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 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 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