>Here are some statistics about the random-entry trading system. One reason I’m doing this is so that I have an ordered sense of what this system is capable of. Another is that these results can serve as a benchmark for comparing other systems against. Finally, I want to share this with the world.

## The evaluation system

Since this is a random system, I’ve run a Monte-Carlo simulation of 10,000 iterations and then summarised the results. Each simulation runs over ~10 years of data.

## The statistics

All the following numbers are out of 10,000 total simulations.

- Count of simulations which have ended in a positive number of pips: 9986

- Count of simulations that have made money by the end: 9993

- Note how this is greater than the previous number; this happens because of the position-sizing rules.

- Count of simulations that experienced a balance less than the initial capital sometime during the simulation: 9856

- So be prepared to have your account balance go down sometime during trading instead of steadily going up.

- Count of simulations where there were more losing than winning trades: 10,000

- Yes, that is correct. This system loses more often than it wins. Not eventually, thankfully, but on a per-trade basis.

- Count of simulations with positive R-expectancies: 9997

## Conclusion

Yes, this system isn’t perfect. But this trading system is the one I’m most comfortable with.

### Issues to consider

When speaking to a fund manager, I was asked how the system does during violent/abnormal/investors-start-panicking periods in the financial markets. So far I’ve just run the simulations over the entire 10 years of data & so I don’t know the answer to that question. Here is how I propose to find that out. I’ll just cut up the data into 1.5-2 year chunks, and then have a look at the results. Ideally, the results will be the same throughout the different sets of simulations. If this is the case I can safely state that the random-entry system provides consistent results. If the results are not the same (Or close enough to be similar (Since it would be ridiculously improbable that they would be identical)), then I’ll have to find out what markets the system works better in (And doesn’t).

To look at investors-start-panicking periods, I’ll pay attention to the 2001-ish data-set (The internet/dot-com bubble and the consecutive implosion).

Thanks for reading,

Ravi Desai

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