An Introduction to the Python Programming Language

Daniel ToppoPictet Asset Management

20192 – Financial Econometrics 2

Spring 2017

Part IIntroduction

Python

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Python

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What is Python ?

What is Python ?

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PYTHONProgramming

Language

High-level

Open Source

Multi-paradigm

General-purpose

Interactive

Cross-platform

What is Python ?

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A high-level, general-purpose, interactive programming languageo Designed to communicate instructions to a computero Allows to humans to express what they want the computer to

execute for them

High-levelo Python uses English keywords (aka natural language

elements)o Strong abstraction from the details of the computero Makes the process of creating a program much simpler

General-purposeo Used to create software in a wide variety of domainso Extended through an extensive ecosystem of librarieso Can be used for rapid code development as well as for

building large applications

What is Python ?

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Interactiveo Processed at runtime by Python interpreter (Matlab-like)o The interpreter is a program that reads a program and

carries out its instructionso It translates the code at runtime to executable byte code (i.e.

binary code that the computer can understand and execute)

Cross-platformo Available for the most important Operating Systems

(Windows, Mac OS, Linux, etc.)o Used to build web applications, desktop applications, etc.

Multi-paradigmo Supports different ways to programo Object-oriented, Functional, Imperative, Procedural

programming

Python

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What for ?

Where to find Python ?

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Domainso Software Developmento Art (Movies)o Businesso Educationo Governmento Scienceo Engineering

Available platformso Windowso Mac OSXo Linuxo MS-DOSo Solariso Raspberryo Mobile phone (Android, iOS, Windows)

What is Python used for?

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Web Development

Data Analysis

Machine Learning

What is Python used for ?

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Internet of Things Games

Mobile Development

And LOT

more!

What is Python used for ?

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Web Developmento To create websites with dynamic contento Some popular web framework (packages which allow developers

to write web applications): Django, TurboGears, web2py

Data analysiso Large and excellent scientific libraries: NumPy, SciPy, Statsmodels,

Pandas, Matplotlib, Seaborn, etc.o Specifically, Pandas is probably the most useful data analysis in

Python: easy-to-use data structures and data analysis tools

Machine Learningo Machine Learning is a branch of computing science that studies

algorithms allowing computers to learn without being explicitly programmed

o It is an application of a broader concept of Artificial Intelligence

What is Python used for ?

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Internet of Thingso Python can be used to control and automate your entire

home!o Python can act as a brain for robots to perform actions or act

& react to the environmento Tiny computer like Raspberry Pi can be programmed in

Python

Games Developmentso Entire video games are coded in Pythono From graphical interface, to artificial intelligence algorithmso Specifically, the Pygame library is used to program games

Mobile Developmentso Some library (like Kivy, or PyMob) allow for rapid

development of mobile applications o Still a very young technology though, but might improve

drastically in the near future

Who uses Python ?

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Python

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Tools & Infrastructure

Python Tools & Infrastructure

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Python tools and infrastructure are all the components (software & hardware) that allow for increasing productivity

Anaconda is one of the most popular Python distribution for data scienceo To install Python and scientific librarieso Over 100 of the most important libraries o Cross-platform (Windows, Mac, Linux)

Python Tools & Infrastructure

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iPython is a Python interactive interpretero syntax color highlightingo code completiono variables inspectiono integrated helpo commands historyo cross-platform

Python Tools & Infrastructure

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Spyder IDE (Integrated Development Environment) is a Python editor with: o syntax color highlightingo code auto-completiono variables inspectiono integrated help

Python Tools & Infrastructure

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PyCharm is another Python IDE: o syntax color highlightingo code auto-completiono debugging capabilitieso variables inspectiono built-in web development integrationo free student access for professional edition

Python

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Why use Python ?

Why use Python ?

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Python is easy ! o Easy to read – Easy to learn – Easy to digest!

o In C++ (another very famous programming language) :

o In Python :print (“Ciao Italia!")

#include <iostream.h>void main() {

cout << “Ciao Italia!" << endl;}

Why use Python ?

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Python's ecosystem is one of the largest out of any programming community:

Pyth

on’s

ec

osys

temTools

External libraries

Frameworks

Documentation

Books

Websites

Tutorials

PeopleDevelopers

Trainers

Why use Python ?

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Widely taught in universitieso Top-ranked tech universities (MIT, Berkeley, etc.) switched

their introductory courses to Pythono The largest MOOC (massive open online course) providers

(edX, Coursera, Udacity) offer introductory programming course in Python

Open sourceo Development model that encourages open collaborationo Python source code is freely available to the publico Higher security, higher quality, higher customization

One of the top-ten programming languageso IEEE Spectrum: 3rd out of 20 (July 2016) 1

o TIOBE Programming Community : 5th out of 20 (Feb. 2017) 2

1 http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages2 http://www.tiobe.com/tiobe-index/

Python vs Matlab

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Python Advantageso Open and freeo Low learning curveo Object orientedo High portability: run the

code everywhereo Extensive ecosystem:

libraries (external modules) GUI toolkits (tools to build

Graphical User Interfaces) IDEs (Integrated

Development Environment)

Disadvantageso Must include libraries to

extend standard functionalities

Matlab Advantageso Include IDE, GUI builder,

matrix algebra, data processing, plotting in standard package

o Available and extensive documentation

o Large scientific community

Disadvantageso Proprietary: hidden codeo Expensive: Toolboxes are

usually not freeo Low portability (Matlab

code doesn't run exactly the same way on different platforms)

Python vs Matlab

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Python MatlabCore

Programming language Interpreter Standard library

Libraries

Numpy Scipy Matplotlib Pandas Scikit PyQt4 Etc.

IDEs

Spyder PyCharm Wing IDE PyStudio IDLE Etc.

Core Programming language Interpreter Standard library IDE GUI builder

Toolkits | Toolboxes

Simulink (simulation & modelling)

Parallel computing Image processing Signal processing Etc.

Python

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Python in Finance

Python in Finance

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Financial institutions are evolving into technology companieso Banking and financial institutions is the industry that spends

the most in technology (often spending billions $US per year)o Large banks employ thousands of developers to create and

maintain IT systems

Technology leads to competitive advantageso Increase of speed of trades executionso Increase and automation of controlso Ease of increasing data volumes managemento Financial engineering modelling

However, skilled people are scarce and hard to findo Expert not only need to know technology, but also finance!o Skills shortages are a threat to competitiveness and growth

Python in Finance

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Non-exhaustive list of uses of Python in financeo Analyzing datao Backtesting trades and modelso Financial modelingo Asset pricingo Risk management

Examples of software built with Python in the financial Industryo JP Morgan’s Athena: risk management & analysis, asset

pricing, trading management systemo Bank of America Merrill Lynch’s Quartz: asset pricing,

positions management, risk managemento Bellco Credit Union’s SAFE: online banking systemo Altis Investment Management: risk management system

Python in Finance

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Most requested languages in coding interviews in the financial space (from the HackerRank1 website)

1 https://blog.hackerrank.com/emerging-languages-still-overshadowed-by-incumbents-java-python-in-coding-interviews/

Python

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Part IIExamples

Python Examples – Basic

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Simple mathematical expression and its evaluation: Compound Interest

𝐹𝐹 = 𝑃𝑃 � 1 + 𝑟𝑟 𝑡𝑡

# Defines a function with parameters 'p', 'r' and 't'def compoundInterest (p, r, t) :

return p*(1+r)**t # Return the function result

# Let's initialize the parameters…p = 100r = 0.1t = 10

# … and call the function !f = compoundInterest (p, r, t) # The variable 'f ' yields the

# function result (259.374246…)

Python Examples – Basic

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What does this very simple example shows us:o Python syntax is very similar to the mathematical

syntaxo Python uses English keywords for expressing

scientific and mathematical problemso Python elegant syntax clearly separates blocs of

statements (the function in our example) using adequate indentation

Any financial (or scientific) problem can thus be very easily expressed and translated into algorithms

Examples – Optimization

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Let’s try to minimize the following function over the [-1, 1] x [-1, 1] space:

𝑓𝑓 𝑥𝑥,𝑦𝑦 =12

sin 𝑝𝑝𝑝𝑝 � 𝑥𝑥 � 𝑦𝑦 + 𝑥𝑥2 − 𝑒𝑒𝑥𝑥

Examples – Optimization

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From the plot, minima are located at around:o (x = 1.0, y = -0.5)o (x = -0.5, y = 1.0)

# Defines the function def f(params) :

x, y = paramsreturn sin(pi*x*y) + x**2 - exp(x)

# Let’s fix our search space to [-1, 1]:[-1, 1] bnds = ((-1, 1), (-1, 1))# And our first initial guess for the optimizerinitGuess = [-0.5, 0.5]

# Finally, optimize ‘f ’ using the ‘minimize’ functionoptimization = optimize.minimize(f, initGuess, method="SLSQP", bounds=bnds)

Examples – Optimization

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Results# Prints the result print (optimization)

fun: -2.417917900528793message: 'Optimization terminated successfully.‘nit: 5success: Truex: array([-0.423265, 1.0])

The optimization results is successful, with results o x = [-0.42, 1.0], close to what we were expectingo yielding function value: -2.42

We can do the same exercise with another initial guesses to find another minimum (e.g. [0.75, -0.75])

Examples – Linear Regression

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Dataset is about salaries of professors. The data were collected to monitor salary differences between male and female members

397 observations on the following 6 variableso rank: AssocProf, AsstProf, Profo discipline: A (theoretical), B (applied)

departmento yrs.since.phd: years since PhDo yrs.service: years of serviceo sex: Female, Maleo salary: nine-month salary, in $US

Dependent variable: salary Independent variables: years service, years since PhD

Examples – Linear Regression

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Linear regression function# Read the data series from “salaries.xlsx” Excel filexslx = ExcelFile("salaries.xlsx")# Parse the “salaries” sheet and store the data into the “data” variabledata = xslx.parse("salaries")

regressor = "salary" # responsepredictor = "yearsOfService" # predictor

y = data[regressor] X = data[predictor]X = add_constant(X) # Adds a constant term to the predictor

# OLS is the Ordinary Least Squares functionmodel = OLS(y, X).fit()

Examples – Linear Regression

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Result# Prints the regression modelprint (model)

Examples – Linear Regression

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Plotting salaries regression against years of service

Examples – Linear Regression

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Plot – separating women & men salaries

Examples – Linear Regression

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Plot – separating theoretical to practical departments

Python

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References

Python - References

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Online documentation Official Python website: https://docs.python.org/3/ Wiki-Book: https://en.wikibooks.org/wiki/Python_Programming Byte of Python: http://www.swaroopch.com/notes/Python Python Bibliotheca: http://openbookproject.net/pybiblio/ Tutorialspoint: https://www.tutorialspoint.com/python Google: https://developers.google.com/edu/python/ Pythonspot: https://pythonspot.com/ Pythonlearn: http://www.pythonlearn.com/book.php Python for Fun: http://www.openbookproject.net/py4fun/ Pandas: http://pandas.pydata.org/ Numpy: http://www.numpy.org/ Scipy: https://www.scipy.org/

Python - References

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BooksMark LutzLearning Python, 5th EditionO'Reilly Media; 5 edition (July 6, 2013)

Paul JonesPython: The Fundamentals Of Python ProgrammingCreateSpace Publishing Platform (Oct. 26, 2016)

Eric MatthesPython Crash Course: A Hands-On, Project-Based Introduction to ProgrammingNo Starch Press; 1 edition (November 30, 2015)

Python - References

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Books

Yves HilpischPython for FinanceO′Reilly (Dec. 30, 2014)

Wes McKinneyPython for Data AnalysisO'Reilly Media (Jul. 31, 2016)