In MetaTrader 5 build 6180, we have significantly expanded the capabilities of the AI Assistant for working with the trading platform and Strategy Tester. The assistant can now retrieve and analyze tester reports and logs, check its current settings, help launch optimizations, add indicators to charts with specified parameters, and work with terminal and Expert Advisor logs.

For developers, we have expanded the capabilities for working with complex matrices and vectors in MQL5. Support for additional methods simplifies the processing, conversion, and validation of complex data in mathematical and analytical tasks.

The web terminal now provides improved handling of stop levels on netting accounts. When placing a new trade for an instrument that already has an open position, the terminal preserves the position's current Stop Loss and Take Profit levels, preventing them from being accidentally removed. We have also fixed data loading and quote display issues in Market Watch.

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MetaTrader 5 can open and modify trades, but it lacks a reusable pattern for what happens after entry. This article builds a Position Lifecycle Manager that decouples trade generation from trade management, so different EAs can share the same post-entry logic.

The framework discovers open positions, wraps each one in a CManagedPosition object, and drives it through explicit states: NEW, PROTECTED, BREAKEVEN, and CLOSED. State tracking preserves action history, avoiding repeated terminal queries and preventing duplicate stop or break-even operations.

A CPositionManager coordinates all managed objects, while a CRiskEngine calculates ATR-based protective stops without placing orders itself. Integration is shown with the standard MACD EA: entries stay intact; lifecycle handling becomes a reusable layer.

👉 Read | AlgoBook | @mql5dev

This article builds a compact MT5 position planning tool that turns Entry, Stop-Loss, and Take-Profit into interactive chart lines, so risk and sizing math updates instantly while levels are dragged.

It supports market, limit, and stop scenarios for both BUY and SELL. Market Entry auto-tracks Bid/Ask on every tick, while pending Entry stays user-controlled. Initial SL/TP spacing is derived from ATR to reflect current volatility, with a safe fallback when ATR isn’t available.

The EA validates the price structure (BUY: SL below Entry, TP above; SELL reversed) before computing stop distance, monetary risk from balance and risk %, normalized lot size using tick size/value plus min/max/step rules, reward, and risk-to-reward—without placing or modifying orders.

👉 Read | Docs | @mql5dev

Rare “outlier” bars break the core trading assumption that today resembles yesterday, yet they have no labels. This article implements Isolation Forest for MT5 as a compact MQL5 library that isolates points via random partitions, avoiding density modeling and handling multivariate features efficiently.

Key engineering choices make it testable and fast: a replayable 64‑bit RNG (splitmix64 + xorshift64*) for deterministic forests, iterative array-based trees with in-place partitioning, and the correct truncated-depth path-length correction and normalization. A 100‑tree fit on ~2.4k bars builds in ~2.4 ms; scoring one new bar is ~12 µs.

Feature design is treated as the real lever: no raw prices, no lookahead, and careful column selection because isolation trees sample features uniformly—uninformative columns directly degrade detection. Validation includes b...

👉 Read | Docs | @mql5dev

Dendritic Cell Algorithm (DCA) is a metaheuristic derived from innate immunity, originally published in 2005 for anomaly detection. The model integrates multiple signals over time and uses migration thresholds to avoid reacting to noise in single evaluations.

Optimization mapping treats high fitness as PAMP/Danger and low fitness as Safe, with Inflammation derived from population spread. Cells transform inputs into CSM, Semi, Mature via weighted sums and a shared (1+Inflammation) multiplier; migration triggers context selection (mature vs semi).

Per-solution MCAV aggregates contexts with exponential decay. MCAV drives control flow: above 0.5 triggers local mutation, otherwise either move toward best or reinitialize based on exploration rate. Implementation typically models cells, thresholds, weight matrices, agent assignment, and MCAV bookkeeping w...

👉 Read | AppStore | @mql5dev

Auto ZigZag Fibonacci Golden Zone is an MT5 indicator that derives pullback entry zones from the latest confirmed swing using an internal ZigZag. It scans history to lock the most recent swing high/low, then draws Fibonacci retracements at 50%, 61.8%, and 78.6%.

The 61.8%–78.6% band is marked as the Golden Zone and extended a configurable number of bars. Optional chart labels can trigger when a candle closes inside the zone, with separate handling for uptrend and downtrend measurements.

A compact on-chart panel reports current price, swing points, exact fib levels, and usage steps. Key inputs include ZigZag Length (default 13), Golden Zone Length (15), swing price labels, and signal labels. Calculations are based on closed candles and remain fixed until a new swing is confirmed.

👉 Read | VPS | @mql5dev

Market Structure Shift (CHoCH) is used to confirm a directional reversal.

Bullish CHoCH occurs when price breaks above the last confirmed Lower High (LH), shifting bias from bearish to bullish. Bearish CHoCH occurs when price breaks below the last confirmed Higher Low (HL), shifting bias from bullish to bearish.

After confirmation, Areas of Interest (AOI) are mapped for execution. Common zones include Fibonacci Equilibrium anchored from the origin swing, and a post-CHoCH Fair Value Gap (FVG) formed within the first three candles of the breakout impulse, treated as an imbalance-based entry area.

Risk and targets are defined structurally. For buys, stop loss is placed below the origin swing low (0% Fibonacci anchor). For sells, stop loss is placed above the origin swing high (0% anchor). TP1 is set at the breakout structure level (100%). TP2 targets t...

👉 Read | NeuroBook | @mql5dev

Dynamic flip zones update automatically, converting broken Support into SBR and broken Resistance into RBS on confirmed closes. Optional behavior allows broken zones to be removed instead of flipped.

Zones are drawn as normalized rectangles rather than thin lines. Width is adjusted using average historical volatility, reducing oversized areas and expanding narrow ones to keep sizing consistent across market regimes.

Repaint risk is reduced via confirmation bars that validate pivot points before zones are plotted. Pivot detection is based on ZigZag swing highs and lows, with additional filters to keep levels relevant.

Alerts are gated by a minimum move-away requirement. Price must travel a defined distance from a new zone before a retest can trigger notifications, cutting noise in ranges.

Overlap handling includes Most Extreme and Newest modes to ma...

👉 Read | NeuroBook | @mql5dev

Many indicator specs claim “non-repainting” without a measurable definition. A testable invariant is stricter: once a bar is closed and processed, any drawn object on that bar must never change, move, recolor, change text, or vanish.

A script operationalizes this by recording every object after an initial pass, then appending more bars and forcing a full recalculation so the indicator rebuilds from a longer history. Objects on already-closed bars are matched and compared field by field (anchor times/prices, color, text). This targets failures caused by object names tied to bar index rather than bar time.

Changes and disappearances are tracked separately; only changes falsify the claim. Empty comparisons are reported as inconclusive. Results are written to CSV (one row per symbol/timeframe/step) including compared/changed/vanished counts and the first o...

👉 Read | VPS | @mql5dev

This article builds a practical case for a Cairo-style 2D renderer in pure MQL5 to draw modern UI elements (rounded rectangles, rings, gradients, translucent layers, arbitrary polygons) as a single OBJ_BITMAP_LABEL, avoiding the performance and feature limits of native chart objects and CCanvas.

The core model separates geometry (paths) from paint (sources), then converts paths into a per-pixel coverage mask for true anti-aliasing. Rendering becomes compositing: source through mask onto destination, so new paints (solid, gradients, images) automatically work with all shapes.

Part 1 focuses on the foundation: a strict ARGB uint color pipeline compatible with ResourceCreate, and a reusable pixel surface bound to one bitmap object—keeping later improvements isolated to masking and painting logic.

👉 Read | Calendar | @mql5dev

Manually scrolling to a specific candle in MT5 becomes impractical on lower timeframes and deep history. This History Navigator EA solves it with a small dialog where day/month/year/hour/minute are entered, then the chart jumps to the correct historical area and can return to the live market view in one click.

The design separates concerns: lifecycle code in HistoryNavigator.mq5, and UI + logic in a CNavigatorDialog class built on the Standard Library (CAppDialog, event map, controls). Inputs are validated in two stages: range checks plus real calendar validation with leap-year handling, then converted via MqlDateTime + StructToTime().

Bar location uses CopyTime() and a binary search over available history, selecting the latest bar open not exceeding the requested timestamp. Chart positioning disables auto-scroll/shift, centers the target using CHART_VISIBLE_...

👉 Read | Forum | @mql5dev

Momentum oscillators often become noisy when raw price differences are used directly. A Hull Moving Average layer can smooth the series while staying responsive to direction changes.

An MQL5 implementation combines classic momentum (Close[i] minus Close[i+Length]) with HMA built from multiple WMAs: a fast WMA on half period, a slow WMA on full period, then a final WMA on the rounded square-root period.

The design relies on separate buffers for raw momentum, intermediate HMA values, final output, color indices, a zero reference, and a fixed gray fill between the line and zero.

Interpretation is based on position versus the zero line; crossings indicate state change but degrade in flat regimes due to frequent flips and reduced signal quality.

👉 Read | Forum | @mql5dev

Clustered feature importance depends on a correlation matrix that is both denoised and detoned. Estimation noise inflates spurious correlations, and a dominant first eigenvector from shared regime exposure makes unrelated feature families look similar. Both effects break clustering and bias MDI/MDA via substitution.

Noise is bounded using a Marcenko–Pastur fit, with q = T/N and sigma^2 fit to the empirical eigenvalue density. Two common silent failures are inverting q and using an incompatible KDE bandwidth definition, both yielding plausible but incorrect ceilings.

For serially correlated bars, raw T overstates information. An AR(1)-style effective sample size can shift lambda_max enough to change factor retention near the margin. After denoising (constant residual eigenvalues) and detoning (remove top eigenvector), ONC/K-means clustering recovers...

👉 Read | AlgoBook | @mql5dev

MetaTrader 5 file access stays inside the terminal sandbox, so identical file APIs can yield different internal layouts while producing the same terminal output.

Directory traversal in MQL5 splits into two paths. Interactive selection uses FileSelectDialog with a fixed root, constrained filters, and flags; results come back via a dynamic string array and are typically passed into FileOpen.

Non-interactive enumeration relies on FileFindFirst/FileFindNext. An empty sandbox returns an invalid handle and is not a code error. Basic enumeration lists only the current directory; recursive traversal requires passing subdirectory paths. A trailing slash in returned names can be used to detect directories without FileIsExist, but output may lose full path context unless it is assembled explicitly.

👉 Read | AlgoBook | @mql5dev

A losing EURUSD short exposed a deeper issue: stacking RSI/MACD/Stochastic (and even a neural net trained on them) still reduces a nonlinear, chaotic market into mostly linear price transforms, so the model misses context.

LLMs can discuss that context, but they are nondeterministic: small prompt or temperature changes flip conclusions, which makes probability outputs unusable for trading decisions.

A more reliable path combined CatBoost with quantum feature extraction. Plain indicator features stalled near 59% accuracy; the breakthrough was quantum encoding with 8 qubits plus CZ entanglement, producing a non-uniform state histogram that captured higher-order relationships across indicators.

Four derived quantum statistics (entropy, dominant-state strength, state-count complexity, distribution variance) became numeric features. Adding them to 33 tec...

👉 Read | Calendar | @mql5dev

Backtests often look clean because the tester uses a “current” spread, while the terminal stores a spread value per M1 bar that rarely gets reviewed.

SpreadAudit reads M1 history for a configurable lookback and computes median, p90, and p99 spreads in price units and pips. The same percentiles are also normalized by ATR(H1) to make cost comparisons consistent across instruments. It also aggregates average and maximum spread by hour and can export the hourly table to CSV in MQL5\Files.

This matters when edge is small. A setup averaging 0.28 price units per trade fails if the median spread is 0.37, even if the backtest equity curve looks stable without realistic costs. Hourly stats typically show widening at rollover and near session opens, where a spread filter expressed as a fraction of ATR remains valid as volatility shifts.

Caveat: the recorded fi...

👉 Read | AppStore | @mql5dev

EA initialization requires attaching it to a standard symbol chart (for example, BTCUSD). A custom replay symbol is created automatically and should be opened as a separate chart.

Replay start is controlled from the panel in the top-left. Use SELECT to place a yellow vertical marker, move it to the target start date, then run LOAD DATA and press PLAY.

Playback speed can be adjusted from 0.5x to 20x. Controls include pause and step/skip actions (+10, -10, <<, >>) for fast repositioning.

Trade setups are created from the bottom-right drawing toolbar. Choose LONG or SHORT, click to set entry, move the cursor to define stop distance, then click again to lock; take profit is calculated automatically. Entry, stop, and take-profit lines remain draggable for later adjustments.

For reliable deep history loading, set Tools > Options > Charts and increase Max bars i...

👉 Read | Quotes | @mql5dev

MetaTrader 5 indicators cannot change orders or positions on the trading server. Only Expert Advisors can send trade requests, so UI actions in an indicator must be forwarded to an EA via custom chart events.

A position indicator was extended with a clickable OBJ_BITMAP_LABEL (16x16 resource). On click, it emits a dedicated close-position event with the position ticket in sparam. The EA intercepts it and closes only the targeted position, avoiding NETTING vs HEDGING edge cases tied to “close all” semantics.

The indicator also tracks chart scale changes to recompute bitmap coordinates using price-to-screen conversion, keeping the control aligned with price lines. Next steps generalize the same event pattern for Stop Loss and Take Profit line actions without code duplication.

👉 Read | Calendar | @mql5dev

Dandelion Optimizer (DO) is a 2022 metaheuristic that models how dandelion seeds explore wide areas, then converge where conditions look best. Each seed represents a candidate parameter set; “soil quality” is the objective value.

DO updates a population in three phases: Rising for broad exploration (mostly spiral/vortex moves, sometimes linear drift toward the search-space center), Decline for coordination via the population mean, and Landing for exploitation around the current elite using Lévy-flight steps to keep occasional long jumps.

The MT5-style implementation wraps this into a C_AO_DO class with Init/Moving/Revision, explicit boundary reflection, and adaptive control: step intensity decays over epochs while an attraction ratio increases, shifting from global search to fine-tuning. This fits strategy optimization where stable convergence and ...

👉 Read | VPS | @mql5dev

RL trading often fails for non-algorithmic reasons: weak input signal, leakage, unstable validation, or execution mismatch. A fast supervised baseline should be the first gate: if features cannot rank direction above chance, more complex RL adds cost without information.

A LightGBM check with triple-barrier labels and purged walk-forward CV is a practical filter. On ~144k M15 XAUUSD bars, AUC stayed near 0.50 across horizons and added cross-asset features, aligning with low explained variance and weak live stats.

Engineering controls that reduce train-to-live failure include embargoed walk-forward splits, multi-seed promotion gates, evaluating realized equity/trades (not shaped rewards), and deployment contracts: saved normalization, pinned versions, dataset snapshots, warm-up rules, and broker reconciliation.

👉 Read | Calendar | @mql5dev