[QUOTE=“ClarkFX;578625”]I find this Proximus guy really funny. He seems to know how everything works, refuses to accept any opinions that do not necessarily align with his beliefs and yet, still asks people for their opinion (only to attempt to shut it down of course).[/QUOTE]
I forgot why I stopped commenting on new threads a long time back.
Yes but only an opinion, not a dogma.I can change my opinion.
What makes you think that.I`m just presenting you my view, and there will always be agree-ers and disagree-ers
Technically it does, but since the WR fluctuates, it will more likely fail and will not be very practical.
I would also aim for atleast 60% though.But it was just a thought experiment, you dont have to take it so seriously.
[QUOTE=“pipwhip;578602”] I enjoyed your post a lot. It got me thinking about something from a while back in regards to how the exploitation of inefficiencies leads to the inefficiencies not existing any more. What it got me thinking about is, are there a stable source of inefficiencies? This is a philosophical question I suppose but I was wondering your take on it. I personally would say structural inefficiencies like the January effect are not sustainable. There are some technological ones that HFT has truly killed from over exploitation, I would say these too are not sustainable. There are however people who have consistently made money in the same asset classes and must be deriving their edge from a sustainable source. I can think the only constant is the human condition and what people call predictable irrationality but was wondering what your take was on this question?[/QUOTE]
Well the thing is as I said, the market will have to adapt and reflect the changes in how it moves.
I have been trading for I dont know, six years now? Well the first three was an absolute waste. Ive only been observing what its doing in any sense for the last three or so years.
And in that time I have seen the market change in many subtle ways.
Correlation used to be a given. Its gone now.
Weekend Gap trading was another big thing back than. Market always went to fill the gap, it seems. Good luck with that.
H1 SNR use to be another very predictable trading method. Thats a bye bye now.
So the way I see it, these were all edges that the market adapted to and swallowed, in a sense.
And I believe as the crowd moves to other factors, these once long swallowed anomalies will resurface again.
Now tie that in with the fact that the market is essentially a closed system.
What it means is the market cannot produce money from thin air.
For one to make money, another has to loose it somewhere.
But the market has many facets to it.
I for one honestly believe different faces of the market is reflected in the different TFs.
While the market adapts and changes its behavior on smaller TFs, say up to H1, the fundamental nature of the markets, from DTF onwards is, and will always be fueled by the two most fundamental reasons most of us trade, or get out of a trade with a loss. That would be fear and greed.
The biggest amount of money is lost and equally made when people end up chasing price, and the big fellas taking opposite positions to it.
We see this reflected in our charts as historic price action or key levels.
But I believe the traders psychology is whats really behind it.
In other exchanges, especially commodities, we can see this very clearly, and if we really think about it, I think what the big sharks are doing its criminal.
Why do I say that? Look at Gold.
For the last eight months before gold got wiped from its peak, every newspaper, every investment fund, every bank worth its salt were saying buy gold BUY GOLD BUUUYYYYYY GGGOOOLLLLDDDDDDDDDD,!!!
And let me tell u the sheeps went to slaughter with a grin on their faces.
I told some colleagues of mine to wait, as prices are going to fall.
Ofcause the office know it alls told me their bankers thought otherwise blah blah blah.
A month after my dire warning, it fell and it lost so much.
I read this from a website long ago.
It warned that the entire thing was a set up, and if we want to see the set up, we just had to correlate historical price drops of metals like silver and gold with the news published in main stream media at that time.
I tried googling for silver. It was spot on.
It made sense. The fattening of the turkey for thanks giving.
For every seller there must be a buyer.
Buy bottoms sell at the top.
Thats exactly what the big players been doing.
For those who can bend their minds to this, they seem to be able to pick extremes of price movements and get a good swing. For those whl are stuck trading the smaller TFs, more often than not, they donate their money to the big TF players.
We have to remember, when we say the market, there is no market. There is only other traders against you.
Nope it’s not
Forex Correlation | Myfxbook
Thats BS.The weekend gap was only an illusion.Since markets are open even in weekends, but only your broker doesnt let you trade.And the gap was simply a missing point of data.If your broker would have recorded the weekend activity too, then you would see that there was a nothing uncommon there, it was just a low liquidity period since not many people trade in weekends.So there is & was nothing special about gaps.90% of stock traders agree that not only weekend gaps but all types of gaps are BS.So if you trade tasuki gap patterns or similar you should think about it again.
If it was so predictable they why are you not a billionaire by now.I have 20+ years of historical data , and believe it or not i`ve checked many many strategies there and 99% of “internet” strategies are waste of time.Including this one,it only looked to be a good strategy, because you wanted to look like a good one, when in fact with an objective analysis you would have known by default that it was a waste of time.
Why do I say that? Look at Gold.
For the last eight months before gold got wiped from its peak, every newspaper, every investment fund, every bank worth its salt were saying buy gold BUY GOLD BUUUYYYYYY GGGOOOLLLLDDDDDDDDDD,!!!
And let me tell u the sheeps went to slaughter with a grin on their faces.
On this i agree with you, gold was a little bit of “bubble”, but its still rallying up because of US quantitative easing and in general because of the weakening of the dollar.
The silver also, but the silver is more noisy than the gold so its a bit harder to trust it.
We have to remember, when we say the market, there is no market. There is only other traders against you.
Nope, only 15% of the market is composed of speculators, 85% is composed of the commercial trading (import /export), bank deposits & loans,futures & forwards,mortgages,bonds,other types of fixed maturity date securities,and bank transfers by normal citizens, and the world economy in general.
So the fact that X person from US goes to vacation in Japan and buys some yen has not much to do with forex trading, yet he is also contributing to the USD/JPY forex rate 
Nikitafx has just opened up a whole new can of worms now…must be thinking “why did I even bother replying”, lol
We have to remember, when we say the market, there is no market. There is only other traders against you.
Nope, only 15% of the market is composed of speculators, 85% is composed of the commercial trading (import /export), bank deposits & loans,futures & forwards,mortgages,bonds,other types of fixed maturity date securities,and bank transfers by normal citizens, and the world economy in general
.
Actually Proximus you’re now confusing the Spot market with the futures market and also the forwards market. The Spot market is made of 100% speculators as it involves NO physical delivery of any currency. Therefore trying to combine these individual activities into a single market is wrong.
[QUOTE=“Jezzode;578773”]Nikitafx has just opened up a whole new can of worms now…must be thinking “why did I even bother replying”, lol[/QUOTE]
On the contrary Jezzode.
Im sure u have been on babypips long enough to have met a few know it alls. 
My statement on how gap trading was a false call and the reply to it says a whole lot.
It shows the person replying does not even read what we post before jumping the gun and wanting to make a statement.
Good luck with your ten year data my friend.
Oh and goodluck betting on a basket of currencies based on correlation too! 
I hope that deep down people don’t completely see this as being the case. I know these conversations are pretty painful but I personally enjoyed Nikitafx’s post a lot. I’m new here and I do get the feeling from some of the old hands why bother but honestly some of the things people have brought up are really really important things to discuss especially since we are a community of retail traders who are pray upon by the institutions.
I heard this story a while back about a group of farmers in India. I think it was from one of Bernstien’s books but I cant remember exactly. Anyway the story is something like this.
In spring all the farmers in the area till their soil and prepare to sow their seed. Each farmer has the issue that they don’t want to be the first to sow their seed as if the do all the crows will come and eat their seed. If they leave it too late then they will miss the best time to sow and this will lead to a lower harvest. When the author was being told this he said simply, if you all sow your seed at the same time then there is no way the crows can eat all the seed so you will all benefit. The response was if we would have worked out a way to do that we would all already be rich by now.
What is funny from working inside large institutions and hedge funds is that, banks hate the hedgies because they can pick and choose the trades they do and the funds hate the banks because they have all the insider information.
Now quit b1tching and give me all your IP!!!
The fact that this is your response just goes to show how little you understand of what people are saying to you. By correlation what Nikitafx meant is stable mean reversion within a basket of correlated fx pairs.
Also last time I checked gold has dumped a third of its value in a number of months I wouldn’t describe that as a bit of a bubble. It has also managed to do that while most QE programs were in full swing. It was one of the biggest pump and dump schemes ever legally run I don’t see how you can explain it any other way.
We trade for tomorrow, food for thought
Lmao, get out of here Money. This is out of your scope. Go back to making your 50% a day.
I laughed so hard at this when I saw this reply. Nikita CLEARLY did not mean it literally.
There are 8 major currencies in the FX market, that combined creates a total of 28 commonly traded FX pairs (not including exotics). Mathematical correlation is bound to happen. But correlation/relative-strength-based strategies have been having difficulties in the last few years.
Ha, this is like when people say “We donate a percentage of our profits to charity.”
0% is still a percent
Such an overlooked part of trading currencies.
The amount of people that think this market solely exists for their speculation is staggering, when the fact is, the speculation is a miniscule part of what is actually occurring.
I think you’re the one with the rigid attitude here.
Hi Proximus,
The answer to your problem seems simple. For one, and I say this with the utmost respect, just build the damn thing and stop talking about or involving yourself in dialogue with people that can’t help you to do just that. Sure they have useful opinions, but in the end, none of them really give you anything you can use to go about your potential project. I’ll try to help with a few points, hopefully, being a programmer yourself, you can extrapolate these points and modify them to suite a custom approach to analyzing large amounts of data.
[B][U]BASIC PREMISE OF A NEURAL NETWORK[/U][/B]
I should say that the information I wish to divulge relates to mainly backward propagation neural networks. That simply means that the networks send whatever error was inherent in their calculations to previous neurons in the network for recalculation, more on this later.
The structure of an artificial neuron consists of two units: a summation unit and a unit with a transfer function. A neural network is simply an interconnection of these artificial neurons arranged in layers. There are three primary layers: the input layer, the hidden layer, which is subdivided into a number of secondary layers and finally, the output layer. The connection between layers is as follows:
Input layer->Hidden layer [sub-layers]->output layer.
Each layer has a certain number of neurons. For example, a simple network can have 3 neurons at the input level, 5 neurons at the hidden layers, and three neurons at the output layer. There are also different types of connections, either full or sparse. For instance, in the simple neural network cited, each of the 3 neurons in the input level could be connected in all the five neurons at the hidden layer to form a full connection. These five neurons at the hidden layer could further be connected to another five neurons in a sub-layer, which would finally be connected to all the three neurons in the output layer to form a complete fully connected network.
The connections from one neuron in one layer to another neuron in another layer have bias weights. The neuron itself also has a bias value that is set to a random number once the neural network is initialized. This explains the summation unit within the neuron. It basically sums all the connection weights from the incoming data, adds its own bias and finally sends it to the transfer unit, which would result in the final output of the neuron. Before I write the formula for summing up data within the summation unit and then taking it through the transfer function, please consider the simple network below: [sorry, don’t have much time to upload images]
N1------N3*
* * *
* N5****
* * *
N2------N4*
In the above ‘diagram’ you can see that neurons N1 and N2 form the input layer. Neurons N3 and N4 form a hidden layer with no sub-layers and finally, neuron N5 forms the output layer. That’s a basic feed-forward network, it ‘feeds’ anterior neurons in anterior layers data. A summation unit works as such:
Dim netValue As Single=bias
For Each Input_Neuron connected to This_Neuron
netValue=netValue+(Weight_Associated_With_Input_Neuron*Output_of_Input_Neuron)
Next
To put it in a different way, using N3 as a reference point:
Net Value of N3=N3.bias+(N1.OutputWeight of connection from N1 to N3)+(N2.OutputWeight of connection from N2 to N3).
Do the same for all the neurons in the hidden layers, or receiving data from other neurons. That’s just how the summation unit of a BPN works. Now to the transfer unit, that modifies the Net Value calculated:
Output of Neuron=1/(1+Exp(-NetValue))
As you can see, it’s a sigmoid transfer function. And that’s it really. The next thing is to get the error of the network and use it to modify the bias of each neuron.
–GETTING THE ERROR/DELTA–
The delta or error is simply the difference between the desired or actual output and the calculated output. The steps involved in calculating the delta of each neuron in all the layers are as follows:
As you can see, it propagates the error from the output layer backwards [hence its name, the Back -Propagation-Neural-Network], using it to modify the bias value of each neuron, before the feed-forward process is re-initiated. The general equation for getting the delta of a neuron is:
Neuron.Delta=Neuron.Output*(1-Neuron.Output)*Error_Factor
The Error_Factor of the neurons in the output layer can be calculated directly, because we already know the expected output of each neuron in the output layer:
Error_Factor of An Output Layer Neuron=Expected_Output-Neuron’s Actual Output
The error factor calculation for a neuron in a hidden layer is somewhat different, to calculate it:
In a nutshell, a neuron in a hidden layer is using all the delta of all connected neurons in the next layer, along with the corresponding connection weights, to get the error factor. This is, as can be obviously inferred, due to the lack of direct parameters that can be used to calculate the error of neurons in the hidden layer as we did in the output layer.
So, after getting all the errors of all the neurons in all the layers, correction of both weights and bias with respect to the error or delta is performed, to get more accurate output for the next training/feed-forward cycle. Connection weights and bias are both free parameters. The neuron should update all the forward weights associated with it.
The Pseudo-code to do all this [updating weights and biases (free parameters) in all layers]:
Update free parameters of all hidden-layer neurons
For each layer in HiddenLayers
For each neuron in layer.Neurons
neuron.UpdateFreeParams()
Next
Next
Update free parameters of all neurons in output layer
For each neuron in OutputLayer.Neurons
neuron.UpdateFreeParams()
Next
Anyways, that’s how I’d go about it, you can make up your own way of doing this. Finding the bias value of a neuron is rather easy, if the Learning Rate (I’ll explain what learning rate is later) is constant:
New Bias Value=Old Bias Value+Learning Rate1Delta
The new weight associated with a neuron can be gotten by:
New Weight= Old Weight+ Learning Rate1Output of Input Neuron*Delta
And that’s all. I should say that I’ve left a lot of theoretical information, I wanted to be as direct as I could and also to show using actual formulae and pseudo-code how this could be done. Oh, the learning rate is just that, how fast the delta approaches a certain minimum. In mathematical terms, the gradient vector of the error surface should be gotten. Usually, the step size is proportional to the slope (so that the backward-error-propagation algorithm settles down in a minimum) and to the “special” constant: the learning rate. You should know that the correct setting for the learning rate is application-dependent and is typically gotten via experiment; it could also vary with time, getting smaller as the algorithm ensues.
I don’t know if I just wasted 25 minutes trying to explain this or if I’ve actually managed to tell you anything useful. As a side note, I suggest that before you begin building this thing, or before you try to implement any algorithm that attempts to model the function that associates a certain input to a certain output, you read up on Information theory, and the various characteristics of stochastic data. Because the data obtained from the forex market is random, various patterns within small time frames can be ‘learned’ and these patterns will never change, provided the algorithm ‘changes’ with its market-presence. Also, any efficient market hypothesis loses its value once it becomes public. That means that any ‘Holy-Grail’ that is mass-accepted will eventually fail, due to just that, everybody’s using it. However an algorithm that can potentially withstand this and adapt to any ‘market’ could be called, in my opinion the ‘Holy-Grail’. Traditional Neural Networks, not just BPNN, but any multi-layered perception network from radial basis function networks to generalised regression neural networks will not work in the long run, unless the function that has been modeled by the hidden layers, changes with time, where time is the desired trading time frame, i.e. a second, a minute, an hour, a day etc. How to determine this change with time, you can think about yourself. Lastly, what inputs to connect with what outputs also becomes an issue. Should you use the day’s high as input and the day’s low as output? Should you use the price at time t/2 as input and the price at time t*4 as output? What is the desired inference? That certain prices lead to other prices? That there is a connection between the time of price x and the time of price y? Without certain constant assertions, it becomes rather easy to develop a misconstrued attitude towards random data. But, with various mathematical proofs within Information theory that deal with the nature of random data, you can, by extrapolating these proofs to the forex market (again, because the data is random) develop a system that will always yield consistent results. Then again, you could just ignore all this and use brute-force methods, you could get a program called Pythia Neural Networks (it’s free, google it), use certain inputs with certain ouputs over a long time period, say 10 years, and just see what happens when you use input i with output o. Well, that’s all I have to say, I hope I’ve helped, you can ask where I have not be clear.
Hi, I would like to say welcome. There is help in this thread. ClarkFx raised some points about issues assocaited with training a NN using backprop, i put a load of stuff in about transfer functions and methods connecting layers regarding the NN type. We are not complete douches (apart from clark!), the tone of this thread is just a little tainted becuase of the nature of some of the reponses we have received through time.
I would like to clarify a point you raised. There should be a distinction made between types of NN and the methods used to train them. You can train a feed forward NN using back prop, but you can also train a recurent network with back prop. That doesn’t mean that a feed forward network is the same as a recurrent network.
Great effort, No idea what your talking about, but Liked just for the effort factor.
Great pips to Ya!!