A Hybrid Expert System for Generating Stock Trading Signals

Transcription

1 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Hybrd Expert Syste for Generatng Stock Tradng Sgnals Hosen Hasheh ahar, Mohaad Hossen Fazel Zarand, kbar Esfahanpour Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ bstract In ths paper, a hybrd expert syste s developed by usng fuzzy genetc network prograng wth renforceent learnng (GNP-RL). In ths syste, the frae-based structure of the syste uses the tradng rules extracted by GNP. These rules are extracted by usng techncal ndces of the stock prces n the tranng te perod. For developng ths syste, we appled fuzzy node transton and decson akng n both processng and judgent nodes of GNP-RL. Consequently, usng these ethod not only dd ncrease the accuracy of node transton and decson akng n GNP's nodes, but also extended the GNP's bnary sgnals to ternary tradng sgnals. In the other words, n our proposed Fuzzy GNP-RL odel, a No Trade sgnal s added to conventonal uy or Sell sgnals. Fnally, the obtaned rules are used n a frae-based syste pleented n Kappa-PC software. Ths developed tradng syste has been used to generate tradng sgnals for ten copanes lsted n Tehran Stock Exchange (TSE). The sulaton results n the testng te perod shows that the developed syste has ore favorable perforance n coparson wth the uy and Hold strategy. Keywords Fuzzy genetc network prograng, hybrd expert syste, techncal tradng sgnal, Tehran stock exchange. I. INTRODUCTION XPERT systes as odern technologcal tools havng the Eablty to perfor soe specfc assgned tasks, has ganed ore and ore popularty through the te. ecause of the attractveness of stock prces, they have been under consderaton n recent years, and nuerous researches have been done to forecast the or to realze ther behavor n arkets. In ths regard, techncal ndces and ndcators have been used to generate stock tradng rules based on ther prces and exchange volue. On the ground that choosng an approprate techncal ndex or ndces at the rght te seeed coplcated, researchers tred to apply evolutonary algorths such as rtfcal Neural Networks (NN) [1], Genetc lgorth (G) [2], Genetc Prograng (GP), Genetc Network Prograng (GNP) etc. to overcoe ths challenge. GP [3] as an extenson of G [4] havng a tree structure, has been wdely used for generatng techncal tradng rules [5], [6]. Furtherore, GNP, as an extenson of GP [7], represents each ndvdual as a network havng three knds of nodes naed start node, processng nodes and judgent nodes whch are connected to each other approprately. H. Hasheh ahar s wth the Departent of Industral Engneerng and Manageent Systes, rkabr Unversty of Technology, Tehran, Iran (correspondng author; e-al: hash@aut.ac.r). M. H. F. Zarand and. Esfahanpour are wth the Departent of Industral Engneerng and Manageent Systes, rkabr Unversty of Technology, Tehran, Iran (e-al: zarand@aut.ac.r. esfahaa@aut.ac.r). GNP was used for extractng tradng rules base on candlestck charts n [8] for the frst te. In [9], [10], Renforceent Learnng (RL) was added to GNP by consderng two sub nodes for each node. y addng RL to GNP, the ore desrable the sub node perfors n decson akng, the ore the probablty of vstng t n the followng becoes. fter that n [11] control nodes were added to GNP, and ade t possble to be used for portfolo optzaton. In [12], [13] Chen et al. used Te daptng GNP for the portfolo optzaton. ecause the trend of prces s changng, usng a prce wndow whch shfts through the te durng the evoluton phase akes the ndvduals adapt to changng trends. Fuzzy GNP for the frst te was proposed by Sendar et al. [14]. In ther paper, they used the fuzzy judgent nodes for decdng the next node consderng that fuzzy judgent nodes whch deterne the next node probablstcally nstead of soe judgent nodes wth a threshold, ade the odel ore realstc. In [15], Mabu et al. appled GNP for generatng stock tradng rules. In ther research, durng the evoluton phase dfferent rules were generated out of the elte ndvdual and were accuulated n the approprate rule pool. Dfferent rule pools were created based on the prce's trend and the generated tradng sgnal. t last, the decson akng was based on accuulated rules n the rule pools. In followng years, Chen and Wang proposed a Rsk-djusted GNP for portfolo optzaton [16]. Consequently, they used condtonal Sharp Rato as the ftness functon of ther Rsk-djusted odel. In condtonal Sharp Rato, the devaton of return fro ts average value wll be dvded by Condtonal Value-at-Rsk. In bref, n ths paper usng fuzzy processng nodes and applyng tradng rules n a frae-based structure contrbuted perforance enhanceent to the conventonal GNP-RL. In the followng, the pleentaton of these contrbutons and ther effectveness wll coe under delberaton. The rest of the paper s organzed as follows. In Secton II, the proposed Fuzzy GNP-RL s descrbed. In Secton III, pleentaton of the proposed frae-based expert syste n Kappa-PC s explaned, and n Secton IV, results are reported. Fnally, the paper closes wth our concluson. II. FUZZY GNP In GNP, each ndvdual s a network havng three knds of nodes. s shown n Fg. 1, t conssts of a start node and several judgent and processng nodes. The start node just specfes the frst node, and does not have any other role n perforance of the graph structure. In judgent nodes, a Internatonal Scholarly and Scentfc Research & Innovaton 10(7)

2 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ techncal ndcator wll be used for judgents, and n processng nodes, the decson to uy, Sell or No Trade wll be ade. In vrtue of GNP's graph structure, t s possble to use nodes repeatedly. Consequently, ths possblty culnates n copact structure of GNP, and prevents bloatng phenoenon whch s ore prevalent n GP [9].. Genotype of Fuzzy GNP-RL In Fg. 2, the structure of judgent and processng nodes and the genes correspondng to the are presented. S J P S Start Node Judgent Node Processng Node J1 J2 J5 J4 Fg. 1 asc Structure of GNP In gene representaton, NT represents the node type. In fact, NT 0 eans the node s start node, and NT 1 shows processng nodes, and NT 2 represents judgent nodes. The. RL n Fuzzy GNP-RL The learnng phase of ths odel s based on Sarsa algorth [17]. s shown n Fg. 2, n the proposed GNP odel, each node conssts of two sub nodes, and one of the s chosen accordng to ther Q values. Here, each node s consdered as "state" and the selecton of the sub node s defned as "acton". The sub nodes are chosen usng є-greedy polcy. ased on that, the sub node wth the greater Q value s selected wth probablty of 1-є and the other way around. fter choosng J3 J6 J7 Fg. 2 Judgent and Processng Nodes' Structure d represents the te delay correspondng to each knd of nodes. The te delay s the te that t takes to transt fro node to node j. y settng te delay, the nuber of vsted nodes durng each tradng day can be anaged. The te delay for the start node s zero, and for the processng nodes s equal to 5, and for judgent nodes s consdered 1 te unt. In addton, each day conssts of fve te unts. Therefore, ore than one trade cannot be executed n each tradng day. For each sub node, Q represents the Q value needed for RL. The ID shows the dentfcaton nuber of each sub node. For judgent nodes, t specfes the techncal ndex whch should be used, and n processng nodes t ndcates the functon of the sub node whch can be uy or Sell. The and are paraeters whch are used n ebershp functon of each sub node. In judgent of sub nodes, C and C show the nuber of the next node. ased on the techncal ndex, one of the wll be chosen as the next node. If the judgent result s, then the next node wll be C and the other way around. y contrast, there s not condtonal branch n processng nodes, and they always refer to C as ther next node. the approprate sub node and perforng ts task, the Q values are updated as: where Q p Q p r Q Q ) (1) ( jq p Q shows the Q value of the current sub node, and p Q jq shows the Q value of the next sub node. s learnng rate, s the dscount rate and r s obtaned reward snce the prevous processng node. In ths regard, choosng a sub node whch akes proft wll ncrease ts Q value, and consequently wll Internatonal Scholarly and Scentfc Research & Innovaton 10(7)

3 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ ncrease ts probablty to be chosen n the followng transtons. ddng RL provdes GNP wth ths ablty to have onlne learnng [9]. In ths way, the learnng s not confned to evoluton phase (offlne learnng), and durng calculaton of the ftness functon of each ndvdual the syste can beneft fro that. C. Node Transton n Fuzzy GNP-RL t frst, the procedure starts wth the start node, and then based on the type of the next node the decson vares as explaned n the followng. 1) Judgent Nodes: f the node s a judgent node, a techncal ndex s selected accordng to ts dentfcaton nuber ( ID ). Each techncal ndex has ts own Iportance Index (IMX) whch shows whether buyng or sellng sgnal s ore lkely to appear. The result obtaned by IMX wll be used later n the next processng node to generate tradng sgnal. s an exaple, n Fg. 3 the IMX for Rate of Devaton (ROD) ndex s ndcated. IMX Rate of Devaton Fg. 3 IMX for Rate of Devaton (ROD) [18] In the next step, the odel should deterne the next transton node usng fuzzy judgent ethod. s shown n gene representaton, each sub node has two fuzzy paraeters whch are used to create ts ebershp functon. y havng and the ebershp functon of judgent and processng nodes wll be created as t can be seen n Fg Fg. 4 Mebershp Functon The procedure of deternng the next node usng the ebershp functon s as: - If ndex value < then the next node would be C - If ndex value > then the next node would be C - If < ndex value < then the next node wll be chosen probablstcally. So, the next node would be C wth the probablty of ( x ), and C wll be selected wth the probablty of x ) 1 ( x ), where x ) and ( ( ( x ) show ndex value's ebershp degree n and. 2) Processng Nodes: n processng nodes, t s necessary to calculate as presented n (2) n order to generate tradng t sgnal. 1 ' t IMX ( ) ' (2) I ' ' I ' where I s the set of judgent nodes beng vsted untl the last processng node. In ths paper, nspred by [14], we extended the GNP-RL and appled fuzzy processng nodes. In ths case, we can generate tradng sgnal usng ebershp functon of processng nodes whch are exactly slar to Fg. 4. Usng fuzzy processng nodes oblges by provdng ternary sgnals. In fact, as t can be seen n followng, usng fuzzy processng node akes t possble to generate No Trade sgnal. Ths possblty helps the odel see ore realstc on the ground that n a lot of stuatons not tradng aybe ore benefcal n coparson wth buyng or sellng. To suarze, generatng sgnal n fuzzy processng nodes s as: In processng nodes wth ID 1 (uyng Nodes): - If t then No Trade - If t then uy - If t then No Trade wth the probablty of ( x ) and uy wth probablty of ( x ) 1 ( x ) In processng nodes wth ID 2(Sellng Nodes): - If t then Sell - If t then No Trade - If t then Sell wth the probablty of ( x ) and No Trade wth probablty of x ) 1 ( x ) ( fter generatng the sgnal, the odel transts to C D. Operators of Fuzzy GNP-RL Fuzzy GNP-RL uses genetc operators such as Crossover and Mutaton n the evoluton phase. For executng operatons, the ndvduals are selected as parents by Tournaent Selecton ethod. The Crossover operator s done as explaned below. - Two parents wll be selected usng tournaent selecton. - One node s selected fro the frst parent wth the probablty of p. c - Selected node wll be exchanged wth ts correspondng node n the other parent. - Two obtaned offsprng wll be consdered as ndvduals of next generaton. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)

4 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ The Mutaton operator s executed on one ndvdual. The paraeters ID, C and C are utated as follows. - One ndvdual s selected usng Tournaent selecton as a parent - Each paraeter of selected ndvdual s chosen for utaton wth the probablty of p - Selected paraeters are changed randoly - New ndvdual wll be consdered as an ndvdual of next generaton For utatng and, we use the ethod proposed n [14]. In ths ethod, these paraeter are not utated unforly, but by passng through generaton they change less. The process of utatng fuzzy paraeters s explaned below. - Fuzzy paraeters of each sub node s selected wth the probablty of p - Selected paraeters are utated usng (3). x (, ), 0 ' t U x f x (3) x ( t, x L), f 1 where ' x and x are fuzzy paraeters after and before utaton, and s a rando dgt. U and L are upper bound and lower bound of the paraeter, and t s the current generaton nuber. In addton, ( t, y) s calculated usng (4). 2 t 1 T ( t, y) y 1 r (4) where T s the axu nuber of generatons. - New paraeters consttute the paraeters of utated ndvdual whch transfers to new generaton. Fnally, the flowchart of evoluton phase and extractng tradng rules s ndcated n Fg. 5. E. Desgnng the Frae Structure fter generatng tradng rules obtaned by Fuzzy GNP-RL, we use the n a frae structure to develop our syste. To desgn the frae-based syste, we have used Kappa-PC software. For each stock, a frae s desgned havng slots whch conssts of prce's te seres and extracted rules. ddtonally, there s a frae naed "Syste" whch starts the syste and exposes the generated sgnal. The procedure of generatng tradng sgnals n ths frae-based syste s shown n Fg. 6. III. PPLICTION OF THE SYSTEM IN TEHRN STOCK EXCHNGE In ths secton, we have used the proposed Fuzzy GNP-RL to extract tradng rules for ten stocks traded n TSE. These stocks are selected aong stocks wth the hghest lqudty n the recent years, and are chosen fro dfferent sectors of the arket n order to keep the portfolo well-dversfed. N=N+1 No No No Start Create Intal Populaton N=1 Tradng Tradng Perod Ends? N=Nuber of Indvduals? Yes Mutaton Crossover Last Generaton? Yes Extractng Rules Stop Yes Fg. 5 Flowchart of Evoluton Phase and Extractng Rules [18] Frae of Selected Stock Generatng Tradng Sgnal Tradng Sgnal Rule ase Do You have ths Stock n Hand? Calculatng Indces Syste Frae nnouncng the Sgnal What s Stock s Nae? End Start Fg. 6 Procedure of Generatng Sgnals n the Frae Structure In evoluton phase of the Fuzzy GNP-RL, we use a wndow wth the length of 25 days to evaluate the perforance of ndvduals n ths te perod by calculatng ther ftness functon. To ake the proposed odel ore realstc, ths te wndow shfts through te. In ths case, the ndvduals wll adapt to prce's trend changes. In detal, every two teratons the te wndow shfts one day ahead. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)

5 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ Ftness and Reward The reward that s used for updatng Q values n RL s calculated by (5). In that the Reward needs sellng prce to be calculated, t occurs only n sellng processng nodes. Reward = Sellng Prce uyng Prce (5) Moreover, the ftness functon whch s used to evaluate ndvduals n the evoluton phase s as follows. Ftness = Total Proft n the Te Wndow (6). Techncal Indces Techncal ndces are used n judgent nodes to specfy the next node, and to estate the IMX value. In ths paper, we used eght knds of techncal ndces whch were: Relatve Strength Index (RSI), Rate of Devaton (ROD), Rate of Change (ROC), Volue Rato (VR), Stochastcs, Ranked Correlaton Index (RCI), Golden/Dead Cross, Movng verage Convergence Dvergence. The frst sx of the are calculated n three dfferent te perods wth the length of 5, 10 and 15 days. s a result, totally we had 20 (6*3+2=20) techncal ndces for the judgng task. Furtherore, MCD and Golden/Dead Cross ndcators are calculated by usng 5-day Movng verage and 25-day Movng verage of prces. C. Data In order to generate tradng rules, the odel evolves through tranng te perod, and then the perforance of the syste s evaluated n testng te perod. The tranng and testng te perods' length for selected stocks are clarfed n the followng. - Tranng Perod: conssts of 250 tradng days - Testng Perod: conssts of 125 tradng days endng n January 2, D. Paraeter Settngs The paraeters used n the proposed Fuzzy GNP-RL odel are deterned through related studes [15], [19] and shown n Table I. The ntal connecton between nodes, the dentfcaton nuber of sub nodes and fuzzy paraeters are deterned randoly n frst generaton. Moreover, the ntal Q values are set as zero. E. Results s t can be seen n followng, the proposed odel s appled to generate tradng sgnal for 10 copanes lsted n Table II. t frst, the tradng rules are extracted fro the elte ndvdual evolved over the tranng te perod, and then the rules are appled to generate tradng sgnal n testng perod. The results show that the proposed odel outperfors the uy and Hold (&H) strategy. TLE I PRMETER SETTING Populaton sze 301 Nuber of generatons 500 Mutaton 180 Nuber of nodes 61 Crossover 120 Nuber of judgent nodes 40 Elte 1 Nuber of processng nodes 20 Mutaton rate ( p ) 0.02 Learnng rate ( ) 0.1 Crossover rate ( p ) c 0.1 Dscount rate ( ) 0.4 Tournaent sze 2 Є-greedy paraeter 0.1 TLE II RETURNS OTINED Y USING FUZZY GNP-RL Copany &H &S Pars Khodro Co % -5.56% Telecouncaton Copany of Iran 14.07% 5.07% Shazand Petrochecal Co % 2.81% Ghadr Investent Co % 1.7% ank Saderat Iran -7.58% -6.83% Iran Constructon Investent Co % % zarab Industres Co % 21.26% Darou Pakhsh Pharaceutcal Manufacturng Co % 3.91% Islac Republc of Iran Shppng Lnes (IRISL) 2.43% 7.49% Natonal Iranan Lead & Znc Co % 39.39% verage -1.30% 4.42% The results show that applyng developed syste akes a postve return n ths specfc te perod although the prces have a downward trend. IV. CONCLUSION In ths paper, a hybrd expert syste to generate stock tradng sgnals s developed applyng tradng rules n the frae-based structure desgned for the syste. These tradng rules are extracted by usng Fuzzy GNP-RL. Extracted rules are based on techncal ndces of the stock prces. pplyng fuzzy processng nodes provdes the proposed odel wth the ablty to generate ternary sgnals. In order to evaluate the perforance of the developed syste, t s used to generate sgnals for 10 stocks whch are traded n TSE. The results ndcated that the developed syste outperfors the uy and Hold strategy. In fact, usng fuzzy nodes along wth RL contrbutes to generate ore accurate tradng sgnals. REFERENCES [1] Seng-cho, T.C., et al., stock selecton DSS cobnng I and techncal analyss. nnals of Operatons Research, : p [2] auer, R.J., Genetc algorths and nvestent strateges. Vol : John Wley & Sons. [3] Koza, J.R., Genetc prograng: on the prograng of coputers by eans of natural selecton. Vol : MIT press. [4] Holland, J., dapton n natural and artfcal systes. nn rbor MI: The Unversty of Mchgan Press, [5] Mousav, S.,. Esfahanpour, and M.H.F. Zarand, novel approach to dynac portfolo tradng syste usng ulttree genetc prograng. Knowledge-ased Systes, : p [6] Esfahanpour,. and S. Mousav, genetc prograng odel to generate rsk-adjusted techncal tradng rules n stock arkets. Expert Systes wth pplcatons, (7): p [7] Hrasawa, K., et al. Coparson between genetc network prograng (GNP) and genetc prograng (GP). n Evolutonary Coputaton, Proceedngs of the 2001 Congress on IEEE. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)

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