Simulation Package: Investing & Proprietary Trading (information)

Short description

Maximise your return by transacting; buy and sell at the right times.

Make as much money as you can buy transacting term contracts.

Capacity:

You either act in the capacity of an investor, or as a so-called prop(rietary) trader, taking up the role of speculator.
You act in the capacity of aggressor.

Task:

Your task is to make money, preferably as much as possible.

Objective:

The aim of this simulation is to end up with a positive financial performance (i.e. a profit). This would grow your working capital.

Situation:

You can transact two term contracts (X and Y).
In reality you would start with a certain working capital, which you have to grow by deal-making.
Buy and sell as much as you’d like, but respect your position limit (per product): 5 units (contracts; either long or short).
Once you have an open position (long or short), you’ll be shown your realised profit or loss (“P/L–R”) and your unrealised profit or loss (“P/L–U”).
Assure you have not left any position at the end of the simulation. Hence, make sure you buy and sell equal volume.

Conclusion:

At the end of the simulation, analyse your performance. See what you have done and when you have done this and whether it could have been optimised. This way, you learn and optimise your competences.
Compare your activity to specific elements in the price chart (such as maximum or maximum price levels, as well as price trends).

Notes:

Swap price chart by clicking on product of relevance.
The IT settings are such that one click will lead to aggress a volume of one (contract).
When submitting an order (ticket): It concerns an immediate-or-cancel order. If your price is off-market your order cannot be executed; hence; it cannot be submitted.
Feel free to pause the simulation when it is running. This allows you to temporarily stop the dynamics, which allows you to analyse while the market is frozen.

Study advice:

Follow these online courses (see: guide) at ENTRIMA, to apply this knowledge in this simulation: