Free T(h)r(e)ading: A Trading Systems Journey Beyond the GIL

The release of Python 3.13 with experimental free-threaded mode (PEP 703) represents a fundamental shift in Python's concurrency model. For decades, the Global Interpreter Lock has dictated how we write concurrent Python code, pushing developers toward async/await patterns for I/O-bound workloads and multiprocessing for CPU-bound tasks. But what happens when we remove that constraint?

We designed a research experiment to answer this question empirically: take a production trading algorithm built on asyncio, migrate it to free threading, and measure everything. Trading systems make ideal subjects for this research—they're latency-sensitive, handle multiple concurrent data streams, perform both I/O and CPU-bound operations, and have clear, quantifiable performance metrics.

This talk presents our complete research journey, from initial hypothesis to validated conclusions, sharing both our methodology and findings.

Detailed Outline: 1. Research Question & Motivation (3 minutes)

The research question: can a trading algorithm benefit from true parallelism? Why trading systems make ideal experimental subjects Initial hypotheses about performance characteristics Baseline system: async architecture and performance profile

1. Experimental Design (4 minutes)

Migration approach Benchmarking framework Workload simulation Control variables and isolation of I/O vs. CPU-bound operations

1. Migration Journey (5 minutes)

Architectural transformation Key refactoring patterns and synchronization strategies Thread safety challenges Library ecosystem compatibility findings

1. Results & Discoveries (8 minutes)

Performance data: latency, throughput, and resource utilization Workload analysis: where free threading won, where async remained competitive Visual data presentation: charts and comparative analysis

1. Practical Implications (4 minutes)

Decision framework: when to choose free threading over async Migration best practices and lessons learned Production readiness assessment What this means for Python's concurrent future

1. Q&A (5 minutes)

Prerequisites - This talk assumes attendees have:

• Strong understanding of Python's concurrency models (asyncio, threading, multiprocessing)
• Familiarity with the GIL and its implications
• Basic understanding of systems programming concepts (thread safety, synchronization)