Daily drawdown breaches remain a primary failure point in prop firm challenges, often outweighing strategy quality. A dedicated monitoring layer can reduce operational risk during active trading.

AlphaQuant Prop Firm Daily Drawdown Dashboard is a free Expert Advisor focused on funded-account constraints. It overlays a clean on-screen risk panel rather than adding chart objects, and tracks account equity in real time to quantify remaining loss capacity versus the configured daily limit.

Core functions include equity-based monitoring (balance and equity), large color-coded readouts suited to multi-screen workstations, and status changes from SAFE to WARNING to CRITICAL as the threshold approaches. The baseline can auto-reset at server midnight to match common prop firm rule timing.

Setup is minimal: attach to any chart, set Max Daily DD % (for example 5.0), ...

👉 Read | Quotes | @mql5dev

Hull Moving Average indicator renders up to four configurable periods on a single chart, with direction-based coloring: green on rising values, red on falling values, and grey on flat sections. Line count can be set to 2, 3, or 4 outputs to match the required signal density.

The implementation uses a ring buffer plus incremental running-sum calculations, keeping CPU and memory usage constant regardless of available history. This avoids per-bar recomputation and reduces sensitivity to long backtest ranges.

Buffer layout is documented and stable, supporting reliable access via iCustom() from Expert Advisors and other tooling. Output buffers are aligned for predictable indexing across configurations.

👉 Read | Freelance | @mql5dev

SMC Gold is an intraday Smart Money Concepts EA designed specifically for XAUUSD on M15. It detects a sweep of a confirmed swing high/low, requires a reversal close through the sweep candle body, and only trades in the direction of the H1 trend using a 50 EMA filter.

A key implementation issue surfaced during testing: tick value reporting for XAUUSD can be wrong on some demo feeds. Using SYMBOL_TRADE_TICK_VALUE produced lot sizes around 10x larger than intended, pushing drawdown to 78% on 107 trades. Switching sizing to OrderCalcProfit() aligned actual risk with the configured 1% per trade. Any EA that sizes from tick value should validate XAUUSD contract properties.

Post-fix testing favored M15. M1/M5 underperformed due to spread impact, and H1 produced too few trades. The execution model is single-position, no grid or martingale, with SL/TP attached immedia...

👉 Read | AlgoBook | @mql5dev

Asian Sweep Scorecard adds a self-auditing layer to the common Asian range sweep setup. It marks the Asian session high/low, flags London/NY sweeps that reverse back into the range, then grades each historical signal in R with spread included. Signals are non-repainting and exposed via buffers for EA use.

Range is defined between configurable server-time hours, with days filtered out if the range is too narrow or too wide in ATR terms. A sweep requires a closed bar to wick beyond one side by a minimum ATR amount and close back inside. Confirmation must arrive within a fixed bar count by closing beyond the opposite end of the sweep bar; entry is assumed next bar open, SL is ATR-buffered beyond the wick, TP is set by an R multiple, and trades are force-closed at a daily cutoff.

The panel reports signal counts, win rate, profit factor, average R, and to...

👉 Read | VPS | @mql5dev

A single LLM prompt wired into MT5 tends to overcommit: when asked for buy/sell, it optimizes for producing a signal, not for admitting uncertainty. This mirrors confirmation bias in trading and becomes an architectural flaw, not a data issue.

The article proposes a debate pipeline: three parallel model roles (bull, bear, risk manager) analyze the same market brief, then a low-temperature judge applies hard rules. If risk is HIGH, the result is always hold; trades require aligned direction from at least two voices.

Indicators are computed locally in pure NumPy (MAs/EMAs, multi-period RSI, stochastic, ATR, StdDev, Bollinger position, momentum, candle anatomy) and fed as a structured briefing. A new DEBATE command keeps V17 compatibility, returns JSON plus full rationale for logging, enabling later prompt tuning from stored outcomes.

👉 Read | Quotes | @mql5dev

This article builds a full Hawkes-process toolkit in MQL5 to measure volatility clustering as event timing, not return size. Large moves are converted into an event train, fitted by maximum likelihood, and summarized by the branching ratio n: the expected number of follow-on events per event, interpreting reflexivity on a 0–1 scale.

Key implementation choices make it practical in MetaTrader: event times are measured in bars for numerical stability, and a trailing volatility window of ~100 bars avoids “normalizing away” clusters during event detection.

An exponential kernel enables O(N) intensity updates via recursion and a closed-form compensator, keeping likelihood evaluations fast. The fitter optimizes in (mu, n, beta) to enforce stationarity (n<1) as a simple bound, and verification relies on brute-force checks, numerical integration, Python cross...

👉 Read | VPS | @mql5dev

Most platforms report a single lifetime Profit Factor. That hides time structure and can make different risk profiles look identical.

Rolling Profit Factor restores chronology by recomputing PF over a fixed window of N consecutive closed trades, sliding forward one trade at a time. Trade-count windows keep sample size constant, unlike calendar windows.

Implementation notes for MQL5: compute Gross Profit and absolute Gross Loss per window. Handle GrossLoss≈0 with a high finite sentinel, and mark all-zero windows as undefined. If history < window size, return failure.

An incremental update subtracts the outgoing trade and adds the incoming trade, reducing complexity from O(N×W) to O(N). Architecture splits trade sourcing, history reading, rolling computation, scaling, reference lines, canvas rendering, and test sources via an ITradeSource interface.

👉 Read | Calendar | @mql5dev

Extralonger targets a common pain point in market forecasting: most models handle minutes or hours, but fail on multi-day horizons. It treats market data as spatiotemporal, aiming to stay stable over long histories while keeping compute and memory costs low.

The core idea is a unified space-time representation that merges price “space” and time into one encoding, avoiding duplicated passes across both dimensions. Architecture-wise, it runs three parallel routes (temporal, spatial, mixed) and uses a Global-Local Spatial Transformer to capture both distant cross-asset links and short-range session-level interactions, with a full receptive field over history.

The MQL5 implementation centers on CNeuronExtralonger, adding adaptive attention pooling so route weights are computed per input, not fixed. Modules include shared temporal projection, separate ...

👉 Read | Docs | @mql5dev

A consensus of LLM agents tends to average away rare but valuable signals and stays static even when specific agents underperform. This article replaces voting with a market-style selection mechanism: each agent trades independently and is judged by PnL, not agreement.

The framework runs 10 distinct trading “philosophies” in parallel, tracks per-agent capital and streaks, and rebuilds each system prompt every cycle via build_prompt() to reflect risk posture. Results persist across sessions, and closed trades trigger a REWARD protocol that updates agent state without extra model calls.

On MT5, true separation is achieved with per-agent magic numbers, allowing simultaneous long/short positions on the same symbol. Three modes cover research and production: observer, full parallel trading, and selection that disables bankrupt agents.

Backtest (EURUSD M15) sho...

👉 Read | AlgoBook | @mql5dev

Most spread indicators average over ticks, which underweights quiet minutes and smooths out the spreads paid during rollover, pre-open, and low-liquidity sessions. A timer-based sampler avoids that bias by taking one spread snapshot per second so each minute has equal weight.

PropSpread is built as a monitoring utility for prop firm and funded accounts. It shows live spread in broker points, as raw price difference, and in the symbol’s natural unit derived from digits and calculation mode (Forex pips, index points for CFD indices, otherwise price units). A legend is always printed to verify conversions.

The panel reports rolling stats over a configurable window (min/avg/median/max and time above threshold), plus session stats since server midnight. It adds spread as a percent of ATR from the last closed bar, and includes threshold alerts with hold time...

👉 Read | Freelance | @mql5dev

EMA Cross Demo EA for MetaTrader 5 was released as a compact reference Expert Advisor focused on development method rather than trading advantage. Signals are evaluated once per new bar using closed candles only, preventing repainting. A fast/slow EMA cross triggers entry; an opposite signal closes the current position and opens the new one on the next tick of the new bar.

Risk is based on ATR: stop-loss uses ATR multiplied by a configurable factor, take-profit is derived from the stop distance via a reward:risk input. The EA enforces the broker’s minimum stop and freeze distances, normalizes volume to symbol limits and step size, and skips entries when spread exceeds a configured maximum.

Position handling is safe for both hedging and netting accounts via magic number and symbol lookup. Recent fixes include correct stop-distance flooring against Bid/Ask on...

👉 Read | VPS | @mql5dev

ATR Trailing Stop ported from TradingView Pine Script to MQL5, kept consistent with a Python reference implementation and validated bar-by-bar.

Rules across all versions:
nLoss = ATR multiplier x SMA(true range, InpATRLength). The stop ratchets under price while closes remain above it, and above price while closes remain below it. A close through the stop flips it to the other side and draws an arrow.

Translation details:
True range on the first bar uses high-low due to missing previous close. SMA remains undefined until enough bars exist (first value at bar n-1). Previous stop uses “previous stop else current price” while undefined. Persistent state maps to an indicator buffer in MQL5 and an array in Python. Crossovers are strictly false when either side is undefined, affecting initial bars.

Repository: github.com/RAo797bit/pine-python-mql5-conversion

👉 Read | Forum | @mql5dev

Trade Reporter Lite sends MetaTrader 5 trade events to a Telegram chat in real time. It only reports activity and does not open, close, or modify orders. Monitoring covers all positions on the account, including manual trades and any EA. Restarts do not re-announce positions already open.

Open messages include side, symbol, entry, stop, target, pip distances, and lot size. Close messages include pips, R-multiple based on the first stop, P/L in account currency, entry-to-exit, and time held. Failed deliveries retry with back-off at 5 s, 15 s, 45 s, and 135 s. An on-chart status box shows sent, queued, and failed counts plus a readable error.

Setup requires a Telegram bot token from @BotFather and a chat ID (channel @name or numeric ID). WebRequest must allow https://api.telegram.org. In Strategy Tester, messages are written to the journal as TRL[tester].

👉 Read | Quotes | @mql5dev

CME Gap Tracker for gold and Bitcoin measures weekend gaps using CME Friday close and Sunday open, mapped into broker server time with Chicago DST handling. It plots each gap as a box, keeps unfilled gaps extended until price trades back to the Friday close, then marks the fill and records hours-to-fill. Gaps below a configurable minimum percent are ignored.

A panel summarizes recent history: counts by direction, fill rates within 24 hours and 7 days, median and slow-tail fill times, and “heat before fill” in gap-multiples. A built-in fade test enters at the CME open toward the fill, uses a stop sized by gap multiple, times out after N days, charges entry-bar spread, and treats stop/target conflicts conservatively.

Gold backtests on broker spot data showed high fill rates (roughly 83%–97%), but negative results for a fixed fade strategy due to freque...

👉 Read | AppStore | @mql5dev

Gold breakout EA for XAUUSD on H4 uses a Turtle-style entry: buy when the last closed H4 bar finishes above the prior 20-bar high, only if price is above the 200 EMA. Default is long-only, one position at a time, with a 2x ATR(20) stop and a fixed 2R take-profit. Orders are opened at market, then SL/TP are attached to the fill; failure to attach triggers an immediate close. Risk is position-sized from stop distance, spread is filtered (skip when spread >10% of stop), and entries are blocked near session close.

Backtest (Jan 2020–Sep 2026, 10k deposit, 1% risk, spread+swap, random delay) reported +9,029.72 net, PF 1.77, max DD 11.38%, 198 trades, 46.97% win rate. Returns were concentrated in 2024–2025; 2020–2023 ranged from -2.9% to +5.1% per year. Enabling shorts reduced performance in this sample, with sells negative while buys remained positive.

👉 Read | NeuroBook | @mql5dev

Breakout Exit Lab scores Donchian channel breakouts with three exits applied in parallel: ATR trailing stop, Turtle 10-bar exit channel, and a fixed 2R target. Signals are non-repainting and exposed via buffers for EA use, with a panel reporting results in R with spread deducted.

Entry is the first close beyond the prior 20-bar high/low (Turtle System 1), with trades opened on the next bar. Initial stop is 2.0 x ATR(20). Optional 200 EMA trend filter is available. Exit logic includes ATR trail that never loosens, channel exit using prior lows/highs, and fixed target versus the initial stop. Gaps fill at bar open; stop is assumed first when stop and target fall inside one bar.

Tests on XAUUSD showed exit B leading on H4 in 2025 (+66.20R) and 2026 (+22.35R), while H1 leadership varied by year. A 2026 check on EURUSD, GBPUSD, USDJPY was flat to negative...

👉 Read | Freelance | @mql5dev

Renderer changes after Part 4 add a fill-rule switch to the rasterizer, enabling reliable holes and fill-based borders without rewriting the pipeline.

The scanline sweep already computes a signed winding sum from edge crossings. Non-zero treats any non-zero winding as inside. Even-odd treats an odd number of crossings as inside, ignoring direction, so inset contours produce holes without reversing vertex order.

Implementation impact stays minimal: a stable enum with explicit values, a defaulted Fill(rule) parameter to preserve old call sites, and one inside-test change. Even-odd is implemented by testing winding & 1; parity remains correct even with signed increments in two’s complement.

This fixes common UI shapes: glyph counters, donut gauges, ring borders, and stroke-like shapes built as filled contours, while avoiding double-composited anti-alia...

👉 Read | CodeBase | @mql5dev

Most MQL5 position sizers ask for a risk number that is independent of strategy quality. Fixed percent rules size the same whether win rate and payoff are favorable or not.

Kelly sizing derives the risk fraction from the strategy edge: win probability and average win versus loss. A CKelly class can measure these from closed deal history (net of costs), refuse to act on small samples, and return zero sizing when no edge is detected.

Monte Carlo sweeps show the key trade-off: growth peaks at full Kelly, while drawdown and large-loss frequency keep rising. Fractional Kelly, often half or quarter, retains most growth while materially reducing drawdown and ruin risk.

The code is native MQL5: one reusable class plus a sweep script that outputs CSV. Limits remain: edge is estimated, non-stationary, and correlation or overlapping positions are not covered.

👉 Read | AppStore | @mql5dev

“Historical volatility” hides a real implementation choice: close-only, range-based, or full OHLC estimators embed different assumptions. This study benchmarks five variance estimators (Close-to-Close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang) under a strict persistence forecast: today’s rolling variance becomes tomorrow’s forecast.

Targets are next-session realized-variance proxies built from chronological M1 closes plus the close-to-open jump, with hard rules for session boundaries, gap limits, and endpoint checks. A common target mask ensures every estimator is scored on the same 1,472 EURUSD sessions (2020–2025), avoiding sample drift.

The MQL5 toolkit emphasizes reproducibility: shared OHLC transforms, formula validation, strict chronology safeguards, and paired moving-block bootstrap to test whether observed QLIKE differences (notab...

👉 Read | NeuroBook | @mql5dev

This article replaces clock-based bars with intrinsic time: the market “ticks” only when price reverses by a chosen threshold. An online MQL5 directional-change operator tracks extremes, confirms turns, and splits each move into a directional-change leg plus an overshoot, building a measurable “coastline” from raw ticks.

Using 17.8M EUR/USD ticks, a multi-threshold sweep reproduces key power-law relationships and shows the mean overshoot is roughly the threshold at fine resolutions. A volatility-matched random walk produces nearly identical exponents, suggesting these laws are robust but not a reliable market-structure detector.

The Alpha Engine is then implemented as an MT5 Expert Advisor: a contrarian cascade/de-cascade scheme that adds in fixed steps at intrinsic events, trims on reversals, and controls trend risk with asymmetric thresholds and ...

👉 Read | AppStore | @mql5dev

A non-repainting swing high/low indicator for chart structure and backtesting.

Swings are confirmed only after the required bars to the right have closed. The current forming bar is excluded, so confirmed marks remain unchanged on future candles.

Logic: a swing high is a bar whose high is above the highs of N bars on the left and not below the highs of N bars on the right. Swing lows apply the mirrored rule. This design adds an unavoidable lag of N bars, which is the trade-off for non-repainting behavior.

Key inputs: InpStrength (N, default 5), InpMaxBars (scan depth, default 2000, 0 = all), optional popup alert and mobile push on confirmation.

EA integration: Buffer 0 returns swing high price, Buffer 1 returns swing low price, with EMPTY_VALUE otherwise. Suitable as a neutral building block for BOS/CHoCH logic, not a trading signal.

👉 Read | Forum | @mql5dev