Williams %R (WPR) is used as an oscillator in the chart window, with additional TPSL levels calculated from recent high-low ranges.
The setup derives take-profit and stop-loss reference points from the latest swing extremes, aligning exits with nearby liquidity zones rather than fixed pip targets.
This combination can support more consistent risk placement by anchoring stops beyond local structure and setting targets based on measured range, while WPR provides timing via overbought/oversold conditions.
As with any oscillator-based approach, parameter selection and market regime filtering remain critical to reduce false signals in trending phases.
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #Indicator
The setup derives take-profit and stop-loss reference points from the latest swing extremes, aligning exits with nearby liquidity zones rather than fixed pip targets.
This combination can support more consistent risk placement by anchoring stops beyond local structure and setting targets based on measured range, while WPR provides timing via overbought/oversold conditions.
As with any oscillator-based approach, parameter selection and market regime filtering remain critical to reduce false signals in trending phases.
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #Indicator
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GUI groundwork is being extended around a new CForm object built on the existing canvas element base. The update adds theme/style infrastructure, refactors element properties, and prepares shadow rendering via a fixed pixel offset around forms.
Core changes include trimming two unused integer properties from the element property enum, adding a canvas offset constant for shadows, and extending the base graph object with visibility control through OBJPROP_TIMEFRAMES using OBJ_ALL_PERIODS and OBJ_NO_PERIODS, plus a foreground operation implemented as hide-then-show.
GCnvElement now stores background color and opacity as members, introduces HSL-based lightness adjustment from ARGB for consistent shading, and adds a protected parametric constructor for descendant types. GraphINI.mqh defines initial color themes and style parameter tables, with InpData.mqh expos...
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
Core changes include trimming two unused integer properties from the element property enum, adding a canvas offset constant for shadows, and extending the base graph object with visibility control through OBJPROP_TIMEFRAMES using OBJ_ALL_PERIODS and OBJ_NO_PERIODS, plus a foreground operation implemented as hide-then-show.
GCnvElement now stores background color and opacity as members, introduces HSL-based lightness adjustment from ARGB for consistent shading, and adds a protected parametric constructor for descendant types. GraphINI.mqh defines initial color themes and style parameter tables, with InpData.mqh expos...
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
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An indicator implementing Austin Passamonte’s pivot concept with multi-timeframe support, requiring the selected pivot timeframe to be higher than the active chart timeframe.
Two calculation modes are provided. Original mode follows the referenced pivot formula. Alternate mode adjusts the levels to behave more like traditional support and resistance bands.
Operational guidance remains standard for pivot-based workflows: treat levels as potential reaction zones, validate with price action and context, and avoid assuming deterministic reversals.
The implementation is optimized for runtime efficiency by recalculating only the latest bar on incoming ticks. This makes it suitable for reuse inside other scripts and EAs through iCustom(), without adding significant per-tick overhead.
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
Two calculation modes are provided. Original mode follows the referenced pivot formula. Alternate mode adjusts the levels to behave more like traditional support and resistance bands.
Operational guidance remains standard for pivot-based workflows: treat levels as potential reaction zones, validate with price action and context, and avoid assuming deterministic reversals.
The implementation is optimized for runtime efficiency by recalculating only the latest bar on incoming ticks. This makes it suitable for reuse inside other scripts and EAs through iCustom(), without adding significant per-tick overhead.
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
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Work continues on a base GUI canvas element for an MQL5 library. The focus is adding thin wrappers over CCanvas primitives and text, plus property persistence hooks for later use by an object collection.
Canvas clearing is corrected for alpha: Erase(0) drops transparency and causes artifacts. A NULL_COLOR constant uses 0x00FFFFFF to preserve the alpha channel during clears.
Text anchoring is normalized via an ENUM_TEXT_ANCHOR that encodes TA_LEFT/TA_CENTER/TA_RIGHT with TA_TOP/TA_VCENTER/TA_BOTTOM combinations, removing ambiguity in flag ordering.
Color utilities are centralized by making CColors static and extending it with RGBtoLab(). State persistence is prepared using a struct + StructToCharArray/CharArrayToStruct, with virtual ObjectToStruct/StructToObject and file read/write methods.
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
Canvas clearing is corrected for alpha: Erase(0) drops transparency and causes artifacts. A NULL_COLOR constant uses 0x00FFFFFF to preserve the alpha channel during clears.
Text anchoring is normalized via an ENUM_TEXT_ANCHOR that encodes TA_LEFT/TA_CENTER/TA_RIGHT with TA_TOP/TA_VCENTER/TA_BOTTOM combinations, removing ambiguity in flag ordering.
Color utilities are centralized by making CColors static and extending it with RGBtoLab(). State persistence is prepared using a struct + StructToCharArray/CharArrayToStruct, with virtual ObjectToStruct/StructToObject and file read/write methods.
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
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A multi-metric indicator can be used to assess whether a timeframe is tradeable by quantifying structure versus randomness.
Signal-to-Noise Ratio is derived from a rolling linear regression, comparing explained variance to residual variance to separate trend from noise.
A memory check is added via lag-1 return autocorrelation. Values above 0.1 are treated as persistence, while readings near zero are flagged as noise.
Long-range dependence is estimated using the Hurst exponent: ~0.5 suggests random walk, >0.55 trending bias, <0.45 mean reversion.
Volatility clustering is presented as a 0–1 oscillator to indicate variance stability. Shannon entropy is computed from binned returns and normalized to 0–1, where higher values imply increased randomness.
Suggested presets include balanced defaults, trend-following for breakouts, fast intraday settings, a...
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
Signal-to-Noise Ratio is derived from a rolling linear regression, comparing explained variance to residual variance to separate trend from noise.
A memory check is added via lag-1 return autocorrelation. Values above 0.1 are treated as persistence, while readings near zero are flagged as noise.
Long-range dependence is estimated using the Hurst exponent: ~0.5 suggests random walk, >0.55 trending bias, <0.45 mean reversion.
Volatility clustering is presented as a 0–1 oscillator to indicate variance stability. Shannon entropy is computed from binned returns and normalized to 0–1, where higher values imply increased randomness.
Suggested presets include balanced defaults, trend-following for breakouts, fast intraday settings, a...
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
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Machine learning trading research benefits from consistent structure: reproducibility, traceability, cache efficiency, validation gates, and automatic documentation. Without this, results become non-auditable as data, features, and parameters drift across experiments.
A production-grade pipeline standardizes the path from tick ingestion to deployable ONNX artifacts for MetaTrader 5. Key elements include time-aware caching to prevent leakage, dependency-based cache invalidation, structured experiment storage keyed by configuration, and logs plus reports generated at each stage.
A practical data architecture keeps ticks in session RAM while persisting derived bars and features in Parquet. This reduces duplication, improves iteration speed, and stabilizes schemas compared with CSV workflows. ONNX export should be blocked unless runtime predictions ma...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AITrading
A production-grade pipeline standardizes the path from tick ingestion to deployable ONNX artifacts for MetaTrader 5. Key elements include time-aware caching to prevent leakage, dependency-based cache invalidation, structured experiment storage keyed by configuration, and logs plus reports generated at each stage.
A practical data architecture keeps ticks in session RAM while persisting derived bars and features in Parquet. This reduces duplication, improves iteration speed, and stabilizes schemas compared with CSV workflows. ONNX export should be blocked unless runtime predictions ma...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AITrading
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Part 20 extends the MQL5 chart UI work from a tools palette to a canvas regression and correlation view for two symbols. The module targets pair analysis with scatter points, a fitted line, and metrics such as slope, intercept, Pearson r, and R-squared.
Implementation uses ALGLIB for linear regression and a Canvas bitmap label for rendering. The UI supports dragging and resizing via state tracking and a ResizeDirection enum, plus theme-based color adjustments using LightenColor and DarkenColor.
Data is loaded with SymbolSelect and CopyClose, normalized to the minimum bar count, then plotted with dynamic tick generation, anti-aliased points and lines, and overlay panels for statistics and a legend. Mouse events drive hover cues and resize indicators.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #EA
Implementation uses ALGLIB for linear regression and a Canvas bitmap label for rendering. The UI supports dragging and resizing via state tracking and a ResizeDirection enum, plus theme-based color adjustments using LightenColor and DarkenColor.
Data is loaded with SymbolSelect and CopyClose, normalized to the minimum bar count, then plotted with dynamic tick generation, anti-aliased points and lines, and overlay panels for statistics and a legend. Mouse events drive hover cues and resize indicators.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #EA
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Part 8 upgrades a hybrid TPO Market Profile indicator in MQL5 by integrating volume per price level. This enables volume-based POC, volume-derived value area, and optional session VWAP, making profiles reflect participation rather than just time-at-price.
The implementation expands inputs and data structures to store per-level volume, initial balance high/low, VWAP, and a separate volume profile mapped from a dynamically chosen lower timeframe. It distributes bar volume across quantized price steps, then finds the max-volume node and aligns it to the nearest TPO level.
Rendering is extended with configurable TPO glyphs (letters, squares, circles), period-open marking, split-profile padding, initial balance highlighting, border and background drawing, key-level extension lines (high/low, VA, midpoint, volume POC), and volume labels for fixed ranges.
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
The implementation expands inputs and data structures to store per-level volume, initial balance high/low, VWAP, and a separate volume profile mapped from a dynamically chosen lower timeframe. It distributes bar volume across quantized price steps, then finds the max-volume node and aligns it to the nearest TPO level.
Rendering is extended with configurable TPO glyphs (letters, squares, circles), period-open marking, split-profile padding, initial balance highlighting, border and background drawing, key-level extension lines (high/low, VA, midpoint, volume POC), and volume labels for fixed ranges.
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
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Risk-based lot calculation module
A utility function for automated position sizing by risk percentage. The goal is to keep the maximum loss at stop loss equal to a fixed share of the account balance, supporting consistent money management.
Inputs are the risk percentage (for example, 2%) and the stop loss price. The function computes the allowed monetary loss, converts it into trade volume, and outputs the lot size based on the instrument’s tick value, contract size, and volume step.
Logic accounts for common pricing differences such as EURUSD versus USDJPY. Validation is included for price correctness and broker limits, including min/max lot and rounding rules.
Designed for direct use in EAs and scripts: a single call returns the final volume suitable for sending with a market order.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #EA
A utility function for automated position sizing by risk percentage. The goal is to keep the maximum loss at stop loss equal to a fixed share of the account balance, supporting consistent money management.
Inputs are the risk percentage (for example, 2%) and the stop loss price. The function computes the allowed monetary loss, converts it into trade volume, and outputs the lot size based on the instrument’s tick value, contract size, and volume step.
Logic accounts for common pricing differences such as EURUSD versus USDJPY. Validation is included for price correctness and broker limits, including min/max lot and rounding rules.
Designed for direct use in EAs and scripts: a single call returns the final volume suitable for sending with a market order.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #EA
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Overbought/oversold indicators are commonly used to estimate where price may reverse.
In the classic Williams %R (WPR), readings around -20 are typically treated as overbought. When price pushes into this zone, traders often watch for conditions that could support a downward reversal and a potential sell setup.
Readings around -70 are typically treated as oversold. When price reaches this area, some traders look for signs of a reversal upward and a potential buy setup.
These levels are context-dependent and work best with risk controls and confirmation, since no indicator can reliably predict outcomes.
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
In the classic Williams %R (WPR), readings around -20 are typically treated as overbought. When price pushes into this zone, traders often watch for conditions that could support a downward reversal and a potential sell setup.
Readings around -70 are typically treated as oversold. When price reaches this area, some traders look for signs of a reversal upward and a potential buy setup.
These levels are context-dependent and work best with risk controls and confirmation, since no indicator can reliably predict outcomes.
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
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Session Range Boxes draws filled high/low range boxes for the Asian, London, and New York Forex sessions directly on the chart. For each day in the lookback, it scans M1 bars inside each session window and marks the exact high and low, with an optional dotted midpoint and per-box range label. All sessions can be shown at once to compare range size and overlap.
A stats panel reports average range in pips per session across the configured DaysBack. Optional breakout alerts trigger when price exits today’s session box, once per session per day, resetting at midnight server time.
Key settings include session start/end times (server time), DaysBack, box transparency, colors, and alert delivery. M1 history must be available; missing data can require loading more history and re-attaching.
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
A stats panel reports average range in pips per session across the configured DaysBack. Optional breakout alerts trigger when price exits today’s session box, once per session per day, resetting at midnight server time.
Key settings include session start/end times (server time), DaysBack, box transparency, colors, and alert delivery. M1 history must be available; missing data can require loading more history and re-attaching.
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
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Swing Extremes and Pullback logic was converted into an automated, rule-driven EA built on multi-timeframe structure (HTF bias + LTF execution). Swing highs/lows are detected via SwingBars scanning, stored as structured objects, and updated only on new bars for efficiency.
Trade triggers are defined as structural displacement: sell when bid is above both last LTF swing high and low; buy when price drops below the last LTF swing low. Take-profit inherits the opposite swing level, with risk-based fallbacks and broker-compliant sizing. One-trade-per-swing is enforced via timestamp/price guards.
Runtime flow uses OnTick bar-gating, structure-break resets, optional HTF filtering, and chart visualization that renders swings, bias labels, and key price levels. Backtest reference: H1, 01 Dec 2025–01 Jan 2026, defaults mentioned.
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
Trade triggers are defined as structural displacement: sell when bid is above both last LTF swing high and low; buy when price drops below the last LTF swing low. Take-profit inherits the opposite swing level, with risk-based fallbacks and broker-compliant sizing. One-trade-per-swing is enforced via timestamp/price guards.
Runtime flow uses OnTick bar-gating, structure-break resets, optional HTF filtering, and chart visualization that renders swings, bias labels, and key price levels. Backtest reference: H1, 01 Dec 2025–01 Jan 2026, defaults mentioned.
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
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This article upgrades a liquidity-zone strategy that can signal both directions by adding a simple directional anchor: an EMA(50) trend filter. The goal is to treat liquidity zones as trend-continuation pullbacks, not reversal points, reducing low-probability countertrend trades.
Core rule set: place buy limits only when a demand zone forms above the EMA; place sell limits only when a supply zone forms below it. Profit targets are focused on recent swing highs/lows aligned with market structure.
On the MQL5 side, the filter is implemented as a dedicated, stateful module (TrendFilter.mqh) that owns the iMA handle, reads closed-bar values (shift 1), exposes “buy allowed/sell allowed” permissions, initializes in OnInit, and releases resources in OnDeinit. This keeps OnTick clean, reusable, and safer during testing.
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #Strategy
Core rule set: place buy limits only when a demand zone forms above the EMA; place sell limits only when a supply zone forms below it. Profit targets are focused on recent swing highs/lows aligned with market structure.
On the MQL5 side, the filter is implemented as a dedicated, stateful module (TrendFilter.mqh) that owns the iMA handle, reads closed-bar values (shift 1), exposes “buy allowed/sell allowed” permissions, initializes in OnInit, and releases resources in OnDeinit. This keeps OnTick clean, reusable, and safer during testing.
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #Strategy
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Smash Day reversals tend to generate noisy signals when traded without timing, context, and validity rules. A rule-based EA converts the pattern into measurable conditions that can be automated and backtested across symbols and timeframes.
Signal definition: Buy when a bar closes below the lows of N prior bars, Sell when a bar closes above the highs of N prior bars. Outside bars are excluded. The Smash bar is only a setup, not an entry.
Execution: entries trigger on a break of the Smash level or on a confirmed close beyond it. Each setup expires after a fixed number of bars. Optional filters include Supertrend trend alignment and day-of-week gating.
Risk: SL at the Smash bar extreme, TP via fixed R:R, with manual lots or percent-risk sizing. The EA enforces one position at a time for test clarity.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #EA
Signal definition: Buy when a bar closes below the lows of N prior bars, Sell when a bar closes above the highs of N prior bars. Outside bars are excluded. The Smash bar is only a setup, not an entry.
Execution: entries trigger on a break of the Smash level or on a confirmed close beyond it. Each setup expires after a fixed number of bars. Optional filters include Supertrend trend alignment and day-of-week gating.
Risk: SL at the Smash bar extreme, TP via fixed R:R, with manual lots or percent-risk sizing. The EA enforces one position at a time for test clarity.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #EA
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ICE Pro memakai arsitektur tiga lapis untuk validasi sinyal. Layer Detection memadukan rasio volume dan body candle terhadap ATR untuk menangkap impuls awal. Layer Confirmation menambahkan adaptive trend filter berbasis slope MA serta volatility guard agar sistem tidak aktif saat pasar low activity atau spread melebar. Layer Execution menjalankan tiga skenario entry: breakout, pullback, dan velocity untuk mengurangi slippage tanpa kehilangan momentum.
Hasil refactor menambahkan ATR auto-point calibration agar logika tetap konsisten di XAUUSD, forex, dan indeks tanpa penyesuaian desimal manual. Modul momentum velocity memungkinkan entry lebih cepat saat percepatan harga terdeteksi.
Karakter trading cenderung aggressive impulse follower dengan timeframe M15–H1, lot adaptif berbasis ATR, serta filter sesi yang menghindari Asia dan jam rollover. Parameter utama...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
Hasil refactor menambahkan ATR auto-point calibration agar logika tetap konsisten di XAUUSD, forex, dan indeks tanpa penyesuaian desimal manual. Modul momentum velocity memungkinkan entry lebih cepat saat percepatan harga terdeteksi.
Karakter trading cenderung aggressive impulse follower dengan timeframe M15–H1, lot adaptif berbasis ATR, serta filter sesi yang menghindari Asia dan jam rollover. Parameter utama...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
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Breakeven in MT5 is a practical stop-management technique: once price moves into profit by a defined threshold, Stop Loss is shifted to the entry price (optionally plus extra points) to avoid turning a winning move into a full loss.
The article outlines three activation models: fixed points, ATR-based distance that adapts to volatility, and Risk-Reward triggers that protect trades at milestones like 1:1.
On the implementation side, it builds a reusable CBreakEvenBase: a ticket registry with price_to_beat and breakeven_price, automatic vs manual modes, and OnTradeTransaction integration to track entries/exits and call CTrade::PositionModify safely.
The first derived class, CBreakEvenSimple, implements the fixed-points variant with two parameters (trigger distance and extra offset) using MqlParam-based configuration for extensibility.
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Strategy
The article outlines three activation models: fixed points, ATR-based distance that adapts to volatility, and Risk-Reward triggers that protect trades at milestones like 1:1.
On the implementation side, it builds a reusable CBreakEvenBase: a ticket registry with price_to_beat and breakeven_price, automatic vs manual modes, and OnTradeTransaction integration to track entries/exits and call CTrade::PositionModify safely.
The first derived class, CBreakEvenSimple, implements the fixed-points variant with two parameters (trigger distance and extra offset) using MqlParam-based configuration for extensibility.
👉 Read | CodeBase | @mql5dev
#MQL5 #MT5 #Strategy
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MetaTrader 5 build 5660 improves operations with proxy servers. We have enhanced HTTP and SOCKS5 protocol support to reduce connection errors. The outdated SOCKS4 protocol is no longer supported.
We have also revised operations with the OpenBLAS linear algebra library. Its implementation is now delivered as a separate file: openblas.dll.
• The size of the trading terminal and tester executables has been reduced, helping you save bandwidth during platform updates. Since OpenBLAS is updated far less frequently, you will not need to download it each time.
• If multiple platform instances are installed on a computer, they will all use a single shared library file, saving disk space.
• If OpenBLAS functionality is not used, the library is not loaded into memory, reducing resource consumption.
Discuss the update...
We have also revised operations with the OpenBLAS linear algebra library. Its implementation is now delivered as a separate file: openblas.dll.
• The size of the trading terminal and tester executables has been reduced, helping you save bandwidth during platform updates. Since OpenBLAS is updated far less frequently, you will not need to download it each time.
• If multiple platform instances are installed on a computer, they will all use a single shared library file, saving disk space.
• If OpenBLAS functionality is not used, the library is not loaded into memory, reducing resource consumption.
Discuss the update...
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Close_Orders is a universal trade-management routine for MetaTrader 5 that consolidates 14 common tasks into a single call. It supports closing open positions and deleting pending orders with filtering by direction, profitability, and magic number.
Position handling includes: all positions, only profitable, only losing, BUY only, SELL only, BUY profitable, BUY losing, SELL profitable, and SELL losing. Pending order handling includes: all pending, Buy Stop only, Buy Limit only, Sell Stop only, and Sell Limit only.
Key parameters are p_o ("Position" or "Order"), type_po (filter mode), and magicNumber (0 applies to all). Implementation notes include deviation=5 for price tolerance, reverse iteration for safe removal, and local variables for isolated queries. Suitable for Expert Advisors, scripts, and indicators.
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #EA
Position handling includes: all positions, only profitable, only losing, BUY only, SELL only, BUY profitable, BUY losing, SELL profitable, and SELL losing. Pending order handling includes: all pending, Buy Stop only, Buy Limit only, Sell Stop only, and Sell Limit only.
Key parameters are p_o ("Position" or "Order"), type_po (filter mode), and magicNumber (0 applies to all). Implementation notes include deviation=5 for price tolerance, reverse iteration for safe removal, and local variables for isolated queries. Suitable for Expert Advisors, scripts, and indicators.
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #EA
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A cyclical life-development model is being circulated as a numeric matrix (3, 7, 39, 49), positioned as a synthesized framework combining psychological and sociological views, with esoteric references. It is presented as conceptual rather than scientific.
The 3-year unit is described as a repeating learning loop: initial exposure, friction and correction, then integration and stable competence. The 7-year unit is framed as an identity shift that restructures priorities and values, not just skills.
Composite periods include 49 years as seven consecutive 7-year phases (from early dependency through social integration, crisis, responsibility, and meaning), and 39 years as 13 completed 3-year cycles, mapped to peak material/social consolidation with risk of stagnation without new goals.
The model treats crises as boundary conditions between cycles and ...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #Strategy
The 3-year unit is described as a repeating learning loop: initial exposure, friction and correction, then integration and stable competence. The 7-year unit is framed as an identity shift that restructures priorities and values, not just skills.
Composite periods include 49 years as seven consecutive 7-year phases (from early dependency through social integration, crisis, responsibility, and meaning), and 39 years as 13 completed 3-year cycles, mapped to peak material/social consolidation with risk of stagnation without new goals.
The model treats crises as boundary conditions between cycles and ...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #Strategy
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Swing Extremes and Pullback is an MT5 indicator that treats reversals as measurable overextension, not countertrend guessing. It tracks the latest lower-timeframe swing high/low to form a structural range, then flags price displacements beyond those bounds as mean-reversion candidates.
Signals are filtered by higher-timeframe structure (bullish/bearish/neutral) and gated by an ATR-based distance, so only volatility-adjusted extremes qualify. Code design uses SwingPoint structs, structure-state enums, and “fire once per swing” tracking to avoid duplicate alerts, with efficient recalculation only on new LTF bars.
Chart output combines arrow buffers, swing markers, key levels, and bias/condition labels, supporting practical entries, invalidation-based stops beyond swing extremes, and targets back into prior structure.
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
Signals are filtered by higher-timeframe structure (bullish/bearish/neutral) and gated by an ATR-based distance, so only volatility-adjusted extremes qualify. Code design uses SwingPoint structs, structure-state enums, and “fire once per swing” tracking to avoid duplicate alerts, with efficient recalculation only on new LTF bars.
Chart output combines arrow buffers, swing markers, key levels, and bias/condition labels, supporting practical entries, invalidation-based stops beyond swing extremes, and targets back into prior structure.
👉 Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
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CSV trade logs are often reviewed as static tables, which makes it hard to assess balance progression, drawdowns, and per-trade impact inside MetaTrader 5. Strategy Tester curves do not represent real account history, especially with manual trades.
A practical approach is an MQL5 indicator that reads a CSV from MQL5\Files, extracts the Profit($) column, and builds a cumulative balance curve in a separate indicator window. The subwindow auto-scales to min/max values, draws axes, and optionally prints per-trade P/L labels.
Implementation focuses on separating concerns: load and parse data in a dedicated function called from OnInit and OnTimer, while OnCalculate only redraws when the total trade count changes. This reduces file I/O per tick and avoids unnecessary object updates.
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Indicator
A practical approach is an MQL5 indicator that reads a CSV from MQL5\Files, extracts the Profit($) column, and builds a cumulative balance curve in a separate indicator window. The subwindow auto-scales to min/max values, draws axes, and optionally prints per-trade P/L labels.
Implementation focuses on separating concerns: load and parse data in a dedicated function called from OnInit and OnTimer, while OnCalculate only redraws when the total trade count changes. This reduces file I/O per tick and avoids unnecessary object updates.
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Indicator
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