今天想和大家分享一下如何利用 Python 拟合具有非平稳特征的神经网络，从而对股票进行预测。

建筑行业市值前六公司

中国建筑 - 601668.SH 中国交建 - 601800.SH 中国中铁 - 601390.SH 中国铁建 - 601186.SH 中国中冶 - 601618.SH 中国电建 - 601669.SH

建模计算分析

import math import numpy as np import pandas as pd import seaborn as sns sns.set_style('whitegrid') import sklearn.neural_network from datetime import datetime import matplotlib.pyplot as plt from sklearn import preprocessing from pandas import Series,DataFrame from statsmodels.tsa.stattools import adfuller from scipy.stats import norm, t, skew, kurtosis, kurtosistest, beta 

对中国电建 - 601669.SH 进行预测

# 前复权数据 data = pd.read_csv('建筑.csv',index_col=0) data.head(3).append(data.tail(3)) 

China_DJ = data['601669'] new_index = pd.to_datetime(China_DJ.index) Y= Series(China_DJ.values,new_index) Y.head(6) 

#收益率 Y_pct = Y.pct_change() Y_pct= Y_pct[1:].copy() Y_pct.head() 

#转换到 0 、 1 f = lambda x: 1 if x > 0 else -1 Y_pct = Y_pct.apply(f) Y_pct.head() 

Y_pct = Y_pct.shift(-1,freq='1d') Y_pct.head() 

#用 X 表示每日价格，来预测未来 601669 的收益 new_index1 = pd.to_datetime(data.index) X = DataFrame(data.values,new_index1) X.tail() 

X = X[:-2] X.index 

Y.index 

NN = sklearn.neural_network.MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(10, 5)) 

NN = NN.fit(X, Y) NN 

MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(10, 5), learning_rate='constant', learning_rate_init=0.001, max_iter=200, momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=None, shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False, warm_start=False)

NN.predict(X) 

array([ 1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1], dtype=int64)

def check_accuracy(predictions, Y): correct = len(Y4[predictiOns== Y]) return correct / float(len(Y)) 

predictiOns= NN.predict(X) check_accuracy(predictions, Y) 

0.61

imputer = preprocessing.Imputer() scaler = preprocessing.MinMaxScaler() X = imputer.fit_transform(X) X = scaler.fit_transform(X) NN = NN.fit(X, Y) NN 

MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(10, 5), learning_rate='constant', learning_rate_init=0.001, max_iter=200, momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=None, shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False, warm_start=False)

NN.predict(X) 

array([-1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, -1], dtype=int64)

predictiOns= NN.predict(X) check_accuracy(predictions, Y4) 

0.71

可以预测第二天的方向超过 71%的时间

# 前复权数据 OOS_pricing_data = pd.read_csv('建筑 2.csv',index_col=0) OOS_pricing_data.head(3).append(OOS_pricing_data.tail(3)) 

Y1 = OOS_pricing_data['601669'] new_index = pd.to_datetime(Y1.index) Y5 = Series(Y1.values,new_index) Y5 = Y5.pct_change() Y5 = Y5[1:] Y5.head() 

Y5 = Y5.shift(-1,freq='1d') Y5.head() 

new_index2 = pd.to_datetime(OOS_pricing_data.index) X11 = DataFrame(OOS_pricing_data.values,new_index2) X11.head() 

X11 = X11[:-1] X11.index 

Y5.index 

X11 = imputer.fit_transform(X11) X11 = scaler.fit_transform(X11) OOS_predictiOns= NN.predict(X11) 

check_accuracy(OOS_predictions, OOS_Y) 

result: 0.5034013605442177

50%

只有 50%的准确率

可能是在不同时期之间的不稳定造成的，这导致学习神经网络,很适合现在的条件训练数据,但不适合在不同条件下测试数据。也有可能是神经网络是适合噪声而没有体现出真正的信号，很难讲。

new_index3 = pd.to_datetime(data.index) Y6 = pd.DataFrame(data.values,new_index3) Y6.columns = ['601668','601800','601390','601186','601618','601669'] Y6.head() 

corr_df = pd.rolling_corr(Y6 , window=30) corr_df 

看看平稳性

fig = plt.figure(figsize=(16,8.5)) plt.plot(corr_df[:,'601668','601669']) plt.plot(corr_df[:,'601800','601669']) plt.plot(corr_df[:,'601390','601669']) plt.plot(corr_df[:,'601186','601669']) plt.plot(corr_df[:,'601618','601669']) ts = corr_df[:, '601618','601669'] plt.hlines(ts.mean(), ts.index[30-1], ts.index[-1], linestyles='dashed') plt.ylabel('Pearson Correlation Coefficient') plt.legend(['601668 x 601669', '601800 x 601669', '601390 x 601669', '601186 x 601669', '601618 x 601669','601618 x 601669 AVG']) 

adfuller(data['601668']) 

adfuller(data['601800']) 

adfuller(data['601390']) 

adfuller(data['601186']) 

adfuller(data['601618']) 

adfuller(data['601669']) 

源地址： https://uqer.io/community/share/587db6aa23a7d6004da3665b