Python for Finance：Financial modeling and quantitative analysis explained-经管之家官网！

Table of Contents
Chapter 1: Python Basics 1
Python installation 1
Installation of Python via Anaconda 2
Launching Python via Spyder 3
Direct installation of Python 4
Variable assignment, empty space, and writing our own programs 7
Writing a Python function 9
Python loops 10
Python loops, if...else conditions 11
Data input 15
Data manipulation 19
Data output 25
Exercises 27
Summary 29Chapter 2: Introduction to Python Modules 31
What is a Python module? 32
Introduction to NumPy 38
Introduction to SciPy 41
Introduction to matplotlib 45
How to install matplotlib 45
Several graphical presentations using matplotlib 45
Introduction to statsmodels 49
Introduction to pandas 51
Python modules related to finance 59
Introduction to the pandas_reader module 60
Two financial calculators 61
How to install a Python module 64
Module dependency 67
Exercises 68
Summary 69
Chapter 3: Time Value of Money 71
Introduction to time value of money 72
Writing a financial calculator in Python 81
Definition of NPV and NPV rule 86
Definition of IRR and IRR rule 88
Definition of payback period and payback period rule 90
Writing your own financial calculator in Python 91
Two general formulae for many functions 92
Appendix A – Installation of Python, NumPy, and SciPy 96
Appendix B – visual presentation of time value of money 98
Appendix C – Derivation of present value of annuity from present value
of one future cash flow and present value of perpetuity 99
Appendix D – How to download a free financial calculator written
in Python 101
Appendix E – The graphical presentation of the relationship between
NPV and R 102
Appendix F – graphical presentation of NPV profile with two IRRs 104
Appendix G – Writing your own financial calculator in Python 105
Exercises 106
Summary 108
Chapter 4: Sources of Data 109
Diving into deeper concepts 110
Retrieving data from Yahoo!Finance 113
Retrieving data from Google Finance 125
Retrieving data from FRED 126
Retrieving data from Prof. French's data library 127
Retrieving data from the Census Bureau, Treasury, and BLS 128
Generating two dozen datasets 130
Several datasets related to CRSP and Compustat 132
Appendix A – Python program for return distribution versus a
normal distribution 137
Appendix B – Python program to a draw
candle-stick picture 138
Appendix C – Python program for price movement 140
Appendix D – Python program to show a picture of a stock's
intra-day movement 141
Appendix E –properties for a pandas DataFrame 142
Appendix F –how to generate a Python dataset with an extension of
.pkl or .pickle 144
Appendix G – data case #1 -generating several Python datasets 145
Exercises 145
Summary 147
Chapter 5: Bond and Stock Valuation 149
Introduction to interest rates 149
Term structure of interest rates 159
Bond evaluation 166
Stock valuation 171
A new data type – dictionary 176
Appendix A – simple interest rate versus compounding interest rate 176
Appendix B – several Python functions related to interest conversion 178
Appendix C – Python program for rateYan.py 179
Appendix D – Python program to estimate stock price based on an
n-period model 180
Appendix E – Python program to estimate the duration for a bond 181
Appendix F – data case #2 – fund raised from a new bond issue 182
Summary 184
Chapter 6: Capital Asset Pricing Model 185
Introduction to CAPM 186
Moving beta 192
Adjusted beta 193
Scholes and William adjusted beta 194
Extracting output data 197
Outputting data to text files 198
Saving our data to a .csv file 198
Saving our data to an Excel file 199
Saving our data to a pickle dataset 199
Saving our data to a binary file 200
Reading data from a binary file 200
Simple string manipulation 201
Python via Canopy 204
References 207
Exercises 209
Summary 212
Chapter 7: Multifactor Models and Performance Measures 213
Introduction to the Fama-French three-factor model 214
Fama-French three-factor model 218
Fama-French-Carhart four-factor model and Fama-French
five-factor model 221
Implementation of Dimson (1979) adjustment for beta 223
Performance measures 225
How to merge different datasets 228
Appendix A – list of related Python datasets 235
Appendix B – Python program to generate ffMonthly.pkl 236
Appendix C – Python program for Sharpe ratio 237
Appendix D – data case #4 – which model is the best, CAPM, FF3,
FFC4, or FF5, or others? 238
References 239
Exercises 240
Summary 242
Chapter 8: Time-Series Analysis 243
Introduction to time-series analysis 244
Merging datasets based on a date variable 246
Using pandas.date_range() to generate one dimensional time-series 246
Return estimation 250
Converting daily returns to monthly ones 252
Merging datasets by date 253
Understanding the interpolation technique 254
Merging data with different frequencies 256
Tests of normality 258
Estimating fat tails 260
T-test and F-test 262
Tests of equal variances 263
Testing the January effect 264
52-week high and low trading strategy 265
Estimating Roll's spread 266
Estimating Amihud's illiquidity 267
Estimating Pastor and Stambaugh (2003) liquidity measure 268
Fama-MacBeth regression 269
Durbin-Watson 270
Python for high-frequency data 273
Spread estimated based on high-frequency data 277
Introduction to CRSP 279
References 280
Appendix A – Python program to generate GDP dataset
usGDPquarterly2.pkl 281
Appendix B – critical values of F for the 0.05 significance level 282
Appendix C – data case #4 - which political party manages the
economy better? 283
Exercises 285
Summary 288
Chapter 9: Portfolio Theory 289
Introduction to portfolio theory 290
A 2-stock portfolio 290
Optimization – minimization 294
Forming an n-stock portfolio 301
Constructing an optimal portfolio 307
Constructing an efficient frontier with n stocks 310
References 322
Appendix A – data case #5 - which industry portfolio do you prefer? 322
Appendix B – data case #6 - replicate S&P500 monthly returns 323
Exercises 325
Summary 331
Chapter 10: Options and Futures 333
Introducing futures 334
Payoff and profit/loss functions for call and put options 341
European versus American options 346
Understanding cash flows, types of options, rights and obligations 346
Black-Scholes-Merton option model on non-dividend paying stocks 347
Generating our own module p4f 348
European options with known dividends 349
Various trading strategies 350
Covered-call – long a stock and short a call 351
Straddle – buy a call and a put with the same exercise prices 352
Butterfly with calls 353
The relationship between input values and option values 355
Greeks 356
Put-call parity and its graphic presentation 359
The put-call ratio for a short period with a trend 363
Binomial tree and its graphic presentation 364
Binomial tree (CRR) method for European options 371
Binomial tree (CRR) method for American options 372
Hedging strategies 373
Implied volatility 374
Binary-search 377
Retrieving option data from Yahoo! Finance 378
Volatility smile and skewness 379
References 381
Appendix A – data case 6: portfolio insurance 382
Exercises 384
Summary 387
Chapter 11: Value at Risk 389
Introduction to VaR 390
Normality tests 400
Skewness and kurtosis 402
Modified VaR 403
VaR based on sorted historical returns 405
Simulation and VaR 408
VaR for portfolios 409
Backtesting and stress testing 411
Expected shortfall 413
Appendix A – data case 7 – VaR estimation for individual stocks
and a portfolio 415
References 418
Exercises 418
Summary 420
Chapter 12: Monte Carlo Simulation 421
Importance of Monte Carlo Simulation 422
Generating random numbers from a standard normal distribution 422
Drawing random samples from a normal distribution 423
Generating random numbers with a seed 424
Random numbers from a normal distribution 425
Histogram for a normal distribution 425
Graphical presentation of a lognormal distribution 426
Generating random numbers from a uniform distribution 428
Using simulation to estimate the pi value 429
Generating random numbers from a Poisson distribution 431
Selecting m stocks randomly from n given stocks 432
With/without replacements 433
Distribution of annual returns 435
Simulation of stock price movements 437
Graphical presentation of stock prices at options' maturity dates 439
Replicating a Black-Scholes-Merton call using simulation 441
Exotic option #1 – using the Monte Carlo Simulation to price average 442
Exotic option #2 – pricing barrier options using the Monte Carlo
Simulation 443
Liking two methods for VaR using simulation 445
Capital budgeting with Monte Carlo Simulation 446
Python SimPy module 449
Comparison between two social policies – basic income and basic job 450
Finding an efficient frontier based on two stocks by using simulation 454
Constructing an efficient frontier with n stocks 457
Long-term return forecasting 460
Efficiency, Quasi-Monte Carlo, and Sobol sequences 462
Appendix A – data case #8 - Monte Carlo Simulation and blackjack 463
References 464
Exercises 464
Summary 466
Chapter 13: Credit Risk Analysis 467
Introduction to credit risk analysis 468
Credit rating 468
Credit spread 475
YIELD of AAA-rated bond, Altman Z-score 477
Using the KMV model to estimate the market value of total assets
and its volatility 479
Term structure of interest rate 482
Distance to default 485
Credit default swap 486
Appendix A – data case #8 - predicting bankruptcy by using Z-score 487
References 488
Exercises 488
Summary 490
Chapter 14: Exotic Options 491
European, American, and Bermuda options 492
Chooser options 494
Shout options 496
Binary options 497
Rainbow options 498
Pricing average options 505
Pricing barrier options 507
Barrier in-and-out parity 509
Graph of up-and-out and up-and-in parity 510
Pricing lookback options with floating strikes 512
Appendix A – data case 7 – hedging crude oil 514
References 516
Exercises 516
Summary 519
Chapter 15: Volatility, Implied Volatility, ARCH, and GARCH 521
Conventional volatility measure – standard deviation 522
Tests of normality 522
Estimating fat tails 524
Lower partial standard deviation and Sortino ratio 526
Test of equivalency of volatility over two periods 528
Test of heteroskedasticity, Breusch, and Pagan 529
Volatility smile and skewness 532
Graphical presentation of volatility clustering 534
The ARCH model 535
Simulating an ARCH (1) process 536
The GARCH model 537
Simulating a GARCH process 538
Simulating a GARCH (p,q) process using modified garchSim() 539
GJR_GARCH by Glosten, Jagannanthan, and Runkle 542
References 545
Appendix A – data case 8 - portfolio hedging using VIX calls 545
References 546
Appendix B – data case 8 - volatility smile and its implications 546

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