def get_fin_regressors(ticker_code):
# TEST : get financial data for one ticker
data = si.get_financials(
ticker=ticker_code)['yearly_income_statement'].transpose()
data['co_name'] = tickers_df.loc[ticker_code]['name']
data = data.sort_values(by='endDate')
#data.loc['ds'] = data.loc['endDate']
# remove duplicates
data.drop_duplicates(subset=['totalRevenue'], inplace=True)
data = data.reset_index()
data.index = data.endDate
# calculate aggregates
data['grossProfit_margin'] = data.apply(
lambda x: margin(x['grossProfit'], x['totalRevenue']), axis=1)
data['operatingIncome_margin'] = data.apply(
lambda x: margin(x['operatingIncome'], x['totalRevenue']), axis=1)
data['netIncome_margin'] = data.apply(
lambda x: margin(x['netIncome'], x['totalRevenue']), axis=1)
# calculate % growth
revenue_growth = ['NaN']
for i in range(1, len(data)):
revenue_growth.append(
per_growth(data['totalRevenue'][i], data['totalRevenue'][i - 1]))
data['revenue_growth'] = revenue_growth
grossProfit_growth = ['NaN']
for i in range(1, len(data)):
grossProfit_growth.append(
per_growth(data['grossProfit'][i], data['grossProfit'][i - 1]))
data['grossProfit_growth'] = grossProfit_growth
operatingIncome_growth = ['NaN']
for i in range(1, len(data)):
operatingIncome_growth.append(
per_growth(data['operatingIncome'][i],
data['operatingIncome'][i - 1]))
data['operatingIncome_growth'] = operatingIncome_growth
netIncome_growth = ['NaN']
for i in range(1, len(data)):
netIncome_growth.append(
per_growth(data['netIncome'][i], data['netIncome'][i - 1]))
data['netIncome_growth'] = netIncome_growth
return data
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
interestExpense2020 = int(-interestExpense["2020"])
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 get_fundamental_data(self):
""" all fundamental data """
try:
self.fundamental = si.get_quote_data(self.ticker)
except:
pass
try:
self.quote = si.get_quote_table(self.ticker)
except:
pass
try:
self.stats = si.get_stats(self.ticker)
except:
pass
try:
self.earnings = si.get_earnings(self.ticker)
except:
pass
try:
self.financials = si.get_financials(self.ticker)
except:
pass
# Individual fundamental factors
try:
self.earnings_up_count = sum(
(self.earnings['quarterly_revenue_earnings'].earnings.diff() >
0).iloc[1:])
except:
self.errorCount += 1
try:
self.earnings_up_latest = int(
(self.earnings['quarterly_revenue_earnings'].earnings.diff() >
0).iloc[-1])
except:
self.errorCount += 1
try:
self.trailingPE = self.quote['PE Ratio (TTM)']
except:
self.errorCount += 1
try:
self.EPS = self.quote['EPS (TTM)']
except:
self.errorCount += 1
try:
self.marketCap = self.fundamental['marketCap']
except:
self.errorCount += 1
try:
self.TotalDebtEquity = self.stats[
self.stats.Attribute == 'Total Debt/Equity (mrq)'].iloc[0, 1]
except:
self.errorCount += 1
try:
self.shortRatio = float(self.stats[
self.stats.Attribute.str.contains('Short Ratio')].iloc[0, 1])
except:
self.errorCount += 1
try:
self.institutionPct = float(
self.stats[self.stats.Attribute.str.contains(
'Held by Institutions')].iloc[0, 1][:-1])
except:
self.errorCount += 1
def __init__(self, name):
quote_table = si.get_quote_table(name, dict_result=False)
open = quote_table.at[12, 'value']
price = quote_table.at[15, 'value']
prePrice = quote_table.at[14, 'value']
pe = quote_table.at[13, 'value']
avgVol = quote_table.at[3, 'value']
vol = quote_table.at[6, 'value']
ticker = yf.Ticker(name)
info = ticker.info
mktCap = info['marketCap']
mktCapNum = mktCap
mktCap = mktCap / 1000000000
mktCap = "{:.1f}".format(mktCap)
self.mktCap = mktCap + 'B'
symbol = info['symbol']
self.ave50 = info['fiftyDayAverage']
self.name = info['shortName']
country = get_dict_item(info, 'country')
employeeRaw = get_dict_item(info, 'fullTimeEmployees')
if employeeRaw is not None:
employee = format(employeeRaw, ',d')
else:
employee = '-'
instHoldPctRaw = get_dict_item(info, 'heldPercentInstitutions')
if instHoldPctRaw is not None:
instHoldPct = "{:.1%}".format(instHoldPctRaw)
else:
instHoldPct = '-'
fin = si.get_financials(name)
# fin_bs_q=fin["quarterly_balance_sheet"]
fin_bs_q = fin["yearly_balance_sheet"]
fin_bs_q_dates = fin_bs_q.columns.values
date = fin_bs_q_dates[0]
dateStr = str(date)
self.finDate = dateStr[0:10]
fin_year_y = fin["yearly_balance_sheet"]
fin_year_dates_y = fin_year_y.columns.values
date_y = fin_year_dates_y[0]
dateStr_y = str(date_y)
self.finDate_y = dateStr_y[0:10]
sharesOutstandingRaw = get_dict_item(info, 'sharesOutstanding')
sharesOutstanding = number2M_pure(sharesOutstandingRaw)
## General
# Total Asset
totalAssetsRaw, totalAssets = get_dataframe_item(
fin_bs_q, 'totalAssets', country, date, 0)
# Total Liabilities
totalLiabRaw, totalLiab = get_dataframe_item(fin_bs_q, 'totalLiab',
country, date, 0)
totalLiab_pct = addPct(totalLiabRaw, totalLiab, totalAssetsRaw)
# Total Equity
totalEquityRaw = totalAssetsRaw - totalLiabRaw
totalEquity = number2M(totalEquityRaw, country, date)
totalEquityRaw_pct = addPct(totalEquityRaw, totalEquity,
totalAssetsRaw)
## ASSET
# Total Current Assets
totalCurrentAssetsRaw, totalCurrentAssets = get_dataframe_item(
fin_bs_q, 'totalCurrentAssets', country, date, 0)
if totalCurrentAssetsRaw is not None:
pct = "{:.1%}".format(totalCurrentAssetsRaw / totalAssetsRaw)
totalCurrentAssets = totalCurrentAssets + ' (' + pct + ')'
# Cash
cashRaw, cash = get_dataframe_item(fin_bs_q, 'cash', country, date, 0)
cash_pct = addPct(cashRaw, cash, totalCurrentAssetsRaw)
# Short Term Investment
shortTermInvestmentsRaw, shortTermInvestments = get_dataframe_item(
fin_bs_q, 'shortTermInvestments', country, date, 0)
shortTermInvestments_pct = addPct(shortTermInvestmentsRaw,
shortTermInvestments,
totalCurrentAssetsRaw)
# Receivables
netReceivablesRaw, netReceivables = get_dataframe_item(
fin_bs_q, 'netReceivables', country, date, 0)
netReceivables_pct = addPct(netReceivablesRaw, netReceivables,
totalCurrentAssetsRaw)
# Inventory
inventoryRaw, inventory = get_dataframe_item(fin_bs_q, 'inventory',
country, date, 0)
inventory_pct = addPct(inventoryRaw, inventory, totalCurrentAssetsRaw)
# Other Current Asset
otherCurrentAssetsRaw, otherCurrentAssets = get_dataframe_item(
fin_bs_q, 'otherCurrentAssets', country, date, 0)
otherCurrentAssets_pct = addPct(otherCurrentAssetsRaw,
otherCurrentAssets,
totalCurrentAssetsRaw)
# Total Long Term Asset
totalLongTermAssetRaw = totalAssetsRaw - totalCurrentAssetsRaw
totalLongTermAsset = number2M(totalLongTermAssetRaw, country, date)
totalLongTermAsset_pct = addPct(totalLongTermAssetRaw,
totalLongTermAsset, totalAssetsRaw)
# Property, Plant, and Equipment
propertyPlantEquipmentRaw, propertyPlantEquipment = get_dataframe_item(
fin_bs_q, 'propertyPlantEquipment', country, date, 0)
propertyPlantEquipment_pct = addPct(propertyPlantEquipmentRaw,
propertyPlantEquipment,
totalLongTermAssetRaw)
# Long-term Investment
longTermInvestmentsRaw, longTermInvestments = get_dataframe_item(
fin_bs_q, 'longTermInvestments', country, date, 0)
longTermInvestments_pct = addPct(longTermInvestmentsRaw,
longTermInvestments,
totalLongTermAssetRaw)
# Net Intangible Asset
netIntangibleAssetsRaw, netIntangibleAssets = get_dataframe_item(
fin_bs_q, 'intangibleAssets', country, date, 0)
netIntangibleAssets_pct = addPct(netIntangibleAssetsRaw,
netIntangibleAssets,
totalLongTermAssetRaw)
# Goodwill
goodWillRaw, goodWill = get_dataframe_item(fin_bs_q, 'goodWill',
country, date, 0)
goodWill_pct = addPct(goodWillRaw, goodWill, totalLongTermAssetRaw)
# Intangible Asset
intangibleAssetsRaw = netIntangibleAssetsRaw + goodWillRaw
intangibleAssets = number2M(intangibleAssetsRaw, country, date)
intangibleAssets_pct = addPct(intangibleAssetsRaw, intangibleAssets,
totalLongTermAssetRaw)
# Other Long-term Asset
otherAssetsRaw, otherAssets = get_dataframe_item(
fin_bs_q, 'otherAssets', country, date, 0)
otherAssets_pct = addPct(otherAssetsRaw, otherAssets,
totalLongTermAssetRaw)
# Tangible
tangibleAssetsRaw = totalAssetsRaw - intangibleAssetsRaw
tangibleAssets = number2M(tangibleAssetsRaw, country, date)
tangibleAssets_pct = addPct(tangibleAssetsRaw, tangibleAssets,
totalAssetsRaw)
## LIABILITY
# Total Current Liabilities
totalCurrentLiabilitiesRaw, totalCurrentLiabilities = get_dataframe_item(
fin_bs_q, 'totalCurrentLiabilities', country, date, 0)
totalCurrentLiabilities_pct = addPct(totalCurrentLiabilitiesRaw,
totalCurrentLiabilities,
totalLiabRaw)
# Account Payable
accountsPayableRaw, accountsPayable = get_dataframe_item(
fin_bs_q, 'accountsPayable', country, date, 0)
accountsPayable_pct = addPct(accountsPayableRaw, accountsPayable,
totalCurrentLiabilitiesRaw)
# Other Current Liabilities
otherCurrentLiabRaw, otherCurrentLiab = get_dataframe_item(
fin_bs_q, 'otherCurrentLiab', country, date, 0)
otherCurrentLiab_pct = addPct(otherCurrentLiabRaw, otherCurrentLiab,
totalCurrentLiabilitiesRaw)
# Total Long-term Liablities
totalLongTermLiabRaw = totalLiabRaw - totalCurrentLiabilitiesRaw
totalLongTermLiab = number2M(totalLongTermLiabRaw, country, date)
totalLongTermLiab_pct = addPct(totalLongTermLiabRaw, totalLongTermLiab,
totalLiabRaw)
# Long-term Debt
longTermDebtRaw, longTermDebt = get_dataframe_item(
fin_bs_q, 'longTermDebt', country, date, 0)
longTermDebt_pct = addPct(longTermDebtRaw, longTermDebt,
totalLongTermLiabRaw)
shortLongTermDebtRaw, shortLongTermDebt = get_dataframe_item(
fin_bs_q, 'shortLongTermDebt', country, date, 0)
## EQUITY
# Minority Interest
minorityInterestRaw, minorityInterest = get_dataframe_item(
fin_bs_q, 'minorityInterest', country, date, 0)
minorityInterest_pct = addPct(minorityInterestRaw, minorityInterest,
totalEquityRaw)
# Total Shareholder's Equity
totalShareholderEquityRaw = totalEquityRaw - minorityInterestRaw
totalShareholderEquity = number2M(totalShareholderEquityRaw, country,
date)
totalShareholderEquity_pct = addPct(totalShareholderEquityRaw,
totalShareholderEquity,
totalEquityRaw)
# Common Stock
commonStockRaw, commonStock = get_dataframe_item(
fin_bs_q, 'commonStock', country, date, 0)
# Retained Earnings
retainedEarningsRaw, retainedEarnings = get_dataframe_item(
fin_bs_q, 'retainedEarnings', country, date, 0)
# Gains Losses Not Affecting Retained Earnings (Treasury Stock)
treasuryStockRaw, treasuryStock = get_dataframe_item(
fin_bs_q, 'treasuryStock', country, date, 0)
# Common Stock Equity
commonStockEquityRaw, commonStockEquity = get_dataframe_item(
fin_bs_q, 'totalStockholderEquity', country, date, 0)
commonStockEquity_pct = addPct(commonStockEquityRaw, commonStockEquity,
totalShareholderEquityRaw)
# Preferred Stock Equity
preferredStockEquityRaw = totalShareholderEquityRaw - commonStockEquityRaw
preferredStockEquity = number2M(preferredStockEquityRaw, country, date)
preferredStockEquity_pct = addPct(preferredStockEquityRaw,
preferredStockEquity,
totalShareholderEquityRaw)
# Book Value
bookValueRaw = tangibleAssetsRaw - totalLiabRaw
bookValue = number2M(bookValueRaw, country, date)
# Common Book Value
commonBookValueRaw = commonStockEquityRaw - intangibleAssetsRaw
commonBookValue = number2M(commonBookValueRaw, country, date)
capitalSurplusRaw, capitalSurplus = get_dataframe_item(
fin_bs_q, 'capitalSurplus', country, date, 0)
floatSharesRaw = info["floatShares"]
floatShares = number2M(floatSharesRaw, country, date)
floatSharesPct = "{:.1%}".format(floatSharesRaw / sharesOutstandingRaw)
# FUNDAMENTALS
workingCapitalRaw = totalCurrentAssetsRaw - totalCurrentLiabilitiesRaw
if (workingCapitalRaw
is not None) & (not math.isnan(workingCapitalRaw)):
workingCapital = number2M(workingCapitalRaw, country, date)
# Basic Ratios
currentRatioRaw = totalCurrentAssetsRaw / totalCurrentLiabilitiesRaw
currentRatio = "{:.2f}".format(currentRatioRaw)
quickRatioRaw = (totalCurrentAssetsRaw -
inventoryRaw) / totalCurrentLiabilitiesRaw
quickRatio = "{:.2f}".format(quickRatioRaw)
deRaw = totalLiabRaw / totalShareholderEquityRaw
de = "{:.2f}".format(deRaw)
# BVPS
bvpsRaw = commonStockEquityRaw / sharesOutstandingRaw
bvps = "{:.2f}".format(bvpsRaw)
tanBvpsRaw = (commonStockEquityRaw -
intangibleAssetsRaw) / sharesOutstandingRaw
tanBvps = "{:.2f}".format(tanBvpsRaw)
## Income
in_quart = fin["quarterly_income_statement"]
netIncomeRaw, netIncome = get_dataframe_item(in_quart, 'netIncome',
country, date, 0)
# roeRaw=4*netIncomeRaw/((totalStockholderEquityRaw+totalStockholderEquityRawPre1)/2)
# roe="{:.1%}".format(roeRaw)
totalRevenueRaw, totalRevenue = get_dataframe_item(
in_quart, 'totalRevenue', country, date, 0)
# dfsize=in_quart.shape
# colNum=dfsize[1]
# if colNum>1:
# sum = totalRevenueRaw
# for i in range(1,colNum):
# tempRaw,temp=get_dataframe_item(in_quart,'totalRevenue',country,date,i)
# sum = sum + tempRaw
# totalRevenueRawTTM = sum / colNum
# totalRevenueTTM=number2M(totalRevenueRawTTM,country,date)
grossProfitRaw, grossProfit = get_dataframe_item(
in_quart, 'grossProfit', country, date, 0)
rd_q0Raw, rd_q0 = get_dataframe_item(in_quart, 'researchDevelopment',
country, date, 0)
in_year = fin["yearly_income_statement"]
rd_y = in_year.loc['researchDevelopment']
rd_y0 = rd_y.iloc[0]
if rd_y0 is not None:
rd_y0 = convert_currency(rd_y0, country, date)
rd_y0 = int(rd_y0 / 1000000)
rd_y0 = format(rd_y0, ',d')
rd_y0 = str(rd_y0) + 'M'
BalanceSheetBasic = {
'Symbol': symbol,
'MktCapNum': [mktCapNum], # Used for data reorder
'Tot Asset': totalAssets,
'Tot Liab': totalLiab_pct,
'Tot Equity': totalEquityRaw_pct
}
df_BalanceSheetBasic = pd.DataFrame(BalanceSheetBasic, index=[0])
d = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Price': [("{:.2f}".format(price))],
'EMPL No.': employee,
'Qtly Date': self.finDate,
'Annu Date': self.finDate_y,
'Shares Outsdg': sharesOutstanding
}
df_Old = pd.DataFrame(d, index=[0])
incomeDetail = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Net Income': netIncome
}
df_incomeDetail = pd.DataFrame(incomeDetail, index=[0])
assetDetail = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Tot Asset': totalAssets,
'Total Current / Tot': totalCurrentAssets,
'Cash / Cr': cash_pct,
'ShrtT Invest / Cr': shortTermInvestments_pct,
'Receivables / Cr': netReceivables_pct,
'Inventory / Cr': inventory_pct,
'Other Curr Asset / Cr': otherCurrentAssets_pct,
'Total Long-term / Tot': totalLongTermAsset_pct,
'Property,ect / Lng': propertyPlantEquipment_pct,
'LongT Invest / Lng': longTermInvestments_pct,
'Intangible / Lng': intangibleAssets_pct,
'Net Intangible / Lng': netIntangibleAssets_pct,
'Goodwill / Lng': goodWill_pct,
'Other LongT Asset / Lng': otherAssets_pct,
'Tangible / Tot': tangibleAssets_pct,
}
df_assetDetail = pd.DataFrame(assetDetail, index=[0])
liabilityDetail = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Tot Liab': totalLiab,
'Total Current / Tot': totalCurrentLiabilities_pct,
'Acc Payable / Cr': accountsPayable_pct,
'Other Curr / Cr': otherCurrentLiab_pct,
'Total Long / Tot': totalLongTermLiab_pct,
'Long Debt / Lng': longTermDebt_pct,
'shortLongTermDebt': shortLongTermDebt
}
df_liabilityDetail = pd.DataFrame(liabilityDetail, index=[0])
equityDetail = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Tot Eqty': totalEquity,
'Mnrty Int / Tot': minorityInterest_pct,
'Tot Sh Eqty / Tot': totalShareholderEquity_pct,
'Commn Eqty / ShH': commonStockEquity_pct,
'Prffd Eqty / ShH': preferredStockEquity_pct,
'Book Val': bookValue,
'Comn Book Val': commonBookValue,
'Cap Surplus': capitalSurplus
}
df_equityDetail = pd.DataFrame(equityDetail, index=[0])
fundamentals = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Wrk Cap': workingCapital,
}
df_fundamentals = pd.DataFrame(fundamentals, index=[0])
baiscRatios = {
'Symbol': symbol,
'MktCapNum': mktCapNum, # Used for data reorder
'Current Rt': currentRatio,
'Quick Rt': quickRatio,
'Debt-Equity': de,
'BVPS': bvps,
'TanBVPS': tanBvps
}
df_baiscRatios = pd.DataFrame(baiscRatios, index=[0])
self.output = {
'General Information': df_Old,
'Balance Sheet Basic': df_BalanceSheetBasic,
'Income': df_incomeDetail,
'Assets Details': df_assetDetail,
'Liability Details': df_liabilityDetail,
'Equity Details': df_equityDetail,
'Fundamentals': df_fundamentals,
'Basic Ratios': df_baiscRatios
}
def getQuarterlyFinancials(ticker):
return si.get_financials(ticker, yearly=False, quarterly=True)
from yahoo_fin import stock_info as si import pandas as pd import xlsxwriter stock = 'NVDA' directory = "/users/satish/Desktop/" + stock + "_fundamentals.xlsx" writer = pd.ExcelWriter(directory, engine='xlsxwriter') balance_sheet = si.get_balance_sheet(stock, True) income_statement = si.get_income_statement(stock, True) cash_flow = si.get_cash_flow(stock, True) financials = si.get_financials(stock, yearly=True) balance_sheet.to_excel(writer, sheet_name='Balance Sheet') income_statement.to_excel(writer, sheet_name='Income Statement') cash_flow.to_excel(writer, sheet_name='Cash Flow') #financials.to_excel(writer, sheet_name = 'Financials') writer.save()