MQL5 Algo Trading

Classic trading robots often fail because indicators trigger without context: session liquidity, widened spreads, day-of-week effects, and event risk. ML models can score direction, but still miss timing and execution constraints. A practical mitigation is a local LLM layer that can veto trades when conditions are unfavorable. Self-hosting reduces API cost, avoids rate limits, and keeps strategy data off third-party servers. Ollama enables a local setup in minutes: pull a base model, run it, then create a custom model via a Modelfile with risk rules, low temperature, and a defined context window. Extend it with larger models, longer context, multi-timeframe inputs, calendar data, and post-trade review logs to reduce false entries and drawdown. #MQL5 #MT5 #AlgoTrading #Ollama https://lnkd.in/dtU6vXZ8

This article builds a compact MQL5 implementation of the Avellaneda–Stoikov market-making model, replacing fixed symmetric grids with quotes that adapt to inventory and market conditions. The core outputs are a reservation price (mid adjusted against current position) and an optimal spread that expands with volatility and tightens with liquidity. Inputs are estimated online from a rolling window: volatility from price increments, and a practical retail proxy for order-flow intensity derived from mean absolute moves. The code is structured as a testable model class plus a self-check harness to catch sign and scaling errors. A chart indicator plots reservation, bid, and ask in real time, making inventory skew and spread changes visible. A controlled simulation on EURUSD H1 shows the adaptive quotes keeping inventory near flat and improving P&L versus a fi... #MQL5 #MT5 #AlgoTrading #HFT https://lnkd.in/dQcYrv2r

Multi-timeframe Renko for MT5 consolidates M5/M15/H1/H4 into a single custom symbol updated from ticks. Each timeframe maintains EMA state, Renko direction, last update time, and a signal-strength metric, then a synthesizer builds a unified price stream and writes bricks via CustomRatesUpdate(). Combination options include simple average, weighted average (default weights 1/2/3/4), and a consensus rule requiring 3-of-4 agreement with weighted fallback. Brick sizing can be fixed or ATR-adaptive using daily ATR with a multiplier (commonly 0.3–0.7). Supports historical rebuild from M5 and real-time updates, enabling indicators, strategy testing, and EAs on the synthetic symbol. #MQL5 #MT5 #AlgoTrading #Renko https://lnkd.in/dmyUpvRP

MetaTrader 5 ships with many built-in indicators, but no terminal-level search. Access still depends on Navigator or Insert menus, which adds friction during frequent indicator switching and increases accidental selection risk. A practical workaround is a searchable indicator panel implemented in MQL5 with clear separation of concerns. Core modules: IndicatorCatalog (name to ENUM_INDICATOR map), SearchEngine (case-insensitive substring filtering), ChartLauncher (IndicatorCreate plus ChartIndicatorAdd with window selection), and SearchPanel (Standard Library UI with timer-driven updates). This structure keeps catalog data centralized, search logic reusable, and platform API calls isolated, while allowing indicators to be located and attached from one panel without manual category browsing. #MQL5 #MT5 #Indicator #EA https://lnkd.in/d7GuwZ8g

OrderSend() logic tends to sprawl across an EA, duplicating lot rounding, stop validation, retry lists, and slippage checks. The article consolidates these concerns into a CExecutionGateway that becomes the only execution entry point, keeping strategy code focused on trade decisions. The gateway normalizes volume via CLotNormalizer using symbol min/max/step with caching and float-safe rounding, and enforces broker stop-distance rules via CSlTpValidator, including a spread-based fallback when stops level is reported as zero. A structured CGatewayResult replaces fragile bool/retcode handling, reporting success, slippage rejection, submitted SL/TP, fill price/volume, attempts used, and a human-readable reason. Filling-mode resolution and centralized retryable retcodes reduce avoidable rejections across brokers. A thin demo EA shows integration, while a test s... #MQL5 #MT5 #EA #Strategy https://lnkd.in/dNNTu5j8

Time-MoE reframes time-series forecasting as a scalable, decoder-only Transformer tuned for trading data. It keeps strict causality (no future leakage), supports variable history/forecast lengths, and is designed for real-time streams. Instead of window aggregation, it tokenizes every time step, preserving tick-level detail. Tokens are embedded with SwiGLU, combining smooth nonlinear features with gating to stay stable under noise. The core adds Sparse Mixture-of-Experts: only a small set of specialist subnetworks activates per token, so capacity scales to billions of parameters without exploding inference cost. Forecasting is multiscale via parallel heads trained for multiple horizons, with dynamic head selection at runtime based on market conditions. The MQL5 path breaks this into testable modules: tokenization via 1D convolution, an OpenCL-... #MQL5 #MT5 #AlgoTrading #Forecasting https://lnkd.in/d4G4gmRQ

Static take-profits and fixed trailing stops often fail for the same reason: they ignore market structure. Price frequently turns at swing extremes, order blocks, and liquidity zones, so round-number targets get missed and tight trails exit on normal pullbacks. The article proposes a hybrid exit model: take partial profit at a first structural level, then trail only the remaining position using CRT-derived anchors. Stops move only when price reaches new structural levels, reducing noise-driven stop-outs while banking realized gains. Implementation in MQL5 centers on a reusable class using Standard Library trade/position APIs. It detects CRT patterns from higher-timeframe OHLC mapping, computes range/signal extremes, executes volume-safe partial closes, and applies one-way stop improvements, with an EA wrapper exposing inputs and chart visualization for vali... #MQL5 #MT5 #Strategy #EA https://lnkd.in/dQUnNf3j

Financial forecasting remains difficult due to noise, non-stationarity, regime shifts, and event shocks. Classical models assume stability; LSTM/GRU often overfit and degrade under asset or regime changes. TimeFound targets zero-shot forecasting via a Transformer trained on diverse cross-domain time series. Multi-Resolution Patching converts sequences into multi-scale tokens; independent projection modules with masks improve robustness to padding, missing data, and phase shifts. The Encoder–Decoder block uses bidirectional self-attention in the Encoder and causal decoding with cross-attention in the Decoder. Forecasting is autoregressive: one next-token segment per step, appended back into history. Implementation notes: a central unit coordinates both streams. The Encoder uses per-variable attention (CNeuronMVMHAttentionMLKV) with MLKV reuse; the... #MQL5 #MT5 #AlgoTrading #AITrading https://lnkd.in/duvfenRQ

This article shows how to generate a portable, one-page trading performance PDF directly from MQL5, without DLL imports or external converters. The approach writes a minimal PDF (catalog, pages, page, font, content stream) using plain strings and the standard file API. The core constraint is correctness of the cross-reference table: every object’s byte offset must be exact. Offsets are precomputed from ASCII lengths and the file is written in FILE_BIN to avoid newline translation that would corrupt positions. The solution is modular: one component extracts closed-trade net results (profit+swap+commission) and builds stats plus an equity curve; another renders text and vector graphics via PDF operators, handling string escaping and drawing the curve as a single polyline for efficiency. #MQL5 #MT5 #AlgoTrading #script https://lnkd.in/d4gKaS_k

This article ports level-2 path signatures to MQL5 to measure lead–lag without scanning lags. Instead of shifting series and correlating, it treats two aligned streams as a 2D path and uses the Levy area (the antisymmetric part of level 2) as an ordering-sensitive statistic: sign indicates which channel tends to move first, and zero-crossings mark flips. The implementation builds a correct incremental engine via Chen’s identity with O(d^2) work per bar, plus a shuffle-relation residual to verify results. A facade class handles preprocessing (time augmentation, window normalization, optional lead–lag staircase transform), and exposes compact features. Practical output is an indicator plotting a rolling Levy-area oscillator and an EA using the reading directly, with notes on scaling choices and non-stationary behavior in real markets. #MQL5 #MT5 #AlgoTrading #Strategy https://lnkd.in/d68ZrSF6