Boris’s Formula For Trading Success

Some of you may be familiar with the Kelly criterion which is a formula used to calculate an optimal bet size allowing gamblers and traders to maximize their winnings. The proof for the concept can be complicated, especially if you are mathematically challenged like me, but the net takeaway of the Kelly formula is that on 1 to 1 payout you bet 2 times the probability percentage minus 100% of your capital. So that if probability is 60% you bet (2X60% -100%) =20% of your capital on any given trade to maximize your profits.

In real world of trading betting 20% of your capital on any one idea is insane and Kelly has been criticised for creating wildly inappropriate bet sizes for traders enamoured with the math but utterly unfamiliar with the non standard distribution properties of capital markets. (In plain English – there is no such thing as fixed odds when it comes to trading.The game could literally morph from roulette with a tiny negative 49%-51% edge to the 1 out 1000 odds of a daily scratch lottery – and this is the key point – all on the exact same trading strategy.)

So given the fact that odds in trading are essentially an illusion, I have my own rather crude but I think more effective formula designed not to optimize gains but rather to contain losses to a manageable level.

Anyone who has ever traded for more than a month quickly realises that to survive in the market the key is to control losses, as gains pretty much take care of themselves. The simple math is that it takes 200% profit to get back to even from 50% loss, so the key to long term winning is to never, ever get to that -50% level in the first place.

So here is my day traders formula for controlling size.
Ask yourself the following questions:
What is the maximum amount I am willing to lose in a day?
What is my stop on my day trading strategy?
How many trades will I make per day?
What is my expected losing percentage?
How much money will I trade with?

On a hypothetical example let’s say I never want to lose more that 1.5% in a day (I am assuming a worst case scenario of 10 straight days of losses would equal to -15% of my account. Make your own assumptions on this- there is no wrong answers – as long as you assume at least 5 losing days in a row minimum)
My stop is 25 pips
I will make 10 trades/day
My expected loss is only 20% ( 2 losers out of 10)
I have 10000 dollars to trade with.

In stress testing these variables I assume that my expected losses will be three times what they should be ( The good old rule of thumb that in any business plan you must double your expected costs and half your expected profits comes in very handy in trading. In other words ALWAYS assume that things will be at least twice as bad as you initially think. In my case I assume that they will be three times as bad)

So when you put all of these ideas together I basically trade at 10,000 units (one mini-lot) per 10,000 dollars of capital. This way if I lose six times out of ten I lose 150 dollars or about 1.5% of equity. (Yes I know I still have 4 more trades left, that could reduce my loses, but as I said I am assuming the worst, ALWAYS).

Now for those of you with a mathematical mind or an engineering degree this “formula” is laughably imprecise and full of utterly subjective assumptions. But that is exactly the point. This a formula is produced by the school of hard knocks rather than crafted through the elegance of Big Data algorithms. Its designed to be as robust as possible against a non deterministic distribution ( simple English – I have no f-ing idea what will happen) rather than a beautifully written model that may ultimately bankrupt you.

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In any case I hope you find it useful.

Boris Schlossberg

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