在外汇数据上使用NEAT python演化神经网络

时间:2018-10-19 13:29:20

标签: python tensorflow artificial-intelligence genetic-algorithm neat

我正尝试在时间序列外汇数据上实现一个不断发展的神经网络,其中该模型将在特定时间范围内接收3种不同的汇率作为输入,并且所有3种输入中的基础货币都相同(例如,USD / CHF, USD / JPY和USDZAR都具有相同的基础货币,即用作输入的美元。然后,网络将不得不基于输入使用预测未来几周或几天的基础货币的走势(基于数据中使用的时间范围)。它具有以下可供选择的预测选项:

  • 出售
  • 强卖
  • 购买
  • 强买

我已尝试使用非常相似的配置文件在https://neat-python.readthedocs.io/en/latest/xor_example.html的最简单示例的基础上,在NEAT python框架中实现此目标,该配置文件表示“卖”为“ 0”,“强卖”为“ 1”,“在代码中,“购买”为“ 2”,“强烈购买”为“ 3”。但是,自从我在84个观察值的训练集上可以达到的最高适应水平(大约75%)之后,我一直没有成功,这是在我决定更改NEAT以仅预测“ 0”代表的“卖出”和“买入”之后和“ 1”。 我尝试使用循环以及前馈型网络来实现此目的,但是并没有太大的改进。我也尝试过修改突变率。请协助我增加模型适用性水平以及测试集的预测准确性。请参见下面的代码,谢谢。

以下使用的数据是此处使用的数据,运行时间为 3年

from __future__ import print_function
import neat
import visualize
import random

# 3-input, inputs and expected outputs(training set).
xor_inputs = [(1.1241,1.2976,1.02923013585838), 
(1.1201,1.2434,1.0231225700839),(1.0971,1.2187,1.00999899000101), 
(1.0884,1.2235,1.00644122383253),(1.0987,1.2185,1.01142914938809), 
(1.1138,1.2519,1.03241792277514),(1.0856,1.2602,1.01163378856854),             
(1.0588,1.2346,0.99009900990099),(1.0587,1.2476,0.986679822397632), 
(1.0673,1.2729,0.988630746416214),(1.0562,1.2576,0.982318271119843), 
(1.0451,1.2497,0.974563882662508),(1.0456,1.2292,0.973994350832765), 
(1.0516,1.2338,0.981932443047918),(1.0533,1.2287,0.982028871648826), 
(1.0644,1.2179,0.990982063224656),(1.0703,1.2375,0.998203234178479), 
(1.0699,1.2555,1.000100010001),(1.0784,1.2488,1.00745516824501), 
(1.0641,1.2491,0.997506234413965),(1.0614,1.2414,0.997207818109294), 
(1.0563,1.2468,0.992457324335054),(1.0623,1.2295,0.992457324335053), 
(1.0672,1.2171,0.989119683481701),(1.0738,1.2394,1.00130169219986), 
(1.0799,1.247,1.00867460157353),(1.0652,1.255,0.996909580301067), 
(1.059,1.2373,0.991080277502478),(1.0612,1.2524,0.994629003381739), 
(1.0728,1.2813,1.00411687920474),(1.0897,1.2951,1.0048231511254), 
(1.0998,1.2981,1.01317122593718),(1.0931,1.2891,0.99930048965724), 
(1.1207,1.3035,1.02838338132456),(1.1183,1.2804,1.02616726526424), 
(1.1282,1.2887,1.0391769718383),(1.1196,1.2745,1.03135313531353), 
(1.1198,1.2776,1.02690490860546),(1.1194,1.2718,1.03145951521403), 
(1.1426,1.3027,1.04351455702807),(1.1401,1.2892,1.03734439834025), 
(1.1469,1.3096,1.03788271925272),(1.1663,1.2995,1.05741778576716), 
(1.1752,1.3135,1.03220478943022),(1.1772,1.3036,1.02785486689279), 
(1.1821,1.3012,1.03960910697578),(1.1762,1.2876,1.03626943005181), 
(1.1926,1.2887,1.04515050167224),(1.1859,1.2951,1.03659168653467), 
(1.2035,1.3198,1.05876124933827),(1.1943,1.3593,1.0417751849151), 
(1.1954,1.3493,1.03177878662815),(1.1814,1.3397,1.03284445362528), 
(1.1734,1.3066,1.02249488752556),(1.1823,1.3287,1.02616726526424), 
(1.1785,1.319,1.01595042161943),(1.161,1.3129,1.00230530219505), 
(1.1609,1.3076,0.99930048965724),(1.1665,1.3191,1.00421771440048), 
(1.1795,1.3214,1.01153145862836),(1.193,1.3337,1.02061645233721), 
(1.1891,1.3473,1.02396067990989),(1.1764,1.3392,1.00704934541793), 
(1.1754,1.3321,1.00959111559818),(1.1859,1.3362,1.01081572829273), 
(1.1998,1.3515,1.02627257799672),(1.203,1.3571,1.02543068088597), 
(1.2187,1.3729,1.03284445362528),(1.2222,1.3852,1.03842159916926), 
(1.2421,1.417,1.07100781835707),(1.2462,1.4123,1.07376785139053), 
(1.2235,1.3838,1.06371662589086),(1.2406,1.4042,1.07781849536538), 
(1.2293,1.3967,1.06746370623399),(1.2317,1.3805,1.06587081645705), 
(1.2307,1.3849,1.05130361648444),(1.2289,1.3941,1.0501995379122), 
(1.2353,1.4134,1.05574324324324),(1.2323,1.4018,1.0481081647626), 
(1.2283,1.4092,1.04242676951944),(1.2331,1.4241,1.03906899418121), 
(1.2288,1.4004,1.02606197414324),(1.213,1.378,1.01224820325944), 
(1.196,1.3533,1)]
xor_outputs = [(3,),(3,),(2,),(0,),(1,),(2,),(3,),(2,),(1,),(3,),(3,), 
(2,),(1,),(0,),(0,),(1,),(0,),(0,),(2,),(3,),(2,),(2,),(2,),(1,),(1,), 
(2,),(3,),(1,),(1,),(1,),(1,),(3,),(1,),(3,),(1,),(3,),(0,),(2,),(1,), 
(3,),(1,),(0,),(0,),(2,),(0,),(3,),(1,),(2,),(1,),(2,),(2,),(2,),(3,), 
(1,),(3,),(3,),(3,),(1,),(1,),(1,),(0,),(3,),(2,),(1,),(1,),(0,),(1,), 
(1,),(1,),(0,),(3,),(1,),(3,),(2,),(2,),(2,),(1,),(3,),(2,),(0,),(3,), 
(3,),(3,),(2,)
]
# 3-input, inputs and expected outputs(test set).
xor_inputs2 = [(1.1944,1.3543,0.99940035978413), 
(1.1778,1.3474,1.00220485067148),(1.1652,1.3309,1.01030511214387), 
(1.1661,1.3348,1.0120433154539),(1.1768,1.3412,1.01502233049127), 
(1.1609,1.3285,1.00240577385726),(1.1657,1.327,1.01214574898785), 
(1.1685,1.3209,1.00938730190774),(1.1747,1.3285,1.01020305081321), 
(1.1685,1.3235,0.998302885095338),(1.172,1.3134,1.00745516824501), 
(1.1658,1.3104,1.00553041729512),(1.1567,1.3008,1.00573267625465), 
(1.1411,1.2769,1.00462125778582),(1.1439,1.2752,1.00411687920474), 
(1.1623,1.2845,1.01698362656361),(1.1601,1.2963,1.03220478943022), 
(1.1553,1.2924,1.03167234086454),(1.163,1.3068,1.03359173126615), 
(1.175,1.3077,1.04307916970898),(1.1782,1.3159,1.03626943005181)
]
xor_outputs2 = [(2,),(2,),(1,),(1,),(3,),(0,),(2,),(1,),(3,),(0,), 
(3,),(2,),(3,),(2,),(1,),(0,),(3,),(1,),(1,),(0,),(3,)
]


def eval_genomes(genomes, config): #function Used for training model 
using the training set
   for genome_id, genome in genomes:
       genome.fitness = 84.0
       net = neat.nn.RecurrentNetwork.create(genome, config)
       for xi, xo in zip(xor_inputs, xor_outputs):
           output = net.activate(xi)
           genome.fitness -= (output[0] - xo[0]) ** 2 #Distance from 
the correct output summed for all 84 inputs patterns


# Load configuration.
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                 neat.DefaultSpeciesSet, neat.DefaultStagnation,
                 'config-feedforward-research_nn')

# Create the population, which is the top-level object for a NEAT run.
p = neat.Population(config)

# Add a stdout reporter to show progress in the terminal.
p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
#p.add_reporter(neat.Checkpointer(100))

# Run until a solution is found.
winner = p.run(eval_genomes, 30000)  # run for 30000 to test 
generations

# Display the winning genome.
print('\nBest genome:\n{!s}'.format(winner))

# Make and show prediction on unseen data (test set) using winner NN's 
genome.
print('\nOutput:')
winner_net = neat.nn.RecurrentNetwork.create(winner, config)
for xi, xo in zip(xor_inputs2, xor_outputs2):
  output = winner_net.activate(xi)
  print("  input {!r}, expected output {!r}, got {!r}".format(
    xi, xo, output))

node_names = {-1: 'Input 1', -2: 'Input 2', -3: 'Input 3', 0: 
'Prediction'}
visualize.draw_net(config, winner, True, node_names=node_names,
               filename="winner-feedforward-research_nn.gv")

配置文件:

[NEAT]
# changed
fitness_criterion     = max
fitness_threshold     = 75 
pop_size              = 500 
reset_on_extinction   = False

[DefaultGenome]
# node activation options 
# changed
activation_default      = sigmoid 
activation_mutate_rate  = 0.0
activation_options      = sigmoid, square 

# node aggregation options
aggregation_default     = sum
aggregation_mutate_rate = 0.0
aggregation_options     = sum

# node bias options
bias_init_mean          = 0.0
bias_init_stdev         = 1.0
bias_max_value          = 30.0
bias_min_value          = -30.0
bias_mutate_power       = 0.5
bias_mutate_rate        = 0.7
bias_replace_rate       = 0.1

# genome compatibility options
compatibility_disjoint_coefficient = 1.0
compatibility_weight_coefficient   = 0.5

# connection add/remove rates
conn_add_prob           = 0.5
conn_delete_prob        = 0.5

# connection enable options
# changed
enabled_default         = True
enabled_mutate_rate     = 0.04 

feed_forward            = false
initial_connection      = full_direct

# node add/remove rates
# changed
node_add_prob           = 0.5 
node_delete_prob        = 0.5 

# network parameters
# changed
num_hidden              = 0
num_inputs              = 3 
num_outputs             = 1

# node response options
response_init_mean      = 1.0
response_init_stdev     = 0.0
response_max_value      = 30.0
response_min_value      = -30.0
response_mutate_power   = 0.0
response_mutate_rate    = 0.0
response_replace_rate   = 0.0

# connection weight options
# changed
weight_init_mean        = 0.0
weight_init_stdev       = 1.0
weight_max_value        = 30
weight_min_value        = -30
weight_mutate_power     = 0.5
weight_mutate_rate      = 0.9
weight_replace_rate     = 0.1

[DefaultSpeciesSet]
compatibility_threshold = 3.0

[DefaultStagnation]
species_fitness_func = max
max_stagnation       = 20
species_elitism      = 2

[DefaultReproduction]
elitism            = 2
survival_threshold = 0.2

0 个答案:

没有答案