def get_stocks_data(stocks: list, finance_key: str, sa_dict: dict,
stocks_data: dict, period: str):
"""Get stocks data based on a list of stocks and the finance key. The function searches for the correct
financial statement automatically. [Source: StockAnalysis]
Parameters
----------
stocks: list
A list of tickers that will be used to collect data for.
finance_key: str
The finance key used to search within the sa_dict for the correct name of item
on the financial statement
sa_dict: dict
A dictionary that includes BS, IS and CF, the abbreviations and names of items
on the financial statements. I.e: {"BS": {"ce": "Cash & Equivalents"}}
stocks_data : dict
A dictionary that is empty on initialisation but filled once data is collected
for the first time.
period : str
Whether you want annually, quarterly or trailing financial statements.
Returns
-------
dict
Dictionary of filtered stocks data separated by financial statement
"""
no_data = []
for symbol in tqdm(stocks):
for statement in sa_dict.keys():
if finance_key in sa_dict[statement]:
if statement not in stocks_data:
stocks_data[statement] = {}
used_statement = statement
symbol_statement = create_dataframe(symbol, statement,
period.lower())
if symbol_statement[0].empty:
no_data.append(symbol)
continue
stocks_data[statement][symbol] = (
change_type_dataframes(symbol_statement[0]) *
symbol_statement[1])
if period in ["Quarterly", "Trailing"]:
for symbol in stocks_data[used_statement]:
stocks_data[used_statement][symbol].columns = (
stocks_data[used_statement][symbol].columns.map(
lambda x: pd.Period(x, "Q")).astype(str))
stocks_data[used_statement] = match_length_dataframes(
stocks_data[used_statement])
if no_data:
console.print(
f"No data available for {', '.join(str(symbol) for symbol in no_data)}"
)
return stocks_data
def get_data(self, statement: str, row: int, header: bool) -> pd.DataFrame:
df, rounding, _ = dcf_model.create_dataframe(self.info["ticker"],
statement)
if df.empty:
raise ValueError("Could generate a dataframe for the ticker")
self.info["rounding"] = rounding
if not self.info["len_data"]:
self.info["len_data"] = len(df.columns)
self.ws[1][f"A{row}"] = dcf_static.statement_titles[statement]
self.ws[1][f"A{row}"].font = dcf_static.bold_font
rowI = row + 1
names = df.index.values.tolist()
for name in names:
self.ws[1][f"A{rowI}"] = name
if name in dcf_static.sum_rows:
length = self.info["len_data"] + (self.info["len_pred"]
if statement != "CF" else 0)
for i in range(length):
if statement == "CF" and name == "Net Income":
pass
else:
self.ws[1][
f"{dcf_static.letters[i+1]}{rowI}"].font = dcf_static.bold_font
self.ws[1][
f"{dcf_static.letters[i+1]}{rowI}"].border = dcf_static.thin_border_top
rowI += 1
column = 1
for key, value in df.iteritems():
rowI = row
if header:
dcf_model.set_cell(
self.ws[1],
f"{dcf_static.letters[column]}{rowI}",
float(key),
font=dcf_static.bold_font,
)
for item in value:
rowI += 1
m = 0 if item is None else float(item.replace(",", ""))
dcf_model.set_cell(
self.ws[1],
f"{dcf_static.letters[column]}{rowI}",
m,
num_form="[$$-409]#,##0.00;[RED]-[$$-409]#,##0.00",
)
column += 1
return df
def get_stocks_data(
stocks: list,
finance_key: str,
sa_dict: dict,
stocks_data: dict,
period: str,
convert_currency: str = "USD",
):
"""Get stocks data based on a list of stocks and the finance key. The function searches for the correct
financial statement automatically. [Source: StockAnalysis]
Parameters
----------
stocks: list
A list of tickers that will be used to collect data for.
finance_key: str
The finance key used to search within the sa_dict for the correct name of item
on the financial statement
sa_dict: dict
A dictionary that includes BS, IS and CF, the abbreviations and names of items
on the financial statements. I.e: {"BS": {"ce": "Cash & Equivalents"}}
stocks_data : dict
A dictionary that is empty on initialisation but filled once data is collected
for the first time.
period : str
Whether you want annually, quarterly or trailing financial statements.
convert_currency : str
Choose in what currency you wish to convert each company's financial statement. Default is USD (US Dollars).
Returns
-------
dict
Dictionary of filtered stocks data separated by financial statement
"""
no_data = []
for symbol in tqdm(stocks):
for statement in sa_dict.keys():
if finance_key in sa_dict[statement]:
if statement not in stocks_data:
stocks_data[statement] = {}
used_statement = statement
symbol_statement, rounding, currency = create_dataframe(
symbol, statement, period.lower())
if symbol_statement.empty:
no_data.append(symbol)
continue
symbol_statement_rounded = (
change_type_dataframes(symbol_statement) * rounding)
if convert_currency and convert_currency != currency:
currency_data = yf.download(
f"{currency}{convert_currency}=X",
start=f"{symbol_statement_rounded.columns[0]}-01-01",
end=f"{symbol_statement_rounded.columns[-1]}-12-31",
progress=False,
)["Adj Close"]
for year in symbol_statement_rounded:
# Since fiscal year can differ, I take the median of the currency and not the last value
# of the year
symbol_statement_rounded[year] = (
symbol_statement_rounded[year] *
currency_data.loc[year].median())
stocks_data[statement][symbol] = symbol_statement_rounded
if period in ["Quarterly", "Trailing"]:
for symbol in stocks_data[used_statement]:
stocks_data[used_statement][symbol].columns = (
stocks_data[used_statement][symbol].columns.map(
lambda x: pd.Period(x, "Q")).astype(str))
stocks_data[used_statement] = match_length_dataframes(
stocks_data[used_statement])
if no_data:
console.print(
f"No data available for {', '.join(str(symbol) for symbol in no_data)}"
)
return stocks_data