MQL5 Algo Trading
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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 520 912 підписників, посідаючи 149 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 520 912 підписників.
За останніми даними від 13 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 11 174, а за останні 24 години на 692, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.36%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.87% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 17 508 переглядів. Протягом першої доби публікація в середньому набирає 9 729 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 38.
- 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 липня, 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.
520 912
Підписники
+69224 години
+3 7007 днів
+11 17430 день
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| Дата | Залучення підписників | Згадування | Канали | |
| 13 липня | +692 | |||
| 12 липня | +366 | |||
| 11 липня | +397 | |||
| 10 липня | +476 | |||
| 09 липня | +475 | |||
| 08 липня | +567 | |||
| 07 липня | +735 | |||
| 06 липня | +691 | |||
| 05 липня | +311 | |||
| 04 липня | +184 | |||
| 03 липня | +207 | |||
| 02 липня | +459 | |||
| 01 липня | +296 |
Дописи каналу
Indicator buffer data is being formalized as a first-class timeseries: per indicator, maintain a list containing all buffer values for every bar used in calculations. This enables consistent storage, search, sort, and later statistical analysis across library collections.
Core library changes: the NewBar class is relocated to Services; SeriesDE include paths are updated; new message indices/texts are added; a dedicated collection ID and timer constants are introduced. Indicator data gains an extra integer property for indicator handle, including sorting/search criteria support.
New class CSeriesDataInd stores CDataInd objects per bar/buffer, supports creation, refresh on current bar, append on new bar, and trimming to required depth. Indicator objects are extended with total buffer count and an embedded buffer-data series; the indicators collection and eng...
👉 Read | AlgoBook | @mql5dev
| 2 |  BBandsPsar is a custom hybrid indicator that merges a volatility model (Bollinger Bands) with a trend-following signal (Parabolic SAR) into one output.
The core calculation measures the gap between the Parabolic SAR value and the current candle’s open or close, reflecting SAR’s alternating behavior during trend changes. That gap is then normalized using Bollinger Bands, producing a standardized histogram rather than raw price-distance values.
Because the output is scaled, the same threshold logic can be applied across instruments with different price ranges, improving cross-asset comparability.
BBandsPsar inherits the full input sets of both Bollinger Bands and Parabolic SAR, allowing adjustment of period, deviation, price source, step, and maximum settings to control sensitivity.
For dynamic parameter testing, the iBands call can be refactored so ...
👉 Read | Freelance | @mql5dev | 10 295 |
| 3 |  Backtests only charge the costs that were configured, so net profit and profit factor do not show how close a strategy is to the execution-cost boundary. This MQL5 tool measures the limit directly: how much cost the strategy can absorb before the edge disappears.
It reads a CSV (Date,Profit,Volume; one row per closing deal) and prints a report to the Experts tab. Output includes breakeven cost per deal, cushion versus an assumed realistic cost, net and profit factor re-priced at the assumed cost, a cost-sensitivity curve from 0 to 3x, and win erosion where costs flip winners into losers. A composite A+ to F score aggregates cushion, profit-factor resilience, and erosion, with recommendations.
Input file goes to MQL5\Files; Volume is optional. If missing on first run, a reproducible sample file is generated. A helper ExportCost.mq5 can export deals from ...
👉 Read | CodeBase | @mql5dev | 10 512 |
| 4 |  Spreadsheets can be used as a lightweight research harness for algorithmic strategies: import MT5 history from CSV/TXT, normalize numeric formats, and keep the dataset small enough (around a few thousand rows) to avoid slow recalculation with minimal impact on results.
The workflow builds indicators directly in cells. A separate “variables” sheet stores parameters, while functions like IF, AVERAGE and INDIRECT generate a configurable SMA over dynamic ranges. Relative vs absolute references ($) make formulas safe to copy across long columns.
A moving-average crossover is then modeled as stateful trade logic: one column detects crossings, another persists position status bar-by-bar, and a signal column maps actions (Buy/Sell/Close) with correct next-bar execution. Additional columns propagate entry price (including spread) and compute pip outcomes for late...
👉 Read | AppStore | @mql5dev | 16 479 |
| 5 |  BBandsPsar is a hybrid indicator combining Bollinger Bands volatility metrics with Parabolic SAR trend signaling in a single output.
The calculation tracks the distance between the Parabolic SAR level and the candle open/close, capturing SAR’s alternating behavior during trend changes. This gap is then normalized through Bollinger Bands and rendered as a histogram.
Band-based standardization targets more stable thresholds across instruments, supporting cross-asset comparison on a unified scale. All configuration inputs from both Bollinger Bands and Parabolic SAR are retained, allowing adjustment of sensitivity, smoothing, and switching behavior to match market conditions.
👉 Read | Quotes | @mql5dev | 14 001 |
| 6 |  This article upgrades the classic SuperTrend by making its ATR multiplier responsive to momentum divergence, using MPO4 (or optionally RSI) as the trigger. When a validated bullish/bearish divergence appears, a “shrinking” rule reduces the multiplier by a sensitivity factor, pulling the band closer to price. The result is tighter trailing stops near exhaustion and earlier flips for capturing the next trend.
A key focus is non-repainting behavior in MT5 indicators. Instead of fragile global flags, the design persists divergence context per bar via state buffers (last pivot prices/osc values, last divergence type, bars-since counter). State is propagated forward each iteration and reset on trend flips, keeping historical signals stable across ticks, refreshes, and restarts.
Implementation details cover a chart-window MQL5 indicator with a colored SuperTrend...
👉 Read | Docs | @mql5dev | 14 568 |
| 7 |  Price-window indicators in MQL5 often shift arrays with ArrayCopy() on every bar to keep index 0 as “latest”. That design is O(n) per update and scales poorly with large periods, multi-symbol sets, and dense OnCalculate() workloads.
A circular buffer removes the shift. Only a head index advances with modulo arithmetic, and one slot is overwritten. Push and Get become O(1) regardless of window size; allocation is fixed at construction.
An MQL5 template CCircularBuffer<T> fits this pattern but comes with constraints: no specialization, template code must stay in-header, and capacity must be runtime. Statistical routines should use two-pass variance to avoid cancellation common in price series.
Result: less memory movement per bar, improved backtest throughput, and reduced live tick latency under load.
👉 Read | Signals | @mql5dev | 12 814 |
| 8 |  False breaks of recent N-bar highs/lows often act as liquidity sweeps: price runs stops beyond an obvious extreme, then closes back inside the range. The article turns this into a fully testable Turtle Soup contract: minimum sweep depth (points and % of range), minimum age of the swept level, sweep-window rules, and close-based rejection confirmation (multiple closes, optional reversal body).
The MQL5 implementation focuses on robustness: bar-close execution, duplicate-signal guards, per-direction position caps, and broker stop-level compliance. Stops can be dynamic beyond the sweep extreme or fixed-point; targets support R-multiple projection or the opposite side of the lookback range, with optional trailing.
It also builds chart tooling (levels, sweep zones, risk/reward boxes) and a scan engine using iHighest/iLowest and close checks, ready for S...
👉 Read | CodeBase | @mql5dev | 12 037 |
| 9 |  Mean-reversion implementation in MetaTrader 5: weekly channel breaks using two moving averages on High and Low with a shared period. Break above the upper band triggers short bias; break below the lower band triggers long bias.
Signal confirmation uses an ONNX-backed statistical model plus daily open/close moving averages for directional filtering. Data pipeline exports EURUSD daily bars to CSV via MQL5, then trains in Python on 2011–2019 and tests on 2020–2026. Model selection highlights overfitting in flexible learners; a linear regressor is chosen and exported to ONNX.
The EA loads the ONNX model, manages trades via magic number filtering, updates indicator buffers, and applies ATR-based symmetric SL/TP with trailing stops. Backtests with real ticks and random delay show modest positive expectancy, profit factor near 1.1, and larger average win...
👉 Read | Calendar | @mql5dev | 11 179 |
| 10 |  Most MT5 risk tools implicitly cap worst-case planning to what the recent window already contains. ATR, bootstrapped trade resampling, and historical VaR all operate on observed volatility and outcomes, so tail losses beyond the sample are systematically understated.
Financial returns are fat-tailed, so rare moves occur more often than normal assumptions imply. Extreme Value Theory addresses this by modeling only the tail, allowing extrapolation beyond the historical maximum loss.
An MQL5-native implementation can use Peaks-Over-Threshold with a Generalized Pareto fit via ALGLIB MLE. Outputs include EVT VaR, Expected Shortfall, and a shape parameter that quantifies tail heaviness, with hard refusal to report metrics when exceedances are insufficient.
👉 Read | Signals | @mql5dev | 10 832 |
| 11 |  Protecting profit after a position is opened typically requires rules that react to price movement, not just the entry signal. Common approaches include moving the stop loss to breakeven after a defined profit threshold, then switching to a trailing stop to lock in gains as volatility expands or contracts.
A structured method uses staged stop management: initial fixed stop, breakeven activation, and trailing based on points, ATR, or recent swing levels. Partial closes can reduce risk while keeping exposure for continuation. Controls such as minimum distance, step size, and spread filters help avoid premature stop-outs in noisy conditions.
👉 Read | AlgoBook | @mql5dev | 10 811 |
| 12 |  Replay/simulation work in MQL5 is moving past sockets and SQL basics into chart-side tooling needed for realistic interaction.
Current components (mouse pointer, Chart Trade indicator, EA messaging) can submit market execution, but cross-orders and replay symbols lack reliable visual feedback for positions and orders.
Next step is a minimal position-visualization indicator. An indicator can read position state and print details, without attempting trade actions, which remain EA-only. This also highlights a platform constraint: indicators share a single chart thread, so the logic must avoid blocking and unnecessary updates.
Focus shifts to controlled chart objects for position lines, with attention to object lifecycle and cleanup to prevent long-term chart degradation.
👉 Read | Docs | @mql5dev | 10 232 |
| 13 |  MetaTrader 5 hides powerful “indicator-like” behavior inside chart objects. By programmatically repurposing OBJ_FIBO, the article builds a custom risk tool that visually marks entry, stop, and target using only a few configured levels.
The core technique sets a fixed number of Fibonacci levels, then assigns each level its value, color, and style via parallel arrays. Using values outside 0..1 turns Fibonacci into a projection engine, making it easy to express 1:1, 1:1.5, and partial-exit layouts that can’t be recreated manually from the UI.
Practical refinements include locking selection to prevent accidental moves, hiding unwanted guide lines, exposing a stop/target ratio as an input, and optionally disabling right-side extension so multiple analyses can coexist cleanly. The article also explores switching drawing flow toward left-click interaction, ...
👉 Read | Signals | @mql5dev | 9 349 |
| 14 |  This article extends the MCP approach from trading execution to the full development workflow by connecting AI agents to MQL5 Algo Forge, a Git-based host backed by Forgejo. Using its HTTPS REST API, an assistant can programmatically create repos, commit EA files, manage branches, open pull requests, file issues, and publish releases.
The server is a portable Python project (no MetaTrader/Windows dependencies) built in layers: JSON config + token, an httpx-based API client with consistent error normalization, domain handlers, and a FastMCP tool surface exposing 12 tools.
Key implementation details include safe token handling (config or environment variable) and Base64 encoding/decoding for file endpoints. A single “commit file” tool abstracts POST vs PUT by detecting existing files and retrieving sha automatically, enabling reliable updates without t...
👉 Read | Docs | @mql5dev | 8 882 |
| 15 |  Reliable MT5 Expert Advisors require systematic validation before sending trade requests. Common constraints include max positions/orders, volume min/max/step, SL/TP distance, margin, session limits, symbol permissions, and news filters. Market publication tests already enforce many of these cases, so reusable checks reduce duplication and regressions.
A compact MQL5 validator set can cover: lot sizing with step-based normalization (avoids retcode 10014), SL/TP distance vs SYMBOL_TRADE_STOPS_LEVEL, price digit normalization (avoids retcode 10015), freeze-level checks for order modifications, margin/funds checks, and “no changes” guards to prevent TRADE_RETCODE_NO_CHANGES.
Additional utilities include: pending-order limits, lightweight new-bar detection, tradability checks for SYMBOL_TRADE_MODE_DISABLED, calendar-based news windows (live only), and UTC sess...
👉 Read | Signals | @mql5dev | 9 481 |
| 16 |  This part adds a pinned-tools ribbon to complement the deep, tabbed sidebar: the sidebar stays optimized for discovery, while the ribbon provides one-click access to a small set of frequently used drawing tools. The ribbon auto-hides when empty and preserves pin order.
Pinned tools are stored in the engine as an ordered list with a minimal API (count, get, contains, pin/unpin, toggle). Pinning appends; unpinning compacts the array without reordering, keeping behavior predictable.
A shared anti-aliased pushpin glyph is rendered via polygon coverage and alpha blending, reused consistently across flyout rows, the Pinned sidebar tile, and the ribbon.
The ribbon is a floating surface with drag, resize, and horizontal scrolling. It clips overflowing icons using an offscreen canvas and shows a proportional scrollbar thumb, keeping interaction smooth eve...
👉 Read | NeuroBook | @mql5dev | 10 467 |
| 17 |  Walk-Forward Analysis is reframed as a measurable robustness test for MT5 EAs: optimize on in-sample windows, score degradation on forward windows, and summarize it as a reproducible metric instead of eyeballing equity curves.
The core score is Walk-Forward Efficiency (WFE): per-window ratio of out-of-sample to in-sample Sharpe. Windows pass only if they retain at least 50% efficiency, with guards that force failure when in-sample Sharpe is non-positive or too small to be meaningful.
A native MQL5 pipeline implements the full loop: an EA logs per-bar equity to CSV, a fast reader ingests it, WFE_Engine.mqh computes Sharpe/WFE with numerical stability and validity flags, and a CCanvas histogram renders pass/fail windows and reference lines directly on-chart for immediate diagnosis.
👉 Read | Calendar | @mql5dev | 10 550 |
| 18 |  KCI Embedded Sniper is an algorithmic reversal-entry EA built around a fully embedded Kinetic Compression Index engine. KCI math (Velocity Quotients, Kinetic Displacement, Energy Dispersion, Phase Velocity) runs inside the EA, avoiding iCustom() latency and thread desynchronization. Signals are computed on closed bars only, targeting non-repainting execution with Singularity exhaustion validation and a Williams %R momentum gate.
The design removes external indicator files, reducing memory overhead and eliminating indicator path, loading, and buffer read errors. Computation is event-driven via rates_total and internal matrices, optimized for multi-asset operation in a single process loop.
Risk and filtering are parameterized: fixed lot sizing, ED-based dynamic SL/TP scaling, WPR period and extreme levels, plus KCI sensitivity controls (ZScorePeriod, Compress...
👉 Read | Docs | @mql5dev | 10 976 |
| 19 |  The Kinetic Compression Index (KCI) is a custom oscillator for detecting market exhaustion and localized compression events. It computes kinematic-style metrics directly inside the indicator loop, reducing reliance on multiple external indicator handles and simplifying EA buffer management.
The design exposes reproducible components such as Velocity, Deviation, and Dispersion, with Z-Score normalization applied over a rolling window before forming a composite KCI value. Signal buffers are built to validate on closed bars to reduce intrabar repainting and async tick timing issues.
A unified buffer map is central to integration: KCI main line and color index, buy/sell signal buffers, plus internal calculation arrays including Velocity Quotient, Kinetic Displacement, Energy Dispersion (usable as a volatility proxy for SL/TP logic), Phase Velocity, and R...
👉 Read | Forum | @mql5dev | 12 276 |
| 20 |  A Smart Money Concepts (SMC/ICT) market-structure indicator for MetaTrader 5 that derives structure from swing-high/swing-low sequencing and renders the current state directly on the chart.
Swing points are detected via N-bar fractals and plotted as arrows. Breaks are classified as BOS when price closes beyond the prior swing in the trend direction, and as CHoCH when the close exceeds the prior swing against the trend. On each structure break, the last opposite candle is flagged as an Order Block. Fair Value Gaps are identified as 3-candle imbalance zones. After a CHoCH, QML draws a dotted retrace level at the left-shoulder swing.
Key inputs include swing sensitivity, close vs wick confirmation, lookback bars, and per-feature toggles with configurable colors. Runs on any symbol and timeframe, recalculating on every new bar with close-confirmed markings.
👉 Read | Docs | @mql5dev | 13 059 |
