Skyrocket: Trading Expert Utilizing Price Action Strategy and Neural Network
1. Introduction:
Skyrocket is a unique trading expert that combines the Price Action strategy with neural network technologies to optimize trading decisions. The expert provides accurate analytical capabilities by employing a structured analysis of price movements in conjunction with innovative machine learning methods.
MT4 version https://www.mql5.com/en/market/product/108823
MT5 version https://www.mql5.com/en/market/product/108822
2. Price Action Strategy:
Skyrocket actively analyzes candlestick patterns, trendlines, and support and resistance levels using the Price Action strategy. This approach allows it to identify potential entry and exit points based on historical data and key price levels.
3. Neural Network Training:
Training the neural network is a fundamental stage in the development of Skyrocket. Key steps include:
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Data Preparation: Historical data undergoes thorough processing, including normalization and outlier removal.
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Choosing Network Parameters: Determining the network architecture, activation functions, and learning parameters.
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Training the Network: The process through which the network adapts to market dynamics.
4. Avoiding Overfitting:
A significant achievement of Skyrocket is the avoidance of overfitting, ensuring stability in various market conditions. This is achieved through:
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Ensemble Methods: Introducing different strategies and neural networks to create an ensemble, reducing the risk of overfitting.
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Cross-Validation: Regularly checking the model's effectiveness on independent time intervals to confirm its generalization ability.
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Hyperparameter Optimization: Regularly updating parameters to maintain a balance between accuracy and generalization.
5. Neural Network + Price Action Code Example:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
from sklearn.preprocessing import MinMaxScaler
import numpy as np
# Load and preprocess data
# ...
# Build the neural network
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
# Train the model
model.fit(X_train, y_train, batch_size=1, epochs=1)
# Make predictions using the neural network
predictions = model.predict(X_test)
predictions = scaler.inverse_transform(predictions)
# Apply Price Action strategy
# ...
# Pseudocode example for implementing the Price Action strategy
def apply_price_action_strategy(data):
# Price Action strategy logic
# ...
# Integrate the strategy with predictions
combined_strategy = apply_price_action_strategy(predictions)
Conclusion:
Skyrocket is a trading expert that successfully combines the Price Action strategy with neural networks, avoiding overfitting and ensuring stability in the dynamics of financial markets. The combination of precise Price Action analysis and the flexibility of neural network learning makes Skyrocket a powerful tool for traders seeking effective portfolio management.
MT4 version https://www.mql5.com/en/market/product/108823
MT5 version https://www.mql5.com/en/market/product/108822
You can ask me any questions in private messages
https://www.mql5.com/en/users/darksidefx
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darksidefx777@gmail.com🔷TELEGRAM https://t.me/beast_trade_fx
