def __init__(self, ticker: str, audit: bool):
self.audit: bool = audit
self.wb: Workbook = Workbook()
self.ws1: worksheet = self.wb.active
self.ws2: worksheet = self.wb.create_sheet("Free Cash Flows")
self.ws3: worksheet = self.wb.create_sheet("Explanations")
self.ws4: worksheet = self.wb.create_sheet("Ratios")
self.ws1.title = "Financials"
self.ticker: str = ticker
self.now: str = datetime.now().strftime("%Y-%m-%d")
self.letter: int = 0
self.is_start: int = 4
self.bs_start: int = 18
self.cf_start: int = 47
self.len_data: int = 0
self.len_pred: int = 10
self.years: List[str] = []
self.rounding: int = 0
self.df_bs: pd.DataFrame = self.get_data("BS", self.bs_start, False)
self.df_is: pd.DataFrame = self.get_data("IS", self.is_start, True)
self.df_cf: pd.DataFrame = self.get_data("CF", self.cf_start, False)
self.info: pd.DataFrame = yf.Ticker(ticker).info
self.t_bill: float = get_rf()
self.r_ff: float = dcf_model.get_fama_coe(self.ticker)
self.sisters: List[str] = dcf_model.others_in_sector(
self.ticker, self.info["sector"], self.info["industry"]
)
self.sister_data: List[List[pd.DataFrame]] = [[pd.DataFrame()]]
def __init__(
self,
ticker: str,
audit: bool = False,
ratios: bool = True,
len_pred: int = 10,
max_similars: int = 3,
no_filter: bool = False,
):
"""
Creates a detialed DCF for a given company
Parameters
----------
ticker : str
The ticker to create a DCF for
audit : bool
Whether or not to show that the balance sheet and income statement tie-out
ratios : bool
Whether to show ratios for the company and for similar companies
len_pred : int
The number of years to make predictions for before assuming a terminal value
max_similars : int
The maximum number of similar companies to show, will be less if there are not enough similar companies
no_filter : bool
Disable filtering of similar companies to being in the same market cap category
"""
self.info: Dict[str, Any] = {
"len_data": 0,
"len_pred": len_pred,
"max_similars": max_similars,
"rounding": 0,
"ticker": ticker,
"audit": audit,
"ratios": ratios,
"no_filter": no_filter,
}
self.letter: int = 0
self.starts: Dict[str, int] = {"IS": 4, "BS": 18, "CF": 47}
self.wb: Workbook = Workbook()
self.ws: Dict[int, Any] = {
1: self.wb.active,
2: self.wb.create_sheet("Free Cash Flows"),
3: self.wb.create_sheet("Explanations"),
}
self.df: Dict[str, pd.DataFrame] = {
"BS": self.get_data("BS", self.starts["BS"], False),
"IS": self.get_data("IS", self.starts["IS"], True),
"CF": self.get_data("CF", self.starts["CF"], False),
}
self.data: Dict[str, Any] = {
"now": datetime.now().strftime("%Y-%m-%d"),
"info": yf.Ticker(ticker).info,
"t_bill": get_rf(),
"r_ff": dcf_model.get_fama_coe(self.info["ticker"]),
}
def test_get_fama_coe():
coef = dcf_model.get_fama_coe(ticker="TSLA")
assert isinstance(coef, np.float64)
