神经网络（python源代码）

神经网络的逻辑应该都是熟知的了，在这里想说明一下交叉验证

交叉验证方法：

看图大概就能理解了，大致就是先将数据集分成K份，对这K份中每一份都取不一样的比例数据进行训练和测试。得出K个误差，将这K个误差平均得到最终误差

这第一个部分是BP神经网络的建立

参数选取参照论文：基于数据挖掘技术的股价指数分析与预测研究_胡林林

import math
import random
import tushare as ts
import pandas as pd

random.seed(0)

def getData(id,start,end):
    df = ts.get_hist_data(id,start,end)
    DATA=pd.DataFrame(columns=['rate1', 'rate2','rate3','pos1','pos2','pos3','amt1','amt2','amt3','MA20','MA5','r'])
    P1 = pd.DataFrame(columns=['high','low','close','open','volume'])
    DATA2=pd.DataFrame(columns=['R'])
    DATA['MA20']=df['ma20']
    DATA['MA5']=df['ma5']
    P=df['close']
    P1['high']=df['high']
    P1['low']=df['low']
    P1['close']=df['close']
    P1['open']=df['open']
    P1['volume']=df['volume']

    DATA['rate1']=(P1['close'].shift(1)-P1['open'].shift(1))/P1['open'].shift(1)
    DATA['rate2']=(P1['close'].shift(2)-P1['open'].shift(2))/P1['open'].shift(2)
    DATA['rate3']=(P1['close'].shift(3)-P1['open'].shift(3))/P1['open'].shift(3)
    DATA['pos1']=(P1['close'].shift(1)-P1['low'].shift(1))/(P1['high'].shift(1)-P1['low'].shift(1))
    DATA['pos2']=(P1['close'].shift(2)-P1['low'].shift(2))/(P1['high'].shift(2)-P1['low'].shift(2))
    DATA['pos3']=(P1['close'].shift(3)-P1['low'].shift(3))/(P1['high'].shift(3)-P1['low'].shift(3))
    DATA['amt1']=P1['volume'].shift(1)/((P1['volume'].shift(1)+P1['volume'].shift(2)+P1['volume'].shift(3))/3)
    DATA['amt2']=P1['volume'].shift(2)/((P1['volume'].shift(2)+P1['volume'].shift(3)+P1['volume'].shift(4))/3)
    DATA['amt3']=P1['volume'].shift(3)/((P1['volume'].shift(3)+P1['volume'].shift(4)+P1['volume'].shift(5))/3)
    templist=(P-P.shift(1))/P.shift(1)
    tempDATA = []
    for indextemp in templist:
        tempDATA.append(1/(1+math.exp(-indextemp*100)))
    DATA['r'] = tempDATA
    DATA=DATA.dropna(axis=0)
    DATA2['R']=DATA['r']
    del DATA['r']
    DATA=DATA.T
    DATA2=DATA2.T
    DATAlist=DATA.to_dict("list")
    result = []
    for key in DATAlist:
        result.append(DATAlist[key])
    DATAlist2=DATA2.to_dict("list")
    result2 = []
    for key in DATAlist2:
        result2.append(DATAlist2[key])
    return result

def getDataR(id,start,end):
    df = ts.get_hist_data(id,start,end)
    DATA=pd.DataFrame(columns=['rate1', 'rate2','rate3','pos1','pos2','pos3','amt1','amt2','amt3','MA20','MA5','r'])
    P1 = pd.DataFrame(columns=['high','low','close','open','volume'])
    DATA2=pd.DataFrame(columns=['R'])
    DATA['MA20']=df['ma20'].shift(1)
    DATA['MA5']=df['ma5'].shift(1)
    P=df['close']
    P1['high']=df['high']
    P1['low']=df['low']
    P1['close']=df['close']
    P1['open']=df['open']
    P1['volume']=df['volume']

    DATA['rate1']=(P1['close'].shift(1)-P1['open'].shift(1))/P1['open'].shift(1)
    DATA['rate2']=(P1['close'].shift(2)-P1['open'].shift(2))/P1['open'].shift(2)
    DATA['rate3']=(P1['close'].shift(3)-P1['open'].shift(3))/P1['open'].shift(3)
    DATA['pos1']=(P1['close'].shift(1)-P1['low'].shift(1))/(P1['high'].shift(1)-P1['low'].shift(1))
    DATA['pos2']=(P1['close'].shift(2)-P1['low'].shift(2))/(P1['high'].shift(2)-P1['low'].shift(2))
    DATA['pos3']=(P1['close'].shift(3)-P1['low'].shift(3))/(P1['high'].shift(3)-P1['low'].shift(3))
    DATA['amt1']=P1['volume'].shift(1)/((P1['volume'].shift(1)+P1['volume'].shift(2)+P1['volume'].shift(3))/3)
    DATA['amt2']=P1['volume'].shift(2)/((P1['volume'].shift(2)+P1['volume'].shift(3)+P1['volume'].shift(4))/3)
    DATA['amt3']=P1['volume'].shift(3)/((P1['volume'].shift(3)+P1['volume'].shift(4)+P1['volume'].shift(5))/3)
    templist=(P-P.shift(1))/P.shift(1)
    tempDATA = []
    for indextemp in templist:
        tempDATA.append(1/(1+math.exp(-indextemp*100)))
    DATA['r'] = tempDATA
    DATA=DATA.dropna(axis=0)
    DATA2['R']=DATA['r']
    del DATA['r']
    DATA=DATA.T
    DATA2=DATA2.T
    DATAlist=DATA.to_dict("list")
    result = []
    for key in DATAlist:
        result.append(DATAlist[key])
    DATAlist2=DATA2.to_dict("list")
    result2 = []
    for key in DATAlist2:
        result2.append(DATAlist2[key])
    return result2

def rand(a, b):
    return (b - a) * random.random() + a

def make_matrix(m, n, fill=0.0):
    mat = []
    for i in range(m):
        mat.append([fill] * n)
    return mat

def sigmoid(x):
    return 1.0 / (1.0 + math.exp(-x))

def sigmod_derivate(x):
    return x * (1 - x)

class BPNeuralNetwork:
    def __init__(self):
        self.input_n = 0
        self.hidden_n = 0
        self.output_n = 0
        self.input_cells = []
        self.hidden_cells = []
        self.output_cells = []
        self.input_weights = []
        self.output_weights = []
        self.input_correction = []
        self.output_correction = []

    def setup(self, ni, nh, no):
        self.input_n = ni + 1
        self.hidden_n = nh
        self.output_n = no
        # init cells
        self.input_cells = [1.0] * self.input_n
        self.hidden_cells = [1.0] * self.hidden_n
        self.output_cells = [1.0] * self.output_n
        # init weights
        self.input_weights = make_matrix(self.input_n, self.hidden_n)
        self.output_weights = make_matrix(self.hidden_n, self.output_n)
        # random activate
        for i in range(self.input_n):
            for h in range(self.hidden_n):
                self.input_weights[i][h] = rand(-0.2, 0.2)
        for h in range(self.hidden_n):
            for o in range(self.output_n):
                self.output_weights[h][o] = rand(-2.0, 2.0)
        # init correction matrix
        self.input_correction = make_matrix(self.input_n, self.hidden_n)
        self.output_correction = make_matrix(self.hidden_n, self.output_n)

    def predict(self, inputs):
        # activate input layer
        for i in range(self.input_n - 1):
            self.input_cells[i] = inputs[i]
        # activate hidden layer
        for j in range(self.hidden_n):
            total = 0.0
            for i in range(self.input_n):
                total += self.input_cells[i] * self.input_weights[i][j]
            self.hidden_cells[j] = sigmoid(total)
        # activate output layer
        for k in range(self.output_n):
            total = 0.0
            for j in range(self.hidden_n):
                total += self.hidden_cells[j] * self.output_weights[j][k]
            self.output_cells[k] = sigmoid(total)
        return self.output_cells[:]

    def back_propagate(self, case, label, learn, correct):
        # feed forward
        self.predict(case)
        # get output layer error
        output_deltas = [0.0] * self.output_n
        for o in range(self.output_n):
            error = label[o] - self.output_cells[o]
            output_deltas[o] = sigmod_derivate(self.output_cells[o]) * error
        # get hidden layer error
        hidden_deltas = [0.0] * self.hidden_n
        for h in range(self.hidden_n):
            error = 0.0
            for o in range(self.output_n):
                error += output_deltas[o] * self.output_weights[h][o]
            hidden_deltas[h] = sigmod_derivate(self.hidden_cells[h]) * error
        # update output weights
        for h in range(self.hidden_n):
            for o in range(self.output_n):
                change = output_deltas[o] * self.hidden_cells[h]
                self.output_weights[h][o] += learn * change + correct * self.output_correction[h][o]
                self.output_correction[h][o] = change
        # update input weights
        for i in range(self.input_n):
            for h in range(self.hidden_n):
                change = hidden_deltas[h] * self.input_cells[i]
                self.input_weights[i][h] += learn * change + correct * self.input_correction[i][h]
                self.input_correction[i][h] = change
        # get global error
        error = 0.0
        for o in range(len(label)):
            error += 0.5 * (label[o] - self.output_cells[o]) ** 2
        return error

    def train(self, cases, labels, limit=10000, learn=0.05, correct=0.1):
        for i in range(limit):
            error = 0.0
            for i in range(len(cases)):
                label = labels[i]
                case = cases[i]
                error += self.back_propagate(case, label, learn, correct)

    def test(self,id):
        result=getData("", "2015-01-05", "2015-01-09")
        result2=getDataR("", "2015-01-05", "2015-01-09")
        self.setup(11, 5, 1)
        self.train(result, result2, 10000, 0.05, 0.1)

        for t in resulttest:
            print(self.predict(t))

下面是选取14-15年数据进行训练，16年数据作为测试集，调仓周期为20个交易日，大约1个月，对上证50中的股票进行预测，选取预测的涨幅前10的股票买入，对每只股票分配一样的资金,初步运行没有问题，但就是太慢了，等哪天有空了再运行

import BPnet
import tushare as ts
import pandas as pd
import math
import xlrd
import datetime as dt
import time

#
#nn =BPnet.BPNeuralNetwork()
#nn.test('000001')
#for i in ts.get_sz50s()['code']:
holdList=pd.DataFrame(columns=['time','id','value'])
share=ts.get_sz50s()['code']
time2=ts.get_k_data('')['date']
newtime = time2[400:640]
newcount=0
for itime in newtime:
    print(itime)
    if newcount % 20 == 0:

        sharelist = pd.DataFrame(columns=['time','id','value'])
        for ishare in share:
            backwardtime = time.strftime('%Y-%m-%d',time.localtime(time.mktime(time.strptime(itime,'%Y-%m-%d'))-432000*4))
            trainData = BPnet.getData(ishare, '2014-05-22',itime)
            trainDataR = BPnet.getDataR(ishare, '2014-05-22',itime)
            testData = BPnet.getData(ishare, backwardtime,itime)
            try:
                print(testData)
                testData = testData[-1]
                print(testData)
                nn = BPnet.BPNeuralNetwork()
                nn.setup(11, 5, 1)
                nn.train(trainData, trainDataR, 10000, 0.05, 0.1)
                value = nn.predict(testData)
                newlist= pd.DataFrame({'time':itime,"id":ishare,"value":value},index=[""])
                sharelist = sharelist.append(newlist,ignore_index=True)
            except:
                pass
        sharelist=sharelist.sort(columns ='value',ascending=False)
        sharelist = sharelist[:10]
        holdList=holdList.append(sharelist,ignore_index=True)
    newcount+=1
    print(holdList)