Python for Finance

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Chapter 3The associated graph is shown here:To make our graph better, we could add a title, both labels, and one horizon line; seethe following code:import scipy as spfrom matplotlib.pyplot import *cashflows=[-120,50,60,70]rate=[]npv=[]x=(0,0.7)y=(0,0)for i in range(1,70):rate.append(0.01*i)npv.append(sp.npv(0.01*i,cashflows))title("NPV profile")xlabel("Discount Rate")ylabel("NPV (Net Present Value)")plot(rate,npv)plot(x,y)show()[ 103 ]
Time Value of MoneyThe output is shown here:Appendix F – graphical presentation of NPVprofile with two IRRsSince the direction of cash flow changes twice, we might have two IRRs:import scipy as spimport matplotlib.pyplot as pltcashflows=[504,-432,-432,-432,832]rate=[]npv=[]x=[0,0.3]y=[0,0]for i in range(1,30):rate.append(0.01*i)npv.append(sp.npv(0.01*i,cashflows))plt.plot(x,y),plt.plot(rate,npv)plt.show()[ 104 ]
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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,
Page 80 and 81: Chapter 2'core', 'crosstab', 'cut',
Page 82 and 83: x[bad] = np.nan# missing code is np
Page 84 and 85: [ 57 ]Chapter 2For finance, time se
Page 86 and 87: Chapter 2Stock A Stock B0 0.452820
Page 88 and 89: Chapter 22010-01-08 110.929466>> >d
Page 90 and 91: Chapter 2After clicking the logo sh
Page 92 and 93: For a Mac, we have the following co
Page 94 and 95: Chapter 2After answering y, the fol
Page 96: Chapter 213. What is wrong with the
Page 99 and 100: Time Value of MoneyIntroduction to
Page 101 and 102: Time Value of MoneyLater in this ch
Page 103 and 104: Time Value of MoneyNote that in the
Page 105 and 106: Time Value of MoneyFor perpetuity,
Page 107 and 108: Time Value of MoneyExample 1: >>>pv
Page 109 and 110: Time Value of MoneyThe following ta
Page 111 and 112: Time Value of MoneyExample 1: >>>pv
Page 113 and 114: Time Value of MoneyIf using the sam
Page 115 and 116: Time Value of MoneyWhile using just
Page 117 and 118: Time Value of MoneyThe related grap
Page 119 and 120: Time Value of MoneyNow, let's write
Page 121 and 122: Time Value of MoneyFor annuity due,
Page 123 and 124: Time Value of MoneyThe total future
Page 125 and 126: Time Value of MoneyNormally, if a s
Page 127 and 128: Time Value of MoneyAn annuity is de
Page 129: Time Value of MoneyTo find out the
Page 133 and 134: Time Value of Moneydef pvGrowingAnn
Page 135 and 136: Time Value of Money15. We know that
Page 137 and 138: Sources of DataDiving into deeper c
Page 139 and 140: Sources of DataTo find the usage of
Page 141 and 142: Sources of DataTo view the first an
Page 143 and 144: Sources of DataIn the preceding pro
Page 145 and 146: Sources of Data>>>ret[0:3]array([-0
Page 147 and 148: Sources of DataAssume that two pric
Page 149 and 150: Sources of Datad0=x.datefor i in ra
Page 151 and 152: Sources of DataThe so-called candle
Page 153 and 154: Sources of DataRetrieving data from
Page 155 and 156: Sources of DataAssume that the data
Page 157 and 158: Sources of DataGenerating two dozen
Page 159 and 160: Sources of Data15 spreadBBB Moody's
Page 161 and 162: Sources of Data0 201512311 20130228
Page 163 and 164: Sources of DataAssume that TORQcq.p
Page 165 and 166: Sources of Datasigma = np.std(ret)x
Page 167 and 168: Sources of DataAppendix C - Python
Page 169 and 170: Sources of DataThe corresponding gr
Page 171 and 172: Sources of DataAppendix F -how to g
Page 173 and 174: Sources of Data3. Manually download
Page 176 and 177: Bond and Stock ValuationBond or fix
Page 178 and 179: 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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Chapter 14The final price would be
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Chapter 14total+=sTprice_average=to
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Chapter 14#if sT<barrier:in_=True#p
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Chapter 14c=p4f.bs_call(s,x,T,r,sig
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Chapter 14The Python code for this
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Chapter 14If we use futures to hedg
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Chapter 14Here, T is the maturity d
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Chapter 14SummaryThe options we've
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Volatility, Implied Volatility, ARC
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IndexSymbols2-Step Approach 1522-st
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merging 228-234merging, based on da
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Monte Carlo Simulationabout 408, 42
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Python SimPy module 449QQuasi Monte
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