def get_div_yield(symbol):
stock = YahooFinancials(symbol)
if stock.get_dividend_yield() != None:
y = stock.get_dividend_yield()
elif stock.get_summary_data()[symbol] != None:
y = stock.get_summary_data()[symbol]['yield']
else:
y = 0
if y == None:
return 0
else:
return y
def calculate_dividend_yield(self):
yahoo_financials = YahooFinancials(self.symbols)
logging.debug(
"[calculate_dividend_yield] YahooFinancials - Fetching get_dividend_yield"
)
divs = yahoo_financials.get_dividend_yield()
logging.debug(
"[calculate_dividend_yield] YahooFinancials - Finished fetching get_exdividend_date"
)
for symbol in self.symbols:
self.fin_data[symbol]["dividend_yield"] = divs[symbol]
def fundamentals(tickers, begin="2020-05-26", end="2020-06-26",):
format_header = '{:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>10} {:>10}'
format_numbers = '{:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6} {:>10.2e} {:>10.2E}'
print(format_header.format('ticker', 'P/E', 'EARNSH', 'BK/PR', 'DY', 'DR', 'VAR', 'PEG', 'PS', 'PCI', 'VOLPR', 'CM',))
for ticker in tickers:
yf = YF(ticker)
try:
pe = get_number_for_None(get_number_for_None(yf.get_pe_ratio()))
prices = yf.get_historical_price_data(begin, end, "daily")[ticker]['prices']
stat_pr = calc_statistics(prices)
var_pr = get_number_for_None(100 * stat_pr[1] / stat_pr[0])
volume = get_number_for_None(stat_pr[4])
es = get_number_for_None(yf.get_earnings_per_share())
sh = get_number_for_None(yf.get_num_shares_outstanding(price_type='current'))
''' "pegRatio" "priceToBook" "heldPercentInstitutions" '''
statistics = yf.get_key_statistics_data()[ticker]
summary = yf.get_summary_data()[ticker]
peg = get_number_for_None(statistics["pegRatio"])
PCI = get_number_for_None(statistics["heldPercentInstitutions"])
bv = yf.get_key_statistics_data()[ticker]['bookValue']
pr = yf.get_current_price()
if pr is not None and bv is not None:
pr_bv = get_number_for_None(pr/bv)
else:
pr_bv = '-99.99'
f_s = yf.get_financial_stmts('annual', 'income')['incomeStatementHistory'][ticker][0]
f_s_key = list(f_s.keys())[0]
totalRevenue = f_s[f_s_key]['totalRevenue']
outstanding = statistics["sharesOutstanding"]
rev_per_share = totalRevenue / outstanding
if pr is not None and es is not None:
p_s = get_number_for_None(rev_per_share/float(es))
else:
p_s = '99'
dy = get_number_for_None(yf.get_dividend_yield())
dr = get_number_for_None(yf.get_dividend_rate())
volume10days = summary['averageVolume10days']
marketCap = summary['marketCap']
# float(volume)*pr
# float(sh)*pr)
print(format_numbers.format(ticker, pe, es, pr_bv, dy, dr, var_pr, peg, p_s, PCI, volume10days, marketCap))
except Exception as e:
print(ticker, e)
def getMarketData():
tickers = 'AAPL'
financials = YahooFinancials(tickers)
# company_stock_price = financials.get_stock_price_data() #gets stock price information
historical_stock_prices_data = financials.get_historical_price_data(
'2015-11-21', '2020-11-21',
'daily') #gets historical daily stock price of company
# get_Div_data(historical_stock_prices_data[tickers])
get_stock_price_data(historical_stock_prices_data[tickers])
# company_balance_sheet_data_qt = financials.get_financial_stmts('quarterly', 'balance') #get balance sheet
# company_income_statement_data_qt = financials.get_financial_stmts('quarterly', 'income') #get income statement
company_key_statistics_data = financials.get_key_statistics_data(
) #includes profit margins, forward eps, yearly change etc.
# get_forward_pe(company_key_statistics_data[tickers])
# get_trailing_eps(company_key_statistics_data[tickers])
# get_foward_eps(company_key_statistics_data[tickers])
# get_ytdReturn(company_key_statistics_data[tickers])
company_earnings_data = financials.get_stock_earnings_data(
) #historical eps only for 1 year span
# get_earnings_data(company_earnings_data[tickers])
company_dividend_yield = financials.get_dividend_yield(
) #current dividends yield
company_dividend = financials.get_dividend_rate() #current dividends rate
company_avg_div_yield_1year = financials.get_annual_avg_div_yield(
) #average 1 year div yield
company_avg_div_yield_5year = financials.get_five_yr_avg_div_yield(
) #average 5 year div yield
company_eps = financials.get_earnings_per_share() #current eps
company_pe = financials.get_pe_ratio() #current pe ratio
company_beta = financials.get_beta() #current beta
company_current_stock_price = financials.get_current_price(
) #current stock price
company_revenue = financials.get_total_revenue() #current company revenue
company_operating_income = financials.get_operating_income(
) #current company operating income
company_net_income = financials.get_net_income() #current net income
company_yearly_high = financials.get_yearly_high() #get yearly high
company_yearly_low = financials.get_yearly_low() #get yearly low
company_moving_50 = financials.get_50day_moving_avg(
) #50 day moving average of stock
company_moving_200 = financials.get_200day_moving_avg(
) #200 day moving average of stock
def update_shares(request, portfolio_pk):
portfolio = Portfolio.objects.get(pk=portfolio_pk)
check_ownership(request, portfolio)
pstocks = PortfolioStock.objects.filter(portfolio=portfolio)
for pstock in pstocks:
stock = pstock.stock
yf = YahooFinancials(quote_name)
stock.last_price = convert_number(yf.get_current_price())
stock.price_sales_ratio = convert_number(yf.get_price_to_sales())
stock.dividend_yield = convert_number(yf.get_dividend_yield())
stock.save()
return http.HttpResponseRedirect('..')
def index(response):
form = EnterMarketCap
form2 = EnterDividend
form3 = EnterIndustry
form4 = EnterPrice
form5 = EnterPE
form9 = ""
form10 = ""
form11 = ""
bla = ""
mk = 0
div = 0
ind = ""
price = 0
if response.method == "GET" and response.GET.get('apply') == 'apply':
form = EnterMarketCap(response.GET)
form2 = EnterDividend(response.GET)
form3 = EnterIndustry(response.GET)
form4 = EnterPrice(response.GET)
form5 = EnterPE(response.GET)
#payload = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
#first_table = payload[0]
#df = first_table
#df.head()
#symbols = df['Symbol'].values.tolist()
ticker_list = [
'FB', 'AAPL', 'FRT', 'AMZN', 'FIS', 'FITB', 'FE', 'FRC', 'FISV',
'FLT', 'FLIR', 'FLS', 'FMC', 'F', 'FTNT', 'FTV', 'FBHS', 'FOXA',
'FOX', 'BEN', 'FCX', 'GPS', 'GRMN', 'IT', 'GD', 'GE', 'GIS', 'GM',
'L', 'LOW', 'LUMN', 'LYB', 'MTB', 'MRO', 'MPC', 'MKTX', 'MAR',
'MMC', 'MLM', 'MAS', 'MA', 'MKC', 'MXIM', 'MCD', 'MCK', 'MDT',
'TSLA', 'MET', 'MTD', 'MGM', 'MCHP', 'MU', 'MSFT', 'MAA', 'MHK'
]
if form.is_valid():
mk = (form.cleaned_data["marketcap"])
ck = form.cleaned_data["check"]
sign = form.cleaned_data["sign"]
else:
mk = 0
ck = False
sign = ""
if form2.is_valid():
div = form2.cleaned_data["dividend"]
ck2 = form2.cleaned_data["check2"]
sign2 = form2.cleaned_data["sign2"]
else:
div = 0.0
ck2 = False
sign2 = ""
if form3.is_valid():
ind = form3.cleaned_data["industry"]
ck3 = form3.cleaned_data["check3"]
else:
ind = ""
ck3 = False
if form4.is_valid():
p = form4.cleaned_data["price"]
ck4 = form4.cleaned_data["check4"]
sign4 = form4.cleaned_data["sign4"]
else:
p = 0
ck4 = False
sign4 = ""
if form5.is_valid():
pe_ratio = form5.cleaned_data["pe_ratio"]
ck5 = form5.cleaned_data["check5"]
sign5 = form5.cleaned_data["sign5"]
else:
pe_ratio = 0
ck5 = False
sign5 = ""
if ck is True:
form10 = mk
else:
form10 = ""
if ck4 is True:
form9 = p
else:
form9 = ""
resultMK = []
form11 = ""
if mk != 0 and ck is True and ck5 is False and ck4 is False and ck2 is False:
for i in ticker_list:
ticker = yf.Ticker(i)
b = ticker.info["marketCap"]
if sign == "Above":
if b > (mk * 1000000000):
resultMK.append(i)
elif sign == "Below":
if b < (mk * 1000000000):
resultMK.append(i)
resultDIV = []
if div != 0 and ck is False and ck5 is False and ck4 is False and ck2 is True:
for i in ticker_list:
yahoo_financials = YahooFinancials(i)
d = yahoo_financials.get_dividend_yield()
if sign2 == "Above":
if d > (div / 100):
resultDIV.append(i)
elif sign2 == "Below":
if d < (div / 100):
resultDIV.append(i)
resultP = []
if p != 0 and ck is False and ck5 is False and ck4 is True and ck2 is False:
for i in ticker_list:
price = si.get_live_price(i)
if sign4 == "Above":
if price > p:
resultP.append(i)
elif sign4 == "Below":
if price < p:
resultP.append(i)
resultPE = []
if div != 0 and ck is False and ck5 is True and ck4 is False and ck2 is False:
for i in ticker_list:
ticker = yf.Ticker(i)
pe = ticker.info["forwardPE"]
if sign5 == "Above":
if pe > pe_ratio:
resultPE.append(i)
elif sign5 == "Below":
if pe < pe_ratio:
resultPE.append(i)
intermediate = []
resultP_MK = []
if ck is True and ck5 is False and ck4 is True and ck2 is False:
for i in ticker_list:
price = si.get_live_price(i)
if sign4 == "Above":
if price > p:
intermediate.append(i)
elif sign4 == "Below":
if price < p:
intermediate.append(i)
for i in intermediate:
ticker = yf.Ticker(i)
b = ticker.info["marketCap"]
if sign == "Above":
if b > (mk * 1000000000):
resultP_MK.append(i)
elif sign == "Below":
if b < (mk * 1000000000):
resultP_MK.append(i)
if ck is True and ck5 is False and ck4 is False and ck2 is False:
if not resultMK:
form11 = "Your filters match no stock (From my list of 50 stocks :)"
else:
form11 = resultMK
elif ck is False and ck4 is True and ck5 is False and ck2 is False:
if not resultP:
form11 = "Your filters match no stock (From my list of 50 stocks :)"
else:
form11 = resultP
elif ck is True and ck5 is False and ck4 is True and ck2 is False:
if not resultP_MK:
form11 = "Your filters match no stock (From my list of 50 stocks :)"
else:
form11 = resultP_MK
elif ck is False and ck5 is True and ck4 is False and ck2 is False:
form11 = resultPE
#if not resultDIV:
# form11 = "Your filters match no stock (From my list of 50 stocks :)"
#else:
# form11 = resultDIV
elif ck is False and ck5 is False and ck4 is False and ck2 is True:
form11 = resultDIV
elif ck is False and ck2 is False and ck4 is False and ck2 is False:
form11 = "You didn't enter any filter, Click use to use a filter!"
return render(
response, "main/home.html", {
"form": form,
"form2": form2,
"form3": form3,
"form4": form4,
"form5": form5,
"form9": form9,
"form10": form10,
"form11": form11
})
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)
def run(self):
tickers = ['ATVI', 'GOOGL', 'AMED', 'CHWY', 'C', 'CRWD', 'FB', 'FTHM', 'VEEV']
today = date.today()
writeToday = str(today)
writeToday = writeToday + ".txt"
outF = open(writeToday, "w")
for i in tickers:
ticker = "$" + i + ":"
addedDate = getBuyDate(ticker)
#get market cap + stock price
yearold = today.year - 1
yago = datetime.date(yearold, today.month, today.day)
stock = web.DataReader(i, 'yahoo', yago, today)
outF.write("\n\nDISPLAYING DATA FOR " + ticker)
outF.write("\n\tAdded to the portfolio on: " + addedDate.strftime('%m/%d/%Y'))
outF.write("\n\tShare price as of "+ today.strftime('%m/%d/%Y') + " for "+ ticker + " ${0:.2f}".format(stock['Close'][-1])+ " per share")
market_cap = int(data.get_quote_yahoo(i)['marketCap'])
outF.write("\n\tMarket cap is: ${:,}".format(market_cap))
yahoo_financials = YahooFinancials([i])
#get dividend yield
dividend_yield = str(yahoo_financials.get_dividend_yield())
dividend_yield = dividend_yield[(len(i) + 5):len(dividend_yield) - 1]
if (dividend_yield == "None"):
dividend_yield = 0.00
dividend_yield = float(dividend_yield) * 100
outF.write("\n\tDividend yield: "+"{0:.4g}".format(dividend_yield)+ "%")
#Get daily return
stock['Daily Return'] = stock['Close'].pct_change(1)
daily_return = float(stock['Daily Return'][-1]) * 100
outF.write("\n\tDaily return as of " + today.strftime('%m/%d/%Y') + "is: " + "{0:.3f}".format(daily_return) + "%")
#Get week to date return
lastMonday = today - datetime.timedelta(days=today.weekday())
weekToDate = web.DataReader(i, 'yahoo', lastMonday, today)
firstPrice = float(weekToDate['Open'][0])
lastPrice = float(weekToDate['Close'][-1])
weekToDateReturn = (lastPrice/firstPrice - 1) * 100
#weekToDate['Daily Return'] = weekToDate['Close'].pct_change(1)
#weekToDate['Cumulative Return'] = ((1 + weekToDate['Daily Return']).cumprod() - 1) * 100
#returnWeekly = float(weekToDate['Cumulative Return'][1])
outF.write("\n\tWeek-to-date return: "+"{0:.4g}".format(weekToDateReturn)+ "%")
#get month to date return
first_day_of_month = today.replace(day=1)
monthToDate = web.DataReader(i, 'yahoo', first_day_of_month, today)
firstPrice = float(monthToDate['Open'][0])
lastPrice = float(monthToDate['Close'][-1])
monthToDateReturn = (lastPrice/firstPrice - 1) * 100
outF.write("\n\tMonth-to-date return: " + "{0:.4g}".format(monthToDateReturn)+ "%")
#get trailing month change
if today.month == 3 and today.day > 28:
day = 28
else:
day = today.day
if today.month == 1:
year = today.year - 1
month = 12
else:
year = today.year
month = today.month - 1
lastMonth = datetime.date(year, month, day)
trailingMonth = web.DataReader(i, 'yahoo', lastMonth, today)
firstPrice = float(trailingMonth['Open'][0])
lastPrice = float(monthToDate['Close'][-1])
trailingMonthReturn = (lastPrice/firstPrice - 1) * 100
outF.write("\n\tTrailing month return: "+"{0:.5g}".format(trailingMonthReturn)+ "%")
#get YTD return
firstDay = datetime.date(today.year, 1, 4)
yearToDate = web.DataReader(i, 'yahoo', firstDay, today)
firstPrice = float(yearToDate['Open'][0])
lastPrice = float(yearToDate['Close'][-1])
yearToDateReturn = (lastPrice/firstPrice - 1) * 100
outF.write("\n\tYear-to-date return: {0:.3g}".format(yearToDateReturn)+ "%")
#get percent down from 52 week high
yearAgo = datetime.date(today.year - 1, 1, 1)
oneYear = web.DataReader(i, 'yahoo', yearAgo, today)
high = float(oneYear['High'][0])
count = int(oneYear['High'].count())
for j in range (1,count):
if float(oneYear['High'][j]) > high:
high = float(oneYear['High'][j])
priceToday = float(oneYear['Close'][-1])
down52wk = (high/priceToday - 1) * 100
outF.write("\n\tPercent down from 52-week-high is: {0:.5g}".format(down52wk) + "%")
# get percent down from peak since buy
buyDate = getBuyDate(ticker)
holdingPeriod = web.DataReader(i, 'yahoo', buyDate, today)
peakHigh = float(holdingPeriod['High'][0])
count = int(holdingPeriod['High'].count())
for j in range(1, count):
if float(holdingPeriod['High'][j]) > peakHigh:
peakHigh = float(holdingPeriod['High'][j])
priceToday = float(holdingPeriod['Close'][-1])
downPeak = (peakHigh / priceToday - 1) * 100
outF.write("\n\tPercent down from peak since purchase is: {0:.5g}".format(downPeak) + "%")
#get 50 day SMA
stock['SMA10'] = talib.MA(stock['Close'].values, timeperiod=10, matype=0)
SMA10 = float(stock['SMA10'][-1])
stock['SMA50'] = talib.MA(stock['Close'].values, timeperiod=50, matype=0)
SMA50 = float(stock['SMA50'][-1])
if SMA10 > SMA50:
outF.write("\n\tThe SMA10 is: " + "${0:.5g}, ".format(SMA10) + " which is above the SMA50 of: ${0:.5g}".format(SMA50))
elif SMA10 == SMA50:
outF.write("\n\tThe SMA10 is:" + "${0:.5g}".format(SMA10) + "which is equal the SMA50 of: ${0:.5g}".format(SMA50))
else:
outF.write("\n\tThe SMA10 is:"+ "${0:.5g}".format(SMA10)+ "which is below the SMA50 of: ${0:.5g}".format(SMA50))
#get the RSI
stock['RSI'] = talib.RSI(stock['Close'].values)
rsiVal = float(stock['RSI'][-1])
outF.write("\n\tThe RSI is "+ "{0:.5g}".format(rsiVal))
#get the MACD
stock['MACD'], stock['MACDsignal'], stock['MACDhist'] = talib.MACD(stock["Close"], fastperiod=12, slowperiod=26, signalperiod=9)
fig, axes = plt.subplots(2, 1)
stock[['MACD', 'MACDsignal', 'MACDhist']].plot(ax=axes[1], grid=True)
plt.legend(loc='best', shadow=True)
symbol = ticker + " MACD graph"
plt.title(symbol)
plt.show()
print("\tDone with", i)
time.sleep(8)
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
companiesData['Dividend Yield'] = dividend_yields
#print(companiesData)