VenomEngine: Ultra-Low Latency LOB & Risk Suite

1. Project Overview & Objectives

VenomEngine is a high-performance, C++23-based matching engine and quantitative risk suite. It is designed to simulate the environment of a High-Frequency Trading (HFT) firm, focusing on nanosecond-level execution and hardware-aware mathematical computations.

Key Objectives:

• Deterministic Latency: Sub-microsecond order matching via cache-optimized data structures.
• Quantitative Signal Generation: Real-time calculation of VPIN (Volume-synchronized Probability of Informed Trading) using SIMD.
• HPC Integration: Utilization of Lock-free SPSC queues, CPU pinning, and AVX-512 intrinsics.

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2. Project Etymology: Why "Venom"?

The name VenomEngine is a play on the project's primary mathematical focus: Order Flow Toxicity.

• The Concept: In high-frequency markets, "Toxic Flow" occurs when liquidity providers are adversely selected by informed traders.
• The "Venom": This engine is specifically architected to detect the "venom" (toxic informed flow) in real-time using the VPIN metric.
• The Engine: Just as an antivenom must act instantly, this engine utilizes AVX-512 vectorization to process volume buckets at hardware speeds, allowing the system to identify toxic shifts in the limit order book before price discovery "bites" the market maker.

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3. Technical Architecture

A. Memory Management

• Zero-Allocation Policy: No std::malloc, new, or smart pointers (std::shared_ptr) are permitted during the matching loop.
• Memory Pooling: All Order objects must be retrieved from a pre-allocated OrderPool (Arena).
• Data-Oriented Design (DOD): Structures are designed to fit within a 64-byte L1 cache line to prevent cache misses and false sharing.

B. Core Data Structures

• Intrusive Doubly-Linked List: Price levels maintain head and tail indices. Orders store next_idx and prev_idx as int32_t to save space and avoid pointer chasing.
• Sparse Array Price Ladder: Indexing price levels via $O(1)$ direct mapping rather than binary search trees.
• Bitset Optimization: Uses std::bitset combined with std::countr_zero (hardware-accelerated TZCNT) to identify the best bid/ask instantly.

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4. Mathematical Specifications

The risk engine calculates VPIN to measure order flow toxicity.

VPIN Derivation

The engine groups trade flow into volume buckets of size $V$. The VPIN metric is calculated as: $$VPIN = \frac{\sum_{\tau=1}^n |V_\tau^B - V_\tau^S|}{n \cdot V}$$

Vectorization Strategy

• SIMD Batching: Volume imbalances across 16+ buckets are calculated in parallel using AVX-512 registers.
• Floating Point Precision: Use float for SIMD throughput unless double is strictly required for precision.

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5. Hardware & Concurrency Layout

The system is partitioned into isolated execution cores to prevent OS context switching.

Component	Responsibility	Technical Requirement
Matching Engine	Order entry, Price-Time priority	Core Pinning, std::atomic
SPSC Queue	Inter-thread message passing	Lock-free, Cache-line padding
Risk Engine	VPIN, Micro-price, Greeks	AVX-512, SIMD Intrinsics
Market Feed	UDP/TCP Simulation	mmap / Shared Memory