def update_current_ratio(entity):
ticker = Ticker(entity)
df = ticker.balance_sheet()
print("df: ", df)
df = df.reset_index(drop=True)
cols = df.columns
totalCurrentAssets = df["totalCurrentAssets"][0]
totalCurrentLiabilities = df["totalCurrentLiabilities"][0]
ratio = float(totalCurrentAssets) / float(totalCurrentLiabilities)
if ratio > 2:
financial_status = "Conservatively financed"
elif ratio > 1:
financial_status = "Be careful"
else:
financial_status = "indebted"
data = dict(
asset_and_liability=[financial_status, "totalCurrentAssets", "totalCurrentLiabilities"],
parent=["", financial_status, financial_status],
value=[ratio, totalCurrentAssets, totalCurrentLiabilities]
)
fig = px.sunburst(
data,
names='asset_and_liability',
parents='parent',
values='value',
)
return fig
def update_current_ratio(active_cell):
df = pd.read_csv(DATA_PATH + 'tickers_september_2017_red.csv')
row = df.iloc[[active_cell.get("row")]]
ticker = row['Ticker'].values[0]
ticker = Ticker(ticker)
df = ticker.balance_sheet()
df = df.reset_index(drop=True)
cols = df.columns
totalCurrentAssets = df["totalCurrentAssets"][0]
totalCurrentLiabilities = df["totalCurrentLiabilities"][0]
ratio = float(totalCurrentAssets) / float(totalCurrentLiabilities)
if ratio > 2:
financial_status = "Conservatively financed"
elif ratio > 1:
financial_status = "Be careful"
else:
financial_status = "indebted"
data = dict(asset_and_liability=[
financial_status, "totalCurrentAssets", "totalCurrentLiabilities"
],
parent=["", financial_status, financial_status],
value=[ratio, totalCurrentAssets, totalCurrentLiabilities])
fig = px.sunburst(data,
names='asset_and_liability',
parents='parent',
values='value')
return fig
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()
# Add CSV File Implementation
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
def get(self):
s = Ticker(self.stock.symbol, timeout=15)
# all numbers convert to million
df = s.balance_sheet(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 between model field (left) and data json key (right)
mapping = {
"ap": "AccountsPayable",
"cash_and_cash_equivalent": "CashAndCashEquivalents",
"cash_cash_equivalents_and_short_term_investments":
"CashCashEquivalentsAndShortTermInvestments",
"common_stock": "CommonStock",
"common_stock_equity": "CommonStockEquity",
"invested_capital": "InvestedCapital",
"long_term_debt_and_capital_lease_obligation":
"LongTermDebtAndCapitalLeaseObligation",
"machinery_furniture_equipment": "MachineryFurnitureEquipment",
"net_ppe": "NetPPE",
"net_tangible_assets": "NetTangibleAssets",
"payables": "Payables",
"payables_and_accrued_expenses": "PayablesAndAccruedExpenses",
"retained_earnings": "RetainedEarnings",
"stockholders_equity": "StockholdersEquity",
"tangible_book_value": "TangibleBookValue",
"total_assets": "TotalAssets",
"total_capitalization": "TotalCapitalization",
"total_debt": "TotalDebt",
"total_non_current_assets": "TotalNonCurrentAssets",
"working_capital": "WorkingCapital",
"gross_ppe": "GrossPPE",
"current_assets": "CurrentAssets",
"current_liabilities": "CurrentLiabilities",
"receivables": "Receivables",
"other_short_term_investments": "OtherShortTermInvestments",
"long_term_debt": "LongTermDebt",
"other_current_liabilities": "OtherCurrentLiabilities",
"land_and_improvements": "LandAndImprovements",
"ar": "AccountsReceivable",
"other_current_assets": "OtherCurrentAssets",
"investments_and_advances": "InvestmentsAndAdvances",
"current_debt": "CurrentDebt",
"inventory": "Inventory",
"total_tax_payable": "TotalTaxPayable",
"available_for_sale_securities": "AvailableForSaleSecurities",
"other_receivables": "OtherReceivables",
"other_current_borrowings": "OtherCurrentBorrowings",
"investmentin_financial_assets": "InvestmentinFinancialAssets",
"cash_equivalents": "CashEquivalents",
"cash_financial": "CashFinancial",
"commercial_paper": "CommercialPaper",
"current_deferred_liabilities": "CurrentDeferredLiabilities",
"current_deferred_revenue": "CurrentDeferredRevenue",
"leases": "Leases",
"net_debt": "NetDebt",
"accumulated_depreciation": "AccumulatedDepreciation",
"allowance_for_doubtful_accounts_receivable":
"AllowanceForDoubtfulAccountsReceivable",
"properties": "Properties",
"goodwill": "Goodwill",
"goodwill_and_other_intangible_assets":
"GoodwillAndOtherIntangibleAssets",
"other_intangible_assets": "OtherIntangibleAssets",
"other_non_current_assets": "OtherNonCurrentAssets",
"current_debt_and_capital_lease_obligation":
"CurrentDebtAndCapitalLeaseObligation",
"other_current_liabilities": "OtherCurrentLiabilities",
"total_non_current_liabilities_net_minority_interest":
"TotalNonCurrentLiabilitiesNetMinorityInterest",
"non_current_deferred_liabilities":
"NonCurrentDeferredLiabilities",
"tradeand_other_payables_non_current":
"TradeandOtherPayablesNonCurrent",
"other_non_current_liabilities": "OtherNonCurrentLiabilities",
"non_current_deferred_taxes_liabilities":
"NonCurrentDeferredTaxesLiabilities",
"non_current_deferred_revenue": "NonCurrentDeferredRevenue",
"share_issued": "ShareIssued",
}
# enumerate data frame
for row in df.itertuples(index=False):
i, created = BalanceSheet.objects.get_or_create(
stock=self.stock, on=row.asOfDate.date())
for key, val in mapping.items():
try:
# value can be None
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 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
