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An Introduction to the Python Programming Language Daniel Toppo Pictet Asset Management An Introduction to the Python Programming Language Daniel Toppo Pictet Asset Management 20192 – Financial Econometrics 2 Spring 2017 Python Part I Introduction Intro Lecture 2 Python What is Python ? Intro Lecture 3 What is Python ? High-level Cross- Open platform Source PYTHON Programming Language Multi- Interactive paradigm General- purpose Intro Lecture 4 What is Python ? . A high-level, general-purpose, interactive programming language o Designed to communicate instructions to a computer o Allows to humans to express what they want the computer to execute for them . High-level o Python uses English keywords (aka natural language elements) o Strong abstraction from the details of the computer o Makes the process of creating a program much simpler . General-purpose o Used to create software in a wide variety of domains o Extended through an extensive ecosystem of libraries o Can be used for rapid code development as well as for building large applications Intro Lecture 5 What is Python ? . Interactive o Processed at runtime by Python interpreter (Matlab-like) o The interpreter is a program that reads a program and carries out its instructions o It translates the code at runtime to executable byte code (i.e. binary code that the computer can understand and execute) . Cross-platform o Available for the most important Operating Systems (Windows, Mac OS, Linux, etc.) o Used to build web applications, desktop applications, etc. Multi-paradigm o Supports different ways to program o Object-oriented, Functional, Imperative, Procedural programming Intro Lecture 6 Python What for ? Intro Lecture 7 Where to find Python ? . Domains o Software Development o Art (Movies) o Business o Education o Government o Science o Engineering . Available platforms o Windows o Mac OSX o Linux o MS-DOS o Solaris o Raspberry o Mobile phone (Android, iOS, Windows) Intro Lecture 8 What is Python used for? Web Data Development Analysis Machine Learning Intro Lecture 9 What is Python used for ? Internet of Things Games And Mobile LOT Development more! Intro Lecture 10 What is Python used for ? . Web Development o To create websites with dynamic content o Some popular web framework (packages which allow developers to write web applications): Django, TurboGears, web2py . Data analysis o 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 Learning o 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 Intro Lecture 11 What is Python used for ? . Internet of Things o 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 environment o Tiny computer like Raspberry Pi can be programmed in Python . Games Developments o Entire video games are coded in Python o From graphical interface, to artificial intelligence algorithms o Specifically, the Pygame library is used to program games . Mobile Developments o 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 Intro Lecture 12 Who uses Python ? Intro Lecture 13 Python Tools & Infrastructure Intro Lecture 14 Python Tools & Infrastructure . 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 science o To install Python and scientific libraries o Over 100 of the most important libraries o Cross-platform (Windows, Mac, Linux) Intro Lecture 15 Python Tools & Infrastructure . iPython is a Python interactive interpreter o syntax color highlighting o code completion o variables inspection o integrated help o commands history o cross-platform Intro Lecture 16 Python Tools & Infrastructure . Spyder IDE (Integrated Development Environment) is a Python editor with: o syntax color highlighting o code auto-completion o variables inspection o integrated help Intro Lecture 17 Python Tools & Infrastructure . PyCharm is another Python IDE: o syntax color highlighting o code auto-completion o debugging capabilities o variables inspection o built-in web development integration o free student access for professional edition Intro Lecture 18 Python Why use Python ? Intro Lecture 19 Why use Python ? . Python is easy ! o Easy to read – Easy to learn – Easy to digest! o In C++ (another very famous programming language) : #include <iostream.h> void main() { cout << “Ciao Italia!" << endl; } o In Python : print (“Ciao Italia!") Intro Lecture 20 Why use Python ? . Python's ecosystem is one of the largest out of any programming community: External libraries Tools Frameworks Books Documentation Websites Tutorials Python’s Python’s ecosystem Developers People Trainers Intro Lecture 21 Why use Python ? . Widely taught in universities o Top-ranked tech universities (MIT, Berkeley, etc.) switched their introductory courses to Python o The largest MOOC (massive open online course) providers (edX, Coursera, Udacity) offer introductory programming course in Python . Open source o Development model that encourages open collaboration o Python source code is freely available to the public o Higher security, higher quality, higher customization . One of the top-ten programming languages rd 1 o IEEE Spectrum: 3 out of 20 (July 2016) th 2 o TIOBE Programming Community : 5 out of 20 (Feb. 2017) 1 http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages 2 http://www.tiobe.com/tiobe-index/ Intro Lecture 22 Python vs Matlab Python Matlab . Advantages . Advantages Include IDE, GUI builder, o Open and free o matrix algebra, data o Low learning curve processing, plotting in o Object oriented standard package o High portability: run the code everywhere o Available and extensive documentation o Extensive ecosystem: libraries (external modules) o Large scientific community GUI toolkits (tools to build Graphical User Interfaces) . Disadvantages IDEs (Integrated Proprietary: hidden code Development Environment) o o Expensive: Toolboxes are . Disadvantages usually not free Low portability (Matlab o Must include libraries to o extend standard code doesn't run exactly the functionalities same way on different platforms) Intro Lecture 23 Python vs Matlab Python Matlab Core Core . Programming language . Programming language . Interpreter . Interpreter . Standard library . Standard library . IDE . GUI builder Libraries IDEs Toolkits | Toolboxes . Numpy . Spyder . Simulink (simulation & . Scipy . PyCharm . Matplotlib . Wing IDE modelling) . Pandas . PyStudio . Parallel computing . Scikit . IDLE . Image processing . PyQt4 . Etc. Signal processing . Etc. Etc. Intro Lecture 24 Python Python in Finance Intro Lecture 25 Python in Finance . Financial institutions are evolving into technology companies o 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 advantages o Increase of speed of trades executions o Increase and automation of controls o Ease of increasing data volumes management o Financial engineering modelling . However, skilled people are scarce and hard to find o Expert not only need to know technology, but also finance! o Skills shortages are a threat to competitiveness and growth Intro Lecture 26 Python in Finance . Non-exhaustive list of uses of Python in finance o Analyzing data o Backtesting trades and models o Financial modeling o Asset pricing o Risk management . Examples of software built with Python in the financial Industry o JP Morgan’s Athena: risk management & analysis, asset pricing, trading management system o Bank of America Merrill Lynch’s Quartz: asset pricing, positions management, risk management o Bellco Credit Union’s SAFE: online banking system o Altis Investment Management: risk management system Intro Lecture 27 Python in Finance . 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/ Intro Lecture 28 Python Part II Examples Intro Lecture 29 Python Examples – Basic . Simple mathematical expression and its evaluation: Compound Interest = 1 + # 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 = 100 r = 0.1 t = 10 # … and call the function ! f = compoundInterest (p, r, t) # The variable 'f' yields the # function result (259.374246…) Intro Lecture 30 Python Examples – Basic . What does this very simple example shows us: o Python syntax is very similar to the mathematical syntax o Python uses English keywords for expressing scientific and mathematical problems o 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 Intro Lecture 31 Examples – Optimization . Let’s try to minimize the following function over the [-1, 1] x [-1,
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