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Preprocessor


 

Preprocessor is a part of the system that work up input flow of ticks and gives out the following:

  • forms bar chart .
  • finds moment when trend changes its direction.
  • calculates different types of indicators.
  • prepares values of indicators for neural network builder.

Bar Charting

When user defines bar chart he can request any time dimension (or interval) of bar. For example, if user would like to work with time interval in 15 minutes then each bar represents 15 minutes of trading.

 

Trend Tracing

Trend tracing is one of keystone problem. When trend changes preprocessor determines trend and opens new trade period. Trend can last more than one interval. When trend changes preprocessor issues trade signal, closes period, and prepares data for input into receptor layer of associative self-building neural network. Preprocessor should generate trade signals only when trend really changes. In that case Interpreter will have less problems with predicting if next trade period is favorable for trade or not.

 

Calculation of Indicators and Moving Averages

Main objective of preprocessor is to work out description of situation at market during open trade period. One of serious problem was to generate description of the situation that does not depend on absolute values of prices. It is very important because associative self-building neural network should store knowledge about trades based on data from different years. However, prices can differ greatly year from year. For example, if we compare prices of contracts of S&P 500 in 1994 against contracts in 2002 we can see that prices in 2002 were in 2 times bigger than in 1994. That is why it was worked out a set of indicators that track rate of up trade or down trade, levels of support or resistance, and so on.

Sensors

Preprocessor has a layer that consists of a set of sensors. Each sensor corresponds to one indicator, moving average, or any other information that was selected as a criterion for trade analysis. Sensors are divided into two types. Qualitative sensors belong to the first type of sensors, and quantitative sensors - to the second type of sensors. Each qualitative sensor is confronted with only one receptor of associative self-organazed neural network (see >>). Qualitative sensor generates only one signal that activates corresponding receptor. Quantitative sensor is confronted with a set of receptors. Range of values that sensor receives is divided into a set of sub ranges. Current version of preprocessor can form from 3 up to 50 sub ranges. Accordingly, quantitative sensor can combine up to 50 receptors. In that case receptor becomes activated only if sensor sends a signal that value corresponds a sub range of the receptor.

As an example of qualitative sensor we can use the following indicator: Cn-1 > Cn, where Cn-1 is close value of (n-1) interval, and Cn - close value of (n) interval. If Cn-1 > Cn then sensor send signal to receptor that activates it.

As an example of quantitative sensor we can use RSI. Suppose its values are in a range of values between 10 and 100 and set of receptor consists of 4 receptors. In that case sensor sends signal that activates receptor R1 if value is less than or equal 10, R2 - if value is in a range between 10 and 50th inclusive, R3 - if value is in a range between 50 and 100th inclusive, and R4 - if value is more than 100.

 

At the same time preprocessor processes well known traders indicators and moving averages and displays them on the screen together with bar chart. It will greatly helps trader to analyze situation at market and assess advise that Artificial Foreteller generates.

 

 

Home

Finance forecast

Preprocessor

Neural Net Builder

Interpreter

GUI

Test Results

Latest News

 

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