import tensorflow as tf

from tensorflow.keras.fashions import Sequential

from tensorflow.keras.layers import Dense, Dropout, LSTM

from sklearn.preprocessing import MinMaxScaler

import numpy as np

# Load and preprocess information

# …

# Construct the neural community

mannequin = Sequential()

mannequin.add(LSTM(50, return_sequences=True, input_shape=(X_train.form[1], 1)))

mannequin.add(LSTM(50, return_sequences=False))

mannequin.add(Dense(25))

mannequin.add(Dense(1))

# Compile the mannequin

mannequin.compile(optimizer=”adam”, loss=”mean_squared_error”)

# Prepare the mannequin

mannequin.match(X_train, y_train, batch_size=1, epochs=1)

# Make predictions utilizing the neural community

predictions = mannequin.predict(X_test)

predictions = scaler.inverse_transform(predictions)

# Apply Value Motion technique

# …

# Pseudocode instance for implementing the Value Motion technique

def apply_price_action_strategy(information):

    # Value Motion technique logic

    # …

# Combine the technique with predictions

combined_strategy = apply_price_action_strategy(predictions)

Conclusion:

Skyrocket is a buying and selling professional that efficiently combines the Value Motion technique with neural networks, avoiding overfitting and guaranteeing stability within the dynamics of monetary markets. The mix of exact Value Motion evaluation and the flexibleness of neural community studying makes Skyrocket a robust device for merchants searching for efficient portfolio administration.