EUR/USD Paper Trades - Is This Typical

[dwaddell]

Hello,

I'm looking for a little advice from any FX guys/gals who have a bit of experience and have seen the ups and downs of the market over at least the course of a couple of years.

I started paper trading the EUR/USD pair around early June using two seperate strategies, each taking positions for 2-7 days, when the time was right (is this swing trading?).

Both strategies use some EMA's on the 1H and 4H charts, with some exit rules of my own devising.

Strategy 1, my 4H chart strategy, paper traded since 10/6/05 has produced 13 trades:

+157
-49
-67
+155
+94
-40
-83
-3
+236
+143
-43
+140
+119 (Currently still in this trade, short from 1.2403)

Strategy 2, my 1H chart strategy, paper traded since 15/8/05 has produced 7 trades:

+200
0
-24
+56
+56
+276
+210 (Currently still in this trade, short from 1.2500)

What I want to know, before I start to throw my hard earned into the market is:

IS THIS TYPICAL?

Have I hit a nice trending period over the last few months which has trended more than it has historically, will the flat times come and wipe out my gains? I know FX markets 'should' be trending 70% of the time....is it realistic to think that the above results (or even 50% of those values) are consistently acheivable?

Appreciate your input.

Dave

 

[Mr Chill]

Hi Dave
Good results , if anything the fx markets have been pretty hard to trade the last few months so your doing very well ,what EMA's are you using ?if you don't mind my asking .

 

[Bangbang]

Using EMA

Use to be easy get the results of strategy number 1 with emas the problem is when you trade in real live if you have the patience to see your are loosing 3 days and u close positions when the next two days you will be earning.. just remenber at paper you are right so dont give up trades in real live thats it.


i will use strategy number 2 , seem is better even though you have less entries for that one.

And i will close the shorts positions of eur/usd...

 

[Spreadbetteur]

I think your sample size is a little small to make a decision.

If I try anything new, a system or a tweak to the current one, I always like to make a sample of 20 trades. I think even 20 is to small.

 

[Orion]

I think your sample size is a little small to make a decision.

If I try anything new, a system or a tweak to the current one, I always like to make a sample of 20 trades. I think even 20 is to small.

If memory serves me correctly, I recall 30 being the minimum number to assure "statistical significance".

 

[Spreadbetteur]

Out of interest, where did you get that 30 figure from ?

 

[onoufris]

If memory serves me correctly, I recall 30 being the minimum number to assure "statistical significance".

You are right according to the "CENTRAL LIMIT THEORY"

 

[Orion]

Out of interest, where did you get that 30 figure from ?

My recollection of statistical and probability theory is rather dim and distant so I bow to superior knowledge, but try a Google search on those key words, plus "statistical significance". The figure of 30 I'm sure will be revealed.

 

[Orion]

Take a look also at http://www.adaptrade.com/sigtest.htm

 

[dwaddell]

Thank you all for your input, I will continue to paper trade for the next couple of months and report back when I get to the magic '30' trades.

David

 

[Orion]

Thank you all for your input, I will continue to paper trade for the next couple of months and report back when I get to the magic '30' trades.

Good luck! I look forward to hearing how you got on.

 

[onoufris]

Out of interest, where did you get that 30 figure from ?

Central Limit Theorem:

If a random sample of n observation is selected from any population, then, when the sample size is sufficiently large (n>=30) the sampling distribution of the mean tends to approximate the normal distribution. The larger the sample size, n, the better will be the normal approximation to the sampling distribution of the mean. Then, again in this case it can be shown that the mean of the sample means is same as population mean, and the standard error of the mean is smaller than the population standard deviation.

The real advantage of the central limit theorem is that sample data drawn from populations not normally distributed or from populations of unknown shape also can be analyzed by using the normal distribution, because the sample means are normally distributed for sample sizes of n>=30.

Since the central limit theorem states that sample means are normally distributed regardless of the shape of the population for large samples and for any sample size with normally distributed population, thus sample means can be analyzed by using Z scores.


Q So the Central Limit Theorem says that the means of large samples are always normally distributed. How large is large?
A Actually a difficult question to answer: the larger the sample size, the closer a distribution is to being normal. There is no exact point where we can say that the sample size is large enough to warrant an assumption that the sampling distribution is normal. In practice, we tend to use n = 30 as a cutoff point: for samples of size n „ 30, we assume that a sampling distribution is approximately normal, otherwise we do not.


The C.L.T. describes the distribution of sample means (or of any other sample statistics) of any population, no matter what shape, mean or standard deviation it has.
The distribution of sample means approaches normality very rapidly. By the time the sample reaches n-30, the distribution is almost perfectly normal.


Normal Approximations

The central limit theorem implies that if the sample size n is "large," then the distribution of the sample mean is approximately normal, with the same mean and standard deviation as the underlying basic distribution. This fact is of fundamental importance in statistics, because it means that we can approximate the probability of an event involving the sample mean, even if we know very little about the underlying distribution.

Of course, the term "large" is relative. Roughly, the more "abnormal" the basic distribution, the larger n must be for normal approximations to work well. The rule of thumb is that a sample size n of at least 30 will suffice.

 

[Orion]

And there you have it!