The RIT simulation-based-learning product includes the RIT Market Simulator (an order-matching platform which allows users to transact financial securities with each other on a real-time basis) plus a sequence of RIT Decision Cases which focus on specific decision tasks associated with financial securities, market dynamics, and investment or risk management strategies.
The RIT Decision Cases have been designed to complement finance curricula at both the undergraduate and graduate levels. The decision tasks are presented in an easy-to-understand manner so that students can explore, learn, and practice strategies that achieve their desired goals. Note that most cases have an associated start-up decision-support template that applies the relevant theory and links to the order-driven market in real-time. This reflects our mission to integrate theory and practice.
The RIT Decision Cases also sequence from introductory (generally one source of risk) to capstone cases for which the decision maker must manage several, potentially correlated, risks. In this way, participants acquire and practice one skill at a time before combining those skills in a comprehensive and robust strategy. This simulation approach to learning in an uncertain environment is analogous to using a flight simulator to train pilots. Additional motivations for simulation-based learning and the associated learning objectives using the RIT package are available here.
The RIT Market Simulator package is structured on a server/client basis:
Depending on the RIT case, students are required to fulfil many different roles, for example, hedgers, arbitrageurs, speculators, analysts, etc. The RIT cases offer the possibility to simulate both buy-side and sell-side professional roles, such as, agency trading, liability trading, algorithmic trading, fixed income trading, equity valuation, M&A, commodities trading, hedging and speculating using futures and options, volatility trading, portfolio management, risk management, etc.
One of the key features of the RIT Market Simulator platform is that market clearing prices are determined, and recorded at millisecond frequencies, based on orders from market participants: both students and the AI Order Flow. As explained below, the AI Order Flow can be turned off or programmed to achieve specific learning objectives. Market clearing, based on actions of all the participants in the simulated market, ensures that students are not exogenous to the market. In other words, price impact due to liquidity issues, behavioural effects (endogenous uncertainty), and other realistic interactions and feedback from markets can be experienced.
Before class, instructors choose the case(s) and inform their students to download the RIT Case Brief(s) and any additional support material that they should read in advance. Professors also have access to the instructors’ notes contained in the RIT Case Brief – SOLUTION. The RIT cases have been developed for sequential progression for each topic, from introductory cases to more advanced cases that build on strategies mastered in the cases earlier in the sequence. Details of the case topic sequences, and case summaries are outlined on the RIT webpages. More detailed information is included in the RIT Case Briefs.
Once the RIT Server Application has been installed on the instructor’s workstation, instructors can run the market in a few clicks:
While the simulation is running, instructors can monitor participants’ decisions and have full control over the market.
When each iteration of the case simulation ends, instructors can display the results in different ways depending on the depth of the discussion that they would like to have with their class. For example:
Instructors can fully customize the simulations. The RIT Server Casefiles are open source and available to all the RIT instructors. The RIT team has developed a user guide that explains the role of each server casefile variable and how it affects the simulation. For more details please refer to the RIT – User Guide – Server Casefile Variable Description.
Students open the RIT Client Application by double clicking on the RIT Interactive Trader 2.0 Client’ icon and connect to the RIT Decision Case which has been loaded by their instructor. Based on prior instructions, students will have the option of anonymous log-in or, alternatively, will be required to use credentials (‘Trader ID’ and ‘Password’) specified in a ‘User File’ preloaded by the instructor. In order to login to the RIT Client Application, students must also enter the ‘Server’ address and port number to access the RIT Decision Case that the instructor has loaded on the RIT Server Application. Once connected, students will be able to take part in the simulation and will be able to monitor the market and implications of their own and other participants’ decisions in real-time. For example:
Students study the case brief and the associated start-up decision support template to establish their understanding of the case objective (including the necessary decisions to achieve their assigned job task) and the case implementation (including the available securities, the mapping from real time to simulation time, the stochastic structure, etc.).
They then use the start-up decision support template to build-out a decision support model to derive their real-time decisions, which they then submit manually or automatically to the markets using various order types. Based on immediate feedback from the market, they can revise their decisions in order to develop a decision strategy that is robust to the sources of uncertainty and complexity associated with their job task. Multiple iterations of the case support their adaptive learning-by-doing.
Students will also be able to monitor their own accounts in real-time. For example:
In order to perform well in the simulation, students have to identify and manage risks and opportunities relevant for the RIT Decision Case and formulate strategies that work well across the entire range of possible outcomes. Instructors should require students to use a financial model linked to the simulated market – in order to help them make decisions. The ability to use RTD (real-time data) and/or API links to the markets is one of the key features of the RIT package, as students can export securities’ prices, volumes, open and filled orders, etc. They are provided with all of the tools needed in order to perform valuations and risk management calculations that will help to make good decisions in the simulated stochastic (uncertain) environment.
For most RIT Decision Cases, instructors are provided with start-up templates that will provide introductory models of the relevant finance theory and, as such, suggest strategies for decisions that should be implemented for that case. These templates include RTD and/or API links to the simulated market(s) associated with the corresponding RIT Decision Case. These models will not only help students process information and make decisions, for example, identifying mispriced securities, implement a Monte-Carlo to evaluate the potential range of outcomes, etc., but also monitor their own performance, for example. risk management effectiveness, etc.
Depending on the decision of the instructor, these support templates can be distributed to the students prior to running the case as a starting point for developing their strategies. Alternatively, students can be asked to develop their own support models. If the provided support templates are distributed, it is highly recommended that students be required to further develop the templates in order to demonstrate their mastery of the finance theory relevant for implementing good decisions.
Students are normally asked to submit their decision manually to the simulated market, using order entry screens and/or fast order entries inside the limit-order books. Alternatively, given the API support for low latency trading built into the RIT application, students can design and program algorithms to automate the entire decision and execution process for cases for which an automated strategy is appropriate. This option is useful for students to further develop their programming skills. The RIT platform provides VBA API and REST API functionality, so students can choose Excel VBA or any programming application that supports a REST API such as Matlab, Python, R, Node, or C#. API order submission speed can also be controlled by instructors.
Most of our RIT Decision Cases use an AI Order Flow to add liquidity in order to maintain reasonable market conditions (particularly when a small number of students are practicing). These orders can be turned-off by the instructor or, alternatively, depending on the case, allowed to participate as noise traders, liquidity traders, options traders, and/or buy-side institutions. They can be programmed to be uninformed, in which case the AI orders are submitted randomly (according to a specified distribution) on either side of the mid-market price; or, alternatively, they can be programmed to be fully or partially informed, in which case they will push the market towards a pre-generated price path.
The ability to program that AI Order Flow with adjustable parameters allows for a very flexible market structure. For example, liquidity can be parameterized such that students have an impact on the market when they are acting as institutional investors and have sufficient market power to cause price impact. Alternatively, in some cases liquidity risk is not a primary concern in which case the AI Order Flow is used to generate sufficient liquidity so that students’ actions will not have an impact on the price paths, that is, they will be price takers as in most retail investor situations.
The RIT platform provides similar order management systems as those used by major financial institutions. Students have full control over the order types including market orders and limit orders. Furthermore, the RIT platform can accommodate sophisticated order entry systems including spread trades and transportation arbitrage trades that allow users to simultaneously submit multiple orders to capture profit opportunities. The RIT platform also features Over-the-Counter (OTC) trades that allow market participants to negotiate and book a trade with counterparty participants off market.
The RIT application also includes a built-in risk management system similar to those used at institutional trading desks. Portfolio position margin is used to calculate both net and gross risk/exposure based on participants’ current portfolio holdings, and risk management restrictions can be strictly enforced, or limit breaches can be logged using a penalty system. For example, the RIT Decision Cases include a Risk Management (VaR) case which challenges students to manage their portfolio VaR exposure (subject to penalties) while allocating their funds to multiple ETFs.
Multi-marketplace simulations are supported allowing cross-listed securities; and multi-venue market making, arbitrage, and liquidity risk management strategies.
The RIT Market Simulator platform has a built-in news delivery system that can announce news based on a predetermined script (time, ticker, title, news body), or news can be input and disseminated in real-time by the instructor. News can also be programmed with specific targets so either all traders receive a news item, or only particular traders receive the news.
The RIT Server Application allows the instructor to monitor, in real time, all the activities of all users.
One of the unique features of the RIT Market Simulator platform is that it allows simulations to run 24/7 so that students can remotely connect from home and practice RIT Decision Cases. This is easy to set up and only requires a computer running the relevant RIT Server Casefile 24/7 and access to that computer via a port through any firewalls.
The RIT Market Simulator package allows students to experience how various financial instruments are priced and traded. Securities currently supported by the RIT package, and their properties, are summarized below.
Equities – Instructors can design tradable equity instruments that are either traded solely by the RIT users or also by AI Order Flow. Payoffs for equities such as dividends, as well as the final value of the equity, can be specified prior to the start of the case. Alternatively, instructors can set the final value of the equity security to be determined based on the last price of the period. Trading costs can be specified in different measures.
Foreign currency – multi-currency and dynamic cash interest rates can be simulated allowing cases using forward contracts on foreign currency, money market swaps, etc.
Fixed income – RIT supports both discount bonds and coupon bonds. Discount bonds are quoted and traded based on their price. Coupon bonds are quoted and traded based on their clean price and accrued interest is added to the transaction once a trade is made. AI Order Flow can participate in fixed income markets as informed or uninformed traders.
Options – Both call and put options can be implemented in RIT Decision Cases to add speculative and hedging opportunities. Options are traded based on the equity convention of 1 contract being associated with 100 shares of the underlying. Options can be settled either in cash or in underlying shares upon expiration: in case of the cash settlement, the payoff is based on the difference between the strike price and last price of the underlying at the expiration of the contract.
Futures – Futures contracts can be traded using RIT and marked-to-market depending on the pre- determined case parameters. Futures can be also linked to an underlying security for their final mark-to-market value and can be either cash-settled or physically-settled (in the underlying security) upon expiry (delivery).
Commodities – The trading and processing of physical commodities is supported by the RIT application. Students can be required to lease storage prior to purchasing physical products, and can then use different physical assets (such as pipelines, refineries, production facilities, etc.) to convert (for example, crude oil to heating oil or gasoline) or move these physical assets from one market to another. In addition to pricing, the commodity cases feature location and product arbitrage as well as speculation (news trading) and risk management.
Synthetic financial securities – RIT allows instructors to create tradable synthetic financial products. Students can also use the RIT ‘converter’ technology, for example, to create or redeem ETF units. Synthetic securities from non-traditional asset classes can be designed, modelled, and traded on the RIT Market Simulator platform with appropriate settlement features.