124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
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="01/01/2020", enddate = "01/01/2021", long=False):
|
|
"""
|
|
https://www.marketwatch.com/investing/
|
|
"""
|
|
if long == False:
|
|
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
|
|
else:
|
|
tmp_url = url["moneywatch"] + "currency/{}/downloaddatapartial".format(instrument)
|
|
ua = UserAgent(verify_ssl=False)
|
|
request_header = {"User-Agent": ua.random}
|
|
df = pd.DataFrame()
|
|
for i in range(int(startdate[6:10]), int(enddate[6:10])):
|
|
tmp_startdate = "01/01/" + str(i) + " 00:00:00"
|
|
if i+1 == int(enddate[6:10]):
|
|
tmp_enddate = enddate[0:6] + str((i+1)) + " 00:00:00"
|
|
else:
|
|
tmp_enddate = "01/01" + str((i+1)) + " 00:00:00"
|
|
request_params = urlencode({
|
|
"startdate": r"{}".format(tmp_startdate),
|
|
"enddate": r"{}".format(tmp_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
|
|
tmp_df = pd.read_csv(io.StringIO(data_text.decode('utf-8')))
|
|
Date = []
|
|
for i in range(0, len(tmp_df)):
|
|
Date.append(datetime.strptime(tmp_df["Date"][i], "%m/%d/%Y"))
|
|
|
|
tmp_df["Date"] = Date
|
|
if i == int(startdate[6:10]):
|
|
df = tmp_df
|
|
else:
|
|
df = pd.concat([tmp_df, df], axis=0)
|
|
|
|
df.reset_index(drop=True, inplace = 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 |