SNR Sentinel is an adaptive support and resistance engine for MT5 that maintains only the nearest active levels to current price. It uses strict left/right bar confirmation to validate swing highs and lows, filtering out low-quality structure and reducing chart noise.

Breakouts are recognized only on candle close beyond a level. Once breached, the prior level is marked and queued for replacement, and the engine selects the next historically relevant level likely to receive a reaction.

Active levels are projected forward and updated per bar using controlled line extensions rather than rays. Output is limited to one support and one resistance at any time, with no repainting and no redundant objects.

Typical applications include breakout and retest workflows, reaction-zone mapping for scalping or swing trading, market structure validation, and clean S/...

👉 Read | AppStore | @mql5dev

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A utility script is available to export all locally available tick data from the terminal into files stored under the Common directory. Existing files are appended with new ticks since the last recorded timestamp, enabling incremental updates and later reuse for building custom symbols.

Setup requires creating a destination folder in Common (default: “MW Download History”) or adjusting the MW_DH_DEST_FOLDER compiler define. After compiling, add the desired instruments to Market Watch, open any chart, and attach the script.

Inputs support exporting a single symbol or all Market Watch symbols, plus selecting a timeframe (M1 recommended). Separate files are created per symbol and timeframe. Execution progress is reported in the Experts log, and periodic reruns keep datasets current.

👉 Read | CodeBase | @mql5dev

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Candle Close Timer is an MT5 indicator that displays the remaining time until the active bar closes. The countdown is rendered in real time above the current candle and stays aligned with the latest price action.

Settings allow customization of text color and font size without affecting chart readability. The indicator is lightweight and designed to remain accurate across symbols and all timeframes.

Deployment is straightforward: attach it to any chart, then adjust visual parameters in the inputs. It supports timing-sensitive workflows such as scalping and intraday execution where decisions depend on the exact close of the current candle.

👉 Read | NeuroBook | @mql5dev

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Quantitative trend analysis in FX replaces subjective chart reading with measurable statistics: trend duration, magnitude in points, and frequency distributions by pair and timeframe. This enables hypothesis testing and parameter selection based on observed data.

A common implementation uses Python with MetaTrader 5 data, pandas for time series handling, and a sliding-window algorithm to detect local highs/lows and form uptrend/downtrend segments. Window size controls sensitivity and noise filtering.

Example EURUSD H1 (Feb 2018–May 2025, ~45k bars, 100-bar window): 471 trends, split 236 up and 235 down. Average duration 5.11 days (~120 hours), average magnitude 167 points. 51.6% of trends fall in 100–200 points; 85.6% end within 10 days; moves >500 points are ~1.3%, creating a heavy tail that is a key risk factor for grid averaging systems.

👉 Read | NeuroBook | @mql5dev

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ARIMA forecasts prices by combining three ideas: autoregression (recent prices influence the next one), integration via differencing (model daily changes to handle non-stationary series), and moving average (use past forecast errors to correct bias).

In MetaTrader 5, a practical ARIMA indicator needs more than formulas: separate buffers for prices, residuals, and forecasts; dynamic arrays; and an OnCalculate pipeline that updates efficiently on new ticks and restores forecasts back from differenced space.

Parameters (lookback, forecast horizon, p/d/q, learning rate) are fit by maximizing log-likelihood, optimizing AR/MA coefficients with gradient descent. Reliability depends on validation: MAE/RMSE, residual diagnostics (Ljung-Box, Jarque-Bera), and sliding-window tests. ARIMA is strongest in stable regimes and degrades around news, policy shifts, and s...

👉 Read | NeuroBook | @mql5dev

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Utility script for removing chart annotations and drawing objects on the active chart. Intended to clear items such as comments, horizontal and vertical lines, arrows, and other objects created by indicators or manual markup.

No user input is required. When executed, it deletes all objects on the current chart, so it should be validated on a clean, newly opened chart before running on an active workspace.

Operational notes: assign a keyboard hotkey via the platform’s script list (context menu: Set hotkey) or run it by dragging the script onto the chart. The code can be adjusted to keep specific object types if needed.

👉 Read | Signals | @mql5dev

#MQL4 #MT4 #script

Structured vs organized code remains a frequent source of confusion. A practical boundary appears when structures start carrying context through internal procedures, before transitioning to classes and OOP.

A struct with public data can compute average/median, but it also allows external writes. After a reset, memory may be reused incorrectly, producing wrong medians and hard-to-trace faults. This is a typical encapsulation error.

Marking internal arrays as private blocks direct access and even prevents blanket memory wipes, forcing explicit API methods for set/reset/print. The result is tighter invariants, clearer intent, and fewer hidden state bugs, at the cost of more deliberate design.

👉 Read | VPS | @mql5dev

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Risk-management module update: new enums, structures, and constants added to support daily limits, position accounting, and dynamic per-trade risk.

Dynamic risk now adjusts assigned_percentage based on balance drawdown from an initial balance. Initial balance can be fixed (prop firm) or captured at startup (personal account). Thresholds must be sorted; a loop-free counter-based method requires ordered activation levels. A CHashMap approach is recommended to preserve key-value mapping during sorting.

Balance-check timing is configurable via enum: per-tick checks (accurate but noisy) or checks on trade close (lighter but less granular). Dynamic-risk modes include full custom strings, fixed numeric parameters (up to four steps, optimizer-friendly), or disabled.

Daily limit exceedance criteria are selectable: equity only, closed P/L only, or combined. Trade ac...

👉 Read | Calendar | @mql5dev

#MQL5 #MT5 #Strategy

This article turns slanted, swing-anchored trendlines into a rule-based breakout signal tool for MetaTrader 5. Trendlines are treated as dynamic support/resistance built from market structure, not discretionary drawing.

Signals come only from validated lines: two swing anchors define the slope, and a third swing confirms that price actually respects the level. Breakouts are detected via true cross logic (with optional close vs wick selection and a configurable buffer), filtering minor intrusions and reducing false triggers.

The MQL5 EA is modular: ATR-assisted swing filtering, ordered swing storage, scored selection of best support/resistance candidates by touches and recency, automatic chart objects, and persistent breakout records with optional alerts. Practical result: consistent trendline breakout execution suitable for automation and clean cha...

👉 Read | Docs | @mql5dev

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Part 18 extends the MQL5 canvas vector toolkit by composing a rounded rectangle body with a triangular pointer to form a speech bubble, with pointer orientation set to up, down, left, or right via an enum.

Geometry is precomputed per orientation, including body bounds, pointer vertices, apex rounding, tangents, and scanline mode selection. Filling uses supersampled high-res rendering, scanline triangle fill with arc inclusion checks, and quadrant circle fills for rounded corners, then bicubic downsampling.

Border rendering is segmented to avoid overdrawing at the pointer junction. A configurable borderExtensionMultiplier scales edge extensions by thickness to keep joins clean across wider strokes. Initialization integrates additional canvases, redraw handling, and cleanup, with backtest visuals confirming output.

👉 Read | Forum | @mql5dev

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This part extends an MQL5 trading journal from basic file creation to exporting real account history into a structured log. It selects deals within a defined date range, then extracts core fields needed for audit and analysis: ticket, symbol, side, volume, open/close times and prices, SL/TP, profit, and a final result flag.

The journal becomes readable by writing a consistent header row first, ensuring every subsequent value lands in the right column and can be parsed later by scripts or spreadsheets.

History processing is done safely: HistorySelect scopes the dataset, HistoryDealsTotal drives iteration, and DEAL_ENTRY_IN filters to true position entries. Ticket IDs are collected into a resized dynamic ulong array, using a separate index to avoid gaps when non-entry deals are skipped.

👉 Read | NeuroBook | @mql5dev

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Market Memory Zones move from a chart-only indicator to a MetaTrader 5 Expert Advisor that turns price behavior into executable rules. The system models three zone types: displacement (ATR-qualified impulse candles), structure transitions (CHoCH via swing breaks), and liquidity sweeps (stop-hunts that reverse back into range).

Each zone is stored as a structured object with boundaries, direction, ATR context, expiry, and activation state, managed in bounded arrays for stable long runs. OnTick updates ATR and swing structure, detects new zones only on fresh bars, removes expired zones, and continuously evaluates returns into active zones.

Entries avoid blind level trading: a zone must be revisited and confirmed on a lower timeframe (engulfing or strong close). Trades use risk-based sizing, optional dynamic stops beyond zone invalidation, structure-aware targ...

👉 Read | Forum | @mql5dev

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Final part of a risk management series builds an MT5 Expert Advisor around an optimized Order Blocks indicator, then measures how risk controls change backtest results across four parameter sets (with/without loss and profit limits, plus dynamic per-trade risk).

Indicator speed is improved by reducing recalculation loops via prev_calculated, simplifying mitigation checks to highs/lows, switching to predefined OHLC arrays, and using template helpers to add/remove array elements. Mitigated blocks are now removed from the main list and deleted from the chart using auxiliary name arrays.

The EA is assembled with clear inputs: Order Block/TP-SL modes (ATR or points), fixed vs risk-based lots, PropFirm-style constraints, max daily/weekly/total loss, max daily profit with strict/non-strict logic, and dynamic GMLPO risk adjustment checked on ticks or on trade close...

👉 Read | Quotes | @mql5dev

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Multi-timeframe RSI scanners can reduce false positives versus single-chart RSI, especially on volatile symbols like XAUUSD and fast FX crosses. This build tracks RSI across up to seven timeframes (M1 through D1) and renders a compact dashboard with color-coded states plus per-timeframe overbought/oversold alerts via push, email, or sound.

The main signal is convergence: when three or more timeframes reach the same extreme zone, a dedicated warning is raised. This is intended as a confirmation layer for reversal or continuation setups, not an entry system.

v1.6 (Feb 2026) focuses on operational stability: alert retry (up to 2 retries with 100ms delay, skipping configuration errors), verbose logging toggle for VPS usage, extracted constants, throttled warnings, safer dashboard positioning, and handle cleanup. Live usage since Oct 2025 reported no crashes o...

👉 Read | CodeBase | @mql5dev

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This article upgrades a basic Python echo server into a multi-client TCP server suitable as a bridge concept for Excel and MetaTrader 5 workflows.

The core issue is blocking I/O: accept() and recv() halt execution, and when Python runs inside Excel via VBA it competes for CPU time, making the spreadsheet sluggish.

The solution shown is a threaded design: the main loop keeps accepting connections, while each client gets its own handler thread with an independent receive loop. This enables reconnects and simultaneous clients, and mirrors earlier MT5 mini-chat ideas previously implemented with a C++ server.

It also notes practical limits: threads consume scheduling resources, so scaling requires restraint and careful design before adding client-to-client message routing.

👉 Read | Signals | @mql5dev

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Basic news filters that only pause entries at release time miss the real risk: spreads can blow out, slippage increases, and stop-losses get triggered by volatility spikes. Open trades stay exposed, prop-firm timing rules get violated, and many EAs lose their logic after restarts or can’t be tested without live events.

The article builds a structured MQL5 news filter around the native Economic Calendar, replacing fragile web scraping with cached, deterministic terminal data that can be simulated in the Strategy Tester.

Key mechanics: load and cache a rolling 24-hour event set, map events to the traded symbol via currency relevance (base/quote for FX, heuristics for indices/commodities, manual overrides for odd symbols), filter by impact, and enforce configurable pre/post buffers. The result is a state-driven system that blocks entries and can optio...

👉 Read | VPS | @mql5dev

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In the MetaTrader 4 Beta Build 1457, we have implemented several important security enhancements, fixed known issues, and improved overall platform stability.

We invite all traders to participate in testing. Your experience and feedback help us further improve the platform reliability.

To receive the beta update, connect to our public demo server MetaQuotes-Demo — enter the server name in the search field and open a demo account.

Discuss the update...

This article tackles a practical integration issue: a threaded Python socket server works fine standalone, but becomes disruptive when launched from Excel via xlwings. The root cause isn’t threading itself—it’s blocking accept() and an always-running loop that still contends with Excel’s execution.

A safer pattern replaces blocking accept() with select() using a short timeout, letting the server poll for events without freezing the host application. Logging back into Excel is simplified with a small helper that writes status and messages into worksheet rows.

The server is then refactored into a class, separating connection checks and message handling into callable methods. Moving the main loop outside the class makes the component easier to package, test, and later adapt for MetaTrader 5 ↔ Excel data exchange.

👉 Read | VPS | @mql5dev

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An indicator implements pivot calculations for any selectable timeframe, provided the pivot timeframe is higher than the current chart timeframe. This supports multi-timeframe levels without switching charts.

Two calculation modes are available. Original mode follows the approach described by Austin Passamonte. Alternate mode adjusts the level logic to emphasize support and resistance behavior more strongly.

Operational use is consistent with standard pivot workflows, including plotting key levels for planning entries, exits, and risk boundaries across sessions.

👉 Read | Docs | @mql5dev

#MQL4 #MT4 #Indicator

Part 41 shifts from writing trade journals to reading them back in MQL5, using a practical target: an indicator that ingests a CSV trading history and plots a balance curve from profit/loss over a selected period.

The core technique is treating CSV content as sequential elements (cell-like values), not “lines of text”. The workflow starts by opening the file in FILE_READ with CSV parsing and defined encoding, validating the handle, and using FileIsEnding + incremental reads to count total elements for safe loop bounds.

After counting, the file pointer is reset, then all elements are loaded into dynamic arrays. This enables scalable grouping by column (TradeID, LotSize, etc.), avoids fixed-size structures, and prevents out-of-range errors when processing real trading logs.

👉 Read | CodeBase | @mql5dev

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A Python socket server can act as a bridge between Excel (VBA/COM) and MetaTrader 5, enabling Excel-driven actions without placing orders directly from Excel.

The implementation uses only the Python standard library, with a class-based server, explicit host/port parsing, and defensive exception handling for Excel session, worksheet, and cell access.

Data flow is kept minimal: one control cell for requests and one log area for responses. The server reads MT5 messages, writes results into Excel, and forwards Excel cell content back to MT5.

Connection gating is handled via an initial header plus simple command parsing (shutdown, disconnect). Unknown clients are rejected, and missing worksheets trigger a clean shutdown.

👉 Read | Quotes | @mql5dev

#MQL5 #MT5 #AlgoTrading