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Neural Crossover System

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This information is then trained based on data selected by the user ... user can create the network topology by hand, can manually set Momentum rate, ... – PowerPoint PPT presentation

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Title: Neural Crossover System


1
Neural Crossover System
  • By Kyle Serra

2
General Overview
  • Trading Systems consist of three main things
  • Entry Criteria
  • Stop placement
  • Exit Criteria

3
Entry Criteria
  • The entry Criteria is the setup which alerts the
    trader to buy/sell a specific stock
  • There are many types of entries
  • The following is an example of how This System
    Enters trades

4
Entry Criteria
5
Stop Definition
  • The stop is the maximum amount of risk the trader
    is taking on any given trade
  • Can also be thought of the point where the trader
    will get out of the trade when X amount of money
    has been lost

6
Exit Criteria
  • The exit is the point where the trade is gotten
    out of.
  • The exit can depend on anything, but is usually
    either a time stop or some sort of profit target
    that has been achieved

7
  • Currently this system has a timed exit point, it
    will always exit after two bars have elapsed.
    This exit is not very effective.

8
Neuro-Lab
  • Neuro-Lab allows the user to create a customer
    indicator using the criteria that the user wants
  • This information is then trained based on data
    selected by the user
  • The user can create the network topology by hand,
    can manually set Momentum rate, training rate,
    hidden layers, input and output layer neurons

9
  • My goal was to create a custom indicator that
    would allow me to predict future exits to a
    certain degree using Neuro-Lab. These results
    would then be compared to the old method of a
    timed stop to see which was more profitable.
    Multiple data sets were used to avoid curve
    fitting measures.

10
Network Topology of Exit NN
  • Blue Dots Input Layer
  • Red Dots Hidden layer
  • Green Dot Output Layer

11
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12
Output Script
13
Training
14
Performance EvaluationTraining Set
15
Performance EvaluationValidation Set
16
Creating the Indicator
  • The indicator created by the neural net showed a
    correlation that when readings were high in the
    indicator the market was likely to go up and vice
    versa.
  • This information was used to set a threshold for
    exit criteria, when the indicator reached 15 or
    below for long positions, the position was then
    executed, there is also a timed to stop so that
    after 6 bars if the indicator has not hit 15 or
    lower and has not been stopped out the position
    is automatically exited.

17
ResultsOld System, Tested on Dow Index from
1/1/2001 12/31/2001
18
Results New System, Tested on Dow Components
Index from 1/1/2001 12/31/2001
19
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