All about MQL5 Wizard : create robots without programming. - page 12

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 87): Volatility-Scaled Money Management with Monotonic Queue in MQL5

This article proposes a practical, deployable remedy: a zero-lag money-management pipeline that (1) computes Donchian-style range extremes in O(N) using a Monotonic Queue (deque) to eliminate sliding-window latency, and (2) applies a lightweight Radial Basis Function (RBF) gatekeeper to nonlinearly validate signal quality before finalizing lots. The deliverable is a ready‑to‑integrate MQL5 module (CMoneyMonotonicQueueRBF::Optimize) with tunable inputs (window size, m base volatility, use rbf) that (a) produces an instantaneous current volatility, (b) computes scale factor = base volatility / current_volatility, (c) normalizes lots to broker step/min/max, and (d) — optionally — multiplies by an RBF output in [0,1]. The approach targets EA developers who require preventive, real‑time risk scaling and a reproducible test protocol for Strategy Tester comparisons.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 88): Using Blooms Filter with a Custom Trailing Class

Regular trailing stops often depend on distance-based logic, that processes every tick/bar linearly. This memoryless method does struggle to set apart real structural trends from repetitive market noise. In this article we explore the Bloom Filter as nother novel alternative for this form of trade management. Rather than tracking pip distances, we use a probability-based membership testing to only move the stops on prices analysed to be unique. We pair this with an RNN in MQL5, that acts as a state-aware gatekeeper reacting only to novel price moves while also resolving secondary problems of computational latency.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 89): Using Bitwise Vectorization with Perceptron Classifiers

To achieve this, we use CSignalBitwisePerceptron: a compact MQL5 Signal class that vectorizes 64 periods into one 64-bit ‘word’, applying a O(1) bitwise map gate giving us ultra-fast pattern matching and also using a lightweight perceptron gate (hardware-level dot-product over bits) as a contextual filter. This pairing keeps floating-point work low,provides a single tunable confidence parameter (m_threshold) for trade off frequency vs precision, and - per forward walk tests - has potential to reduce drawdown while keeping execution latency near zero.

[Freanlink Mustafa]

Sergey Golubev #:

MQL5 Wizard Techniques you should know (Part 89): Using Bitwise Vectorization with Perceptron Classifiers

To achieve this, we use CSignalBitwisePerceptron: a compact MQL5 Signal class that vectorizes 64 periods into one 64-bit ‘word’, applying a O(1) bitwise map gate giving us ultra-fast pattern matching and also using a lightweight perceptron gate (hardware-level dot-product over bits) as a contextual filter. This pairing keeps floating-point work low,provides a single tunable confidence parameter (m_threshold) for trade off frequency vs precision, and - per forward walk tests - has potential to reduce drawdown while keeping execution latency near zero.

Interesting approach! Lightweight perceptron via bit masks - great idea for latency reduction.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 90): Fenwick Tree Money Management with 1D CNN in MQL5

In the last article of the MQL5 Wizard Series, we explored market entry signals by merging a bitwise vectorization algorithm with a perceptron classifier. We got some hints that the pairing has potential for further development into a usable trade system based on the test reports from some forward walks we made. Nonetheless, entry signals, though fundamental, are only half the battle. We look to a custom money management class usable within the MQL5 wizard to put together an Expert Advisor. Our approach when exploring unique money management systems is to reduce position sizing when it is pertinent. Even with precise entry signals whether navigating the turbulent swings of NVDA or very liquid forex pairs using a rigid lot size can leave some alpha on the table. Worse, the drawdown risks tend to follow suit.

A default way of adjusting position size can be to proportion it inversely to the prevalent tick volume. These indicative volume values are usually surmised from trailing averages. These averages can be over large periods, which can be taxing to compute resources and lead to some latency. Thus as a theme, we delve into another approach at building a system that fluidly responds to real-time volume pressure without suffocating execution speed while relying on the Fenwick Tree.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 91): Using Skip Lists and a Hopfield Network in a Custom Trailing Class

In the last article, in MQL5-Wizard Techniques, we constructed a robust capital proportioning framework that used the Fenwick Trees as well as a 1-D CNN. Optimizing position size in tandem with volatility is an important aspect to trading but so is guarding profits in live markets. If traders use standard linear trailing stops they could be susceptible to the latency gap. In flash events, which tend to happen more frequently these days and in less predictable periods, price action is seldom contiguous.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 92): Using B-Tree Indexing and a Bayesian NN in a Custom Signal Class

In the last article in this series, we added to our toolkit a trailing stop risk shield that utilized the Skip-List algorithm together with Hopfield networks. Trailing stops help in defending floating profits not just by having a stop loss but also by ensuring it is moved only at the optimal time and not in response to market noise or erratic swings. Constructing entry signals for multiple symbols can expose structural limits in standard toolkits, especially when running multiple strategies. First, the default MQL5 library does not provide relational state management. So, inbuilt data structures such as MQLRates are symbol-specific and if comparison across symbols is key in any strategy, this could be a speed-bump.

Secondly, there is the danger of deterministic certainty. Regular indicators and traditional neural networks do not have a safety net in that they are always issuing rigid buy and sell orders while callously treating chaotic market noise with the same conviction as clear trends. In other words, they are always exploiting. However, today one can argue that traders can use more sophisticated toolkits - ones that not only allow multi-symbol mapping, but afford a degree of exploration by perhaps incorporating a probability-based safety net in order to better appraise all market states with an appropriate conviction.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 93): Using Suffix Automation and an Auto Encoder in a Custom Money Management Class

Here's a short summary: the Suffix Automaton looks for pattern familiarity, while the Autoencoder checks the integrity of the structure.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 94): Using Reservoir Sampling and Linear Regression in a Custom Trailing Stop Class

In the last article on trailing stops within this series, we explored the Skip-List algorithm pairing to a Hopfield Network, with an associative memory approach, customized for sharp momentum environments. One can say it suits aggressive traders who are after fast-moving markets that are also gap-heavy. Non-contiguous price action would often be the norm in these cases.

However, different models tend to favor different market environments and trading styles. Trying to develop a universal solution is often a non-starter. In this article, we pivot to Reservoir Sampling paired with a Linear Regression network. This is a move from associative memory to statistical normalization. The approach here is particularly suited to markets where filtering micro-noise while monitoring a smooth memory-efficient average-buffer outperforms rapid gap detection.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 95): Using Disjoint Set Union and Deep Belief Network in a Custom Signal Class

In the last MQL5 Wizard article that showcased Custom Signals, we used the pairing of a B-Tree Index algorithm and a Bayesian Network. The system we had then was meant for arbitrage by maneuvering inefficiencies over co-dependent assets. It showed some potential in classifying, multi-symbol market states. That model, though, could be unsuitable for single-asset traders that track momentum in volatile environments. Those who trade the news. A multi-asset index is not helpful in trading breakouts. For that, you could use a time-based noise filter. 

In realizing this, therefore, this article pivots to the Disjoint Set Union (DSU) algorithm that we pair with a Deep Belief Network (DBN). While the last custom signal article had the Bayesian model mapping static relationships, this approach uses generative DBN filters for noise. We are not in any way touting a universal solution; we are proposing an alternative approach to a specific issue some breakout traders face. How to group high-volatility bars into a cluster via a DSU and then identify if the cluster stands for a regime shift or a transient trap.