MQL5 Algo Trading

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📈 Analytical overview of Telegram channel MQL5 Algo Trading

Channel MQL5 Algo Trading (@mql5dev) in the English language segment is an active participant. Currently, the community unites 511 565 subscribers, ranking 153 in the Technologies & Applications category and 5 in the United Kingdom region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 511 565 subscribers.

According to the latest data from 17 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 8 615 over the last 30 days and by 84 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 3.44%. Within the first 24 hours after publication, content typically collects 1.98% reactions from the total number of subscribers.
Post reach: On average, each post receives 17 586 views. Within the first day, a publication typically gains 10 124 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 39.
Thematic interests: Content is focused on key topics such as indicator, chart, mql5, candle, range.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“The best publications of the largest community of algotraders. Subscribe to stay up-to-date with modern technologies and trading programs development.”

Thanks to the high frequency of updates (latest data received on 18 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

511 565

Subscribers

+8424 hours

+2 0627 days

+8 61530 days

17 586

Post views

~ 10 12424 hours

~ 12 29248 hours

3.44%

Engagement rate

~ 5

Posts per day

Ads index

beta

Posts Archive

511 565

Standard volatility bands such as Bollinger Bands and Keltner Channels often rely on symmetric, time-independent assumptions. Standard deviations around an arithmetic mean do not test whether price has a statistical tendency to revert, which can cause mean-reversion systems to keep trading into sustained drift and accumulate drawdowns. A more robust filter is the Ornstein-Uhlenbeck process: dX_t = θ(μ − X_t)dt + σ dW_t. Estimating μ, θ, and σ turns “bands” into an equilibrium model: μ as the baseline, θ as the reversion or drift regime flag, and σ as diffusion limits derived from residual variance rather than rolling deviation. Operationally, θ > 0 supports mean reversion, while θ < 0 indicates trend drift and should disable reversion logic. For liquid instruments on M30/H1, trades are taken only when price exceeds diffusion boundaries, targeting con... 👉 Read | CodeBase | @mql5dev

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MSNR v5.31Plus AEU EA targets XAUUSD execution on M5, while scanning W1, D1, H4 and H1 to map support and resistance zones. Entries are gated by multi-layer price action confirmation rather than a single trigger. Core logic includes Malaysian SNR body-level detection, liquidity sweep with MISS and engulfing validation, plus trendline confluence, QML, breakout-retest, CRT, and a confluence cluster filter. Trading can be restricted by Asia, Europe, and US sessions. Risk controls cover percentage-based sizing, partial close and break-even handling, and protection against losing streaks and drawdown expansion. Output includes an on-chart dashboard with key levels and CSV exports for backtest deals, signal logs, and closed trades. Targets prioritize DOL references such as prior day/week highs and lows and nearby swing liquidity, with risk-reward projection as fal... 👉 Read | Calendar | @mql5dev

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An Expert Advisor has been released to test the “Turnaround Tuesday” hypothesis using daily candle data. The rule set is simple: if Monday closes below its open, a Buy is opened at the start of Tuesday; if Monday closes above its open, a Sell is opened. Trades are always taken against Monday’s direction, with one position per symbol. Optional controls include an ATR-based volatility filter and SL/TP derived from daily ATR. Lot sizing supports fixed volume or risk from start balance, with the limitation that risk sizing cannot be computed when Stop Loss is disabled. Positions are force-closed at a configurable CloseHour if exits are not hit. Backtests on EURUSD, XAUUSD, and SP500 (2016–2026) showed no stable edge in the baseline variant, while ATR filtering and management rules improved XAUUSD to +38% and SP500 to +40%, with 11% max drawdown on SP500. Full r... 👉 Read | VPS | @mql5dev

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HiSSD (Hierarchical and Separate Skill Discovery) is being adapted from offline multi-agent training to trading, targeting fast regime shifts without full retraining. The design splits behavior into common skills and task-specific skills, supporting stability under changing market conditions and coordination across many agents. Implementation in MQL5 continues with a dedicated Controller object (CNeuronHiSSDLowLevelControler) built on convolutional layers to run independent agents in parallel. Inputs combine local observations, Planner-provided common skills, and encoder-generated specific skills, with row-wise concatenation, transposition for univariate streams, and batch normalization. Training defines four models: a Planner-style environmental state encoder for multi-step forecasting and common skill output, the Controller for agent actions, a ... 👉 Read | Calendar | @mql5dev

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Part III adds a regression gate on top of the Part II artifacts without changing their contracts. The profiler CSV and deterministic TestLite report stay stable, while the gate compares current evidence to an accepted baseline and emits PASS, WARN, SKIP, or FAIL. The workflow stays file-based and sequential: run UnitTestRunner.mq5, run ProfilerExampleEA.mq5 in Strategy Tester, promote a clean profile to ProfilerExampleEA_Baseline.csv, then run DiagnosticGateRunner.mq5 after changes. Outputs include delta and failure CSVs plus a final machine-readable status. RegressionGate.mqh matches profiler rows by section name, computes call-count and timing deltas, and classifies movement using combined absolute and percentage thresholds to avoid microsecond noise. New or missing sections warn by default. TradeAssertions.mqh reuses TestLite to add symbol-awar... 👉 Read | Freelance | @mql5dev

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This article replaces gap-focused trailing logic (Skip-List + Hopfield) with a two-stage trailing stop built for smoother, continuous trends: Reservoir Sampling as a memory-fixed price “engine”, plus an OLS Linear Regression “filter” that decides when trailing should pause. Reservoir Sampling maintains a statistically fair buffer of k prices from an unbounded stream using replacement probability k/i, avoiding full-history moving-average storage. For Strategy Tester repeatability, randomness is isolated with an internal deterministic LCG instead of MathRand(). The class offers four reservoir evaluation modes (mean, min/max midpoint, volatility-adjusted via standard deviation, and a recent-biased blend). Linear Regression predicts the next bar; if the forecast moves against the position, stop updates freeze to avoid micro-pullback stopouts. Broker stop/f... 👉 Read | Docs | @mql5dev

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Stop losses automate exits at a trigger price, reducing downside risk and removing emotional decision-making. In leveraged forex/CFDs, they also support profit protection by adjusting the stop without reducing position size. Exchange-side stops expose the stop level, enabling stop runs around clustered liquidity, broker-imposed minimum stop distances, and spread widening that can force premature exits. A local stop-loss system keeps the stop price only in the MetaTrader 5 terminal. The EA monitors bid/ask correctly (bid for closing buys, ask for closing sells) and closes via CTrade when conditions are met. The implementation uses OOP with per-position stop objects, CPositionInfo for position access, and CHashMap keyed by ticket for O(1) tracking and scalable multi-position management in OnInit/OnTick/OnDeinit. 👉 Read | VPS | @mql5dev

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MetaTrader 5 backtests and optimizations generate rich statistics, but exporting them into Python, Excel, or a database often turns into repetitive manual work. MQL5 file I/O can convert the tester into a repeatable research pipeline, with CSV as a practical interchange format. File access is sandboxed. Without FILE_COMMON, paths resolve under the terminal data folder. With FILE_COMMON, output is redirected to a shared directory usable by multiple terminal instances and external tools. Local sandbox fits internal logs; common sandbox fits cross-process workflows. FileOpen flags define behavior. FILE_WRITE truncates; append needs FILE_WRITE|FILE_READ plus FileSeek to end. FILE_CSV reads and writes field-by-field. FILE_ANSI maximizes compatibility for ASCII data; FILE_UNICODE is needed for non-English symbols. A dedicated CCSVExporter class keeps file... 👉 Read | AppStore | @mql5dev

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Version 2.3 continues the 2.x line with bug fixes, code refinement, and added Price-to-MA Distance reporting. Trade value is tied to price positioning relative to a moving average. That positioning can be characterized by MA speed and the price-to-MA distance, giving two measurable signals for pressure and trend conditions. The indicator outputs MA speed as a histogram normalized against its historical average, and price-to-MA distance as a percentage line. Both metrics are packaged in one view for operational convenience rather than strict signal compatibility. In practical use, the combined display helps separate directional pressure from mean-reversion behavior around a trending moving average. 👉 Read | Forum | @mql5dev

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MetaTrader 5 custom symbols can turn raw market data into synthetic instruments, and 3DBarCustomSymbol.mq5 is a concrete example. It builds “3D bars” that encode price action alongside volume behavior, time-cycle features, and volatility measures, making anomalies and regime shifts easier to spot than on standard candles. The indicator is structured like a solid MQL5 project: init validates inputs and loads history; symbol-creation configures precision, sessions, and metadata; processing computes normalized metrics (returns/acceleration, volume changes, volatility); an update loop refreshes the current bar on a timer for near real-time use. Practical benefits: traders can monitor volatility bursts, volume spikes, and acceleration changes on a dedicated custom chart, while developers can feed the synthetic symbol into indicators or EAs. Performance is... 👉 Read | CodeBase | @mql5dev

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Most MT5 EA reviews rely on account history or post-trade CSV exports. Both omit intratrade behavior because history stores only endpoints, not what happened while a position was open. Tick-level maximum adverse excursion (MAE) and maximum favorable excursion (MFE) capture worst drawdown and best run-up in account currency. Trades with identical final PnL can have materially different MAE/MFE, changing conclusions about entry timing, stop placement, and whether mean reversion is doing the work. A reusable MQL5 class, CTradeJournal, tracks all open positions on every tick, stores initial risk at entry for R-multiple normalization, and writes a structured CSV on close. Integration stays minimal: Init on OnInit, NotifyOpen on fill, Update on every tick, NotifyClose from OnTradeTransaction. 👉 Read | Freelance | @mql5dev

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MetaTrader 5 Strategy Tester optimization output remains transient and fragmented, which complicates comparative studies across SMA, EMA, KAMA and other filter families. Native reports do not persist custom metrics and the XML report is not practical for downstream tooling. A reusable include, CSVExportEngine.mqh, exports standard TesterStatistics() fields plus derived metrics such as Sortino Ratio from deal history, average trade duration via DEAL_POSITION_ID matching, and signal-quality measures tracked per bar (lag on turns, whipsaws, price-to-line distance, intersection frequency). Multi-core optimization introduces file-lock contention: parallel agents can lose rows when FileOpen() returns INVALID_HANDLE, and Sleep() does nothing in the Tester. The fix is an iteration-based spin-lock retry around FileOpen(), with phase-aware filenames (Baselin... 👉 Read | Quotes | @mql5dev

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Regression replaces buy/sell labels with a continuous target: expected price change. This removes a key weakness of binary models in trading—directional accuracy ignores move size, so it cannot rank opportunities or avoid low-payoff trades. The article shows two target designs: point-to-point future delta and an averaged future-price delta over a horizon. Averaging captures intermediate excursions and reduced overfitting in tests. A meta “causal inference” layer trains multiple regressors on random subsets and filters trades when the main forecast is likely to be unreliable, using a tolerance on prediction error. Models are exported to MetaTrader 5 via ONNX (Random Forest used due to CatBoost ONNX limits). In-terminal thresholds for buy/sell magnitude and meta deviation turn raw regression outputs into tradable signals, improving forward stability. 👉 Read | Docs | @mql5dev

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Recurrence matrices in RQA are reinterpreted as adjacency matrices: each time index becomes a node, each recurrence becomes an undirected edge, and the diagonal is cleared to remove self-loops. This turns pattern counting into graph topology analysis, exposing structure like clustering, shortest paths, centrality, and degree correlation. The MQL5 library adds CRNAMetrics to compute 12 network measures (degree stats, clustering/transitivity, path length/diameter, betweenness/closeness, assortativity, density). Rolling analyzers (CRNAWindow, CJRNAWindow) apply the metrics per window for single-series and joint recurrence, with facades (CRNA, CJRNA) for one-call use. Two indicators plot five live metrics (ACC, transitivity, normalized APL, assortativity, avg betweenness). The approach is practical but CPU-bound; betweenness and triangle counting scal... 👉 Read | Docs | @mql5dev

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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

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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

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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

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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

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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

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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