def ticker_object(pytestconfig):
global ticker_symbol
ticker_symbol = pytestconfig.getoption("--ticker").upper()
ticker_symbol_object = ticker.get_ticker(ticker_symbol)
assert ticker_symbol_object is not None
return ticker_symbol_object
def get_cash_burn_number(ticker_symbol):
global most_recent_cash_flow
ticker_symbol_object = ticker.get_ticker(
ticker_symbol) # Gets the ticker_symbol object so you can access the various objects
balance_sheet_data_frame = balance_sheet.get_balance_sheet_data(ticker_symbol_object, 'a')
cash_flow_data_frame = cash_flow_page.get_cash_flow_data(ticker_symbol_object, 'a')
cash_and_cash_equivalents = balance_sheet.get_cash_and_expenses(balance_sheet_data_frame,ticker_symbol)
"""
because balance sheet does not have a TTM.## if the size of the cash flow data frame > balance sheet select
the most recent year(not TTM)
"""
try:
if cash_flow_data_frame.shape[0] > balance_sheet_data_frame.shape[0]:
most_recent_cash_flow = cash_flow_page.get_most_recent_cash_flow_total(cash_flow_data_frame, -2)
cash_burn = cash_flow_page.calculate_cash_burn(cash_and_cash_equivalents, most_recent_cash_flow)
ticker_symbol_name = get_stock_name(ticker_symbol)
if cash_burn < 0:
print(f'{ticker_symbol_name} is already running out of money. Their cash burn is:{cash_burn:,.1f} months')
else:
print(f'It will take {cash_burn:,.1f} months before {ticker_symbol_name} runs out of money')
except Exception as e:
print(f'yikes, seems like I cannot get the cash burn for {ticker_symbol} because "{e}"')
def get_company_sector(ticker_symbol):
try:
ticker_symbol_object = ticker.get_ticker(
ticker_symbol) # Gets the ticker_symbol object so you can access the various objects
company_sector = company_profile.get_company_sector(ticker_symbol_object, ticker_symbol)
return company_sector
except TypeError:
return error_message
def get_stock_name(ticker_symbol_symbol):
try:
ticker_symbol_object = ticker.get_ticker(
ticker_symbol_symbol) # Gets the ticker_symbol object so you can access the various objects
stock_name = price_data.get_company_name(ticker_symbol_object, ticker_symbol_symbol)
return stock_name
except TypeError:
return error_message
def plot_revenue(self, container, ticker_symbol, frequency):
ticker_object = ticker.get_ticker(
ticker_symbol
) # Gets the ticker object so you can access the various objects
earnings_data = earning.get_earnings_data(ticker_object)
company_name = price_data.get_company_name(ticker_object,
ticker_symbol)
data = self.get_freqency(earnings_data, ticker_symbol, frequency)
earnings_data_frame = pd.DataFrame(data)
dates = earnings_data_frame['date'].astype(str)
revenue = earnings_data_frame['revenue']
revenue = revenue / 100 # This scales the earnings value to Millions or Billions depends on how you want to read the chart
ax = self.fig.add_subplot(111)
yLabelText = "Amount in $"
graph_title = company_name + " " + 'Revenue'
ax.set_title(graph_title)
ax.set_xlabel('Period')
ax.set_ylabel(yLabelText)
ax.get_yaxis().set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
ax.bar(dates, revenue,
color=['r' if v < 0 else 'g'
for v in revenue]) ## adding color to bar graphs
for i, v in enumerate(revenue):
ax.text(i,
v * 0.75,
f'${v:,.0f}',
fontweight='bold',
va='center',
ha='center') ## printing values into the bar graphs
legend_handles = [
Line2D([0], [0],
linewidth=0,
marker='o',
markerfacecolor=color,
markersize=12,
markeredgecolor='none') for color in ['g', 'r']
]
ax.legend(legend_handles, ['positive revenue', 'negative revenue'])
# ax.legend()
if not self.canvas:
self.canvas = FigureCanvasTkAgg(self.fig, container)
self.canvas.get_tk_widget().pack(side="top",
fill="both",
expand=True)
self.canvas.draw_idle()
def plot_net_income(self, container, ticker_symbol, Frequency):
ticker_object = ticker.get_ticker(
ticker_symbol
) ## Gets the ticker object so you can access the various objects
income_statements_data_drame = income.get_income_statement(
ticker_object, Frequency)
income_statement = income.get_net_income(income_statements_data_drame)
company_name = priceData.get_company_name(ticker_object, ticker_symbol)
dates = date.get_dates(income_statements_data_drame)
income_statement_title = 'Net income'
ax = self.fig.add_subplot(111)
yLabelText = "Net Income in $"
graph_title = company_name + " " + income_statement_title
ax.set_title(graph_title)
ax.set_xlabel('Period')
ax.set_ylabel(yLabelText)
ax.get_yaxis().set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
ax.bar(dates,
income_statement,
color=['r' if v < 0 else 'g' for v in income_statement])
for i, v in enumerate(income_statement):
ax.text(i,
v * 0.75,
f'${v:,.0f}',
fontweight='bold',
va='center',
ha='center',
color='#0A0A0A')
legend_handles = [
Line2D([0], [0],
linewidth=0,
marker='o',
markerfacecolor=color,
markersize=12,
markeredgecolor='none') for color in ['g', 'r']
]
ax.legend(legend_handles,
['positive net income', 'negative net income'])
if not self.canvas:
self.canvas = FigureCanvasTkAgg(self.fig, container)
self.canvas.get_tk_widget().pack(side="top",
fill="both",
expand=True)
self.canvas.draw_idle()
def plotCashGraph(self, container, ticker_symbol, frequency):
ticker_object = ticker.get_ticker(
ticker_symbol
) ## Gets the ticker object so you can access the various objects
cash_flow_data_frame = cash_flow_page.get_cash_flow_data(
ticker_object, frequency)
free_cash_flow = cash_flow_page.get_free_cash_flow(
cash_flow_data_frame)
company_name = price_data.get_company_name(ticker_object,
ticker_symbol)
dates = date.get_dates(cash_flow_data_frame)
cash_flow_graph_title = 'Free Cash Flow'
ax = self.fig.add_subplot(111)
yLabelText = "Free Cash Flow in $"
graphTitle = company_name + " " + cash_flow_graph_title
ax.set_title(graphTitle)
ax.set_xlabel('Period')
ax.set_ylabel(yLabelText)
ax.get_yaxis().set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
ax.bar(dates,
free_cash_flow,
color=['r' if v < 0 else 'g' for v in free_cash_flow])
for i, v in enumerate(free_cash_flow):
ax.text(i,
v * 0.75,
f'${v:,.0f}',
fontweight='bold',
va='center',
ha='center')
legend_handles = [
Line2D([0], [0],
linewidth=0,
marker='o',
markerfacecolor=color,
markersize=12,
markeredgecolor='none') for color in ['g', 'r']
]
ax.legend(legend_handles, ['positive cash flow', 'negative cash flow'])
if not self.canvas:
self.canvas = FigureCanvasTkAgg(self.fig, container)
self.canvas.get_tk_widget().pack(side="top",
fill="both",
expand=True)
self.canvas.draw_idle()
def plotCashToEarnings(self, container, ticker_symbol, frequency):
ticker_object = ticker.get_ticker(
ticker_symbol
) # Gets the ticker object so you can access the various objects
income_statements_data_frame = income.get_income_statement(
ticker_object, frequency)
net_income = income.get_net_income(income_statements_data_frame)
cash_flow_data_frame = cash_flow_page.get_cash_flow_data(
ticker_object, frequency)
operating_cash_flow = cash_flow_page.get_operating_cash_flow(
cash_flow_data_frame)
company_name = price_data.get_company_name(ticker_object,
ticker_symbol)
all_dates = dates.get_dates(cash_flow_data_frame)
if operating_cash_flow.iloc[-1] > net_income.iloc[-1]:
print(f'{company_name} has high quality earnings')
else:
print(f'{company_name} has low quality earnings')
title = 'Cash Based Earnings'
ax = self.fig.add_subplot(111)
yLabelText = "Amount in $"
graph_title = company_name + " " + title
ax.set_title(graph_title)
ax.set_xlabel('Period')
ax.set_ylabel(yLabelText)
ax.get_yaxis().set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
ax.plot(all_dates,
operating_cash_flow,
'-o',
label='Cash From Operations')
ax.plot(all_dates, net_income, '-o', label='Net Income')
ax.legend()
if not self.canvas:
self.canvas = FigureCanvasTkAgg(self.fig, container)
self.canvas.get_tk_widget().pack(side="top",
fill="both",
expand=True)
self.canvas.draw_idle()
def plot_debt_graph(self, container, ticker_symbol, frequency):
ticker_object = ticker.get_ticker(
ticker_symbol
) ## Gets the ticker object so you can access the various objects
balance_sheet_data_frame = balancesheet.get_balance_sheet_data(
ticker_object, frequency)
long_term_debt = balancesheet.get_long_term_debt(
balance_sheet_data_frame)
company_name = price_data.get_company_name(ticker_object,
ticker_symbol)
dates = date.get_dates(balance_sheet_data_frame)
graph_title = 'Long Term Debt'
ax = self.fig.add_subplot(111)
yLabelText = "Long term Debt $"
graph_title = company_name + " " + graph_title
ax.set_title(graph_title)
ax.set_xlabel('Period')
ax.set_ylabel(yLabelText)
ax.get_yaxis().set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
ax.bar(dates, long_term_debt, color='r')
for i, v in enumerate(long_term_debt):
ax.text(i,
v * 0.75,
f'${v:,.0f}',
fontweight='bold',
va='center',
ha='center')
if not self.canvas:
self.canvas = FigureCanvasTkAgg(self.fig, container)
self.canvas.get_tk_widget().pack(side="top",
fill="both",
expand=True)
self.canvas.draw_idle()
def get_profit_margin(ticker_symbol):
ticker_symbol_object = ticker.get_ticker(
ticker_symbol) # Gets the ticker_symbol object so you can access the various objects
profit_margin = statistics_tab.get_profit_margins(ticker_symbol_object, ticker_symbol)
return profit_margin
def get_return_on_equity(ticker_symbol):
ticker_symbol_object = ticker.get_ticker(
ticker_symbol) # Gets the ticker_symbol object so you can access the various objects
return_on_equity = statistics_tab.get_return_on_equity(ticker_symbol_object, ticker_symbol)
return return_on_equity
def get_debt_to_equity(ticker_symbol):
ticker_symbol_object = ticker.get_ticker(
ticker_symbol) # Gets the ticker_symbol object so you can access the various objects
debt_to_equity_ratio = statistics_tab.get_debt_to_equity(ticker_symbol_object, ticker_symbol)
return debt_to_equity_ratio
def get_pe_ratio(ticker_symbol_symbol):
ticker_symbol_object = ticker.get_ticker(
ticker_symbol_symbol) # Gets the ticker_symbol object so you can access the various objects
pe_ratio = summary_page.get_pe_ratio(ticker_symbol_object, ticker_symbol_symbol)
return pe_ratio
def get_eps(ticker_symbol_symbol):
ticker_symbol_object = ticker.get_ticker(
ticker_symbol_symbol) # Gets the ticker_symbol object so you can access the various objects
eps = summary_page.earnings_per_share(ticker_symbol_object, ticker_symbol_symbol)
return eps
def get_current_stock_price(ticker_symbol_symbol):
ticker_symbol_object = ticker.get_ticker(
ticker_symbol_symbol) # Gets the ticker_symbol object so you can access the various objects
current_stock_price = price_data.get_current_stock_price(ticker_symbol_object, ticker_symbol_symbol)
return current_stock_price