1. EDDIE for Investment Opportunities Forecasting Michael Kampouridis http://kampouridis.net/ Email: mkampo [at] essex [dot] ac [dot] uk

2. Outline Presentation of EDDIE 8 EDDIE 8-TEACH demonstration Comprehensive exercises

3. 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

4. How EDDIE works Financial Expert Genetic Decision Tree (GDT) Genetic Decision Tree (GDT) EDDIE 5. Approval / rejection 1. Suggestion of indicators 3. Evaluate Training Data 2. Output Training Data Testing Data 4. Apply

5. How the training data is created Given Daily closing 90998782….. Expert adds: 50 days M.A. 80828382….. More input: 12 days Vol 50525351….. Define target:  4% in 20 days? 1011…..…..

6. A typical GDT: EDDIE 8 Functions VarConstructor > If-then-else Buy (1)Not Buy (0) If-then-else Buy (1) 6.4 < Terminals VarConstructor 5.57 MA 12 Momentum 50

7. EDDIE 8: Technical Indicators Technical Indicator (Abbreviation) Moving Average (MA) Trade Break Out (TBR) Filter (FLR) Volatility (Vol) Momentum (Mom) Momentum Moving Average (MomMA)

8. 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

9. Performance Measures Rate of Correctness (RC) = (TN + TP)  Total Rate of Correctness (RC) = (TN + TP)  Total Rate of Failure (RF) = FP  (FP + TP) Rate of Failure (RF) = FP  (FP + TP) Rate of Missing Chances (RMC) = FN  (FN+TP) Rate of Missing Chances (RMC) = FN  (FN+TP) Fitness Function (ff) = w1*RC-w2*RMC-w3*RF Fitness Function (ff) = w1*RC-w2*RMC-w3*RF Fitness Function (ff) w1 Fitness Function (ff) w1 Negative True Negative False Negative Predictions Positive False Positive True Positive Reality Negative Positive

10. Thanks You can find these slides on my website, under the teaching tab: – http://kampouridis.net/teaching/cf963 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

11. 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

12. Supplementary Material

13. 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