Quantora Spread Monitor MT5 is a real-time spread monitoring indicator for MetaTrader 5. It continuously tracks current spread and records minimum, maximum, and average values to quantify changing transaction costs over time.

A built-in history graph highlights sudden spread spikes and periods of instability. Spread levels are classified from low to extreme, with configurable thresholds to match specific risk and cost limits.

Alerts trigger when the spread exceeds a defined level, with popup, sound, and push options. A cooldown mechanism reduces repeated notifications during volatile bursts.

The dashboard presents current spread, statistics, symbol details, and server time in a compact layout. It supports all symbols, updates automatically, and is designed strictly for monitoring without trade execution or signal generation.

👉 Read | Freelance | @mql5dev

Quantora Trading Cost Calculator for MT5 estimates total trade cost before entry using live symbol data and user inputs. It aggregates spread, commission, and swap into a single view to reflect expected costs under current conditions.

Inputs include lot size, direction, commission per lot, and holding period. Output breaks down spread cost, commission cost, and swap impact, then totals the estimate in account currency. The cost is also shown as a percentage of account balance.

The panel displays tick size, tick value, and contract size to adapt calculations across instruments and broker settings. Works on all symbols and timeframes with automatic updates. Analysis-only utility with no order placement and no signal generation.

👉 Read | NeuroBook | @mql5dev

Adaptive Kalman Trend Filter uses a single-state Kalman estimate with process noise (Q) recalculated on every bar. Q is scaled between min and max multipliers using Kaufman’s Efficiency Ratio (ER), avoiding fixed-parameter lag versus whipsaw trade-offs. High ER raises Q and the Kalman gain, keeping the line closer to price; low ER reduces Q and increases smoothing during range conditions.

Regime bands are derived from rolling residual volatility (price minus Kalman line). Band width is the residual standard deviation times a multiplier that is further expanded as ER falls, producing tighter bands in directional markets and wider bands in chop. Color state changes are confirmed on closed bars only, so signals do not repaint.

Key inputs include base Q, measurement noise (R), ER/band lookbacks, regime threshold, and band multiplier. Typical tuning targets...

👉 Read | Freelance | @mql5dev

SessionORB_EA automates an opening-range breakout around a configurable session start (London, New York, or any custom broker-time open). It records the high/low of the first N minutes using M1 data, then maintains two trigger levels at the range boundaries plus an optional buffer.

A trade is placed only after a bar closes beyond a trigger, not on a wick. One market position per session is allowed by default, with position sizing based on account risk percent or an optional fixed lot. Stop loss is set beyond the opposite side of the range with an added buffer, and take profit is defined by a reward-to-risk multiple.

The range measurement stays minute-accurate regardless of chart timeframe, while signal confirmation follows the attached chart period. Chart objects can draw the opening-range box, forward trigger lines, and a session-start marker, with automat...

👉 Read | VPS | @mql5dev

Reverse RSI Bands is a custom indicator that reverse-engineers the RSI equation and plots the exact price levels where RSI will reach selected overbought and oversold thresholds. Instead of a separate oscillator window, the main chart shows the calculated upper and lower price bands, enabling advance identification of potential extremes and dynamic support/resistance zones.

The implementation uses algebraic deductions of Wilder’s recursive smoothing and computes the required price for the current bar from the verified previous-bar state, preventing repainting. The resulting levels are designed to match the platform’s standard RSI output exactly.

Key inputs include RSI period (default 14), overbought target (70), oversold target (30), and applied price (close). Common usage focuses on major FX pairs on H1/H4, watching for touches or pierces of the ban...

👉 Read | AppStore | @mql5dev

K²VAE targets time-series forecasting under high uncertainty by combining linear latent dynamics (Koopman), adaptive error filtering (KalmanNet), and probabilistic sampling (VAE). Output is a full distribution of future states, with variance reflecting confidence rather than a single trajectory.

Architecture splits into patching, encoder, and decoder. The decoder returns mean and variance to model P(Y|Z) and preserve uncertainty end-to-end.

The encoder chains KoopmanNet for latent transitions and retrospective reconstruction, attention over reconstruction error (not the raw sequence), KalmanNet to generate a covariance matrix from error-derived control signals, and VAE sampling using Koopman mean plus Kalman dispersion.

KoopmanNet can be extended from dual MLPs to a sparse Mixture-of-Experts block to separate local vs global dynamics and improve robustness on vo...

👉 Read | Signals | @mql5dev

This article outlines an MVP “LLM-driven trader” built with Python + MetaTrader 5 + an OpenRouter-connected language model. MT5 supplies recent OHLCV candles, the script packages them into a prompt, and the LLM returns a structured decision: BUY/SELL/WAIT plus entry, SL/TP, and a short rationale.

Key implementation details: cloud inference keeps hardware requirements low; model choice is swappable via a single API parameter; responses are parsed with regex to tolerate formatting drift; orders are placed as MT5 market trades with a 1:2 risk/reward, and each cycle runs on a timer (e.g., every 5 minutes) with full logging.

Practical takeaways: strategy iteration happens mostly in prompt design and model settings (temperature, max_tokens), multi-timeframe inputs can improve context, and production use needs guardrails for open-position handling, latency, and...

👉 Read | Forum | @mql5dev

Most MT5 indicators lean on mean, standard deviation, and least-squares regression, which collapse under outliers: a single bad tick or gap can drag the mean, inflate volatility, and even flip a regression slope.

A robust alternative replaces those estimators with median, MAD (scaled by 1.4826 to match sigma on clean data), and Theil-Sen slope (median of pairwise slopes). These keep meaning until a large fraction of the window is corrupted, unlike classical tools with effectively zero tolerance.

The core is a single include, RobustStats.mqh, using a ring buffer for O(1) updates and a one-pass ComputeAll() that returns robust and classical metrics over the same window for fair comparison. Three drop-in indicators mirror bands, channels, and oscillators, plus an overlay and a breakdown-point script to quantify stability and highlight edge cases (NaNs, ...

👉 Read | VPS | @mql5dev

Running one EA per symbol hides portfolio risk: correlated pairs can stack exposure, turning multiple “safe” trades into one concentrated drawdown. The article proposes a master–slave architecture to coordinate trading across symbols.

A Portfolio Controller (master) holds no positions; it computes equity-based risk budget, enforces drawdown kill switches, and broadcasts limits. Instrument Agents (slaves) generate signals per symbol but must query shared limits before any order, separating strategy code from capital governance.

State sharing uses MT5 global variables for low-latency scalar flags (budget, max lots, halt), with named pipes or files reserved for structured, slower updates. A readiness handshake prevents trading on uninitialized state, timers drive controller updates, and degraded mode keeps agents running conservatively if the controller dis...

👉 Read | Freelance | @mql5dev

Aggregate stats in MQL5 signals can hide trade sequencing. Win rate, profit factor, drawdown, and a smooth equity curve summarize outcomes, not sizing and exposure mechanics.

A native MT5 auditor is proposed to grade “Hidden Risk-of-Ruin” from A to F using four checks on reconstructed closed positions: volume escalation after losses (martingale), overlapping same-direction entries at worsening prices (grid), payoff asymmetry (small wins vs rare large losses), and a classical risk-of-ruin estimate for a chosen risk-per-trade.

Implementation is split into two scripts: an exporter that rebuilds positions from deal history into CSV, and an auditor that loads CSV or runs a reproducible demo and prints findings in the Experts tab. No external dependencies.

👉 Read | CodeBase | @mql5dev

CustomAverage implements a two-stage adaptive moving average: a selectable base MA on price, followed by an independent smoothing MA applied to the base output. This setup improves responsiveness versus a single long MA while keeping the line more stable than a short MA.

The plot is slope-colored, switching based on bar-to-bar direction. Optional arrows mark close/average crossovers: bullish when the close moves from below to above the line, bearish on the reverse. A corner label prints the current average value.

The calculation runs left-to-right on closed bars only, with no future data usage and no historical repainting. Updates use prev_calculated logic to recompute only changed bars, keeping runtime low on deep histories and small timeframes.

Key inputs include MA periods, MA methods (SMA/EMA/SMMA/LWMA), applied price, signal toggles, arrow code...

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MetaTrader 5 trendlines are purely graphical, so EAs can’t natively detect touches, bounces, or meaningful breakouts. This article bridges that gap by wrapping each chart trendline into a managed runtime entity with identity, memory, and a controlled lifecycle.

The design is event-driven for user actions (create/drag/modify) and confirmation-driven for market logic, using closed candles to avoid intrabar noise. States progress through active, touched/pending, bounced, and broken, with configurable thresholds for proximity, volatility, and consecutive closes.

Responsibilities are split cleanly: chart synchronization, geometry projection, lifecycle decisions, and visual debugging (color-coded states). A central manager discovers existing objects, maintains a collection of managed trendlines, and coordinates updates across multiple lines.

👉 Read | Freelance | @mql5dev

Order reject 130 (“Invalid stops”) is often caused by server contract limits, not EA logic. Key constraints are stop level, freeze level, and volume rules (min/step/max lot). These values are per symbol, broker-specific, and can change without notice.

A lightweight script can print symbol specifications without placing, modifying, or closing trades, and without requiring algo trading to be enabled. It can read a comma-separated symbol list or use the current Market Watch set.

Output includes digits, point, tick size/value, contract size, lot limits, spread mode, execution mode, swaps, and margin required for one minimum lot vs free margin. The most actionable line computes the nearest stop level the server should accept, returned in price units to avoid pip/point mistakes on 5-digit symbols.

Stop validation should use the larger of stop level and freeze leve...

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DoEasy indicator handling in MQL5 received a custom indicator object to complement the standard indicator set.

Standard indicators use fixed, known inputs and can be instantiated via dedicated constructors. Custom indicators require an MqlParam[] passed to a creation method, including a mandatory TYPE_STRING element with the indicator path/name. A new indicator group “any” covers unknown type until the user assigns trend/oscillator/volume/arrow.

The indicator base class adds an ID property, ID-based sorting, and data access helpers that fetch a single value via CopyBuffer() by bar index or time. Parameter descriptions for custom indicators are printed sequentially from MqlParam[].

Indicator collection creation now checks ID uniqueness, supports custom indicator lookup by group+MqlParam[], and provides GetByID/SetID. On timeframe changes, duplicate handles...

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A new MT5 chart wallpaper background indicator is available with BMP support.

Place an image named background.bmp in the terminal’s FILES directory, then attach the indicator to a chart. The indicator reads the BMP file and renders it as the chart background.

Two layout modes have been added to control scaling behavior: BMP_FIT and BMP_FILL. FIT keeps the entire image visible with possible margins, while FILL covers the full chart area and may crop edges.

👉 Read | Signals | @mql5dev

Neural-network trading workflow shifts from Matlab to Python, using TensorFlow plus Keras with MetaTrader 5 integration. Focus moves to input preparation, dataset splitting by direction, and training operations for EURUSD H1.

A two-stage model is used: Net1 reproduces indicator-like features from quotes, Net2 generates the signal target. The system runs four networks (buy/sell, max/min). MQL5 scripts export CSVs; daily extreme markers set -1 at first high/low touch. Net2 targets are hour open vs day open (or day close vs hour open), emphasizing achieved outcomes over event prediction.

Training is handled in a Python script: pandas ingestion, standardization, a Sequential model (22 inputs, 60 outputs), 10 epochs, batch 10, 30% validation, then saving .h5 models. Strategy Tester data generation feeds a separate test dataset.

Results are optimized via an EA ...

👉 Read | CodeBase | @mql5dev

Volume-Weighted Price Displacement Oscillator measures mean reversion against a rolling VWAP instead of a simple moving average. Higher-volume bars influence the anchor more, so the reference tracks where trading concentrated, not just closes.

The oscillator is Close minus rolling VWAP, normalized by a rolling standard deviation over a separate volatility window. This produces a z-score style histogram: near 0 indicates trading around volume-weighted fair value, beyond ±1.0 indicates an impulse, and beyond ±2.0 flags statistical stretch where consolidation or reversion becomes more likely.

Key inputs: VWAP period (default 20), volatility period (14), signal smoothing (5), impulse level (1.0), exhaustion level (2.0), applied price (typical). Practical use: in trends, sustained impulse readings can support continuation; in ranges, exhaustion plus the signa...

👉 Read | Freelance | @mql5dev

MetaEditor’s profiler finds slow code, but it won’t catch indicators that draw “correct-looking” lines with wrong values. This walkthrough focuses on the debugger: pausing execution at breakpoints, stepping line-by-line, inspecting variables in Watch, and using the call stack to trace how a bad state was reached.

A rolling z-score indicator is used with two intentional bugs: an off-by-one loop in the mean that can read past the array edge, and a variance formula dividing by period+1, producing a plausible yet consistently biased result.

Key workflow: start debugging on real data (F5) or in Strategy Tester history mode (Ctrl+F5), place breakpoints before suspect reads, then step until the exact variable (like an index) becomes invalid or the math deviates silently.

👉 Read | VPS | @mql5dev

Partial position closing in MQL5 often fails in production due to three implementation errors: lot-step rounding, applying close percentages to remaining volume, and leaving the stop at the original risk after the first scale-out.

A CPartialCloseEngine design addresses these directly. It freezes entry state in CPositionRecord, drives an R-multiple profit ladder, normalizes and clamps volumes via CVolumeNormalizer, executes reductions with TRADE_ACTION_DEAL through a dedicated executor, and moves SL with TRADE_ACTION_SLTP in a breakeven manager.

The ladder applies percentages to the original entry volume, tracks per-level hit state, optionally triggers a one-time breakeven shift, and draws chart HLINE markers for verification. A companion script validates rounding rules, R calculations, trigger logic, and remainder clamping.

👉 Read | Docs | @mql5dev

This article replaces fragile, hand-tuned trade filters and “train-once” ML with an online logistic regression that updates after every closed trade. The goal is simple: keep the EMA crossover, but adapt the filter when market behavior changes.

A shared MQL5 library implements logistic regression with a minimal SGD update, L2 regularization, optional learning-rate decay, and CSV persistence. The EA builds six explicitly scaled features, gates entries by predicted win probability, then labels outcomes using net profit (including costs) while handling partial closes via position IDs.

Validation uses a synthetic generator to prove the update rule learns, quantify warm-up needs, test feature ablations, check probability calibration, and compare online learning against frozen and periodic/rolling retrain baselines under regime shifts. Practical notes cover visualizati...

👉 Read | CodeBase | @mql5dev