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Deep Moving Average MT4

Every indicator has its advantages and disadvantages. Trending ones show good signals during a trend, but lag during a flat. Flat ones thrive in the flat, but die off as soon as a trend comes. All this would not be a problem, if it was easy to predict when a flat changes to a trend and when a trend changes to a flat, but in practice it is an extremely serious task.

What if you develop such an algorithm, which could eliminate an indicator's flaws and enhance its strengths? What if such an algorithm could improve the operation of a trend indicator during a flat, but at the same time increase the efficiency during a trend, and fix the signals of a flat indicator during a trend and perfect them during a flat?

Deep is the algorithm that enhances the advantages of an indicator and reduces its disadvantages. This algorithm improves indicator quality as a result of combining neural networks with indicator algorithm.

About the Deep algorithm

The specified task, due to its non-reproducibility and complex regularity, are best soled by non-linear algorithms. Neural networks are a very versatile and powerful tool, which allows to achieve a lot, if the task is set correctly and the solution is formed reasonable. The algorithm is based on this tool.

This algorithm calculates the optimal periods of the moving average, depending on the state of the trend and the flat. The algorithm trains the neural network to predict the optimal moving average for each market condition, so the indicator adapts to the flat and trend, eliminating the shortcomings of the basic algorithm. In the flat condition, the neural network adapts the period of the moving average in the direction of the flat, in the trending condition in the direction of the trend.

The neural network architecture and the heuristic search algorithm were designed specifically for the optimal solution of the task. For convenience, there are Presets of the neural network structure, as well as all the necessary parameters for fine-tuning the neural network.

Advantages

  • The algorithm has a self-learning system. This is an adaptive system that helps to learn flexibly all the time using a neural network so that the algorithm always keeps up with all market changes.
  • Graphical control panel. Using the panel directly on the chart, you can flexibly control and manage the learning system.
  • Parameters for fine-tuning all functions.
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    Version 6.18 2022.05.16
    Fixed bugs in the Neural Network.
    Version 6.17 2022.05.16
    Model: C117

    1. The indicator has been completely redesigned.

    2. New neural network architecture (NN).
    2.2. Convolutional layers have been added.
    2.3. Improved architecture of hidden layers.
    2.5. Now the structure of the NN is set by fixed values.
    2.6. Added PreSets of the NN structure.
    It is configured using the input parameter 'PreSet'.
    If 'PreSet' = 'CUSTOM SET', then the structure is configured manually using a group of input parameters "Custom Set".

    3. The MTF mode has been removed.
    This is done to improve the performance and stability of calculations.

    4. The learning system has been improved.
    4.1. An adaptive learning system has been implemented.
    4.1. The learning process has been optimized.
    4.2. Now the learning process is saved when the indicator is restarted. This allows you to continue training the indicator after it is restarted.
    4.3. New parameters for learning:
    - 'Signal Mode' - signal calculation method (for line color, arrows and learning).
    Options:
    'Direction (MA line)' - the line direction is used.
    'Crossing (Price with MA line)' - the intersection of the bar closing price with the MA is used.

    - 'Criterion(for Quality)' - criteria for learning (adapting process).
    Options:
    'Balance max' - the learning system will strive to optimize net profit.
    'Profit Factor max' - the learning system will strive to optimize the profit factor.
    'Recovery Factor max' - the learning system will strive to optimize the recovery factor.

    5. The "Deep" algorithm has been improved.
    5.1. Unnecessary parameters have been removed.
    5.2. New parameter 'Sensitivity in % [1...100]' - the sensitivity of the MA period change. More precisely, it is the rate of change.
    The lower the value, the slower the MA period will change. Use to avoid fast changes of the MA line.
    5.3. New parameter 'Period(in bars) for Re-Adapt(0-Adapt once)' - time period (number of bars) for automatic start of repeat learning (adaptation).
    5.4. New parameter 'Maximum Iterations [1...)' is the maximum number of iterations for the running learning process (adaptation).
    5.5. Now the MA period is set through the minimum and maximum values.
    For reference: if 'Minimum Period [1...)' = 'Maximum Period [1...)', then the "Deep" algorithm will not be activated and the training system (adaptations) will not be used.

    6. Improved visual panel.
    6.1. Visual improvements.
    6.2. Increased functionality.
    6.3. Added more visual information.
    6.4. A button has been added for manually stopping the learning process (adaptation), as well as for starting the learning process (adaptation) again.
    Version 5.57 2021.07.20
    - Improved: the 'Visual Info' function.
    Added new input parameter 'Starting Bar for Arrows'.
    If value = 'Current Bar', then the arrows are drawn from the current bar.
    If value = 'Closed Bar', then the arrows are drawn from the last closed bar (use this to wait for more stable arrows).

    - Improved: the 'Notify' function.
    -- The notification text was shortened and improved.
    -- Added new input parameters:
    1) 'Use Prefix Info'.
    If = true, then the value of the input parameter 'Graphic objects prefix name' will be added to the notification description.
    2) 'Use Currency Pair Info'.
    If = true, then the chart Symbol (Currency Pair) name will be added to the notification description.
    3) 'Use Chart Period Info'.
    If = true, then the chart Time Frame will be added to the notification description.