Вже доступно! Дослідження Telegram за 2025 — головні інсайти року 
MQL5 Algo Trading
Відкрити в Telegram
The best publications of the largest community of algotraders. Subscribe to stay up-to-date with modern technologies and trading programs development.
Показати більше📈 Аналітичний огляд Telegram-каналу MQL5 Algo Trading
Канал MQL5 Algo Trading (@mql5dev) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 509 935 підписників, посідаючи 152 місце в категорії Технології та додатки та 5 місце у регіоні Об'єднане королівство.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 509 935 підписників.
За останніми даними від 13 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 8 581, а за останні 24 години на 7, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.58%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.01% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 18 276 переглядів. Протягом першої доби публікація в середньому набирає 10 262 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 40.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як indicator, chart, mql5, candle, range.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“The best publications of the largest community of algotraders.
Subscribe to stay up-to-date with modern technologies and trading programs development.”
Завдяки високій частоті оновлень (останні дані отримано 14 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
509 935
Підписники
+724 години
+2 0887 днів
+8 58130 день
Архів дописів
509 951
Part 2 extends an MQL5 chart object detector beyond basic time/price pairs to support complex analytical objects used in rule-based strategies.
Focus areas include extracting Fibonacci level arrays via OBJPROP_LEVELS and OBJPROP_LEVELVALUE, collecting three-point geometry for OBJ_CHANNEL and OBJ_PITCHFORK, and normalizing the results into an extended data structure suitable for deterministic checks such as 0.618 retracement alignment or median-line contact conditions.
Implementation targets OBJ_FIBO, OBJ_FIBOTIMES, OBJ_FIBOFAN, OBJ_FIBOARC, OBJ_CHANNEL, and OBJ_PITCHFORK, with the same approach applicable to cycles and Gann tools. Delivery consists of ComplexObjectDataCollector.mqh (derived from the Part 1 detector) and a timer-based TestComplexObjectsEA.mq5 that logs extracted levels and geometry for validation.
👉 Read | Signals | @mql5dev
509 951
Part 35 upgrades the custom MT5 canvas layer from simple two-point line tools to a full analytical toolkit with 15 new objects: channels (parallel, equidistant, regression), pitchforks (Andrews, Schiff, modified Schiff), Gann tools (line, fan, box), multiple Fibonacci variants, plus delete-all.
The core change is support for multi-anchor geometry, derived handles, and per-tool “level arrays” (ratios, styles, visibility) stored with each object. Three-click placement and dual-state rubber-band previews standardize interaction across complex tools.
Key implementation details include OLS regression channels that recompute endpoints on redraw, sigma bands with proper alpha compositing, scanline quadrilateral fills, dashed/AA line reuse, and hit-testing that recalculates live geometry so tools stay aligned with evolving price data.
👉 Read | Forum | @mql5dev
509 951
Microstructural features treat each OHLCV bar as an aggregation of trades and attempt to recover spread, price impact, and order-flow imbalance at bar close.
A two-layer design is used. compute_all_microfeatures() runs on OHLCV only and produces Roll spread, Corwin-Schultz spread and sigma, plus Kyle, Amihud, and Hasbrouck lambdas with rolling t-stats where applicable. Kernels are Numba-accelerated and avoid Pandas in inner loops.
When tick data exists, bar_microstructure_features() and vpin() add per-bar imbalance stats and VPIN from volume-synchronized buckets. Tick preprocessing and bar boundary indexing via searchsorted enable parallel bar kernels while keeping VPIN bucket filling sequential.
Outputs converge to a bar-indexed feature frame suitable for downstream ML pipelines.
👉 Read | Signals | @mql5dev
509 951
Long-only trend breakout EA for MetaTrader 5 with fixed-currency risk per trade and execution restricted to the open of a new bar.
Signal logic: on the last completed bar, a buy setup triggers when Close(Bar 1) is strictly above High(Bar 2). Stop loss is set at Low(Bar 1). Take profit is derived from a configurable risk-to-reward ratio. Designed for indices with persistent upside bias, including NAS100, and typically evaluated on H4.
Risk management uses tick value and tick size to convert the stop distance into position volume, normalized to broker volume steps. Trades are skipped if the required size is below the minimum.
Backtests referenced: NAS100, H4, ~3.5 years, M1 OHLC modelling. Real-tick validation and broker-specific spread/contract checks remain required before live use.
👉 Read | AppStore | @mql5dev
509 951
This article adds “discipline” to MetaTrader 5 EAs by separating strategy signals from an equity governance layer that supervises execution during stress. It tracks peak equity, drawdown, daily loss, and rapid equity drops, then drives a four-state machine: NORMAL, CAUTION, RESTRICTED, LOCKDOWN.
The design is modular: monitoring and analysis feed a state engine with cooldown and recovery checks (hysteresis to avoid flip-flopping). CAUTION scales risk via lot multipliers, position caps, and trade spacing; stricter states can block entries while still allowing position management.
Implementation uses a clean MQL5 library of specialized classes plus a thin EA wrapper. A single Authorize() call becomes the trade gate, with hard limits as an immediate backstop. A built-in SMA test engine and on-chart dashboard make behavior easy to validate in backtests.
👉 Read | Signals | @mql5dev
509 951
Cooperative MARL is moving toward offline multi-task training, driven by the cost of new environment interaction and poor scaling when agent counts or objectives change. Transformer-based models help, but many pipelines still miss task-specific structure and the temporal buildup of coordination.
HiSSD (Hierarchical and Separable Skill Discovery) proposes joint, hierarchical learning of shared cooperation skills and task-specific skills from offline datasets. Modules are trained end-to-end, with a multi-agent IQL-style value model, a planner selecting high-level skills, and a controller trained via VAE objectives plus contrastive separation across tasks.
An MQL5-oriented adaptation maps agents to univariate market sequences, adds controlled cross-sequence information exchange via masked attention (CATCH), then produces per-agent skill vectors with independent conv...
👉 Read | AlgoBook | @mql5dev
509 951
This part turns multifractal diagnostics into simulation-ready inputs for MMAR. A new Spectrum Fitter converts the scaling curve τ(q) into the singularity spectrum f(α) via a discrete Legendre transform (central differences, low-q filtering, and rejection of invalid negative f(α) points).
The empirical f(α) is then matched to four parametric cascade models (Normal, Binomial, Poisson, Gamma). Each fit minimizes SSE using ALGLIB’s BLEIC optimizer with strict parameter bounds to keep solutions physically meaningful.
A CSpectrumFitter class encapsulates the pipeline and selects the best distribution by lowest SSE. On EURUSD M10, the Binomial model wins, yielding α_min/α_max that quantify spectrum width and directly control cascade heterogeneity, supporting realistic volatility clustering with negligible extra runtime versus partition analysis.
👉 Read | Calendar | @mql5dev
509 951
Many EAs treat overnight swap as an afterthought, even though it can materially change expectancy on multi-day trades, especially on high-differential pairs like AUDJPY. Swap is effectively daily interest: long positions can earn credit, shorts often pay, and triple-swap rollovers must be projected inside the holding window.
The article shows how to make swap actionable in MQL5 by reading SYMBOL_SWAP_LONG/SHORT plus SYMBOL_SWAP_MODE, then converting broker-specific units into a per-lot, per-day value in account currency. It handles common retail modes (points, deposit currency, interest-based using the 360-day convention), warns and returns 0 for reopening/margin modes, and notes where currency conversion or open-price accuracy is required.
Four drop-in building blocks tie this into real EA decisions: daily swap calculation, expected swap over a planned hol...
👉 Read | CodeBase | @mql5dev
509 951
Exchange Market Algorithm (EMA) is a population metaheuristic modeled as a trading session: agents are ranked by fitness, split into elite, middle, and beginners, then updated with group-specific rules while elites remain fixed to preserve the best solutions.
Each iteration runs two modes. In a balanced market, the middle moves toward a random elite mentor, while beginners blend guidance from elite and middle. In a fluctuating market, the middle either copies the global best or samples around the elite centroid; beginners use resets, wide local searches, or opposition moves away from poor regions.
Exploration-to-exploitation is controlled by a sigmoid decay rate. An MQL5 C_AO_EMA class encapsulates parameters (r1/r2, riskAlpha), initialization, movement, and revision/sorting. Tests show modest but consistent improvement as problem size grows under a ...
👉 Read | Signals | @mql5dev
509 951
Quantum XAUUSD Silver Trader is a multi-indicator Expert Advisor for XAUUSD and XAGUSD on MT5, combining RSI, ADX, fast/slow moving averages, and a volatility filter into a single weighted decision. Separate presets are applied per metal to account for different volatility profiles, while a configurable threshold gates trade entries.
The weighting is adaptive: recent closed-trade outcomes adjust the contribution of each component, aiming to reduce overreliance on any single signal.
Risk controls are part of the core design. Orders use ATR-based stop loss and take profit, plus an adaptive ATR trailing stop. Account protection includes daily and total drawdown limits, daily loss limits, a position-size cap, and an optional hard stop. A micro/cent mode targets small deposits, with conservative defaults of 0.5% risk on Gold and 0.3% on Silver, typically on M15 an...
👉 Read | AlgoBook | @mql5dev
509 951
Traditional money management in MT5 often reduces risk only after closed losses, which can be too slow for pattern-based strategies. The article builds CMoneySuffixAE, a custom Wizard-compatible money module that sizes positions proactively using current market structure instead of loss counters.
The model combines two real-time checks. A suffix automaton indexes “Price DNA” (Up/Down/Flat sequences) in linear time, letting the EA measure how familiar the latest 16-bar pattern is and scale lot size from exact historical repetition without expensive O(n²) scans.
An autoencoder adds structural risk control. It reconstructs normalized recent prices and uses reconstruction error as an anomaly signal; when geometry breaks (news shocks), confidence collapses and position size is aggressively cut before losses accumulate.
👉 Read | Freelance | @mql5dev
509 951
This article builds an MT5 weekend-gap indicator that turns Friday-close vs Monday-open discontinuities into persistent chart structures, so older gaps stay readable as the week evolves.
Gaps are detected from session time breaks (>= 48 hours) rather than day-name rules, then filtered by a minimum pip threshold with digit-aware pip sizing. Each gap is stored as a record with boundaries, midpoint, and an active-week flag.
Instead of buffers, the framework uses chart objects (rectangles, lines, labels) to keep multiple gaps visible and independently editable. A simple state machine updates the active gap in real time (fresh, partial, reaction, filled) and restyles objects on transitions.
Lifecycle handling is explicit: one-time historical scan on first calculate, tick-by-tick state updates, week rollover promotion to historical zones, and full cleanup...
👉 Read | NeuroBook | @mql5dev
509 951
Fixed RSI/Stochastic thresholds break in trends because they ignore volatility, leaving oscillators pinned at extremes and triggering repeated counter-trend entries. A Z-Score normalizes price deviation by rolling standard deviation, giving a volatility-aware mean-reversion signal: (Price - SMA) / StdDev, with extremes treated as anomalies rather than guaranteed reversals.
The implementation defines a strict “signal contract”: symmetric ±2.5 thresholds, calculations on closed bars only (shift=1), and safeguards for missing history and zero StdDev. The math is isolated in a CZScore class (include file) to reuse identical logic in both an indicator and an EA, avoid code duplication, and manage object lifetime to prevent memory leaks.
An indicator validates behavior visually and optimizes CPU using prev_calculated. The EA uses CTrade, runs once per ba...
👉 Read | AlgoBook | @mql5dev
509 951
CATCH extends multivariate time-series anomaly detection with frequency-domain processing. Data is transformed via FFT, partitioned into frequency patches, and analyzed to separate low-frequency structure from high-frequency deviations. Anomalies are flagged by reconstruction error after the inverse transform.
Channel dependencies are handled by an adaptive masked-attention module that learns inter-series correlations while suppressing noise. Attention is constrained to matching frequency patches across variables, improving robustness for coupled signals such as financial instruments.
An MQL5/OpenCL implementation continues with a complex-valued multi-head masked attention object. Q/K/V are generated by complex convolution, masks by a real-valued conv layer with sigmoid and zero-initialized weights. Kernel scheduling merges segment and head dimensions to apply at...
👉 Read | NeuroBook | @mql5dev
509 951
The article moves a MetaTrader 5 market simulation project into a database-driven stage, using the platform’s built-in SQLite to avoid DLLs or sockets while keeping full SQL control.
It demonstrates creating/opening a database via DatabaseOpen, choosing storage scope (RAM vs terminal Files vs COMMON\Files) through flags, and handling failures cleanly. Table creation is then executed with DatabaseExecute, separating simple commands from future query workflows.
A key design choice is wrapping SQLite access in a class. This encapsulation centralizes the database handle, prevents misuse, and prepares the codebase to switch later from file-based SQLite to a DLL build or client-server databases over sockets with minimal refactoring.
👉 Read | NeuroBook | @mql5dev
509 951
Part 2 extends the breakeven subsystem with two dynamic modes and a selector to standardize configuration.
CBreakEvenAtr implements ATR-based levels. Fixed-point distances are replaced by two ATR multipliers: one for activation distance and one for the final breakeven stop offset. The class manages an ATR handle, a one-element buffer, and an ATR index for CopyBuffer reads. SetSimple supports either a ready handle or period-based handle creation, while Set(MqlParam[]) switches using a HANDLE_INSTEAD_OF_PERIOD flag.
CBreakEvenRR adds an RRR trigger and optionally computes the breakeven offset via points or ATR, controlled by an enum. Add() validates StopLoss presence, reads ATR when enabled, checks stop distance vs activation distance, then computes break_even_price and price_to_beat.
A CBreakEven selector class stores per-mode MqlParam arrays, allocates th...
👉 Read | Signals | @mql5dev
509 951
A common approach for consuming news in a trading or automation stack is a scheduled web request to a remote endpoint, followed by local persistence and XML export.
Implementation typically includes HTTP GET with explicit timeouts, response code validation, and UTF-8 handling. Payloads are parsed as JSON, XML, or HTML depending on the source, then normalized into a compact internal structure with stable keys, timestamps, and de-duplication based on ID or checksum.
Stored records can be exported to XML using a fixed schema, proper escaping, and deterministic ordering. Production setups also add retry with backoff, rate limiting, and a cache layer to reduce repeated downloads.
👉 Read | Signals | @mql5dev
509 951
Multi-Head Attention extends self-attention by running several parallel attention “heads” with independent weights, then concatenating their outputs and applying a final projection matrix to combine perspectives. This helps a model capture different dependencies in market sequences before producing a single decision tensor.
The article shows a practical MT5/OpenCL implementation that reduces computation by removing the explicit Keys tensor and training a single matrix where possible, while noting the trade-off when dimension reduction makes separate matrices cheaper.
For time-series, positional encoding is added to preserve distance information between candles using deterministic sine/cosine vectors, summed with inputs to avoid widening tensors.
Implementation details include a dedicated multi-head class, OpenCL kernels for concatenation/deconcatenation, and opt...
👉 Read | Docs | @mql5dev
509 951
Stochastic of Moving Average indicator implemented as a reusable class for EA integration. Intended for use as a filter and can be combined with the free HLPeak_Trend indicator.
No iCustom() dependency. Runtime updates are handled through .Update(int shift), with shift=0 typically used in Expert Advisors. The design supports access to current and previous values via buffer indexing (0=current, 1=previous).
Input parameters cover period configuration and price source: parPeriod, parKPeriod, parDPeriod, parSlowingPeriod, and parPrice (ENUM_APPLIED_PRICE).
Outputs include bufStoc (stochastic), bufStoK (K line), bufStoD (D line), and bufStoDir for directional state mapping: 2 strong up, 1 weak up, -1 weak down, -2 strong down.
👉 Read | VPS | @mql5dev
509 951
This article replaces large indicator vectors with chart screenshots, letting a CNN learn directly from price context. Images are labeled Buy/Sell using trend-extreme events and daily highs/lows, then split into train/validation and a separate sequential “response” set for later strategy work.
A compact CNN is built in Keras/TensorFlow (449x449 RGB input, 3 conv+pool blocks, 64-neuron dense layer, dropout, sigmoid output) and trained with binary cross-entropy. GPU support via Anaconda dramatically reduces iteration time, making architecture tuning practical.
Model outputs are exported to CSV, plotted in MT5 as an indicator, and converted into tradable signals (e.g., line crossover) for EA optimization. Feature-map visualization helps debug chart design; consistent, distinct colors improve what the CNN can separate.
👉 Read | AppStore | @mql5dev
