GS,
Thank you for the app answer. btw.
There are many things that I don’t do in public: making love, praying, and trading are three of them.
By the way, Aaron, [I]“siege”[/I] is spelled [I]“i before e”.[/I]
Thank you, sir. I hope you’re right.
If you find any errors or omissions in it, give me a shout.
Clint, we type LOL so easily, but I literally just laughed out loud! 
Thanks for the applause. — I’m delighted to entertain.
Somewhere, back in the first 25 or 30 pages of this thread, there were a lot of questions about the exact risk involved in this portfolio methodology. Clearly, the posters who were asking those questions were hoping for hard numbers — for example, [I]“if you open n positions, you will be risking x% of your account.”[/I] Answers that specific were never given, because such answers are not possible. The risk associated with opening multiple positions in a forex portfolio is dependent on a number of variables, some of which must be estimated.
The final step in forex portfolio risk analysis involves some hairy mathematics called binomial probability distribution, involving laborious calculations which you don’t even want to attempt to do by hand. But, those calculations are a snap for computers. And fortunately there are free online Binomial Calculators which will spit out binomial probabilities to 15 decimal places in just a few seconds, with just a few inputs.
And, therein lies the problem: those 15-decimal outputs are only as good as the inputs we provide; and, as it turns out, we have to make a lot of assumptions, in order to come up with those inputs. So, before we resort to a Binomial Calculator, let’s talk about trade risk in general terms, and let’s see whether we can make the necessary assumptions with an adequate level of confidence.
It’s common for forex traders to refer to risk as the [I]loss[/I] (in dollars or in percentage of account balance) which will result, if things go wrong — without regard to the [I]probability[/I] of things going wrong.
For example, two traders open [I]different[/I] positions in GBP/USD. Each trader has a $20,000 account. Each trader’s position size is one standard lot. And each trader protects his position with a 40-pip stop-loss.
Almost everyone would agree that these two traders are taking the same amount of risk; specifically, each trader is risking 40 pips, which is $400 (in a 1-lot position), and this $400 risk represents 2% of each trader’s account.
But, look what happens when we take into account just one additional metric — [I]win-ratio[/I] — involved in these trades.
Let’s say that one trader has an average win-ratio (in trades similar to his GPB/USD trade) of 55%. And let’s say that the other trader has an average win-ratio of 80%. The trader whose strategy has a 55% win-ratio is much more likely to lose $400 on [I]his[/I] GBP/USD trade, than the trader whose win-ratio is 80%.
Specifically, one trader has a 45% probability of a $400 loss, and the other trader has a 20% probability of a $400 loss.
Furthermore, over the course of many trades, the trader with the 55% win-ratio is much more vulnerable than the other trader to devastating [I]strings of losses.[/I]* (see the note at the bottom of this post)
Shouldn’t these realities be included in our concept of “risk”?
The idea that [I]“risk is simply the loss that will result if price hits your stop-loss”[/I] is so commonly held that we might as well just accept it and use it, when we’re discussing individual trades. But, for the more complex situation where several trades (or many trades) are running simultaneously in a portfolio, the winners and losers offset one another — so, we know intuitively that there has to be more to this idea of risk than just stop-losses.
If we know the number of positions in a portfolio, and if we know the [I]average win-ratio[/I] and the [I]average reward/risk ratio[/I] for the trades in the portfolio, we can calculate the number of losers required to tilt the overall portfolio from profit into loss.
If most, or all, of the positions in a portfolio were to result in losses, the overall portfolio loss would be substantially larger than most traders are accustomed to. So, it would be a good idea for us to determine the probability of such an event. Which brings us back to the binomial probability distribution mentioned above.
Let’s start with the metrics which Mastergunner has posted in this thread for his portfolio trading so far this year. Then, let’s plug his numbers into a Binomial Calculator to determine the probability of various numbers of losses.
Mastergunner has posted the following numbers:
• number of trades = 56
• net pips = 3434.4 pips
• average winner = 239.7 pips
• average loser = 93.26 pips
• 100 pips = approximately 1% of account
• broker leverage = 50:1 (maximum allowable leverage in the U.S.)
• typical portfolio size = 20 positions
Using his numbers, we can calculate the following:
• number of winners = 26
• number of losers = 30
• win-ratio = 46.4%
• reward/risk ratio = 2.57/1
• approximate average risk per trade = 0.9326% of account
• [I]implied[/I] average risk per 20 trades = 0.9326% risk per trade x 20 trades = 18.65% of account
• required margin per trade = 2% of account (approximate average)
• required margin per 20 trades = 40% of account (approximate average)
From these numbers, we can generalize that, in a typical portfolio of 20 positions, 40% of the account balance is “escrowed” as margin, and 18.65% of the account is nominally “at risk”. That is, if all 20 positions were to result in losses, averaging 93.26 pips each, then 18.65% of the account would be lost. (Even in this worst-case scenario, more than 40% of the account would be untouched by margins or losses; so, margin calls would not be an issue.)
If [I]x[/I] trades result in average profits (of 239.7 pips each), and [I]20-x[/I] trades result in average losses (of 93.26 pips each), then the “break-even” ratio of winners/losers is 6 winners/14 losers. That is, if the number of winners is 6 or more, then the 20 trades in the portfolio represent an [I]aggregate[/I] profit; if the number of winners is 5 or fewer, then the 20 trades in the portfolio represent an [I]aggregate[/I] loss.
We want to use a Binomial Calculator to determine the probability of 5 or fewer winners, in a portfolio of 20 positions, given Mastergunner’s win-ratio of 46.4%. This calculator works well.
We plug in the numbers, and get this result: Cumulative probability: P(X =/< 5) = 0.0427… (to 15 decimal places) — which means that there is a 4.27% probability that the number of winners (X) will be equal to, or less than, 5.
So, given the numbers which Mastergunner has posted, his portfolio has (on average) at any given time a 4.27% probability of being in aggregate loss, and a 95.73% probability of being in aggregate profit. This is an “idealized” result, which does not take into account the frequent removal and replacement of positions within the portfolio.
However, this result demonstrates that there is [I]a low probability of realizing overall portfolio loss,[/I] when compared to the way we normally evaluate risk.
And this result [I][U]implies[/U] that there must be an extremely low probability of realizing a total loss[/I] of all 20 positions in the portfolio (which would result in the 18.65% account loss previously calculated).
We can quickly calculate the probability of [I]total loss,[/I] using the Binomial Calculator. And we find that, given a 46.4% win-ratio, the probability of [I]zero winners and 20 losers[/I] in a 20-position portfolio is 3.83…E-06 (note the exponential notation) — which equals 0.00000383%. In other words, [I]the probability of total loss is less than 4 ten-thousandths of 1%.[/I]
As long as Mastergunner’s portfolio maintains a reward/risk ratio of 2.57/1, the “break-even” ratio of winners/losers for a 20-position portfolio will remain at 6 winners/14 losers.
However, his portfolio’s [I]win-ratio[/I] could improve. And relatively small improvements in the win-ratio of his portfolio would significantly reduce the probability of overall portfolio loss.
• Mastergunner’s current 46.4% win-ratio corresponds to a 4.27% cumulative probability of
achieving no more than 5 winners out of 20 positions, resulting in overall portfolio loss.
• Increasing his win-ratio to 50% would reduce the probability of overall portfolio loss to 2.07%.
• Increasing his win-ratio to 53% would reduce the probability of overall loss to 1.05%.
• Increasing his win-ratio to 56% would reduce the probability of overall loss to 0.5%.
• And increasing his win-ratio to 59% would reduce the probability of overall loss to 0.2%.
You can verify these results, by plugging the relevant numbers into the Binomial Calculator.
- Note concerning strings of losses for the two traders in the GBP/USD example —
For the trader with the 55% win-ratio, a string of 5 losses in a row will occur, on average, once in every 54 trades; whereas for the trader with the 80% win-ratio, a string of 5 losses in a row will occur, on average, once in every 3,125 trades. In general, for any number of consecutive losses specified, the average frequency of occurrence will be much higher for the trader with the 55% win-ratio, than for the trader with the 80% win-ratio.
The equation for calculating strings of losses can be found here.
Strings of losses should not be confused with binomial probability distributions. Strings of losses are, by definition, consecutive losses; whereas binomial probability distributions refer to numbers of losses (or wins) occurring in any order, whether consecutive or not.
Hi Clint,
This was an eye-opener of a post, thank you for the effort. I’m almost having the overload of ideas the same way I had it when I truly started paying attention to money management algorithms, the possibilities that was right in front of me all the time is amazing.
I’m very intrigued by your post and will have to read it again a few times before beginning to apply it to my own stats. Good job man, thanks again.
Cheers.
Clint that was a wonderful post. Many will appreciate that and it certainly helps warm me up to swing trading even moreso.
Kick ass post, Clint… Thanks. I now have to fully digest it.
Clint,
It will take me a few times reading your post for me to fully digest it. A great one, Clint. Very informative. Thank you for opening me up to binomial probability distributions and calculations.
Nice Clint, very helpful stats to have in consideration when a trader has a portfolio. Very enlightening indeed.
I started applying MG99 methodology in my paper trading (I’m still a newbie) and I got some grade of success, and also I discover some mistakes I could fix.
However I still have an strong feeling about many things that escape to my understanding about price action, and that makes me feel uneasy. I got a copy of Al Brooks book and I started it. Very dense material, but very rich. I’m not going to carry on trading until I progress with the book. Anyone can trade, but not anyone can be a trader 
[B]cyanidez, soul786, abrsive, Prince of Pips,[/B] and [B]alfonsomg[/B] —
Thanks for the kind remarks, guys.
I hope that nobody will take that post as the final word on portfolio risk; it’s just the starting point for an investigation of a complex subject.
At first glance, portfolios of positions, utilizing actual leverage of 20:1 or more, look very risky. And, if we just multiply our normal metric for risk by the number of positions in the portfolio, we get some scary numbers — like the 18.65% loss of account [I]seemingly[/I] being risked by Mastergunner’s portfolio.
Yet, as Mastergunner has indicated, we know intuitively that the actual risk is less than that. And, if the math in my post is accurate, it appears that the actual risk is [I]much[/I] less than that.
The best way to find out whether the math is accurate is to evaluate lots of forex portfolios, to see whether binomial probability distribution describes what’s going on in those portfolios.
I’m quite sure of one thing: The best way to make your trading more robust — including portfolio trading — is to improve your win-ratio (provided you don’t do it at the expense of your reward:risk ratio). Your win-ratio (the percentage of your trades which result in profit) is negatively correlated with (1) your risk in individual positions, (2) your overall risk in a portfolio of positions, and (3) the risk associated with devastating strings of losses.
Regarding portfolio risk specifically, as the Mastergunner numbers in my previous post illustrate, improving your win-ratio will make your portfolio significantly safer.
I’ve gotten through reading this thread the whole way through again and have one question regarding the methodology and specifically the trade entries.
Looking for PA setups on a long term basis don’t come all that often, so are we always looking for the best setup to enter a trade, or are we looking for existing PA to continue and IF we have a bias (rather than a PA signal) just enter in that direction?
Reason I ask is because looking for specific PA signals means I see one or two signals a week, how could I possibly enter even 10 positions across different pairs let alone 28?
Any opinion on this is greatly appreciated.
I’ve been using this methodology with my own “style” and I try to enter on a pullback if possible. If the overall trend is up, there are quite a few “best” areas to get in that may not ever come. I usually try to get in on a retracement on D1 if I can get it, or on a smaller TF.
Have you found yourself managing a ‘portfolio’ per say, of pairs that you manage over time? I like the idea of having risk spread across the board. At one time are you in xx number of trades at once?
I too use my own interpretation of PA on the daily t/f and the criteria I look for is continuation based after a pullback with a pin or engulfing bar to confirm. I never have trades going on all 28 pairs at the same time…I may get a signal on 2 or 3 pairs at the same time, and then end up with up to 10 in the days that follow if/when those pairs give their signal. Like you’ve noticed, it may be awhile on the daily t/f before you see a signal on any pair, but at least you only have to check all 28 pairs once a day… at the close of your daily candle.
I never seem to have trades open “all the time”. It’s more like I have a batch of trades open, then they eventually all get closed before I start getting any new signals again which could be more or less a week apart. That could be due to my filtering out certain pairs based on another method.
Once you decide what your criteria will be, then stick to it while you test it out.
Somewhat, yes. I’ve been using this method for only 2 months now, and given it’s nature of holding time I’ve made less than 50 trades. That being said I’ve been active in as few as 5 pairs and as many as 14; I have yet to manage anything close to the 28 limit.
I am similar to Sweet Pip in that I can very quickly go from 10 open trades to 4 open trades, and it will take a week or two before I decide to open up more. I feel like entry is the most important aspect of the trade, so I have no problem waiting out price movements to get into an area that I like. I think somewhere between 1 to 3 signals a week is perfectly fine with this style of trading.
Hello guys,
It’s good to see many people are trying this methodology. I have also been trying this for more than three months now. The main problem I am having now is when all the open trades go against me. Most of the times they will change direction and become favourable in a few days, but hanging on to the losing positions for a while appear quite difficult for me, at least psycologically. This usually happens when the whole portfolio is heavily burish or bullish towards one currency, as balance can go down by 10-20 percent.
What do you do in this situation? Do you think there could be something wrong with my position sizing?
Thanks for your input.
You said that [I]most of the time,[/I] a group of losing trades (which happen to be in correlated pairs) will turn in your favor. This indicates that you have identified the trends correctly, but your entries were not perfect.
[B]Rather than messing around with your position sizing, I would suggest that you work on timing your entries.[/B]
An emerging trend, suitable for trading with Mastergunner’s methodology, will generally offer you more than one optimum entry point. If you can nail one of those [I]optimum[/I] entry points — and that’s the big challenge, isn’t it? — then your position will move immediately into profit, never showing you a loss on paper.
As I see the task for all of us who trade this methodology, there are three main parts to each trade, and we have to do a good job (at least, overall, on average) with each one them. They are (1) identify a suitable trend [I]early,[/I] (2) identify and grab a [I]workable[/I] entry price, and (3) identify a breakdown in the trend [I]early,[/I] and get out at that point.
From what you’ve told us so far, it appears that you’re doing (1) and (3) well enough.
But, it’s probably being too early or too late with your entries — item (2) — that’s causing those scary drawdowns.
Thanks Clint,
I couldn’t agree more with your three points. As MG mentioned he was entering the trades on retracement. At times I see a strong trend which doesn’t show indications of retracements any time soon and I want to jump on that. Some times it works like recent movement of AUD/USD and some times it doesn’t as price retraces and if this retracement is big the drawdowns can be big. It appears also quite important to recognise the market we are in. This goes back to your first point. As I see it, and I might be wrong, the methodology can only be applicable in trending market and not in many ranging pairs we see recently.
As you mentioned I should work on my entry points among other things like when to take the profit or how much give room for the trade. These could be quite challenging as there could be some rollercoaster movements in the balance.
Thanks again.