MQL5.community

Automated trading on MetaTrader

[MQL5.community @mql5com]

Self-Aware Trend System (SATS) is a multi-engine SuperTrend for MT5 built around four parts: adaptive ATR SuperTrend bands, a Trend Quality Index (TQI), composite signal scoring, and on-chart risk levels (entry, SL, TP1–TP3).

Band width adapts with Kaufman Efficiency Ratio: tighter in directional moves, wider in chop. TQI (0–1) combines efficiency, volatility regime (ATR vs baseline), pivot-based structure, and momentum persistence. A character-flip module flags sharp TQI drops during an active trend as an early warning before a line flip.

Signals are gated by a configurable score (momentum, ER, volume Z-score, RSI zone, pivots). The dashboard tracks TQI components plus rolling win rate, average R, drawdown, streaks, and cumulative R. Optional auto-calibration adjusts “Quality Influence” from recent R results. Non-repainting uses confirmed closed bar...
#MQL5 #MT5 #Indicator #Strategy

https://t.co/ZCevdtQhaB

[MQL5.community @mql5com]

Daily Risk Monitor Lite is a free, open-source MT5 indicator that renders intraday account risk directly on the chart with a small, explainable feature set. The panel shows Daily Realized P/L, Floating P/L, Daily Total, and current drawdown percent, plus SAFE/WARNING/DANGER status via configurable colors.

Daily Realized P/L counts only exit deals within the active day range, with optional commission and swap inclusion. Floating P/L uses the current result of all open positions, optionally including swap. Daily Total is realized plus floating. Drawdown is max((Balance-Equity)/Balance*100, 0), or N/A when Balance<=0.

Day boundaries can follow broker 00:00 or a manual server-hour start. This is read-only: no auto-close, no trade blocking, no enforcement engine. Intended as a lightweight sample for monitoring and further development.
#MQL5 #MT5 #Indicator #RiskMgmt

https://t.co/leW5WcdglB

[MQL5.community @mql5com]

A confluence detector identifies price zones where Fibonacci retracement levels from multiple swing pairs align.

The algorithm selects 4–5 significant swing points, calculates 38.2%, 50%, 61.8%, and 78.6% levels for each swing pair, then scans for clusters where two or more levels converge within a configurable tolerance. Zones are ranked by density, with more overlapping levels treated as higher strength.

Confluence areas are rendered as shaded rectangles and color-coded by strength: yellow for 2 matches, orange for 3, and red for 4 or more. Labels list the contributing levels, while individual Fibonacci lines can be enabled or hidden. An alert triggers when price enters a strong zone.

Runs on all timeframes, with typical use on H1, H4, and D1.
#MQL4 #MT4 #Indicator #Strategy

https://t.co/39Jawps46F

[MQL5.community @mql5com]

MQL5 EAs often persist input changes by rewriting a single settings file, which removes history and makes past test results hard to reproduce.

A practical alternative is an append-only binary log. Each parameter snapshot is stored as a fixed-size struct record, enabling fast reads of the latest configuration while keeping prior versions intact.

The struct is typically split into metadata (version, timestamp), adjustable trading inputs, and an integrity field. A checksum is computed only from adjustable inputs so timestamps, counters, and performance metrics do not trigger new versions.

On startup, create version 1 only if the file is empty. On later runs, read the last record, recompute the checksum from current inputs, and append a new record only when the checksum differs.
#MQL5 #MT5 #EA #MQL5

https://t.co/VZCTYoRpEk

[MQL5.community @mql5com]

MetaTrader 5 keeps trade history inside the terminal, so external analytics need an explicit export path. This article completes that link by adding a lightweight EA that emits a trade record the moment a position is closed.

The core design is event-driven: OnTradeTransaction filters only “deal added” events that represent exits, avoiding fragile OnTick polling and ensuring only final, complete trades are sent.

The EA reconstructs a full trade by pairing the closing deal with its opening deal, normalizes enums (reason, direction) into readable strings, builds JSON manually (no native MQL5 JSON), then POSTs it via WebRequest to a versioned Flask API endpoint.

Local testing covers server startup, MT5 WebRequest URL whitelisting, and verifying 200 responses and server logs. Known gaps include no retries, no payload validation, and no local queueing for outa...
#MQL5 #MT5 #EA #WebRequest

https://t.co/t51uJ3cUyy

[MQL5.community @mql5com]

Price action systems often fail on breakout filtering, with liquidity sweeps creating false continuation signals. A structured model is required to locate likely resting orders, validate breaks, and keep risk rules consistent.

An MQL5 automation is outlined for order block trading in consolidation zones, using higher-timeframe swing structure as the trend filter. Setups require inducement first, then break of structure, with fair value gap alignment inside the impulse leg.

Implementation details cover enums for trade mode, FVG state, trailing type, and mitigated-zone handling. Core structs track OB metadata, FVG links, and open-trade trailing state, plus chart rendering utilities for zones, labels, BoS lines, and mitigation marks. Swing and consolidation functions drive detection, followed by zone creation, mitigation tracking, and risk-based execu...
#MQL5 #MT5 #AlgoTrading #Strategy

https://t.co/mIZluTIABI

[MQL5.community @mql5com]

Strategy Tester gaps around CalendarValueHistory() make news-driven EAs hard to verify. Historical events are often missing, so entry blocks, SL/TP suspension, and pre-news closes never trigger, producing misleading curves and no audit trail in logs.

A deterministic fix is a static CSV of events (time, currency, importance, name) loaded once in OnInit() when MQL_TESTER is true. Tester mode switches isUpcomingNews()/IsNewsTime() to an in-memory scan, while live trading keeps the terminal calendar API unchanged.

Implementation points: strict CSV format compatible with StringToTime(), FILE_COMMON access for tester sandbox, a symbol currency cache built at init, and an optional script that exports the terminal calendar to CSV for chosen date ranges.
#MQL5 #MT5 #AlgoTrading #Strategy

https://t.co/30vnyFUe3y

[MQL5.community @mql5com]

CATCH targets subtle anomalies in multivariate market series by moving analysis to the frequency domain, where volatility bursts and regime shifts separate into different bands. Its key idea is “frequency patching”: split the complex FFT spectrum into overlapping patches, learn normal behavior per band, then reconstruct with iFFT and flag anomalies via reconstruction error.

A channel-aware fusion stage uses a masked Transformer to model cross-asset dependencies without letting irrelevant instruments dilute attention. The mask is learned and refined with an explicit objective to strengthen meaningful inter-channel links while suppressing noise.

The article then maps these ideas into MQL5 with OpenCL acceleration: complex-valued convolution and masked attention are implemented using float2 buffers for real/imag parts, reusing existing layer infrastr...
#MQL5 #MT5 #AlgoTrading #AITrading

https://t.co/KaPQVS546Q

[MQL5.community @mql5com]

L1 trend filtering can be used to estimate piecewise-linear price trends while suppressing short-term noise. The method keeps turning points and slope changes that are often lost with moving averages or heavy smoothing.

An example implementation in MQL5 demonstrates L1 Trend Filter routines for float and double vectors, validated on random-walk simulated series. This setup helps verify numerical stability, parameter sensitivity, and output consistency across data types.

In trading workflows, the filtered trend can support regime detection, adaptive risk controls, and signal preconditioning by separating structural movement from micro-variations. The same code is packaged as a shared project under MQL5\Shared Projects\L1Trend.
#MQL5 #MT5 #Indicator #Strategy

https://t.co/3Rqqia12t4

[MQL5.community @mql5com]

An Expert Advisor can automate session control by recalculating daily start, end, and close times, clearing internal state, and initializing the price range used for breakout logic.

During the range window, minute-level highs and lows are sampled to compute the session maximum and minimum. A chart rectangle is updated in real time to reflect the current consolidation zone.

After the range window completes, the close of the latest finished candle is compared to the stored boundaries, independent of the range end timestamp. On confirmation, a market order is placed in the breakout direction, with take-profit set to the measured range size and stop-loss set to the opposite boundary.
#MQL5 #MT5 #EA #Strategy

https://t.co/SOKKPPfszG

[MQL5.community @mql5com]

DADA (Adaptive Bottlenecks + Dual Adversarial Decoders) targets time-series anomaly detection with adaptive compression, dual reconstructions for normal vs anomalous regimes, and patching/masking to reduce noise and improve generalization.

The Adaptive Bottlenecks block is implemented as CNeuronAdaBN inheriting CNeuronMoE. It reuses top-k gating and expert routing, but populates experts with autoencoders at multiple latent sizes. A convolution stage produces a latent vector whose segments map to each autoencoder, followed by multi-window conv, tensor transpositions, and per-autoencoder decoder weights.

Instead of a monolith, the system is assembled from library components into three jointly trained models: a state encoder autoencoder with patching/masking and AdaBN, an Actor replacing the anomalous decoder for action selection, and a direction predictor as a deci...
#MQL5 #MT5 #AI #EA

https://t.co/rCuUkwzSoT

[MQL5.community @mql5com]

Deterministic Oscillatory Search (DOS) is a reproducible metaheuristic for global optimization that removes randomness entirely. Particles start from deterministically distributed points, making runs repeatable under identical inputs—useful for research and trading system validation.

Search is driven by a “fitness slope” state: improving, worsening, or unknown. When fitness deteriorates, a particle reflects (reverses direction) and halves velocity, producing controlled oscillations that refine local extrema without derivatives.

If oscillations stall (worsening persists), DOS switches to a swarm step, pushing particles toward the current global best using a configurable movement factor. An MT5-style implementation centers on per-particle velocity vectors, range clamping, best-solution tracking, and adaptive velocity updates.

Tests show stronger behav...
#MQL5 #MT5 #algorithm #Strategy

https://t.co/KXYKp5QKJL

[MQL5.community @mql5com]

Manual liquidity zone work on higher timeframes misses the internal structure of the base candle. Verifying whether a zone was built by a triangle, rectangle, or double top/bottom requires repeated lower-timeframe zooming, which is slow and produces inconsistent labeling.

An automation module is proposed to classify the lower-timeframe geometry inside each base candle as ascending triangle, descending triangle, symmetrical triangle, rectangle, M, W, or undefined, then annotate the zone and alerts with the result.

Implementation outline targets MQL5: isolate detection logic in an include file (CGeometryDetector), add configurable tolerances and swing-distance filtering, compute slopes for symmetry, and integrate by extracting intrabar data for each base candle interval and storing the detected shape per zone.
#MQL5 #MT5 #Indicator #Strategy

https://t.co/1fM5itdaOB

[MQL5.community @mql5com]

Trading losses often come from trading at the wrong time: low-liquidity hours and scheduled news can invalidate otherwise solid entries via spread expansion, slippage, and regime shifts. This system turns “discipline” into enforceable rules by blocking execution outside defined sessions and during configurable pre/post news blackout windows.

The MQL5 design uses a modular control layer: a permission engine (single boolean allow/deny), an enforcement EA that intercepts transactions so neither manual trades nor other EAs can bypass limits, and a dashboard for visibility.

Key implementation details include external session/news files for no-recompile updates, smart caching via file timestamps to avoid per-tick I/O, robust parsing with validation, fast minute-based session checks with early exits, and correct handling of day rollover plus “next allowed time” ca...
#MQL5 #MT5 #EA #Strategy

https://t.co/8I3tGGTE6L

[MQL5.community @mql5com]

A MetaTrader 5 canvas butterfly curve renderer is extended from a four-segment anti-aliased outline to a full illustration with wing fills, texture, and anatomical body details. Rendering remains based on precomputed parametric points mapped to pixel space and processed through the same supersampled pipeline.

Wing interiors are filled via scanline polygon rasterization with a nonzero winding rule to handle self-intersections. Three layers are added: outer vertical gradient, an inward-scaled vertical gradient, and an innermost inward-scaled radial gradient. Veins are rendered as thin anti-aliased lines from the curve origin to sampled boundary points.

Scale texture is added using dense boundary sampling and small filled circles, colored per parametric segment via t ranges (3π, 6π, 9π), then slightly brightened toward edges. The body is composed fro...
#MQL5 #MT5 #AlgoTrading #Indicator

https://t.co/WPA9BQD3QF

[MQL5.community @mql5com]

MQL5 port of four bet-sizing methods lands with a five-file stack designed for a tick-driven EA without NumPy/SciPy.

BetSizingUtils.mqh adds missing statistical primitives: normal CDF/ICDF via Hart minimax rational approximation, raw-moment computation, shared structs, plus an O(N log N) sweep-line counter to replace O(N²) overlap scans for concurrent positions. AvgActiveSignals remains O(N²) by necessity when averaging signal values across active intervals.

Sizing methods map to include files: probability sizing (z-score, averaging, discretization), dynamic forecast-price sizing (sigmoid/power, closed-form calibration, limit price via inverse sizing), budget sizing (exposure normalization with seeded running maxima), and reserve sizing using EF3M-3 mixture fitting from three moments with multi-start analytic solves and log-likelihood selection. Output...
#MQL5 #MT5 #AlgoTrading #MQL5

https://t.co/ELAckD5KJG

[MQL5.community @mql5com]

No source text was included. Provide the text to convert, and optionally note the target platform and any key points that must be kept or removed (product names, dates, numbers).
#MQL4 #MT4 #EA #Strategy

https://t.co/oEl3VlxgXI

[MQL5.community @mql5com]

Position entry is split into multiple legs, with signals derived from reversal probability by block scale. Trend and flat are treated as probabilistic states across concurrent scales, so analysis starts from the smallest blocks and escalates until a working scale is justified.

Long one-direction moves require starting new position series before the previous series closes. A second series is allowed only after the first series basic timeframe exceeds a threshold, then a start signal is found on the second series TF1 and confirmed by detecting a flat segment on the second series basic timeframe using an adaptive Bmin–Bmax range and a probability-based flat criterion.

Additional series profits are accumulated into a reserve and used to close the most loss-making erroneous positions when coverage rules are met. Profit from series N+1 compensates errors...
#MQL5 #MT5 #AlgoTrading #Strategy

https://t.co/yZFnk39C7z

[MQL5.community @mql5com]

A next-generation SuperTrend variant adds an internal ML layer to classic trend-following. Core parameters are adjusted bar-by-bar from recent signal outcomes, and entries are gated by a multi-stage confirmation path before any arrow is plotted. No external libraries are required, and a hidden Confidence buffer is exposed for automated systems to read directly.

Two bands are computed (Bull/Bear) from an RMA-smoothed ATR envelope on a selectable price basis. Band width and ATR length start from inputs and are periodically re-tuned by an optimizer, widening in high volatility and tightening in calmer conditions.

Signals run in Reversal mode (confirmed trend flip with pivot) or Breakout mode (new extreme inside trend, contrarian trigger). Candidates must pass RSI state checks, optional tick-volume surge validation, and an optional key-level depth test ...
#MQL5 #MT5 #Indicator #AITrading

https://t.co/LJKbajiazM

[MQL5.community @mql5com]

XANDER Adaptive Cross overlays two adaptive moving averages designed to react to different market properties. A crossover triggers a color-coded arrow to flag a potential trend shift. When both lines remain aligned, the chart provides continuous visual confirmation of directional bias.

The fast line is range-aware, adjusting speed based on where price sits within the recent high/low band. It accelerates near extremes to react quicker to breakouts and slows near the midpoint to reduce noise. The slow line is efficiency-aware, tightening during clean directional moves and flattening during choppy action.

Color states indicate bias: fast above slow signals bullish, fast below slow signals bearish, and gray indicates transition or undefined conditions. Trading use cases typically wait for an arrow, confirm both lines have flipped to the same bias, and re...
#MQL5 #MT5 #Indicator #Strategy

https://t.co/DC5hcLtPx0