def get_df_1(ticker):
yahoo_financials = YahooFinancials(ticker)
stats_1 = yahoo_financials.get_key_statistics_data()
df_1 = pd.DataFrame(stats_1).loc[['enterpriseValue'], :].transpose()
df_1['marketCap'] = [yahoo_financials.get_market_cap()]
return df_1
def calculate_ps_metrics(stock):
yahoo_stock = YahooFinancials(stock.symbol)
market_cup = yahoo_stock.get_market_cap()
if stock.target_avg_sales:
multiplier = FilteredStocks.get_multiplier(
stock.target_avg_sales[-1])
target_avg_sales = float(stock.target_avg_sales[:-1]) * multiplier
try:
stock.price_to_sell_ratio = round(
market_cup / target_avg_sales, 2)
stock.ps_to_growth_ratio = round(
stock.price_to_sell_ratio /
float(stock.target_sales_growth[:-1]), 2)
ev = (yahoo_stock.get_key_statistics_data()[stock.symbol]
["enterpriseValue"])
profit_margins = (yahoo_stock.get_key_statistics_data()[
stock.symbol]["profitMargins"])
stock.ev_to_sell_ratio = round(ev / target_avg_sales, 2)
stock.gross_margins = round(
yahoo_stock.get_gross_profit() /
yahoo_stock.get_total_revenue() * 100, 2)
ev_to_profit_ratio = round(
stock.ev_to_sell_ratio / profit_margins, 2)
print("gross_margins : " + str(stock.gross_margins))
print("ev sells : " + str(stock.ev_to_sell_ratio))
print("ev / earnings : " + str(ev_to_profit_ratio))
print("ps_to_growth : " + str(stock.ps_to_growth_ratio))
print("sales targets:" + str(stock.target_sales_growth[:-1]))
print("growth adujsted ev / sells: " + str(
round(
stock.ev_to_sell_ratio /
float(stock.target_sales_growth[:-1]), 2)))
except Exception as e:
print(str(e))
def calc_b2m(tickers):
total = {}
for index, ticker in enumerate(tickers):
if index % 1 == 0:
print('Fetching B2M Data for %d out of %d' % (index, len(tickers)))
yahoo_financials = YahooFinancials(ticker)
total[ticker] = yahoo_financials.get_book_value(
) / yahoo_financials.get_market_cap()
return total
def portfolio(request, portfolio_pk):
portfolio = Portfolio.objects.get(pk=portfolio_pk)
check_ownership(request, portfolio)
stock_not_found = False
quote_name = request.POST.get('quote', None)
if quote_name:
quote_name = quote_name.upper()
yf = YahooFinancials(quote_name)
exchange = yf.get_stock_exchange()
try:
stock = Stock.objects.get(name=quote_name)
except Stock.DoesNotExist:
stock = Stock.objects.create(
name=quote_name,
stock_exchange=yf.get_stock_exchange(),
market_cap=convert_number(yf.get_market_cap()),
last_price=convert_number(yf.get_current_price())
)
PortfolioStock.objects.get_or_create(stock=stock,
portfolio=portfolio)
stocks = PortfolioStock.objects.filter(portfolio=portfolio).order_by("-stock__value_score")
operations = Operation.objects.filter(portfolio_stock__portfolio=portfolio)[0:10]
total_value, total_spent, profit = portfolio.balance_sheet()
if total_spent != 0:
profit_purcent = round(profit / total_spent * 100, 2)
else:
profit_purcent = 0
total_cash_value = total_value + portfolio.cash
c = {
'quote_name': quote_name,
'stock_not_found': stock_not_found,
'portfolio':portfolio,
'stocks': stocks,
'operations':operations,
'total_value':total_value,
'total_spent':total_spent,
'profit':profit,
'profit_purcent':profit_purcent,
'total_cash_value':total_cash_value,
}
c.update(csrf(request))
return render_to_response('portfolio.html', c)
async def stock(ctx, ticker, info):
yahoo_financials = YahooFinancials(ticker)
if (info == "current"):
await ctx.send(ticker.upper() + " current share price: $" +
str(yahoo_financials.get_current_price()))
if (info == "open"):
await ctx.send(ticker.upper() + " share price at opening: $" +
str(yahoo_financials.get_open_price()))
if (info == "prevclose"):
await ctx.send(ticker.upper() + " share priced at previous close: $" +
str(yahoo_financials.get_prev_close_price()))
if (info == "cap"):
await ctx.send(ticker.upper() + " market cap: $" +
str("{:,}".format(yahoo_financials.get_market_cap())))
if (info == "dailylow"):
await ctx.send(ticker.upper() + " daily low: $" +
str(yahoo_financials.get_daily_low()))
if (info == "dailyhigh"):
await ctx.send(ticker.upper() + " daily high: $" +
str(yahoo_financials.get_daily_high()))
if (info == "yearlow"):
await ctx.send(ticker.upper() + " yearly low: $" +
str(yahoo_financials.get_yearly_low()))
if (info == "yearhigh"):
await ctx.send(ticker.upper() + " yearly high: $" +
str(yahoo_financials.get_yearly_high()))
if (info == "rev"):
await ctx.send(ticker.upper() + " total revenue: $" +
str("{:,}".format(yahoo_financials.get_total_revenue()))
)
if (info == "net"):
await ctx.send(ticker.upper() + " net income: $" +
str("{:,}".format(yahoo_financials.get_net_income())))
if (info == "op"):
await ctx.send(
ticker.upper() + " operating income: $" +
str("{:,}".format(yahoo_financials.get_operating_income())))
if (info == "profit"):
await ctx.send(ticker.upper() + " gross profit: $" +
str("{:,}".format(yahoo_financials.get_gross_profit())))
def create_retrieve_thread(tickers, metric, file_name, data_dict, batch_no):
start_loop = time.time()
print(
f"Batch/thread {batch_no + 1}: Tickers to be retrieved are: {tickers}")
yahoo_financials = YahooFinancials(tickers)
if metric == "balance":
print(f"Retrieving annual balance sheets from Yahoo Finance...")
financial_statement = yahoo_financials.get_financial_stmts(
'annual', 'balance')['balanceSheetHistory']
elif metric == "income":
print(
f"Retrieving annual income statement history from Yahoo Finance..."
)
financial_statement = yahoo_financials.get_financial_stmts(
'annual', 'income')['incomeStatementHistory']
elif metric == "cap":
print(f"Retrieving market cap information from Yahoo Finance...")
financial_statement = yahoo_financials.get_market_cap()
else:
print("Metric entered is not recognized.")
financial_statement = {}
dict_lock.acquire()
data_dict.update(financial_statement)
end_loop = time.time()
print(f"Saving batch {batch_no + 1} to JSON file...")
json.dump(data_dict, open(file_name + '.json', 'w'))
dict_lock.release()
print(
f"Time elapsed for batch {batch_no + 1}: {end_loop - start_loop}, metric: {metric}"
)
print()
def Statement():
page_bg_img = '''
<style>
body {
background-image: url("https://images.pexels.com/photos/2748757/pexels-photo-2748757.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=1000");
background-size: cover;
}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)
symbols = 'https://raw.githubusercontent.com/Moly-malibu/Stocks/main/bxo_lmmS1.csv'
df = pd.read_csv(symbols)
ticker = st.sidebar.selectbox('Stocks by Company', (df['Symbol']))
tickerData = YahooFinancials(ticker)
def get_symbol(symbol):
url = "http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}®ion=1&lang=en".format(
symbol)
result = requests.get(url).json()
for x in result['ResultSet']['Result']:
if x['symbol'] == symbol:
return x['name']
company_name = get_symbol(ticker.upper())
st.write("""# Analysis of """, company_name)
company = yf.Ticker(ticker)
# st.write(company.info)
company_general = st.sidebar.checkbox("Financial Ratio")
if company_general:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Financial Ratios</h1>",
unsafe_allow_html=True)
st.write('***Payout Ratio:*** ', company.info["payoutRatio"])
st.write('***Trailing Annual Dividend Yield:*** ',
company.info["trailingAnnualDividendYield"])
st.write('***Dividend Rate:*** ', company.info["dividendRate"])
st.write('***Profit Margins: ***', company.info["profitMargins"])
st.write('***Peg Ratio: ***', company.info["pegRatio"])
yahoo_financials = YahooFinancials(ticker)
marketcap = yahoo_financials.get_market_cap()
price_to_sales = yahoo_financials.get_current_price()
dividend_yield = yahoo_financials.get_dividend_yield()
income_balance = si.get_income_statement(ticker)
transpose_income = income_balance.transpose()
balance_income = si.get_balance_sheet(ticker)
transpose_balance = balance_income.transpose()
st.write("""**Dividends**""", company.dividends)
income = si.get_income_statement(ticker)
transpose = income.transpose()
interest_coverage1 = transpose['operatingIncome']
interest_coverage2 = transpose['interestExpense']
st.write(
'***Interest Coverage:*** Operating Income / interest Expenses',
interest_coverage1 / interest_coverage2)
gross_profit_margin1 = transpose['totalRevenue']
gross_profit_margin2 = transpose['costOfRevenue']
st.write(
'***Gross Profit Margin:*** Total Revenue / Gross Profit Margin',
(gross_profit_margin1 - gross_profit_margin2) /
gross_profit_margin1)
balance = si.get_balance_sheet(ticker)
transpose = balance.transpose()
current_ratio1 = transpose['totalCurrentAssets']
current_ratio2 = transpose['totalCurrentLiabilities']
debt_to_assets1 = transpose['otherCurrentAssets']
debt_to_assets2 = transpose['totalAssets']
st.write('***Debit Assets:*** Total Debit / Total Assets',
(debt_to_assets1 / debt_to_assets2))
debt_to_equity1 = transpose['otherCurrentAssets']
debt_to_equity2 = transpose['totalStockholderEquity']
st.write(
'***Debit to Equity:*** Total Debit / Total Stock Holders Equity',
(debt_to_equity1 / debt_to_equity2))
ROE1 = transpose_income['netIncome']
ROE2 = transpose_balance['totalStockholderEquity']
st.write(
'***Return On Equity ROE:*** Net Income / (Total Stock Holder Equity + Total Stock Holder Equity)/2',
(ROE1 / ((ROE2 + ROE2) / 2)))
ROA1 = transpose_income['netIncome']
ROA2 = transpose_balance['totalAssets']
st.write('***Return On Assets:*** Net Income / Total Assets',
(ROA1 / ROA2))
company_simulation = st.sidebar.checkbox("Monte Carlo Simulation")
if company_simulation:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Monte Carlo Simulation Price</h1>",
unsafe_allow_html=True)
st.write(
"""Monte Carlo Simulation project future price for the stocks. """)
yahoo_financials = YahooFinancials(ticker)
price = yahoo_financials.get_current_price()
st.write('***Current Price:***', price)
marketcap = yahoo_financials.get_market_cap()
st.write('***Market Capital***', marketcap)
income_balance = si.get_income_statement(ticker)
transpose_income = income_balance.transpose()
revenue = transpose_income['totalRevenue']
st.write('***Price to sales:*** (Market Capital / Revenue',
marketcap / revenue)
price_to_earnings = transpose_income['netIncome']
st.write('***Price to Earnings:*** (Market Capital/ Net Income',
marketcap / price_to_earnings)
balance_income = si.get_balance_sheet(ticker)
transpose_balance = balance_income.transpose()
price_to_book = transpose_balance['totalStockholderEquity']
st.write('***Price to book:*** (marketcap/Total Stock Holders Equity',
marketcap / price_to_book)
start = st.date_input("Please enter date begin Analysis: ")
price = yf.download(ticker, start=start, end=None)['Close']
returns = price.pct_change()
last_price = price[-1]
num_simulations = 1000
num_days = 252
num_simulations_df = pd.DataFrame()
for x in range(num_simulations):
count = 0
daily_vol = returns.std()
price_series = []
price = last_price * (1 + np.random.normal(0, daily_vol))
price_series.append(price)
for y in range(num_days):
if count == 251:
break
price = price_series[count] * (1 +
np.random.normal(0, daily_vol))
price_series.append(price)
count += 1
num_simulations_df[x] = price_series
fig = plt.figure()
plt.title('Monte Carlo Simulation')
plt.plot(num_simulations_df)
plt.axhline(y=last_price, color='r', linestyle='-')
plt.xlabel('Day')
plt.ylabel('Price')
st.set_option('deprecation.showPyplotGlobalUse', False)
st.pyplot()
st.write('Price Series Predict: ', num_simulations_df)
# company_general = st.sidebar.checkbox("Quick_Ratio")
# if company_general:
# st.subheader("""**Quick Ratio**""")
# balance=si.get_balance_sheet(ticker)
# transpose=balance.transpose()
# quick_ratio1 = transpose['otherCurrentAssets']
# quick_ratio2 = transpose['inventory']
# quick_ratio3 = transpose['otherCurrentLiab']
# quick_ratio = ((quick_ratio1-quick_ratio2)/quick_ratio3)
# if not quick_ratio2:
# st.write("No data available")
# else:
# st.write('(***Quick Ratio:*** CurrentAssets - Inventory)/Current Liabilities)', (quick_ratio1-quick_ratio2)/quick_ratio3)
company_hist = st.sidebar.checkbox("Cash Flow")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Cash Flow</h1>",
unsafe_allow_html=True)
display_cash = si.get_cash_flow(ticker)
if display_cash.empty == True:
st.write("No data available")
else:
st.write(display_cash)
company_hist = st.sidebar.checkbox("Income Statement")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Income Statement</h1>",
unsafe_allow_html=True)
display_income_stat = si.get_income_statement(ticker)
if display_income_stat.empty == True:
st.write("No data available")
else:
st.write(display_income_stat)
company_hist = st.sidebar.checkbox("Balance Sheet")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Balance Sheet</h1>",
unsafe_allow_html=True)
display_balance = si.get_balance_sheet(ticker)
if display_balance.empty == True:
st.write("No data available")
else:
st.write(display_balance)
company_hist = st.sidebar.checkbox("Quote Table")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Quote Table</h1>",
unsafe_allow_html=True)
display_table = si.get_quote_table(ticker, dict_result=False)
if display_table.empty == True:
st.write("No data available")
else:
st.write(display_table)
quote_table = si.get_quote_table(ticker)
t = quote_table["Forward Dividend & Yield"]
st.write('Forward Dividend & Yield:', t)
display_capital = si.get_quote_table(ticker)["Market Cap"]
st.write('Market Capital', display_capital)
company_hist = st.sidebar.checkbox("Call Option")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Call Option</h1>",
unsafe_allow_html=True)
c = ops.get_calls(ticker)
transpose = c.transpose()
st.write(transpose)
print("Loading ticker list...")
with open('ticker_list.json') as json_list:
ticker_list = json.load(json_list)
# ticker_list = ticker_list[0:20] # For debugging purposes, when we want a smaller ticker_list to work with
# Refresh market cap info without retrieving all the other info about the stocks
if not args.refresh and args.refresh_market_caps:
with open('market_cap_info.json') as json_list:
market_cap = json_list
cap_keys = market_cap.keys()
print(f"Keys in market_cap dictionary: {cap_keys}")
yahoo_financials = YahooFinancials(cap_keys)
print("Retrieving market cap information from Yahoo Finance...")
market_cap = yahoo_financials.get_market_cap()
json.dump(market_cap, open('market_cap_info.json', 'w'))
# Retrieve the ticker information (balance sheets, income statements, and market caps), scrape the data in
# batches, and save in batches.
if args.refresh and not args.continue_refresh:
print("Refreshing all stock data...")
balance_sheet = {}
income_statement = {}
market_cap = {}
retrieve_data(batch_size, ticker_list, "balance", fn_balance,
balance_sheet)
retrieve_data(batch_size, ticker_list, "income", fn_income,
income_statement)
retrieve_data(batch_size, ticker_list, "cap", fn_cap, market_cap)
clean_tickers()
from yahoofinancials import YahooFinancials
ticker = 'KO'
YF = YahooFinancials(ticker)
balance_sheet_data_qt = YF.get_financial_stmts('annual', 'balance')
income_statement_data_qt = YF.get_financial_stmts('annual', 'income')
cash_statement_data_qt = YF.get_financial_stmts('annual', 'cash')
all_statement_data_qt = YF.get_financial_stmts('annual',
['income', 'cash', 'balance'])
apple_earnings_data = YF.get_stock_earnings_data()
outstanding_share = YF.get_num_shares_outstanding()
market_cap = YF.get_market_cap()
#apple_net_income = YF.get_net_income()
#historical_stock_prices = YF.get_historical_price_data('2008-08-15', '2018-09-15', 'daily')
print("Balance", balance_sheet_data_qt)
print("income statement", income_statement_data_qt)
print("cash statement", cash_statement_data_qt)
print("earnings", apple_earnings_data)
print("outstanding shares", outstanding_share)
print("market cap", market_cap)
#print ("net income", apple_net_income)
#print( "Div", historical_stock_prices[ticker]['eventsData']['dividends'])
#print (YF.get_total_revenue())
#print (YF.get_50day_moving_avg())
print(income_statement_data_qt)
print(all_statement_data_qt)
companyTickers = companiesData['Ticker'].tolist()
# print(companyTickers)
pe_ratios = []
market_caps = []
dividend_yields = []
sectors = []
industries = []
names = []
# For each company in the S&P 500 list, extract its PE ratio and market cap and add each of them to its respective list
for ticker in companyTickers:
yahoo_financials = YahooFinancials(ticker)
print('Downloading data for ' + ticker + '...')
pe_ratios.append(yahoo_financials.get_pe_ratio())
market_caps.append(yahoo_financials.get_market_cap())
try:
dividend_yields.append(
str(format(yahoo_financials.get_dividend_yield() * 100, ".2f")) +
'%')
except:
dividend_yields.append(str(yahoo_financials.get_dividend_yield()))
sectors.append(yahoo_data.getSector(ticker))
industries.append(yahoo_data.getIndustry(ticker))
names.append(yahoo_data.getName(ticker))
companiesData['Name'] = names
companiesData['Sector'] = sectors
companiesData['Industry'] = industries
companiesData['P/E'] = pe_ratios
companiesData['Market Cap'] = market_caps
