Merge pull request #4 from TerenceLiu98/develop

Develop
2021-05-29 18:32:09 +08:00 · 2021-05-29 18:32:09 +08:00 · 50f1f6ba74
parent ba530b253b cd9ea45b19
commit 50f1f6ba74
8 changed files with 670 additions and 220 deletions
--- a/CEDA/MacroEcon/cn.py
+++ b/CEDA/MacroEcon/cn.py
--- a/CEDA/MacroEcon/eu.py
+++ b/CEDA/MacroEcon/eu.py
@ -0,0 +1,74 @@
+import pandas as pd
+import numpy as np
+import io
+import demjson
+import requests
+from fake_useragent import UserAgent
+
+
+url = {
+    "eurostat": "http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/",
+    "ecb": "https://sdw-wsrest.ecb.europa.eu/service/data/"
+}
+
+class ecb_data(object):
+    def __init__(self, url=url["ecb"]):
+        self.url = url
+
+    def codebook(self):
+        return "please follow the ECB's codebook: https://sdw.ecb.europa.eu/browse.do?node=9691101"
+
+    def get_data(self,
+                 datacode="ICP", 
+                 key="M.U2.N.000000.4.ANR", 
+                 startdate="2000-01-01", 
+                 enddate="2020-01-01"):
+        """
+        """
+        tmp_url = self.url + "{}/".format(datacode) + "{}".format(key)
+        ua = UserAgent()
+        request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'}
+        request_params = {
+            "startPeriod": "{}".format(startdate),
+            "endPeriod": "{}".format(enddate)
+        }
+        r = requests.get(tmp_url, params = request_params, headers = request_header)
+        data_text = r.content
+        df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
+        return df
+
+class eurostat_data(object):
+    def __init__(self, url=url["eurostat"]):
+        self.url = url
+
+    def codebook(self):
+        return "please follow the EuroStat's codebook: \nhttps://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic"
+        
+    def get_data(self,
+                 datasetcode="nama_10_gdp", 
+                 precision="1",
+                 unit="CP_MEUR",
+                 na_item="B1GQ",
+                 time="2020"):
+        """
+        """
+        tmp_url = self.url + "{}".format(datasetcode)
+        ua = UserAgent()
+        request_header = {"User-Agent": ua.random, 'Accept': 'text/csv'}
+        request_params = {
+            "precision": "{}".format(precision),
+            "unit": "{}".format(unit),
+            "na_item": "{}".format(na_item),
+            "time": "{}".format(time)
+        }
+        r = requests.get(tmp_url, params = request_params, headers = request_header)
+        data_text = r.text
+        data_json = demjson.decode(data_text)
+        value = data_json['value']
+        abb = data_json['dimension']['geo']['category']['index']
+        abb = {abb[k]:k for k in abb}
+        geo = data_json['dimension']['geo']['category']['label']
+        geo_list = [abb[int(k)] for k in list(value.keys())]
+        geo = [geo[k] for k in geo_list]
+        df = pd.DataFrame({"Geo":geo, "{}".format(na_item): list(value.values())})
+        return df
--- a/CEDA/MacroEcon/us.py
+++ b/CEDA/MacroEcon/us.py
@ -3,10 +3,14 @@ import numpy as np
 import requests
 from fake_useragent import UserAgent
 import io
+import os 
+import demjson

 # Main Economic Indicators: https://alfred.stlouisfed.org/release?rid=205
 url = {
-    "fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?"
+    "fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?",
+    "philfed": "https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/",
+    "chicagofed": "https://www.chicagofed.org/~/media/publications/"
 }

 def gdp_quarterly(startdate="1947-01-01", enddate="2021-01-01"):
@ -681,4 +685,95 @@ def bir(startdate="2003-01-01", enddate="2021-01-01"):
    df_10y["DATE"] = pd.to_datetime(df_10y["DATE"], format="%Y-%m-%d")
    df = pd.merge_asof(df_5y, df_10y, on = "DATE", direction = "backward")
    df.columns = ["Date", "BIR_5y", "BIR_10y"]
-    return df
+    return df
+
+def adsbci():
+    """
+    An index designed to track real business conditions at high observation frequency
+    """
+    ua = UserAgent()
+    request_header = {"User-Agent": ua.random}
+    tmp_url = url["philfed"] + "ads"
+    r = requests.get(tmp_url, headers = request_header)
+    file = open("ads_temp.xls", "wb")
+    file.write(r.content)
+    file.close()
+    df = pd.read_excel("ads_temp.xls")
+    df.columns = ["Date", "ADS_Index"]
+    df['Date'] = pd.to_datetime(df["Date"], format="%Y:%m:%d")
+    os.remove("ads_temp.xls")
+    return df
+
+def pci():
+    """
+    Tracks the degree of political disagreement among U.S. politicians at the federal level, Monthly
+    """
+    df = pd.read_excel("https://www.philadelphiafed.org/-/media/frbp/assets/data-visualizations/partisan-conflict.xlsx")
+    df["Date"] = df["Year"].astype(str) + df["Month"]
+    df["Date"] = pd.to_datetime(df["Date"], format = "%Y%B")
+    df = df.drop(["Year", "Month"], axis=1)
+    df = df[["Date", "Partisan Conflict"]]
+    return df
+
+def inflation_noewcasting():
+    """
+    
+    """
+    ua = UserAgent()
+    request_header = {"User-Agent": ua.random}
+    tmp_url = "https://www.clevelandfed.org/~/media/files/charting/%20nowcast_quarter.json"
+
+    r = requests.get(tmp_url, headers = request_header)
+    tmp_df = pd.DataFrame(demjson.decode(r.text))
+    df = pd.DataFrame()
+    for i in range(0, len(tmp_df)):
+        date = tmp_df['chart'][i]['subcaption'][:4] + "/"  + \
+            pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[0] + "/"  + \
+            pd.DataFrame(tmp_df["dataset"][i][0]['data'])['tooltext'].str.extract(r"\b(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])\b")[1]
+        CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[0])["value"]
+        C_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[1])["value"]
+        PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[2])["value"]
+        C_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[3])["value"]
+        A_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[4])["value"]
+        A_C_CPI_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[5])["value"]
+        A_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[6])["value"]
+        A_C_PCE_I = pd.DataFrame((pd.DataFrame(tmp_df["dataset"][i])['data'])[7])["value"]
+        tmp_df2 = pd.DataFrame({"date": date,
+                                "CPI_I": CPI_I,
+                                "C_CPI_I": C_CPI_I,
+                                "PCE_I": PCE_I,
+                                "C_PCE_I": C_PCE_I,
+                                "A_CPI_I": A_CPI_I,
+                                "A_C_CPI_I": A_C_CPI_I,
+                                "A_PCE_I": A_PCE_I,
+                                "A_C_PCE_I": A_C_PCE_I})
+        df = pd.concat([df,tmp_df2], axis=0)
+        df.reset_index(drop=True, inplace=True)
+    
+    df.replace('', np.nan, inplace = True)
+    return df
+
+def bbki():
+    tmp_url = url["chicagofed"] + "bbki/bbki-monthly-data-series-csv.csv"
+    df = pd.read_csv(tmp_url)
+    return df
+
+def cfnai():
+    tmp_url = url["chicagofed"] + "cfnai/cfnai-data-series-csv.csv"
+    df = pd.read_csv(tmp_url)
+    return df
+
+def cfsbc():
+    tmp_url = url["chicagofed"] + "cfsbc-activity-index-csv.csv"
+    df = pd.read_csv(tmp_url)
+    return df
+
+def nfci():
+    tmp_url = url["chicagofed"] + "nfci/decomposition-nfci-csv.csv"
+    df = pd.read_csv(tmp_url)
+    return df
+
+def nfci():
+    tmp_url = url["chicagofed"] + "nfci/decomposition-anfci-csv.csv"
+    df = pd.read_csv(tmp_url)
+    return df
--- a/CEDA/Market/init.py
+++ b/CEDA/Market/init.py
@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+# time: 05/29/2021 UTC+8
+# author: terencelau
+# email: t_lau@uicstat.com
--- a/CEDA/Market/market.py
+++ b/CEDA/Market/market.py
@ -47,9 +47,18 @@ def market_data(

 if __name__ == "__main__":
    data = market_data(instrument="eurusd",
+<<<<<<< HEAD
+                       startdate="2020-01-01",
+                       enddate="2021-01-01",
+                       timeframe="d1",
+                       pricetype="bid",
+                       volume=True,
+                       flat=True)
+=======
                        startdate="2020-01-01",
                        enddate="2021-01-01",
                        timeframe="d1",
                        pricetype="bid",
                        volume=True,
                        flat=True)
+>>>>>>> master
--- a/CEDA/init.py
+++ b/CEDA/init.py
@ -1,29 +1 @@
-from CEDA.MacroEcon.cn import (
-    gdp_quarterly,
-    ppi_monthly,
-    cpi_monthly,
-    pmi_monthly,
-    fai_monthly,
-    hi_old_monthly,
-    hi_new_monthly,
-    ci_eei_monthly,
-    ig_monthly,
-    cgpi_monthly,
-    cci_csi_cei_monthly,
-    trscg_monthly,
-    ms_monthly,
-    ie_monthly,
-    stock_monthly,
-    fgr_monthly,
-    ctsf_monthly,
-    sao_monthly,
-    fdi_monthly,
-    gr_monthly,
-    ti_monthly,
-    nl_monthly,
-    dfclc_monthly,
-    fl_monthly,
-    drr_monthly,
-    interest_monthly,
-    gdc_daily
-)
+from CEDA import * 
--- a/CEDA/config.py
+++ b/CEDA/config.py
@ -0,0 +1,15 @@
+import requests
+
+def config(http:str, https:str, auth:bool, user:str, passwd:str):
+    if auth == False:
+        proxies = {
+            "http": "{}".format(http),
+            "https": "{}".format(https)
+        }
+        return proxies
+    if auth == True:
+        proxies = {
+            "http": "http://{}:{}@{}".format(user, passwd, http),
+            "https": "https://{}:{}@{}".format(user, passwd, https),
+        }
+        return proxies
--- a/setup.py
+++ b/setup.py
@ -2,7 +2,11 @@ from setuptools import setup, find_packages
 import os
 setup(
    name = "CEDApy",
-    version = "1.0.1",
+<<<<<<< HEAD
+    version = "1.0.3",
+=======
+    version = "1.0.3",
+>>>>>>> master
    keywords = "quantitative economic data",
    long_description = open(
    os.path.join(