Type of module:

Summary module description:

In this module you will be introduced to Python, a programming language that has become

an industry standard and is widely used to produce innovative financial products and

services. Common applications include big data analysis and manipulation, algorithmic

trading, portfolio analysis, and machine learning algorithms. Students who complete this

course will be able to write programming functions in Python, process data files including

reading, modifying and writing data to external files. Specifically, students will be able to

read and write to Excel, CSV and Text files, connect to databases, obtain and process data

from the Web, as well as use Python for Finance and Econometrics applications including

developing event based trading strategies and back testing with historical data. By the end

of the module students are expected to produce a simple Python application to solve real

world financial problems. No prior programming experience is required.


Aims:

The module focuses on (1) what is computational programming and why it is useful (2) fundamentals of object-oriented programming (3) Python step by step: conditional statements, functions, sequences and loops (4) key Python libraries for data manipulation, visualisation and statistical analysis which include financial time series regressions and portfolio optimisation (5) input / output operations with excel integration (6) rapid web applications and web services integration (7) finance applications: efficient portfolio frontier, multivariate regressions, algorithmic trading, option pricing and Monte Carlo simulations (8) Build your own finance tool. 



 


Assessable learning outcomes:

By the end of the module it is expected that students will: 





  • Understand the principles behind object-oriented programming 




  • Know how to formulate a problem and use divide-and-rule techniques to solve it with Python 




  • Understand Python’s syntax and be able to use conditional statements, functions and loops be able to upload, organise, manipulate, visualise and export databases of various formats  




  • Understand how to retrieve data from a web-based database and complete textual analysis 




  • Understand how to do linear regression analysis in Python 




  • Know how to code portfolio optimisation problems, algorithmic trading, option pricing models and Monte Carlo simulations 




Additional outcomes:

Students will be able to consolidate their knowledge of the tools and strategies learnt in this module by completing a project where they will be asked to build a Python programme to solve a practical finance problem 


Outline content:



  1. Python and object-oriented programming 




  2. Python syntax: conditional statements, functions, sequences and loops 




  3. Data science basics: NumPy and Pandas packages 




  4. Input / Output operations and excel integration  




  5. Financial time series analysis – Linear regressions and data visualisation 







  1. Mathematical tools and statistics – Portfolio optimisation 







  1. Performance Python – Monte Carlo simulations and binomial option pricing 







  1. Derivatives Analytics library – Asset pricing, derivatives valuation and volatility  







  1. Python and systematic trading – Incorporating signals and technical indicators in trading strategies 







  1. GUI and Web integration – Traders chat room and data modelling 




Global context:

The finance application in this module will be based on international examples. Python is one of the most common programming languages for FinTech applications worldwide. For example:  





  • Athena -- J.P. Morgan's cross-market risk management and trading system that provides functionality for traders, salespeople and operations staff globally; 




  • Quartz -- Bank of America Merrill Lynch's integrated trading, position management, pricing and risk management platform; 




  • Venmo -- a mobile payment service owned by PayPal which allows users to transfer money to others using a mobile phone app. 




Brief description of teaching and learning methods:

(1) If Python is your first coding language, do the tutorial at https://www.learnpython.org 



(2) If you have coded before, work through the challenges at https://www.hackerrank.com/domains/python 



The core theory and concepts will be presented during lectures. Problem sets will be solved in workshops. 


Contact hours:
  Autumn Spring Summer
Lectures 20
Seminars 10
Guided independent study:      
    Wider reading (independent) 50
    Wider reading (directed) 20
    Preparation for seminars 20
    Revision and preparation 30
    Carry-out research project 30
    Reflection 20
       
Total hours by term 200 0 0
       
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Project output other than dissertation 50
Set exercise 25
Class test administered by School 25

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Students will be asked to complete: 





  • a set exercise (25%) to be submitted in week 8 of the autumn term,  




  • an in class multiple choice tests (25%) in week 11 of the autumn term and  




  • an individual project (50%) to be submitted in week 1 of the spring term. 




Formative assessment methods:

Seminar questions are assigned for each class. The seminar leader will facilitate discussion and offer feedback. 


Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy.

Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx


Assessment requirements for a pass:

50% weighted average mark


Reassessment arrangements:

By individual project to be submitted in August/September


Additional Costs (specified where applicable):

Last updated: 8 April 2019

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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