TerenceLiu
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CEDApy
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add new functions

This commit is contained in:
TerenceLiu98 2021-05-27 11:15:25 +08:00
parent 1aa1a8e984
commit 4fe577148c
2 changed files with 404 additions and 28 deletions
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27
CEDA/__init__.py
View File

@ -1,4 +1,29 @@
from CEDA.economic.macro import (
cn_gdp_quarter,
cn_ig_monthly
cn_ppi_monthly,
cn_cpi_monthly,
cn_pmi_monthly,
cn_fai_monthly,
cn_hi_old_monthly,
cn_hi_new_monthly,
cn_ci_eei_monthly,
cn_ig_monthly,
cn_cgpi_monthly,
cn_cci_csi_cei_monthly,
cn_trscg_monthly,
cn_ms_monthly,
cn_ie_monthly,
cn_stock_monthly,
cn_fgr_monthly,
cn_ctsf_monthly,
cn_sao_monthly,
cn_fdi_monthly,
cn_gr_monthly,
cn_ti_monthly,
cn_nl_monthly,
cn_dfclc_monthly,
cn_fl_monthly,
cn_drr_monthly,
cn_interest_monthly,
cn_gdc_daily
)

405
CEDA/economic/macro.py
View File

@ -217,6 +217,46 @@ def cn_hi_old_monthly(): # house index old version (2008-2010)
]
return df
# mkt=1&stat=2&city1=%E5%B9%BF%E5%B7%9E&city2=%E4%B8%8A%E6%B5%B7
def cn_hi_new_monthly(city1:str, city2:str): # newly built commercial housing & second-hand commercial housing
"""
Man: manufacturing
Non-Man: Non-manufacturing
"""
tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getnewhousechartdata?"
ua = UserAgent()
request_header = {"User-Agent": ua.random}
request_params_nbch = {
"mkt": "1",
"stat": "2",
"city1": "{}".format(city1),
"city2": "{}".format(city2)
}
request_params_shch = {
"mkt": "1",
"stat": "3",
"city1": "{}".format(city1),
"city2": "{}".format(city2)
}
r_nbch = requests.get(tmp_url, params = request_params_nbch, headers = request_header)
r_shch = requests.get(tmp_url, params = request_params_shch, headers = request_header)
data_text_nbch = r_nbch.text
data_text_shch = r_shch.text
data_json_nbch = demjson.decode(data_text_nbch)
data_json_shch = demjson.decode(data_text_shch)
date_nbch = data_json_nbch['chart']['series']['value']
data1_nbch = data_json_nbch['chart']['graphs']['graph'][0]['value']
data2_nbch = data_json_nbch['chart']['graphs']['graph'][1]['value']
data1_shch = data_json_shch['chart']['graphs']['graph'][0]['value']
data2_shch = data_json_shch['chart']['graphs']['graph'][1]['value']
df = pd.DataFrame({"Date": date_nbch,
"City1":data1_nbch,
"City2":data2_nbch,
"City1":data1_shch,
"City2":data2_shch})
return df
def cn_ci_eei_monthly(): # Climate Index & Entrepreneur Expectation Index
"""
Man: manufacturing
@ -461,45 +501,47 @@ def cn_ie_monthly(): # Import & Export
"Accumulation_Export",
"Accumulation_Export_YoY",
"Accumulation_Import",
"Accumulation_Import_YoY",
"Accumulation_Import_YoY"
]
return df
def cn_ie_monthly(): # Import & Export
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": "datatable3818891",
"cb": "jQuery112308659690274138041_1622084599455",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "200",
"mkt": "1",
"_": "1622044292103"
"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])
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",
"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",
"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
@ -512,7 +554,7 @@ def cn_fgr_monthly(): # Forex and Gold Reserve
request_header = {"User-Agent": ua.random}
tmp_url = url["eastmoney"]
request_params = {
"cb": "atatable6260802",
"cb": "tatable6260802",
"type": "GJZB",
"sty": "ZGZB",
"js": "({data:[(x)],pages:(pc)})",
@ -535,7 +577,7 @@ def cn_fgr_monthly(): # Forex and Gold Reserve
"Gold_MoM"
]
return df
#TODO: SPECIAL CASE
def cn_ctsf_monthly(): # Client Transaction Settlement Funds
"""
@ -553,36 +595,345 @@ def cn_ctsf_monthly(): # Client Transaction Settlement Funds
df = pd.DataFrame(data_json)
return df
# TODO: needs help (missing two tables)
# 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": "jQuery1123014377091065513636_1622046865705",
"callback": "datatable4006236",
"type": "GPKHData",
"st": "HdDate",
"js" : "({data:[(x)],pages:(pc)})",
"st": "SDATE",
"sr": "-1",
"sty": "Chart",
"token": "894050c76af8597a853f5b408b759f5d",
"p": "1",
"ps": "2000",
"_": "1622046865706"
"_": "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("(")+1:-1])
df = pd.DataFrame(data_json)
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",
"SHIndexClose"
"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__":