Options theory is framed around clear mechanics (call/put rights vs seller obligations), key parameters (underlying, strike, premium, expiration), and exercise/settlement styles. Pricing is anchored to Black-Scholes for European options, with practical caveats: shifting volatility, non-lognormal returns, and limited fit for American exercise. Sensitivities are handled via Greeks, especially Delta for replication.

The core idea is emulating options by trading the underlying to match an option portfolio’s Delta, enabling synthetic contracts with custom strikes/expirations—even when listed options don’t exist or are illiquid. Delta-hedged rebalancing (time-based or delta-step) keeps the synthetic payoff aligned, but requires systematic automation, low friction costs, and continuous monitoring to avoid tracking error and missed adjustments.

👉 Read | Signals | @mql5dev

#MQL5 #MT5 #EA

CAPM emerged from Markowitz’s 1952 portfolio theory and the efficient frontier. In 1964, Sharpe reduced the computational burden of covariance-heavy optimization into a market equilibrium model; Lintner and Mossin reached similar results. By the 1970s, CAPM became standard for cost of capital estimates.

Classical CAPM links expected return to the risk-free rate and beta, with beta defined via covariance with the market over market variance. Assumptions include frictionless markets, shared expectations, and diversified portfolios pricing only systematic risk.

A MetaTrader 5 adaptation for FX replaces beta with a volatility-driven dynamic risk premium, uses unbiased variance, handles low-data cases, and annualizes volatility with a 252 factor. Two buffers expose expected return and risk premium, using CopyClose for efficient data access. Limitations includ...

👉 Read | NeuroBook | @mql5dev

#MQL5 #MT5 #Indicator

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👉 Read | Freelance | @mql5dev

#MQL4 #MT4 #EA

AI agents expanded quickly in early 2026, with mature automation on the crypto side. MetaTrader 5 remains largely unsupported by agent toolchains, despite visible demand in OpenClaw feature requests and trader forums.

A practical approach is an MCP (Model Context Protocol) server that bridges AI clients to MT5 via stdio. MCP standardizes tool discovery and typed calls, avoiding per-client plugin formats while keeping execution local.

The proposed Python design uses the official MetaTrader5 library plus FastMCP, exposing 14 tools across account, market data, positions, orders, and history. A wrapper layer manages initialization, login checks, timeouts, constant mapping, and order request normalization for mt5.order_send().

👉 Read | VPS | @mql5dev

#MQL5 #MT5 #AI

MetaTrader 5 canvas work can exceed static rendering by adding a timer-driven animation state machine. A four-phase sequence can progressively draw the parametric outline (t from 0 to 12π), fade in wing fills, fade in veins/scales/body, then switch to continuous flight.

Flight motion can be composed from independent oscillators: sine-based wing flap scaling on X toward the body axis, vertical bobbing and horizontal sway offsets, and a small tilt shear. Visual enhancements can include multi-pass glow strokes and per-frame HSV hue rotation.

Implementation in MQL5 typically adds an animation enum, input groups for speeds/amplitudes, and global accumulators for phase, opacity, oscillator phases, and hue shift. Core helpers include ShiftHue (RGB↔HSV), glow pixel falloff, a single ApplyFlyingTransform, and centralized point collection feeding outlines, ...

👉 Read | CodeBase | @mql5dev

#MQL5 #MT5 #AlgoTrading

ARCH/GARCH models handle volatility clustering but are symmetric by construction. Squared shocks remove sign, so rallies and drawdowns have identical impact on forward variance. Empirical data shows leverage effects where negative returns increase conditional risk more than positive returns.

Two common fixes are GJR-GARCH and TARCH. GJR-GARCH adds an indicator term that activates on negative returns, scaling shock impact via an asymmetry parameter. TARCH applies the threshold idea to conditional standard deviation, using absolute shocks with a sign-dependent slope.

An MQL5 volatility library can implement both as CGarchProcess derivatives, selectable through an ENUM_VOLATILITY_MODEL flag. Example scripts and an indicator can recalibrate on a rolling window and emit one-step forecasts plus a volatility z-score for anomaly detection.

Model choice is pract...

👉 Read | Quotes | @mql5dev

#MQL5 #MT5 #GARCH

Manual horizontal support and resistance remains effective, but it is static and requires continuous chart watching. This creates a monitoring gap on fast markets or multi-chart setups.

An MQL5 Expert Advisor can track manually drawn OBJ_HLINE levels in real time, compare bid price to each level, and classify events as touch, breakout, reversal, and retest. Levels are treated as discrete prices for deterministic checks, with per-line state (side, touched, breakout flags) to avoid duplicate alerts.

Implementation uses OnInit/OnDeinit for setup and cleanup, OnChartEvent for sync and deletion handling, and OnTick as the main loop. UI buttons manage sync and reset. Signals include labels, arrows with cooldown and one-per-bar rules, plus alerts and optional candlestick reversal pattern filters.

👉 Read | Calendar | @mql5dev

#MQL5 #MT5 #EA

This article replaces Comment()/Print() output with a reusable full-screen console-style dialog for MetaTrader 5 charts. The goal is readable, structured, multi-line text with proper font control and scrollable navigation, without polluting the journal.

The core is CConsoleDialog, derived from CAppDialog, using CCanvas to render monospaced text (Consolas) suitable for table-like layouts. It supports vertical/horizontal scrolling via mouse wheel (Shift for horizontal), font scaling with Ctrl+wheel, programmatic font/color settings, and automatic resizing when the chart dimensions change.

Implementation details include splitting incoming text into lines, tracking longest line for scroll bounds, measuring character size for layout, and freeing canvas resources on minimize/maximize. Integration into an EA requires including ConsoleDialog.mqh, creating the dial...

👉 Read | Freelance | @mql5dev

#MQL5 #MT5 #EA

Backtests often fail live when volatility expands and position sizing stays static, especially when volatility estimates arrive late due to costly sliding-window scans. On low timeframes or multi-symbol EAs, even small VPS delays can translate into oversized lots during spikes, worse slippage, and deeper drawdowns.

A zero-lag money-management module for MT5 addresses this by computing Donchian-style highs/lows with a monotonic deque: amortized O(1) per update, O(N) total. It avoids repeated CopyHigh/CopyLow loops and uses min(x) = -max(-x) to reuse the same max-queue logic for lows.

Lots are inversely scaled by current range versus a baseline, then normalized to broker min/max/step. An optional RBF gate multiplies volume by a 0–1 quality score using volatility plus indicators (e.g., RSI), reducing risk in anomalous regimes.

Strategy Tester comparison on...

👉 Read | Docs | @mql5dev

#MQL5 #MT5 #AlgoTrading

Post-2008 and post-2020 policy expanded central bank toolkits beyond rates into QE, credit facilities, swaps, and fiscal backstops. Balance sheets became primary telemetry for liquidity regimes and FX repricing.

A practical workflow aggregates Fed, ECB, BOJ, and PBoC series into a composite global liquidity index, then models relative momentum to explain cross rates when expansions occur in parallel.

A modular system design separates ingestion/standardization (frequency sync, FX conversion, interpolation, reliability scoring) from forecasting. Features combine liquidity lags with technical indicators; Random Forest captures non-linear effects.

Execution connects signals to MetaTrader 5, with sub-indices for short/long horizons plus acceleration and volatility. Known constraints include PBoC data gaps and shock-driven model failure modes.

👉 Read | AppStore | @mql5dev

#MQL5 #MT5 #AlgoTrading

The CalculateLot function computes position volume using a fixed risk percentage of account balance, producing a lot size aligned with broker limits and symbol specifics.

Inputs typically include the risk percent, with internal reads for ACCOUNT_BALANCE, SYMBOL_TRADE_TICK_VALUE, plus VOLUME_MIN, VOLUME_MAX, and VOLUME_STEP for the current symbol.

Processing flow: derive the risk amount in deposit currency, convert it to volume using tick value, then normalize the result by rounding to the nearest VOLUME_STEP.

Validation clamps the final value to VOLUME_MIN/VOLUME_MAX. If the raw calculation is outside limits, the function returns the nearest allowed minLot or maxLot.

Typical usage patterns include direct calls from an Expert Advisor and script execution with explicit error checking and parameter validation.

👉 Read | Docs | @mql5dev

#MQL5 #MT5 #EA

Frontend EA A is a single MT5 EA that focuses on chart UI cleanup plus a compact quick-trading layer. Default grid, volumes, ticker bar, and the native one-click panel are hidden on load, replaced by larger custom price and time scales designed for readability.

The price scale uses round-number steps (1/2/2.5/5 × 10^k). The time scale prints the date once per day at 00:00 and shows time elsewhere. A compact tiled mode limits labels to 5 price levels and 3 time labels. Auto-padding around Bid and endpoint labels is in progress.

Trading controls include top-left BUY/SELL with an editable volume box persisted via GlobalVariable. Open positions render as entry-price plates with price, signed volume, and live PnL including swap and commission, refreshed at 1 Hz. Plates merge for overlapping entries and support one-click close per cluster.

Included indicators: H1...

👉 Read | Forum | @mql5dev

#MQL5 #MT5 #EA

This article builds a Liquidity Spectrum Volume Profile indicator for MetaTrader 5 that shows where volume concentrates across price, not just per bar. The design makes clear assumptions: binning uses candle close to assign volume, prefers tick volume with a fallback to real volume, and always operates on explicitly copied history for consistent recalculation.

Implementation centers on three engineering problems. First, reliably collecting a fixed lookback dataset via CopyHigh/CopyLow/CopyClose and CopyTickVolume, with safety checks for missing history. Second, scanning that window for the highest high/lowest low, splitting the range into equal price bins, accumulating volume per bin (with slight bin overlap to avoid boundary misses), then normalizing widths against the maximum bin. Third, rendering stable chart objects: rectangles for bins and horizont...

👉 Read | Calendar | @mql5dev

#MQL5 #MT5 #Indicator

Recurrence Quantification Analysis (RQA) brings nonlinear dynamics into MetaTrader 5 by measuring whether price revisits prior states and how those recurrences are structured. It converts closes into time-delay embedded vectors, builds a thresholded recurrence matrix using selectable distance norms (Euclidean, Chebyshev, Manhattan), then derives metrics like RR, DET, LAM, ENTR, and TREND to quantify similarity, determinism, consolidation, complexity, and drift.

The implementation focuses on reuse: a modular MQL5 library plus a facade API, a rolling-window engine that outputs a time series of metrics, and an indicator that plots live values on-chart for any symbol/timeframe.

A key engineering feature is automatic epsilon selection, including a target-recurrence-rate method using bisection with subsampled distance estimates, avoiding repeated full m...

👉 Read | Docs | @mql5dev

#MQL5 #MT5 #AlgoTrading

Trade evaluation based only on win rate and net profit misses execution quality. A script is available that rebuilds historical positions and calculates excursion and time-based return metrics, exporting results to a CSV compatible with Excel.

Metrics include MAE/MFE in points to quantify adverse and favorable movement during each trade, plus forward returns at fixed horizons (T+30m, T+1h, T+4h, T+12h, T+1d, T+1w). When a horizon is not available, the output is n/a. A 1-week forward worst-case adverse move (T+1w MIN) is also reported, returning 0 when no adverse movement occurs.

Output columns cover position metadata, timestamps, duration, prices, PnL in $ and points, MAE/MFE, forward returns, and forward risk. Calculations use M1 OHLC data; insufficient M1 history can produce zeros in some fields.

👉 Read | AppStore | @mql5dev

#MQL5 #MT5 #script

Open-source MQL5 order execution library and demo EA for MT5 adds configurable retry logic, separate requote vs rejection handling, and slippage measurement in pips with violation detection. SL/TP values are normalized to broker stop levels, and fill policy is auto-detected across FOK, IOC, and RETURN.

Supports market and pending orders, plus position operations including close, partial close, SL/TP modification, and close-all-by-magic. Execution reporting includes success rate, average slippage, average execution time, and volume, with 10 human-readable result states and optional verbose logging.

Demo EA provides a chart dashboard with BUY/SELL/CLOSE ALL controls, live color-coded metrics, gauges, and a stats reset function. Two files placed in one folder compile without additional setup. Compatible with all brokers, instruments, and timeframes.

👉 Read | Quotes | @mql5dev

#MQL5 #MT5 #EA

Supertrend is an ATR-based trend-following indicator that plots a trailing support/resistance line on the main chart. The line switches state with price direction, allowing trend alignment without additional oscillator panels.

This MetaTrader 5 custom implementation provides the full Supertrend band logic with two ATR modes: the built-in iATR calculation or a manual Simple Moving Average of True Range. Both approaches adjust band width with volatility and keep bands fixed once price has moved away, reducing noise during sustained moves.

Trend reversals can optionally print arrows at the crossover bar: upward for bullish flips and downward for bearish flips. Inputs cover ATR period, ATR multiplier, ATR method selection, and signal visibility.

Use is intended for trend identification and testing; signals should be validated in a demo environment befor...

👉 Read | Freelance | @mql5dev

#MQL5 #MT5 #Indicator

The article outlines two non-standard money-management techniques aimed at improving exit quality and stabilizing performance in MetaTrader systems.

First, it proposes partial position closing driven by the previous bar’s impulse size. A power function maps last-bar movement (in points) to the volume to close, with parameters chosen so developers tune behavior via intuitive anchors rather than raw coefficients. The goal is to harvest rollbacks after strong candles and reduce spread impact when the final exit is uncertain.

Second, it describes a hybrid lot-variation model combining martingale and reverse-martingale logic based on equity “waves.” Lots are increased during drawdown phases and reduced during recovery, constrained within a min/max corridor and pulled back toward the midpoint to avoid sticking at extremes. Practical value depends on markets domin...

👉 Read | Forum | @mql5dev

#MQL5 #MT5 #EA

Панель «Калькулятор корреляции» представляет собой индикатор в формате таблицы для оценки взаимосвязей валютных пар и выбора точек входа без дополнительного набора инструментов. Подход ориентирован в первую очередь на кросс-курсы.

При закреплении панели на графике EURJPY сравниваются EURUSD и USDJPY. Если в таблице выше находится EURUSD, кросс, как правило, повторяет его движение. Если выше USDJPY, кросс чаще следует за этой парой. Сигналом считается момент, когда основные пары меняются местами в таблице, после чего открывается сделка по кроссу.

Рекомендуемые параметры: таймфрейм H4, глубина расчета 10 баров. Наиболее подходящие кроссы: GBPCHF, EURCHF, GBPCAD, EURCAD, AUDCHF, AUDJPY, EURJPY, GBPJPY, NZDJPY, NZDCHF. Такие кроссы формируются из валют с прямыми и обратными котировками и обычно повторяют динамику одной из базовых пар.

👉 Read | Freelance | @mql5dev

#MQL4 #MT4 #Indicator

DoEasy’s next step toward Depth of Market in MT5 starts by modeling a single DOM entry as a first-class library object fed by MarketBookGet() on OnBookEvent() updates.

The article implements an abstract DOM order class backed by MqlBookInfo fields (type, price, volume, volume with extended precision) and adds an explicit side flag (Buy/Sell) to quickly split demand vs supply regardless of market/limit type.

Four derived classes represent the concrete book types (buy, buy market, sell, sell market), while the base class provides property enums, sorting criteria, full/partial comparison, and structured logging.

A test EA subscribes to book events, snapshots the book into a price-sorted list, and validates handling by printing the best bid/ask and dumping the first snapshot to the journal.

👉 Read | Forum | @mql5dev

#MQL5 #MT5 #AlgoTrading