This article shifts from socket-based MT5/Excel exchange to building the SQL foundation needed for reliable data workflows in algorithmic trading.

It compares practical ways to run SQL: command line, MySQL Workbench, MetaEditor, embedded MQL5, or sending SQL via sockets to a server. The key constraint is portability: stick to standard SQL to avoid vendor-specific features that break across engines.

Core setup focuses on CREATE DATABASE and USE, plus Workbench execution mechanics (run selection vs current statement). For repeatable scripts, it recommends conditional creation (IF/EXISTS patterns) instead of failing on reruns.

Table design is treated as the real challenge: data types and schema planning matter more than column order. A quotes example shows why a “price-only” table cannot store history, then uses ALTER TABLE to add a date field, emphasizing c...

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#MQL5 #MT5 #SQL

Inheritance in MQL5 is not limited to classes. Structs can share a base struct to remove duplicated fields and functions, such as a common k_value and Get_K logic across st_Reg and st_Bio.

Refactoring to a st_Base struct and inheriting publicly centralizes behavior. Updates to shared members then propagate to all derived structs, reducing maintenance risk and inconsistent behavior.

The next step combines structs, templates, and inheritance. A templated base forces derived structs to also become templates, enabling type-specific instantiations generated by the compiler and allowing a reusable list-like container with overloads.

👉 Read | Forum | @mql5dev

#MQL5 #MT5 #AlgoTrading

ICT-style time segmentation keeps algorithmic price delivery tied to specific operating windows. Liquidity often accumulates during Asia, gets engineered around the London open, and sees distribution during the New York kill window. Time alignment is treated as a primary variable alongside price.

MT4 provides limited native support for consistent session visualization, so ranges and windows are often marked manually, with additional effort spent converting broker server time to market time.

An ICT Killzones indicator automates the session map. It draws the Asian range from the session high/low to frame liquidity pools, shades the London and New York high-volatility windows, and includes a broker GMT shift input so windows line up with institutional timing across GMT+2, GMT+3, EST, and other feeds.

Execution is kept lightweight by limiting calculations to...

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#MQL4 #MT4 #Indicator

Moving averages remain common at the retail level, while many execution systems benchmark fills against VWAP. Large order execution is typically evaluated versus the volume-weighted average price, with accumulation more likely below VWAP and distribution more likely above it.

An anchored VWAP implementation for MT4 is positioned to address the lack of a native session-based VWAP. The calculation anchors to the start of a selected session (Daily, Weekly, Monthly) and maintains a cumulative VWAP without repainting on subsequent bars.

The VWAP line is derived from Typical Price (H+L+C)/3 weighted by tick volume to approximate session “fair value”. Standard deviation bands are added above and below VWAP to highlight statistically stretched conditions; touches near the second deviation are commonly treated as overextension in quantitative workflows.

Configur...

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#MQL4 #MT4 #VWAP

Institutional execution logic often clusters around round-number levels where liquidity is consistently available. These zones commonly become reference points for block placement, order matching, and risk management, especially during macro-driven sessions.

The Institutional Psychological Levels indicator renders these levels directly on MT4, replacing the default grid with structured round-number mapping. It emphasizes major 1000 and 500-point levels (BRN) and keeps the layout adaptive to the symbol’s price scale across FX, metals, and indices.

Implementation details focus on terminal stability: optimized MQL4, zero-lag rendering, and calculations limited to the visible chart range to reduce overhead during volatility spikes.

Operational use cases include profit targets and protective stop placement around 000/500 areas. Low-volume approaches o...

👉 Read | AppStore | @mql5dev

#MQL4 #MT4 #Indicator

MetaTrader 4’s built-in trailing stop uses a fixed pip distance. In changing volatility regimes, fixed offsets create inconsistent risk: stops can be oversized in quiet markets and overly tight during fast moves, increasing early stop-outs.

A volatility-based alternative is to trail using Average True Range (ATR). The stop distance is recalculated from current ATR and applied as ATR × multiplier, adjusting to current conditions while tightening as price advances.

Operational features typically include auto-breakeven logic, moving Stop Loss to entry plus a small buffer once a defined profit threshold is reached. Trade coverage can be configured to manage manual orders on the symbol or positions opened by other EAs via Magic Number filtering.

Common inputs: ATR period (often 14), multiplier (commonly 2.0–3.0), breakeven enablement and activation pips, and Magic N...

👉 Read | Signals | @mql5dev

#MQL4 #MT4 #EA

Intermarket analysis remains a core workflow in quantitative and institutional trading, where cross-asset correlation helps infer liquidity conditions. The US Dollar Index (DXY) is often monitored as a proxy for broad USD strength and its impact across FX and metals.

An Institutional DXY Overlay consolidates this view by plotting the Dollar Index directly on the active chart, reducing reliance on multi-window layouts and enabling faster correlation checks during execution.

Key capabilities include multi-symbol synchronization via iClose-based data requests, with alignment to the current chart timeframe. Dynamic scaling adjusts the plotted index to the visible price range for clearer comparison across instruments with different magnitudes.

SMT divergence spotting focuses on correlation breaks, such as DXY printing a higher high while a related pair fail...

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#MQL5 #MT5 #AlgoTrading

MetaTrader 5 uses SQLite, so standard SQL fits naturally inside MQL5 executables. One exception is execution from MetaEditor: it blocks DROP DATABASE for safety, even though DROP TABLE works. The takeaway is to treat destructive statements as production-grade risks.

The article then builds a clean workflow for quote storage: always modify data via SQL, not manual file edits. INSERT INTO is order-sensitive between column lists and VALUES, and careless table design permits unlimited duplicates.

To enforce consistency, define a PRIMARY KEY (e.g., trading day for daily bars). INSERT always creates rows; to change existing rows, use UPDATE ... SET ... WHERE keyed by the primary key. This pattern enables reliable incremental writes for trading data pipelines.

👉 Read | Calendar | @mql5dev

#MQL5 #MT5 #SQL

Naive sizing breaks in ML trading because it ignores confidence, overlaps trades into accidental leverage, and forces constant rebalancing that turns edge into fees. It also misses payoff asymmetry, where the same win rate can justify different risk.

The AFML Chapter 10 toolkit in afml.bet_sizing implements four complementary sizers. bet_size_probability turns classifier probabilities into bounded signals via a z-score-to-normal-CDF mapping, then fixes triple-barrier overlap by averaging concurrently active bets and discretizing changes to reduce churn.

For non-probabilistic models, bet_size_dynamic sizes from forecast-vs-market divergence using calibrated sigmoid/power curves and can output limit prices for execution. bet_size_budget and bet_size_reserve handle directional-only signals, with the reserve method learning a sizing curve from empirica...

👉 Read | CodeBase | @mql5dev

#MQL5 #MT5 #AlgoTrading

Market structure logic that relies on higher highs and lower lows treats every extreme as equal, which increases false signals during consolidation, minor retracements, and liquidity grabs.

A five-layer EA design addresses this with raw swing candidates plus a Structural Validation Engine. Swings become valid only with Break of Structure, displacement, liquidity sweep behavior, or time-based respect.

A Liquidity Interaction Layer tracks equal highs/lows and untouched validated swings, marking whether liquidity is taken. A state machine classifies phases as accumulation, expansion, distribution, or reversal based on validated swings and liquidity behavior.

Execution requires confluence: validated structure, liquidity interaction, and state alignment. Stops anchor to validated structure; targets use fixed risk:reward or next liquidity/structure level.

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#MQL5 #MT5 #AlgoTrading

This part shows a practical path from raw MT5 tick exports to queryable data: request ticks in MetaTrader 5, save to CSV, then import the file into a clean .db using MetaEditor’s table import. Key details are choosing the correct delimiter (MT5 uses tabs by default) and naming the new table so the CSV header becomes the table schema.

With the data structured as a real table, SQL becomes usable for analysis and simulation. The article introduces SELECT as the core inspection tool, then moves to filtering with a WHERE clause, using column criteria (e.g., FLAGS = 88) to reduce large result sets.

MetaEditor’s result paging (blocks of 1,000 rows) is highlighted as a simple way to navigate big tick datasets during exploration.

👉 Read | VPS | @mql5dev

#MQL5 #MT5 #SQL

ASQ SuperTrend is an ATR-based trend-following indicator for MT5, drawing a SuperTrend line that flips between bullish and bearish states in real time. The line is green below price in uptrends and red above price in downtrends, with reversal arrows. Signals do not repaint on completed bars, and alerts support popup, sound (custom file), email, and push.

Version 2.0 adds three ATR calculation modes: Wilder smoothing (recursive ATR), SMA of true range, and EMA of true range. Mode selection changes responsiveness and is shown in the short name and dashboard.

The dashboard reports trend direction, SuperTrend level, price, pip distance to the flip point, trend duration, strength vs ATR, reversal count over the last 100 bars, active settings, current ATR in pips, plus symbol/timeframe.

Implementation uses 7 buffers, band locking (upper only decreases, lower o...

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#MQL5 #MT5 #Indicator

ASQ_FlowDesk is an MT5 expert advisor panel focused on manual execution with automated risk and exit handling. It supports one-click LONG/SHORT market orders plus pending entries: Buy/Sell Limit and Buy/Sell Stop.

Risk can be set via manual lots, % of equity, or fixed cash risk. Exits support three scaled take-profit targets (T1/T2/T3) with configurable close fractions, plus four trailing modes: off, fixed distance, ATR-adaptive, or prior-bar structure. Auto-breakeven can shift SL to entry plus a cushion after a defined profit threshold.

Operations include Flatten All, Close Long/Short, Cancel Pendings, and Lock Entry. An on-chart panel shows spread, positions, exposure, P&L, target progress, and trail status.

Install by copying ASQ_FlowDesk.mq5 into MQL5/Experts, compiling, attaching to a chart, and enabling Algo Trading.

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#MQL5 #MT5 #EA

ASQ_RiskGuard for MT5 adds account-level risk controls designed to enforce drawdown and daily loss rules. Equity drawdown protection closes positions when thresholds are hit, with daily loss caps that can disable trading for the day. A trailing max drawdown mode measures from peak equity to match typical prop firm constraints.

Operational filters include session hours enforcement with optional forced close, spread-based entry blocking, and position limits for count, lots, and daily order volume. Lot sizing supports fixed size, percent risk, or fixed currency risk per trade. Alerts trigger at configurable warning levels and at limit breach across popup, sound, push, and email. Logging to file is available for audit.

Installation: place ASQ_RiskGuard.mq5 in MQL5/Experts, compile, attach to a chart, and enable algo trading. Monitoring can cover all positions o...

👉 Read | AlgoBook | @mql5dev

#MQL5 #MT5 #EA

Library work starts on symbol chart support with a new Chart object that stores chart properties as int/double/string parameters. The parameter set will evolve as functionality expands.

Signal handling is corrected in MQL5.com Signals integration. Collection updates will now refresh properties of existing signal objects, not only append newly discovered signals. Added methods include SetProperties(), lookup of signal index by ID, and selection by ID to avoid unstable database indices.

A new CChartObj class is added under DoEasy\Objects\Chart. It initializes from ChartGetInteger/Double/String, supports comparisons, property descriptions, and journal output. Flag setters use ChartSet* with optional ChartRedraw batching to account for asynchronous updates. Read-only chart parameters are mirrored into object state for later synchronization.

👉 Read | Freelance | @mql5dev

#MQL5 #MT5 #AlgoTrading

Building a profitable model is only the first phase in algorithmic trading. A common failure point is execution: standard terminal trade calls are synchronous, blocking the next instruction until the server responds. During high-impact news or when a grid needs to close many positions at once, this wait time can turn into platform stalls and significant negative slippage.

The Asynchronous Institutional Trade Engine is a header-only library that routes order, modify, and close requests through non-blocking channels. Execution commands can be queued and dispatched without tying up the main strategy loop, separating trading logic from network latency.

The library also adds risk controls, including spread checks that can halt async bursts when liquidity degrades. It provides a partial-close routine for closing exact position percentages without manual lot r...

👉 Read | Calendar | @mql5dev

#MQL5 #MT5 #AlgoTrading

A new product version reaches baseline functionality for extracting working trading settings, with usability changes aimed at lowering the entry barrier and expanding participation in parameter search and market research.

Key updates include a redesigned UI, a second polynomial for brute force based on a modified Fourier series, improved RNG modes, spread capture and noise reduction, spread-aware optimization, lot variation for ultra-short segments, and multiple bug fixes.

A new workflow generates a TXT configuration read by a single “settings receiver” EA for MT4/MT5, avoiding .set limits with variable-length arrays.

Internals cover coefficient generation, tick-based spread recording for OHLC, use of mid-price to reduce broker dependence, and notes on instability under spread noise plus overfitting risk.

👉 Read | Signals | @mql5dev

#MQL5 #MT5 #EA

An RSI variant has been released that applies a Kalman Filter to the input series while keeping the standard RSI interpretation. The intent is to reduce short-term noise in the oscillator without changing common threshold logic such as overbought and oversold levels.

The indicator is displayed in a separate subwindow, consistent with typical RSI layouts. This build is described as the latest fixed version, addressing prior issues and aiming for stable behavior in live charts and backtests.

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#MQL4 #MT4 #Indicator

Smart Money Concepts setups without strict session alignment tend to fail because directional moves often concentrate around defined macroeconomic windows rather than persisting through the full trading day.

The ICT Silver Bullet framework targets those narrow periods where liquidity conditions change and execution becomes more consistent. This indicator automates the detection of those time windows, then limits on-chart output to the active session to reduce noise and common retail traps.

Execution zones are plotted directly on the chart, while Fair Value Gaps are scanned and projected only when they form inside the configured windows. Broker server offsets are handled via dynamic GMT adjustment to keep session alignment consistent without manual calculation.

The MQL5 implementation is optimized to process only recent visible history to reduce CPU ...

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#MQL5 #MT5 #Indicator

ASQ SafeScalping v1.20 is a free open-source breakout scalping Expert Advisor for MT5 from AlgoSphere Quant. Entries use a strict seven-condition gate: EMA(150/510) direction, ATR-based EMA separation, close relative to both EMAs, N-bar breakout with ATR buffer, RSI zone filter, momentum vs prior close, and optional H1 EMA(50/200) confirmation. If any check fails, no trade.

v1.20 fixes repeated TP1 partial closes via ticket tracking, disables GlobalVariables-based peak drawdown tracking in Strategy Tester, and removes a legacy MT4 directive for clean MT5 builds.

Risk and trade management are conservative: single position only, fixed SL/TP, percent sizing, max drawdown auto-pause, daily trade cap, breakeven, trailing stop, and one-time partial close. Filters cover session hours, spread, news, and Friday cutoff. Five .set presets support phased optimization, w...

👉 Read | AlgoBook | @mql5dev

#MQL5 #MT5 #EA