def test_rate_ratings(self):
self.assertEqual(-1, rate_ratings(AnalystRatings(1, 1, 0)),
"rate ratings - test 1")
self.assertEqual(0, rate_ratings(AnalystRatings(1, 0, 1)),
"rate ratings - test 2")
self.assertEqual(1, rate_ratings(AnalystRatings(0, 1, 1)),
"rate ratings - test 3")
def rate_small_ratings(ratings: AnalystRatings):
count = ratings.count()
sum = ratings.sum_weight()
if count == 0:
return -1
rating = round(sum / count, 1)
if rating <= 1.5: return 1
if rating >= 2.5: return -1
return 0
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 scrap_analysen(soup: BeautifulSoup):
h3s = soup.findAll("h3", {"class": "box-headline"})
for h3 in h3s:
headline = h3.get_text().strip()
if headline.startswith("Kursziele ") and headline.endswith(" Aktie"):
ratings = {}
ratingLegend = h3.parent.find("div", {"class": "ratingLegend"})
rows = ratingLegend.findAll("div", {"class": "clearfix"})
for row in rows:
row_content = row.get_text().strip().split(": ")
if len(row_content) == 2:
ratings[row_content[0]] = row_content[1]
return AnalystRatings(int(ratings["Buy"]), int(ratings["Hold"]), int(ratings["Sell"]))
return None
def scrap_ratings(stock, stock_storage: StockStorage):
filename = stock_storage.getStoragePath("ratings", "html")
ratings = {"kaufen": 0, "halten": 0, "verkaufen": 0}
content = stock_storage.storage_repository.load(filename)
if content:
soup = BeautifulSoup(content, 'html.parser')
for row in soup.findAll("tr"):
columns = row.findAll("td")
type = columns[0].get_text().strip()
count = columns[1]
count.div.decompose()
ratings[type] = int(count.get_text().strip())
stock.ratings = AnalystRatings(ratings["kaufen"], ratings["halten"],
ratings["verkaufen"])
return stock