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Articles by Dmitriy Gizlyk
Нейросети в трейдинге: Habi — быстрое принятие решений без сложного планирования (Окончание)
MetaTrader 5 — Торговые системы | 6 ноября 2026, 10:22 5 0 Повторное диффузионное планирование требует вычислений на каждом шаге. Идея Habi — перенести этот опыт в нейросетевую политику, которая после обучения предлагает действия без повторного построения плана. В первой статье мы разобрали подход и торговую адаптацию. Во второй части подготовили обработку наблюдений, параметры распределения и выборку латентных векторов. В третьей статье реализовали формирование действий и их оценку.
Neural Networks in Trading: Recurrent Modeling of Market Micromovements (Conclusion)
The market remains that same complex, multi-layered system where every micro-event — whether a spike in volume or a shift in the liquidity balance — forms its own trajectory. We observe this fabric of events every day, but few architectural solutions are capable of handling it as naturally as an event-driven neural network does. That is precisely why the EV-MGRFlowNet framework has become the foundation for developing a truly modern approach to analyzing market micromovements.
Neural Networks in Trading: Recurrent Modeling of Market Micromovements (Encoder)
In the world of machine learning, there is a special class of architectural solutions capable not only of analyzing data, but also of sensing its underlying dynamics. Among them, the EV-MGRFlowNet framework holds a special place — the result of meticulous engineering work in which researchers proposed an unconventional method for modeling motion based on event streams. Its introduction marked a significant step forward in the field of computer vision.
Нейросети в трейдинге: Habi — быстрое принятие решений без сложного планирования (формирование и оценка действий)
Диффузионные модели показывают хорошие результаты в задачах генерации и планирования. Однако последовательное уточнение результата требует значительных вычислений. Это ограничивает их применение там, где важна скорость принятия решения. Авторы фреймворка Habi предлагают перенести знания диффузионного планировщика в компактную модель, способную выбирать действия без повторения этого затратного процесса. В основе такого переноса лежит условный вариационный автоэнкодер — VAE.
Neural Networks in Trading: Recurrent Modeling of Market Micromovements (EV-MGRFlowNet)
Today's financial markets resemble a gigantic stream of micro-events — an endless sequence of minute changes, each of which bears the imprint of past transactions and a foreshadowing of future ones. Prices fluctuate, volumes ebb and flow, and orders follow one another at a frequency beyond human perception.
Neural Networks in Trading: Temporal Motion Aggregation (Conclusion)
Financial markets are like a living organism, pulsing to the rhythm of expectations, fear, and hope. Quote streams, like blood in the veins of the system, carry information encoded in every price movement, every surge of liquidity, and every minor fluctuation that, to an outside observer, may seem random. However, a careful observer knows that nothing here is random. There are only patterns hidden within the chaos.
Neural Networks in Trading: Temporal Motion Aggregation (Key Components)
The financial market, like a living organism, breathes, changes, and moves. It doesn't take snapshots; it creates waves. And to understand it, it is not enough to record individual points — you need to see the trajectory. It is important not only to know where the price has been, but also to get a sense of where it is headed and exactly how the price movement has unfolded.
Нейросети в трейдинге: Habi — быстрое принятие решений без сложного планирования (вероятностное представление)
На каждом новом баре торговой системе предстоит решить, сохранить ли позицию, изменить её объём, пересмотреть защитные уровни или выйти из рынка. Ответ зависит от движения цены и состояния счёта. Подробное планирование помогает оценить последствия разных действий, но его повторение на каждом баре требует дополнительных вычислений. В предыдущей статье мы рассмотрели фреймворк Habi. Он переносит опыт сложного планировщика в компактную генеративную политику.
Neural Networks in Trading: Temporal Motion Aggregation (TMA)
The financial market is, in many ways, like a living organism. Its life pulses to the rhythm of trades, surges in liquidity, and shifts in market sentiment. Its breathing is irregular: at times it quickens under the pressure of news and emotional impulses, and at other times it seems to stop during long periods of waiting. Every moment here is filled with events, yet not every moment matters.
Нейросети в трейдинге: Habi — быстрое принятие решений без сложного планирования
При разработке торговой стратегии мы регулярно выбираем между несколькими действиями. Можно открыть позицию, изменить её объём, пересмотреть защитные уровни или остаться вне рынка. Каждое решение зависит не только от движения цены, но и от уже принятых обязательств. Чем подробнее алгоритм оценивает возможные последствия, тем больше вычислений требуется на очередном баре. Особенно заметна эта проблема в системах, которые строят несколько сценариев будущего и подбирают действия для каждого из них.
Neural Networks in Trading: Iterative Forecast Refinement Models (Conclusion)
The market is in no hurry to reveal its intentions. It is constantly in motion — flexible, fluid, and often unpredictable. Every price change is not a random spike, but a reflection of the many interactions that take place between market participants and information streams. And while classical models attempt to capture this moment in a single snapshot, the modern RAFT (Recurrent All-Pairs Field Transforms) approach looks deeper.
Neural Networks in Trading: Iterative Forecast Refinement Models (Key Components)
The modern financial market is more than just a collection of quotes and indices. It is a complex, dynamic system in which every price change reflects a whole network of causes, interactions, and expectations. The stream of data arriving every second resembles a living flow in which every moment carries meaning. To see this movement is to learn to read the market as a process, not as a result.
Нейросети в трейдинге: Диффузионная генерация торговых планов (Окончание)
Отдельное торговое действие может выглядеть привлекательным, но последовательность таких решений способна привести к нехватке маржи и глубокой просадке. Поэтому при построении торговой политики необходимо оценивать не только следующую сделку, но и возможные изменения состояния счёта. В серии статей по DPCC рассматриваем планирование на несколько шагов. Модель формирует последовательности действий.
Neural Networks in Trading: Iterative Forecast Refinement Models (RAFT)
The world of financial markets is always one step ahead of theoretical models. Each new wave of technological innovation only confirms that traditional forecasting methods are too simplistic compared with real price dynamics. The market does not operate as a series of simple linear trends, but rather as a continuous field of multiple interactions, in which any local impulse is immediately reflected in the overall picture.
Neural Networks in Trading: Adaptive Perception of Market Dynamics (Conclusion)
MetaTrader 5 — Trading systems | 22 October 2026, 07:41 2 1 The financial market is not an easy-to-read table. It is a stream. Continuous, sparse, and at times explosive. Market events do not occur on a schedule. They arise where liquidity shifts, orders break down, and volume spikes flicker past. Understanding this stream is essential for building a reliable, responsive, and adaptive forecasting system.
Neural Networks in Trading: Adaptive Perception of Market Dynamics (Encoder)
The behavior of modern financial markets increasingly resembles a continuous stream of events in which the magnitude of price changes and the exact moment they occur, the time intervals between impulses, and the underlying fabric of the market’s micro-movements are all significant. It was precisely this idea that formed the basis for the transition from a static view to an event-driven approach to data processing, capable of capturing the market’s dynamic pulse in real time.
Neural Networks in Trading: Adaptive Perception of Market Dynamics (STE-FlowNet)
In recent years, financial markets have been undergoing a rapid transformation driven by deep learning. The challenge of creating models capable not only of identifying patterns in data but also of forming a robust representation of the temporal, contextual, and structural aspects of market dynamics is becoming increasingly pressing.
Neural Networks in Trading: A Spike-Semantic Approach to Spatiotemporal Identification (Conclusion)
Just because the market is quiet does not mean it is calm. Beneath its smooth surface, life is constantly bubbling: bursts of orders, tremors in liquidity, and shifts in trading volume. All of this consists of brief, almost imperceptible impulses that give rise to price movements. Every tick is a signal in the living fabric of the market. And it is precisely in those moments, when most people still see only silence, that the real process of trend formation begins.
Нейросети в трейдинге: Диффузионная генерация торговых планов (Планировщик)
Запланированный донабор позиции может стать невыполнимым ещё до исполнения. Сейчас свободной маржи достаточно, но движение цены против сделки уменьшит средства счёта. Прежний объём потребует пересмотра. Сокращение позиции на раннем шаге изменит условия последующих действий. Поэтому проверять нужно весь план и соответствующие состояния счёта. Для решения этой задачи мы продолжаем торговую адаптацию фреймворка Diffusion Predictive Control with Constraints — DPCC.
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (Key Components)
In the world of algorithmic trading and financial analysis, the idea of seeing not just numbers but events has always captured the attention of researchers. Streams of quotes and orders, bursts of activity, crashes, and lulls — all of this resembles a living organism pulsing with the rhythm of global liquidity. However, most traditional models view this merely as statistics — a set of sequential values. They analyze the price, but they do not feel it.
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (S3CE-Net)
In today's financial world, the market has long since ceased to be a place where only assets are traded. Here, people trade information, time, and attention. Reaction speed is becoming the new currency, and the ability to identify patterns in a continuous stream of data is the key advantage. Every second, millions of quotes, orders, and news reports form a seething ocean of events in which chance and pattern are so closely intertwined that the boundaries between them sometimes blur.
Нейросети в трейдинге: Диффузионная генерация торговых планов (Сценарный расчёт)
Предположим, через несколько баров планируется донабор позиции. Сейчас свободной маржи достаточно. Но после движения цены против сделки средства могут уменьшиться. Прежний объём окажется слишком большим. Поэтому нужно проверять всю последовательность действий с учётом будущих состояний счёта. Продолжим адаптацию фреймворка Diffusion Predictive Control with Constraints (DPCC). В авторском методе диффузионная модель генерирует состояния и действия.
Neural Networks in Trading: Training Deep Spiking Models (Conclusion)
The financial market, like any complex nonlinear system, operates according to its own laws. It breathes, fluctuates, accumulates energy, and releases it in the form of sharp price movements. And the more closely we study its dynamics, the clearer it becomes that classical analytical methods can no longer always keep pace with the volatility of modern trading scenarios.
Neural Networks in Trading: Training Deep Spiking Models (Spike Integration)
Financial markets have always been a battleground of opposing forces: order and chaos, rational calculation and irrational emotional outbursts. They resemble a stormy sea, where the surface reveals the play of waves — price fluctuations — while complex currents lie hidden beneath the water: fundamental factors, macroeconomic shifts, and the psychological sentiment of market participants. In this world, every trader, investor, or researcher is searching for the key to understanding patterns.
Neural Networks in Trading: Training Deep Spiking Models (SEW-ResNet)
Financial markets are an arena where the interests of millions of participants clash, and every decision results in either a profit or a loss. In such circumstances, the ability to quickly extract information from complex data streams becomes a critical factor for success. In recent decades, artificial neural networks have taken the lead in information processing tasks, proving their effectiveness in a wide variety of fields — from computer vision to machine translation.
Neural Networks in Trading: From Transformers to Spiking Neurons (Conclusion)
MetaTrader 5 — Trading systems | 9 October 2026, 12:50 6 0 Financial markets can be compared to a vast ocean, where the waves rise and fall every second. Sometimes the sea is calm and predictable; sometimes it rages and threatens to capsize a ship; and at other times, it appears deceptively smooth, hiding powerful currents beneath the surface.
Нейросети в трейдинге: Диффузионная генерация торговых планов (Вычислительная основа)
Представим торговый план с донабором позиции через несколько баров. Сейчас средств для него достаточно. Но движение цены против сделки сократит свободную маржу ещё до намеченной покупки. Прежний объём окажется слишком большим, а последующее снижение цены может привести к превышению лимита просадки. Проверка такого плана должна охватывать всю последовательность действий: текущее решение меняет возможности счёта на следующих шагах. Для решения этой задачи продолжим торговую адаптацию DPCC.
Neural Networks in Trading: From Transformers to Spiking Neurons (Key Components)
Financial markets are like a stormy sea, where a ship’s captain must make split-second decisions, relying not only on charts and compasses but also on intuition honed through years of experience. In today's world, computational models are increasingly taking on this role, and the researcher's task is to create an algorithm capable of rivaling human flexibility and foresight.
Neural Networks in Trading: From Transformers to Spiking Neurons (SpikingBrain)
MetaTrader 5 — Trading systems | 7 October 2026, 13:35 1 0 Financial markets have always been a place where chance coexists with regularity, and an immediate reaction is often more important than lengthy analysis. They have been compared to the ocean, where a calm surface can give way to a storm in an instant, and to an organism, where nerve impulses determine the rhythm of life.
Нейросети в трейдинге: Диффузионная генерация торговых планов (DPCC)
Торговое решение зависит от прогноза рынка и текущего состояния счёта. Открытие позиции меняет свободную маржу и возможный убыток при движении цены. Накопленные потери сокращают запас до установленного лимита. Поэтому один и тот же сигнал может потребовать разного объёма позиции или отказа от входа. В предыдущих статьях мы учитывали распределение возможных результатов при обучении торговой политики. Спектральная оценка усиливала внимание к неблагоприятным исходам.
Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Conclusion)
Can anyone advise on how to deal with this – 'Math' is not a class, struct or union VAE.mqh 93 8 'MathRandomNormal' – some operator expected VAE.mqh 93 14 Parameter conversion from type 'int[1]' to 'const uint[] &' is not permitted VAE.mqh 135 55 Incorrect number of parameters: 4 passed, but 5 are required VAE.mqh 135 15 could be one of 2 function(s) VAE.mqh 135 15 bool COpenCL::Execute(const int, const int, const uint&[], const uint&[]) OpenCL.mqh 82 22 bool COpenCL::Execute(const int, const...
Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Attention Modules)
MetaQuotes: An article has been published entitled ‘Neural Networks in Trading: Reliable Trading Signals in All Market Conditions (Attention Modules)’: Author: Dmitriy Gizlyk Hello, Dmitriy! Could you perhaps create a simple script for us mere mortals to demonstrate how the model works? Using very simple synthetic data, for example 10 data points and 2 features. It should be able to plot the loss function or output the results to a log file. If possible, just using the CPU.
Neural Networks in Trading: Robust Trading Signals in Any Market Regime (ST-Expert)
Today's financial markets are a dynamic ecosystem in which millions of transactions, regulatory decisions, corporate reports, and macroeconomic signals come together to form a complex pattern. For a trader or analyst, the key task is to identify regularities in this pattern and use them to build a forecast. But this is precisely where the main contradiction arises: the market is changing faster than traditional models can adapt.
Нейросети в трейдинге: Управление риском через распределение результатов (Окончание)
На финансовом рынке средняя прибыль ещё не описывает характер стратегии. Две системы могут показать близкую доходность и совсем по-разному прийти к этому результату. Одна чаще приносит умеренную прибыль. Другая покрывает глубокие убытки редкими крупными выигрышами. Для трейдера за этим стоят размер необходимого капитала и способность пережить неудачную серию. Поэтому доходность приходится оценивать вместе с возможными потерями.
Neural Networks in Trading: A Unified View of Space and Time (Conclusion)
Financial markets can be compared to a living organism, where every price movement resembles a pulse, and every news item or macroeconomic decision is like a heartbeat that sends waves of change throughout the entire system. They breathe, fluctuate, rise, and fall in a complex rhythm that reflects the behavior of millions of participants.
Neural Networks in Trading: A Unified View of Space and Time (Global-Local Attention)
Financial markets are a complex and dynamic system in which space and time are closely intertwined. Every price movement reflects not only the instantaneous balance of supply and demand, but also the traces of previous events, as well as the influence of related instruments, sectors, and even entire economies. Predicting the behavior of such a system using traditional methods has always been a highly complex task.
Neural Networks in Trading: A Unified View of Space and Time (Extralonger)
Predicting the behavior of complex systems has traditionally been one of the key tasks of data analysis. The quality and depth of forecasts determine not only researchers’ academic interest but also very practical outcomes: optimizing flows, reducing costs, and managing risks. This problem is most evident in two areas that might seem unrelated — intelligent transportation systems and financial markets.
Neural Networks in Trading: The Adaptive Graph Diffusion Model (Conclusion)
Today we are wrapping up our work on implementing the approaches proposed by the authors of the SAGDFN framework. The framework's authors proposed one possible solution to the most pressing problem in spatio-temporal forecasting — the inexorably increasing complexity of processing large graphs.
Нейросети в трейдинге: Управление риском через распределение результатов (Объект верхнего уровня)
При построении торговой модели мало смотреть только на средний результат. Две стратегии могут показать одинаковую доходность, но прийти к ней совершенно разными путями. Одна зарабатывает относительно ровно. Другая компенсирует глубокие убытки редкими крупными прибылями. Для трейдера это разные профили риска. Вместе с ожидаемой прибылью нужно видеть и неблагоприятный хвост распределения.
Neural Networks in Trading: The Adaptive Graph Diffusion Model (Attention Module)
In the previous article, we were introduced to the SAGDFN framework. We discussed the key principles behind its operation and highlighted its strengths. But the market isn't a museum of architectural models. Here, there is no tolerance for excessive hesitation or lifeless schemes. For a building to stand, it must be built — brick by brick — while checking each level for stability.
Neural Networks in Trading: The Adaptive Graph Diffusion Model (SAGDFN)
Multivariate time series are characterized not only by the familiar temporal dependence (trends, seasonality, sudden spikes in activity), but also by spatial correlation, which is far more insidious. It describes how changes in one sequence affect others. And these connections take on a life of their own: they appear and disappear, and their strength and direction change depending on global news, shifts in national monetary policy, political crises, or technological breakthroughs.
Нейросети в трейдинге: Управление риском через распределение результатов (Вычислительная основа)
При оценке торговой стратегии нас интересует не только величина прибыли. Важно понимать, какие потери могут сопровождать ее получение. Стратегия способна регулярно закрывать сделки с небольшим положительным результатом, а затем отдать значительную часть заработанного за один неудачный период. Средняя доходность при этом может оставаться привлекательной. Но для практической торговли такой результат уже требует другого отношения к риску.
Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (Conclusion)
MetaTrader 5 — Trading systems | 22 September 2026, 06:01 8 0 Algorithmic trading rarely forgives excessive complexity or unjustifiably simplistic solutions. Any model — whether it is a highly complex neural network ensemble or a minimalist autoregressive filter — must ultimately stand the test of time and the market.
Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (Key Components)
In the previous article, we looked at the HimNet framework — a practical tool that not only analyzes data but also understands its context. In trading, this means that the model distinguishes between periods of high liquidity and periods of low activity. HimNet automatically adapts to these changes. The HimNet architecture is simple and logical: time series segments of length T across N locations are fed as input. First, the model generates two types of trainable embeddings — temporal and spatial.
Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (HimNet)
The emergence of a wide range of tools for collecting streaming data has led to the large-scale accumulation of spatio-temporal series in a wide variety of areas of human activity. Trading systems generate streams of quotes, orders, and trades across different trading venues, tickers, and instrument types. As data volumes grow, accurate forecasting of such series becomes a tool for reducing costs, managing risks, and improving the execution of trading strategies.
Нейросети в трейдинге: Управление риском через распределение результатов (AC-SRM)
На финансовом рынке одной высокой средней доходности недостаточно. Важно понимать, какой ценой она получена. Две стратегии могут показывать одинаковый средний результат. Первая дает умеренную прибыль при небольшом разбросе исходов. Вторая чередует крупные выигрыши с глубокими просадками. Для практической торговли это уже две разные стратегии. Та же проблема возникает в обучении с подкреплением. В классическом Actor–Critic решение обычно строится вокруг ожидаемого вознаграждения.
Neural Networks in Trading: The Temporal Query Model (Conclusion)
The TQNet framework is one of the most elegant and flexible approaches to building neural network models capable of effectively processing time series and structurally organized data. Its key advantage lies in its ability to combine local and global dependencies, creating a distinctive balance between depth of analysis and computational efficiency.
Neural Networks in Trading: The Temporal Query Model (TQNet)
Multivariate time-series forecasting in finance is a race against time, where accuracy and speed are key. Exchange rates, indices, trading volumes, interest rates, and liquidity indicators — all of these factors constantly interact with one another, forming a complex, ever-changing system. Traders, asset managers, and analysts know that to stay one step ahead, you need to understand how all the variables influence one another in real time.
Нейросети в трейдинге: Адаптация прогноза при смене рыночного режима (Окончание)
MetaTrader 5 — Торговые системы | 15 сентября 2026, 12:24 11 0 На финансовом рынке модель редко перестаёт работать в один момент. Обычно всё выглядит спокойнее. Советник продолжает получать данные и формировать прогнозы, но прежние связи постепенно ослабевают. Волатильность меняет масштаб, ликвидность перераспределяется между торговыми сессиями, а знакомая комбинация признаков начинает приводить к другому продолжению движения. Несколько неудачных сделок ещё можно объяснить шумом.
Neural Networks in Trading: Decomposition Instead of Scaling (Conclusion)
Today's financial markets are not just a place where buyers and sellers meet, but a complex, dynamic ecosystem where every price tick is the result of the instantaneous interaction of hundreds of factors. There is no room here for chance in the usual sense of the word: behind the apparent chaos lie patterns, albeit ones clothed in complex, multilayered forms.
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