MQL5 Algo Trading

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A utility script can be used to display full trading account information in one place, including balances, equity, margin figures, account currency, leverage, and server identifiers. This helps reduce manual checks and supports faster validation during setup and support cases. Operational guidance is required before deployment. If account information is restricted or unavailable for a client, the script may fail or return partial fields depending on permissions and broker settings. Email handling should be defined explicitly. Decide whether the email is added as an input parameter, read from platform settings, or stored in an external config, and ensure no sensitive account data is sent without consent and audit logging. 👉 Read | Forum | @mql5dev #MQL4 #MT4 #script

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Chart Replay Pro (MT5 Strategy Tester, visual mode) received a major update focused on manual backtesting parity with live-trading controls. Changes include runtime lot adjustment without restarting tests, per-position trade management, and an order workflow supporting buy/sell stop and buy/sell limit orders. UI is built with bitmap-based CbmpButton plus #resource inclusion, and a read-only edit field for lots. Key implementation notes: OnChartEvent is not processed in Strategy Tester, so entry and TP/SL are adjusted via plus/minus buttons instead of draggable lines. Global state variables are used to prevent tick-driven UI bugs and to keep selected ticket, TP, and SL stable while actions execute. 👉 Read | Quotes | @mql5dev

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MetaTrader 5 chart panels can host self-contained documentation rendered directly in-terminal, without external files. A canvas-based engine built on CCanvas supports tabs, scrollable rich text, inline images, theming, and supersampled anti-aliased UI elements. Implementation details include embedding bitmaps via #resource, defining paragraph types, and parsing inline markup into styled runs. Rendering uses a two-pass alpha reconstruction method to preserve glyph transparency on any background, then blends via Porter-Duff over. Images are loaded from resources, scaled with bicubic interpolation, and cached per panel width. Paragraphs are wrapped into encoded display lines with helpers to decode type, indent, and image slots during paint and scroll. 👉 Read | Quotes | @mql5dev

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Part 3 ported Python get_time_features into MQL5 as CTimeFeatures.mqh, producing a flat double array with Python-identical feature order and names for direct OnnxRun input. Key MQL5 issues addressed: broker time vs UTC, incremental rolling volatility, and a timeframe-driven frequency gate that must match the feature-name registry. UTC is derived once at init via TimeCurrent()-TimeGMT(); this fixes systematic session misclassification but cannot track mid-run DST shifts. Architecture: CTimeFeatures exposes Initialize/Update/Calculate, backed by CRingBuffer for population std (ddof=0) and forward-filled session/calendar vol. Session detection handles cross-midnight Sydney via OR logic. Day-of-week and day-of-year are re-indexed to match pandas conventions. Frequency gating uses PeriodSeconds to support custom periods; calendar flags appear only for D... 👉 Read | Quotes | @mql5dev #MQL5 #MT5 #AITrading

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Reinforcement learning in algo trading shifts strategy creation from fixed rules to continuous updating from trade outcomes. An MQL5/MetaTrader 5 implementation uses a multi-agent layout with experience stored as discrete “memory neurons” and aggregated into independent decision agents. Each neuron encodes a quantized 32-bit market state, a continuous action value (sell to buy), activation stats, and a score combining hit rate and usage frequency. Inputs are built from mostly stationary features (RSI, CCI, stochastics, MACD, ATR, Bollinger position, MA relations, fractals, ADX, WPR, normalized returns). Learning is modified Q-learning with staged exploration, epsilon control, class-balance correction, and pruning when memory exceeds 1,000 nodes. Ensemble voting weights agents by recent success to reduce single-model bias. 👉 Read | Forum | @mql5dev

DoEasy’s timeseries architecture is extended with a dedicated indicator buffer data object: one object represents one buffer value on one bar, linked to the owning indicator, symbol, timeframe, buffer index, and bar time. These objects are designed to live in typed collection lists, enabling fast selection, filtering, sorting by time, and later cross-indicator comparisons and benchmarking via cached data. The new CDataInd class follows the library’s base object pattern with integer/real/string property arrays, property support flags, a Compare() override for Search()/Sort(), and helpers for readable setters/getters and journal output. Selection/sorting services are updated to handle the new object type. Testing updates the sample EA to instantiate buffer objects for current and previous bars across standard and custom indicators, printing object descriptio... 👉 Read | Forum | @mql5dev

MQL evolved from early “Expert Advisor” concepts into an integrated environment where strategy code runs inside the terminal, with direct access to market series, indicators, and order execution. The key shift was treating automation as native, not an external add-on. Early MQL was minimal, but MetaTrader 2 introduced MQL II with loops, arrays, richer types, and math functions, enabling real program logic per tick. MetaTrader 3 then expanded beyond execution into research by adding custom indicators and file I/O. MetaTrader 4 and MQL4 completed the transition to a developer workflow: a structured language with user functions, scripts, libraries, DLL calls, plus MetaEditor bundling compiler, help, and tooling. Community infrastructure (MQL4.COM, Code Base, forums) and the automated trading championship validated autonomous trading at scale and push... 👉 Read | AppStore | @mql5dev

TimeFound reframes forecasting as a foundation-model problem: a Transformer pre-trained on diverse time series can produce usable predictions even with little or no asset history, enabling zero-shot forecasts for newly traded instruments. Core mechanics combine encoder–decoder attention with multi-resolution patching, capturing fast spikes and slow trends simultaneously. Standardization aligns scales across domains, while relative positional attention preserves temporal relevance and the decoder enforces causal generation. Training blends point accuracy (MSE) with quantile loss to output uncertainty bands alongside forecasts. The MQL5 implementation replaces costly patch replication with parallel multi-window convolution for aligned multi-scale features, then uses a multi-head residual feed-forward block and convolution/max-pooling aggregation for low-l... 👉 Read | VPS | @mql5dev

Backtesting a custom MQL5 indicator in Python often forces a reimplementation that can drift due to bar indexing, rounding, or warm-up rules. A cleaner approach is to export the exact indicator buffers calculated by the terminal and read them in Python as-is. An MQL5 script can attach via iCustom(), wait using BarsCalculated(), fetch rates with CopyRates() and buffers with CopyBuffer(), then write a locale-stable CSV to MQL5/Files/. Avoid FILE_CSV; format numbers with DoubleToString() and write full lines with FileWriteString(). Handle warm-up by converting EMPTY_VALUE to empty fields so pandas loads NaN. iCustom() parameter forwarding needs a fixed-arity dispatch block due to compile-time call signatures. 👉 Read | CodeBase | @mql5dev