To reduce the gap between theory and practice in financial economics.

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.

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).

൦Release Files, Rotman School of Management, University of Toronto-Case Brief (CB)-Trader’s Guide (TG)-Case Tutorial (CT)-Support Sheet (SS)൦-Algorithmic Trading Case•Algorithm 1 (ALGO1) – Arbitrage [CB, CT, SS]•Algorithm 2 (ALGO2) – Market Making [CB, CT, SS]൦-Market Microstructure Case•Market Microstructure 1 (MM1) – Order Driven Markets [CB, TG, SS]•Market Microstructure 2 (MM2) – Liquidity [CB, TG, CT, SS]•Market Microstructure 3 (MM3) – Alternative Trading Venues [CB, TG]൦-Options Case•Options 1 (OP1) – Puts & Calls [CB, TG, SS]•Options 2 (OP2) – Hedging [CB, TG, SS]•Options 3 (OP4) – Trading Volatility [CB, TG, SS]൦-Commodities Case•Commodities 1 (COM1) – Energy Trading [CB, CT, SS]൦-RIT VBA Introduction – Tutorial [RIT VBA]൦MICROSOFT (MS), Visual Basic Developer Center൦VBA Lessons (VBA-L) 1-2, 5-6, 9, 11-12൦VBA Tutorials (VBA-T) 1-16, 20-21, 24-26, 29

Classes and computer-based practice sessions (room 306) through an experential learning approach. Videos will be used during classes for educational purposes.

Four trading competitions in the last four seminars, with 2 practice sessions in each previous week. Final exam.

At the end of the seminar, a team of students will be selected to join the LUISS team attending the Rotman International Trading Competition.