Range Brain Ai

Range BraIn Ai - Enhancing Your Trading Precision:

Range Brain Ai is not just another run-of-the-mill trading tool; it's a sophisticated blend of neural networks, technical indicators, and risk management protocols. What sets Range Brain Ai apart from all the other Time Range advisors on the market is its neural network integration. Harnessing the power of machine learning, the advisor continuously adapts to evolving market conditions, refining its strategies to stay ahead of the curve. By analyzing a myriad of factors, including RSI signals and price action, it identifies high-probability trade setups with unmatched accuracy.

How the strategy works:

Range Brain Ai looks for the break of the user input range hours and minutes. Once the range is broken, we wait for the price to close back inside the range. The EA will then start to look for entries, looking to be above or below our RSI threshold and the Neural Network Outputs are positive or negative according to our optimized strategy inputs. The EA plots a ✔ on the positions the EA will take and and plots an ✖ on the positions that are filtered out by the neural network. All orders are pending stop orders and are placed with a 5 pip gap plus calculated spread.

Default Optimized Inputs Overview:

• Testing Period - 12.02.2013 - 12.02.2023
• Currency Pair - GBP/JPY
• Data Source - Over 20GB of data downloaded from tick data suite, spanning ten years
• Starting Balance - $ 10,000 USD
• Profit - $ 8,531.60 , thats a little over 850% returns over a 10 year period
• Balance Drawdown  - 7.16 %
• Equity Drawdown - 7.41 %

Understanding Our Neural Network:

My implementation is a simple feedforward neural network (FNN) with one hidden layer, designed for regression tasks. It's intended for use within our MT5 Expert Advisor to predict certain market behaviors or trends based on our input and indicator data. "Feedforward neural networks are used in a variety of machine learning tasks including: Pattern recognition. Classification tasks. Regression analysis."

If you want to get more familiarized with Nueral Networks and how they work, check this article out explaining neural networks from theory to practice ------> https://www.mql5.com/en/articles/497

Components of our Neural Network:

Normalizing Inputs

• Normalizes the input data to a range between -1 and 1. Normalization helps to standardize the input data and make it easier for the neural network to learn.

Hidden Layer Calculation

• Computes the output of a hidden layer neuron. It calculates the weighted sum of inputs multiplied by corresponding weights, applies a coefficient, and then applies the hyperbolic tangent (tanh) activation function to introduce non-linearity.

Hyperbolic Tangent Activation Function

• Implements the hyperbolic tangent activation function, which maps the weighted sum of inputs to the range [-1, 1].

Output Layer Calculation

• Computes the final output of the neural network by calling the hidden layer calculation function with normalized inputs and weights.

Backpropagation

• Implements the backpropagation algorithm to train the neural network. It adjusts the weights based on the error between the predicted output and the target output, using the derivative of the activation function to compute the gradient.

Honest overview and things to keep in mind:

This EA does not use any martingale, grid or cost averaging strategies. It will occasionally hedge a position depending on the time frame you use. Often resulting in an excellent trade in the prevailing direction. Please note that this is a real trading strategy and it will incur some losses from time to time. The best strategies have periods of losses.

Suggested Reference for backtesting here ---> https://www.mql5.com/en/blogs/post/746240

If you have any trouble at all, please feel free to reach out to me. I will do my best to guide you to what works with your particular situation. Cheers my friends and wishing everyone success.