Python for Finance

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Monte Carlo Simulationabout 408, 422barrier options, pricing 443-445capital, budgeting 446-449data case 463efficiency 462, 463used, for pricing average 442, 443moving beta 192, 193NNet Present Value (NPV)about 12, 86-88, 422graphical presentation, in relationshipwith R 102-104normal distributionhistogram 425, 426random numbers, generating 422, 423random numbers, generating from 425random samples, drawing 423, 424normality testsabout 258, 259, 522, 523equal variances, testing 263fat tails, estimating 260, 261F-test 262January effect, testing 264T-test 262Normality tests 400, 401NPV profilegraphical presentation, in relationshipwith IRR 104NPV rule 86-88n-stock portfolioforming 301-307NumPyabout 38-40installation 96, 97Oobjective functionminimizing 294-301one dimensional time-seriesgenerating, with pandas.date_range()function 246-249optimal portfolioconstructing 307-310ordinary least square (OLS) 49Ordinary Least Squares (OLS) 214output dataextracting 197extracting, to text files 198reading, from binary file 200saving, to binary file 200saving, to .csv file 198saving, to Excel file 199saving, to pickle dataset 199over the counter (OTC) 491Ppandas.date_range() functionused, for generating one dimensionaltime-series 246-249pandas module 51-59pandas_reader module 60, 61Pastor and Stambaugh (2003) liquiditymeasureestimating 268, 269payback period 90, 91payback period rule 90, 91payoff functionfor call option 341-344for put option 341-344performance measures 225-228pickle datasetoutput data, saving to 199pi valueestimating, with simulation 429, 430Poisson distributionrandom numbers, generating from 431portfoliohedging, with CBOE Volatility Index(VIX) calls 545portfolio insuranceabout 382data case 382-384portfolio theory 290present value (pv)about 94deriving 99, 100Probability of informed (PIN) 431[ 555 ]
profit/loss functionsfor call option 341-344for put option 341-344programswriting 7, 8put-call parityabout 359-362graphic presentation 359-362put-call ratio, with trend forshort period 363, 364URL 361puttable bond 167Pythoncustom financial calculator,writing 91, 92, 105direct installation 4-6financial calculator, writing in 81-85GDP dataset usGDPquarterly2.pkl,generating 281installation 1, 2, 96, 97installation, via Anaconda 2, 3installing, via Canopy 204-206launching, via Spyder 3, 4URL 97used, for high-frequency data 273-276Python datasetsbusinessCycleIndicator2.pkl 235businessCycleIndicator.pkl 235ffcDaily.pkl 235ffcMonthly.pkl 235ffDaily5.pkl 235ffDaily.pkl 235ffMomMonthly.pkl 235ffMonthly5.pkl 235ffMonthly.pkl 235ibm3factor.pkl 235tradingDaysDaily.pkl 235tradingDaysMonthly.pkl 235uniqueWordsBible.pkl 235URL 235usCPImonthly.pkl 235usDebt.pkl 235usGDPquarterly.pkl 235yanMonthly.pkl 235Python functionwriting 9, 10Python loopsabout 10if...else conditions 11-15Python moduleabout 32-37advantages 67dependency 67disadvantages 67installing 64-67related to finance 59, 60Python Module Index (v2.7)reference link 60Python Module Index (v3.5)reference link 60Python module, in financeeconomics 59finance 59FinDates 59googlefinance 59Numpy.lib.financial 59pandas_datareader 59Python_finance 59quant 59tradingmachine 59tstockquote 59yahoo-finance 59Python Package Index (PyPI)reference link 60Python Packaging Index (PIP) 97Python programcandle-stick picture, drawing 139for bond duration estimation 182for displaying stock's intra-daymovement 142for interest conversion 178for price movement 140for rateYan.py 178for stock price based n-periodmodel estimation 180pandas DataFrame, properties 142, 143Python dataset with .pickleextension, generating 144Python dataset with .pkl extension,generating 144return distribution, versus normaldistribution 138several Python datasets, generating 145[ 556 ]
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Python for FinanceSecond EditionFin
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CreditsAuthorYuxing YanProject Coor
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I would like to thank Ben Amoako-Ad
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www.PacktPub.comeBooks, discount of
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Table of ContentsPrefaceixChapter 1
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Table of ContentsAppendix F -how to
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Table of ContentsAppendix C - data
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Table of ContentsCapital budgeting
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PrefaceIt is our firm belief that a
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PrefaceChapter 2, Introduction to P
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PrefaceChapter 13, Credit Risk Anal
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ConventionsIn this book, you will f
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PrefaceErrataAlthough we have taken
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Python BasicsThere are several reas
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Python BasicsThe top-left panel (wi
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Python Basics4. From the IPython sh
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Python BasicsIn the preceding codes
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Python Basics(1+r)^nfv: fture value
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Python BasicsAssuming that we inves
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Python BasicsBy using a loop, we ca
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Python BasicsThe print() function c
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Python BasicsWe could retrieve data
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Python BasicsData typesDescriptioni
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Python BasicsFor example, from the
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Python BasicsIn Chapter 3, Time Val
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Python BasicsTo retrieve the file,
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Python BasicsHere PV is the present
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Introduction toPython ModulesIn thi
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Chapter 2In addition, when a module
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Chapter 2Recall that in Chapter 1,
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Chapter 2To find a specific module,
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Chapter 2In the previous example, d
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Chapter 2----------a : array_likeCa
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Chapter 2If n returns are given, th
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Chapter 2Similarly, we could save a
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Chapter 2The next example presents
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Chapter 2Introduction to statsmodel
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Chapter 2From the preceding output,
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Chapter 2'core', 'crosstab', 'cut',
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x[bad] = np.nan# missing code is np
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[ 57 ]Chapter 2For finance, time se
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Chapter 2Stock A Stock B0 0.452820
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Chapter 22010-01-08 110.929466>> >d
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Chapter 2After clicking the logo sh
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For a Mac, we have the following co
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Chapter 2After answering y, the fol
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Chapter 213. What is wrong with the
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Time Value of MoneyIntroduction to
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Time Value of MoneyLater in this ch
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Time Value of MoneyNote that in the
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Time Value of MoneyFor perpetuity,
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Time Value of MoneyExample 1: >>>pv
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Time Value of MoneyThe following ta
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Time Value of MoneyExample 1: >>>pv
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Time Value of MoneyIf using the sam
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Time Value of MoneyWhile using just
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Time Value of MoneyThe related grap
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Time Value of MoneyNow, let's write
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Time Value of MoneyFor annuity due,
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Time Value of MoneyThe total future
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Time Value of MoneyNormally, if a s
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Time Value of MoneyAn annuity is de
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Time Value of MoneyTo find out the
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Time Value of MoneyThe output is sh
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Time Value of Moneydef pvGrowingAnn
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Time Value of Money15. We know that
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Sources of DataDiving into deeper c
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Sources of DataTo find the usage of
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Sources of DataTo view the first an
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Sources of DataIn the preceding pro
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Sources of Data>>>ret[0:3]array([-0
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Sources of DataAssume that two pric
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Sources of Datad0=x.datefor i in ra
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Sources of DataThe so-called candle
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Sources of DataRetrieving data from
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Sources of DataAssume that the data
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Sources of DataGenerating two dozen
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Sources of Data15 spreadBBB Moody's
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Sources of Data0 201512311 20130228
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Sources of DataAssume that TORQcq.p
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Sources of Datasigma = np.std(ret)x
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Sources of DataAppendix C - Python
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Sources of DataThe corresponding gr
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Sources of DataAppendix F -how to g
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Sources of Data3. Manually download
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Bond and Stock ValuationBond or fix
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Chapter 5In Chapter 3, Time Value o
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Chapter 5On the bottom, we have the
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Chapter 5By applying equation (8),
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Chapter 5Note that Rm=APR/m is from
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Chapter 5Interest rate quotationEff
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Chapter 5The related graph is given
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Chapter 5For this reason, a Python
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Chapter 5>>>w20.4878049>>>w1*1 + w2
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Chapter 5If a bond holder could con
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Based on this result, the YTM is 7.
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Chapter 5Stock valuationThere are s
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Chapter 5……(23)The selling pric
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Chapter 5Here,Equity is the market
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Here, PV is the present value, R is
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Chapter 5Appendix C - Python progra
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Chapter 5sellingPrice/(1+R)**nselli
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Chapter 5A3/A- 32.00 49.00 59.00 72
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Capital Asset Pricing ModelCapital
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Chapter 6Here is the covariance bet
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Chapter 6Here, yi is the ith observ
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default to the md5 hash or the url
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Chapter 6(beta,alpha,r_value,p_valu
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Chapter 60 1.0 NaN 1.51 1.5 1.0 2.0
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Chapter 6The first 10 lines are sho
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Chapter 6Saving our data to an Exce
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Simple string manipulationFor Pytho
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Chapter 6The output is shown here:T
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Chapter 6The two most used panels a
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Chapter 6ReferencesPlease refer to
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Chapter 6Note 2 - how to download a
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Chapter 616. From this chapter, we
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Multifactor Models andPerformance M
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The pandas DataFrame is used to con
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Chapter 7import statsmodels.api as
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Chapter 7When ratios of equity book
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Chapter 7To save space, we will not
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Chapter 7The corresponding output i
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Chapter 7The output is shown here:P
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Chapter 7The only modification is t
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Chapter 7y = pd.DataFrame({'key': [
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Chapter 7'SP500': [0.1,0.17, -0.05,
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Chapter 7Sometimes, we need to merg
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Appendix A - list of related Python
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Chapter 7The first and last several
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Chapter 7• http://canisius.edu/~y
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14. Data source: http://mba.tuck.da
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Time-Series AnalysisIn finance and
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There is one end of chapter problem
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Chapter 8In the preceding program,
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Function Description Examplesrelati
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Chapter 8The output is shown here:P
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ticker='IBM'begdate=(2013,1,1)endda
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Chapter 8Here, v2(v1) is the second
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2016-09-01 0.0025 0.0200 -0.0134 0.
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Chapter 8import numpy as npticker='
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Chapter 8Some books distinguish the
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Chapter 8ticker='ibm'begdate=(2013,
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Chapter 852-week high and low tradi
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Chapter 8The covariance between the
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ff=pd.read_pickle('c:/temp/ffDaily.
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Chapter 8The following Python progr
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Chapter 8Here, n is the number of o
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Chapter 8x=np.array(pd.read_csv(pat
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Chapter 8The output is shown here:[
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Chapter 8In the preceding example,
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Chapter 8• George, T.J., and Hwan
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The key part of the program used to
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Chapter 8For readers from schools w
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11. In addition, generate the simil
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Portfolio TheoryUnderstanding portf
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Chapter 9Here, is the portfolio var
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Chapter 9var=w1**2*var1 +w2**2*var2
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Chapter 9The graph is shown here:To
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Chapter 9Optimization terminated su
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Chapter 9Here, U is the utility fun
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Chapter 9print("meanAnnual, varAnnj
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[ 303 ]Chapter 9'SCD' 'SEF' 'SI' 'S
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output.columns=[ticker]return outpu
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The key command used is ret.T*ret.
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Chapter 9def sharpe(R,w):var = port
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2. Code for defining two functions:
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Chapter 9In one of the previous pro
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Chapter 9The output is shown here:A
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Chapter 9ret = sp.array(mean_return
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Chapter 9x = getData(ticker,begdate
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Chapter 9Here is the output:Since t
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Chapter 97. Click CSV on the right-
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Chapter 90 10006 19570301 198407181
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Chapter 911. For several stocks suc
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Chapter 910. Update yanMonthly.pkl,
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Chapter 916. Estimating the Sharpe
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Options and Futures• Using the bi
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Options and FuturesTaking the natur
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Options and Futuress0=1.25rHome=0.0
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Options and FuturescontractFuturesS
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Options and FuturesTo create a grap
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Options and FuturesA graphical repr
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Options and FuturesEuropean versus
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Options and FuturesGenerating our o
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Options and FuturesThe first line o
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Options and FuturesThe related grap
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Options and Futuresx3=60; c3=5y1=(a
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Options and FuturesThe correspondin
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Options and FuturesNote that in the
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Options and FuturesOn the other han
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Options and FuturesThe following co
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Options and FuturesThe correspondin
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Options and Futuresplt.figtext(0.75
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Options and FuturesHere, u is the u
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Options and Futures# at level 2su=r
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Options and Futuresd = 1.0 / ua = e
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Options and FuturesFor the currency
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Options and FuturesSimilarly, we co
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Options and FuturesvolHigh=1.0cLow=
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Options and Futuresmin_value=1000fo
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Options and FuturesAppendix A - dat
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Options and FuturesNote that -1 mea
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Options and Futures16. The current
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Value at RiskIn finance, implicitly
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Chapter 11d1=stats.norm.pdf(0,0.1,0
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Chapter 11s=sp.arange(-10,z,delta)f
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Chapter 11ret=f(x)plt.plot(x,ret)x2
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Chapter 11z=norm.ppf(1-confidence_l
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Chapter 11#print(retNdays.head())po
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Chapter 11p =getData(ticker, begdat
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Chapter 11n=5000000#ret = random.no
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Chapter 11VaR based on sorted histo
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leftTail=int(n*(1-confidence_level)
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std=np.std(ret)#n_simulation=5000sp
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for i in np.arange(n):stock=tickers
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Chapter 11Again, we expect to see 2
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Chapter 11#sum=0.0for i in np.arang
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Chapter 11Estimation of an n-day Va
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Chapter 1110. When we estimate a Va
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Monte Carlo SimulationMonte Carlo S
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Chapter 12This program is equivalen
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Chapter 12Here, f(x) is the density
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Chapter 12import matplotlib.pyplot
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Chapter 12Using simulation to estim
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Chapter 12Generating random numbers
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Chapter 12#for i in range(len(nonSt
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Chapter 12The constraint specified
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Chapter 12Simulation of stock price
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Chapter 12Graphical presentation of
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Chapter 12y2=rho*x1+sp.sqrt(1-rho**
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dt=T/n_stepscall=sp.zeros([n_simula
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Chapter 12Based on the preceding re
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Chapter 12We have the following equ
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print("max NPV of project=",round(m
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Chapter 12One alternative is a so-c
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Chapter 12totalCost=salaryCost + ad
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[ 455 ]Chapter 12In other words, we
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Chapter 12Constructing an efficient
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Chapter 12The graph is shown here:I
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Chapter 12The following program ref
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Chapter 12On the other hand, if we
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4. To convert random numbers from a
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Credit Risk AnalysisThe objective o
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Chapter 136 A3 A- A- 1 17 Baa1 BBB+
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Chapter 13Source: Moody's (2007).No
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Chapter 13A secured debt is a debt
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If P is the default probability we
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14 B2/B 455.800 481.600 505.200 531
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Chapter 13Using the KMV model to es
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Chapter 13The following Python prog
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Chapter 13The related graph is show
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Chapter 13output.append(round(R,5))
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Chapter 13CDS P CDS P CDS P CDS P C
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8. What are uses of the term struct
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Exotic OptionsIn Chapter 10, Option
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Chapter 14x=40. # exercise priceT=6
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Chapter 14Here, call (T) is the cal
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Chapter 14v[i][j]=max(v1,v2)return
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Chapter 14n = mu.shape[0]Sigma_det
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Chapter 14First, we should study th
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Chapter 14sigma1=0.15sigma2=0.20pri
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Page 534 and 535: Chapter 14total+=sTprice_average=to
Page 536 and 537: Chapter 14#if sT<barrier:in_=True#p
Page 538 and 539: Chapter 14c=p4f.bs_call(s,x,T,r,sig
Page 540 and 541: Chapter 14The Python code for this
Page 542 and 543: Chapter 14If we use futures to hedg
Page 544 and 545: Chapter 14Here, T is the maturity d
Page 546: Chapter 14SummaryThe options we've
Page 549 and 550: Volatility, Implied Volatility, ARC
Page 578 and 579: IndexSymbols2-Step Approach 1522-st
Page 580 and 581: merging 228-234merging, based on da
Page 584 and 585: Python SimPy module 449QQuasi Monte
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Python for Finance

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