*Conducted in term:*2019L

*Erasmus code:*14.3

*ISCED code:*0311

*ECTS credits:*4

*Language:*English

*Organized by:*Faculty of Economic Sciences

*Related to study programmes:*

# Automatic Transactional Systems 2400-QFU2TSA

Prerequisites:

Econometrics

Time Series Analysis

C++ in Quantitative Finance part I

Course content:

1. Short introduction to Python and its syntax. Preparation of the PyCharm environment/ Installation of Anaconda Python distribution. Organisational matters.

[1]:chapters 1,2,3

Python flow control: conditional expressions (if, else, elif), loops (for ,while), user-defined functions.

[1]:chapters 12,13

2. Object Oriented Programming (OOP) in Python part I (classes, methods, objects, complex built-in structures in Python: list, dictionary, tuple, set)

[1]:chapters 26,27

3. Object Oriented Programming (OOP) in Python part II (inheritance, multiple inheritance, operator overloading)

[1]:chapters 28,29,30

4. Advanced topics in Python: files operation (read/write),regular expressions, lambda function, time series visualisation with matplotlib, use of automatic http requests package.

[1]:chapters 33,34

[2]:chapters 8,

5. Linear algebra with NumPy +Data handling and wrangling with Pandas.

6. Solving mathematical problems in Python part I (root-finding algorithm, factorial calculation). Use of recursive functions.

7. Solving mathematical problems in Python part II (integral calculation). Use of Monte Carlo simulation.

8. Python for finance. The role of normal distribution (and its alternatives) in financial markets (normality tests on examples). Empirical properties of asset returns (stylized facts).

9. Option pricing models.

10. Back testing Minimum Variance portfolios, Maximum Sharpe Portfolio. Measures of trading strategy performance.

11. Trading systems based on Technical Analysis methods.

12. Strategies based on Machine Learning. Artificial Neural Network.

13. Strategies based on Machine Learning. Support Vector Machine.

14. Statistical Arbitrage strategies. Pair Trading.

15. Students presentations.

## Type of course

## Course coordinators

## Learning outcomes

Knowledge:

After finishing the course student knows the fundamentals of Python programming. Student knows how to use Python and its packages to prepare and analyze data to solve financial problems and build own investment strategies.

Skills

Student is able to prepare Python programming environment and install required packages.

Student is able to implement in Python own investment strategies.

Social Competence

Participant understands that the expert use of Python requires continuous practice and improvement of his own skills. This course gives him the skills to seek knowledge ,and update it to constantly changing Python libraries.

KW01, KW02, KU01, KU02

## Assessment criteria

Article Review: 10 points

Short tests: 20 points

Final Test (open-ended questions): 80 points (student must obtain >= 40 points to pass the course)

Project: 90 points [10 points for presentation +80 points for project and report]

Activity: up to 15 extra points

Total Score= (Article Review +Short tests + Final Test + Project+ Activity)/200 * 100%

The class attendance is mandatory. Four or more unjustified absences signify failure of

the course.

Grade Total Score % Description

5 +90% very good

4+ +80% better than good

4 +70% good

3+ +60% satisfactory

3 +50% sufficient

2 Less than 50%

(or less than 40 points at Test) fail

## Bibliography

[1.] Lutz, M. (2013), “Learning Python” 5-th edition , O’Reilly

[2.] McKinney ,W (2012), “Python for Data Analysis”, O’Reilly

[3.] Aldridge, I. (2009), “Measuring Accuracy of Trading Strategies”, Journal of Trading 4,

Summer 2009, pp. 17–25.

[4.] Alexander, C. and Johnson, A. (1992), “Are Foreign Exchange Markets Really Efficient?”,

Economics Letters 40, pp. 449–453.

[5.] Brock, W.A., Lakonishok, J. and LeBaron, B. (1992), “Simple Technical Trading Rules and

the Stochastic Properties of Stock Returns”, Journal of Finance 47, pp. 1731–1764.

[6.] Chan, E. (2008), Quantitative Trading: How to Build Your Own Algorithmic Trading

Business, Wiley Trading

[7.] Hvidkjaer, S. (2006), “A Trade-Based Analysis of Momentum”, Review of Financial

Studies19, pp. 457–491.

[8.] Kissell, R. and Malamut, R. (2006), “Algorithmic Decision Making Framework”, Journal of

Trading 1, pp. 12–21.

## Additional information

Information on *level* of this course, *year of study* and semester when the course
unit is delivered, types and amount of *class hours* - can be found in course structure
diagrams of apropriate study programmes. This course is related to
the following study programmes:

Additional information (*registration* calendar, class conductors,
*localization and schedules* of classes), might be available in the USOSweb system: