def annualIncome(tickerName):
ticker = Ratios.TickerFundamentals(tickerName)
"""
0 - dates
1 - revenues
2 - gross margin
3 - RD & SGA / Gross Margin
4 - op margin
5 - reinvestment rate
6 - profit margin
7 - net income per share
8 - fcf per share
9 - cash/debt
10 - ROIC
"""
temp = Utility.invert(ticker.spreadSheet())
spreadSheet = []
for i in temp:
add = ""
for j in i:
add = str(j) + ":" + add
spreadSheet.append(add)
for i in spreadSheet:
print(i)
def getPerShareValue(tickerName, getData=None):
f = Ratios.TickerFundamentals(tickerName, getData)
fcf = f.getFCF()
ROIC = f.getROIC()[1]
IR = f.reinvestment()
NOPLAT = f.getNOPLAT()
numShares = f.numShares()
cashDebt = f.getCashDebt()
fcfPerShare = []
for i in range(0, len(fcf)):
fcfPerShare.append(
Utility.myFloat(fcf[i]) / Utility.myFloat(numShares[i]))
cashDebtShare = []
for i in range(0, len(cashDebt)):
cashDebtShare.append(
Utility.myFloat(cashDebt[i]) / Utility.myFloat(numShares[i]))
cashDebt = cashDebtShare[0]
fcfNow = fcfPerShare[0]
try:
fcf_3yr = (fcfPerShare[0] + fcfPerShare[1] + fcfPerShare[2]) / 3
except:
fcf_3yr = (fcfPerShare[0] + fcfPerShare[1]) / 2
return fcfNow, fcf_3yr, cashDebt
def screen(tickerName):
multipleFits = RegressionData.getRegressionData(tickerName)
discountedPrice = float(DiscountedModel.discountedCashFlow(tickerName))
todaysPrice, PE, PS, PB, totalDebtToAssetsQ, incomeQualityQ, ROIC, incomeQualityTTM, inventoryTurnoverTTM = Ratios.getStatistics(tickerName)
price_thresholdBuy = todaysPrice + (todaysPrice * 0.05)
_, dividend = Ratios.getDividends(tickerName)
if(dividend < 0.035):
return
print(tickerName + ' Dividend ' + str(dividend))
"""Screen multiple fits. [r^2, regressed, variables] """
buySignals = 0
for i in multipleFits:
r2, regressedPrice = i[0], i[1]
if(r2 > 0.75 and regressedPrice > price_thresholdBuy):
buySignals += 1
if(buySignals < len(multipleFits)/2):
return
"""Screen discounted price """
if(discountedPrice > price_thresholdBuy):
pass
else:
return
"""Screen P/E ratio """
if(PE > 25):
return
"""At this point this is a buy """
print("----- Buy Signal -----")
print("Todays Price = " + "{0:.2f}".format(todaysPrice))
for i in multipleFits:
print("R2 = " + "{0:.2f}".format(i[0]) + " | Regressed Price = " + "{0:.2f}".format(i[1]) + " | " + str(i[2]))
print("Discounted price = " + "{0:.2f}".format(discountedPrice) + " | PE = " + "{0:.2f}".format(PE))
def screen(tickerName):
getData = GetData.getData(tickerName)
f = Ratios.TickerFundamentals(tickerName, getData)
fcfNow, fcfAvg, value, valueAvg = Value.getValue(tickerName, getData)
debtToAssets = f.getDebt()
goodwill = f.getGoodwill()
ROIC = f.getROIC()[1][0]
todaysPrice = HistoricalPrices.getTodaysPriceOffline(tickerName)
price_thresholdBuy = todaysPrice + (todaysPrice) * .20
if (value[0] > price_thresholdBuy):
if (debtToAssets > 0.75):
print("Debt is very high")
if (goodwill > .5):
print("Goodwill very high")
print(tickerName + " - buy signal")
print("Todays Price = " + str(todaysPrice))
print("ROIC = " + str(ROIC))
print("FCF Now : " + "{:,}".format(fcfNow))
print("FCF 3yr Avg : " + "{:,}".format(fcfAvg))
print("Value : " + str(value))
print("Value Avg : " + str(valueAvg))
print("")
return True
def showAnalysis(tickerName):
multipleFits = RegressionData.getRegressionData(tickerName)
discountedPrice = float(DiscountedModel.discountedCashFlow(tickerName))
todaysPrice, PE, PS, PB, totalDebtToAssetsQ, incomeQualityQ, ROIC, incomeQualityTTM, inventoryTurnoverTTM = Ratios.getStatistics(tickerName)
ROIC = float(ROIC) * 100
"""At this point this is a buy """
print(tickerName)
print("Todays Price : " + "{0:.2f}".format(todaysPrice))
print("P/E : " + "{0:.2f}".format(PE))
# print("P/S : " + "{0:.2f}".format(PS))
# print("P/B : " + "{0:.2f}".format(PB))
# print("ROIC : " + "{0:.2f}".format(float(ROIC)) + '%')
regressedPrices = []
for i in multipleFits:
"""MultipleFits returns [0 - radjusted, 1 - regressedPrice, 2 - keywords ]"""
radjusted, regressedPrice, keywords = i[0], float(i[1]), i[2]
regressedPrices.append(regressedPrice)
regressedPrices.sort()
print("Multiple linear analysis gives a target price between " + "{0:.2f}".format(regressedPrices[0]) + " - "
+ "{0:.2f}".format(regressedPrices[len(regressedPrices) - 1]))
print("Multiple linear analysis performed between price and variables such as revenues, dividends, book value, etc")
# print("Todays Price = " + "{0:.2f}".format(todaysPrice))
# for i in multipleFits:
# print("R2 = " + "{0:.2f}".format(i[0]) + " | Regressed Price = " + "{0:.2f}".format(i[1]) + " | " + str(i[2]))
print("Discounted price = " + "{0:.2f}".format(discountedPrice))
print("DCF is done with a conservative 12% discount rate and 2.5% long term growth rate")
fairValue = (sum(regressedPrices)/ len(regressedPrices)) * 0.5 + discountedPrice * 0.5
print("From Multiple linear analysis and DCF, and approximate fair value is = $" + "{0:.2f}".format(fairValue))
print("")
def returnFundamentalData(tickerName):
todaysPrice, PE_ratioTTM, PS_ratioTTM, PB_ratioTTM, totalDebtToAssetsQ, incomeQualityQ, ROIC, incomeQualityTTM, inventoryTurnoverTTM = Ratios.getStatistics(
tickerName)
returnArr = [
todaysPrice, PE_ratioTTM, PS_ratioTTM, PB_ratioTTM, totalDebtToAssetsQ,
incomeQualityQ, ROIC, incomeQualityTTM, inventoryTurnoverTTM
]
for i in range(0, len(returnArr)):
try:
returnArr[i] = "{0:.2f}".format(returnArr[i])
except:
pass
returnArr.insert(0, tickerName)
return returnArr
def discountedCashFlow(tickerName):
data, variables, price, priceAvg, ones = GetData.getData(tickerName)
index_freeCashFlowQ = 0
index_dividendQ = 0
# for i in variables:
# print(i)
count = 0
for i in variables:
if (i == 'Free Cash Flow-QC'):
index_freeCashFlowQ = count
if (i == 'Dividends per Basic Common Share-Q'):
index_dividendQ = count
count += 1
dates = []
freeCashFlowQ = []
dividend = []
for i in data:
dates.append(i[0])
freeCashFlowQ.append(i[index_freeCashFlowQ])
dividend.append(i[index_dividendQ])
datesAnnualized = []
freeCashFlowAnnualized = []
dividendAnnualized = []
count = 0
tempCash = 0
tempDividend = 0
for i in range(0, len(dates)):
if (count == 0):
datesAnnualized.append(dates[i])
if (freeCashFlowQ[i] == '' or freeCashFlowQ[i] == ' '):
continue
else:
tempCash += float(freeCashFlowQ[i])
if (dividend[i] == '' or dividend[i] == ' '):
continue
else:
tempDividend += float(dividend[i])
count += 1
if (count == 4):
freeCashFlowAnnualized.append(tempCash)
dividendAnnualized.append(tempDividend)
tempDividend = 0
count = 0
tempCash = 0
for i in range(0, len(freeCashFlowAnnualized)):
if (i + 1 < len(freeCashFlowAnnualized)):
change = (freeCashFlowAnnualized[i] - freeCashFlowAnnualized[i + 1]
) / freeCashFlowAnnualized[i + 1] * 100
else:
change = 0
# print(datesAnnualized[i] + " : " + str(freeCashFlowAnnualized[i]) + " : " + str(change) + " : " + str(dividendAnnualized[i]))
"""Discounted Cash Flow Analysis.
5 years + residual value discounted to present value.
5 years are estimated with a constant growth rate. Future cash flow = current * (1 + growth rate) ^ years
Residual Calculation : (Final Year * ( 1 + long term growth rate)) / (Discount Rate - Long Term Growth Rate)
Long term growth rate = 2.5 - 3 %
Discouted Rate = 6 - 8 %
Also add dividends into discounted cash flow analysis.
5 years dividend out and estimate dividend growth rate. Future dividend = current * (1 + growth rate) ^ years
Discount dividend back to present value/
"""
"""Step 1: Estimate conservative growth rate of cash flow by using trend analysis of last 5 years. If < 0, use ~5 %"""
i = 0
CAGR = []
while (i + 5 < len(freeCashFlowAnnualized)):
if (freeCashFlowAnnualized[i] <= 0
or freeCashFlowAnnualized[i + 5] <= 0):
i += 1
continue
change = (freeCashFlowAnnualized[i] /
freeCashFlowAnnualized[i + 5])**(1 / 5) - 1
CAGR.append(float(change))
i += 1
"""If cashflows are mostly negative, the company's cash flow is considered to not be growing. """
if (len(CAGR) < 1):
estimatedGrowth = -1
else:
estimatedGrowth = sum(CAGR) / float(len(CAGR))
"""Calculated estimated growth rate of future cash flow. If estimated growth > 10%, use 10% as growth rate. If
growth rate is < 0%, use 4% as growth rate. """
if (estimatedGrowth > 0.1):
estimatedGrowth = 0.08
elif (estimatedGrowth == -1):
estimatedGrowth = 0.005
elif (estimatedGrowth < 0):
estimatedGrowth = 0.015
"""Estimate long term growth rate at 2.75%. Discount is 7%"""
# estimatedGrowth = .05
longTermGrowth = 0.025
discountedRate = 0.12
"""Now calculate discounted cash flow for 5 years """
fiveYrCash = []
fiveYrDiscounted = []
currentCash = freeCashFlowAnnualized[0]
if (currentCash < 0):
currentCash = sum(freeCashFlowAnnualized) / float(
len(freeCashFlowAnnualized))
if (currentCash < 0):
return 0
for i in range(0, 5):
fiveYrCash.append(currentCash * (1 + estimatedGrowth))
currentCash = fiveYrCash[i]
for i in range(0, 5):
fiveYrDiscounted.append(fiveYrCash[i] / (1 + discountedRate)**i)
residual = (fiveYrCash[4] *
(1 + longTermGrowth)) / (discountedRate - longTermGrowth)
discountedResidual = residual / (1 + discountedRate)**5
intrinsic = discountedResidual
for i in fiveYrDiscounted:
intrinsic += i
numShares = Ratios.numShares(tickerName)
"""Intrinsic Price w/o dividends """
intrinsicPrice = intrinsic / numShares
return intrinsicPrice
def getROIC(tickerName):
getData = GetData.getData(tickerName)
f = Ratios.TickerFundamentals(tickerName, getData)
ROIC = f.getROIC()[1][0]
return ROIC
def reproductionEPV(tickerName, getData=None):
if (getData == None):
data, variables, price, priceAvg, ones = GetData.getData(tickerName)
else:
data, variables, price, priceAvg, ones = getData
"""Reproduction value will be calculated first.
Here, reproduction value is very rudimentary. It is the capital required
to reproduce the business by looking at balance sheet. This is the formula:
+ 100% of Cash and Equivalents / Short Term Investments
+ 90% of Inventory TTM
+ x2.5 of SG&A TTM
+ 110% of Trade and Non-Trade Receivables
+ 80% of PP&E
+ x2.5 of R&D TTM
+ 100 % Tax Assets discounted at 10%
+ 50% of Goodwill and Intangible Assets
+ 85% of Investments (similar to cash)
*For financial/insurance firms. This is usually 0 for other companies
- 100% of Current Liabilites
(Account/Trade Payables, Current Debt, Other liabilites all in one)
- Tax Liabilities discounted at 10%
- 100% of Debt non current.
"""
"""All data is in 'data' array. data[0] is most recent earnings data. To
find index of variable, variableIndex[] will be used which is on GetData.py
See below. """
# for i in data:
# print(i)
#
reproduction = 0
try:
reproduction += (1) * float(
data[0][variables.index('Cash and Equivalents-QB')])
reproduction += (1) * float(
data[0][variables.index('Investments Current-QB')])
reproduction += (0.80) * float(
data[0][variables.index('Inventory-QB')])
reproduction += (2) * float(data[0][variables.index(
'Selling, General and Administrative (SG&A) Expenses-T')])
reproduction += (0.8) * float(
data[0][variables.index('Property, Plant & Equipment Net-QB')])
reproduction += (1.1) * float(
data[0][variables.index('Trade and Non-Trade Receivables-QB')])
reproduction += (2.5) * float(data[0][variables.index(
'Research and Development (R&D) Expenses-T')])
reproduction += float(data[0][variables.index('Tax Assets-QB')]) / 1.1
reproduction += (0.20) * float(
data[0][variables.index('Goodwill and Intangible Assets-QB')])
reproduction += (0.90) * float(
data[0][variables.index('Investments Non-Current-QB')])
reproduction -= (1) * float(
data[0][variables.index('Current Liabilities-QB')])
reproduction -= float(
data[0][variables.index('Tax Liabilities-QB')]) / 1.1
reproduction -= (1) * float(
data[0][variables.index('Debt Non-Current-QB')])
except:
try:
reproduction += (1) * float(
data[0][variables.index('Cash and Equivalents-QB')])
reproduction += (0.80) * float(
data[0][variables.index('Inventory-QB')])
reproduction += (2) * float(data[0][variables.index(
'Selling, General and Administrative Expense-T')])
reproduction += (0.8) * float(
data[0][variables.index('Property, Plant & Equipment Net-QB')])
reproduction += (1) * float(
data[0][variables.index('Trade and Non-Trade Receivables-QB')])
reproduction += (2.5) * float(
data[0][variables.index('Research and Development Expense-T')])
reproduction += float(
data[0][variables.index('Tax Assets-QB')]) / 1.1
reproduction += (0.50) * float(
data[0][variables.index('Goodwill and Intangible Assets-QB')])
reproduction += (1) * float(
data[0][variables.index('Investments-QB')])
# print("{:,}".format(reproduction))
# reproduction -= float(data[0][variables.index('Trade and Non-Trade Payables-QB')]) / 1.1
# reproduction -= float(data[0][variables.index('Tax Liabilities-QB')]) / 1.1
# reproduction -= (1) * float(data[0][variables.index('Deposit Liabilities-QB')])
# reproduction -= (1) * float(data[0][variables.index('Total Debt-QB')])
reproduction -= (1) * float(
data[0][variables.index('Total Liabilities-QB')])
except:
reproduction += (1) * float(
data[0][variables.index('Cash and Equivalents-QB')])
reproduction += (0.80) * float(
data[0][variables.index('Inventory-QB')])
reproduction += (2) * float(data[0][variables.index(
'Selling, General and Administrative (SG&A) Expenses-T')])
reproduction += (0.8) * float(
data[0][variables.index('Property, Plant & Equipment Net-QB')])
reproduction += (1.1) * float(
data[0][variables.index('Trade and Non-Trade Receivables-QB')])
reproduction += (2.5) * float(data[0][variables.index(
'Research and Development (R&D) Expenses-T')])
reproduction += float(
data[0][variables.index('Tax Assets-QB')]) / 1.1
reproduction += (0.20) * float(
data[0][variables.index('Goodwill and Intangible Assets-QB')])
reproduction += (1) * float(
data[0][variables.index('Investments-QB')])
reproduction -= (1) * float(
data[0][variables.index('Total Liabilities-QB')])
# print("{:,}".format(reproduction))
# print("{:,}".format(reproduction))
"""
EPV Value will be calculated here by looking at income statments and
making proper adjustments. EPV value is the value that can be extracted
from the business with no growth and still leave operations intact
Start with EBIT
+ 20% Depreciation, Amortization & Accretion
+ 25% R&D
+ 25% SG&A
+ 100% Growth Capex
- 100% Capex
- 100% Interest
Then, apply 37.5 % flat tax rate.
"""
"""Calculate growth CAPEX. Growth CAPEX is the CAPEX required to support
revenue/sales growth. This is ADDED to EPV because it is money that is not needed for operations
at 0 % growth. """
growth = (float(data[0][variables.index('Revenues-T')]) -
float(data[4][variables.index('Revenues-T')]))
revenueT = float(data[0][variables.index('Revenues-T')])
taxRate = 0
"""Get average PPE/Sales """
i = 0
tempRatio = []
while (i < len(data)):
try:
tempRatio.append(
float(data[i][variables.index(
'Property, Plant & Equipment Net-QB')]) /
float(data[i][variables.index('Revenues-T')]))
except:
pass
i += 4
if (len(tempRatio) == 0):
ratioPPEtoRevenue = 0
else:
ratioPPEtoRevenue = sum(tempRatio) / len(tempRatio)
growthCAPEX = 0
if (growth < 0):
growthCAPEX = 0
else:
growthCAPEX = ratioPPEtoRevenue * growth
"""Get Average Ebit and multiple by current revenue-T """
i = 0
tempMargin = []
while (i < len(data) / 2):
# while(i<4):
try:
# print(float(data[i][variables.index('EBIT Margin-T')]))
tempMargin.append(float(data[i][variables.index('EBIT Margin-T')]))
except:
pass
i += 4
ebitMargin = sum(tempMargin) / len(tempMargin)
# print(ebitMargin)
# print(growthCAPEX)
try:
epv = (1.0) * revenueT * ebitMargin
epv += (0.25) * float(
data[0][variables.index('Depreciation & Amortization-QC')])
epv += (0.25) * float(data[0][variables.index(
'Selling, General and Administrative (SG&A) Expenses-T')])
epv += (0.25) * float(data[0][variables.index(
'Research and Development (R&D) Expenses-T')])
epv += (1) * growthCAPEX
"""NOTE: CAPEX is ADDED here because it is already negative"""
epv += (1) * float(data[0][variables.index('Capital Expenditure-TC')])
epv -= (1) * float(data[0][variables.index('Interest Expense-T')])
except:
epv = (1.0) * revenueT * ebitMargin
epv += (0.25) * float(
data[0][variables.index('Depreciation & Amortization-QC')])
epv += (0.25) * float(data[0][variables.index(
'Selling, General and Administrative Expense-T')])
epv += (0.25) * float(
data[0][variables.index('Research and Development Expense-T')])
epv += (1) * growthCAPEX
"""NOTE: CAPEX is ADDED here because it is already negative"""
epv += (1) * float(data[0][variables.index('Capital Expenditure-TC')])
epv -= (1) * float(data[0][variables.index('Interest Expense-T')])
"""Get approximate tax rate"""
try:
taxRate = float(
data[0][variables.index('Income Tax Expense-T')]) / float(
data[0][variables.index('Earnings before Tax-T')])
except:
taxRate = .35
# print(taxRate)
epv = epv * (1 - taxRate)
f = Ratios.TickerFundamentals(tickerName)
g = f.getGrowth()
ROIC = f.getROIC()[1][0]
WACC = f.getWACC()[0]
numShares = f.numShares()
cashDebt = f.getCashDebt()
if (g > 0.03):
g = 0.03
if (WACC < 0.08):
WACC = 0.08
if (g > (0.75 * WACC)):
g = 0.75 * WACC
if (ROIC <= 0):
ROIC = 1
g = 1
if (g > ROIC):
ROIC = 10000
reproduction /= numShares
returnEpv = [
["{0:.4f}".format(WACC - .025) + " WACC", epv / (WACC - .025)],
["{0:.4f}".format(WACC - .01) + " WACC", epv / (WACC - .01)],
["{0:.4f}".format(WACC) + " WACC", epv / WACC],
["{0:.4f}".format(WACC + .01) + " WACC", epv / (WACC + .01)],
["{0:.4f}".format(WACC + .025) + " WACC", epv / (WACC + .025)]
]
"""Make adjustment to add cash and subtract long term debt from EPV too for a shareholders equity value. """
for i in returnEpv:
try:
i[1] += (1) * float(
data[0][variables.index('Cash and Equivalents-QB')])
i[1] -= (1) * float(
data[0][variables.index('Debt Non-Current-QB')])
except:
i[1] += (1) * float(
data[0][variables.index('Cash and Equivalents-QB')])
i[1] -= (1) * float(data[0][variables.index('Total Debt-QB')])
i[1] /= numShares
M = (1 - (g / ROIC)) / (1 - (g / WACC))
marginOfSaftey = []
for i in returnEpv:
marginOfSaftey.append([i[0], i[1] * M])
# print('Reproduction = ' + str(reproduction))
# print('EPV')
# for i in returnEpv:
# print(i)
return [reproduction, returnEpv, marginOfSaftey]
def getValue(tickerName, getData=None):
f = Ratios.TickerFundamentals(tickerName, getData)
fcf = f.getFCF()
ROIC = f.getROIC()[1]
IR = f.reinvestment()
NOPLAT = f.getNOPLAT()
numShares = Utility.myFloat(f.numShares()[0])
cashDebt = Utility.myFloat(f.getCashDebt()[0])
"""Possible growth values """
g = []
for i in range(0, len(ROIC)):
g.append(ROIC[i] * (IR[i] / NOPLAT[i]))
g = sum(g[0:3]) / 3
"""FCF 3 & 5 YR CAGR """
try:
fcf_3yr = (abs(fcf[0]) / abs(fcf[3]))**(1 / 3) - 1
fcf_5yr = (abs(fcf[0]) / abs(fcf[5]))**(1 / 5) - 1
fcf_3yrAverage = sum(fcf[0:3]) / 3
except:
fcf_3yr = g
fcf_5yr = g
fcf_3yrAverage = fcf[0]
"""Value: No Growth, Low Growth, Approximate Growth """
discountRate = .1
lowGrowth = 0.025
"""
No Growth,
Low Growth,
High Growth + Terminal Value
"""
"""Estimate High Growth"""
highGrowth = (g + fcf_3yr + fcf_5yr) / 3
if (highGrowth < 0):
highGrowth = .04
if (highGrowth > .40):
highGrowth = .35
highGrowthMultiplier = 0
for i in range(0, 6):
highGrowthMultiplier += (1 + highGrowth)**(i) / (1 +
discountRate)**(i + 1)
highGrowthMultiplier += (1 + lowGrowth) * (1 + highGrowth)**5 / (
discountRate - lowGrowth) / (1 + discountRate)**5
value = [(fcf[0] / discountRate / numShares) + (cashDebt / numShares),
(fcf[0] /
(discountRate - lowGrowth) / numShares) + (cashDebt / numShares),
highGrowthMultiplier * fcf[0] / numShares + (cashDebt / numShares)
]
valueAvg = [
(fcf_3yrAverage / discountRate / numShares) + (cashDebt / numShares),
(fcf_3yrAverage /
(discountRate - lowGrowth) / numShares) + (cashDebt / numShares),
highGrowthMultiplier * fcf_3yrAverage / numShares +
(cashDebt / numShares)
]
# print("FCF Now : " + "{:,}".format(fcf[0]))
# print("FCF 3yr Avg : " + "{:,}".format(fcf_3yrAverage))
# print("High Growth : " + str(highGrowth))
# print(value)
# print(valueAvg)
return fcf[0], fcf_3yrAverage, value, valueAvg