MQL5 Algo Trading
Open in Telegram
The best publications of the largest community of algotraders. Subscribe to stay up-to-date with modern technologies and trading programs development.
Show moreπ Analytical overview of Telegram channel MQL5 Algo Trading
Channel MQL5 Algo Trading (@mql5dev) in the English language segment is an active participant. Currently, the community unites 529 908 subscribers, ranking 146 in the Technologies & Applications category and 5 in the United Kingdom region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 529 908 subscribers.
According to the latest data from 31 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 14 053 over the last 30 days and by 579 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.22%. Within the first 24 hours after publication, content typically collects 1.94% reactions from the total number of subscribers.
- Post reach: On average, each post receives 17 076 views. Within the first day, a publication typically gains 10 289 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 35.
- Thematic interests: Content is focused on key topics such as indicator, chart, mql5, candle, range.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βThe best publications of the largest community of algotraders.
Subscribe to stay up-to-date with modern technologies and trading programs development.β
Thanks to the high frequency of updates (latest data received on 01 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
529 908
Subscribers
+57924 hours
+3 8497 days
+14 05330 days
Data loading in progress...
Similar Channels
Tags Cloud
Incoming and Outgoing Mentions
---
---
---
---
---
---
Attracting Subscribers
August '26
August '26
+252
in 4 channels
July '26
+15 041
in 6 channels
Get PRO
June '26
+9 701
in 6 channels
Get PRO
May '26
+10 784
in 10 channels
Get PRO
April '26
+10 664
in 10 channels
Get PRO
March '26
+22 886
in 10 channels
Get PRO
February '26
+25 031
in 6 channels
Get PRO
January '26
+26 330
in 10 channels
Get PRO
December '25
+22 353
in 4 channels
Get PRO
November '25
+21 953
in 11 channels
Get PRO
October '25
+24 933
in 4 channels
Get PRO
September '25
+19 967
in 5 channels
Get PRO
August '25
+17 145
in 4 channels
Get PRO
July '25
+18 909
in 10 channels
Get PRO
June '25
+18 135
in 9 channels
Get PRO
May '25
+17 357
in 2 channels
Get PRO
April '25
+19 677
in 0 channels
Get PRO
March '25
+19 081
in 0 channels
Get PRO
February '25
+18 865
in 1 channels
Get PRO
January '25
+19 658
in 0 channels
Get PRO
December '24
+17 881
in 0 channels
Get PRO
November '24
+23 274
in 0 channels
Get PRO
October '24
+25 611
in 3 channels
Get PRO
September '24
+23 810
in 0 channels
Get PRO
August '24
+32 845
in 0 channels
Get PRO
July '24
+39 916
in 0 channels
Get PRO
June '24
+14 429
in 0 channels
Get PRO
May '24
+1 105
in 0 channels
Get PRO
April '24
+1 407
in 0 channels
Get PRO
March '24
+2 180
in 0 channels
Get PRO
February '24
+1 456
in 0 channels
Get PRO
January '24
+1 276
in 0 channels
Get PRO
December '23
+4 228
in 0 channels
| Date | Subscriber Growth | Mentions | Channels | |
| 01 August | +252 |
Channel Posts
This article ports Mark Minerviniβs SEPA Trend Template into a rule-driven MetaTrader 5 EA, turning an 8-point discretionary checklist into a strict pass/fail filter. The core idea is structural selectivity: price and the 50/150/200 SMAs must align, the 200 SMA must be rising, and price must be both well off the yearly low and near the yearly high, filtering for sustained, leader-like trends.
For forex, the missing IBD Relative Strength condition is replaced with RSI(14) > 50, and the VCP entry is simplified to a close above the prior 20-bar high with volume at least 1.5x its 20-bar average. The EA logs exactly which condition blocks each setup, uses ATR-based stop sizing with fixed risk, and exits on a close below the 50 SMA with volume confirmation. Backtests highlight the expected behavior: few signals on D1, long idle periods, and occasional qualified tr...
π Read | Freelance | @mql5dev
| 2 |  The automated MT5 optimization pipeline for a multi-currency EA is refined to reduce friction in real use: cleaner project/library separation, faster iterations, and better visibility into long optimization runs.
A key fix removes hidden coupling between repositories by letting project code define default inputs (like DB name/path) via constants, while the Adwizard library provides safe fallbacks. This keeps the library reusable across strategies without edits.
Optimization tasks can now be time-boxed per stage. A new max-duration field is added to the SQLite tasks table, propagated through task creation, and enforced by checking elapsed time and forcing a stop when limits are exceeded. This trims wasted genetic runs.
Process monitoring is upgraded from Comment() to a full-chart scrollable console dialog, showing detailed current-task info and han...
π Read | AppStore | @mql5dev | 5 018 |
| 3 |  Range breakout research starts with formalization, not opinions. The key step is turning βtrade breakoutsβ into explicit rules for range construction, breakout validation, stops, exits, and position management.
A baseline model should stay minimal: build a time window range, place buy/sell stops outside bounds, then manage with trailing stop and time exit. Filters are excluded to measure whether the breakout mechanic has edge.
An effective framework separates range construction from execution. A dedicated CBoxSession module can encapsulate boundaries, timing, expiration, and breakout state, with GMT-based session inputs for broker portability and logic to skip weekend or irregular bars.
Testing should target trade count, not calendar years. Rough guidance: 300β700 trades for optimization, 50β100 for forward checks. For sizing, fixed risk per trade based on...
π Read | Freelance | @mql5dev | 5 811 |
| 4 |  MetaTrader 5 exposes open positions in the Trade tab, but that view stays inside the terminal and forces constant context switching in multi-monitor setups.
A practical workaround is an Expert Advisor that reads PositionsTotal() on every tick, collects each position via PositionGetTicket(), then pulls fields with PositionGetDouble/Integer/String. Live values like POSITION_PRICE_CURRENT and POSITION_PROFIT update without extra state.
The EA generates a complete HTML document in memory and writes it in one FileWriteString() call using FILE_WRITE to avoid partial renders during browser reloads. The page self-refreshes with a small JavaScript setInterval() timer.
Output is self-contained: inline CSS, no external assets, optional row tinting by profit sign, and HTML escaping for free-text comments to prevent markup breakage.
π Read | Signals | @mql5dev | 6 331 |
| 5 |  Microstructure Matrix v5.1 targets common failure modes in retail SMC indicators: excessive per-tick recalculation, repainting, and unvalidated visual signals. The design centers on an asynchronous scanning engine that filters setups using variance measures and structural checks across multiple instruments.
The core engine uses event-driven CPU caching for high-timeframe data, limiting real-time work to lightweight price verification to reduce terminal load during multi-asset scans. An imbalance validator rejects order blocks that do not produce a measurable fair value gap. Structure is derived from swing fractals rather than candle closes, and CHOCH is confirmed only when tick volume exceeds the 50-period mean plus 1.5 standard deviations; otherwise it is flagged as a low-volatility defect.
A matrix HUD summarizes selected symbols, while validate...
π Read | CodeBase | @mql5dev | 6 679 |
| 6 |  Nikkei 225 Gap Continuation EA is an educational MetaTrader 5 Expert Advisor implementing a cash-session opening-gap continuation model with an opening-range breakout and session VWAP confirmation. It supports bullish and bearish gaps, enforces a βgap less than 50% filledβ invalidation rule, and limits entries to one per day with deadline and forced-exit controls.
Session timing is calculated per trading day, with automatic Japan Standard Time conversion to server time including DST, plus a manual mode for other brokers. The opening range is built from 1-minute bars; VWAP uses real volume when available and falls back to tick volume.
Risk and execution features include equity-based position sizing using OrderCalcProfit for account-currency risk normalization, fixed-lot option, broker volume-step rounding, spread filters, partial close, break-even moves, an...
π Read | Calendar | @mql5dev | 7 366 |
| 7 |  Stop-loss and take-profit placement often defaults to round numbers or fixed ratios. Closed trade history already contains usable measurements for where price moved against and in favor of each entry.
The MAE/MFE Excursion Analyzer EA rebuilds round-trips from MT5 deal records (optionally filtered by magic), then scans M1 candles between entry and exit to compute per-trade MAE, MFE, and efficiency (captured move divided by MFE). Output includes on-chart stats and a CSV per trade.
Key readouts come from distributions split by winners/losers: winnersβ p90 MAE as a stop distance, winnersβ median MFE as a target zone, and efficiency to flag premature exits. The tool reads history only and does not place orders.
π Read | Freelance | @mql5dev | 12 465 |
| 8 |  Forex returns rarely have constant variance, so MSE-based regressors silently optimize the wrong objective. This article replaces that assumption with a probabilistic MLP that outputs both the conditional mean and a feature-dependent variance, trained via Gaussian negative log-likelihood.
The network uses a linear head for the mean and Softplus for strictly positive variance. Backprop is extended with explicit gradients for both outputs, enabling custom-loss training instead of relying on built-in MQL5 loss helpers.
Training is integrated with ALGLIBβs L-BFGS through parameter packing/unpacking plus a separate callback to log true per-iteration loss. A sample MT5 indicator trains on normalized price increments and plots forecasts with 95% confidence intervals, giving traders risk-aware signals, not just point estimates.
π Read | VPS | @mql5dev | 12 290 |
| 9 |  Symbolic Aggregate approXimation (SAX) converts a price window into a short word via z-normalization, PAA segmentation, and Gaussian breakpoint discretization. The result is countable patterns with a lower-bounding distance (MINDIST) for pruning similarity search.
A practical MQL5 implementation fills missing platform primitives: probit-based Gaussian breakpoints, robust PAA for non-divisible lengths, flat-window rejection, and integer-coded symbols to keep distance math stable.
Applied to markets, normalization removes level and volatility, which can invalidate naive trading use. This is handled by evaluating forward outcomes in ATR units and enforcing a strict no-lookahead constraint when collecting precedents.
The analog search uses MINDIST as a fast filter, then computes full distances only for survivors, producing a distribution-based forecas...
π Read | Calendar | @mql5dev | 12 064 |
| 10 |  Chart Replay Pro (MT5 Strategy Tester, visual mode) received a major update focused on manual backtesting parity with live-trading controls.
Changes include runtime lot adjustment without restarting tests, per-position trade management, and an order workflow supporting buy/sell stop and buy/sell limit orders. UI is built with bitmap-based CbmpButton plus #resource inclusion, and a read-only edit field for lots.
Key implementation notes: OnChartEvent is not processed in Strategy Tester, so entry and TP/SL are adjusted via plus/minus buttons instead of draggable lines. Global state variables are used to prevent tick-driven UI bugs and to keep selected ticket, TP, and SL stable while actions execute.
π Read | Quotes | @mql5dev | 11 514 |
| 11 |  Deterministic backtests produce a single realized path and can overstate risk-adjusted metrics. A validation battery addresses this by evaluating distributions, not one sequence.
Permutation testing uses sign-randomization on per-trade P&L to build a no-edge null for order-independent statistics like Sortino. Bootstrap resampling estimates metric stability via percentile and BCa confidence intervals. Monte Carlo trade-sequence shuffling keeps trade outcomes fixed but randomizes order to quantify drawdown sensitivity to sequencing.
All modules consume a trade-level CSV exported from MT5 deal history. An EA-side include writes one row per closed trade and supports multi-core optimization via a contention-safe file-open. Only Trade_Profit_USD and Indicator_Name are required; no strategy logic changes needed.
π Read | Freelance | @mql5dev | 11 309 |
| 12 |  A trend identification indicator is available for classifying buy-side and sell-side conditions using signals derived from the stochastic oscillator.
The logic relies on stochastic readings to determine direction and potential turning points, helping separate upward momentum phases from downward momentum phases.
Typical usage includes filtering entries to align with the detected direction, combining with a separate confirmation method, and validating behavior across multiple market regimes. Parameters such as %K, %D, smoothing, and threshold levels should be reviewed for the target instrument and timeframe.
π Read | AlgoBook | @mql5dev | 11 112 |
| 13 |  The article presents the Bison Algorithm (BIA), a population-based optimizer for single-objective continuous search, built around two behaviors: fast exploration and a defensive clustering phase that stabilizes and refines good candidates.
BIA splits the population into a swarm group (about 80%) that moves toward a target and a runner group (about 20%) that probes new regions with a slightly perturbed direction vector. If a runner outperforms the swarmβs weakest member, it is promoted into the swarm.
The MT5-style implementation exposes clear controls (population size, swarm ratio, elite size, max step). Iterations combine weighted elite-centroid tracking, bounded step updates with rollback on worse fitness, and sorting to preserve top solutionsβuseful for robust parameter optimization in trading systems.
π Read | Forum | @mql5dev | 13 833 |
| 14 |  False breakouts often occur during trend transitions, where velocity changes precede visible structure. A proposed approach pairs Divergence Mapping with a Temporal Fusion Transformer style attention layer to produce an alternative forecasting metric and a Trade Robot output.
The divergence engine computes a lookback slope for price and for momentum indicators, normalizes price slope by the window baseline, then takes the slope differential as a structural discrepancy. A sensitivity threshold gates signal validity rather than single-point breakout confirmation.
The TFT proxy consumes three time states and applies fixed attention weights (0.55/0.30/0.15) to form buy and sell scores with strict mutual exclusivity. An MQL5 implementation emphasizes passing arrays by reference to reduce memory churn.
RSI and DeMarker are used to separate closing consens...
π Read | Quotes | @mql5dev | 13 143 |
| 15 |  Mapper converts a point cloud into a compact graph, but the highest sensitivity sits earlier: the lens and the cover. A lens assigns one scalar per point; a cover slices the lens range into overlapping intervals and records point membership.
Three lens options are used: eccentricity (mean distance), density (Gaussian-weighted neighborhood), and a coordinate projection. Intrinsic lenses cost O(N^2) over an existing distance matrix and can collapse on symmetric clouds, producing zero range and a single-bin cover.
The cover is controlled by resolution (interval count) and gain (overlap). With gain>0, points appear in multiple intervals and later become shared membership for edges. Practical starting values: resolution 5β15 and gain 0.2β0.5, with checks for degenerate lenses and expected double counting from overlap.
π Read | Signals | @mql5dev | 12 738 |
| 16 |  The article builds an MQL5 exporter that turns MetaTrader 5 history into a single JSON report of closed trades over a chosen date range, with optional symbol and magic filtering. It targets trade-level records (not raw deals) so the output loads directly in Python, R, or Excel.
Closed trades are reconstructed by pairing entry/exit deals via DEAL_POSITION_ID. Missing stop-loss or take-profit on deals is fixed with a reliable fallback: if DEAL_SL/DEAL_TP are zero, the script reads ORDER_SL/ORDER_TP from the originating order referenced by DEAL_ORDER.
The JSON includes metadata plus a trade array with ISO timestamps, readable direction, separated profit/commission/swap, and precomputed R-multiple and pip profit, using null when risk is undefined. File output uses ANSI text to avoid UTF-16 parsing issues.
π Read | AlgoBook | @mql5dev | 12 367 |
| 17 |  BlueMoon EA is an MT5 Expert Advisor built around EMA Envelope boundary checks to trigger reversal-oriented entries, with automated basket recovery for adverse movement. Signal generation and basket control are combined with configurable risk limits and account protection.
Core functions include automatic BUY/SELL entries, adaptive basket management, progressive lot sizing, configurable grid distance, per-trade take-profit, and basket-level profit targets. Operational safeguards cover spread filtering, maximum basket size and lot caps, drawdown protection with optional trading lock, and state recovery after terminal restarts. Support is included for broker symbol suffixes and MT5 hedging.
A real-time dashboard reports EMA/envelope levels, spread, basket metrics, drawdown, status, and recent activity. Configuration covers EMA period, deviation, lot model, gr...
π Read | Signals | @mql5dev | 12 043 |
| 18 |  Quant Pro Dashboard consolidates trading KPIs into a single on-chart panel, reducing context switching during analysis.
Account intelligence automatically reads account number and broker details. Performance tracking updates balance, equity, and floating PnL in real time. Trade management shows open positions and closed trade counts at a glance. Market awareness includes live spread, daily PnL percentage, and network latency (ping).
Implementation targets minimal overhead with sub-millisecond execution and layout rules designed to avoid text overlap. UI uses a high-contrast black panel with neon outline for consistent readability across chart themes, plus an optional quote section.
Deployment is drag-and-drop onto any chart, auto-aligned top-right with no additional configuration.
π Read | Quotes | @mql5dev | 12 422 |
| 19 |  BreakoutDistance.mq5 adds an operational readout on top of a standard Donchian channel: current distance to each breakout level, shown in raw price units and normalized by ATR.
The panel prints upper and lower channel values, distance from the current bid to each edge, the same distance in ATR units, and a 10-cell proximity bar that fills as price approaches the trigger. Normalization via ATR makes readings comparable across volatility regimes and symbols, reducing false βclose/farβ interpretations driven by session conditions.
Channel boundaries are computed from closed bars only (Shift >= 1), avoiding repainting. Visual state is managed via chart objects with a unique prefix per instance, optional background, and color thresholds for near (<= 1.0 ATR) and very near (<= 0.5 ATR). Optional alerts can fire when very near, with once-per-bar gating.
π Read | CodeBase | @mql5dev | 12 422 |
| 20 |  Liquidity levels remain a core reference for short-term price action. Previous day high/low and previous week high/low are commonly watched areas where resting orders can accumulate, making them practical anchors for intraday context.
The Draw On Liquidity (DOL) indicator automates plotting of PDHL and PWHL and adds session βkill zoneβ windows for Tokyo, London, and New York. This reduces repetitive manual chart work and standardizes level placement across instruments.
Configuration covers visibility toggles for daily/weekly highs and lows, line style/width/color, and label size/color. Session markers use custom start/end times, with a selectable lookback for prior sessions. Output aims for a clean chart layout with grouped inputs and minimal visual noise.
π Read | Quotes | @mql5dev | 13 166 |
