SpikingBrain reframes market modeling around event-driven spiking signals, matching the irregular nature of price/volume moves. By reacting only to meaningful changes, it reduces constant compute load, stays stable in noisy regimes, and adapts faster with smaller fine-tuning datasets.

The architecture centers on encoding time series into spikes, integrator units that accumulate impulses as a built-in noise filter, and hybrid layers that separate short-term pattern detection from longer-horizon aggregation. Modularity supports mixing spiking blocks with conventional deep learning for broader workflows.

On the MT5/MQL5 side, the focus shifts to implementation details: leveraging pre-trained Transformer components, replacing brute-force window attention with graphon-based sparse attention plus MoE routing, and adding recurrent linear attention with gates to r...

👉 Read | Freelance | @mql5dev

AI interfaces in MQL5 often fail at the same boundary: the model only reasons over typed prompts and cannot see the chart or read terminal state.

An updated panel closes that gap with two mechanisms: chart vision and tool calling. Vision attaches a chart screenshot (base64 + dimensions) to the message, rendered locally as a thumbnail and a full viewer with scrollbars, plus cleanup tied to chat deletion.

Tool calling exposes a controlled set of JSON-defined functions. The model requests indicator values, open positions, chart objects, trade history, and calendar data, the terminal executes the call, and results are returned as plain text for continuation.

Combined usage reduces manual transcription and keeps access scoped to explicitly permitted reads.

👉 Read | Quotes | @mql5dev

Strategy reports show wins and losses, but they do not separate clean entries from trades that survived long adverse moves. MAE and MFE address this by measuring worst adverse and best favorable excursion after entry, then deriving entry efficiency as MFE / (MFE + MAE), independent of final PnL.

An MQL5 dashboard is outlined: read closed positions, compute excursions from tick history, then render a MAE-vs-MFE scatter plot and an efficiency histogram. A summary printer outputs analyzed count, tick-data exclusions, win/loss/breakeven split, mean efficiency, and best/worst entries.

Implementation details cover grouping deals into positions via DEAL_POSITION_ID, tick retrieval with CopyTicksRange in millisecond time, bid/ask handling by direction, undefined ratios on 0/0, histogram bin edge handling at 1.0, and CCanvas rendering. Arithmetic and stats are struc...

👉 Read | Freelance | @mql5dev

Nested CV fixes selection bias in performance evaluation by re-running model selection inside each outer fold. It does not reduce overfitting inside the selection loop, and it does not separate selection issues from regime drift, label noise, or feature instability.

A practical diagnostic is proposed: measure whether the selection criterion can discriminate candidates at all. Use boundary-shifted purged folds (OffsetPurgedKFold) to generate multiple valid repartitions without shuffling, then score every candidate on every offset.

The audit reports noise_floor (per-candidate dispersion across offsets), candidate_spread (dispersion of candidate means), and resolution_ratio = spread/noise. In the example, ratio=0.49, implying the criterion variance is about 2x the signal; the argmax matched the best-on-average candidate in 1/8 partitions. The implication is...

👉 Read | Freelance | @mql5dev

This article turns the Greatest Swing Value breakout concept into a production-grade MQL5 Expert Advisor by enforcing a strict, ordered pipeline: detect directional setup, collect a fixed sample of “failed swings,” compute their average, project a breakout level from the current bar open, then confirm the breakout using M1 close-to-close crossing. Each setup is valid for only the current bar, keeping signals time-bounded and avoiding stale triggers.

Failure swings are measured from bars that moved in one direction intrabar but closed against it (bearish bars for bullish breakouts: High-Open; bullish bars for bearish breakouts: Open-Low). The EA requires an exact count of qualifying swings within a capped search window, rejecting incomplete samples.

Execution includes broker-safe trade construction: configurable stop placement, two take-profit modes...

👉 Read | AlgoBook | @mql5dev

Z-Score is a normalized way to measure how far price is from a rolling mean, scaled by recent standard deviation. It answers whether the current move is statistically unusual versus the last N bars, without implying reversal by itself.

Common thresholds are ±1 (typical), ±2 (unusual), ±3 (extreme), but fat-tailed returns reduce the reliability of Gaussian assumptions. Regime and timeframe selection remain the primary determinants of usefulness.

Three implementation patterns are outlined: mean reversion via re-entry from ±2 back toward 0, breakout confirmation beyond ±2.5 with exit inside ±1, and trend-filtered pullbacks using a 200 EMA plus ±1.5 bands.

MQL5 coverage includes a custom indicator (SMA, population variance, divide-by-zero protection) and EA logic with bar-close gating, indicator handles, Magic Number scoping, and backtest metrics that s...

👉 Read | Forum | @mql5dev

MD Scalper is a high-frequency scalping EA for MT4 built around a short moving-average channel and pending stop execution with a tight trailing stop.

Signals are derived from two 3-period smoothed MAs on High and Low. A break above MA(High) flags UP, and a break below MA(Low) flags DOWN. Entries also require the current bar range to exceed a configurable Filter threshold.

TradeMode controls order placement: Trend places BUYSTOP above price in rising conditions and SELLSTOP below price in falling conditions. Reversal places the opposite. Both submits both orders and cancels the remaining side after a fill. Pending orders are adjusted one-way to stay close to market.

Execution safeguards include stop/freeze-level awareness, modification throttling, retry logic on common trade errors, latency measurement with optional entry blocking, spread/commission filters,...

👉 Read | Quotes | @mql5dev

Backtests and optimisations depend on the bar history currently stored in the terminal, yet that history is rarely audited. A bar-gap is visually indistinguishable from a quiet market, and the tester will model whatever it is given without warning.

A bar-history audit tool runs six checks over the last N closed bars per symbol/timeframe: OHLC consistency, bar time order and grid alignment, missing bars validated against the symbol’s quote sessions, empty (zero-tick) and flat bars, reconstruction from M1 for cross-timeframe consistency, and spread sanity (zero, above limit, max/mean). Each finding can be written as a CSV row keyed by bar time for independent verification.

Example run: five Market Watch symbols, M15, 5,000 bars each, completed in about one second. Session coverage is the key metric: four symbols reported 100.00%, while XAUUSD showed ...

👉 Read | Calendar | @mql5dev

Market Behavior Analyzer is a chart indicator that converts swing movement into an explicit market-structure model. Instead of marking swing points only, it tracks the move between them and labels structure as HH, HL, LH, and LL. Each completed leg is measured in pips, with supporting metrics such as bar count and retracement percentage.

Two interactive panels are included. Market Story summarizes current structure and tags the latest leg as impulse or pullback. Swing Inspector enables selecting a swing point on-chart to view price, classification, leg length, duration, retracement, and adjacent swing relationships.

Key inputs cover lookback and swing validation: InpLookbackCandles, InpDepth, and InpMinDistPoints. Visual controls toggle lines and labels, adjust fonts and colors, and enable or disable each panel with configurable panel themes.

The to...

👉 Read | Calendar | @mql5dev

Prop Firm Daily Loss Tracker is an on-chart monitor for funded and prop firm accounts. It tracks daily loss consumption without generating trade signals or placing orders.

The panel displays day result in account currency, day start balance, current equity, percent of the daily loss limit used, remaining room for the day, and remaining room to the max total loss. A status line reports OK, WARNING, or LIMIT REACHED.

Day result is computed as the closed P/L for the day plus (equity - balance), excluding deposits and withdrawals. Daily limit is account size multiplied by the daily loss percent. The max loss floor is account size multiplied by (1 - max loss percent). The trading day rolls over at a configurable server hour.

Alerts can trigger once per day at the warning level and again at 100%, with optional push notifications. Settings also include pan...

👉 Read | AppStore | @mql5dev

Edge or Luck evaluates closed trades from account history and estimates whether the average trade is distinguishable from chance. Net profit and profit factor describe outcomes, but not the probability that similar results arise randomly when the sample is small.

The script applies bootstrap resampling (Efron, 1979): it resamples the existing trades with replacement, matches the original trade count, computes an average, and repeats this 10,000 times. The central 95% of simulated averages forms a confidence band, while the fraction below zero estimates the probability that the true average trade is negative. No return distribution is assumed, which fits skewed trading results.

Interpretation: a band fully above zero suggests a detectable edge; including zero indicates luck remains plausible; fully below zero suggests underperformance. By default, fewer ...

👉 Read | Calendar | @mql5dev

Arixis Market Regime Map is a market-state classification indicator for MetaTrader 5 focused on answering a single operational question: what regime is currently active. It avoids Buy/Sell signals and labels price behavior as Trend Up, Trend Down, Range, or Transition.

Classification is derived from quantitative inputs such as Efficiency Ratio, normalized directional slope, volatility, market structure, and agreement across multiple horizons. Efficiency Ratio compares net price displacement to total path length to separate directional movement from noise.

The tool uses causal, non-repainting calculations, includes hysteresis to limit regime churn, and provides historical visualization. A Regime Strength metric summarizes how strongly the measurements support the detected regime, without implying a probability or forecasting direction.

Intended use i...

👉 Read | AlgoBook | @mql5dev

Killzones map repeatable intraday volatility windows: Asian range, London open, NY morning, lunch, and NY afternoon. The tool implements the commonly used ICT session schedule on MT5 and fixes a frequent issue on this platform: consistent time alignment across broker server clocks and DST changes (US and EU).

Five configurable session boxes are drawn with per-session names, colors, and NY-time inputs. Session pivots extend the high/low forward until traded through, with optional midpoints, labels, tooltip hit-rate stats, and a one-shot alert on level breaks. Prior day/week/month high, low, and midlines are included with the same statistics.

Key times add up to three NY-time markers, each with a vertical timestamp and optional opening-price line. Setup is driven by broker winter GMT offset and selected DST calendar; defaults target common GMT+2 winter...

👉 Read | AppStore | @mql5dev

Backtest curves are not evidence. A reproducible workflow is required: formal rules, parameter ranges, and post-optimization validation before any live deployment.

A practical pipeline includes artifacts such as an indicator plus one or two EAs, explicit entry/exit and risk rules, in-sample vs forward splits with window criteria, and a stability search for parameter plateaus instead of single best runs.

Validation extends beyond one tester run: forward analysis on unseen data, multi-market and multi-timeframe checks, and clear thresholds for accept/reject and re-optimization cadence.

Example implementations in MQL5 cover PardoSystem (indicator) and PardoEA / Breakout_Bounce (EAs). The Pardo variant trades on bar open in OHLC modeling, using Envelopes breakout, a simple open-to-open trend filter, and a Pardo-style N-bar extremum breakout, with SL/TP, magic ...

👉 Read | AlgoBook | @mql5dev

Matplotlib-style plotting can be added to MQL5 via a thin client/server bridge rather than reimplementing chart rendering inside MetaTrader 5.

The MQL5 side provides CFigure, CAxes, and CPyplot wrappers that capture figure size, titles, labels, limits, grid, and series data. State is serialized to JSON and sent using WebRequest() to a local Flask service.

The Python service accepts routes per plot type, converts JSON into Matplotlib calls, and handles GUI constraints. Direct GUI rendering from Flask handlers can break due to main-thread requirements.

A terminal-friendly path is to render off-screen, return the figure as image bytes, save as BMP, and display via OBJ_BITMAP_LABEL. This keeps plotting deterministic and decouples UI from trading logic.

👉 Read | AlgoBook | @mql5dev

History Navigator update consolidates two gaps: bookmarks now render as on-chart annotations and can be recalled across symbol/timeframe changes that force EA reinitialization in MetaTrader 5.

Bookmarks are drawn as a vertical line plus a short text label at the saved timestamp, filtered by symbol and timeframe to avoid clutter. Object identifiers use an instance prefix and allocation counter, not bookmark text, preventing collisions and staying within platform name limits. Creation is deferred when the target bar is not yet available.

Cross-context navigation uses a pending-request hand-off via terminal Global Variables. Before ChartSetSymbolPeriod(), the EA writes symbol, timeframe, timestamp, version, tick counters, then exits without Sleep(). After REASON_CHARTCHANGE, the new instance restores the request, waits for SERIES_SYNCHRONIZED and CopyRates() a...

👉 Read | Docs | @mql5dev

Correlation across symbols can hide real risk: multiple positions may behave like one trade if their returns move together. A rolling Pearson correlation matrix helps spot these clusters, but it remains a diagnostic tool, not a full portfolio risk model (it ignores size and direction).

The article builds an MT5 Expert Advisor that tracks symbols from open positions, computes bar-to-bar percentage returns from closed candles, and refreshes the pairwise correlation matrix on each new bar or when the position symbol set changes. Results are rendered as a heatmap with a configurable warning border for strong positive or negative relationships.

Implementation details focus on robust MQL5 design: return series and the matrix are stored in compact structures, the matrix uses a flat array with defined/undefined flags, correlations handle zero-variance series as u...

👉 Read | AppStore | @mql5dev

Correlation-based screening misses deterministic non-linear inputs. MIC (Reshef et al., 2011) targets strength over shape, but it is a maximum over many grids and does not read zero on independence; at 200 points, unrelated columns can score near 0.22.

MINE approximates the grid search by freezing one axis via equipartition (tie-aware), reducing candidate cuts to clump boundaries, and solving the remaining partition exactly with a dynamic program. Runtime is dominated by repeated entropy terms, so integer xlogx lookup tables matter.

MIC comes with MAS, MEV, and MCN to describe dependence form and complexity. Evidence requires a permutation null; for time series, iid shuffles are invalid, so block permutations are used. Multi-feature scans must build the null of the maximum, not per-feature p-values.

👉 Read | Quotes | @mql5dev

Session Sweep Boxes plots Asian, London, and New York sessions as range boxes and flags liquidity sweeps of completed session highs/lows. A sweep is defined as price trading beyond a session extreme and then closing back inside it. Red arrows mark high sweeps (bearish), blue arrows mark low sweeps (bullish). Signals trigger only on closed candles, so outputs do not repaint.

Session times are configured in GMT for broker-independent behavior. Server GMT offset can be auto-detected or set manually; Strategy Tester requires the manual value. If sessions shift after DST changes, manual offset should be used.

After a session ends, its high/low can be extended as dotted lines until swept, invalidated by a close beyond the level, or reset by the next day’s same session. Options include filled boxes, labels, history depth, ATR-based arrow spacing, first-swee...

👉 Read | AppStore | @mql5dev

MiniRocket is ported to native MQL5 as a fast, deterministic time-series feature transform: 84 fixed length-9 convolution kernels (weights -1/2) expanded across dilations produce ~10k bounded PPV features per price window, then a linear classifier handles prediction. No gradient descent, ONNX, or Python runtime, with ~2 ms per 128-bar transform.

The implementation focuses on correctness and speed: shared convolution terms reduce each kernel to a few additions, while Configure/Fit/Transform separation prevents leakage. Port accuracy is validated bit-for-bit against independent references, including sktime (accounting for float32 output).

The article also tackles the “no edge vs broken pipeline” problem by adding explicit controls to the audit harness, plus analysis of what zero-sum kernels can and cannot detect, enabling reproducible evaluation of multiple t...

👉 Read | Docs | @mql5dev

Volatility Regime Shader assigns volatility to three regimes using ATR percentile ranking over a configurable lookback window, avoiding fixed ATR thresholds. The same percentile logic adapts across symbols and timeframes, where a 90th percentile reading means current ATR exceeds 90% of prior values in the window.

Compression is flagged at or below a chosen percentile and shaded light blue. Expansion is flagged at or above a chosen percentile and shaded light red. Normal conditions remain unshaded by default, with an optional Normal shade setting. An upper-right label can display the active regime and current ATR percentile.

Typical uses include regime filtering for automated or discretionary systems. Compression can gate breakout activation, while Expansion supports continuation logic and wider stop placement. Mean-reversion setups generally fit bett...

👉 Read | Freelance | @mql5dev