Muhammad Minhas Qamar / Profil

The article addresses online false discovery rate control for streaming signals, where fixed thresholds inflate noise discoveries. We port LORD3, LORD++ and SAFFRON into MQL5, construct a causal bootstrap p‑value source without a resolution floor, and verify correctness before market data. Dependence stress tests, a budget/level indicator and a two-arm Strategy Tester show how to configure, monitor and throttle streams to keep the acted-on noise fraction within target.

Tick volume mostly reflects the time of day and is only complete when a bar closes. We build a per‑quote, live activity gauge from the waits between quote changes, remove the intraday rhythm, and model the remainder with the autoregressive conditional duration (ACD) model. We then test whether this activity measure improves short‑horizon volatility forecasts once live price volatility is already in the model.

This article builds forecast reconciliation in pure MQL5 and applies it to two structures: timeframe aggregation on one symbol and a currency graph linked by no‑arbitrage. The engine projects base forecasts onto linear constraints to make them coherent. We benchmark OLS/WLS and MinT with and without shrinkage on long walk‑forward runs, show when total squared error decreases, and measure the cost that may concentrate in a single node.

We demonstrate live forecast charting with Kronos in MQL5 and an evaluation pipeline designed to be look‑ahead‑safe. Deterministic dumps are scored per horizon against a random‑walk baseline (15,113 predictions), then fed to a simple Strategy Tester rule on EURUSD H1. The findings show no edge, and illustrate how a high threshold can yield a persuasive but low‑information backtest with very few trades.

This article delivers a native MQL5 MiniRocket: 84 fixed convolution kernels yield 9,996 features quickly and deterministically, requiring no training loop and no external runtime. We verify the port against sktime and a float64 reimplementation, then run a reproducible audit of seven classic setups; planted and coin‑flip controls confirm correctness, and a 25%‑flipped control sets the detection threshold.

This article presents a full MQL5 pipeline for Toeplitz Inverse Covariance Clustering: stacked observations, ADMM‑based graphical lasso with block‑Toeplitz constraints, and dynamic‑programming regime assignment. It separates structure from volatility and conditions away the shared USD leg. The fitted regimes are displayed causally as a non-repainting ribbon with a dependency graph for the active state.

We use the MetaTrader 5 AI Assistant to execute the full workflow end to end: create an EA from a natural‑language prompt, compile it, break and watch it self‑correct from compiler output, backtest it, and compare a controlled re‑run. The article details the underlying MCP extensions, configuration and safety limits, and how to connect external AI clients. Readers get a repeatable process for building and testing EAs inside the platform.

We implement Symbolic Fourier Approximation in MQL5 and compare it to SAX under a shared harness on identical price windows. SFA keeps low‑frequency Fourier coefficients and learns per‑position bins (MCB), with a proven, sound lower bound. The measurements show how the same bit budget behaves under different splits of word length and alphabet, and give a practical rule for choosing settings for your symbol.

The article implements intrinsic-time analysis in MQL5: an event-based directional-change operator that splits ticks into directional-change and overshoot sections. We reproduce the core scaling laws on 17.8 million live EUR/USD ticks and compare them to a random-walk baseline. Finally, we build a hedging-account Expert Advisor that trades the Alpha Engine with limit orders, detailing thresholds, inventory skew, and liquidity control for practical reuse.

This article reframes performance from unconditional win rate to conditional probability given survival time. It introduces an MQL5 library, an on‑chart indicator, and a demo Expert Advisor that read deal history, fit Kaplan–Meier and Aalen–Johansen curves with competing risks, and report forward probabilities over a bar‑based horizon. Readers gain a reproducible way to quantify the chance that the current position reaches its target or stop, and to see the bias of the naive censoring approach.

FX options are quoted in delta space, not by strike. This article implements an FX-native smile tool for MetaTrader 5: it converts ATM, risk reversal and butterfly quotes into strike-space pillars, prices with the Garman–Kohlhagen model, handles spot/forward and premium-adjusted delta conventions per pair, and draws the smile with a reconstructed strike ladder and Greeks.

The article builds an MQL5 indicator that recovers the risk-neutral density from an option chain via the Breeden–Litzenberger identity. Quotes are inverted to implied volatilities, the smile is smoothed and priced back to arbitrage‑free calls, and the second derivative yields the density. The tool reports probabilities above any level, the expected move, skew and kurtosis, and overlays the realized-return distribution for comparison.

This article builds a trend-following Expert Advisor that trades momentum spillover across markets, implemented fully in MQL5 without external solvers. It detects leaders with Derivative Dynamic Time Warping, learns a sparse weighted network by convex optimization, and propagates momentum through it with a reverting response. Readers get a step-by-step, reproducible pipeline and a working EA ready to run in the Strategy Tester.

We complete the native MQL5 port of Kronos: the decoder, the predictor's decode_s1 and decode_s2 stages with their cross-attention traps, and the autoregressive loop that produces a multi-bar forecast. Then we profile and make it roughly 4.5x faster with an exact KV-cache and pre-transposed weights, verifying every stage against PyTorch.

We port Daniel Bloch's Relative Moving Average framework into a complete MetaTrader 5 system. Instead of smoothing price, the RMA measures where price sits inside its own recent distribution on a [0,1] fractile scale, and drives four cross-strategies with a regime-adaptive exit. Includes the engine, indicators, and a backtested Expert Advisor.

This article implements BOSS from scratch in MQL5 and applies it to regime classification: SFA turns windows into words, bags record word frequencies, and an ensemble over window lengths votes on labels. We cover the encoding steps, the BOSS distance, training with auto-generated regime labels, and practical parameters. A BTCUSD benchmark versus DTW shows higher macro accuracy on clean data and markedly faster inference.