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In [119]:



    


%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import math

from basic_artificial_market import ArtificialMarket



    











In [220]:



    


import numpy as np
import matplotlib.pyplot as plt
import math

class ArtificialMarket():
    def __init__(self, num_player=1000, ganma=1000, sigma=0.06, P_sigma=30):
        self.num_player = num_player
        self.random_state = np.random.RandomState()
        self.sigma = sigma
        self.P_sigma = P_sigma
    
    def weight(self, w_1_max=1, w_2_max=100, w_3_max=1):
        num_player = self.num_player
        weight_1 = np.zeros(num_player)
        weight_2 = np.zeros(num_player)
        weight_3 = np.zeros(num_player)
        random_state = self.random_state
        for i in range(num_player):
            weight_1[i] = random_state.uniform()*w_1_max
            weight_2[i] = random_state.uniform()*w_2_max
            weight_3[i] = random_state.uniform()*w_3_max
        weight = [weight_1, weight_2, weight_3]
        return weight
    
    def ganma(self, ganma_max=10, num_player=None):
        random_state = self.random_state
        if num_player is None:
            num_player = self.num_player
        ganma = np.array([])
        for i in range(num_player):
            ganma = np.append(ganma, int(random_state.uniform(ganma_max)))
        return ganma
    
    def P_t(self, past_data, delta_l, delta_t):
        if np.sum(delta_t) <= delta_l:
            P_t = past_data[-1]
        else: 
            d = 0
            j = 0
            while d <= delta_l:
                d += delta_t[-j]
                j += 1
            P_t = past_data[-j]
        return P_t
    
    def r_t_h(self, past_data, P_t, ganma):
        if len(past_data) < ganma:
            r_t_h = np.log10(P_t/past_data[0])
        else:
            past_data_ganma = past_data[-ganma]
            r_t_h = np.log10(P_t/past_data_ganma)
        return r_t_h
        
    def one_market_model(self, delta_l, w, ganma, fdmtl=10000.0, b_limit=None, s_limit=None, t=0, delta_t=None, past_data=None, market_order=0, delta=1):
        num_player = self.num_player
        sigma = self.sigma
        P_sigma = self.P_sigma
        P_f = fdmtl
        
        if past_data is None:
            past_data = np.array([P_f])
        if b_limit is None:
            b_limit = np.array([])
        if s_limit is None:
            s_limit = np.array([])
        
        if delta_t is None:
            delta_t = np.array([])
            
        w_1 = w[0]
        w_2 = w[1]
        w_3 = w[2]
        r_t_e = np.zeros(num_player)
                
        for i in range(num_player):
            P_t_1 = past_data[-1]
            #P_t_1 = self.P_t(past_data, delta_l, delta_t)
            
            r_t_h = self.r_t_h(past_data, P_t_1, ganma[i])
            
            e_t = np.random.normal(0, sigma)
            
            r_t_e[i] = (w_1[i]*np.log10(P_f/P_t_1) + w_2[i]*r_t_h + w_3[i]*e_t)/(w_1[i] + w_2[i] + w_3[i])
            P_e = P_t_1*math.exp(r_t_e[i])
            
            P_o = np.random.normal(P_e, P_sigma)
            
            #print w_1[i]*np.log10(P_f/P_t_1), w_2[i]*r_t_h, r_t_e[i]
            
            if P_e > P_o:
                P_o = round(P_o, 1)
                if len(s_limit) > 10 and np.min(s_limit) < P_o:
                    market_order += 1
                    P_t = np.min(s_limit)
                    s_limit = np.delete(s_limit, np.argmin(s_limit))
                else:
                    b_limit = np.append(b_limit, P_o)
                    P_t = P_t_1
            else:
                P_o = round(P_o, 1)+0.1
                if len(b_limit) > 10 and np.max(b_limit) > P_o:
                    market_order += 1
                    P_t = np.max(b_limit)
                    b_limit = np.delete(b_limit, np.argmax(b_limit))
                else:
                    s_limit = np.append(s_limit, P_o)
                    P_t = P_t_1
            past_data = np.append(past_data, P_t)
            delta_t = np.append(delta_t, np.random.exponential(delta))
            t += delta_t[-1]
        return t, delta_t, past_data, b_limit, s_limit, market_order
        
    def one_market_simulation(self, delta_l, o_max=10000, fdmtl=10000):
        o = 0
        w = self.weight()
        ganma = self.ganma()
        t, delta_t, past_data, b_limit, s_limit, market_order = self.one_market_model(delta_l, w, ganma, fdmtl)
        while o < o_max:
            t, delta_t, past_data, b_limit, s_limit, market_order = self.one_market_model(delta_l, w, ganma, fdmtl, b_limit, s_limit, t, delta_t, past_data, market_order)
            o += 1
        return past_data, t, market_order
    
    def rad(self, past_data, fdmtl=10000.0):
        p = 0
        for i in range(len(past_data)):
            p += math.fabs(float(past_data[i]) - fdmtl)/fdmtl
        m = 1.0/float(len(past_data))*p
        return m
    
    def agreed_rate(self, past_data, market_order):
        print market_order
        agreed_rate = float(market_order)/float(len(past_data))
        return agreed_rate



    











In [232]:



    


AM = ArtificialMarket()
k, t, market_order= AM.one_market_simulation(0, 10)
plt.plot(k)
AM.rad(k)



    


















    
Out[232]:








0.0002801908917371384










    
























In [233]:



    


AM.agreed_rate(k, market_order)



    


















    






938










    
Out[233]:








0.0852649759112808

















In [205]:



    


k[-100:]



    


















    
Out[205]:








array([ 10007.5,  10007.5,  10007.5,  10007.5,  10007.5,  10007.5,
        10007.5,  10007.6,  10007.7,  10007.7,  10007.7,  10007.7,
        10007.7,  10007.7,  10007.7,  10002.6,  10002.6,  10002.6,
         9986.1,   9981.8,   9980.4,   9980.4,   9980.4,   9980.4,
         9980.4,   9980.4,   9980.4,   9980.4,   9980.4,   9980.4,
         9980.4,   9980.4,   9980.4,   9980.4,   9979.7,   9979.7,
         9979.7,   9979.7,   9979.7,   9975.3,   9975.3,   9975.3,
         9974.8,   9974.8,   9974.8,   9974.8,   9973.5,   9973.5,
         9973.5,   9973.5,   9973.5,   9973.5,   9973.1,   9973.1,
         9973.1,   9973.1,   9973.1,   9973.1,   9972.8,   9972.8,
         9972.8,   9972.8,   9979.8,   9979.8,   9979.8,   9979.9,
         9994. ,   9994. ,   9974.3,   9974.3,   9972.3,   9972.3,
         9972.3,   9972.3,   9972.3,   9971.2,   9971.2,   9971.2,
         9971.2,   9971.2,   9971.2,   9977.9,   9977.9,   9977.9,
         9977.9,   9977.9,   9984.8,   9984.8,   9984.8,   9976. ,
         9997.4,   9997.4,   9997.4,   9998.3,   9998.3,   9999.1,
         9999.1,   9999.1,   9999.1,   9999.1])

















In [13]:



    


AM = ArtificialMarket()
k, t= AM.one_market_simulation(10, 5)
plt.plot(k)
round(AM.rad(k), 8)



    


















    






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   9925.02591151   9955.25973876   9859.97561989   9971.19247792
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   9603.87685759   9949.45632776   9896.90878477   9964.57888453
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   9972.93139725   9954.47319069   9894.62786427   9960.53041333
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   9582.41757006   9905.88009664   9941.29205988   9976.18409409
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   9878.23180151   9992.78507178   9992.13872094   9981.34623464
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   9952.98955595   9987.96943396   9899.2155643    9962.67758334
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   9965.2029312    9881.70784134   9921.38318591   9994.84803368
   9987.51569627   9956.67615121   9960.27685541  10002.22351252
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   9998.58244292   9987.25496031   9988.52067855   9983.37536254
   9990.06710809   9936.64194275   9952.3702263    9936.80512778
   9946.22032294   9948.88249596  10001.329788     9974.21459933
  10003.04817239   9981.30495479   9963.11217892   9961.57629319
   9991.23864759   9932.97383246   9624.80920976   9963.70482671
   9980.13465742   9999.09549099   9992.71969252   9854.87737824
  10005.11843906   9887.44372414   9977.93372704  10003.36248409
   9804.8161207    9985.22026011   9923.50961307   9962.04698531
   9883.95153908   9999.5430952    9761.26001459  10000.06217223
   9970.07683444   9835.39524146   9956.65284192   9920.43407794
   9979.54031627   9991.58961759   9948.92286566   9990.90481875
   9977.71361642  10004.01299308   9740.17982621   9793.59378596
   9748.91492005   9831.6682247    9890.27125918   9982.20081196
   9994.03225575   9935.17192554   9982.57534251  10000.04952318
   9959.22834814   9989.18377439   9982.66957153   9910.25700598
   9937.14579423   9868.98917903   9954.17905691  10005.40045714
   9998.72112321   9773.47243211  10000.01433281   9944.82955728
   9992.42754852   9975.59173342   9958.05252202   9980.27807183
   9916.81771931   9913.49406417   9944.50139477   9848.38343408
   9996.12154416   9938.95346439   9965.19143073   9895.04080034
   9939.55149185   9929.78832339   9960.46902854   9985.27325278
   9915.67155379   9892.58698954   9931.8588127    9986.60722698
   9747.79254443   9955.76477926  10003.61503049   9985.49693985
   9939.46125681  10002.9394607    9999.6204625    9930.96533929
   9990.60160379   9730.64562562   9998.29782501   9962.68286563
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   9932.7978683   10004.02135757   9944.26621792   9968.01328299
   9947.93801641   9917.64122503   9995.40604985   9914.4706049
   9957.95995446  10004.76752969   9965.71807954   9989.8458797
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   9740.04869867   9990.57970743   9971.09977174   9854.01517466
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   9971.31964207   9928.92139399   9852.42634523   9949.87662555
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  10014.3860709    9856.75022021  10007.75522939  10015.54669538
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   9958.88599977   9817.82430837   9999.3415174    9984.56824869
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  10035.44538897  10049.88135141  10041.23988909  10050.35154217
   9994.61113764  10031.00944992  10017.33026455] [ 10155.57019688  10096.26369191  10090.48854606  10305.85577591
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  10145.57794989  10086.42042175  10176.06627841  10146.17946076
  10114.76412161  10082.00409551  10166.37061041  10128.07898349
  10363.04519844  10084.60869167  10094.10458996  10074.24513728
  10078.56766291  10153.18392221  10096.91784959  10131.37287792
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  10100.40970718  10253.22403851  10108.99824478  10075.21524546
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  10187.58269111  10151.26516305  10083.10302879  10190.46126366
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  10082.46423326  10114.42884818  10095.79116825  10093.64875882
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Out[13]:








0.00775557










    
























In [218]:



    


j = np.where(k == min(k))
j



    


















    
Out[218]:








(array([14, 15, 16, 17, 18, 19, 20]),)

















In [221]:



    


k[20:1000]



    


















    
Out[221]:








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In [311]:



    


for i in range:
    np.random.exponential(1)



    


















    
Out[311]:








1.4960887944860213

















In [464]:



    


k = [1, 3, 4, 5, 1, 5]
k = np.delete(k, np.max(k))
k



    


















    
Out[464]:








array([1, 3, 4, 5, 1])

















In [ ]:



    


if past_data[-1] > past_data[-2] + 100:
                print past_data[-1], past_data[-2], i, P_t_1, P_e, P_o, r_t_e[i], math.exp(r_t_e[i])
            elif past_data[-1] < past_data[-2] - 100:
                print past_data[-1], past_data[-2], i, P_t_1, P_e, P_o, r_t_e[i], math.exp(r_t_e[i]



    











In [24]:



    


random_state = np.random.RandomState()
random_state.uniform()



    


















    
Out[24]:








0.8936064001824369

















In [28]:



    


i = 0
for i in range(100):
    i += random_state.uniform()
i/100



    


















    
Out[28]:








0.9972606483423683

















In [ ]:

Content source: NlGG/ArtificialMarket

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