add XHData

CEDA/economic/XHData.py

@@ -0,0 +1,223 @@
+import ast
+import requests
+import pandas as pd
+
+url = {
+    "CNFIN": "https://api.cnfin.com/roll/charts/"
+}
+
+class XHData(object):
+    def __init__(self):
+        pass
+    
+    def download(self, id:int=None):
+        tmp_url = url["CNFIN"] + "getContent?ids={}".format(id)
+        r = requests.get(tmp_url)
+        if r.ok:
+            raw_data = r.json()
+            data = pd.DataFrame(ast.literal_eval(raw_data["data"]["list"][0]["content"]))
+            return data
+        else:
+            return ValueError("Something went wrong, try again later")
+
+    
+    def GDP(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12006)
+        data.columns = ["date", "data"]
+        return data
+    
+    def Household_Consumption_SPLY(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12073)
+        data.columns = ["date", "data"]
+        return data
+
+    def Household_Consumption(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12074)
+        data.columns = ["date", "data"]
+        return data
+
+    def Per_capita_Disposable_Income(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12071)
+        data.columns = ["date", "data"]
+        return data
+
+    def Urban_Average_Salary_Annual(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12070)
+        data.columns = ["date", "data"]
+        return data
+
+    def Urban_Uneployment_Rate(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12069)
+        data.columns = ["date", "data"]
+        return data
+
+    def Government_Bound_Return_Rate_10_Year(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12068)
+        data.columns = ["date", "data"]
+        return data
+
+    def Government_Bound_Return_Rate_3_Year(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12067)
+        data.columns = ["date", "data"]
+        return data
+
+    def Government_Bound_Return_Rate_1_Year(self):
+        """
+        quarterly
+        """
+        data = self.download(id=12066)
+        data.columns = ["date", "data"]
+        return data
+
+    def LPR_1_Year(self):
+        """
+        Monthly
+        """
+        data = self.download(id=12065)
+        data.columns = ["date", "data"]
+        return data
+
+    def SHIBOR_3_Month(self):
+        """
+        Daily
+        """
+        data = self.download(id=12064)
+        data.columns = ["date", "data"]
+        return data
+
+    def SHIBOR_2_Week(self):
+        """
+        Daily
+        """
+        data = self.download(id=12063)
+        data.columns = ["date", "data"]
+        return data
+
+    def SHIBOR_1_Day(self):
+        """
+        Daily
+        """
+        data = self.download(id=12063)
+        data.columns = ["date", "data"]
+        return data
+
+    def Foreign_Exchange_Options(self):
+        data = self.download(id=12060)
+        data.columns = ["date", "data"]
+        return data     
+
+    def Foreign_Exchange_Swaps(self):
+        data = self.download(id=12059)
+        data.columns = ["date", "data"]
+        return data  
+
+    def Foreign_Exchange_Forward(self):
+        data = self.download(id=12058)
+        data.columns = ["date", "data"]
+        return data       
+
+    def Foreign_Exchange_Spot(self):
+        data = self.download(id=12057)
+        data.columns = ["date", "data"]
+        return data       
+
+    def Loan_to_Deposit(self):
+        data = self.download(id=12056)
+        data.columns = ["date", "data"]
+        return data         
+
+    def RMB_Deposits(self):
+        data = self.download(id=12055)
+        data.columns = ["date", "data"]
+        return data     
+
+    def RMB_Loan(self):
+        data = self.download(id=12054)
+        data.columns = ["date", "data"]
+        return data  
+
+    def M0_SPLY(self):
+        data = self.download(id=12053)
+        data.columns = ["date", "data"]
+        return data  
+
+    def M1_SPLY(self):
+        data = self.download(id=12052)
+        data.columns = ["date", "data"]
+        return data  
+
+    def M2_SPLY(self):
+        data = self.download(id=12051)
+        data.columns = ["date", "data"]
+        return data  
+
+    def M0(self):
+        data = self.download(id=12050)
+        data.columns = ["date", "data"]
+        return data  
+
+    def M1(self):
+        data = self.download(id=12049)
+        data.columns = ["date", "data"]
+        return data  
+
+    def M2(self):
+        data = self.download(id=12048)
+        data.columns = ["date", "data"]
+        return data  
+
+    def Total_Retail_Sales_of_Consumer_Goods_LP(self):
+        data = self.download(id=12047)
+        data.columns = ["date", "data"]
+        return data  
+
+    def Total_Retail_Sales_of_Consumer_Goods_SPLY(self):
+        data = self.download(id=12046)
+        data.columns = ["date", "data"]
+        return data  
+
+"""
+import json
+import requests
+from tqdm import tqdm
+
+urls, titles = [], []
+for i in tqdm(range(5000, 20000)):
+    url = "https://api.cnfin.com/roll/charts/getContent?ids={}".format(i)
+    r = requests.get(url)
+    if r.ok:
+        data = r.json()
+        if data["data"] == "图表数据不存在":
+            pass
+        else:
+            urls.append(url)
+            titles.append(json.loads(data["data"]["list"][0]["modelCode"])["title"]["text"])
+"""
+
+
+            
+