DoEasy’s timeseries architecture is extended with a dedicated indicator buffer data object: one object represents one buffer value on one bar, linked to the owning indicator, symbol, timeframe, buffer index, and bar time. These objects are designed to live in typed collection lists, enabling fast selection, filtering, sorting by time, and later cross-indicator comparisons and benchmarking via cached data.

The new CDataInd class follows the library’s base object pattern with integer/real/string property arrays, property support flags, a Compare() override for Search()/Sort(), and helpers for readable setters/getters and journal output. Selection/sorting services are updated to handle the new object type.

Testing updates the sample EA to instantiate buffer objects for current and previous bars across standard and custom indicators, printing object descriptio...

👉 Read | Forum | @mql5dev

Smart Session Breakout is a session-range breakout indicator for MetaTrader 4. It tracks the Asian session high/low (default 00:00–08:00 broker time), draws the range box, then monitors London/New York for confirmed closes beyond the projected levels.

Signal quality is tightened with volatility checks. Beyond a close outside the range, the breakout candle must meet configurable ATR expansion rules, including a minimum body size relative to ATR. Min/Max range height filters skip unusually small or large ranges.

Signals are non-repainting when confirmation on bar close is enabled. An on-chart panel reports session phase, spread, ATR in pips, and time to bar close. Alerts support pop-up, sound, email, and mobile push. Version 1.10 fixes midnight-crossing sessions, dashboard corner placement, alert firing in intrabar mode, input validation, and reduces p...

👉 Read | Calendar | @mql5dev

Dashboard stats summarize historical signal outcomes on the chart.

BUY shows the count of Buy signals split into Paid (target TP reached) and Loss (3-Grid SL hit), along with the resulting Buy win rate percentage.

SELL provides the same breakdown for Sell signals: Paid versus Loss, plus the Sell win rate percentage.

OVERALL WINRATE aggregates Buy and Sell results into a single success percentage across the available chart history.

InpRiskReward controls the target Risk:Reward ratio in the indicator settings, for example 1.0 for 1:1, 1.5 for 1:1.5, and 2.0 for 1:2.

👉 Read | Docs | @mql5dev

Running multiple Expert Advisors on one account, or mixing manual trades with automation, makes the account statement less actionable. The equity curve aggregates everything and cannot show which systems generated the return, which ones stopped trading, or which ones are subsidised by others.

A script can separate history by magic number and print an audit table. It reads history only, does not place or modify orders, does not require algo trading to be enabled, and scans the full account regardless of the chart symbol. As a script, it runs once and removes itself.

Output is one row per magic number (magic 0 groups manual trades and EAs without a magic), sorted by net result, plus totals. Columns include closed round turns, net P/L with swap and commission, win rate, profit factor, average win/loss, current open volume, pending orders, first/last activity, an...

👉 Read | Forum | @mql5dev

Daily Zone Recovery is an Expert Advisor for MetaTrader 5 that trades around the previous day’s high and low levels.

The logic combines three independent strategies: a breakout followed by a return to the level, a level test that precedes a breakout, and a test–pullback–retest sequence.

Risk and execution controls include an ATR-based volatility filter and spread limits. Configuration is separated by timeframe, with distinct zone parameters plus Take Profit and Stop Loss settings per strategy.

Optional grid handling supports fixed-lot position series and closure at the average price. The system is tuned for gold and can be configured for scaling-style trade management.

👉 Read | Calendar | @mql5dev

Previous Week High/Low is a higher-timeframe reference that reduces intraday noise to two W1 levels: the prior week’s extreme high and low. These values are read from the last completed weekly bar and plotted as horizontal lines across the current week, without switching chart timeframes.

PWH is commonly treated as resistance or a breakout trigger after a clean break. PWL is commonly treated as support or a breakdown trigger once price trades through. With multiple weeks enabled, each week’s levels are drawn only across the week they belonged to and labeled with a -N suffix to avoid overlap.

Inputs cover weeks to display (1–8), per-level visibility, colors, line style/width, and label options including price and font size. Levels refresh automatically when a new weekly bar opens, and objects are cleaned up on detach. Week boundaries depend on broker ...

👉 Read | Freelance | @mql5dev

A scalping dashboard can reduce execution friction by showing spread status and momentum state in real time. Spread is validated against a configurable maximum and displayed with clear green/red alerts to block trades when transaction costs spike.

Momentum is derived from a fast and slow EMA pair, producing BULLISH, BEARISH, or FLAT output. This helps keep entries aligned with the prevailing short-term direction and avoids taking setups against the active flow.

The panel refreshes on a timer (default 1s) and removes all chart objects cleanly on exit. Key inputs include Fast EMA (5), Slow EMA (20), Max Spread in points (12.0), refresh rate, panel position, base font size, and a full color theme for consistent visibility.

Suggested usage: take BUY/SELL prompts only when spread is OK and momentum agrees, and tune max spread per symbol to reduce slippage...

👉 Read | Forum | @mql5dev

A range-context panel can be built from the last N closed candles, excluding the current bar. It calculates the highest High and lowest Low in the lookback, then defines upper and lower buffer zones as a percentage of the range span. The latest close is classified as breakout above the high, breakdown below the low, testing the resistance/support zone, or mid-range.

The display updates on a timer rather than on every tick, reducing UI churn and making it practical for scanning multiple symbols.

Key inputs include lookback length, zone thickness, and timer interval, plus panel placement, font-based scaling, and color theming. Signal colors for breakout/breakdown/testing can remain fixed.

Usage is best treated as a filter and context layer: avoid mid-range entries, validate breakouts with volume/volatility confirmation, apply breakout-retest or range-...

👉 Read | VPS | @mql5dev

An MT5 indicator for XAUUSD built around Smart Money Concepts, combining multi-timeframe liquidity, order blocks, sweeps, and M5 close-based confirmation to filter entries.

Liquidity and order-block levels are derived from a strongest day-wise scan plus H4, M30, M15, and M5, then confluence is used to flag strong buy-side and sell-side zones on the M5 chart. Sweeps are marked and setups trigger only after an M5 candle close confirms rejection or a break-and-close beyond a level.

Risk management is tied to M1 structure: stops reference the relevant M1 swing high/low, with TP1/TP2/TP3 plotted alongside entry and SL. A compact position tool renders risk/reward, while same-side signal locking prevents repeated entries until TP3 or SL, yet opposite-direction setups can still be displayed. Levels are recalculated every 5 minutes, keeping historical annotat...

👉 Read | Forum | @mql5dev

MT5 Cash Risk Probe is a read-only MetaTrader 5 script that validates a single cash-risk sizing boundary before order logic is added. It never sends, modifies, or closes orders, and reports order_sent=false.

It uses OrderCalcProfit() on a hypothetical 1.0-lot trade between entry and stop to estimate loss in the account deposit currency. A user-defined cash reserve for fees or slippage is deducted, the remaining budget is divided by the one-lot loss, then floored to the broker’s volume grid. The script recalculates final loss and prints symbol, side, entry, stop, cash budget, reserve, raw/final volume, estimated loss/total, and min/max/step/limit with tick size.

Inputs include symbol, side, entry (or current Bid/Ask), required stop, cash budget, and reserve. Optional self-tests cover flooring, min rejection, max cap, 0.10 steps, and non-aligned min/step ...

👉 Read | AlgoBook | @mql5dev

A Python fractal feature module was found to leak future data due to centered rolling windows. The fix shifts centered outputs by n bars and keeps hindsight only for label building. Porting this to MQL5 requires separating “where the fractal occurred” from “when features may be written” to keep buffers strictly causal.

The solution is a stateful, event-driven engine (CFractalFeatures.mqh) that processes one closed bar at a time, maintains rings for confirmed fractals and breakout history, and publishes 18 iCustom-readable features. FractalViewer splits chart markers (drawn at the center bar) from feature buffers (written at confirmation), avoiding silent look-ahead.

The port also documents three practical failure modes: reversed series indexing can reintroduce 2n-bar look-ahead, CSV precision can break parity on derived signals, and tick-driven recom...

👉 Read | CodeBase | @mql5dev

ECEA (Elite Crystal Evolution Algorithm) maintains a population split into elite and regular agents. Elite agents run an intensive local search with a decreasing step size. Regular agents select one of three moves (40/30/30): toward the global best, toward the nearest elite plus the elite centroid, or stochastic exploratory jumps. A periodic “wind” resets the worst non-elite with 10% probability.

Key operators include FindNearestElite (Euclidean distance), MoveTowardsEliteCluster (r1*0.3 toward nearest elite + r2*0.2 toward elite centroid with bounds/step enforcement), two-scale ExploratoryMove (70% small, 30% large), elite influence radii (half nearest elite distance), and a progress-based exploration-rate decay to 30% by 100 iterations.

Benchmarking used Hilly/Forest/Megacity at 5/25/500D, 10,000 evaluations, 10 runs. Overall score was 36.46%, abov...

👉 Read | NeuroBook | @mql5dev

Standard GARCH estimation can converge with parameters pinned near zero and p-values near 1.0. This typically reflects positivity constraints (omega>0, alpha>=0, beta>=0) forcing the optimizer to a boundary rather than matching the data.

EGARCH resolves this by modeling log(variance), keeping variance positive without non-negativity restrictions and supporting leverage effects through an explicit asymmetry term. Negative omega or alpha becomes admissible and can materially change the fitted dynamics.

An MQL5 implementation adds VOL_EGARCH, implements CEgarchProcess with log-space backcasting, grid-search starting values, stationarity constraints on summed beta, recursion-based variance, and simulation-based multi-step forecasts. Mean models are updated to recognize the new volatility process.

Leverage diagnostics include a leverage correlation test, a non-pa...

👉 Read | AlgoBook | @mql5dev

Channel Breakout Dashboard is a chart-side informational panel for MetaTrader 5, built to consolidate conditions that typically require multiple indicators. It shows current and higher-timeframe trend, RSI momentum with OB/OS states, volatility via current ATR vs average ATR, an N-bar high/low price channel with breakout status, ADR used vs remaining, live spread classified by thresholds, and a combined bias label: BUY SETUP, SELL SETUP, or WAIT. Updates run on a configurable timer.

Operational impact focuses on faster validation and fewer low-quality entries. Breakouts can be filtered by higher-timeframe alignment, RSI extremes, and volatility regime, while ADR context helps avoid late-day trend entries and supports realistic TP/SL placement. Spread state adds an execution gate during illiquid hours or event spikes.

Multiple instances are supported ...

👉 Read | Freelance | @mql5dev

News Trading Dashboard is an on-chart informational panel focused on real-time volatility monitoring. It flags “news spike” conditions by comparing the current bar range (High–Low) to ATR multiplied by a configurable threshold, highlighting abnormal movement typically seen on scheduled macro releases.

Key controls include ATR period as the baseline, spike multiplier as the trigger level, and a UI refresh timer for the footer clock. Panel options cover visibility, theme, font-based scaling, auto-width to prevent clipping, minimum width, and screen anchoring with X/Y offsets.

Multi-instance support uses an InstanceID to run parallel configurations per chart or symbol, with automatic deconfliction if IDs collide.

Common usage patterns include a no-trade filter during red status, momentum entries after the first spike candle closes, mean-reversion on extreme...

👉 Read | Calendar | @mql5dev

Financial forecasting exposes the limits of “one task, one model” pipelines, especially under regime shifts, volatility changes, and mixed sampling rates. Large TS foundation models add scale, but often trade deployment cost for marginal accuracy.

LightGTS proposes period-aware tokenization. Instead of fixed-length patches, sequences are split into full-cycle segments derived from known frequency or FFT-based period detection, keeping token semantics stable across timeframes.

A flex projection layer adapts weights to variable patch sizes via pseudoinverse-based resizing, avoiding destructive interpolation. Forecasting uses non-autoregressive parallel decoding from the final encoder token, reducing error accumulation while keeping the parameter count under 5M.

👉 Read | Freelance | @mql5dev

A range panel automates session High/Low calculation from a configurable lookback, removing manual level drawing. It provides a clear status for consolidation versus confirmed breakout using a point buffer, with optional chart lines and floating price tags for on-chart verification.

Multiple instances can run in parallel with separate IDs and lookbacks to add multi-timeframe confirmation without changing charts. Updates run on a timer rather than every tick to reduce terminal load during long sessions. Display controls include theme, text sizing, auto-scaling, corner anchoring, and pixel offsets, plus line style and width.

Common usage includes fading range extremes when price remains within bounds, breakout entries after buffered confirmation with stops back inside the range, opening-range breakout using a short lookback at session start, and conflu...

👉 Read | Signals | @mql5dev

In 2001, MetaQuotes was only beginning to introduce algorithmic trading. The first version of MQL was remarkably simple: its entire description fit on a single small HTML page, and the language didn't even have loops or arrays.

Today, MetaTrader 5 is a full-fledged platform for trading and development:

✓ MQL5 with Python, ONNX, OpenBLAS, and Git-based Algo Forge storage.
✓ A powerful Strategy Tester and cloud computing network.
✓ An integrated AI Assistant that can write code, analyze markets, and execute multi-step tasks.

We dug through MetaQuotes archives, old forums, release notes, and algorithmic trading championship materials to reconstruct the journey — how, over 25 years, a trading terminal evolved into a powerful platform for algorithmic trading, application development, data analysis, and machine learning.

Read the full story of how MetaTrader and MQL evolved...

Rademacher functions are being evaluated as a practical tool for noisy, nonlinear market series where common filters and regressions can lose signal. Key limitations remain: the basis is incomplete and periods are constrained to powers of two, but linear trend components can still be captured with high accuracy.

A period-scaling tweak (order P with multiplier M) reduces sensitivity of higher-order terms, improving robustness to outliers. Using the resulting multi-level smoothing, price is partitioned into discrete levels that behave like support/resistance, with strength derived from the distribution of prices around each level.

Forecasting can be built from empirical PDFs/CDFs of Rademacher coefficients, including SMA range projection by treating SMA deltas as a first-order Rademacher series. A higher-level model pairs a Rademacher-based oscillato...

👉 Read | CodeBase | @mql5dev

A compact trading panel aggregates trend, momentum, volatility, carry, and upcoming macro events in one view. Sentiment is a technical proxy built from EMA, MACD, and Stochastic. Trend Riding reports direction plus stage (nascent, established, aging, reversal risk). Volatility/Breakout combines ATR, Bollinger squeeze percentile, and Donchian breakout state (testing, confirmed, false). Carry displays long/short swap, while News shows the next relevant calendar event.

Chart overlays include EMA fast/mid/slow, Bollinger Bands, and Donchian channels. Typical usage focuses on filtering: avoid ranges when SIDEWAYS or FALSE BREAK – FADE appears, avoid entries near high-impact events, and prefer positive-swap direction on H4/D1 when aligned with an established trend.

Limitations remain: no automatic entries/exits, sentiment is not COT/fundamentals, and calen...

👉 Read | VPS | @mql5dev

Classical moving averages compress a window into one value and miss where the latest close sits within its own recent distribution. Bloch’s RMA ranks the current price inside a sliding window and outputs a fractile on a consistent [0,1] scale across instruments. This differs from Wilder-style “RMA” smoothing despite the shared acronym.

An MT5 port is structured as an engine indicator publishing multi-buffer outputs under a strict buffer-index contract, plus read-only panels and an EA. Core code computes SMA as local equilibrium, normalized deviations for window landmarks, then empirical fractiles for the current close and extremes.

A regime detector uses the window’s normalized-return range to classify expansion, contraction, and transition, feeding four cross-strategies implemented in the EA and validated in Strategy Tester.

👉 Read | Docs | @mql5dev