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神经网络python源码分享
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("000001", "2015-01-05", "2015-01-09")
result2=getDataR("000001", "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('000001')['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=["0"])
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)
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