def bs_chart_input(search_btn,search):
if search_btn:
balance_sheet = si.get_balance_sheet(search)
bs = balance_sheet.T
else:
balance_sheet = si.get_balance_sheet("aapl")
bs = balance_sheet.T
bs["date"] = bs.index
date= bs["date"].dt.year
ta = (bs["totalAssets"]/1000000000).astype(float).round(2)
tl = (bs["totalLiab"]/1000000000).astype(float).round(2)
# color=np.array(['rgb(255,255,255)']*ta.shape[0])
# color[ta<0]='red'
# color[ta>=0]='green'
fig3 = go.Figure(data=[
go.Bar(name='Total Asset', x=date, y=bs["totalAssets"],marker_color="green", text = ta,textfont_size=8, textposition='outside',),
go.Bar(name='Total Liability', x=date, y=bs["totalLiab"],marker_color="crimson", text = tl,textfont_size=8, textposition='outside',),
])
# Change the bar mode
fig3.update_layout(barmode='group',width = 444,title_text='Asset vs Liability',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)')
fig3.update_layout(legend=dict(orientation="h",yanchor="bottom",y=1,xanchor="right",x=0.75,font=dict(size=9,),))
return fig3
def gearing(self, tck):
equity = int(
get_balance_sheet(tck).loc[get_balance_sheet(
tck)['Breakdown'] == "Total stockholders' equity"].iloc[:, 1])
debt = int(
get_balance_sheet(tck).loc[get_balance_sheet(tck)['Breakdown'] ==
"Total Liabilities"].iloc[:, 1])
gear = debt / equity
return gear
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 balance(request):
ticker = json.loads(request.body)['ticker']
if not ticker:
return HttpResponse('ticker not received')
else:
info = si.get_balance_sheet(ticker)
# print info to a text buffer
with io.StringIO() as buf, redirect_stdout(buf):
print(info)
output = buf.getvalue()
return HttpResponse(output)
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))
def balance_sheet(self):
bundle = [
self.stock_one, self.stock_two, self.stock_three, self.stock_four
]
for symbol in bundle:
try:
sheet = si.get_balance_sheet(stock_class)
stats = si.get_stats(symbol)
sheet.cash
sheet.longTermInvestments
#balancesheet
ebitda = stats[stats.Attribute == 'EBITDA']
ebitda_value = ebitda['Value'].values[0]
company = stock.upper()
except KeyError as err:
sector = 'N/A'
symbol = company.info['symbol']
print(f'{symbol} sector not found {sector}')
except TypeError as err:
print(f'{symbol} showing {err}')
PS_TTM = 'Nan'
print('setting Nan for PS_TTM')
pass
except IndexError as err:
print(f'{symbol} showing {err}')
except Exception as e:
print(e)
tweet = f"DAILY STOCK\nStock: ${company}\nMarket Cap: ${market_cap}\n52 Week Range: ${fiftytwo_wk}\nClose: ${previous_close}\n1y Target Est: ${one_year_target}\nEPS (TTM): ${eps_ttm}\nPE Ratio (TTM) {pe_ratio}"
# response = tweet_api.update_status(status=tweet)
# original_tweet = original_tweet.id
# tweet_id = response.id
### to respond to a tweet ####
# status=textforreply,
# in_reply_to_status_id=original_twee.id
# auto_populate_reply_metadata=True#
print(f'tweet sent!\n{tweet}')
print(f'tweet Char Count: {len(tweet)}')
return tweet
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)
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 get_yahoo_fin(prod, outdir):
"""
Query exhaustive information of a stock on Yahoo finance and output to a worksheet
Args:
prod Product object, containing basic info about the stock such as symbol, name, currency etc.
start_date start date string such as '1/1/2018' or '2018-1-1T00:00:00'
end_date end date string such as '30/12/2018' ...
outdir output dir to save the plot
Return:
no return
"""
ticker = prod._symbol
out_path = os.path.join(outdir, f'{prod._name}.xlsx')
print(f'Retrieving data for {prod._name} ...')
writer = pd.ExcelWriter(out_path,engine='xlsxwriter') # Creating Excel Writer Object from Pandas
# summary, quote table, stats
print('\tQuerying summary/quote table/stats ...')
row_count = 0
summ = {'name': prod._name, 'id': prod._id, 'symbol': prod._symbol,
'close price': prod._closeprice,
'close date': prod._closedate,
'current price': si.get_live_price(ticker),
'vwdId': prod._vwdId}
df_summ = dict2dataframe(summ, 'info')
df_summ.rename_axis('Summary', axis='index', inplace=True)
df_summ.to_excel(writer, sheet_name='Summary', startrow=row_count, startcol=0)
row_count = row_count + len(df_summ) + 2
df_quote = dict2dataframe(si.get_quote_table(ticker))
df_quote.rename_axis('Quote table', axis='index', inplace=True)
df_quote.to_excel(writer, sheet_name='Summary', startrow=row_count, startcol=0)
row_count = row_count + len(df_quote) + 2
df_stats = si.get_stats(ticker)
df_stats.rename_axis('Stats', axis='index', inplace=True)
df_stats.to_excel(writer, sheet_name='Summary', startrow=row_count, startcol=0)
row_count = row_count + len(df_stats) + 2
# analyst
print('\tQuerying analyst ...')
ana = si.get_analysts_info(ticker) # this return a dict of pandas dataframes
row_count = 0
for key, df in ana.items():
df.name = key
df.to_excel(writer, sheet_name='Analyst Info', startrow=row_count, startcol=0)
row_count = row_count + len(df) + 2
# balance sheet
print('\tQuerying balance ...')
df_bal = si.get_balance_sheet(ticker)
df_bal.to_excel(writer,sheet_name='Balance', startrow=0 , startcol=0)
# cash flow
print('\tQuerying cash flow ...')
df_cash = si.get_cash_flow(ticker)
df_cash.to_excel(writer,sheet_name='Cash flow', startrow=0 , startcol=0)
# data
print('\tQuerying historic data ...')
df_data = si.get_data(ticker)
df_data.sort_index(ascending=False, inplace=True)
df_data.to_excel(writer,sheet_name='Data', startrow=0 , startcol=0)
# financial
print('\tQuerying financial ...')
fin = si.get_financials(ticker) # this return a dict of dataframes
row_count = 0
for key, df in fin.items():
df.rename_axis(key, axis='index', inplace=True)
df.to_excel(writer, sheet_name='Financial', startrow=row_count, startcol=0)
row_count = row_count + len(df) + 2
# save
writer.save()
print(f'Data saved to {out_path}')
def update_yearly_balance_sheet(self, symbol):
yearly_balance_sheet = si.get_balance_sheet(symbol).to_dict(orient="dict")
self.update(symbol, yearly_balance_sheet, 'Stocks', 'Yearly_Balance_Sheet')
def NCAV(self, tck):
df = get_balance_sheet(tck)
NCAV_last = (int(df.loc[df['Breakdown'] == 'Total Current Assets'].iloc[:,1]) - \
int(df.loc[df['Breakdown'] == 'Total Liabilities'].iloc[:,1]))*1000 / \
self.market_cap(tck)
return NCAV_last
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()
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 __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 get_balance_sheet(tickers, yearly=True):
dict_data = {
ticker: si.get_balance_sheet(ticker, yearly)
for ticker in tickers
}
return dict_data
def down_bs(self, ticker):
balance = si.get_balance_sheet(ticker)
filename = ticker + 'BalanceSheet.txt'
f = open(filename, "x")
print(balance, file=f)
def getYearlyBalanceSheet(ticker):
return si.get_balance_sheet(ticker)
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))
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
while True:
while (True):
import yahoo_fin.stock_info as info
import datetime
ticker = []
test = 'ZM'
date = datetime.date.today()
print(date)
file = open('guide/tickers.txt', 'r').readlines()
for line in file:
line = line.replace('\n', '')
ticker.append(line)
print(info.get_data(test, start_date="1/2/2018"))
print(info.get_balance_sheet(test))
print(info.get_analysts_info(test))
def get_balance_sheet(self):
if self.storage == None:
return self.format1D(stock_info.get_balance_sheet(self.stock))
else:
self.storage['balance_sheet'] = self.format1D(stock_info.get_balance_sheet(self.stock))
]
A = open('InformationTech.csv', 'w')
writer1 = csv.writer(A)
Col = ['Company'] + column_names
writer1.writerow(Col)
#this for loop cycles through every company
for companies in list_of_companies:
print(companies)
#add a csv writer that adds these to rows
data_list = [companies]
#this is the information from the balance sheet
Loaded = 0
company_data = get_balance_sheet(companies)
#no matter how many times I try to redownload the data, the key is always missing
#this line is being problematic
Loaded += sum(
company_data['Breakdown'].str.count("Total stockholders' equity"))
while Loaded == 0.0:
company_data = get_balance_sheet(companies)
Loaded += sum(
company_data['Breakdown'].str.count("Total stockholders' equity"))
SE1 = float(company_data.iloc[company_data[
company_data['Breakdown'] == "Total stockholders' equity"].index[0],
1])
return np.nan
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
def getQuarterlyBalanceSheet(ticker):
return si.get_balance_sheet(ticker, yearly=False)
async def balance_sheet(ctx, *, ticker):
await ctx.send(si.get_balance_sheet(ticker))
all_historical = {}
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)
WAM = int(
input(
'What is the Weighted Average Maturity of Debt Found in 10k Report (if unsure write 5):'
))
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
symbol = symbol.upper()
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'
