Andrew posted a great comment on the ダフ屋 EA noting how he increased profits dramatically with position sizing. You can think of the expert advisor as the orange and the potential profit as the juice. We want to squeeze out every last drop.
Squeeze more profit out of your expert advisor with position sizing rules
I always talk about winning percentage and the R multiple together. It doesn’t matter that if win 99% または 15% 時間の. It matters how much you win compared to how much you lose. Winning frequently does not correspond to profitable trading.
Only when you know the percent accuracy and R multiple can you determine expectancy: do I expect to make a profit or not following this expert advisor?
The explanation reveals two assumptions:
- I know the real accuracy of the system
- I know the real R multiple
NinjaTrader backtest reports show the percent accuracy and the R Multiple (ratio of avg win to avg loss)
I don’t know either for a certain fact. The real accuracy and real R multiple may be above or below the numbers seen in backtesting.
It’s important that we consider how we developed these numbers. I found them after analyzing an entire year’s worth of chart data. There’s a real risk that they overestimate future performance.
The forward test for 2012 discounted that notion – the R multiple and percent accuracy numbers actually improved on the blind data. しかし, as most of you know, I’m extraordinarily risk averse.
I assume the worst case scenario in nearly everything that I do. If the worst case isn’t so bad and the average case is fantastic, that’s the only threshold that pushes me past my risk inhibition. It has to be a really amazing opportunity for me to pull the trigger.
A pessimist’s view on position sizing
The goal is ない to maximize profits. It’s more complex than that. The real goal is to maximize profits with a view to how many losses you can stomach.
That number is pretty low for me. Use bigger numbers if you can tolerate more pain.
I operate with the same attitude when selecting my position sizing strategy. It’s important to make reasonable, yet pessimistic, assumptions when selecting your money management approach.
ステップ 1: Decrease the strategy’s accuracy by 5%
Strategies vary in their accuracy with the chart and instrument where it operates. I have not done an analysis, but my rule of thumb is that accuracy fluctuates ±5%. The worst percent accuracy seen is usually within 5% of the best percent accuracy seen.
The rule generally holds across multiple instruments and time frames. Once you get a feel for the accuracy, you take the observed accuracy and subtract 5%.
The scalping strategy profited 75.93% of the time after including trading costs. Removing 5% off of that number leaves a reasonable worst case assumption of 70.93% 精度.
ステップ 2: Decrease the R multiple by 20%
The method used here depends on the expert advisor’s trading style. Range trading expert advisors usually come out with an R multiple (average win/average loss) within a consistently narrow band. Trending EAs exhibit wild fluctuations in the R multiple.
This should make sense to readers with several years of trading experience. Range trades earn profits on the majority of trades. No individual trade contributes dramatically to the final outcome.
Trend trades do exactly the opposite. A small handful of trades contribute enormously to the overall result. The traders faces the risk of overemphasizing how well volatility played to his in advantage in the past. If trends of similar magnitude fail to materialize, the trader’s estimate for the R multiple will be wildly off the mark.
Rules of thumb for adjusting R multiple assumptions:
- Cut the observed R multiple for a ranging strategy by 20%
- Cut the observed R multiple for a trending strategy by 33-50%
The scalper EA trades ranges and showed an R multiple of 0.53. Cutting that number by 20% reduces it to 0.424.
Apply pessimistic assumptions to model money management
The adjusted numbers for the scalping strategy are 70.93% accuracy and an R multiple of 0.424. I can now take these numbers and start playing with the money management software.
The first thing that I did was to reduce the number of trades in the test to 100. That equates to roughly 1 year of trading, which is a meaningful period of time for this expert advisor.
固定小数マネーマネジメント using 1% risk and the pessimistic assumptions puts the trader slightly ahead of breakeven after trading costs (、 1% 利益). The worst case scenario shows an annual loss of 25%. Nobody wants a loss like that. しかし, it’s something that I can handle as a plausible worst case.
Final numbers
The pessimistic money management assumptions still show an average profit.
Increasing the percent number to 2% doubles the minimum and maximum outcomes. Tripling it to 3% triples the worst and best outcomes. You should select a number whose worst outcome strikes you as something personally acceptable to you.
I selected 1%. The worst case scenario is something that I can handle. The next step is to split the difference between the observed numbers and the pessimistic number for the percent accuracy and R multiple.
The numbers are 73.43% accuracy and 0.477 for the accuracy and R multiple. When I plug the numbers into the software, it spits out an average return of 8.9% with a maximum upside of 40.9% 後 100 取引.
Moderate assumptions for position sizing show an average return of 8.9%.
Using fixed fractional money management squeezed more juice out of the orange. We brought the worst case scenario up to -25% and the best case scenario on moderate assumptions to over +40%. もっと重要なこと, the average return increases without affecting the best and worst cases.
What position sizing ideas do you have? Leave your thoughts in the comments section below.
