Pending Order Inspector MT5 is a read-only indicator for pre-validating BUY_STOP and SELL_STOP inputs against the current symbol contract. It converts common server-side rejections into a client-side report before Send is pressed. No trading functions are used: no OrderSend, OrderCheck, CTrade, price suggestion, auto-normalization, or risk sizing in v1.00.

It reads Point, tick size, volume min/max/step, stops and freeze levels, Bid/Ask/spread, trade mode, order permissions, and quote age. Point and tick size are shown separately to catch prices that have valid digits but violate the tick grid.

Checks include order side vs Bid/Ask, tick-grid validity for entry/SL/TP, volume bounds and step, stops-level distances, permission for stop orders and SL/TP, and stale quotes. Results are PASS, INVALID, CHECK, UNKNOWN, or INFO, with adjacent grid values shown for of...

👉 Read | CodeBase | @mql5dev

A SuperTrend port is only useful if it matches the reference numerically, not just visually. This implementation aligns with TradingView output bar-for-bar and includes a reproducible verifier based on exported OHLC plus indicator outputs.

Ports typically drift for three reasons. Pine’s ta.rma uses Wilder smoothing with alpha=1/n and specific seeding, not EMA with 2/(n+1). Platform ATR functions also differ in warm-up and true range handling, creating a permanent offset inside the recursion. A common logic error is ignoring “sticky” bands that only tighten and only flip after a close through the band, which can change the trend on roughly half the bars.

Validation compares MQL5 results against a Python recomputation from identical inputs and reports the first failing bar. Tests across EURUSD, USDJPY, XAUUSD, GER40, and BTCUSD show zero direction mi...

👉 Read | AlgoBook | @mql5dev

This part closes a common gap in MT5 EAs: risk-based position sizing can still generate lots the account cannot margin, especially when ATR stops get tight and computed volume rises. Risk (loss at stop) and margin (capital locked at entry) are independent constraints, and ignoring margin leads to rejected orders or trades that consume nearly all free margin.

The fix adds a margin-aware lot cap using OrderCalcMargin() to price 1 lot, then converts free margin into a broker-valid maximum volume with step-aware rounding. If even the minimum lot exceeds the cap, the trade is skipped; otherwise the risk-based lot is reduced.

An optional adaptive layer scales the cap by ACCOUNT_MARGIN_LEVEL, tightening exposure as margin level deteriorates and blocking new trades below a danger threshold.

Finally, checks are consolidated into a named pre-trade validation gate (pos...

👉 Read | CodeBase | @mql5dev

LLM trading models can decay faster than they can be retrained: strong short-term accuracy collapses as regimes shift, and standard fine-tuning overwrites older behaviors that may return in cyclic markets.

SEAL (Self-Evolving Adaptive Learning) reframes the loop into continual learning from live outcomes. Each closed trade becomes feedback, but examples are weighted by move size and “confident but wrong” cases to correct false pattern detection.

A prioritized ring-buffer memory blends freshness with retention of rare, high-value events, while retraining is triggered by quality/regime-change signals (volatility, volume, error distribution) and runs asynchronously to avoid blocking execution. Switching training data from raw JSON to narrative context improves indicator relationship learning.

Operational safeguards cover black swans (anomaly stop), overfitti...

👉 Read | Calendar | @mql5dev

Forum demand for indicator alerts and multi-timeframe signals remains constrained by a common limitation: many users only have compiled .ex5 files, not source, and maintaining custom edits across multiple indicators does not scale.

A standalone approach uses iCustom to load indicators by name and read their buffers without source. EXPLORE mode probes buffers, counts non-empty/zero/blank values across recent bars, and prints a per-bar table so the visible plot or sparse signal buffer can be identified reliably.

WATCH mode then applies triggers to the selected buffer: value appears, level cross, buffer-to-buffer cross, or value change. Closed bars are evaluated by default, with optional tick-by-tick evaluation on the forming bar, still limited to one alert per bar.

A separate change audit distinguishes late values (blank becomes populated later) from repain...

👉 Read | Quotes | @mql5dev

An Expert Advisor built around a SuperTrend port where correctness is defined by matching TradingView output and by matching trades to signals, not by profit claims.

Execution is limited to closed bars to prevent transient intra-bar flips from generating trades that do not exist at bar close. The EA reads the SuperTrend direction via iCustom, trades one position at a time, tags orders with a magic number, and places the stop on the line with an optional points offset. On a flip, the position is closed and reversed.

A known edge case is handled: if the next-tick entry would place the stop on the wrong side (price already beyond the line), the signal is dropped and logged rather than forcing an invalid or near-instant stop-out.

An audit logs the indicator value on every closed bar, then a Python check verifies one entry per flip and zero mismatches (example:...

👉 Read | CodeBase | @mql5dev

Trading-system costs are measurable, but commonly underestimated. A read-only Expert Advisor calculates one round-trip cost per broker and symbol using tick-sampled live spread, swap for long and short including the triple-charge day, and commission derived from closed deals. It also prints the spread value many tools rely on next to the measured one.

Bar “spread” fields are per-bar summaries, not the current quote. On XAUUSD, 60 live readings showed 20 points while the M1 field median was 6, which breaks spread filters and flatters backtests, especially with high trade counts.

Swap is often omitted in custom simulators. It is asymmetric and includes a triple weekday. Example on gold per 0.01 lot: −0.5842 long, +0.4098 short, Wednesday tripled.

Implementation details include counting distinct quotes, refusing closed-market sampling via TimeCurrent vs TimeTra...

👉 Read | AppStore | @mql5dev

Prop Firm Rule Checker is a read-only diagnostic script that evaluates closed-trade history from a live/demo account or Strategy Tester runs against common prop-firm evaluation constraints. Results are printed as a PASS/FAIL report in the Experts/Journal log, reducing manual post-backtest calculations.

Checks include profit target versus starting balance, max daily loss by calendar day, peak-to-valley overall drawdown, a consistency limit to flag single-day profit concentration, and minimum distinct trading days.

Usage involves attaching the script to any chart or running it after a tester pass, then configuring inputs for balance, thresholds, and date range. It uses HistorySelect() only and does not open, modify, or close trades. Rules differ by firm and must be set from the official rule sheet.

👉 Read | VPS | @mql5dev

Most position size calculators evaluate only the next trade and ignore existing exposure. That approach can overstate available risk when multiple symbols are correlated, such as stacking EURUSD and GBPUSD in the same direction and effectively increasing USD exposure.

This script estimates a standard risk-based lot size, then reviews all open positions across the account. It computes recent correlation between each open symbol and the target symbol using the last N bars on a selected timeframe, evaluates whether directions amplify or offset, and aggregates risk already allocated to correlated positions. If combined exposure approaches or exceeds a configurable basket limit, the recommended lot size is reduced.

Output is written to the Experts/Journal log with the positions flagged, correlation values, direction impact, and the adjusted lot size. If any ...

👉 Read | Signals | @mql5dev

Risk-Based Lot Size Calculator is a lightweight MQL5 script designed to standardize position sizing using fixed account risk.

Inputs are limited to InpRiskPercent (risk as a percent of balance, default 1.0) and InpStopLossPips (stop distance in pips, default 20). The script reads the current account balance and symbol trading properties including tick value, tick size, and volume limits (min, max, step).

Lot size is calculated so that a stop-out at the specified pip distance equals the configured risk amount. Output volume is rounded down to the nearest valid step and constrained to broker limits. The calculated lot size and a full breakdown are displayed on-chart and written to the Experts log. Live broker parameters are used, avoiding hard-coded pip assumptions across symbols and account types.

👉 Read | Docs | @mql5dev

MQL5 datetime is an integer epoch (seconds since 1970), but broker server timestamps are the broker’s wall clock stored as if it were UTC. When exported as a raw epoch and parsed as “1970 UTC” in Python or spreadsheets, timestamps shift by the server’s UTC offset. On a UTC-3 server, a daily bar at 00:00 becomes 21:00 of the prior day, and session boundaries move without any parsing errors.

A diagnostic script prints the available clocks and deltas: TimeTradeServer() (server wall clock, works without ticks), TimeCurrent() (last tick time, freezes when ticks stop), TimeGMT() (UTC from the PC), and TimeLocal() (PC local time). It reports server-GMT, local-GMT, server-local, tick lag, plus optional last bar time in server time and real UTC (InpShowLastBar=true).

Guidance: export server timestamps as wall-clock text, or export epoch together with the measured...

👉 Read | Calendar | @mql5dev

An account statement reports realised results, but omits what each trade offered intrabar. This indicator reads closed trades from account history and measures Maximum Favourable Excursion (MFE) and Maximum Adverse Excursion (MAE) using the bar high/low across the trade’s lifespan on the active timeframe.

Each position is rendered with a vertical span from worst to best excursion, an entry marker, and an exit line. A summary panel aggregates medians for winners (MFE, MAE, capture ratio), losers (MAE), trade count, and median bars per trade.

The key metric is capture ratio: realised profit versus the best unrealised profit seen on winning trades. Low medians often indicate exits that consistently give back gains, while tight stops can appear noisy when MAE barely exceeds the stop despite eventual direction being correct.

Limits are stated: intra-bar ...

👉 Read | AlgoBook | @mql5dev

A utility script generates a CSV report with one row per closed position, grouped by DEAL_POSITION_ID, supporting hedging and netting accounts. Output columns include ticket, symbol, direction, entry/exit timestamps, entry/exit prices, profit in points, MFE/MAE in points, capture ratio, bars spanned, and holding time.

Key inputs: InpDias (history days, default 365), InpSoEsteAtivo (restrict to chart symbol or read all symbols), InpTF (timeframe used for excursion measurement, default current), and InpArquivo (CSV name under MQL5\Files).

A summary is printed to the Experts log: trade count, winners/losers, net result, median MFE/MAE for winners, median MAE for losers, median capture ratio, median bars per trade, single-bar trade count, and a count of trades not measurable due to missing historical bars (reported explicitly).

Excursions are computed from bar...

👉 Read | Docs | @mql5dev

CME gold has a daily maintenance break. Across an 11-year hourly sample, the first hour after the reopen shows a repeatable upward drift, while other hours are near flat.

An EA was built around a single rule: buy at the reopen, hold a fixed time, use a server-side stop sized by volatility, then stand down. One trade per session. No averaging, grid, martingale, or recovery logic.

Initial research assumed ~19 points round-trip cost and measured +3.34 bps with t=7.40 (59.3% wins, PF 1.63, 11/11 positive years). Real-tick testing showed the reopen cost is closer to ~60 points because bar-level spread summaries understate the reopen spread. After correction: +1.60 bps, t=3.42, ~50.8% wins, PF 1.30, 10/11 positive years.

Sizing for ~20% drawdown gives ~3.3%/yr with ~4.6% max drawdown, Sharpe ~1.23, ~200 trades/yr, implying non-trivial negative-year risk. Validat...

👉 Read | Docs | @mql5dev

Reusable MQL5 trade-management blocks were added to the Bootstrap library to standardize trailing-stop and break-even handling across EAs. The helpers focus on safe stop updates: validating broker stop levels, preventing “reverse” stop loosening when indicator values change, converting money targets into price distances correctly, and applying consistent symbol/magic and BUY/SELL filtering.

Trailing is covered in multiple styles: fixed points with step control, moving-average, ATR (volatility-adaptive with anti-reverse protection), Parabolic SAR, monetary trailing based on account currency, and periodic trailing that tightens stops over time rather than by price movement.

Break-even is implemented as a one-time stop move after an activation threshold, with optional offsets, available in both point-based and money-based forms. The result is less duplicated c...

👉 Read | Freelance | @mql5dev

M1 OHLC backtests rely on an implicit intra-bar path model. Since OHLC does not record the sequence of prints, the assumed order only becomes material when the bar that closes a trade touches both stop and target, leaving the outcome dependent on the model rather than the market.

A script quantifies this error rate by opening a virtual bracket each minute around the bar open, then advancing until one level is reached. If the closing minute touches both levels, the assumed OHLC path is checked against real tick history. Multiple bracket sizes are swept to produce an error curve.

XAUUSD results over 30 days (27,844 virtual trades): 20pt 62.6% contested, 23.84% wrong; 50pt 30.1%, 8.25%; 100pt 8.9%, 1.66%; 200pt 1.5%, 0.18%; 500pt 0.1%, 0.00%; 1000pt none.

Inputs include symbol, days, bracket list, horizon, sampling step, and optional CSV of mis-resolved cases...

👉 Read | Freelance | @mql5dev

This article turns Jesse Livermore’s “Market Key” into a rule-driven MT5 Expert Advisor using a state machine: uptrend, natural reaction, natural rally, and downtrend. Signals come from a frozen consolidation “pivot” that must break by an ATR-based clearance on expanding volume, with a strict one-bar reversal filter.

Position building is explicit pyramiding across four tranches, sized from a single stored full-lot calculation to keep risk consistent. Adds occur only after follow-through and a low-volume reaction, then a high-volume resumption; exits trigger immediately on “abnormal” against-trend ATR moves with elevated volume, not just stop hits.

Correctness is enforced in OnInit(): tranche percents must total 100, parameters must be sane, and the account must support hedging so each tranche remains a separate ticket. Known gap: no state recovery after ter...

👉 Read | AlgoBook | @mql5dev