2020-21, 2019-20, 2018-19, 2017-18, 2016-17, 2015-16, 2014-15, 2013-14 ACADEMIC YEAR

Course: Seminar – Master degree in Financial Economics

Subject: Algorithmic Trading

Semester: 1st

Credits: 4

In computerized financial markets, algorithmic trading (also known as algo trading, automated trading, black-box trading or robo trading) is the use of applications which allow the automatic entering of buy or sell (market and / or limit) orders. It is the algorithm developed from programmers which decides crucial aspects of orders as timing, price and / or quantity.
Algorithmic trading is growing massively – it’s cheaper, faster and better to control than standard trading. It enables financial institutions to ‘pre-think’ the market, executing complex math in real time, and take the required decisions based on the strategy defined.
The cost alone (estimated at 6 cents per share manual, 1 cent per share algorithmic) is a sufficient driver to power the growth of algo trading. According to some estimates, high frequency trading firms alone account for 73% of all US equity trading volume.
To learn how securities are actually traded in financial markets, we will use trading cases (simulations) based on the Rotman Interactive Trader (RIT) platform.
Finance theory will help us to understand the risk / return tradeoff inherent in particular trading strategies.
Excel applications linked to the real-time data-feeds from the simulated market will guide our decision making and allow us to develop effective trading strategies.
These strategies will also be implemented by developing algorithms written in Visual Basic for Application (VBA).

Educational goals
1. – To develop trading strategies with various contracts and various investment objectives.
2. – To identify and manage risks associated with those strategies.
3. – To turn the trading strategies into algorithms.
These objectives require us to understand how financial markets work. For example:
– how traders generate liquidity, volatility, and profits/losses;
– how security prices get determined reflecting information, news, investor behavior, etc.
We also need to understand:
– the role of various market participants, including dealers, brokers, arbitrageurs, buy-side traders (institutions) and retail investors;
– different order types, such as market versus limit orders, stop orders, etc.
Cases will cover various securities and various derivatives (futures, options) and a range of investment objectives.

Teaching method
Lectures and exercises in computer lab (301 hall), with an “experiential learning” approach.
Several videos, available on the web, will be used in classroom for teaching purposes.

Lecture Schedule
1st: Getting a grip on trading, market vs. limit orders, bid-ask prices, Rotman Interactive Trader (RIT), selection criteria for the Rotman International Trading Competition (RITC).
2nd: Introduction to VBA macros. Social Outcry (live simulation).
3rd: Market microstructure: instructions (RTD function, orders from institutional investors).
4th: Algorithmic trading: arbitrages, market-making.
5th: Options trading: instructions (arbitrages, delta-neutral strategies).
6th: Commodities case: instructions (producers, refiners, traders).
7th: Market microstructure: competition.
8th: Commodities trading: competition.
9th: Options trading: competition.
10th: Algorithmic trading: competition.

Assessment Method
Individual computer-based assignments, based on RIT (Rotman Interactive Trader) platform.
Students will be ranked according to the scoring methodology used for the Rotman International Trading Competition (pdf).
The main purpose of the system is to reward consistently high performance, i.e. a student who places 8th, 5th, and 10th will have a higher final score than a student who places 1st, 10th, and 35th.

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