在我正式放弃将深度学习应用于股票预测的希望之前，希望第二眼很快。
目标是使用LSTM预测两个类别之一。正类对应于导致接下来六个周期内价格上涨5％或更高的序列；负类对应于不引起价格上涨的序列。不出所料，这导致了一些阶级失衡，负数与正数之比约为6：1。 现在的问题是，该模型在所有时期都显示出相同的准确性，并且仅预测负数类。这使我认为我的模型结构可能有问题。输入是一个数据框，其中包含价格数据和少量移动平均线：
price_open price_high price_low price_close ma_8 ma_13 ma_21 ma_55 6prd_pctchange entry_flag
time_period_start
11-02-2016 23:00 10.83280 10.98310 10.72591 10.96000 10.932415 10.855693 10.960608 11.087525 0.008535 0.0
11-03-2016 03:00 10.96016 11.02560 10.96000 11.00003 10.937569 10.873219 10.948081 11.075059 0.004544 0.0
11-03-2016 07:00 11.00007 11.14997 10.91000 11.00006 10.954170 10.919378 10.929689 11.062878 -0.007442 0.0
11-03-2016 11:00 11.05829 11.14820 10.90001 10.99208 10.959396 10.923376 10.912183 11.057317 0.008392 0.0
11-03-2016 15:00 10.90170 11.03112 10.70000 10.91529 10.938490 10.933783 10.890906 11.048504 0.006289 0.0
11-03-2016 19:00 10.89420 10.95000 10.82460 10.94980 10.944640 10.947429 10.882745 11.041227 0.005234 0.0
11-03-2016 23:00 10.94128 11.08475 10.88404 11.08475 10.974350 10.957118 10.888859 11.032288 0.011382 0.0
11-04-2016 03:00 11.02761 11.22778 10.94360 10.99813 10.987517 10.967185 10.893531 11.023518 -0.000173 0.0
11-04-2016 07:00 10.95076 11.01814 10.92000 10.92100 10.982642 10.964934 10.904055 11.011691 -0.007187 0.0
11-04-2016 11:00 10.94511 11.06298 10.89000 10.99557 10.982085 10.958244 10.914692 11.000365 0.000318 0.0
并已转换为长度为6个周期的numpy数组，并使用scikit-learn方法MinMaxScaler进行了规范化。例如，第一个序列如下所示：
array([[0. , 0.16552483, 0.09965385, 0.52742716, 0. ,
0. , 1. , 1. ],
[0.5648144 , 0.37805671, 1. , 0.9996461 , 0.19101228,
0.19104958, 0.83911884, 0.73073358],
[0.74180673, 1. , 0.80769231, 1. , 0.80630067,
0.69421501, 0.60290376, 0.46764059],
[1. , 0.99114867, 0.76926923, 0.90586292, 1. ,
0.73780155, 0.37807623, 0.34751414],
[0.30555679, 0.40566085, 0. , 0. , 0.22515636,
0.85124563, 0.104818 , 0.15716305],
[0.27229589, 0. , 0.47923077, 0.40710157, 0.45309243,
1. , 0. , 0. ]])
在这些序列上构建，编译和拟合模型时，我的结果很快达到平稳状态，并且该模型最终只能预测负类。
# Constants:
loss = 'binary_crossentropy'
optimizer = 'adam'
epochs = 12
batch_size = 300
# Complie model:
model = Sequential()
model.add(LSTM(100))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
results = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1, validation_data=(X_test, y_test), shuffle=False)
model.summary()
它输出：
Epoch 1/12
22/22 [==============================] - 0s 16ms/step - loss: 0.5696 - accuracy: 0.8410 - val_loss: 0.3953 - val_accuracy: 0.8885
Epoch 2/12
22/22 [==============================] - 0s 10ms/step - loss: 0.4355 - accuracy: 0.8473 - val_loss: 0.3569 - val_accuracy: 0.8885
Epoch 3/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4379 - accuracy: 0.8473 - val_loss: 0.3612 - val_accuracy: 0.8885
Epoch 4/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4320 - accuracy: 0.8473 - val_loss: 0.3554 - val_accuracy: 0.8885
Epoch 5/12
22/22 [==============================] - 0s 10ms/step - loss: 0.4338 - accuracy: 0.8473 - val_loss: 0.3577 - val_accuracy: 0.8885
Epoch 6/12
22/22 [==============================] - 0s 10ms/step - loss: 0.4297 - accuracy: 0.8473 - val_loss: 0.3554 - val_accuracy: 0.8885
Epoch 7/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4303 - accuracy: 0.8473 - val_loss: 0.3570 - val_accuracy: 0.8885
Epoch 8/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4273 - accuracy: 0.8473 - val_loss: 0.3558 - val_accuracy: 0.8885
Epoch 9/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4285 - accuracy: 0.8473 - val_loss: 0.3577 - val_accuracy: 0.8885
Epoch 10/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4254 - accuracy: 0.8473 - val_loss: 0.3565 - val_accuracy: 0.8885
Epoch 11/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4270 - accuracy: 0.8473 - val_loss: 0.3581 - val_accuracy: 0.8885
Epoch 12/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4243 - accuracy: 0.8473 - val_loss: 0.3569 - val_accuracy: 0.8885
Model: "sequential_6"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_6 (LSTM) (None, 100) 42400
_________________________________________________________________
dense_6 (Dense) (None, 1) 101
=================================================================
快速检查表明它只是在预测否定类别：
predictions = model.predict(X_test)
predictions_round = [1 if x > 0.5 else 0 for x in predictions]
pd.Series(predictions_round).value_counts()
0 1641
dtype: int64
我将第一个说这可能是因为预测股价进入点是一项充满噪音的任务。但我也希望模型至少会犯一些错误猜测而不是简单地猜测所有相同的类。在我看来，这似乎与我建立模型或构建输入的方式有关。
X_train.shape和y_train.shape分别给我(6561, 6, 8)和(6561, )。
在此先感谢您的帮助！