2021-05-26 13:57:29 +00:00
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import pandas as pd
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import numpy as np
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import re
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import demjson
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import requests
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from fake_useragent import UserAgent
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2021-05-26 16:57:20 +00:00
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# TODO need add comments
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2021-05-26 13:57:29 +00:00
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url = {
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"eastmoney": "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
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}
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def cn_gdp_quarter():
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"""
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ABS: absolute value (per 100 million CNY)
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YoY: year on year growth
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"""
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable7519513",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "20",
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"_": "1622020352668"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Absolute_Value",
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"YoY",
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"Primary_Industry_ABS",
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"Primary_Industry_YoY",
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"Secondary_Industry_ABS",
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"Secondary_Industry_YoY",
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"Tertiary_Industry_ABS",
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"Tertiary_Industry_YoY",
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]
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#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
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return df
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2021-05-26 16:57:20 +00:00
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def cn_ppi_monthly():
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"""
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ABS: absolute value (per 100 million CNY)
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YoY: year on year growth
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"""
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable9051497",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "22",
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"_": "1622047940401"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Current_Month",
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"Current_Month_YoY",
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"Current_Month_Accum"
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]
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#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
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return df
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2021-05-26 13:57:29 +00:00
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def cn_cpi_monthly():
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"""
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Accum: Accumulation
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YoY: year on year growth
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MoM: month on month growth
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable2790750",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "19",
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"_": "1622020352668"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Notion_Monthly",
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"Notion_YoY",
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"Notion_MoM",
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"Notion_Accum",
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"Urban_Monthly",
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"Urban_YoY",
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"Urban_MoM",
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"Urban_Accum",
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"Rural_Monthly",
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"Rural_YoY",
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"Rural_MoM",
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"Rural_Accum",
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]
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return df
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def cn_pmi_monthly():
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable4515395",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "2",
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"ps": "200",
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"mkt": "21",
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"_": "162202151821"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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temp_df.columns = [
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"Date",
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"Man_Industry_Index",
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"Man_Index_YoY",
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"Non-Man_Industry_Index",
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"Non-Man_Index_YoY",
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]
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return temp_df
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def cn_fai_monthly(): # fix asset investment
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable607120",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "12",
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"_": "1622021790947"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Current_Month",
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"YoY",
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"MoM",
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"Current_Year_Accum"
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]
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return df
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def cn_hi_old_monthly(): # house index old version (2008-2010)
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable1895714",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "10",
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"_": "1622022794457"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Housing_Prosperity_Index",
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"HPI_YoY",
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"Land_Development_Area_Index",
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"LDAI_YoY",
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"Sales_Price_Index",
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"SPI_YoY"
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]
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return df
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2021-05-26 16:57:20 +00:00
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def cn_ci_eei_monthly(): # Climate Index & Entrepreneur Expectation Index
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2021-05-26 13:57:29 +00:00
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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2021-05-26 16:57:20 +00:00
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tmp_url = url["eastmoney"]
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2021-05-26 13:57:29 +00:00
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request_params = {
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2021-05-26 16:57:20 +00:00
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"cb": "datatable7709842",
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2021-05-26 13:57:29 +00:00
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"type": "GJZB",
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2021-05-26 16:57:20 +00:00
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"sty": "ZGZB",
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2021-05-26 13:57:29 +00:00
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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2021-05-26 16:57:20 +00:00
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"ps": "200",
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"mkt": "8",
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"_": "1622041485306"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Climate_Index",
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"CI_YoY",
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"CI_MoM",
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"Entrepreneur_Expectation_Index",
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"EEI_YoY",
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"EEI_MoM"
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]
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return df
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def cn_ig_monthly(): # Industry Growth
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable4577327",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "0",
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"_": "1622042259898"
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2021-05-26 13:57:29 +00:00
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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2021-05-26 16:57:20 +00:00
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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2021-05-26 13:57:29 +00:00
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df.columns = [
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"Date",
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2021-05-26 16:57:20 +00:00
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"IG_YoY",
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"IG_Accum",
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]
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return df
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def cn_cgpi_monthly(): # Corporate Goods Price Index
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable7184534",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "9",
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"_": "1622042652353"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"General_Index",
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"General_Index_YoY",
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"Total_Index_MoM",
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"Agricultural_Product",
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"Agricultural_Product_YoY",
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"Agricultural_PRoduct_MoM",
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"Mineral_Product",
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"Mineral_Product_YoY",
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"Mineral_Product_MoM",
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"Coal_Oil_Electricity",
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"Coal_Oil_Electricity_YoY",
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"Coal_Oil_Electricity_MoM"
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]
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return df
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def cn_cci_csi_cei_monthly(): # Consumer Confidence Index & Consumer Satisfaction Index & Consumer Expectation Index
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable1243218",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "4",
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"_": "1622043704818"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"CCI",
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"CCI_YoY",
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"CCI_MoM",
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"CSI",
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"CSI_YoY",
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"CSI_MoM",
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"CEI",
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"CEI_YoY",
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"CEI_MoM"
|
|
|
|
]
|
|
|
|
return df
|
|
|
|
|
|
|
|
def cn_trscg_monthly(): # Total Retail Sales of Consumer Goods
|
|
|
|
"""
|
|
|
|
Man: manufacturing
|
|
|
|
Non-Man: Non-manufacturing
|
|
|
|
"""
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
request_params = {
|
|
|
|
"cb": "datatable3665821",
|
|
|
|
"type": "GJZB",
|
|
|
|
"sty": "ZGZB",
|
|
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
|
|
"p": "1",
|
|
|
|
"ps": "200",
|
|
|
|
"mkt": "5",
|
|
|
|
"_": "1622044011316"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
|
|
|
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
|
|
|
df.columns = [
|
|
|
|
"Date",
|
|
|
|
"Current_Month",
|
|
|
|
"TRSCG_YoY",
|
|
|
|
"TRSCG_MoM",
|
|
|
|
"TRSCG_Accum",
|
|
|
|
"TRSCG_Accum_YoY"
|
|
|
|
]
|
|
|
|
return df
|
|
|
|
|
|
|
|
def cn_ms_monthly(): # monetary Supply
|
|
|
|
"""
|
|
|
|
Man: manufacturing
|
|
|
|
Non-Man: Non-manufacturing
|
|
|
|
"""
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
request_params = {
|
|
|
|
"cb": "datatable3818891",
|
|
|
|
"type": "GJZB",
|
|
|
|
"sty": "ZGZB",
|
|
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
|
|
"p": "1",
|
|
|
|
"ps": "200",
|
|
|
|
"mkt": "11",
|
|
|
|
"_": "1622044292103"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
|
|
|
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
|
|
|
df.columns = [
|
|
|
|
"Date",
|
|
|
|
"M2",
|
|
|
|
"M2_YoY",
|
|
|
|
"M2_MoM",
|
|
|
|
"M1",
|
|
|
|
"M1_YoY",
|
|
|
|
"M1_MoM",
|
|
|
|
"M0",
|
|
|
|
"M0_YoY",
|
|
|
|
"M0_MoM"
|
|
|
|
]
|
|
|
|
return df
|
|
|
|
|
|
|
|
def cn_ie_monthly(): # Import & Export
|
|
|
|
"""
|
|
|
|
|
|
|
|
"""
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
request_params = {
|
|
|
|
"cb": "datatable3818891",
|
|
|
|
"type": "GJZB",
|
|
|
|
"sty": "ZGZB",
|
|
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
|
|
"p": "1",
|
|
|
|
"ps": "200",
|
|
|
|
"mkt": "1",
|
|
|
|
"_": "1622044292103"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
|
|
|
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
|
|
|
df.columns = [
|
|
|
|
"Date",
|
|
|
|
"Current_Month_Export",
|
|
|
|
"Current_Month_Export_YoY",
|
|
|
|
"Current_Month_Export_MoM",
|
|
|
|
"Current_Month_Import",
|
|
|
|
"Current_Month_Import_YoY",
|
|
|
|
"Current_Month_Import_MoM",
|
|
|
|
"Accumulation_Export",
|
|
|
|
"Accumulation_Export_YoY",
|
|
|
|
"Accumulation_Import",
|
|
|
|
"Accumulation_Import_YoY",
|
|
|
|
]
|
|
|
|
return df
|
|
|
|
|
|
|
|
|
|
|
|
def cn_ie_monthly(): # Import & Export
|
|
|
|
"""
|
|
|
|
|
|
|
|
"""
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
request_params = {
|
|
|
|
"cb": "datatable3818891",
|
|
|
|
"type": "GJZB",
|
|
|
|
"sty": "ZGZB",
|
|
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
|
|
"p": "1",
|
|
|
|
"ps": "200",
|
|
|
|
"mkt": "1",
|
|
|
|
"_": "1622044292103"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
|
|
|
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
|
|
|
df.columns = [
|
|
|
|
"Date",
|
|
|
|
"Current_Month_Export",
|
|
|
|
"Current_Month_Export_YoY",
|
|
|
|
"Current_Month_Export_MoM",
|
|
|
|
"Current_Month_Import",
|
|
|
|
"Current_Month_Import_YoY",
|
|
|
|
"Current_Month_Import_MoM",
|
|
|
|
"Accumulation_Export",
|
|
|
|
"Accumulation_Export_YoY",
|
|
|
|
"Accumulation_Import",
|
|
|
|
"Accumulation_Import_YoY",
|
|
|
|
]
|
|
|
|
return df
|
|
|
|
|
|
|
|
def cn_fgr_monthly(): # Forex and Gold Reserve
|
|
|
|
"""
|
|
|
|
|
|
|
|
"""
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
tmp_url = url["eastmoney"]
|
|
|
|
request_params = {
|
|
|
|
"cb": "atatable6260802",
|
|
|
|
"type": "GJZB",
|
|
|
|
"sty": "ZGZB",
|
|
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
|
|
"p": "1",
|
|
|
|
"ps": "200",
|
|
|
|
"mkt": "16",
|
|
|
|
"_": "1622044863548"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
|
|
|
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
|
|
|
df.columns = [
|
|
|
|
"Date",
|
|
|
|
"Forex",
|
|
|
|
"Forex_YoY",
|
|
|
|
"Forex_MoM",
|
|
|
|
"Gold",
|
|
|
|
"Gold_YoY",
|
|
|
|
"Gold_MoM"
|
|
|
|
]
|
|
|
|
return df
|
|
|
|
|
|
|
|
def cn_ctsf_monthly(): # Client Transaction Settlement Funds
|
|
|
|
"""
|
|
|
|
|
|
|
|
"""
|
|
|
|
tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getbanktransferdata?"
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
request_params = {
|
|
|
|
"p": "1",
|
|
|
|
"ps": "200"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("["):-11])
|
|
|
|
df = pd.DataFrame(data_json)
|
|
|
|
return df
|
|
|
|
|
|
|
|
# TODO: needs help (missing two tables)
|
|
|
|
def cn_sao_monthly(): # Stock Account Overview
|
|
|
|
"""
|
|
|
|
"""
|
|
|
|
tmp_url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?"
|
|
|
|
ua = UserAgent()
|
|
|
|
request_header = {"User-Agent": ua.random}
|
|
|
|
request_params = {
|
|
|
|
"callback": "jQuery1123014377091065513636_1622046865705",
|
|
|
|
"type": "GPKHData",
|
|
|
|
"st": "HdDate",
|
|
|
|
"sr": "-1",
|
|
|
|
"sty": "Chart",
|
|
|
|
"token": "894050c76af8597a853f5b408b759f5d",
|
|
|
|
"ps": "2000",
|
|
|
|
"_": "1622046865706"
|
|
|
|
}
|
|
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
|
|
data_text = r.text
|
|
|
|
data_json = demjson.decode(data_text[data_text.find("(")+1:-1])
|
|
|
|
df = pd.DataFrame(data_json)
|
|
|
|
df.columns = [
|
|
|
|
"Date",
|
|
|
|
"New_Investor",
|
|
|
|
"Active_Investor",
|
|
|
|
"SHIndexClose"
|
2021-05-26 13:57:29 +00:00
|
|
|
]
|
2021-05-26 16:57:20 +00:00
|
|
|
df.Date = pd.to_datetime(df.Date, format = "%Y年%m月")
|
2021-05-26 13:57:29 +00:00
|
|
|
return df
|
|
|
|
|
2021-05-26 16:57:20 +00:00
|
|
|
|
2021-05-26 13:57:29 +00:00
|
|
|
"""
|
|
|
|
if __name__ == "__main__":
|
|
|
|
"""
|