Getting the edge of the Forex market

Statistical Trading - Getting the edge of the Forex market

Statistical trading consists of using statistical tools of historical price data in order to improve trading returns. The idea behind the trade statistics is that if a trader can find even a small statistical advantage, then the expected return on a large number of trades will be positive. We'll talk about exactly how to calculate the expected return below, but for now let's just concentrate on what it means to be a statistical "edge."

I'm talking about the same kind of edge that the casino owner or insurance company has. This statistical edge is based on the law of large numbers. The casino does not know whether a particular spin of the roulette wheel will be a win or loss, but they know that after 1000 turns very likely they will be richer. Their advantage is simple to describe using the game of roulette as an example. The player has a 1 / 38 chance of winning on any given spin, but will only receive 36 times their money if they win. So for 3800 turns, the player will win 100 of them on average, gives $ 3600th However, the player will lose the other turns the 3700 one dollar for each $ 3,700 loss. So what is the average house? It is $ 100 for every 3800 turns, or a little under 3 cents per spin. It adds up to ... and all other casino games of pure chance (it does not involve poker or blackjack that may include some skills) are variations on this theme. That is why the casinos are rich and gamblers are broke.

Insurance companies are very rich in the same way. The company has no idea if a person will die this year, but they are fairly accurate idea how many people out of 1,000,000 insured since the account will die this year. Let's say that statistically the death rate of a certain class of people (men over 55, smokers, and in moderate health, for example) is 4%, so we expect 40,000 to die this year. If each policy pays $ 10,000 for death, the company expects to pay $ 400 million dollars in favor ... Wow! So how should the company charge in premiums for those one million policies every year then? So how about $ 500 each? It gives the company 500 million dollars in revenue to $ 400 million expected benefit payments, leaving $ 100,000,000 for salaries, expenses, gains and the like. It is their statistical advantage.

Now let's look at some ways that you can use this idea of ​​"Statistical edge" in the trade.

A very common way that retailers are trying to apply the ideas of Statistics is planning trades in such a way that the potential gain exceeds the potential loss. This is a classic "cut losses short and let profits run" argument. For example, if the trade so that you only lose $ 100 if you're wrong, but gain $ 300 if you're right, you just need to be right one fourth of the time to break even. This is because for every four trades (on average) will lose $ 100 three times and get $ 300 a time that is washing (not counting commissions). And all numbskull to be right more than a quarter of the time right?

Right. Certainly. So why are not all rich? Mon trading currencies during 2004, I figured out what was wrong. Solid and wide stop order will tend to make wrong very simply because it is easier for the guests to guess. On the other extreme, suppose you decide you want to have a lot of winning trades, so place a very wide stops very close and cost targets. Well, now you get a lot of time, but amounts will be small. And a loss, although uncommon, will tend to remove very few victories. So no matter where you are on "trade adjustment" range, wide and narrow goals stops, tight stops and broad goals, or any combination in between, statistically it ends up being a wash. There are substantial "edge" in any given setting trading scheme, including "cutting losses short and letting profits run." Here, I know.

Getting a real statistical edge required to be able to identify situations in which the price tends to move in such a way that you can set up trades that have a positive expected return. Expected return is only a percentage of wins multiplied by win amount, minus the percentage of loss multiplied by the loss amount. An example will make this clearer.

Suppose you know that every time USD / JPY rate crosses above its 20 day moving average, the price tends to move up more often than it goes down. Examining this in more detail using historical data to determine that there is a 40% probability that prices for 25 pips before it ever drops 10 pips. Now even though this happens only less than half the time, it still allows you to place trades with a positive expected return. This is because if you set your target at 25 pips and your guests in 10 pips, you get 25 pips 40% of the time and lose only 10 pips during the other 60% of the time. Keep in mind that I am greatly simplifying this trading example of clarity. Stops and targets should be placed in locations that make sense to the table, but I discuss such details in other articles. For our purposes here, we are only concerned with calculating the expected return, which is:

(40% x 25 pips) - (60% x 10 pips) = 10 pips - 6 pips 4 pips =

So on average you can expect to get four pips on the trade using this strategy, even though it lost most of the time! But remember that this whole example is the knowledge that a positive crossover at 20 day moving average tends to skew is expected back in your favor. It's your preference in this example.



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