An MQL5 signal class is set up to test whether a neural confirmation adds measurable value to a trend-continuation ruleset on EURUSD H4. The rule engine proposes entries from ADX/DI, an HMM gates them by regime probability, and an optional GRU vetoes unless direction and confidence thresholds are met.

The HMM estimates range, trend, or high-volatility states from normalized ADX and standardized ATR/price using a 3-state Gaussian model with Baum-Welch fitting and posterior gating (>= 0.6). The GRU is regime-specific and predicts near-term direction; it must agree with the rule and exceed |output| >= 0.05.

Evaluation is framed as a three-way comparison: raw rule, HMM-gated rule, and HMM+GRU. The added complexity is justified only if rejected trades have worse expectancy and risk metrics, rather than simply reducing trade count.

👉 Read | AppStore | @mql5dev

Walsh functions provide an orthogonal, lightweight alternative to sine/cosine transforms for decomposing price series into components. Symmetric terms act as smoothers, while antisymmetric terms emphasize trend, making them useful for extracting structure that common indicators often miss.

A practical approach builds probability distributions for Walsh-derived features: order-0 aligns with SMA, and order-1 (with doubled period) captures SMA change. Combining these distributions yields an expected future SMA/trend level plus an explicit uncertainty measure; large forecast error becomes a volatility/regime signal.

Two key implementation issues are addressed: periods constrained to powers of two, and repainting from windowed calculation. Periods can be scaled by integer factors, and repainting can be removed by locking values at bar close, enabling a ...

👉 Read | Freelance | @mql5dev

H1 Container MTF Boxes is a display-only MT4 indicator that maps a single H1 candle into lower timeframes on the active chart. It renders an H1 container and optional nested boxes for M30, M15, M5, and M1 candles that occur within that hour.

The H1 range can include 25%, 50%, and 75% levels. Lower timeframe boxes use a single 50% midline to reduce visual noise. AutoFollowCurrentH1 tracks the current H1 candle, or a historical H1 can be selected via H1Shift.

Visibility toggles are provided for each nested timeframe, along with controls for colors, widths, line styles, labels, and an ObjectPrefix to avoid naming collisions. M1 mode can generate many objects and may be disabled if the chart becomes crowded. Values update while candles are still forming. An EA version based on the same H1 container logic is in development.

👉 Read | Forum | @mql5dev

A CISD-based market microstructure tool is designed to flag potential turning points using ICT/SMC-style logic. It monitors consecutive delivery candles, derives reference levels from the delivery open, and confirms a Change in State of Delivery when price reclaims and closes beyond those levels.

Confirmed CISD events extend pending levels forward, then render dashed or solid lines with mid-line labels and directional entry arrows. Liquidity sweep logic marks wick expansions that run swing highs or lows and then reject, highlighting potential stop runs.

Market Structure Shift detection tracks swing breaks and annotates them with dots. Candle overlay options apply Emerald Green and Red bull/bear colors either directly or conditioned on the confirmed CISD direction.

👉 Read | CodeBase | @mql5dev

Session Range Desk MT5 v4.01 is a source-available, educational session-range breakout EA for MT5 hedging accounts. It combines a trailing completed-bar range, ATR-banded range validation, stop-distance position sizing, and an on-chart panel. Multiple symbols can share a desk view via terminal global variables. No grid, martingale, or averaging logic is used.

At the configured start time, the EA builds a high/low range from the previous LookbackCandles bars. If the range width falls outside the Min/Max ATR multiplier band, the day is skipped. Otherwise, a touch or a bar-close breakout can place a market order with the opposite range edge as the stop. Optional management supports fixed R targets, break-even, and partial closes, with one intended entry per symbol per server day.

Risk controls include RiskPercent sizing and a MaxDailyRiskPct desk threshold, wh...

👉 Read | Forum | @mql5dev

A fixed 3-qubit quantum circuit is used as a nonlinear feature generator for MT5 price windows, modeling “uncertainty structure” rather than claiming any quantum computing advantage. Mean return, volatility, and range drive RY rotations; a CNOT chain adds dependencies; 1000-shot simulation yields an 8-bin outcome histogram.

Seven metrics are extracted from that histogram: entropy, dominant-state probability, superposition width, outcome coherence, neighbor-bit correlation (entanglement proxy), variance, and count of significant states. Caching via window hashing is required to keep sliding-window runs practical.

These features are fused with classical OHLCV-derived inputs in a bidirectional LSTM pipeline (BatchNorm, ReLU, Dropout), trained with Focal Loss plus weighted sampling to handle directional imbalance. On a small EURUSD H1 sample, reported ga...

👉 Read | Quotes | @mql5dev

Position replay/simulation indicator update: C_IndicatorPosition is removed to eliminate an intermediate layer between the indicator and C_ElementsTrade. Methods and state move into the indicator so the new P/L label can access the opening-price component directly.

Profit/loss is computed as points to the best close price, not POSITION_PROFIT. Spread makes POSITION_PROFIT unreliable for exit viability, so closing logic uses Bid for long exits and Ask for short exits, sourced via SymbolInfoTick in OnCalculate.

C_ElementsTrade gains a label anchored to the opening line, updated with a formatted points delta using the symbol’s digits. The label color indicates positive vs negative, and ChartRedraw is used to avoid delayed UI refresh.

👉 Read | VPS | @mql5dev

Time-series forecasting is seeing diminishing returns from scaling Transformer-style models. Reported MSE/MAE gains stay under ~30% while parameter counts rise by orders of magnitude, and progress after PatchTST appears incremental.

A key cost driver is patching with attention: fragmenting sequences removes temporal/spatial identifiers, then position/identity encodings are added back, expanding latent dimensionality and increasing overfitting risk on limited datasets.

SSCNN (“Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting”) argues for structured decomposition over patching, with formal analysis. The model extracts long-term, seasonal, short-term, plus spatial components via temporal/spatial attention normalization and selection maps, then extrapolates and combines them using polynomial regression.

Benchmarks repor...

👉 Read | Freelance | @mql5dev

MetaTrader 5 projects that rely on structs often end up with warnings about undeleted instances or unreleased memory. A common root cause is skipped initialization routines, which turns structured code into a source of inconsistent state.

In MQL5, a class is effectively a struct with lifecycle hooks. Constructors run automatically on instance creation and can take parameters; destructors run when an instance ends and are intended for explicit resource release. Multiple constructors are allowed, but only one destructor.

Replacing manual Init() calls with constructors removes the dependency on callers remembering setup steps. If no constructor is defined, MQL5 generates a default one, and member state may remain effectively uninitialized from a logic standpoint. Initialization lists can also be used to set members before the constructor body executes.

👉 Read | Forum | @mql5dev

Update to an MQL5 replay/simulation position view focuses on UI robustness and event-driven interaction.

OBJ_EDIT fields now auto-adjust size based on font metrics computed in the C_ElementsTrade constructor, removing manual width/height tuning and preventing text clipping after font changes. Font name/size are centralized via macros, and a deliberate compile-stop guard avoids conflicting macro redefinitions.

Distance readouts reuse the existing P/L display logic: take-profit and stop-loss lines show point distance from the open price without overwriting the open-line P/L value.

Runtime behavior is fixed at the indicator layer. Update logic is extracted into a helper and invoked from both OnCalculate and OnChartEvent, enabling live distance updates during drag and proper cleanup when creation is canceled with ESC.

👉 Read | CodeBase | @mql5dev

Part 2 extends the Cairo-style split between geometry and rendering by adding Path.mqh as a common shape representation.

A path is geometry only: points grouped into outlines, with no color, thickness, fill rule, or destination buffer. This removes shape-specific render loops and enables reuse across different fills and transforms.

Points are stored as doubles to preserve sub-pixel coordinates. This is required for later anti-aliasing, smooth animation, and curve flattening without quantization.

A path can contain multiple contours (open or closed) to support glyph holes, rings, borders, and multi-part icons. Storage uses one flat vertex array plus a contour start index array and a closed-flag array for contiguous traversal and low overhead.

👉 Read | AlgoBook | @mql5dev

Gopalakrishnan Range Index (GRI), published in TASC in 2001, measures market activity using the closing-price range over N bars, normalized on a log scale. It remains a practical volatility gauge and is easy to implement in MetaTrader 5.

Implementation computes High/Low of Close over ChaoticPeriod, takes log10(range)/log10(N), returning 0 when range is zero. For range < 1, values become negative; a shifted range can clamp the minimum toward zero. Scaling by Point makes thresholds usable in EA inputs.

In testing, an EA using TEMA for direction and AMA for confirmation improved behavior when trades were filtered by rising GRI, avoiding low-activity phases. GRI is a condition filter, not a directional signal, and benefits from per-symbol threshold calibration and backtesting.

👉 Read | Quotes | @mql5dev

Post-trade stats typically report what a trade earned. This utility records what each trade had available, and what it returned.

On first detection it captures the initial stop distance as 1R, then measures max favorable and adverse excursion in R until the position closes. Trailing stops do not alter the reference, so the measurement stays consistent.

One CSV row per closed position is written to MQL5\Files: ticket, symbol, side, open/close time, entry, risk distance, peak R, worst R, realised R, and profit. Trades without a stop are skipped and counted.

Use case: equity curves that spike and fade can come from trades reaching high peak R and giving it back, or from trades never traveling far. Comparing average peak R vs realised R, plus average giveback on the chart panel, separates these cases.

The EA is read-only: no open/modify/close. Attach alongsid...

👉 Read | VPS | @mql5dev

A read-only MQL5 Expert Advisor logs each closed position to CSV with market-state context at entry and exit. Recorded fields include RSI, Stochastic, EMA20/EMA50 distance, Bollinger %B, ATR, and ADX on two timeframes, enabling post-trade rule reconstruction and strategy evaluation without relying on undocumented logic.

Several data-loss traps are handled explicitly. Deduplication avoids position_id and close_time watermarks; position_id reflects open order, not close order, and datetime has 1-second resolution. A set of logged position IDs is rebuilt from the CSV on restart. HistorySelectByPosition() is isolated from the main scan to prevent global history selection from being overwritten during iteration.

Close detection includes DEAL_ENTRY_OUT and DEAL_ENTRY_OUT_BY. Magic filtering reads the opening deal’s magic, not the closing deal, to avoid dropping m...

👉 Read | AlgoBook | @mql5dev

MetaTrader 5 includes an economic calendar accessible from MQL5 via CalendarValueHistory(), but external tooling cannot query it. The Python MetaTrader5 package exposes market data and trading state, yet provides no calendar API, leaving research scripts, dashboards, bots, and spreadsheets blind to upcoming releases.

A read-only Expert Advisor can bridge this gap by exporting calendar data to a CSV file under MQL5\Files on a fixed interval. It never opens, closes, or modifies positions.

Key implementation points: UTF-8 output to preserve localized event names, chronological sorting across currencies, atomic write (build in memory, then write once), HTML entity decoding (e.g., S&amp;P), and a freshness header with server time, event count, and UTC offset. Update time is taken from the trade server clock, not the last tick.

Inputs include currency filter, ho...

👉 Read | AlgoBook | @mql5dev

Trade Guardian is a defensive Expert Advisor designed for risk supervision rather than trade entry. It monitors existing positions and reacts when configured limits are breached. Each function is independent, and position closing is disabled by default to allow warning-only operation on live charts.

Stop-loss enforcement checks open positions on timer events and flags orders without a stop. If enabled, it places a stop at an ATR-based distance using the chart timeframe, respecting a minimum distance in points and the broker stop level. Stops are applied only to the chart symbol.

Risk limits include a daily loss cap referenced to the balance at the day’s start, auto-refreshed on each new day, and a total drawdown cap referenced to the equity peak. A cooldown can rebase the peak to current equity so the guard can re-arm, with rebase events logged.

Scope can ...

👉 Read | Forum | @mql5dev

EOSA (Ebola Optimization Search Algorithm, 2021) is a bio-inspired metaheuristic derived from a SEIR-HDVQ epidemic model, mapping short-range transmission to exploitation and long-range transmission to exploration. Quarantine is used to reduce premature convergence by keeping part of the population static.

A literal implementation exposes issues in the paper’s core equations: missing direction vectors in movement updates, identical exploitation/exploration forms, and a bounds bug in initialization (L + rand(U+L) instead of L + rand(U-L)). These defects can cause non-improving motion and rapid population collapse.

A practical variant keeps the intended mechanics but simplifies to active agents only, adds directed moves, pBest memory, fitness-biased strategy selection, Lévy flights for global search, and a ρ schedule from 1.0 to 0.5, with boundary re...

👉 Read | NeuroBook | @mql5dev

This article focuses on practical debugging in MQL5 using MetaEditor and the terminal logs, bridging the gap between basic syntax and building reliable Expert Advisors.

It breaks down compiler diagnostics (warnings vs errors, file/line/column navigation) and shows why fixing issues top-to-bottom reduces cascading messages, especially when porting MQL4 code with mismatched function signatures.

Runtime failures get equal attention: dynamic arrays without sizing, “array out of range” cases from unloaded history or off-by-one indexing, and silent logic bugs where an EA simply never trades.

The workflow starts from entry points (OnInit/OnStart, OnTick/OnCalculate, OnTimer), encourages structural analysis, buffer/property checks for indicators, and uses targeted Print/PrintFormat output to pinpoint control-flow mistakes like an accidental semicolon after a loop.

👉 Read | AlgoBook | @mql5dev

This article builds a Fisher Transform–style oscillator for MetaTrader 5, showing how to turn recent price position into a bounded value, smooth it, clamp it near ±1 to keep the logarithm stable, then apply the log transform and recursive smoothing to make extremes and reversals visually sharp.

Key implementation work focuses on correctness in MQL5: preserving recursive state via a calculation buffer, handling series indexing consistently, guarding minimum bars and zero-range windows, and recalculating only new bars for performance.

For trading logic, the signal is not a threshold cross. It waits for the line to exceed an extreme (often ±1.5 to ±2) and then turn back toward zero on closed bars, enabling a non-repainting EA to act on confirmed peaks/troughs across symbols.

👉 Read | Forum | @mql5dev

This article turns the reusable CSwingEngine into a double top/bottom detector that prioritizes market structure over chart “shapes”. The EA first confirms H4 trend (up for double top, down for double bottom) using labeled swings (HH/HL or LL/LH); if the context is range, it refuses to evaluate the pattern.

Validation is based on confirmed swing points: two matching swings with exactly one opposite swing between them, peaks/troughs within a configurable H4-ATR tolerance, a minimum pattern height in H4-ATR, and time width measured from swing timestamps. ATR is always taken from the swing timeframe to keep thresholds scaled to structure.

Execution is separated from detection via a three-state machine: scan, lock a single pattern once, then wait for a neckline break on the trading chart timeframe with expiry and “no re-entry” identity tracking. Entries add pra...

👉 Read | Calendar | @mql5dev

Mamba4Cast is presented as a modular time-series forecasting core for high-frequency market data, combining compact feature extraction, multi-window convolutions for noise-resistant signal detection, and an SSM-based long-memory block to keep context across dozens of candles. A key design choice is forecasting across the full planning horizon rather than only the next step, improving stability for trading decisions.

The framework is integrated into an Actor–Director–Critic agent. The Environment State Encoder normalizes raw OHLCV and indicators (using noisy batch norm for better generalization), adds H1/D1 harmonic time embeddings, then applies stacked convolution + pooling + Chimera SSM blocks, followed by a convolutional decoder and denormalization.

The Actor aligns account state with per-feature latent embeddings via stacked cross-attention before...

👉 Read | Forum | @mql5dev