Why it’s nearly impossible to trade Currencies with success

(Elite E Services) — 9/1/2017 — As we have explained in our book Splitting Pennies – trading FX is nearly impossible; or at least, it may be possible for some time, but in the long run, it’s a near certainty that without the use of professional algorithmic trading systems you will blow up your account.  That’s because of the dynamics of how FX works vs. other markets.  In traditional markets, there is a bias towards positive movement; all CEOs of public companies want their stock to go higher.  Bull traders, 401k investors, pension funds – basically everyone wants the stock market to go up.  The short sellers aren’t ‘pessimists’ so much as ‘realists’ that over-inflated P/E ratios are a sign for a crash from unrealistic levels.  This is NOT the case in FX.  Currency markets have opposing forces like ‘gravity’ and ‘anti-gravity’ – every country wants both a strong currency and a weak currency.  This may seem illogical, welcome to the world of Currency!  The reason is simple – exporters want a cheap currency and importers want a strong currency.  Politicians usually favor a weak currency because it’s good domestically and big business favors a strong currency (at least in the USA) because USA is a net importer.  Let’s have a look at today’s USD action most noticed in EUR/USD:

On the surface this looks like a great trading opportunity – but is it?  EUR went up on poor US Payroll data; and then fell on dovish jawboning from the ECB.  Planned conspiracy to manipulate FX or just random brownian movement?  Believe what fits into your mind that helps you sleep at night, either way – would you have been able to buy EUR at 1.1924, sell near the high at 1.1980 and then reverse, covering near 1.19 handle?  All within 10 minutes?  Maybe someone did it, even if by accident, but the point is that any trading plan or investment strategy shouldn’t rely on the ability of such skills because even if as a trader you were able to achieve this great feat – would it be able to repeat it, day in and day out – for years?  Probably not.

Enter more paradox such as “Triffin Dilemma”:

The Triffin dilemma or Triffin paradox is the conflict of economic interests that arises between short-term domestic and long-term international objectives for countries whose currencies serve as global reserve currencies. This dilemma was first identified in a 1929 book, Gold and Central Banks, by Polish economist Feliks Młynarski,[1] who identified a fundamental instability in a gold-based international monetary system, that the reserve currency countries would tend to accumulate foreign reserves, but as the volume of these grew relative to the country’s gold reserves, international investors would begin to fear suspension of convertibility; later in the 1960s, it was rediscovered in the context of the Bretton Woods system by Belgian–American economist Robert Triffin, who pointed out that the country whose currency, being the global reserve currency, foreign nations wish to hold, must be willing to supply the world with an extra supply of its currency to fulfill world demand for these foreign exchange reserves, thus leading to a trade deficit. Due to Młynarski’s precedence in articulating the problem, Barry Eichengreen has suggested renaming the problem to “the Młynarski dilemma“.[1]

This is not only true for a reserve currency – any currency has a conflict between short term and long term interests.  For example, if a currency is weaker it can help exporters in the short term to boost sales, but hurt the same exporters in the medium term when they need to go out into the world and buy raw materials for higher prices.  This push and pull is what defines modern Forex on a systemic level.  While average investors certainly don’t need to know this unless you’re planning on getting a job with a central bank, it can help any investor understand how and why Currency markets fluctuate the way they do.  It should also be noted that these forces maintain ‘bounds’ naturally, establishing a sort of ‘high’ and ‘low’ limit for any FX pair.  For example the EUR/USD now trading around 1.19, it can go in next days to 1.20 or 1.21 but not 1.90, for example.  Even in rare cases such as the “Brexit” the GBP/USD went down by less than 10% – which is a lot, for a major Currency.  So let it be known to all that these risks in FX are investable (with the help of algorithms) and hedgeable.  Looking from a risk management perspective, it is a lot more manageable than securities, commodities, or bonds – which have the finality of the ‘ulimate’ risk (default) – as Currency is ‘money’ the Euro can’t ‘default’.

A final note to all you Bitcoiners – Bitcoin is a Currency it’s only a matter of time before it’s integrated into the Forex system, because BTC/USD is an FX pair.  Good time to brush up on your FX and understand the broader market (not just the microcosm of Cryptocurrencies).

So now for the good news, the Currency Market provide a number of opportunities for algorithmic trading systems that continually profit, making FX a new budding asset class.

Today’s move is a blip on the radar, a non-event for hedgers – and a potential huge trading opportunity for algos.  Game on!

For a pocket guide to make you a Currency Genius checkout Splitting Pennies.

Money Management Modeling

We recently developed software to model the affects of random chance on money management. Although computers are capable of producing pseudo-random numbers, the pseudo-random procedure introduces bias into the random distribution.

We determined to source our random numbers from random.org. The web site obtains purely random streams of bits taken from atmospheric noise. We then use binary mathematics to change those bits into numbers ranging from 1-10,000. Say, for example, that we want to model a trading system with a winning percentage of 50%. Whenever a number comes out between 1-5,000, we consider that a winner. Anything above 5,000 marks a loser.

Modifying the winning percentage to 65% works the same way. Any number less than 6,500 represents a winning trade. Numbers above 6,500 signal a loss. The modeling quality is accurate to the thousandth decimal place. That type of accuracy is way more accurate than the "known" accuracy of your trading system, which can only be known within a few whole percentage points.

Most traders fall into the trap of thinking about their trades as individual outcomes. The more necessary way to view returns is as the sum of all individual outcomes. Losing on any given trade does not matter. It only matters whether the sum of all your winners is greater than all of the losers.

It gets more complicated, unfortunately. A system with 50% wins and a 1:1 payout will almost never come out at exactly breakeven. The mathematical expectation is that we expect to see a degree of drift in the returns solely due to random chance. I suggest reading more in the random trade outcomes and dollar profits section to get a better understanding of drift.

Lastly, we must delimit a sampling period for evaluating the final result. I arbitrarily set the default value to 200. That means that the software tells us the range of outcomes after 200 total trades. That may take more than a year for some traders. Daytraders may reach that benchmark in several weeks of trading. The question that we are answering is "what is my account balance likely to be after placing 200 trades?"

Coin Toss Trading Experiment

The first experiment is to analyze how dollar returns vary with a coin toss game and the most basic money management method. A starting balance of $100,000 is used with a risk of 1%. The risk will not change as in the fixed fractional method. Instead, we will leave the lots fixed in order to strictly understand random chance. Wins always earn $1,000. Losses always lose $1,000. The odds of a coin toss are 50% wins with 50% losses and a 1:1 reward risk ratio.

The average trade comes out to $99,868.36, almost exactly $100,000. It's what we expect for a 50-50 game with a 1:1 reward risk ratio. What I find interesting is the standard deviation of $14,377 and how it changes. I don't want to cover scary math topics. The layman's explanation is that the standard deviation is the "normal" range from the average that you might expect. The $100,000 balance, in most cases, would either lose $14,000 or make $14,000.

Everything beyond those standard deviation boundaries represent the less likely wild scenarios. The minimum outcome comes out to $58,o00, a massive 42% loss. This had a 0.54% chance of occuring. The maximum outcome shows as $158,000, a monster 58% return. This had an even smaller chance of occurring, only 0.1% (1 in every 1,000 trials).

Changing the account risk dramatically affects the standard deviation. 1% strikes most traders as sane and reasonable. Yet, there is a small chance of losing half the account to drawdown strictly because of terrible luck. Decreasing the overall risk by a fourth to 0.25% drops the standard deviation by exactly one fourth. The worst case scenario shrinks to a highly tolerable $11,500 drawdown (11.5%). Most traders would find a number between 10%-20% reasonable. The consequence of the reduced risk is that the best case scenario drops correspondingly down to a 14.5% gain.

Stretching the risk out to 2%, a normal industry practice, turns out to be risking accounting suicide with the coin toss game. The worst case scenario drops the final account balance down to $8,000, a staggering 92% loss.

The goal is to help you define risk from a gut feeling matter into something more tangible and calculated. Too many traders enter the market day dreaming about profits. Risk enters the picture, but too few traders actually understand the relationship between risk and reward. Hopefully, the picture of best, worst and average scenarios is starting to become more clear for you.