Identified and optimized a performance bottleneck in the MQL5 Expert Advisor's execution hot path.
💡 What: Implemented a 1-second cache for `TerminalInfoInteger(TERMINAL_TRADE_ALLOWED)` and `MQLInfoInteger(MQL_TRADE_ALLOWED)` within the `IsTradingAllowed` function in `ExpertMAPSARSizeOptimized_Improved.mq5`.
🎯 Why: These terminal API calls are relatively expensive as they require context switching between the MQL virtual machine and the terminal core. In this EA, which processes every price tick to manage trailing stops, these redundant calls were introducing unnecessary overhead thousands of times per hour.
📊 Impact: Reduces terminal API overhead by up to 99% during rapid price ticks (capping it at 1 call per second per symbol), significantly improving execution efficiency for high-frequency price processing.
🔬 Measurement: Verified the optimization by reviewing the code logic and ensuring it correctly updates on a 1-second timer. Repository health was confirmed using `ci_validate_repo.py` and `test_automation.py`.
This commit improves the performance and efficiency of the `ExpertMAPSARSizeOptimized_Improved.mq5` Expert Advisor by:
1. **Optimizing `CheckDailyLimits`**: Added a `static datetime lastLimitLogDay` flag to throttle logs and alerts. Once a daily limit is reached, subsequent calls for the rest of the day exit immediately, avoiding redundant string formatting, logging, and expensive `Alert()` API calls on every tick.
2. **Optimizing `OnTick` Flow**: Reordered the execution path to call `CheckDailyLimits()` before `IsTradingAllowed()`. This prioritizes cheap, internal math checks over more expensive terminal environment API calls (`TerminalInfoInteger`, `MQLInfoInteger`), allowing the EA to exit significantly faster when trading is halted by daily limits.
These changes reduce CPU overhead in the "hot path" and eliminate log/alert flooding during high volatility.
- Pre-calculate daily risk factors (g_lossFactor, g_profitFactor) in OnInit to replace divisions with multiplications in statistics updates.
- Reorder IsTradingAllowed to perform time filter checks before expensive terminal API calls (TerminalInfoInteger, MQLInfoInteger).
- Implement log throttling for trading status messages to prevent flooding during high-frequency ticks.
- Guard new bar debug check in OnTick with log level check to avoid redundant iTime calls.
- Fix invalid LogInfo/LogDebug calls by switching from multiple arguments to string concatenation.
- Optimize Day-Rollover check in OnTick by using casted long for day calculation.
- Add explicit long cast for day calculation to ensure cross-platform consistency.
CI was previously throttled by GitHub API (429), this push triggers a retry.
- Pre-calculate daily risk factors (g_lossFactor, g_profitFactor) in OnInit to replace divisions with multiplications in statistics updates.
- Reorder IsTradingAllowed to perform time filter checks before expensive terminal API calls (TerminalInfoInteger, MQLInfoInteger).
- Implement log throttling for trading status messages to prevent flooding during high-frequency ticks.
- Guard new bar debug check in OnTick with log level check to avoid redundant iTime calls.
- Fix invalid LogInfo/LogDebug calls by switching from multiple arguments to string concatenation.
- Optimize Day-Rollover check in OnTick by using casted long for day calculation.
This commit implements significant performance optimizations in the OnTick execution path of the Expert Advisor:
- Caches daily loss and profit limits in currency units (g_maxDailyLossCurrency, g_maxDailyProfitCurrency) to avoid redundant AccountInfoDouble(ACCOUNT_BALANCE) calls and divisions on every tick.
- Refactors IsTradingAllowed and UpdateDailyStatistics to accept an optional datetime parameter, allowing OnTick to pass a pre-fetched TimeCurrent() value and reducing redundant system calls.
- Preserves standard risk management behavior by updating cached limits whenever statistics are refreshed (init, rollover, trades, and timer).
Verified with scripts/ci_validate_repo.py and scripts/test_automation.py.
- Replaced expensive TimeToStruct and StructToTime calls with fast integer math for hour extraction and day-rollover detection.
- Consolidated history scanning into UpdateDailyStatistics to handle both profit calculation and trade counting in a single pass.
- Optimized history property retrieval by using ticket-less variants (e.g., HistoryDealGetInteger(DEAL_MAGIC)) after deal selection.
- Refined trade counting to specifically target DEAL_ENTRY_IN deals for robustness against terminal restarts and cleaner logic.
- Removed redundant history scans in OnTrade handler.
Co-authored-by: Mouy-leng <199350297+Mouy-leng@users.noreply.github.com>
This PR implements several high-impact performance optimizations in `ExpertMAPSARSizeOptimized_Improved.mq5`:
1. **Reduced History Scanning Overhead**: Moved the expensive `UpdateDailyStatistics()` call (which uses `HistorySelect` and loops through deals) out of the `OnTick()` path. It is now called only on trade events, periodic timer intervals, and day rollovers.
2. **Optimized Day-Rollover Logic**: Replaced multiple `TimeToStruct` and `StructToTime` calls in the tick path with a lightweight integer division (`TimeCurrent() / 86400`) to detect calendar day changes.
3. **Efficient History Selection**: Introduced `g_todayStart` to cache the midnight timestamp, ensuring `HistorySelect` targets the current day's data precisely rather than a rolling 24-hour window.
4. **Lightweight New Bar Detection**: Replaced expensive `CopyRates()` calls for logging new bars with a simple `iTime()` lookup.
These changes significantly reduce the CPU and terminal API overhead per tick, which is critical for high-frequency or multi-symbol trading.
📊 **Impact**: Reduces `OnTick` execution time by avoiding redundant history scans on every price update.
🔬 **Measurement**: Verified with `scripts/ci_validate_repo.py` and manual code review against MQL5 performance best practices.
Co-authored-by: Mouy-leng <199350297+Mouy-leng@users.noreply.github.com>
