Add High-Frequency Trading Market-Making Example

Contribution Proposal: High-Frequency Trading Market-Making Example

Hi NVIDIA cuOpt team,

I would like to propose adding a new example to cuopt-examples: a high-frequency trading market-making optimization example that demonstrates how GPU-accelerated optimization can enable real-time decision-making in financial systems.

Proposed Example

High-Frequency Trading Market-Making with Non-Linear Optimization

This example demonstrates real-time quote optimization for 50+ cryptocurrency trading pairs. The goal is to optimize bid/ask spread, inventory skew, and notional size under a strict real-time budget.

The key point of the example is not only that GPU is faster, but that the CPU baseline cannot meet the real-time requirement, while GPU acceleration makes the workflow feasible.

Problem Description

A market-maker continuously posts buy and sell quotes. For a realistic high-frequency trading system, the optimizer needs to:

• Update quotes roughly 10 times per second
• Optimize across 50+ trading pairs
• Account for fill probability, transaction costs, slippage, and inventory risk
• Solve within a tight optimization budget of approximately 50ms per cycle

The optimization problem includes 150 decision variables:

• Spread per pair
• Skew per pair
• Notional size per pair

The objective includes non-linear terms such as:

• Fill probability: P(fill) = exp(-k * spread)
• Slippage cost: cost = alpha * (size / depth)^2
• Inventory risk: lambda * volatility * abs(position) * abs(skew)

Why This Example Is Useful

This example would show a real-world use case where GPU acceleration is required for the application to be practical.

Current benchmark from the CPU baseline:

• CPU baseline using scipy.optimize: approximately 200–300ms per solve
• Real-time target: less than 50ms
• CPU result: about 5x over budget
• Expected GPU/cuOpt target: approximately 7ms
• Expected speedup: about 35x

This makes the example a strong “CPU cannot do this in real time, GPU makes it possible” demonstration.

What Would Be Included

The contribution can include:

• A complete Python implementation
• CPU baseline using SciPy/SLSQP
• cuOpt-based implementation or adapter
• Sample data generation, so no API keys or proprietary market data are required
• A README with setup and running instructions
• Optional Streamlit dashboard for visualizing solve-time performance
• Benchmarking output comparing CPU timing, budget violations, and expected GPU timing

Educational Value

This example would be useful for:

• Financial engineers and quantitative developers
• Operations research practitioners
• Students learning optimization in finance
• Developers evaluating cuOpt for real-time decision systems

It demonstrates:

1. A realistic financial optimization workflow
2. Non-linear optimization concepts
3. Performance benchmarking
4. Real-time decision constraints
5. Why GPU acceleration matters for latency-sensitive applications