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Associative Self-Building Neural Net Builder


 

Associative self-building neural net builder generates neural network only during train. When the system foretells future trade periods it only activates receptors in the receptor layer based on result of work of the preprocessor. The builder contains a set of learning rules that define way of the network construction. These rules provide Artificial Foreteller with mechanism to extract regularities that can be used to predict future price of a stock from the tick data itself. When associative self-building neural network learns it fixes historical trading data in its structure that it met during train. It can be said that network remembers and compares them with new case trying to find analogies and make conclusion if it is favorable for trade or not. Attempt to implement this approach with conventional programs failed because of dimension problem. Associative neural network is able to overcome this problem because it can extract only that part of data that have some associations with processed case.

Train of Network

Main objective of train is to develop network that can wrongly generated by preprocessor trade signal and advise user not to trade during this trade periods.

As it can be seen from figure 1, initially associative self-organazed neural network consists of only receptor layer. Quantity of receptors are limited only with quantity of criteria that user wants to use and PC processing power. Each receptor represents one of values of indicators or moving averages, or any other useful information that preprocessor defines when it processes tick data. Receptor becomes activated only when its relevant indicator gets a value.

figure 1.

During training session we should use historic tick data. Why do we use them? There exists very simple answer - because in that case Preprocessor can peep at "future" trade periods. In other words, it processes historic data like a real one but with one very important difference. After it processed current trade period it verifies next adjoining one and determines if next period is favorable for trade or not. If next period is favorable for trade then Preprocessor gives current trade period positive value. If it is bad for trade then Preprocessor gives current trade period negative value. If next period is on-balance period then Preprocessor gives current trade period neutral value.

Each period is described with a set of indicators and/or moving averages. Trainer can select different indicators. Range of values of each indicator or moving average is split up to 50 sub ranges and can be presented as shown below.

Indicator A - A1, A2, ... , An.,where Indicator 1 is a range of values, and A1, A2, ... , An - set of sub ranges.

Indicator B - B1, B2, ... , Bm.

........

Indicator Z - Z1, Z2, ... , Zs.

When preprocessor generates trade signal it describes current trade period with set of values of indicators like A1,B2, ... , Zm. For the period of trades preprocessor can generate as a set of trade periods:

Trade Period 1 - A1,B2, ... , Zm.

Trade Period 2 - A9, Bw, ... , Zr.

........

Trade Period m - Ak, B6, ... , Zl , where A1,A9,..., Ak - values of indicator A, B2, Bw,..., B6 - values of indicator B, ...., and Zm, Zr,...,Zl - values of indicator Z.

When preprocessor works on historical trade data it derives trade signal and describes current trade period as Train Case 1 - A1, B2, ... , Zm. At same time, it assesses next trade period as positive/negative/neutral, where "positive" means that the description of the trade period followed change of trend, "negative" means that trade period did not followed with change of trade. Preprocessor forms train set of examples like:

Train Case 1 - A1, B2, ... , Zm. - positive

Train Case 2 - A9, Bw, ... , Zr. - positive

........

Train Case m - Ak, B6, ... , Zl - negative

This set of cases is used for train of Artificial Foreteller and generating of associative self-building neural network. During train Artificial Foreteller processes the set to find combinations of values of different features that are typical for positive and negative train cases. Let's see how the Artificial Foreteller processes shown below train set. Trade period 1 describes situation at the market, and its description is marked as positive if following trade period 2 is favorable for trade. Trade period 2 describes situation at the market, and its description is marked as positive if following trade period 3 is favorable for trade. Trade period 3 describes situation at the market, and its description is marked as positive if following trade period 4 is not favorable for trade.

Neural Net Builder derives combinations of indicator values and represents them with nodes and their connections in the neural network. As follows from example give above, Neural Net Builder forms four main nodes that represent two positive, and two negative trade periods. At same time it will generate three new nodes that are common for more than one trade period. One of them ( A1&B2&C5&G11&K13 ) belongs to positive trade periods, one - belongs to negative trade period (A10&B21), and one node (D11&E13&G11) belongs both positive and negative trade periods. As a whole, generated network can be represented like sum of products and dispersions

((A1 & B2 & C5 & G11 & K13) & D1 & E23 & F43 & L7 & M8 & N22 & O11 )OR((A1 & B2 & C5 & G11 & K13) & (D11 & E13 & G11) & F33 & L8 & M19 & N21 & O17)OR NOT((A10 & B21) & C15 & (D11 & E13 & G11) & F23 & K18 & L5 & M14 & N11 & O2)OR NOT((A10 & B21) & C14 & D8 & E11 & F3 & G1 & K19 & L6 & M4 & N1 & O4)

Associative self-building neural network is not a "black box". The formula given above can be analyzed by a user to separate most important combinations of indicators from minor ones. It can be used as a powerful tool for technical analysis. Based on analysis of indicators user selects only those indicators that were used in positive periods and removes those indicators that were used in both positive and negative trade periods. Then user trains Artificial Foreteller on a new set of indicators and assesses quality of prediction. Using this approach user can select most important indicators.
In example shown above a combination of indicator values A
1&B2&C5&G11&K13 is common for two trade periods. Combination of features A10&B21 is common for two trade periods. And combination of features D11&E13&G11 is common for two trade periods that belong both positive and negative trade periods. When Artificial Foreteller predicts situation it assesses activated nodes and if activated node represents combination of features A1&B2&C5&G11&K13 then its prediction is positive. If a node that represents combination of feature's values A10&B21 is activated then Artificial Foreteller predicts that next trade period is not favorable for trade. In case when a node D11&E13&G11 is activated Artificial Foreteller can give only assessment of future trade period. It can predict that the trade will be profitable with % probability.

User who trains the system can analyze network and decide if it necessary to keep features D, E, and G, or remove only sub ranges D11, E13, and G11. After that Artificial Foreteller can be trained again to check if its prediction becomes better or not. This way user can select most important and representative features that describe market condition and remove less important ones.

Associative Self-Building Neural Network Generation

Network Builder receives result of analysis made by Preprocessor on its input. According to description of trade period Network Builder activates a set of corresponding receptors and forms fragment of network that represents not only current trade period but also finds associations with periods that have already are in the network. Then Network Builder applies a set of learning rules to attach relevant weights and stimulus to the activated part of the network. In this way Network Builder forms associations and patterns that will be used for predictions. On figure 2 a small fragment of associative self-building neural network is demonstrated.<

figure 2.

Quantity of layers and nodes does not predefined in the associated self-building neural network. Their quantity depends on complexity and quantity of train data. If relationships in the data are well defined then network will be compact. If there are no any relationship in train data then network will be a collection of not correlated data cases. User has no way to interfere directly into forming of neural network. He cannot order number of nodes of layers. But user can influence on the network structure with selecting different sets of indicators. The more representative set of indicators was selected the more compact network will be generated by Network Builder.

 

 

 

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