def income_bar(search_btn,search):
if search_btn:
balance_sheet = si.get_income_statement(search)
bs = balance_sheet.T
else:
balance_sheet = si.get_income_statement("aapl")
bs = balance_sheet.T
# print(bs.columns)
# print(bs)
bs["date"] = bs.index
date= bs["date"].dt.year
netincome = (bs["netIncome"]/1000000000).astype(float).round(2)
totalrev = (bs["totalRevenue"]/1000000000).astype(float).round(2)
opincome = (bs["operatingIncome"]/1000000000).astype(float).round(2)
color=np.array(['rgb(255,255,255)']*netincome.shape[0])
color[netincome<0]='red'
color[netincome>=0]='green'
fig2 = go.Figure(data=[
go.Bar(name='Net Income', x=date, y=bs["netIncome"],marker=dict(color=color.tolist()), text = netincome,textfont_size=8, textposition='outside'),
go.Bar(name='Total Revenue', x=date, y=bs["totalRevenue"], text = totalrev,textfont_size=8, textposition='outside',visible='legendonly'),
go.Bar(name='Operating Income', x=date, y=bs["operatingIncome"], text = opincome,textfont_size=8, textposition='outside',visible='legendonly')
])
# Change the bar mode
fig2.update_layout(barmode='group',width = 444,title_text='Income Statement',title_x=0.5,margin=dict(t=70,b=20,l=55,r=40),paper_bgcolor='rgba(0,0,0,0)',plot_bgcolor='rgba(0,0,0,0)')
fig2.update_layout(legend=dict(orientation="h",yanchor="bottom",y=1,xanchor="right",x=0.95,font=dict(size=9,),))
return fig2
def is_available(stock_nm):
try:
income_statement = si.get_income_statement(stock_nm)
balance_sheet = si.get_balance_sheet(stock_nm)
cash_flow_statement = si.get_cash_flow(stock_nm)
my_stock_price(stock_nm)
except (IOError, KeyError):
None
def getIncomeStatement(self, symbol):
incomeStatement = si.get_income_statement(symbol)
data = self.yahooFinanceDataModification.formatSheetData(incomeStatement)
# typescript cloud not recognize
if 'Net_Income_from_Continuing_&_Discontinued_Operation' in data:
data['Net_Income_from_Continuing_And_Discontinued_Operation'] = data['Net_Income_from_Continuing_&_Discontinued_Operation']
del data['Net_Income_from_Continuing_&_Discontinued_Operation']
return {'incomeStatement': data}
def fair_value(ticker, years=1, wish_return=0.4):
netincome = Decimal(si.get_income_statement(ticker)[si.get_income_statement(ticker).columns[0]].netIncome)
shares_outstanding = number_encoder(str(si.get_stats(ticker)["Value"][9]))
current_eps = netincome / shares_outstanding
all_growth_rates = [Decimal(x[:-1]) for x in si.get_analysts_info(ticker)["Revenue Estimate"][
si.get_analysts_info(ticker)["Revenue Estimate"].columns[
:-2]].iloc[5][1:].dropna()]
growth_rate = sum(all_growth_rates) / len(all_growth_rates) / 100
projected_eps = current_eps * (1 + growth_rate) ** years
projected_pe_ratio = Decimal(si.get_live_price(ticker)) / projected_eps
forwarded_pe_ratio = Decimal(si.get_stats_valuation(ticker)[si.get_stats_valuation(ticker).columns[1]][2]) * (
1 + growth_rate) ** (years - 1)
return (forwarded_pe_ratio * projected_eps) / Decimal(1 + wish_return)
def income(request):
ticker = json.loads(request.body)['ticker']
if not ticker:
return HttpResponse('ticker not received')
else:
info = si.get_income_statement(ticker)
# print info to a text buffer
with io.StringIO() as buf, redirect_stdout(buf):
print(info)
output = buf.getvalue()
return HttpResponse(output)
def income_stat(self, company):
statement = get_income_statement(company)
df = pd.DataFrame(statement,
columns=[
'Breakdown', 'ttm', '6/30/2019', '6/30/2018',
'6/30/2017'
])
array = []
for ind in df.index:
data = {
'breakdown': df['Breakdown'][ind],
'current': df['ttm'][ind],
'2019': df['6/30/2019'][ind],
'2018': df['6/30/2018'][ind],
'2017': df['6/30/2017'][ind]
}
array.append(data)
print(array)
return array
def my_stock_rank(stock_nm):
#print(stock_nm)
stock_nm = "NESTLEIND.NS"
stock_stats = si.get_stats(stock_nm)
low_52 = float(
stock_stats[stock_stats.Attribute == '52 Week Low 3'].Value.item())
dma_200 = float(stock_stats[stock_stats.Attribute ==
'200-Day Moving Average 3'].Value.item())
avg_price = (low_52 + dma_200) / 2
cmp = si.get_live_price(stock_nm)
buy_price_gap = ((cmp - avg_price) / avg_price) * 100
#print (buy_price_gap)
curr_row = stock_nm + ',' + str(round(buy_price_gap, 2))
print(curr_row)
#df = df.append(curr_row)
income_statement = si.get_income_statement(stock_nm)
balance_sheet = si.get_balance_sheet(stock_nm)
cash_flow_statement = si.get_cash_flow(stock_nm)
income_statement.fillna(0, inplace=True)
income_statement = income_statement.div(10000000).astype(int)
balance_sheet.fillna(0, inplace=True)
balance_sheet = balance_sheet.div(10000000).astype(int)
cash_flow_statement.fillna(0, inplace=True)
cash_flow_statement = cash_flow_statement.div(10000000).astype(int)
income_statement_df = income_statement.transpose()
balance_sheet_df = balance_sheet.transpose()
cash_flow_statement_df = cash_flow_statement.transpose()
revenue = income_statement_df['totalRevenue']
revenue.sort_index()
end_value = float(revenue.iloc[0])
mcap = stock_stats[stock_stats.Attribute ==
'Market Cap (intraday) 5'].Value.item()
if mcap.find("T") == -1:
print("No 'is' here!")
else:
print("Found 'is' in the string.")
def format_object(ticker):
# The following function will pass in our ticker
# and format the dataframe files from Panda and from our API
try:
# the following will format the ticker enter above into a more readable dataframe format form our pandas import
bs_stock_1 = yf.get_balance_sheet(ticker)
# format original columns into these easy to read columns
bs_stock_1.columns = ["2020", "2019", "2018", "2017"]
bs_stock_1.index.name = "Account"
# formatting for the income statement portion
is_stock_1 = yf.get_income_statement(ticker)
is_stock_1.columns = ["2020", "2019", "2018", "2017"]
is_stock_1.index.name = "Account"
# Calling the function to format shares from a dictionary to get a float
# We also want the daily stock and market cap
shares_outstanding = sy.shares(ticker)
daily_share_price = yf.get_live_price(ticker)
market_cap = shares_outstanding * daily_share_price
# this calls the year function
year = sy.year_call()
stock_attributes = {
"Income Statement": is_stock_1,
"Balance Sheet": bs_stock_1,
"total Shares": shares_outstanding,
"Daily Shares": daily_share_price,
"market_cap": market_cap,
"year": year,
"ticker": ticker,
"daily_share_price": daily_share_price
}
control(stock_attributes)
except:
# if they did not enter a ticker
# this will be a pseudo recursive approach and take us back to the main page
print(
"you did not enter a ticker with a correct symbol,you will be taken back to the ticker login section"
)
format_ticker()
def getAcctgInfo(ticker):
global balance_sheet
global income_statement
global cfs
global periods
balance_sheet = yf.get_balance_sheet(ticker, yearly=False)
income_statement = yf.get_income_statement(ticker, yearly=False)
cfs = yf.get_cash_flow(ticker, yearly=False)
periods = balance_sheet.columns
cq = periods[0]
pq = periods[1]
cq_rev = income_statement[periods[0]]['totalRevenue']
pq_rev = income_statement[periods[1]]['totalRevenue']
cq_ni = income_statement[periods[0]]['netIncome']
pq_ni = income_statement[periods[1]]['netIncome']
cq_cash = balance_sheet[periods[0]]['cash']
pq_cash = balance_sheet[periods[1]]['cash']
cq_ocf = cfs[periods[0]]['totalCashFromOperatingActivities']
pq_ocf = cfs[periods[1]]['totalCashFromOperatingActivities']
acctginfo.append((ticker, cq, pq, cq_rev, pq_rev, cq_ni, pq_ni, cq_cash,
pq_cash, cq_ocf, pq_ocf))
import time
qt = qy.get_German_quotes()
ncav = {}
tickers = qt.DE_tickers("Jan17_CS.csv")
start_time = dt.datetime.now()
count = 0
for i in tickers:
if count == 100 or count == 200 or count == 300 or count == 400 or count == 500 or count == 600:
time.sleep(180)
try:
df = get_income_statement(i)
if int(df.loc[df['Breakdown'] == 'Net Income'].iloc[:, 1]) >= 0:
ncav[i] = qt.NCAV(i)
print(i + " is successfully appended.")
count += 1
else:
print(i + " has negative net income.")
count += 1
except:
print(i + " is N/A.")
count += 1
else:
try:
df = get_income_statement(i)
if int(df.loc[df['Breakdown'] == 'Net Income'].iloc[:, 1]) >= 0:
ncav[i] = qt.NCAV(i)
def update_income_statement(self, symbol):
income_statement = si.get_income_statement(symbol).to_dict(orient="dict")
self.update(symbol, income_statement, 'Stocks', 'Income_Statement')
rev_score = 0 pat_score = 0 nprof_score = 0 avg_rec_score = 0 roe_score = 0 avg_prof_cap_emp_score = 0 cfo_score = 0 cfo_gt_10_score = 0 ccflo_to_cnpat_score = 0 debt_score = 0 #def my_stock_price(stock_nm): stock_nm = "TCS.NS" income_statement = si.get_income_statement(stock_nm) balance_sheet = si.get_balance_sheet(stock_nm) cash_flow_statement = si.get_cash_flow(stock_nm) income_statement.fillna(0, inplace=True) income_statement = income_statement.div(10000000).astype(int) balance_sheet.fillna(0, inplace=True) balance_sheet = balance_sheet.div(10000000).astype(int) cash_flow_statement.fillna(0, inplace=True) cash_flow_statement = cash_flow_statement.div(10000000).astype(int) income_statement_df = income_statement.transpose() balance_sheet_df = balance_sheet.transpose() cash_flow_statement_df = cash_flow_statement.transpose()
def get_income_statement(tickers, yearly=True):
dict_data = {
ticker: si.get_income_statement(ticker, yearly)
for ticker in tickers
}
return dict_data
company_data[company_data['Breakdown'] ==
"Net property plant and equipment"].index[0], 2])
PPE3 = float(company_data.iloc[
company_data[company_data['Breakdown'] ==
"Net property plant and equipment"].index[0], 3])
PPE4 = float(company_data.iloc[
company_data[company_data['Breakdown'] ==
"Net property plant and equipment"].index[0], 4])
##################################################
##################################################
##################################################
#this is the information from the income statement
Loaded = 0
company_income_data = get_income_statement(companies)
Loaded += sum(company_income_data['Breakdown'].str.count(
'Net Income Common Stockholders'))
while Loaded == 0.0:
company_income_data = get_income_statement(companies)
Loaded += sum(company_income_data['Breakdown'].str.count(
'Net Income Common Stockholders'))
NI1 = float(company_income_data.iloc[
company_income_data[company_income_data['Breakdown'] ==
'Net Income Common Stockholders'].index[0], 2])
NI2 = float(company_income_data.iloc[
company_income_data[company_income_data['Breakdown'] ==
'Net Income Common Stockholders'].index[0], 3])
def __init__(self, ticker):
self.income_statement = si.get_income_statement(ticker)
self.balance_sheet = si.get_balance_sheet(ticker)
self.cash_flow_statement = si.get_cash_flow(ticker)
self.inputs = self.get_inputs_df()
def my_stock_price(stock_nm):
#stock_nm="DMART.NS"
income_statement = si.get_income_statement(stock_nm)
balance_sheet = si.get_balance_sheet(stock_nm)
cash_flow_statement = si.get_cash_flow(stock_nm)
income_statement.fillna(0, inplace=True)
income_statement=income_statement.div(10000000).astype(int)
balance_sheet.fillna(0, inplace=True)
balance_sheet=balance_sheet.div(10000000).astype(int)
cash_flow_statement.fillna(0, inplace=True)
cash_flow_statement=cash_flow_statement.div(10000000).astype(int)
income_statement_df=income_statement.transpose()
balance_sheet_df=balance_sheet.transpose()
cash_flow_statement_df=cash_flow_statement.transpose()
revenue=income_statement_df['totalRevenue']
pat=income_statement_df['incomeBeforeTax']
nprof=income_statement_df['netIncomeFromContinuingOps']
if "netReceivables" in balance_sheet_df:
avg_recei=balance_sheet_df['netReceivables'].mean()
avg_recei_latest=balance_sheet_df['netReceivables']
else:
avg_recei_df = revenue
avg_recei_df = avg_recei_df.replace(avg_recei_df, 0)
avg_recei=avg_recei_df.mean()
avg_recei_latest=avg_recei_df
#avg_recei=balance_sheet_df['netReceivables'].mean()
avg_rev_20_pct=(income_statement_df['totalRevenue'].mean()*(20/100))
#avg_recei_latest=balance_sheet_df['netReceivables']
tot_share_hold=balance_sheet_df['totalStockholderEquity'].sort_index()
net_income=income_statement_df['netIncome'].sort_index()
roe=(net_income/tot_share_hold)*100
roe_mean=roe.mean()
np_share_hold=income_statement_df['netIncomeApplicableToCommonShares'].sort_index()
prof_cap_emp=(np_share_hold/tot_share_hold)*100
avg_prof_cap_emp=prof_cap_emp.mean()
cfo=cash_flow_statement_df['totalCashFromOperatingActivities']
CCFO=cfo.sum()
CNPAT=income_statement_df['netIncomeApplicableToCommonShares'].sum()
CCFO_to_CNPAT=round(CCFO/CNPAT,2)
if "shortLongTermDebt" in balance_sheet_df:
short_debt=balance_sheet_df['shortLongTermDebt']
else:
#print ("notfound")
short_debt = revenue
short_debt = short_debt.replace(short_debt, 0)
if "longTermDebt" in balance_sheet_df:
long_debt=balance_sheet_df['longTermDebt']
else:
long_debt = revenue
long_debt = long_debt.replace(long_debt, 0)
total_debt=short_debt+long_debt
# sorting.......................
revenue.sort_index()
pat.sort_index()
nprof.sort_index()
avg_recei_latest.sort_index()
cfo.sort_index()
total_debt.sort_index()
""" Values updates"""
end_value = float(revenue.iloc[0])
start_value = float(revenue.iloc[-1])
num_periods = len(revenue)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
rev_grw=0
else:
rev_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
end_value = float(pat.iloc[0])
start_value = float(pat.iloc[-1])
num_periods = len(pat)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
pat_grw=0
else:
pat_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
end_value = float(nprof.iloc[0])
start_value = float(nprof.iloc[-1])
num_periods = len(nprof)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
nprof_grw=0
else:
nprof_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
end_value = float(nprof.iloc[0])
start_value = float(nprof.iloc[-1])
num_periods = len(nprof)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
nprof_grw=0
else:
nprof_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
end_value = float(avg_recei_latest.iloc[0])
start_value = float(avg_recei_latest.iloc[-1])
num_periods = len(avg_recei_latest)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
avg_recei_grw=0
else:
avg_recei_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
latest_roe = float(roe.iloc[-1])
end_value = float(cfo.iloc[0])
start_value = float(cfo.iloc[-1])
num_periods = len(cfo)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
cfo_grw=0
else:
cfo_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
end_value = float(total_debt.iloc[0])
start_value = float(total_debt.iloc[-1])
num_periods = len(total_debt)
if start_value == 0:
start_value=0.01
else:
start_value=start_value
if start_value == end_value:
total_debt_grw=0
else:
total_debt_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
total_debt_mean=total_debt.mean()
total_rev_mean=revenue.mean()
total_rev_mean_20_pct=total_rev_mean*(20/100)
""" Values updates"""
rev_simple=rev_grw-(rev_grw*(5/100))
if rev_simple > 8:
rev_score=1
else:
rev_score=0
if pat_grw > (rev_simple - (rev_simple*(10/100))):
pat_score=1
else:
pat_score=0
if nprof_grw > (rev_simple - (rev_simple*(10/100))):
nprof_score=1
else:
nprof_score=0
if avg_recei < avg_rev_20_pct or avg_recei_grw <=(rev_simple - (rev_simple*(9/100))):
avg_rec_score=1
else:
avg_rec_score=0
if latest_roe > (roe_mean - (roe_mean*(10/100))):
roe_score=1
else:
roe_score=0
if avg_prof_cap_emp > 15 :
avg_prof_cap_emp_score=1
else:
avg_prof_cap_emp_score=0
if cfo_grw > (rev_simple - (rev_simple*(10/100))):
cfo_score=1
else:
cfo_score=0
if cfo_grw > 10:
cfo_gt_10_score=1
else:
cfo_gt_10_score=0
if CCFO_to_CNPAT > 0.8:
ccflo_to_cnpat_score=1
else:
ccflo_to_cnpat_score=0
if total_debt_mean < total_rev_mean_20_pct or total_debt_grw <=(rev_simple - (rev_simple*(10/100))) or total_debt_grw <0 :
debt_score=1
else:
debt_score=0
stock_stats = si.get_stats(stock_nm)
opm=float(stock_stats[stock_stats.Attribute=='Profit Margin'].Value.item().replace('%', ''))
book_per_share=float(stock_stats[stock_stats.Attribute=='Book Value Per Share (mrq)'].Value.item())
promoter_hold=float(stock_stats[stock_stats.Attribute=='% Held by Insiders 1'].Value.item().replace('%', ''))
roe=float(stock_stats[stock_stats.Attribute=='Return on Equity (ttm)'].Value.item().replace('%', ''))
eps=float(stock_stats[stock_stats.Attribute=='Diluted EPS (ttm)'].Value.item())
if opm >=0 and book_per_share>=0 and promoter_hold>=45 and roe>=10 and eps>=0 :
#print(stock_nm,opm,book_per_share,promoter_hold,roe,eps)
base_validation=1
total_score=(rev_score+pat_score+nprof_score+avg_rec_score+roe_score+avg_prof_cap_emp_score+cfo_score+cfo_gt_10_score+ccflo_to_cnpat_score+debt_score)
if total_score >=8 and base_validation>0 :
print (stock_nm, "Good For Investment" , total_score)
selected_list.
else:
print (stock_nm, "Please skip", total_score)
stats = [
"Net income available to common shareholders", "Total assets",
"Net cash provided by operating activities", "Long-term debt",
"Other long-term liabilities", "Total current assets",
"Total current liabilities", "Common stock", "Total revenue",
"Gross profit"
]
for ticker in tickers:
temp_dict = {}
temp_dict2 = {}
temp_dict3 = {}
bal = si.get_balance_sheet(ticker)
cf = si.get_cash_flow(ticker)
income = si.get_income_statement(ticker)
print("getting financial data for: ", ticker)
try:
temp_dict["Net income available to common shareholders"] = income.loc[
"netIncomeApplicableToCommonShares", :][0]
temp_dict2["Net income available to common shareholders"] = income.loc[
"netIncomeApplicableToCommonShares", :][1]
temp_dict3["Net income available to common shareholders"] = income.loc[
"netIncomeApplicableToCommonShares", :][2]
except:
temp_dict["Net income available to common shareholders"] = np.nan
temp_dict2["Net income available to common shareholders"] = np.nan
temp_dict3["Net income available to common shareholders"] = np.nan
try:
temp_dict["Total Current Assets"] = bal.loc["totalCurrentAssets", :][0]
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(si.get_data(input_arguments.symbol))
elif input_arguments.information == 'quote':
pp.pprint(si.get_quote_table(input_arguments.symbol))
elif input_arguments.information == 'stats':
pp.pprint(si.get_stats(input_arguments.symbol))
elif input_arguments.information == 'holders':
pp.pprint(si.get_holders(input_arguments.symbol))
elif input_arguments.information == 'analysis':
pp.pprint(si.get_analysts_info(input_arguments.symbol))
elif input_arguments.information == 'income':
pp.pprint(si.get_income_statement(input_arguments.symbol))
elif input_arguments.information == 'balance':
pp.pprint(si.get_balance_sheet(input_arguments.symbol))
elif input_arguments.information == 'cashflow':
pp.pprint(si.get_cash_flow(input_arguments.symbol))
sys.exit()
previous_price = 0
profit = 0
current_price = 0
def getYearlyIncomeStatement(ticker):
return si.get_income_statement(ticker, yearly=True)
def getQuarterlyIncomeStatement(ticker):
return si.get_income_statement(ticker, yearly=False)
def financial_stmt_checks(stock_nm):
stock_nm="VSTIND.NS"
income_statement = si.get_income_statement(stock_nm)
balance_sheet = si.get_balance_sheet(stock_nm)
cash_flow_statement = si.get_cash_flow(stock_nm)
income_statement.fillna(0, inplace=True)
income_statement=income_statement.div(10000000).astype(int)
balance_sheet.fillna(0, inplace=True)
balance_sheet=balance_sheet.div(10000000).astype(int)
cash_flow_statement.fillna(0, inplace=True)
cash_flow_statement=cash_flow_statement.div(10000000).astype(int)
income_statement_df=income_statement.transpose()
balance_sheet_df=balance_sheet.transpose()
cash_flow_statement_df=cash_flow_statement.transpose()
revenue=income_statement_df['totalRevenue']
pat=income_statement_df['incomeBeforeTax']
nprof=income_statement_df['netIncomeFromContinuingOps']
net_prof=income_statement_df['netIncomeApplicableToCommonShares']
#op_exp=income_statement_df['totalOperatingExpenses']
#########Aggregation##########
tot_revenue=income_statement_df['totalRevenue'].sum()
tot_exp=income_statement_df['totalOperatingExpenses'].sum()
tot_np=income_statement_df['netIncomeFromContinuingOps'].sum()
avg_revenue=income_statement_df['totalRevenue'].mean()
cfo=cash_flow_statement_df['totalCashFromOperatingActivities']
CCFO=cfo.sum()
CNPAT=income_statement_df['netIncomeApplicableToCommonShares'].sum()
CCFO_to_CNPAT=round(CCFO/CNPAT,2)
ebit=income_statement_df['ebit'].sort_index()
net_prof_sort=income_statement_df['netIncomeApplicableToCommonShares'].sort_index()
ebit_to_net_prof=(ebit/net_prof_sort)
avg_ebit_to_net_prof=ebit_to_net_prof.mean()
#tot_int=income_statement_df['interestExpense'].sum()
#############################
if "netReceivables" in balance_sheet_df:
avg_recei=balance_sheet_df['netReceivables'].mean()
avg_recei_latest=balance_sheet_df['netReceivables']
else:
#print ("notfound")
avg_recei_df = revenue
avg_recei_df = avg_recei_df.replace(avg_recei_df, 0)
avg_recei=avg_recei_df.mean()
avg_recei_latest=avg_recei_df
revenue.sort_index()
pat.sort_index()
nprof.sort_index()
net_prof.sort_index()
def int_to_net_prof_lt():
tot_int=income_statement_df['interestExpense'].sum()
if tot_int ==0:
tot_int=0.001
if (tot_int/CNPAT) <= 0.5:
print ("All checks passed", stock_nm)
def ebit_to_net_prof_check():
latest_ebit_to_net_prof = float(ebit_to_net_prof.iloc[-1])
if latest_ebit_to_net_prof >= (avg_ebit_to_net_prof - (avg_ebit_to_net_prof*10/100)):
#print ("All checks passed")
int_to_net_prof_lt()
def Ccfo_to_Cpat():
if CCFO_to_CNPAT > 0.8:
#print ("All checks passed")
ebit_to_net_prof_check()
def receivables_sales():
if avg_recei <= ((avg_revenue*30)/100):
#print ("All checks passed")
Ccfo_to_Cpat()
def net_prof_sales_gt_8pct():
if tot_np >= ((tot_revenue*8)/100):
receivables_sales()
def expense_less_sales():
if tot_revenue>=tot_exp:
net_prof_sales_gt_8pct()
def net_prof_growth():
print ("inside net_prof_growth" )
end_value = float(net_prof.iloc[0])
start_value = float(net_prof.iloc[-1])
num_periods = len(net_prof)
if end_value < 0:
end_value=0.001
else:
end_value=end_value
if start_value <= 0:
start_value=0.001
else:
start_value=start_value
if start_value == end_value:
net_prof_grw=0
else:
net_prof_grw=((end_value / start_value) ** (1 / (num_periods )) - 1)*100
print ("Net profit growth", net_prof_grw)
if int(net_prof_grw) > 7:
expense_less_sales()
#net_prof_growth()
def income_stmt():
print ("income",avg_recei)
net_prof_growth()
balance_sheet()
def balance_sheet():
print ("balance",avg_recei)
cashflow_stmt()
def cashflow_stmt():
print ("cashflow",avg_recei)
def dummy():
net_prof_growth()
dummy()
def page_5():
failures = 0
for i in Tickers5:
if pd.isna(StockInfo.loc[i, 'Revenue GLY']):
time.sleep(5)
# This IF statement is for error handling. Sometimes it fails and just needs some time to rest before it can work again. IDK why.
if failures > 4:
time.sleep(120)
failures = 0
else:
try:
income_statement = sp.get_income_statement(i)
income_statement.reset_index(inplace = True)
# Calculate growth from income statements - GLY indicates "Growth Last Year"
try:
StockInfo.loc[i, 'Revenue GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Total Revenue'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Total Revenue'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Total Revenue'].iloc[:,3])[0]))
StockInfo.loc[i, 'Revenue GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Total Revenue'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Total Revenue'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Total Revenue'].iloc[:,5])[0]))
failures = 0
Tickers5.remove(i)
print('page 5 successful for {}.'.format(i))
except:
print('page 5 failed for {}.'.format(i))
pass
failures = failures + 1
try:
StockInfo.loc[i, 'Cost of Revenue GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Cost of Revenue'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Cost of Revenue'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Cost of Revenue'].iloc[:,3])[0]))
StockInfo.loc[i, 'Cost of Revenue GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Cost of Revenue'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Cost of Revenue'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Cost of Revenue'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Gross Profit GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Gross Profit'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Gross Profit'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Gross Profit'].iloc[:,3])[0]))
StockInfo.loc[i, 'Gross Profit GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Gross Profit'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Gross Profit'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Gross Profit'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Operating Expense GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Operating Expense'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Operating Expense'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Operating Expense'].iloc[:,3])[0]))
StockInfo.loc[i, 'Operating Expense GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Operating Expense'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Operating Expense'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Operating Expense'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Operating Income GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Operating Income'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Operating Income'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Operating Income'].iloc[:,3])[0]))
StockInfo.loc[i, 'Operating Income GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Operating Income'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Operating Income'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Operating Income'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Net Income GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Net Income Common Stockholders'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Net Income Common Stockholders'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Net Income Common Stockholders'].iloc[:,3])[0]))
StockInfo.loc[i, 'Net Income GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Net Income Common Stockholders'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Net Income Common Stockholders'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Net Income Common Stockholders'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Basic EPS GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Basic EPS'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Basic EPS'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Basic EPS'].iloc[:,3])[0]))
StockInfo.loc[i, 'Basic EPS GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Basic EPS'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Basic EPS'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Basic EPS'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Diluted EPS GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Diluted EPS'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Diluted EPS'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Diluted EPS'].iloc[:,3])[0]))
StockInfo.loc[i, 'Diluted EPS GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Diluted EPS'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Diluted EPS'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Diluted EPS'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'EBIT GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'EBIT'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'EBIT'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'EBIT'].iloc[:,3])[0]))
StockInfo.loc[i, 'EBIT GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'EBIT'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'EBIT'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'EBIT'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'EBITDA GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'EBITDA'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'EBITDA'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'EBITDA'].iloc[:,3])[0]))
StockInfo.loc[i, 'EBITDA GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'EBITDA'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'EBITDA'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'EBITDA'].iloc[:,5])[0]))
except:
pass
try:
StockInfo.loc[i, 'Normalized EBITDA GLY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Normalized EBITDA'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Normalized EBITDA'].iloc[:,3])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Normalized EBITDA'].iloc[:,3])[0]))
StockInfo.loc[i, 'Normalized EBITDA GTY'] = (float(list(income_statement[income_statement['Breakdown'] == 'Normalized EBITDA'].iloc[:,2])[0]) - float(list(income_statement[income_statement['Breakdown'] == 'Normalized EBITDA'].iloc[:,5])[0])) / abs(float(list(income_statement[income_statement['Breakdown'] == 'Normalized EBITDA'].iloc[:,5])[0]))
except:
pass
except:
pass
else:
print('Page 5 for {} is complete.'.format(i))
pass
with pd.ExcelWriter(download_path + '\\Stock Rankings ' + str(today) + '.xlsx') as writer:
StockInfo.to_excel(writer, sheet_name='Stats', index = False)
from yahoo_fin import stock_info as si import pprint as pp symbol = 'HDFCBANK.NS' print(si.get_live_price(symbol)) print(si.get_data(symbol)) pp.pprint(si.get_quote_table(symbol)) pp.pprint(si.get_stats(symbol)) pp.pprint(si.get_holders(symbol)) pp.pprint(si.get_analysts_info(symbol)) pp.pprint(si.get_income_statement(symbol)) pp.pprint(si.get_balance_sheet(symbol)) pp.pprint(si.get_cash_flow(symbol))
def get_income_statement(self):
if self.storage == None:
return self.format1D(stock_info.get_income_statement(self.stock))
else:
self.storage['income_statement'] = self.format1D(stock_info.get_income_statement(self.stock))
def income_bar(ses_data, search_btn, search):
if search_btn:
try:
income_sheet = si.get_income_statement(search)
except KeyError:
income_sheet = si.get_income_statement("aapl")
elif ses_data:
income_sheet = si.get_income_statement(ses_data["test"])
else:
income_sheet = si.get_income_statement("aapl")
inc = income_sheet.T
inc["date"] = inc.index
date = inc["date"].dt.year
netincome = (inc["netIncome"] / 1000000000).astype(float).round(2)
totalrev = (inc["totalRevenue"] / 1000000000).astype(float).round(2)
opincome = (inc["operatingIncome"] / 1000000000).astype(float).round(2)
color = np.array(['rgb(255,255,255)'] * netincome.shape[0])
color[netincome < 0] = 'crimson'
color[netincome >= 0] = 'green'
fig2 = go.Figure(data=[
go.Bar(name='Net Income',
x=date,
y=inc["netIncome"],
marker=dict(color=color.tolist()),
text=netincome,
textfont_size=8,
textposition='outside'),
go.Bar(name='Total Revenue',
x=date,
y=inc["totalRevenue"],
text=totalrev,
textfont_size=8,
textposition='outside',
visible='legendonly'),
go.Bar(name='Opt Income',
x=date,
y=inc["operatingIncome"],
text=opincome,
textfont_size=8,
textposition='outside',
visible='legendonly')
])
# Change the bar mode
fig2.update_layout(barmode='group',
height=395,
title_text='Income Statement',
title_x=0.5,
margin=dict(t=70, b=20, l=55, r=40),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig2.update_layout(legend=dict(
orientation="h",
yanchor="bottom",
y=1,
xanchor="right",
x=0.95,
font=dict(size=9, ),
))
return fig2
async def income_statement(ctx, *, ticker):
await ctx.send(si.get_income_statement(ticker))
if symbol == 'Q':
break
if Company_name(symbol) == None:
print('Company not found')
print('')
col += 1
continue
print('Company: ' + Company_name(symbol))
print('Collecting Data: ', end = '')
start_time = time.time()
Balance_Sheet = si.get_balance_sheet(symbol)
Income_Statement = si.get_income_statement(symbol)
Cash_Flow = si.get_cash_flow(symbol)
Quote = si.get_quote_table(symbol)
Valuation_Stats = si.get_stats_valuation(symbol)
print(round(time.time() - start_time,2), 'seconds')
##column 2
print('Insert Data: ', end = '')
start_time = time.time()
total_assets = int(Balance_Sheet.loc[list(Balance_Sheet.iloc[:,0]).index('Total Assets')][1])
total_debt = int(Balance_Sheet.loc[list(Balance_Sheet.iloc[:,0]).index('Total Debt')][1])
try: ebitda_anual = int(Income_Statement.loc[list(Income_Statement.iloc[:,0]).index('Normalized EBITDA')][2])
except: ebitda_anual = 'Fail'
if ebitda_anual == 'Fail': Debt_Ebitda = 'Fail'
print(int(input('Stock Based Compensation:')))
Ticker = input("Insert Ticker:")
quote = yf.get_quote_table(Ticker)
# indexing market cap
MarketCap = quote["Market Cap"]
# print market cap
beta = quote["Beta (5Y Monthly)"]
print('Market Cap:', "{:,}".format(conv_mrktcap(MarketCap)), '$')
print('Beta:', beta)
stats = yf.get_stats_valuation(Ticker)
Data = yf.get_data(Ticker)
Balance_Sheet = yf.get_balance_sheet(Ticker)
financials = yf.get_financials(Ticker)
analyst = yf.get_analysts_info(Ticker)
# import company's valuations as stats
income = yf.get_income_statement(Ticker)
Cash = yf.get_cash_flow(Ticker)
# import comapny's income statement as income
ebit = income.loc["ebit"]
# indexing ebit in icnome statement
ebit2020 = int(ebit["2020"])
# indexing latest ebit in income statement
print('Latest Calender Ebit:', "{:,}".format(ebit2020), "$")
interestExpense = income.loc['interestExpense']
# indexing interest expense in imcome statement
interestExpense2020 = int(-interestExpense["2020"])
# indexing latest interest expemse in income statement
print('Latest Calendar Interest Expense:',
"{:,}".format(interestExpense2020), '$')
for ticker in all_tickers:
all_historical[ticker] = si.get_data(ticker)
print(ticker)
dow_historical = {}
for ticker in dow_list:
dow_historical = si.get_data(ticker, start_date='1990-01-01', end_date='2020-01-01', interval='1d')
print(ticker)
print(si.get_quote_table('AAPL'))
print(si.get_stats('AAPL')[si.get_stats('AAPL')['Attribute'] == 'EBITDA']['Value'].iloc[0])
"""
all_data = {}
for ticker in {'AMZN'}:
balance_sheet = si.get_balance_sheet(ticker, False)
income_statement = si.get_income_statement(ticker, False)
cash_flow = si.get_cash_flow(ticker, False)
stats = si.get_stats(ticker)
analysts_info = si.get_analysts_info(ticker)
earnings = si.get_earnings(ticker)
oneyear_return = (si.get_data(ticker, start_date=datetime.today()-timedelta(days=365), end_date=datetime.today())['close'].iloc[-1] - si.get_data(ticker, start_date=datetime.today()-timedelta(days=365), end_date=datetime.today())['close'][0])/si.get_data(ticker, start_date=datetime.today()-timedelta(days=365), end_date=datetime.today())['close'][0]
stats.columns = ['Labels', 'Values']
stats_labels = []
stats_values = []
for i in range(stats.shape[0]):
stats_labels.append(stats.iat[i, 0])
stats_values.append(stats.iat[i, 1])
stats_df = pd.DataFrame({'Values': stats_values}, index=stats_labels)
