MQL5 Algo Trading

Trend detection gets a practical upgrade by reworking the Abbe criterion for market data. The core idea is comparing dispersion of prices with dispersion of bar-to-bar changes: trends inflate overall spread while short-step movement stays relatively stable. To reduce spike sensitivity on short windows, variance is replaced with robust estimates: median, MAD, and absolute differences. Trend “strength” is then ranked using historical distributions (CDF), turning raw deviations into comparable probabilities. Direction is handled separately with a Kendall-based measure, then combined with the robust Abbe signals for entry/exit rules. Further variants address edge anomalies, add a linearity ratio (net move vs total path), and explore hybrid non-parametric criteria using sorting and SMA-order comparisons to capture both direction and magnitude. 👉 Read | Calendar | @mql5dev #MQL5 #MT5 #Strategy

Nested structs in MQL5 were extended from a fixed double payload to a more reusable mapping pattern, similar to key-to-value translation used in database indexing. A practical issue surfaced: compiler warnings that can mask real defects later. The immediate fix is explicit conversion, which keeps builds clean without changing behavior. The main refactor is separation of concerns. Set/Get were moved out of the payload struct and placed into the registry layer, enabling st_Data to store st_Reg elements without hard coupling. Because MQL5 cannot return pointers or null, the Set logic reserves a sentinel slot to represent “not found”. st_Data then becomes a template over type T, with a remaining dependency through Get_K, which can later be overloaded for flexible key extraction. 👉 Read | AppStore | @mql5dev #MQL5 #MT5 #AlgoTrading

This part completes the Excel–Python–MetaTrader 5 bridge by moving the MT5 side into MQL5 as a Service, avoiding the “script must stay on a chart” limitation. Running as a service keeps the connector independent of charts while still able to open/close charts and control terminal state. The system is deliberately split into three cooperating processes: a Python socket server, VBA in Excel, and an MQL5 client. The recommended workflow is incremental testing: validate Python first, then Excel↔server messaging, then attach MT5. On the MQL5 side, the core is a persistent TCP client loop with explicit socket cleanup on failed connects, configurable host/port parameters, and a defensive receive buffer strategy to prevent losing data when reads arrive in bursts. A simple text protocol is parsed character-by-character to extract a symbol and a command, return... 👉 Read | Quotes | @mql5dev #MQL5 #MT5 #AlgoTrading

Liquidity zones and flips can be identified accurately in MQL5, yet many trades fail because risk is applied mechanically instead of modeling how price sweeps liquidity around those levels. The article proposes an engineered, reproducible risk framework implemented as an MT5 Expert Advisor that manages the full trade lifecycle: pre-trade zone qualification (impulse/base quality, size constraints, higher-timeframe alignment), context-aware stop placement with buffers beyond expected sweep areas, and dynamic position sizing from a fixed account risk percent and actual stop distance. Key implementation details include a LiquidityZone state model (triggered, flipped, expiry), ATR-based normalization to reject weak zones, pending limits for fresh zones, and confirmation-based market entries for flipped zones with reduced risk. Zones are monitored for expiry, ord... 👉 Read | NeuroBook | @mql5dev #MQL5 #MT5 #EA

Manual Fibonacci retracements stay discretionary, but their monitoring can be automated without losing context. This EA detects manually drawn OBJ_FIBO objects, extracts each retracement level, converts them into fixed price references, and tracks them independently. An event-driven workflow avoids noisy auto-detection: the trader draws, then explicitly syncs and converts levels for monitoring. Internally, each level is stored with state so interactions are interpreted over time, not as one-off checks. On every tick, a state machine classifies approach, touch, breakout, and post-touch reversal using configurable tolerances, instrument-aware pip scaling, and level-mapping options. Alerts and a compact panel provide persistent awareness across charts, while lifecycle handling keeps monitored structures aligned with edits and deletions. 👉 Read | AppStore | @mql5dev #MQL5 #MT5 #AlgoTrading

ZigZag-based SNR Detection calculates support and resistance from confirmed pivot highs and lows. Levels are derived from ZigZag extremes and plotted as horizontal references to outline recent market structure. Configuration covers Lookback bars and the core ZigZag settings: Depth, Deviation, and Backstep. Optional rendering includes broken (closed) levels for historical context, open levels extending to the current bar, the ZigZag line, and per-level labels. Labels mark level type (S/R) and the active chart timeframe. The tool runs across standard symbols and timeframes from intraday through daily, and is intended to highlight pivot clusters where multiple levels align into practical zones for further analysis. 👉 Read | Quotes | @mql5dev #MQL5 #MT5 #Indicator

An Expert Advisor implements a moving average crossover with close-based confirmation. Long entries trigger when the fast MA crosses above the slow MA and the bar closes above the fast MA. Short entries trigger when the fast MA crosses below the slow MA and the bar closes below the fast MA. Execution is evaluated on new bars to limit noise-driven churn. An optional multi-timeframe MA filter can be enabled to align entries with a higher-timeframe trend and reduce false positives. Risk controls include configurable take profit and stop loss (both optional), a maximum lot cap, and an equity threshold gate. Exit logic can optionally close positions when price crosses back over the fast MA for adaptive trade management. Trades are isolated via a MagicNumber. Backtests referenced EURUSD on D1 with fast/slow periods of 100/200, showing comparatively stable result... 👉 Read | Calendar | @mql5dev #MQL5 #MT5 #EA

An Expert Advisor targets swing highs and lows confirmed on the H4 timeframe, then watches the M15 chart for liquidity sweeps. A sweep is detected when price temporarily breaches a swing level and the closing price resolves beyond that level, triggering an automatic buy or sell entry. Position sizing is derived from a fixed monetary risk, stop-loss distance in points, and tick value. This keeps per-trade risk consistent regardless of volatility or instrument specifics. Executed signals are annotated on-chart with arrows for audit and review. Swing levels are stored in arrays and updated continuously. New H4 candles can confirm additional swing points, while M15 sweep events can invalidate existing levels. Helper routines handle candle data retrieval, array maintenance, and lot-size calculation. Example settings referenced include EUR/USD with range 21, SL 1... 👉 Read | Quotes | @mql5dev #MQL5 #MT5 #EA

CRT Indicator (MTF): Explorer extends a single-timeframe CRT implementation into a multi-timeframe indicator for MQL5, aligning higher timeframe structure with lower timeframe execution on one chart. The logic monitors a user-selected HTF, validates bullish and bearish CRT conditions with strict price-action rules, then projects the resulting CRH/CRL zones onto the active LTF. Zones are rendered with standard library Fibonacci objects via CChartObjectFibo, providing labeled, color-coded levels for quick context around macro-defined areas. Updates are event-driven: CRT evaluation and redraw occur only on formation of a new HTF bar. This reduces redundant recalculation while keeping the overlay responsive. The design stays compact (under 130 lines) and focuses on deterministic synchronization between HTF data and LTF visualization. 👉 Read | Signals | @mql5dev #MQL5 #MT5 #Indicator

In live execution, entry logic alone does not control risk. Broker microstructure, order book visibility, and short-lived price spikes can make visible Stop Loss and Take Profit levels predictable and vulnerable. Stealth Trade Manager is a protection utility that manages existing positions rather than opening trades. It keeps SL/TP virtual: the broker sees positions without attached stops, while the local terminal tracks real thresholds and closes at market only when levels are breached. Core functions include Virtual SL/TP and a Spread Protector. If a virtual stop is reached during abnormal spread expansion, such as news events or server rollover, the position is held until spreads normalize, reducing exits caused by artificial widening. Key inputs: Virtual_SL_Points, Virtual_TP_Points, Max_Spread_To_Close, and Magic_Number for managing manual trades or se... 👉 Read | Calendar | @mql5dev #MQL5 #MT5 #EA

Chart object work in MQL5 is being extended with a window layer and indicator descriptors. Two new classes are added in ChartWnd.mqh: CWndInd for an indicator attached to a window, and CChartWnd for a chart window that stores size and a list of attached indicators. CWndInd keeps chart ID, window index, short name, and handle, with comparison logic based on handle/window index or short name. CChartWnd manages per-window properties previously skipped at chart level: Y-distance to main window and window height, plus height changes via ChartSetInteger with forced redraw and event-queue checks. Indicator enumeration uses ChartIndicatorGet by short name, with immediate handle release to avoid leaking usage counters. The window object can build/sync its indicator list, verify real presence, and remove stale entries. 👉 Read | NeuroBook | @mql5dev #MQL5 #MT5 #Indicator

An Expert Advisor runs DOOM (1993) inside a MetaTrader 5 chart by rendering frames into a bitmap label created with ObjectCreate and updated via ResourceCreate. Keyboard input is handled through OnChartEvent and forwarded to a DOOM engine hosted in a custom Windows DLL. The EA file alone is insufficient. Deployment requires a package containing doomlib.dll and the shareware doom1.wad. Without these files in MQL5\Libraries and MQL5\Experts\Doom, initialization fails. DLL imports must be enabled. Implementation uses a DLL wrapper around PureDOOM (single-header C port). The game loop runs on a background thread with double-buffered 320x200 ARGB output. The EA triggers updates about every 33ms using EventSetMillisecondTimer, copies the framebuffer to a chart bitmap, and handles key-up by polling GetAsyncKeyState due to missing CHARTEVENT_KEYUP support. 👉 Read | Quotes | @mql5dev #MQL5 #MT5 #EA

DoEasy’s chart handling is consolidated into a chart object collection, giving structured access to every open MT5 chart, its subwindows, and attached indicators. The design targets safer iteration and selection across charts by exposing searchable, sortable lists keyed by chart properties. Change detection avoids relying on CHARTEVENT_CHART_CHANGE (too generic) and instead uses a timer: twice per second it compares the current count of open charts and chart windows to cached values, rebuilding lists only when counts differ. This reduces event noise while keeping state consistent. The update logic rebuilds window lists when indices shift, fixes loops to use list sizes (not terminal counts), and prevents leaks when list insertion fails. The collection enumerates charts via ChartFirst/ChartNext, supports add/remove sync, and refreshes each chart object to re... 👉 Read | CodeBase | @mql5dev #MQL5 #MT5 #EA

A price-action indicator for Smart Money Concepts workflows automates detection and plotting of Fair Value Gaps (FVGs) in real time. An FVG is defined as a three-candle sequence where the wick range of the first and third candles does not overlap, leaving an imbalance across the second candle. Bullish (demand) and bearish (supply) zones are marked on the close of the third candle. The calculation is based only on closed bars, so plotted zones do not repaint or shift after confirmation. Performance is kept predictable by scanning a user-defined history window. Parameters include HistoryBars (default 500), plus separate color settings for bullish and bearish zones. Typical use is M5/M15 for execution-level entries and H1/H4 for broader liquidity targets and mitigation levels. 👉 Read | Docs | @mql5dev #MQL5 #MT5 #Indicator

A custom MACD implementation for MQL5 extends functionality beyond the standard indicator. The code mirrors the calculation logic and feature set found in the Pine Script version, keeping outputs consistent across platforms. This approach can add configuration options and signal behavior not present in the built-in MQL5 MACD, while retaining expected MACD line, signal line, and histogram behavior. It also supports closer parity for teams maintaining strategies that need matching indicator semantics between TradingView studies and MT5 execution code. 👉 Read | Freelance | @mql5dev #MQL5 #MT5 #Indicator

Coral Reef Optimization (CRO, 2014) models a 2D N×M reef where each occupied cell holds a candidate solution with an objective-based fitness. Initialization uses rho0 to set starting occupancy, then iterates reproduction, settlement, cloning, and depredation until a stop criterion. Core stages: broadcast spawning selects an Fb fraction for paired crossover (often averaging plus small noise). The remaining corals brood via mutation. Each larva attempts settlement up to k times, taking empty cells or replacing weaker incumbents. Asexual reproduction clones the top Fa fraction. Depredation removes the worst corals with probability Pd. An updated CROm variant modifies destruction using inverse power law sampling near top solutions, targeting faster convergence on multimodal benchmarks. 👉 Read | CodeBase | @mql5dev #MQL5 #MT5 #Strategy

Footprint charts expose volume at each price level per bar, but lack a compact bar-level summary. This forces manual aggregation and slows both discretionary reads and systematic logic. An MQL5 footprint indicator extension adds a per-bar sentiment box updated on every tick. It caches delta, total volume, buy/sell percentages, and precomputed colors in the bar structure to keep redraw paths lightweight. Rendering uses rounded rectangle geometry with arc and quadrilateral rasterization, supersampled draw then downsampled anti-aliased output, and ARGB alpha compositing so price-level text remains readable. Practical use: net delta as a first filter, delta/price divergence as a reversal signal, volume to qualify significance, and percentage imbalance to quantify participation at key levels. 👉 Read | AppStore | @mql5dev #MQL5 #MT5 #Indicator

A Market Entropy Indicator formalizes market randomness using Shannon entropy on discretized price states (up/down/flat). A normalized rolling score classifies regimes: TREND (<0.35), TRANSITION, and CHAOTIC (>0.65), reducing reliance on price-only heuristics. Implementation in MQL5 typically uses separate-window plots with fast/slow horizons (e.g., 20/100), momentum, divergence, and compression/decompression detection. Buffers and flags drive a color histogram plus markers for regime shifts and 25%+ “information shocks.” Signal logic is rule-based: buys align with compression to decompression, fast entropy crossing above slow, and improving momentum outside chaotic zones. Sells reverse those conditions, with rising disorder and negative momentum. Visual outputs are spaced to prevent clustering. 👉 Read | CodeBase | @mql5dev #MQL5 #MT5 #Indicator

MT5 EAs lose all in-memory state on terminal close, chart refresh, or removal, making multi-terminal coordination hard. A practical fix is persisting a single parameter/state record in a shared binary file so restarts resume cleanly. The approach packs EA settings into an MQL5 struct and writes/reads it as raw bytes using FileWriteStruct/FileReadStruct with FILE_BIN, stored under FILE_COMMON for cross-terminal access on the same PC. Key constraint: only simple, fixed-layout structs (int/double/bool, optionally nested) are safe for binary persistence. Strings, dynamic arrays, pointers, and objects require extra handling because their memory layout isn’t stable. Saving typically happens in OnDeinit(); loading on start restores the exact parameter set without text conversion, reducing code and type/order errors compared to CSV. 👉 Read | Docs | @mql5dev #MQL5 #MT5 #EA