "/cost-of-living/index/africa", "oceania": "/cost-of-living/index/oceania", "world": "/cost-of-living/index", } url = f"https://www.expatistan.com{name_url_map[region]}" r = requests.get(url) temp_df = pd.read_html(r.text)[0] temp_df.columns = ["rank", "city", "index"] return temp_df

18,755

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/news/news_cctv.py

news_cctv

(date: str = "20130308")

新闻联播文字稿 https://tv.cctv.com/lm/xwlb/?spm=C52056131267.P4y8I53JvSWE.0.0 :param date: 需要获取数据的日期; 目前 20160203 年后 :type date: str :return: 新闻联播文字稿 :rtype: pandas.DataFrame

16

176

def news_cctv(date: str = "20130308") -> pd.DataFrame: """ 新闻联播文字稿 https://tv.cctv.com/lm/xwlb/?spm=C52056131267.P4y8I53JvSWE.0.0 :param date: 需要获取数据的日期; 目前 20160203 年后 :type date: str :return: 新闻联播文字稿 :rtype: pandas.DataFrame """ if int(date) <= int("20130708"): url = f"http://cctv.cntv.cn/lm/xinwenlianbo/{date}.shtml" r = requests.get(url) r.encoding = "gbk" raw_list = re.findall(r"title_array_01\((.*)", r.text) page_url = [ re.findall("(http.*)", item)[0].split("'")[0] for item in raw_list[1:] ] title_list = [] content_list = [] headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Cookie": "cna=DLYSGBDthG4CAbRVCNxSxGT6", "Host": "tv.cctv.com", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36", } for page in tqdm(page_url, leave=False): try: r = requests.get(page, headers=headers) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") title = soup.find("h3").text content = soup.find("div", attrs={"class": "cnt_bd"}).text title_list.append( title.strip("[视频]").strip().replace("\n", " ") ) content_list.append( content.strip() .strip("央视网消息(新闻联播)：") .strip("央视网消息（新闻联播）：") .strip("(新闻联播)：") .strip() .replace("\n", " ") ) except: continue temp_df = pd.DataFrame( [[date] * len(title_list), title_list, content_list], index=["date", "title", "content"], ).T return temp_df elif int(date) < int("20160203"): url = f"http://cctv.cntv.cn/lm/xinwenlianbo/{date}.shtml" r = requests.get(url) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") page_url = [ item.find("a")["href"] for item in soup.find( "div", attrs={"id": "contentELMT1368521805488378"} ).find_all("li")[1:] ] title_list = [] content_list = [] headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Cookie": "cna=DLYSGBDthG4CAbRVCNxSxGT6", "Host": "tv.cctv.com", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36", } for page in tqdm(page_url, leave=False): try: r = requests.get(page, headers=headers) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") title = soup.find("h3").text content = soup.find("div", attrs={"class": "cnt_bd"}).text title_list.append( title.strip("[视频]").strip().replace("\n", " ") ) content_list.append( content.strip() .strip("央视网消息(新闻联播)：") .strip("央视网消息（新闻联播）：") .strip("(新闻联播)：") .strip() .replace("\n", " ") ) except: continue temp_df = pd.DataFrame( [[date] * len(title_list), title_list, content_list], index=["date", "title", "content"], ).T return temp_df elif int(date) > int("20160203"): url = f"https://tv.cctv.com/lm/xwlb/day/{date}.shtml" r = requests.get(url) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") page_url = [item.find("a")["href"] for item in soup.find_all("li")[1:]] title_list = [] content_list = [] headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Cookie": "cna=DLYSGBDthG4CAbRVCNxSxGT6", "Host": "tv.cctv.com", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36", } for page in tqdm(page_url, leave=False): try: r = requests.get(page, headers=headers) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") if soup.find("h3"): title = soup.find("h3").text else: title = soup.find("div", attrs={"class": "tit"}).text if soup.find("div", attrs={"class": "cnt_bd"}): content = soup.find("div", attrs={"class": "cnt_bd"}).text else: content = soup.find( "div", attrs={"class": "content_area"} ).text title_list.append( title.strip("[视频]").strip().replace("\n", " ") ) content_list.append( content.strip() .strip("央视网消息(新闻联播)：") .strip("央视网消息（新闻联播）：") .strip("(新闻联播)：") .strip() .replace("\n", " ") ) except: continue temp_df = pd.DataFrame( [[date] * len(title_list), title_list, content_list], index=["date", "title", "content"], ).T return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/news/news_cctv.py#L16-L176

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

5.590062

[ 9, 10, 11, 12, 13, 14, 18, 19, 20, 32, 33, 34, 35, 36, 37, 38, 39, 42, 50, 51, 52, 56, 58, 59, 60, 61, 62, 63, 69, 70, 71, 83, 84, 85, 86, 87, 88, 89, 90, 93, 101, 102, 103, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 140, 143, 146, 154, 155, 156, 160 ]

43.478261

false

10

161

15

56.521739

6

def news_cctv(date: str = "20130308") -> pd.DataFrame: if int(date) <= int("20130708"): url = f"http://cctv.cntv.cn/lm/xinwenlianbo/{date}.shtml" r = requests.get(url) r.encoding = "gbk" raw_list = re.findall(r"title_array_01\((.*)", r.text) page_url = [ re.findall("(http.*)", item)[0].split("'")[0] for item in raw_list[1:] ] title_list = [] content_list = [] headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Cookie": "cna=DLYSGBDthG4CAbRVCNxSxGT6", "Host": "tv.cctv.com", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36", } for page in tqdm(page_url, leave=False): try: r = requests.get(page, headers=headers) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") title = soup.find("h3").text content = soup.find("div", attrs={"class": "cnt_bd"}).text title_list.append( title.strip("[视频]").strip().replace("\n", " ") ) content_list.append( content.strip() .strip("央视网消息(新闻联播)：") .strip("央视网消息（新闻联播）：") .strip("(新闻联播)：") .strip() .replace("\n", " ") ) except: continue temp_df = pd.DataFrame( [[date] * len(title_list), title_list, content_list], index=["date", "title", "content"], ).T return temp_df elif int(date) < int("20160203"): url = f"http://cctv.cntv.cn/lm/xinwenlianbo/{date}.shtml" r = requests.get(url) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") page_url = [ item.find("a")["href"] for item in soup.find( "div", attrs={"id": "contentELMT1368521805488378"} ).find_all("li")[1:] ] title_list = [] content_list = [] headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Cookie": "cna=DLYSGBDthG4CAbRVCNxSxGT6", "Host": "tv.cctv.com", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36", } for page in tqdm(page_url, leave=False): try: r = requests.get(page, headers=headers) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") title = soup.find("h3").text content = soup.find("div", attrs={"class": "cnt_bd"}).text title_list.append( title.strip("[视频]").strip().replace("\n", " ") ) content_list.append( content.strip() .strip("央视网消息(新闻联播)：") .strip("央视网消息（新闻联播）：") .strip("(新闻联播)：") .strip() .replace("\n", " ") ) except: continue temp_df = pd.DataFrame( [[date] * len(title_list), title_list, content_list], index=["date", "title", "content"], ).T return temp_df elif int(date) > int("20160203"): url = f"https://tv.cctv.com/lm/xwlb/day/{date}.shtml" r = requests.get(url) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") page_url = [item.find("a")["href"] for item in soup.find_all("li")[1:]] title_list = [] content_list = [] headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Cookie": "cna=DLYSGBDthG4CAbRVCNxSxGT6", "Host": "tv.cctv.com", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36", } for page in tqdm(page_url, leave=False): try: r = requests.get(page, headers=headers) r.encoding = "utf-8" soup = BeautifulSoup(r.text, "lxml") if soup.find("h3"): title = soup.find("h3").text else: title = soup.find("div", attrs={"class": "tit"}).text if soup.find("div", attrs={"class": "cnt_bd"}): content = soup.find("div", attrs={"class": "cnt_bd"}).text else: content = soup.find( "div", attrs={"class": "content_area"} ).text title_list.append( title.strip("[视频]").strip().replace("\n", " ") ) content_list.append( content.strip() .strip("央视网消息(新闻联播)：") .strip("央视网消息（新闻联播）：") .strip("(新闻联播)：") .strip() .replace("\n", " ") ) except: continue temp_df = pd.DataFrame( [[date] * len(title_list), title_list, content_list], index=["date", "title", "content"], ).T return temp_df

18,756

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/news/news_stock.py

stock_news_em

(symbol: str = "601628")

return temp_df

东方财富-个股新闻-最近 100 条新闻 https://so.eastmoney.com/news/s?keyword=%E4%B8%AD%E5%9B%BD%E4%BA%BA%E5%AF%BF&pageindex=1&searchrange=8192&sortfiled=4 :param symbol: 股票代码 :type symbol: str :return: 个股新闻 :rtype: pandas.DataFrame

14

82

def stock_news_em(symbol: str = "601628") -> pd.DataFrame: """ 东方财富-个股新闻-最近 100 条新闻 https://so.eastmoney.com/news/s?keyword=%E4%B8%AD%E5%9B%BD%E4%BA%BA%E5%AF%BF&pageindex=1&searchrange=8192&sortfiled=4 :param symbol: 股票代码 :type symbol: str :return: 个股新闻 :rtype: pandas.DataFrame """ url = "https://search-api-web.eastmoney.com/search/jsonp" params = { "cb": "jQuery3510875346244069884_1668256937995", "param": '{"uid":"",' + f'"keyword":"{symbol}"' + ',"type":["cmsArticleWebOld"],"client":"web","clientType":"web","clientVersion":"curr","param":{"cmsArticleWebOld":{"searchScope":"default","sort":"default","pageIndex":1,"pageSize":100,"preTag":"","postTag":""}}}', "_": "1668256937996", } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text.strip("jQuery3510875346244069884_1668256937995(")[:-1] ) temp_df = pd.DataFrame(data_json["result"]["cmsArticleWebOld"]) temp_df.rename( columns={ "date": "发布时间", "mediaName": "文章来源", "code": "-", "title": "新闻标题", "content": "新闻内容", "url": "新闻链接", "image": "-", }, inplace=True, ) temp_df["关键词"] = symbol temp_df = temp_df[ [ "关键词", "新闻标题", "新闻内容", "发布时间", "文章来源", "新闻链接", ] ] temp_df["新闻标题"] = ( temp_df["新闻标题"] .str.replace(r"\(", "", regex=True) .str.replace(r"\)", "", regex=True) ) temp_df["新闻标题"] = ( temp_df["新闻标题"] .str.replace(r"", "", regex=True) .str.replace(r"", "", regex=True) ) temp_df["新闻内容"] = ( temp_df["新闻内容"] .str.replace(r"\(", "", regex=True) .str.replace(r"\)", "", regex=True) ) temp_df["新闻内容"] = ( temp_df["新闻内容"] .str.replace(r"", "", regex=True) .str.replace(r"", "", regex=True) ) temp_df["新闻内容"] = temp_df["新闻内容"].str.replace(r"\u3000", "", regex=True) temp_df["新闻内容"] = temp_df["新闻内容"].str.replace(r"\r\n", " ", regex=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/news/news_stock.py#L14-L82

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

13.043478

[ 9, 10, 17, 18, 19, 22, 23, 35, 36, 46, 51, 56, 61, 66, 67, 68 ]

23.188406

false

25

69

1

76.811594

6

def stock_news_em(symbol: str = "601628") -> pd.DataFrame: url = "https://search-api-web.eastmoney.com/search/jsonp" params = { "cb": "jQuery3510875346244069884_1668256937995", "param": '{"uid":"",' + f'"keyword":"{symbol}"' + ',"type":["cmsArticleWebOld"],"client":"web","clientType":"web","clientVersion":"curr","param":{"cmsArticleWebOld":{"searchScope":"default","sort":"default","pageIndex":1,"pageSize":100,"preTag":"","postTag":""}}}', "_": "1668256937996", } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text.strip("jQuery3510875346244069884_1668256937995(")[:-1] ) temp_df = pd.DataFrame(data_json["result"]["cmsArticleWebOld"]) temp_df.rename( columns={ "date": "发布时间", "mediaName": "文章来源", "code": "-", "title": "新闻标题", "content": "新闻内容", "url": "新闻链接", "image": "-", }, inplace=True, ) temp_df["关键词"] = symbol temp_df = temp_df[ [ "关键词", "新闻标题", "新闻内容", "发布时间", "文章来源", "新闻链接", ] ] temp_df["新闻标题"] = ( temp_df["新闻标题"] .str.replace(r"\(", "", regex=True) .str.replace(r"\)", "", regex=True) ) temp_df["新闻标题"] = ( temp_df["新闻标题"] .str.replace(r"", "", regex=True) .str.replace(r"", "", regex=True) ) temp_df["新闻内容"] = ( temp_df["新闻内容"] .str.replace(r"\(", "", regex=True) .str.replace(r"\)", "", regex=True) ) temp_df["新闻内容"] = ( temp_df["新闻内容"] .str.replace(r"", "", regex=True) .str.replace(r"", "", regex=True) ) temp_df["新闻内容"] = temp_df["新闻内容"].str.replace(r"\u3000", "", regex=True) temp_df["新闻内容"] = temp_df["新闻内容"].str.replace(r"\r\n", " ", regex=True) return temp_df

18,757

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/news/news_baidu.py

news_economic_baidu

(date: str = "20220502")

return big_df

百度股市通-经济数据 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 经济数据 :rtype: pandas.DataFrame

12

73

def news_economic_baidu(date: str = "20220502") -> pd.DataFrame: """ 百度股市通-经济数据 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 经济数据 :rtype: pandas.DataFrame """ start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "economic_data", 'rn': '500', 'pn': '0', } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "日期", "时间", "-", "事件", "重要性", "前值", "预期", "公布", "-", "-", "地区", "-", ] temp_df = temp_df[ [ "日期", "时间", "地区", "事件", "公布", "预期", "前值", "重要性", ] ] temp_df["公布"] = pd.to_numeric(temp_df["公布"], errors="coerce") temp_df["预期"] = pd.to_numeric(temp_df["预期"], errors="coerce") temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["重要性"] = pd.to_numeric(temp_df["重要性"], errors="coerce") temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/news/news_baidu.py#L12-L73

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

14.516129

[ 9, 10, 11, 12, 20, 21, 22, 23, 24, 25, 26, 40, 52, 53, 54, 55, 56, 58, 60, 61 ]

32.258065

false

9.411765

62

3

67.741935

6

def news_economic_baidu(date: str = "20220502") -> pd.DataFrame: start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "economic_data", 'rn': '500', 'pn': '0', } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "日期", "时间", "-", "事件", "重要性", "前值", "预期", "公布", "-", "-", "地区", "-", ] temp_df = temp_df[ [ "日期", "时间", "地区", "事件", "公布", "预期", "前值", "重要性", ] ] temp_df["公布"] = pd.to_numeric(temp_df["公布"], errors="coerce") temp_df["预期"] = pd.to_numeric(temp_df["预期"], errors="coerce") temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["重要性"] = pd.to_numeric(temp_df["重要性"], errors="coerce") temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

18,758

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/news/news_baidu.py

news_trade_notify_suspend_baidu

(date: str = "20220513")

return big_df

百度股市通-交易提醒-停复牌 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 交易提醒-停复牌 :rtype: pandas.DataFrame

76

125

def news_trade_notify_suspend_baidu(date: str = "20220513") -> pd.DataFrame: """ 百度股市通-交易提醒-停复牌 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 交易提醒-停复牌 :rtype: pandas.DataFrame """ start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "notify_suspend", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "股票代码", "-", "交易所", "股票简称", "停牌时间", "复牌时间", "-", "停牌事项说明", ] temp_df = temp_df[ [ "股票代码", "股票简称", "交易所", "停牌时间", "复牌时间", "停牌事项说明", ] ] temp_df["停牌时间"] = pd.to_datetime(temp_df["停牌时间"]).dt.date temp_df["复牌时间"] = pd.to_datetime(temp_df["复牌时间"]).dt.date big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/news/news_baidu.py#L76-L125

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

18

[ 9, 10, 11, 12, 18, 19, 20, 21, 22, 23, 24, 34, 44, 45, 46, 48, 49 ]

34

false

9.411765

50

3

66

6

def news_trade_notify_suspend_baidu(date: str = "20220513") -> pd.DataFrame: start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "notify_suspend", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "股票代码", "-", "交易所", "股票简称", "停牌时间", "复牌时间", "-", "停牌事项说明", ] temp_df = temp_df[ [ "股票代码", "股票简称", "交易所", "停牌时间", "复牌时间", "停牌事项说明", ] ] temp_df["停牌时间"] = pd.to_datetime(temp_df["停牌时间"]).dt.date temp_df["复牌时间"] = pd.to_datetime(temp_df["复牌时间"]).dt.date big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

18,759

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/news/news_baidu.py

news_trade_notify_dividend_baidu

(date: str = "20220916")

return big_df

百度股市通-交易提醒-分红派息 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 交易提醒-停复牌 :rtype: pandas.DataFrame

128

182

def news_trade_notify_dividend_baidu(date: str = "20220916") -> pd.DataFrame: """ 百度股市通-交易提醒-分红派息 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 交易提醒-停复牌 :rtype: pandas.DataFrame """ start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "notify_divide", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "股票代码", "-", "交易所", "股票简称", "除权日", "报告期", "分红", "送股", "转增", "实物", ] temp_df = temp_df[ [ "股票代码", "除权日", "分红", "送股", "转增", "实物", "交易所", "股票简称", "报告期", ] ] temp_df["除权日"] = pd.to_datetime(temp_df["除权日"]).dt.date temp_df["报告期"] = pd.to_datetime(temp_df["报告期"]).dt.date big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/news/news_baidu.py#L128-L182

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

16.363636

[ 9, 10, 11, 12, 18, 19, 20, 21, 22, 23, 24, 36, 49, 50, 51, 53, 54 ]

30.909091

false

9.411765

55

3

69.090909

6

def news_trade_notify_dividend_baidu(date: str = "20220916") -> pd.DataFrame: start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "notify_divide", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "股票代码", "-", "交易所", "股票简称", "除权日", "报告期", "分红", "送股", "转增", "实物", ] temp_df = temp_df[ [ "股票代码", "除权日", "分红", "送股", "转增", "实物", "交易所", "股票简称", "报告期", ] ] temp_df["除权日"] = pd.to_datetime(temp_df["除权日"]).dt.date temp_df["报告期"] = pd.to_datetime(temp_df["报告期"]).dt.date big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

18,760

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/news/news_baidu.py

news_report_time_baidu

(date: str = "20220514")

return big_df

百度股市通-财报发行 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 财报发行 :rtype: pandas.DataFrame

185

230

def news_report_time_baidu(date: str = "20220514") -> pd.DataFrame: """ 百度股市通-财报发行 https://gushitong.baidu.com/calendar :param date: 查询日期 :type date: str :return: 财报发行 :rtype: pandas.DataFrame """ start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "report_time", 'finClientType': 'pc', } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "股票代码", "-", "交易所", "-", "股票简称", "-", "财报期", ] temp_df = temp_df[ [ "股票代码", "交易所", "股票简称", "财报期", ] ] big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/news/news_baidu.py#L185-L230

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

19.565217

[ 9, 10, 11, 12, 19, 20, 21, 22, 23, 24, 25, 34, 42, 44, 45 ]

32.608696

false

9.411765

46

3

67.391304

6

def news_report_time_baidu(date: str = "20220514") -> pd.DataFrame: start_date = "-".join([date[:4], date[4:6], date[6:]]) end_date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://finance.pae.baidu.com/api/financecalendar" params = { "start_date": start_date, "end_date": end_date, "market": "", "cate": "report_time", 'finClientType': 'pc', } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() for item in data_json["Result"]: if not item["list"] == []: temp_df = pd.DataFrame(item["list"]) temp_df.columns = [ "股票代码", "-", "交易所", "-", "股票简称", "-", "财报期", ] temp_df = temp_df[ [ "股票代码", "交易所", "股票简称", "财报期", ] ] big_df = pd.concat([big_df, temp_df], ignore_index=True) else: continue return big_df

18,761

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dividents_cninfo.py

stock_dividents_cninfo

(symbol: str = "600009")

return temp_df

巨潮资讯-个股-历史分红 http://webapi.cninfo.com.cn/#/company?companyid=600009 :param symbol: 股票代码 :type symbol: str :return: 历史分红 :rtype: pandas.DataFrame

45

100

def stock_dividents_cninfo(symbol: str = "600009") -> pd.DataFrame: """ 巨潮资讯-个股-历史分红 http://webapi.cninfo.com.cn/#/company?companyid=600009 :param symbol: 股票代码 :type symbol: str :return: 历史分红 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1139" params = { 'scode': symbol } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "实施方案公告日期", "送股比例", "转增比例", "派息比例", "股权登记日", "除权日", "派息日", "股份到账日", "实施方案分红说明", "分红类型", "报告时间", ] temp_df["实施方案公告日期"] = pd.to_datetime(temp_df["实施方案公告日期"]).dt.date temp_df["送股比例"] = pd.to_numeric(temp_df["送股比例"], errors="coerce") temp_df["转增比例"] = pd.to_numeric(temp_df["转增比例"], errors="coerce") temp_df["派息比例"] = pd.to_numeric(temp_df["派息比例"], errors="coerce") temp_df["股权登记日"] = pd.to_datetime(temp_df["股权登记日"], errors="coerce").dt.date temp_df["除权日"] = pd.to_datetime(temp_df["除权日"], errors="coerce").dt.date temp_df["派息日"] = pd.to_datetime(temp_df["派息日"], errors="coerce").dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dividents_cninfo.py#L45-L100

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

16.071429

[ 9, 10, 13, 14, 15, 16, 17, 32, 33, 34, 35, 48, 49, 50, 51, 52, 53, 54, 55 ]

33.928571

false

27.586207

56

1

66.071429

6

def stock_dividents_cninfo(symbol: str = "600009") -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1139" params = { 'scode': symbol } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "实施方案公告日期", "送股比例", "转增比例", "派息比例", "股权登记日", "除权日", "派息日", "股份到账日", "实施方案分红说明", "分红类型", "报告时间", ] temp_df["实施方案公告日期"] = pd.to_datetime(temp_df["实施方案公告日期"]).dt.date temp_df["送股比例"] = pd.to_numeric(temp_df["送股比例"], errors="coerce") temp_df["转增比例"] = pd.to_numeric(temp_df["转增比例"], errors="coerce") temp_df["派息比例"] = pd.to_numeric(temp_df["派息比例"], errors="coerce") temp_df["股权登记日"] = pd.to_datetime(temp_df["股权登记日"], errors="coerce").dt.date temp_df["除权日"] = pd.to_datetime(temp_df["除权日"], errors="coerce").dt.date temp_df["派息日"] = pd.to_datetime(temp_df["派息日"], errors="coerce").dt.date return temp_df

18,762

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_industry_cninfo.py

stock_industry_category_cninfo

(symbol: str = "巨潮行业分类标准") -> pd.DataFrame

return temp_df

巨潮资讯-行业分类数据 http://webapi.cninfo.com.cn/#/apiDoc 查询 p_public0002 接口 :param symbol: 行业类型; choice of {"证监会行业分类标准", "巨潮行业分类标准", "申银万国行业分类标准", "新财富行业分类标准", "国资委行业分类标准", "巨潮产业细分标准", "天相行业分类标准", "全球行业分类标准"} :type symbol: str :return: 行业分类数据 :rtype: pandas.DataFrame

47

115

def stock_industry_category_cninfo(symbol: str = "巨潮行业分类标准") -> pd.DataFrame: """ 巨潮资讯-行业分类数据 http://webapi.cninfo.com.cn/#/apiDoc 查询 p_public0002 接口 :param symbol: 行业类型; choice of {"证监会行业分类标准", "巨潮行业分类标准", "申银万国行业分类标准", "新财富行业分类标准", "国资委行业分类标准", "巨潮产业细分标准", "天相行业分类标准", "全球行业分类标准"} :type symbol: str :return: 行业分类数据 :rtype: pandas.DataFrame """ symbol_map = { "证监会行业分类标准": "008001", "巨潮行业分类标准": "008002", "申银万国行业分类标准": "008003", "新财富行业分类标准": "008004", "国资委行业分类标准": "008005", "巨潮产业细分标准": "008006", "天相行业分类标准": "008007", "全球行业分类标准": "008008", } url = "http://webapi.cninfo.com.cn/api/stock/p_public0002" params = {"indcode": "", "indtype": symbol_map[symbol]} random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) cols_map = { "PARENTCODE": "父类编码", "SORTCODE": "类目编码", "SORTNAME": "类目名称", "F001V": "类目名称英文", "F002D": "终止日期", "F003V": "行业类型编码", "F004V": "行业类型", } temp_df.rename(columns=cols_map, inplace=True) # 行业按分级排序 tmp = temp_df[["类目编码"]].copy() tmp["len"] = temp_df["类目编码"].str.len() tmp["Level"] = 0 g = tmp.groupby("len") level = 0 for k in g.groups.keys(): temp_df.loc[ temp_df["类目编码"].isin(g.get_group(k)["类目编码"]), "Level" ] = level level += 1 temp_df["Level"] = temp_df["Level"].astype(int) temp_df.rename(columns={"Level": "分级"}, inplace=True) temp_df["终止日期"] = pd.to_datetime(temp_df["终止日期"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_industry_cninfo.py#L47-L115

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]

14.492754

[ 10, 20, 21, 22, 23, 24, 25, 26, 41, 42, 43, 44, 53, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68 ]

36.231884

false

17.857143

69

2

63.768116

7

def stock_industry_category_cninfo(symbol: str = "巨潮行业分类标准") -> pd.DataFrame: symbol_map = { "证监会行业分类标准": "008001", "巨潮行业分类标准": "008002", "申银万国行业分类标准": "008003", "新财富行业分类标准": "008004", "国资委行业分类标准": "008005", "巨潮产业细分标准": "008006", "天相行业分类标准": "008007", "全球行业分类标准": "008008", } url = "http://webapi.cninfo.com.cn/api/stock/p_public0002" params = {"indcode": "", "indtype": symbol_map[symbol]} random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) cols_map = { "PARENTCODE": "父类编码", "SORTCODE": "类目编码", "SORTNAME": "类目名称", "F001V": "类目名称英文", "F002D": "终止日期", "F003V": "行业类型编码", "F004V": "行业类型", } temp_df.rename(columns=cols_map, inplace=True) # 行业按分级排序 tmp = temp_df[["类目编码"]].copy() tmp["len"] = temp_df["类目编码"].str.len() tmp["Level"] = 0 g = tmp.groupby("len") level = 0 for k in g.groups.keys(): temp_df.loc[ temp_df["类目编码"].isin(g.get_group(k)["类目编码"]), "Level" ] = level level += 1 temp_df["Level"] = temp_df["Level"].astype(int) temp_df.rename(columns={"Level": "分级"}, inplace=True) temp_df["终止日期"] = pd.to_datetime(temp_df["终止日期"]).dt.date return temp_df

18,763

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_industry_cninfo.py

stock_industry_change_cninfo

( symbol: str = "002594", start_date: str = "20091227", end_date: str = "20220713", )

return data_df

巨潮资讯-上市公司行业归属的变动情况 http://webapi.cninfo.com.cn/#/apiDoc 查询 p_stock2110 接口 :param symbol: 股票代码 :type symbol: str :param start_date: 开始变动日期 :type start_date: str :param end_date: 结束变动日期 :type end_date: str :return: 行业归属的变动情况 :rtype: pandas.DataFrame

118

183

def stock_industry_change_cninfo( symbol: str = "002594", start_date: str = "20091227", end_date: str = "20220713", ) -> pd.DataFrame: """ 巨潮资讯-上市公司行业归属的变动情况 http://webapi.cninfo.com.cn/#/apiDoc 查询 p_stock2110 接口 :param symbol: 股票代码 :type symbol: str :param start_date: 开始变动日期 :type start_date: str :param end_date: 结束变动日期 :type end_date: str :return: 行业归属的变动情况 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/stock/p_stock2110" params = { "scode": symbol, "sdate": "-".join([start_date[:4], start_date[4:6], start_date[6:]]), "edate": "-".join([end_date[:4], end_date[4:6], end_date[6:]]), } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) cols_map = { "ORGNAME": "机构名称", "SECCODE": "证券代码", "SECNAME": "新证券简称", "VARYDATE": "变更日期", "F001V": "分类标准编码", "F002V": "分类标准", "F003V": "行业编码", "F004V": "行业门类", "F005V": "行业次类", "F006V": "行业大类", "F007V": "行业中类", "F008C": "最新记录标识", } ignore_cols = ["最新记录标识"] temp_df.rename(columns=cols_map, inplace=True) temp_df.fillna(np.nan, inplace=True) temp_df["变更日期"] = pd.to_datetime(temp_df["变更日期"]).dt.date data_df = temp_df[[c for c in temp_df.columns if c not in ignore_cols]] return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_industry_cninfo.py#L118-L183

25

[ 0 ]

1.515152

[ 18, 19, 24, 25, 26, 27, 28, 43, 44, 45, 46, 60, 61, 62, 63, 64, 65 ]

25.757576

false

17.857143

66

2

74.242424

11

def stock_industry_change_cninfo( symbol: str = "002594", start_date: str = "20091227", end_date: str = "20220713", ) -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/stock/p_stock2110" params = { "scode": symbol, "sdate": "-".join([start_date[:4], start_date[4:6], start_date[6:]]), "edate": "-".join([end_date[:4], end_date[4:6], end_date[6:]]), } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) cols_map = { "ORGNAME": "机构名称", "SECCODE": "证券代码", "SECNAME": "新证券简称", "VARYDATE": "变更日期", "F001V": "分类标准编码", "F002V": "分类标准", "F003V": "行业编码", "F004V": "行业门类", "F005V": "行业次类", "F006V": "行业大类", "F007V": "行业中类", "F008C": "最新记录标识", } ignore_cols = ["最新记录标识"] temp_df.rename(columns=cols_map, inplace=True) temp_df.fillna(np.nan, inplace=True) temp_df["变更日期"] = pd.to_datetime(temp_df["变更日期"]).dt.date data_df = temp_df[[c for c in temp_df.columns if c not in ignore_cols]] return data_df

18,764

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_new_cninfo.py

stock_new_gh_cninfo

()

return temp_df

巨潮资讯-数据中心-新股数据-新股过会 http://webapi.cninfo.com.cn/#/xinguList :return: 新股过会 :rtype: pandas.DataFrame

45

85

def stock_new_gh_cninfo() -> pd.DataFrame: """ 巨潮资讯-数据中心-新股数据-新股过会 http://webapi.cninfo.com.cn/#/xinguList :return: 新股过会 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1098" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "公司名称", "上会日期", "审核类型", "审议内容", "审核结果", "审核公告日", ] temp_df["上会日期"] = pd.to_datetime(temp_df["上会日期"]).dt.date temp_df["审核公告日"] = pd.to_datetime(temp_df["审核公告日"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_new_cninfo.py#L45-L85

25

[ 0, 1, 2, 3, 4, 5, 6 ]

17.073171

[ 7, 8, 9, 10, 11, 12, 27, 28, 29, 30, 38, 39, 40 ]

31.707317

false

18

41

1

68.292683

4

def stock_new_gh_cninfo() -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1098" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "公司名称", "上会日期", "审核类型", "审议内容", "审核结果", "审核公告日", ] temp_df["上会日期"] = pd.to_datetime(temp_df["上会日期"]).dt.date temp_df["审核公告日"] = pd.to_datetime(temp_df["审核公告日"]).dt.date return temp_df

18,765

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_new_cninfo.py

stock_new_ipo_cninfo

()

return temp_df

巨潮资讯-数据中心-新股数据-新股发行 http://webapi.cninfo.com.cn/#/xinguList :return: 新股发行 :rtype: pandas.DataFrame

88

165

def stock_new_ipo_cninfo() -> pd.DataFrame: """ 巨潮资讯-数据中心-新股数据-新股发行 http://webapi.cninfo.com.cn/#/xinguList :return: 新股发行 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1097" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "timetype": "36", "market": "ALL", } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "摇号结果公告日", "中签公告日", "证券简称", "上市日期", "中签缴款日", "申购日期", "发行价", "证劵代码", "上网发行中签率", "总发行数量", "发行市盈率", "上网发行数量", "网上申购上限", ] temp_df = temp_df[ [ "证劵代码", "证券简称", "上市日期", "申购日期", "发行价", "总发行数量", "发行市盈率", "上网发行中签率", "摇号结果公告日", "中签公告日", "中签缴款日", "网上申购上限", "上网发行数量", ] ] temp_df["摇号结果公告日"] = pd.to_datetime(temp_df["摇号结果公告日"]).dt.date temp_df["中签公告日"] = pd.to_datetime(temp_df["中签公告日"]).dt.date temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"]).dt.date temp_df["中签缴款日"] = pd.to_datetime(temp_df["中签缴款日"]).dt.date temp_df["申购日期"] = pd.to_datetime(temp_df["申购日期"]).dt.date temp_df["发行价"] = pd.to_numeric(temp_df["发行价"]) temp_df["上网发行中签率"] = pd.to_numeric(temp_df["上网发行中签率"]) temp_df["总发行数量"] = pd.to_numeric(temp_df["总发行数量"]) temp_df["发行市盈率"] = pd.to_numeric(temp_df["发行市盈率"]) temp_df["上网发行数量"] = pd.to_numeric(temp_df["上网发行数量"]) temp_df["网上申购上限"] = pd.to_numeric(temp_df["网上申购上限"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_new_cninfo.py#L88-L165

25

[ 0, 1, 2, 3, 4, 5, 6 ]

8.974359

[ 7, 8, 9, 10, 11, 12, 27, 31, 32, 33, 34, 49, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77 ]

30.769231

false

18

78

1

69.230769

4

def stock_new_ipo_cninfo() -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1097" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "timetype": "36", "market": "ALL", } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "摇号结果公告日", "中签公告日", "证券简称", "上市日期", "中签缴款日", "申购日期", "发行价", "证劵代码", "上网发行中签率", "总发行数量", "发行市盈率", "上网发行数量", "网上申购上限", ] temp_df = temp_df[ [ "证劵代码", "证券简称", "上市日期", "申购日期", "发行价", "总发行数量", "发行市盈率", "上网发行中签率", "摇号结果公告日", "中签公告日", "中签缴款日", "网上申购上限", "上网发行数量", ] ] temp_df["摇号结果公告日"] = pd.to_datetime(temp_df["摇号结果公告日"]).dt.date temp_df["中签公告日"] = pd.to_datetime(temp_df["中签公告日"]).dt.date temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"]).dt.date temp_df["中签缴款日"] = pd.to_datetime(temp_df["中签缴款日"]).dt.date temp_df["申购日期"] = pd.to_datetime(temp_df["申购日期"]).dt.date temp_df["发行价"] = pd.to_numeric(temp_df["发行价"]) temp_df["上网发行中签率"] = pd.to_numeric(temp_df["上网发行中签率"]) temp_df["总发行数量"] = pd.to_numeric(temp_df["总发行数量"]) temp_df["发行市盈率"] = pd.to_numeric(temp_df["发行市盈率"]) temp_df["上网发行数量"] = pd.to_numeric(temp_df["上网发行数量"]) temp_df["网上申购上限"] = pd.to_numeric(temp_df["网上申购上限"]) return temp_df

18,766

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_us_pink.py

stock_us_pink_spot_em

()

return temp_df

东方财富网-行情中心-美股市场-粉单市场 http://quote.eastmoney.com/center/gridlist.html#us_pinksheet :return: 粉单市场实时行情 :rtype: pandas.DataFrame

12

100

def stock_us_pink_spot_em() -> pd.DataFrame: """ 东方财富网-行情中心-美股市场-粉单市场 http://quote.eastmoney.com/center/gridlist.html#us_pinksheet :return: 粉单市场实时行情 :rtype: pandas.DataFrame """ url = "http://23.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:153", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f26,f22,f33,f11,f62,f128,f136,f115,f152", "_": "1631271634231", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ "_", "最新价", "涨跌幅", "涨跌额", "_", "_", "_", "_", "_", "_", "_", "简称", "编码", "名称", "最高价", "最低价", "开盘价", "昨收价", "总市值", "_", "_", "_", "_", "_", "_", "_", "_", "市盈率", "_", "_", "_", "_", "_", ] temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename(columns={"index": "序号"}, inplace=True) temp_df["代码"] = temp_df["编码"].astype(str) + "." + temp_df["简称"] temp_df = temp_df[ [ "序号", "名称", "最新价", "涨跌额", "涨跌幅", "开盘价", "最高价", "最低价", "昨收价", "总市值", "市盈率", "代码", ] ] temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce") temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce") temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce") temp_df["昨收价"] = pd.to_numeric(temp_df["昨收价"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["市盈率"] = pd.to_numeric(temp_df["市盈率"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_us_pink.py#L12-L100

25

[ 0, 1, 2, 3, 4, 5, 6 ]

7.865169

[ 7, 8, 21, 22, 23, 24, 59, 60, 61, 62, 63, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88 ]

23.595506

false

17.857143

89

1

76.404494

4

def stock_us_pink_spot_em() -> pd.DataFrame: url = "http://23.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:153", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f26,f22,f33,f11,f62,f128,f136,f115,f152", "_": "1631271634231", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ "_", "最新价", "涨跌幅", "涨跌额", "_", "_", "_", "_", "_", "_", "_", "简称", "编码", "名称", "最高价", "最低价", "开盘价", "昨收价", "总市值", "_", "_", "_", "_", "_", "_", "_", "_", "市盈率", "_", "_", "_", "_", "_", ] temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename(columns={"index": "序号"}, inplace=True) temp_df["代码"] = temp_df["编码"].astype(str) + "." + temp_df["简称"] temp_df = temp_df[ [ "序号", "名称", "最新价", "涨跌额", "涨跌幅", "开盘价", "最高价", "最低价", "昨收价", "总市值", "市盈率", "代码", ] ] temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce") temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce") temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce") temp_df["昨收价"] = pd.to_numeric(temp_df["昨收价"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["市盈率"] = pd.to_numeric(temp_df["市盈率"], errors="coerce") return temp_df

18,767

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_cg_equity_mortgage.py

stock_cg_equity_mortgage_cninfo

(date: str = "20210930")

return temp_df

巨潮资讯-数据中心-专题统计-公司治理-股权质押 http://webapi.cninfo.com.cn/#/thematicStatistics :param date: 开始统计时间 :type date: str :return: 股权质押 :rtype: pandas.DataFrame

45

111

def stock_cg_equity_mortgage_cninfo(date: str = "20210930") -> pd.DataFrame: """ 巨潮资讯-数据中心-专题统计-公司治理-股权质押 http://webapi.cninfo.com.cn/#/thematicStatistics :param date: 开始统计时间 :type date: str :return: 股权质押 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1094" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "tdate": "-".join([date[:4], date[4:6], date[6:]]), } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "质押解除数量", "股票简称", "公告日期", "质押事项", "质权人", "出质人", "股票代码", "占总股本比例", "累计质押占总股本比例", "质押数量", ] temp_df = temp_df[ [ "股票代码", "股票简称", "公告日期", "出质人", "质权人", "质押数量", "占总股本比例", "质押解除数量", "质押事项", "累计质押占总股本比例", ] ] temp_df["公告日期"] = pd.to_datetime(temp_df["公告日期"]).dt.date temp_df["质押数量"] = pd.to_numeric(temp_df["质押数量"]) temp_df["占总股本比例"] = pd.to_numeric(temp_df["占总股本比例"]) temp_df["质押解除数量"] = pd.to_numeric(temp_df["质押解除数量"]) temp_df["累计质押占总股本比例"] = pd.to_numeric(temp_df["累计质押占总股本比例"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_cg_equity_mortgage.py#L45-L111

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

13.432836

[ 9, 10, 11, 12, 13, 14, 29, 32, 33, 34, 35, 47, 61, 62, 63, 64, 65, 66 ]

26.865672

false

28.571429

67

1

73.134328

6

def stock_cg_equity_mortgage_cninfo(date: str = "20210930") -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1094" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "tdate": "-".join([date[:4], date[4:6], date[6:]]), } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "质押解除数量", "股票简称", "公告日期", "质押事项", "质权人", "出质人", "股票代码", "占总股本比例", "累计质押占总股本比例", "质押数量", ] temp_df = temp_df[ [ "股票代码", "股票简称", "公告日期", "出质人", "质权人", "质押数量", "占总股本比例", "质押解除数量", "质押事项", "累计质押占总股本比例", ] ] temp_df["公告日期"] = pd.to_datetime(temp_df["公告日期"]).dt.date temp_df["质押数量"] = pd.to_numeric(temp_df["质押数量"]) temp_df["占总股本比例"] = pd.to_numeric(temp_df["占总股本比例"]) temp_df["质押解除数量"] = pd.to_numeric(temp_df["质押解除数量"]) temp_df["累计质押占总股本比例"] = pd.to_numeric(temp_df["累计质押占总股本比例"]) return temp_df

18,768

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_industry.py

stock_sector_spot

(indicator: str = "新浪行业") -> pd.D

return temp_df

新浪行业-板块行情 http://finance.sina.com.cn/stock/sl/ :param indicator: choice of {"新浪行业", "启明星行业", "概念", "地域", "行业"} :type indicator: str :return: 指定 indicator 的数据 :rtype: pandas.DataFrame

18

79

def stock_sector_spot(indicator: str = "新浪行业") -> pd.DataFrame: """ 新浪行业-板块行情 http://finance.sina.com.cn/stock/sl/ :param indicator: choice of {"新浪行业", "启明星行业", "概念", "地域", "行业"} :type indicator: str :return: 指定 indicator 的数据 :rtype: pandas.DataFrame """ if indicator == "新浪行业": url = "http://vip.stock.finance.sina.com.cn/q/view/newSinaHy.php" r = requests.get(url) if indicator == "启明星行业": url = "http://biz.finance.sina.com.cn/hq/qmxIndustryHq.php" r = requests.get(url) r.encoding = "gb2312" if indicator == "概念": url = "http://money.finance.sina.com.cn/q/view/newFLJK.php" params = { "param": "class" } r = requests.get(url, params=params) if indicator == "地域": url = "http://money.finance.sina.com.cn/q/view/newFLJK.php" params = { "param": "area" } r = requests.get(url, params=params) if indicator == "行业": url = "http://money.finance.sina.com.cn/q/view/newFLJK.php" params = { "param": "industry" } r = requests.get(url, params=params) text_data = r.text json_data = json.loads(text_data[text_data.find("{"):]) temp_df = pd.DataFrame([value.split(",") for key, value in json_data.items()]) temp_df.columns = [ "label", "板块", "公司家数", "平均价格", "涨跌额", "涨跌幅", "总成交量", "总成交额", "股票代码", "个股-涨跌幅", "个股-当前价", "个股-涨跌额", "股票名称", ] temp_df['公司家数'] = pd.to_numeric(temp_df['公司家数']) temp_df['平均价格'] = pd.to_numeric(temp_df['平均价格']) temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额']) temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) temp_df['总成交量'] = pd.to_numeric(temp_df['总成交量']) temp_df['总成交额'] = pd.to_numeric(temp_df['总成交额']) temp_df['个股-涨跌幅'] = pd.to_numeric(temp_df['个股-涨跌幅']) temp_df['个股-当前价'] = pd.to_numeric(temp_df['个股-当前价']) temp_df['个股-涨跌额'] = pd.to_numeric(temp_df['个股-涨跌额']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_industry.py#L18-L79

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

14.516129

[ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 27, 28, 29, 30, 33, 34, 35, 36, 37, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ]

53.225806

false

11.627907

62

7

46.774194

6

def stock_sector_spot(indicator: str = "新浪行业") -> pd.DataFrame: if indicator == "新浪行业": url = "http://vip.stock.finance.sina.com.cn/q/view/newSinaHy.php" r = requests.get(url) if indicator == "启明星行业": url = "http://biz.finance.sina.com.cn/hq/qmxIndustryHq.php" r = requests.get(url) r.encoding = "gb2312" if indicator == "概念": url = "http://money.finance.sina.com.cn/q/view/newFLJK.php" params = { "param": "class" } r = requests.get(url, params=params) if indicator == "地域": url = "http://money.finance.sina.com.cn/q/view/newFLJK.php" params = { "param": "area" } r = requests.get(url, params=params) if indicator == "行业": url = "http://money.finance.sina.com.cn/q/view/newFLJK.php" params = { "param": "industry" } r = requests.get(url, params=params) text_data = r.text json_data = json.loads(text_data[text_data.find("{"):]) temp_df = pd.DataFrame([value.split(",") for key, value in json_data.items()]) temp_df.columns = [ "label", "板块", "公司家数", "平均价格", "涨跌额", "涨跌幅", "总成交量", "总成交额", "股票代码", "个股-涨跌幅", "个股-当前价", "个股-涨跌额", "股票名称", ] temp_df['公司家数'] = pd.to_numeric(temp_df['公司家数']) temp_df['平均价格'] = pd.to_numeric(temp_df['平均价格']) temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额']) temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) temp_df['总成交量'] = pd.to_numeric(temp_df['总成交量']) temp_df['总成交额'] = pd.to_numeric(temp_df['总成交额']) temp_df['个股-涨跌幅'] = pd.to_numeric(temp_df['个股-涨跌幅']) temp_df['个股-当前价'] = pd.to_numeric(temp_df['个股-当前价']) temp_df['个股-涨跌额'] = pd.to_numeric(temp_df['个股-涨跌额']) return temp_df

18,769

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_industry.py

stock_sector_detail

(sector: str = "gn_gfgn")

return big_df

新浪行业-板块行情-成份详情 http://finance.sina.com.cn/stock/sl/#area_1 :param sector: stock_sector_spot 返回的 label 值, choice of {"新浪行业", "概念", "地域", "行业"}; "启明星行业" 无详情 :type sector: str :return: 指定 sector 的板块详情 :rtype: pandas.DataFrame

82

131

def stock_sector_detail(sector: str = "gn_gfgn") -> pd.DataFrame: """ 新浪行业-板块行情-成份详情 http://finance.sina.com.cn/stock/sl/#area_1 :param sector: stock_sector_spot 返回的 label 值, choice of {"新浪行业", "概念", "地域", "行业"}; "启明星行业" 无详情 :type sector: str :return: 指定 sector 的板块详情 :rtype: pandas.DataFrame """ url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount" params = { "node": sector } r = requests.get(url, params=params) total_num = int(r.json()) total_page_num = math.ceil(int(total_num) / 80) big_df = pd.DataFrame() url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData" for page in tqdm(range(1, total_page_num+1), leave=True): params = { "page": str(page), "num": "80", "sort": "symbol", "asc": "1", "node": sector, "symbol": "", "_s_r_a": "page", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text) temp_df = pd.DataFrame(data_json) big_df = big_df.append(temp_df, ignore_index=True) big_df['trade'] = pd.to_numeric(big_df['trade']) big_df['pricechange'] = pd.to_numeric(big_df['pricechange']) big_df['changepercent'] = pd.to_numeric(big_df['changepercent']) big_df['buy'] = pd.to_numeric(big_df['buy']) big_df['sell'] = pd.to_numeric(big_df['sell']) big_df['settlement'] = pd.to_numeric(big_df['settlement']) big_df['open'] = pd.to_numeric(big_df['open']) big_df['high'] = pd.to_numeric(big_df['high']) big_df['low'] = pd.to_numeric(big_df['low']) big_df['volume'] = pd.to_numeric(big_df['volume']) big_df['amount'] = pd.to_numeric(big_df['amount']) big_df['per'] = pd.to_numeric(big_df['per']) big_df['pb'] = pd.to_numeric(big_df['pb']) big_df['mktcap'] = pd.to_numeric(big_df['mktcap']) big_df['nmc'] = pd.to_numeric(big_df['nmc']) big_df['turnoverratio'] = pd.to_numeric(big_df['turnoverratio']) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_industry.py#L82-L131

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

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[ 9, 10, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 ]

62

false

11.627907

50

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38

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def stock_sector_detail(sector: str = "gn_gfgn") -> pd.DataFrame: url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount" params = { "node": sector } r = requests.get(url, params=params) total_num = int(r.json()) total_page_num = math.ceil(int(total_num) / 80) big_df = pd.DataFrame() url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData" for page in tqdm(range(1, total_page_num+1), leave=True): params = { "page": str(page), "num": "80", "sort": "symbol", "asc": "1", "node": sector, "symbol": "", "_s_r_a": "page", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text) temp_df = pd.DataFrame(data_json) big_df = big_df.append(temp_df, ignore_index=True) big_df['trade'] = pd.to_numeric(big_df['trade']) big_df['pricechange'] = pd.to_numeric(big_df['pricechange']) big_df['changepercent'] = pd.to_numeric(big_df['changepercent']) big_df['buy'] = pd.to_numeric(big_df['buy']) big_df['sell'] = pd.to_numeric(big_df['sell']) big_df['settlement'] = pd.to_numeric(big_df['settlement']) big_df['open'] = pd.to_numeric(big_df['open']) big_df['high'] = pd.to_numeric(big_df['high']) big_df['low'] = pd.to_numeric(big_df['low']) big_df['volume'] = pd.to_numeric(big_df['volume']) big_df['amount'] = pd.to_numeric(big_df['amount']) big_df['per'] = pd.to_numeric(big_df['per']) big_df['pb'] = pd.to_numeric(big_df['pb']) big_df['mktcap'] = pd.to_numeric(big_df['mktcap']) big_df['nmc'] = pd.to_numeric(big_df['nmc']) big_df['turnoverratio'] = pd.to_numeric(big_df['turnoverratio']) return big_df

18,770

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hk_sina.py

stock_hk_spot

()

return data_df

新浪财经-港股的所有港股的实时行情数据 http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk :return: 实时行情数据 :rtype: pandas.DataFrame

23

58

def stock_hk_spot() -> pd.DataFrame: """ 新浪财经-港股的所有港股的实时行情数据 http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk :return: 实时行情数据 :rtype: pandas.DataFrame """ res = requests.get(hk_sina_stock_list_url, params=hk_sina_stock_dict_payload) data_json = [ demjson.decode(tt) for tt in [ item + "}" for item in res.text[1:-1].split("},") if not item.endswith("}") ] ] data_df = pd.DataFrame(data_json) data_df = data_df[ [ "symbol", "name", "engname", "tradetype", "lasttrade", "prevclose", "open", "high", "low", "volume", "amount", "ticktime", "buy", "sell", "pricechange", "changepercent", ] ] return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hk_sina.py#L23-L58

25

[ 0, 1, 2, 3, 4, 5, 6 ]

19.444444

[ 7, 8, 14, 15, 35 ]

13.888889

false

7.317073

36

3

86.111111

4

def stock_hk_spot() -> pd.DataFrame: res = requests.get(hk_sina_stock_list_url, params=hk_sina_stock_dict_payload) data_json = [ demjson.decode(tt) for tt in [ item + "}" for item in res.text[1:-1].split("},") if not item.endswith("}") ] ] data_df = pd.DataFrame(data_json) data_df = data_df[ [ "symbol", "name", "engname", "tradetype", "lasttrade", "prevclose", "open", "high", "low", "volume", "amount", "ticktime", "buy", "sell", "pricechange", "changepercent", ] ] return data_df

18,771

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hk_sina.py

stock_hk_daily

(symbol: str = "00981", adjust: str = "")

新浪财经-港股-个股的历史行情数据 https://stock.finance.sina.com.cn/hkstock/quotes/02912.html :param symbol: 可以使用 stock_hk_spot 获取 :type symbol: str :param adjust: "": 返回未复权的数据 ; qfq: 返回前复权后的数据; qfq-factor: 返回前复权因子和调整; :type adjust: str :return: 指定 adjust 的数据 :rtype: pandas.DataFrame

61

203

def stock_hk_daily(symbol: str = "00981", adjust: str = "") -> pd.DataFrame: """ 新浪财经-港股-个股的历史行情数据 https://stock.finance.sina.com.cn/hkstock/quotes/02912.html :param symbol: 可以使用 stock_hk_spot 获取 :type symbol: str :param adjust: "": 返回未复权的数据 ; qfq: 返回前复权后的数据; qfq-factor: 返回前复权因子和调整; :type adjust: str :return: 指定 adjust 的数据 :rtype: pandas.DataFrame """ res = requests.get(hk_sina_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", res.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]).dt.date del data_df["date"] data_df = data_df.astype("float") if adjust == "": data_df.reset_index(inplace=True) return data_df if adjust == "hfq": res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol)) try: hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if len(hfq_factor_df) == 1: data_df.reset_index(inplace=True) return data_df except SyntaxError as e: data_df.reset_index(inplace=True) return data_df hfq_factor_df.columns = ["date", "hfq_factor", "cash"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] # 处理复权因子 temp_date_range = pd.date_range( "1900-01-01", hfq_factor_df.index[0].isoformat() ) temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range) new_range = pd.merge( temp_df, hfq_factor_df, left_index=True, right_index=True, how="outer" ) new_range = new_range.fillna(method="ffill") new_range = new_range.iloc[:, [1, 2]] temp_df = pd.merge( data_df, new_range, left_index=True, right_index=True, how="outer" ) temp_df.fillna(method="ffill", inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df = temp_df.astype(float) temp_df["open"] = temp_df["open"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df["high"] = temp_df["high"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df = temp_df.apply(lambda x: round(x, 4)) temp_df.dropna(how="any", inplace=True) temp_df = temp_df.iloc[:, :-2] temp_df.reset_index(inplace=True) temp_df.rename({"index": "date"}, axis='columns', inplace=True) temp_df['date'] = temp_df['date'].astype(str) return temp_df if adjust == "qfq": res = requests.get(hk_sina_stock_hist_qfq_url.format(symbol)) try: qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if len(qfq_factor_df) == 1: data_df.reset_index(inplace=True) return data_df except SyntaxError as e: data_df.reset_index(inplace=True) return data_df qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] temp_date_range = pd.date_range( "1900-01-01", qfq_factor_df.index[0].isoformat() ) temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range) new_range = pd.merge( temp_df, qfq_factor_df, left_index=True, right_index=True, how="outer" ) new_range = new_range.fillna(method="ffill") new_range = new_range.iloc[:, [1]] temp_df = pd.merge( data_df, new_range, left_index=True, right_index=True, how="outer" ) temp_df.fillna(method="ffill", inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df = temp_df.astype(float) temp_df["open"] = temp_df["open"] * temp_df["qfq_factor"] temp_df["high"] = temp_df["high"] * temp_df["qfq_factor"] temp_df["close"] = temp_df["close"] * temp_df["qfq_factor"] temp_df["low"] = temp_df["low"] * temp_df["qfq_factor"] temp_df = temp_df.apply(lambda x: round(x, 4)) temp_df.dropna(how="any", inplace=True) temp_df = temp_df.iloc[:, :-1] temp_df.reset_index(inplace=True) temp_df.rename({"index": "date"}, axis='columns', inplace=True) temp_df['date'] = temp_df['date'].astype(str) return temp_df if adjust == "hfq-factor": res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) hfq_factor_df.columns = ["date", "hfq_factor", "cash"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] hfq_factor_df.reset_index(inplace=True) hfq_factor_df['date'] = hfq_factor_df['date'].astype(str) return hfq_factor_df if adjust == "qfq-factor": res = requests.get(hk_sina_stock_hist_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] qfq_factor_df.reset_index(inplace=True) qfq_factor_df['date'] = qfq_factor_df['date'].astype(str) return qfq_factor_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hk_sina.py#L61-L203

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

7.692308

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66.433566

false

7.317073

143

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33.566434

8

def stock_hk_daily(symbol: str = "00981", adjust: str = "") -> pd.DataFrame: res = requests.get(hk_sina_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", res.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]).dt.date del data_df["date"] data_df = data_df.astype("float") if adjust == "": data_df.reset_index(inplace=True) return data_df if adjust == "hfq": res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol)) try: hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if len(hfq_factor_df) == 1: data_df.reset_index(inplace=True) return data_df except SyntaxError as e: data_df.reset_index(inplace=True) return data_df hfq_factor_df.columns = ["date", "hfq_factor", "cash"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] # 处理复权因子 temp_date_range = pd.date_range( "1900-01-01", hfq_factor_df.index[0].isoformat() ) temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range) new_range = pd.merge( temp_df, hfq_factor_df, left_index=True, right_index=True, how="outer" ) new_range = new_range.fillna(method="ffill") new_range = new_range.iloc[:, [1, 2]] temp_df = pd.merge( data_df, new_range, left_index=True, right_index=True, how="outer" ) temp_df.fillna(method="ffill", inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df = temp_df.astype(float) temp_df["open"] = temp_df["open"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df["high"] = temp_df["high"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"] + temp_df["cash"] temp_df = temp_df.apply(lambda x: round(x, 4)) temp_df.dropna(how="any", inplace=True) temp_df = temp_df.iloc[:, :-2] temp_df.reset_index(inplace=True) temp_df.rename({"index": "date"}, axis='columns', inplace=True) temp_df['date'] = temp_df['date'].astype(str) return temp_df if adjust == "qfq": res = requests.get(hk_sina_stock_hist_qfq_url.format(symbol)) try: qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if len(qfq_factor_df) == 1: data_df.reset_index(inplace=True) return data_df except SyntaxError as e: data_df.reset_index(inplace=True) return data_df qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] temp_date_range = pd.date_range( "1900-01-01", qfq_factor_df.index[0].isoformat() ) temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range) new_range = pd.merge( temp_df, qfq_factor_df, left_index=True, right_index=True, how="outer" ) new_range = new_range.fillna(method="ffill") new_range = new_range.iloc[:, [1]] temp_df = pd.merge( data_df, new_range, left_index=True, right_index=True, how="outer" ) temp_df.fillna(method="ffill", inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df = temp_df.astype(float) temp_df["open"] = temp_df["open"] * temp_df["qfq_factor"] temp_df["high"] = temp_df["high"] * temp_df["qfq_factor"] temp_df["close"] = temp_df["close"] * temp_df["qfq_factor"] temp_df["low"] = temp_df["low"] * temp_df["qfq_factor"] temp_df = temp_df.apply(lambda x: round(x, 4)) temp_df.dropna(how="any", inplace=True) temp_df = temp_df.iloc[:, :-1] temp_df.reset_index(inplace=True) temp_df.rename({"index": "date"}, axis='columns', inplace=True) temp_df['date'] = temp_df['date'].astype(str) return temp_df if adjust == "hfq-factor": res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) hfq_factor_df.columns = ["date", "hfq_factor", "cash"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] hfq_factor_df.reset_index(inplace=True) hfq_factor_df['date'] = hfq_factor_df['date'].astype(str) return hfq_factor_df if adjust == "qfq-factor": res = requests.get(hk_sina_stock_hist_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] qfq_factor_df.reset_index(inplace=True) qfq_factor_df['date'] = qfq_factor_df['date'].astype(str) return qfq_factor_df

18,772

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_us_js.py

stock_price_js

(symbol: str = "us")

return temp_df

美股目标价 or 港股目标价 https://www.ushknews.com/report.html :param symbol: choice of {"us", "hk"} :type symbol: str :return: 美股目标价 or 港股目标价 :rtype: pandas.DataFrame

12

74

def stock_price_js(symbol: str = "us") -> pd.DataFrame: """ 美股目标价 or 港股目标价 https://www.ushknews.com/report.html :param symbol: choice of {"us", "hk"} :type symbol: str :return: 美股目标价 or 港股目标价 :rtype: pandas.DataFrame """ url = "https://calendar-api.ushknews.com/getWebTargetPriceList" params = { 'limit': '20', 'category': symbol, } headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://www.ushknews.com", "pragma": "no-cache", "referer": "https://www.ushknews.com/", "sec-ch-ua": '"Google Chrome";v="107", "Chromium";v="107", "Not=A?Brand";v="24"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36", "x-app-id": "BNsiR9uq7yfW0LVz", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) json_data = r.json() temp_df = pd.DataFrame(json_data["data"]["list"]) temp_df.columns = [ "_", "_", "评级", "_", "最新目标价", "先前目标价", "机构名称", "日期", "_", "个股名称", "_", "_", ] temp_df = temp_df[ [ "日期", "个股名称", "评级", "先前目标价", "最新目标价", "机构名称", ] ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["先前目标价"] = pd.to_numeric(temp_df["先前目标价"], errors="coerce") temp_df["最新目标价"] = pd.to_numeric(temp_df["最新目标价"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_us_js.py#L12-L74

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

14.285714

[ 9, 10, 14, 32, 33, 34, 35, 49, 59, 60, 61, 62 ]

19.047619

false

23.809524

63

1

80.952381

6

def stock_price_js(symbol: str = "us") -> pd.DataFrame: url = "https://calendar-api.ushknews.com/getWebTargetPriceList" params = { 'limit': '20', 'category': symbol, } headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://www.ushknews.com", "pragma": "no-cache", "referer": "https://www.ushknews.com/", "sec-ch-ua": '"Google Chrome";v="107", "Chromium";v="107", "Not=A?Brand";v="24"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36", "x-app-id": "BNsiR9uq7yfW0LVz", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) json_data = r.json() temp_df = pd.DataFrame(json_data["data"]["list"]) temp_df.columns = [ "_", "_", "评级", "_", "最新目标价", "先前目标价", "机构名称", "日期", "_", "个股名称", "_", "_", ] temp_df = temp_df[ [ "日期", "个股名称", "评级", "先前目标价", "最新目标价", "机构名称", ] ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["先前目标价"] = pd.to_numeric(temp_df["先前目标价"], errors="coerce") temp_df["最新目标价"] = pd.to_numeric(temp_df["最新目标价"], errors="coerce") return temp_df

18,773

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_industry_pe_cninfo.py

stock_industry_pe_ratio_cninfo

(symbol: str = "证监会行业分类", date: str = "20210910") -> pd.DataFra

return temp_df

巨潮资讯-数据中心-行业分析-行业市盈率 http://webapi.cninfo.com.cn/#/thematicStatistics :param symbol: choice of {"证监会行业分类", "国证行业分类"} :type symbol: str :param date: 查询日期 :type date: str :return: 行业市盈率 :rtype: pandas.DataFrame

45

122

def stock_industry_pe_ratio_cninfo(symbol: str = "证监会行业分类", date: str = "20210910") -> pd.DataFrame: """ 巨潮资讯-数据中心-行业分析-行业市盈率 http://webapi.cninfo.com.cn/#/thematicStatistics :param symbol: choice of {"证监会行业分类", "国证行业分类"} :type symbol: str :param date: 查询日期 :type date: str :return: 行业市盈率 :rtype: pandas.DataFrame """ sort_code_map = { "证监会行业分类": "008001", "国证行业分类": "008200" } url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1087" params = {"tdate": "-".join([date[:4], date[4:6], date[6:]]), "sortcode": sort_code_map[symbol], } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "行业层级", "静态市盈率-算术平均", "静态市盈率-中位数", "静态市盈率-加权平均", "净利润-静态", "行业名称", "行业编码", "行业分类", "总市值-静态", "纳入计算公司数量", "变动日期", "公司数量", ] temp_df = temp_df[[ "变动日期", "行业分类", "行业层级", "行业编码", "行业名称", "公司数量", "纳入计算公司数量", "总市值-静态", "净利润-静态", "静态市盈率-加权平均", "静态市盈率-中位数", "静态市盈率-算术平均", ]] temp_df["行业层级"] = pd.to_numeric(temp_df["行业层级"], errors="coerce") temp_df["公司数量"] = pd.to_numeric(temp_df["公司数量"], errors="coerce") temp_df["纳入计算公司数量"] = pd.to_numeric(temp_df["纳入计算公司数量"], errors="coerce") temp_df["总市值-静态"] = pd.to_numeric(temp_df["总市值-静态"], errors="coerce") temp_df["净利润-静态"] = pd.to_numeric(temp_df["净利润-静态"], errors="coerce") temp_df["静态市盈率-加权平均"] = pd.to_numeric(temp_df["静态市盈率-加权平均"], errors="coerce") temp_df["静态市盈率-中位数"] = pd.to_numeric(temp_df["静态市盈率-中位数"], errors="coerce") temp_df["静态市盈率-算术平均"] = pd.to_numeric(temp_df["静态市盈率-算术平均"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_industry_pe_cninfo.py#L45-L122

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

14.102564

[ 11, 15, 16, 19, 20, 21, 22, 23, 38, 39, 40, 41, 55, 69, 70, 71, 72, 73, 74, 75, 76, 77 ]

28.205128

false

25

78

1

71.794872

8

def stock_industry_pe_ratio_cninfo(symbol: str = "证监会行业分类", date: str = "20210910") -> pd.DataFrame: sort_code_map = { "证监会行业分类": "008001", "国证行业分类": "008200" } url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1087" params = {"tdate": "-".join([date[:4], date[4:6], date[6:]]), "sortcode": sort_code_map[symbol], } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "行业层级", "静态市盈率-算术平均", "静态市盈率-中位数", "静态市盈率-加权平均", "净利润-静态", "行业名称", "行业编码", "行业分类", "总市值-静态", "纳入计算公司数量", "变动日期", "公司数量", ] temp_df = temp_df[[ "变动日期", "行业分类", "行业层级", "行业编码", "行业名称", "公司数量", "纳入计算公司数量", "总市值-静态", "净利润-静态", "静态市盈率-加权平均", "静态市盈率-中位数", "静态市盈率-算术平均", ]] temp_df["行业层级"] = pd.to_numeric(temp_df["行业层级"], errors="coerce") temp_df["公司数量"] = pd.to_numeric(temp_df["公司数量"], errors="coerce") temp_df["纳入计算公司数量"] = pd.to_numeric(temp_df["纳入计算公司数量"], errors="coerce") temp_df["总市值-静态"] = pd.to_numeric(temp_df["总市值-静态"], errors="coerce") temp_df["净利润-静态"] = pd.to_numeric(temp_df["净利润-静态"], errors="coerce") temp_df["静态市盈率-加权平均"] = pd.to_numeric(temp_df["静态市盈率-加权平均"], errors="coerce") temp_df["静态市盈率-中位数"] = pd.to_numeric(temp_df["静态市盈率-中位数"], errors="coerce") temp_df["静态市盈率-算术平均"] = pd.to_numeric(temp_df["静态市盈率-算术平均"], errors="coerce") return temp_df

18,774

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hot_rank_em.py

stock_hot_rank_em

()

return temp_df

东方财富-个股人气榜-人气榜 https://guba.eastmoney.com/rank/ :return: 人气榜 :rtype: pandas.DataFrame

12

61

def stock_hot_rank_em() -> pd.DataFrame: """ 东方财富-个股人气榜-人气榜 https://guba.eastmoney.com/rank/ :return: 人气榜 :rtype: pandas.DataFrame """ url = "https://emappdata.eastmoney.com/stockrank/getAllCurrentList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "marketType": "", "pageNo": 1, "pageSize": 100, } r = requests.post(url, json=payload) data_json = r.json() temp_rank_df = pd.DataFrame(data_json["data"]) temp_rank_df["mark"] = [ "0" + "." + item[2:] if "SZ" in item else "1" + "." + item[2:] for item in temp_rank_df["sc"] ] ",".join(temp_rank_df["mark"]) + "?v=08926209912590994" params = { "ut": "f057cbcbce2a86e2866ab8877db1d059", "fltt": "2", "invt": "2", "fields": "f14,f3,f12,f2", "secids": ",".join(temp_rank_df["mark"]) + ",?v=08926209912590994", } url = "https://push2.eastmoney.com/api/qt/ulist.np/get" r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = ["最新价", "涨跌幅", "代码", "股票名称"] temp_df["当前排名"] = temp_rank_df["rk"] temp_df["代码"] = temp_rank_df["sc"] temp_df = temp_df[ [ "当前排名", "代码", "股票名称", "最新价", "涨跌幅", ] ] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hot_rank_em.py#L12-L61

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14

[ 7, 8, 15, 16, 17, 19, 23, 24, 31, 32, 33, 34, 35, 36, 37, 38, 47, 48, 49 ]

38

false

11.363636

50

2

62

4

def stock_hot_rank_em() -> pd.DataFrame: url = "https://emappdata.eastmoney.com/stockrank/getAllCurrentList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "marketType": "", "pageNo": 1, "pageSize": 100, } r = requests.post(url, json=payload) data_json = r.json() temp_rank_df = pd.DataFrame(data_json["data"]) temp_rank_df["mark"] = [ "0" + "." + item[2:] if "SZ" in item else "1" + "." + item[2:] for item in temp_rank_df["sc"] ] ",".join(temp_rank_df["mark"]) + "?v=08926209912590994" params = { "ut": "f057cbcbce2a86e2866ab8877db1d059", "fltt": "2", "invt": "2", "fields": "f14,f3,f12,f2", "secids": ",".join(temp_rank_df["mark"]) + ",?v=08926209912590994", } url = "https://push2.eastmoney.com/api/qt/ulist.np/get" r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = ["最新价", "涨跌幅", "代码", "股票名称"] temp_df["当前排名"] = temp_rank_df["rk"] temp_df["代码"] = temp_rank_df["sc"] temp_df = temp_df[ [ "当前排名", "代码", "股票名称", "最新价", "涨跌幅", ] ] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") return temp_df

18,775

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hot_rank_em.py

stock_hot_rank_detail_em

(symbol: str = "SZ000665")

return temp_df

东方财富-个股人气榜-历史趋势及粉丝特征 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 个股的历史趋势及粉丝特征 :rtype: pandas.DataFrame

64

96

def stock_hot_rank_detail_em(symbol: str = "SZ000665") -> pd.DataFrame: """ 东方财富-个股人气榜-历史趋势及粉丝特征 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 个股的历史趋势及粉丝特征 :rtype: pandas.DataFrame """ url_rank = "https://emappdata.eastmoney.com/stockrank/getHisList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "marketType": "", "srcSecurityCode": symbol, } r = requests.post(url_rank, json=payload) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]) temp_df["证券代码"] = symbol temp_df.columns = ["时间", "排名", "证券代码"] temp_df = temp_df[["时间", "排名", "证券代码"]] url_follow = "https://emappdata.eastmoney.com/stockrank/getHisProfileList" r = requests.post(url_follow, json=payload) data_json = r.json() temp_df["新晋粉丝"] = ( pd.DataFrame(data_json["data"])["newUidRate"].str.strip("%").astype(float) / 100 ) temp_df["铁杆粉丝"] = ( pd.DataFrame(data_json["data"])["oldUidRate"].str.strip("%").astype(float) / 100 ) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hot_rank_em.py#L64-L96

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

27.272727

[ 9, 10, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 29, 32 ]

42.424242

false

11.363636

33

1

57.575758

6

def stock_hot_rank_detail_em(symbol: str = "SZ000665") -> pd.DataFrame: url_rank = "https://emappdata.eastmoney.com/stockrank/getHisList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "marketType": "", "srcSecurityCode": symbol, } r = requests.post(url_rank, json=payload) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]) temp_df["证券代码"] = symbol temp_df.columns = ["时间", "排名", "证券代码"] temp_df = temp_df[["时间", "排名", "证券代码"]] url_follow = "https://emappdata.eastmoney.com/stockrank/getHisProfileList" r = requests.post(url_follow, json=payload) data_json = r.json() temp_df["新晋粉丝"] = ( pd.DataFrame(data_json["data"])["newUidRate"].str.strip("%").astype(float) / 100 ) temp_df["铁杆粉丝"] = ( pd.DataFrame(data_json["data"])["oldUidRate"].str.strip("%").astype(float) / 100 ) return temp_df

18,776

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hot_rank_em.py

stock_hot_rank_detail_realtime_em

(symbol: str = "SZ000665")

return temp_df

东方财富-个股人气榜-实时变动 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 实时变动 :rtype: pandas.DataFrame

99

119

def stock_hot_rank_detail_realtime_em(symbol: str = "SZ000665") -> pd.DataFrame: """ 东方财富-个股人气榜-实时变动 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 实时变动 :rtype: pandas.DataFrame """ url = "https://emappdata.eastmoney.com/stockrank/getCurrentList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "marketType": "", "srcSecurityCode": symbol, } r = requests.post(url, json=payload) data_json = r.json() temp_df = pd.DataFrame(data_json['data']) temp_df.columns = ['时间', '排名'] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hot_rank_em.py#L99-L119

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

42.857143

[ 9, 10, 16, 17, 18, 19, 20 ]

33.333333

false

11.363636

21

1

66.666667

6

def stock_hot_rank_detail_realtime_em(symbol: str = "SZ000665") -> pd.DataFrame: url = "https://emappdata.eastmoney.com/stockrank/getCurrentList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "marketType": "", "srcSecurityCode": symbol, } r = requests.post(url, json=payload) data_json = r.json() temp_df = pd.DataFrame(data_json['data']) temp_df.columns = ['时间', '排名'] return temp_df

18,777

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hot_rank_em.py

stock_hot_keyword_em

(symbol: str = "SZ000665")

return temp_df

东方财富-个股人气榜-关键词 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 关键词 :rtype: pandas.DataFrame

122

142

def stock_hot_keyword_em(symbol: str = "SZ000665") -> pd.DataFrame: """ 东方财富-个股人气榜-关键词 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 关键词 :rtype: pandas.DataFrame """ url = "https://emappdata.eastmoney.com/stockrank/getHotStockRankList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "srcSecurityCode": symbol, } r = requests.post(url, json=payload) data_json = r.json() temp_df = pd.DataFrame(data_json['data']) del temp_df['flag'] temp_df.columns = ['时间', '股票代码', '概念名称', '概念代码', '热度'] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hot_rank_em.py#L122-L142

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

42.857143

[ 9, 10, 15, 16, 17, 18, 19, 20 ]

38.095238

false

11.363636

21

1

61.904762

6

def stock_hot_keyword_em(symbol: str = "SZ000665") -> pd.DataFrame: url = "https://emappdata.eastmoney.com/stockrank/getHotStockRankList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "srcSecurityCode": symbol, } r = requests.post(url, json=payload) data_json = r.json() temp_df = pd.DataFrame(data_json['data']) del temp_df['flag'] temp_df.columns = ['时间', '股票代码', '概念名称', '概念代码', '热度'] return temp_df

18,778

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hot_rank_em.py

stock_hot_rank_relate_em

(symbol: str = "SZ000665")

return temp_df

东方财富-个股人气榜-相关股票 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 相关股票 :rtype: pandas.DataFrame

169

191

def stock_hot_rank_relate_em(symbol: str = "SZ000665") -> pd.DataFrame: """ 东方财富-个股人气榜-相关股票 https://guba.eastmoney.com/rank/stock?code=000665 :param symbol: 带市场表示的证券代码 :type symbol: str :return: 相关股票 :rtype: pandas.DataFrame """ url = "https://emappdata.eastmoney.com/stockrank/getFollowStockRankList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "srcSecurityCode": symbol, } r = requests.post(url, json=payload) data_json = r.json() temp_df = pd.DataFrame.from_dict(data_json['data']) temp_df.columns = ['时间', '-', '股票代码', '-', '相关股票代码', '涨跌幅', '-'] temp_df = temp_df[['时间', '股票代码', '相关股票代码', '涨跌幅']] temp_df['涨跌幅'] = temp_df['涨跌幅'].str.strip('%') temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hot_rank_em.py#L169-L191

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

39.130435

[ 9, 10, 15, 16, 17, 18, 19, 20, 21, 22 ]

43.478261

false

11.363636

23

1

56.521739

6

def stock_hot_rank_relate_em(symbol: str = "SZ000665") -> pd.DataFrame: url = "https://emappdata.eastmoney.com/stockrank/getFollowStockRankList" payload = { "appId": "appId01", "globalId": "786e4c21-70dc-435a-93bb-38", "srcSecurityCode": symbol, } r = requests.post(url, json=payload) data_json = r.json() temp_df = pd.DataFrame.from_dict(data_json['data']) temp_df.columns = ['时间', '-', '股票代码', '-', '相关股票代码', '涨跌幅', '-'] temp_df = temp_df[['时间', '股票代码', '相关股票代码', '涨跌幅']] temp_df['涨跌幅'] = temp_df['涨跌幅'].str.strip('%') temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) return temp_df

18,779

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_ah_tx.py

_get_zh_stock_ah_page_count

()

return page_count

腾讯财经-港股-AH-总页数 https://stockapp.finance.qq.com/mstats/#mod=list&id=hk_ah&module=HK&type=AH&sort=3&page=3&max=20 :return: 总页数 :rtype: int

24

38

def _get_zh_stock_ah_page_count() -> int: """ 腾讯财经-港股-AH-总页数 https://stockapp.finance.qq.com/mstats/#mod=list&id=hk_ah&module=HK&type=AH&sort=3&page=3&max=20 :return: 总页数 :rtype: int """ hk_payload_copy = hk_payload.copy() hk_payload_copy.update({"reqPage": 1}) res = requests.get(hk_url, params=hk_payload_copy, headers=hk_headers) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) page_count = data_json["data"]["page_count"] return page_count

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_ah_tx.py#L24-L38

25

[ 0, 1, 2, 3, 4, 5, 6 ]

46.666667

[ 7, 8, 9, 10, 13, 14 ]

40

false

13.186813

15

1

60

4

def _get_zh_stock_ah_page_count() -> int: hk_payload_copy = hk_payload.copy() hk_payload_copy.update({"reqPage": 1}) res = requests.get(hk_url, params=hk_payload_copy, headers=hk_headers) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) page_count = data_json["data"]["page_count"] return page_count

18,780

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_ah_tx.py

stock_zh_ah_spot

()

return big_df

腾讯财经-港股-AH-实时行情 https://stockapp.finance.qq.com/mstats/#mod=list&id=hk_ah&module=HK&type=AH&sort=3&page=3&max=20 :return: 腾讯财经-港股-AH-实时行情 :rtype: pandas.DataFrame

41

109

def stock_zh_ah_spot() -> pd.DataFrame: """ 腾讯财经-港股-AH-实时行情 https://stockapp.finance.qq.com/mstats/#mod=list&id=hk_ah&module=HK&type=AH&sort=3&page=3&max=20 :return: 腾讯财经-港股-AH-实时行情 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() page_count = _get_zh_stock_ah_page_count() + 1 for i in tqdm(range(1, page_count), leave=False): hk_payload.update({"reqPage": i}) res = requests.get(hk_url, params=hk_payload, headers=hk_headers) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) big_df = pd.concat( [ big_df, pd.DataFrame(data_json["data"]["page_data"]) .iloc[:, 0] .str.split("~", expand=True), ], ignore_index=True, ) big_df.columns = [ "代码", "名称", "最新价", "涨跌幅", "涨跌额", "买入", "卖出", "成交量", "成交额", "今开", "昨收", "最高", "最低", "-", ] big_df = big_df[ [ "代码", "名称", "最新价", "涨跌幅", "涨跌额", "买入", "卖出", "成交量", "成交额", "今开", "昨收", "最高", "最低", ] ] big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["涨跌额"] = pd.to_numeric(big_df["涨跌额"], errors="coerce") big_df["买入"] = pd.to_numeric(big_df["买入"], errors="coerce") big_df["卖出"] = pd.to_numeric(big_df["卖出"], errors="coerce") big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce") big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce") big_df["今开"] = pd.to_numeric(big_df["今开"], errors="coerce") big_df["昨收"] = pd.to_numeric(big_df["昨收"], errors="coerce") big_df["最高"] = pd.to_numeric(big_df["最高"], errors="coerce") big_df["最低"] = pd.to_numeric(big_df["最低"], errors="coerce") return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_ah_tx.py#L41-L109

25

[ 0, 1, 2, 3, 4, 5, 6 ]

10.144928

[ 7, 8, 9, 10, 11, 12, 15, 24, 40, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68 ]

30.434783

false

13.186813

69

2

69.565217

4

def stock_zh_ah_spot() -> pd.DataFrame: big_df = pd.DataFrame() page_count = _get_zh_stock_ah_page_count() + 1 for i in tqdm(range(1, page_count), leave=False): hk_payload.update({"reqPage": i}) res = requests.get(hk_url, params=hk_payload, headers=hk_headers) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) big_df = pd.concat( [ big_df, pd.DataFrame(data_json["data"]["page_data"]) .iloc[:, 0] .str.split("~", expand=True), ], ignore_index=True, ) big_df.columns = [ "代码", "名称", "最新价", "涨跌幅", "涨跌额", "买入", "卖出", "成交量", "成交额", "今开", "昨收", "最高", "最低", "-", ] big_df = big_df[ [ "代码", "名称", "最新价", "涨跌幅", "涨跌额", "买入", "卖出", "成交量", "成交额", "今开", "昨收", "最高", "最低", ] ] big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["涨跌额"] = pd.to_numeric(big_df["涨跌额"], errors="coerce") big_df["买入"] = pd.to_numeric(big_df["买入"], errors="coerce") big_df["卖出"] = pd.to_numeric(big_df["卖出"], errors="coerce") big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce") big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce") big_df["今开"] = pd.to_numeric(big_df["今开"], errors="coerce") big_df["昨收"] = pd.to_numeric(big_df["昨收"], errors="coerce") big_df["最高"] = pd.to_numeric(big_df["最高"], errors="coerce") big_df["最低"] = pd.to_numeric(big_df["最低"], errors="coerce") return big_df

18,781

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_ah_tx.py

stock_zh_ah_name

()

return big_df

腾讯财经-港股-AH-股票名称 :return: 股票代码和股票名称的字典 :rtype: dict

112

156

def stock_zh_ah_name() -> dict: """ 腾讯财经-港股-AH-股票名称 :return: 股票代码和股票名称的字典 :rtype: dict """ big_df = pd.DataFrame() page_count = _get_zh_stock_ah_page_count() + 1 for i in tqdm(range(1, page_count), leave=False): hk_payload.update({"reqPage": i}) res = requests.get(hk_url, params=hk_payload, headers=hk_headers) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) big_df = pd.concat( [ big_df, pd.DataFrame(data_json["data"]["page_data"]) .iloc[:, 0] .str.split("~", expand=True), ], ignore_index=True, ).iloc[:, :-1] big_df.columns = [ "代码", "名称", "最新价", "涨跌幅", "涨跌额", "买入", "卖出", "成交量", "成交额", "今开", "昨收", "最高", "最低", ] big_df = big_df[ [ "代码", "名称", ] ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_ah_tx.py#L112-L156

25

[ 0, 1, 2, 3, 4, 5 ]

13.333333

[ 6, 7, 8, 9, 10, 11, 14, 23, 38, 44 ]

22.222222

false

13.186813

45

2

77.777778

3

def stock_zh_ah_name() -> dict: big_df = pd.DataFrame() page_count = _get_zh_stock_ah_page_count() + 1 for i in tqdm(range(1, page_count), leave=False): hk_payload.update({"reqPage": i}) res = requests.get(hk_url, params=hk_payload, headers=hk_headers) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) big_df = pd.concat( [ big_df, pd.DataFrame(data_json["data"]["page_data"]) .iloc[:, 0] .str.split("~", expand=True), ], ignore_index=True, ).iloc[:, :-1] big_df.columns = [ "代码", "名称", "最新价", "涨跌幅", "涨跌额", "买入", "卖出", "成交量", "成交额", "今开", "昨收", "最高", "最低", ] big_df = big_df[ [ "代码", "名称", ] ] return big_df

18,782

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_ah_tx.py

stock_zh_ah_daily

( symbol: str = "02318", start_year: str = "2000", end_year: str = "2019", adjust: str = "", )

return big_df

腾讯财经-港股-AH-股票历史行情 http://gu.qq.com/hk01033/gp :param symbol: 股票代码 :type symbol: str :param start_year: 开始年份; e.g., “2000” :type start_year: str :param end_year: 结束年份; e.g., “2019” :type end_year: str :param adjust: 'qfq': 前复权, 'hfq': 后复权 :type adjust: str :return: 指定股票在指定年份的日频率历史行情数据 :rtype: pandas.DataFrame

159

258

def stock_zh_ah_daily( symbol: str = "02318", start_year: str = "2000", end_year: str = "2019", adjust: str = "", ) -> pd.DataFrame: """ 腾讯财经-港股-AH-股票历史行情 http://gu.qq.com/hk01033/gp :param symbol: 股票代码 :type symbol: str :param start_year: 开始年份; e.g., “2000” :type start_year: str :param end_year: 结束年份; e.g., “2019” :type end_year: str :param adjust: 'qfq': 前复权, 'hfq': 后复权 :type adjust: str :return: 指定股票在指定年份的日频率历史行情数据 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() for year in tqdm(range(int(start_year), int(end_year)), leave=False): # year = "2003" hk_stock_payload_copy = hk_stock_payload.copy() hk_stock_payload_copy.update({"_var": f"kline_day{adjust}{year}"}) if adjust == "": hk_stock_payload_copy.update( { "param": f"hk{symbol},day,{year}-01-01,{int(year) + 1}-12-31,640," } ) else: hk_stock_payload_copy.update( { "param": f"hk{symbol},day,{year}-01-01,{int(year) + 1}-12-31,640,{adjust}" } ) hk_stock_payload_copy.update({"r": str(random.random())}) if adjust == "": headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "web.ifzq.gtimg.cn", "Pragma": "no-cache", "Referer": "http://gu.qq.com/hk01033/gp", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36", } res = requests.get( "http://web.ifzq.gtimg.cn/appstock/app/kline/kline", params=hk_stock_payload_copy, headers=headers, ) else: res = requests.get( "http://web.ifzq.gtimg.cn/appstock/app/hkfqkline/get", params=hk_stock_payload_copy, headers=hk_stock_headers, ) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) try: if adjust == "": temp_df = pd.DataFrame(data_json["data"][f"hk{symbol}"]["day"]) else: temp_df = pd.DataFrame( data_json["data"][f"hk{symbol}"][f"{adjust}day"] ) except: continue if adjust != "" and not temp_df.empty: temp_df.columns = [ "日期", "开盘", "收盘", "最高", "最低", "成交量", "_", "_", "_", ] temp_df = temp_df[["日期", "开盘", "收盘", "最高", "最低", "成交量"]] elif not temp_df.empty: try: temp_df.columns = ["日期", "开盘", "收盘", "最高", "最低", "成交量", "_"] except: temp_df.columns = ["日期", "开盘", "收盘", "最高", "最低", "成交量"] temp_df = temp_df[["日期", "开盘", "收盘", "最高", "最低", "成交量"]] big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["开盘"] = pd.to_numeric(big_df["开盘"]) big_df["收盘"] = pd.to_numeric(big_df["收盘"]) big_df["最高"] = pd.to_numeric(big_df["最高"]) big_df["最低"] = pd.to_numeric(big_df["最低"]) big_df["成交量"] = pd.to_numeric(big_df["成交量"]) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_ah_tx.py#L159-L258

25

[ 0 ]

1

[ 20, 21, 23, 24, 25, 26, 32, 37, 38, 39, 50, 56, 61, 64, 65, 66, 68, 71, 72, 73, 74, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 ]

36

false

13.186813

100

10

64

12

def stock_zh_ah_daily( symbol: str = "02318", start_year: str = "2000", end_year: str = "2019", adjust: str = "", ) -> pd.DataFrame: big_df = pd.DataFrame() for year in tqdm(range(int(start_year), int(end_year)), leave=False): # year = "2003" hk_stock_payload_copy = hk_stock_payload.copy() hk_stock_payload_copy.update({"_var": f"kline_day{adjust}{year}"}) if adjust == "": hk_stock_payload_copy.update( { "param": f"hk{symbol},day,{year}-01-01,{int(year) + 1}-12-31,640," } ) else: hk_stock_payload_copy.update( { "param": f"hk{symbol},day,{year}-01-01,{int(year) + 1}-12-31,640,{adjust}" } ) hk_stock_payload_copy.update({"r": str(random.random())}) if adjust == "": headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "web.ifzq.gtimg.cn", "Pragma": "no-cache", "Referer": "http://gu.qq.com/hk01033/gp", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36", } res = requests.get( "http://web.ifzq.gtimg.cn/appstock/app/kline/kline", params=hk_stock_payload_copy, headers=headers, ) else: res = requests.get( "http://web.ifzq.gtimg.cn/appstock/app/hkfqkline/get", params=hk_stock_payload_copy, headers=hk_stock_headers, ) data_json = demjson.decode( res.text[res.text.find("{") : res.text.rfind("}") + 1] ) try: if adjust == "": temp_df = pd.DataFrame(data_json["data"][f"hk{symbol}"]["day"]) else: temp_df = pd.DataFrame( data_json["data"][f"hk{symbol}"][f"{adjust}day"] ) except: continue if adjust != "" and not temp_df.empty: temp_df.columns = [ "日期", "开盘", "收盘", "最高", "最低", "成交量", "_", "_", "_", ] temp_df = temp_df[["日期", "开盘", "收盘", "最高", "最低", "成交量"]] elif not temp_df.empty: try: temp_df.columns = ["日期", "开盘", "收盘", "最高", "最低", "成交量", "_"] except: temp_df.columns = ["日期", "开盘", "收盘", "最高", "最低", "成交量"] temp_df = temp_df[["日期", "开盘", "收盘", "最高", "最低", "成交量"]] big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["开盘"] = pd.to_numeric(big_df["开盘"]) big_df["收盘"] = pd.to_numeric(big_df["收盘"]) big_df["最高"] = pd.to_numeric(big_df["最高"]) big_df["最低"] = pd.to_numeric(big_df["最低"]) big_df["成交量"] = pd.to_numeric(big_df["成交量"]) return big_df

18,783

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_us_zh_hx.py

stock_us_zh_spot

()

return temp_df

美股-中概股的实时行情数据 http://quote.hexun.com/default.htm#ustock_3 :return: 中概股的实时行情数据 :return: pandas.DataFrame

15

64

def stock_us_zh_spot() -> pd.DataFrame: """ 美股-中概股的实时行情数据 http://quote.hexun.com/default.htm#ustock_3 :return: 中概股的实时行情数据 :return: pandas.DataFrame """ url = "http://quote.hexun.com/usastock/data/getdjstock.aspx" params = { "type": "1", "market": "3", "sorttype": "4", "updown": "up", "page": "1", 'count': "200", "time": "203450" } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "quote.hexun.com", "Pragma": "no-cache", "Referer": "http://quote.hexun.com/usastock/xqstock.aspx?market=3", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.120 Safari/537.36" } r = requests.get(url, params=params, headers=headers) data_list = eval(r.text.split("=")[1].strip().rsplit(";")[0]) # eval 出列表 data_df = pd.DataFrame( data_list, columns=[ "代码", "名称", "最新价(美元)", "涨跌幅", "d_1", "d_2", "最高", "最低", "昨收", "d_3", "成交量", "d_4", ], ) temp_df = data_df[["代码", "名称", "最新价(美元)", "涨跌幅", "最高", "最低", "昨收", "成交量"]] temp_df = temp_df.rename({"最新价(美元)": "最新价"}, axis=1) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_us_zh_hx.py#L15-L64

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14

[ 7, 8, 17, 28, 29, 30, 47, 48, 49 ]

18

false

20

50

1

82

4

def stock_us_zh_spot() -> pd.DataFrame: url = "http://quote.hexun.com/usastock/data/getdjstock.aspx" params = { "type": "1", "market": "3", "sorttype": "4", "updown": "up", "page": "1", 'count': "200", "time": "203450" } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "quote.hexun.com", "Pragma": "no-cache", "Referer": "http://quote.hexun.com/usastock/xqstock.aspx?market=3", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.120 Safari/537.36" } r = requests.get(url, params=params, headers=headers) data_list = eval(r.text.split("=")[1].strip().rsplit(";")[0]) # eval 出列表 data_df = pd.DataFrame( data_list, columns=[ "代码", "名称", "最新价(美元)", "涨跌幅", "d_1", "d_2", "最高", "最低", "昨收", "d_3", "成交量", "d_4", ], ) temp_df = data_df[["代码", "名称", "最新价(美元)", "涨跌幅", "最高", "最低", "昨收", "成交量"]] temp_df = temp_df.rename({"最新价(美元)": "最新价"}, axis=1) return temp_df

18,784

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_us_zh_hx.py

stock_us_zh_daily

(symbol: str = 'BABA')

return data_df

美股-中概股的日频率历史行情数据 http://stockdata.stock.hexun.com/us/BABA.shtml :return: 中概股的日频率历史行情数据 :return: pandas.DataFrame

67

108

def stock_us_zh_daily(symbol: str = 'BABA') -> pd.DataFrame: """ 美股-中概股的日频率历史行情数据 http://stockdata.stock.hexun.com/us/BABA.shtml :return: 中概股的日频率历史行情数据 :return: pandas.DataFrame """ url = "http://webusstock.hermes.hexun.com/usa/kline" params = { "code": f"NYSE{symbol}", "start": "20201218223000", "number": "-1000", "type": "5", } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Cookie": "UM_distinctid=1758d5cddd49c-0fc7e2c2612624-303464-1fa400-1758d5cddd592f; ADVC=38ffe1fbf97465; cn_1263247791_dplus=%7B%22distinct_id%22%3A%20%221758d5cddd49c-0fc7e2c2612624-303464-1fa400-1758d5cddd592f%22%2C%22userFirstDate%22%3A%20%2220201103%22%2C%22userID%22%3A%20%220%22%2C%22userName%22%3A%20%22%22%2C%22userType%22%3A%20%22loginuser%22%2C%22userLoginDate%22%3A%20%2220201103%22%2C%22%24_sessionid%22%3A%200%2C%22%24_sessionTime%22%3A%201604394498%2C%22%24dp%22%3A%200%2C%22%24_sessionPVTime%22%3A%201604394498%2C%22initial_view_time%22%3A%20%221604392649%22%2C%22initial_referrer%22%3A%20%22https%3A%2F%2Fwww.baidu.com%2Flink%3Furl%3DRaFkqqESxpi2iDV4Q7Men69HaM9QOkW5KKUtQakjQzkfkygaOuGzJBFcHSg35wmfSKFA26xUDad7jHwCCv1ksa%26wd%3D%26eqid%3Da38e871500004846000000065fa11d78%22%2C%22initial_referrer_domain%22%3A%20%22www.baidu.com%22%2C%22%24recent_outside_referrer%22%3A%20%22www.baidu.com%22%7D; HexunTrack=SID=20200722150527074dded739ba5e24fd2915f1f692d4ad9c7&CITY=51&TOWN=510100; ADVS=392342a9ca40f5; ASL=18614,00rzr,abdfc0a27d461d18b68a5530; __jsluid_h=35f649169d5fb027d0e857b8ecd24d5b", "Host": "webusstock.hermes.hexun.com", "Pragma": "no-cache", "Referer": "http://stockdata.stock.hexun.com/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36", "X-Requested-With": "ShockwaveFlash/33.0.0.432", } r = requests.get(url, params=params, headers=headers) r.encoding = "utf-8" data_dict = json.loads(r.text[1:-2]) data_df = pd.DataFrame( data_dict["Data"][0], columns=[list(item.values())[0] for item in data_dict["KLine"]], ) data_df['时间'] = data_df['时间'].astype(str).str.slice(0, 8) data_df['前收盘价'] = round(data_df['前收盘价'] / 100, 2) data_df['开盘价'] = round(data_df['开盘价'] / 100, 2) data_df['收盘价'] = round(data_df['收盘价'] / 100, 2) data_df['最高价'] = round(data_df['最高价'] / 100, 2) data_df['最低价'] = round(data_df['最低价'] / 100, 2) del data_df['成交额'] return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_us_zh_hx.py#L67-L108

25

[ 0, 1, 2, 3, 4, 5, 6 ]

16.666667

[ 7, 8, 14, 27, 28, 29, 30, 34, 35, 36, 37, 38, 39, 40, 41 ]

35.714286

false

20

42

2

64.285714

4

def stock_us_zh_daily(symbol: str = 'BABA') -> pd.DataFrame: url = "http://webusstock.hermes.hexun.com/usa/kline" params = { "code": f"NYSE{symbol}", "start": "20201218223000", "number": "-1000", "type": "5", } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Cookie": "UM_distinctid=1758d5cddd49c-0fc7e2c2612624-303464-1fa400-1758d5cddd592f; ADVC=38ffe1fbf97465; cn_1263247791_dplus=%7B%22distinct_id%22%3A%20%221758d5cddd49c-0fc7e2c2612624-303464-1fa400-1758d5cddd592f%22%2C%22userFirstDate%22%3A%20%2220201103%22%2C%22userID%22%3A%20%220%22%2C%22userName%22%3A%20%22%22%2C%22userType%22%3A%20%22loginuser%22%2C%22userLoginDate%22%3A%20%2220201103%22%2C%22%24_sessionid%22%3A%200%2C%22%24_sessionTime%22%3A%201604394498%2C%22%24dp%22%3A%200%2C%22%24_sessionPVTime%22%3A%201604394498%2C%22initial_view_time%22%3A%20%221604392649%22%2C%22initial_referrer%22%3A%20%22https%3A%2F%2Fwww.baidu.com%2Flink%3Furl%3DRaFkqqESxpi2iDV4Q7Men69HaM9QOkW5KKUtQakjQzkfkygaOuGzJBFcHSg35wmfSKFA26xUDad7jHwCCv1ksa%26wd%3D%26eqid%3Da38e871500004846000000065fa11d78%22%2C%22initial_referrer_domain%22%3A%20%22www.baidu.com%22%2C%22%24recent_outside_referrer%22%3A%20%22www.baidu.com%22%7D; HexunTrack=SID=20200722150527074dded739ba5e24fd2915f1f692d4ad9c7&CITY=51&TOWN=510100; ADVS=392342a9ca40f5; ASL=18614,00rzr,abdfc0a27d461d18b68a5530; __jsluid_h=35f649169d5fb027d0e857b8ecd24d5b", "Host": "webusstock.hermes.hexun.com", "Pragma": "no-cache", "Referer": "http://stockdata.stock.hexun.com/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36", "X-Requested-With": "ShockwaveFlash/33.0.0.432", } r = requests.get(url, params=params, headers=headers) r.encoding = "utf-8" data_dict = json.loads(r.text[1:-2]) data_df = pd.DataFrame( data_dict["Data"][0], columns=[list(item.values())[0] for item in data_dict["KLine"]], ) data_df['时间'] = data_df['时间'].astype(str).str.slice(0, 8) data_df['前收盘价'] = round(data_df['前收盘价'] / 100, 2) data_df['开盘价'] = round(data_df['开盘价'] / 100, 2) data_df['收盘价'] = round(data_df['收盘价'] / 100, 2) data_df['最高价'] = round(data_df['最高价'] / 100, 2) data_df['最低价'] = round(data_df['最低价'] / 100, 2) del data_df['成交额'] return data_df

18,785

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_special.py

stock_zh_a_st_em

()

return temp_df

东方财富网-行情中心-沪深个股-风险警示板 http://quote.eastmoney.com/center/gridlist.html#st_board :return: 风险警示板 :rtype: pandas.DataFrame

17

106

def stock_zh_a_st_em() -> pd.DataFrame: """ 东方财富网-行情中心-沪深个股-风险警示板 http://quote.eastmoney.com/center/gridlist.html#st_board :return: 风险警示板 :rtype: pandas.DataFrame """ url = 'http://40.push2.eastmoney.com/api/qt/clist/get' params = { 'pn': '1', 'pz': '2000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f3', 'fs': 'm:0 f:4,m:1 f:4', 'fields': 'f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152', '_': '1631107510188', } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.reset_index(inplace=True) temp_df['index'] = range(1, len(temp_df)+1) temp_df.columns = [ '序号', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '换手率', '市盈率-动态', '量比', '_', '代码', '_', '名称', '最高', '最低', '今开', '昨收', '_', '_', '_', '市净率', '_', '_', '_', '_', '_', '_', '_', '_', '_', ] temp_df = temp_df[[ '序号', '代码', '名称', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '最高', '最低', '今开', '昨收', '量比', '换手率', '市盈率-动态', '市净率', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors="coerce") temp_df['成交量'] = pd.to_numeric(temp_df['成交量'], errors="coerce") temp_df['成交额'] = pd.to_numeric(temp_df['成交额'], errors="coerce") temp_df['振幅'] = pd.to_numeric(temp_df['振幅'], errors="coerce") temp_df['最高'] = pd.to_numeric(temp_df['最高'], errors="coerce") temp_df['最低'] = pd.to_numeric(temp_df['最低'], errors="coerce") temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors="coerce") temp_df['量比'] = pd.to_numeric(temp_df['量比'], errors="coerce") temp_df['换手率'] = pd.to_numeric(temp_df['换手率'], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_special.py#L17-L106

25

[ 0, 1, 2, 3, 4, 5, 6 ]

7.777778

[ 7, 8, 21, 22, 23, 24, 25, 26, 59, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89 ]

23.333333

false

9.183673

90

1

76.666667

4

def stock_zh_a_st_em() -> pd.DataFrame: url = 'http://40.push2.eastmoney.com/api/qt/clist/get' params = { 'pn': '1', 'pz': '2000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f3', 'fs': 'm:0 f:4,m:1 f:4', 'fields': 'f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152', '_': '1631107510188', } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.reset_index(inplace=True) temp_df['index'] = range(1, len(temp_df)+1) temp_df.columns = [ '序号', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '换手率', '市盈率-动态', '量比', '_', '代码', '_', '名称', '最高', '最低', '今开', '昨收', '_', '_', '_', '市净率', '_', '_', '_', '_', '_', '_', '_', '_', '_', ] temp_df = temp_df[[ '序号', '代码', '名称', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '最高', '最低', '今开', '昨收', '量比', '换手率', '市盈率-动态', '市净率', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors="coerce") temp_df['成交量'] = pd.to_numeric(temp_df['成交量'], errors="coerce") temp_df['成交额'] = pd.to_numeric(temp_df['成交额'], errors="coerce") temp_df['振幅'] = pd.to_numeric(temp_df['振幅'], errors="coerce") temp_df['最高'] = pd.to_numeric(temp_df['最高'], errors="coerce") temp_df['最低'] = pd.to_numeric(temp_df['最低'], errors="coerce") temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors="coerce") temp_df['量比'] = pd.to_numeric(temp_df['量比'], errors="coerce") temp_df['换手率'] = pd.to_numeric(temp_df['换手率'], errors="coerce") return temp_df

18,786

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_special.py

stock_zh_a_new_em

()

return temp_df

东方财富网-行情中心-沪深个股-新股 http://quote.eastmoney.com/center/gridlist.html#newshares :return: 新股 :rtype: pandas.DataFrame

109

198

def stock_zh_a_new_em() -> pd.DataFrame: """ 东方财富网-行情中心-沪深个股-新股 http://quote.eastmoney.com/center/gridlist.html#newshares :return: 新股 :rtype: pandas.DataFrame """ url = 'http://40.push2.eastmoney.com/api/qt/clist/get' params = { 'pn': '1', 'pz': '2000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f26', 'fs': 'm:0 f:8,m:1 f:8', 'fields': 'f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152', '_': '1631107510188', } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.reset_index(inplace=True) temp_df['index'] = range(1, len(temp_df)+1) temp_df.columns = [ '序号', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '换手率', '市盈率-动态', '量比', '_', '代码', '_', '名称', '最高', '最低', '今开', '昨收', '_', '_', '_', '市净率', '_', '_', '_', '_', '_', '_', '_', '_', '_', ] temp_df = temp_df[[ '序号', '代码', '名称', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '最高', '最低', '今开', '昨收', '量比', '换手率', '市盈率-动态', '市净率', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors="coerce") temp_df['成交量'] = pd.to_numeric(temp_df['成交量'], errors="coerce") temp_df['成交额'] = pd.to_numeric(temp_df['成交额'], errors="coerce") temp_df['振幅'] = pd.to_numeric(temp_df['振幅'], errors="coerce") temp_df['最高'] = pd.to_numeric(temp_df['最高'], errors="coerce") temp_df['最低'] = pd.to_numeric(temp_df['最低'], errors="coerce") temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors="coerce") temp_df['量比'] = pd.to_numeric(temp_df['量比'], errors="coerce") temp_df['换手率'] = pd.to_numeric(temp_df['换手率'], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_special.py#L109-L198

25

[ 0, 1, 2, 3, 4, 5, 6 ]

7.777778

[ 7, 8, 21, 22, 23, 24, 25, 26, 59, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89 ]

23.333333

false

9.183673

90

1

76.666667

4

def stock_zh_a_new_em() -> pd.DataFrame: url = 'http://40.push2.eastmoney.com/api/qt/clist/get' params = { 'pn': '1', 'pz': '2000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f26', 'fs': 'm:0 f:8,m:1 f:8', 'fields': 'f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152', '_': '1631107510188', } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.reset_index(inplace=True) temp_df['index'] = range(1, len(temp_df)+1) temp_df.columns = [ '序号', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '换手率', '市盈率-动态', '量比', '_', '代码', '_', '名称', '最高', '最低', '今开', '昨收', '_', '_', '_', '市净率', '_', '_', '_', '_', '_', '_', '_', '_', '_', ] temp_df = temp_df[[ '序号', '代码', '名称', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '最高', '最低', '今开', '昨收', '量比', '换手率', '市盈率-动态', '市净率', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors="coerce") temp_df['成交量'] = pd.to_numeric(temp_df['成交量'], errors="coerce") temp_df['成交额'] = pd.to_numeric(temp_df['成交额'], errors="coerce") temp_df['振幅'] = pd.to_numeric(temp_df['振幅'], errors="coerce") temp_df['最高'] = pd.to_numeric(temp_df['最高'], errors="coerce") temp_df['最低'] = pd.to_numeric(temp_df['最低'], errors="coerce") temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors="coerce") temp_df['量比'] = pd.to_numeric(temp_df['量比'], errors="coerce") temp_df['换手率'] = pd.to_numeric(temp_df['换手率'], errors="coerce") return temp_df

18,787

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_special.py

stock_zh_a_stop_em

()

return temp_df

东方财富网-行情中心-沪深个股-两网及退市 http://quote.eastmoney.com/center/gridlist.html#staq_net_board :return: 两网及退市 :rtype: pandas.DataFrame

201

290

def stock_zh_a_stop_em() -> pd.DataFrame: """ 东方财富网-行情中心-沪深个股-两网及退市 http://quote.eastmoney.com/center/gridlist.html#staq_net_board :return: 两网及退市 :rtype: pandas.DataFrame """ url = 'http://40.push2.eastmoney.com/api/qt/clist/get' params = { 'pn': '1', 'pz': '2000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f3', 'fs': 'm:0 s:3', 'fields': 'f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152', '_': '1631107510188', } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.reset_index(inplace=True) temp_df['index'] = range(1, len(temp_df)+1) temp_df.columns = [ '序号', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '换手率', '市盈率-动态', '量比', '_', '代码', '_', '名称', '最高', '最低', '今开', '昨收', '_', '_', '_', '市净率', '_', '_', '_', '_', '_', '_', '_', '_', '_', ] temp_df = temp_df[[ '序号', '代码', '名称', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '最高', '最低', '今开', '昨收', '量比', '换手率', '市盈率-动态', '市净率', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors="coerce") temp_df['成交量'] = pd.to_numeric(temp_df['成交量'], errors="coerce") temp_df['成交额'] = pd.to_numeric(temp_df['成交额'], errors="coerce") temp_df['振幅'] = pd.to_numeric(temp_df['振幅'], errors="coerce") temp_df['最高'] = pd.to_numeric(temp_df['最高'], errors="coerce") temp_df['最低'] = pd.to_numeric(temp_df['最低'], errors="coerce") temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors="coerce") temp_df['量比'] = pd.to_numeric(temp_df['量比'], errors="coerce") temp_df['换手率'] = pd.to_numeric(temp_df['换手率'], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_special.py#L201-L290

25

[ 0, 1, 2, 3, 4, 5, 6 ]

7.777778

[ 7, 8, 21, 22, 23, 24, 25, 26, 59, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89 ]

23.333333

false

9.183673

90

1

76.666667

4

def stock_zh_a_stop_em() -> pd.DataFrame: url = 'http://40.push2.eastmoney.com/api/qt/clist/get' params = { 'pn': '1', 'pz': '2000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f3', 'fs': 'm:0 s:3', 'fields': 'f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152', '_': '1631107510188', } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.reset_index(inplace=True) temp_df['index'] = range(1, len(temp_df)+1) temp_df.columns = [ '序号', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '换手率', '市盈率-动态', '量比', '_', '代码', '_', '名称', '最高', '最低', '今开', '昨收', '_', '_', '_', '市净率', '_', '_', '_', '_', '_', '_', '_', '_', '_', ] temp_df = temp_df[[ '序号', '代码', '名称', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '振幅', '最高', '最低', '今开', '昨收', '量比', '换手率', '市盈率-动态', '市净率', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors="coerce") temp_df['成交量'] = pd.to_numeric(temp_df['成交量'], errors="coerce") temp_df['成交额'] = pd.to_numeric(temp_df['成交额'], errors="coerce") temp_df['振幅'] = pd.to_numeric(temp_df['振幅'], errors="coerce") temp_df['最高'] = pd.to_numeric(temp_df['最高'], errors="coerce") temp_df['最低'] = pd.to_numeric(temp_df['最低'], errors="coerce") temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors="coerce") temp_df['量比'] = pd.to_numeric(temp_df['量比'], errors="coerce") temp_df['换手率'] = pd.to_numeric(temp_df['换手率'], errors="coerce") return temp_df

18,788

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_special.py

stock_zh_a_new

()

return big_df

新浪财经-行情中心-沪深股市-次新股 http://vip.stock.finance.sina.com.cn/mkt/#new_stock :return: 次新股行情数据 :rtype: pandas.DataFrame

293

338

def stock_zh_a_new() -> pd.DataFrame: """ 新浪财经-行情中心-沪深股市-次新股 http://vip.stock.finance.sina.com.cn/mkt/#new_stock :return: 次新股行情数据 :rtype: pandas.DataFrame """ url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount" params = {"node": "new_stock"} r = requests.get(url, params=params) total_page = math.ceil(int(r.json()) / 80) url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData" big_df = pd.DataFrame() for page in range(1, total_page + 1): params = { "page": str(page), "num": "80", "sort": "symbol", "asc": "1", "node": "new_stock", "symbol": "", "_s_r_a": "page", } r = requests.get(url, params=params) r.encoding = "gb2312" data_json = r.json() temp_df = pd.DataFrame(data_json) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df = big_df[ [ "symbol", "code", "name", "open", "high", "low", "volume", "amount", "mktcap", "turnoverratio", ] ] big_df['open'] = pd.to_numeric(big_df['open']) big_df['high'] = pd.to_numeric(big_df['high']) big_df['low'] = pd.to_numeric(big_df['low']) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_special.py#L293-L338

25

[ 0, 1, 2, 3, 4, 5, 6 ]

15.217391

[ 7, 8, 9, 10, 11, 12, 13, 14, 23, 24, 25, 26, 27, 28, 42, 43, 44, 45 ]

39.130435

false

9.183673

46

2

60.869565

4

def stock_zh_a_new() -> pd.DataFrame: url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount" params = {"node": "new_stock"} r = requests.get(url, params=params) total_page = math.ceil(int(r.json()) / 80) url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData" big_df = pd.DataFrame() for page in range(1, total_page + 1): params = { "page": str(page), "num": "80", "sort": "symbol", "asc": "1", "node": "new_stock", "symbol": "", "_s_r_a": "page", } r = requests.get(url, params=params) r.encoding = "gb2312" data_json = r.json() temp_df = pd.DataFrame(data_json) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df = big_df[ [ "symbol", "code", "name", "open", "high", "low", "volume", "amount", "mktcap", "turnoverratio", ] ] big_df['open'] = pd.to_numeric(big_df['open']) big_df['high'] = pd.to_numeric(big_df['high']) big_df['low'] = pd.to_numeric(big_df['low']) return big_df

18,789

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dzjy_em.py

stock_dzjy_sctj

()

return big_df

东方财富网-数据中心-大宗交易-市场统计 http://data.eastmoney.com/dzjy/dzjy_sctj.aspx :return: 市场统计表 :rtype: pandas.DataFrame

12

61

def stock_dzjy_sctj() -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-市场统计 http://data.eastmoney.com/dzjy/dzjy_sctj.aspx :return: 市场统计表 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'TRADE_DATE', 'sortTypes': '-1', 'pageSize': '500', 'pageNumber': '1', 'reportName': 'PRT_BLOCKTRADE_MARKET_STA', 'columns': 'TRADE_DATE,SZ_INDEX,SZ_CHANGE_RATE,BLOCKTRADE_DEAL_AMT,PREMIUM_DEAL_AMT,PREMIUM_RATIO,DISCOUNT_DEAL_AMT,DISCOUNT_RATIO', 'source': 'WEB', 'client': 'WEB', } r = requests.get(url, params=params) data_json = r.json() total_page = int(data_json['result']["pages"]) big_df = pd.DataFrame() for page in range(1, total_page+1): params.update({'pageNumber': page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df['index'] + 1 big_df.columns = [ "序号", "交易日期", "上证指数", "上证指数涨跌幅", "大宗交易成交总额", "溢价成交总额", "溢价成交总额占比", "折价成交总额", "折价成交总额占比", ] big_df["交易日期"] = pd.to_datetime(big_df["交易日期"]).dt.date big_df["上证指数"] = pd.to_numeric(big_df["上证指数"]) big_df["上证指数涨跌幅"] = pd.to_numeric(big_df["上证指数涨跌幅"]) big_df["大宗交易成交总额"] = pd.to_numeric(big_df["大宗交易成交总额"]) big_df["溢价成交总额"] = pd.to_numeric(big_df["溢价成交总额"]) big_df["溢价成交总额占比"] = pd.to_numeric(big_df["溢价成交总额占比"]) big_df["折价成交总额"] = pd.to_numeric(big_df["折价成交总额"]) big_df["折价成交总额占比"] = pd.to_numeric(big_df["折价成交总额占比"]) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dzjy_em.py#L12-L61

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14

[ 7, 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 41, 42, 43, 44, 45, 46, 47, 48, 49 ]

48

false

5.586592

50

2

52

4

def stock_dzjy_sctj() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'TRADE_DATE', 'sortTypes': '-1', 'pageSize': '500', 'pageNumber': '1', 'reportName': 'PRT_BLOCKTRADE_MARKET_STA', 'columns': 'TRADE_DATE,SZ_INDEX,SZ_CHANGE_RATE,BLOCKTRADE_DEAL_AMT,PREMIUM_DEAL_AMT,PREMIUM_RATIO,DISCOUNT_DEAL_AMT,DISCOUNT_RATIO', 'source': 'WEB', 'client': 'WEB', } r = requests.get(url, params=params) data_json = r.json() total_page = int(data_json['result']["pages"]) big_df = pd.DataFrame() for page in range(1, total_page+1): params.update({'pageNumber': page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df['index'] + 1 big_df.columns = [ "序号", "交易日期", "上证指数", "上证指数涨跌幅", "大宗交易成交总额", "溢价成交总额", "溢价成交总额占比", "折价成交总额", "折价成交总额占比", ] big_df["交易日期"] = pd.to_datetime(big_df["交易日期"]).dt.date big_df["上证指数"] = pd.to_numeric(big_df["上证指数"]) big_df["上证指数涨跌幅"] = pd.to_numeric(big_df["上证指数涨跌幅"]) big_df["大宗交易成交总额"] = pd.to_numeric(big_df["大宗交易成交总额"]) big_df["溢价成交总额"] = pd.to_numeric(big_df["溢价成交总额"]) big_df["溢价成交总额占比"] = pd.to_numeric(big_df["溢价成交总额占比"]) big_df["折价成交总额"] = pd.to_numeric(big_df["折价成交总额"]) big_df["折价成交总额占比"] = pd.to_numeric(big_df["折价成交总额占比"]) return big_df

18,790

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dzjy_em.py

stock_dzjy_mrmx

(symbol: str = '基金', start_date: str = '20220104', end_date: str = '20220104') ->

return temp_df

东方财富网-数据中心-大宗交易-每日明细 http://data.eastmoney.com/dzjy/dzjy_mrmxa.aspx :param symbol: choice of {'A股', 'B股', '基金', '债券'} :type symbol: str :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日明细 :rtype: pandas.DataFrame

64

186

def stock_dzjy_mrmx(symbol: str = '基金', start_date: str = '20220104', end_date: str = '20220104') -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-每日明细 http://data.eastmoney.com/dzjy/dzjy_mrmxa.aspx :param symbol: choice of {'A股', 'B股', '基金', '债券'} :type symbol: str :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日明细 :rtype: pandas.DataFrame """ symbol_map = { 'A股': '1', 'B股': '2', '基金': '3', '债券': '4', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'SECURITY_CODE', 'sortTypes': '1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_DATA_BLOCKTRADE', 'columns': 'TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,CLOSE_PRICE,DEAL_PRICE,PREMIUM_RATIO,DEAL_VOLUME,DEAL_AMT,TURNOVER_RATE,BUYER_NAME,SELLER_NAME,CHANGE_RATE_1DAYS,CHANGE_RATE_5DAYS,CHANGE_RATE_10DAYS,CHANGE_RATE_20DAYS,BUYER_CODE,SELLER_CODE', 'source': 'WEB', 'client': 'WEB', 'filter': f"""(SECURITY_TYPE_WEB={symbol_map[symbol]})(TRADE_DATE>='{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<='{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}')""" } r = requests.get(url, params=params) data_json = r.json() if not data_json['result']["data"]: return pd.DataFrame() temp_df = pd.DataFrame(data_json['result']["data"]) temp_df.reset_index(inplace=True) temp_df['index'] = temp_df.index + 1 if symbol in {'A股'}: temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交量", "成交额", "成交额/流通市值", "买方营业部", "卖方营业部", "_", "_", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df = temp_df[[ "序号", "交易日期", "证券代码", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交量", "成交额", "成交额/流通市值", "买方营业部", "卖方营业部", ]] temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) temp_df['收盘价'] = pd.to_numeric(temp_df['收盘价']) temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['折溢率'] = pd.to_numeric(temp_df['折溢率']) temp_df['成交量'] = pd.to_numeric(temp_df['成交量']) temp_df['成交额'] = pd.to_numeric(temp_df['成交额']) temp_df['成交额/流通市值'] = pd.to_numeric(temp_df['成交额/流通市值']) if symbol in {'B股', '基金', '债券'}: temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "-", "-", "成交价", "-", "成交量", "成交额", "-", "买方营业部", "卖方营业部", "_", "_", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df = temp_df[[ "序号", "交易日期", "证券代码", "证券简称", "成交价", "成交量", "成交额", "买方营业部", "卖方营业部", ]] temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['成交量'] = pd.to_numeric(temp_df['成交量']) temp_df['成交额'] = pd.to_numeric(temp_df['成交额']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dzjy_em.py#L64-L186

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ]

10.569106

[ 13, 19, 20, 31, 32, 33, 34, 35, 36, 37, 38, 39, 61, 62, 77, 78, 79, 80, 81, 82, 83, 84, 85, 107, 108, 119, 120, 121, 122 ]

23.577236

false

5.586592

123

4

76.422764

10

def stock_dzjy_mrmx(symbol: str = '基金', start_date: str = '20220104', end_date: str = '20220104') -> pd.DataFrame: symbol_map = { 'A股': '1', 'B股': '2', '基金': '3', '债券': '4', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'SECURITY_CODE', 'sortTypes': '1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_DATA_BLOCKTRADE', 'columns': 'TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,CLOSE_PRICE,DEAL_PRICE,PREMIUM_RATIO,DEAL_VOLUME,DEAL_AMT,TURNOVER_RATE,BUYER_NAME,SELLER_NAME,CHANGE_RATE_1DAYS,CHANGE_RATE_5DAYS,CHANGE_RATE_10DAYS,CHANGE_RATE_20DAYS,BUYER_CODE,SELLER_CODE', 'source': 'WEB', 'client': 'WEB', 'filter': f"""(SECURITY_TYPE_WEB={symbol_map[symbol]})(TRADE_DATE>='{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<='{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}')""" } r = requests.get(url, params=params) data_json = r.json() if not data_json['result']["data"]: return pd.DataFrame() temp_df = pd.DataFrame(data_json['result']["data"]) temp_df.reset_index(inplace=True) temp_df['index'] = temp_df.index + 1 if symbol in {'A股'}: temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交量", "成交额", "成交额/流通市值", "买方营业部", "卖方营业部", "_", "_", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df = temp_df[[ "序号", "交易日期", "证券代码", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交量", "成交额", "成交额/流通市值", "买方营业部", "卖方营业部", ]] temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) temp_df['收盘价'] = pd.to_numeric(temp_df['收盘价']) temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['折溢率'] = pd.to_numeric(temp_df['折溢率']) temp_df['成交量'] = pd.to_numeric(temp_df['成交量']) temp_df['成交额'] = pd.to_numeric(temp_df['成交额']) temp_df['成交额/流通市值'] = pd.to_numeric(temp_df['成交额/流通市值']) if symbol in {'B股', '基金', '债券'}: temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "-", "-", "成交价", "-", "成交量", "成交额", "-", "买方营业部", "卖方营业部", "_", "_", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df = temp_df[[ "序号", "交易日期", "证券代码", "证券简称", "成交价", "成交量", "成交额", "买方营业部", "卖方营业部", ]] temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['成交量'] = pd.to_numeric(temp_df['成交量']) temp_df['成交额'] = pd.to_numeric(temp_df['成交额']) return temp_df

18,791

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dzjy_em.py

stock_dzjy_mrtj

(start_date: str = '20220105', end_date: str = '20220105')

return temp_df

东方财富网-数据中心-大宗交易-每日统计 http://data.eastmoney.com/dzjy/dzjy_mrtj.aspx :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日统计 :rtype: pandas.DataFrame

189

259

def stock_dzjy_mrtj(start_date: str = '20220105', end_date: str = '20220105') -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-每日统计 http://data.eastmoney.com/dzjy/dzjy_mrtj.aspx :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日统计 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'TURNOVERRATE', 'sortTypes': '-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_STA', 'columns': 'TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,CLOSE_PRICE,AVERAGE_PRICE,PREMIUM_RATIO,DEAL_NUM,VOLUME,DEAL_AMT,TURNOVERRATE,D1_CLOSE_ADJCHRATE,D5_CLOSE_ADJCHRATE,D10_CLOSE_ADJCHRATE,D20_CLOSE_ADJCHRATE', 'source': 'WEB', 'client': 'WEB', 'filter': f"(TRADE_DATE>='{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<='{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}')" } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) temp_df.reset_index(inplace=True) temp_df['index'] = temp_df.index + 1 temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交笔数", "成交总量", "成交总额", "成交总额/流通市值", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df = temp_df[[ "序号", "交易日期", "证券代码", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交笔数", "成交总量", "成交总额", "成交总额/流通市值", ]] temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) temp_df['收盘价'] = pd.to_numeric(temp_df['收盘价']) temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['折溢率'] = pd.to_numeric(temp_df['折溢率']) temp_df['成交笔数'] = pd.to_numeric(temp_df['成交笔数']) temp_df['成交总量'] = pd.to_numeric(temp_df['成交总量']) temp_df['成交总额'] = pd.to_numeric(temp_df['成交总额']) temp_df['成交总额/流通市值'] = pd.to_numeric(temp_df['成交总额/流通市值']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dzjy_em.py#L189-L259

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

15.492958

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26.760563

false

5.586592

71

1

73.239437

8

def stock_dzjy_mrtj(start_date: str = '20220105', end_date: str = '20220105') -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'TURNOVERRATE', 'sortTypes': '-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_STA', 'columns': 'TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,CLOSE_PRICE,AVERAGE_PRICE,PREMIUM_RATIO,DEAL_NUM,VOLUME,DEAL_AMT,TURNOVERRATE,D1_CLOSE_ADJCHRATE,D5_CLOSE_ADJCHRATE,D10_CLOSE_ADJCHRATE,D20_CLOSE_ADJCHRATE', 'source': 'WEB', 'client': 'WEB', 'filter': f"(TRADE_DATE>='{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<='{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}')" } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) temp_df.reset_index(inplace=True) temp_df['index'] = temp_df.index + 1 temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交笔数", "成交总量", "成交总额", "成交总额/流通市值", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df = temp_df[[ "序号", "交易日期", "证券代码", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交笔数", "成交总量", "成交总额", "成交总额/流通市值", ]] temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅']) temp_df['收盘价'] = pd.to_numeric(temp_df['收盘价']) temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['折溢率'] = pd.to_numeric(temp_df['折溢率']) temp_df['成交笔数'] = pd.to_numeric(temp_df['成交笔数']) temp_df['成交总量'] = pd.to_numeric(temp_df['成交总量']) temp_df['成交总额'] = pd.to_numeric(temp_df['成交总额']) temp_df['成交总额/流通市值'] = pd.to_numeric(temp_df['成交总额/流通市值']) return temp_df

18,792

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dzjy_em.py

stock_dzjy_hygtj

(symbol: str = '近三月') -> pd

return big_df

东方财富网-数据中心-大宗交易-活跃 A 股统计 http://data.eastmoney.com/dzjy/dzjy_hygtj.aspx :param symbol: choice of {'近一月', '近三月', '近六月', '近一年'} :type symbol: str :return: 活跃 A 股统计 :rtype: pandas.DataFrame

262

352

def stock_dzjy_hygtj(symbol: str = '近三月') -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-活跃 A 股统计 http://data.eastmoney.com/dzjy/dzjy_hygtj.aspx :param symbol: choice of {'近一月', '近三月', '近六月', '近一年'} :type symbol: str :return: 活跃 A 股统计 :rtype: pandas.DataFrame """ period_map = { '近一月': '1', '近三月': '3', '近六月': '6', '近一年': '12', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'DEAL_NUM,SECURITY_CODE', 'sortTypes': '-1,-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_ACSTA', 'columns': 'SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CLOSE_PRICE,CHANGE_RATE,TRADE_DATE,DEAL_AMT,PREMIUM_RATIO,SUM_TURNOVERRATE,DEAL_NUM,PREMIUM_TIMES,DISCOUNT_TIMES,D1_AVG_ADJCHRATE,D5_AVG_ADJCHRATE,D10_AVG_ADJCHRATE,D20_AVG_ADJCHRATE,DATE_TYPE_CODE', 'source': 'WEB', 'client': 'WEB', 'filter': f'(DATE_TYPE_CODE={period_map[symbol]})', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page)+1): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df.index + 1 big_df.columns = [ "序号", "证券代码", "_", "证券简称", "最新价", "涨跌幅", "最近上榜日", "总成交额", "折溢率", "成交总额/流通市值", "上榜次数-总计", "上榜次数-溢价", "上榜次数-折价", "上榜日后平均涨跌幅-1日", "上榜日后平均涨跌幅-5日", "上榜日后平均涨跌幅-10日", "上榜日后平均涨跌幅-20日", "_", ] big_df = big_df[[ "序号", "证券代码", "证券简称", "最新价", "涨跌幅", "最近上榜日", "上榜次数-总计", "上榜次数-溢价", "上榜次数-折价", "总成交额", "折溢率", "成交总额/流通市值", "上榜日后平均涨跌幅-1日", "上榜日后平均涨跌幅-5日", "上榜日后平均涨跌幅-10日", "上榜日后平均涨跌幅-20日", ]] big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"]).dt.date big_df["最新价"] = pd.to_numeric(big_df["最新价"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["上榜次数-总计"] = pd.to_numeric(big_df["上榜次数-总计"]) big_df["上榜次数-溢价"] = pd.to_numeric(big_df["上榜次数-溢价"]) big_df["上榜次数-折价"] = pd.to_numeric(big_df["上榜次数-折价"]) big_df["总成交额"] = pd.to_numeric(big_df["总成交额"]) big_df["折溢率"] = pd.to_numeric(big_df["折溢率"]) big_df["成交总额/流通市值"] = pd.to_numeric(big_df["成交总额/流通市值"]) big_df["上榜日后平均涨跌幅-1日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-1日"]) big_df["上榜日后平均涨跌幅-5日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-5日"]) big_df["上榜日后平均涨跌幅-10日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-10日"]) big_df["上榜日后平均涨跌幅-20日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-20日"]) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dzjy_em.py#L262-L352

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

9.89011

[ 9, 15, 16, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 59, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90 ]

34.065934

false

5.586592

91

2

65.934066

6

def stock_dzjy_hygtj(symbol: str = '近三月') -> pd.DataFrame: period_map = { '近一月': '1', '近三月': '3', '近六月': '6', '近一年': '12', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'DEAL_NUM,SECURITY_CODE', 'sortTypes': '-1,-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_ACSTA', 'columns': 'SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CLOSE_PRICE,CHANGE_RATE,TRADE_DATE,DEAL_AMT,PREMIUM_RATIO,SUM_TURNOVERRATE,DEAL_NUM,PREMIUM_TIMES,DISCOUNT_TIMES,D1_AVG_ADJCHRATE,D5_AVG_ADJCHRATE,D10_AVG_ADJCHRATE,D20_AVG_ADJCHRATE,DATE_TYPE_CODE', 'source': 'WEB', 'client': 'WEB', 'filter': f'(DATE_TYPE_CODE={period_map[symbol]})', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page)+1): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df.index + 1 big_df.columns = [ "序号", "证券代码", "_", "证券简称", "最新价", "涨跌幅", "最近上榜日", "总成交额", "折溢率", "成交总额/流通市值", "上榜次数-总计", "上榜次数-溢价", "上榜次数-折价", "上榜日后平均涨跌幅-1日", "上榜日后平均涨跌幅-5日", "上榜日后平均涨跌幅-10日", "上榜日后平均涨跌幅-20日", "_", ] big_df = big_df[[ "序号", "证券代码", "证券简称", "最新价", "涨跌幅", "最近上榜日", "上榜次数-总计", "上榜次数-溢价", "上榜次数-折价", "总成交额", "折溢率", "成交总额/流通市值", "上榜日后平均涨跌幅-1日", "上榜日后平均涨跌幅-5日", "上榜日后平均涨跌幅-10日", "上榜日后平均涨跌幅-20日", ]] big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"]).dt.date big_df["最新价"] = pd.to_numeric(big_df["最新价"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["上榜次数-总计"] = pd.to_numeric(big_df["上榜次数-总计"]) big_df["上榜次数-溢价"] = pd.to_numeric(big_df["上榜次数-溢价"]) big_df["上榜次数-折价"] = pd.to_numeric(big_df["上榜次数-折价"]) big_df["总成交额"] = pd.to_numeric(big_df["总成交额"]) big_df["折溢率"] = pd.to_numeric(big_df["折溢率"]) big_df["成交总额/流通市值"] = pd.to_numeric(big_df["成交总额/流通市值"]) big_df["上榜日后平均涨跌幅-1日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-1日"]) big_df["上榜日后平均涨跌幅-5日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-5日"]) big_df["上榜日后平均涨跌幅-10日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-10日"]) big_df["上榜日后平均涨跌幅-20日"] = pd.to_numeric(big_df["上榜日后平均涨跌幅-20日"]) return big_df

18,793

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dzjy_em.py

stock_dzjy_hyyybtj

(symbol: str = '近3日') ->

return big_df

东方财富网-数据中心-大宗交易-活跃营业部统计 https://data.eastmoney.com/dzjy/dzjy_hyyybtj.html :param symbol: choice of {'当前交易日', '近3日', '近5日', '近10日', '近30日'} :type symbol: str :return: 活跃营业部统计 :rtype: pandas.DataFrame

355

425

def stock_dzjy_hyyybtj(symbol: str = '近3日') -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-活跃营业部统计 https://data.eastmoney.com/dzjy/dzjy_hyyybtj.html :param symbol: choice of {'当前交易日', '近3日', '近5日', '近10日', '近30日'} :type symbol: str :return: 活跃营业部统计 :rtype: pandas.DataFrame """ period_map = { '当前交易日': '1', '近3日': '3', '近5日': '5', '近10日': '10', '近30日': '30', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'BUYER_NUM,TOTAL_BUYAMT', 'sortTypes': '-1,-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_OPERATEDEPTSTATISTICS', 'columns': 'OPERATEDEPT_CODE,OPERATEDEPT_NAME,ONLIST_DATE,STOCK_DETAILS,BUYER_NUM,SELLER_NUM,TOTAL_BUYAMT,TOTAL_SELLAMT,TOTAL_NETAMT,N_DATE', 'source': 'WEB', 'client': 'WEB', 'filter': f'(N_DATE=-{period_map[symbol]})', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page)+1): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df.index + 1 big_df.columns = [ "序号", "_", "营业部名称", "最近上榜日", "买入的股票", "次数总计-买入", "次数总计-卖出", "成交金额统计-买入", "成交金额统计-卖出", "成交金额统计-净买入额", "_", ] big_df = big_df[[ "序号", "最近上榜日", "营业部名称", "次数总计-买入", "次数总计-卖出", "成交金额统计-买入", "成交金额统计-卖出", "成交金额统计-净买入额", "买入的股票", ]] big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"]).dt.date big_df["次数总计-买入"] = pd.to_numeric(big_df["次数总计-买入"]) big_df["次数总计-卖出"] = pd.to_numeric(big_df["次数总计-卖出"]) big_df["成交金额统计-买入"] = pd.to_numeric(big_df["成交金额统计-买入"]) big_df["成交金额统计-卖出"] = pd.to_numeric(big_df["成交金额统计-卖出"]) big_df["成交金额统计-净买入额"] = pd.to_numeric(big_df["成交金额统计-净买入额"]) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dzjy_em.py#L355-L425

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

12.676056

[ 9, 16, 17, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 53, 64, 65, 66, 67, 68, 69, 70 ]

33.802817

false

5.586592

71

2

66.197183

6

def stock_dzjy_hyyybtj(symbol: str = '近3日') -> pd.DataFrame: period_map = { '当前交易日': '1', '近3日': '3', '近5日': '5', '近10日': '10', '近30日': '30', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'BUYER_NUM,TOTAL_BUYAMT', 'sortTypes': '-1,-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_OPERATEDEPTSTATISTICS', 'columns': 'OPERATEDEPT_CODE,OPERATEDEPT_NAME,ONLIST_DATE,STOCK_DETAILS,BUYER_NUM,SELLER_NUM,TOTAL_BUYAMT,TOTAL_SELLAMT,TOTAL_NETAMT,N_DATE', 'source': 'WEB', 'client': 'WEB', 'filter': f'(N_DATE=-{period_map[symbol]})', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page)+1): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df.index + 1 big_df.columns = [ "序号", "_", "营业部名称", "最近上榜日", "买入的股票", "次数总计-买入", "次数总计-卖出", "成交金额统计-买入", "成交金额统计-卖出", "成交金额统计-净买入额", "_", ] big_df = big_df[[ "序号", "最近上榜日", "营业部名称", "次数总计-买入", "次数总计-卖出", "成交金额统计-买入", "成交金额统计-卖出", "成交金额统计-净买入额", "买入的股票", ]] big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"]).dt.date big_df["次数总计-买入"] = pd.to_numeric(big_df["次数总计-买入"]) big_df["次数总计-卖出"] = pd.to_numeric(big_df["次数总计-卖出"]) big_df["成交金额统计-买入"] = pd.to_numeric(big_df["成交金额统计-买入"]) big_df["成交金额统计-卖出"] = pd.to_numeric(big_df["成交金额统计-卖出"]) big_df["成交金额统计-净买入额"] = pd.to_numeric(big_df["成交金额统计-净买入额"]) return big_df

18,794

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_dzjy_em.py

stock_dzjy_yybph

(symbol: str = '近三月') -> pd

return big_df

东方财富网-数据中心-大宗交易-营业部排行 http://data.eastmoney.com/dzjy/dzjy_yybph.aspx :param symbol: choice of {'近一月', '近三月', '近六月', '近一年'} :type symbol: str :return: 营业部排行 :rtype: pandas.DataFrame

428

515

def stock_dzjy_yybph(symbol: str = '近三月') -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-营业部排行 http://data.eastmoney.com/dzjy/dzjy_yybph.aspx :param symbol: choice of {'近一月', '近三月', '近六月', '近一年'} :type symbol: str :return: 营业部排行 :rtype: pandas.DataFrame """ period_map = { '近一月': '30', '近三月': '90', '近六月': '120', '近一年': '360', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'D5_BUYER_NUM,D1_AVERAGE_INCREASE', 'sortTypes': '-1,-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_OPERATEDEPT_RANK', 'columns': 'OPERATEDEPT_CODE,OPERATEDEPT_NAME,D1_BUYER_NUM,D1_AVERAGE_INCREASE,D1_RISE_PROBABILITY,D5_BUYER_NUM,D5_AVERAGE_INCREASE,D5_RISE_PROBABILITY,D10_BUYER_NUM,D10_AVERAGE_INCREASE,D10_RISE_PROBABILITY,D20_BUYER_NUM,D20_AVERAGE_INCREASE,D20_RISE_PROBABILITY,N_DATE,RELATED_ORG_CODE', 'source': 'WEB', 'client': 'WEB', 'filter': f'(N_DATE=-{period_map[symbol]})', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page)+1): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df.index + 1 big_df.columns = [ "序号", "_", "营业部名称", "上榜后1天-买入次数", "上榜后1天-平均涨幅", "上榜后1天-上涨概率", "上榜后5天-买入次数", "上榜后5天-平均涨幅", "上榜后5天-上涨概率", "上榜后10天-买入次数", "上榜后10天-平均涨幅", "上榜后10天-上涨概率", "上榜后20天-买入次数", "上榜后20天-平均涨幅", "上榜后20天-上涨概率", "_", "_", ] big_df = big_df[[ "序号", "营业部名称", "上榜后1天-买入次数", "上榜后1天-平均涨幅", "上榜后1天-上涨概率", "上榜后5天-买入次数", "上榜后5天-平均涨幅", "上榜后5天-上涨概率", "上榜后10天-买入次数", "上榜后10天-平均涨幅", "上榜后10天-上涨概率", "上榜后20天-买入次数", "上榜后20天-平均涨幅", "上榜后20天-上涨概率", ]] big_df['上榜后1天-买入次数'] = pd.to_numeric(big_df['上榜后1天-买入次数']) big_df['上榜后1天-平均涨幅'] = pd.to_numeric(big_df['上榜后1天-平均涨幅']) big_df['上榜后1天-上涨概率'] = pd.to_numeric(big_df['上榜后1天-上涨概率']) big_df['上榜后5天-买入次数'] = pd.to_numeric(big_df['上榜后5天-买入次数']) big_df['上榜后5天-平均涨幅'] = pd.to_numeric(big_df['上榜后5天-平均涨幅']) big_df['上榜后5天-上涨概率'] = pd.to_numeric(big_df['上榜后5天-上涨概率']) big_df['上榜后10天-买入次数'] = pd.to_numeric(big_df['上榜后10天-买入次数']) big_df['上榜后10天-平均涨幅'] = pd.to_numeric(big_df['上榜后10天-平均涨幅']) big_df['上榜后10天-上涨概率'] = pd.to_numeric(big_df['上榜后10天-上涨概率']) big_df['上榜后20天-买入次数'] = pd.to_numeric(big_df['上榜后20天-买入次数']) big_df['上榜后20天-平均涨幅'] = pd.to_numeric(big_df['上榜后20天-平均涨幅']) big_df['上榜后20天-上涨概率'] = pd.to_numeric(big_df['上榜后20天-上涨概率']) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_dzjy_em.py#L428-L515

25

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false

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def stock_dzjy_yybph(symbol: str = '近三月') -> pd.DataFrame: period_map = { '近一月': '30', '近三月': '90', '近六月': '120', '近一年': '360', } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'D5_BUYER_NUM,D1_AVERAGE_INCREASE', 'sortTypes': '-1,-1', 'pageSize': '5000', 'pageNumber': '1', 'reportName': 'RPT_BLOCKTRADE_OPERATEDEPT_RANK', 'columns': 'OPERATEDEPT_CODE,OPERATEDEPT_NAME,D1_BUYER_NUM,D1_AVERAGE_INCREASE,D1_RISE_PROBABILITY,D5_BUYER_NUM,D5_AVERAGE_INCREASE,D5_RISE_PROBABILITY,D10_BUYER_NUM,D10_AVERAGE_INCREASE,D10_RISE_PROBABILITY,D20_BUYER_NUM,D20_AVERAGE_INCREASE,D20_RISE_PROBABILITY,N_DATE,RELATED_ORG_CODE', 'source': 'WEB', 'client': 'WEB', 'filter': f'(N_DATE=-{period_map[symbol]})', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page)+1): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json['result']["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df['index'] = big_df.index + 1 big_df.columns = [ "序号", "_", "营业部名称", "上榜后1天-买入次数", "上榜后1天-平均涨幅", "上榜后1天-上涨概率", "上榜后5天-买入次数", "上榜后5天-平均涨幅", "上榜后5天-上涨概率", "上榜后10天-买入次数", "上榜后10天-平均涨幅", "上榜后10天-上涨概率", "上榜后20天-买入次数", "上榜后20天-平均涨幅", "上榜后20天-上涨概率", "_", "_", ] big_df = big_df[[ "序号", "营业部名称", "上榜后1天-买入次数", "上榜后1天-平均涨幅", "上榜后1天-上涨概率", "上榜后5天-买入次数", "上榜后5天-平均涨幅", "上榜后5天-上涨概率", "上榜后10天-买入次数", "上榜后10天-平均涨幅", "上榜后10天-上涨概率", "上榜后20天-买入次数", "上榜后20天-平均涨幅", "上榜后20天-上涨概率", ]] big_df['上榜后1天-买入次数'] = pd.to_numeric(big_df['上榜后1天-买入次数']) big_df['上榜后1天-平均涨幅'] = pd.to_numeric(big_df['上榜后1天-平均涨幅']) big_df['上榜后1天-上涨概率'] = pd.to_numeric(big_df['上榜后1天-上涨概率']) big_df['上榜后5天-买入次数'] = pd.to_numeric(big_df['上榜后5天-买入次数']) big_df['上榜后5天-平均涨幅'] = pd.to_numeric(big_df['上榜后5天-平均涨幅']) big_df['上榜后5天-上涨概率'] = pd.to_numeric(big_df['上榜后5天-上涨概率']) big_df['上榜后10天-买入次数'] = pd.to_numeric(big_df['上榜后10天-买入次数']) big_df['上榜后10天-平均涨幅'] = pd.to_numeric(big_df['上榜后10天-平均涨幅']) big_df['上榜后10天-上涨概率'] = pd.to_numeric(big_df['上榜后10天-上涨概率']) big_df['上榜后20天-买入次数'] = pd.to_numeric(big_df['上榜后20天-买入次数']) big_df['上榜后20天-平均涨幅'] = pd.to_numeric(big_df['上榜后20天-平均涨幅']) big_df['上榜后20天-上涨概率'] = pd.to_numeric(big_df['上榜后20天-上涨概率']) return big_df

18,795

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_cg_guarantee.py

stock_cg_guarantee_cninfo

( symbol: str = "全部", start_date: str = "20180630", end_date: str = "20210927" )

return temp_df

巨潮资讯-数据中心-专题统计-公司治理-对外担保 http://webapi.cninfo.com.cn/#/thematicStatistics :param symbol: choice of {"全部", "深市主板", "沪市", "创业板", "科创板"} :type symbol: str :param start_date: 开始统计时间 :type start_date: str :param end_date: 结束统计时间 :type end_date: str :return: 对外担保 :rtype: pandas.DataFrame

45

119

def stock_cg_guarantee_cninfo( symbol: str = "全部", start_date: str = "20180630", end_date: str = "20210927" ) -> pd.DataFrame: """ 巨潮资讯-数据中心-专题统计-公司治理-对外担保 http://webapi.cninfo.com.cn/#/thematicStatistics :param symbol: choice of {"全部", "深市主板", "沪市", "创业板", "科创板"} :type symbol: str :param start_date: 开始统计时间 :type start_date: str :param end_date: 结束统计时间 :type end_date: str :return: 对外担保 :rtype: pandas.DataFrame """ symbol_map = { "全部": '', "深市主板": '012002', "沪市": '012001', "创业板": '012015', "科创板": '012029', } url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1054" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "sdate": "-".join([start_date[:4], start_date[4:6], start_date[6:]]), "edate": "-".join([end_date[:4], end_date[4:6], end_date[6:]]), "market": symbol_map[symbol], } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "公告统计区间", "担保金融占净资产比例", "担保金额", "担保笔数", "证券简称", "证券代码", "归属于母公司所有者权益", ] temp_df = temp_df[ [ "证券代码", "证券简称", "公告统计区间", "担保笔数", "担保金额", "归属于母公司所有者权益", "担保金融占净资产比例", ] ] temp_df["担保笔数"] = pd.to_numeric(temp_df["担保笔数"]) temp_df["担保金额"] = pd.to_numeric(temp_df["担保金额"]) temp_df["归属于母公司所有者权益"] = pd.to_numeric(temp_df["归属于母公司所有者权益"]) temp_df["担保金融占净资产比例"] = pd.to_numeric(temp_df["担保金融占净资产比例"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_cg_guarantee.py#L45-L119

25

[ 0 ]

1.333333

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def stock_cg_guarantee_cninfo( symbol: str = "全部", start_date: str = "20180630", end_date: str = "20210927" ) -> pd.DataFrame: symbol_map = { "全部": '', "深市主板": '012002', "沪市": '012001', "创业板": '012015', "科创板": '012029', } url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1054" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "sdate": "-".join([start_date[:4], start_date[4:6], start_date[6:]]), "edate": "-".join([end_date[:4], end_date[4:6], end_date[6:]]), "market": symbol_map[symbol], } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "公告统计区间", "担保金融占净资产比例", "担保金额", "担保笔数", "证券简称", "证券代码", "归属于母公司所有者权益", ] temp_df = temp_df[ [ "证券代码", "证券简称", "公告统计区间", "担保笔数", "担保金额", "归属于母公司所有者权益", "担保金融占净资产比例", ] ] temp_df["担保笔数"] = pd.to_numeric(temp_df["担保笔数"]) temp_df["担保金额"] = pd.to_numeric(temp_df["担保金额"]) temp_df["归属于母公司所有者权益"] = pd.to_numeric(temp_df["归属于母公司所有者权益"]) temp_df["担保金融占净资产比例"] = pd.to_numeric(temp_df["担保金融占净资产比例"]) return temp_df

18,796

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_fund.py

stock_individual_fund_flow

( stock: str = "600094", market: str = "sh" )

return temp_df

东方财富网-数据中心-资金流向-个股 http://data.eastmoney.com/zjlx/detail.html :param stock: 股票代码 :type stock: str :param market: 股票市场; 上海证券交易所: sh, 深证证券交易所: sz :type market: str :return: 近期个股的资金流数据 :rtype: pandas.DataFrame

15

94

def stock_individual_fund_flow( stock: str = "600094", market: str = "sh" ) -> pd.DataFrame: """ 东方财富网-数据中心-资金流向-个股 http://data.eastmoney.com/zjlx/detail.html :param stock: 股票代码 :type stock: str :param market: 股票市场; 上海证券交易所: sh, 深证证券交易所: sz :type market: str :return: 近期个股的资金流数据 :rtype: pandas.DataFrame """ market_map = {"sh": 1, "sz": 0} url = "http://push2his.eastmoney.com/api/qt/stock/fflow/daykline/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "lmt": "0", "klt": "101", "secid": f"{market_map[market]}.{stock}", "fields1": "f1,f2,f3,f7", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64,f65", "ut": "b2884a393a59ad64002292a3e90d46a5", "cb": "jQuery183003743205523325188_1589197499471", "_": int(time.time() * 1000), } r = requests.get(url, params=params, headers=headers) text_data = r.text json_data = json.loads(text_data[text_data.find("{") : -2]) content_list = json_data["data"]["klines"] temp_df = pd.DataFrame([item.split(",") for item in content_list]) temp_df.columns = [ "日期", "主力净流入-净额", "小单净流入-净额", "中单净流入-净额", "大单净流入-净额", "超大单净流入-净额", "主力净流入-净占比", "小单净流入-净占比", "中单净流入-净占比", "大单净流入-净占比", "超大单净流入-净占比", "收盘价", "涨跌幅", "-", "-", ] temp_df = temp_df[ [ "日期", "收盘价", "涨跌幅", "主力净流入-净额", "主力净流入-净占比", "超大单净流入-净额", "超大单净流入-净占比", "大单净流入-净额", "大单净流入-净占比", "中单净流入-净额", "中单净流入-净占比", "小单净流入-净额", "小单净流入-净占比", ] ] temp_df["主力净流入-净额"] = pd.to_numeric(temp_df["主力净流入-净额"]) temp_df["小单净流入-净额"] = pd.to_numeric(temp_df["小单净流入-净额"]) temp_df["中单净流入-净额"] = pd.to_numeric(temp_df["中单净流入-净额"]) temp_df["大单净流入-净额"] = pd.to_numeric(temp_df["大单净流入-净额"]) temp_df["超大单净流入-净额"] = pd.to_numeric(temp_df["超大单净流入-净额"]) temp_df["主力净流入-净占比"] = pd.to_numeric(temp_df["主力净流入-净占比"]) temp_df["小单净流入-净占比"] = pd.to_numeric(temp_df["小单净流入-净占比"]) temp_df["中单净流入-净占比"] = pd.to_numeric(temp_df["中单净流入-净占比"]) temp_df["大单净流入-净占比"] = pd.to_numeric(temp_df["大单净流入-净占比"]) temp_df["超大单净流入-净占比"] = pd.to_numeric(temp_df["超大单净流入-净占比"]) temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"]) temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_fund.py#L15-L94

25

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70

8

def stock_individual_fund_flow( stock: str = "600094", market: str = "sh" ) -> pd.DataFrame: market_map = {"sh": 1, "sz": 0} url = "http://push2his.eastmoney.com/api/qt/stock/fflow/daykline/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "lmt": "0", "klt": "101", "secid": f"{market_map[market]}.{stock}", "fields1": "f1,f2,f3,f7", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64,f65", "ut": "b2884a393a59ad64002292a3e90d46a5", "cb": "jQuery183003743205523325188_1589197499471", "_": int(time.time() * 1000), } r = requests.get(url, params=params, headers=headers) text_data = r.text json_data = json.loads(text_data[text_data.find("{") : -2]) content_list = json_data["data"]["klines"] temp_df = pd.DataFrame([item.split(",") for item in content_list]) temp_df.columns = [ "日期", "主力净流入-净额", "小单净流入-净额", "中单净流入-净额", "大单净流入-净额", "超大单净流入-净额", "主力净流入-净占比", "小单净流入-净占比", "中单净流入-净占比", "大单净流入-净占比", "超大单净流入-净占比", "收盘价", "涨跌幅", "-", "-", ] temp_df = temp_df[ [ "日期", "收盘价", "涨跌幅", "主力净流入-净额", "主力净流入-净占比", "超大单净流入-净额", "超大单净流入-净占比", "大单净流入-净额", "大单净流入-净占比", "中单净流入-净额", "中单净流入-净占比", "小单净流入-净额", "小单净流入-净占比", ] ] temp_df["主力净流入-净额"] = pd.to_numeric(temp_df["主力净流入-净额"]) temp_df["小单净流入-净额"] = pd.to_numeric(temp_df["小单净流入-净额"]) temp_df["中单净流入-净额"] = pd.to_numeric(temp_df["中单净流入-净额"]) temp_df["大单净流入-净额"] = pd.to_numeric(temp_df["大单净流入-净额"]) temp_df["超大单净流入-净额"] = pd.to_numeric(temp_df["超大单净流入-净额"]) temp_df["主力净流入-净占比"] = pd.to_numeric(temp_df["主力净流入-净占比"]) temp_df["小单净流入-净占比"] = pd.to_numeric(temp_df["小单净流入-净占比"]) temp_df["中单净流入-净占比"] = pd.to_numeric(temp_df["中单净流入-净占比"]) temp_df["大单净流入-净占比"] = pd.to_numeric(temp_df["大单净流入-净占比"]) temp_df["超大单净流入-净占比"] = pd.to_numeric(temp_df["超大单净流入-净占比"]) temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"]) temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"]) return temp_df

18,797

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_fund.py

stock_individual_fund_flow_rank

(indicator: str = "5日") -

return temp_df

东方财富网-数据中心-资金流向-排名 http://data.eastmoney.com/zjlx/detail.html :param indicator: choice of {"今日", "3日", "5日", "10日"} :type indicator: str :return: 指定 indicator 资金流向排行 :rtype: pandas.DataFrame

97

306

def stock_individual_fund_flow_rank(indicator: str = "5日") -> pd.DataFrame: """ 东方财富网-数据中心-资金流向-排名 http://data.eastmoney.com/zjlx/detail.html :param indicator: choice of {"今日", "3日", "5日", "10日"} :type indicator: str :return: 指定 indicator 资金流向排行 :rtype: pandas.DataFrame """ indicator_map = { "今日": [ "f62", "f12,f14,f2,f3,f62,f184,f66,f69,f72,f75,f78,f81,f84,f87,f204,f205,f124", ], "3日": [ "f267", "f12,f14,f2,f127,f267,f268,f269,f270,f271,f272,f273,f274,f275,f276,f257,f258,f124", ], "5日": [ "f164", "f12,f14,f2,f109,f164,f165,f166,f167,f168,f169,f170,f171,f172,f173,f257,f258,f124", ], "10日": [ "f174", "f12,f14,f2,f160,f174,f175,f176,f177,f178,f179,f180,f181,f182,f183,f260,f261,f124", ], } url = "http://push2.eastmoney.com/api/qt/clist/get" params = { "fid": indicator_map[indicator][0], "po": "1", "pz": "5000", "pn": "1", "np": "1", "fltt": "2", "invt": "2", "ut": "b2884a393a59ad64002292a3e90d46a5", "fs": "m:0+t:6+f:!2,m:0+t:13+f:!2,m:0+t:80+f:!2,m:1+t:2+f:!2,m:1+t:23+f:!2,m:0+t:7+f:!2,m:1+t:3+f:!2", "fields": indicator_map[indicator][1], } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) if indicator == "今日": temp_df.columns = [ "序号", "最新价", "今日涨跌幅", "代码", "名称", "今日主力净流入-净额", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", "_", "今日主力净流入-净占比", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "今日涨跌幅", "今日主力净流入-净额", "今日主力净流入-净占比", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", ] ] elif indicator == "3日": temp_df.columns = [ "序号", "最新价", "代码", "名称", "_", "3日涨跌幅", "_", "_", "_", "3日主力净流入-净额", "3日主力净流入-净占比", "3日超大单净流入-净额", "3日超大单净流入-净占比", "3日大单净流入-净额", "3日大单净流入-净占比", "3日中单净流入-净额", "3日中单净流入-净占比", "3日小单净流入-净额", "3日小单净流入-净占比", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "3日涨跌幅", "3日主力净流入-净额", "3日主力净流入-净占比", "3日超大单净流入-净额", "3日超大单净流入-净占比", "3日大单净流入-净额", "3日大单净流入-净占比", "3日中单净流入-净额", "3日中单净流入-净占比", "3日小单净流入-净额", "3日小单净流入-净占比", ] ] elif indicator == "5日": temp_df.columns = [ "序号", "最新价", "代码", "名称", "5日涨跌幅", "_", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "5日涨跌幅", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", ] ] elif indicator == "10日": temp_df.columns = [ "序号", "最新价", "代码", "名称", "_", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", ] ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_fund.py#L97-L306

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

4.285714

[ 9, 27, 28, 40, 41, 42, 43, 44, 45, 46, 67, 86, 87, 108, 127, 128, 149, 168, 169, 190, 209 ]

10

false

7.8125

210

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90

6

def stock_individual_fund_flow_rank(indicator: str = "5日") -> pd.DataFrame: indicator_map = { "今日": [ "f62", "f12,f14,f2,f3,f62,f184,f66,f69,f72,f75,f78,f81,f84,f87,f204,f205,f124", ], "3日": [ "f267", "f12,f14,f2,f127,f267,f268,f269,f270,f271,f272,f273,f274,f275,f276,f257,f258,f124", ], "5日": [ "f164", "f12,f14,f2,f109,f164,f165,f166,f167,f168,f169,f170,f171,f172,f173,f257,f258,f124", ], "10日": [ "f174", "f12,f14,f2,f160,f174,f175,f176,f177,f178,f179,f180,f181,f182,f183,f260,f261,f124", ], } url = "http://push2.eastmoney.com/api/qt/clist/get" params = { "fid": indicator_map[indicator][0], "po": "1", "pz": "5000", "pn": "1", "np": "1", "fltt": "2", "invt": "2", "ut": "b2884a393a59ad64002292a3e90d46a5", "fs": "m:0+t:6+f:!2,m:0+t:13+f:!2,m:0+t:80+f:!2,m:1+t:2+f:!2,m:1+t:23+f:!2,m:0+t:7+f:!2,m:1+t:3+f:!2", "fields": indicator_map[indicator][1], } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) if indicator == "今日": temp_df.columns = [ "序号", "最新价", "今日涨跌幅", "代码", "名称", "今日主力净流入-净额", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", "_", "今日主力净流入-净占比", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "今日涨跌幅", "今日主力净流入-净额", "今日主力净流入-净占比", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", ] ] elif indicator == "3日": temp_df.columns = [ "序号", "最新价", "代码", "名称", "_", "3日涨跌幅", "_", "_", "_", "3日主力净流入-净额", "3日主力净流入-净占比", "3日超大单净流入-净额", "3日超大单净流入-净占比", "3日大单净流入-净额", "3日大单净流入-净占比", "3日中单净流入-净额", "3日中单净流入-净占比", "3日小单净流入-净额", "3日小单净流入-净占比", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "3日涨跌幅", "3日主力净流入-净额", "3日主力净流入-净占比", "3日超大单净流入-净额", "3日超大单净流入-净占比", "3日大单净流入-净额", "3日大单净流入-净占比", "3日中单净流入-净额", "3日中单净流入-净占比", "3日小单净流入-净额", "3日小单净流入-净占比", ] ] elif indicator == "5日": temp_df.columns = [ "序号", "最新价", "代码", "名称", "5日涨跌幅", "_", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "5日涨跌幅", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", ] ] elif indicator == "10日": temp_df.columns = [ "序号", "最新价", "代码", "名称", "_", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", ] ] return temp_df

18,798

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_fund.py

stock_market_fund_flow

()

return temp_df

东方财富网-数据中心-资金流向-大盘 http://data.eastmoney.com/zjlx/dpzjlx.html :return: 近期大盘的资金流数据 :rtype: pandas.DataFrame

309

389

def stock_market_fund_flow() -> pd.DataFrame: """ 东方财富网-数据中心-资金流向-大盘 http://data.eastmoney.com/zjlx/dpzjlx.html :return: 近期大盘的资金流数据 :rtype: pandas.DataFrame """ url = "http://push2his.eastmoney.com/api/qt/stock/fflow/daykline/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "lmt": "0", "klt": "101", "secid": "1.000001", "secid2": "0.399001", "fields1": "f1,f2,f3,f7", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64,f65", "ut": "b2884a393a59ad64002292a3e90d46a5", "cb": "jQuery183003743205523325188_1589197499471", "_": int(time.time() * 1000), } r = requests.get(url, params=params, headers=headers) text_data = r.text json_data = json.loads(text_data[text_data.find("{") : -2]) content_list = json_data["data"]["klines"] temp_df = pd.DataFrame([item.split(",") for item in content_list]) temp_df.columns = [ "日期", "主力净流入-净额", "小单净流入-净额", "中单净流入-净额", "大单净流入-净额", "超大单净流入-净额", "主力净流入-净占比", "小单净流入-净占比", "中单净流入-净占比", "大单净流入-净占比", "超大单净流入-净占比", "上证-收盘价", "上证-涨跌幅", "深证-收盘价", "深证-涨跌幅", ] temp_df = temp_df[ [ "日期", "上证-收盘价", "上证-涨跌幅", "深证-收盘价", "深证-涨跌幅", "主力净流入-净额", "主力净流入-净占比", "超大单净流入-净额", "超大单净流入-净占比", "大单净流入-净额", "大单净流入-净占比", "中单净流入-净额", "中单净流入-净占比", "小单净流入-净额", "小单净流入-净占比", ] ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["上证-收盘价"] = pd.to_numeric(temp_df["上证-收盘价"], errors="coerce") temp_df["上证-涨跌幅"] = pd.to_numeric(temp_df["上证-涨跌幅"], errors="coerce") temp_df["深证-收盘价"] = pd.to_numeric(temp_df["深证-收盘价"], errors="coerce") temp_df["深证-涨跌幅"] = pd.to_numeric(temp_df["深证-涨跌幅"], errors="coerce") temp_df["主力净流入-净额"] = pd.to_numeric(temp_df["主力净流入-净额"], errors="coerce") temp_df["主力净流入-净占比"] = pd.to_numeric(temp_df["主力净流入-净占比"], errors="coerce") temp_df["超大单净流入-净额"] = pd.to_numeric(temp_df["超大单净流入-净额"], errors="coerce") temp_df["超大单净流入-净占比"] = pd.to_numeric( temp_df["超大单净流入-净占比"], errors="coerce" ) temp_df["大单净流入-净额"] = pd.to_numeric(temp_df["大单净流入-净额"], errors="coerce") temp_df["大单净流入-净占比"] = pd.to_numeric(temp_df["大单净流入-净占比"], errors="coerce") temp_df["中单净流入-净额"] = pd.to_numeric(temp_df["中单净流入-净额"], errors="coerce") temp_df["中单净流入-净占比"] = pd.to_numeric(temp_df["中单净流入-净占比"], errors="coerce") temp_df["小单净流入-净额"] = pd.to_numeric(temp_df["小单净流入-净额"], errors="coerce") temp_df["小单净流入-净占比"] = pd.to_numeric(temp_df["小单净流入-净占比"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_fund.py#L309-L389

25

[ 0, 1, 2, 3, 4, 5, 6 ]

8.641975

[ 7, 8, 11, 22, 23, 24, 25, 26, 27, 44, 63, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 76, 77, 78, 79, 80 ]

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false

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67.901235

4

def stock_market_fund_flow() -> pd.DataFrame: url = "http://push2his.eastmoney.com/api/qt/stock/fflow/daykline/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "lmt": "0", "klt": "101", "secid": "1.000001", "secid2": "0.399001", "fields1": "f1,f2,f3,f7", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64,f65", "ut": "b2884a393a59ad64002292a3e90d46a5", "cb": "jQuery183003743205523325188_1589197499471", "_": int(time.time() * 1000), } r = requests.get(url, params=params, headers=headers) text_data = r.text json_data = json.loads(text_data[text_data.find("{") : -2]) content_list = json_data["data"]["klines"] temp_df = pd.DataFrame([item.split(",") for item in content_list]) temp_df.columns = [ "日期", "主力净流入-净额", "小单净流入-净额", "中单净流入-净额", "大单净流入-净额", "超大单净流入-净额", "主力净流入-净占比", "小单净流入-净占比", "中单净流入-净占比", "大单净流入-净占比", "超大单净流入-净占比", "上证-收盘价", "上证-涨跌幅", "深证-收盘价", "深证-涨跌幅", ] temp_df = temp_df[ [ "日期", "上证-收盘价", "上证-涨跌幅", "深证-收盘价", "深证-涨跌幅", "主力净流入-净额", "主力净流入-净占比", "超大单净流入-净额", "超大单净流入-净占比", "大单净流入-净额", "大单净流入-净占比", "中单净流入-净额", "中单净流入-净占比", "小单净流入-净额", "小单净流入-净占比", ] ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["上证-收盘价"] = pd.to_numeric(temp_df["上证-收盘价"], errors="coerce") temp_df["上证-涨跌幅"] = pd.to_numeric(temp_df["上证-涨跌幅"], errors="coerce") temp_df["深证-收盘价"] = pd.to_numeric(temp_df["深证-收盘价"], errors="coerce") temp_df["深证-涨跌幅"] = pd.to_numeric(temp_df["深证-涨跌幅"], errors="coerce") temp_df["主力净流入-净额"] = pd.to_numeric(temp_df["主力净流入-净额"], errors="coerce") temp_df["主力净流入-净占比"] = pd.to_numeric(temp_df["主力净流入-净占比"], errors="coerce") temp_df["超大单净流入-净额"] = pd.to_numeric(temp_df["超大单净流入-净额"], errors="coerce") temp_df["超大单净流入-净占比"] = pd.to_numeric( temp_df["超大单净流入-净占比"], errors="coerce" ) temp_df["大单净流入-净额"] = pd.to_numeric(temp_df["大单净流入-净额"], errors="coerce") temp_df["大单净流入-净占比"] = pd.to_numeric(temp_df["大单净流入-净占比"], errors="coerce") temp_df["中单净流入-净额"] = pd.to_numeric(temp_df["中单净流入-净额"], errors="coerce") temp_df["中单净流入-净占比"] = pd.to_numeric(temp_df["中单净流入-净占比"], errors="coerce") temp_df["小单净流入-净额"] = pd.to_numeric(temp_df["小单净流入-净额"], errors="coerce") temp_df["小单净流入-净占比"] = pd.to_numeric(temp_df["小单净流入-净占比"], errors="coerce") return temp_df

18,799

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_fund.py

stock_sector_fund_flow_rank

( indicator: str = "10日", sector_type: str = "行业资金流" )

return temp_df

东方财富网-数据中心-资金流向-板块资金流-排名 http://data.eastmoney.com/bkzj/hy.html :param indicator: choice of {"今日", "5日", "10日"} :type indicator: str :param sector_type: choice of {"行业资金流", "概念资金流", "地域资金流"} :type sector_type: str :return: 指定参数的资金流排名数据 :rtype: pandas.DataFrame

392

576

def stock_sector_fund_flow_rank( indicator: str = "10日", sector_type: str = "行业资金流" ) -> pd.DataFrame: """ 东方财富网-数据中心-资金流向-板块资金流-排名 http://data.eastmoney.com/bkzj/hy.html :param indicator: choice of {"今日", "5日", "10日"} :type indicator: str :param sector_type: choice of {"行业资金流", "概念资金流", "地域资金流"} :type sector_type: str :return: 指定参数的资金流排名数据 :rtype: pandas.DataFrame """ sector_type_map = {"行业资金流": "2", "概念资金流": "3", "地域资金流": "1"} indicator_map = { "今日": [ "f62", "1", "f12,f14,f2,f3,f62,f184,f66,f69,f72,f75,f78,f81,f84,f87,f204,f205,f124", ], "5日": [ "f164", "5", "f12,f14,f2,f109,f164,f165,f166,f167,f168,f169,f170,f171,f172,f173,f257,f258,f124", ], "10日": [ "f174", "10", "f12,f14,f2,f160,f174,f175,f176,f177,f178,f179,f180,f181,f182,f183,f260,f261,f124", ], } url = "http://push2.eastmoney.com/api/qt/clist/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "pn": "1", "pz": "5000", "po": "1", "np": "1", "ut": "b2884a393a59ad64002292a3e90d46a5", "fltt": "2", "invt": "2", "fid0": indicator_map[indicator][0], "fs": f"m:90 t:{sector_type_map[sector_type]}", "stat": indicator_map[indicator][1], "fields": indicator_map[indicator][2], "rt": "52975239", "cb": "jQuery18308357908311220152_1589256588824", "_": int(time.time() * 1000), } r = requests.get(url, params=params, headers=headers) text_data = r.text json_data = json.loads(text_data[text_data.find("{") : -2]) temp_df = pd.DataFrame(json_data["data"]["diff"]) if indicator == "今日": temp_df.columns = [ "-", "今日涨跌幅", "_", "名称", "今日主力净流入-净额", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", "-", "今日主力净流入-净占比", "今日主力净流入最大股", "今日主力净流入最大股代码", "是否净流入", ] temp_df = temp_df[ [ "名称", "今日涨跌幅", "今日主力净流入-净额", "今日主力净流入-净占比", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", "今日主力净流入最大股", ] ] temp_df.sort_values(["今日主力净流入-净额"], ascending=False, inplace=True) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename({"index": "序号"}, axis=1, inplace=True) elif indicator == "5日": temp_df.columns = [ "-", "_", "名称", "5日涨跌幅", "_", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", "5日主力净流入最大股", "_", "_", ] temp_df = temp_df[ [ "名称", "5日涨跌幅", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", "5日主力净流入最大股", ] ] temp_df.sort_values(["5日主力净流入-净额"], ascending=False, inplace=True) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename({"index": "序号"}, axis=1, inplace=True) elif indicator == "10日": temp_df.columns = [ "-", "_", "名称", "_", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", "10日主力净流入最大股", "_", "_", ] temp_df = temp_df[ [ "名称", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", "10日主力净流入最大股", ] ] temp_df.sort_values(["10日主力净流入-净额"], ascending=False, inplace=True) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename({"index": "序号"}, axis=1, inplace=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_fund.py#L392-L576

25

[ 0 ]

0.540541

[ 13, 14, 31, 32, 35, 51, 52, 53, 54, 55, 56, 77, 94, 95, 96, 97, 98, 99, 120, 137, 138, 139, 140, 141, 142, 163, 180, 181, 182, 183, 184 ]

16.756757

false

7.8125

185

4

83.243243

8

def stock_sector_fund_flow_rank( indicator: str = "10日", sector_type: str = "行业资金流" ) -> pd.DataFrame: sector_type_map = {"行业资金流": "2", "概念资金流": "3", "地域资金流": "1"} indicator_map = { "今日": [ "f62", "1", "f12,f14,f2,f3,f62,f184,f66,f69,f72,f75,f78,f81,f84,f87,f204,f205,f124", ], "5日": [ "f164", "5", "f12,f14,f2,f109,f164,f165,f166,f167,f168,f169,f170,f171,f172,f173,f257,f258,f124", ], "10日": [ "f174", "10", "f12,f14,f2,f160,f174,f175,f176,f177,f178,f179,f180,f181,f182,f183,f260,f261,f124", ], } url = "http://push2.eastmoney.com/api/qt/clist/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "pn": "1", "pz": "5000", "po": "1", "np": "1", "ut": "b2884a393a59ad64002292a3e90d46a5", "fltt": "2", "invt": "2", "fid0": indicator_map[indicator][0], "fs": f"m:90 t:{sector_type_map[sector_type]}", "stat": indicator_map[indicator][1], "fields": indicator_map[indicator][2], "rt": "52975239", "cb": "jQuery18308357908311220152_1589256588824", "_": int(time.time() * 1000), } r = requests.get(url, params=params, headers=headers) text_data = r.text json_data = json.loads(text_data[text_data.find("{") : -2]) temp_df = pd.DataFrame(json_data["data"]["diff"]) if indicator == "今日": temp_df.columns = [ "-", "今日涨跌幅", "_", "名称", "今日主力净流入-净额", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", "-", "今日主力净流入-净占比", "今日主力净流入最大股", "今日主力净流入最大股代码", "是否净流入", ] temp_df = temp_df[ [ "名称", "今日涨跌幅", "今日主力净流入-净额", "今日主力净流入-净占比", "今日超大单净流入-净额", "今日超大单净流入-净占比", "今日大单净流入-净额", "今日大单净流入-净占比", "今日中单净流入-净额", "今日中单净流入-净占比", "今日小单净流入-净额", "今日小单净流入-净占比", "今日主力净流入最大股", ] ] temp_df.sort_values(["今日主力净流入-净额"], ascending=False, inplace=True) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename({"index": "序号"}, axis=1, inplace=True) elif indicator == "5日": temp_df.columns = [ "-", "_", "名称", "5日涨跌幅", "_", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", "5日主力净流入最大股", "_", "_", ] temp_df = temp_df[ [ "名称", "5日涨跌幅", "5日主力净流入-净额", "5日主力净流入-净占比", "5日超大单净流入-净额", "5日超大单净流入-净占比", "5日大单净流入-净额", "5日大单净流入-净占比", "5日中单净流入-净额", "5日中单净流入-净占比", "5日小单净流入-净额", "5日小单净流入-净占比", "5日主力净流入最大股", ] ] temp_df.sort_values(["5日主力净流入-净额"], ascending=False, inplace=True) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename({"index": "序号"}, axis=1, inplace=True) elif indicator == "10日": temp_df.columns = [ "-", "_", "名称", "_", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", "10日主力净流入最大股", "_", "_", ] temp_df = temp_df[ [ "名称", "10日涨跌幅", "10日主力净流入-净额", "10日主力净流入-净占比", "10日超大单净流入-净额", "10日超大单净流入-净占比", "10日大单净流入-净额", "10日大单净流入-净占比", "10日中单净流入-净额", "10日中单净流入-净占比", "10日小单净流入-净额", "10日小单净流入-净占比", "10日主力净流入最大股", ] ] temp_df.sort_values(["10日主力净流入-净额"], ascending=False, inplace=True) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename({"index": "序号"}, axis=1, inplace=True) return temp_df

18,800

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_stop.py

stock_staq_net_stop

()

return temp_df

东方财富网-行情中心-沪深个股-两网及退市 https://quote.eastmoney.com/center/gridlist.html#staq_net_board :return: 两网及退市 :rtype: pandas.DataFrame

12

39

def stock_staq_net_stop() -> pd.DataFrame: """ 东方财富网-行情中心-沪深个股-两网及退市 https://quote.eastmoney.com/center/gridlist.html#staq_net_board :return: 两网及退市 :rtype: pandas.DataFrame """ url = "http://5.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:0 s:3", "fields": "f12,f14", "_": "1622622663841", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = ["序号", "代码", "名称"] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_stop.py#L12-L39

25

[ 0, 1, 2, 3, 4, 5, 6 ]

25

[ 7, 8, 21, 22, 23, 24, 25, 26, 27 ]

32.142857

false

31.25

28

1

67.857143

4

def stock_staq_net_stop() -> pd.DataFrame: url = "http://5.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:0 s:3", "fields": "f12,f14", "_": "1622622663841", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = ["序号", "代码", "名称"] return temp_df

18,801

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_kcb_report.py

_stock_zh_kcb_report_em_page

()

return page_num

科创板报告的页数 http://data.eastmoney.com/notices/kcb.html :return: 科创板报告的页数 :rtype: int

13

35

def _stock_zh_kcb_report_em_page() -> int: """ 科创板报告的页数 http://data.eastmoney.com/notices/kcb.html :return: 科创板报告的页数 :rtype: int """ url = "http://np-anotice-stock.eastmoney.com/api/security/ann" params = { "sr": "-1", "page_size": "100", "page_index": "1", "ann_type": "KCB", "client_source": "web", "f_node": "0", "s_node": "0", } r = requests.get(url, params=params) data_json = r.json() page_num = int( int(data_json["data"]["total_hits"]) / int(data_json["data"]["page_size"]) ) return page_num

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_kcb_report.py#L13-L35

25

[ 0, 1, 2, 3, 4, 5, 6 ]

30.434783

[ 7, 8, 17, 18, 19, 22 ]

26.086957

false

24.137931

23

1

73.913043

4

def _stock_zh_kcb_report_em_page() -> int: url = "http://np-anotice-stock.eastmoney.com/api/security/ann" params = { "sr": "-1", "page_size": "100", "page_index": "1", "ann_type": "KCB", "client_source": "web", "f_node": "0", "s_node": "0", } r = requests.get(url, params=params) data_json = r.json() page_num = int( int(data_json["data"]["total_hits"]) / int(data_json["data"]["page_size"]) ) return page_num

18,802

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_kcb_report.py

stock_zh_kcb_report_em

(from_page: int = 1, to_page: int = 100)

return big_df

科创板报告内容 http://data.eastmoney.com/notices/kcb.html :param from_page: 开始获取的页码 :type from_page: int :param to_page: 结束获取的页码 :type to_page: int :return: 科创板报告内容 :rtype: pandas.DataFrame

38

89

def stock_zh_kcb_report_em(from_page: int = 1, to_page: int = 100) -> pd.DataFrame: """ 科创板报告内容 http://data.eastmoney.com/notices/kcb.html :param from_page: 开始获取的页码 :type from_page: int :param to_page: 结束获取的页码 :type to_page: int :return: 科创板报告内容 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() url = "http://np-anotice-stock.eastmoney.com/api/security/ann" total_page = _stock_zh_kcb_report_em_page() if to_page >= total_page: to_page = total_page for i in tqdm(range(from_page, to_page + 1), leave=False): params = { "sr": "-1", "page_size": "100", "page_index": i, "ann_type": "KCB", "client_source": "web", "f_node": "0", "s_node": "0", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame( [ [item["codes"][0]["stock_code"] for item in data_json["data"]["list"]], [item["codes"][0]["short_name"] for item in data_json["data"]["list"]], [item["title"] for item in data_json["data"]["list"]], [ item["columns"][0]["column_name"] for item in data_json["data"]["list"] ], [item["notice_date"] for item in data_json["data"]["list"]], [item["art_code"] for item in data_json["data"]["list"]], ] ).T big_df = big_df.append(temp_df, ignore_index=True) big_df.columns = [ "代码", "名称", "公告标题", "公告类型", "公告日期", "公告代码", ] big_df['公告日期'] = pd.to_datetime(big_df['公告日期']).dt.date return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_kcb_report.py#L38-L89

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

21.153846

[ 11, 12, 13, 14, 15, 16, 17, 26, 27, 28, 41, 42, 50, 51 ]

26.923077

false

24.137931

52

9

73.076923

8

def stock_zh_kcb_report_em(from_page: int = 1, to_page: int = 100) -> pd.DataFrame: big_df = pd.DataFrame() url = "http://np-anotice-stock.eastmoney.com/api/security/ann" total_page = _stock_zh_kcb_report_em_page() if to_page >= total_page: to_page = total_page for i in tqdm(range(from_page, to_page + 1), leave=False): params = { "sr": "-1", "page_size": "100", "page_index": i, "ann_type": "KCB", "client_source": "web", "f_node": "0", "s_node": "0", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame( [ [item["codes"][0]["stock_code"] for item in data_json["data"]["list"]], [item["codes"][0]["short_name"] for item in data_json["data"]["list"]], [item["title"] for item in data_json["data"]["list"]], [ item["columns"][0]["column_name"] for item in data_json["data"]["list"] ], [item["notice_date"] for item in data_json["data"]["list"]], [item["art_code"] for item in data_json["data"]["list"]], ] ).T big_df = big_df.append(temp_df, ignore_index=True) big_df.columns = [ "代码", "名称", "公告标题", "公告类型", "公告日期", "公告代码", ] big_df['公告日期'] = pd.to_datetime(big_df['公告日期']).dt.date return big_df

18,803

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_summary.py

stock_szse_summary

(date: str = "20200619")

return temp_df

深证证券交易所-总貌-证券类别统计 https://www.szse.cn/market/overview/index.html :param date: 最近结束交易日 :type date: str :return: 证券类别统计 :rtype: pandas.DataFrame

18

46

def stock_szse_summary(date: str = "20200619") -> pd.DataFrame: """ 深证证券交易所-总貌-证券类别统计 https://www.szse.cn/market/overview/index.html :param date: 最近结束交易日 :type date: str :return: 证券类别统计 :rtype: pandas.DataFrame """ url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1803_sczm", "TABKEY": "tab1", "txtQueryDate": "-".join([date[:4], date[4:6], date[6:]]), "random": "0.39339437497296137", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl") temp_df["证券类别"] = temp_df["证券类别"].str.strip() temp_df.iloc[:, 2:] = temp_df.iloc[:, 2:].applymap(lambda x: x.replace(",", "")) temp_df.columns = ["证券类别", "数量", "成交金额", "总市值", "流通市值"] temp_df["数量"] = pd.to_numeric(temp_df["数量"]) temp_df["成交金额"] = pd.to_numeric(temp_df["成交金额"]) temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_summary.py#L18-L46

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

31.034483

[ 9, 10, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 ]

48.275862

false

7.228916

29

2

51.724138

6

def stock_szse_summary(date: str = "20200619") -> pd.DataFrame: url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1803_sczm", "TABKEY": "tab1", "txtQueryDate": "-".join([date[:4], date[4:6], date[6:]]), "random": "0.39339437497296137", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl") temp_df["证券类别"] = temp_df["证券类别"].str.strip() temp_df.iloc[:, 2:] = temp_df.iloc[:, 2:].applymap(lambda x: x.replace(",", "")) temp_df.columns = ["证券类别", "数量", "成交金额", "总市值", "流通市值"] temp_df["数量"] = pd.to_numeric(temp_df["数量"]) temp_df["成交金额"] = pd.to_numeric(temp_df["成交金额"]) temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") return temp_df

18,804

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_summary.py

stock_szse_area_summary

(date: str = "202203")

return temp_df

深证证券交易所-总貌-地区交易排序 https://www.szse.cn/market/overview/index.html :param date: 最近结束交易日 :type date: str :return: 地区交易排序 :rtype: pandas.DataFrame

49

80

def stock_szse_area_summary(date: str = "202203") -> pd.DataFrame: """ 深证证券交易所-总貌-地区交易排序 https://www.szse.cn/market/overview/index.html :param date: 最近结束交易日 :type date: str :return: 地区交易排序 :rtype: pandas.DataFrame """ url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1803_sczm", "TABKEY": "tab2", "DATETIME": "-".join([date[:4], date[4:6]]), "random": "0.39339437497296137", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl") temp_df.columns = ["序号", "地区", "总交易额", "占市场", "股票交易额", "基金交易额", "债券交易额"] temp_df["总交易额"] = temp_df["总交易额"].str.replace(",", "") temp_df["总交易额"] = pd.to_numeric(temp_df["总交易额"]) temp_df["占市场"] = pd.to_numeric(temp_df["占市场"]) temp_df["股票交易额"] = temp_df["股票交易额"].str.replace(",", "") temp_df["股票交易额"] = pd.to_numeric(temp_df["股票交易额"], errors="coerce") temp_df["基金交易额"] = temp_df["基金交易额"].str.replace(",", "") temp_df["基金交易额"] = pd.to_numeric(temp_df["基金交易额"], errors="coerce") temp_df["债券交易额"] = temp_df["债券交易额"].str.replace(",", "") temp_df["债券交易额"] = pd.to_numeric(temp_df["债券交易额"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_summary.py#L49-L80

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

28.125

[ 9, 10, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ]

53.125

false

7.228916

32

2

46.875

6

def stock_szse_area_summary(date: str = "202203") -> pd.DataFrame: url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1803_sczm", "TABKEY": "tab2", "DATETIME": "-".join([date[:4], date[4:6]]), "random": "0.39339437497296137", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl") temp_df.columns = ["序号", "地区", "总交易额", "占市场", "股票交易额", "基金交易额", "债券交易额"] temp_df["总交易额"] = temp_df["总交易额"].str.replace(",", "") temp_df["总交易额"] = pd.to_numeric(temp_df["总交易额"]) temp_df["占市场"] = pd.to_numeric(temp_df["占市场"]) temp_df["股票交易额"] = temp_df["股票交易额"].str.replace(",", "") temp_df["股票交易额"] = pd.to_numeric(temp_df["股票交易额"], errors="coerce") temp_df["基金交易额"] = temp_df["基金交易额"].str.replace(",", "") temp_df["基金交易额"] = pd.to_numeric(temp_df["基金交易额"], errors="coerce") temp_df["债券交易额"] = temp_df["债券交易额"].str.replace(",", "") temp_df["债券交易额"] = pd.to_numeric(temp_df["债券交易额"], errors="coerce") return temp_df

18,805

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_summary.py

stock_szse_sector_summary

(symbol: str = "当月", date: str = "202203") ->

return temp_df

深圳证券交易所-统计资料-股票行业成交 https://docs.static.szse.cn/www/market/periodical/month/W020220511355248518608.html :param symbol: choice of {"当月", "当年"} :type symbol: str :param date: 交易年月 :type date: str :return: 股票行业成交 :rtype: pandas.DataFrame

83

157

def stock_szse_sector_summary(symbol: str = "当月", date: str = "202203") -> pd.DataFrame: """ 深圳证券交易所-统计资料-股票行业成交 https://docs.static.szse.cn/www/market/periodical/month/W020220511355248518608.html :param symbol: choice of {"当月", "当年"} :type symbol: str :param date: 交易年月 :type date: str :return: 股票行业成交 :rtype: pandas.DataFrame """ url = "https://www.szse.cn/market/periodical/month/index.html" r = requests.get(url) r.encoding = "utf8" soup = BeautifulSoup(r.text, "lxml") tags_list = soup.find_all("div", attrs={"class": "g-container"})[4].find_all( "script" ) tags_dict = [ eval( item.string[item.string.find("{") : item.string.find("}") + 1] .replace("\n", "") .replace(" ", "") .replace("value", "'value'") .replace("text", "'text'") ) for item in tags_list ] date_url_dict = dict( zip( [item["text"] for item in tags_dict], [item["value"][2:] for item in tags_dict], ) ) date_format = "-".join([date[:4], date[4:]]) url = f"http://www.szse.cn/market/periodical/month/{date_url_dict[date_format]}" r = requests.get(url) r.encoding = "utf8" soup = BeautifulSoup(r.text, "lxml") url = soup.find("a", text="股票行业成交数据")["href"] if symbol == "当月": temp_df = pd.read_html(url, encoding="gbk")[0] temp_df.columns = [ "项目名称", "项目名称-英文", "交易天数", "成交金额-人民币元", "成交金额-占总计", "成交股数-股数", "成交股数-占总计", "成交笔数-笔", "成交笔数-占总计", ] else: temp_df = pd.read_html(url, encoding="gbk")[1] temp_df.columns = [ "项目名称", "项目名称-英文", "交易天数", "成交金额-人民币元", "成交金额-占总计", "成交股数-股数", "成交股数-占总计", "成交笔数-笔", "成交笔数-占总计", ] temp_df["交易天数"] = pd.to_numeric(temp_df["交易天数"]) temp_df["成交金额-人民币元"] = pd.to_numeric(temp_df["成交金额-人民币元"]) temp_df["成交金额-占总计"] = pd.to_numeric(temp_df["成交金额-占总计"]) temp_df["成交股数-股数"] = pd.to_numeric(temp_df["成交股数-股数"]) temp_df["成交股数-占总计"] = pd.to_numeric(temp_df["成交股数-占总计"]) temp_df["成交笔数-笔"] = pd.to_numeric(temp_df["成交笔数-笔"]) temp_df["成交笔数-占总计"] = pd.to_numeric(temp_df["成交笔数-占总计"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_summary.py#L83-L157

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

14.666667

[ 11, 12, 13, 14, 15, 18, 28, 34, 35, 36, 37, 38, 39, 40, 41, 42, 54, 55, 67, 68, 69, 70, 71, 72, 73, 74 ]

34.666667

false

7.228916

75

5

65.333333

8

def stock_szse_sector_summary(symbol: str = "当月", date: str = "202203") -> pd.DataFrame: url = "https://www.szse.cn/market/periodical/month/index.html" r = requests.get(url) r.encoding = "utf8" soup = BeautifulSoup(r.text, "lxml") tags_list = soup.find_all("div", attrs={"class": "g-container"})[4].find_all( "script" ) tags_dict = [ eval( item.string[item.string.find("{") : item.string.find("}") + 1] .replace("\n", "") .replace(" ", "") .replace("value", "'value'") .replace("text", "'text'") ) for item in tags_list ] date_url_dict = dict( zip( [item["text"] for item in tags_dict], [item["value"][2:] for item in tags_dict], ) ) date_format = "-".join([date[:4], date[4:]]) url = f"http://www.szse.cn/market/periodical/month/{date_url_dict[date_format]}" r = requests.get(url) r.encoding = "utf8" soup = BeautifulSoup(r.text, "lxml") url = soup.find("a", text="股票行业成交数据")["href"] if symbol == "当月": temp_df = pd.read_html(url, encoding="gbk")[0] temp_df.columns = [ "项目名称", "项目名称-英文", "交易天数", "成交金额-人民币元", "成交金额-占总计", "成交股数-股数", "成交股数-占总计", "成交笔数-笔", "成交笔数-占总计", ] else: temp_df = pd.read_html(url, encoding="gbk")[1] temp_df.columns = [ "项目名称", "项目名称-英文", "交易天数", "成交金额-人民币元", "成交金额-占总计", "成交股数-股数", "成交股数-占总计", "成交笔数-笔", "成交笔数-占总计", ] temp_df["交易天数"] = pd.to_numeric(temp_df["交易天数"]) temp_df["成交金额-人民币元"] = pd.to_numeric(temp_df["成交金额-人民币元"]) temp_df["成交金额-占总计"] = pd.to_numeric(temp_df["成交金额-占总计"]) temp_df["成交股数-股数"] = pd.to_numeric(temp_df["成交股数-股数"]) temp_df["成交股数-占总计"] = pd.to_numeric(temp_df["成交股数-占总计"]) temp_df["成交笔数-笔"] = pd.to_numeric(temp_df["成交笔数-笔"]) temp_df["成交笔数-占总计"] = pd.to_numeric(temp_df["成交笔数-占总计"]) return temp_df

18,806

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_summary.py

stock_sse_summary

()

return temp_df

上海证券交易所-总貌 https://www.sse.com.cn/market/stockdata/statistic/ :return: 上海证券交易所-总貌 :rtype: pandas.DataFrame

160

201

def stock_sse_summary() -> pd.DataFrame: """ 上海证券交易所-总貌 https://www.sse.com.cn/market/stockdata/statistic/ :return: 上海证券交易所-总貌 :rtype: pandas.DataFrame """ url = "http://query.sse.com.cn/commonQuery.do" params = { "sqlId": "COMMON_SSE_SJ_GPSJ_GPSJZM_TJSJ_L", "PRODUCT_NAME": "股票,主板,科创板", "type": "inParams", "_": "1640855495128", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]).T temp_df.reset_index(inplace=True) temp_df["index"] = [ "流通股本", "总市值", "平均市盈率", "上市公司", "上市股票", "流通市值", "报告时间", "-", "总股本", "项目", ] temp_df = temp_df[temp_df["index"] != "-"].iloc[:-1, :] temp_df.columns = [ "项目", "股票", "主板", "科创板", ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_summary.py#L160-L201

25

[ 0, 1, 2, 3, 4, 5, 6 ]

16.666667

[ 7, 8, 14, 18, 19, 20, 21, 22, 34, 35, 41 ]

26.190476

false

7.228916

42

1

73.809524

4

def stock_sse_summary() -> pd.DataFrame: url = "http://query.sse.com.cn/commonQuery.do" params = { "sqlId": "COMMON_SSE_SJ_GPSJ_GPSJZM_TJSJ_L", "PRODUCT_NAME": "股票,主板,科创板", "type": "inParams", "_": "1640855495128", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]).T temp_df.reset_index(inplace=True) temp_df["index"] = [ "流通股本", "总市值", "平均市盈率", "上市公司", "上市股票", "流通市值", "报告时间", "-", "总股本", "项目", ] temp_df = temp_df[temp_df["index"] != "-"].iloc[:-1, :] temp_df.columns = [ "项目", "股票", "主板", "科创板", ] return temp_df

18,807

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_summary.py

stock_sse_deal_daily

(date: str = "20220331")

上海证券交易所-数据-股票数据-成交概况-股票成交概况-每日股票情况 https://www.sse.com.cn/market/stockdata/overview/day/ :return: 每日股票情况 :rtype: pandas.DataFrame

204

438

def stock_sse_deal_daily(date: str = "20220331") -> pd.DataFrame: """ 上海证券交易所-数据-股票数据-成交概况-股票成交概况-每日股票情况 https://www.sse.com.cn/market/stockdata/overview/day/ :return: 每日股票情况 :rtype: pandas.DataFrame """ if int(date) <= 20211224: url = "http://query.sse.com.cn/commonQuery.do" params = { "searchDate": "-".join([date[:4], date[4:6], date[6:]]), "sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_DAYCJGK_C", "stockType": "90", "_": "1616744620492", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df.T temp_df.reset_index(inplace=True) temp_df.columns = [ "单日情况", "主板A", "股票", "主板B", "_", "股票回购", "科创板", ] temp_df = temp_df[ [ "单日情况", "股票", "主板A", "主板B", "科创板", "股票回购", ] ] temp_df["单日情况"] = [ "流通市值", "流通换手率", "平均市盈率", "_", "市价总值", "_", "换手率", "_", "挂牌数", "_", "_", "_", "_", "_", "成交笔数", "成交金额", "成交量", "次新股换手率", "_", "_", ] temp_df = temp_df[temp_df["单日情况"] != "_"] temp_df["单日情况"] = temp_df["单日情况"].astype("category") list_custom_new = [ "挂牌数", "市价总值", "流通市值", "成交金额", "成交量", "成交笔数", "平均市盈率", "换手率", "次新股换手率", "流通换手率", ] temp_df["单日情况"].cat.set_categories(list_custom_new) temp_df.sort_values("单日情况", ascending=True, inplace=True) temp_df.reset_index(drop=True, inplace=True) temp_df["股票"] = pd.to_numeric(temp_df["股票"], errors="coerce") temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce") temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce") temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce") temp_df["股票回购"] = pd.to_numeric(temp_df["股票回购"], errors="coerce") return temp_df elif int(date) <= 20220224: url = "http://query.sse.com.cn/commonQuery.do" params = { "sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_MRGK_C", "SEARCH_DATE": "-".join([date[:4], date[4:6], date[6:]]), "_": "1640836561673", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df.T temp_df.reset_index(inplace=True) temp_df.columns = [ "单日情况", "主板A", "主板B", "科创板", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "单日情况", "主板A", "主板B", "科创板", ] ] temp_df["单日情况"] = [ "市价总值", "成交量", "平均市盈率", "换手率", "成交金额", "-", "流通市值", "流通换手率", "报告日期", "挂牌数", "-", ] temp_df = temp_df[temp_df["单日情况"] != "-"] temp_df["单日情况"] = temp_df["单日情况"].astype("category") list_custom_new = [ "挂牌数", "市价总值", "流通市值", "成交金额", "成交量", "平均市盈率", "换手率", "流通换手率", ] temp_df["单日情况"].cat.set_categories(list_custom_new) temp_df.sort_values("单日情况", ascending=True, inplace=True) temp_df.reset_index(inplace=True, drop=True) temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce") temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce") temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce") return temp_df else: url = "http://query.sse.com.cn/commonQuery.do" params = { "sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_MRGK_C", "PRODUCT_CODE": "01,02,03,11,17", "type": "inParams", "SEARCH_DATE": "-".join([date[:4], date[4:6], date[6:]]), "_": "1640836561673", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df.T temp_df.reset_index(inplace=True) if len(temp_df.T) == 5: temp_df.columns = [ "单日情况", "主板A", "主板B", "科创板", "股票", ] temp_df["股票回购"] = "-" else: temp_df.columns = [ "单日情况", "主板A", "主板B", "科创板", "股票回购", "股票", ] temp_df = temp_df[ [ "单日情况", "股票", "主板A", "主板B", "科创板", "股票回购", ] ] temp_df["单日情况"] = [ "市价总值", "成交量", "平均市盈率", "换手率", "成交金额", "-", "流通市值", "流通换手率", "报告日期", "挂牌数", "-", ] temp_df = temp_df[temp_df["单日情况"] != "-"] temp_df["单日情况"] = temp_df["单日情况"].astype("category") list_custom_new = [ "挂牌数", "市价总值", "流通市值", "成交金额", "成交量", "平均市盈率", "换手率", "流通换手率", ] temp_df["单日情况"].cat.set_categories(list_custom_new) temp_df.sort_values("单日情况", ascending=True, inplace=True) temp_df.reset_index(inplace=True, drop=True) temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce") temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce") temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce") temp_df["股票"] = pd.to_numeric(temp_df["股票"], errors="coerce") temp_df["股票回购"] = pd.to_numeric(temp_df["股票回购"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_summary.py#L204-L438

25

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def stock_sse_deal_daily(date: str = "20220331") -> pd.DataFrame: if int(date) <= 20211224: url = "http://query.sse.com.cn/commonQuery.do" params = { "searchDate": "-".join([date[:4], date[4:6], date[6:]]), "sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_DAYCJGK_C", "stockType": "90", "_": "1616744620492", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df.T temp_df.reset_index(inplace=True) temp_df.columns = [ "单日情况", "主板A", "股票", "主板B", "_", "股票回购", "科创板", ] temp_df = temp_df[ [ "单日情况", "股票", "主板A", "主板B", "科创板", "股票回购", ] ] temp_df["单日情况"] = [ "流通市值", "流通换手率", "平均市盈率", "_", "市价总值", "_", "换手率", "_", "挂牌数", "_", "_", "_", "_", "_", "成交笔数", "成交金额", "成交量", "次新股换手率", "_", "_", ] temp_df = temp_df[temp_df["单日情况"] != "_"] temp_df["单日情况"] = temp_df["单日情况"].astype("category") list_custom_new = [ "挂牌数", "市价总值", "流通市值", "成交金额", "成交量", "成交笔数", "平均市盈率", "换手率", "次新股换手率", "流通换手率", ] temp_df["单日情况"].cat.set_categories(list_custom_new) temp_df.sort_values("单日情况", ascending=True, inplace=True) temp_df.reset_index(drop=True, inplace=True) temp_df["股票"] = pd.to_numeric(temp_df["股票"], errors="coerce") temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce") temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce") temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce") temp_df["股票回购"] = pd.to_numeric(temp_df["股票回购"], errors="coerce") return temp_df elif int(date) <= 20220224: url = "http://query.sse.com.cn/commonQuery.do" params = { "sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_MRGK_C", "SEARCH_DATE": "-".join([date[:4], date[4:6], date[6:]]), "_": "1640836561673", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df.T temp_df.reset_index(inplace=True) temp_df.columns = [ "单日情况", "主板A", "主板B", "科创板", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "单日情况", "主板A", "主板B", "科创板", ] ] temp_df["单日情况"] = [ "市价总值", "成交量", "平均市盈率", "换手率", "成交金额", "-", "流通市值", "流通换手率", "报告日期", "挂牌数", "-", ] temp_df = temp_df[temp_df["单日情况"] != "-"] temp_df["单日情况"] = temp_df["单日情况"].astype("category") list_custom_new = [ "挂牌数", "市价总值", "流通市值", "成交金额", "成交量", "平均市盈率", "换手率", "流通换手率", ] temp_df["单日情况"].cat.set_categories(list_custom_new) temp_df.sort_values("单日情况", ascending=True, inplace=True) temp_df.reset_index(inplace=True, drop=True) temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce") temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce") temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce") return temp_df else: url = "http://query.sse.com.cn/commonQuery.do" params = { "sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_MRGK_C", "PRODUCT_CODE": "01,02,03,11,17", "type": "inParams", "SEARCH_DATE": "-".join([date[:4], date[4:6], date[6:]]), "_": "1640836561673", } headers = { "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df.T temp_df.reset_index(inplace=True) if len(temp_df.T) == 5: temp_df.columns = [ "单日情况", "主板A", "主板B", "科创板", "股票", ] temp_df["股票回购"] = "-" else: temp_df.columns = [ "单日情况", "主板A", "主板B", "科创板", "股票回购", "股票", ] temp_df = temp_df[ [ "单日情况", "股票", "主板A", "主板B", "科创板", "股票回购", ] ] temp_df["单日情况"] = [ "市价总值", "成交量", "平均市盈率", "换手率", "成交金额", "-", "流通市值", "流通换手率", "报告日期", "挂牌数", "-", ] temp_df = temp_df[temp_df["单日情况"] != "-"] temp_df["单日情况"] = temp_df["单日情况"].astype("category") list_custom_new = [ "挂牌数", "市价总值", "流通市值", "成交金额", "成交量", "平均市盈率", "换手率", "流通换手率", ] temp_df["单日情况"].cat.set_categories(list_custom_new) temp_df.sort_values("单日情况", ascending=True, inplace=True) temp_df.reset_index(inplace=True, drop=True) temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce") temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce") temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce") temp_df["股票"] = pd.to_numeric(temp_df["股票"], errors="coerce") temp_df["股票回购"] = pd.to_numeric(temp_df["股票回购"], errors="coerce") return temp_df

18,808

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_allotment_cninfo.py

stock_allotment_cninfo

( symbol: str = "600030", start_date: str = "19700101", end_date: str = "22220222" )

return temp_df

巨潮资讯-个股-配股实施方案 http://webapi.cninfo.com.cn/#/dataBrowse :param symbol: 股票代码 :type symbol: str :param start_date: 开始查询的日期 :type symbol: str :param end_date: 结束查询的日期 :type symbol: str :return: 配股实施方案 :rtype: pandas.DataFrame

45

205

def stock_allotment_cninfo( symbol: str = "600030", start_date: str = "19700101", end_date: str = "22220222" ) -> pd.DataFrame: """ 巨潮资讯-个股-配股实施方案 http://webapi.cninfo.com.cn/#/dataBrowse :param symbol: 股票代码 :type symbol: str :param start_date: 开始查询的日期 :type symbol: str :param end_date: 结束查询的日期 :type symbol: str :return: 配股实施方案 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/stock/p_stock2232" params = { "scode": symbol, "sdate": start_date if not start_date else f"{start_date[0:4]}-{start_date[4:6]}-{start_date[6:8]}", "edate": end_date if not end_date else f"{end_date[0:4]}-{end_date[4:6]}-{end_date[6:8]}", } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() columns = [ "记录标识", "证券简称", "停牌起始日", "上市公告日期", "配股缴款起始日", "可转配股数量", "停牌截止日", "实际配股数量", "配股价格", "配股比例", "配股前总股本", "每股配权转让费(元)", "法人股实配数量", "实际募资净额", "大股东认购方式", "其他配售简称", "发行方式", "配股失败，退还申购款日期", "除权基准日", "预计发行费用", "配股发行结果公告日", "证券代码", "配股权证交易截止日", "其他股份实配数量", "国家股实配数量", "委托单位", "公众获转配数量", "其他配售代码", "配售对象", "配股权证交易起始日", "资金到账日", "机构名称", "股权登记日", "实际募资总额", "预计募集资金", "大股东认购数量", "公众股实配数量", "转配股实配数量", "承销费用", "法人获转配数量", "配股后流通股本", "股票类别", "公众配售简称", "发行方式编码", "承销方式", "公告日期", "配股上市日", "配股缴款截止日", "承销余额(股)", "预计配股数量", "配股后总股本", "职工股实配数量", "承销方式编码", "发行费用总额", "配股前流通股本", "股票类别编码", "公众配售代码", ] if data_json["records"]: # 有配股记录 temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = columns dates = ( "停牌起始日", "上市公告日期", "配股失败，退还申购款日期", "配股缴款起始日", "停牌截止日", "除权基准日", "配股发行结果公告日", "配股权证交易截止日", "配股权证交易起始日", "资金到账日", "股权登记日", "公告日期", "配股上市日", "配股缴款截止日", ) for s in dates: temp_df[s] = pd.to_datetime(temp_df[s], errors="coerce").dt.date nums = ( "可转配股数量", "实际配股数量", "配股价格", "配股比例", "配股前总股本", "每股配权转让费(元)", "法人股实配数量", "实际募资净额", "预计发行费用", "其他股份实配数量", "国家股实配数量", "公众获转配数量", "实际募资总额", "预计募集资金", "大股东认购数量", "公众股实配数量", "转配股实配数量", "承销费用", "法人获转配数量", "配股后流通股本", "承销余额(股)", "预计配股数量", "配股后总股本", "职工股实配数量", "发行费用总额", "配股前流通股本", ) for s in nums: temp_df[s] = pd.to_numeric(temp_df[s], errors="coerce") else: # 没有配股数据 temp_df = pd.DataFrame(columns=columns) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_allotment_cninfo.py#L45-L205

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86.956522

10

def stock_allotment_cninfo( symbol: str = "600030", start_date: str = "19700101", end_date: str = "22220222" ) -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/stock/p_stock2232" params = { "scode": symbol, "sdate": start_date if not start_date else f"{start_date[0:4]}-{start_date[4:6]}-{start_date[6:8]}", "edate": end_date if not end_date else f"{end_date[0:4]}-{end_date[4:6]}-{end_date[6:8]}", } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() columns = [ "记录标识", "证券简称", "停牌起始日", "上市公告日期", "配股缴款起始日", "可转配股数量", "停牌截止日", "实际配股数量", "配股价格", "配股比例", "配股前总股本", "每股配权转让费(元)", "法人股实配数量", "实际募资净额", "大股东认购方式", "其他配售简称", "发行方式", "配股失败，退还申购款日期", "除权基准日", "预计发行费用", "配股发行结果公告日", "证券代码", "配股权证交易截止日", "其他股份实配数量", "国家股实配数量", "委托单位", "公众获转配数量", "其他配售代码", "配售对象", "配股权证交易起始日", "资金到账日", "机构名称", "股权登记日", "实际募资总额", "预计募集资金", "大股东认购数量", "公众股实配数量", "转配股实配数量", "承销费用", "法人获转配数量", "配股后流通股本", "股票类别", "公众配售简称", "发行方式编码", "承销方式", "公告日期", "配股上市日", "配股缴款截止日", "承销余额(股)", "预计配股数量", "配股后总股本", "职工股实配数量", "承销方式编码", "发行费用总额", "配股前流通股本", "股票类别编码", "公众配售代码", ] if data_json["records"]: # 有配股记录 temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = columns dates = ( "停牌起始日", "上市公告日期", "配股失败，退还申购款日期", "配股缴款起始日", "停牌截止日", "除权基准日", "配股发行结果公告日", "配股权证交易截止日", "配股权证交易起始日", "资金到账日", "股权登记日", "公告日期", "配股上市日", "配股缴款截止日", ) for s in dates: temp_df[s] = pd.to_datetime(temp_df[s], errors="coerce").dt.date nums = ( "可转配股数量", "实际配股数量", "配股价格", "配股比例", "配股前总股本", "每股配权转让费(元)", "法人股实配数量", "实际募资净额", "预计发行费用", "其他股份实配数量", "国家股实配数量", "公众获转配数量", "实际募资总额", "预计募集资金", "大股东认购数量", "公众股实配数量", "转配股实配数量", "承销费用", "法人获转配数量", "配股后流通股本", "承销余额(股)", "预计配股数量", "配股后总股本", "职工股实配数量", "发行费用总额", "配股前流通股本", ) for s in nums: temp_df[s] = pd.to_numeric(temp_df[s], errors="coerce") else: # 没有配股数据 temp_df = pd.DataFrame(columns=columns) return temp_df

18,809

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_tick_tx_163.py

stock_zh_a_tick_tx_js

(symbol: str = "sz000001")

return big_df

腾讯财经-历史分笔数据 http://gu.qq.com/sz300494/gp/detail :param symbol: 股票代码 :type symbol: str :return: 股票代码 :rtype: pandas.DataFrame

17

68

def stock_zh_a_tick_tx_js(symbol: str = "sz000001") -> pd.DataFrame: """ 腾讯财经-历史分笔数据 http://gu.qq.com/sz300494/gp/detail :param symbol: 股票代码 :type symbol: str :return: 股票代码 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() page = 0 warnings.warn("正在下载数据，请稍等") while True: try: url = "http://stock.gtimg.cn/data/index.php" params = { "appn": "detail", "action": "data", "c": symbol, "p": page, } r = requests.get(url, params=params) text_data = r.text temp_df = ( pd.DataFrame(eval(text_data[text_data.find("[") :])[1].split("|")) .iloc[:, 0] .str.split("/", expand=True) ) page += 1 big_df = pd.concat([big_df, temp_df], ignore_index=True) except: break if not big_df.empty: big_df = big_df.iloc[:, 1:] big_df.columns = ["成交时间", "成交价格", "价格变动", "成交量", "成交金额", "性质"] big_df.reset_index(drop=True, inplace=True) property_map = { "S": "卖盘", "B": "买盘", "M": "中性盘", } big_df["性质"] = big_df["性质"].map(property_map) big_df = big_df.astype({ '成交时间': str, '成交价格': float, '价格变动': float, '成交量': int, '成交金额': int, '性质': str, }) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_tick_tx_163.py#L17-L68

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

17.307692

[ 9, 10, 11, 12, 13, 14, 15, 21, 22, 23, 28, 29, 31, 32, 33, 34, 35, 36, 37, 42, 43, 51 ]

42.307692

false

12.5

52

4

57.692308

6

def stock_zh_a_tick_tx_js(symbol: str = "sz000001") -> pd.DataFrame: big_df = pd.DataFrame() page = 0 warnings.warn("正在下载数据，请稍等") while True: try: url = "http://stock.gtimg.cn/data/index.php" params = { "appn": "detail", "action": "data", "c": symbol, "p": page, } r = requests.get(url, params=params) text_data = r.text temp_df = ( pd.DataFrame(eval(text_data[text_data.find("[") :])[1].split("|")) .iloc[:, 0] .str.split("/", expand=True) ) page += 1 big_df = pd.concat([big_df, temp_df], ignore_index=True) except: break if not big_df.empty: big_df = big_df.iloc[:, 1:] big_df.columns = ["成交时间", "成交价格", "价格变动", "成交量", "成交金额", "性质"] big_df.reset_index(drop=True, inplace=True) property_map = { "S": "卖盘", "B": "买盘", "M": "中性盘", } big_df["性质"] = big_df["性质"].map(property_map) big_df = big_df.astype({ '成交时间': str, '成交价格': float, '价格变动': float, '成交量': int, '成交金额': int, '性质': str, }) return big_df

18,810

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_tick_tx_163.py

stock_zh_a_tick_tx

( symbol: str = "sz000001", trade_date: str = "20210316" )

return temp_df

http://gu.qq.com/sz000001/gp/detail 成交明细-每个交易日 16:00 提供当日数据 :param symbol: 带市场标识的股票代码 :type symbol: str :param trade_date: 需要提取数据的日期 :type trade_date: str :return: 返回当日股票成交明细的数据 :rtype: pandas.DataFrame

71

94

def stock_zh_a_tick_tx( symbol: str = "sz000001", trade_date: str = "20210316" ) -> pd.DataFrame: """ http://gu.qq.com/sz000001/gp/detail 成交明细-每个交易日 16:00 提供当日数据 :param symbol: 带市场标识的股票代码 :type symbol: str :param trade_date: 需要提取数据的日期 :type trade_date: str :return: 返回当日股票成交明细的数据 :rtype: pandas.DataFrame """ url = "http://stock.gtimg.cn/data/index.php" params = { "appn": "detail", "action": "download", "c": symbol, "d": trade_date, } r = requests.get(url, params=params) r.encoding = "gbk" temp_df = pd.read_table(StringIO(r.text)) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_tick_tx_163.py#L71-L94

25

[ 0 ]

4.166667

[ 13, 14, 20, 21, 22, 23 ]

25

false

12.5

24

1

75

8

def stock_zh_a_tick_tx( symbol: str = "sz000001", trade_date: str = "20210316" ) -> pd.DataFrame: url = "http://stock.gtimg.cn/data/index.php" params = { "appn": "detail", "action": "download", "c": symbol, "d": trade_date, } r = requests.get(url, params=params) r.encoding = "gbk" temp_df = pd.read_table(StringIO(r.text)) return temp_df

18,811

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_tick_tx_163.py

stock_zh_a_tick_163

( symbol: str = "sz000001", trade_date: str = "20220429" )

return temp_df

成交明细-每个交易日 22:00 提供当日数据; 该接口目前还不支持北交所的股票 http://quotes.money.163.com/trade/cjmx_000001.html#01b05 :param symbol: 带市场标识的股票代码 :type symbol: str :param trade_date: 需要提取数据的日期 :type trade_date: str :return: 返回当日股票成交明细的数据 :rtype: pandas.DataFrame

97

127

def stock_zh_a_tick_163( symbol: str = "sz000001", trade_date: str = "20220429" ) -> pd.DataFrame: """ 成交明细-每个交易日 22:00 提供当日数据; 该接口目前还不支持北交所的股票 http://quotes.money.163.com/trade/cjmx_000001.html#01b05 :param symbol: 带市场标识的股票代码 :type symbol: str :param trade_date: 需要提取数据的日期 :type trade_date: str :return: 返回当日股票成交明细的数据 :rtype: pandas.DataFrame """ name_code_map = {"sh": "0", "sz": "1"} url = f"http://quotes.money.163.com/cjmx/{trade_date[:4]}/{trade_date}/{name_code_map[symbol[:2]]}{symbol[2:]}.xls" r = requests.get(url) r.encoding = "utf-8" temp_df = pd.read_excel(BytesIO(r.content), engine="xlrd") temp_df.columns = [ "时间", "成交价", "价格变动", "成交量", "成交额", "性质", ] temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['价格变动'] = pd.to_numeric(temp_df['价格变动']) temp_df['成交量'] = pd.to_numeric(temp_df['成交量']) temp_df['成交额'] = pd.to_numeric(temp_df['成交额']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_tick_tx_163.py#L97-L127

25

[ 0 ]

3.225806

[ 13, 14, 15, 16, 17, 18, 26, 27, 28, 29, 30 ]

35.483871

false

12.5

31

1

64.516129

8

def stock_zh_a_tick_163( symbol: str = "sz000001", trade_date: str = "20220429" ) -> pd.DataFrame: name_code_map = {"sh": "0", "sz": "1"} url = f"http://quotes.money.163.com/cjmx/{trade_date[:4]}/{trade_date}/{name_code_map[symbol[:2]]}{symbol[2:]}.xls" r = requests.get(url) r.encoding = "utf-8" temp_df = pd.read_excel(BytesIO(r.content), engine="xlrd") temp_df.columns = [ "时间", "成交价", "价格变动", "成交量", "成交额", "性质", ] temp_df['成交价'] = pd.to_numeric(temp_df['成交价']) temp_df['价格变动'] = pd.to_numeric(temp_df['价格变动']) temp_df['成交量'] = pd.to_numeric(temp_df['成交量']) temp_df['成交额'] = pd.to_numeric(temp_df['成交额']) return temp_df

18,812

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_tick_tx_163.py

stock_zh_a_tick_163_now

(symbol: str = "000001")

return big_df

成交明细-收盘后获取, 补充 stock_zh_a_tick_163 接口, 用来尽快获取数据 http://quotes.money.163.com/trade/cjmx_000001.html#01b05 :param symbol: 带市场标识的股票代码 :type symbol: str :return: 返回当日股票成交明细的数据 :rtype: pandas.DataFrame

130

187

def stock_zh_a_tick_163_now(symbol: str = "000001") -> pd.DataFrame: """ 成交明细-收盘后获取, 补充 stock_zh_a_tick_163 接口, 用来尽快获取数据 http://quotes.money.163.com/trade/cjmx_000001.html#01b05 :param symbol: 带市场标识的股票代码 :type symbol: str :return: 返回当日股票成交明细的数据 :rtype: pandas.DataFrame """ time_list_one = [ item.isoformat().split("T")[1] for item in pd.date_range("09:30:00", "11:30:00", freq="5min").tolist() ][1:] time_list_two = [ item.isoformat().split("T")[1] for item in pd.date_range("13:00:00", "15:00:00", freq="5min").tolist() ][1:] time_list_one.extend(time_list_two) big_df = pd.DataFrame() for item in tqdm(time_list_one): url = "http://quotes.money.163.com/service/zhubi_ajax.html" params = {"symbol": symbol, "end": item} r = requests.get(url, params=params) data_json = r.json() if not data_json['zhubi_list']: break temp_df = pd.DataFrame(data_json["zhubi_list"]) del temp_df["_id"] del temp_df["TRADE_TYPE"] del temp_df["DATE"] temp_df.reset_index(inplace=True) temp_df.sort_values( by="index", ascending=False, ignore_index=True, inplace=True ) big_df = pd.concat([big_df,temp_df], ignore_index=True) del big_df["index"] big_df.columns = [ "_", "成交量", "成交价", "成交额", "价格变动", "成交时间", "性质", ] big_df = big_df[ [ "成交时间", "成交价", "价格变动", "成交量", "成交额", "性质", ] ] big_df["成交量"] = big_df["成交量"] / 100 return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_tick_tx_163.py#L130-L187

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

15.517241

[ 9, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 36, 37, 46, 56, 57 ]

39.655172

false

12.5

58

5

60.344828

6

def stock_zh_a_tick_163_now(symbol: str = "000001") -> pd.DataFrame: time_list_one = [ item.isoformat().split("T")[1] for item in pd.date_range("09:30:00", "11:30:00", freq="5min").tolist() ][1:] time_list_two = [ item.isoformat().split("T")[1] for item in pd.date_range("13:00:00", "15:00:00", freq="5min").tolist() ][1:] time_list_one.extend(time_list_two) big_df = pd.DataFrame() for item in tqdm(time_list_one): url = "http://quotes.money.163.com/service/zhubi_ajax.html" params = {"symbol": symbol, "end": item} r = requests.get(url, params=params) data_json = r.json() if not data_json['zhubi_list']: break temp_df = pd.DataFrame(data_json["zhubi_list"]) del temp_df["_id"] del temp_df["TRADE_TYPE"] del temp_df["DATE"] temp_df.reset_index(inplace=True) temp_df.sort_values( by="index", ascending=False, ignore_index=True, inplace=True ) big_df = pd.concat([big_df,temp_df], ignore_index=True) del big_df["index"] big_df.columns = [ "_", "成交量", "成交价", "成交额", "价格变动", "成交时间", "性质", ] big_df = big_df[ [ "成交时间", "成交价", "价格变动", "成交量", "成交额", "性质", ] ] big_df["成交量"] = big_df["成交量"] / 100 return big_df

18,813

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_fund_hold.py

stock_report_fund_hold

( symbol: str = "基金持仓", date: str = "20210331" )

return big_df

东方财富网-数据中心-主力数据-基金持仓 http://data.eastmoney.com/zlsj/2020-12-31-1-2.html :param symbol: choice of {"基金持仓", "QFII持仓", "社保持仓", "券商持仓", "保险持仓", "信托持仓"} :type symbol: str :param date: 财报发布日期, xxxx-03-31, xxxx-06-30, xxxx-09-30, xxxx-12-31 :type date: str :return: 基金持仓数据 :rtype: pandas.DataFrame

14

108

def stock_report_fund_hold( symbol: str = "基金持仓", date: str = "20210331" ) -> pd.DataFrame: """ 东方财富网-数据中心-主力数据-基金持仓 http://data.eastmoney.com/zlsj/2020-12-31-1-2.html :param symbol: choice of {"基金持仓", "QFII持仓", "社保持仓", "券商持仓", "保险持仓", "信托持仓"} :type symbol: str :param date: 财报发布日期, xxxx-03-31, xxxx-06-30, xxxx-09-30, xxxx-12-31 :type date: str :return: 基金持仓数据 :rtype: pandas.DataFrame """ symbol_map = { "基金持仓": "1", "QFII持仓": "2", "社保持仓": "3", "券商持仓": "4", "保险持仓": "5", "信托持仓": "6", } date = "-".join([date[:4], date[4:6], date[6:]]) url = "http://data.eastmoney.com/dataapi/zlsj/list" params = { "date": date, "type": symbol_map[symbol], "zjc": "0", "sortField": "HOULD_NUM", "sortDirec": "1", "pageNum": "1", "pageSize": "500", "p": "1", "pageNo": "1", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["pages"] big_df = pd.DataFrame() for page in range(1, total_page + 1): params = { "date": date, "type": symbol_map[symbol], "zjc": "0", "sortField": "HOULD_NUM", "sortDirec": "1", "pageNum": page, "pageSize": "500", "p": page, "pageNo": page, } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "_", "股票简称", "_", "_", "持有基金家数", "持股总数", "持股市值", "_", "持股变化", "持股变动数值", "持股变动比例", "_", "_", "_", "_", "_", "_", "_", "_", "股票代码", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "持有基金家数", "持股总数", "持股市值", "持股变化", "持股变动数值", "持股变动比例", ] ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_fund_hold.py#L14-L108

25

[ 0 ]

1.052632

[ 13, 21, 22, 23, 34, 35, 36, 37, 38, 39, 50, 51, 52, 53, 54, 55, 56, 81, 94 ]

20

false

12.727273

95

2

80

8

def stock_report_fund_hold( symbol: str = "基金持仓", date: str = "20210331" ) -> pd.DataFrame: symbol_map = { "基金持仓": "1", "QFII持仓": "2", "社保持仓": "3", "券商持仓": "4", "保险持仓": "5", "信托持仓": "6", } date = "-".join([date[:4], date[4:6], date[6:]]) url = "http://data.eastmoney.com/dataapi/zlsj/list" params = { "date": date, "type": symbol_map[symbol], "zjc": "0", "sortField": "HOULD_NUM", "sortDirec": "1", "pageNum": "1", "pageSize": "500", "p": "1", "pageNo": "1", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["pages"] big_df = pd.DataFrame() for page in range(1, total_page + 1): params = { "date": date, "type": symbol_map[symbol], "zjc": "0", "sortField": "HOULD_NUM", "sortDirec": "1", "pageNum": page, "pageSize": "500", "p": page, "pageNo": page, } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "_", "股票简称", "_", "_", "持有基金家数", "持股总数", "持股市值", "_", "持股变化", "持股变动数值", "持股变动比例", "_", "_", "_", "_", "_", "_", "_", "_", "股票代码", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "持有基金家数", "持股总数", "持股市值", "持股变化", "持股变动数值", "持股变动比例", ] ] return big_df

18,814

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_fund_hold.py

stock_report_fund_hold_detail

( symbol: str = "008286", date: str = "20220331" )

return temp_df

东方财富网-数据中心-主力数据-基金持仓-明细 http://data.eastmoney.com/zlsj/ccjj/2020-12-31-008286.html :param symbol: 基金代码 :type symbol: str :param date: 财报发布日期, xxxx-03-31, xxxx-06-30, xxxx-09-30, xxxx-12-31 :type date: str :return: 基金持仓-明细数据 :rtype: pandas.DataFrame

111

179

def stock_report_fund_hold_detail( symbol: str = "008286", date: str = "20220331" ) -> pd.DataFrame: """ 东方财富网-数据中心-主力数据-基金持仓-明细 http://data.eastmoney.com/zlsj/ccjj/2020-12-31-008286.html :param symbol: 基金代码 :type symbol: str :param date: 财报发布日期, xxxx-03-31, xxxx-06-30, xxxx-09-30, xxxx-12-31 :type date: str :return: 基金持仓-明细数据 :rtype: pandas.DataFrame """ date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "SECURITY_CODE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_MAINDATA_MAIN_POSITIONDETAILS", "columns": "ALL", "quoteColumns": "", "filter": f"""(HOLDER_CODE="{symbol}")(REPORT_DATE='{date}')""", "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = [ "序号", "-", "股票代码", "-", "股票简称", "_", "_", "_", "_", "_", "_", "_", "_", "持股数", "持股市值", "占总股本比例", "占流通股本比例", "_", "_", ] temp_df = temp_df[ [ "序号", "股票代码", "股票简称", "持股数", "持股市值", "占总股本比例", "占流通股本比例", ] ] temp_df["持股数"] = pd.to_numeric(temp_df["持股数"]) temp_df["持股市值"] = pd.to_numeric(temp_df["持股市值"]) temp_df["占总股本比例"] = pd.to_numeric(temp_df["占总股本比例"]) temp_df["占流通股本比例"] = pd.to_numeric(temp_df["占流通股本比例"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_fund_hold.py#L111-L179

25

[ 0 ]

1.449275

[ 13, 14, 15, 27, 28, 29, 30, 31, 32, 53, 64, 65, 66, 67, 68 ]

21.73913

false

12.727273

69

1

78.26087

8

def stock_report_fund_hold_detail( symbol: str = "008286", date: str = "20220331" ) -> pd.DataFrame: date = "-".join([date[:4], date[4:6], date[6:]]) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "SECURITY_CODE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_MAINDATA_MAIN_POSITIONDETAILS", "columns": "ALL", "quoteColumns": "", "filter": f"""(HOLDER_CODE="{symbol}")(REPORT_DATE='{date}')""", "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = [ "序号", "-", "股票代码", "-", "股票简称", "_", "_", "_", "_", "_", "_", "_", "_", "持股数", "持股市值", "占总股本比例", "占流通股本比例", "_", "_", ] temp_df = temp_df[ [ "序号", "股票代码", "股票简称", "持股数", "持股市值", "占总股本比例", "占流通股本比例", ] ] temp_df["持股数"] = pd.to_numeric(temp_df["持股数"]) temp_df["持股市值"] = pd.to_numeric(temp_df["持股市值"]) temp_df["占总股本比例"] = pd.to_numeric(temp_df["占总股本比例"]) temp_df["占流通股本比例"] = pd.to_numeric(temp_df["占流通股本比例"]) return temp_df

18,815

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_zrbg_hx.py

stock_zh_a_scr_report

(year: str = "2018", need_page: str = "1")

return big_df

和讯财经-上市公司社会责任报告, 从 2010- 年至今因为股票数量大, 所以获取某年需要遍历所有页 http://stockdata.stock.hexun.com/zrbg/Plate.aspx# :param year: 报告年份 :type year: str :param need_page: 需要获取的天数 :type need_page: str :return: 上市公司社会责任报告数据 :rtype: pandas.DataFrame

16

86

def stock_zh_a_scr_report(year: str = "2018", need_page: str = "1") -> pd.DataFrame: """ 和讯财经-上市公司社会责任报告, 从 2010- 年至今因为股票数量大, 所以获取某年需要遍历所有页 http://stockdata.stock.hexun.com/zrbg/Plate.aspx# :param year: 报告年份 :type year: str :param need_page: 需要获取的天数 :type need_page: str :return: 上市公司社会责任报告数据 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() for page in tqdm(range(1, int(need_page)+1)): hx_params_copy = hx_params.copy() hx_params_copy.update({"date": "{}-12-31".format(year)}) hx_params_copy.update({"page": page}) r = requests.get(hx_url, headers=hx_headers, params=hx_params_copy) data_text = r.text temp_df = data_text[data_text.find("(") + 1: data_text.rfind(")")] py_obj = demjson.decode(temp_df) industry = [item["industry"] for item in py_obj["list"]] stock_number = [item["stockNumber"] for item in py_obj["list"]] industry_rate = [item["industryrate"] for item in py_obj["list"]] price_limit = [item["Pricelimit"] for item in py_obj["list"]] looting_chips = [item["lootingchips"] for item in py_obj["list"]] r_scramble = [item["rscramble"] for item in py_obj["list"]] strong_stock = [item["Strongstock"] for item in py_obj["list"]] s_cramble = [item["Scramble"] for item in py_obj["list"]] temp_df = pd.DataFrame( [ industry, stock_number, industry_rate, price_limit, looting_chips, r_scramble, strong_stock, s_cramble, ], index=["股票名称", "股东责任", "总得分", "等级", "员工责任", "环境责任", "社会责任", "供应商、客户和消费者权益责任"], ).T big_df = big_df.append(temp_df, ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.rename({"index": "序号"}, axis="columns", inplace=True) big_df["股票代码"] = ( big_df["股票名称"].str.split("(", expand=True).iloc[:, 1].str.strip(")") ) big_df["股票名称"] = big_df["股票名称"].str.split("(", expand=True).iloc[:, 0] big_df["股东责任"] = pd.to_numeric(big_df["股东责任"]) big_df["总得分"] = pd.to_numeric(big_df["总得分"]) big_df["员工责任"] = pd.to_numeric(big_df["员工责任"]) big_df["环境责任"] = pd.to_numeric(big_df["环境责任"]) big_df["社会责任"] = pd.to_numeric(big_df["社会责任"]) big_df["供应商、客户和消费者权益责任"] = pd.to_numeric(big_df["供应商、客户和消费者权益责任"]) big_df = big_df[ [ "序号", "股票名称", "股票代码", "总得分", "等级", "股东责任", "员工责任", "供应商、客户和消费者权益责任", "环境责任", "社会责任", ] ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_zrbg_hx.py#L16-L86

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ]

16.901408

[ 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 42, 43, 44, 45, 46, 49, 50, 51, 52, 53, 54, 55, 56, 70 ]

45.070423

false

19.047619

71

10

54.929577

9

def stock_zh_a_scr_report(year: str = "2018", need_page: str = "1") -> pd.DataFrame: big_df = pd.DataFrame() for page in tqdm(range(1, int(need_page)+1)): hx_params_copy = hx_params.copy() hx_params_copy.update({"date": "{}-12-31".format(year)}) hx_params_copy.update({"page": page}) r = requests.get(hx_url, headers=hx_headers, params=hx_params_copy) data_text = r.text temp_df = data_text[data_text.find("(") + 1: data_text.rfind(")")] py_obj = demjson.decode(temp_df) industry = [item["industry"] for item in py_obj["list"]] stock_number = [item["stockNumber"] for item in py_obj["list"]] industry_rate = [item["industryrate"] for item in py_obj["list"]] price_limit = [item["Pricelimit"] for item in py_obj["list"]] looting_chips = [item["lootingchips"] for item in py_obj["list"]] r_scramble = [item["rscramble"] for item in py_obj["list"]] strong_stock = [item["Strongstock"] for item in py_obj["list"]] s_cramble = [item["Scramble"] for item in py_obj["list"]] temp_df = pd.DataFrame( [ industry, stock_number, industry_rate, price_limit, looting_chips, r_scramble, strong_stock, s_cramble, ], index=["股票名称", "股东责任", "总得分", "等级", "员工责任", "环境责任", "社会责任", "供应商、客户和消费者权益责任"], ).T big_df = big_df.append(temp_df, ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.rename({"index": "序号"}, axis="columns", inplace=True) big_df["股票代码"] = ( big_df["股票名称"].str.split("(", expand=True).iloc[:, 1].str.strip(")") ) big_df["股票名称"] = big_df["股票名称"].str.split("(", expand=True).iloc[:, 0] big_df["股东责任"] = pd.to_numeric(big_df["股东责任"]) big_df["总得分"] = pd.to_numeric(big_df["总得分"]) big_df["员工责任"] = pd.to_numeric(big_df["员工责任"]) big_df["环境责任"] = pd.to_numeric(big_df["环境责任"]) big_df["社会责任"] = pd.to_numeric(big_df["社会责任"]) big_df["供应商、客户和消费者权益责任"] = pd.to_numeric(big_df["供应商、客户和消费者权益责任"]) big_df = big_df[ [ "序号", "股票名称", "股票代码", "总得分", "等级", "股东责任", "员工责任", "供应商、客户和消费者权益责任", "环境责任", "社会责任", ] ] return big_df

18,816

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_sz_name_code

(indicator: str = "A股列表") -> pd

深圳证券交易所-股票列表 http://www.szse.cn/market/product/stock/list/index.html :param indicator: choice of {"A股列表", "B股列表", "CDR列表", "AB股列表"} :type indicator: str :return: 指定 indicator 的数据 :rtype: pandas.DataFrame

17

115

def stock_info_sz_name_code(indicator: str = "A股列表") -> pd.DataFrame: """ 深圳证券交易所-股票列表 http://www.szse.cn/market/product/stock/list/index.html :param indicator: choice of {"A股列表", "B股列表", "CDR列表", "AB股列表"} :type indicator: str :return: 指定 indicator 的数据 :rtype: pandas.DataFrame """ url = "http://www.szse.cn/api/report/ShowReport" indicator_map = { "A股列表": "tab1", "B股列表": "tab2", "CDR列表": "tab3", "AB股列表": "tab4", } params = { "SHOWTYPE": "xlsx", "CATALOGID": "1110", "TABKEY": indicator_map[indicator], "random": "0.6935816432433362", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content)) if len(temp_df) > 10: if indicator == "A股列表": temp_df["A股代码"] = ( temp_df["A股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df = temp_df[ [ "板块", "A股代码", "A股简称", "A股上市日期", "A股总股本", "A股流通股本", "所属行业", ] ] elif indicator == "B股列表": temp_df["B股代码"] = ( temp_df["B股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df = temp_df[ [ "板块", "B股代码", "B股简称", "B股上市日期", "B股总股本", "B股流通股本", "所属行业", ] ] elif indicator == "AB股列表": temp_df["A股代码"] = ( temp_df["A股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df["B股代码"] = ( temp_df["B股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df = temp_df[ [ "板块", "A股代码", "A股简称", "A股上市日期", "B股代码", "B股简称", "B股上市日期", "所属行业", ] ] return temp_df else: return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L17-L115

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

9.090909

[ 9, 10, 16, 22, 23, 24, 25, 26, 27, 28, 36, 47, 48, 56, 67, 68, 76, 84, 96, 98 ]

20.20202

false

11.538462

99

6

79.79798

6

def stock_info_sz_name_code(indicator: str = "A股列表") -> pd.DataFrame: url = "http://www.szse.cn/api/report/ShowReport" indicator_map = { "A股列表": "tab1", "B股列表": "tab2", "CDR列表": "tab3", "AB股列表": "tab4", } params = { "SHOWTYPE": "xlsx", "CATALOGID": "1110", "TABKEY": indicator_map[indicator], "random": "0.6935816432433362", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content)) if len(temp_df) > 10: if indicator == "A股列表": temp_df["A股代码"] = ( temp_df["A股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df = temp_df[ [ "板块", "A股代码", "A股简称", "A股上市日期", "A股总股本", "A股流通股本", "所属行业", ] ] elif indicator == "B股列表": temp_df["B股代码"] = ( temp_df["B股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df = temp_df[ [ "板块", "B股代码", "B股简称", "B股上市日期", "B股总股本", "B股流通股本", "所属行业", ] ] elif indicator == "AB股列表": temp_df["A股代码"] = ( temp_df["A股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df["B股代码"] = ( temp_df["B股代码"] .astype(str) .str.split(".", expand=True) .iloc[:, 0] .str.zfill(6) .str.replace("000nan", "") ) temp_df = temp_df[ [ "板块", "A股代码", "A股简称", "A股上市日期", "B股代码", "B股简称", "B股上市日期", "所属行业", ] ] return temp_df else: return temp_df

18,817

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_sh_name_code

(indicator: str = "主板A股") -> pd

return temp_df

上海证券交易所-股票列表 http://www.sse.com.cn/assortment/stock/list/share/ :param indicator: choice of {"主板A股": "1", "主板B股": "2", "科创板": "8"} :type indicator: str :return: 指定 indicator 的数据 :rtype: pandas.DataFrame

118

187

def stock_info_sh_name_code(indicator: str = "主板A股") -> pd.DataFrame: """ 上海证券交易所-股票列表 http://www.sse.com.cn/assortment/stock/list/share/ :param indicator: choice of {"主板A股": "1", "主板B股": "2", "科创板": "8"} :type indicator: str :return: 指定 indicator 的数据 :rtype: pandas.DataFrame """ indicator_map = {"主板A股": "1", "主板B股": "2", "科创板": "8"} url = "http://query.sse.com.cn/sseQuery/commonQuery.do" headers = { "Host": "query.sse.com.cn", "Pragma": "no-cache", "Referer": "http://www.sse.com.cn/assortment/stock/list/share/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "STOCK_TYPE": indicator_map[indicator], "REG_PROVINCE": "", "CSRC_CODE": "", "STOCK_CODE": "", "sqlId": "COMMON_SSE_CP_GPJCTPZ_GPLB_GP_L", "COMPANY_STATUS": "2,4,5,7,8", "type": "inParams", "isPagination": "true", "pageHelp.cacheSize": "1", "pageHelp.beginPage": "1", "pageHelp.pageSize": "10000", "pageHelp.pageNo": "1", "pageHelp.endPage": "1", "_": "1653291270045", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) columns = [ "-", "-", "-", "-", "-", "-", "证券简称", "扩位证券简称", "-", "上市日期", "-", "-", "-", ] # column index 3=A_STOCK_CODE, 8=B_STOCK_CODE, 11=COMPANY_CODE if indicator == "主板B股": columns[8] = "证券代码" else: columns[3] = "证券代码" temp_df.columns = columns temp_df = temp_df[ [ "证券代码", "证券简称", "扩位证券简称", "上市日期", ] ] temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L118-L187

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

12.857143

[ 9, 10, 11, 17, 33, 34, 35, 36, 53, 54, 56, 58, 60, 68, 69 ]

21.428571

false

11.538462

70

2

78.571429

6

def stock_info_sh_name_code(indicator: str = "主板A股") -> pd.DataFrame: indicator_map = {"主板A股": "1", "主板B股": "2", "科创板": "8"} url = "http://query.sse.com.cn/sseQuery/commonQuery.do" headers = { "Host": "query.sse.com.cn", "Pragma": "no-cache", "Referer": "http://www.sse.com.cn/assortment/stock/list/share/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36", } params = { "STOCK_TYPE": indicator_map[indicator], "REG_PROVINCE": "", "CSRC_CODE": "", "STOCK_CODE": "", "sqlId": "COMMON_SSE_CP_GPJCTPZ_GPLB_GP_L", "COMPANY_STATUS": "2,4,5,7,8", "type": "inParams", "isPagination": "true", "pageHelp.cacheSize": "1", "pageHelp.beginPage": "1", "pageHelp.pageSize": "10000", "pageHelp.pageNo": "1", "pageHelp.endPage": "1", "_": "1653291270045", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) columns = [ "-", "-", "-", "-", "-", "-", "证券简称", "扩位证券简称", "-", "上市日期", "-", "-", "-", ] # column index 3=A_STOCK_CODE, 8=B_STOCK_CODE, 11=COMPANY_CODE if indicator == "主板B股": columns[8] = "证券代码" else: columns[3] = "证券代码" temp_df.columns = columns temp_df = temp_df[ [ "证券代码", "证券简称", "扩位证券简称", "上市日期", ] ] temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"]).dt.date return temp_df

18,818

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_bj_name_code

()

return big_df

北京证券交易所-股票列表 https://www.bse.cn/nq/listedcompany.html :return: 股票列表 :rtype: pandas.DataFrame

190

283

def stock_info_bj_name_code() -> pd.DataFrame: """ 北京证券交易所-股票列表 https://www.bse.cn/nq/listedcompany.html :return: 股票列表 :rtype: pandas.DataFrame """ url = "https://www.bse.cn/nqxxController/nqxxCnzq.do" payload = { "page": "0", "typejb": "T", "xxfcbj[]": "2", "xxzqdm": "", "sortfield": "xxzqdm", "sorttype": "asc", } r = requests.post(url, data=payload) data_text = r.text data_json = json.loads(data_text[data_text.find("[") : -1]) total_page = data_json[0]["totalPages"] big_df = pd.DataFrame() for page in tqdm(range(total_page), leave=False): payload.update({"page": page}) r = requests.post(url, data=payload) data_text = r.text data_json = json.loads(data_text[data_text.find("[") : -1]) temp_df = data_json[0]["content"] temp_df = pd.DataFrame(temp_df) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "上市日期", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "流通股本", "-", "-", "-", "-", "-", "所属行业", "-", "-", "-", "-", "报告日期", "-", "-", "-", "-", "-", "-", "地区", "-", "-", "-", "-", "-", "券商", "总股本", "-", "证券代码", "-", "证券简称", "-", "-", "-", "-", "-", "-", "-", ] big_df = big_df[ [ "证券代码", "证券简称", "总股本", "流通股本", "上市日期", "所属行业", "地区", "报告日期", ] ] big_df["报告日期"] = pd.to_datetime(big_df["报告日期"]).dt.date big_df["上市日期"] = pd.to_datetime(big_df["上市日期"]).dt.date return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L190-L283

25

[ 0, 1, 2, 3, 4, 5, 6 ]

7.446809

[ 7, 8, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 79, 91, 92, 93 ]

21.276596

false

11.538462

94

2

78.723404

4

def stock_info_bj_name_code() -> pd.DataFrame: url = "https://www.bse.cn/nqxxController/nqxxCnzq.do" payload = { "page": "0", "typejb": "T", "xxfcbj[]": "2", "xxzqdm": "", "sortfield": "xxzqdm", "sorttype": "asc", } r = requests.post(url, data=payload) data_text = r.text data_json = json.loads(data_text[data_text.find("[") : -1]) total_page = data_json[0]["totalPages"] big_df = pd.DataFrame() for page in tqdm(range(total_page), leave=False): payload.update({"page": page}) r = requests.post(url, data=payload) data_text = r.text data_json = json.loads(data_text[data_text.find("[") : -1]) temp_df = data_json[0]["content"] temp_df = pd.DataFrame(temp_df) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "上市日期", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "流通股本", "-", "-", "-", "-", "-", "所属行业", "-", "-", "-", "-", "报告日期", "-", "-", "-", "-", "-", "-", "地区", "-", "-", "-", "-", "-", "券商", "总股本", "-", "证券代码", "-", "证券简称", "-", "-", "-", "-", "-", "-", "-", ] big_df = big_df[ [ "证券代码", "证券简称", "总股本", "流通股本", "上市日期", "所属行业", "地区", "报告日期", ] ] big_df["报告日期"] = pd.to_datetime(big_df["报告日期"]).dt.date big_df["上市日期"] = pd.to_datetime(big_df["上市日期"]).dt.date return big_df

18,819

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_sh_delist

()

return temp_df

上海证券交易所-终止上市公司 http://www.sse.com.cn/assortment/stock/list/delisting/ :return: 终止上市公司 :rtype: pandas.DataFrame

286

349

def stock_info_sh_delist() -> pd.DataFrame: """ 上海证券交易所-终止上市公司 http://www.sse.com.cn/assortment/stock/list/delisting/ :return: 终止上市公司 :rtype: pandas.DataFrame """ url = "http://query.sse.com.cn/commonQuery.do" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "query.sse.com.cn", "Pragma": "no-cache", "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } params = { "sqlId": "COMMON_SSE_CP_GPJCTPZ_GPLB_GP_L", "isPagination": "true", "STOCK_CODE": "", "CSRC_CODE": "", "REG_PROVINCE": "", "STOCK_TYPE": "1,2", "COMPANY_STATUS": "3", "type": "inParams", "pageHelp.cacheSize": "1", "pageHelp.beginPage": "1", "pageHelp.pageSize": "500", "pageHelp.pageNo": "1", "pageHelp.endPage": "1", "_": "1643035608183", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df.columns = [ "-", "-", "公司简称", "-", "暂停上市日期", "-", "-", "-", "-", "上市日期", "-", "公司代码", "-", ] temp_df = temp_df[ [ "公司代码", "公司简称", "上市日期", "暂停上市日期", ] ] temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"]).dt.date temp_df["暂停上市日期"] = pd.to_datetime(temp_df["暂停上市日期"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L286-L349

25

[ 0, 1, 2, 3, 4, 5, 6 ]

10.9375

[ 7, 8, 19, 35, 36, 37, 38, 53, 61, 62, 63 ]

17.1875

false

11.538462

64

1

82.8125

4

def stock_info_sh_delist() -> pd.DataFrame: url = "http://query.sse.com.cn/commonQuery.do" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "query.sse.com.cn", "Pragma": "no-cache", "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } params = { "sqlId": "COMMON_SSE_CP_GPJCTPZ_GPLB_GP_L", "isPagination": "true", "STOCK_CODE": "", "CSRC_CODE": "", "REG_PROVINCE": "", "STOCK_TYPE": "1,2", "COMPANY_STATUS": "3", "type": "inParams", "pageHelp.cacheSize": "1", "pageHelp.beginPage": "1", "pageHelp.pageSize": "500", "pageHelp.pageNo": "1", "pageHelp.endPage": "1", "_": "1643035608183", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df.columns = [ "-", "-", "公司简称", "-", "暂停上市日期", "-", "-", "-", "-", "上市日期", "-", "公司代码", "-", ] temp_df = temp_df[ [ "公司代码", "公司简称", "上市日期", "暂停上市日期", ] ] temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"]).dt.date temp_df["暂停上市日期"] = pd.to_datetime(temp_df["暂停上市日期"]).dt.date return temp_df

18,820

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_sz_delist

(indicator: str = "暂停上市公司") -> pd.DataF

深证证券交易所-暂停上市公司-终止上市公司 http://www.szse.cn/market/stock/suspend/index.html :param indicator: choice of {"暂停上市公司", "终止上市公司"} :type indicator: str :return: 暂停上市公司 or 终止上市公司的数据 :rtype: pandas.DataFrame

352

374

def stock_info_sz_delist(indicator: str = "暂停上市公司") -> pd.DataFrame: """ 深证证券交易所-暂停上市公司-终止上市公司 http://www.szse.cn/market/stock/suspend/index.html :param indicator: choice of {"暂停上市公司", "终止上市公司"} :type indicator: str :return: 暂停上市公司 or 终止上市公司的数据 :rtype: pandas.DataFrame """ indicator_map = {"暂停上市公司": "tab1", "终止上市公司": "tab2"} url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1793_ssgs", "TABKEY": indicator_map[indicator], "random": "0.6935816432433362", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content)) temp_df["证券代码"] = temp_df["证券代码"].astype("str").str.zfill(6) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L352-L374

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

39.130435

[ 9, 10, 11, 17, 18, 19, 20, 21, 22 ]

39.130435

false

11.538462

23

2

60.869565

6

def stock_info_sz_delist(indicator: str = "暂停上市公司") -> pd.DataFrame: indicator_map = {"暂停上市公司": "tab1", "终止上市公司": "tab2"} url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1793_ssgs", "TABKEY": indicator_map[indicator], "random": "0.6935816432433362", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content)) temp_df["证券代码"] = temp_df["证券代码"].astype("str").str.zfill(6) return temp_df

18,821

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_sz_change_name

(indicator: str = "全称变更") -> pd.D

深证证券交易所-更名公司 http://www.szse.cn/market/companys/changename/index.html :param indicator: choice of {"全称变更": "tab1", "简称变更": "tab2"} :type indicator: str :return: 全称变更 or 简称变更的数据 :rtype: pandas.DataFrame

377

399

def stock_info_sz_change_name(indicator: str = "全称变更") -> pd.DataFrame: """ 深证证券交易所-更名公司 http://www.szse.cn/market/companys/changename/index.html :param indicator: choice of {"全称变更": "tab1", "简称变更": "tab2"} :type indicator: str :return: 全称变更 or 简称变更的数据 :rtype: pandas.DataFrame """ indicator_map = {"全称变更": "tab1", "简称变更": "tab2"} url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "SSGSGMXX", "TABKEY": indicator_map[indicator], "random": "0.6935816432433362", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content)) temp_df["证券代码"] = temp_df["证券代码"].astype("str").str.zfill(6) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L377-L399

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

39.130435

[ 9, 10, 11, 17, 18, 19, 20, 21, 22 ]

39.130435

false

11.538462

23

2

60.869565

6

def stock_info_sz_change_name(indicator: str = "全称变更") -> pd.DataFrame: indicator_map = {"全称变更": "tab1", "简称变更": "tab2"} url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "SSGSGMXX", "TABKEY": indicator_map[indicator], "random": "0.6935816432433362", } r = requests.get(url, params=params) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(BytesIO(r.content)) temp_df["证券代码"] = temp_df["证券代码"].astype("str").str.zfill(6) return temp_df

18,822

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_change_name

(symbol: str = "000503")

新浪财经-股票曾用名 http://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CorpInfo/stockid/300378.phtml :param symbol: 股票代码 :type symbol: str :return: 股票曾用名 :rtype: list

402

427

def stock_info_change_name(symbol: str = "000503") -> pd.DataFrame: """ 新浪财经-股票曾用名 http://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CorpInfo/stockid/300378.phtml :param symbol: 股票代码 :type symbol: str :return: 股票曾用名 :rtype: list """ url = f"http://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CorpInfo/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(r.text)[3].iloc[:, :2] temp_df.dropna(inplace=True) temp_df.columns = ["item", "value"] temp_df["item"] = temp_df["item"].str.split("：", expand=True)[0] try: name_list = ( temp_df[temp_df["item"] == "证券简称更名历史"].value.tolist()[0].split(" ") ) big_df = pd.DataFrame(name_list) big_df.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.columns = ["index", "name"] return big_df except IndexError as e: return pd.DataFrame()

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L402-L427

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

34.615385

[ 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25 ]

57.692308

false

11.538462

26

2

42.307692

6

def stock_info_change_name(symbol: str = "000503") -> pd.DataFrame: url = f"http://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CorpInfo/stockid/{symbol}.phtml" r = requests.get(url) temp_df = pd.read_html(r.text)[3].iloc[:, :2] temp_df.dropna(inplace=True) temp_df.columns = ["item", "value"] temp_df["item"] = temp_df["item"].str.split("：", expand=True)[0] try: name_list = ( temp_df[temp_df["item"] == "证券简称更名历史"].value.tolist()[0].split(" ") ) big_df = pd.DataFrame(name_list) big_df.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.columns = ["index", "name"] return big_df except IndexError as e: return pd.DataFrame()

18,823

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info.py

stock_info_a_code_name

()

return big_df

沪深京 A 股列表 :return: 沪深京 A 股数据 :rtype: pandas.DataFrame

431

457

def stock_info_a_code_name() -> pd.DataFrame: """ 沪深京 A 股列表 :return: 沪深京 A 股数据 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() stock_sh = stock_info_sh_name_code(indicator="主板A股") stock_sh = stock_sh[["证券代码", "证券简称"]] stock_sz = stock_info_sz_name_code(indicator="A股列表") stock_sz["A股代码"] = stock_sz["A股代码"].astype(str).str.zfill(6) big_df = pd.concat([big_df, stock_sz[["A股代码", "A股简称"]]], ignore_index=True) big_df.columns = ["证券代码", "证券简称"] stock_kcb = stock_info_sh_name_code(indicator="科创板") stock_kcb = stock_kcb[["证券代码", "证券简称"]] stock_bse = stock_info_bj_name_code() stock_bse = stock_bse[["证券代码", "证券简称"]] stock_bse.columns = ["证券代码", "证券简称"] big_df = pd.concat([big_df, stock_sh], ignore_index=True) big_df = pd.concat([big_df, stock_kcb], ignore_index=True) big_df = pd.concat([big_df, stock_bse], ignore_index=True) big_df.columns = ["code", "name"] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info.py#L431-L457

25

[ 0, 1, 2, 3, 4, 5 ]

22.222222

[ 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26 ]

62.962963

false

11.538462

27

1

37.037037

3

def stock_info_a_code_name() -> pd.DataFrame: big_df = pd.DataFrame() stock_sh = stock_info_sh_name_code(indicator="主板A股") stock_sh = stock_sh[["证券代码", "证券简称"]] stock_sz = stock_info_sz_name_code(indicator="A股列表") stock_sz["A股代码"] = stock_sz["A股代码"].astype(str).str.zfill(6) big_df = pd.concat([big_df, stock_sz[["A股代码", "A股简称"]]], ignore_index=True) big_df.columns = ["证券代码", "证券简称"] stock_kcb = stock_info_sh_name_code(indicator="科创板") stock_kcb = stock_kcb[["证券代码", "证券简称"]] stock_bse = stock_info_bj_name_code() stock_bse = stock_bse[["证券代码", "证券简称"]] stock_bse.columns = ["证券代码", "证券简称"] big_df = pd.concat([big_df, stock_sh], ignore_index=True) big_df = pd.concat([big_df, stock_kcb], ignore_index=True) big_df = pd.concat([big_df, stock_bse], ignore_index=True) big_df.columns = ["code", "name"] return big_df

18,824

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_sina.py

_get_zh_a_page_count

()

所有股票的总页数 http://vip.stock.finance.sina.com.cn/mkt/#hs_a :return: 需要采集的股票总页数 :rtype: int

29

41

def _get_zh_a_page_count() -> int: """ 所有股票的总页数 http://vip.stock.finance.sina.com.cn/mkt/#hs_a :return: 需要采集的股票总页数 :rtype: int """ res = requests.get(zh_sina_a_stock_count_url) page_count = int(re.findall(re.compile(r"\d+"), res.text)[0]) / 80 if isinstance(page_count, int): return page_count else: return int(page_count) + 1

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_sina.py#L29-L41

25

[ 0, 1, 2, 3, 4, 5, 6 ]

53.846154

[ 7, 8, 9, 10, 12 ]

38.461538

false

6.550218

13

2

61.538462

4

def _get_zh_a_page_count() -> int: res = requests.get(zh_sina_a_stock_count_url) page_count = int(re.findall(re.compile(r"\d+"), res.text)[0]) / 80 if isinstance(page_count, int): return page_count else: return int(page_count) + 1

18,825

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_sina.py

stock_zh_a_spot

()

return big_df

新浪财经-所有 A 股的实时行情数据; 重复运行本函数会被新浪暂时封 IP http://vip.stock.finance.sina.com.cn/mkt/#hs_a :return: 所有股票的实时行情数据 :rtype: pandas.DataFrame

44

125

def stock_zh_a_spot() -> pd.DataFrame: """ 新浪财经-所有 A 股的实时行情数据; 重复运行本函数会被新浪暂时封 IP http://vip.stock.finance.sina.com.cn/mkt/#hs_a :return: 所有股票的实时行情数据 :rtype: pandas.DataFrame """ big_df = pd.DataFrame() page_count = _get_zh_a_page_count() zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy() for page in tqdm( range(1, page_count + 1), leave=False, desc="Please wait for a moment" ): zh_sina_stock_payload_copy.update({"page": page}) r = requests.get( zh_sina_a_stock_url, params=zh_sina_stock_payload_copy ) data_json = demjson.decode(r.text) big_df = pd.concat( [big_df, pd.DataFrame(data_json)], ignore_index=True ) big_df = big_df.astype( { "trade": "float", "pricechange": "float", "changepercent": "float", "buy": "float", "sell": "float", "settlement": "float", "open": "float", "high": "float", "low": "float", "volume": "float", "amount": "float", "per": "float", "pb": "float", "mktcap": "float", "nmc": "float", "turnoverratio": "float", } ) big_df.columns = [ "代码", "_", "名称", "最新价", "涨跌额", "涨跌幅", "买入", "卖出", "昨收", "今开", "最高", "最低", "成交量", "成交额", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "代码", "名称", "最新价", "涨跌额", "涨跌幅", "买入", "卖出", "昨收", "今开", "最高", "最低", "成交量", "成交额", ] ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_sina.py#L44-L125

25

[ 0, 1, 2, 3, 4, 5, 6 ]

8.536585

[ 7, 8, 9, 10, 13, 14, 17, 18, 22, 42, 64, 81 ]

14.634146

false

6.550218

82

2

85.365854

4

def stock_zh_a_spot() -> pd.DataFrame: big_df = pd.DataFrame() page_count = _get_zh_a_page_count() zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy() for page in tqdm( range(1, page_count + 1), leave=False, desc="Please wait for a moment" ): zh_sina_stock_payload_copy.update({"page": page}) r = requests.get( zh_sina_a_stock_url, params=zh_sina_stock_payload_copy ) data_json = demjson.decode(r.text) big_df = pd.concat( [big_df, pd.DataFrame(data_json)], ignore_index=True ) big_df = big_df.astype( { "trade": "float", "pricechange": "float", "changepercent": "float", "buy": "float", "sell": "float", "settlement": "float", "open": "float", "high": "float", "low": "float", "volume": "float", "amount": "float", "per": "float", "pb": "float", "mktcap": "float", "nmc": "float", "turnoverratio": "float", } ) big_df.columns = [ "代码", "_", "名称", "最新价", "涨跌额", "涨跌幅", "买入", "卖出", "昨收", "今开", "最高", "最低", "成交量", "成交额", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "代码", "名称", "最新价", "涨跌额", "涨跌幅", "买入", "卖出", "昨收", "今开", "最高", "最低", "成交量", "成交额", ] ] return big_df

18,826

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_sina.py

stock_zh_a_daily

( symbol: str = "sh603843", start_date: str = "19900101", end_date: str = "21000118", adjust: str = "", )

新浪财经-A 股-个股的历史行情数据, 大量抓取容易封 IP https://finance.sina.com.cn/realstock/company/sh603843/nc.shtml :param start_date: 20201103; 开始日期 :type start_date: str :param end_date: 20201103; 结束日期 :type end_date: str :param symbol: sh600000 :type symbol: str :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子 :type adjust: str :return: specific data :rtype: pandas.DataFrame

128

293

def stock_zh_a_daily( symbol: str = "sh603843", start_date: str = "19900101", end_date: str = "21000118", adjust: str = "", ) -> pd.DataFrame: """ 新浪财经-A 股-个股的历史行情数据, 大量抓取容易封 IP https://finance.sina.com.cn/realstock/company/sh603843/nc.shtml :param start_date: 20201103; 开始日期 :type start_date: str :param end_date: 20201103; 结束日期 :type end_date: str :param symbol: sh600000 :type symbol: str :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子 :type adjust: str :return: specific data :rtype: pandas.DataFrame """ def _fq_factor(method: str) -> pd.DataFrame: if method == "hfq": res = requests.get(zh_sina_a_stock_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if hfq_factor_df.shape[0] == 0: raise ValueError("sina hfq factor not available") hfq_factor_df.columns = ["date", "hfq_factor"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] hfq_factor_df.reset_index(inplace=True) return hfq_factor_df else: res = requests.get(zh_sina_a_stock_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if qfq_factor_df.shape[0] == 0: raise ValueError("sina hfq factor not available") qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] qfq_factor_df.reset_index(inplace=True) return qfq_factor_df if adjust in ("hfq-factor", "qfq-factor"): return _fq_factor(adjust.split("-")[0]) res = requests.get(zh_sina_a_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", res.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]).dt.date del data_df["date"] data_df = data_df.astype("float") r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol)) amount_data_json = demjson.decode( r.text[r.text.find("[") : r.text.rfind("]") + 1] ) amount_data_df = pd.DataFrame(amount_data_json) amount_data_df.index = pd.to_datetime(amount_data_df.date) del amount_data_df["date"] temp_df = pd.merge( data_df, amount_data_df, left_index=True, right_index=True, how="outer" ) temp_df.fillna(method="ffill", inplace=True) temp_df = temp_df.astype(float) temp_df["amount"] = temp_df["amount"] * 10000 temp_df["turnover"] = temp_df["volume"] / temp_df["amount"] temp_df.columns = [ "open", "high", "low", "close", "volume", "outstanding_share", "turnover", ] if adjust == "": temp_df = temp_df[start_date:end_date] temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.drop_duplicates(inplace=True) temp_df.reset_index(inplace=True) return temp_df if adjust == "hfq": res = requests.get(zh_sina_a_stock_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) hfq_factor_df.columns = ["date", "hfq_factor"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] temp_df = pd.merge( temp_df, hfq_factor_df, left_index=True, right_index=True, how="outer", ) temp_df.fillna(method="ffill", inplace=True) temp_df = temp_df.astype(float) temp_df.dropna(inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = temp_df["open"] * temp_df["hfq_factor"] temp_df["high"] = temp_df["high"] * temp_df["hfq_factor"] temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"] temp_df = temp_df.iloc[:, :-1] temp_df = temp_df[start_date:end_date] temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.reset_index(inplace=True) return temp_df if adjust == "qfq": res = requests.get(zh_sina_a_stock_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] temp_df = pd.merge( temp_df, qfq_factor_df, left_index=True, right_index=True, how="outer", ) temp_df.fillna(method="ffill", inplace=True) temp_df = temp_df.astype(float) temp_df.dropna(inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = temp_df["open"] / temp_df["qfq_factor"] temp_df["high"] = temp_df["high"] / temp_df["qfq_factor"] temp_df["close"] = temp_df["close"] / temp_df["qfq_factor"] temp_df["low"] = temp_df["low"] / temp_df["qfq_factor"] temp_df = temp_df.iloc[:, :-1] temp_df = temp_df[start_date:end_date] temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.reset_index(inplace=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_sina.py#L128-L293

25

[ 0 ]

0.60241

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60.240964

false

6.550218

166

9

39.759036

12

def stock_zh_a_daily( symbol: str = "sh603843", start_date: str = "19900101", end_date: str = "21000118", adjust: str = "", ) -> pd.DataFrame: def _fq_factor(method: str) -> pd.DataFrame: if method == "hfq": res = requests.get(zh_sina_a_stock_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if hfq_factor_df.shape[0] == 0: raise ValueError("sina hfq factor not available") hfq_factor_df.columns = ["date", "hfq_factor"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] hfq_factor_df.reset_index(inplace=True) return hfq_factor_df else: res = requests.get(zh_sina_a_stock_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) if qfq_factor_df.shape[0] == 0: raise ValueError("sina hfq factor not available") qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] qfq_factor_df.reset_index(inplace=True) return qfq_factor_df if adjust in ("hfq-factor", "qfq-factor"): return _fq_factor(adjust.split("-")[0]) res = requests.get(zh_sina_a_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", res.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]).dt.date del data_df["date"] data_df = data_df.astype("float") r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol)) amount_data_json = demjson.decode( r.text[r.text.find("[") : r.text.rfind("]") + 1] ) amount_data_df = pd.DataFrame(amount_data_json) amount_data_df.index = pd.to_datetime(amount_data_df.date) del amount_data_df["date"] temp_df = pd.merge( data_df, amount_data_df, left_index=True, right_index=True, how="outer" ) temp_df.fillna(method="ffill", inplace=True) temp_df = temp_df.astype(float) temp_df["amount"] = temp_df["amount"] * 10000 temp_df["turnover"] = temp_df["volume"] / temp_df["amount"] temp_df.columns = [ "open", "high", "low", "close", "volume", "outstanding_share", "turnover", ] if adjust == "": temp_df = temp_df[start_date:end_date] temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.drop_duplicates(inplace=True) temp_df.reset_index(inplace=True) return temp_df if adjust == "hfq": res = requests.get(zh_sina_a_stock_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) hfq_factor_df.columns = ["date", "hfq_factor"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] temp_df = pd.merge( temp_df, hfq_factor_df, left_index=True, right_index=True, how="outer", ) temp_df.fillna(method="ffill", inplace=True) temp_df = temp_df.astype(float) temp_df.dropna(inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = temp_df["open"] * temp_df["hfq_factor"] temp_df["high"] = temp_df["high"] * temp_df["hfq_factor"] temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"] temp_df = temp_df.iloc[:, :-1] temp_df = temp_df[start_date:end_date] temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.reset_index(inplace=True) return temp_df if adjust == "qfq": res = requests.get(zh_sina_a_stock_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(res.text.split("=")[1].split("\n")[0])["data"] ) qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] temp_df = pd.merge( temp_df, qfq_factor_df, left_index=True, right_index=True, how="outer", ) temp_df.fillna(method="ffill", inplace=True) temp_df = temp_df.astype(float) temp_df.dropna(inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = temp_df["open"] / temp_df["qfq_factor"] temp_df["high"] = temp_df["high"] / temp_df["qfq_factor"] temp_df["close"] = temp_df["close"] / temp_df["qfq_factor"] temp_df["low"] = temp_df["low"] / temp_df["qfq_factor"] temp_df = temp_df.iloc[:, :-1] temp_df = temp_df[start_date:end_date] temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.reset_index(inplace=True) return temp_df

18,827

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_sina.py

stock_zh_a_cdr_daily

( symbol: str = "sh689009", start_date: str = "19900101", end_date: str = "22201116", )

return temp_df

新浪财经-A股-CDR个股的历史行情数据, 大量抓取容易封 IP # TODO 观察复权情况 https://finance.sina.com.cn/realstock/company/sh689009/nc.shtml :param start_date: 20201103; 开始日期 :type start_date: str :param end_date: 20201103; 结束日期 :type end_date: str :param symbol: sh689009 :type symbol: str :return: specific data :rtype: pandas.DataFrame

296

331

def stock_zh_a_cdr_daily( symbol: str = "sh689009", start_date: str = "19900101", end_date: str = "22201116", ) -> pd.DataFrame: """ 新浪财经-A股-CDR个股的历史行情数据, 大量抓取容易封 IP # TODO 观察复权情况 https://finance.sina.com.cn/realstock/company/sh689009/nc.shtml :param start_date: 20201103; 开始日期 :type start_date: str :param end_date: 20201103; 结束日期 :type end_date: str :param symbol: sh689009 :type symbol: str :return: specific data :rtype: pandas.DataFrame """ res = requests.get(zh_sina_a_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", res.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]) del data_df["date"] data_df = data_df.astype("float") temp_df = data_df[start_date:end_date].copy() temp_df["open"] = pd.to_numeric(temp_df["open"]) temp_df["high"] = pd.to_numeric(temp_df["high"]) temp_df["low"] = pd.to_numeric(temp_df["low"]) temp_df["close"] = pd.to_numeric(temp_df["close"]) temp_df.reset_index(inplace=True) temp_df["date"] = temp_df["date"].dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_sina.py#L296-L331

25

[ 0 ]

2.777778

[ 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35 ]

44.444444

false

6.550218

36

1

55.555556

11

def stock_zh_a_cdr_daily( symbol: str = "sh689009", start_date: str = "19900101", end_date: str = "22201116", ) -> pd.DataFrame: res = requests.get(zh_sina_a_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", res.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]) del data_df["date"] data_df = data_df.astype("float") temp_df = data_df[start_date:end_date].copy() temp_df["open"] = pd.to_numeric(temp_df["open"]) temp_df["high"] = pd.to_numeric(temp_df["high"]) temp_df["low"] = pd.to_numeric(temp_df["low"]) temp_df["close"] = pd.to_numeric(temp_df["close"]) temp_df.reset_index(inplace=True) temp_df["date"] = temp_df["date"].dt.date return temp_df

18,828

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_zh_a_sina.py

stock_zh_a_minute

( symbol: str = "sh600519", period: str = "1", adjust: str = "" )

股票及股票指数历史行情数据-分钟数据 http://finance.sina.com.cn/realstock/company/sh600519/nc.shtml :param symbol: sh000300 :type symbol: str :param period: 1, 5, 15, 30, 60 分钟的数据 :type period: str :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; :type adjust: str :return: specific data :rtype: pandas.DataFrame

334

457

def stock_zh_a_minute( symbol: str = "sh600519", period: str = "1", adjust: str = "" ) -> pd.DataFrame: """ 股票及股票指数历史行情数据-分钟数据 http://finance.sina.com.cn/realstock/company/sh600519/nc.shtml :param symbol: sh000300 :type symbol: str :param period: 1, 5, 15, 30, 60 分钟的数据 :type period: str :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; :type adjust: str :return: specific data :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/cn/api/jsonp_v2.php/=/CN_MarketDataService.getKLineData" params = { "symbol": symbol, "scale": period, "ma": "no", "datalen": "36580", } r = requests.get(url, params=params) data_text = r.text try: data_json = json.loads(data_text.split("=(")[1].split(");")[0]) temp_df = pd.DataFrame(data_json).iloc[:, :6] except: url = f"https://quotes.sina.cn/cn/api/jsonp_v2.php/var%20_{symbol}_{period}_1658852984203=/CN_MarketDataService.getKLineData" params = { "symbol": symbol, "scale": period, "ma": "no", "datalen": "30000", } r = requests.get(url, params=params) data_text = r.text data_json = json.loads(data_text.split("=(")[1].split(");")[0]) temp_df = pd.DataFrame(data_json).iloc[:, :6] if temp_df.empty: print(f"{symbol} 股票数据不存在，请检查是否已退市") return try: stock_zh_a_daily(symbol=symbol, adjust="qfq") except: return temp_df if adjust == "": return temp_df if adjust == "qfq": temp_df[["date", "time"]] = temp_df["day"].str.split(" ", expand=True) # 处理没有最后一分钟的情况 need_df = temp_df[ [ True if "09:31:00" <= item <= "15:00:00" else False for item in temp_df["time"] ] ] need_df.drop_duplicates(subset=["date"], keep="last", inplace=True) need_df.index = pd.to_datetime(need_df["date"]) stock_zh_a_daily_qfq_df = stock_zh_a_daily(symbol=symbol, adjust="qfq") stock_zh_a_daily_qfq_df.index = pd.to_datetime( stock_zh_a_daily_qfq_df["date"] ) result_df = stock_zh_a_daily_qfq_df.iloc[-len(need_df) :, :][ "close" ].astype(float) / need_df["close"].astype(float) temp_df.index = pd.to_datetime(temp_df["date"]) merged_df = pd.merge( temp_df, result_df, left_index=True, right_index=True ) merged_df["open"] = ( merged_df["open"].astype(float) * merged_df["close_y"] ) merged_df["high"] = ( merged_df["high"].astype(float) * merged_df["close_y"] ) merged_df["low"] = ( merged_df["low"].astype(float) * merged_df["close_y"] ) merged_df["close"] = ( merged_df["close_x"].astype(float) * merged_df["close_y"] ) temp_df = merged_df[["day", "open", "high", "low", "close", "volume"]] temp_df.reset_index(drop=True, inplace=True) return temp_df if adjust == "hfq": temp_df[["date", "time"]] = temp_df["day"].str.split(" ", expand=True) # 处理没有最后一分钟的情况 need_df = temp_df[ [ True if "09:31:00" <= item <= "15:00:00" else False for item in temp_df["time"] ] ] need_df.drop_duplicates(subset=["date"], keep="last", inplace=True) need_df.index = pd.to_datetime(need_df["date"]) stock_zh_a_daily_hfq_df = stock_zh_a_daily(symbol=symbol, adjust="hfq") stock_zh_a_daily_hfq_df.index = pd.to_datetime( stock_zh_a_daily_hfq_df["date"] ) result_df = stock_zh_a_daily_hfq_df.iloc[-len(need_df) :, :][ "close" ].astype(float) / need_df["close"].astype(float) temp_df.index = pd.to_datetime(temp_df["date"]) merged_df = pd.merge( temp_df, result_df, left_index=True, right_index=True ) merged_df["open"] = ( merged_df["open"].astype(float) * merged_df["close_y"] ) merged_df["high"] = ( merged_df["high"].astype(float) * merged_df["close_y"] ) merged_df["low"] = ( merged_df["low"].astype(float) * merged_df["close_y"] ) merged_df["close"] = ( merged_df["close_x"].astype(float) * merged_df["close_y"] ) temp_df = merged_df[["day", "open", "high", "low", "close", "volume"]] temp_df.reset_index(drop=True, inplace=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_zh_a_sina.py#L334-L457

25

[ 0 ]

0.806452

[ 15, 16, 22, 23, 24, 25, 26, 27, 28, 29, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 50, 51, 53, 59, 60, 61, 62, 65, 68, 69, 72, 75, 78, 81, 84, 85, 86, 87, 88, 90, 96, 97, 98, 99, 102, 105, 106, 109, 112, 115, 118, 121, 122, 123 ]

45.967742

false

6.550218

124

9

54.032258

10

def stock_zh_a_minute( symbol: str = "sh600519", period: str = "1", adjust: str = "" ) -> pd.DataFrame: url = "https://quotes.sina.cn/cn/api/jsonp_v2.php/=/CN_MarketDataService.getKLineData" params = { "symbol": symbol, "scale": period, "ma": "no", "datalen": "36580", } r = requests.get(url, params=params) data_text = r.text try: data_json = json.loads(data_text.split("=(")[1].split(");")[0]) temp_df = pd.DataFrame(data_json).iloc[:, :6] except: url = f"https://quotes.sina.cn/cn/api/jsonp_v2.php/var%20_{symbol}_{period}_1658852984203=/CN_MarketDataService.getKLineData" params = { "symbol": symbol, "scale": period, "ma": "no", "datalen": "30000", } r = requests.get(url, params=params) data_text = r.text data_json = json.loads(data_text.split("=(")[1].split(");")[0]) temp_df = pd.DataFrame(data_json).iloc[:, :6] if temp_df.empty: print(f"{symbol} 股票数据不存在，请检查是否已退市") return try: stock_zh_a_daily(symbol=symbol, adjust="qfq") except: return temp_df if adjust == "": return temp_df if adjust == "qfq": temp_df[["date", "time"]] = temp_df["day"].str.split(" ", expand=True) # 处理没有最后一分钟的情况 need_df = temp_df[ [ True if "09:31:00" <= item <= "15:00:00" else False for item in temp_df["time"] ] ] need_df.drop_duplicates(subset=["date"], keep="last", inplace=True) need_df.index = pd.to_datetime(need_df["date"]) stock_zh_a_daily_qfq_df = stock_zh_a_daily(symbol=symbol, adjust="qfq") stock_zh_a_daily_qfq_df.index = pd.to_datetime( stock_zh_a_daily_qfq_df["date"] ) result_df = stock_zh_a_daily_qfq_df.iloc[-len(need_df) :, :][ "close" ].astype(float) / need_df["close"].astype(float) temp_df.index = pd.to_datetime(temp_df["date"]) merged_df = pd.merge( temp_df, result_df, left_index=True, right_index=True ) merged_df["open"] = ( merged_df["open"].astype(float) * merged_df["close_y"] ) merged_df["high"] = ( merged_df["high"].astype(float) * merged_df["close_y"] ) merged_df["low"] = ( merged_df["low"].astype(float) * merged_df["close_y"] ) merged_df["close"] = ( merged_df["close_x"].astype(float) * merged_df["close_y"] ) temp_df = merged_df[["day", "open", "high", "low", "close", "volume"]] temp_df.reset_index(drop=True, inplace=True) return temp_df if adjust == "hfq": temp_df[["date", "time"]] = temp_df["day"].str.split(" ", expand=True) # 处理没有最后一分钟的情况 need_df = temp_df[ [ True if "09:31:00" <= item <= "15:00:00" else False for item in temp_df["time"] ] ] need_df.drop_duplicates(subset=["date"], keep="last", inplace=True) need_df.index = pd.to_datetime(need_df["date"]) stock_zh_a_daily_hfq_df = stock_zh_a_daily(symbol=symbol, adjust="hfq") stock_zh_a_daily_hfq_df.index = pd.to_datetime( stock_zh_a_daily_hfq_df["date"] ) result_df = stock_zh_a_daily_hfq_df.iloc[-len(need_df) :, :][ "close" ].astype(float) / need_df["close"].astype(float) temp_df.index = pd.to_datetime(temp_df["date"]) merged_df = pd.merge( temp_df, result_df, left_index=True, right_index=True ) merged_df["open"] = ( merged_df["open"].astype(float) * merged_df["close_y"] ) merged_df["high"] = ( merged_df["high"].astype(float) * merged_df["close_y"] ) merged_df["low"] = ( merged_df["low"].astype(float) * merged_df["close_y"] ) merged_df["close"] = ( merged_df["close_x"].astype(float) * merged_df["close_y"] ) temp_df = merged_df[["day", "open", "high", "low", "close", "volume"]] temp_df.reset_index(drop=True, inplace=True) return temp_df

18,829

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hot_search_baidu.py

stock_hot_search_baidu

(symbol: str = "A股", date: str = "20221014", time: str = "0"):

return temp_df

百度股市通-热搜股票 https://gushitong.baidu.com/expressnews :param symbol: choice of {"全部", "A股", "港股", "美股"} :type symbol: str :param date: 日期 :type date: str :param time: 默认 time=0，则为当天的排行；如 time="16"，则为 date 的 16 点的热门股票排行 :type time: str :return: 股东人数及持股集中度 :rtype: pandas.DataFrame

12

52

def stock_hot_search_baidu(symbol: str = "A股", date: str = "20221014", time: str = "0"): """ 百度股市通-热搜股票 https://gushitong.baidu.com/expressnews :param symbol: choice of {"全部", "A股", "港股", "美股"} :type symbol: str :param date: 日期 :type date: str :param time: 默认 time=0，则为当天的排行；如 time="16"，则为 date 的 16 点的热门股票排行 :type time: str :return: 股东人数及持股集中度 :rtype: pandas.DataFrame """ symbol_map = { "全部": "all", "A股": "ab", "港股": "hk", "美股": "us", } url = "https://finance.pae.baidu.com/vapi/v1/hotrank" params = { "tn": "wisexmlnew", "dsp": "iphone", "product": "stock", "day": date, "hour": time, "pn": "0", "rn": "1000", "market": symbol_map[symbol], "type": "day" if time == 0 else "hour", "finClientType": "pc", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame( data_json["Result"]["body"], columns=data_json["Result"]["header"] ) temp_df["综合热度"] = pd.to_numeric(temp_df["综合热度"]) temp_df["排名变化"] = pd.to_numeric(temp_df["排名变化"]) temp_df["是否连续上榜"] = pd.to_numeric(temp_df["是否连续上榜"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hot_search_baidu.py#L12-L52

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ]

31.707317

[ 13, 19, 20, 32, 33, 34, 37, 38, 39, 40 ]

24.390244

false

29.411765

41

1

75.609756

10

def stock_hot_search_baidu(symbol: str = "A股", date: str = "20221014", time: str = "0"): symbol_map = { "全部": "all", "A股": "ab", "港股": "hk", "美股": "us", } url = "https://finance.pae.baidu.com/vapi/v1/hotrank" params = { "tn": "wisexmlnew", "dsp": "iphone", "product": "stock", "day": date, "hour": time, "pn": "0", "rn": "1000", "market": symbol_map[symbol], "type": "day" if time == 0 else "hour", "finClientType": "pc", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame( data_json["Result"]["body"], columns=data_json["Result"]["header"] ) temp_df["综合热度"] = pd.to_numeric(temp_df["综合热度"]) temp_df["排名变化"] = pd.to_numeric(temp_df["排名变化"]) temp_df["是否连续上榜"] = pd.to_numeric(temp_df["是否连续上榜"]) return temp_df

18,830

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_info_em.py

stock_individual_info_em

(symbol: str = "603777")

return temp_df

东方财富-个股-股票信息 https://quote.eastmoney.com/concept/sh603777.html?from=classic :param symbol: 股票代码 :type symbol: str :return: 股票信息 :rtype: pandas.DataFrame

14

61

def stock_individual_info_em(symbol: str = "603777") -> pd.DataFrame: """ 东方财富-个股-股票信息 https://quote.eastmoney.com/concept/sh603777.html?from=classic :param symbol: 股票代码 :type symbol: str :return: 股票信息 :rtype: pandas.DataFrame """ code_id_dict = code_id_map_em() url = "http://push2.eastmoney.com/api/qt/stock/get" params = { "ut": "fa5fd1943c7b386f172d6893dbfba10b", "fltt": "2", "invt": "2", "fields": "f120,f121,f122,f174,f175,f59,f163,f43,f57,f58,f169,f170,f46,f44,f51,f168,f47,f164,f116,f60,f45,f52,f50,f48,f167,f117,f71,f161,f49,f530,f135,f136,f137,f138,f139,f141,f142,f144,f145,f147,f148,f140,f143,f146,f149,f55,f62,f162,f92,f173,f104,f105,f84,f85,f183,f184,f185,f186,f187,f188,f189,f190,f191,f192,f107,f111,f86,f177,f78,f110,f262,f263,f264,f267,f268,f255,f256,f257,f258,f127,f199,f128,f198,f259,f260,f261,f171,f277,f278,f279,f288,f152,f250,f251,f252,f253,f254,f269,f270,f271,f272,f273,f274,f275,f276,f265,f266,f289,f290,f286,f285,f292,f293,f294,f295", "secid": f"{code_id_dict[symbol]}.{symbol}", "_": "1640157544804", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json) temp_df.reset_index(inplace=True) del temp_df["rc"] del temp_df["rt"] del temp_df["svr"] del temp_df["lt"] del temp_df["full"] code_name_map = { "f57": "股票代码", "f58": "股票简称", "f84": "总股本", "f85": "流通股", "f127": "行业", "f116": "总市值", "f117": "流通市值", "f189": "上市时间", } temp_df["index"] = temp_df["index"].map(code_name_map) temp_df = temp_df[pd.notna(temp_df["index"])] if "dlmkts" in temp_df.columns: del temp_df["dlmkts"] temp_df.columns = [ "item", "value", ] temp_df.reset_index(inplace=True, drop=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_info_em.py#L14-L61

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

18.75

[ 9, 10, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 38, 39, 40, 41, 42, 46, 47 ]

41.666667

false

21.428571

48

2

58.333333

6

def stock_individual_info_em(symbol: str = "603777") -> pd.DataFrame: code_id_dict = code_id_map_em() url = "http://push2.eastmoney.com/api/qt/stock/get" params = { "ut": "fa5fd1943c7b386f172d6893dbfba10b", "fltt": "2", "invt": "2", "fields": "f120,f121,f122,f174,f175,f59,f163,f43,f57,f58,f169,f170,f46,f44,f51,f168,f47,f164,f116,f60,f45,f52,f50,f48,f167,f117,f71,f161,f49,f530,f135,f136,f137,f138,f139,f141,f142,f144,f145,f147,f148,f140,f143,f146,f149,f55,f62,f162,f92,f173,f104,f105,f84,f85,f183,f184,f185,f186,f187,f188,f189,f190,f191,f192,f107,f111,f86,f177,f78,f110,f262,f263,f264,f267,f268,f255,f256,f257,f258,f127,f199,f128,f198,f259,f260,f261,f171,f277,f278,f279,f288,f152,f250,f251,f252,f253,f254,f269,f270,f271,f272,f273,f274,f275,f276,f265,f266,f289,f290,f286,f285,f292,f293,f294,f295", "secid": f"{code_id_dict[symbol]}.{symbol}", "_": "1640157544804", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json) temp_df.reset_index(inplace=True) del temp_df["rc"] del temp_df["rt"] del temp_df["svr"] del temp_df["lt"] del temp_df["full"] code_name_map = { "f57": "股票代码", "f58": "股票简称", "f84": "总股本", "f85": "流通股", "f127": "行业", "f116": "总市值", "f117": "流通市值", "f189": "上市时间", } temp_df["index"] = temp_df["index"].map(code_name_map) temp_df = temp_df[pd.notna(temp_df["index"])] if "dlmkts" in temp_df.columns: del temp_df["dlmkts"] temp_df.columns = [ "item", "value", ] temp_df.reset_index(inplace=True, drop=True) return temp_df

18,831

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_profile_cninfo.py

stock_profile_cninfo

(symbol: str = "600030")

return temp_df

巨潮资讯-个股-公司概况 http://webapi.cninfo.com.cn/#/company :param symbol: 股票代码 :type symbol: str :return: 公司概况 :rtype: pandas.DataFrame :raise: Exception，如果服务器返回的数据无法被解析

45

126

def stock_profile_cninfo(symbol: str = "600030") -> pd.DataFrame: """ 巨潮资讯-个股-公司概况 http://webapi.cninfo.com.cn/#/company :param symbol: 股票代码 :type symbol: str :return: 公司概况 :rtype: pandas.DataFrame :raise: Exception，如果服务器返回的数据无法被解析 """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1133" params = { "scode": symbol, } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() columns = [ "公司名称", "英文名称", "曾用简称", "A股代码", "A股简称", "B股代码", "B股简称", "H股代码", "H股简称", "入选指数", "所属市场", "所属行业", "法人代表", "注册资金", "成立日期", "上市日期", "官方网站", "电子邮箱", "联系电话", "传真", "注册地址", "办公地址", "邮政编码", "主营业务", "经营范围", "机构简介", ] count = data_json["count"] if count == 1: # 有公司概况的 redundant_json = data_json["records"][0] records_json = {} i = 0 for k, v in redundant_json.items(): if i == (len(redundant_json) - 4): break records_json[k] = v i += 1 del i temp_df = pd.Series(records_json).to_frame().T temp_df.columns = columns elif count == 0: # 没公司概况的 temp_df = pd.DataFrame(columns=columns) else: raise Exception("数据错误！") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_profile_cninfo.py#L45-L126

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]

12.195122

[ 10, 11, 14, 15, 16, 17, 18, 32, 33, 34, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 80, 81 ]

32.926829

false

21.621622

82

5

67.073171

7

def stock_profile_cninfo(symbol: str = "600030") -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1133" params = { "scode": symbol, } random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() columns = [ "公司名称", "英文名称", "曾用简称", "A股代码", "A股简称", "B股代码", "B股简称", "H股代码", "H股简称", "入选指数", "所属市场", "所属行业", "法人代表", "注册资金", "成立日期", "上市日期", "官方网站", "电子邮箱", "联系电话", "传真", "注册地址", "办公地址", "邮政编码", "主营业务", "经营范围", "机构简介", ] count = data_json["count"] if count == 1: # 有公司概况的 redundant_json = data_json["records"][0] records_json = {} i = 0 for k, v in redundant_json.items(): if i == (len(redundant_json) - 4): break records_json[k] = v i += 1 del i temp_df = pd.Series(records_json).to_frame().T temp_df.columns = columns elif count == 0: # 没公司概况的 temp_df = pd.DataFrame(columns=columns) else: raise Exception("数据错误！") return temp_df

18,832

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hist_163.py

stock_zh_a_hist_163

( symbol: str = "sh601318", start_date: str = "10700101", end_date: str = "20500101", )

return temp_df

网易财经-行情首页-沪深 A 股-每日行情注意：该接口只返回未复权数据 https://quotes.money.163.com/trade/lsjysj_601318.html?year=2022&season=2 :param symbol: 带市场表示的股票代码 :type symbol: str :param start_date: 开始时间 :type start_date: str :param end_date: 结束时间 :type end_date: str :return: 每日行情 :rtype: pandas.DataFrame

12

94

def stock_zh_a_hist_163( symbol: str = "sh601318", start_date: str = "10700101", end_date: str = "20500101", ) -> pd.DataFrame: """ 网易财经-行情首页-沪深 A 股-每日行情注意：该接口只返回未复权数据 https://quotes.money.163.com/trade/lsjysj_601318.html?year=2022&season=2 :param symbol: 带市场表示的股票代码 :type symbol: str :param start_date: 开始时间 :type start_date: str :param end_date: 结束时间 :type end_date: str :return: 每日行情 :rtype: pandas.DataFrame """ url = "http://quotes.money.163.com/service/chddata.html" params = { "code": "0601318", "start": start_date, "end": end_date, "fields": "TCLOSE;HIGH;LOW;TOPEN;LCLOSE;CHG;PCHG;TURNOVER;VOTURNOVER;VATURNOVER;TCAP;MCAP", } if "sh" in symbol: params.update({"code": f"0{symbol[2:]}"}) else: params.update({"code": f"1{symbol[2:]}"}) headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "quotes.money.163.com", "Pragma": "no-cache", "Referer": "http://quotes.money.163.com/trade/lsjysj_300254.html", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36", } r = requests.get(url, params=params, headers=headers) r.encoding = "gbk" data_text = r.text temp_df = pd.DataFrame( [item.split(",") for item in data_text.split("\r\n")[1:]] ) temp_df.columns = [ "日期", "股票代码", "名称", "收盘价", "最高价", "最低价", "开盘价", "前收盘", "涨跌额", "涨跌幅", "换手率", "成交量", "成交金额", "总市值", "流通市值", ] temp_df["股票代码"] = temp_df["股票代码"].str.strip("'").str.strip() temp_df["名称"] = temp_df["名称"].str.strip() temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce") temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce") temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce") temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce") temp_df["前收盘"] = pd.to_numeric(temp_df["前收盘"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交金额"] = pd.to_numeric(temp_df["成交金额"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df.dropna(subset=["日期"], inplace=True) temp_df.sort_values("日期", inplace=True) temp_df.reset_index(inplace=True, drop=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hist_163.py#L12-L94

25

[ 0 ]

1.204819

[ 18, 19, 25, 26, 28, 29, 41, 42, 43, 44, 47, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82 ]

36.144578

false

12.820513

83

3

63.855422

11

def stock_zh_a_hist_163( symbol: str = "sh601318", start_date: str = "10700101", end_date: str = "20500101", ) -> pd.DataFrame: url = "http://quotes.money.163.com/service/chddata.html" params = { "code": "0601318", "start": start_date, "end": end_date, "fields": "TCLOSE;HIGH;LOW;TOPEN;LCLOSE;CHG;PCHG;TURNOVER;VOTURNOVER;VATURNOVER;TCAP;MCAP", } if "sh" in symbol: params.update({"code": f"0{symbol[2:]}"}) else: params.update({"code": f"1{symbol[2:]}"}) headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "quotes.money.163.com", "Pragma": "no-cache", "Referer": "http://quotes.money.163.com/trade/lsjysj_300254.html", "Upgrade-Insecure-Requests": "1", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36", } r = requests.get(url, params=params, headers=headers) r.encoding = "gbk" data_text = r.text temp_df = pd.DataFrame( [item.split(",") for item in data_text.split("\r\n")[1:]] ) temp_df.columns = [ "日期", "股票代码", "名称", "收盘价", "最高价", "最低价", "开盘价", "前收盘", "涨跌额", "涨跌幅", "换手率", "成交量", "成交金额", "总市值", "流通市值", ] temp_df["股票代码"] = temp_df["股票代码"].str.strip("'").str.strip() temp_df["名称"] = temp_df["名称"].str.strip() temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce") temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce") temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce") temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce") temp_df["前收盘"] = pd.to_numeric(temp_df["前收盘"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交金额"] = pd.to_numeric(temp_df["成交金额"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df.dropna(subset=["日期"], inplace=True) temp_df.sort_values("日期", inplace=True) temp_df.reset_index(inplace=True, drop=True) return temp_df

18,833

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_hold_num_cninfo.py

stock_hold_num_cninfo

(date: str = "20210630")

return temp_df

巨潮资讯-数据中心-专题统计-股东股本-股东人数及持股集中度 http://webapi.cninfo.com.cn/#/thematicStatistics :param date: choice of {"XXXX0331", "XXXX0630", "XXXX0930", "XXXX1231"}; 从 20170331 开始 :type date: str :return: 股东人数及持股集中度 :rtype: pandas.DataFrame

45

111

def stock_hold_num_cninfo(date: str = "20210630") -> pd.DataFrame: """ 巨潮资讯-数据中心-专题统计-股东股本-股东人数及持股集中度 http://webapi.cninfo.com.cn/#/thematicStatistics :param date: choice of {"XXXX0331", "XXXX0630", "XXXX0930", "XXXX1231"}; 从 20170331 开始 :type date: str :return: 股东人数及持股集中度 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1034" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "rdate": date, } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "本期人均持股数量", "股东人数增幅", "上期股东人数", "本期股东人数", "证券简称", "证券代码", "人均持股数量增幅", "变动日期", "上期人均持股数量", ] temp_df = temp_df[ [ "证券代码", "证券简称", "变动日期", "本期股东人数", "上期股东人数", "股东人数增幅", "本期人均持股数量", "上期人均持股数量", "人均持股数量增幅", ] ] temp_df["变动日期"] = pd.to_datetime(temp_df["变动日期"]).dt.date temp_df["本期人均持股数量"] = pd.to_numeric(temp_df["本期人均持股数量"]) temp_df["股东人数增幅"] = pd.to_numeric(temp_df["股东人数增幅"]) temp_df["上期股东人数"] = pd.to_numeric(temp_df["上期股东人数"]) temp_df["本期股东人数"] = pd.to_numeric(temp_df["本期股东人数"]) temp_df["人均持股数量增幅"] = pd.to_numeric(temp_df["人均持股数量增幅"]) temp_df["上期人均持股数量"] = pd.to_numeric(temp_df["上期人均持股数量"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_hold_num_cninfo.py#L45-L111

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

13.432836

[ 9, 10, 11, 12, 13, 14, 29, 32, 33, 34, 35, 46, 59, 60, 61, 62, 63, 64, 65, 66 ]

29.850746

false

26.666667

67

1

70.149254

6

def stock_hold_num_cninfo(date: str = "20210630") -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1034" random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } params = { "rdate": date, } r = requests.post(url, headers=headers, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "本期人均持股数量", "股东人数增幅", "上期股东人数", "本期股东人数", "证券简称", "证券代码", "人均持股数量增幅", "变动日期", "上期人均持股数量", ] temp_df = temp_df[ [ "证券代码", "证券简称", "变动日期", "本期股东人数", "上期股东人数", "股东人数增幅", "本期人均持股数量", "上期人均持股数量", "人均持股数量增幅", ] ] temp_df["变动日期"] = pd.to_datetime(temp_df["变动日期"]).dt.date temp_df["本期人均持股数量"] = pd.to_numeric(temp_df["本期人均持股数量"]) temp_df["股东人数增幅"] = pd.to_numeric(temp_df["股东人数增幅"]) temp_df["上期股东人数"] = pd.to_numeric(temp_df["上期股东人数"]) temp_df["本期股东人数"] = pd.to_numeric(temp_df["本期股东人数"]) temp_df["人均持股数量增幅"] = pd.to_numeric(temp_df["人均持股数量增幅"]) temp_df["上期人均持股数量"] = pd.to_numeric(temp_df["上期人均持股数量"]) return temp_df

18,834

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_rank_forecast.py

stock_rank_forecast_cninfo

(date: str = "20210910")

return temp_df

巨潮资讯-数据中心-评级预测-投资评级 http://webapi.cninfo.com.cn/#/thematicStatistics?name=%E6%8A%95%E8%B5%84%E8%AF%84%E7%BA%A7 :param date: 查询日期 :type date: str :return: 投资评级 :rtype: pandas.DataFrame

44

105

def stock_rank_forecast_cninfo(date: str = "20210910") -> pd.DataFrame: """ 巨潮资讯-数据中心-评级预测-投资评级 http://webapi.cninfo.com.cn/#/thematicStatistics?name=%E6%8A%95%E8%B5%84%E8%AF%84%E7%BA%A7 :param date: 查询日期 :type date: str :return: 投资评级 :rtype: pandas.DataFrame """ url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1089" params = {"tdate": "-".join([date[:4], date[4:6], date[6:]])} random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "证券简称", "发布日期", "前一次投资评级", "评级变化", "目标价格-上限", "是否首次评级", "投资评级", "研究员名称", "研究机构简称", "目标价格-下限", "证券代码", ] temp_df = temp_df[[ "证券代码", "证券简称", "发布日期", "研究机构简称", "研究员名称", "投资评级", "是否首次评级", "评级变化", "前一次投资评级", "目标价格-下限", "目标价格-上限", ]] temp_df["目标价格-上限"] = pd.to_numeric(temp_df["目标价格-上限"], errors="coerce") temp_df["目标价格-下限"] = pd.to_numeric(temp_df["目标价格-下限"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_rank_forecast.py#L44-L105

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

14.516129

[ 9, 10, 11, 12, 13, 14, 15, 30, 31, 32, 33, 46, 59, 60, 61 ]

24.193548

false

32

62

1

75.806452

6

def stock_rank_forecast_cninfo(date: str = "20210910") -> pd.DataFrame: url = "http://webapi.cninfo.com.cn/api/sysapi/p_sysapi1089" params = {"tdate": "-".join([date[:4], date[4:6], date[6:]])} random_time_str = str(int(time.time())) js_code = py_mini_racer.MiniRacer() js_code.eval(js_str) mcode = js_code.call("mcode", random_time_str) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Content-Length": "0", "Host": "webapi.cninfo.com.cn", "mcode": mcode, "Origin": "http://webapi.cninfo.com.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://webapi.cninfo.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.post(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["records"]) temp_df.columns = [ "证券简称", "发布日期", "前一次投资评级", "评级变化", "目标价格-上限", "是否首次评级", "投资评级", "研究员名称", "研究机构简称", "目标价格-下限", "证券代码", ] temp_df = temp_df[[ "证券代码", "证券简称", "发布日期", "研究机构简称", "研究员名称", "投资评级", "是否首次评级", "评级变化", "前一次投资评级", "目标价格-下限", "目标价格-上限", ]] temp_df["目标价格-上限"] = pd.to_numeric(temp_df["目标价格-上限"], errors="coerce") temp_df["目标价格-下限"] = pd.to_numeric(temp_df["目标价格-下限"], errors="coerce") return temp_df

18,835

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_board_concept_em.py

stock_board_concept_name_em

()

return temp_df

东方财富网-沪深板块-概念板块-名称 https://quote.eastmoney.com/center/boardlist.html#concept_board :return: 概念板块-名称 :rtype: pandas.DataFrame

12

91

def stock_board_concept_name_em() -> pd.DataFrame: """ 东方财富网-沪深板块-概念板块-名称 https://quote.eastmoney.com/center/boardlist.html#concept_board :return: 概念板块-名称 :rtype: pandas.DataFrame """ url = "http://79.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:90 t:3 f:!50", "fields": "f2,f3,f4,f8,f12,f14,f15,f16,f17,f18,f20,f21,f24,f25,f22,f33,f11,f62,f128,f124,f107,f104,f105,f136", "_": "1626075887768", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.columns = [ "排名", "最新价", "涨跌幅", "涨跌额", "换手率", "_", "板块代码", "板块名称", "_", "_", "_", "_", "总市值", "_", "_", "_", "_", "_", "_", "上涨家数", "下跌家数", "_", "_", "领涨股票", "_", "_", "领涨股票-涨跌幅", ] temp_df = temp_df[ [ "排名", "板块名称", "板块代码", "最新价", "涨跌额", "涨跌幅", "总市值", "换手率", "上涨家数", "下跌家数", "领涨股票", "领涨股票-涨跌幅", ] ] temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["上涨家数"] = pd.to_numeric(temp_df["上涨家数"], errors="coerce") temp_df["下跌家数"] = pd.to_numeric(temp_df["下跌家数"], errors="coerce") temp_df["领涨股票-涨跌幅"] = pd.to_numeric(temp_df["领涨股票-涨跌幅"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_board_concept_em.py#L12-L91

25

[ 0, 1, 2, 3, 4, 5, 6 ]

8.75

[ 7, 8, 21, 22, 23, 24, 25, 26, 55, 71, 72, 73, 74, 75, 76, 77, 78, 79 ]

22.5

false

7.920792

80

1

77.5

4

def stock_board_concept_name_em() -> pd.DataFrame: url = "http://79.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:90 t:3 f:!50", "fields": "f2,f3,f4,f8,f12,f14,f15,f16,f17,f18,f20,f21,f24,f25,f22,f33,f11,f62,f128,f124,f107,f104,f105,f136", "_": "1626075887768", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.columns = [ "排名", "最新价", "涨跌幅", "涨跌额", "换手率", "_", "板块代码", "板块名称", "_", "_", "_", "_", "总市值", "_", "_", "_", "_", "_", "_", "上涨家数", "下跌家数", "_", "_", "领涨股票", "_", "_", "领涨股票-涨跌幅", ] temp_df = temp_df[ [ "排名", "板块名称", "板块代码", "最新价", "涨跌额", "涨跌幅", "总市值", "换手率", "上涨家数", "下跌家数", "领涨股票", "领涨股票-涨跌幅", ] ] temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["上涨家数"] = pd.to_numeric(temp_df["上涨家数"], errors="coerce") temp_df["下跌家数"] = pd.to_numeric(temp_df["下跌家数"], errors="coerce") temp_df["领涨股票-涨跌幅"] = pd.to_numeric(temp_df["领涨股票-涨跌幅"], errors="coerce") return temp_df

18,836

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_board_concept_em.py

stock_board_concept_hist_em

( symbol: str = "数字货币", period: str = "daily", start_date: str = "20220101", end_date: str = "20221128", adjust: str = "", )

return temp_df

东方财富网-沪深板块-概念板块-历史行情 https://quote.eastmoney.com/bk/90.BK0715.html :param symbol: 板块名称 :type symbol: str :type period: 周期; choice of {"daily", "weekly", "monthly"} :param period: 板块名称 :param start_date: 开始时间 :type start_date: str :param end_date: 结束时间 :type end_date: str :param adjust: choice of {'': 不复权, "qfq": 前复权, "hfq": 后复权} :type adjust: str :return: 历史行情 :rtype: pandas.DataFrame

94

182

def stock_board_concept_hist_em( symbol: str = "数字货币", period: str = "daily", start_date: str = "20220101", end_date: str = "20221128", adjust: str = "", ) -> pd.DataFrame: """ 东方财富网-沪深板块-概念板块-历史行情 https://quote.eastmoney.com/bk/90.BK0715.html :param symbol: 板块名称 :type symbol: str :type period: 周期; choice of {"daily", "weekly", "monthly"} :param period: 板块名称 :param start_date: 开始时间 :type start_date: str :param end_date: 结束时间 :type end_date: str :param adjust: choice of {'': 不复权, "qfq": 前复权, "hfq": 后复权} :type adjust: str :return: 历史行情 :rtype: pandas.DataFrame """ period_map = { "daily": "101", "weekly": "102", "monthly": "103", } stock_board_concept_em_map = stock_board_concept_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] adjust_map = {"": "0", "qfq": "1", "hfq": "2"} url = "http://91.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"90.{stock_board_code}", "ut": "fa5fd1943c7b386f172d6893dbfba10b", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period_map[period], "fqt": adjust_map[adjust], "beg": start_date, "end": end_date, "smplmt": "10000", "lmt": "1000000", "_": "1626079488673", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]]) temp_df.columns = [ "日期", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "振幅", "涨跌幅", "涨跌额", "换手率", ] temp_df = temp_df[ [ "日期", "开盘", "收盘", "最高", "最低", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", ] ] temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_board_concept_em.py#L94-L182

25

[ 0 ]

1.123596

[ 23, 28, 29, 32, 33, 34, 47, 48, 49, 50, 63, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88 ]

24.719101

false

7.920792

89

2

75.280899

14

def stock_board_concept_hist_em( symbol: str = "数字货币", period: str = "daily", start_date: str = "20220101", end_date: str = "20221128", adjust: str = "", ) -> pd.DataFrame: period_map = { "daily": "101", "weekly": "102", "monthly": "103", } stock_board_concept_em_map = stock_board_concept_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] adjust_map = {"": "0", "qfq": "1", "hfq": "2"} url = "http://91.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"90.{stock_board_code}", "ut": "fa5fd1943c7b386f172d6893dbfba10b", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period_map[period], "fqt": adjust_map[adjust], "beg": start_date, "end": end_date, "smplmt": "10000", "lmt": "1000000", "_": "1626079488673", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]]) temp_df.columns = [ "日期", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "振幅", "涨跌幅", "涨跌额", "换手率", ] temp_df = temp_df[ [ "日期", "开盘", "收盘", "最高", "最低", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", ] ] temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df

18,837

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_board_concept_em.py

stock_board_concept_hist_min_em

( symbol: str = "长寿药", period: str = "5" )

return temp_df

东方财富网-沪深板块-概念板块-分时历史行情 https://quote.eastmoney.com/bk/90.BK0715.html :param symbol: 板块名称 :type symbol: str :param period: choice of {"1", "5", "15", "30", "60"} :type period: str :return: 分时历史行情 :rtype: pandas.DataFrame

185

255

def stock_board_concept_hist_min_em( symbol: str = "长寿药", period: str = "5" ) -> pd.DataFrame: """ 东方财富网-沪深板块-概念板块-分时历史行情 https://quote.eastmoney.com/bk/90.BK0715.html :param symbol: 板块名称 :type symbol: str :param period: choice of {"1", "5", "15", "30", "60"} :type period: str :return: 分时历史行情 :rtype: pandas.DataFrame """ stock_board_concept_em_map = stock_board_concept_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] url = "http://91.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"90.{stock_board_code}", "ut": "fa5fd1943c7b386f172d6893dbfba10b", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period, "fqt": "1", "end": "20500101", "lmt": "1000000", "_": "1647760607065", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]]) temp_df.columns = [ "日期时间", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "振幅", "涨跌幅", "涨跌额", "换手率", ] temp_df = temp_df[ [ "日期时间", "开盘", "收盘", "最高", "最低", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", ] ] temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_board_concept_em.py#L185-L255

25

[ 0 ]

1.408451

[ 13, 14, 17, 18, 29, 30, 31, 32, 45, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70 ]

28.169014

false

7.920792

71

2

71.830986

8

def stock_board_concept_hist_min_em( symbol: str = "长寿药", period: str = "5" ) -> pd.DataFrame: stock_board_concept_em_map = stock_board_concept_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] url = "http://91.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"90.{stock_board_code}", "ut": "fa5fd1943c7b386f172d6893dbfba10b", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period, "fqt": "1", "end": "20500101", "lmt": "1000000", "_": "1647760607065", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]]) temp_df.columns = [ "日期时间", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "振幅", "涨跌幅", "涨跌额", "换手率", ] temp_df = temp_df[ [ "日期时间", "开盘", "收盘", "最高", "最低", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", ] ] temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df

18,838

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_board_concept_em.py

stock_board_concept_cons_em

(symbol: str = "车联网") -> pd

return temp_df

东方财富-沪深板块-概念板块-板块成份 https://quote.eastmoney.com/center/boardlist.html#boards-BK06551 :param symbol: 板块名称 :type symbol: str :return: 板块成份 :rtype: pandas.DataFrame

258

359

def stock_board_concept_cons_em(symbol: str = "车联网") -> pd.DataFrame: """ 东方财富-沪深板块-概念板块-板块成份 https://quote.eastmoney.com/center/boardlist.html#boards-BK06551 :param symbol: 板块名称 :type symbol: str :return: 板块成份 :rtype: pandas.DataFrame """ stock_board_concept_em_map = stock_board_concept_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] url = "http://29.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": f"b:{stock_board_code} f:!50", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152,f45", "_": "1626081702127", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.columns = [ "序号", "_", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", "市盈率-动态", "_", "_", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "_", "_", "_", "市净率", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "最高", "最低", "今开", "昨收", "换手率", "市盈率-动态", "市净率", ] ] temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce") temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce") temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_board_concept_em.py#L258-L359

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

8.823529

[ 9, 10, 13, 14, 27, 28, 30, 31, 32, 33, 68, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101 ]

24.509804

false

7.920792

102

1

75.490196

6

def stock_board_concept_cons_em(symbol: str = "车联网") -> pd.DataFrame: stock_board_concept_em_map = stock_board_concept_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] url = "http://29.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "2000", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": f"b:{stock_board_code} f:!50", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152,f45", "_": "1626081702127", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.columns = [ "序号", "_", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", "市盈率-动态", "_", "_", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "_", "_", "_", "市净率", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "最高", "最低", "今开", "昨收", "换手率", "市盈率-动态", "市净率", ] ] temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce") temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce") temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce") return temp_df

18,839

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock/stock_us_sina.py

get_us_page_count

()

return page_count

新浪财经-美股-总页数 https://finance.sina.com.cn/stock/usstock/sector.shtml :return: 美股总页数 :rtype: int

25

49

def get_us_page_count() -> int: """ 新浪财经-美股-总页数 https://finance.sina.com.cn/stock/usstock/sector.shtml :return: 美股总页数 :rtype: int """ page = "1" us_js_decode = f"US_CategoryService.getList?page={page}&num=20&sort=&asc=0&market=&id=" js_code = py_mini_racer.MiniRacer() js_code.eval(js_hash_text) dict_list = js_code.call("d", us_js_decode) # 执行js解密代码 us_sina_stock_dict_payload.update({"page": "{}".format(page)}) res = requests.get( us_sina_stock_list_url.format(dict_list), params=us_sina_stock_dict_payload, ) data_json = json.loads( res.text[res.text.find("({") + 1 : res.text.rfind(");")] ) if not isinstance(int(data_json["count"]) / 20, int): page_count = int(int(data_json["count"]) / 20) + 1 else: page_count = int(int(data_json["count"]) / 20) return page_count

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock/stock_us_sina.py#L25-L49

25

[ 0, 1, 2, 3, 4, 5, 6 ]

28

[ 7, 8, 9, 10, 11, 12, 13, 17, 20, 21, 23, 24 ]

48

false

10.447761

25

2

52

4

def get_us_page_count() -> int: page = "1" us_js_decode = f"US_CategoryService.getList?page={page}&num=20&sort=&asc=0&market=&id=" js_code = py_mini_racer.MiniRacer() js_code.eval(js_hash_text) dict_list = js_code.call("d", us_js_decode) # 执行js解密代码 us_sina_stock_dict_payload.update({"page": "{}".format(page)}) res = requests.get( us_sina_stock_list_url.format(dict_list), params=us_sina_stock_dict_payload, ) data_json = json.loads( res.text[res.text.find("({") + 1 : res.text.rfind(");")] ) if not isinstance(int(data_json["count"]) / 20, int): page_count = int(int(data_json["count"]) / 20) + 1 else: page_count = int(int(data_json["count"]) / 20) return page_count

18,840