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. - PowerPoint PPT PresentationTRANSCRIPT
EDDIE for Investment
Opportunities Forecasting
Michael Kampouridishttp://kampouridis.net/
Email: mkampo [at] essex [dot] ac [dot] uk
OutlinePresentation of EDDIE 8
EDDIE 8-TEACH demonstration
Comprehensive exercises
EDDIE’s goalEDDIE 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
Financial ExpertFinancial Expert
Genetic Decision Tree(GDT)
Genetic Decision Tree(GDT)
EDDIEEDDIE5. Approval / rejection
1. Suggestion of indicators
3. Evaluate
Training DataTraining Data
2. Output
Training DataTraining Data
Testing DataTesting Data
4. Apply
How the training data is created
GivenGiven
Daily Daily closingclosing
9090999987878282
……....
Expert Expert adds:adds:
50 50 days days M.A.M.A.
8080
8282
8383
8282
…….. ..
More More input:input:
12 12 days days VolVol
5050
5252
5353
5151
…….. ..
Define Define targettarget
::
4% in 4% in 20 20
days?days?
11
00
11
11
…….. ..
…….. ..
A typical GDT: EDDIE 8Fu
nctio
ns
VarConstructor >
If-then-else
Buy (1)Not Buy (0)
If-then-else
Buy (1)
6.4
<
Term
inal
s
VarConstructor
5.57MA 12
Momentum 50
EDDIE 8: Technical Indicators
Technical Indicator (Abbreviation)
Moving Average (MA)
Trade Break Out (TBR)
Filter (FLR)
Volatility (Vol)
Momentum (Mom)
Momentum Moving Average (MomMA)
GP ProcessInitialise 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
Rate of Correctness (RC) = (TN + TP) Rate of Correctness (RC) = (TN + TP) TotalTotal
Rate of Failure (RF) = FP Rate of Failure (RF) = FP (FP + TP) (FP + TP) Rate of Missing Chances (RMC) = FN Rate of Missing Chances (RMC) = FN (FN+TP)(FN+TP)
Fitness Function (ff) = = w1*RC-w2*RMC-*RC-w2*RMC-w3*RFw3*RF
Negative
True Negative
False Negative
Predictions
Positive
False Positive
True Positive
Reality
Negative
Positive
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• 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
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 casesPmin is the minimum number of positive predictions requiredPmax 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