Python Programming in Finance

Objectives

• Time-series data processing: stocks/derivatives/macroenocomic factors
• Quantitative methods
• Pricing models

Prerequisites

• Statistics (basic)
• Financial markets (basic)

Text

• Yves Hilpisch, Python for Finance: Analyze Big Financial Data, O'Reilly Media, 2014 [PFF]
• William McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly Media, October 2012 [PDA]
• Joel Grus, Data Science from Scratch: First Principles with Python, O'Reilly Media, April 2015 [DSS]
• Yves Hilpisch, Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging, Wiley, 2015 [DAP]
• Bill Lubanovic, Introducing Python: Modern Computing in Simple Packages, O'Reilly Media, 2014 [IP]

Overview

Python crash course

• Variable, simple data types (int, float, str, boolean), and operators (arithmetic, rational, logical)
• Built-in data structures: list, tuple, dictionary, and set
• Selections (if-elif-else)
• Iterations (for-in loop, while loop) with jump statements (break, continue)
• Iterables: range, map, zip, enumerate
• Comprehensions: list, tuple, dictionary, set
• Analysis of algorithms
• Numpy [PDA ch. 4 and 10]
• Function, scope, arguments (positional, keyword)
• Functional programming: lambda expression, map, filter, generator (yield), decorator
• Script, module and package
• Python Standard Library [IP ch.5 (partial)]
• Class and object: attributes and methods
• Object-oriented paradigm: encapsulation and inheritance
• Exception handling (try-except-finally, raise, with)
• Systems: files and directories [IP ch. 10]

Data preprocessing

• IO with panda
• Regular expression
• Automatic data crawler using urllib and requests [DSS ch. 9 (partial)]
• Data cleansing

Data visualization and technical analysis

• 2D plotting with matplotlib and seaborn
• Financial plots
• 3D plotting

Mathematical tools and time series analysis

• Probability and random number generation
• Descriptive statistics, hypothesis testing using statsmodel
• Principal component analysis (PCA)
• Regression and interpolation
• Portfolio optimization
• Backtest framework using backtrader

Financial models

• Tutorial on financial markets: stocks, futures, and options
• Valuation framework: fundamental theorem of asset pricing
• Simulation of financial models: geometric Brownian motion (Black-Scholes model), jump diffusion, square-root diffusion
• Derivatives valuation: European option and American option
• Monte Carlo simulation: European options and American options (LSM)
• Binomial lattice methods
• Greeks

Model calibration and dynamic hedging

• The VSTOXX data
• Calibration procedure
• Risk measure: Value at Risk

Performance issues

• Python paradigm
• Memory layout
• Parallel computing
• multiprocessing
• Generation of random numbers on GPUs

Machine learning

• Tutorial on machine learning using scikit-learn
• Supervised learning: linear/multiple regression, logistic regression, decision tree, SVM
• Unsupervised learning: K-mean, K-clustering
• Deep learning using tensorflow

References

Python programming

• Python 3 Reference, Python Software Foundation
• Mark Lutz, Learning Python, 5/e, 2013, O’Reilly Media, Inc.
• Sam, CS41 happy code the python programming language, Stanford University, 2017sp
• Python Practice, University of California, Berkeley
• Caitlin Carnahan, CIS4930 Python Programming, Department of Computer Science, Florida State University, 2017sp
• CS 61A: Structure and Interpretation of Computer Programs, University of California, Berkeley
• Ana Bell, Eric Grimson, and John Guttag, Introduction to Computer Science and Programming in Python, Massachusetts Institute of Technology, 2016
• Python 基础教程, http://www.runoob.com/
• Jin Li, 中文Python笔记, github
• Top 8 resources for learning data analysis with panda
• Matplotlib
• Statsmodels: statistical modeling and econometrics in Python, github

Financial markets and trading systems

• John C. Hull, Options, Futures, and Other Derivatives, 9/e, 2014
• Perry J. Kaufman, Trading Systems and Methods, 5/e, 2013

Quantitative methods in finance and economics

• Thomas J. Sargent and John Stachurski, QuantEcon.lectures-python3, Lectures in Quantitative Economics, 2018.4.10
• backtrader, github

Machine learning

• What is Machine Learning?, OxfordSparks
• Sunil Ray, 7 Types of Regression Techniques you should know!
• sklearn.svm.SVC, scikit-learn: Machine Learning in Python
• Alice Zhao, Support Vector Machines: A Visual Explanation with Sample Python Code
• Thales Sehn Körting, How SVM (Support Vector Machine) algorithm works
• Hung-yi Lee, ML Lecture 13: Unsupervised Learning - Linear Methods, Department of Electrical Engineering, National Taiwan University
• Hung-yi Lee, ML Lecture 20: Support Vector Machine (SVM), Department of Electrical Engineering, National Taiwan University
• 3Blue1Brown, But what *is* a Neural Network? | Chapter 1, deep learning
• BUILDING DECISION TREE ALGORITHM IN PYTHON WITH SCIKIT LEARN, http://dataaspirant.com
• Hung-yi Lee, Hung-yi Lee's Youtube Channel
• 24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)
• Kunle Olukotun and Ardavan Pedram, Hardware Accelerators for Machine Learning (CS 217), Stanford University, 2018fa

Optimization

• Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Stanford University

Misc

• Learning with Kaggle Kernels, Kaggle

Installation & Wi-Fi

• Anaconda with Python 3.6.x
• Wifi access
• SSID: CSIE_guest
• account: guest_PD9YZ
• pwd: 6PXUXYF5

Schedule [ 298, 300 ]

Sample code

• python/298/code/

Gradebook