一個(gè)神經(jīng)網(wǎng)絡(luò)的EA的示例備課講稿

1、Good is good, but better carries it.精益求精，善益求善。一個(gè)神經(jīng)網(wǎng)絡(luò)的EA的示例-一個(gè)神經(jīng)網(wǎng)絡(luò)的EA的示例(含源碼)Combo_Right.mq4軟件簡(jiǎn)介：去年年底結(jié)束的國(guó)際大賽的第一名為Better所奪得他采用的就是神經(jīng)網(wǎng)絡(luò)原理的EA這使得用神經(jīng)網(wǎng)絡(luò)方法做EA成為不少人關(guān)注的焦點(diǎn)這里翻譯一篇采用神經(jīng)網(wǎng)絡(luò)做EA的不錯(cuò)的示例文章當(dāng)然附有源碼是吸引人的地方不過(guò)也許作者提出了研究神經(jīng)網(wǎng)絡(luò)EA的一些思考更為值得注意作者提出了1?！叭绻酗w機(jī)，為什么還要教人類去飛？”意思是研究是經(jīng)網(wǎng)絡(luò)不必從零起步。MT4里已有了不錯(cuò)的“遺傳算法”文中介紹了如何利用MT4已有的“遺傳算

2、法”2。大家都說(shuō)做單子最重要的是“順勢(shì)而為”，但更需要解決的是“一個(gè)基于趨勢(shì)的交易系統(tǒng)是不能成功交易在盤整(sidewaystrends)，也不能識(shí)別市場(chǎng)的回調(diào)(setbacks)和逆轉(zhuǎn)(reversals.,反向走勢(shì))！”這可是抓到不少人心中的“癢處”，有多少人不是到了該逆勢(shì)時(shí)沒(méi)轉(zhuǎn)向而產(chǎn)生虧損呢？3。訓(xùn)練神經(jīng)網(wǎng)絡(luò)需要用多長(zhǎng)的歷史數(shù)據(jù)，提出了并不是用的歷史數(shù)據(jù)越長(zhǎng)越好，另外也不是訓(xùn)練的間隔越短越好，文中提出了什么情況下有需再訓(xùn)練它。等等。下面是譯文和作者的源碼Theproblemisstatedforthisautomatedtradingsystem(ATS)asfollows:(ATS)自

3、動(dòng)的(智能的，采用神經(jīng)網(wǎng)絡(luò)的)交易系統(tǒng)的問(wèn)題表述如下Letsconsiderwehaveabasictradingsystem-BTS.ItisnecessarytocreateandteachaneuralnetworkinorderittodothingsthatcannotbedonewiththeBTS.ThismustresultincreationofatradingsystemconsistingoftwocombinedandmutuallycomplementaryBTSandNN(neuralnetwork).如果我們有一個(gè)(BTS,basictradingsystem)，

4、同時(shí)需要用創(chuàng)建一個(gè)神經(jīng)網(wǎng)絡(luò)系統(tǒng)并教會(huì)它做BTS所不能做的事，按這個(gè)思路就是要?jiǎng)?chuàng)建這樣一個(gè)交易系統(tǒng)它由互相補(bǔ)充(配合)的兩部分組成，BTS和NN(神經(jīng)網(wǎng)絡(luò))。Or,theEnglishofthisis:Thereisnoneedtodiscoverthecontinentsagain,theywerealldiscovered.Whytoteachsomebodytorunfast,ifwehaveacar,ortofly,ifwehaveaplane?呃，英語(yǔ)說(shuō)，我們不需要再去發(fā)現(xiàn)“新大陸”，它們是已經(jīng)存在的東西！進(jìn)一步說(shuō)，如果我們已經(jīng)有了汽車，那為什么還要教人如何跑得快？如果有飛機(jī)，為什么還

5、要教人類去飛？Oncewehaveatrend-followingATS,wejusthavetoteachtheneuralnetworkincountertrendstrategy.Thisisnecessary,becauseasystemintendedfortrend-basedtradingcannottradeonsidewaystrendsorrecognizemarketsetbacksorreversals.Youcan,ofcourse,taketwoATSes-atrend-followingoneandacountertrendone-andattachthemtot

6、hesamechart.Ontheotherhand,youcanteachaneuralnetworktocomplementyourexistingtradingsystem.一旦有一個(gè)趨勢(shì)交易系統(tǒng)的ATS，我們僅需要教會(huì)這個(gè)神經(jīng)網(wǎng)絡(luò)如何逆勢(shì)(反趨勢(shì))交易的策略。這一點(diǎn)是非常必要的，因?yàn)橐粋€(gè)基于趨勢(shì)的交易系統(tǒng)是不能成功交易在盤整(sidewaystrends)，也不能識(shí)別市場(chǎng)的回調(diào)(setbacks)和逆轉(zhuǎn)(reversals.,反向走勢(shì))！當(dāng)然，你可以采用兩個(gè)ATS，一個(gè)基于“趨勢(shì)”，一個(gè)基于“反趨勢(shì)”(逆向)，然后把它們掛到同一圖表上。另一個(gè)辦法是，你能教會(huì)神經(jīng)網(wǎng)絡(luò)如何與你現(xiàn)有的系統(tǒng)“

7、互補(bǔ)地”協(xié)調(diào)工作！Forthispurpose,wedesignedatwo-layerneuralnetworkconsistingoftwoperceptronsinthelowerlayerandoneperceptronintheupperlayer.為實(shí)現(xiàn)這個(gè)目標(biāo)，我們?cè)O(shè)計(jì)了一個(gè)兩層的神經(jīng)網(wǎng)絡(luò)，下層有兩個(gè)感知機(jī)(perceptrons)上層有一個(gè)感知機(jī)。Theoutputoftheneuralnetworkcanbeinoneofthesethreestates:這個(gè)神經(jīng)網(wǎng)絡(luò)的能輸出下列三種狀態(tài)之一Enteringthemarketwithalongposition(Entering

8、)市場(chǎng)是處在多向倉(cāng)Enteringthemarketwithashortposition(Entering)市場(chǎng)是處在空向倉(cāng)Indeterminatestate不確定的,(不明確的,模糊的)狀態(tài)Actually,thethirdstateisthestateofpassingcontrolovertotheBTS,whereasinthefirsttwostatesthetradesignalsaregivenbytheneuralnetwork.實(shí)際上，第三種狀態(tài)是就把控制權(quán)交給BTS，反之前兩種狀態(tài)是交易信號(hào)由神經(jīng)網(wǎng)絡(luò)給出。Theteachingoftheneuralnetworkisdi

9、videdintothreestages,eachstageforteachingoneperceptron.Atanystage,theoptimizedBTSmustbepresentforperceptronstoknowwhatitcando.神經(jīng)網(wǎng)絡(luò)的“教育”分成三步驟，每一步驟“教育”一個(gè)感知機(jī)，在任何一步驟，這個(gè)優(yōu)化了的BTS必須存在為的是“感知機(jī)們”知道它自己能做什么。Theseparateteachingofperceptronsbyageneticalgorithmisdeterminedbythelackofthisalgorithm,namely:Theamountof

10、inputssearchedinwiththehelpofsuchalgorithmislimited.However,eachteachingstageiscoherentandtheneuralnetworkisnottoolarge,sothewholeoptimizationdoesnottaketoomuchtime.感知機(jī)們分別的“教育”由遺傳算法來(lái)承擔(dān)，由于這樣的算法的缺乏，換句話說(shuō)，搜索到的這樣的算法有限，限制了“輸入”(參數(shù)變量)的數(shù)量(借助這樣算法得到的參數(shù)變量的值)，然而，每一步驟的“教育”是密切配合補(bǔ)充的。(因此效果還是不錯(cuò))，這樣這個(gè)神經(jīng)網(wǎng)絡(luò)不會(huì)太大，整個(gè)的優(yōu)化也不會(huì)

11、耗費(fèi)太多的時(shí)間。Theveryfirststage,precedingtheteachingofanNN,consistsinoptimizationoftheBTS.在“教育”NN之前的一步是對(duì)BTS進(jìn)行優(yōu)化。Inordernottoloseourselves,wewillrecordthestagenumberintheinputoftheATSidentifiedaspass.Identifiersofinputscorrespondingwiththestagenumberwillandinthenumberequaltothisstagenumber.為了不使我們自己也被搞糊涂了，我

12、們將已經(jīng)測(cè)試通過(guò)的ATS的輸入（參數(shù)變量）記錄上(”通過(guò)”(pass)的步驟號(hào)(stagenumber).，輸入s（參數(shù)變量）的標(biāo)識(shí)符將和stagenumber(步驟號(hào))一致(等同)。Thus,letsstartpreparationsforoptimizationandteachingtheNN.Letssettheinitialdepositas$1000000(inordernottocreateanartificialmargincallduringoptimization)andtheinputtobeoptimizedasBalanceinExpertAdvisorproperti

13、esonthetabofTestingintheStrategyTester,andstartgeneticalgorithm.這樣，我們開始對(duì)這個(gè)NN進(jìn)行優(yōu)化和“教育”的準(zhǔn)備。存入初始保證金為$100萬(wàn)(以便于在優(yōu)化期間不產(chǎn)生人為的補(bǔ)充保證金的通知)。Input(參數(shù)變量)是按“余額”進(jìn)行優(yōu)化，設(shè)置EA的StrategyTester的測(cè)試的屬性tab為Testing。開始運(yùn)行遺傳算法。LetsgototheInputstaboftheEAspropertiesandspecifythevolumeofpositionstobeopenedbyassigningthevalue1totheid

14、entifierlots.在這個(gè)EA的開倉(cāng)量lots.的值設(shè)為1lot。Optimizationwillbeperformedaccordingtothemodel:Openpricesonly(fastestmethodtoanalyzethebarjustcompleted,onlyforEAsthatexplicitlycontrolbaropening),sincethismethodisavailableintheATSalgorithm.從這個(gè)ATS算法明確地有效開始，實(shí)施優(yōu)化，所采用復(fù)盤模型是“僅用開盤價(jià)(以最快速的方法分析剛形成的柱線)”。Stage1ofoptimizatio

15、n.OptimizationoftheBTS:優(yōu)化步驟1，BTS的優(yōu)化Setthevalue1fortheinputpass.設(shè)置為1為這input（參數(shù)變量）“為通過(guò)”(theinputpass)。Wewilloptimizeonlyinputsthatcorrespondwiththefirststage,i.e.,thatendin1.Thus,wecheckonlytheseinputsforoptimization,anduncheckallothers.我們僅僅優(yōu)化步驟1相關(guān)的那些inputs（參數(shù)變量），即，尾標(biāo)為1的參數(shù)變量，于是，我們僅僅測(cè)試優(yōu)化有關(guān)的inputs而不測(cè)試其他

16、的變量參數(shù)tp1-TakeProfitoftheBTS.Itisoptimizedwiththevalueswithintherangeof10to100,step1tp1，BTS的所取的止盈值（TakeProfit）。在step1，優(yōu)化的值的范圍在10到100，sl1-StopLossoftheBTS.Itisoptimizedwiththevalueswithintherangeof10to100,step1sl1，BTS的所取的止損值（StopLoss）。在step1，優(yōu)化的值的范圍在10到100。p1-periodofCCIusedintheBTS.Itisoptimizedwitht

17、hevalueswithintherangeof3to100,step1p1，用于BTS的CCI的周期值。在step1，優(yōu)化的值的范圍在3到100BelowaretheresultsoftheBTSoptimization:下面是BTS優(yōu)化的結(jié)果Stage2.Teachingtheperceptronresponsibleforshortpositions:步驟2，“教育負(fù)責(zé)管“開空倉(cāng)”(shortpositions)的感知機(jī)Setthevalue2(accordingtothestagenumber)fortheinputpass.根據(jù)步驟的步驟號(hào)，設(shè)置(input，參數(shù)變量)的pass的值

18、為2。Unchecktheinputscheckedforoptimizationinthepreviousstage.Justincase,saveinafiletheinputsobtainedatthepreviousstage.不測(cè)試那些已經(jīng)測(cè)試過(guò)的優(yōu)化了的以前步驟的inputs.（變量參數(shù)）。以防萬(wàn)一，保存以前步驟獲得的inputs（變量參數(shù)值）到一個(gè)文件中去Checktheinputsforoptimizationaccordingtoourrule:theiridentifiersmustendin2:根據(jù)我們的規(guī)則，必須是測(cè)試那些是在尾標(biāo)為2的inputs（變量參數(shù)）。x12,

19、x22,x32,x42-weightnumbersoftheperceptronthatrecognizesshortpositions.Itisoptimizedwiththevalueswithintherangeof0to200,step1x12,x22,x32,x42是識(shí)別并開空倉(cāng)的感知機(jī)的權(quán)重，它們的值在step1時(shí)被優(yōu)化在范圍0to200tp2-TakeProfitofpositionsopenedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof10to100,step1tp2(TakeProfit)是感知機(jī)所開的

20、倉(cāng)的止盈值，它們的值在step1時(shí)被優(yōu)化在范圍10to100。sl2-StopLossofpositionsopenedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof10to100,step1sl2(StopLos)在step1它是感知機(jī)所開的倉(cāng)的止損值，被優(yōu)化值的范圍在10to100p2-theperiodofthevaluesofpricedifferencetobeanalyzedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof3to100,step1

21、.p2感知機(jī)所分析的價(jià)格差的周期值(iiCCI()函數(shù)的一個(gè)參數(shù)period-Averagingperiodforcalculation)，在step1它的值所優(yōu)化的范圍在3to100Letsstartteachingitusingoptimizationwithageneticalgorithm.Theobtainedresultsaregivenbelow:現(xiàn)在，開始用遺傳算法來(lái)優(yōu)化“教育”NN（讓它“學(xué)習(xí)”市場(chǎng)），獲得的結(jié)果如下Stage3.Teachingtheperceptronresponsibleforlongpositions:步驟3“教育”負(fù)責(zé)開多倉(cāng)的感知機(jī)（“學(xué)習(xí)”市場(chǎng)）。

22、Setthevalue3(accordingtothestagenumber)fortheinputpass.設(shè)置值3(根據(jù)步驟的步驟號(hào))說(shuō)明這些input(變量參數(shù))已經(jīng)“通過(guò)”(theinputpass)Unchecktheinputscheckedforoptimizationinthepreviousstage.Justincase,saveinafiletheinputsobtainedatthepreviousstage.同樣，不測(cè)試，那些已經(jīng)測(cè)試過(guò)的優(yōu)化了的，以前步驟的inputs.(變量參數(shù)值)，以防萬(wàn)一，保存以前步驟獲得的inputs.(變量參數(shù)值)到一個(gè)文件中去Checkt

23、heinputsforoptimizationaccordingtoourrule:theiridentifiersmustendin3:根據(jù)我們的規(guī)則，優(yōu)化測(cè)試的inputs(變量參數(shù)值)必須是尾標(biāo)為3的那些變量參數(shù)。x13,x23,x33,x43-weightnumbersoftheperceptronthatrecognizeslongpositions.Itisoptimizedwiththevalueswithintherangeof0to200,step1.x13,x23,x33,x43是識(shí)別多倉(cāng)的感知機(jī)的權(quán)重，它們的值在step1時(shí)被優(yōu)化時(shí)得到的范圍在0to200tp3-Take

24、Profitofpositionsopenedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof10to100,step1tp3(TakeProfit)是感知機(jī)所開的倉(cāng)的“止盈值”，它的值在step1時(shí)被優(yōu)化時(shí)的范圍是在10to100。sl3-StopLossofpositionsopenedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof10to100,step1sl3(StopLoss)是感知機(jī)所開的倉(cāng)的“止盈值”，它們的值在step1時(shí)被優(yōu)化為范圍是10t

25、o100。p3-theperiodofthevaluesofpricedifferencetobeanalyzedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof3to100,step1.p3-感知機(jī)所分析的價(jià)差的周期值。它在步驟1優(yōu)化時(shí)得到的值的范圍是3to100。Letsstartteachingitusingoptimizationwithageneticalgorithm.Theobtainedresultsaregivenbelow:啟動(dòng)采用遺傳算法的優(yōu)化來(lái)“教育”NN，所獲得的結(jié)果如下Stage4(final).T

26、eachingthefirstlayer,i.e.,teachingtheperceptronthatisintheupperlayer:步驟4(最終步驟)“教育”第一層，即“教育”在上層的感知機(jī)。Setthevalue4(accordingtothestagenumber)fortheinputpass.根據(jù)步驟的步驟號(hào)，設(shè)置值4為輸入通過(guò)(fortheinputpass)Unchecktheinputscheckedforoptimizationinthepreviousstage.Justincase,saveinafiletheinputsobtainedatthepreviousst

27、age.不測(cè)試那些在之前步驟已經(jīng)測(cè)試過(guò)的優(yōu)化了的“輸入”(inputs)（意思是已經(jīng)在之前步驟優(yōu)化過(guò)的變量的參數(shù)值就不再優(yōu)化它們了)。以防萬(wàn)一，將之前步驟獲得的這些變量的參數(shù)值存到一個(gè)文件中去。Checktheinputsforoptimizationaccordingtoourrule:theiridentifiersmustendin4:根據(jù)我們的規(guī)則，只測(cè)試優(yōu)化標(biāo)識(shí)符最后位是4的那些inputs(變量的參數(shù)值)x14,x24,x34,x44-weightnumbersoftheperceptronofthefirstlayer.Itisoptimizedwiththevalueswith

28、intherangeof0to200,step1.x14,x24,x34,x44是第一層感知機(jī)參數(shù)的權(quán)重值。在步驟1時(shí)它們被優(yōu)化的值的范圍在0to200。p4-theperiodofthevaluesofpricedifferencetobeanalyzedbytheperceptron.Itisoptimizedwiththevalueswithintherangeof3to100,step1.p4被感知機(jī)分析的價(jià)差的值的周期。在步驟1它的值的范圍被優(yōu)化在3to100。Letsstartteachingitusingoptimizationwithageneticalgorithm.Theo

29、btainedresultsaregivenbelow:采用遺傳算法來(lái)優(yōu)化，啟動(dòng)“教育”來(lái)教它“學(xué)習(xí)”。所獲得結(jié)果如下Thatsall,theneuralnetworkhasbeentaught.這就是全部，神經(jīng)網(wǎng)絡(luò)已經(jīng)被“教育”了。TheATShasonemorenon-optimizableinput,mn-MagicNumber.ItistheidentifierofpositionsforatradingsystemnottomixitsorderswiththeordersopenedmanuallyorbyotherATSes.Thevalueofthemagicnumbermus

30、tbeuniqueandnotcoincidewiththemagicnumbersofpositionsthathavenotbeenopenedbythisspecificExpertAdvisor.這個(gè)ATS有一個(gè)不能被優(yōu)化的input(參數(shù))mn-MagicNumber.(魔法號(hào))它是一個(gè)交易系統(tǒng)它所開的倉(cāng)位的識(shí)別符，為的是不和手動(dòng)開倉(cāng)或其他ATSes開的倉(cāng)位混淆。這個(gè)MagicNumber的值必須是唯一的并且和這個(gè)特別的ea尚未開倉(cāng)的magicnumbers不一致。P.S.Thesizeoftheinitialdepositisfoundasthedoubledabsolutedra

31、wdown,i.e.,weconsidersomesafetyresourcesforit.出于保證有一些安全保險(xiǎn)的考慮，初始保證金的金額設(shè)置是考慮為絕對(duì)最大回落的兩倍TheEAgiveninthesourcecodesisnotoptimized.這個(gè)ea的源代碼沒(méi)有優(yōu)化。Ifyouneedtoreplacethebuilt-inBTSwiththealgorithmofanothertradingsystem,youmustmodifythecontentsofthefunctionbasicTradingSystem().如果你需要置換嵌入另一個(gè)交易系統(tǒng)算法的BTS，你必須修改BTS功能

32、的內(nèi)部。Inordernottoentertheinitialandthefinalvaluesandthevaluesofstepsforoptimization,youcantakethereadyfilecombo.set,placeitinthefoldertesterMT4,anduploadtotheEAspropertiesinTester.以便于不輸入優(yōu)化時(shí)的初值，終值和步長(zhǎng)，你可采用已備好的combo.set文件，把它放置到MT4的tester目錄并加載這個(gè)ea的屬性(properties)到StrategyTester。Re-optimizationoftheEAistob

33、eperformedataweekend,i.e.,onSaturdayoronSunday,butonlyiftheresultsoftheprecedingweekwereunprofitable.Thepresenceoflossesmeansthatthemarkethaschanged,andthere-optimizationisnecessary.ThepresenceofprofitsmeansthattheATSdoesnotneedanyre-optimizationandrecognizesmarketpatternsquitewell.這個(gè)ea的再優(yōu)化可在周末進(jìn)行，即周

34、六和周日，但僅在前面一周的結(jié)果是不盈利的。虧損的出現(xiàn)意味著市場(chǎng)已經(jīng)改變，于是需要重新優(yōu)化，若是仍然獲利意味著這個(gè)ATS不需要重新優(yōu)化，它對(duì)市場(chǎng)目前的模型的識(shí)別繼續(xù)有效！附源代碼：/+-+/|Combo_Right.mq4|/|Copyright?2008,YuryV.Reshetov|/|/load/2-1-0-171|/+-+#propertycopyrightCopyright?2008,YuryV.Reshetov#propertylink/load/2-1-0-171/-inputparametersexterndoubletp1=50;externdoublesl1=50;exter

35、nintp1=10;externintx12=100;externintx22=100;externintx32=100;externintx42=100;externdoubletp2=50;externdoublesl2=50;externintp2=20;externintx13=100;externintx23=100;externintx33=100;externintx43=100;externdoubletp3=50;externdoublesl3=50;externintp3=20;externintx14=100;externintx24=100;externintx34=1

36、00;externintx44=100;externintp4=20;externintpass=1;externdoublelots=0.01;externintmn=888;staticintprevtime=0;staticdoublesl=10;staticdoubletp=10;/+-+/|expertstartfunction|/+-+intstart()if(Time0=prevtime)return(0);prevtime=Time0;if(!IsTradeAllowed()again();return(0);/-inttotal=OrdersTotal();for(inti=0;i0)ticket=OrderSend(Symbol(),OP_BU