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model v1

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 413
  3. batch_size: 128

model v2

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: if epochs < 500 3e-4 else 3e-5
  2. epochs: 1000
  3. batch_size: 128

model v3

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, padding='same', activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.2), tf.keras.layers.Convolution1D(32, 3, padding='same', activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: if epochs < 500 3e-4 else 3e-5
  2. epochs: 800
  3. batch_size: 128

model v4

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, padding='same', activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: if epochs < 300 3e-4 elif epochs < 200 1e-4 else 3e-5
  2. epochs: 600
  3. batch_size: 128

model v5 —— 99 stocks 311 factors

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 400
  3. batch_size: 128

model v6 —— 99 stocks 81 factors

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu', kernel_initializer=tf.keras.initializers.GlorotNormal(), input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True, kernel_initializer=tf.keras.initializers.GlorotNormal()), tf.keras.layers.LSTM(32, dropout=0.2, kernel_initializer=tf.keras.initializers.GlorotNormal()), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 400
  3. batch_size: 128

model v7 —— 99 stocks 81 factors

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 400
  3. batch_size: 128

model v8 —— 99 stocks backtrader

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 400
  3. batch_size: 128

model v9

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, padding='same', activation='relu', input_shape=(x_train.shape[-2], x_train.shape[-1])), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 2000
  3. batch_size: 128

model v10 double

model v11 threshold_slope=0.008

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu'), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 400
  3. batch_size: 128

model v12 threshold_slope=0.008

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu'), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 2000
  3. batch_size: 128

model v13 threshold_slope=0.005 分季度训练

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu'), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 400
  3. batch_size: 128

model v14 threshold_slope=0.005 分季度训练

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(128, 3, padding='same', activation='relu'), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(64, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(32, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

  1. lr: 3e-4
  2. epochs: 5000 --- 400
  3. batch_size: 128

model v15 分季度训练 future_steps=10

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, activation='relu'), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(16, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ]) return model

  1. lr: 3e-4
  2. epochs: 1000 --- 50
  3. batch_size: 512 --- 128

model v16 分季度训练 future_steps=5

model = tf.keras.Sequential([ tf.keras.layers.Convolution1D(64, 3, activation='relu'), tf.keras.layers.Dropout(0.4), tf.keras.layers.LSTM(32, dropout=0.2, return_sequences=True), tf.keras.layers.LSTM(16, dropout=0.2), tf.keras.layers.Dense(1, activation='sigmoid') ]) return model

  1. lr: 3e-4
  2. epochs: 1000 --- 50
  3. batch_size: 512 --- 128

v20 ==> 预测一天

v21 ==> 预测三天

v23 ==> 预测三天 使用60天历史

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