Programming Neural Networks and Fuzzy Systems in FOREX Trading

Programming Neural Networks and Fuzzy Systems in FOREX Trading Presentation 0 Balázs Kovács (Terminator 2), PhD Student Faculty of Economics, University of Pécs E-mail:kovacs.balazs.ktk@gmail.com Dr. Gabor Pauler, Associate Professor Department of Information Technology Faculty of Science, University of Pécs E-mail: pauler@t-online.hu

Content of the Presentation • Basic course info • Purpose of the Course • Course Agenda • Accessibility of course materials • FOREX Trading Bot Building Contest • Requirements, Grading and Consultation • Introduction • Basic terms of Stock Market/FOREX • Fractal Theory of Stock/Currency Pair Prices • Basic terms of Distribution Free Estimators (DFE) • Basic terms of Rule-Based Systems (RBS) • Basic terms of Learning Algorithms (LA) • Basic terms of Artificial Neural Networks (ANN) • Biologic analogy: Neurons in Human brain • Comparison with silicon-based hardware • Biologic Neuron and its Mathematical Model • Neural references

Basic course info: Purpose of the Course • Nowadays there are wide range of fancy FOREX „course providers” which promise that you can be a millionaire within 4 weeks, without any serious economic and mathematic training, just completing 1 week rapid course where you learn drawing curves to charts visually. • By contrast, we do not teach how you can be rapidly millionaire with FOREX. Instead of it we teach how to avoid loosing everything you have very rapidly with FOREX: • Participants will be trained besides basics of FOREX and how to use Meta Trader 4 (MT4) FOREX Platform: • Recognizing their psychologic limits and assembling customized trading strategies accordingly, • The MQL programming language of MT4 to create your own indicators, • Theoretical basics of Artificial Neural Networks and Fuzzy Systems, • Using the Joone open-source Neural Shell under GNU license, programming it in Java and set up its data link with MT4. • To motivate gifted students, we organize a FOREX Trading Bot Contest paralel with our course, where team 3-4 students can tune their software to reach maximum amount of return from limited amount of investment, within limited time frame making limited number of trades • As a unique option Trading Bots with extremely high computational requirement can be run at the new University of Pecs Supercomputer in C++ environment • The course has English language course material, even if it presented in Hungarian: • To close out simple-minded Gamblers (Szerencsejátékos) • Because – even if you have trade platforms and courses in Hungarian - almost every additional resource in FOREX you really need (eg. indicator source codes, economic analysises, user guides of trading bots) are most freshly available only in English! • So the main outcome of this course is not being a millionaire in 4 weeks (which is unrealistic at FOREX anyway) but to develope proficiency using Artificial Neural Networks and Fuzzy Systems in a difficult simulated battleground called FOREX. And that knowledge can result getting better paid positions in many areas of engineeering or business

Basic course info: Course Agenda

Basic course info: Accessibility of course materials • All course materials are available at PTE-TTK Szentágothai Szakkollégium website: ftp://szentagothai.ttk.pte.hu/pub/pauler/Forex/ in form of PowerPoint presentations and practices • These are NOT conventional „three sentences/slide” projectable presenta-tions, but almost full-text materials with: • Linked-in case study materials • Step-by step animated software usage usable at computer lab • However it is highly recommended for students to print them out in handout format and taking notes to slides, as questions in quiz may be represented from oral comments of tutor also • All course materials are in English to cap- ture Business English • But presentations are in Hungarian, and we have Hungarian Notes • MT4 GUI can be both We can’t use TAB here!

Basic course info: FOREX Trading Bot Building Contest • To motivate gifted students, we organize a FOREX Trading Bot Contest paralel with our course, where team 3-4 students can tune their software to reach. Rules of the contest are: • Server: FxPro MT4 • Base currency: USD • Maximum Leverage: 1:50 • Demo account capital: 5000 USD http://usd.kurs24.com/huf/?q=5000 • Platform: FxPro MT4 Client Terminal http://www.fxpro.com/hu/downloads/platforms/client-terminal • Operating system of trading bot: Windows 2000, XP, Vista, Windows 7 • Time range: 2011.11.10. 8:00:01 - 2011.12.08. 7:59:59 • Trading hours: whenever markets are open • Currency pairs: all possible pairs of EUR, USD, GBP, CHF, JPY, CAD, AUD, NZD can be traded in demo account • Who can participate: registered students of current course • Performance benchmarks: • Passively managed static currency portfolio, • Tutors demo account • Maximal number of modifications on a trading bot: 5 • Minimal number of trades completed by bot: 10 (without closing) • Using any foreign code in bots without referencing it will result in immedaiate exclusion from contest • Opened positions will be closed at the end of time range by tutors • Winner team will be the one with the highest balance at the closing • Identical balances among more teams will result in deuce • Relative result% = Team balance/Tutor benchmark balance of teams compared to benchmarks can be published in university media/certificates

Basic course info: Requirements, Grading and Consultation • Mid-semester requirements: • Max. 10 × 3points = 30 points from simple 5-question quizes written at the beginning of presentations where students are evaluated individually • Quizes are from the last presentation and practice • Missed quizes can be substituted by one extra 6 point quiz ad the end of semester • Max. 10 × 3points = 30 points from home assignments evaluated at project team-level. Teams are free to reallocate their home assignment points internally to proportionate it to contribution of their members! • Home assignments are due to the beginning of next practice • Missed home assignments cannot be replaced after deadline as they are group assignments • Max 40 team points from trading bot contest = 40 × Relative result% • Grading of individual students: • 0-29points:Reject signing course(0), 30-49points: Fail(1), 50-59points: Sufficit(2), 60-69points: Medium(3), 70-79points: Good(4), 80-points: Excellent(5) • In case of Fail(1), there are 2 possibilities for correction at oral exam from course material of presentations to get credit • Consultations: • Tutors will provide consultation at Department of Informatics, PTE-TTK, at times prearranged at pauler@t-online.hu or kovacs.balazs.ktk@gmail.com • Results: • Students can track their mid-semester results at ftp://szentagothai.ttk.pte.hu/pub/pauler/Forex/ExamForex/

Basic terms of Stock Market/FOREX 1 • The Stock Exchange (részvénytőzsde) is a Non-profit Company (non-profit társaság ), what is Exclusive (Kizárólagos) trading place of stocks of Publicly Quoted (tőzsdére bevezetett, nyílvánosan árfolyam-jegyzett)Companies (részvénytársaságok). These are larger, stabile firms complying strict Accounting (Számviteli) rules. Stocks of smaller companies not quoted publicly are traded at Over The Counter (OTC) market. • Macroeconomic (Makrogazdasági) function of stock exchange is Effective Allocation of Investment Resources (hatékonyan ossza el a vállalkozások közt a beruházási erőforrásokat) allocating more money to more profitable companies with larger growth in a public, open competition. • Microoeconomic (Mikrogazdasági, vállalati szintű) functions of Stocks/Equity (Rész-vénytőke): It helps Raise Funds (Tőkét gyűjt) necessary for operating a company and: • Represents proportional Ownership/share (tulajdonrésze) in a company, giving the right to Vote (Szavaz) in Board of Directors (igazgatótanács) governing it, except: non-voting stocks • Profitable companies pay Dividend (osztalék) of profit for that, but it is not guaranteed, except: if it is Preferred Stock (elsőbbségi/aranyrészvény): always pays dividend, but cannot be sold and usually does not have vote • Ordinary stocks can be Sold (eladható) on the stock exchange any time at Spot Stock Price (aktuális árfolyam), or at Futures (határidős árfolyam)except: if the company has Pre-emptive Option (elővételi jog), to block Hostile Takeover (támadó célú részvényfelvásárlás) by competitor firms • If we Bought (vettük) or Underwrite (Lejegyeztük) a stock in the past, and there was Hausse, Bull (árfolyamemelkedés) we can earn Yield (árfolyam-nyereség). If there was Baisse, Bear (csökkenés) then we Loose (veszt).

Basic terms of Stock Market/FOREX 2 • Equity is the most profitablebutmostriskytool of InvestmentPortfolio Management (Tőke befektetési portfolió menedzsment): Youcanbuy and hold (Long) stocks of profitable and less riskycompanies (Blue Chips) tomake profit fromdividendorpriceincrease, and liquidatestocks (Short) of badcompaniestoavoidloss. Itcanuse 3 basictechniques: • Hedge (Fedezeti ügylet): toshort/long a stockwhosepricetendencyouslymovesagainstaprice of anotherstockorCurrency (Valuta) longed/shortedtoeliminaterisk of lossfromadversepricemovement. Less risky, lessprofitable. • Arbitrage (Arbitrázs): short/long a stockveryrapidly (1day-some hours) tomake profit from minor pricefluctuations. Mediumrisk, mediumprofitable. • Speculation (Spekuláció): open a short/longPosition (Pozíció) forlongertimeframeagainstthepriceExpectations (Várakozás) of thewhole market, and trytoinfluencethemwithtrickstorapidlychangetheirexpectations. Veryrisky, veryprofitable. • Actors of stockexchange: • Broker (bróker): doesnotownstockjusttradesitbyComission (Megbízás) of theownerfor a Fee (Díj), • Dealer (díler): canownstocks • Underwriter (undervrájter): canbuyallstocks of a newcompanyforre-sale. Frombrokertounderwritertheyhave more rightstoperformdifficult and riskytrades, buttheyhavetocomply more and more strict accounting and stockexchangerules • FOREX, FOReignEXchange (Devizatőzsde) differsfromordinarystockexchange 2 ways: • Instead of tradingstocksagainstoneCurrency (Valuta) eg. (Sell IBMforUSD), severalForeignExchanges (Deviza, valutára szóló számlakövetelés) aretradedagainsteachotherinCurrencyPairs (Valutapárok): eg. USDEUR, GBPCHF, JPYEUR, etc. • ThereareonlybrokerscalledFOREX companies/providerstradingwithsomeoneelse’s money, whowanttohedge, arbitrageorspeculate

Fractal Theory of Stock/Currency Pair Prices • Both at Stock Exchange/FOREX there is strongInformationAsimmetry (Információ aszimmetria): most investorsdonothaveanydirectinformationabout: • Changingtechnologylevel and marketing efficiency of a company (denotedwithgreen) • Plans of Governments (Kormány) and CentralBanks (Központi Bank) of 2 countries determiningat most price of a givenstock/currencypairlongterm • Theyhavetodecideallocation of theirmoneyamongstocks/currenciesfrompartialinformation and theirexpectations, sotheytendtofallinselling/buyingpanicatsuddenbigchanges. • Therefore, both Stock Exchange/FOREX arestrictlycontrolledmarketswithmanysafetyrules. • Butthiswillresultin a Stepped (Lépcsős) price (denotedwithred) update behavior: • Withoutstrongexternalimpulsebrokerstendtobuild „dreamworlds” settinguppricesbytheirexpectationsignoringslow and smallchanges of reality (eg. In „.com boom” of 2000s, smallinternet-basedcompanieswereworth more than General Electric and otherindustrialgiants) • Butwhenthedifferencebetweenthemgetstobig, they update priceinsmaller-biggersuddensteps, instead of continouschange • Aspricesareinfluencedbymanydifferentlenghtcycles (eg. 1 year:seasons .. 1day:dailyclose), suddenstepsareEmbedded (Beágyaz) intoeachotheratseverallevels, itcreatesFractal (Fraktál)-typestructures: pricestepsintimehaveself-similardetailsembeddedintoeachother • ItmakesPrice Forecasting (Árelőrejelzés) necessaryfortradingextremelydifficultFunctionEstimation (Függvény becslési) problem: • Prices of stocks/currencypairsare influencedbynumerousparameters creatingcomplexmultivariate (Sokvál- tozós) functions • Price data is veryNoisy (Zajos) dis- tortedby random disturbances, soSto- chastic (Sztochasztikus) function esti- mation is necessaryfrom a Sample (Minta) of prices • Sometimesit is hardtoassemble anyfunctionfromfuturepriceexpecta- tionscollectedfromdifferentinformati- onsources: spot price( ) canadaptto realityin more alternativefractalpath Price 400$ 300$ 200$ 100$ 12 16 8 24 20 4 Week

Basic terms of Distribution Free Estimatiors (DFE) y A x t • Distribution Free Estimators (Eloszlásfüggetlen becslési rendszer) can estimate output of a complex, multivariate function from inputs. Functional transformation is estimated from previously observed (Megfigyelt) Sample (Minta) of noisy input-output values, and it does not make any assumptions on Probability Distribution (Valószínűségi Eloszlás) of sample. It means that the function can be reasonably complex. • There are Analytic (Függvénytani) methods of Approxi-mating (Közelít) complex functions: • Taylor Series (Taylor-sor): it approximates a non-linear function (Eg. Sin(x)) with a suitably paramete-red higher order polynom in a given range • Fourier Transform (Fourier-transzformáció): a complex nonlinar function (eg. Stock price, sound wave, etc.) is assembled as weighted sum of Sin(x) typefunctions with different wavelenght and phase. • Evaluation of analytic methods: •  They have relatively low Computational requirement (Számolásigény) •  They require high level analytic mathematical knowledge •  They are not Modular (Moduláris): modeling any additional local „bumps” or „steps” will result exponentially more complex global formulation • Therefore, we will not deal with analytic approximation methods in this course. Instead of them, we will use Rule Based Systems, RBS (Szabályalapú rendszerek)

Basic terms of Rule-Based Systems (RBS) 1 vju IF x[vil,viu] THEN y [vjl,vju] y vjl x2 vil viu y mx(rk) = 1 x x x1 • Rule-Based Distribution Free Estimators(szabály-alapú eloszlásfüggetlen becslési rendszerek) approximate Control Function (Vezérlési függvény) ( ∕ ) among Input-Output Variables (I/O változók) of Decision Space (Döntési tér) with the help of Rule Basis (Szabálybázis) containing k=1..l finite set ofrkRules(Szabály): • They Associate (Egymáshoz rendel) vi, vj values, or [vil, viu]intervals of xi i=1..n input and yo o=1..O output variables • They have Linguistic(Nyelvi) representation: IF InputVar1 = Intreval AND InputVar2 = Interval AND.. THEN OutputVar = Interval • They have Graphic (Grafikus) representation: multi-dimensional Hyperbars (Hipertéglatest) in decision space (we denote them Yellow █) • Rules of a rule basis can be Mutually Exclusive (kölcsönösen egymást kizáróak): they have no Intersection (Metszet) = Common subset (Közös részhalmaz) in decision space. Alternatively, they can be Overlapping (Átlapolóak) • All rules of the basis has mx(rk) Validity (érvényes-ségi) value, which shows whether the rule is Valid/ Fires (Tüzel) (Red█) at a given x vector (GreenO) of input variables: x rk If there is only one rule in the base to fire at any x input vector then rule basis is Non-Contradictive (Ellentmondásmentes), else Contradictive (Önel-lentmondó)

Basic terms of Rule-Based Systems (RBS) 2 mx(rk) P(xrk)= 0.025 P(xrk)= 0.025 mx(rk) P(xrk)= 0.945 P(xrk)= 0.945 mx(rk) mx(rk) P(xrk)= 0.020 P(xrk)= 0.020 mx(rk) y x2 y P(xrk)= 0.020 P(xrk)= 0.025 mx(rk) P(xrk)= 0.945 mx(rk) P(xrk)= 0.945 mx(rk) y* P(xrk)= 0.020 P(xrk)= 0.020 mx(rk) x x1 x1 • Effectiveapproximation of continousfunctionswouldrequirelargenumberofrulesinthebasetogetrea-sonableResolution (Felbontás), eatingupresources • Toavoidthis, rulescanhavewk[0,1]importanceweights. Estimatedotputyx* is computedasweighted sum of output values of firingrules. This is calledInterpolation (Interpoláció) amongrules: yx* = Skwk× mx(rk) × yk (0.1) • Interpolationenablestomodelcontinouscontrolfunc-tionswith less rules more effectively. It has 2 methods: • BayesianProbability (Bayes-i valószínűség) rules: • Itusesmutuallyexclusive, Crisp (Éles) rulebase • Wheremultiplerulescanfirebinarymx(rk){0,1}for a givenx input vector • ButsimutaneousfiringrulesOccour (Bekövetkez) onlywith a pk[0,1]Probabilityweight (Valószínűségi súly), where sum of theirprobabilities is 1creatingProbabilitydistribution (Valószínűségeloszlás): Skpk× mx(rk) = 1 (0.2) •  It is supportedbyProbabilitytheory (Elmélet) •  Itrequiresdataaboutprobabilities of relativelylargenumberofmutuallyexclusiverules, which is unrealistictogetinthepractice • Fuzzy RuleInference (Fuzzy szabály következtetés): • Rulebasis has overlappingrules: Boundary (Határ) of Support (Tartó) of oneruleareinthemiddleofsupportofneighbouredrules • mx(rk)[0,1]validity of a rulecanchangecontinously:It is 1inthemiddle of support and 0atboundary (wedenoteitwithyellowshading), formingnotcrisp/fuzzy rules: theyoccourcertainlybuttheirvalidity is uncertain/changinggradually • wkweightsdonotformprobabilitydistribution •  Theoreticallyit is less soundmethod •  Butcanmodelcomplexnonlinearcontinousfunctionsusingmuch less rules/weightstotune

Basic terms of Learning Algorithms (LA) Profit/ Assets 0.18 Liabilities/ Assets 0.14 $ 0.10 $ $ 0.80 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 0.69 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 0.30 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 0.10 $ $ $ $ $ 0.13 0.16 CashFlow/Liabilities • Manualdefinition of severalthousandrules and theirweightswiththehelp of experts is expensive, slow • ThatswhyExpert System Shells, ESS (Szakértői rendszer shell) - usingmanualBayesianprobabilisticrulebases - failedtobecomethemainstream of ArtificialIntelligence, AI (Mesterséges Intelligencia) • SoweneedLearningAlgorithms(Tanuló Algoritmus) whichcansetuprules and theirweightsautomati-callyform an X,YSampledatabase (Minta adat-bázis) of pre-viouslyObserved (Megfigyelt) j=1..m xj,yjvectors of xi i=1..n input/yo o=1..O output vars. Theyhave 2 groups: • Classification and RegressionTrees, CRT (Klasszifikációs és regressziós fák) algorithms: • Theycanestimateonlydiscretevalued (Diszkrét értékű) output variablesfromcontinous/discreteinputs (Eg. EstimateBankrupcy/Survival of a companyfromitsfinancialrates) • Building Decisiontree (Döntési fa) of connectedcrispBayesianprobabilityrules • Tryingtosetupruleboundaryvaluesateach input variable, whichseparatebest output values •  Lowcomputationalreqirement •  Canuseonlycrisphyperbarrules, whichareineffective modelling complexnonlinearTransversal (Átlós) controlfunctions • ArtificialNeuralNetworks, ANN (Mesterséges Neurális Hálózatok): theycanestimatecontinous/discreteoutputsfromcontinous/discreteinputs • Building kind of „implicte fuzzy rules”, withoutliguisticrepresntation and directaccesbyuser • From random initialboundaries and ruleweights • Theycanmodelcomplexnonlinear, transversalcontrolfunctions (Eg. Recognizing a letter „N” fromdots of inkscannedin a picture) effectively • At a price of difficultparametering and brutallyhighcomputationalrequirement

Neurons in Biology • Human brain contains 1011Neurons (Idegsejt) connected with 1016Synapses (Szinapszis) organized in Hemi-spheres (Félteke) > Cortexes (Kéreg) > Layers (Mező) > Blocks • Unisolated short Dend-rits(Ág) transmit inco-ming electric signals at 2.3m/s to Cell membra-ne(Sejtfal)of neuron col-lecting electric charge • At certain mV potential Treshold (Határérték), neuron emits electric signal by its Signal function (Jelzési függvény), which is tranmitted at 90m/s on long synapses covered with isolator Myelin (Mielin) jumping over the Ranvier-gaps (Rés) • Excited Sytaptic termi-nals (Végbunkó) emit Neurotransmitter(Ingerü-letátvivő) molecules (Eg. Acetilcolin, Opiats) • Opening ion channels on other neurons mem-brane making them ac-cumulat electric charge

wik Comparison of Neuron with Silicon-based Hardware wik bi Si(xit) ui ai xit li Sj wji wji • In the electron microscope image above we can see a neuron laid on leads of a modern microchip. Neuron is 10-12 times bigger than condensers and transistors of basic logic gates, however it can perform such a non-linear computing function, which requires hundreeds of basic logic gates in a math cooprocessor. • Moreover, neurons require much less energy and produce much less heat than silicon-based chips. Currently a 100TByte blade-supercomputer compa-rable in storage capacity with human brain - but still inferior in speed, as brain can share work among 1011 simple processors instead of 103 more difficult ones - consumes 2-3 m3space, 380V industrial current and cooling capacity of a supermarket • Human brain consumes 1500cm3 volume even storing oxygene and glucose for 15-20 secs of work, and requires 5-10 Watts of power input and cooling

wik Biologic Neuron and its Mathematical Model wik bi Si(xit) ui ai xit li Sj wji wji • Fuctions of a neuron in ANN MathematicalModel: • Non-volatileMemory (Permanens memória): jisynapsesconnectingj=1..mneuronswithi=1..nneuronsinthenetworkduringt=0..TtimeperiodstransmitsjtRsignals of jth neuron intthperiodwithchangingwjitIntensity/ Weight(Súly). Teaching/ Training(Tanítás) of net meanschangingtheinitially random wji0Rweights. Allinformationlearnt is storedassynapticweights • VolatileMemory (Rövid távú memória): a neuron aggregateswjit×sjtweightedsignals of incomingsynapsesinto a xitMembranevalue (Membrán érték) intheActivationProcess (aktivációs folya-mat),additionallytheyPassivelydecay (Passzív lecsengés) membranvalueby(1-di)DecayRate (Lecsengési ráta) tokeepmembranevaluewithin[li, ui]Lower/Upperbounds (Alsó/Felső Korlát) and smooth (Simít) itschangesintime. Thereare 2 methods of membranevalueaggregation: • Additive (Additív):xit=di(Sj(wjit×sjt)/Sj(wjit))+ +(1-di)×xit-1i=1..n, j=1..m, t=1..T (0.3) • Multiplicative (Multiplikatív): xit=diPj(sjtwjit)(1/ Sj(wjit))+ +(1-di)×xit-1, i=1..n, j=1..m, t=1..T (0.4) • Aggregatedmembranvalueemitssignalbymono-tonicincreasing (Monoton növekvő) signalfunctionwithaiinflexionpointassignaltreshold and bislope: sit = 1/ (1+e-bi×(xit-ai)), i=1..n, t=1..T (0.5)

References 1 • Hungarian language course notes: Notes • Neural networks biologic analogy: http://health.howstuffworks.com/brain.htm • Neural networks chatroom: http://www.geocities.com/siliconvalley/lakes/6007/Neural.htm • GNU-licensed neural software: • Source code libraries in C++, without install utility: • SNNS: http://www-ra.informatik.uni-tuebingen.de/SNNS/ (+install and user guide) • http://www.generation5.org/xornet.shtml • http://www.netwood.net/~edwin/Matrix/ • http://www.netwood.net/~edwin/svmt/ • http://www.geocities.com/Athens/Agora/7256/c-plus-p.html • http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/faces.html • http://www.cog.brown.edu/~rodrigo/neural_nets_library.html • http://www.agt.net/public/bmarshal/aiparts/aiparts.htm • http://www.geocities.com/CapeCanaveral/1624/ • http://www.neuroquest.com/ • http://www.grobe.org/LANE • http://www.neuro-fuzzy.de/ • http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/cascor/ • http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/qprop/ • http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/rcc/

References 2 • GNU-licensed neural software: • Source code libraries in Java: • Java Neural Networks by Jochen Frölich: http://fbim.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html (Java Class, Internet applet about Kohonen-nets, free, no GUI, Tutorial in HTML) • http://www.philbrierley.com/code • http://rfhs8012.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html • http://neuron.eng.wayne.edu/software.html • http://www.aist.go.jp/NIBH/~b0616/Lab/Links.html • http://www.aist.go.jp/NIBH/~b0616/Lab/BSOM1/ • http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/loos • http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html • http://www.isbiel.ch/I/Projects/janet/index.html • http://www.born-again.demon.nl/software.html • http://www.patol.com/java/NN/index.html • http://www-isis.ecs.soton.ac.uk/computing/neural/laboratory/laboratory.html • http://www.neuro-fuzzy.de/ • http://openai.sourceforge.net/ • http://www.geocities.com/aydingurel/neural/ • http://www-eco.enst-bretagne.fr/~phan/emergence/complexe/neuron/mlp.html • Biologic modelling software: • Neuron: http://www.neuron.yale.edu/neuron/ (free, GUI, Win XP install, Tutorial in HTML) • Genesis: http://www.genesis-sim.org/GENESIS/ (free, GUI, Win XP install, Tutorial in HTML) • PDP++: http://www.cnbc.cmu.edu/Resources/PDP++//PDP++.html (C++ source code library, GUI, Win XP install, Tutorial in HTML)

References 3 • Decision support software: • JNNS: http://www-ra.informatik.uni-tuebingen.de/software/JavaNNS (Simplified SNNS in Java, GUI, Win XP install, Tutorial in PDF) • JOONE: http://www.joone.org (Java, GUI, Win XP install, Tutorial in PDF) • Commercial neural decision support software: • NeuroSolutions: http://www.neurosolutions.com/download.html (60 days shareware, no save, GUI, Win XP install, Excel Add-in, Excel Wizard, MATLAB modul, Tutorial in PDF Medical, automotive appliacations) • NeurOK: http://soft.neurok.com/dm/download.shtml (Excel Add-in, C forráskód, XML-es felület, Win XP install, financial applications) • EasyNN: http://www.easynn.com/dlennp.htm (30 days shareware, GUI, Win XP install, Tutorial in HTML, financial forecasting applications) • ALNFit Pro: http://www.dendronic.com/downloadalnfit_pro.shtml (30 days shareware, GUI, Win XP install, Tutorial in PDF, pénzügyi előrejelzési applications) • Trajan: http://www.trajan-software.demon.co.uk/Downloads.htm (30 days shareware, GUI, Win XP install, Tutorial in HTML, no real application) • AINet: http://www.ainet-sp.si/NN/En/nn.htm (1 days shareware, GUI, Win 95 install, Tutorial in PDF, nincs még valós alkalmazása) • NeNet: http://koti.mbnet.fi/~phodju/nenet/Nenet/Download.html (performance limited shareware, GUI, Win 95 install, Tutorial in HTML, SOM networks oriented) • Add-Ons for Statistical Packages: • Statistica Neural Networks: https://www.statsoft.com/downloads/maintenance/download.html (no shareware, GUI, Win XP install, Tutorial in MPEG)

References 4 • Add-Ons for MATLAB: • Matlab Neural Toolbox: http://www.mathworks.com/products/neuralnet/ (No shareware) • SOM ToolBox: http://www.cis.hut.fi/projects/somtoolbox/download/ (Matlab 5, free, GUI, Tutorial in PDF) • FastICA: http://www.cis.hut.fi/projects/ica/fastica/code/dlcode.shtml (Matlab 7, free, GUI, Tutorial in PDF) • NetLab: http://www.ncrg.aston.ac.uk/netlab/down.php (Matlab 5, free, GUI, Tutorial in PDF) • NNSysID: http://www.iau.dtu.dk/research/control/nnsysid.html (Matlab 7, free, GUI, Tutorial in PDF) • Excel Add-Ins in Financial Forecasting: • NeuroShell: http://www.neuroshell.com/ (no shareware) • NeuroXL: http://www.neuroxl.com/ (no shareware) • Comparison of 50 commercial licensed neural software: http://wwwcs.uni-paderborn.de/~IFS/Tools/neural_network_tools.htm

Programming Neural Networks and Fuzzy Systems in FOREX Trading