def test_negativ_1_year_performance_calculation(self): # given: history = History(95, 100, 100) # when: performance = history.performance_1_year() # then: self.assertEqual(-0.05, performance, "performance of 5%")
def test_6_month_performance_calculation(self): #given: history = History(105, 100, 100) #when: performance = history.performance_6_month() #then: self.assertEqual(0.05, performance, "performance of 5%")
def fromJson(self, json_str: str) -> Stock: stock_json = json.loads(json_str) stock = Stock(stock_json["stock_id"], stock_json["name"], None) for attr in stock_json.keys(): if attr == "stock_id" or attr == "stock_name": continue if attr == "history": history = stock_json["history"] stock.history = History(history["today"], history["half_a_year"], history["one_year"]) elif attr == "monthClosings": stock.monthClosings = MonthClosings() stock.monthClosings.closings = stock_json["monthClosings"].get("closings") elif attr == "ratings": ratings = stock_json["ratings"] stock.ratings = AnalystRatings(ratings["buy"], ratings["hold"], ratings["sell"]) elif attr == "reaction_to_quarterly_numbers": reaction = stock_json["reaction_to_quarterly_numbers"] stock.reaction_to_quarterly_numbers = \ ReactionToQuarterlyNumbers(reaction["price"], reaction["price_before"], reaction["index_price"], reaction["index_price_before"], reaction["date"]) else: stock.__setattr__(attr, stock_json[attr]) return stock
def test_index_group_to_json(self): # given: index_group = IndexGroup("isin", "index_name", "source_id", "source") index_group.stocks = [Stock("stock_id", "stock_name", index_group)] index_group.history = History(1, 2, 3) index_group.monthClosings = MonthClosings() index_group.monthClosings.closings = [0, 0, 0, 0]
def scrap_index(indexGroup: IndexGroup, index_storage: IndexStorage): date = index_storage.date if sameDay(date, datetime.now()): date = date - relativedelta(days=1) index_price_today = get_latest_price(index_storage, date) index_price_6month = get_historical_price(index_storage, (date - relativedelta(months=6))) index_price_1year = get_historical_price(index_storage, (date - relativedelta(months=12))) indexGroup.history = History(index_price_today, index_price_6month, index_price_1year) indexGroup.monthClosings = get_month_closings(index_storage)
def fromJson(self, json_str: str) -> IndexGroup: index_json = json.loads(json_str) # backward compatibilities isin = index_json["isin"] if "isin" in index_json else index_json["index"] name = index_json["name"] sourceID = index_json["sourceId"] if "sourceId" in index_json else name source = index_json["source"] if "source" in index_json else "onvista" indexGroup = IndexGroup(isin, name, sourceID, source) history = index_json["history"] indexGroup.history = History(history["today"], history["half_a_year"], history["one_year"]) indexGroup.monthClosings = MonthClosings() indexGroup.monthClosings.closings = index_json["monthClosings"].get("closings") indexGroup.stocks = list(map(lambda s: Stock(s.id, s.name, indexGroup)), index_json["stocks"]) return indexGroup
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