def __init__(self, symbol):
# TODO gestire il caso in cui il simbolo è sbagliato
ticker = Ticker(symbol)
self.symbol = symbol
self.name = ticker.quote_type[symbol]['longName']
self.currency = ticker.financial_data[symbol]['financialCurrency']
self.exchange = ticker.quote_type[symbol]['exchange']
self.industry = ticker.asset_profile[symbol]['industry']
self.sector = ticker.asset_profile[symbol]['sector']
self.current_price = ticker.financial_data[symbol]['currentPrice']
if 'dividendRate' in ticker.summary_detail[symbol]:
self.dividend_rate = ticker.summary_detail[symbol]['dividendRate']
else:
self.dividend_rate = 0
self.eps = ticker.key_stats[symbol]['trailingEps']
self.roe = ticker.financial_data[symbol]['returnOnEquity']
# Calcolo del growth rate sulla base della crescita dei dividendi degli ultimi 5 anni e del core growth stimato
self.dividends = ticker.cash_flow()['CashDividendsPaid']
self.dividend_perc_growth = self.dividends.pct_change().dropna().mean()
# TODO calcolare il core gorwth,per ora usiamo la media dei dividendi negli ultimi 5 anni
self.core_growth = self.dividend_perc_growth
#! da correggere quando si inserirà il core growth, non tiene conto di rendimenti superiori al 10%
i = (self.core_growth * 100).round()
if i > 10:
i = 10
req_return = _hurdle_return[i]
self.buy_value = (self.dividend_rate / (req_return - self.core_growth))
self.actual_return = (self.dividend_rate /
self.current_price) + self.core_growth
def get(self):
s = Ticker(self.stock.symbol, timeout=15)
# all numbers convert to million
df = s.cash_flow(frequency="q")
if "unavailable" in df or "error" in df:
logger.error("{}: {}".format(self.stock.symbol, df))
return
# DB doesn't like NaN
df = df.where(pd.notnull(df), 0)
mapping = {
"beginning_cash": "BeginningCashPosition",
"ending_cash": "EndCashPosition",
"free_cash_flow": "FreeCashFlow",
"investing_cash_flow": "InvestingCashFlow",
"net_income": "NetIncome",
"operating_cash_flow": "OperatingCashFlow",
"da": "DepreciationAndAmortization",
"capex": "CapitalExpenditure",
"from_continuing_financing_activity": "CashFlowFromContinuingFinancingActivities",
"change_in_working_capital": "ChangeInWorkingCapital",
"stock_based_compensation": "StockBasedCompensation",
"change_in_cash_supplemental_as_reported": "ChangeInCashSupplementalAsReported",
"sale_of_investment": "SaleOfInvestment",
"purchase_of_investment": "PurchaseOfInvestment",
"common_stock_issuance": "CommonStockIssuance",
"repurchase_of_capital_stock": "RepurchaseOfCapitalStock",
"change_in_inventory": "ChangeInInventory",
"dividend_paid": "CashDividendsPaid",
"change_in_account_payable": "ChangeInAccountPayable",
"change_in_account_receivable": "ChangesInAccountReceivables",
"purchase_of_business": "PurchaseOfBusiness",
"net_other_financing_charges": "NetOtherFinancingCharges",
"net_other_investing_changes": "NetOtherInvestingChanges",
}
# enumerate data frame
for row in df.itertuples(index=False):
i, created = CashFlow.objects.get_or_create(
stock=self.stock, on=row.asOfDate.date()
)
for key, val in mapping.items():
try:
tmp = float(getattr(row, val))
except AttributeError:
tmp = 0
# if tmp is a large number, it's unlikely a rate,
# eg. tax rate, thus convert it to B.
if abs(tmp) > M:
tmp = tmp / B
# set value
setattr(i, key, tmp)
i.save()
def fetch(self, dataset: AnyStr, ticker: Ticker, **kwargs):
if dataset == "income_statement":
output = ticker.income_statement(frequency=kwargs.get("period"),
trailing=False)
elif dataset == "cash_flow":
output = ticker.cash_flow(frequency=kwargs.get("period"),
trailing=False)
elif dataset == "balance_sheet":
output = ticker.balance_sheet(frequency=kwargs.get("period"),
trailing=False)
else:
return DataFrame()
if type(output) == DataFrame:
return output
else:
self.logger.warning(output)
return DataFrame()
df_stocks = pd.read_csv(r"/Users/matthew/Documents/UNI/Coding/Stock.csv")
df_stocks.columns = ["Stocks"]
stocklist = df_stocks["Stocks"].values.tolist()
tickers = Ticker(stocklist)
# Key Stats Data
keystats = tickers.key_stats
esg_scores = tickers.summary_detail
# Storing the income statement data into a dataframe
df_balance = tickers.balance_sheet().transpose()
df_income = tickers.income_statement().transpose()
df_cashflow = tickers.cash_flow().transpose()
df_keystats = pd.DataFrame.from_dict(keystats)
df_summary = pd.DataFrame(tickers.summary_detail)
# Defining all the empty directories
all_stat_income = {}
all_stat_balance = {}
all_stat_cashflow = {}
all_stat_keystat = {}
all_stat_summary = {}
# All the data directories
stats_income = ["asOfDate", "EBIT", "NetIncome", "NetIncomeCommonStockholders"]
stats_balance = ["asOfDate", "CurrentAssets", "CurrentLiabilities", "NetPPE","StockholdersEquity","TotalDebt"]
def calcIntVal(self):
outst_shrs_list = []
Eps_1to5y_list = []
discount_rate_list = []
last_close_list = []
intrisic_per_share_list = []
for tick in self.ticks:
#### Retrieve Stock ####
stock_yq = Ticker(tick)
stock_fv = fv.get_stock(tick)
#### Cash Flow ####
cash_fl = stock_yq.cash_flow(frequency="a")
op_cash = cash_fl.tail(1).loc[:, "OperatingCashFlow"]
#### Balance Sheet ####
bal_sheet = stock_yq.balance_sheet(frequency="q")
if "TotalDebt" in bal_sheet:
total_debt = bal_sheet.tail(1).loc[:, "TotalDebt"]
else:
total_debt = 0
cash_short_term_investment = bal_sheet.tail(
1).loc[:, "CashCashEquivalentsAndShortTermInvestments"]
#### Growth Rate ####
try:
Eps_1to5y = float(stock_fv["EPS next 5Y"].strip(' \t\n\r%'))
except ValueError:
Eps_1to5y = 0
if Eps_1to5y >= 15:
Eps_6to10y = 15
else:
Eps_6to10y = Eps_1to5y
#### Last Close ####
last_close = stock_yq.price[tick]["regularMarketPrice"]
#### Outstanding Shares ###
outst_shrs = self.text_to_num(stock_fv["Shs Outstand"])
#### Discount Rate ####
try:
beta = float(stock_fv["Beta"])
except ValueError:
beta = 0
risk_free_rate = 0.64
mrkt_risk_prem = 5
discount_rate = round(risk_free_rate + beta * mrkt_risk_prem, 1)
#### Present Value of 10 year Cash Flows ####
PV_10y_CF_li = []
for x in range(10):
if x == 0:
Op_cash_flow_proj = op_cash * (1 + (Eps_1to5y / 100))
elif x > 5:
Op_cash_flow_proj = op_cash * (1 + (Eps_6to10y / 100))
else:
Op_cash_flow_proj = Op_cash_flow_proj * (1 +
(Eps_1to5y / 100))
if x == 0:
discount_factor = (1 / (1 + discount_rate / 100))
else:
discount_factor = (discount_factor /
(1 + discount_rate / 100))
Op_cash_flow_proj_discounted = Op_cash_flow_proj * discount_factor
PV_10y_CF_li.append(Op_cash_flow_proj_discounted)
PV_10y_CF = sum(PV_10y_CF_li)
#### Intrinsic Value before cash/debt ####
if outst_shrs != 0:
intrisic_bfr_cash_dbt = round(PV_10y_CF / outst_shrs, 2)
else:
intrisic_bfr_cash_dbt = 0
#### Less Debt per Share ####
if outst_shrs != 0:
less_dbt_per_shr = round(total_debt / outst_shrs, 2)
else:
less_dbt_per_shr = 0
#### Plus (+) Cash Per Share ####
if outst_shrs != 0:
plus_cash_per_shr = round(
cash_short_term_investment / outst_shrs, 2)
else:
plus_cash_per_shr = 0
#### Final Intrinsic Value Per Share ####
intrisic_per_share = intrisic_bfr_cash_dbt - less_dbt_per_shr + plus_cash_per_shr
#### Discount/Premium ####
#discount_premium = (last_close - intrisic_per_share)/intrisic_per_share
outst_shrs_list.append(self.num_to_text(outst_shrs))
Eps_1to5y_list.append(Eps_1to5y)
discount_rate_list.append(discount_rate)
last_close_list.append(last_close)
intrisic_per_share_list.append(
round(intrisic_per_share.get(tick), 2))
data = {
'symbol': self.ticks,
'outst_shrs': outst_shrs_list,
'Eps_1to5y': Eps_1to5y_list,
'discount_rate': discount_rate_list,
'last_close': last_close_list,
'intrisic_per_share': intrisic_per_share_list
}
df = pd.DataFrame.from_dict(data)
return df
