Discussing the article: "MQL5 Wizard Techniques you should know (Part 89): Using Bitwise Vectorization with Perceptron Classifiers"
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Check out the new article: MQL5 Wizard Techniques you should know (Part 89): Using Bitwise Vectorization with Perceptron Classifiers.
This article presents a custom MQL5 signal class, CSignalBitwisePerceptron, for ultra-lightweight entry logic. It packs 64 bars into a single uint64 via bitwise vectorization and evaluates them with a perceptron that sums weights only for active bits. A two-gate flow (algorithmic hash map plus neural threshold) minimizes array iteration and heavy math. Readers get a practical template to cut latency and refine entry validation.
The entry logic for algorithms is usually a bottleneck with real-time trading systems: with every incoming tick or new bar, an Expert Advisor can do dozens of floating-point reads and loop iterations(up to 64 historical reads) to put together OHLC context, fill indicator buffers and prepare ML inputs. In quiet markets this cost can be acceptable; when having bursts of volume and tick activity it turns into latency that one can track, meaning - later order submission, worse fills, higher slippage. Our engineering goal therefore becomes simple: restructure the entry path so that a 64-bar context is read and evaluated almost instantly, without using expensive double arrays or matrix math while waiting on a decision.
To achieve this, we use CSignalBitwisePerceptron: a compact MQL5 Signal class that vectorizes 64 periods into one 64-bit ‘word’, applying a O(1) bitwise map gate giving us ultra-fast pattern matching and also using a lightweight perceptron gate (hardware-level dot-product over bits) as a contextual filter. This pairing keeps floating-point work low,provides a single tunable confidence parameter (m_threshold) for trade off frequency vs precision, and - per forward walk tests - has potential to reduce drawdown while keeping execution latency near zero.
Author: Stephen Njuki