Python Programming in Finance

"All money is a matter of belief."
-- Adam Smith

"Investing is simple, not easy."
-- Warren Buffett

"In the business world, the rearview mirror
is always clearer than the windshield."
-- Warren Buffett

"It's a Marathon, not a sprint."
-- Anonymous

"Money often costs too much."
-- Ralph Waldo Emerson

Class Information

Instructor

• Name: 盧政良 (Zheng-Liang Lu, Arthur)
• Email address: arthurzllu@gmail.com

Objectives

This course is an inter-disciplinary course in the fields of computer science, finance, and (a lot of) math:

• Python programming
• Debut of quantative research: data acquisition, visualization, strategy development, and backtesting
• Selected math tools
• Modern portfolio theory
• Financial time series analysis
• Pricing theory
• Risk management
• (Optional) Machine learning

These techniques are essential both in P & Q quant. Just for the record, this course is not to teach you how to get rich in your life but get rich in knowledge (such that you may get rich in the future life).

Prerequisites

• Basic knowledge about financial markets
• Basic level of Python programming
  • You may consider my Python 101 course.

Working Environment

• Google Colab with Python 3.6 link
  • You need a Google account, of course.

Syllabus

• Course overview
  • 2021 FRM Study Guide
  • Preliminary knowledge about CS link
  • (FYR) Kronos Research, Kronos Webinar Trilogy #1 - Quant Trading 101, 2020.5.16

Python Crash Course

• Variable notebook pdf
• Basic data types: int/float, str, bool
• Assignment/arithmetic/rational/logical operators
• List & slicing
• Branching (if-elif-else)
• Iterations (for/while loops)
  • Monte Carlo simulation
  • Bisection method for root-finding
• Function & anonymous expression (lambda)

Debut of Quantitative Research

• Data acquisition
  • Easy-to-use data collector: ffn notebook
    • Another easy-to-use data collector: yfinance notebook
  • DataFrame of pandas notebook
• Data visualization: matplotlib notebook
• Strategy development
  • Technical analysis: ta/talib notebook
    • Rodolfo Toríbio Farias Nazário, Jéssica Lima e Silva, Vinicius Amorim Sobreiro, Herbert Kimura, A literature review of technical analysis on stock markets, 2017
• Backtesting: backtesting notebook

Selected Mathematical Tools

• Package numpy notebook
  • Vectorization
  • Matrix computation
    • Inner product
    • Inverse matrix
• Package scipy
  • Interpolation
    • Linear interpolation
    • Cubic spline
  • Optimization
    • Gradient descent
• Essentials of Statistics notebook pdf
  • Probability models
    • You need to know at least the following four distribution: normal distribution, \(\chi^2\) distribution, Student's t-distribution, F distribution.
  • Random variable & random number generator
  • Hypothesis testing
    • Null hypothesis, significant level \(\alpha\) and \(p\)-value.
  • Parameter/interval estimation
  • Simple linear regression pdf

Modern Portfolio Theory

• Mean-variance framework and the efficient frontier notebook
• Capital asset pricing model (CAPM) notebook
• Multi-factor models notebook
  • Arbitrage pricing theory (APT)
  • Fama-French 3-factor model
  • Barra risk factor analysis
  • AQR: Buffett's Alpha

Financial Time Series Analysis

• Autocorrelation notebook
• Stationary process, order of integration, and the mean-reverting property
• Autoregressive moving-average (ARMA) model
• Generalized autoregressive conditional heteroskedasticity (GARCH) model notebook
• Vector autoregression (VAR) model notebook
• Cointegration & vector error correction model (VECM) notebook
• Granger causality notebook
• Structural break notebook

Pricing Theory

• Introduction to futures and options pdf notebook
• Arbitrage-free principle
• Fundamental theorems of asset pricing
• Binomial option pricing model (BOPM)
• Risk-neutral valuation
• Monte Carlo simulation for option pricing
• Black-Scholes formula for European options
  • (FYR) Case study: Ai Jun Hou, Weining Wang, Cathy Y H Chen, Wolfgang Karl Härdle, Pricing Cryptocurrency Options, 2020
• Model calibration: implied volatility
  • CBOE VIX Index
• Simulation of financial models: geometric Brownian motion, mean-reverting process, stochastic volatility process, jump-diffusion process

Risk Management

• Value at risk (VaR) & expected shortfall (ES) pdf notebook
• Futures Hedging notebook new
• Sensitivity analysis: Greeks pdf notebook
  • Delta hedging

Machine Learning notebook1 notebook2

• Supervised learning
  • Ridge/LASSO regression notebook
  • Logistic regression notebook
  • Support vector machine (SVM) notebook
  • Decision tree, random forest, AdaBoost notebook
• Unsupervised learning
  • K-means clustering notebook
  • Principal component analysis (PCA) notebook
• Reinforcement learning
  • Q-learning notebook
• Deep learning
  • Recurrent net (RNN) notebook
  • Long/Short-Term Memeory Model (LSTM) notebook

Programming Labs

References

Computer Science

Python Programming

• CS41 happy code the python programming language, Stanford University IN PROGRESS
• Python Practice, University of California, Berkeley IN PROGRESS
• CS 61A: Structure and Interpretation of Computer Programs, University of California, Berkeley IN PROGRESS
• CMU 15-112: Fundamentals of Programming and Computer Science, Carnegie Mellon University, 2021sp IN PROGRESS

Quantitative Finance by Python

• https://python-advanced.quantecon.org/

Machine Learning for Finance

• Marcos Lopez de Prado, Advances in Financial Machine Learning, 2018
  

Mathematical Foundations

Linear Algebra

• Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence, Linear Algebra, 5/e, 2018
  

Probability Models

• Sheldon Ross, Introduction to Probability Models, 12/e, 2019
  

Statistics

• Svetlozar T. Rachev, Markus Hoechstoetter, Frank J. Fabozzi, and Sergio M. Focardi, Probability and Statistics for Finance, 2010
  
• Robert V. Hogg, Joseph W. McKean, and Allen T. Craig, Introduction to Mathematical Statistics, 8/e, 2019
  
• George Casella and Roger L. Berger, Statistical Inference, 2/e, 2001
  
• 陳旭昇，統計學：應用與進階，第三版
  
• 陳旭昇，時間序列分析 - 總體經濟與財務金融之應用，第二版
  

Time Series

• Kutner, Nachtsheim, Neter, and Li, Applied Linear Statistical Models: Applied Linear Regression Models, 5/e, 2019
  

Numerical Methods

• Paul Glasserman, Monte Carlo Methods in Financial Engineering, 2003
  

Finance

Financial Market

• Frank J. Fabozzi, Frank J. Jones, Francesco A. Fabozzi, and Steven V. Mann, Foundation of Global Financial Markets and Institutions, 5/e, 2019
  
• Stanley Eakins Frederic Mishkin, Financial Markets & Institutions, 9/e, 2018
  
• Frank Fabozzi, Bond Markets, Analysis, and Strategies, 9/e, 2014
  

Investment

• Zvi Bodie, Alex Kane, Alan J. Marcus, Investments, 12/e, 2020
  

Derivatives

• John C. Hull, Options, Futures, and Other Derivatives, 10/e, 2017
  

Portfolio Management

• Frank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm, Financial Modeling of the Equity Market: From CAPM to Cointegration, 2012
  
• 石川，因子投資：方法與實踐，2020
  
• Fischer Black and Robert Litterman, Global Portfolio Optimization, 1992: pdf
• Heinz Zimmermann, Wolfgang Drobetz, Peter Oertmann, Global Asset Allocation: New Methods and Applications, 2002
  
• Bob Litterman and Quantitative Resources Group, Modern Investment Management: An Equilibrium Approach, 2003
  
• Igor Tulchinsky, Finding Alphas: A Quantitative Approach to Building Trading Strategies, 2019
  

Trading System

• Perry J. Kaufman, Trading Systems and Methods, 5/e, 2013
  

Risk Management

• John C. Hull, Risk Management and Financial Institutions, 5/e, 2018
  
• Emanuel Derman, Michael B. Miller, and David Park, The Volatility Smile, 2016
  
• Colin Bennett, Trading Volatility: Trading Volatility, Correlation, Term Structure and Skew, 2014
  
• Dan Passarelli, Trading Option Greeks: How Time, Volatility, and Other Pricing Factors Drive Profits, 2/e, 2012
  
• Rama Cont, Frontiers in Quantitative Finance: Volatility and Credit Risk Modeling, 2012
  
• Global Association of Risk Professionals, Financial Risk Manager (FRM®)
  • You may refer to the official study guide: https://www.csie.ntu.edu.tw/~d00922011/python/misc/FRM-StudyGuide-092520_annotated.pdf

Financial Engineering: Pricing Theory & Financial Mathematics

• Tomas Björk, Arbitrage Theory in Continuous Time, 3/e, 2009
  
• Steven Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, 2004
  
• Steven Shreve, Stochastic Calculus for Finance II: Continuous-Time Models, 2004
  
• Brigo, Damiano, Mercurio, Fabio, Interest Rate Models Theory and Practice with Smile, Inflation and Credit, 2/e, 2006
  

Additional Reading Materials

• Burton G. Malkiel，漫步華爾街：超越股市漲跌的成功投資策略
  
• James Owen Weatherall，華爾街的物理學
  
• Nassim Nicholas Taleb，隨機騙局
  

MISC

• https://personal.ntu.edu.sg/nprivault/indext.html