使用keras的LSTM进行预测----实战练习

代码

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

import marksix_1
import talib as ta


lt = marksix_1.Marksix()
lt.load_data(period=500)

# 指标序列
m = 2
series = lt.adapter(loc='0000001', zb_name='mod', args=(m, lt.get_mod_list(m)), tf_n=0)

# 实时线
close = np.cumsum(series).astype(float)

# 布林线
timeperiod = 5
upper, middle, lower = ta.BBANDS(close, timeperiod=timeperiod, nbdevup=2, nbdevdn=2, matype=0)

# 趋势
qushi1 = np.where(close-middle < 0, 0, 1)# 实时线在均线上、下方
qushi2 = np.where(middle[1:] - middle[:-1] < 0, 0, 1) # 均线上、下行（长度少了1）

# 标签转化为0,1
y = np.where(series==-1, 0, 1)

# 构造特征（注意，已经归一化，全部为非负数）
f = upper-lower
f = f[timeperiod:] # 去掉了前面timeperiod个nan数据！！！
f = (f - f.min()) / (f.max() - f.min()) # 归一化
y = y[timeperiod:]
qushi1 = qushi1[timeperiod:]
qushi2 = qushi2[timeperiod-1:]
features = np.column_stack([y, qushi1, qushi2, f]) # 特征:[标签、趋势1、趋势2、布林宽度]

# 
data_len = len(series)
time_steps = 3

# 将数据转化为[样本数, 时间步数, 特征数]的形式
X = [features[i:i+time_steps] for i in range(data_len-time_steps-timeperiod)] # [samples, time steps * features]
X = np.reshape(X, (data_len - time_steps-timeperiod, time_steps, -1)) # [samples, time steps, features]

# 标签长度一致
y = y[time_steps:]

# one-hot编码
y = np.eye(2)[y]

# 划分训练数据、测试数据
train_X, test_X = X[:-20], X[-20:]
train_y, test_y = y[:-20], y[-20:]

# =================================
model = Sequential()
model.add(LSTM(64, input_shape=(X.shape[1], X.shape[2])))
model.add(Dense(y.shape[1], activation='softmax')) # 输出各类的概率(softmax)
model.compile(loss='categorical_crossentropy',     # 单标签，多分类(categorical_crossentropy)
              optimizer='adam', 
              metrics=['accuracy'])

model.fit(train_X, train_y, epochs=500, batch_size=1, verbose=2)

#检查模型在测试集上的表现是否良好
test_loss, test_acc = model.evaluate(test_X, test_y)
print('test_acc:', test_acc)

 

效果图

 

 

结论

只测试了mod 2的情况，效果不好.

训练数据精度可以达到三分之二左右，测试数据的精度只有四分之一。头脑风暴，几乎可以反其道而行之！可能不失为可行之策。

 

下一步：

1.画出后20个数据k线图，看是否是震荡区间，亦或是趋势区间

2.换别的指标看看