Daily Loss Guard enforces an account-level daily loss cap by closing exposure when the configured limit is reached. It can be attached to any single chart, on any symbol and timeframe, and operates independently from trade entry logic. A timer is used, so tick activity on the host chart is not required.

Daily P/L is computed from today’s closed trades plus the change in open positions since the defined day start. Deposits and withdrawals are excluded. On limit hit, all positions are closed, pending orders are removed, and any newly opened trade is closed until the next trading day.

The trading day start is set by server hour. Start balance and limit-hit state are stored in terminal global variables to survive restarts and re-attaches, then reset automatically on the next day.

Configuration supports percent or fixed-currency limits, symbol filtering, magic ...

👉 Read | Signals | @mql5dev

Session Range and Spread is a chart annotation tool that marks the Asia, London, and New York sessions. Each day it draws a box from the session high to low and prints the range on the box. A compact panel shows current spread and which sessions are currently open.

Session times are entered in UTC. On live charts, the broker UTC offset is detected from server time versus GMT, with an option to set it manually. Sessions crossing midnight are supported. Range labels can be Auto, Price, Points, or Pips; forex pairs use pips by default, while metals and indices use price units.

The active session updates on every tick and is marked with an asterisk. Labels are positioned to reduce overlap. Boxes render on H1 and lower; higher timeframes show only the panel. The indicator draws objects only and provides no trading signals.

👉 Read | Freelance | @mql5dev

Currency Exposure and Leverage Monitor aggregates every open position into currency legs and reports net exposure per currency in account currency. It highlights when several trades that look unrelated are effectively the same directional risk, such as multiple USD shorts created via EURUSD, GBPUSD, and XAUUSD.

The panel ranks currencies by gross contribution and visualizes long vs short exposure around a zero axis. It also calculates effective leverage as gross notional divided by equity, compared with broker leverage, with configurable warning thresholds and concentration flags when a single currency exceeds a set share of total exposure.

For the three largest exposures, a sensitivity section estimates equity impact for a configurable currency move (default 1%), in money and as a percentage of equity. Opposing legs are netted to expose hidden offse...

👉 Read | Quotes | @mql5dev

RSI extremes label momentum, not direction. A 70+ reading can align with bullish continuation; a 30- reading can persist during bearish continuation. Directional intent requires market structure.

A closed-bar analytical EA can separate “threshold crossing detected” from “trade bias validated.” It detects RSI crossings on completed candles, reconstructs the last two confirmed swing highs and lows, and classifies structure as bullish, bearish, or range.

Signals are validated only when structure and a break of structure (BOS) agree. BOS is confirmed by a completed close beyond the latest swing level, with a configurable buffer and a prior close that was not already beyond.

Mixed structure arms a time-limited candidate. Opposite BOS within the confirmation window validates reversal; otherwise the candidate expires. Output stays diagnostic: comments, uni...

👉 Read | Forum | @mql5dev

MT5 alerts can only beep or play fixed WAV files, so key events stay trapped on-screen. The article builds a single MQL5 include that enables runtime text-to-speech from EAs and scripts via a Speak() function, while deliberately blocking indicators and refusing unsafe contexts like Strategy Tester.

Instead of a COM wrapper DLL, MQL5 launches a hidden PowerShell process through WinAPI CreateProcessW. PowerShell uses Windows SAPI to select an installed voice, set rate (-10..10) and volume (0..100), and speak synchronously.

Safety and robustness come from UTF-16 Base64 (-EncodedCommand) to prevent command-line injection, XML-aware handling (plain vs tagged text), strict length limits, timeouts with forced termination, and exit codes to report missing voices, malformed markup, or execution errors.

👉 Read | AppStore | @mql5dev

EV-MGRFlowNet is being ported to MQL5 with OpenCL to handle market microstructure as an event stream rather than fixed time windows. Core blocks remain FERE-Net (ConvGRU/ST-ConvGRU) for long temporal context, FGD-Net for multi-scale reconstruction of a motion vector, and HMC-Loss for self-supervised training without labels.

Current work shifts from event encoding to reconstruction via the FGD-Net decoder. Decoder inputs are built from concatenated signals: prior decoder output plus the matching encoder level, enabling continuous correction against the original event structure.

A CNeuronFGDModule is implemented as a standalone decoder unit: batch norm, cD conv with TANH, flow projection, strict concatenation, and cXH multi-window conv producing GRU gates in one OpenCL pass. Forward pass follows deterministic sequencing, explicit state handling, and gradient...

👉 Read | AlgoBook | @mql5dev

LearnedStrategy exposes signal generation and metadata, but no interface for post-trade outcomes. After deployment, evidence arrives one trade at a time, and retirement rules become sequential tests with repeated looks.

A fixed 0.95 Beta-posterior trigger is statistically valid per look, but not per stream. Simulated at a 48.5% win rate, checking after every trade retired healthy runs 33.4% of the time over 300 trades; a single look at trade 300 was 5.2%. Raising the trigger to 0.9956 restored ~5% false alarms but reduced sensitivity.

WinRateSPRT adds two modes: Wald terminating SPRT for fixed batches, and Page reflecting CUSUM for continuous monitoring. CUSUM requires a planned horizon; calibrating the boundary to 5% false alarms over 300 trades raised detection delay (ARL1 ~99 to ~173 trades) but materially improved break detection versus posteri...

👉 Read | Forum | @mql5dev

This MQL5 article builds a trend stop that adapts to market regime instead of trailing at a fixed distance. On each flip, the stop is reseeded a configurable number of slow-ATR multiples beyond the flip bar’s high/low, then accelerates toward price bar-by-bar with a capped speed so early noise is tolerated and stalled trends are exited sooner.

Acceleration is boosted by how far price is from a reference VWAP (session/week/month, or combinations). That same VWAP relationship grades flips as confirmed or filtered, publishes the stop/trend/grade via buffers, and optionally alerts only on live bars.

Visualization is treated as a first-class feature: persistent flip levels remain until invalidated by a close, while a blended canvas draws an anti-aliased stop line, a non-obscuring cloud, and precise labels without performance-heavy floating point work.

👉 Read | Docs | @mql5dev

This article tackles two common failure modes in ML trading: noisy price-derived features that overfit, and equity curves dominated by a single instrument’s regime.

It builds a Python+MT5 pipeline around a causal L1 Trend Filter solved with ADMM (banded O(N) iterations, adaptive rho, correct residual tracking). A sliding window makes the filter usable live, producing a piecewise-linear trend and explicit breakpoints.

Features come from trend geometry and indicators computed on the filtered series, with targets defined over a structural horizon (3 L1 segments) rather than fixed time. Models are trained per symbol with CatBoost and strict walk-forward (purge/embargo, internal early stopping).

Portfolio stability is improved by trading 29 instruments and sizing lots via 95% parametric VaR, allocating inversely to volatility and backtesting the weight...

👉 Read | VPS | @mql5dev

Volatility tools built on close-to-close returns discard intrabar dispersion. OHLC-based estimators reduce that loss and react faster to regime shifts, with direct impact on sizing, stops, and breakout timing.

The Garman–Klass (1980) variance estimator uses ln(H/L)^2 plus a corrected ln(C/O)^2 term. It is theoretically more efficient than close-to-close under zero drift, but it ignores prior-close to current-open gaps, which can understate risk in instruments with frequent jumps.

Testing on synthetic GBM in Python showed materially lower MSE for GK, but also exposed a failure mode: GK-to-MA crossovers do not classify regimes. Percentile rank over a lookback window produced stable squeeze (<30th) to expansion (>60th) signals, filtered by a directional SMA.

In MQL5, correct series indexing, incremental recalculation, and storing recursive squeeze s...

👉 Read | Signals | @mql5dev

This article builds an MT5 “signal router” that mimics discretionary filtering: a lightweight online classifier scores direction, Shannon entropy converts the score into uncertainty, then signals are auto-traded, skipped, or sent to a human review step. In Strategy Tester, the human step is replaced with deterministic proxies (always confirm/reject) or seeded randomness.

Key engineering choices: rolling online logistic regression with warm-start refits on a 300-bar window, careful no-leak label indexing, and a P² streaming quantile to keep a fixed review budget under distribution drift without storing all history. Execution code enforces broker constraints (stops/freeze, min lot, fill modes) and attributes PnL per decision tag.

Validation is treated as the core problem: thresholds are tuned on one window and judged on a held-out window against mult...

👉 Read | AppStore | @mql5dev