add new functions
This commit is contained in:
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1aa1a8e984
commit
4fe577148c
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@ -1,4 +1,29 @@
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from CEDA.economic.macro import (
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from CEDA.economic.macro import (
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cn_gdp_quarter,
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cn_gdp_quarter,
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cn_ig_monthly
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cn_ppi_monthly,
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cn_cpi_monthly,
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cn_pmi_monthly,
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cn_fai_monthly,
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cn_hi_old_monthly,
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cn_hi_new_monthly,
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cn_ci_eei_monthly,
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cn_ig_monthly,
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cn_cgpi_monthly,
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cn_cci_csi_cei_monthly,
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cn_trscg_monthly,
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cn_ms_monthly,
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cn_ie_monthly,
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cn_stock_monthly,
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cn_fgr_monthly,
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cn_ctsf_monthly,
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cn_sao_monthly,
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cn_fdi_monthly,
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cn_gr_monthly,
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cn_ti_monthly,
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cn_nl_monthly,
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cn_dfclc_monthly,
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cn_fl_monthly,
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cn_drr_monthly,
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cn_interest_monthly,
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cn_gdc_daily
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)
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)
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@ -217,6 +217,46 @@ def cn_hi_old_monthly(): # house index old version (2008-2010)
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]
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]
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return df
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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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def cn_ci_eei_monthly(): # Climate Index & Entrepreneur Expectation Index
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"""
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"""
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Man: manufacturing
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Man: manufacturing
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@ -461,45 +501,47 @@ def cn_ie_monthly(): # Import & Export
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"Accumulation_Export",
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"Accumulation_Export",
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"Accumulation_Export_YoY",
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"Accumulation_Export_YoY",
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"Accumulation_Import",
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"Accumulation_Import",
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"Accumulation_Import_YoY",
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"Accumulation_Import_YoY"
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]
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]
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return df
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return df
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def cn_ie_monthly(): # Import & Export
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def cn_stock_monthly(): # Import & Export
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"""
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"""
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&type=GJZB&sty=ZGZB&js=(%5B(x)%5D)&p=1&ps=200&mkt=2&_=1622084599456
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"""
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"""
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tmp_url = url["eastmoney"]
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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tmp_url = url["eastmoney"]
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request_params = {
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request_params = {
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"cb": "datatable3818891",
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"cb": "jQuery112308659690274138041_1622084599455",
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"type": "GJZB",
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"type": "GJZB",
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"sty": "ZGZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"p": "1",
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"ps": "200",
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"ps": "200",
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"mkt": "1",
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"mkt": "2",
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"_": "1622044292103"
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"_": "1622084599456"
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}
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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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_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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data_json = demjson.decode(data_text[data_text.find("(")+1:-1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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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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df.columns = [
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"Date",
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"Date",
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"Current_Month_Export",
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"SH_Total_Stock_issue",
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"Current_Month_Export_YoY",
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"SZ_Total_Stock_Issue",
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"Current_Month_Export_MoM",
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"SH_Total_Market_Capitalization",
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"Current_Month_Import",
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"SZ_Total_Market_Capitalization",
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"Current_Month_Import_YoY",
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"SH_Turnover",
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"Current_Month_Import_MoM",
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"SZ_Turnover",
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"Accumulation_Export",
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"SH_Volume",
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"Accumulation_Export_YoY",
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"SZ_Volume",
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"Accumulation_Import",
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"SH_Highest",
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"Accumulation_Import_YoY",
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"SZ_Highest",
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"SH_lowest",
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"SZ_lowest"
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]
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]
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return df
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return df
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@ -512,7 +554,7 @@ def cn_fgr_monthly(): # Forex and Gold Reserve
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request_header = {"User-Agent": ua.random}
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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tmp_url = url["eastmoney"]
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request_params = {
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request_params = {
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"cb": "atatable6260802",
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"cb": "tatable6260802",
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"type": "GJZB",
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"type": "GJZB",
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"sty": "ZGZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"js": "({data:[(x)],pages:(pc)})",
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@ -535,7 +577,7 @@ def cn_fgr_monthly(): # Forex and Gold Reserve
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"Gold_MoM"
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"Gold_MoM"
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]
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]
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return df
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return df
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#TODO: SPECIAL CASE
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def cn_ctsf_monthly(): # Client Transaction Settlement Funds
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def cn_ctsf_monthly(): # Client Transaction Settlement Funds
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"""
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"""
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@ -553,36 +595,345 @@ def cn_ctsf_monthly(): # Client Transaction Settlement Funds
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df = pd.DataFrame(data_json)
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df = pd.DataFrame(data_json)
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return df
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return df
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# TODO: needs help (missing two tables)
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# TODO: SPECIAL CASE
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def cn_sao_monthly(): # Stock Account Overview
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def cn_sao_monthly(): # Stock Account Overview
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"""
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"""
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"""
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"""
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tmp_url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?"
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tmp_url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?"
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ua = UserAgent()
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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request_header = {"User-Agent": ua.random}
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request_params = {
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request_params = {
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"callback": "jQuery1123014377091065513636_1622046865705",
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"callback": "datatable4006236",
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"type": "GPKHData",
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"type": "GPKHData",
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"st": "HdDate",
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"js" : "({data:[(x)],pages:(pc)})",
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"st": "SDATE",
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"sr": "-1",
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"sr": "-1",
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"sty": "Chart",
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"token": "894050c76af8597a853f5b408b759f5d",
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"token": "894050c76af8597a853f5b408b759f5d",
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"p": "1",
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"ps": "2000",
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"ps": "2000",
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"_": "1622046865706"
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"_": "1622079339035"
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}
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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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_text = r.text
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data_json = demjson.decode(data_text[data_text.find("(")+1:-1])
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data_json = demjson.decode(data_text[data_text.find("{")+6 : -14])
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df = pd.DataFrame(data_json)
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df = pd.DataFrame(data_json[0])
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df.columns = [
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df.columns = [
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"Date",
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"Date",
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"New_Investor",
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"New_Investor",
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"New_Investor_MoM",
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"New_Investor_YoY",
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"Active_Investor",
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"Active_Investor",
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"SHIndexClose"
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"Active_Investor_A_Share",
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"Active_Investor_B_share",
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"SHIndex_Close",
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"SHIndex_Rate",
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"SHSZ_Market_Capitalization",
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"SHSZ_Average_Capitalization"
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]
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]
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df.Date = pd.to_datetime(df.Date, format = "%Y年%m月")
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df.Date = pd.to_datetime(df.Date, format = "%Y年%m月")
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return df
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return df
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def cn_fdi_monthly(): # Foreign Direct Investment
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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": "datatable1477466",
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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": "15",
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"_": "1622044863548"
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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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"Accumulation",
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"Accum_YoY"
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]
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return df
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def cn_gr_monthly(): # Government Revenue
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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": "datatable7840652",
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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": "14",
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"_": "1622080618625"
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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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"Accumulation",
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"Accum_YoY"
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]
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return df
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def cn_ti_monthly(): # Tax Income
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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": "datatable8280567",
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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": "3",
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"_": "1622080669713"
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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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]
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return df
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def cn_nl_monthly(): # New Loan
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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": "datatable2533707",
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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": "7",
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"_": "1622080800162"
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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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"Accumulation",
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"Accum_YoY"
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]
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return df
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def cn_dfclc_monthly(): # Deposit of Foreign Currency and Local Currency
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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": "datatable2899877",
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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": "18",
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"_": "1622081057370"
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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
|
||||||
|
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__":
|
if __name__ == "__main__":
|
||||||
|
|
Loading…
Reference in New Issue