Retail trading systems often base stop-loss distance and position sizing on Average True Range. ATR is a trailing statistic, so it can understate risk during regime changes and react late during shock events.
A common institutional approach is conditional variance forecasting with GARCH(1,1). Rather than averaging ranges, it models next-period variance from return shocks and volatility persistence, capturing clustering where large moves tend to be followed by large moves.
A practical implementation computes log returns and updates variance using standard parameters often seen in finance (alpha 0.09, beta 0.90), while keeping them configurable for calibration. Running the calculation natively in C++ avoids external runtime dependencies.
In execution logic, the forecasted variance stream can drive adaptive risk controls: expand stop distances or reduce...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #AlgoTrading
A common institutional approach is conditional variance forecasting with GARCH(1,1). Rather than averaging ranges, it models next-period variance from return shocks and volatility persistence, capturing clustering where large moves tend to be followed by large moves.
A practical implementation computes log returns and updates variance using standard parameters often seen in finance (alpha 0.09, beta 0.90), while keeping them configurable for calibration. Running the calculation natively in C++ avoids external runtime dependencies.
In execution logic, the forecasted variance stream can drive adaptive risk controls: expand stop distances or reduce...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€38π4π2β‘1π1
Forex forecasting sits between βno edgeβ randomness and exploitable structure. The article starts with an EA that flips direction randomly each new bar; results stay negative once spread is accounted for, showing why execution costs matter.
Adding realistic constraints (hold time, single position, SL/TP exits) turns the same random core into something that can be optimized. Seed selection becomes a search problem, with the secretary problem suggested to reduce brute-force runs.
From there, it builds data-driven signals: an empirical distribution of price increments (trim extremes to form a probabilistic channel), a simplified linear-trend rate updated to stabilize forecast error and tied to SMA, and a random-walk exponent estimated via log-scaled path length vs time to classify flat vs trend.
Finally, it explores polygonal-number weighted averages as...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #AlgoTrading
Adding realistic constraints (hold time, single position, SL/TP exits) turns the same random core into something that can be optimized. Seed selection becomes a search problem, with the secretary problem suggested to reduce brute-force runs.
From there, it builds data-driven signals: an empirical distribution of price increments (trim extremes to form a probabilistic channel), a simplified linear-trend rate updated to stabilize forecast error and tied to SMA, and a random-walk exponent estimated via log-scaled path length vs time to classify flat vs trend.
Finally, it explores polygonal-number weighted averages as...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€42π4π4π3π3
A next-generation trading EA concept combines three components: Markov state modeling, an MLP used on transition probabilities, and a hedging-aware execution layer.
Market βstateβ is defined as a multi-factor vector, not raw price. Daily change is normalized by ATR to classify three regimes (up, down, flat), keeping thresholds volatility-adjusted across instruments.
A 3x3 transition matrix is built from the last window of bars, with fallback to uniform probabilities when a state is absent. The MLP consumes the 9 matrix elements (9-40-2, ReLU) and outputs up/down probabilities. Retraining runs every 48 hours.
Execution can hold long and short simultaneously when both probabilities are elevated, with constraints on max positions and minimum entry distance. Reported tests cite ~28.7% annual return at 14.2% max drawdown (Sharpe 1.65), with EURUSD optimization...
π Read | VPS | @mql5dev
#MQL5 #MT5 #EA
Market βstateβ is defined as a multi-factor vector, not raw price. Daily change is normalized by ATR to classify three regimes (up, down, flat), keeping thresholds volatility-adjusted across instruments.
A 3x3 transition matrix is built from the last window of bars, with fallback to uniform probabilities when a state is absent. The MLP consumes the 9 matrix elements (9-40-2, ReLU) and outputs up/down probabilities. Retraining runs every 48 hours.
Execution can hold long and short simultaneously when both probabilities are elevated, with constraints on max positions and minimum entry distance. Reported tests cite ~28.7% annual return at 14.2% max drawdown (Sharpe 1.65), with EURUSD optimization...
π Read | VPS | @mql5dev
#MQL5 #MT5 #EA
β€53π4π4π4β3π3π€―1
Market Clock Pro is a MetaTrader 5 indicator that adds a candle-close countdown HUD with contextual market data. It shows time remaining to the next bar close, active trading sessions in UTC (Tokyo, London, New York, Sydney), live spread, daily range versus ADR, and an explicit market-closed state per symbol.
Session logic supports overlaps and counts down to the nearest session end. Market status can report reasons such as NYSE CLOSED (holiday), LSE CLOSED (weekend), or XETRA CLOSED (session). Symbol-to-market mapping covers NYSE, LSE, XETRA, TSE, HKEX, ASX, and SSE, with a manual override for non-standard broker tickers. Holidays are provided via an optional plain-text calendar (2026β2027).
Implementation notes include a millisecond timer as the only render driver, no indicator buffers, and label de-duplication before redraw. No DLLs, WebRequest, S...
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Indicator
Session logic supports overlaps and counts down to the nearest session end. Market status can report reasons such as NYSE CLOSED (holiday), LSE CLOSED (weekend), or XETRA CLOSED (session). Symbol-to-market mapping covers NYSE, LSE, XETRA, TSE, HKEX, ASX, and SSE, with a manual override for non-standard broker tickers. Holidays are provided via an optional plain-text calendar (2026β2027).
Implementation notes include a millisecond timer as the only render driver, no indicator buffers, and label de-duplication before redraw. No DLLs, WebRequest, S...
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Indicator
β€39π2π2β1
Machine learning can be applied to grid and martingale trading by changing the labeling from single-trade direction to group P/L across all triggered orders in a fixed horizon (for example, 15 bars). Labels can be assigned by max total profit, weighted profit per triggered order, or max triggered orders with net profit.
Implementation uses configurable GRID_SIZE, GRID_DISTANCES, and GRID_COEFFICIENTS (regular grid vs martingale). Each sample tracks grid state, triggered levels, and aggregated buy/sell profit, then discards uninformative segments.
A sequential tester is required to avoid future leakage and to close grids on opposite signals. CatBoost models can be trained on synthetic GMM samples, selected by R^2, exported to MQL5 with saved grid settings, and executed via an EA that places pending orders using per-level multipliers. Results show regime d...
π Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
Implementation uses configurable GRID_SIZE, GRID_DISTANCES, and GRID_COEFFICIENTS (regular grid vs martingale). Each sample tracks grid state, triggered levels, and aggregated buy/sell profit, then discards uninformative segments.
A sequential tester is required to avoid future leakage and to close grids on opposite signals. CatBoost models can be trained on synthetic GMM samples, selected by R^2, exported to MQL5 with saved grid settings, and executed via an EA that places pending orders using per-level multipliers. Results show regime d...
π Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€52π11π6β‘3π3π3
XANDER Adaptive Cross overlays two adaptive moving averages designed to react to different market properties. A crossover triggers a color-coded arrow to flag a potential trend shift. When both lines remain aligned, the chart provides continuous visual confirmation of directional bias.
The fast line is range-aware, adjusting speed based on where price sits within the recent high/low band. It accelerates near extremes to react quicker to breakouts and slows near the midpoint to reduce noise. The slow line is efficiency-aware, tightening during clean directional moves and flattening during choppy action.
Color states indicate bias: fast above slow signals bullish, fast below slow signals bearish, and gray indicates transition or undefined conditions. Trading use cases typically wait for an arrow, confirm both lines have flipped to the same bias, and re...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
The fast line is range-aware, adjusting speed based on where price sits within the recent high/low band. It accelerates near extremes to react quicker to breakouts and slows near the midpoint to reduce noise. The slow line is efficiency-aware, tightening during clean directional moves and flattening during choppy action.
Color states indicate bias: fast above slow signals bullish, fast below slow signals bearish, and gray indicates transition or undefined conditions. Trading use cases typically wait for an arrow, confirm both lines have flipped to the same bias, and re...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
β€47π₯4π3π―1
A next-generation SuperTrend variant adds an internal ML layer to classic trend-following. Core parameters are adjusted bar-by-bar from recent signal outcomes, and entries are gated by a multi-stage confirmation path before any arrow is plotted. No external libraries are required, and a hidden Confidence buffer is exposed for automated systems to read directly.
Two bands are computed (Bull/Bear) from an RMA-smoothed ATR envelope on a selectable price basis. Band width and ATR length start from inputs and are periodically re-tuned by an optimizer, widening in high volatility and tightening in calmer conditions.
Signals run in Reversal mode (confirmed trend flip with pivot) or Breakout mode (new extreme inside trend, contrarian trigger). Candidates must pass RSI state checks, optional tick-volume surge validation, and an optional key-level depth test ...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
Two bands are computed (Bull/Bear) from an RMA-smoothed ATR envelope on a selectable price basis. Band width and ATR length start from inputs and are periodically re-tuned by an optimizer, widening in high volatility and tightening in calmer conditions.
Signals run in Reversal mode (confirmed trend flip with pivot) or Breakout mode (new extreme inside trend, contrarian trigger). Candidates must pass RSI state checks, optional tick-volume surge validation, and an optional key-level depth test ...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
β€43β‘2π2
Position entry is split into multiple legs, with signals derived from reversal probability by block scale. Trend and flat are treated as probabilistic states across concurrent scales, so analysis starts from the smallest blocks and escalates until a working scale is justified.
Long one-direction moves require starting new position series before the previous series closes. A second series is allowed only after the first series basic timeframe exceeds a threshold, then a start signal is found on the second series TF1 and confirmed by detecting a flat segment on the second series basic timeframe using an adaptive BminβBmax range and a probability-based flat criterion.
Additional series profits are accumulated into a reserve and used to close the most loss-making erroneous positions when coverage rules are met. Profit from series N+1 compensates errors...
π Read | Calendar | @mql5dev
#MQL5 #MT5 #AlgoTrading
Long one-direction moves require starting new position series before the previous series closes. A second series is allowed only after the first series basic timeframe exceeds a threshold, then a start signal is found on the second series TF1 and confirmed by detecting a flat segment on the second series basic timeframe using an adaptive BminβBmax range and a probability-based flat criterion.
Additional series profits are accumulated into a reserve and used to close the most loss-making erroneous positions when coverage rules are met. Profit from series N+1 compensates errors...
π Read | Calendar | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€65π10β2π1
MQL5 port of four bet-sizing methods lands with a five-file stack designed for a tick-driven EA without NumPy/SciPy.
BetSizingUtils.mqh adds missing statistical primitives: normal CDF/ICDF via Hart minimax rational approximation, raw-moment computation, shared structs, plus an O(N log N) sweep-line counter to replace O(NΒ²) overlap scans for concurrent positions. AvgActiveSignals remains O(NΒ²) by necessity when averaging signal values across active intervals.
Sizing methods map to include files: probability sizing (z-score, averaging, discretization), dynamic forecast-price sizing (sigmoid/power, closed-form calibration, limit price via inverse sizing), budget sizing (exposure normalization with seeded running maxima), and reserve sizing using EF3M-3 mixture fitting from three moments with multi-start analytic solves and log-likelihood selection. Output...
π Read | Signals | @mql5dev
#MQL5 #MT5 #AlgoTrading
BetSizingUtils.mqh adds missing statistical primitives: normal CDF/ICDF via Hart minimax rational approximation, raw-moment computation, shared structs, plus an O(N log N) sweep-line counter to replace O(NΒ²) overlap scans for concurrent positions. AvgActiveSignals remains O(NΒ²) by necessity when averaging signal values across active intervals.
Sizing methods map to include files: probability sizing (z-score, averaging, discretization), dynamic forecast-price sizing (sigmoid/power, closed-form calibration, limit price via inverse sizing), budget sizing (exposure normalization with seeded running maxima), and reserve sizing using EF3M-3 mixture fitting from three moments with multi-start analytic solves and log-likelihood selection. Output...
π Read | Signals | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€46
A MetaTrader 5 canvas butterfly curve renderer is extended from a four-segment anti-aliased outline to a full illustration with wing fills, texture, and anatomical body details. Rendering remains based on precomputed parametric points mapped to pixel space and processed through the same supersampled pipeline.
Wing interiors are filled via scanline polygon rasterization with a nonzero winding rule to handle self-intersections. Three layers are added: outer vertical gradient, an inward-scaled vertical gradient, and an innermost inward-scaled radial gradient. Veins are rendered as thin anti-aliased lines from the curve origin to sampled boundary points.
Scale texture is added using dense boundary sampling and small filled circles, colored per parametric segment via t ranges (3Ο, 6Ο, 9Ο), then slightly brightened toward edges. The body is composed fro...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #AlgoTrading
Wing interiors are filled via scanline polygon rasterization with a nonzero winding rule to handle self-intersections. Three layers are added: outer vertical gradient, an inward-scaled vertical gradient, and an innermost inward-scaled radial gradient. Veins are rendered as thin anti-aliased lines from the curve origin to sampled boundary points.
Scale texture is added using dense boundary sampling and small filled circles, colored per parametric segment via t ranges (3Ο, 6Ο, 9Ο), then slightly brightened toward edges. The body is composed fro...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€38π4
Trading losses often come from trading at the wrong time: low-liquidity hours and scheduled news can invalidate otherwise solid entries via spread expansion, slippage, and regime shifts. This system turns βdisciplineβ into enforceable rules by blocking execution outside defined sessions and during configurable pre/post news blackout windows.
The MQL5 design uses a modular control layer: a permission engine (single boolean allow/deny), an enforcement EA that intercepts transactions so neither manual trades nor other EAs can bypass limits, and a dashboard for visibility.
Key implementation details include external session/news files for no-recompile updates, smart caching via file timestamps to avoid per-tick I/O, robust parsing with validation, fast minute-based session checks with early exits, and correct handling of day rollover plus βnext allowed timeβ ca...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
The MQL5 design uses a modular control layer: a permission engine (single boolean allow/deny), an enforcement EA that intercepts transactions so neither manual trades nor other EAs can bypass limits, and a dashboard for visibility.
Key implementation details include external session/news files for no-recompile updates, smart caching via file timestamps to avoid per-tick I/O, robust parsing with validation, fast minute-based session checks with early exits, and correct handling of day rollover plus βnext allowed timeβ ca...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
β€40π1
Manual liquidity zone work on higher timeframes misses the internal structure of the base candle. Verifying whether a zone was built by a triangle, rectangle, or double top/bottom requires repeated lower-timeframe zooming, which is slow and produces inconsistent labeling.
An automation module is proposed to classify the lower-timeframe geometry inside each base candle as ascending triangle, descending triangle, symmetrical triangle, rectangle, M, W, or undefined, then annotate the zone and alerts with the result.
Implementation outline targets MQL5: isolate detection logic in an include file (CGeometryDetector), add configurable tolerances and swing-distance filtering, compute slopes for symmetry, and integrate by extracting intrabar data for each base candle interval and storing the detected shape per zone.
π Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
An automation module is proposed to classify the lower-timeframe geometry inside each base candle as ascending triangle, descending triangle, symmetrical triangle, rectangle, M, W, or undefined, then annotate the zone and alerts with the result.
Implementation outline targets MQL5: isolate detection logic in an include file (CGeometryDetector), add configurable tolerances and swing-distance filtering, compute slopes for symmetry, and integrate by extracting intrabar data for each base candle interval and storing the detected shape per zone.
π Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
β€51π₯4
Deterministic Oscillatory Search (DOS) is a reproducible metaheuristic for global optimization that removes randomness entirely. Particles start from deterministically distributed points, making runs repeatable under identical inputsβuseful for research and trading system validation.
Search is driven by a βfitness slopeβ state: improving, worsening, or unknown. When fitness deteriorates, a particle reflects (reverses direction) and halves velocity, producing controlled oscillations that refine local extrema without derivatives.
If oscillations stall (worsening persists), DOS switches to a swarm step, pushing particles toward the current global best using a configurable movement factor. An MT5-style implementation centers on per-particle velocity vectors, range clamping, best-solution tracking, and adaptive velocity updates.
Tests show stronger behav...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #algorithm
Search is driven by a βfitness slopeβ state: improving, worsening, or unknown. When fitness deteriorates, a particle reflects (reverses direction) and halves velocity, producing controlled oscillations that refine local extrema without derivatives.
If oscillations stall (worsening persists), DOS switches to a swarm step, pushing particles toward the current global best using a configurable movement factor. An MT5-style implementation centers on per-particle velocity vectors, range clamping, best-solution tracking, and adaptive velocity updates.
Tests show stronger behav...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #algorithm
β€29π5
DADA (Adaptive Bottlenecks + Dual Adversarial Decoders) targets time-series anomaly detection with adaptive compression, dual reconstructions for normal vs anomalous regimes, and patching/masking to reduce noise and improve generalization.
The Adaptive Bottlenecks block is implemented as CNeuronAdaBN inheriting CNeuronMoE. It reuses top-k gating and expert routing, but populates experts with autoencoders at multiple latent sizes. A convolution stage produces a latent vector whose segments map to each autoencoder, followed by multi-window conv, tensor transpositions, and per-autoencoder decoder weights.
Instead of a monolith, the system is assembled from library components into three jointly trained models: a state encoder autoencoder with patching/masking and AdaBN, an Actor replacing the anomalous decoder for action selection, and a direction predictor as a deci...
π Read | Calendar | @mql5dev
#MQL5 #MT5 #AI
The Adaptive Bottlenecks block is implemented as CNeuronAdaBN inheriting CNeuronMoE. It reuses top-k gating and expert routing, but populates experts with autoencoders at multiple latent sizes. A convolution stage produces a latent vector whose segments map to each autoencoder, followed by multi-window conv, tensor transpositions, and per-autoencoder decoder weights.
Instead of a monolith, the system is assembled from library components into three jointly trained models: a state encoder autoencoder with patching/masking and AdaBN, an Actor replacing the anomalous decoder for action selection, and a direction predictor as a deci...
π Read | Calendar | @mql5dev
#MQL5 #MT5 #AI
β€39π2π2β‘1
An Expert Advisor can automate session control by recalculating daily start, end, and close times, clearing internal state, and initializing the price range used for breakout logic.
During the range window, minute-level highs and lows are sampled to compute the session maximum and minimum. A chart rectangle is updated in real time to reflect the current consolidation zone.
After the range window completes, the close of the latest finished candle is compared to the stored boundaries, independent of the range end timestamp. On confirmation, a market order is placed in the breakout direction, with take-profit set to the measured range size and stop-loss set to the opposite boundary.
π Read | VPS | @mql5dev
#MQL5 #MT5 #EA
During the range window, minute-level highs and lows are sampled to compute the session maximum and minimum. A chart rectangle is updated in real time to reflect the current consolidation zone.
After the range window completes, the close of the latest finished candle is compared to the stored boundaries, independent of the range end timestamp. On confirmation, a market order is placed in the breakout direction, with take-profit set to the measured range size and stop-loss set to the opposite boundary.
π Read | VPS | @mql5dev
#MQL5 #MT5 #EA
β€35π―3π1π1
L1 trend filtering can be used to estimate piecewise-linear price trends while suppressing short-term noise. The method keeps turning points and slope changes that are often lost with moving averages or heavy smoothing.
An example implementation in MQL5 demonstrates L1 Trend Filter routines for float and double vectors, validated on random-walk simulated series. This setup helps verify numerical stability, parameter sensitivity, and output consistency across data types.
In trading workflows, the filtered trend can support regime detection, adaptive risk controls, and signal preconditioning by separating structural movement from micro-variations. The same code is packaged as a shared project under MQL5\Shared Projects\L1Trend.
π Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
An example implementation in MQL5 demonstrates L1 Trend Filter routines for float and double vectors, validated on random-walk simulated series. This setup helps verify numerical stability, parameter sensitivity, and output consistency across data types.
In trading workflows, the filtered trend can support regime detection, adaptive risk controls, and signal preconditioning by separating structural movement from micro-variations. The same code is packaged as a shared project under MQL5\Shared Projects\L1Trend.
π Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
β€31π2π1
CATCH targets subtle anomalies in multivariate market series by moving analysis to the frequency domain, where volatility bursts and regime shifts separate into different bands. Its key idea is βfrequency patchingβ: split the complex FFT spectrum into overlapping patches, learn normal behavior per band, then reconstruct with iFFT and flag anomalies via reconstruction error.
A channel-aware fusion stage uses a masked Transformer to model cross-asset dependencies without letting irrelevant instruments dilute attention. The mask is learned and refined with an explicit objective to strengthen meaningful inter-channel links while suppressing noise.
The article then maps these ideas into MQL5 with OpenCL acceleration: complex-valued convolution and masked attention are implemented using float2 buffers for real/imag parts, reusing existing layer infrastr...
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
A channel-aware fusion stage uses a masked Transformer to model cross-asset dependencies without letting irrelevant instruments dilute attention. The mask is learned and refined with an explicit objective to strengthen meaningful inter-channel links while suppressing noise.
The article then maps these ideas into MQL5 with OpenCL acceleration: complex-valued convolution and masked attention are implemented using float2 buffers for real/imag parts, reusing existing layer infrastr...
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€37π5π2π2
Strategy Tester gaps around CalendarValueHistory() make news-driven EAs hard to verify. Historical events are often missing, so entry blocks, SL/TP suspension, and pre-news closes never trigger, producing misleading curves and no audit trail in logs.
A deterministic fix is a static CSV of events (time, currency, importance, name) loaded once in OnInit() when MQL_TESTER is true. Tester mode switches isUpcomingNews()/IsNewsTime() to an in-memory scan, while live trading keeps the terminal calendar API unchanged.
Implementation points: strict CSV format compatible with StringToTime(), FILE_COMMON access for tester sandbox, a symbol currency cache built at init, and an optional script that exports the terminal calendar to CSV for chosen date ranges.
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
A deterministic fix is a static CSV of events (time, currency, importance, name) loaded once in OnInit() when MQL_TESTER is true. Tester mode switches isUpcomingNews()/IsNewsTime() to an in-memory scan, while live trading keeps the terminal calendar API unchanged.
Implementation points: strict CSV format compatible with StringToTime(), FILE_COMMON access for tester sandbox, a symbol currency cache built at init, and an optional script that exports the terminal calendar to CSV for chosen date ranges.
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€32π4π2
Price action systems often fail on breakout filtering, with liquidity sweeps creating false continuation signals. A structured model is required to locate likely resting orders, validate breaks, and keep risk rules consistent.
An MQL5 automation is outlined for order block trading in consolidation zones, using higher-timeframe swing structure as the trend filter. Setups require inducement first, then break of structure, with fair value gap alignment inside the impulse leg.
Implementation details cover enums for trade mode, FVG state, trailing type, and mitigated-zone handling. Core structs track OB metadata, FVG links, and open-trade trailing state, plus chart rendering utilities for zones, labels, BoS lines, and mitigation marks. Swing and consolidation functions drive detection, followed by zone creation, mitigation tracking, and risk-based execu...
π Read | Docs | @mql5dev
#MQL5 #MT5 #AlgoTrading
An MQL5 automation is outlined for order block trading in consolidation zones, using higher-timeframe swing structure as the trend filter. Setups require inducement first, then break of structure, with fair value gap alignment inside the impulse leg.
Implementation details cover enums for trade mode, FVG state, trailing type, and mitigated-zone handling. Core structs track OB metadata, FVG links, and open-trade trailing state, plus chart rendering utilities for zones, labels, BoS lines, and mitigation marks. Swing and consolidation functions drive detection, followed by zone creation, mitigation tracking, and risk-based execu...
π Read | Docs | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€27β3π2