MT5 historical demonstration on XAUUSD (RoboForex-ECN), 1 Mayβ8 Sep 2026, using 100% real ticks. Test settings: USD 10,000 deposit, 1:100 leverage, fixed 0.01 lot, default parameters (v0.11 defaults match the demonstrated set; trading logic unchanged vs v0.10). Result is optimized history, not a forward test.
Performance summary: 220 trades, net profit USD 1,293.99, profit factor 1.67, max equity drawdown USD 203.00 (1.91%), win rate 28.64%. Both long and short sides were net positive. Low hit rate included 13 consecutive losses, with expectancy driven by larger winners.
Inputs: brick size 6.0 (price units), momentum 8, threshold 5.0, TP 10 bricks, SL 2 bricks, max hold 1440 min, cooldown 3 bricks, max spread/brick 0.35, Magic 26091043.
Operational notes: validate in Strategy Tester. Brick size is price units, not points. Virtual SL/TP require terminal uptim...
π Read | Forum | @mql5dev
Performance summary: 220 trades, net profit USD 1,293.99, profit factor 1.67, max equity drawdown USD 203.00 (1.91%), win rate 28.64%. Both long and short sides were net positive. Low hit rate included 13 consecutive losses, with expectancy driven by larger winners.
Inputs: brick size 6.0 (price units), momentum 8, threshold 5.0, TP 10 bricks, SL 2 bricks, max hold 1440 min, cooldown 3 bricks, max spread/brick 0.35, Magic 26091043.
Operational notes: validate in Strategy Tester. Brick size is price units, not points. Virtual SL/TP require terminal uptim...
π Read | Forum | @mql5dev
β€11π6β‘2π1
TQNet targets multivariate market forecasting by combining fast reaction to current conditions with a learned βglobal memoryβ of stable cross-asset relationships. Instead of building attention queries from raw prices, it uses trainable vectors that shift cyclically over time, while keys/values still come from the live input window. This balances persistent structure (seasonality, recurring liquidity cycles) with local shocks and noise.
Architecturally, it stays lightweight: one multi-head attention block plus a shallow MLP with residual connections, then a linear projection to any forecast horizon. RevIN normalization is used to handle distribution shifts common in finance, keeping the model focused on patterns rather than changing scale/volatility.
The article also outlines an MQL5-oriented implementation path and positions TQNet as a practical a...
π Read | AppStore | @mql5dev
Architecturally, it stays lightweight: one multi-head attention block plus a shallow MLP with residual connections, then a linear projection to any forecast horizon. RevIN normalization is used to handle distribution shifts common in finance, keeping the model focused on patterns rather than changing scale/volatility.
The article also outlines an MQL5-oriented implementation path and positions TQNet as a practical a...
π Read | AppStore | @mql5dev
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AurumNeuro Vanguard is an Expert Advisor focused on XAUUSD, built around a hybrid neural risk design and a Unified Market Dynamics Engine. Signal generation combines causal price analysis, online neural learning, and ATR-based risk controls rather than relying only on standard indicators.
The UMDE layer evaluates direction using price velocity, entropy, and Causal Price Dynamics to filter weaker conditions. A 5-12-3 neural network trains online on prior bar data and is used for directional confirmation plus dynamic TP/SL guidance.
Risk handling supports fixed lot or risk-percent sizing with auto sizing based on SL distance, commission, and tick value. Trade management includes ATR trailing with optional aggressive behavior, volatility-aware stop widening, RR-based profit hard close, and early loss exits when neural confidence drops. Execution filters include ...
π Read | Freelance | @mql5dev
The UMDE layer evaluates direction using price velocity, entropy, and Causal Price Dynamics to filter weaker conditions. A 5-12-3 neural network trains online on prior bar data and is used for directional confirmation plus dynamic TP/SL guidance.
Risk handling supports fixed lot or risk-percent sizing with auto sizing based on SL distance, commission, and tick value. Trade management includes ATR trailing with optional aggressive behavior, volatility-aware stop widening, RR-based profit hard close, and early loss exits when neural confidence drops. Execution filters include ...
π Read | Freelance | @mql5dev
β€10π3π₯1π€©1π1
Butterfly Optimization Algorithm (BOA), proposed in 2019 by Arora and Singh, models movement using a fragrance term f = cΒ·I^a and a switch p between global and local search. Fitness is mapped to stimulus intensity I, then converted to fragrance via the power law, with a increasing toward 1 over epochs to shift from broad search to stronger exploitation.
Implementation review found a critical issue in the paperβs update equations. Using x_new = x + (rΒ²Β·g* - x)Β·f biases steps toward rΒ²Β·g*, which tends to pull the population toward the origin and only looks correct when the optimum is at 0.
A corrected form preserves components but fixes geometry: x_new = x + rΒ²Β·(g* - x)Β·f, and locally x_new = x + rΒ²Β·(x_j - x_k)Β·f. Testing should include optima away from the origin to catch this class of error.
π Read | NeuroBook | @mql5dev
Implementation review found a critical issue in the paperβs update equations. Using x_new = x + (rΒ²Β·g* - x)Β·f biases steps toward rΒ²Β·g*, which tends to pull the population toward the origin and only looks correct when the optimum is at 0.
A corrected form preserves components but fixes geometry: x_new = x + rΒ²Β·(g* - x)Β·f, and locally x_new = x + rΒ²Β·(x_j - x_k)Β·f. Testing should include optima away from the origin to catch this class of error.
π Read | NeuroBook | @mql5dev
β€16π6π₯1π€©1π1
ZetaBurst is a tick-driven scalper that evaluates a short rolling burst window (default 4 seconds) and builds a live baseline from recent returns. Trades trigger only on statistically abnormal moves using a z-score against the last InpStatsSampleCount samples, with a default threshold of 3.0Ο. A prior ATR-fraction trigger was removed after it over-fired on normal tick noise and produced broad losses.
Two execution modes are provided: momentum (trade with the burst) and reversion (trade against it). Symbol fit is not inferred automatically; both modes require separate testing per instrument.
Order handling accounts for real execution delay. Positions open without an attached stop, then SL/TP are computed from the confirmed fill price, clamped to the symbol minimum stop level. If stop attachment fails, the position is closed immediately. Entries also require a...
π Read | AlgoBook | @mql5dev
Two execution modes are provided: momentum (trade with the burst) and reversion (trade against it). Symbol fit is not inferred automatically; both modes require separate testing per instrument.
Order handling accounts for real execution delay. Positions open without an attached stop, then SL/TP are computed from the confirmed fill price, clamped to the symbol minimum stop level. If stop attachment fails, the position is closed immediately. Entries also require a...
π Read | AlgoBook | @mql5dev
β€11π9π₯4π¨βπ»3π€©2
Modern FX trading stacks still face a trade-off between deterministic indicators and ML models that overfit, especially when train/test boundaries are weak. Information leakage remains a primary source of inflated backtest results.
A hybrid design is described: a fine-tuned Llama 3.2 model paired with Self-Evolving Adversarial Learning (SEAL), deployed on MetaTrader 5. The pipeline enforces a forward-test split (last 7 days held out) to keep validation unbiased.
Data generation labels 24h direction on M15 across eight major pairs, applies a 0.05% move threshold, and uses active class balancing near 50/50. Prompts include RSI, MACD, ATR, Bollinger position, stochastic, and volume ratio with a strict parsable output format.
SEAL adds adversarial self-play, prioritized replay, curriculum difficulty, and evolutionary updates to improve robustness und...
π Read | Freelance | @mql5dev
A hybrid design is described: a fine-tuned Llama 3.2 model paired with Self-Evolving Adversarial Learning (SEAL), deployed on MetaTrader 5. The pipeline enforces a forward-test split (last 7 days held out) to keep validation unbiased.
Data generation labels 24h direction on M15 across eight major pairs, applies a 0.05% move threshold, and uses active class balancing near 50/50. Prompts include RSI, MACD, ATR, Bollinger position, stochastic, and volume ratio with a strict parsable output format.
SEAL adds adversarial self-play, prioritized replay, curriculum difficulty, and evolutionary updates to improve robustness und...
π Read | Freelance | @mql5dev
β€17π5π€©3β‘1π1π1
MetaTrader 5 Strategy Tester can produce a strong equity curve that fails to reproduce in forward or live trading. A single backtest reflects one ordering of trades and hides the range of possible equity paths, including tail drawdowns.
Monte Carlo simulation reshuffles the closed-trade PnL sequence to generate many alternative equity curves. Monte Carlo analysis then extracts metrics from that distribution, including bust rate, profit rate, and worst observed drawdown.
A practical Python pipeline can parse the MT5 HTML report, extract initial balance and βDirection=outβ PnL rows, run N shuffled paths, and output a mean curve with 5β95% bands plus summary stats. This supports position sizing based on tail risk rather than one curve.
π Read | Quotes | @mql5dev
Monte Carlo simulation reshuffles the closed-trade PnL sequence to generate many alternative equity curves. Monte Carlo analysis then extracts metrics from that distribution, including bust rate, profit rate, and worst observed drawdown.
A practical Python pipeline can parse the MT5 HTML report, extract initial balance and βDirection=outβ PnL rows, run N shuffled paths, and output a mean curve with 5β95% bands plus summary stats. This supports position sizing based on tail risk rather than one curve.
π Read | Quotes | @mql5dev
β€22π₯5π4β‘2π€©1π1π1
Moving from point-to-point bridges to an event-bus design, this article builds a native Kafka producer directly in MQL5 so one terminal can publish signals to a topic while any number of consumers independently subscribe, replay, and track offsets.
The core work is implementing Kafkaβs wire protocol over raw TCP: big-endian framing with a 4-byte length prefix, RecordBatch v2 encoding, base-128 varints with zigzag for deltas, and CRC32C (Castagnoli) with a runtime-built lookup table. RecordBatch encoding is handled as a two-pass βwrite placeholders then patchβ process for lengths and checksums.
On the trading side, signals are versioned and schema-checked at init, keyed by symbol+timeframe for partition ordering, queued and flushed on a timer or batch size, with configurable acks and exponential-backoff retries. Logging via FILE_COMMON produces tester-friendly,...
π Read | Quotes | @mql5dev
The core work is implementing Kafkaβs wire protocol over raw TCP: big-endian framing with a 4-byte length prefix, RecordBatch v2 encoding, base-128 varints with zigzag for deltas, and CRC32C (Castagnoli) with a runtime-built lookup table. RecordBatch encoding is handled as a two-pass βwrite placeholders then patchβ process for lengths and checksums.
On the trading side, signals are versioned and schema-checked at init, keyed by symbol+timeframe for partition ordering, queued and flushed on a timer or batch size, with configurable acks and exponential-backoff retries. Logging via FILE_COMMON produces tester-friendly,...
π Read | Quotes | @mql5dev
β€17π11π€©3π¨βπ»2π₯1π€£1
Part 15βs decision-forest classifier is turned into a tradable EA by separating prediction from execution. The model produces three class scores (bearish/neutral/bullish) from normalized bar features, but a decision layer gates whether that output is allowed to influence positions.
The classifier is refactored into a reusable module that encapsulates feature building, training stats, ALGLIB forest objects, and safe lifecycle rules (no inference until training succeeds, completed-bar history only, reproducible seeding, OOB error exposed for diagnostics).
The EA runs once per new bar, converts raw votes into a stable βdirectional regimeβ using a minimum-confidence filter, multi-bar confirmation, and a post-change cooldown. Trade policy is intentionally simple: long-only in bullish, short-only in bearish, otherwise no-entry, with strict risk sizing, spread/...
π Read | CodeBase | @mql5dev
The classifier is refactored into a reusable module that encapsulates feature building, training stats, ALGLIB forest objects, and safe lifecycle rules (no inference until training succeeds, completed-bar history only, reproducible seeding, OOB error exposed for diagnostics).
The EA runs once per new bar, converts raw votes into a stable βdirectional regimeβ using a minimum-confidence filter, multi-bar confirmation, and a post-change cooldown. Trade policy is intentionally simple: long-only in bullish, short-only in bearish, otherwise no-entry, with strict risk sizing, spread/...
π Read | CodeBase | @mql5dev
β€20π13π₯7π€©3π2π€£2π1
Most trading anomaly detection watches price; this design watches execution quality: slippage, fill latency, spread paid, partial fills, and clustered requotes. A native Isolation Forest is implemented entirely in MQL5 and scored on every confirmed entry inside OnTradeTransaction(), acting as a monitoring/circuit-breaker layer rather than a signal generator.
The EA logs a 5D feature vector per fill, trains on a rolling window, and flags multivariate anomalies that single thresholds miss. Isolation Forest is chosen for unsupervised training, low parameter load, and fast scoring (trees x height), with configurable timing logs to verify runtime.
Implementation details that matter: strict feature-count enforcement to prevent invalid scoring, flat-array tree storage for serialization, subsampling without replacement, leaf-size path-length correction, binary p...
π Read | Signals | @mql5dev
The EA logs a 5D feature vector per fill, trains on a rolling window, and flags multivariate anomalies that single thresholds miss. Isolation Forest is chosen for unsupervised training, low parameter load, and fast scoring (trees x height), with configurable timing logs to verify runtime.
Implementation details that matter: strict feature-count enforcement to prevent invalid scoring, flat-array tree storage for serialization, subsampling without replacement, leaf-size path-length correction, binary p...
π Read | Signals | @mql5dev
β€27π12π₯9π€©6π€£2π1
TQNet targets time-series problems where both short-term moves and long-range structure matter. It does this by maintaining a global correlation tensor that acts as periodic memory, letting relationships form across the sequence without a fixed direction.
The implementation continues in MQL5 with a TQ-MHA module: multi-head attention where queries come from stored correlation parameters while keys/values come from current market data. This merges global context with fresh price action more safely than standard cross-attention.
A new CNeuronTQMHA class reuses cross-attention internals but changes residual/normalization to avoid overwriting local signals. Init is explicit and OpenCL-backed: GeLU for smoother training on noisy quotes, bounded timeframe indexing for carousel switching, and zeroed correlation buffers to prevent biased starts.
π Read | Signals | @mql5dev
The implementation continues in MQL5 with a TQ-MHA module: multi-head attention where queries come from stored correlation parameters while keys/values come from current market data. This merges global context with fresh price action more safely than standard cross-attention.
A new CNeuronTQMHA class reuses cross-attention internals but changes residual/normalization to avoid overwriting local signals. Init is explicit and OpenCL-backed: GeLU for smoother training on noisy quotes, bounded timeframe indexing for carousel switching, and zeroed correlation buffers to prevent biased starts.
π Read | Signals | @mql5dev
β€13π₯10π9π€£4π1
MQL5 signal experiment shifts focus from detecting setups to timing entries under volatility regime changes. A dual-engine design combines a GARCH(1,1) volatility expansion gate with an optional volatility-scaled LSTM that scores short feature sequences.
Seven execution modes cover breakout, squeeze release, re-entry, mid-band impulse, band walk, pullback continuation, and range escape. Modes share a graded 0.5-centered score, then pass common LongCondition/ShortCondition gates: minimum GARCH expansion, raw pattern threshold, and final entry probability.
LSTM inputs are volatility-normalized (returns scaled by GARCH sigma, ATR and band metrics, expansion ratio). The key test is redundancy: ATR/Bollinger and GARCH can overlap, so validation requires switching algorithm-only vs blended LSTM and comparing backtests plus forward walks.
π Read | CodeBase | @mql5dev
Seven execution modes cover breakout, squeeze release, re-entry, mid-band impulse, band walk, pullback continuation, and range escape. Modes share a graded 0.5-centered score, then pass common LongCondition/ShortCondition gates: minimum GARCH expansion, raw pattern threshold, and final entry probability.
LSTM inputs are volatility-normalized (returns scaled by GARCH sigma, ATR and band metrics, expansion ratio). The key test is redundancy: ATR/Bollinger and GARCH can overlap, so validation requires switching algorithm-only vs blended LSTM and comparing backtests plus forward walks.
π Read | CodeBase | @mql5dev
β€15π9π€£3π₯1π€©1π1
Running multiple MQL5 Expert Advisors in one terminal means no shared state beyond GlobalVariables. That namespace is untyped, schema-free, and cannot distinguish stale from current values.
A named-pipe message bus provides explicit message framing and a fixed, typed schema agreed by all participants. The broker EA owns the pipe server, message registry, risk aggregator, and a chart dashboard. Slave EAs connect as clients, report per-magic position state, and receive a centrally computed portfolio risk figure plus the symbol identified as the cause.
Implementation details matter: kernel32.dll imports, message-mode pipes, non-blocking accept via PIPE_NOWAIT, and connection confirmation via PeekNamedPipe due to unreliable last-error reads in MQL5. Messages serialize to a fixed 52-byte little-endian layout, with doubles copied bit-exact.
Limits remain expl...
π Read | CodeBase | @mql5dev
A named-pipe message bus provides explicit message framing and a fixed, typed schema agreed by all participants. The broker EA owns the pipe server, message registry, risk aggregator, and a chart dashboard. Slave EAs connect as clients, report per-magic position state, and receive a centrally computed portfolio risk figure plus the symbol identified as the cause.
Implementation details matter: kernel32.dll imports, message-mode pipes, non-blocking accept via PIPE_NOWAIT, and connection confirmation via PeekNamedPipe due to unreliable last-error reads in MQL5. Messages serialize to a fixed 52-byte little-endian layout, with doubles copied bit-exact.
Limits remain expl...
π Read | CodeBase | @mql5dev
β€13π4π₯2π€£2
Supertrend MTF Indicator is a free multi-timeframe trend tool for MT4. It plots bullish and bearish conditions on the chart using colored Supertrend lines and signal arrows.
Key capabilities include multi-timeframe trend confirmation, Buy/Sell arrow signals, and parameter controls for adapting sensitivity. It can be applied to Forex, Gold, indices, and other MT4 instruments, across different chart timeframes.
This is a visual indicator only and does not place or manage orders. An Expert Advisor based on the same Supertrend MTF logic is available separately for automated execution, while the indicator source is suited to manual analysis, education, and custom modifications.
Any trading tool should be validated on a demo account before live use.
π Read | Signals | @mql5dev
Key capabilities include multi-timeframe trend confirmation, Buy/Sell arrow signals, and parameter controls for adapting sensitivity. It can be applied to Forex, Gold, indices, and other MT4 instruments, across different chart timeframes.
This is a visual indicator only and does not place or manage orders. An Expert Advisor based on the same Supertrend MTF logic is available separately for automated execution, while the indicator source is suited to manual analysis, education, and custom modifications.
Any trading tool should be validated on a demo account before live use.
π Read | Signals | @mql5dev
π11β€9π₯5π€©3π2π¨βπ»2π€£1
A Renko indicator for MT5 that renders fixed-size bricks using BID ticks and outputs only completed bricks. Reversal logic follows the classic two-brick rule, with square, equal-width brick rendering.
The chart window is used only as a host. Visible brick count is calculated automatically, the Renko range is vertically centered, and the real price scale remains on the right.
Implementation avoids offline charts, custom symbols, DLLs, and external libraries. A single primary input is used: Brick Size. Original chart visual settings are restored after removal.
Published as a CodeBase example aimed at studying Renko construction and MQL5 Canvas rendering.
π Read | Calendar | @mql5dev
The chart window is used only as a host. Visible brick count is calculated automatically, the Renko range is vertically centered, and the real price scale remains on the right.
Implementation avoids offline charts, custom symbols, DLLs, and external libraries. A single primary input is used: Brick Size. Original chart visual settings are restored after removal.
Published as a CodeBase example aimed at studying Renko construction and MQL5 Canvas rendering.
π Read | Calendar | @mql5dev
β€19π6π₯2π€£2π¨βπ»2π€©1
Incoming BID ticks are routed to a classic Renko builder using a two-brick reversal rule. ADX, +DI, and -DI update only after a brick is completed. ADX acts as a strength gate, while +DI/-DI restrict trade direction. Entries require the newest completed brick to confirm direction and meet the configured Renko run. Exits can be requested by opposite DI, plus virtual TP/SL and a maximum holding time.
Default demo profile: brick size 16, ADX(14) threshold 8.5, min DI separation 2.5, entry run 3 bricks, TP 9.5 bricks, SL 42 bricks, max hold 1060 minutes, cooldown 6 bricks, max spread/brick 0.35, lot 0.01.
Real-tick test: XAUUSD M1, 2026-05-01 to 2026-09-08, 100% quality, 48,458,994 ticks. 46 trades, net +$1,095.53, PF 3.14, max equity DD 1.36%. Educational backtest only; outcomes depend on symbol specs, tick data, spread, execution, and broker conditions....
π Read | Forum | @mql5dev
Default demo profile: brick size 16, ADX(14) threshold 8.5, min DI separation 2.5, entry run 3 bricks, TP 9.5 bricks, SL 42 bricks, max hold 1060 minutes, cooldown 6 bricks, max spread/brick 0.35, lot 0.01.
Real-tick test: XAUUSD M1, 2026-05-01 to 2026-09-08, 100% quality, 48,458,994 ticks. 46 trades, net +$1,095.53, PF 3.14, max equity DD 1.36%. Educational backtest only; outcomes depend on symbol specs, tick data, spread, execution, and broker conditions....
π Read | Forum | @mql5dev
β€21π5π2π₯1π€£1
A Renko + Bollinger EA design ships with four modes enabled by default: Breakout (brick close beyond outer band), Re-entry/mean reversion (outside then back inside), Midline Cross (state change on middle line cross), and Squeeze Breakout (band-width filter before accepting breakout). Each mode has its own demonstration profile: brick size, Bollinger period/deviation, entry run, TP/SL in bricks, max hold time, and cooldown.
Portfolio controls allow per-mode enable/disable, one position per mode on hedging accounts, and an effective single net position per symbol on netting accounts. An optional conflict filter skips entries when modes signal opposite directions on the same tick. Separate magic numbers identify which mode opened a position after restarts.
A historical educational run on XAUUSD M1 (2026-05-01 to 2026-09-08, real ticks) reported 108 trades wit...
π Read | AppStore | @mql5dev
Portfolio controls allow per-mode enable/disable, one position per mode on hedging accounts, and an effective single net position per symbol on netting accounts. An optional conflict filter skips entries when modes signal opposite directions on the same tick. Separate magic numbers identify which mode opened a position after restarts.
A historical educational run on XAUUSD M1 (2026-05-01 to 2026-09-08, real ticks) reported 108 trades wit...
π Read | AppStore | @mql5dev
β€21π6β1π₯1π€£1
New CodeBase indicator example demonstrates custom Renko brick rendering with a causal Bollinger Bands overlay.
Parameters include Brick Size in price units, Bollinger lookback measured in completed Renko bricks, and the standard-deviation multiplier.
Rendering uses cyan up bricks and red down bricks, with blue outer Bollinger bands and a gold middle band. Bricks are square with automatic scaling. Drawing is handled through a single Canvas bitmap layer rather than large sets of rectangle objects, with automatic redraw on chart resize.
No DLLs, custom symbols, offline charts, or external indicators are required. Intended for education and implementation reference only, with no trading signals and no performance claims.
π Read | Docs | @mql5dev
Parameters include Brick Size in price units, Bollinger lookback measured in completed Renko bricks, and the standard-deviation multiplier.
Rendering uses cyan up bricks and red down bricks, with blue outer Bollinger bands and a gold middle band. Bricks are square with automatic scaling. Drawing is handled through a single Canvas bitmap layer rather than large sets of rectangle objects, with automatic redraw on chart resize.
No DLLs, custom symbols, offline charts, or external indicators are required. Intended for education and implementation reference only, with no trading signals and no performance claims.
π Read | Docs | @mql5dev
β€17π₯5π3π€©2π€£1
A Renko statistics panel is available for monitoring recent price action using only completed bricks and the classic two-brick reversal rule.
Displayed metrics include up/down brick counts with percentages, directional balance, reversal rate, average and maximum run length, current run direction with length, bricks formed per hour, and average minutes per completed brick.
Key inputs are Brick Size (fixed Renko brick size in price units) and Lookback Bricks (number of recent completed bricks used to compute statistics).
Implementation does not create offline charts or custom symbols and requires no DLLs or external indicators. The tool is intended for educational and statistical use, provides no BUY/SELL signals, and makes no claims of predictability or future profitability.
π Read | Signals | @mql5dev
Displayed metrics include up/down brick counts with percentages, directional balance, reversal rate, average and maximum run length, current run direction with length, bricks formed per hour, and average minutes per completed brick.
Key inputs are Brick Size (fixed Renko brick size in price units) and Lookback Bricks (number of recent completed bricks used to compute statistics).
Implementation does not create offline charts or custom symbols and requires no DLLs or external indicators. The tool is intended for educational and statistical use, provides no BUY/SELL signals, and makes no claims of predictability or future profitability.
π Read | Signals | @mql5dev
β€18π4π€©4π₯2π€£1
Renko charting is rendered on a sequence axis rather than time. The internal Renko builder consumes incoming BID ticks and applies the classic two-brick reversal rule.
The Donchian Channel is calculated causally. On each newly completed brick, upper and lower bounds are derived only from the previous N completed Renko bricks. The active brick is excluded from its own channel window. Inputs include fixed Brick Size in price units and Donchian Period as the lookback in completed bricks.
Implementation details include an internal fixed-size Renko, square bricks, and stepped Donchian boundaries with a midpoint. Rendering uses a single CCanvas bitmap layer instead of many chart objects, with no offline chart, custom symbol, DLL, or external indicator dependencies.
Provided as an educational visualization, not a trading system or a recommendation to trade br...
π Read | Signals | @mql5dev
The Donchian Channel is calculated causally. On each newly completed brick, upper and lower bounds are derived only from the previous N completed Renko bricks. The active brick is excluded from its own channel window. Inputs include fixed Brick Size in price units and Donchian Period as the lookback in completed bricks.
Implementation details include an internal fixed-size Renko, square bricks, and stepped Donchian boundaries with a midpoint. Rendering uses a single CCanvas bitmap layer instead of many chart objects, with no offline chart, custom symbol, DLL, or external indicator dependencies.
Provided as an educational visualization, not a trading system or a recommendation to trade br...
π Read | Signals | @mql5dev
β€14π9π₯5π€©5π€£2β‘1
This update to the MT5 replay/simulation position indicator adds switchable display modes so traders can avoid focusing on monetary P/L while still monitoring risk during volatile periods.
A scoped enum inside the position structure defines modes (money, ticks, points, percent), letting the ViewValue routine format output via a clean switch and scope resolution. An extra βticks-to-priceβ support value is computed once and passed into the class to keep formatting fast and consistent.
Mode changes are triggered by clicking the OBJ_EDIT field: a custom event cycles the enum and forces a refresh of all indicator segments (entry, SL, TP) to prevent mixed units. On hedging accounts, updates are isolated per position via a ticket check, with an easy path to broadcast changes if desired.
π Read | NeuroBook | @mql5dev
A scoped enum inside the position structure defines modes (money, ticks, points, percent), letting the ViewValue routine format output via a clean switch and scope resolution. An extra βticks-to-priceβ support value is computed once and passed into the class to keep formatting fast and consistent.
Mode changes are triggered by clicking the OBJ_EDIT field: a custom event cycles the enum and forces a refresh of all indicator segments (entry, SL, TP) to prevent mixed units. On hedging accounts, updates are isolated per position via a ticket check, with an easy path to broadcast changes if desired.
π Read | NeuroBook | @mql5dev
β€15π8π€©6π₯4β‘2π€£1