# 神经网络交易的训练部分

``````import pybrain as brain
training_set = ("20050101", "20130101")       # 训练集（六年）
testing_set  = ("20150101", "20150525")       # 测试集（2015上半年数据）
universe     = ['000001']
# 目标股票池

HISTORY      = 10                             # 通过前十日数据预测
``````
``````from pybrain.datasets import SupervisedDataSet
### 建立数据集
def make_training_data():
ds = SupervisedDataSet(HISTORY, 1)
for ticker in universe: # 遍历每支股票
raw_data = DataAPI.MktEqudGet(ticker=ticker, beginDate=training_set[0], endDate=training_set[1], field=[
], pandas="1")
plist = list(raw_data['closePrice'])
for idx in range(1, len(plist) - HISTORY - 1):
sample = []
for i in range(HISTORY):
sample.append(plist[idx + i - 1] / plist[idx + i] - 1)
answer = plist[idx + HISTORY - 1] / plist[idx + HISTORY] - 1

return ds

### 建立测试集
def make_testing_data():
ds = SupervisedDataSet(HISTORY, 1)
for ticker in universe: # 遍历每支股票
raw_data = DataAPI.MktEqudGet(ticker=ticker, beginDate=testing_set[0], endDate=testing_set[1], field=[
], pandas="1")
plist = list(raw_data['closePrice'])
for idx in range(1, len(plist) - HISTORY - 1):
sample = []
for i in range(HISTORY):
sample.append(plist[idx + i - 1] / plist[idx + i] - 1)
answer = plist[idx + HISTORY - 1] / plist[idx + HISTORY] - 1

return ds
``````
``````from pybrain.supervised.trainers import BackpropTrainer
### 构造BP训练实例
def make_trainer(net, ds, momentum = 0.1, verbose = True, weightdecay = 0.01): # 网络, 训练集, 训练参数
trainer = BackpropTrainer(net, ds, momentum = momentum, verbose = verbose, weightdecay = weightdecay)
return trainer
``````
``````### 开始训练
def start_training(trainer, epochs = 15): # 迭代次数
trainer.trainEpochs(epochs)

def start_testing(net, dataset):
return net.activateOnDataset(dataset)
``````
``````### 保存参数
from pybrain.tools.customxml import NetworkWriter
def save_arguments(net):
NetworkWriter.writeToFile(net, 'huge_data.csv')
print 'Arguments save to file net.csv'
``````
``````from pybrain.tools.shortcuts import buildNetwork
### 初始化神经网络
fnn = buildNetwork(HISTORY, 15, 7, 1)

training_dataset = make_training_data()
testing_dataset  = make_testing_data()
trainer = make_trainer(fnn, training_dataset)
start_training(trainer, 5)
save_arguments(fnn)
print start_testing(fnn, testing_dataset)

Total error:  0.00226884924246
Total error:  0.00058242191557
Total error:  0.00058089738079
Total error:  0.000581061747831
Total error:  0.000580708420341
Arguments save to file net.csv
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``````