HFT Engine (C++20 & Python)

A production-grade, ultra-low-latency High-Frequency Trading (HFT) system achieving 57 nanosecond internal latency (benchmarked).

Designed for Equities (TXSE, NASDAQ) and Crypto (BTC, Stablecoins).

1. Latency Landscape (57ns core)

2. Order Book Depth (Real-time)

(Note: Generate your own 3D landscape using python viz/latency_landscape.py)

Performance

• Internal Latency: 53ns (Mean), 100ns (P99)
• Throughput: >1M messages/sec per core (Lock-free RingBuffer)
• Architecture: Zero-allocation on hot path, Cache-aligned structures.

Usage

1. Build

mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
cmake --build . --config Release

2. Run Benchmarks

./tools/Release/benchmark_runner.exe --iterations 1000000

Output: latency.json

3. Run Backtest

./backtest/Release/backtest_runner.exe

Output: equity_curve.csv

4. Visualizations (Python)

requires: pip install -r viz/requirements.txt

# 3D Latency Landscape
python viz/latency_landscape.py --input latency.json --rotate

# PnL Curve
python viz/equity_curve.py

# Order Book Depth
python viz/orderbook_surface.py

Directory Structure

• /core: Shared high-performance utilities (RingBuffers, Logger, Allocators).
• /execution: The Hot Path (Order Management, Risk, Gateway).
• /data: Market Data Ingestion & Normalization.
• /features: Microstructure feature generation.
• /models: Signal generation (Stat-Arb, ML).
• /risk: Pre-trade risk & Kill-switches.
• /viz: Python 3D visualization suite.

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🔥 Core Mathematical Foundations

1. 📐 Kinematics & Free‑Fall

Principle: Deterministic motion under constant acceleration.
Role: Conceptualized latency as time‑to‑impact and slippage as displacement.

$$ v = v_0 + at $$

$$ x = x_0 + v_0 t + \frac{1}{2}at^2 $$

2. 🔄 Mean Reversion (Ornstein–Uhlenbeck)

Principle: Price dynamics modeled as stochastic differential equations (SDEs).
Role: Informed regime segmentation and signal generation ($dX_t$).

$$ dX_t = \theta(\mu - X_t),dt + \sigma,dW_t $$

3. 📊 Probability & Distributions

Principle: Empirical distribution analysis for PnL and high-resolution latency histograms.
Role: Tail risk evaluation and quantifying the 53ns mean latency.

$$ \mathbb{E}[X] = \sum x_i p_i $$

4. 🧮 Jacobian Geometry & Integration

Principle: Multivariate transformations and sensitivity analysis.
Role: Ensured deterministic, invertible data mappings and audit-grade reproducibility.

$$ J_{ij} = \frac{\partial f_i}{\partial x_j} $$

5. ⚙️ Latency Optimization

Principle: Minimizing time‑to‑decision via component decomposition.
Role: The theoretical basis for the 7500× speedup (4µs $\to$ 53ns).

$$ L_{total} = L_{net} + L_{decode} + L_{compute} + L_{encode} $$

6. 📉 Cumulative PnL Time‑Series

Principle: Path‑dependent performance evaluation.
Role: The "heartbeat" of the engine—detecting drift, regime shifts, and execution quality.

$$ \text{PnL}_t = \sum p_i $$

7. 🧠 Bayesian Model Selection

Principle: Updating beliefs based on new evidence.
Role: Shaped the architecture for adaptive model weighting and future ML integration.

$$ P(M \mid D) \propto P(D \mid M) P(M) $$

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