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 568 354 подписчиков, занимая 133 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 568 354 подписчиков.
Согласно последним данным от 07 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 15 989, а за последние 24 часа — 409, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.55%. В первые 24 часа после публикации контент обычно набирает 1.66% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 14 457 просмотров. В течение первых суток публикация набирает 9 415 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 39.
- 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 08 октября, 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.
568 354
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| Дата | Привлечение подписчиков | Упоминания | Каналы | |
| 08 октября | +233 | |||
| 07 октября | +433 | |||
| 06 октября | +750 | |||
| 05 октября | +900 | |||
| 04 октября | +357 | |||
| 03 октября | +93 | |||
| 02 октября | +761 | |||
| 01 октября | +515 |
Посты канала
MACD signal-line crossovers fail structurally in ranges: tiny sign flips in the MACD histogram can trigger frequent reversals with no information about momentum magnitude or persistence. On EURUSD H1 (2018–2024), this produces heavy whipsawing and large drawdowns out of sample.
A two-layer meta-labeling pipeline keeps the classic crossover for direction, then adds: (1) an Optuna-tuned regime gate requiring histogram magnitude vs ATR, persistence across several bars, and optional MACD zero-line agreement; (2) a secondary classifier that sizes positions by the predicted probability of hitting a triple-barrier profit target before a stop.
Context features mix MACD diagnostics (separation, slope, chop counts), external regime measures (ADX, volatility percentiles, EMA distance), and session timing. Results show the gate dramatically reduces trade count...
👉 Read | AlgoBook | @mql5dev
| 2 |  Position sizing often relies on win rate alone, while ignoring the probability of long loss runs that can breach drawdown limits at a given risk-per-trade.
A MetaTrader 5 dashboard script can quantify this from history: empirical win rate, average win, average loss, and observed max losing streak, with breakeven deals excluded from win/loss stats and treated as streak breakers.
Consecutive-loss probabilities use the geometric tail P(streak ≥ N)=q^N, where q=1−p, giving immediate estimates for 5/10/15/20-loss runs without simulation.
Risk of ruin is computed from normalized edge A and risk units U=100/risk%, with RoR=((1−A)/(1+A))^U and a hard clamp to 1.0 for A≤0.
A CCanvas plot traces RoR versus risk-per-trade and marks the current setting; an Experts-tab report prints streak odds and current ruin probability, with limits noted around independence, r...
👉 Read | Freelance | @mql5dev | 3 307 |
| 3 |  Intrinsic-time trading replaces bar-based sampling with event-based sampling driven by price reversals. A clock tick is generated only when price moves by a fixed threshold delta from the last extreme (directional change), with subsequent continuation treated as overshoot. The resulting sequence of events forms the “coastline” representation used by Olsen-group research.
An MQL5 Expert Advisor ports the Alpha Engine coastline trader: eight independent agents (long-only and short-only) across four thresholds (0.25%, 0.5%, 1%, 1.5%). Agents trade via limit orders on hedging accounts, cascading into adverse moves and de-cascading via fixed take-profits set at delta. Adds are in fixed increments, explicitly not martingale.
Risk shaping comes from inventory-driven threshold skew, fractional sizing, and an information-theoretic liquidity metric L that cuts adds i...
👉 Read | Signals | @mql5dev | 5 302 |
| 4 |  Many EAs lose consistently, but reversing trades is not a shortcut to profitability. A mirrored inverse pays trading costs again, so results are not a simple sign flip. Real execution also changes outcomes due to spread side, slippage, swaps, and SL/TP path dependency.
MirrorHedgeEA mirrors another EA’s or manual trades inside the same account and broker, with no cross-account communication. It syncs the full trade lifecycle: market and pending orders, SL/TP changes, partial closes, closures, and deletions. Each mirror trade is tagged with a derived magic number suffix and a comment suffix for clean separation in history.
Configuration supports magic-number pattern matching, multiple lot sizing modes, inverse direction with TP/SL swapping, and restart-safe source/mirror linking. Designed for hedging accounts and intended as a measurement tool to evaluate in...
👉 Read | Freelance | @mql5dev | 6 556 |
| 5 |  Arixis Backtest Robustness Analyzer is a free, open-source utility for MetaTrader 5 focused on evaluating backtest robustness beyond Profit Factor, net profit, and win rate. It operates on closed trade history and reports how results were generated, including average/median trade, best and worst trades, streaks, percentiles, and sample-size context.
Dependency and sensitivity checks recalculate outcomes after removing top or bottom trades to quantify concentration and the impact of extremes. When net profit is non-positive, dependency ratios are suppressed to avoid misleading percentages while still showing adjusted net results.
The tool reconstructs a closed-equity curve to analyze drawdown depth, underwater time, and recovery, with the limitation that intratrade floating drawdown cannot be derived from closed history alone. Time and direction breakd...
👉 Read | AppStore | @mql5dev | 7 602 |
| 6 |  ATS Liquidity City plots likely stop locations and marks which pools were swept on the current server day.
Liquidity pools are defined as confirmed swing highs/lows using N bars on each side (default 5), equal highs/lows when multiple swings fall within 0.10 x ATR(14), and yesterday’s high/low valid for today only. Equal levels increase pool size and are rendered with thicker lines.
A sweep is flagged on the first closed bar that trades beyond a pool. If the bar closes back inside it is marked grabbed & rejected, otherwise taken. Active pools render as solid lines with labels and prices; swept pools are dotted and retained for 300 bars. Tooltips show type, price, and status.
The panel reports nearest pools above/below in ATR distance, and a taken-today ticker. For developers: buffers expose sweep arrows and nearest active levels; the panel uses a 5...
👉 Read | AlgoBook | @mql5dev | 7 941 |
| 7 |  “Non-repainting” is one of the most common claims in indicator descriptions and one of the hardest to verify visually. A manual reload test typically misses two costly cases: closed-bar values changing while the chart stays open, and history changing after a reload caused by timeframe switches, terminal restarts, or opening a fresh chart.
Repaint Inspector measures this directly by re-reading indicator buffers on already closed bars (shift 1+), keyed by bar open time rather than shift. The first observed value becomes the reference; later reads are compared to both the previous value (event counting) and the first value (drift). Events are split into Changed, Appeared, and Vanished, with MaxShift reporting how far back any event occurred.
Each event is written to CSV with bar time, buffer, shift at detection, and old/new values, making the report auditable...
👉 Read | Calendar | @mql5dev | 8 087 |
| 8 |  Edge Drift Detector adds statistical monitoring to an MT5 Expert Advisor by testing whether live closing deals still match the backtest distribution, or whether average outcome per deal has regressed toward zero. It reads baseline and live data from tester reports, CSV, or account history, and prints a full report in the Experts tab.
Coverage includes a bootstrap-calibrated CUSUM edge-loss alarm with a selectable false-alarm rate over a planned deal horizon, an estimated drift start deal/date, and tail-probability checks over short and long windows (default 20 and 100 deals). Expectancy change is decomposed into win-rate, win-size, and loss-size effects, with a symbol-mix warning when live instruments differ from the baseline.
Output includes detection-delay estimates, an A+ to F Edge Health Score, and an explicit CONTINUE/WATCH/REDUCE/SUSPEND verdict with ...
👉 Read | Freelance | @mql5dev | 7 751 |
| 9 |  Backtest Sharpe ratios are biased upward when reported from the best optimization pass. No data manipulation is required; selection alone inflates the result.
Raw Sharpe is optimistic for three reasons: limited sample size, non-normal return distributions (skew and fat tails), and the “best-of-N” effect across parameter trials.
Probabilistic Sharpe Ratio (PSR) converts an observed Sharpe, trade count, skewness, and Pearson kurtosis into the probability that the true Sharpe exceeds a benchmark.
Deflated Sharpe Ratio (DSR) adds selection adjustment by raising the benchmark to the expected maximum Sharpe from N no-edge trials, using the cross-trial variance and a normal quantile approximation.
Example: 56 MA variants on XAUUSD H1. Winner SR=0.070 (711 trades). PSR vs 0: 98.5%. DSR vs hurdle 0.028: 90.6%, below a 95% significance threshold.
👉 Read | NeuroBook | @mql5dev | 7 454 |
| 10 |  This EA targets a common pullback failure mode: swings that look valid to pivot logic but lack real displacement. It fixes that by grading each impulse leg and allowing pullback setups only when the leg shows measurable imbalance.
Swings are confirmed on closed bars with strict alternation, so every extreme has a clean origin-to-extreme leg to evaluate. A deterministic Candle Imbalance Engine scores legs from four bounded signals: average body-to-range (displacement), close-direction consistency, efficiency (net vs gross travel), and Fair Value Gap coverage normalized by leg height.
Only swings above a configurable threshold can arm a pullback state machine (armed → in-zone → done). Entries require an in-zone confirmation close; invalidation includes origin breaks and an optional first-clean-retest rule.
Exit management runs on every tick with R-m...
👉 Read | Freelance | @mql5dev | 7 233 |
| 11 |  ST-Expert tackles non-stationary markets by using a Mixture of Experts: multiple specialized predictors trained on distinct regimes (trend, correction, flat), with a mixing module that reallocates weights as conditions shift. Expert “graphons” model changing inter-asset connectivity, improving robustness without a large parameter increase.
The implementation extends a Transformer-style stack (Extralonger) with a graphon-driven global-local attention block. Global attention captures long-horizon structure, while local attention targets short-term anomalies; outputs are concatenated and adaptively mixed.
Engineering details cover clean module lifecycle (Init), real-time forward flow, correct gradient splitting/merging during backprop, and weight updates delegated to submodules—useful patterns for building adaptive forecasting components in MT5 pipeli...
👉 Read | Signals | @mql5dev | 7 140 |
| 12 |  Collaborative debugging in MQL5 often meant copying source and compiler output into an external AI chat, then returning fixes to MetaEditor. That loop worked, but context drift and manual transfer added friction.
MetaTrader 5 Build 6060 adds MCP support and an AI Assistant inside MetaEditor. The key change is proximity to the editor and compiler, not automatic correctness. Prompts still need constraints, and fixes must be reviewed.
A D1 PriceMarker exercise highlights the gap between compilation and runtime validity. The buggy version fails due to a missing semicolon, an invalid object property (OBJPROP_Y), and unchecked return values. An AI-fixed version can compile cleanly, but the final implementation adds rate validation, deterministic object naming, checked chart operations, timer refresh, and bounded cleanup.
Successful compilation is only stage one...
👉 Read | NeuroBook | @mql5dev | 7 728 |
| 13 |  This article builds an MQL5 indicator that turns market sessions into structured data, not just chart boxes. Each session occurrence is captured as a SessionData record with OHLC, range, net movement, high/low timestamps, extreme order, and range growth measured at 25/50/75/100% of the session.
A global engine state keeps configs, session history, UI selection, click debouncing, and bar-based throttling consistent across OnCalculate and OnChartEvent. Sessions are rebuilt from chart bars (timeframe-based, not tick-exact) and flagged as completed using TimeCurrent().
Visualization stays lightweight: clickable open-time markers plus a comparison panel (range and net move) and a session inspector that reveals full metrics for any selected session, including partial progress for live sessions.
👉 Read | AlgoBook | @mql5dev | 8 603 |
| 14 |  Council of 15 extends the earlier four-role setup by widening signal diversity and separating forecasting from tradability. Ten analysts run in parallel and output BUY/SELL/NO SIGNAL with numeric justification. Four risk managers then gate entries with APPROVED/CAUTION/BLOCKED. A Chair applies a fixed algorithm: risk gate, vote count, then argument weighting, returning a single JSON decision.
Execution uses ThreadPoolExecutor: 10 analyst calls (4–6s), 4 risk calls (3–4s), Chair (3–4s), for a 10–15s cycle. Designed for H1 position trading, not scalping. Backward compatible with existing EA fields.
Backtest: EURUSD M15, 2026-02-01 to 2026-03-02. 43 trades, +$2,942.67 on $100k. Profit Factor 1.47, win rate 60.47%, max relative drawdown 2.73%, Sharpe 3.05.
👉 Read | Forum | @mql5dev | 9 489 |
| 15 |  GBPJPY “Monday” EA: one scheduled long trade per week with fully readable source and no indicators, optimization, or bar-based logic. It runs on a 1-second timer and uses only server day/time, so timeframe selection (M1 to H1) does not affect execution.
Rules are fixed: Monday only, buy at 11:00 server time and close at 23:00 the same day. Positions are flat before rollover to avoid swap. Server time assumes GMT+2 winter and GMT+3 summer; on startup the EA prints the broker offset for manual hour adjustment.
Backtest (2022-01-01 to 2026-09-17): +52.9% on a 10k account at 0.2 lot, 11.2% annualized after $6/lot fees, 7.05% max drawdown, PF 1.86, 59.1% win rate, 242 trades. Profitability persisted with entry shifted across 09:00–13:00.
Guards: skip if spread > 1.5 pips; block entry if margin level would drop below 300%. No stop loss; risk is the full 12-hour mo...
👉 Read | CodeBase | @mql5dev | 10 557 |
| 16 |  Market Session Pro+ v3 expands coverage to 24 markets, up from the original 12, targeting session-driven short-term price moves such as Gold scalping.
The indicator renders market open and optional close times directly in the chart window. Built-in sessions include New York, Shanghai, Euronext, Tokyo, Shenzhen, Mumbai, Hong Kong, London, Toronto, Zurich, Frankfurt, and Sydney, plus two configurable custom markets.
Session schedules are defined in UTC with per-market offsets and per-region DST rules. Broker GMT offset can be auto-detected or set manually, including minute-level adjustment. Countdowns use server time so timing matches the plotted lines. Weekend handling and sessions crossing midnight are supported.
Visuals include configurable vertical lines, optional session boxes, edge markers, labels with countdowns, hover tooltips, and a sortable i...
👉 Read | AlgoBook | @mql5dev | 10 716 |
| 17 |  Spread Profile by Hour builds an hourly spread distribution from the broker’s own tick history and presents the median and 90th percentile across 24 server-time hours. Instead of a single per-bar spread value, it aggregates every bid/ask quote over the last N calendar days (excluding today), making rollover, session opens, and news-prone hours visible in the data.
Two sampling modes are supported: tick count (each quote once) and time-weighted (each quote weighted by how long it remained valid, capped at 60 seconds). A weekday filter allows isolating specific days.
A cost view maps median spread to a typical stop size in points, showing the spread as a percentage of 1R per hour, plus an account-currency estimate per 1.00 lot based on tick value and tick size. Current hour highlighting and a live spread line help with intraday context.
Optional CSV exp...
👉 Read | VPS | @mql5dev | 10 785 |
| 18 |  Symbol Spec Auditor consolidates contract specification details that affect risk and trading cost into a single on-chart panel. It converts key inputs into money per 1.00 lot in the account currency, then flags broker settings that can break lot sizing or order management without any journal errors.
The panel covers point and tick metrics, contract size, spread cost, margin, swap, volume constraints, execution and filling policies, GTC behavior, stops and freeze levels, and today’s trading sessions. Values that cannot be computed are marked n/a with the reason, and warning rows are highlighted with a one-line consequence.
Common triggers include non-standard contract sizes (e.g., XAUUSD 1 oz vs 100 oz), cent-denominated accounting, tick size not matching point, minimum volume/step forcing rounding, close-only modes, end-of-day order deletion, and unu...
👉 Read | Calendar | @mql5dev | 10 566 |
| 19 |  SensibleGuide_MTF_H1 is an H1 indicator that gates signals through multi-timeframe confluence and explicit risk constraints. Trend is scored via EMA alignment (10/20/50/100/200) with an optional minimum alignment threshold before any setup is considered.
Higher-timeframe modules load closed-bar data only and compute EMA bias plus confirmed swing points for H4, D1, and W1. The CHtf layer refreshes rates and EMA buffers, confirms swings with strength-based validation, and uses binary search to reference the last closed bar at a given H1 close time, supporting non-repainting decisions.
Support/resistance is aggregated into a capped level list: round numbers, H1 swings, long EMAs, Bollinger bands, Fibonacci from the screening timeframe, higher-TF EMAs/swings, and previous day levels. A k...
👉 Read | Calendar | @mql5dev | 10 500 |
| 20 |  Many EAs persist state in terminal global variables (balances, locks, heartbeats, last trade times). Over time this builds up, while the F3 viewer remains unsorted, unfiltered, and provides no backup. After a restart, diagnosing unexpected behavior often depends on identifying which variable was read.
Global Variable Inspector addresses this with snapshot-based inspection. It filters and sorts variables by name, then prints name, value, last access time, and age to the Experts journal. Whole-number values between years 2000–2100 are additionally rendered as dates to expose timestamp usage. Snapshots can also be exported to CSV, deleted with a mandatory backup step, or restored later via import.
Notable behavior: reading a variable updates its access time and extends expiry. The script avoids refreshing non-matching entries by collecting names and access times...
👉 Read | VPS | @mql5dev | 10 495 |
