MQL5 Algo Trading
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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 575 999 subscribers, ranking 139 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 575 999 subscribers.
According to the latest data from 28 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 13 563 over the last 30 days and by 571 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.40% reactions from the total number of subscribers.
- Post reach: On average, each post receives 12 063 views. Within the first day, a publication typically gains 8 057 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 40.
- 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 28 October, 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.
576 204
Subscribers
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+2 6087 days
+13 56330 days
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| Date | Subscriber Growth | Mentions | Channels | |
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Channel Posts
LLM-driven trading in MetaTrader breaks quickly when the EA only asks for BUY/SELL. Practical constraints include M15βH1 operation, 30β90s inference latency, upcoming news within 10 minutes, max 3 open positions, and ~1% risk per trade.
A workable design separates thinking from execution and enforces sequential filters: Signal β News β Risk. The MQL5 EA becomes pure I/O on new-bar events, while a local Python server (LangChain) handles indicators, agents, and SQLite logging with decision_id plus a RESULT feedback loop.
Transport runs over a persistent local WebSocket on 127.0.0.1:8977 using a small command set (ANALYZE, RESULT, MEM_STATUS, STATUS, STOP) with separate long/short timeouts. Every decision, including hold, is recorded, and closed-trade PnL (with swap/commission) is returned to update agent_stats.
π Read | CodeBase | @mql5dev
| 2 |  TesterStats.mqh computes Strategy Tester report statistics directly from deal history, including metrics not available via TesterStatistics(): Z-Score, AHPR, GHPR, LR Correlation, and LR Standard Error. Output can be used for custom optimization criteria or to match backtest report values on a live account.
Validation was done across 5 scenarios (EURUSD, USDJPY, XAUUSD; fixed lots and percent risk; with and without TesterWithdrawal) by comparing 44 fields against TesterStatistics() and the HTML report. With β1 minute OHLCβ and βEvery tickβ, all 44 matched. With βEvery tick based on real ticksβ, equity is sampled less frequently, producing equity drawdown 0.05%β0.26% deeper, while other values remained identical.
Report rules implemented: 0.00 result counts as profit; post-start deposits/withdrawals excluded from balance drawdown; equity drawdown re...
π Read | Calendar | @mql5dev | 4 927 |
| 3 |  ICT kill zones define time windows where higher activity is expected, expressed in New York local time: Asian 20:00-00:00, London 02:00-05:00, New York 07:00-10:00, and London Close 10:00-12:00. Each session window can be customized (HH:MM-HH:MM, NY time).
A common platform issue is broker server time versus New York time, including mismatched US and EU DST switch dates. The indicator converts candles to New York time so session boxes remain aligned year-round. Server clock handling supports Auto (reads terminal GMT offset), the typical βNew York + 7hβ forex server setup (GMT+2/GMT+3 with EU DST rules), or a fixed GMT offset.
It draws each kill zone box from high to low with labels, extends finished session highs/lows until traded through, and marks the 00:00 New York open as a dashed line. It can display recent trading days, supports M1-H1, and show...
π Read | AlgoBook | @mql5dev | 6 483 |
| 4 |  Order blocks in SMC/ICT are typically defined as the last opposite candle before a displacement that breaks market structure. A bullish OB is the final down candle near the low of the leg that precedes a close above the last swing high. A bearish OB is the final up candle near the high of the leg that precedes a close below the last swing low. Swings are confirmed using an N-candle strength setting (default 10).
The indicator plots active OB zones as right-extended boxes (green bullish, red bearish) and tracks failures as breaker blocks (blue). Used and broken zones can be kept as grey boxes terminating at the touch/break candle for review. βUsed upβ logic is configurable: touch, 50% mitigation, or only on a closing break.
Signals are non-repainting due to closed-candle swing and break confirmation. Zones can use full candle range or body, with an optiona...
π Read | NeuroBook | @mql5dev | 7 541 |
| 5 |  XlsxWriter.mqh generates valid .xlsx workbooks directly from MQL5. No DLL dependency and no local Excel installation are required. Output is a ZIP container of Office Open XML parts, stored uncompressed with CRC-32 per part.
Supports multi-sheet workbooks with text, numbers, and proper Excel date serials for correct sorting and filtering. Each sheet can be created with a bold header row, frozen panes, and auto-filters. Column widths are configurable, with eight built-in number formats: date-time, date, month, money, integer, price (5 decimals), percent, and 2-decimal.
Unicode text is handled as UTF-8. Designed to run in scripts, Expert Advisors, and the Strategy Tester, including writing to FILE_COMMON from OnTester.
Example: ExportDealsToXlsx.mq5 exports account history deals into a Deals sheet and aggregates daily results into a Daily sheet. Place XlsxWr...
π Read | Calendar | @mql5dev | 7 796 |
| 6 |  Failed Breakout Detector flags range breakouts and highlights those that reverse back into the prior range. It is a price-action utility focused on identifying trapped breakout traders, not forecasting direction.
The range is defined as the highest high and lowest low of the last N bars (default 12), filtered by range height (0.5β4.0 ATR). A breakout requires a close outside the range by an ATR-based buffer (default 0.1 ATR) and is marked with a blue dot.
If any of the next K bars (default 3) closes back inside the range, the breakout is classified as failed: red arrow for failed breakout up, green arrow for failed breakout down. If price holds outside for K bars, the breakout is confirmed and no further marks are added. Calculations use closed bars only, so signals do not repaint.
A corner label counts failed breakouts for the current day (up/down)...
π Read | AppStore | @mql5dev | 7 758 |
| 7 |  Prop Firm Guard is a risk-control Expert Advisor designed to enforce two common prop firm constraints: daily loss limit and maximum drawdown. It does not open trades and can run alongside manual trading or other EAs, attached to any single chart while monitoring the entire account.
Equity is checked every second. When a limit is reached, it closes all open positions, deletes pending orders, and can optionally close any new trade opened while the breach persists. Alerts and optional push notifications are supported, with all actions recorded in the Experts log.
Daily loss can reference either balance or the higher of balance and equity at the daily reset. Maximum drawdown supports static or trailing modes, with an optional safety margin to act before the threshold. State is persisted across restarts and reconnections, and the chart displays live usage plus n...
π Read | CodeBase | @mql5dev | 7 462 |
| 8 |  A Risk of Ruin script estimates the probability of hitting a defined drawdown floor before a strategyβs edge shows up. It reads closed trades from account history, converts each result into an R-multiple (net P&L divided by average absolute loss), then runs a bootstrap simulation.
Each simulated path resamples historical R-multiples with replacement, applies a user-selected risk % per trade, compounds equity trade by trade, and flags βruinβ if equity drops below the configured floor. The method is independent of the original traded lot sizes; only the win/loss distribution and current risk settings drive outcomes.
Reported metrics include chance of hitting the floor, chance of ending profitable, median ending balance, and the 95% range of final balances. Median is used to reduce distortion from long-tail winners. Higher risk per trade typically increases th...
π Read | AppStore | @mql5dev | 7 162 |
| 9 |  This EA builds signals on a classic two-brick Renko reversal. Oscillators update only on completed bricks, with one active mode: RSI Re-entry, RSI Midline, MACD Signal, or MACD Zero. RSI uses Wilder smoothing; MACD is EMA-diff with an SMA signal line. Periods count completed bricks. Multi-brick completion in one tick updates per brick, but only the last brick can trigger an entry. Orders execute at market quotes; blocked signals are not queued.
Entry is blocked if a position or order already exists for the symbol/Magic. Exits rely on virtual SL/TP or a holding-time limit; opposite signals do not close positions. A brick-based cooldown applies after exit; spread-to-brick filtering affects entries only.
A included test example reports XAUUSD M1 real-tick backtest results (JanβOct 2026): 178 trades, PF 1.49, max DD 5.99%, net profit $2,348.57. Parameters were chos...
π Read | VPS | @mql5dev | 7 465 |
| 10 |  Spread Monitor is a lightweight information indicator for MetaTrader 5 focused on real-time spread visibility.
It shows the current spread in pips along with Bid and Ask values. The display uses configurable color states (green, yellow, red) to reflect low, medium, and high spread conditions. Language support is available in Russian and English. The UI is limited to a single text panel to keep chart rendering clean.
Key settings include language selection, X/Y offsets for panel placement, MidThreshold and HighThreshold in points, per-state colors (ColorLow/ColorMid/ColorHigh), and font size.
Typical use is spread verification before order placement, especially around news releases and during low-liquidity sessions where execution costs can change quickly.
π Read | NeuroBook | @mql5dev | 8 444 |
| 11 |  A source text is required to generate the post. Only βby Grok AIβ was provided, which is not enough to rewrite into an 800-character technical news-style update.
Send the full text to convert, and include any constraints such as platform (X/LinkedIn), product/topic, and whether company or project names must be kept or removed.
π Read | Forum | @mql5dev | 10 312 |
| 12 |  A trend-following trading system built around OpenAI models typically separates signal generation from execution and risk controls. Market data is normalized into features, prompts are constrained to structured outputs, and model responses are treated as probabilistic inputs rather than deterministic rules.
Key design points include strict data boundaries to prevent leakage, reproducible backtests with walk-forward validation, and hard limits on position sizing, leverage, and drawdown. Execution should rely on deterministic order logic, while the model is limited to regime classification, filtering, or parameter selection.
Operational requirements include latency and rate-limit handling, audit logs of inputs and outputs, and continuous monitoring for drift. Any deployment should include fail-closed behavior and clear rules for disabling automation un...
π Read | NeuroBook | @mql5dev | 11 268 |
| 13 |  DeepSeek Quantum Apex Color is a chart-based indicator intended to visualize the dominant trend while reducing noise through a rules-based filter stack.
Regime filtering is driven by ADX. Trend-following logic is active only when directional strength exceeds a threshold (default >20), limiting signals during sideways conditions.
Momentum and exhaustion checks combine RSI and Z-Score. RSI provides directional bias (RSI >50 long, <50 short), while Z-Score measures price deviation from a moving average to reject statistical outliers and reduce early entries.
Signal history is computed on the last closed bar (Bar 1) to avoid repainting and keep backtest results consistent.
π Read | Forum | @mql5dev | 10 794 |
| 14 |  Mistral Trend Candles color-code trend state using Hull MA slope and an ADX regime filter.
Green marks a confirmed uptrend when the slope is above the configured threshold and ADX indicates a trend regime. Red marks a confirmed downtrend under the same conditions.
In weak or lateral markets, candles retain the last confirmed trend color to keep continuous green/red zones for faster visual reading.
The logic is non-repainting: closed candles keep their final color. Only the currently forming candle can change until close.
Rendering uses chart objects placed above native candles, fully covering them.
For M1βM5, lowering ADX_TrendMin to ~18β19 can improve responsiveness. Higher timeframes typically need a higher ADX_TrendMin. HMA_Period and Slope_MinATR control reactivity and noise tolerance.
π Read | AlgoBook | @mql5dev | 10 891 |
| 15 |  Perplexity AI is being used to accelerate development of rule-based trading systems by generating code drafts, test cases, and documentation for common patterns such as trend filters, mean reversion triggers, and risk controls. Output still requires strict validation: confirm indicator math, verify order sizing, and reconcile bar indexing to avoid lookahead.
A production-ready setup typically includes deterministic backtests, walk-forward checks, and Monte Carlo analysis on execution assumptions. Logging, parameter constraints, and fail-safe handling for connectivity and rejected orders are mandatory.
AI assistance can reduce iteration time, but strategy quality remains dependent on data hygiene, robust statistics, and disciplined change control.
π Read | Signals | @mql5dev | 10 591 |
| 16 |  A new indicator package is available featuring a βGood Tendency Estimatorβ combined with coloured candlesticks. The estimator is designed to quantify directional bias and highlight potential transitions in trend conditions, while the candle colouring provides a fast visual filter for momentum and regime changes.
Typical usage focuses on aligning entries with the estimatorβs direction and using colour shifts as confirmation or early warning. Key points to validate during testing include behaviour in low-volatility ranges, sensitivity to parameter changes, repainting risks, and performance across symbols and timeframes.
π Read | Freelance | @mql5dev | 10 904 |
| 17 |  Multi-period indicators with shifted lines require converting the shift into the current chartβs bar scale. A 1-bar shift on H4 becomes 4 bars on H1, and buffer copying must start from a shift-adjusted reference index to align values correctly.
Library updates: buffer shift setters now allow shifts on calculated buffers, and calculated buffer copy uses the line shift with an inverted sign to select the correct source start. Indicator factory methods (MA/AMA/DEMA/TEMA/VIDYA/Bands/Alligator, etc.) compute shiftBars = shift * periodBarRatio, pass that to the native handle, and apply the same shift to drawn and calculated buffers.
Common logic was consolidated: standard line types were normalized across indicators, universal preparation/clearing paths were reduced to 1/2/3-line cases, and a buffer-clear routine prevents artefacts from inactive indicators...
π Read | AppStore | @mql5dev | 14 535 |
| 18 |  Market structure can be quantified by tracking sequences of higher highs/higher lows or lower highs/lower lows, and flagging when that pattern changes. This indicator applies fixed swing rules and prints non-repainting labels.
Swing highs/lows are confirmed only after N candles close to the right (default N=5), so markings appear with a delay and never shift. Each confirmed swing is classified as HH/LH versus the prior swing high, and HL/LL versus the prior swing low.
Break logic supports BOS and CHoCH. BOS is triggered when a candle closes beyond the last swing in the current trend direction and is drawn as a dashed line. CHoCH is triggered when a candle closes beyond the last swing against the active trend and is drawn as a solid line. Break detection can use close (default) or wick.
Inputs include swing strength, bars to scan (default 3000), disp...
π Read | CodeBase | @mql5dev | 14 148 |
| 19 |  Spread Monitor is a lightweight information indicator for MetaTrader 5 that shows the current spread in pips in real time.
The data is presented as a compact on-chart panel with colour-coded status markers and a level bar for quick visual assessment.
Designed for low overhead and continuous monitoring, it helps track spread changes during varying liquidity conditions and around news-driven volatility.
π Read | CodeBase | @mql5dev | 14 993 |
| 20 |  A refreshed design-oriented market price view has been released, focused on clearer trend visibility and a cleaner chart presentation.
Deployment requires adding a bitmap asset. Download Mr.Wave.zip, extract surfer.bmp, and place it in the platformβs Images directory so the interface renders as intended.
If the image is missing or stored in a different path, the UI will fall back to a blank placeholder or fail to display the visual element, which can lead to inconsistent layout across installations.
π Read | Quotes | @mql5dev | 15 129 |
