MetaTrader 5 optimization can be made visual by attaching tester handlers to an EA and using frame mode. Agents run passes, OnTester() collects pass metrics and deal-by-deal balance, then FrameAdd() sends a frame to the terminal-side EA where OnTesterPass() renders and stores results.
The UI design uses a separate chart with a five-tab control: live pass chart plus post-run tabs for top-3 by Sharpe, Net Profit, Profit Factor, and Recovery Factor. Each tab pairs balance charts with two tables: pass results and EA inputs, plus a replay control.
Implementation is organized into MQL5 control classes (canvas, panels, labels, buttons, tab control), a refined table control, and three core components: progress bar, special chart renderer, and frame viewer, backed by sortable frame objects for ranking and selection.
π Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
The UI design uses a separate chart with a five-tab control: live pass chart plus post-run tabs for top-3 by Sharpe, Net Profit, Profit Factor, and Recovery Factor. Each tab pairs balance charts with two tables: pass results and EA inputs, plus a replay control.
Implementation is organized into MQL5 control classes (canvas, panels, labels, buttons, tab control), a refined table control, and three core components: progress bar, special chart renderer, and frame viewer, backed by sortable frame objects for ranking and selection.
π Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€26β‘6π2π1
Larry Williamsβ approach reduces chart interpretation and focuses on short-term price events that can be defined and tested. Patterns are expressed as rules built from OHLC relationships, designed for automation and statistical validation.
A single MQL5 Expert Advisor is structured to run one strategy at a time, hold one position at a time, and apply uniform risk and exit handling. Signals are confirmed via a volatility-based breakout level, with the baseline mode entering directly at the open.
The strategy set includes: buy on open, buy after a down close, buy after three down closes, buy after a measured pullback, buy after a bearish outside bar, and a short setup fading three bullish closes. A day-of-week filter is applied to measure time-based effects.
Stops are configurable (range-based or prior-bar extreme). Exits support profit-on-next-open, fixed h...
π Read | Docs | @mql5dev
#MQL5 #MT5 #EA
A single MQL5 Expert Advisor is structured to run one strategy at a time, hold one position at a time, and apply uniform risk and exit handling. Signals are confirmed via a volatility-based breakout level, with the baseline mode entering directly at the open.
The strategy set includes: buy on open, buy after a down close, buy after three down closes, buy after a measured pullback, buy after a bearish outside bar, and a short setup fading three bullish closes. A day-of-week filter is applied to measure time-based effects.
Stops are configurable (range-based or prior-bar extreme). Exits support profit-on-next-open, fixed h...
π Read | Docs | @mql5dev
#MQL5 #MT5 #EA
β€31π4β‘1
Part 15 extends an MQL5 canvas dashboard with header blur/shadow rendering and corrected mouse wheel scrolling behavior for text panels.
Blur is used for fog-like gradients via per-pixel opacity blending. Shadows are rendered with multi-pass rounded rectangles, offset and alpha-faded by distance and blur radius. Bicubic interpolation and antialiased primitives are applied for higher quality scaling and lines.
Implementation adds grouped inputs for shadow color, opacity percent, distance, and blur radius. Header drawing is reworked to allocate extra padding, prevent clipping, and keep borders/icons aligned. Hit testing expands to include the shadow area while excluding button regions.
Minimize/maximize and OnInit resize the header canvas using the same extra padding. OnChartEvent removes wheel logic that altered chart scale, so wheel scrolling affect...
π Read | Forum | @mql5dev
#MQL5 #MT5 #Indicator
Blur is used for fog-like gradients via per-pixel opacity blending. Shadows are rendered with multi-pass rounded rectangles, offset and alpha-faded by distance and blur radius. Bicubic interpolation and antialiased primitives are applied for higher quality scaling and lines.
Implementation adds grouped inputs for shadow color, opacity percent, distance, and blur radius. Header drawing is reworked to allocate extra padding, prevent clipping, and keep borders/icons aligned. Hit testing expands to include the shadow area while excluding button regions.
Minimize/maximize and OnInit resize the header canvas using the same extra padding. OnChartEvent removes wheel logic that altered chart scale, so wheel scrolling affect...
π Read | Forum | @mql5dev
#MQL5 #MT5 #Indicator
β€26β6π2π2
A quantified liquidity model is turned into an MT5 tool: consolidation zones (AβB) are measured, then validated only when a breakout impulse (to C) reaches a configurable multiple of the zone depth, with 1:3 as the tested baseline.
The indicator encodes this as deterministic rules in MQL5. It separates logic from presentation via inputs (LookbackBars, RatioMultiplier, colors, projection length), manages its own OHLC/time arrays for consistent indexing, and combines chart rectangles with eight buffers so zones are both visible and EA-consumable.
Lifecycle handling is treated as a first-class concern: strict object naming, cleanup on deinit, buffer clearing by time validity, and forward projection for retest tracking.
Live testing on XAUUSD showed zones mapped cleanly to real consolidations and many breakouts meeting or exceeding the 1:3 threshold, su...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #Indicator
The indicator encodes this as deterministic rules in MQL5. It separates logic from presentation via inputs (LookbackBars, RatioMultiplier, colors, projection length), manages its own OHLC/time arrays for consistent indexing, and combines chart rectangles with eight buffers so zones are both visible and EA-consumable.
Lifecycle handling is treated as a first-class concern: strict object naming, cleanup on deinit, buffer clearing by time validity, and forward projection for retest tracking.
Live testing on XAUUSD showed zones mapped cleanly to real consolidations and many breakouts meeting or exceeding the 1:3 threshold, su...
π Read | Quotes | @mql5dev
#MQL5 #MT5 #Indicator
β€40π7π4β‘3π2
An MT5 indicator example demonstrates sending trade signals through terminal global variables, leaving execution logic to external code. It targets quick testing workflows rather than a complete trading system.
Recommended usage is a 5βsecond chart on any liquid instrument. The signal is derived by counting candle colors over the lookback window: the majority color selects the direction, mapped to CALL or PUT. A configurable reverse mode flips the rule so the minority color drives the output.
Signal publication can be verified in the terminal by opening the Global Variables window (F3) and checking the values being written and updated in real time.
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
Recommended usage is a 5βsecond chart on any liquid instrument. The signal is derived by counting candle colors over the lookback window: the majority color selects the direction, mapped to CALL or PUT. A configurable reverse mode flips the rule so the minority color drives the output.
Signal publication can be verified in the terminal by opening the Global Variables window (F3) and checking the values being written and updated in real time.
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
β€28π2π2
A standalone MQL5 include library is available for adding balance/equity mini-charts and extra optimization criteria to existing Expert Advisors. After placing Advanced Optimization Report Saver.mqh into \MQL5\Include\, the file can be included and its exported functions called from standard EA handlers to save frames and custom metrics.
The library adds inputs such as toggling statistics collection and setting the chart width in pixels. Using the Moving Average example EA, the required changes are limited to small additions near the end of the code after existing trade checks.
For large parameter grids, readability and runtime can be improved by replacing linear steps with an enum scale (e.g., 1,2,3,5,7,10,15,20,30,50,70,100). This reduces optimization passes, speeds up processing, and shrinks saved output while making average lines clearer. Update 2026-02...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #EA
The library adds inputs such as toggling statistics collection and setting the chart width in pixels. Using the Moving Average example EA, the required changes are limited to small additions near the end of the code after existing trade checks.
For large parameter grids, readability and runtime can be improved by replacing linear steps with an enum scale (e.g., 1,2,3,5,7,10,15,20,30,50,70,100). This reduces optimization passes, speeds up processing, and shrinks saved output while making average lines clearer. Update 2026-02...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #EA
β€26β‘3π3π2
A chart-side service extends the default terminal view of open positions by rendering clearer trade context directly on price charts. It marks entry time and price, and can also annotate related deals such as partial closes or volume adds in netting accounts.
The service polls account state on a configurable interval (default 5 seconds) and draws objects only on the active chart when its symbol matches an existing position. Supported objects include trend lines or vertical lines, optional deal arrows, and profit/loss coloring with configurable style and width.
Chart objects are tracked via global variables to manage cleanup. When KeepLinesOnCharts is disabled, objects are removed on position close. When enabled, objects remain for manual control, while related global variables are still cleared. Tooltips and on-chart text expose key position and deal prope...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
The service polls account state on a configurable interval (default 5 seconds) and draws objects only on the active chart when its symbol matches an existing position. Supported objects include trend lines or vertical lines, optional deal arrows, and profit/loss coloring with configurable style and width.
Chart objects are tracked via global variables to manage cleanup. When KeepLinesOnCharts is disabled, objects are removed on position close. When enabled, objects remain for manual control, while related global variables are still cleared. Tooltips and on-chart text expose key position and deal prope...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #Indicator
β€27π4β‘3
Forex swap carry remains underused in systematic trading, despite being a direct function of rate differentials and broker rollover terms.
Backtests on 2015β2025 data indicate 5β8% annualized return contribution from swaps in optimized portfolios, with slower swap repricing versus FX spot moves supporting longer holding periods.
A swap-aware allocation model can combine expected market return, average swap, and volatility, then apply correlation and covariance constraints to reduce portfolio variance. Sharpe optimization with a positive cumulative swap constraint filters high-return portfolios with negative carry.
Implementation details typically include MT5 data ingestion (swap_long/swap_short, spreads), cost ratios such as swap-to-spread, business-day swap application, and SLSQP optimization for nonlinear constraints.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
Backtests on 2015β2025 data indicate 5β8% annualized return contribution from swaps in optimized portfolios, with slower swap repricing versus FX spot moves supporting longer holding periods.
A swap-aware allocation model can combine expected market return, average swap, and volatility, then apply correlation and covariance constraints to reduce portfolio variance. Sharpe optimization with a positive cumulative swap constraint filters high-return portfolios with negative carry.
Implementation details typically include MT5 data ingestion (swap_long/swap_short, spreads), cost ratios such as swap-to-spread, business-day swap application, and SLSQP optimization for nonlinear constraints.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€26π₯8π3π1
Part 38 finalizes the API workflow by building an MQL5 Expert Advisor that pulls Binance candlestick data and evaluates it inside MetaTrader 5, without relying on MT5βs symbol history or indicators.
The EA requests the last three closed 30-minute candles via WebRequest, then converts the raw byte response into text and extracts OHLC and candle open time by cleaning and splitting Binanceβs JSON array structure into ordered fields and arrays.
To respect exchange rate limits and avoid incomplete bars, the EA triggers the API call only when a new 30-minute candle opens, using the current candleβs opening timestamp as a simple state check. This creates a reusable pattern for external-market data analysis and pattern detection (e.g., bullish engulfing) that can be extended to other timeframes and signals.
π Read | Signals | @mql5dev
#MQL5 #MT5 #EA
The EA requests the last three closed 30-minute candles via WebRequest, then converts the raw byte response into text and extracts OHLC and candle open time by cleaning and splitting Binanceβs JSON array structure into ordered fields and arrays.
To respect exchange rate limits and avoid incomplete bars, the EA triggers the API call only when a new 30-minute candle opens, using the current candleβs opening timestamp as a simple state check. This creates a reusable pattern for external-market data analysis and pattern detection (e.g., bullish engulfing) that can be extended to other timeframes and signals.
π Read | Signals | @mql5dev
#MQL5 #MT5 #EA
β€37π5
The article presents βcompression readiness,β a non-repainting MT5 indicator that turns discretionary price-action compression reading into closed-bar rules. It targets the key problem: separating meaningful absorption ranges from low-information sideways drift.
Detection compares a recent window to a prior one, scoring range contraction, candle overlap, boundary tests, and wick rejections. Robustness comes from optional wick-trimmed boxes, close-out invalidation when a bar closes outside the range, and a dead-drift filter to reject ultra-quiet markets.
Compression is graded (none/early/building/mature) via transparent thresholds. Directional bias is estimated without indicators, using CLV and rejection asymmetry, with optional smoothing and state-change visuals for practical chart context.
π Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
Detection compares a recent window to a prior one, scoring range contraction, candle overlap, boundary tests, and wick rejections. Robustness comes from optional wick-trimmed boxes, close-out invalidation when a bar closes outside the range, and a dead-drift filter to reject ultra-quiet markets.
Compression is graded (none/early/building/mature) via transparent thresholds. Directional bias is estimated without indicators, using CLV and rejection asymmetry, with optional smoothing and state-change visuals for practical chart context.
π Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
β€33π6π3
An MQL5 Expert Advisor can model liquidity zones using a minimal two-candle structure: a βbaseβ candle representing compression and an βimpulseβ candle representing expansion, with evaluation restricted to closed candles for non-repainting behavior.
Execution logic typically relies on candle high-low range comparisons (not indicators), a configurable impulse-to-base ratio, and directional alignment to bias continuation setups.
Automation details include CTrade-based order handling, spread checks, magic number tagging, and one-bar-one-execution timing. Entries can be split into multiple limit orders across the base candle range, with stop-loss buffered beyond the zone and targets anchored to the impulse extreme.
Testing in MT5 Strategy Tester should first validate mechanical correctness: order placement, fills, stop/target behavior, and pending expiry when ...
π Read | VPS | @mql5dev
#MQL5 #MT5 #EA
Execution logic typically relies on candle high-low range comparisons (not indicators), a configurable impulse-to-base ratio, and directional alignment to bias continuation setups.
Automation details include CTrade-based order handling, spread checks, magic number tagging, and one-bar-one-execution timing. Entries can be split into multiple limit orders across the base candle range, with stop-loss buffered beyond the zone and targets anchored to the impulse extreme.
Testing in MT5 Strategy Tester should first validate mechanical correctness: order placement, fills, stop/target behavior, and pending expiry when ...
π Read | VPS | @mql5dev
#MQL5 #MT5 #EA
β€59π8π€1
A standard deviation variant based on price momentum rather than raw price. The calculation is optimized for minimal CPU load while keeping output values broadly comparable to conventional standard deviation.
Like standard deviation, it can be applied to measure deviation for different input series, not only price. This makes it suitable for custom data streams such as indicator buffers or derived signals.
In side-by-side comparison, the upper plot represents the momentum-based deviation, while the lower plot shows the classic standard deviation. Usage guidance remains the same as for standard deviation indicators across volatility filtering, normalization, and regime detection.
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
Like standard deviation, it can be applied to measure deviation for different input series, not only price. This makes it suitable for custom data streams such as indicator buffers or derived signals.
In side-by-side comparison, the upper plot represents the momentum-based deviation, while the lower plot shows the classic standard deviation. Usage guidance remains the same as for standard deviation indicators across volatility filtering, normalization, and regime detection.
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
β€27π3
AdaptiveQ Enhanced is an MQL5 Expert Advisor built around DQN reinforcement learning, game theory, and causal analysis for FX trading under uncertainty.
Market state is modeled as 531,441 discrete states across seven major currency pairs, with inter-symbol dependency handled via Nash-equilibrium action selection. Correlations are applied only when |corr| > 0.3 to reduce noise.
The action space expands to six actions: buy, sell, add-to-buy, add-to-sell, close-profitable-buys, close-profitable-sells. Inputs include multi-timeframe signals (M15/H1/H4), MA deltas/flags, RSI, Stochastic, and momentum, mapped into a single state index.
Implementation focuses on performance: cached prices, correlations, indicator values, persistent indicator handles, and periodic refresh. Learning adds an opportunity-cost term plus cross-learning between correlated pair...
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
Market state is modeled as 531,441 discrete states across seven major currency pairs, with inter-symbol dependency handled via Nash-equilibrium action selection. Correlations are applied only when |corr| > 0.3 to reduce noise.
The action space expands to six actions: buy, sell, add-to-buy, add-to-sell, close-profitable-buys, close-profitable-sells. Inputs include multi-timeframe signals (M15/H1/H4), MA deltas/flags, RSI, Stochastic, and momentum, mapped into a single state index.
Implementation focuses on performance: cached prices, correlations, indicator values, persistent indicator handles, and periodic refresh. Learning adds an opportunity-cost term plus cross-learning between correlated pair...
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€24π3
This workshop rebuilds the Supertrend indicator in MQL5 with a focus on correctness and live-trading safety. It targets a common pain point: many retail Supertrend versions repaint, flip inconsistently, or hide the logic, making them unreliable for EAs.
Supertrend is treated as a volatility-driven state machine: ATR defines dynamic trailing bands, and a single bullish/bearish state is determined by closes relative to the band. The implementation leverages MT5βs built-in iATR via an indicator handle, then layers clear trend logic and plotting on top.
The design emphasizes practical usability: one line that flips sides, consistent candle recoloring for fast trend recognition, user inputs for ATR period/multiplier, documented buffers for automation, and proper handle lifecycle management to keep the indicator stable and extensible.
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
Supertrend is treated as a volatility-driven state machine: ATR defines dynamic trailing bands, and a single bullish/bearish state is determined by closes relative to the band. The implementation leverages MT5βs built-in iATR via an indicator handle, then layers clear trend logic and plotting on top.
The design emphasizes practical usability: one line that flips sides, consistent candle recoloring for fast trend recognition, user inputs for ATR period/multiplier, documented buffers for automation, and proper handle lifecycle management to keep the indicator stable and extensible.
π Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
β€46β4π4π€£2π2π2β‘1
MQL5 Community OAuth can be configured from the Apps section and used as an identity provider for external software, including Android clients. OAuth 2.0 is implemented with standard authorization-code flow and endpoints for login, token exchange, and user_info.
Compared with generic social auth, the returned profile can include trading-focused signals such as rank, reputation, products, and forum activity. This is useful for apps that need domain-specific user context and basic trust signals.
Mobile clients should only initiate authorization and receive the code via a redirect URI or deep link. Token exchange requires a server component because the client secret must not ship in the APK. Some free PHP hosts block outbound POST, which breaks token exchange even when redirects work.
π Read | Calendar | @mql5dev
#MQL5 #MT5 #AlgoTrading
Compared with generic social auth, the returned profile can include trading-focused signals such as rank, reputation, products, and forum activity. This is useful for apps that need domain-specific user context and basic trust signals.
Mobile clients should only initiate authorization and receive the code via a redirect URI or deep link. Token exchange requires a server component because the client secret must not ship in the APK. Some free PHP hosts block outbound POST, which breaks token exchange even when redirects work.
π Read | Calendar | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€70π4β‘3π3β1π1
An indicator is available to chart the spread between two instruments as either a difference or a ratio.
Configuration includes Instrument1 and Instrument2, which must match the brokerβs symbol strings exactly. Multiplier1 and Multiplier2 apply scaling factors to each instrument to normalize contract sizes or pricing units.
The ChartDifernce boolean selects the output mode. When enabled, the indicator plots the difference between the two instruments. When disabled, it plots the ratio.
The spread series is rendered in a separate subwindow. Once plotted, standard technical indicators can be applied to the spread line for additional analysis.
π Read | Forum | @mql5dev
#MQL4 #MT4 #Indicator
Configuration includes Instrument1 and Instrument2, which must match the brokerβs symbol strings exactly. Multiplier1 and Multiplier2 apply scaling factors to each instrument to normalize contract sizes or pricing units.
The ChartDifernce boolean selects the output mode. When enabled, the indicator plots the difference between the two instruments. When disabled, it plots the ratio.
The spread series is rendered in a separate subwindow. Once plotted, standard technical indicators can be applied to the spread line for additional analysis.
π Read | Forum | @mql5dev
#MQL4 #MT4 #Indicator
β€37β3π3β‘1π1
MetaTrader 5 indicators rely on event-driven flow: timeframe changes trigger OnDeinit, then OnInit after the chart rebuild. During this cycle, indicator memory is cleared, so runtime counters reset unless state is stored externally.
One approach is to handle OnDeinit(reason) and preserve the counter only when reason equals REASON_CHARTCHANGE; otherwise reset to zero. This keeps logic local, but file-based persistence can introduce race conditions when multiple charts or rapid period changes contend for the same state.
An alternative is terminal global variables. They persist across reinitializations while the terminal is running, store only double, and can be adapted via unions for packed data. GlobalVariableTemp enables session-scoped state without filesystem dependency, but shifts control to terminal-level storage semantics.
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #AlgoTrading
One approach is to handle OnDeinit(reason) and preserve the counter only when reason equals REASON_CHARTCHANGE; otherwise reset to zero. This keeps logic local, but file-based persistence can introduce race conditions when multiple charts or rapid period changes contend for the same state.
An alternative is terminal global variables. They persist across reinitializations while the terminal is running, store only double, and can be adapted via unions for packed data. GlobalVariableTemp enables session-scoped state without filesystem dependency, but shifts control to terminal-level storage semantics.
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€25π₯8π2π―2
Unidirectional ML trading is often a better fit for instruments with persistent trend, where buy/sell classes are not separable and bidirectional classifiers generate systematic errors.
A one-direction sampler replaces buy/sell markup with a single label: 1 means βopen trade in selected directionβ, 0 means βskipβ. Trade duration is randomized between min/max bars.
The tester is updated for one-direction logic with Numba-accelerated processing, supporting exits by model signal, stop loss, or take profit, whichever occurs first.
Meta learners are used as preprocessing, not as final models. Cross-validated or bootstrapped βheadsβ identify consistently misclassified rows and set meta_labels and labels to 0 to filter low-quality entries. This simplifies learning under trend non-stationarity and reduces false entries for the chosen direction.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
A one-direction sampler replaces buy/sell markup with a single label: 1 means βopen trade in selected directionβ, 0 means βskipβ. Trade duration is randomized between min/max bars.
The tester is updated for one-direction logic with Numba-accelerated processing, supporting exits by model signal, stop loss, or take profit, whichever occurs first.
Meta learners are used as preprocessing, not as final models. Cross-validated or bootstrapped βheadsβ identify consistently misclassified rows and set meta_labels and labels to 0 to filter low-quality entries. This simplifies learning under trend non-stationarity and reduces false entries for the chosen direction.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€32π2
Classic Ilan popularized grid averaging with Martingale-like sizing: add positions against price to improve the average entry. It can look robust in range markets, but it predictably fails in sustained trends as exposure grows exponentially and margin collapses.
Ilan 2.0 added volatility-based grid spacing, multi-symbol correlation checks, and extra filters, yet remained a static rule engine with no way to learn when to stop averaging.
Ilan 3.0 AI reframes the EA as a reinforcement learning agent. Q-learning drives decisions via a dynamic Q-table over discretized market/position states, updated with the Bellman equation and an Ξ΅-greedy explore/exploit schedule. Rewards favor profitable closes and penalize averaging, with persistence by saving/loading the Q-table.
Tests still show drawdown risk typical of Martingale, motivating a next step: neural...
π Read | AppStore | @mql5dev
#MQL5 #MT5 #AlgoTrading
Ilan 2.0 added volatility-based grid spacing, multi-symbol correlation checks, and extra filters, yet remained a static rule engine with no way to learn when to stop averaging.
Ilan 3.0 AI reframes the EA as a reinforcement learning agent. Q-learning drives decisions via a dynamic Q-table over discretized market/position states, updated with the Bellman equation and an Ξ΅-greedy explore/exploit schedule. Rewards favor profitable closes and penalize averaging, with persistence by saving/loading the Q-table.
Tests still show drawdown risk typical of Martingale, motivating a next step: neural...
π Read | AppStore | @mql5dev
#MQL5 #MT5 #AlgoTrading
β€57π3π3π3π3
Price Action Day Trader EA executes on each new bar using three setups: pin-bar rejection with configurable wick-to-body thresholds, engulfing candles for reversal momentum, and inside-bar breakouts from a defined mother bar.
Entries are gated by a dual EMA trend filter (20/50 by default) and a support/resistance lookback that validates signals against recent structural levels.
Risk is position-sized by balance percentage (default 1.5%) with a max daily loss cap (3%). Trade management includes break-even automation and trailing stops, plus a session window that can block entries and force end-of-day flat.
Execution includes automatic detection of broker filling mode (FOK, IOC, Return). Key inputs cover RiskPercent, StopLossPips, TakeProfitRatio, PinBarRatio, SRLookback, EMA periods, and trading hours. Recommended for M15/H1/H4 on majors and XAUUSD, with SL...
π Read | AppStore | @mql5dev
#MQL5 #MT5 #EA
Entries are gated by a dual EMA trend filter (20/50 by default) and a support/resistance lookback that validates signals against recent structural levels.
Risk is position-sized by balance percentage (default 1.5%) with a max daily loss cap (3%). Trade management includes break-even automation and trailing stops, plus a session window that can block entries and force end-of-day flat.
Execution includes automatic detection of broker filling mode (FOK, IOC, Return). Key inputs cover RiskPercent, StopLossPips, TakeProfitRatio, PinBarRatio, SRLookback, EMA periods, and trading hours. Recommended for M15/H1/H4 on majors and XAUUSD, with SL...
π Read | AppStore | @mql5dev
#MQL5 #MT5 #EA
β€40π5
A computer-vision pipeline is built around MetaTrader 5 data to predict EURUSD direction without hand-crafted indicators. The system pulls ~2,000 H1 bars, converts rolling 48-candle OHLC windows into normalized multi-channel βimages,β and labels each sample by the move 24 bars ahead.
A Keras Functional CNN (two Conv blocks with batch normalization, then dense layers) is trained with early stopping to reduce overfitting. Functional design also enables introspection.
Interpretability is treated as a core feature: intermediate feature maps are visualized alongside candlesticks, and an attention-style heatmap highlights the chart regions driving the decision, often aligning with reversals and key levels.
Outputs are presented as both direction and probability, supporting confidence-based position sizing and server-side execution via headless plotting.
π Read | Quotes | @mql5dev
#MQL5 #MT5 #AI
A Keras Functional CNN (two Conv blocks with batch normalization, then dense layers) is trained with early stopping to reduce overfitting. Functional design also enables introspection.
Interpretability is treated as a core feature: intermediate feature maps are visualized alongside candlesticks, and an attention-style heatmap highlights the chart regions driving the decision, often aligning with reversals and key levels.
Outputs are presented as both direction and probability, supporting confidence-based position sizing and server-side execution via headless plotting.
π Read | Quotes | @mql5dev
#MQL5 #MT5 #AI
β€36π3π1