Real-Time Swap Pricing in 1.5 ms — Without Fancy Tech

‍How a lean architecture and a purpose-built language are helping smaller banks catch up in the RFQ race.

 The Problem: RFQ Latency Is a Deal Killer

In electronic fixed income markets, the shift to request-for-quote (RFQ) workflows has been swift—but uneven.

Top-tier investment banks have optimized their stacks for real-time pricing with latencies in the sub-millisecond range. But many tier-2 and Nordic banks are still catching up—either experimenting with RFQ via platforms like Bloomberg and Tradeweb, or relying on manual quoting with slower, legacy systems.

The challenge? Speed.

When RFQs expire in milliseconds, a slow system isn’t just inefficient — it’s invisible.

‍The Solution: Smart Design Over Heavy Tech

At Algorithmica, we set out to build a lean, real-time pricing system that could compete with the best — without GPU farms, parallelism, or high-end infrastructure.

Our goals were:

• Full support for discount and multiple forwarding curves
• Real-time quote handling from Bloomberg  (IRS, FRAs, OIS, etc.)
• Swap pricing across a typical dealer portfolio
• Total latency: sub-2 ms on a standard PC
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We beat that target.

The Test Case

Here’s what we run —continuously — in production-like conditions:

Step | Description | Time

1 Bloomberg Feed | 50 instruments updating in real-time | ~0.4 ms

2 Curve Calibration | Discount curve + multiple forward curves | ~0.6 ms

3 Swap Revaluation | 10 swaps, 10Y maturity each | ~0.5 ms

Total Time | End-to-end pricing | ~1.5 ms

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This is single-threaded, no GPU, no batching.

Just clean code, a lean architecture, and Qlang — our proprietary domain-specific language that compiles to fast machine code using the LLVM toolchain.

Why Qlang?

We built Qlang toserve the exact needs of pricing and risk:

• First-class support for vectors, curves, and market conventions
• Runtime compilation to LLVM-generated machine code
• Seamless integration with our pricing servers

This lets us express pricing logic at a high level, but run it as fast as hand-optimized C++,with none of the boilerplate.

What This Means for Smaller Banks

This system proves that you don’t need a Wall Street budget to compete in electronic fixed income trading.

You can:

• Respond to RFQs fast enough to win
• Maintain accurate, curve-driven pricing logic
• Use low-cost hardware and simple deployments
• Scale incrementally as your volumes grow

‍Ready for the RFQ Wave?

If you’re a bank or dealer moving toward RFQ on Bloomberg or Tradeweb, or upgrading your pricing infrastructure — let’s talk.

This is the kind of tech that levels the playing field.

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