Many Forex EAs still execute trades using legacy indicators despite low signal-to-noise, long dependencies, and regime shifts that break stationarity assumptions.
An N-BEATS pipeline was implemented in MQL5 from scratch: matrix tensors with gradient storage, SiLU with stable exponent bounds, Adam, quantile loss for uncertainty, robust median/MAD normalization, anomaly handling, and hysteresis to prevent signal churn. Production concerns included caching on OnTick(), memory control, profiling, continuous training, and concept-drift monitoring with automated response.
Backtests on Jan–Aug 2025 did not produce stable profitability, unlike prior Mamba and PatchTST variants. Key gaps were market noise, missing microstructure effects, and unfavorable compute-to-edge ratio, while the engineering stack remains reusable for further research.
👉 Read | NeuroBook | @mql5dev
An N-BEATS pipeline was implemented in MQL5 from scratch: matrix tensors with gradient storage, SiLU with stable exponent bounds, Adam, quantile loss for uncertainty, robust median/MAD normalization, anomaly handling, and hysteresis to prevent signal churn. Production concerns included caching on OnTick(), memory control, profiling, continuous training, and concept-drift monitoring with automated response.
Backtests on Jan–Aug 2025 did not produce stable profitability, unlike prior Mamba and PatchTST variants. Key gaps were market noise, missing microstructure effects, and unfavorable compute-to-edge ratio, while the engineering stack remains reusable for further research.
👉 Read | NeuroBook | @mql5dev
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KCI Volatility Distance is an adaptive quantitative engine designed to measure momentum and trend direction using a matrix-based calculation. It focuses on filtering market noise and outputting a standalone directional strength value suitable for systematic decision-making. The codebase uses an OOP core intended to stay lightweight on VPS deployments and stable across multi-asset workloads.
Core components include a Matrix Momentum Engine to evaluate internal price dynamics for early directional strength, a Dynamic Noise Filter that recalibrates to reduce consolidation spikes and false triggers, and a Directional Strength Output that can drive entries, continuation validation, or position sizing.
Integration into automated systems is supported via two paths: embedding the OOP class directly in an EA for lower overhead and easier data collection, o...
👉 Read | Docs | @mql5dev
Core components include a Matrix Momentum Engine to evaluate internal price dynamics for early directional strength, a Dynamic Noise Filter that recalibrates to reduce consolidation spikes and false triggers, and a Directional Strength Output that can drive entries, continuation validation, or position sizing.
Integration into automated systems is supported via two paths: embedding the OOP class directly in an EA for lower overhead and easier data collection, o...
👉 Read | Docs | @mql5dev
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KCI-Directional Matrix (KCI-DX) is a quantitative directional indicator that models market “kinematics” via price path length variance and energy-weighted movement scoring. It applies dynamic Z-Score normalization plus Sigmoid scaling, keeping outputs bounded in a stable 0–100 range and avoiding distortion from historical extremes.
The matrix exposes three signals: KCI Main (trend/compression strength), +KDI (bullish directional dominance), and -KDI (bearish directional dominance). Typical zones: below 20 indicates compression/no-trade conditions, 20–80 signals trend expansion where direction follows +KDI vs -KDI, and above 80 flags exhaustion risk and potential correction.
For automation, entry logic is based on +KDI/-KDI crossovers gated by KCI Main > 20, with exit or tighter trailing behavior when KCI Main approaches 80. Parameters can be adjusted...
👉 Read | CodeBase | @mql5dev
The matrix exposes three signals: KCI Main (trend/compression strength), +KDI (bullish directional dominance), and -KDI (bearish directional dominance). Typical zones: below 20 indicates compression/no-trade conditions, 20–80 signals trend expansion where direction follows +KDI vs -KDI, and above 80 flags exhaustion risk and potential correction.
For automation, entry logic is based on +KDI/-KDI crossovers gated by KCI Main > 20, with exit or tighter trailing behavior when KCI Main approaches 80. Parameters can be adjusted...
👉 Read | CodeBase | @mql5dev
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Fixed-window correlation matrices in multi-symbol EAs lag regime shifts. With a 60-bar window, pre-shift data contaminates estimates for the full 60 bars, degrading hedges and risk budgets when covariance changes fastest.
Exponentially weighted covariance updates every bar, overweights recent returns, and uses constant memory. Each cell updates recursively with decay factor lambda and current return products, avoiding history buffers and re-scans.
MQL5 runtime sizing limits require storing an N×N matrix as a flat 1D array. Production code needs warm-up gating, near-zero variance guards for correlation normalization, and clamping to [-1, 1]. A live heatmap renderer must pack colors as 0x00BBGGRR and skip correlation calls during cold start to avoid log storms.
👉 Read | NeuroBook | @mql5dev
Exponentially weighted covariance updates every bar, overweights recent returns, and uses constant memory. Each cell updates recursively with decay factor lambda and current return products, avoiding history buffers and re-scans.
MQL5 runtime sizing limits require storing an N×N matrix as a flat 1D array. Production code needs warm-up gating, near-zero variance guards for correlation normalization, and clamping to [-1, 1]. A live heatmap renderer must pack colors as 0x00BBGGRR and skip correlation calls during cold start to avoid log storms.
👉 Read | NeuroBook | @mql5dev
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Multi-strategy MT5 accounts can hide strategy-level risk behind aggregate account metrics. Terminal reports remain account-wide and do not provide attribution, date filtering, or cross-strategy correlation.
A standalone EA, Portfolio Analyzer, reads deal history, reconstructs closed positions, attributes them by magic number or normalized comment, and renders a dashboard without modifying any trading EA.
Architecture centers on a self-contained CPortfolioAnalyzer class with explicit setters instead of direct access to global inputs. Closed positions are paired from DEAL_POSITION_ID, sorted by exit time for equity and drawdown, and filtered via tokenized comment normalization.
UI uses CCanvas with manual ARGB blending and DPI scaling. Analytics include portfolio stats, an equity curve, and a Pearson correlation matrix based on daily strategy returns.
👉 Read | CodeBase | @mql5dev
A standalone EA, Portfolio Analyzer, reads deal history, reconstructs closed positions, attributes them by magic number or normalized comment, and renders a dashboard without modifying any trading EA.
Architecture centers on a self-contained CPortfolioAnalyzer class with explicit setters instead of direct access to global inputs. Closed positions are paired from DEAL_POSITION_ID, sorted by exit time for equity and drawdown, and filtered via tokenized comment normalization.
UI uses CCanvas with manual ARGB blending and DPI scaling. Analytics include portfolio stats, an equity curve, and a Pearson correlation matrix based on daily strategy returns.
👉 Read | CodeBase | @mql5dev
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Part 4 moves from UT Bot Alerts signals to an MQL5 Expert Advisor that can execute trades reliably.
Execution is gated by new-bar detection. Signals are read only from buffer index 1 (last closed candle), using CopyBuffer() with timeseries arrays to avoid acting on unstable values from the forming bar and to prevent repeated triggers inside one candle.
Position control is single-direction. Opposite positions are closed before opening a new one, filtered by a magic number. Trade direction is configurable (buy, sell, both). Optional risk controls use ATR from the last closed bar for stop-loss, and take-profit is computed from risk via a reward-to-risk ratio. Trade actions are isolated into buy/sell functions for testable, modular logic.
👉 Read | Calendar | @mql5dev
Execution is gated by new-bar detection. Signals are read only from buffer index 1 (last closed candle), using CopyBuffer() with timeseries arrays to avoid acting on unstable values from the forming bar and to prevent repeated triggers inside one candle.
Position control is single-direction. Opposite positions are closed before opening a new one, filtered by a magic number. Trade direction is configurable (buy, sell, both). Optional risk controls use ATR from the last closed bar for stop-loss, and take-profit is computed from risk via a reward-to-risk ratio. Trade actions are isolated into buy/sell functions for testable, modular logic.
👉 Read | Calendar | @mql5dev
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MetaTrader 5 file handling in MQL5 is constrained by the sandbox and by function choice. FileSave/FileLoad operate on whole-file buffers, which is safe for small data but unsuitable for random access and partial updates.
A common symptom is unexpected overwrite: two consecutive FileSave calls to the same name keep only the second payload. Another issue is unwanted NUL bytes when treating binary-style strings as plain text.
A practical pattern is read-modify-write: load the existing file into a buffer, append new lines, then save once. For repeated runs, either delete the file in a synchronized sandbox context, or use a static flag to clear on first write while appending afterward.
👉 Read | Signals | @mql5dev
A common symptom is unexpected overwrite: two consecutive FileSave calls to the same name keep only the second payload. Another issue is unwanted NUL bytes when treating binary-style strings as plain text.
A practical pattern is read-modify-write: load the existing file into a buffer, append new lines, then save once. For repeated runs, either delete the file in a synchronized sandbox context, or use a static flag to clear on first write while appending afterward.
👉 Read | Signals | @mql5dev
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MetaTrader 5 replication/simulation stack moves to component integration: Expert Advisor, Mouse Study, Chart Trade, and a dedicated position indicator.
The position indicator is no longer user-attached. The EA dynamically adds and removes one indicator instance per open position, while keeping the indicator decoupled from EA code to allow shared updates without recompiling multiple EAs.
Indicator changes include parameters supplied by the EA and additional validation: confirm the position exists and prevent duplicate instances via object-name checks.
EA changes are minimal but highlight a common defect: attaching indicators for all open positions without filtering by the chart’s effective symbol. In multi-symbol setups, this can surface only in live trading. A safe fix should leverage existing terminal symbol resolution without breaking encapsulation in C...
👉 Read | Signals | @mql5dev
The position indicator is no longer user-attached. The EA dynamically adds and removes one indicator instance per open position, while keeping the indicator decoupled from EA code to allow shared updates without recompiling multiple EAs.
Indicator changes include parameters supplied by the EA and additional validation: confirm the position exists and prevent duplicate instances via object-name checks.
EA changes are minimal but highlight a common defect: attaching indicators for all open positions without filtering by the chart’s effective symbol. In multi-symbol setups, this can surface only in live trading. A safe fix should leverage existing terminal symbol resolution without breaking encapsulation in C...
👉 Read | Signals | @mql5dev
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MetaTrader 5 charts remain symbol-centric, which limits visibility into correlated moves across related instruments. A practical workaround is a synthetic custom symbol built from multiple markets by averaging aligned OHLC bars.
An MQL5 Expert Advisor can load OHLCV for selected source symbols, match candles by timestamp, and compute per-bar averages for open, high, low, close, and optionally volume. Only bars present across all sources are included to avoid skew from missing sessions.
The EA then creates or reuses a custom symbol, copies formatting from a template symbol, disables trading mode, clears prior history, and writes the reconstructed series. Live synchronization is handled via a timer that recalculates only the most recent bars to keep updates lightweight.
👉 Read | Quotes | @mql5dev
An MQL5 Expert Advisor can load OHLCV for selected source symbols, match candles by timestamp, and compute per-bar averages for open, high, low, close, and optionally volume. Only bars present across all sources are included to avoid skew from missing sessions.
The EA then creates or reuses a custom symbol, copies formatting from a template symbol, disables trading mode, clears prior history, and writes the reconstructed series. Live synchronization is handled via a timer that recalculates only the most recent bars to keep updates lightweight.
👉 Read | Quotes | @mql5dev
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Trend and breakout EAs often assume returns are always predictable. When the series is near-random, signals become noise and performance degrades through spreads and commissions. Standard MQL5 indicators track price, momentum, volatility, or volume, but do not quantify predictability of returns.
Approximate Entropy (ApEn) is presented as a native MQL5 measure of short-term serial structure on closed-bar log-returns. It is implemented as a standalone CApEnCalculator class, plus a subwindow indicator that marks regime zones via configurable thresholds, and a test script for synthetic validation.
ApEn is positioned as a gating filter, not a trade trigger. EAs can read it via iCustom/CopyBuffer with shift=1 and disable directional entries when ApEn exceeds an upper threshold. Parameters m=2, r=0.2·SD, and window sizes around 50–200 balance stability a...
👉 Read | Calendar | @mql5dev
Approximate Entropy (ApEn) is presented as a native MQL5 measure of short-term serial structure on closed-bar log-returns. It is implemented as a standalone CApEnCalculator class, plus a subwindow indicator that marks regime zones via configurable thresholds, and a test script for synthetic validation.
ApEn is positioned as a gating filter, not a trade trigger. EAs can read it via iCustom/CopyBuffer with shift=1 and disable directional entries when ApEn exceeds an upper threshold. Parameters m=2, r=0.2·SD, and window sizes around 50–200 balance stability a...
👉 Read | Calendar | @mql5dev
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This indicator implements a volatility breakout model using two independent envelopes around a moving-average baseline. The inner envelope defines the normal range, while the outer envelope models expanded volatility for target placement.
Entry logic uses Envelope 1 as the trigger. A long setup occurs when a candle closes above the inner upper band after the prior candle closed at or below that band. A short setup occurs when a candle closes below the inner lower band after the prior candle closed at or above that band.
Exit logic uses Envelope 2 as a dynamic take-profit. Positions are closed when price touches the corresponding outer band, treating the higher deviation as a statistically consistent exhaustion zone.
Risk is controlled with a structural stop at the prior bar’s opposite inner band. A return through the full inner channel invalidates the...
👉 Read | Calendar | @mql5dev
Entry logic uses Envelope 1 as the trigger. A long setup occurs when a candle closes above the inner upper band after the prior candle closed at or below that band. A short setup occurs when a candle closes below the inner lower band after the prior candle closed at or above that band.
Exit logic uses Envelope 2 as a dynamic take-profit. Positions are closed when price touches the corresponding outer band, treating the higher deviation as a statistically consistent exhaustion zone.
Risk is controlled with a structural stop at the prior bar’s opposite inner band. A return through the full inner channel invalidates the...
👉 Read | Calendar | @mql5dev
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Kronos brings foundation-model ideas to candlesticks: a tokenizer compresses each 6-field bar into two discrete tokens, and a decoder-only transformer predicts future tokens autoregressively, then decodes them back to OHLCV(+amount).
The key engineering focus is running inference entirely inside MetaTrader 5. Weights are exported once from PyTorch into flat float32 .bin tensors with a manifest, then loaded in MQL5 and executed with native matrix/vector ops—no Python at runtime.
This part implements the front pipeline: exact z-score normalization per window (with correct ddof=0), careful timestamp features (pandas weekday remap), and Binary Spherical Quantization where tokens depend only on latent sign bits, avoiding unnecessary normalization.
Correctness is established via golden-reference, bit-for-bit verification against the original model, making later ...
👉 Read | Signals | @mql5dev
The key engineering focus is running inference entirely inside MetaTrader 5. Weights are exported once from PyTorch into flat float32 .bin tensors with a manifest, then loaded in MQL5 and executed with native matrix/vector ops—no Python at runtime.
This part implements the front pipeline: exact z-score normalization per window (with correct ddof=0), careful timestamp features (pandas weekday remap), and Binary Spherical Quantization where tokens depend only on latent sign bits, avoiding unnecessary normalization.
Correctness is established via golden-reference, bit-for-bit verification against the original model, making later ...
👉 Read | Signals | @mql5dev
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This update extends an MT5 drawing toolkit that creates chart objects from keyboard shortcuts, using the mouse position to pick the nearest Highs or Lows as anchor points. It adds configurable object presets and consistent naming via prefix arrays, making later automation like deleting compound objects practical.
The drawing layer now covers infinite horizontal/vertical lines, trend lines as rays or segments (including controlled extension into the future), fixed-length horizontal levels (length by pixels or bars, with scalable “extended” variants), vertical lines with labels, a configurable Fibonacci fan, and an Andrews’ Pitchfork set (regular, Schiff, reverse) built from shared point calculations.
A key engineering focus is reliable “future time” placement. MT5 time-based endpoints can shrink across weekends or break near chart boundaries, so th...
👉 Read | Quotes | @mql5dev
The drawing layer now covers infinite horizontal/vertical lines, trend lines as rays or segments (including controlled extension into the future), fixed-length horizontal levels (length by pixels or bars, with scalable “extended” variants), vertical lines with labels, a configurable Fibonacci fan, and an Andrews’ Pitchfork set (regular, Schiff, reverse) built from shared point calculations.
A key engineering focus is reliable “future time” placement. MT5 time-based endpoints can shrink across weekends or break near chart boundaries, so th...
👉 Read | Quotes | @mql5dev
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A practical example shows how a traditional trend indicator such as SuperTrend can be converted into a profitable trading EA when the logic is engineered beyond entry signals.
Results are driven primarily by the exit model: stop placement, trailing rules, and conditions for closing on trend weakening or volatility shifts. Risk sizing and slippage handling remain core to expectancy.
The takeaway is that indicator-based automation should treat the indicator as a state filter, while exits and risk management define the profit profile and drawdown behavior.
👉 Read | Quotes | @mql5dev
Results are driven primarily by the exit model: stop placement, trailing rules, and conditions for closing on trend weakening or volatility shifts. Risk sizing and slippage handling remain core to expectancy.
The takeaway is that indicator-based automation should treat the indicator as a state filter, while exits and risk management define the profit profile and drawdown behavior.
👉 Read | Quotes | @mql5dev
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The article explains why FileSave/FileLoad are convenient for logging but awkward for true random access, since they encourage sequential reads or full file reloads.
It walks through MQL5’s lower-level file API, showing how FileOpen flags change what actually lands on disk. In text mode, extra bytes like format markers, tabs, and CR/LF can be inserted, breaking position-based reads and causing FileReadString to stop early when it treats separators as delimiters.
By adjusting open/read flags and forcing FileFlush before reading, the code reliably repositions the file pointer and retrieves expected content.
The final step shifts toward binary-style access: treating the file as a byte array, using FileReadInteger with CHAR_VALUE to control 1-byte reads and predictable indexing—essential groundwork for fast, block-based random access in trading tools.
👉 Read | Quotes | @mql5dev
It walks through MQL5’s lower-level file API, showing how FileOpen flags change what actually lands on disk. In text mode, extra bytes like format markers, tabs, and CR/LF can be inserted, breaking position-based reads and causing FileReadString to stop early when it treats separators as delimiters.
By adjusting open/read flags and forcing FileFlush before reading, the code reliably repositions the file pointer and retrieves expected content.
The final step shifts toward binary-style access: treating the file as a byte array, using FileReadInteger with CHAR_VALUE to control 1-byte reads and predictable indexing—essential groundwork for fast, block-based random access in trading tools.
👉 Read | Quotes | @mql5dev
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Work on a market replay/simulation stack continues with four components: Expert Advisor, position indicator, Chart Trade, and Mouse Study. Current guidance is demo-first; the EA and position indicator still require stability work, while Chart Trade and Mouse Study are safe but depend on the EA for execution.
Two cleanup issues are addressed: removing the EA leaves orphaned position indicators, and switching the tracked contract can leave misleading visuals. Handling DeInit in OnDeinit and deleting indicators by their short name (derived from the position ticket) resolves both.
A separate failure appears on timeframe changes due to pointer state across OnInit reinitialization. Explicitly resetting pointers in OnInit prevents runtime unloads.
Next focus is NETTING vs HEDGING behavior. NETTING changes average price on volume increases, but indicators...
👉 Read | Forum | @mql5dev
Two cleanup issues are addressed: removing the EA leaves orphaned position indicators, and switching the tracked contract can leave misleading visuals. Handling DeInit in OnDeinit and deleting indicators by their short name (derived from the position ticket) resolves both.
A separate failure appears on timeframe changes due to pointer state across OnInit reinitialization. Explicitly resetting pointers in OnInit prevents runtime unloads.
Next focus is NETTING vs HEDGING behavior. NETTING changes average price on volume increases, but indicators...
👉 Read | Forum | @mql5dev
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CKS Position Risk Dashboard is a lightweight MT5 chart indicator focused on pre-trade risk review and position visibility. It is informational only and does not open, modify, or close orders.
The panel shows account and symbol metrics including balance, equity, free margin, margin level, bid/ask, spread, and broker volume constraints (min/max/step). It also reports open-position count and floating P/L for the current chart symbol, plus estimated protected risk when a stop loss is present. Tick size, tick value, and symbol digits are handled automatically. Panel colors, placement, width, and refresh interval are configurable.
Key inputs include risk percent, planned stop distance in points, balance vs equity selection, and a maximum cap for suggested lot size. The suggested volume remains an estimate and should be verified against final margin and sym...
👉 Read | AppStore | @mql5dev
The panel shows account and symbol metrics including balance, equity, free margin, margin level, bid/ask, spread, and broker volume constraints (min/max/step). It also reports open-position count and floating P/L for the current chart symbol, plus estimated protected risk when a stop loss is present. Tick size, tick value, and symbol digits are handled automatically. Panel colors, placement, width, and refresh interval are configurable.
Key inputs include risk percent, planned stop distance in points, balance vs equity selection, and a maximum cap for suggested lot size. The suggested volume remains an estimate and should be verified against final margin and sym...
👉 Read | AppStore | @mql5dev
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A multi-timeframe, multi-symbol SuperTrend setup can simplify monitoring when it is implemented with strict data handling and clear output.
Key requirements include per-symbol and per-timeframe state separation, deterministic bar indexing, and consistent ATR/SuperTrend parameterization across feeds. Updates should be event-driven to avoid redundant recalculation, with safeguards for missing history and session gaps.
For usability, dashboards should prioritize current direction, last flip time, and distance to the band. Alerts need debouncing and a cooldown window to prevent repeated signals during consolidation.
👉 Read | CodeBase | @mql5dev
Key requirements include per-symbol and per-timeframe state separation, deterministic bar indexing, and consistent ATR/SuperTrend parameterization across feeds. Updates should be event-driven to avoid redundant recalculation, with safeguards for missing history and session gaps.
For usability, dashboards should prioritize current direction, last flip time, and distance to the band. Alerts need debouncing and a cooldown window to prevent repeated signals during consolidation.
👉 Read | CodeBase | @mql5dev
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Building time-aware EAs starts with timezone hygiene. Session-based logic breaks when broker server time shifts for DST, and MT5 testing does not provide reliable GMT via TimeGMT(). Without a verified broker UTC offset and DST rule, session windows cannot be mapped correctly.
A practical DST detector can be built from NFP timestamps in the MQL5 Economic Calendar plus EURUSD M15 volatility spikes. When the expected spike alignment flips by one hour, a DST transition is inferred and matched against EU/US/AU transition calendars computed from weekday-occurrence rules.
The implementation uses modular MQL5 architecture: indicator layer (multi-AMA pairwise voting across timeframes), strategy layer (signal-to-direction mapping), and a dedicated time layer (DST-aware session conversion, calendar filters, intraday open/mid/close windows). TimeTradeServer() ...
👉 Read | VPS | @mql5dev
A practical DST detector can be built from NFP timestamps in the MQL5 Economic Calendar plus EURUSD M15 volatility spikes. When the expected spike alignment flips by one hour, a DST transition is inferred and matched against EU/US/AU transition calendars computed from weekday-occurrence rules.
The implementation uses modular MQL5 architecture: indicator layer (multi-AMA pairwise voting across timeframes), strategy layer (signal-to-direction mapping), and a dedicated time layer (DST-aware session conversion, calendar filters, intraday open/mid/close windows). TimeTradeServer() ...
👉 Read | VPS | @mql5dev
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The article breaks Forex arbitrage into a graph problem: currencies are vertices, tradable pairs are directed edges weighted by executable bid/ask prices. Profitable “cycles” are those where the rate product stays above 1 after subtracting relative spreads, enabling near-zero market risk when executed correctly.
It outlines an MT5 Expert Advisor built as modular components: real-time graph construction, cycle discovery using a modified Floyd–Warshall (maximize products, track spread growth, reconstruct paths) plus a DFS pass to enumerate alternative cycles while avoiding reuse of the same symbol.
A key engineering focus is zero-exposure sizing: lots are derived by propagating a base notional through the cycle, then normalized to broker constraints (contract size, min lot, step), with proportional downscaling to cap risk. Execution and fault handling are tr...
👉 Read | NeuroBook | @mql5dev
It outlines an MT5 Expert Advisor built as modular components: real-time graph construction, cycle discovery using a modified Floyd–Warshall (maximize products, track spread growth, reconstruct paths) plus a DFS pass to enumerate alternative cycles while avoiding reuse of the same symbol.
A key engineering focus is zero-exposure sizing: lots are derived by propagating a base notional through the cycle, then normalized to broker constraints (contract size, min lot, step), with proportional downscaling to cap risk. Execution and fault handling are tr...
👉 Read | NeuroBook | @mql5dev
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MetaTrader 5 ships with a single-timeframe volume histogram, but multi-timeframe volume context and anchoring require custom tooling. An MQL5 implementation can render synchronized profiles across the main chart and a subwindow using objects, not indicator plots.
The design uses a draggable vertical anchor to define the start of analysis, with the viewport’s right edge as the end. Anchor time is normalized to valid bar times, restored if deleted, and auto-centered when needed. HTF selection is validated to ensure it is above the chart timeframe.
Bin sizing is interactive and stateful. Edit mode activates only when the anchor is selected: double-click E to enter numeric input, double-click S to commit. Invalid or empty input falls back to the last valid value. OnChartEvent drives recalculation on zoom, scroll, drag, and keystrokes, while rendering POC and...
👉 Read | AlgoBook | @mql5dev
The design uses a draggable vertical anchor to define the start of analysis, with the viewport’s right edge as the end. Anchor time is normalized to valid bar times, restored if deleted, and auto-centered when needed. HTF selection is validated to ensure it is above the chart timeframe.
Bin sizing is interactive and stateful. Edit mode activates only when the anchor is selected: double-click E to enter numeric input, double-click S to commit. Invalid or empty input falls back to the last valid value. OnChartEvent drives recalculation on zoom, scroll, drag, and keystrokes, while rendering POC and...
👉 Read | AlgoBook | @mql5dev
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