Eddie for investment opportunities forecasting
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EDDIE for Investment Opportunities Forecasting. Michael Kampouridis http://kampouridis.net/ Email: mkampo [at] essex [dot] ac [dot] uk. Outline. Presentation of EDDIE 8 EDDIE 8-TEACH demonstration Comprehensive exercises. EDDIE ’ s goal.

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EDDIE for Investment Opportunities Forecasting

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Eddie for investment opportunities forecasting

EDDIE for Investment Opportunities Forecasting

Michael Kampouridis

http://kampouridis.net/

Email: mkampo [at] essex [dot] ac [dot] uk


Outline

Outline

  • Presentation of EDDIE 8

  • EDDIE 8-TEACH demonstration

  • Comprehensive exercises


Eddie s goal

EDDIE’s goal

  • EDDIE is a GP tool that attempts to answer the following question:

    • “Will the price of the X stock go up by r% within the next n days”?

    • Users specify X, r, and n


How eddie works

Training Data

1. Suggestion

of indicators

5. Approval / rejection

2. Output

3. Evaluate

How EDDIE works

Financial Expert

EDDIE

Testing Data

4. Apply

Training Data

Genetic Decision Tree

(GDT)


How the training data is created

How the training data is created

Given

Daily closing

90

99

87

82

…..

Expert adds:

50 days M.A.

80

82

83

82

…..

More input:

12 days Vol

50

52

53

51

…..

…..

Define target:

4% in 20 days?

1

0

1

1

…..


Eddie for investment opportunities forecasting

A typical GDT: EDDIE 8

If-then-else

Functions

<

Buy (1)

If-then-else

6.4

>

VarConstructor

Not Buy (0)

Buy (1)

Terminals

VarConstructor

12

MA

5.57

50

Momentum


Eddie 8 technical indicators

EDDIE 8: Technical Indicators


Gp process

GP Process

  • Initialise population

  • Calculate fitness of each tree in the population

  • Selection of individuals for producing new offspring by the means of different genetic operators (e.g. crossover, mutation). These offspring form the new population

  • Repeat the previous two steps for a number of generations N


Performance measures

Performance Measures

Predictions

Reality

Negative

Positive

Negative

True Negative

False Negative

Positive

False Positive

True Positive

  • Rate of Correctness (RC) = (TN + TP)  Total

  • Rate of Failure (RF) = FP  (FP + TP)

  • Rate of Missing Chances (RMC) = FN  (FN+TP)

  • Fitness Function (ff) = w1*RC-w2*RMC-w3*RF


Thanks

Thanks 

  • You can find these slides on my website, under the teaching tab:

    • http://kampouridis.net/teaching/cf963

  • Any other material that we use today (EDDIE 8-Teaching, Lab sheet) can also be found there

  • If you have any questions, feel free to email me. I’m happy to arrange a meeting

  • EDDIE 8-Teaching Demo + Comprehensive exercises


Msc dissertation topic

MSc dissertation topic

  • There are a couple of extensions to EDDIE 8, which would fit very well as an MSc dissertation topic

  • You would be given the source code of EDDIE and be asked to add some new java code, which would be related to heuristic search methods

    • Java knowledge is required

    • No need to have implemented heuristics algorithms before.

  • You would then apply EDDIE 8 to a different stocks and investigate on the advantages of the introduction of heuristics to the search process of EDDIE 8

  • Opportunity for those who are interested in a project that has real-life/industry application

    • Attract industry’s interest

    • Do actual research

    • Possibility of publishing the results in a paper


Supplementary material

Supplementary Material


Constraints in the fitness function

Constraints in the Fitness Function

  • ff = w1’*RC-w2*RMC-w3*RF

  • Constraint R = [Cmin, Cmax]

    where Cmin = (Pmin/Ntr) x 100%,Cmax = (Pmax/Ntr) x 100%, 0<= Cmin <= Cmax <= 100%

    Ntr is the total number of training data cases

    Pmin is the minimum number of positive predictions required

    Pmax is the maximum number of positive predictions required

    If the percentage of positive signals predicted falls in the range of constraint R, then w1’ = w1. If not, then w1’ = 0.

    In the latter case, the GDT is heavily penalized and ends up with a negative fitness function


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