Cortex Artificial Neural Networks Software
ver.5.0 Tutorial.
Contents
Neural Network Program and more (what is Cortex?)
Using Neural Networks (Features)
Installation of Cortex Neural Network Application
Uninstallation of Cortex Neural Network Application
Registration of Cortex Neural Network Application
Cortex Neural Network Tutorial
Databases
Other features: images and so on. Sample scripts
Neural Network Program and more (what is "Cortex"?)
Cortex neural networks software is an integrated package, built around the scripting
engine. It means, that you can write scripts to automate tasks.
Cortex is a general purpose neural network program, it can be used for different tasks. However,
as most visitors are interested in neural network forex trading, almost every neural network example
that comes with Cortex has something to do with using neural networks for trading.
To some extent, Neural network trading is what Cortex was created for. It includes:
Neural Networks
Feedforward Backpropagation Neural Networks, with an easy to use interface and large number
of additional features, like charting, for example.
On this site, you will find an example, Neural Network Forex Trading Tutorial,
that gives you detailed, step by step instructions.
Neural Network Genetic Algorithm Module
Genetic computations, when applied to neural networks, result in a powerfull data analysis tool.
Add here the scripting language, that gives you an ability to write your own genetic algorithms,
and you will get amazing results...
On this site, you will find an example, that teaches, step by step,
using Neural Network Genetic Algorithm for FOREX trading.
Kohonen Self Organizing Maps
SOM (Self Organizing Maps, classification neural network) are now included as part of Cortex neural network software.
With SOM you can perform network teaching and to use a resulting net for data classification.
On this site, you will find an example, that teaches, step by step,
how to use Kohonen Neural Network for FOREX trading.
Scripting
A built-in scripting language, that allows you to perform
routine tasks automatically. Loading and converting data, optimizations, creating charts, as well as web pages containing
these charts, and much more can be created by scripts, with no interference from the user.
Extras
Additional features, that include databases, image processing, HTML/XML based forms and data import-export.
This tutorial illustrates the use of UI (non-scripting) features of Cortex. For the complete information on scripting language,
see scripting tutorial and scripting reference guide.
Using Neural Networks (Features)
Let's outline the steps that we need to take, to use the Neural Network as the data analysing tool (I assume that you are already
familiar with an introduction to neural networks theory). The Cortex will help you to do these steps faster and
with less frustration, something you may want to keep in mind while reading.
1. First of all, we need data, anything from stock quotes to the sound patterns for a speech recognition software. The
only criteria is - the data must be sequential (a table with numbers in it's cells is a good example).
2.These data need to be fed to the artificial neural networks application one row in a time. Let's say, we want
to do stock trading. One row of data is simply not enough! You need a HISTORY, not just a current OHLC information. Can you
predict the tomorrow's stock price based on today's price? Not unless you have the "historical price" information for at least
couple of prior days.
Therefore, to use a "one row in a time" approach, we need to make sure that this row contains all the historical data we need, for
example, it can contain the today's data in the column one, the yesterday's data in a column two, and so on. This kind of file is
called (at least in this tutorial) a lag-file, it can be automatically generated by the Cortex neural network program. As a matter of fact,
it can be either generated by the Cortex, OR by its built-in scripting language, if you need to
have more flexibility (for example, having Close, you may want to calculate moving average of Close, as well as couple of indicators that
are used in stock trading, and only then create lags of this advanced input.
3. We need to choose a Neural Network configuration - number of neurons, activation type and so on. The Cortex
neural network software presents a simple visual interface, that allows to do just that. (Again, you can use scripting
language to do it in automated way. When you only need one neural network, it does not make a lot of sense, but sometimes,
you need to try many different combinations of input data, with different networks - automating this task is a real time saver).
4. What wedo next is training neural network. To do it, we run it against part of the data in the "backpropagation" mode, using
another part of the data to test the performance of the net. The first part of the data will be (in this tutorial) called a
learning data set, the second part is called a "testing" data set. As we doing it, a neural networks optimization occures.
As the "Cortex" is fine-tuning the Neural Network, it is displaying both values and charts for learning and testing errors.
5. After the neural networks optimization (training) is completed, we can use it on the "real" data. For example,
we may teach it on the stock qoutes for the last year, and then we expect it to predict the tomorrow's price, based on the price
for today, and couple of days of history.
To do it we need to generate a lag file, and to run the data rows we want to analyze through the Neural Network. The resulting
file will have the following columns:
Columns for the input. What we present to the Neural Network.
Columns for the output - what we were trying to predict.
And the prediction: the column(s), generated by the Neural Net.
Here is an example:
|
Inputs |
Output |
Predicted |
| No |
Close-1 |
Close-2 |
Close-3 |
Close-4 |
Close |
NN: Close |
| 0 |
5.150000 |
5.180000 |
5.120000 |
5.100000 |
5.200000 |
5.070705 |
| 1 |
5.180000 |
5.120000 |
5.100000 |
4.950000 |
5.150000 |
5.094328 |
| ... |
As was already mentioned, the Cortex Neural Network Application does just that - and much more
(see Cortex Built-in Scripting Language).
6. Finally, after the Neural Network is created, we need to somehow call it from the trading software of our choice:
TradeStation, MetaStocks, MetaTrader... In the article on Neural Network FOREX Trading you will
find a working neural network trading system that is created, step by step, and then moved from Cortex
to the trading platform, capable to place real trades with the real brocker.
Installation of Cortex Neural Network Application.
Download the Cortex archive.
Some programs that you can download from this site can work
together. If you want (strongly recommended) this kind of functionality
to be available, you should create a common folder, called (recommended)
S_PROJECTS, and to unzip all software in this folder. The sub-folders
will be created for you automatically during the unzip procedure.
When specifying options in your Winzip software, make sure that
all subdirectories (subfolders) are restored. Usually it is the default
setting for the WinZip. Installation is complete.
Uninstallation of Cortex Neural Network Application.
Delete the folder containing the Cortex files.
Registration of Cortex Neural Network Application
Cortex is distributed as feature-limited shareware.
If you choose to
register the Cortex software, you will need to enter
the password (provided in the e.mail that you will receive
after the registration) into the registration prompt.
Cortex Neural Network Tutorial
The following chapters will walk you through a simple example
of using the Cortex Neural Networks Software - we are going to
build the stock price predicting net.
Run the Cortex.exe. From the main menu select File - New NN File.
The program comes with three sample data file groups - genz.*,
msft.* and eurusd.*. First two contain data for Genzyme and Microsoft
stocks, the third one contains FOREX (Foreign exchange) quotes for
conversion between US dollar and EURO.
From the neural network forecasting point of view, there is no
difference between stock market and FOREX (except, stocks sometimes split), whatever you learn
for one area, usually applies to the other.
Click the "..." button to open the data/stocks/genz.txt file.
Note that you can specify what are the start and end line patterns,
and how many lines should we skip before and after the start line
pattern. Use it if the data are in the middle of the text file
(for example, the file has a header and a footer).
For this example, leave the first field blank.
If you look inside the GENZ.TXT file, that you have received with
the Cortex archive, you will notice that the last line has
nothing to do with the stocks - it is some kind of a commercial.
This is exactly the way it looked when I got this file from the
Yahoo quote server. To deal with the problem (we do not want to
feed THESE data to the program!), copy this last line, or at least
its beginning, and paste it to the "End line" field of the Input
dialog. Now the program will stop extracting the data, when it finds
the string you provided.
Important note: the stock quote file that
I have downloaded from the Internet contains newest data FIRST.
The neural network code (as many other data analyzing packages) expects the newest
data LAST. So we need to click the "Reverse"
checkbox to make sure data arrays are reversed before we do
the processing.
Also note that no matter if the "Reverse" is checked or not,
the resulting file that the program will produce, will have
records in the same order as the original file had. This way
if you have some other program that can work with stock price
file having new data at the beginning, it will also be able to
work with whatever output we produce with Cortex. Also
it is much less confusing, especially when you work with the
sequence of files.
What that means? Lets say you have a GENZ.TXT file. You checked the
"Reverse" box, and then (see below for details) you clicked
"Generate LAG file". The resulting lag file will still be reversed.
But if you select Date and Close, and click Chart, the program will
load the data, and then reverse them.
To put it differently, "reverse" checkbox only works for the current
tab in a dialog box. To have it at the next tab, check it there.
Still, if you want, you can use the scripting language instead,
it will give you the full control over the data.
Click the "Select fields...". The first
(after we found the start pattern and skipped extra lines) line
will be broken into the column names and presented in the list
box for inputs and outputs.
Select "Adj. Close*" both for the input and
output. We are going to use past values of the stock
closing price, to predict future values.
By the way, "adj" means "adjusted, the file I have is adjusted for
stock splits. Some data sources do not have this nice feature -
what shall we do then?
One of the possible solutions is to use the
Built-in scripting language to
pre-process the file.
As we are trying to create a network to PREDICT the future
values for the line (represented by data in genz.txt), we need to
provide the input in the form of HISTORICAL PATTERNS - not only
today's data, but yesterdays, and so on.
The reason we need it is simple. Can we predict the future value
of the stock price by the current value ONLY? No. We need to
know what the price was yesterday, and the day before yesterday -
we need to know what is going on with this price.
The "Adj. Close*" is now selected in the list
boxes. We are about to generate what is called a lag file.
The idea of the lag file is to represent today's data in the same
table, side by side with the data for yesterday and so on.
Press the "lag file" button. You will have the file with .lgg
extention containing something like:
| No |
Close
| Close-1
| Close-2
| ...
|
| 10
| 5.200000
| 5.150000
| 5.180000
| ...
|
| 11
| 5.150000
| 5.180000
| 5.120000
| ...
|
| 12
| 5.180000
| 5.120000
| 5.100000
| ...
|
As you can see, the first value in Close-1 column was removed, and
the entire column moved up. For the Close-2, TWO first values
were removed and so on. Therefore, each line of this new file
contains data for the current day AND data for nine previous days.
Let's select the new inputs. Click on the
"Select fields" again, and select "Adj. Close*-1", "Adj. Close*-2"...
"Adj. Close*-9" as inputs and "Adj. Close*" as output.
This way we will be using nine PREVIOUS days to predict the
coming price.
Click on the "Network" tab.
As you can see, you can specify the number of layers, number of
neurons in hidden layers (see
Introduction to Neural Networks for details on what the
elements of a Neural Net are), one of two activation functions
(standard for almost any NN package), and stop criteria, if you
want the learning process to stop automatically.
For this particular task let's select 7
neurons in the hidden layer.
Click on the "Processing" tab.
Here you can specify one of two ways of breaking the data to the
"learning" and "testing" parts (see
Introduction to Neural Networks for details). You can use first
N records (patterns) as a "learning" material and the rest -
as a "testing" data. Or you can randomly select N % of the
data.
The random selection does not work with the prediction - it is called
cheating ;) But it works well when you are trying to do a line
fitting. So for this example we will choose the
"First N records" option.
How do we know how many records we have in our .lgg file? We can
open the file in a text editor and find out. Or we can click the
button on the right from the data entry field. I got 1228 records,
and I have decided to use 1000 of them for "learning", and the rest - for
the "testing".
The "adjust range" combo box is important if there is a chance
for our "test" data to get out from the range where the "learning"
data are. To compensate, we can extend the range.
Let's leave it at 1.0 as in our case the
last 228 records are not out of range of the first 1000.
Click the "Learning" tab, select all check boxes
and press Run. The Neural Network will begin the learning
process. The number of epochs (how many times the entire data set
was presented to the network), and the best (smallest) learning
and testing errors will be displayed.
As the learning continues, the error (representing the difference
between the actual output of the network and the desired output)
is decreasing. When we decide that it is small enough (and we can
always go back and continue the training) we
click "Stop" and go to the "Apply" tab.
The "Apply" tab is an exact copy of a "Input" tab except for the
"Chart" and "Apply" buttons. The functionality is different,
however.
Click the "..." and open the .LGG file.
Select same fields you used as the inputs and outputs.
When you press the "Apply" button, the file
with the .apl extention is generated. It contains all data that the
input contained, plus extra fields for the output, generated by the
Neural Network.
Finally, switch to the "Output" tab,
click the "..." and open the .APL file.
Select the No (record number) as the input and
Close and NN:Close as outputs - we are going to plot the Close
and Predicted Close together on the same chart, to be able to compare them
visually.
Use the "Chart" button, to plot the
desired output (Close) vs. the output, produced by the Neural Net.
The following image is produced by the undertrained Neural Net.
The approximation is not very poor (for a one-day prediction).
If you continue training, you will get better results.
Other features: images and so on. Sample scripts
There are many other features in Cortex application. It can load
pages from the Internet, it can parse these pages using regular expression syntax,
extracting data according to your requirements.
It has a built-in image processor, so you can process all images in a selected
folder, making them sharper, for example.
It can access additional functions in third-party DLLs, so you can write extensions to
the built-in scripting language.
And more. As new features are introduced, new samples are added to the ZIP
archive, so that you can see it in action. Also, there is a complete list
available in Cortex Built-in Scripting Language Reference Guide.
To get fully enabled version of the Cortex, you need
to
register.
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