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scrape_data.py
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scrape_data.py
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import pandas as pd
import datetime as dt
from datetime import timedelta
from datetime import datetime
from pandas_datareader import data as wb
import requests
import requests_cache
from bs4 import BeautifulSoup
import re
from settings import *
import random
def scrape_indexes():
session = requests_cache.CachedSession(cache_name='cache', backend='sqlite', expire_after=3600)
session.headers = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:89.0) Gecko/20100101 Firefox/89.0',
'Accept': 'application/json;charset=utf-8'}
tickers = ['^DJI','^HSI', '^GSPC', '^IXIC', '^N225', '^GDAXI' , '^FCHI']
index_data = pd.DataFrame()
today = dt.date.today()
week_ago = today - dt.timedelta(days=7)
for t in tickers:
index_data[t]= wb.DataReader(t, data_source='yahoo', start=week_ago, session=session)['Adj Close']
return index_data
def scrape_ftse_mib():
url_fsteMib = "https://markets.businessinsider.com/index/ftse_mib?op=1"
page_ftseMib = requests.get(url_fsteMib)
soup = BeautifulSoup(page_ftseMib.content, "html.parser")
mib_index_perf = soup.find_all('span', class_="price-section__relative-value")[0].get_text()
perfs = []
session = requests_cache.CachedSession(cache_name='cache', backend='sqlite', expire_after=3600)
session.headers = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:89.0) Gecko/20100101 Firefox/89.0',
'Accept': 'application/json;charset=utf-8'}
ftse_tickers_df = pd.read_csv(f"{ROOT_DIR}/ftse_mib_stocks.csv", sep=";", header=0)
tickers = ftse_tickers_df['Ticker'].to_list()
ftse_mib_data = pd.DataFrame()
today = dt.date.today()
week_ago = today - dt.timedelta(days=7)
for t in tickers:
ftse_mib_data[t]= wb.DataReader(t, data_source='yahoo', start=week_ago, session=session)['Adj Close']
ftse_tickers_df['Return'] = round((ftse_mib_data.pct_change().iloc[-1]*100),2).to_list()
top3 = ftse_tickers_df.sort_values('Return')[-3:]
bottom3 = ftse_tickers_df.sort_values('Return')[:3]
return ftse_tickers_df, mib_index_perf, top3, bottom3
def scrape_macro():
months_dict = {'gennaio' : 1,
'febbraio' : 2,
'marzo': 3,
'aprile': 4,
'maggio': 5,
'giugno': 6,
'luglio': 7,
'agosto' : 8,
'settembre': 9,
'ottobre': 10,
'novembre': 11,
'dicembre': 12}
url_macro = "https://www.teleborsa.it/Agenda/?mode=calendar"
page_macro = requests.get(url_macro)
soup = BeautifulSoup(page_macro.content, "html.parser")
table = soup.find_all("table", class_="grid")
text_data_soups = []
for j in range(len(table[0].findAll('tr'))):
for i in table[0].findAll('tr')[j].find_all("td", class_="l"):
text_data_soups.append(i)
text_data_soups = BeautifulSoup(str(text_data_soups), "html.parser")
number_data_soups = []
for j in range(len(table[0].findAll('tr'))):
for i in table[0].findAll('tr')[j].find_all("td", class_="c"):
number_data_soups.append(i)
number_data_soups = BeautifulSoup(str(number_data_soups), "html.parser")
##################################### retrieving days
full_text_soup = []
for i in table[0].findAll('tr'):
full_text_soup.append(i)
full_text_soup = BeautifulSoup(str(full_text_soup), "html.parser")
hours_and_days =[i.get_text() for i in full_text_soup.find_all("span", id=re.compile("lblDate"))]
#####################################
for i, val in enumerate(hours_and_days):
if i < (len(hours_and_days)-1):
if len(val)> 10 and len(hours_and_days[i+1])<10:
hours_and_days[i+1] = val
del hours_and_days[i]
else:
continue
################################## first cycle removes the "header" row with the date of each day
for i, val in enumerate(hours_and_days):
if i < (len(hours_and_days)-1):
if len(val)> 10 and len(hours_and_days[i+1])<10:
hours_and_days[i+1] = val
else:
continue
################################## second cycle transforms each time in the date of the event
macro_df = pd.DataFrame()
macro_df['Time'] = [i.get_text() for i in text_data_soups.find_all("span", id=re.compile("lblDateFrom"))]
hours_and_days = hours_and_days[:len(macro_df['Time'])] #get date for every event in string then convert to datetime
years = [i[-5:].strip() for i in hours_and_days]
daymonth = [i[:-5].split(" ") for i in hours_and_days]
datetimes = [dt.date(int(years[i]), months_dict[daymonth[i][2]], int(daymonth[i][1])) for i in range(len(daymonth))]
macro_df['DateTime'] = datetimes
macro_df['Country'] = [i.get_text() for i in text_data_soups.find_all("span", id=re.compile("lblSource"))]
macro_df['EventName'] = [i.get_text() for i in text_data_soups.find_all("span", id=re.compile("lblDescription"))]
macro_df['Period'] = [i.get_text() for i in number_data_soups.find_all("span", id=re.compile("lblReference"))]
macro_df['Actual'] = [i.get_text() for i in number_data_soups.find_all("span", id=re.compile("lblActual"))]
macro_df['Previous'] = [i.get_text() for i in number_data_soups.find_all("span", id=re.compile("lblPrevious"))]
macro_df['Forecast'] = [i.get_text() for i in number_data_soups.find_all("span", id=re.compile("lblForecast"))]
macro_df['Unit'] = [i.get_text() for i in text_data_soups.find_all("span", id=re.compile("lblUnits"))]
macro_df = macro_df[macro_df['DateTime'] == dt.date.today()]
return macro_df
def retrieve_event_time_formatted():
macro_data = scrape_macro()
time_format_str = '%H.%M'
event_time = [datetime.strptime(macro_data['Time'].iloc[i], time_format_str) for i in range(len(macro_data))]
n = random.randrange(-30, -45)
final_time = [i + timedelta(minutes=n) for i in event_time] # Add n+60 minutes to the event time
final_time_formatted = [i.strftime('%H:%M') for i in final_time]
final_time_series = pd.Series(final_time_formatted, name = 'time')
return final_time_series.to_csv(f"{ROOT_DIR}/event_time_today.csv", index = False, header = True)
def read_event_time():
event_time_df = pd.read_csv(f"{ROOT_DIR}/event_time_today.csv", header=0, sep=",") #header on first row
event_time = event_time_df['time'].to_list()
return event_time
def retrieve_markets_in_holiday():
holiday_df = pd.read_csv(f"{ROOT_DIR}/bank_holidays.csv")
tickers = ['DAX', 'CAC40', 'HSI', 'FTSEMIB', 'N225']
today=dt.date.today()
holiday_df['datetime'] = holiday_df['Holiday'].apply(lambda x: dt.date(int(str(x)[0:4]), int(str(x)[4:6]), int(str(x)[6:8])))
markets_in_holiday = holiday_df[holiday_df['datetime']==today]['Ticker']
markets_in_holiday.to_csv(f"{ROOT_DIR}/markets_in_holiday_today.csv", index = False, header = True)
return
def read_markets_in_holiday():
markets_in_holiday_df = pd.read_csv(f"{ROOT_DIR}/markets_in_holiday_today.csv", header=0) #header on first row
markets_in_holiday = markets_in_holiday_df['Ticker'].to_list()
return markets_in_holiday