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Mostrar más📈 Análisis del canal de Telegram MQL5 Algo Trading
El canal MQL5 Algo Trading (@mql5dev) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 512 054 suscriptores, ocupando la posición 153 en la categoría Tecnologías y Aplicaciones y el puesto 5 en la región Reino Unido.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 512 054 suscriptores.
Según los últimos datos del 19 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 8 965, y en las últimas 24 horas de 370, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.42%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.91% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 17 520 visualizaciones. En el primer día suele acumular 9 780 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 39.
- Intereses temáticos: El contenido se centra en temas clave como indicator, chart, mql5, candle, range.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“The best publications of the largest community of algotraders.
Subscribe to stay up-to-date with modern technologies and trading programs development.”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 20 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
512 054
Suscriptores
+37024 horas
+2 1507 días
+8 96530 días
Archivo de publicaciones
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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
#MQL5 #MT5 #Indicator
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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
#MQL5 #MT5 #Indicator
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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
#MQL5 #MT5 #Indicator
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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
#MQL5 #MT5 #Strategy
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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
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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
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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
#MQL5 #MT5 #Indicator
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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
#MQL5 #MT5 #Indicator
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Dominance EA is a daily, bias-driven system that derives direction from the prior session’s control rather than intraday tick activity. Execution is limited to once per trading day at the session open, with Mondays excluded to reduce weekend gap effects.
Bias is validated through two checks. Structural dominance counts prior-day bullish versus bearish candles to determine control. Context confirmation requires the prior day’s final close to be on the correct side of a moving average. Optional inverted mode flips the signal for contrarian testing.
Trade flow enforces one position per symbol per day. Volume uses SYMBOL_VOLUME_MIN. Stops are set from the prior day’s high/low widened by an ATR multiple, with take profit fixed at 2x stop distance. Pre-trade checks cover stop level rules, margin, and tick pricing. Configuration includes mode, MA/ATR parameters, f...
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #EA
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ASQ SafeScalping v1.20 is a free open-source breakout scalping Expert Advisor for MT5 from AlgoSphere Quant. Entries use a strict seven-condition gate: EMA(150/510) direction, ATR-based EMA separation, close relative to both EMAs, N-bar breakout with ATR buffer, RSI zone filter, momentum vs prior close, and optional H1 EMA(50/200) confirmation. If any check fails, no trade.
v1.20 fixes repeated TP1 partial closes via ticket tracking, disables GlobalVariables-based peak drawdown tracking in Strategy Tester, and removes a legacy MT4 directive for clean MT5 builds.
Risk and trade management are conservative: single position only, fixed SL/TP, percent sizing, max drawdown auto-pause, daily trade cap, breakeven, trailing stop, and one-time partial close. Filters cover session hours, spread, news, and Friday cutoff. Five .set presets support phased optimization, w...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #EA
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Smart Money Concepts setups without strict session alignment tend to fail because directional moves often concentrate around defined macroeconomic windows rather than persisting through the full trading day.
The ICT Silver Bullet framework targets those narrow periods where liquidity conditions change and execution becomes more consistent. This indicator automates the detection of those time windows, then limits on-chart output to the active session to reduce noise and common retail traps.
Execution zones are plotted directly on the chart, while Fair Value Gaps are scanned and projected only when they form inside the configured windows. Broker server offsets are handled via dynamic GMT adjustment to keep session alignment consistent without manual calculation.
The MQL5 implementation is optimized to process only recent visible history to reduce CPU ...
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
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An RSI variant has been released that applies a Kalman Filter to the input series while keeping the standard RSI interpretation. The intent is to reduce short-term noise in the oscillator without changing common threshold logic such as overbought and oversold levels.
The indicator is displayed in a separate subwindow, consistent with typical RSI layouts. This build is described as the latest fixed version, addressing prior issues and aiming for stable behavior in live charts and backtests.
👉 Read | Signals | @mql5dev
#MQL4 #MT4 #Indicator
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A new product version reaches baseline functionality for extracting working trading settings, with usability changes aimed at lowering the entry barrier and expanding participation in parameter search and market research.
Key updates include a redesigned UI, a second polynomial for brute force based on a modified Fourier series, improved RNG modes, spread capture and noise reduction, spread-aware optimization, lot variation for ultra-short segments, and multiple bug fixes.
A new workflow generates a TXT configuration read by a single “settings receiver” EA for MT4/MT5, avoiding .set limits with variable-length arrays.
Internals cover coefficient generation, tick-based spread recording for OHLC, use of mid-price to reduce broker dependence, and notes on instability under spread noise plus overfitting risk.
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #EA
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Building a profitable model is only the first phase in algorithmic trading. A common failure point is execution: standard terminal trade calls are synchronous, blocking the next instruction until the server responds. During high-impact news or when a grid needs to close many positions at once, this wait time can turn into platform stalls and significant negative slippage.
The Asynchronous Institutional Trade Engine is a header-only library that routes order, modify, and close requests through non-blocking channels. Execution commands can be queued and dispatched without tying up the main strategy loop, separating trading logic from network latency.
The library also adds risk controls, including spread checks that can halt async bursts when liquidity degrades. It provides a partial-close routine for closing exact position percentages without manual lot r...
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #AlgoTrading
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Library work starts on symbol chart support with a new Chart object that stores chart properties as int/double/string parameters. The parameter set will evolve as functionality expands.
Signal handling is corrected in MQL5.com Signals integration. Collection updates will now refresh properties of existing signal objects, not only append newly discovered signals. Added methods include SetProperties(), lookup of signal index by ID, and selection by ID to avoid unstable database indices.
A new CChartObj class is added under DoEasy\Objects\Chart. It initializes from ChartGetInteger/Double/String, supports comparisons, property descriptions, and journal output. Flag setters use ChartSet* with optional ChartRedraw batching to account for asynchronous updates. Read-only chart parameters are mirrored into object state for later synchronization.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #AlgoTrading
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ASQ_RiskGuard for MT5 adds account-level risk controls designed to enforce drawdown and daily loss rules. Equity drawdown protection closes positions when thresholds are hit, with daily loss caps that can disable trading for the day. A trailing max drawdown mode measures from peak equity to match typical prop firm constraints.
Operational filters include session hours enforcement with optional forced close, spread-based entry blocking, and position limits for count, lots, and daily order volume. Lot sizing supports fixed size, percent risk, or fixed currency risk per trade. Alerts trigger at configurable warning levels and at limit breach across popup, sound, push, and email. Logging to file is available for audit.
Installation: place ASQ_RiskGuard.mq5 in MQL5/Experts, compile, attach to a chart, and enable algo trading. Monitoring can cover all positions o...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #EA
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ASQ_FlowDesk is an MT5 expert advisor panel focused on manual execution with automated risk and exit handling. It supports one-click LONG/SHORT market orders plus pending entries: Buy/Sell Limit and Buy/Sell Stop.
Risk can be set via manual lots, % of equity, or fixed cash risk. Exits support three scaled take-profit targets (T1/T2/T3) with configurable close fractions, plus four trailing modes: off, fixed distance, ATR-adaptive, or prior-bar structure. Auto-breakeven can shift SL to entry plus a cushion after a defined profit threshold.
Operations include Flatten All, Close Long/Short, Cancel Pendings, and Lock Entry. An on-chart panel shows spread, positions, exposure, P&L, target progress, and trail status.
Install by copying ASQ_FlowDesk.mq5 into MQL5/Experts, compiling, attaching to a chart, and enabling Algo Trading.
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #EA
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ASQ SuperTrend is an ATR-based trend-following indicator for MT5, drawing a SuperTrend line that flips between bullish and bearish states in real time. The line is green below price in uptrends and red above price in downtrends, with reversal arrows. Signals do not repaint on completed bars, and alerts support popup, sound (custom file), email, and push.
Version 2.0 adds three ATR calculation modes: Wilder smoothing (recursive ATR), SMA of true range, and EMA of true range. Mode selection changes responsiveness and is shown in the short name and dashboard.
The dashboard reports trend direction, SuperTrend level, price, pip distance to the flip point, trend duration, strength vs ATR, reversal count over the last 100 bars, active settings, current ATR in pips, plus symbol/timeframe.
Implementation uses 7 buffers, band locking (upper only decreases, lower o...
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
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This part shows a practical path from raw MT5 tick exports to queryable data: request ticks in MetaTrader 5, save to CSV, then import the file into a clean .db using MetaEditor’s table import. Key details are choosing the correct delimiter (MT5 uses tabs by default) and naming the new table so the CSV header becomes the table schema.
With the data structured as a real table, SQL becomes usable for analysis and simulation. The article introduces SELECT as the core inspection tool, then moves to filtering with a WHERE clause, using column criteria (e.g., FLAGS = 88) to reduce large result sets.
MetaEditor’s result paging (blocks of 1,000 rows) is highlighted as a simple way to navigate big tick datasets during exploration.
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #SQL
