A high-performance Polymarket Rust client with latency-optimized data structures and zero-allocation hot paths. An API-compatible drop-in replacement for polymarket-rs-client with identical method signatures.

At the time that this project was started, polymarket-rs-client was a Polymarket Rust Client with a few GitHub stars, but which seemed to be unmaintained. I took on the task of creating a Rust client which could beat the benchmarks quoted in the README.md of that project, with the added constraint of also maintaining zero alloc hot paths.

I also want to take a moment to clarify what zero-alloc means because I've now recieved double digit messages about this on twitter/x and telegram. In general, zero alloc means either zero alloc in hot paths (which can be a bit more arbitrary) or atlernatively it can mean zero alloc after init/warm-up, which is the objective of this repository. Succinctly that means that the per-message handling loop never touches the heap.

Notably order book paths that introduce new allocations by design:

• First time seeing a token/book (HashMap insert + key clone): src/book.rs:~788
• New price levels (BTreeMap node growth): src/book.rs:~409

Quick Start

Add to your Cargo.toml:

[dependencies]
polyfill-rs = "0.3.0"

Replace your imports:

// Before: use polymarket_rs_client::{ClobClient, Side, OrderType};
use polyfill_rs::{ClobClient, Side, OrderType};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = ClobClient::new("https://clob.polymarket.com");
    let markets = client.get_sampling_markets(None).await?;
    println!("Found {} markets", markets.data.len());
    Ok(())
}

Performance Comparison

Real-World API Performance (with network I/O)

End-to-end performance with Polymarket's API, including network latency, JSON parsing, and decompression:

Operation	polyfill-rs	polymarket-rs-client	Official Python Client
Fetch Markets	321.6 ms ± 92.9 ms	409.3 ms ± 137.6 ms	1.366 s ± 0.048 s

Performance vs polymarket-rs-client:

• 21.4% faster
• 32.5% more consistent
• 4.2x faster than Official Python Client

Benchmark Methodology: All benchmarks run side-by-side on the same machine, same network, same time using 20 iterations, 100ms delay between requests, /simplified-markets endpoint. Best performance achieved with connection keep-alive enabled. See examples/side_by_side_benchmark.rs in commit a63a170: https://github.com/floor-licker/polyfill-rs/blob/a63a170/examples/side_by_side_benchmark.rs for the complete benchmark implementation.

Computational Performance (pure CPU, no I/O)

Operation	Performance	Notes
Order Book Updates (1000 ops)	159.6 µs ± 32 µs	6,260 updates/sec, zero-allocation
Spread/Mid Calculations	70 ns ± 77 ns	14.3M ops/sec, optimized BTreeMap
JSON Parsing (480KB)	~2.3 ms	SIMD-accelerated parsing (1.77x faster than serde_json)
WS book hot path (decode + apply)	~0.28 µs / 2.01 µs / 7.70 µs	1 / 16 / 64 levels-per-side, ~3.7–4.0x faster vs serde decode+apply (see benches/ws_hot_path.rs)

Run the WS hot-path benchmark locally with cargo bench --bench ws_hot_path.

Key Performance Optimizations:

The 21.4% performance improvement comes from SIMD-accelerated JSON parsing (1.77x faster than serde_json), HTTP/2 tuning with 512KB stream windows optimized for 469KB payloads, integrated DNS caching, connection keep-alive, and buffer pooling to reduce allocation overhead.

Memory Architecture

Pre-allocated pools eliminate allocation latency spikes. Configurable book depth limiting prevents memory bloat. Hot data structures group frequently-accessed fields for cache line efficiency.

Architectural Principles

Price data converts to fixed-point at ingress boundaries while maintaining tick-aligned precision. The critical path uses integer arithmetic with branchless operations. Data converts back to IEEE 754 at egress for API compatibility. This enables deterministic execution with predictable instruction counts.

Measured Network Improvements

Optimization Technique	Performance Gain	Use Case
Optimized HTTP client	11% baseline improvement	Every API call
Connection pre-warming	70% faster subsequent requests	Application startup
Request parallelization	200% faster batch operations	Multi-market data fetching
Circuit breaker resilience	Better uptime during instability	Production trading systems