def get_closing_price(storage, month):
ref_month = get_reference_date(storage) - relativedelta(months=month)
path = get_path_to_historical_prices(storage, ref_month)
content = storage.storage_repository.load(path)
if content:
history = csv.DictReader(content.splitlines(), delimiter=';')
last_price = "0"
first_day_next_month = (ref_month +
relativedelta(months=1)).replace(day=1)
for day in history:
if "Datum" not in day:
continue
date_str = day["Datum"].strip()
if date_str == "":
continue
if datetime.strptime(date_str, "%d.%m.%Y") >= first_day_next_month:
break
if day["Schluss"]:
last_price = day["Schluss"]
return asFloat(last_price)
return 0
def read_price_from_csv(filename: str, storage, date_for_price):
content = storage.storage_repository.load(filename)
if content:
history = csv.DictReader(content.splitlines(), delimiter=';')
last_price = None
for day in history:
if day["Datum"].strip() == "":
continue
date = datetime.strptime(day["Datum"].strip(), "%d.%m.%Y")
if date > date_for_price and last_price is not None:
break
if day["Schluss"]:
last_price = day["Schluss"]
if last_price is None:
return 0
return asFloat(last_price)
else:
return 0
def get_market_capitalization(fundamentals, last_year, last_cross_year):
market_capitalization = asFloat(
get_for_year(
fundamentals["Marktkapitalisierung"]
["Marktkapitalisierung in Mio. EUR"],
[last_year, last_cross_year]))
if market_capitalization > 0:
market_capitalization = market_capitalization * 1000000
return market_capitalization
def calc_per_5_years(pers, ref_year):
counter = 0
per_sum = 0.0
for key in pers.keys():
if key <= ref_year and pers[key].strip() != "-":
counter += 1
per_sum += asFloat(pers[key])
if counter == 0:
return 0
return per_sum / counter
def calc_per_5_years(fundamentals, col_names: []):
pers = fundamentals["Gewinn"]["KGV"]
ref_year = find_existing_column(pers, col_names)
if ref_year is None:
return 0
counter = 0
per_sum = 0.0
for key in pers.keys():
if key <= ref_year and pers[key].strip() != "-":
counter += 1
per_sum += asFloat(pers[key])
if counter == 0:
return 0
return per_sum / counter
def scrap(stock: Stock, stock_storage: StockStorage, util: OnVistaDateUtil = OnVistaDateUtil()):
with open(stock_storage.getStoragePath("profil", "html"), mode="r") as f:
soup = BeautifulSoup(f, 'html.parser')
currencies = scrap_currencies(soup)
price = scrap_price(soup)
fundamentals = scrap_fundamentals(soup)
company_details = scrap_company_details(soup)
with open(stock_storage.getStoragePath("bilanz_guv", "html"), mode="r") as f:
soup = BeautifulSoup(f, 'html.parser')
bilanz_guv = scrap_bilanz_guv(soup)
with open(stock_storage.getStoragePath("schaetzungen", "html"), mode="r") as f:
soup = BeautifulSoup(f, 'html.parser')
schaetzung = scrap_schaetzung(soup)
with open(stock_storage.getStoragePath("analysen", "html"), mode="r") as f:
soup = BeautifulSoup(f, 'html.parser')
stock.ratings = scrap_analysen(soup)
last_year = util.get_last_year()
current_year = util.get_current_year(estimated=False)
next_year = util.get_next_year(estimated=False)
stock.price = asFloat(price)
stock.field = company_details["Branchen"]
GuV = findIn(bilanz_guv, "GuV")
gewinn = asFloat(GuV["Ergebnis nach Steuer"][last_year])
ebit = asFloat(GuV["Ergebnis vor Steuern"][last_year])
erloes = asFloat(GuV["Umsatzerlöse"][last_year])
bilanz = findIn(bilanz_guv, "Bilanz")
eigenkapital = asFloat(bilanz["Eigenkapital"][last_year])
stock.roi = gewinn / eigenkapital * 100
stock.ebit_margin = ebit / erloes * 100
unternehmenskennzahlen = findIn(bilanz_guv, "Unternehmenskennzahlen")
stock.equity_ratio = asFloat(unternehmenskennzahlen["Eigenkapitalquote in %"][last_year])
stock.per = asFloat(schaetzung["KGV"][current_year])
hist_pers = unternehmenskennzahlen["KGV (Jahresendkurs)"]
per_5_years = stock.per
number_of_year = 1
for year in list(hist_pers.keys())[-4:]:
if hist_pers[year] != "-":
per_5_years += asFloat(hist_pers[year])
number_of_year += 1
stock.per_5_years = (per_5_years / number_of_year)
eps_row_name = "Ergebnis/Aktie (reported)" if ("Ergebnis/Aktie (reported)" in schaetzung) else "Ergebnis/Aktie"
stock.eps_current_year = asFloat(schaetzung[eps_row_name][current_year])
stock.eps_next_year = asFloat(schaetzung[eps_row_name][next_year])
stock.per_fallback = stock.price / stock.eps_current_year if stock.eps_current_year != 0 else 0
stock.market_capitalization = asFloat(fundamentals["Marktkapitalisierung in Mrd. EUR"]) * 1000000000
stock_price_today = 0
stock_price_6month = 0
stock_price_1year = 0
stock.history = History(stock_price_today, stock_price_6month, stock_price_1year)
stock.monthClosings = MonthClosings()
stock.historical_eps_current_year = 0
stock.historical_eps_date = 0
stock.historical_eps_next_year = 0
stock.reaction_to_quarterly_numbers = ReactionToQuarterlyNumbers(0, 0, 0, 0, "")
return stock
def scrap(stock: Stock,
stock_storage: StockStorage,
util: OnVistaDateUtil = OnVistaDateUtil()):
path = stock_storage.getStoragePath("fundamental", "html")
content = stock_storage.storage_repository.load(path)
if content:
soup = BeautifulSoup(content, 'html.parser')
stock.symbol = scrap_symbol(soup)
fundamentals = scrap_fundamentals(soup)
last_year_est = util.get_last_year(estimated=True)
last_cross_year_est = util.get_last_cross_year(estimated=True)
fallback_to_last_year_values = last_year_est in fundamentals[
"Rentabilität"][
"Eigenkapitalrendite"] or last_cross_year_est in fundamentals[
"Rentabilität"]["Eigenkapitalrendite"]
if fallback_to_last_year_values:
last_year = util.get_last_year(min_years=2)
last_cross_year = util.get_last_cross_year(min_years=2)
current_year = util.get_last_year(estimated=True)
current_cross_year = util.get_last_cross_year()
current_cross_year_est = util.get_last_cross_year(estimated=True)
next_year = util.get_current_year()
next_cross_year = util.get_current_cross_year()
else:
last_year = util.get_last_year()
last_cross_year = util.get_last_cross_year()
current_year = util.get_current_year()
current_cross_year = util.get_current_cross_year(estimated=False)
current_cross_year_est = util.get_current_cross_year()
next_year = util.get_next_year()
next_cross_year = util.get_next_cross_year()
stock.price = asFloat(
soup.find("ul", {
"class": "KURSDATEN"
}).find("li").find("span").get_text().strip())
stock.roi = asFloat(
get_for_year(fundamentals["Rentabilität"]["Eigenkapitalrendite"],
[last_year, last_cross_year]))
stock.ebit_margin = asFloat(
get_for_year(fundamentals["Rentabilität"]["EBIT-Marge"],
[last_year, last_cross_year]))
stock.equity_ratio = asFloat(
get_for_year(fundamentals["Bilanz"]["Eigenkapitalquote"],
[last_year, last_cross_year]))
stock.per_5_years = calc_per_5_years(
fundamentals,
[current_year, current_cross_year_est, current_cross_year])
stock.per = asFloat(
get_for_year(
fundamentals["Gewinn"]["KGV"],
[current_year, current_cross_year_est, current_cross_year]))
date = stock_storage.indexStorage.date
if sameDay(date, datetime.now()):
date = date - relativedelta(days=1)
stock_price_today = get_latest_price(stock_storage, date)
stock_price_6month = get_historical_price(
stock_storage, (date - relativedelta(months=6)))
stock_price_1year = get_historical_price(
stock_storage, (date - relativedelta(months=12)))
stock.history = History(stock_price_today, stock_price_6month,
stock_price_1year)
stock.monthClosings = get_month_closings(stock_storage)
stock.eps_current_year = asFloat(
get_for_year(
fundamentals["Gewinn"]["Gewinn pro Aktie in EUR"],
[current_year, current_cross_year_est, current_cross_year]))
stock.per_fallback = stock.price / stock.eps_current_year if stock.eps_current_year != 0 else 0
stock.eps_next_year = asFloat(
get_for_year(fundamentals["Gewinn"]["Gewinn pro Aktie in EUR"],
[next_year, next_cross_year]))
stock.market_capitalization = get_market_capitalization(
fundamentals, last_year, last_cross_year)
stock = scrap_ratings(stock, stock_storage)
add_historical_eps(stock, stock_storage)
add_reaction_to_quarterly_numbers(stock, stock_storage)
return stock