FZ3006 Financial Programming
- Juan Carlos Arismendi Zambrano
- Sep 13, 2017
- 3 min read
Objectives
In this course of advanced level in finance, we develop the student programming skills for solving financial and economics problems. The methodology for solving the financial problems requires the understanding of financial models as prerequisite, then an algorithm is elaborated to address a required computer/numerical solution of a mathematical problem. We focus on the implementation of the algorithm, rather than in the understanding of the models. We are going to develop algorithms in MATLAB with some interfaces and algorithms in C++, and Excel VBA.
Course Program
1. INTRODUCTION TO PROGRAMMING WITH MATLAB (2 weeks) (a) Basic Features (b) The MATLAB Desktop (c) Script M-files
(d) Arrays and Array Operations (e) Multidimensional Arrays (f) Numeric Data Types (g) Cell Arrays and Structures (h) Character Strings (i) Relational and Logical Operations (j) Control Flow (k) Functions (l) Matrix Algebra
2. TRADING MECHANISMS (2 weeks) (a) Limit Order Markets (b) Floor Markets (c) Dealers (d) Auctions and Other Clearing Mechanisms (e) Bargaining (f) Crossing Networks and Derivative Pricing
3. BACKTESTING AND AUTOMATED EXECUTION (7 weeks) (a) The Importance of Backtesting (b) Common Pitfalls of Backtesting (c) Statistical Significance of Backtesting: Hypothesis Testing (d) When Not to Backtest a Strategy (e) Will a Backtest Be Predictive of Future Returns? (f) Choosing a Backtesting and Automated Execution Platform
4. EXECUTION SYSTEMS (2 weeks) (a) Algorithmic Penetration (b) Implementation Shortfall Measurement (c) Volume-Weighted Average Price (d) VWAP Definitions
(e) Time-Weighted Average Price
5. ALGORITHMIC FEASIBILITY (1 week) (a) Trade Structure (b) Algorithmic Feasibility (c) Algorithmic Trading Checklist (d) High Opportunity Cost (e) News ow Algorithms (f) Black Box Trading for Fixed-Income Instruments
6. TRANSACTION COST RESEARCH (1 week) (a) Post-Trade TCR (b) Pre-Trade TCR (c) The Future of Transaction Cost Research
Evaluation
Course evaluation will be done as following:
1. One case study (Goldman ITESM Investment Banking - Trading and Algorithmic Execution Department) presented and evaluated by 6 trading execution algorithms, and three written reports in English, that occur during the partial and final exam dates. There will be 3 (three) evaluations during the semester. The final grade will be a weighted average of the three exams:
1. Semana i - From 26/09/2016 to 30/09/2016 - 5% 2. Partial Exam 1 - 12/09/2016 - 30% (Trading Execution Algorithms of: 29/08/2016, 10%; and 12/09/2016, 20%). 3. Partial Exam 2 - 24/10/2016 - 30% (Trading Execution Algorithms of: 10/10/2016, 10%; and 24/10/2016, 20%). 4. Final Exam - 28/11/2016 - 35% (Trading Execution Algorithms of: 14/11/2016, 10%; and 28/11/2016, 25%).
It will be considered approved the student with minimum assistance that obtains a total grade superior to 70 from the three evaluations. Will be considered failed the student with less than 70 points (69.5, for example). The student with more than 3 weeks of nonattendance will not be able to participate in the final exam.
References
[1] HANSELMAN, Duane; LITTLEFIELD, Bruce. Mastering MATLAB. Essex, Pearson, 2012. [2] HASBROUCK, Joel. Empirical Market Microstructure, The Institutions, Economics, and Econometrics of Securities Trading. New York, Oxford University Press, 2007. [3] CHAN, Ernest P. Algorithmic Trading, Winning strategies and their Rationale. New Jersey, John Wiley & Sons, 2013. [4] KIM, Kendall. Electronic and Algorithmic Trading Technology, The Complete Guide. Burlington, San Diego, London, Elsevier, 2007. [5] CHAN, Ernest P. Quantitative Trading, How to Build Your Own Algorithmic Trading Business. New Jersey, John Wiley & Sons, 2009.