CEDApy/CEDA/Market/market.py

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",
    "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")
    df = pd.DataFrame()

    def _FX(instrument = "eurusd", startdate = "01/01/2020", enddate = "01/01/2021"):
        """
        https://www.marketwatch.com/investing/
        """
        tmp_url = url["moneywatch"] + "currency/{}/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",
                       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