Mql5 Algo Trading

Dandelion Optimizer (DO) is a 2022 metaheuristic that models how dandelion seeds explore wide areas, then converge where conditions look best. Each seed represents a candidate parameter set; “soil quality” is the objective value. DO updates a population in three phases: Rising for broad exploration (mostly spiral/vortex moves, sometimes linear drift toward the search-space center), Decline for coordination via the population mean, and Landing for exploitation around the current elite using Lévy-flight steps to keep occasional long jumps. The MT5-style implementation wraps this into a C_AO_DO class with Init/Moving/Revision, explicit boundary reflection, and adaptive control: step intensity decays over epochs while an attraction ratio increases, shifting from global search to fine-tuning. This fits strategy optimization where stable convergence and ... 👉 Read | VPS |@mql5algotrading122

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MetaTrader 5 indicators cannot change orders or positions on the trading server. Only Expert Advisors can send trade requests, so UI actions in an indicator must be forwarded to an EA via custom chart events. A position indicator was extended with a clickable OBJ_BITMAP_LABEL (16x16 resource). On click, it emits a dedicated close-position event with the position ticket in sparam. The EA intercepts it and closes only the targeted position, avoiding NETTING vs HEDGING edge cases tied to “close all” semantics. The indicator also tracks chart scale changes to recompute bitmap coordinates using price-to-screen conversion, keeping the control aligned with price lines. Next steps generalize the same event pattern for Stop Loss and Take Profit line actions without code duplication. 👉 Read | Calendar |@mql5algotrading122

Ok.. please wait for a minute

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Hello, good morning admin

EA initialization requires attaching it to a standard symbol chart (for example, BTCUSD). A custom replay symbol is created automatically and should be opened as a separate chart. Replay start is controlled from the panel in the top-left. Use SELECT to place a yellow vertical marker, move it to the target start date, then run LOAD DATA and press PLAY. Playback speed can be adjusted from 0.5x to 20x. Controls include pause and step/skip actions (+10, -10, <<, >>) for fast repositioning. Trade setups are created from the bottom-right drawing toolbar. Choose LONG or SHORT, click to set entry, move the cursor to define stop distance, then click again to lock; take profit is calculated automatically. Entry, stop, and take-profit lines remain draggable for later adjustments. For reliable deep history loading, set Tools > Options > Charts and increase Max bars i... 👉 Read | Quotes |@mql5algotrading122

Backtests often look clean because the tester uses a “current” spread, while the terminal stores a spread value per M1 bar that rarely gets reviewed. SpreadAudit reads M1 history for a configurable lookback and computes median, p90, and p99 spreads in price units and pips. The same percentiles are also normalized by ATR(H1) to make cost comparisons consistent across instruments. It also aggregates average and maximum spread by hour and can export the hourly table to CSV in MQL5\Files. This matters when edge is small. A setup averaging 0.28 price units per trade fails if the median spread is 0.37, even if the backtest equity curve looks stable without realistic costs. Hourly stats typically show widening at rollover and near session opens, where a spread filter expressed as a fraction of ATR remains valid as volatility shifts. Caveat: the recorded fi... 👉 Read | AppStore |@mql5algotrading122

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A losing EURUSD short exposed a deeper issue: stacking RSI/MACD/Stochastic (and even a neural net trained on them) still reduces a nonlinear, chaotic market into mostly linear price transforms, so the model misses context. LLMs can discuss that context, but they are nondeterministic: small prompt or temperature changes flip conclusions, which makes probability outputs unusable for trading decisions. A more reliable path combined CatBoost with quantum feature extraction. Plain indicator features stalled near 59% accuracy; the breakthrough was quantum encoding with 8 qubits plus CZ entanglement, producing a non-uniform state histogram that captured higher-order relationships across indicators. Four derived quantum statistics (entropy, dominant-state strength, state-count complexity, distribution variance) became numeric features. Adding them to 33 tec... 👉 Read | Calendar |@mql5algotrading122

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