# The Exponential Benefits of Pyramiding – with numbers

Had the time to do some simulations of my own. I’ve been interested to understand the risk profile of pyramiding and the potential outputs of such a technique. This post will help you understand the exponential potential of scaling in, especially for big time trend followers.

The Initial Conditions

The initial conditions are arbitrary but it is a start: Starting price of xxx/USD of 1.0000. Risk of initial trade is 2%, with stop at 20 pips. Volatility N = 0.0010 (A measure of weighted true average range ATR). Plan is to scale in at every N, with additional of 1% or 2% per position added. Thus stop loss is at -2N, while every N growth is at 10 pips.

The Data

The table below shows the numbers of pyramiding, the potential profit, risk and net equity if the trade is stopped out.

Based on this numbers, I’ve scaled in at every N growth till N=10, where price is 1.0100. I then plotted the equity growth if stopped vs N growth, equity growth if taking profit at N vs N growth, and lastly reward:risk ratio if taking profit at N vs N growth. The variable here is the %risk taken for every added position.

The blue line shows the results for additional 1% risk scaled into our position when price moves in our favor, while the red line shows %2 risk scaled in.

This graph shows a couple of things:
1) exponential equity growth at high N (good trending market)
2) At low N, where N<4, lower risk profile downside if you scale in with smaller position. 3) At higher N, where N>5, higher risk equates higher gain.

This second graph shows profit taken at N. It tells a few things as well:
1) Exponential growth for scaling in. Higher growth for higher risk taken.
2) Decent locking in of profits even at very low N.

From the above graphs, it may seem that risking a higher % per scaling in results in higher reward, which makes sense. But the next graph may tell a different story, in terms of risk:reward.

Now this graph paints a different picture:
1) Risk:reward ratio is consistently higher if you use a smaller risk% to scale in.
2) It is contradictory: if you risk lesser, your absolute gain is lesser, but your relative gain is higher.

Conclusion

The interesting thing is the last graph for me. As a long term trader, I’ll seek to minimise risk in my trades so as to preserve my capital, my number 1 priority, despite giving lower absolute returns. Secondly, this study shows the great power of scaling in especially for trend followers. What this means is that you just need one big trend per year and you scale in accordingly, you will make your trade of the year. At N = 10, this represents only a 1:5 risk reward ratio which is highly achievable if you study your charts. Strong defined trends can often give more than 1:5 RR.

However, the rosy picture in this study does not tell you about the drawdowns expected in a whipsaw market, or when you are stopped out. I believe this is how the turtle traders traded, enduring plenty of small losses until something big come their way and recouping their losses plus more.

Interested to hear some comments on this.