The single equity curve generated by most system testing environments (or your own real life trading) is “one-dimensional” in that it does not say much about the expected variability of the results.  It is therefore difficult to effectively position-size a trading system to meet your objectives simply from a single (hypothetical or real) equity curve.

One useful technique for assessing what “good and bad” could look like for your trading system or method is to simulate the variability of your results to generate a series of “what if” situations (i.e. a set of equally likely equity curves).  This gives you a much better indication of the possible range of results of your trading simply by showing what an equity curve would look like by randomly sampling the trades (or sequences of trades) in different orders.

In this mini-eBook we look at simple but effective techniques for simulating the variability of your trading results in order to more effectively position-size a trading system to stay within your targets for reward and risk.

The main caveats of simulation are also discussed and these include:

• Sequential versus concurrent trades as part of the simulation
• Intra-trade simulation versus “atomic” trade simulation
• Serial dependency between trades being “lost” in simulation

If done properly, simulation of the variability of results can be a great benefit in setting sensible position-sizing rules and also setting your expectation for “what good looks like” and knowing when a trading system or method is operating within or outside normal parameters.  If you know what the likely range of results is for your system you can have rules in place that monitor, detect, and actually do something about situations where your trading is not acting “normally”.