MQL5 Algo Trading
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Больше📈 Аналитический обзор Telegram-канала MQL5 Algo Trading
Канал MQL5 Algo Trading (@mql5dev) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 556 890 подписчиков, занимая 140 место в категории Технологии и приложения и 5 место в регионе Великобритания.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 556 890 подписчиков.
Согласно последним данным от 17 сентября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 17 265, а за последние 24 часа — 535, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.16%. В первые 24 часа после публикации контент обычно набирает 1.67% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 17 590 просмотров. В течение первых суток публикация набирает 9 277 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 42.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как indicator, chart, mql5, candle, range.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“The best publications of the largest community of algotraders.
Subscribe to stay up-to-date with modern technologies and trading programs development.”
Благодаря высокой частоте обновлений (последние данные получены 18 сентября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
556 890
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Архив постов
556 890
Part 15’s decision-forest classifier is turned into a tradable EA by separating prediction from execution. The model produces three class scores (bearish/neutral/bullish) from normalized bar features, but a decision layer gates whether that output is allowed to influence positions.
The classifier is refactored into a reusable module that encapsulates feature building, training stats, ALGLIB forest objects, and safe lifecycle rules (no inference until training succeeds, completed-bar history only, reproducible seeding, OOB error exposed for diagnostics).
The EA runs once per new bar, converts raw votes into a stable “directional regime” using a minimum-confidence filter, multi-bar confirmation, and a post-change cooldown. Trade policy is intentionally simple: long-only in bullish, short-only in bearish, otherwise no-entry, with strict risk sizing, spread/...
👉 Read | CodeBase | @mql5dev
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Moving from point-to-point bridges to an event-bus design, this article builds a native Kafka producer directly in MQL5 so one terminal can publish signals to a topic while any number of consumers independently subscribe, replay, and track offsets.
The core work is implementing Kafka’s wire protocol over raw TCP: big-endian framing with a 4-byte length prefix, RecordBatch v2 encoding, base-128 varints with zigzag for deltas, and CRC32C (Castagnoli) with a runtime-built lookup table. RecordBatch encoding is handled as a two-pass “write placeholders then patch” process for lengths and checksums.
On the trading side, signals are versioned and schema-checked at init, keyed by symbol+timeframe for partition ordering, queued and flushed on a timer or batch size, with configurable acks and exponential-backoff retries. Logging via FILE_COMMON produces tester-friendly,...
👉 Read | Quotes | @mql5dev
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MetaTrader 5 Strategy Tester can produce a strong equity curve that fails to reproduce in forward or live trading. A single backtest reflects one ordering of trades and hides the range of possible equity paths, including tail drawdowns.
Monte Carlo simulation reshuffles the closed-trade PnL sequence to generate many alternative equity curves. Monte Carlo analysis then extracts metrics from that distribution, including bust rate, profit rate, and worst observed drawdown.
A practical Python pipeline can parse the MT5 HTML report, extract initial balance and “Direction=out” PnL rows, run N shuffled paths, and output a mean curve with 5–95% bands plus summary stats. This supports position sizing based on tail risk rather than one curve.
👉 Read | Quotes | @mql5dev
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Modern FX trading stacks still face a trade-off between deterministic indicators and ML models that overfit, especially when train/test boundaries are weak. Information leakage remains a primary source of inflated backtest results.
A hybrid design is described: a fine-tuned Llama 3.2 model paired with Self-Evolving Adversarial Learning (SEAL), deployed on MetaTrader 5. The pipeline enforces a forward-test split (last 7 days held out) to keep validation unbiased.
Data generation labels 24h direction on M15 across eight major pairs, applies a 0.05% move threshold, and uses active class balancing near 50/50. Prompts include RSI, MACD, ATR, Bollinger position, stochastic, and volume ratio with a strict parsable output format.
SEAL adds adversarial self-play, prioritized replay, curriculum difficulty, and evolutionary updates to improve robustness und...
👉 Read | Freelance | @mql5dev
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ZetaBurst is a tick-driven scalper that evaluates a short rolling burst window (default 4 seconds) and builds a live baseline from recent returns. Trades trigger only on statistically abnormal moves using a z-score against the last InpStatsSampleCount samples, with a default threshold of 3.0σ. A prior ATR-fraction trigger was removed after it over-fired on normal tick noise and produced broad losses.
Two execution modes are provided: momentum (trade with the burst) and reversion (trade against it). Symbol fit is not inferred automatically; both modes require separate testing per instrument.
Order handling accounts for real execution delay. Positions open without an attached stop, then SL/TP are computed from the confirmed fill price, clamped to the symbol minimum stop level. If stop attachment fails, the position is closed immediately. Entries also require a...
👉 Read | AlgoBook | @mql5dev
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Butterfly Optimization Algorithm (BOA), proposed in 2019 by Arora and Singh, models movement using a fragrance term f = c·I^a and a switch p between global and local search. Fitness is mapped to stimulus intensity I, then converted to fragrance via the power law, with a increasing toward 1 over epochs to shift from broad search to stronger exploitation.
Implementation review found a critical issue in the paper’s update equations. Using x_new = x + (r²·g* - x)·f biases steps toward r²·g*, which tends to pull the population toward the origin and only looks correct when the optimum is at 0.
A corrected form preserves components but fixes geometry: x_new = x + r²·(g* - x)·f, and locally x_new = x + r²·(x_j - x_k)·f. Testing should include optima away from the origin to catch this class of error.
👉 Read | NeuroBook | @mql5dev
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AurumNeuro Vanguard is an Expert Advisor focused on XAUUSD, built around a hybrid neural risk design and a Unified Market Dynamics Engine. Signal generation combines causal price analysis, online neural learning, and ATR-based risk controls rather than relying only on standard indicators.
The UMDE layer evaluates direction using price velocity, entropy, and Causal Price Dynamics to filter weaker conditions. A 5-12-3 neural network trains online on prior bar data and is used for directional confirmation plus dynamic TP/SL guidance.
Risk handling supports fixed lot or risk-percent sizing with auto sizing based on SL distance, commission, and tick value. Trade management includes ATR trailing with optional aggressive behavior, volatility-aware stop widening, RR-based profit hard close, and early loss exits when neural confidence drops. Execution filters include ...
👉 Read | Freelance | @mql5dev
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TQNet targets multivariate market forecasting by combining fast reaction to current conditions with a learned “global memory” of stable cross-asset relationships. Instead of building attention queries from raw prices, it uses trainable vectors that shift cyclically over time, while keys/values still come from the live input window. This balances persistent structure (seasonality, recurring liquidity cycles) with local shocks and noise.
Architecturally, it stays lightweight: one multi-head attention block plus a shallow MLP with residual connections, then a linear projection to any forecast horizon. RevIN normalization is used to handle distribution shifts common in finance, keeping the model focused on patterns rather than changing scale/volatility.
The article also outlines an MQL5-oriented implementation path and positions TQNet as a practical a...
👉 Read | AppStore | @mql5dev
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MT5 historical demonstration on XAUUSD (RoboForex-ECN), 1 May–8 Sep 2026, using 100% real ticks. Test settings: USD 10,000 deposit, 1:100 leverage, fixed 0.01 lot, default parameters (v0.11 defaults match the demonstrated set; trading logic unchanged vs v0.10). Result is optimized history, not a forward test.
Performance summary: 220 trades, net profit USD 1,293.99, profit factor 1.67, max equity drawdown USD 203.00 (1.91%), win rate 28.64%. Both long and short sides were net positive. Low hit rate included 13 consecutive losses, with expectancy driven by larger winners.
Inputs: brick size 6.0 (price units), momentum 8, threshold 5.0, TP 10 bricks, SL 2 bricks, max hold 1440 min, cooldown 3 bricks, max spread/brick 0.35, Magic 26091043.
Operational notes: validate in Strategy Tester. Brick size is price units, not points. Virtual SL/TP require terminal uptim...
👉 Read | Forum | @mql5dev
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Work continued on hardening an automated MT5 optimization pipeline rather than adding trading logic. Core library and strategy-specific project code were further separated, so new ideas can be tested by changing project parameters without editing the shared library.
Optimization tasks now support time limits to cap end-to-end runtime and stop early once enough strong candidates exist. The optimization EA UI was expanded to show stage, symbol, timeframe, elapsed/remaining time, and overall progress.
CConsoleDialog was fixed to avoid duplicated windows after restarts by correcting OnDeinit cleanup. Chart elements behind the UI were disabled to avoid font rendering issues and remove the need for window minimization, requiring small local copies of standard library dialog classes.
Next focus: running multiple instances of the final multi-currency EA across dif...
👉 Read | Forum | @mql5dev
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Portfolio eigenvalues describe how total variance is split across independent risk factors, but raw spectra don’t clearly indicate whether diversification is real or just cosmetic. This workflow turns the eigenvalue proportions into a single diversification score using spectral entropy (Shannon entropy normalized to [0,1]): high values mean variance is evenly distributed; low values indicate one dominant driver.
A reusable MQL5 script computes the covariance matrix, extracts and sorts eigenvalues safely (ArraySort over unreliable vector.Sort), converts them to variance shares, then outputs H_norm, dominant-factor percentage, an ASCII bar chart, and a thresholded concentration verdict for side-by-side portfolio comparison.
A key takeaway: diversification is governed by covariance structure, not instrument labels. Adding an uncorrelated but high-vola...
👉 Read | AppStore | @mql5dev
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Multi-chart EAs often act as if they are the only process in the account. The broker enforces margin, margin level, and liquidation at account scope, so individually well-sized trades can still combine into a full drawdown.
A shared PortfolioRisk.mqh moves portfolio measurement out of any single EA. It supports account-wide scope or a magic-number filter, scans positions plus pending orders, and builds currency-leg exposure without parsing symbol names.
Before opening a trade, CanOpenPosition() evaluates six limits on the “would be” state: total trades, per-symbol count, distinct symbols, margin use, floating loss, and per-currency net exposure. Pearson correlation is noted but intentionally excluded; currency decomposition is deterministic, history-free, and consistent across EAs.
👉 Read | AlgoBook | @mql5dev
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This MQL5 script turns closed deal history into trade-level analytics that avoid the common win-rate trap. Deals are grouped by position into a single record per round trip, with explicit “defined” flags so missing metrics never masquerade as zeros.
The core metric, Trade Quality Score, uses expectancy but substitutes the Wilson lower bound for win rate to penalize small samples. The conservative expectancy is normalized by average loss as a risk proxy, yielding a dimensionless score comparable across symbols and account sizes.
Architecture is modular: history reader, optional hour-of-day session filter (midnight-safe), pure calculator, and a CCanvas dashboard plus Experts-tab report. A dedicated test script validates pip conversion, edge cases, Wilson math, thresholds, and session boundaries with synthetic trades.
👉 Read | NeuroBook | @mql5dev
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A new MQL5 signal class, CSignalIsotonicPNN, implements a two-stage confidence model for oscillator-based entries. Seven RSI/Stochastic plus price-context interpretations output a bounded directional score in [0,1], with 0.5 as neutral.
Isotonic regression calibrates the score into an ordered probability using a rolling calibration window and forecast horizon, with safeguards against lookahead. An optional PNN then blends a posterior based on similarity to historical bullish/bearish states, controlled by PNNSamples, PNNSigma, and PNNWeight.
The design keeps each mode independently testable and separates ranking quality from calibration. Invalid inputs and degenerate computations return 0.5 to avoid accidental directional bias.
👉 Read | Signals | @mql5dev
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PulseStrike is a tick-driven scalper built around burst detection, not candle close. It maintains a rolling baseline over a short window (default 4s) and treats a move as tradeable only when it is a statistical outlier versus that baseline (z-score, default 3.0). This replaced a fixed ATR-fraction trigger that over-traded normal tick noise.
Two execution modes are supported: momentum (with the burst) and reversion (against it). Symbol fit is not assumed; both modes should be tested.
Entries are gated by spread-aware TP sizing, ATR-scaled SL/TP, a max hold-time force close, plus daily trade and daily loss caps. Single-position operation is used to behave correctly on netting accounts, cycling trades with tick-level checks and a short cooldown.
Backtest (M1, every tick, random delay, 2026-01-01 to 2026-09-08, balance 10k): EURUSD PF 1.30 DD 10.33%; AUDUSD PF ...
👉 Read | NeuroBook | @mql5dev
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EMTOrdersUtility.mq5 implements an on-chart trade monitor that replaces terminal scrolling with a symbol grid. Each symbol is rendered as a button plus up to three stacked label lines, refreshed via EventSetTimer at a user-defined interval.
Symbols can be loaded from Market Watch or a manual CSV list, capped by InpMaxSymbols. Display names can be abbreviated by stripping common broker suffixes, and button width can be fixed or auto-sized based on the longest label.
Runtime updates aggregate positions per symbol, split by BUY and SELL, with swap included in P&L. Pending orders are counted separately and can be highlighted via border settings. Button background reflects net P&L state (profit, loss, flat), with a selected-symbol color override.
Version notes mention alphabetical symbol ordering and removal of the hide/show toggle.
👉 Read | Signals | @mql5dev
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Range volatility can be measured beyond ADR% by using an empirical percentile rank. The metric compares the current period’s range so far to the full ranges of the last N completed periods, reporting what percentage were smaller. This avoids mean-based distortion in instruments with occasional spike sessions.
Readings at or below the compression threshold (default 20) flag an unusually quiet period; readings at or above the expansion threshold (default 80) indicate a session already larger than most historical peers. A 12% value means the current range is smaller than 88% of the last 100 periods.
Key inputs include range timeframe (D1 by default), lookback (100), thresholds (20/80), and update mode (per bar or per tick). The panel includes a 0–100 gauge, uses only chart objects, and does not place or manage orders.
👉 Read | Signals | @mql5dev
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Colliding Bodies Optimization (CBO, 2014) is a population metaheuristic where candidate solutions are ranked, assigned normalized masses from inverse objective values, then split into stationary (best half) and moving (worst half) sets.
Each moving body pairs by rank with a stationary body. Velocities are computed from position differences, then updated with a restitution coefficient epsilon that decreases linearly from 1 to 0 across iterations to shift from broad search to refinement.
Enhanced CBO (ECBO) adds a Colliding Memory archive to reinsert elite solutions and an optional low-probability coordinate reset step to reduce stagnation. Per-iteration cost is dominated by sorting: O(n log n).
👉 Read | Freelance | @mql5dev
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Prop-firm risk limits add a second rulebook on top of the broker account: static and trailing drawdown from a fixed start, daily loss reset in the firm’s time zone, payout consistency, minimum trading days, news blackout, and weekend flat. Violations can void the account even when platform limits are not hit.
This library provides a reusable guard that compiles into any EA. It evaluates eight rules from one frozen account snapshot per pass and emits at most one directive, avoiding multiple reactions to the same event. It does not trade and can work alongside any EA, manual trading, or copied signals.
Archive contents: 24 .mqh headers, a facade class (CPropRulebook), and a demo EA (PropFirmDefense.mq5) with an on-chart panel. State is persisted across restarts, including trailing-floor anchoring and consumed budgets. Optional terminal Algo Trading switching ...
👉 Read | NeuroBook | @mql5dev
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Update on the MT5 replay/simulation series focuses on why the system works: custom events are generated by indicators, routed through MetaTrader 5, and delivered to all apps on a chart via OnChartEvent.
Chart Trade, the Expert Advisor, the Position indicator, and Mouse Study remain decoupled. None of them require direct references to each other. Events are broadcast, and only modules implementing the corresponding handler react; others ignore them.
Two event categories are used: EA-originated update events after trade server replies, and UI-driven events from MT5/indicators (mouse move, click, ESC). Virtual SL/TP dragging is confirmed or canceled by subsequent events.
A requested extension is EA-controlled pause/play for the replay service. Since events are chart-scoped, the service must be reached via its control indicator by adding a custom even...
👉 Read | AlgoBook | @mql5dev
