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MQL5 Algo Trading
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Показати більше📈 Аналітичний огляд Telegram-каналу MQL5 Algo Trading
Канал MQL5 Algo Trading (@mql5dev) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 512 054 підписників, посідаючи 153 місце в категорії Технології та додатки та 5 місце у регіоні Об'єднане королівство.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 512 054 підписників.
За останніми даними від 19 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 8 965, а за останні 24 години на 370, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.42%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.91% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 17 520 переглядів. Протягом першої доби публікація в середньому набирає 9 780 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 39.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як indicator, chart, mql5, candle, range.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“The best publications of the largest community of algotraders.
Subscribe to stay up-to-date with modern technologies and trading programs development.”
Завдяки високій частоті оновлень (останні дані отримано 20 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
512 054
Підписники
+37024 години
+2 1507 днів
+8 96530 день
Архів дописів
512 108
A chart utility for detecting and rendering Fair Value Gaps (price imbalances), commonly referenced in ICT/Smart Money workflows. It identifies both bullish and bearish gaps, then monitors subsequent price action to confirm when gaps are filled.
Visualization is handled through zone rectangles with configurable color and opacity. Filled-gap handling can be configured to remove zones, fade them, or keep them on the chart for review. A minimum gap-size filter is available to reduce low-signal detections, and optional labels can display gap size.
Eventing includes alerts on new gap formation and on fill completion, with support for push notifications and email delivery.
👉 Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
512 108
A utility is available to export full trade history to a CSV file, suitable for direct import into Excel or Google Sheets for further processing.
Per-trade output includes 18 fields such as ticket, symbol, side, open/close timestamps, entry/exit prices, profit, and pip results. Trade duration is formatted as “Xd Xh Xm”, with pip calculations adjusted for 3- and 5-digit pricing.
Filtering supports magic number, date range, and symbol selection. The export also generates summary performance metrics including win rate, profit factor, average R:R, maximum drawdown, Sharpe ratio, and expectancy, plus tracking of maximum consecutive wins and losses.
👉 Read | Quotes | @mql5dev
#MQL4 #MT4 #script
512 108
A Volume Bubble Indicator for MT5 replaces raw volume histograms with a chart overlay that ranks each candle’s activity against recent behavior. It defines a lookback window, computes mean volume plus variance/standard deviation, then normalizes each bar so volume remains comparable as conditions shift.
Normalized scores drive visualization: bubbles plotted above candle highs scale through discrete size tiers, while color reflects whether volume increased or decreased versus the prior bar. Extreme spikes also get compact numeric labels (e.g., 1M) for quick reading without clutter.
The implementation emphasizes practical MQL5 details: choosing tick vs real volume depending on the symbol, using built-in volume arrays, and managing chart-object lifecycle to update in place, avoid duplicates, and clean up when bars roll out of the window or the indicator i...
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
512 108
This MQL5 footprint upgrade adds market-structure and order-flow layers so volume-by-price becomes actionable, not just descriptive. Each bar gains a volume profile with Point of Control (max activity) and a configurable Value Area to separate accepted prices from likely rejections.
Order-flow signals are formalized: stacked bid/ask imbalances across consecutive levels to mark sustained aggression, absorption bars where high volume produces near-zero delta, plus single prints and unfinished extremes that often act as revisit targets.
Implementation focuses on extensible inputs, richer per-bar/per-level metadata, and cached computations feeding a fixed draw-order rendering pipeline. It also adds CVD and per-bar delta histogram panels, dynamic font scaling, and utilities for color blending and dashed markers to keep visuals readable under zoom.
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
512 108
Most retail EAs still size positions with fixed lots or static percent risk. That approach ignores regime changes: when volatility expands, a constant stop distance becomes statistically misaligned with the new distribution, increasing the probability of stop-outs and unstable equity curves.
A Kelly-VAPS position-sizing module addresses this by combining two layers. The Kelly fraction derives an optimal risk budget from observed win rate and payoff ratio. Volatility-adjusted sizing then scales that budget by current ATR and tick-value constraints to map risk into executable volume.
A typical implementation is an object-oriented C++ .mqh include that exposes a risk engine class, enforces broker min/max volume, checks free margin, and returns a final lot size. Inputs are strategy stats plus stop distance; output is a volatility-aware position size intended...
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
512 108
ExMachina CandleTimer Lite is a free MT5 indicator that adds a real-time candle countdown panel to the chart, targeting accurate timing around bar close and new bar open. It supports all symbols and timeframes, with no DLLs and no external dependencies.
The countdown panel provides three modes: digital clock, progress bar with percentage, or a combined view. An optional flash state in the final 10 seconds alternates amber/red each second, and an optional sound can trigger on new bar formation. A secondary line shows elapsed and remaining time in a compact format.
Chart impact is configurable via theme modes (full steel palette, panel-only opacity, or no theme). The panel can be collapsed to a 30px strip showing symbol, timeframe, and live countdown. Layout inputs cover side, margin, Y offset, width, seconds display, flash, and sound.
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #Indicator
512 108
Work continues on restructuring an MQL5 EA generator into a reusable library plus per-project repositories. The library was renamed from Advisor to Adwizard, moved into its own repo, and updated to use relative include paths for portability. A develop branch now serves as the integration point, with feature branches merged into develop and then main.
A new project repo, SymbolsInformer, is proposed as a utility EA rather than a trading system. It gathers cross-symbol, cross-timeframe statistics to guide parameter bounds for the SimpleCandles strategy, reducing optimizer search space and improving comparability across instruments.
The EA inputs include a main timeframe and candle count window, plus comma-separated symbol and timeframe lists. Outputs cover average candle sizes (bull/bear/all), average series length for runs of 2+ candles, and counts of series...
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #EA
512 108
In MetaTrader 5 Beta Build 5770, we have further enhanced integration with the OpenBLAS linear algebra library, by introducing new methods for L1 trend filtering and improvements to the component update system.
MetaEditor now also offers a more convenient way to work with CSV files. The editor automatically displays them in a table format, allowing you to filter data, sort by columns, and delete rows via the context menu.
In addition, we have improved the stability of the Strategy Tester and the web version of the platform.
Read more...
512 108
Most retail volatility bands (Bollinger Bands, Envelopes) are built on linear moving averages and standard deviation. Under extreme shock regimes, variance expands rapidly and these bands can become too wide to provide practical mean-reversion boundaries.
Many institutional quant models reduce reliance on linear averages and shift to non-parametric smoothing to better reflect the current structure of the price feed rather than a fixed distributional assumption.
Nadaraya-Watson kernel regression applies a Gaussian kernel to historical prices, weighting recent local clusters more heavily while damping distant anomalies. The result is a non-linear regression channel that tracks the localized consensus more tightly than standard deviation-based tools.
Key behaviors include dynamic mean-reversion signals on boundary breaches, adaptive smoothing during a...
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #Indicator
512 108
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
#MQL5 #MT5 #Indicator
512 108
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
#MQL5 #MT5 #Indicator
512 108
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
#MQL5 #MT5 #script
512 108
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
#MQL5 #MT5 #Indicator
512 108
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
#MQL4 #MT4 #Indicator
512 108
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
512 108
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
512 108
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
512 108
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
#MQL5 #MT5 #Indicator
512 108
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
#MQL5 #MT5 #Indicator
512 108
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
#MQL5 #MT5 #Indicator
