def get_estimates(stock: str):
# Convert Stock Name to Lowercase
stock = stock.lower()
# Load Data
soup = _make_soup("https://www.finanzen.net/schaetzungen/" + stock)
# Check for Error
_check_site(soup)
# Parse Table containing Yearly Estimates
table_dict = {}
table = soup.find("h1", text=re.compile("^Schätzungen")).parent
years = table.find_all("th")[1:]
years = [x.get_text() for x in years]
rows = table.find_all("tr")[1:]
for row in rows:
fields = row.find_all("td")
fields = [x.get_text() for x in fields]
name = fields[0]
row_data = fields[1:]
row_data = [x if x != "-" else None for x in row_data]
row_data = [
re.sub("[^\d,]", "", x) if x is not None else x for x in row_data
]
row_data = [
re.sub(",", ".", x) if x is not None else x for x in row_data
]
row_data = [float(x) if x is not None else x for x in row_data]
table_dict[name] = dict(zip(years, row_data))
# Return Estimates in Dict
return table_dict
def get_estimates(stock: str, output="dataframe"):
# Check Input
if output not in ["dataframe", "dict"]:
raise ValueError(
"Please choose either 'dict' or 'dataframe' for input")
# Convert Stock Name to Lowercase
stock = stock.lower()
# Load Data
soup = _make_soup("https://www.finanzen.net/schaetzungen/" + stock)
# Check for Error
_check_site(soup)
# Parse Table containing Yearly Estimates
table_dict = {}
table = soup.find("h1", text=re.compile("^Schätzungen")).parent
years = table.find_all("th")[1:]
years = [x.get_text() for x in years]
rows = table.find_all("tr")[1:]
for row in rows:
fields = row.find_all("td")
fields = [x.get_text() for x in fields]
name = fields[0]
row_data = fields[1:]
row_data = [x if x != "-" else None for x in row_data]
row_data = [
re.sub("[^\d,]", "", x) if x is not None else x for x in row_data
]
row_data = [
re.sub(",", ".", x) if x is not None else x for x in row_data
]
row_data = [float(x) if x is not None else x for x in row_data]
table_dict[name] = dict(zip(years, row_data))
# Return Estimates
if output == "dict":
return table_dict
else:
df_list = []
for f in table_dict:
df_tmp = pd.DataFrame({
"Metric": f,
"Year": list(table_dict[f].keys()),
"Value": list(table_dict[f].values())
})
df_list.append(df_tmp)
return pd.concat(df_list)
def search_stock(stock: str, limit: int = -1):
# Convert Stock Name to Lowercase
stock = stock.lower()
# Make Request
soup = _make_soup("https://www.finanzen.net/suchergebnis.asp?_search=" +
stock)
# Check for Error
if soup.find("div", {"class": "red"}) is not None:
if "kein Ergebnis geliefert" in soup.find("div", {
"class": "red"
}).get_text():
return list()
# Define Function to Extract Results
result_list = []
table_outer_div = soup.find("div", {"class": "table-responsive"})
table = table_outer_div.find("table", {"class": "table"})
rows = table.find_all("tr")
for row in rows[1:]:
cells = row.find_all("td")
name = cells[0].get_text()
link = cells[0].find("a")["href"]
link = "https://www.finanzen.net" + link
isin = cells[1].get_text()
wkn = cells[2].get_text()
result_list.append((name, link, isin, wkn))
# Filter Result if limit was given
if limit > 0:
# Decrease limit if bigger than result
result_len = len(result_list)
if limit > result_len:
limit = result_len
result_list = result_list[0:limit]
# Return Result List as formatted String
names = []
for result in result_list:
stock_name = result[0]
short_name = re.search("aktien/(.+)-aktie", result[1]).group(1)
names.append({
'name': stock_name,
'short_name': short_name,
'isin': result[2],
'wkn': result[3]
})
return names
def get_etf_info(stock, output="dict"):
# Parse User Input
if output not in ["dict"]:
raise ValueError("Only 'dict' supportet for now")
# Load Data
soup = _make_soup("https://www.finanzen.net/etf/" + stock)
WKN = soup.find("span", text="WKN:").next_sibling.strip()
ISIN = soup.find("span", text="ISIN:").next_sibling.strip()
currentStockTable = soup.find("div",
class_="table-responsive quotebox").table
currentStockPrice = float(currentStockTable.tr.td.contents[0].replace(
",", "."))
currentStockCurrency = currentStockTable.tr.td.span.contents[0]
currentStockExchange = currentStockTable.find(
"div",
class_="quotebox-time").find_next_sibling("div").next_element.strip()
baseDataTable = soup.find("h2", text="Wichtige Stammdaten").parent
baseDataIssuer = baseDataTable.find("div", text="Emittent").parent.a.text
baseDataBenchmark = baseDataTable.find("div",
text="Benchmark").parent.find(
"div", {
"title": True
}).text
baseDataFondsSize = float(
baseDataTable.find("div", text="Fondsgröße").parent.find(
"div", {
"title": True
}).text.replace(".", "").replace(",", "."))
info = {
"currentStockPrice": currentStockPrice,
"currentStockCurrency": currentStockCurrency,
"currentStockExchange": currentStockExchange,
"WKN": WKN,
"ISIN": ISIN,
"Issuer": baseDataIssuer,
"Benchmark": baseDataBenchmark,
"FondsSize": baseDataFondsSize
}
if output == "dict":
return info
def get_fundamentals(stock: str, output="dataframe"):
# Parse User Input
if output not in ["dataframe", "dict"]:
raise ValueError(
"Please choose either 'dict' or 'dataframe' for input")
# Convert name to lowercase
stock = stock.lower()
# Load Data
soup = _make_soup("https://www.finanzen.net/bilanz_guv/" + stock)
# Check for Error
_check_site(soup)
# Define Function to Parse Table
def _parse_table(soup, signaler: str):
table_dict = {}
table = soup.find("h2", text=re.compile(signaler)).parent
years = [int(x.get_text()) for x in table.find_all("th")[2:]]
rows = table.find_all("tr")[1:]
for row in rows:
name = row.find("td", {"class": "font-bold"}).get_text()
row_data = row.find_all("td")
row_data = row_data[2:]
row_data = [x.get_text() for x in row_data]
row_data = [re.sub(r"\.", "", x) for x in row_data]
row_data = [re.sub(",", ".", x) for x in row_data]
row_data = [float(x) if x != "-" else None for x in row_data]
table_dict[name] = dict(zip(years, row_data))
return table_dict
# Extract Stock Quote Info+
try:
quote_info = _parse_table(soup, "Die Aktie")
except Exception:
quote_info = None
# Extract Key Ratios
try:
key_ratios = _parse_table(soup, "Unternehmenskennzahlen")
except Exception:
key_ratios = None
# Extract Income Statement
try:
income_info = _parse_table(soup, "GuV")
except Exception:
income_info = None
# Extract Balance Sheet
try:
balance_sheet = _parse_table(soup, "Bilanz")
except Exception:
balance_sheet = None
# Extract Other Information
try:
other_info = _parse_table(soup, "sonstige Angaben")
except Exception:
other_info = None
# Collect Fundamentals into single Directory
fundamentals = {
"Quotes": quote_info,
"Key Ratios": key_ratios,
"Income Statement": income_info,
"Balance Sheet": balance_sheet,
"Other": other_info
}
# Return Fundamentals if output is set to dict
if output == "dict":
return fundamentals
else:
df_list = []
for f in fundamentals:
for i in fundamentals[f]:
df_tmp = pd.DataFrame({
"Category":
f,
"Metric":
i,
"Year":
list(fundamentals[f][i].keys()),
"Value":
list(fundamentals[f][i].values())
})
df_list.append(df_tmp)
fundamentals_df = pd.concat(df_list)
return fundamentals_df
def get_fundamentals(stock: str):
# Convert name to lowercase
stock = stock.lower()
# Load Data
soup = _make_soup("https://www.finanzen.net/bilanz_guv/" + stock)
# Check for Error
_check_site(soup)
# Define Function to Parse Table
def _parse_table(soup, signaler: str):
table_dict = {}
table = soup.find("h2", text=re.compile(signaler)).parent
years = [x.get_text() for x in table.find_all("th")[2:]]
rows = table.find_all("tr")[1:]
for row in rows:
name = row.find("td", {"class": "font-bold"}).get_text()
row_data = row.find_all("td")
row_data = row_data[2:]
row_data = [x.get_text() for x in row_data]
row_data = [re.sub(r"\.", "", x) for x in row_data]
row_data = [re.sub(",", ".", x) for x in row_data]
row_data = [float(x) if x != "-" else None for x in row_data]
table_dict[name] = dict(zip(years, row_data))
return table_dict
# Extract Stock Quote Info+
try:
quote_info = _parse_table(soup, "Die Aktie")
except Exception:
quote_info = None
# Extract Key Ratios
try:
key_ratios = _parse_table(soup, "Unternehmenskennzahlen")
except Exception:
key_ratios = None
# Extract Income Statement
try:
income_info = _parse_table(soup, "GuV")
except Exception:
income_info = None
# Extract Balance Sheet
try:
balance_sheet = _parse_table(soup, "Bilanz")
except Exception:
balance_sheet = None
# Extract Other Information
try:
other_info = _parse_table(soup, "sonstige Angaben")
except Exception:
other_info = None
# Collect Fundamentals into single Directory
fundamentals = {
"Quotes": quote_info,
"Key Ratios": key_ratios,
"Income Statement": income_info,
"Balance Sheet": balance_sheet,
"Other": other_info
}
# Return Fundamentals
return fundamentals