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
转载于:https://www.cnblogs.com/hhh5460/p/10217000.html
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