ASQ Trading Journal Export is an MT5 script that exports full closed-trade history to a clean CSV in a single run. Trades are reconstructed from deal history with ticket, symbol, side, open/close time, volume, open/close price, SL/TP, gross P/L, swap, commission, net P/L, duration (minutes), magic number, and comment.
The output is formatted for Excel, Google Sheets, and straightforward ingestion into Python, R, or other analytics pipelines. A summary block is appended with total trades, wins, losses, win rate, gross P/L, and net P/L.
Options include date range (last N days or all history), symbol filtering, and magic-number filtering, plus toggles for swap, commission, duration, magic, and comments. The file is date-stamped and written to MQL5/Files/. Free and open-source.
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#MQL5 #MT5 #script
The output is formatted for Excel, Google Sheets, and straightforward ingestion into Python, R, or other analytics pipelines. A summary block is appended with total trades, wins, losses, win rate, gross P/L, and net P/L.
Options include date range (last N days or all history), symbol filtering, and magic-number filtering, plus toggles for swap, commission, duration, magic, and comments. The file is date-stamped and written to MQL5/Files/. Free and open-source.
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #script
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Traditional SMA/EMA tools remain common in retail workflows, but the core limitation is unavoidable lag. When a crossover finally confirms a reversal, much of the move has often already been priced in, leaving late entries exposed to distribution and mean reversion.
Quant funds mitigate latency using digital signal processing methods adapted from telecommunications. A common approach is a Gaussian-weighted filter based on the ALMA distribution, applying a bell-curve weighting to the series rather than a flat or exponential average.
Operational characteristics typically include faster response to momentum shifts, stronger suppression of high-frequency noise, and smoother structure without relying on long lookbacks. Implementations can be CPU-efficient by precomputing Gaussian weights at initialization to limit per-tick overhead.
Deployment is usually...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #Indicator
Quant funds mitigate latency using digital signal processing methods adapted from telecommunications. A common approach is a Gaussian-weighted filter based on the ALMA distribution, applying a bell-curve weighting to the series rather than a flat or exponential average.
Operational characteristics typically include faster response to momentum shifts, stronger suppression of high-frequency noise, and smoother structure without relying on long lookbacks. Implementations can be CPU-efficient by precomputing Gaussian weights at initialization to limit per-tick overhead.
Deployment is usually...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #Indicator
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MultiSymbolAlertPanel is an MQL5 indicator designed to track High/Low levels across multiple symbols from a single chart. It displays a compact panel with Bid, the selected timeframe High/Low (D1, W1, MN1), and a per-symbol status updated in real time.
Break conditions trigger immediate popup and sound notifications, limited to one alert per bar. Status is color-coded for quick scanning: lime for High breaks, tomato for Low breaks, yellow when price is within a configurable near-zone threshold, and light blue for normal watch state.
For the active chart symbol, optional dashed horizontal High/Low lines can be plotted with price labels. A Date Filter mode switches the panel to historical High/Low for a specified date for review and analysis; alerts are disabled in this mode. The panel supports up to 50 symbols and exposes inputs for fonts, colors, nea...
👉 Read | Calendar | @mql5dev
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Break conditions trigger immediate popup and sound notifications, limited to one alert per bar. Status is color-coded for quick scanning: lime for High breaks, tomato for Low breaks, yellow when price is within a configurable near-zone threshold, and light blue for normal watch state.
For the active chart symbol, optional dashed horizontal High/Low lines can be plotted with price labels. A Date Filter mode switches the panel to historical High/Low for a specified date for review and analysis; alerts are disabled in this mode. The panel supports up to 50 symbols and exposes inputs for fonts, colors, nea...
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
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Most retail volatility bands (Bollinger Bands, Envelopes) are built on linear moving averages and standard deviation. Under extreme shock regimes, variance expands rapidly and these bands can become too wide to provide practical mean-reversion boundaries.
Many institutional quant models reduce reliance on linear averages and shift to non-parametric smoothing to better reflect the current structure of the price feed rather than a fixed distributional assumption.
Nadaraya-Watson kernel regression applies a Gaussian kernel to historical prices, weighting recent local clusters more heavily while damping distant anomalies. The result is a non-linear regression channel that tracks the localized consensus more tightly than standard deviation-based tools.
Key behaviors include dynamic mean-reversion signals on boundary breaches, adaptive smoothing during a...
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #Indicator
Many institutional quant models reduce reliance on linear averages and shift to non-parametric smoothing to better reflect the current structure of the price feed rather than a fixed distributional assumption.
Nadaraya-Watson kernel regression applies a Gaussian kernel to historical prices, weighting recent local clusters more heavily while damping distant anomalies. The result is a non-linear regression channel that tracks the localized consensus more tightly than standard deviation-based tools.
Key behaviors include dynamic mean-reversion signals on boundary breaches, adaptive smoothing during a...
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #Indicator
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In MetaTrader 5 Beta Build 5770, we have further enhanced integration with the OpenBLAS linear algebra library, by introducing new methods for L1 trend filtering and improvements to the component update system.
MetaEditor now also offers a more convenient way to work with CSV files. The editor automatically displays them in a table format, allowing you to filter data, sort by columns, and delete rows via the context menu.
In addition, we have improved the stability of the Strategy Tester and the web version of the platform.
Read more...
MetaEditor now also offers a more convenient way to work with CSV files. The editor automatically displays them in a table format, allowing you to filter data, sort by columns, and delete rows via the context menu.
In addition, we have improved the stability of the Strategy Tester and the web version of the platform.
Read more...
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Work continues on restructuring an MQL5 EA generator into a reusable library plus per-project repositories. The library was renamed from Advisor to Adwizard, moved into its own repo, and updated to use relative include paths for portability. A develop branch now serves as the integration point, with feature branches merged into develop and then main.
A new project repo, SymbolsInformer, is proposed as a utility EA rather than a trading system. It gathers cross-symbol, cross-timeframe statistics to guide parameter bounds for the SimpleCandles strategy, reducing optimizer search space and improving comparability across instruments.
The EA inputs include a main timeframe and candle count window, plus comma-separated symbol and timeframe lists. Outputs cover average candle sizes (bull/bear/all), average series length for runs of 2+ candles, and counts of series...
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #EA
A new project repo, SymbolsInformer, is proposed as a utility EA rather than a trading system. It gathers cross-symbol, cross-timeframe statistics to guide parameter bounds for the SimpleCandles strategy, reducing optimizer search space and improving comparability across instruments.
The EA inputs include a main timeframe and candle count window, plus comma-separated symbol and timeframe lists. Outputs cover average candle sizes (bull/bear/all), average series length for runs of 2+ candles, and counts of series...
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #EA
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ExMachina CandleTimer Lite is a free MT5 indicator that adds a real-time candle countdown panel to the chart, targeting accurate timing around bar close and new bar open. It supports all symbols and timeframes, with no DLLs and no external dependencies.
The countdown panel provides three modes: digital clock, progress bar with percentage, or a combined view. An optional flash state in the final 10 seconds alternates amber/red each second, and an optional sound can trigger on new bar formation. A secondary line shows elapsed and remaining time in a compact format.
Chart impact is configurable via theme modes (full steel palette, panel-only opacity, or no theme). The panel can be collapsed to a 30px strip showing symbol, timeframe, and live countdown. Layout inputs cover side, margin, Y offset, width, seconds display, flash, and sound.
👉 Read | Signals | @mql5dev
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The countdown panel provides three modes: digital clock, progress bar with percentage, or a combined view. An optional flash state in the final 10 seconds alternates amber/red each second, and an optional sound can trigger on new bar formation. A secondary line shows elapsed and remaining time in a compact format.
Chart impact is configurable via theme modes (full steel palette, panel-only opacity, or no theme). The panel can be collapsed to a 30px strip showing symbol, timeframe, and live countdown. Layout inputs cover side, margin, Y offset, width, seconds display, flash, and sound.
👉 Read | Signals | @mql5dev
#MQL5 #MT5 #Indicator
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Most retail EAs still size positions with fixed lots or static percent risk. That approach ignores regime changes: when volatility expands, a constant stop distance becomes statistically misaligned with the new distribution, increasing the probability of stop-outs and unstable equity curves.
A Kelly-VAPS position-sizing module addresses this by combining two layers. The Kelly fraction derives an optimal risk budget from observed win rate and payoff ratio. Volatility-adjusted sizing then scales that budget by current ATR and tick-value constraints to map risk into executable volume.
A typical implementation is an object-oriented C++ .mqh include that exposes a risk engine class, enforces broker min/max volume, checks free margin, and returns a final lot size. Inputs are strategy stats plus stop distance; output is a volatility-aware position size intended...
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
A Kelly-VAPS position-sizing module addresses this by combining two layers. The Kelly fraction derives an optimal risk budget from observed win rate and payoff ratio. Volatility-adjusted sizing then scales that budget by current ATR and tick-value constraints to map risk into executable volume.
A typical implementation is an object-oriented C++ .mqh include that exposes a risk engine class, enforces broker min/max volume, checks free margin, and returns a final lot size. Inputs are strategy stats plus stop distance; output is a volatility-aware position size intended...
👉 Read | Forum | @mql5dev
#MQL5 #MT5 #AlgoTrading
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This MQL5 footprint upgrade adds market-structure and order-flow layers so volume-by-price becomes actionable, not just descriptive. Each bar gains a volume profile with Point of Control (max activity) and a configurable Value Area to separate accepted prices from likely rejections.
Order-flow signals are formalized: stacked bid/ask imbalances across consecutive levels to mark sustained aggression, absorption bars where high volume produces near-zero delta, plus single prints and unfinished extremes that often act as revisit targets.
Implementation focuses on extensible inputs, richer per-bar/per-level metadata, and cached computations feeding a fixed draw-order rendering pipeline. It also adds CVD and per-bar delta histogram panels, dynamic font scaling, and utilities for color blending and dashed markers to keep visuals readable under zoom.
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
Order-flow signals are formalized: stacked bid/ask imbalances across consecutive levels to mark sustained aggression, absorption bars where high volume produces near-zero delta, plus single prints and unfinished extremes that often act as revisit targets.
Implementation focuses on extensible inputs, richer per-bar/per-level metadata, and cached computations feeding a fixed draw-order rendering pipeline. It also adds CVD and per-bar delta histogram panels, dynamic font scaling, and utilities for color blending and dashed markers to keep visuals readable under zoom.
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
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A Volume Bubble Indicator for MT5 replaces raw volume histograms with a chart overlay that ranks each candle’s activity against recent behavior. It defines a lookback window, computes mean volume plus variance/standard deviation, then normalizes each bar so volume remains comparable as conditions shift.
Normalized scores drive visualization: bubbles plotted above candle highs scale through discrete size tiers, while color reflects whether volume increased or decreased versus the prior bar. Extreme spikes also get compact numeric labels (e.g., 1M) for quick reading without clutter.
The implementation emphasizes practical MQL5 details: choosing tick vs real volume depending on the symbol, using built-in volume arrays, and managing chart-object lifecycle to update in place, avoid duplicates, and clean up when bars roll out of the window or the indicator i...
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
Normalized scores drive visualization: bubbles plotted above candle highs scale through discrete size tiers, while color reflects whether volume increased or decreased versus the prior bar. Extreme spikes also get compact numeric labels (e.g., 1M) for quick reading without clutter.
The implementation emphasizes practical MQL5 details: choosing tick vs real volume depending on the symbol, using built-in volume arrays, and managing chart-object lifecycle to update in place, avoid duplicates, and clean up when bars roll out of the window or the indicator i...
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #Indicator
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A utility is available to export full trade history to a CSV file, suitable for direct import into Excel or Google Sheets for further processing.
Per-trade output includes 18 fields such as ticket, symbol, side, open/close timestamps, entry/exit prices, profit, and pip results. Trade duration is formatted as “Xd Xh Xm”, with pip calculations adjusted for 3- and 5-digit pricing.
Filtering supports magic number, date range, and symbol selection. The export also generates summary performance metrics including win rate, profit factor, average R:R, maximum drawdown, Sharpe ratio, and expectancy, plus tracking of maximum consecutive wins and losses.
👉 Read | Quotes | @mql5dev
#MQL4 #MT4 #script
Per-trade output includes 18 fields such as ticket, symbol, side, open/close timestamps, entry/exit prices, profit, and pip results. Trade duration is formatted as “Xd Xh Xm”, with pip calculations adjusted for 3- and 5-digit pricing.
Filtering supports magic number, date range, and symbol selection. The export also generates summary performance metrics including win rate, profit factor, average R:R, maximum drawdown, Sharpe ratio, and expectancy, plus tracking of maximum consecutive wins and losses.
👉 Read | Quotes | @mql5dev
#MQL4 #MT4 #script
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A chart utility for detecting and rendering Fair Value Gaps (price imbalances), commonly referenced in ICT/Smart Money workflows. It identifies both bullish and bearish gaps, then monitors subsequent price action to confirm when gaps are filled.
Visualization is handled through zone rectangles with configurable color and opacity. Filled-gap handling can be configured to remove zones, fade them, or keep them on the chart for review. A minimum gap-size filter is available to reduce low-signal detections, and optional labels can display gap size.
Eventing includes alerts on new gap formation and on fill completion, with support for push notifications and email delivery.
👉 Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
Visualization is handled through zone rectangles with configurable color and opacity. Filled-gap handling can be configured to remove zones, fade them, or keep them on the chart for review. A minimum gap-size filter is available to reduce low-signal detections, and optional labels can display gap size.
Eventing includes alerts on new gap formation and on fill completion, with support for push notifications and email delivery.
👉 Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
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ASQ NeuralNet provides a neural network stack implemented entirely in native MQL5, with no external dependencies. It targets building, training, and running feedforward models directly in MetaTrader 5, including mini-batch training, shuffling, and epoch logging.
Core components include a dense matrix engine with 40+ operations and NaN checks, 13 activation functions with analytical derivatives, dense layers with backpropagation, dropout, and gradient clipping, plus optimizers (SGD with momentum, Adam, AdamW). Learning rate scheduling covers Constant, Step, Exponential, Cosine Annealing, Linear, ReduceOnPlateau, Warmup, and Cyclic.
Loss functions include MSE, MAE, Huber, Cross-Entropy, and Binary Cross-Entropy. Reported characteristics include sub-0.1 ms inference for small models, fixed inference allocation behavior, safe Softmax, and initialization via ...
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #AI
Core components include a dense matrix engine with 40+ operations and NaN checks, 13 activation functions with analytical derivatives, dense layers with backpropagation, dropout, and gradient clipping, plus optimizers (SGD with momentum, Adam, AdamW). Learning rate scheduling covers Constant, Step, Exponential, Cosine Annealing, Linear, ReduceOnPlateau, Warmup, and Cyclic.
Loss functions include MSE, MAE, Huber, Cross-Entropy, and Binary Cross-Entropy. Reported characteristics include sub-0.1 ms inference for small models, fixed inference allocation behavior, safe Softmax, and initialization via ...
👉 Read | NeuroBook | @mql5dev
#MQL5 #MT5 #AI
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Manual closing in MT5 becomes an execution bottleneck during volatility due to sequential Toolbox interactions. An on-chart panel reduces latency by exposing one-click bulk and conditional closes, while showing live floating P/L without external DLLs.
A compact dashboard can be built with the MQL5 Standard Library using CAppDialog plus CTrade, prioritizing maintainability over Canvas-based rendering. Inputs for symbol and Magic Number isolate portfolios to prevent cross-EA liquidation.
Net exposure is computed from position profit plus swap, avoiding deprecated commission fields. Closures should audit CTrade return codes and log ResultRetcodeDescription for rejection diagnostics, including Algo Trading disabled states.
UI event routing via a virtual OnEvent handler avoids conflicts. A 1-second OnTimer loop updates labels and triggers rule-based flattening ...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
A compact dashboard can be built with the MQL5 Standard Library using CAppDialog plus CTrade, prioritizing maintainability over Canvas-based rendering. Inputs for symbol and Magic Number isolate portfolios to prevent cross-EA liquidation.
Net exposure is computed from position profit plus swap, avoiding deprecated commission fields. Closures should audit CTrade return codes and log ResultRetcodeDescription for rejection diagnostics, including Algo Trading disabled states.
UI event routing via a virtual OnEvent handler avoids conflicts. A 1-second OnTimer loop updates labels and triggers rule-based flattening ...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
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BarStats is an MT5 indicator designed to provide per-bar statistics with minimal overhead and clean chart output.
It publishes the bar index for integration in automated logic, along with price difference in points and percentage change for each bar. Calculation is optimized to remain lightweight, and a DRAW_NONE buffer is used to keep the chart free of additional rendering.
The indicator exposes EA-friendly buffers intended for custom strategies, backtesting workflows, and data collection where deterministic bar-by-bar inputs are required.
Contact: Telegram @JohnHansonDev for custom indicators or EA development.
👉 Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
It publishes the bar index for integration in automated logic, along with price difference in points and percentage change for each bar. Calculation is optimized to remain lightweight, and a DRAW_NONE buffer is used to keep the chart free of additional rendering.
The indicator exposes EA-friendly buffers intended for custom strategies, backtesting workflows, and data collection where deterministic bar-by-bar inputs are required.
Contact: Telegram @JohnHansonDev for custom indicators or EA development.
👉 Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
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KSQ Fair Value Gap EA is an automated Expert Advisor built to trade institutional Fair Value Gaps with optional regime filtering to reduce signals during range conditions.
The core logic detects bullish and bearish 3-bar FVG structures and triggers entries on pullbacks into the remaining imbalance zone. Each gap is tracked as a separate object and is marked consumed after one execution to prevent repeat entries.
Regime detection supports higher timeframe EMA50/EMA200 bias, ADX threshold with DI+/DI- direction checks, a combined mode requiring alignment, or an unrestricted mode.
Risk and trade controls include ATR or fixed-point SL/TP, fixed or percent-risk lot sizing, partial exits, trailing stop by ATR or points, and a cap on concurrent positions. Protection options include daily and total drawdown cutoffs, session-hour filters, and alert notifications.
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #EA
The core logic detects bullish and bearish 3-bar FVG structures and triggers entries on pullbacks into the remaining imbalance zone. Each gap is tracked as a separate object and is marked consumed after one execution to prevent repeat entries.
Regime detection supports higher timeframe EMA50/EMA200 bias, ADX threshold with DI+/DI- direction checks, a combined mode requiring alignment, or an unrestricted mode.
Risk and trade controls include ATR or fixed-point SL/TP, fixed or percent-risk lot sizing, partial exits, trailing stop by ATR or points, and a cap on concurrent positions. Protection options include daily and total drawdown cutoffs, session-hour filters, and alert notifications.
👉 Read | Calendar | @mql5dev
#MQL5 #MT5 #EA
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Pivot Points remain a common technical indicator in FX for mapping intraday structure from the prior session’s high, low, and close. The calculation yields a central pivot plus derived support and resistance levels that many desks treat as reference zones for the current trading day.
These levels are often used to frame entries and exits, define stop placement, and set profit targets. Utility tends to increase during higher volatility sessions, where price can rotate quickly between liquidity areas.
Pivot Points are frequently paired with trend and volatility tools. Moving averages can help validate directional bias, while Bollinger Bands can highlight expansion or mean reversion around key levels. Combining signals can reduce false triggers, but requires testing to match risk limits and execution constraints.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
These levels are often used to frame entries and exits, define stop placement, and set profit targets. Utility tends to increase during higher volatility sessions, where price can rotate quickly between liquidity areas.
Pivot Points are frequently paired with trend and volatility tools. Moving averages can help validate directional bias, while Bollinger Bands can highlight expansion or mean reversion around key levels. Combining signals can reduce false triggers, but requires testing to match risk limits and execution constraints.
👉 Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
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ASQ Indicator Manager consolidates 11 built-in MT5 indicators into a single class, removing repetitive handle and buffer code. It provides automatic handle lifecycle management, bar-based caching via Update() with optional ForceUpdate() for tick precision, and consistent getter methods with shift support.
Managed set includes EMA fast/medium, SMA slow, ATR 7/14/50, RSI, Stochastic K/D, CCI, MACD main/signal/histogram, and ADX with +DI/-DI. All periods and settings are configurable through dedicated setters.
The class also adds higher-level signals: trend voting based on MA alignment, DI crossover, and MACD histogram; regime classification (trending up/down, ranging, volatile, quiet) using ADX and ATR ratios; and overbought/oversold flags from RSI and Stochastic.
A demo indicator displays a live dashboard with values, direction, regime, volatility ratio, a...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Indicator
Managed set includes EMA fast/medium, SMA slow, ATR 7/14/50, RSI, Stochastic K/D, CCI, MACD main/signal/histogram, and ADX with +DI/-DI. All periods and settings are configurable through dedicated setters.
The class also adds higher-level signals: trend voting based on MA alignment, DI crossover, and MACD histogram; regime classification (trending up/down, ranging, volatile, quiet) using ADX and ATR ratios; and overbought/oversold flags from RSI and Stochastic.
A demo indicator displays a live dashboard with values, direction, regime, volatility ratio, a...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Indicator
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ASQ Risk Analytics has been released as a free, open-source MQL5 risk library for post-session evaluation of automated strategies. It processes account trade history and generates an institutional-style risk report with no external dependencies.
Core modules include Monte Carlo bootstrapping (1,000–10,000 runs) with expected return, expected max drawdown, probability of ruin, and 95% confidence intervals. Historical VaR and CVaR are provided at 95% and 99%, with daily/weekly/monthly scaling.
Position sizing supports seven methods: Fixed, Percent Risk, full Kelly, Half Kelly, Optimal F, ATR-based, and VaR-constrained sizing, normalized to broker lot limits. Reporting covers risk-adjusted ratios (Sharpe, Sortino, Calmar, Sterling, SQN, Ulcer, Recovery Factor, K-Ratio) plus standard performance stats and a 0–100 risk score with classification and recommendation.
A...
👉 Read | Docs | @mql5dev
#MQL5 #MT5 #EA
Core modules include Monte Carlo bootstrapping (1,000–10,000 runs) with expected return, expected max drawdown, probability of ruin, and 95% confidence intervals. Historical VaR and CVaR are provided at 95% and 99%, with daily/weekly/monthly scaling.
Position sizing supports seven methods: Fixed, Percent Risk, full Kelly, Half Kelly, Optimal F, ATR-based, and VaR-constrained sizing, normalized to broker lot limits. Reporting covers risk-adjusted ratios (Sharpe, Sortino, Calmar, Sterling, SQN, Ulcer, Recovery Factor, K-Ratio) plus standard performance stats and a 0–100 risk score with classification and recommendation.
A...
👉 Read | Docs | @mql5dev
#MQL5 #MT5 #EA
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ASQ Frequency Controller adds a trade-frequency layer on top of an existing EA without changing entry rules. A single frequency factor scales eight filter parameters in sync, allowing consistent control over how often signals are allowed through.
Five presets are provided: Ultra-Conservative (0.5x), Conservative (0.75x), Normal (1.0x), Aggressive (1.5x), and Scalper (2.0x), plus runtime +/- fine-tuning with configurable step size. An auto-frequency option can accept current ATR and adjust the factor based on volatility.
Scaling rules are explicit: higher factor reduces cooldown, minimum bars, minimum ATR, and minimum distance; increases allowed spread; and relaxes signal-strength thresholds using a sqrt divisor with floors (0.2/0.3). Daily entries can be capped via SetMaxTradesPerDay(), with midnight reset. GetSnapshot() returns effective values for dashboar...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
Five presets are provided: Ultra-Conservative (0.5x), Conservative (0.75x), Normal (1.0x), Aggressive (1.5x), and Scalper (2.0x), plus runtime +/- fine-tuning with configurable step size. An auto-frequency option can accept current ATR and adjust the factor based on volatility.
Scaling rules are explicit: higher factor reduces cooldown, minimum bars, minimum ATR, and minimum distance; increases allowed spread; and relaxes signal-strength thresholds using a sqrt divisor with floors (0.2/0.3). Daily entries can be capped via SetMaxTradesPerDay(), with midnight reset. GetSnapshot() returns effective values for dashboar...
👉 Read | Quotes | @mql5dev
#MQL5 #MT5 #EA
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