LUISS

Programma

2012-13 ACADEMIC YEAR

Course: Elective course – Master degree

Subject: Algorithmic Trading

Semester: 1st

Credits: 8

Content
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 (http://rit.rotman.utoronto.ca).
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 (fixed income, equity) 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.

Lectures
[Notation: please see here]

  • 1st ‒ Getting a grip on trading, orders, bid-ask prices, Rotman Interactive Trader (RIT), RIT cases.
  • MM1 (CB), MM2 (CB), MM3 (CB)

  • 2nd ‒ Introduction to VBA macros
  • VBA-L (1), VBA-T (1-3), RIT VBA

  • 3rd ‒ Using variables to perform calculations
  • VBA-T (4-6)

  • 4th ‒ IF statement
  • VBA-T (7-11)

  • 5th ‒ Excel VBA exercises
  • Handouts

  • 6th ‒ Excel string functions
  • VBA-L (6), MS

  • 7th ‒ Loops (For Next and Do Loop)
  • VBA-L (2), VBA-T (12-16)

  • 8th ‒ Excel VBA exercises
  • Handouts

  • 9th ‒ Arrays (part 1)
  • VBA-L (5, 9)

  • 10th ‒ Arrays (part 2)
  • VBA-L (11), VBA-T (25)

  • 11th ‒ Advanced Functions
  • VBA-L (12), VBA-T (20-21, 24, 26, 29)

  • 12th ‒ Excel VBA exercises
  • Handouts

  • 13th ‒ HFT Algorithmic Case – Introduction
  • ALGO1 [CB, CT, SS], ALGO2 [CB, CT, SS]

  • 14th ‒ HFT Algorithmic Case – Practice
  • RIT (ALGO)

  • 15th ‒ Sales & Trader Case – Introduction
  • MM1 [CB, TG, SS], MM2 [CB, TG, CT, SS], MM3 [CB, TG]

  • 16th ‒ Sales & Trader Case – Practice
  • RIT (S&T)

  • 17th ‒ Commodity Trading Case – Introduction
  • COM1 [CB, CT, SS]

  • 18th ‒ Commodity Trading Case – Practice
  • RIT (COM)

  • 19th ‒ Quantitative & Event Driven Case – Introduction
  • QED1

  • 20th ‒ Quantitative & Event Driven Case – Practice
  • RIT (QED)

  • 21st ‒ Sales & Trader Case – Competition
  • RIT (S&T)

  • 22nd ‒ Commodity Trading Case – Competition
  • RIT (COM)

  • 23rd ‒ Quantitative & Event Driven Case – Competition
  • RIT (QED)

  • 24th ‒ HFT Algorithmic Case – Competition
  • RIT (ALGO)

Assessment Method
Class participation 10%
Competitions 30%
Written exam 60%


2011-12 ACADEMIC YEAR

Course Elective course – Master degree

Subject Algorithmic Trading

First semester

Credits: 8

Content
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 (http://rit.rotman.utoronto.ca).
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 (fixed income, equity) and various derivatives (futures, options) and a range of investment objectives.
We will deal with topics as:
– security choice (valuation risk, default risk, …);
– implementing trades (liquidity and price risks);
– performance evaluation and rebalancing (market risks, interest rate risks, etc.);
– speculation (volatility risk);
– risk management (diversification, hedging).

Teaching method
Lectures and exercises in computer lab (301 hall), with an “experiential learning” approach.
In the lectures, arguments will follow the same order as in the textbook, to help improve students’ comprehension.
Several PowerPoint slides, available on the web, will be used in classroom for teaching purposes.

Schedule
1st week: Getting a Grip on Trading (Ch. 1), order arrival, bid-ask prices, Rotman Interactive Trader (RIT), TraderEx, criteria for selecting the LUISS team for the Rotman International Trading Competition (RITC).
2nd week: Social Outcry (live simulation), introduction to electronic trading simulations, RIT – market view, market depth view, order entry, short-cuts, random price close-out, Excel function RealTimeData (RTD).
3rd week: All About Liquidity (Ch. 2), from information to prices, defining liquidity, liquidity and transaction costs.
4th week: How to Use Limit and Market Orders (Ch. 3), handling large orders from institutional investors, an option trader’s view of limit orders, market microstructure case.
5th week: Competition guidelines, exercises.
6th week: Market Intermediaries: Nuts ’n’ Bolts and Challenges (Ch. 5), market maker operations, block trading, market impact, arbitrage case.
7th week: Commodity case, weekly weather report, Department of Energy data, news of information vendors.
8th week: Algorithmic Trading (Ch. 6), dynamic price and quantity discovery, algo history, Visual Basic for Application (VBA), algorithmic trading case.
9th week: Market microstructure: competition.
10th week: Interest rates: competition.
11th week: Commodity trading: competition.
12th week: 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.
Ranks will be translated into grades on a 30-point scale [non passing (0 to 17), and passing grades (18 to 30 cum laude)].
Grading scale: 30 cum laude (top 5%), 29-30 (25%), 26-28 (30%), < 25 (40%).

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