940 lines
26 KiB
Python
940 lines
26 KiB
Python
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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# TODO need add comments
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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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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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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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# mkt=1&stat=2&city1=%E5%B9%BF%E5%B7%9E&city2=%E4%B8%8A%E6%B5%B7
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def cn_hi_new_monthly(city1:str, city2:str): # newly built commercial housing & second-hand commercial housing
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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 = "http://data.eastmoney.com/dataapi/cjsj/getnewhousechartdata?"
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_params_nbch = {
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"mkt": "1",
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"stat": "2",
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"city1": "{}".format(city1),
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"city2": "{}".format(city2)
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}
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request_params_shch = {
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"mkt": "1",
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"stat": "3",
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"city1": "{}".format(city1),
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"city2": "{}".format(city2)
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}
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r_nbch = requests.get(tmp_url, params = request_params_nbch, headers = request_header)
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r_shch = requests.get(tmp_url, params = request_params_shch, headers = request_header)
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data_text_nbch = r_nbch.text
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data_text_shch = r_shch.text
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data_json_nbch = demjson.decode(data_text_nbch)
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data_json_shch = demjson.decode(data_text_shch)
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date_nbch = data_json_nbch['chart']['series']['value']
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data1_nbch = data_json_nbch['chart']['graphs']['graph'][0]['value']
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data2_nbch = data_json_nbch['chart']['graphs']['graph'][1]['value']
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data1_shch = data_json_shch['chart']['graphs']['graph'][0]['value']
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data2_shch = data_json_shch['chart']['graphs']['graph'][1]['value']
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df = pd.DataFrame({"Date": date_nbch,
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"City1":data1_nbch,
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"City2":data2_nbch,
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"City1":data1_shch,
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"City2":data2_shch})
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return df
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def cn_ci_eei_monthly(): # Climate Index & Entrepreneur 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": "datatable7709842",
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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": "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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}
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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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"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"
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]
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return df
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def cn_trscg_monthly(): # Total Retail Sales of Consumer Goods
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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": "datatable3665821",
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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": "5",
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"_": "1622044011316"
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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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"TRSCG_YoY",
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"TRSCG_MoM",
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"TRSCG_Accum",
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"TRSCG_Accum_YoY"
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]
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return df
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def cn_ms_monthly(): # monetary Supply
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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": "datatable3818891",
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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": "11",
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"_": "1622044292103"
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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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"M2",
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"M2_YoY",
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"M2_MoM",
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"M1",
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"M1_YoY",
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"M1_MoM",
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"M0",
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"M0_YoY",
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"M0_MoM"
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]
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return df
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def cn_ie_monthly(): # Import & Export
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"""
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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": "datatable3818891",
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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": "1",
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"_": "1622044292103"
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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_Export",
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"Current_Month_Export_YoY",
|
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"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_stock_monthly(): # Import & Export
|
|
"""
|
|
&type=GJZB&sty=ZGZB&js=(%5B(x)%5D)&p=1&ps=200&mkt=2&_=1622084599456
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "jQuery112308659690274138041_1622084599455",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "2",
|
|
"_": "1622084599456"
|
|
}
|
|
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([item.split(",") for item in data_json["data"]])
|
|
df.columns = [
|
|
"Date",
|
|
"SH_Total_Stock_issue",
|
|
"SZ_Total_Stock_Issue",
|
|
"SH_Total_Market_Capitalization",
|
|
"SZ_Total_Market_Capitalization",
|
|
"SH_Turnover",
|
|
"SZ_Turnover",
|
|
"SH_Volume",
|
|
"SZ_Volume",
|
|
"SH_Highest",
|
|
"SZ_Highest",
|
|
"SH_lowest",
|
|
"SZ_lowest"
|
|
]
|
|
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": "tatable6260802",
|
|
"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
|
|
#TODO: SPECIAL CASE
|
|
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: SPECIAL CASE
|
|
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": "datatable4006236",
|
|
"type": "GPKHData",
|
|
"js" : "({data:[(x)],pages:(pc)})",
|
|
"st": "SDATE",
|
|
"sr": "-1",
|
|
"token": "894050c76af8597a853f5b408b759f5d",
|
|
"p": "1",
|
|
"ps": "2000",
|
|
"_": "1622079339035"
|
|
}
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
data_text = r.text
|
|
data_json = demjson.decode(data_text[data_text.find("{")+6 : -14])
|
|
df = pd.DataFrame(data_json[0])
|
|
df.columns = [
|
|
"Date",
|
|
"New_Investor",
|
|
"New_Investor_MoM",
|
|
"New_Investor_YoY",
|
|
"Active_Investor",
|
|
"Active_Investor_A_Share",
|
|
"Active_Investor_B_share",
|
|
"SHIndex_Close",
|
|
"SHIndex_Rate",
|
|
"SHSZ_Market_Capitalization",
|
|
"SHSZ_Average_Capitalization"
|
|
]
|
|
df.Date = pd.to_datetime(df.Date, format = "%Y年%m月")
|
|
return df
|
|
|
|
def cn_fdi_monthly(): # Foreign Direct Investment
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable1477466",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "15",
|
|
"_": "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",
|
|
"Current_Month",
|
|
"YoY",
|
|
"MoM",
|
|
"Accumulation",
|
|
"Accum_YoY"
|
|
]
|
|
return df
|
|
|
|
def cn_gr_monthly(): # Government Revenue
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable7840652",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "14",
|
|
"_": "1622080618625"
|
|
}
|
|
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",
|
|
"YoY",
|
|
"MoM",
|
|
"Accumulation",
|
|
"Accum_YoY"
|
|
]
|
|
return df
|
|
|
|
def cn_ti_monthly(): # Tax Income
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable8280567",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "3",
|
|
"_": "1622080669713"
|
|
}
|
|
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",
|
|
"YoY",
|
|
"MoM"
|
|
]
|
|
return df
|
|
|
|
|
|
def cn_nl_monthly(): # New Loan
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable2533707",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "7",
|
|
"_": "1622080800162"
|
|
}
|
|
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",
|
|
"YoY",
|
|
"MoM",
|
|
"Accumulation",
|
|
"Accum_YoY"
|
|
]
|
|
return df
|
|
|
|
def cn_dfclc_monthly(): # Deposit of Foreign Currency and Local Currency
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable2899877",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "18",
|
|
"_": "1622081057370"
|
|
}
|
|
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",
|
|
"YoY",
|
|
"MoM",
|
|
"Accumulation"
|
|
]
|
|
return df
|
|
|
|
def cn_fl_monthly(): # Forex Loan
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable636844",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "17",
|
|
"_": "1622081336038"
|
|
}
|
|
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",
|
|
"YoY",
|
|
"MoM",
|
|
"Accumulation"
|
|
]
|
|
return df
|
|
|
|
def cn_drr_monthly(): # Deposit Reserve Ratio
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable4285562",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "23",
|
|
"_": "1622081448882"
|
|
}
|
|
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 = [
|
|
"Announcement Date",
|
|
"Effective Date",
|
|
"Large_Financial_institution_Before",
|
|
"Large_Financial_institution_After",
|
|
"Large_Financial_institution_Adj_Rate",
|
|
"S&M_Financial_institution_Before",
|
|
"S&M_Financial_institution_After",
|
|
"S&M_Financial_institution_Adj_Rate",
|
|
"Comment",
|
|
"SHIndex_Rate",
|
|
"SZIndex_Rate"
|
|
]
|
|
return df
|
|
|
|
def cn_interest_monthly(): # Interest
|
|
"""
|
|
|
|
"""
|
|
tmp_url = url["eastmoney"]
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
tmp_url = url["eastmoney"]
|
|
request_params = {
|
|
"cb": "datatable7591685",
|
|
"type": "GJZB",
|
|
"sty": "ZGZB",
|
|
"js": "({data:[(x)],pages:(pc)})",
|
|
"p": "1",
|
|
"ps": "200",
|
|
"mkt": "13",
|
|
"_": "1622081956464"
|
|
}
|
|
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 = [
|
|
"Announcement Date",
|
|
"Deposit_Benchmark_Interest_Rate_Before",
|
|
"Deposit_Benchmark_Interest_Rate_After",
|
|
"Deposit_Benchmark_Interest_Rate_Adj_Rate",
|
|
"Loan_Benchmark_Interest_Rate_Before",
|
|
"Loan_Benchmark_Interest_Rate_After",
|
|
"Loan_Benchmark_Interest_Rate_Adj_Rate",
|
|
"SHIndex_Rate",
|
|
"SZIndex_Rate",
|
|
"Effective Date"
|
|
]
|
|
df = df[[
|
|
"Announcement Date",
|
|
"Effective Date",
|
|
"Deposit_Benchmark_Interest_Rate_Before",
|
|
"Deposit_Benchmark_Interest_Rate_After",
|
|
"Deposit_Benchmark_Interest_Rate_Adj_Rate",
|
|
"Loan_Benchmark_Interest_Rate_Before",
|
|
"Loan_Benchmark_Interest_Rate_After",
|
|
"Loan_Benchmark_Interest_Rate_Adj_Rate",
|
|
"SHIndex_Rate",
|
|
"SZIndex_Rate"
|
|
]]
|
|
return df
|
|
|
|
#TODO: SPECIAL CASE
|
|
def cn_gdc_daily(): # gasoline, Diesel and Crude Oil
|
|
"""
|
|
"""
|
|
tmp_url = "http://datacenter-web.eastmoney.com/api/data/get?"
|
|
ua = UserAgent()
|
|
request_header = {"User-Agent": ua.random}
|
|
request_params = {
|
|
"callback": "jQuery112302601302322321093_1622082348721",
|
|
"type": "RPTA_WEB_JY_HQ",
|
|
"sty": "ALL",
|
|
"st": "date",
|
|
"sr": "-1",
|
|
"token": "894050c76af8597a853f5b408b759f5d",
|
|
"p": "1",
|
|
"ps": "50000",
|
|
"source": "WEB",
|
|
"_":"1622082348722"
|
|
}
|
|
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
|
data_text = r.text
|
|
data_json = demjson.decode(data_text[data_text.find("{") : -2])
|
|
df = pd.DataFrame(data_json["result"]["data"])
|
|
df.columns = ["Crude_Oil", "Date", "Gasoline", "Diesel"]
|
|
df = df[["Date", "Gasoline", "Diesel", "Crude_Oil"]]
|
|
return df
|
|
|
|
"""
|
|
if __name__ == "__main__":
|
|
""" |