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 568 473 subscribers, ranking 135 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 568 473 subscribers.
According to the latest data from 08 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 15 669 over the last 30 days and by 206 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.57%. Within the first 24 hours after publication, content typically collects 1.64% reactions from the total number of subscribers.
- Post reach: On average, each post receives 14 627 views. Within the first day, a publication typically gains 9 315 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 09 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.
568 473
Subscribers
+20624 hours
+3 4947 days
+15 66930 days
Posts Archive
568 473
Trading data is a finite sample, not the marketβs true distribution. The article reframes strategy research as statistical inference: deciding what is a stable property versus a sample-specific fluctuation, so βpositive meanβ or βnormal returnsβ arenβt mistaken for signal.
LLN and CLT are presented as the bridge from theory to practice. LLN explains why a trading edge shows up only across many trades, while CLT quantifies uncertainty for finite N, enabling confidence bounds and error scaling around estimates.
A key theme is treating the i.i.d. assumption as a hypothesis to test. Using ECDF, histograms, and KDE, EDA surfaces dependence, volatility clustering, and heavy tails before CDA applies parametric/nonparametric estimation and hypothesis tests. MQL5 script examples are positioned as reusable building blocks for a robust research pipeline.
π Read | Docs | @mql5dev
568 473
SpikingBrain reframes market modeling as event-driven computation: neurons stay idle until a threshold is hit, then emit spikes. This mirrors trading realities like Renko-style filtering, news shocks, level breakouts, and stop-loss cascades, where signal quality depends on reacting to discrete events rather than continuous noise.
Technically, it reinterprets Transformer attention as a dynamic trigger: compute is spent only when inputs become meaningful. Threshold adaptation tunes sensitivity to volatility, while local and global connectivity captures both microstructure moves and regime-level shifts.
Two variants show practical trade-offs: SpikingBrain-7B uses linear + sliding-window attention for long contexts with constant memory; SpikingBrain-76B adds hybrid attention, trainable tokens, and sparse MoE to balance coherence, latency, and resource useβenabli...
π Read | AppStore | @mql5dev
568 473
Stochastic Daily Breakout is an XAUUSD H1 EA built around momentum shifts and yesterdayβs range. A stochastic signal-line turn flags a potential change, then a pending stop order is placed at yesterdayβs high or low so execution occurs only if price confirms the move.
Logic is minimal and self-contained. The stochastic is calculated internally using a close-to-close range with SMMA smoothing (not equivalent to iStochastic). The only built-in indicator used is Bollinger Bands. One position is maintained at a time.
Rules: Buy when the signal line peaks on the prior bar and the open three bars ago is below the upper band; place a buy stop at yesterdayβs high. Sell when the signal line troughs and the open three bars ago is above the lower band; place a sell stop at yesterdayβs low. SL and TP are both 1.9% of entry. Pending orders expire after 9 bars and are repl...
π Read | Quotes | @mql5dev
568 473
Shapelets replace hand-coded chart patterns with a subsequence search on labeled price windows. Each shapelet is a z-normalized snippet plus a distance threshold, acting as a decision stump using minimum sliding Euclidean distance.
Labeling comes from forward log returns over a horizon, keeping only moves above k times trailing 1-bar sigma and dropping FLAT. This avoids fixed pip thresholds but makes k horizon-dependent and introduces selection effects.
Core implementation issues: stable rolling variance requires mean-centering to avoid catastrophic cancellation; early-abandon keeps MinDist exact. Candidate scoring uses information gain over sorted distances, enabling fast re-scoring under label permutations.
Sliding-window datasets create duplicate traps: overlap checks must use absolute bar spans and optional z-space similarity, not row indices....
π Read | Docs | @mql5dev
568 473
Prop Firm Risk Calculator is an on-chart panel for funded and prop firm accounts focused on position sizing and loss-limit planning. It is a calculator only and does not generate signals or place orders.
The panel displays risk per trade in account currency, stop distance in price units, and the resulting lot size. It also shows daily loss and maximum total loss limits, plus the number of consecutive losses at the selected risk that fit within each limit. The value of one price unit for one lot is read from the symbol specification.
Risk is computed as account size multiplied by risk percentage. Lot size is derived from risk amount divided by stop distance times the per-lot price-unit value, then rounded down to the volume step and constrained to symbol min/max volume. Stop distance can be manual or ATR-based using the last closed bar.
Inputs include accou...
π Read | Quotes | @mql5dev
568 473
MetaTrader 5 build 6060 turns the AI Assistant into a tool-using agent wired directly into MetaEditor and the terminal via MCP. It can generate an EA from a strategy description, compile with the real MQL5 compiler, interpret diagnostics, apply fixes, and iterate through Strategy Tester runs.
Under the hood it uses the Goose agent framework, loading separate MCP servers for editor tooling, terminal operations (charts, ticks, orders, tester), plus a MetaQuotes-hosted marketdata service for instrument search, fundamentals, and news. Sessions, tool calls, and token usage are locally recorded.
Control is explicit: model/provider can be cloud or local, and permissions gate trading, web access, and shell/file operations. The practical value is a closed loop from code to backtest, with guardrails suitable for live accounts.
π Read | Calendar | @mql5dev
568 473
Flock by Leader (FBL) targets a common swarm-optimization failure mode in trading parameter searches: fast convergence to a single point that kills exploration. It builds multiple concurrent βhotspotsβ while keeping computation bounded.
Agents are split into sub-flocks using ARF, a density metric derived from k-nearest-neighbor direct and reverse neighborhoods. Dense-core agents become centroids, nearby agents join the best-ranked centroid, and the number of sub-flocks is capped to avoid fragmentation.
Leadership is fitness-driven: the leader is the member with the best personal best, not the centroid. Leaders exploit using inertia plus pulls to personal best and global best (phi). Followers combine cohesion, velocity alignment (using saved pre-update velocities), personal-memory pull, global pull, and separation (wSep) to prevent collapse. Outlier...
π Read | Freelance | @mql5dev
568 473
Automatic trendline generation is added to an MT5 framework without diluting the existing βlive objectβ lifecycle (touch, bounce, break). Creation and management stay separate: CTrendlineBuilder converts swing structure into validated bullish (higher lows) and bearish (lower highs) candidates, then creates chart objects with deduplication checks.
CTrendlineManager remains the orchestrator: it updates existing managed lines first, then runs ScanAndBuild(), discovers unowned chart objects, and registers them. After registration, every line becomes a CManagedTrendline and follows the same state machine for proximity, confirmations, resurrection windows, and expirationβusing the same live-drag and closed-bar evaluation paths.
The system also reconciles orphaned chart objects and centralizes direction-aware rendering, so visuals reflect both geometry and ...
π Read | CodeBase | @mql5dev
568 473
MACD signal-line crossovers fail structurally in ranges: tiny sign flips in the MACD histogram can trigger frequent reversals with no information about momentum magnitude or persistence. On EURUSD H1 (2018β2024), this produces heavy whipsawing and large drawdowns out of sample.
A two-layer meta-labeling pipeline keeps the classic crossover for direction, then adds: (1) an Optuna-tuned regime gate requiring histogram magnitude vs ATR, persistence across several bars, and optional MACD zero-line agreement; (2) a secondary classifier that sizes positions by the predicted probability of hitting a triple-barrier profit target before a stop.
Context features mix MACD diagnostics (separation, slope, chop counts), external regime measures (ADX, volatility percentiles, EMA distance), and session timing. Results show the gate dramatically reduces trade count...
π Read | AlgoBook | @mql5dev
568 473
Position sizing often relies on win rate alone, while ignoring the probability of long loss runs that can breach drawdown limits at a given risk-per-trade.
A MetaTrader 5 dashboard script can quantify this from history: empirical win rate, average win, average loss, and observed max losing streak, with breakeven deals excluded from win/loss stats and treated as streak breakers.
Consecutive-loss probabilities use the geometric tail P(streak β₯ N)=q^N, where q=1βp, giving immediate estimates for 5/10/15/20-loss runs without simulation.
Risk of ruin is computed from normalized edge A and risk units U=100/risk%, with RoR=((1βA)/(1+A))^U and a hard clamp to 1.0 for Aβ€0.
A CCanvas plot traces RoR versus risk-per-trade and marks the current setting; an Experts-tab report prints streak odds and current ruin probability, with limits noted around independence, r...
π Read | Freelance | @mql5dev
568 473
Intrinsic-time trading replaces bar-based sampling with event-based sampling driven by price reversals. A clock tick is generated only when price moves by a fixed threshold delta from the last extreme (directional change), with subsequent continuation treated as overshoot. The resulting sequence of events forms the βcoastlineβ representation used by Olsen-group research.
An MQL5 Expert Advisor ports the Alpha Engine coastline trader: eight independent agents (long-only and short-only) across four thresholds (0.25%, 0.5%, 1%, 1.5%). Agents trade via limit orders on hedging accounts, cascading into adverse moves and de-cascading via fixed take-profits set at delta. Adds are in fixed increments, explicitly not martingale.
Risk shaping comes from inventory-driven threshold skew, fractional sizing, and an information-theoretic liquidity metric L that cuts adds i...
π Read | Signals | @mql5dev
568 473
Many EAs lose consistently, but reversing trades is not a shortcut to profitability. A mirrored inverse pays trading costs again, so results are not a simple sign flip. Real execution also changes outcomes due to spread side, slippage, swaps, and SL/TP path dependency.
MirrorHedgeEA mirrors another EAβs or manual trades inside the same account and broker, with no cross-account communication. It syncs the full trade lifecycle: market and pending orders, SL/TP changes, partial closes, closures, and deletions. Each mirror trade is tagged with a derived magic number suffix and a comment suffix for clean separation in history.
Configuration supports magic-number pattern matching, multiple lot sizing modes, inverse direction with TP/SL swapping, and restart-safe source/mirror linking. Designed for hedging accounts and intended as a measurement tool to evaluate in...
π Read | Freelance | @mql5dev
568 473
Arixis Backtest Robustness Analyzer is a free, open-source utility for MetaTrader 5 focused on evaluating backtest robustness beyond Profit Factor, net profit, and win rate. It operates on closed trade history and reports how results were generated, including average/median trade, best and worst trades, streaks, percentiles, and sample-size context.
Dependency and sensitivity checks recalculate outcomes after removing top or bottom trades to quantify concentration and the impact of extremes. When net profit is non-positive, dependency ratios are suppressed to avoid misleading percentages while still showing adjusted net results.
The tool reconstructs a closed-equity curve to analyze drawdown depth, underwater time, and recovery, with the limitation that intratrade floating drawdown cannot be derived from closed history alone. Time and direction breakd...
π Read | AppStore | @mql5dev
568 473
ATS Liquidity City plots likely stop locations and marks which pools were swept on the current server day.
Liquidity pools are defined as confirmed swing highs/lows using N bars on each side (default 5), equal highs/lows when multiple swings fall within 0.10 x ATR(14), and yesterdayβs high/low valid for today only. Equal levels increase pool size and are rendered with thicker lines.
A sweep is flagged on the first closed bar that trades beyond a pool. If the bar closes back inside it is marked grabbed & rejected, otherwise taken. Active pools render as solid lines with labels and prices; swept pools are dotted and retained for 300 bars. Tooltips show type, price, and status.
The panel reports nearest pools above/below in ATR distance, and a taken-today ticker. For developers: buffers expose sweep arrows and nearest active levels; the panel uses a 5...
π Read | AlgoBook | @mql5dev
568 473
βNon-repaintingβ is one of the most common claims in indicator descriptions and one of the hardest to verify visually. A manual reload test typically misses two costly cases: closed-bar values changing while the chart stays open, and history changing after a reload caused by timeframe switches, terminal restarts, or opening a fresh chart.
Repaint Inspector measures this directly by re-reading indicator buffers on already closed bars (shift 1+), keyed by bar open time rather than shift. The first observed value becomes the reference; later reads are compared to both the previous value (event counting) and the first value (drift). Events are split into Changed, Appeared, and Vanished, with MaxShift reporting how far back any event occurred.
Each event is written to CSV with bar time, buffer, shift at detection, and old/new values, making the report auditable...
π Read | Calendar | @mql5dev
568 473
Edge Drift Detector adds statistical monitoring to an MT5 Expert Advisor by testing whether live closing deals still match the backtest distribution, or whether average outcome per deal has regressed toward zero. It reads baseline and live data from tester reports, CSV, or account history, and prints a full report in the Experts tab.
Coverage includes a bootstrap-calibrated CUSUM edge-loss alarm with a selectable false-alarm rate over a planned deal horizon, an estimated drift start deal/date, and tail-probability checks over short and long windows (default 20 and 100 deals). Expectancy change is decomposed into win-rate, win-size, and loss-size effects, with a symbol-mix warning when live instruments differ from the baseline.
Output includes detection-delay estimates, an A+ to F Edge Health Score, and an explicit CONTINUE/WATCH/REDUCE/SUSPEND verdict with ...
π Read | Freelance | @mql5dev
568 473
Backtest Sharpe ratios are biased upward when reported from the best optimization pass. No data manipulation is required; selection alone inflates the result.
Raw Sharpe is optimistic for three reasons: limited sample size, non-normal return distributions (skew and fat tails), and the βbest-of-Nβ effect across parameter trials.
Probabilistic Sharpe Ratio (PSR) converts an observed Sharpe, trade count, skewness, and Pearson kurtosis into the probability that the true Sharpe exceeds a benchmark.
Deflated Sharpe Ratio (DSR) adds selection adjustment by raising the benchmark to the expected maximum Sharpe from N no-edge trials, using the cross-trial variance and a normal quantile approximation.
Example: 56 MA variants on XAUUSD H1. Winner SR=0.070 (711 trades). PSR vs 0: 98.5%. DSR vs hurdle 0.028: 90.6%, below a 95% significance threshold.
π Read | NeuroBook | @mql5dev
568 473
This EA targets a common pullback failure mode: swings that look valid to pivot logic but lack real displacement. It fixes that by grading each impulse leg and allowing pullback setups only when the leg shows measurable imbalance.
Swings are confirmed on closed bars with strict alternation, so every extreme has a clean origin-to-extreme leg to evaluate. A deterministic Candle Imbalance Engine scores legs from four bounded signals: average body-to-range (displacement), close-direction consistency, efficiency (net vs gross travel), and Fair Value Gap coverage normalized by leg height.
Only swings above a configurable threshold can arm a pullback state machine (armed β in-zone β done). Entries require an in-zone confirmation close; invalidation includes origin breaks and an optional first-clean-retest rule.
Exit management runs on every tick with R-m...
π Read | Freelance | @mql5dev
568 473
ST-Expert tackles non-stationary markets by using a Mixture of Experts: multiple specialized predictors trained on distinct regimes (trend, correction, flat), with a mixing module that reallocates weights as conditions shift. Expert βgraphonsβ model changing inter-asset connectivity, improving robustness without a large parameter increase.
The implementation extends a Transformer-style stack (Extralonger) with a graphon-driven global-local attention block. Global attention captures long-horizon structure, while local attention targets short-term anomalies; outputs are concatenated and adaptively mixed.
Engineering details cover clean module lifecycle (Init), real-time forward flow, correct gradient splitting/merging during backprop, and weight updates delegated to submodulesβuseful patterns for building adaptive forecasting components in MT5 pipeli...
π Read | Signals | @mql5dev
568 473
Collaborative debugging in MQL5 often meant copying source and compiler output into an external AI chat, then returning fixes to MetaEditor. That loop worked, but context drift and manual transfer added friction.
MetaTrader 5 Build 6060 adds MCP support and an AI Assistant inside MetaEditor. The key change is proximity to the editor and compiler, not automatic correctness. Prompts still need constraints, and fixes must be reviewed.
A D1 PriceMarker exercise highlights the gap between compilation and runtime validity. The buggy version fails due to a missing semicolon, an invalid object property (OBJPROP_Y), and unchecked return values. An AI-fixed version can compile cleanly, but the final implementation adds rate validation, deterministic object naming, checked chart operations, timer refresh, and bounded cleanup.
Successful compilation is only stage one...
π Read | NeuroBook | @mql5dev
