v1.0.9.1

CEDA/MacroEcon/__init__.py

@@ -1,4 +1,6 @@
 # -*- coding: utf-8 -*-
 # time: 05/25/2021 UTC+8
 # author: terencelau
-# email: t_lau@uicstat.com
+# email: t_lau@uicstat.com
+__all__ = ['cn', 'eu', 'us']
+from CEDA.market import *

CEDA/Market/__init__.py

@@ -2,3 +2,5 @@
 # time: 05/29/2021 UTC+8
 # author: terencelau
 # email: t_lau@uicstat.com
+__all__ = ['duka', 'marketwatch']
+from CEDA.market import *

CEDA/Market/duka.py

@@ -0,0 +1,49 @@
+import re
+import io
+import requests
+import demjson
+import pandas as pd
+from bs4 import BeautifulSoup
+from datetime import datetime
+from urllib.parse import quote, urlencode
+from fake_useragent import UserAgent
+
+url = {
+    "dukascopy": "http://data.uicstat.com/api_1.0"
+}
+
+def dukascopy(
+        instrument: str,
+        startdate: str,
+        enddate: str,
+        timeframe: str,
+        pricetype: str,
+        volume: bool,
+        flat: bool):
+    tmp_url = url["dukascopy"]
+    ua = UserAgent(verify_ssl=False)
+    request_header = {"User-Agent": ua.random}
+    request_params = {
+        "instrument": "{}".format(instrument),
+        "startdate": "{}".format(startdate),
+        "enddate": "{}".format(enddate),
+        "timeframe": "{}".format(timeframe),
+        "pricetype": "{}".format(pricetype),
+        "volume": "{}".format(volume),
+        "flat": "{}".format(flat)
+
+    }
+    r = requests.get(tmp_url, params=request_params, headers=request_header)
+    data_text = r.text
+    data_json = demjson.decode(data_text)
+    df = pd.DataFrame(data_json['result'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    df.columns = [
+        "Date",
+        "Open",
+        "High",
+        "Low",
+        "Close",
+        "Volume"
+    ]
+    return df

CEDA/Market/marketwatch.py

@@ -9,47 +9,9 @@ from urllib.parse import quote, urlencode
 from fake_useragent import UserAgent
 
 url = {
-    "dukascopy": "http://data.uicstat.com/api_1.0",
     "moneywatch": "https://www.marketwatch.com/investing/"
 }
 
-def dukascopy(
-        instrument: str,
-        startdate: str,
-        enddate: str,
-        timeframe: str,
-        pricetype: str,
-        volume: bool,
-        flat: bool):
-    tmp_url = url["dukascopy"]
-    ua = UserAgent(verify_ssl=False)
-    request_header = {"User-Agent": ua.random}
-    request_params = {
-        "instrument": "{}".format(instrument),
-        "startdate": "{}".format(startdate),
-        "enddate": "{}".format(enddate),
-        "timeframe": "{}".format(timeframe),
-        "pricetype": "{}".format(pricetype),
-        "volume": "{}".format(volume),
-        "flat": "{}".format(flat)
-
-    }
-    r = requests.get(tmp_url, params=request_params, headers=request_header)
-    data_text = r.text
-    data_json = demjson.decode(data_text)
-    df = pd.DataFrame(data_json['result'])
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-    df.columns = [
-        "Date",
-        "Open",
-        "High",
-        "Low",
-        "Close",
-        "Volume"
-    ]
-    return df
-
-
 def FX(instrument = "eurusd", startdate = "2019-01-01", enddate = "2021-01-01"):
     startdate = datetime.strptime(startdate, "%Y-%m-%d").strftime("%m/%d/%y")
     enddate = datetime.strptime(enddate, "%Y-%m-%d").strftime("%m/%d/%y")
@@ -99,17 +61,57 @@ def FX(instrument = "eurusd", startdate = "2019-01-01", enddate = "2021-01-01"):
     df = df.reset_index(drop = True)
     return df
 
+def index(instrument = "eurusd", startdate = "2019-01-01", enddate = "2021-01-01"):
+    startdate = datetime.strptime(startdate, "%Y-%m-%d").strftime("%m/%d/%y")
+    enddate = datetime.strptime(enddate, "%Y-%m-%d").strftime("%m/%d/%y")
+    df = pd.DataFrame()
+
+    def _FX(instrument = "eurusd", startdate = "01/01/2020", enddate = "01/01/2021"):
+        """
+        https://www.marketwatch.com/investing/
+        """
+        tmp_url = url["moneywatch"] + "index/{}/downloaddatapartial".format(instrument)
+        ua = UserAgent(verify_ssl=False)
+        request_header = {"User-Agent": ua.random}
+        request_params = urlencode({
+            "startdate": r"{}".format(startdate),
+            "enddate": r"{}".format(enddate),
+            "daterange": "d30",
+            "frequency": "p1d",
+            "csvdownload": "true",
+            "downloadpartial": "false",
+            "newdates": "false"}, quote_via= quote)
+        r = requests.get(tmp_url, params=request_params.replace("%2F", "/").replace("%20", " ").replace("%3A", ":"), headers=request_header)
+        data_text = r.content
+        df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
+        Date = []
+        for i in range(0, len(df)):
+            Date.append(datetime.strptime(df["Date"][i], "%m/%d/%Y"))
+        
+        df["Date"] = Date
+        return df
+
+    for i in range(int(startdate[6:10]), int(enddate[6:10])):
+        if i == int(startdate[6:10]):
+            tmp_startdate = startdate
+        else:
+            tmp_startdate = "01/01/" + str(i) + " 00:00:00"
+        if (i+1) == int(enddate[6:10]):
+            tmp_enddate = enddate
+        else: 
+            tmp_enddate = "01/01/" + str(i+1) + " 00:00:00"
+        
+        tmp_df = _FX(instrument=instrument, startdate = tmp_startdate, enddate = tmp_enddate)
+        if i == int(startdate[6:10]):
+            df = tmp_df
+        else:
+            df = pd.concat([tmp_df, df], axis=0)
+        
+    df = df.reset_index(drop = True)
+    return df
+
 
 
 
 if __name__ == "__main__":
-    data = dukascopy(instrument="eurusd",
+    data = FX()
-                       startdate="2020-01-01",
-                       enddate="2021-01-01",
-                       timeframe="d1",
-                       pricetype="bid",
-                       volume=True,
-                       flat=True)
-
-
-#https://www.marketwatch.com/investing/currency/eurusd/downloaddatapartial?startdate=01/04/1971 00:00:00&enddate=06/04/2021 00:00:00&daterange=d30&frequency=p1d&csvdownload=true&downloadpartial=false&newdates=false

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 import os
 setup(
     name = "CEDApy",
-    version = "1.0.9",
+    version = "1.0.9.1",
     keywords = "quantitative economic data",
     long_description = open(
     os.path.join(