Many strategies still rely on a single trigger such as an EMA crossover, without validating trend strength, momentum, volume, and directional bias. This approach typically increases false entries when context is mixed.

This MT5 indicator applies EMA 9/21 as the trigger, then scores seven conditions on the crossover bar: price vs VWAP, RSI vs 50, MACD line vs signal, crossover direction, ADX strength with price context, volume vs its moving average, and M5 RSI as a short-term momentum check. Signals are printed only when the bull or bear score meets a user-defined minimum.

Risk handling is automated via ATR-based stop-loss and five take-profit levels. A lot size calculator sizes positions by balance and risk percentage, with optional session filtering for Asian/London/New York hours. An on-chart dashboard reports indicator states, bias, trend strength...

👉 Read | AlgoBook | @mql5dev

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Retail decision-making often depends on closed candles, while institutional execution is driven by microstructure signals. Large block execution tends to show up first as a surge in tick velocity at the broker feed, not as an immediate directional price move.

The Institutional Toxic Flow Monitor focuses on tick-speed and tick-density rather than cumulative volume, which often distorts conditions during consolidation. It maintains a moving baseline for tick activity and continuously computes standard deviation. When tick velocity exceeds the statistical threshold, an anomaly spike is printed on a histogram.

This type of signal can be used to validate breakout participation and to filter moves where price is pushed without matching feed acceleration. The implementation uses optimized array calculations to keep the terminal responsive during high-impact news...

👉 Read | VPS | @mql5dev

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Lead/lag analysis measures whether moves in one asset tend to precede moves in another, even when correlation or cointegration already exists. The practical target is low-frequency windows (1 minute and above), where fill quality and slippage are manageable and the edge comes from analysis, not routing latency.

A common method is cross-correlation on log-returns, shifting the leader series by N bars and looking for a statistically consistent peak. This can support mean-reversion setups in cointegrated pairs, and momentum signals in weaker relationships.

Implementation detail matters. SQL engines can be non-deterministic without explicit ordering, especially with columnar storage and parallel reads. Window functions must use OVER (ORDER BY time, row_id). Time-aligned JOINs are required before shifting, to avoid comparing mismatched sessions or missing...

👉 Read | Docs | @mql5dev

#MQL5 #MT5 #Strategy

A production sizer needs more than a confidence-aware probability signal. Three gaps are common: payoff asymmetry (b != 1), hard drawdown constraints, and path-dependence in dynamic sizing backtests.

Kelly adds payoff awareness and has a fixed crossover with z-score based get_signal near p ≈ 0.88. Below that, Kelly is materially more aggressive in the typical 0.52–0.65 range where probability error is highest. Above the crossover, get_signal saturates faster and becomes more aggressive.

A practical architecture is two-stage. Stage 1: get_signal with concurrency correction and discretization. Stage 2: a capped fractional-Kelly multiplier derived from (p, b) to adjust for win/loss ratios while preserving concurrency handling.

For prop firm constraints, map remaining drawdown budget into the bet_size_dynamic sigmoid w, flattening size continuously as loss ...

👉 Read | AppStore | @mql5dev

#MQL5 #MT5 #Kelly

Singular Spectrum Analysis (SSA) is a time-series method for decomposing data into trend, periodic components, and noise without requiring stationarity. The workflow is deterministic and linear-algebra based.

Core stages: build a Hankel trajectory matrix from sliding windows, apply SVD to obtain rank-1 elementary matrices (eigentriples), group components into signal and noise, then reconstruct via diagonal averaging.

Forecasting uses a linear recurrence derived from selected singular vectors, applied to the reconstructed signal only. Practical notes: SSA often extracts cycles hidden by noise, but cannot reliably separate deterministic trends from stochastic drift (e.g., random walk).

In MQL5, Basic-SSA can be implemented with OpenBLAS SVD (SingularValueDecompositionDC); results align with SingularSpectrumAnalysisForecast, suggesting a similar baseli...

👉 Read | VPS | @mql5dev

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Retail chart layouts often retain historical supply and demand zones long after they stop influencing price. In liquid markets, block execution leaves a defined order-block footprint, but once price revisits the level and absorbs resting orders, the zone is effectively neutralized. Keeping mitigated zones on-screen can reinforce incorrect bias and contribute to early stop-outs.

The Unmitigated Order Block Matrix addresses this by plotting only active liquidity pools. It identifies the last counter-trend structural candle preceding a breakout, then validates each impulse against underlying tick volume. Blocks formed in low-activity hours or without a volume anomaly are filtered out.

A mitigation tracker monitors retraces in real time. When price taps a prior block and liquidity is consumed, the zone is marked mitigated and removed from view. This keep...

👉 Read | AppStore | @mql5dev

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Retail traders often lose capital trying to time tops and bottoms with bounded oscillators such as RSI and Stochastic. A fixed 0–100 scale saturates quickly in strong directional flow, creating persistent “overbought/oversold” readings that can mislead participants into fading accelerating trends.

Institutional execution typically focuses on variance and distribution, not capped momentum ratios. A Z-Score model normalizes price relative to a rolling mean and standard deviation, expressing displacement in standard-deviation units and remaining unbounded during extreme volatility.

Readings beyond +2.0 or below -2.0 flag statistically stretched conditions that can support mean-reversion evaluation, especially when aligned with order block and liquidity-sweep criteria. This approach improves context by separating regime-driven trend from outlier deviation.

👉 Read | Forum | @mql5dev

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Large bullish or bearish candles are frequently misread as proof of aggressive participation. Standard volume mostly reports total activity and does not specify whether trades were initiated at the bid or at the ask. This gap is routinely exploited by liquidity providers using passive limit orders to absorb forced selling while price action prints strong bearish bars.

Cumulative Volume Delta (CVD) addresses this by separating estimated buy and sell delta and tracking their net difference over time. When price makes a higher high while CVD makes a lower high, it signals absorption: price is being supported by passive liquidity rather than sustained aggressive buying, a condition often seen before reversals.

The implementation is designed to run on typical broker feeds without heavy tick-data downloads. Delta is continuously aggregated and session r...

👉 Read | AlgoBook | @mql5dev

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ASQ Divergence Detector is an indicator for identifying regular and hidden RSI divergences and marking them with non-repaint, color-coded arrows. Each signal is labeled at the divergence point as Regular Bullish, Regular Bearish, Hidden Bullish, or Hidden Bearish to remove ambiguity during review and execution.

An on-chart dashboard tracks a running count of each divergence type on the active timeframe, providing a quick statistical split between bullish and bearish setups. Core settings cover swing length, lookback depth, and RSI period, with toggles for regular/hidden signals and label visibility.

Alerting supports popup, sound, and push notifications on new divergences. The implementation is designed for low CPU overhead as a single-file indicator with no external dependencies. Distribution is free and open-source, built for MT5 across instruments, bro...

👉 Read | Calendar | @mql5dev

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Candle Body Ratio v2 adds a chart-side strength meter based on each candle’s body-to-wick ratio, presented in a floating dashboard. It reports current candle body percentage, upper and lower wick share, plus a conviction label for fast inspection of candle quality.

A configurable lookback summary provides the average body ratio and a bull versus bear dominance bar, alongside a pressure label derived from recent candle statistics. This keeps the focus on whether follow-through has been present rather than isolated prints.

A heatmap strip ranks the last N candles by body strength, from dark (weak) to bright (strong), ordered oldest to newest. Inputs cover panel position, font sizing, thresholds for strong and weak bodies, and full color control for bullish, bearish, and neutral states.

An optional alert triggers when the current candle meets the stron...

👉 Read | VPS | @mql5dev

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Portfolio Scorer grades multi-EA portfolios across three dimensions: inter-strategy correlation, temporal coverage, and asset class diversity. Results are combined into a composite A+ to F grade, with per-dimension subscores.

The script ingests daily P&L CSV files from the MQL5\Files\ directory. When files are missing, it generates sample data for five EAs spanning forex, index, metal, energy, and JPY forex, allowing immediate end-to-end verification.

Key inputs include a comma-separated file list, minimum required history (default 60 days), correlation alert threshold (default |r| > 0.35), and scoring weights (0.50 correlation, 0.25 coverage, 0.25 diversity).

Output in the Experts tab includes an NxN Pearson correlation matrix with flagged pairs, a 0–23 UTC hourly activity map, weekday coverage (Mon–Fri), a composite score report, and recommendations base...

👉 Read | Signals | @mql5dev

#MQL5 #MT5 #EA

A Renko forecasting pipeline was tested on EURUSD using MetaTrader 5 tick/time-bar data converted into 11,578 Renko bars with ATR-derived brick size 0.00028. Training used 60 days of M5 history and 14 engineered features covering direction sequences and volume stats.

CatBoost delivered 59.27% test accuracy. Feature importance was led by volume: last_volume (18.36), avg_volume (14.23), volume_ratio (12.81), followed by consecutive-move metrics, challenging price-pattern-heavy rule sets.

Implementation stack: Python, numpy, pandas, MetaTrader5 API, catboost. Modules cover data ingest, Renko conversion, feature generation, cross-validated training, and probability-based signaling with a 75% confidence threshold.

👉 Read | AlgoBook | @mql5dev

#MQL5 #MT5 #AlgoTrading

Strong single-EA backtests often understate real portfolio risk because strategies can be correlated, especially when they share the same macro driver (for example, USD moves hitting multiple pairs at once). Portfolio quality depends on interaction, not standalone profit and drawdown.

A practical MT5-native Portfolio Scorer script addresses this by loading daily P&L from backtest-exported CSVs, building an NxN Pearson correlation matrix, and flagging dangerous overlap (|r| >= 0.60) while recognizing negative correlation as a hedge.

It also maps trading coverage by hour and weekday to expose session and day “blind spots,” then adds asset-family classification to reward true market diversity. These inputs roll into a weighted composite grade, guiding EA selection toward lower correlation, better coverage, and broader asset exposure.

👉 Read | AppStore | @mql5dev

#MQL5 #MT5 #EA

An indicator implements a Stochastic oscillator crossover model, generating signals when the %K line crosses the %D line. The logic is designed to highlight momentum shifts and potential reversals with both early warnings and close-confirmed events.

Buy conditions trigger on a bullish crossover where %K moves above %D while positioned in the oversold zone. Sell conditions trigger on a bearish crossover where %K moves below %D while positioned in the overbought zone.

Two alert modes are supported. Real-time alerts display arrows and notifications during the forming bar for provisional setups. Confirmed alerts are produced after bar close, marking validated crossovers with arrows and optional vertical lines for clearer post-signal review.

👉 Read | AlgoBook | @mql5dev

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ASQ Spread Logger is a lightweight MT5 indicator for real-time spread monitoring with persistent logging for later analysis. It is designed for entry filtering, session benchmarking, and documenting trading conditions around volatility.

A separate-window histogram marks spread levels using configurable low/high thresholds, with status shown as green, gold, or red. An on-chart panel reports current spread in points and pips, rolling average, plus minimum and maximum across a defined window.

Optional CSV logging writes DateTime, Symbol, Spread, Bid, Ask, Avg, Min, Max, and Status to MQL5/Files. Logging cadence can be set to every tick, every second, or once per new bar. Alerts trigger when the high threshold is exceeded, with cooldown support and optional push and email notifications.

Single-file MQ5, minimal CPU usage, no external dependencies. Comp...

👉 Read | AppStore | @mql5dev

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ASQ Trading Journal Export is an MT5 script that exports full closed-trade history to a clean CSV in a single run. Trades are reconstructed from deal history with ticket, symbol, side, open/close time, volume, open/close price, SL/TP, gross P/L, swap, commission, net P/L, duration (minutes), magic number, and comment.

The output is formatted for Excel, Google Sheets, and straightforward ingestion into Python, R, or other analytics pipelines. A summary block is appended with total trades, wins, losses, win rate, gross P/L, and net P/L.

Options include date range (last N days or all history), symbol filtering, and magic-number filtering, plus toggles for swap, commission, duration, magic, and comments. The file is date-stamped and written to MQL5/Files/. Free and open-source.

👉 Read | Signals | @mql5dev

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Traditional SMA/EMA tools remain common in retail workflows, but the core limitation is unavoidable lag. When a crossover finally confirms a reversal, much of the move has often already been priced in, leaving late entries exposed to distribution and mean reversion.

Quant funds mitigate latency using digital signal processing methods adapted from telecommunications. A common approach is a Gaussian-weighted filter based on the ALMA distribution, applying a bell-curve weighting to the series rather than a flat or exponential average.

Operational characteristics typically include faster response to momentum shifts, stronger suppression of high-frequency noise, and smoother structure without relying on long lookbacks. Implementations can be CPU-efficient by precomputing Gaussian weights at initialization to limit per-tick overhead.

Deployment is usually...

👉 Read | Quotes | @mql5dev

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MultiSymbolAlertPanel is an MQL5 indicator designed to track High/Low levels across multiple symbols from a single chart. It displays a compact panel with Bid, the selected timeframe High/Low (D1, W1, MN1), and a per-symbol status updated in real time.

Break conditions trigger immediate popup and sound notifications, limited to one alert per bar. Status is color-coded for quick scanning: lime for High breaks, tomato for Low breaks, yellow when price is within a configurable near-zone threshold, and light blue for normal watch state.

For the active chart symbol, optional dashed horizontal High/Low lines can be plotted with price labels. A Date Filter mode switches the panel to historical High/Low for a specified date for review and analysis; alerts are disabled in this mode. The panel supports up to 50 symbols and exposes inputs for fonts, colors, nea...

👉 Read | Calendar | @mql5dev

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