>I’ve broken apart the time frames that I’m studying, and so I now have a better idea of the sort and variety of results I can expect from the random-entry system. First I divided the data into 2 parts, and then I divided it into 6. For both studies, I performed the same simulation and gathered the same data on profitability and risk. The results are below.

Two 5-year periods

All numbers are counts.

Criteria	1st 5-year period	2nd 5-year period
Ended with positive number of pips	9841	9795
Simulations that made money by the end	9839	9920
Experienced absolute drawdown	9823	9839
More losing than winning trades	10000	10000
Positive R-expectancy	9880	9939

6 sub-periods of 10000 hours each (Except the last one)

All numbers are counts (Out of 10,000 simulations), except for the last row, which is the R-expectancy for all the 10,000 simulations combined. 10,000 hours here does not include the time taken for weekends (When the markets are off). So each 10,000 hours is approximately 1.5 years.

Criteria	1st period	2nd period	3rd period	4th period	5th period	6th period
Ended with positive number of pips	7876	9735	8165	9391	9129	7980
Simulations that made money by the end	7631	9595	8484	9730	8934	8256
Experienced absolute drawdown	9838	9148	9607	9136	9254	9146
More losing than winning trades	9999	10000	10000	10000	10000	10000
Positive R-expectancy	7829	9658	8683	9771	9048	8380
R-expectancy	0.0321	0.0801	0.0456	0.0892	0.0558	0.0439

Summary

Now for the good stuff. Lets see what these numbers tell us.

Bad news

Lets begin with the bad news, since its all uphill from there. The bad news is that the numbers are not consistent. During some times, the system does work less well (Than other times). I’m only going to address the 6 sub-periods study since it gives more fine-grained control.

The bad news is that for some 1.5 year periods, only just more than 75% (7631) of the simulations make money. The next best is just above 80% (8256).

Good news

The good news is that on average, even for the worst scenario, this system makes money! Even when only 75% of the simulations make money, the average return per trade (For all 10,000 simulations (Even the ones that lose money at the end of the run)), shown by the R-expectancy, is 3%! Lets put that in real-world terms.

If 10,000 people ran this system independently over this time frame, and used the same position-sizing rules that I did, and they agreed to share all their profits (Positive and negative) evenly, they would have made the equivalent of a 3% increase of the amount they risked per trade, for every trade that they make.

How would you like to be mathematically guaranteed a 3% return on the amount of money you risk, per trade?

Next steps

So now I have a benchmark of the results I would like from a trading system. This is a good starting point; an R-expectancy of 0.03. The next goal is to start practising making neural nets that can figure out better entry points. I am comfortable with the volatility trailing stop as an exit rule (For now).

Thanks for reading,

Ravi Desai