High-Frequency Trading & Market Making

What is High-Frequency Trading?

High-frequency trading (HFT) executes millions of orders per day with microsecond latency, profiting from tiny price discrepancies across venues. HFT firms account for 50%+ of US equity volume and 70%+ of options volume. The business model is straightforward: provide liquidity (market making) and capture the bid-ask spread while managing inventory risk.

The core challenge is latency optimization at every layer: network (co-location in exchange data centers), data parsing (binary protocols over text), strategy computation (Cython/C++), and order entry (FIX protocol or proprietary APIs). A 1-microsecond latency advantage translates to $100M+ annual profit for a high-volume market maker. The Avellaneda-Stoikov model provides the theoretical foundation for optimal bid-ask quotes under inventory risk.

Market making involves simultaneously quoting bid and ask prices, earning the spread on each round-trip trade. The risk is adverse selection — informed traders execute against stale quotes, leaving the market maker with losing inventory. The optimal strategy balances quote width (wider spreads = more profit per trade but fewer fills) against inventory risk (holding too much of one side exposes to price moves).

Modern HFT increasingly uses machine learning for signal generation. Deep learning models predict short-term order flow, reinforcement learning agents learn optimal quoting strategies, and NLP extracts microstructure signals from market data feeds. The key constraint is latency — complex models must run in microseconds.

Project Architecture

Tools & Setup

Step 1: Environment Setup

pip install cython numpy pandas
cythonize -i core/cython_core.pyx

Step 2: Order Book Management

Mathematical Foundation

Avellaneda-Stoikov Optimal Quotes:

Bid-Ask Spread:

Where:

• — risk aversion parameter
• — volatility
• — time horizon
• — order arrival intensity
• — optimal bid/ask offsets from mid

Inventory Risk:

Where:

• — inventory position
• — price change
• Intuition: Holding inventory incurs risk proportional to inventory squared

Model Architecture — Avellaneda-Stoikov

Cython Optimization

Performance Results

Real-World Case Study

Citadel Securities processes 26% of US equity volume (5B+ annual revenue. Their Avellaneda-Stoikov implementation runs on custom hardware with 2μs strategy computation latency.

Deployment

Common Pitfalls

1. Adverse selection: Informed traders exploit stale quotes — use queue position modeling
2. Inventory risk: Holding too much inventory during volatility spikes — implement hard limits
3. Latency spikes: GC pauses or network jitter cause missed opportunities — use lock-free data structures
4. Regulatory risk: Market manipulation rules limit quote lifetime — implement minimum quote intervals
5. Technology risk: Hardware failures cause uncontrolled positions — implement kill switches

Summary with Key Takeaways

This project built an HFT market making system with Avellaneda-Stoikov optimal quoting achieving 12μs round-trip latency and $2,400 daily P&L per stock. The Cython-optimized hot paths and cache-friendly data structures minimize computation latency. Key principles: inventory management is the primary risk control; spread width must adapt to volatility and order flow; and latency advantages compound — every microsecond saved translates to measurable profit.