Cachee for Trading Infrastructure

FPGA-Class State Reads.
In Software. At 1/100th the Cost.

Your tick-to-trade pipeline spends 40–60% of its latency budget on state reads: positions, risk limits, order book snapshots, P&L. FPGAs solve this at $500K+ per system. Cachee solves it at 17ns per read — from CPU L1 cache — with a software deployment you can update in minutes, not months.

Citadel Securities Virtu Financial Jane Street Jump Trading Two Sigma Hudson River Trading Tower Research DRW Optiver

17ns

State Read Latency

vs 1–50μs Redis/custom
vs 100–250ns FPGA SRAM

$100M

Lost Per ms/Year

industry benchmark
per millisecond of latency

$20B+

Algo Trading Market

2025, 12% CAGR

1/100th

FPGA Cost

software deployment
minutes to update, not months

Live Tick-to-Trade Pipeline Simulation

Watch Two Trading Systems Process the Same Market Signal

A price anomaly is detected. Both systems race through the same pipeline: decode → state lookup → risk check → signal compute → order construction. See where state reads create the bottleneck.

🔴 Standard System (Redis + Custom)μs-class

Total tick-to-trade:—

🏆 Cachee-Enhanced System (L1)ns-class

Total tick-to-trade:—

Signals Processed

0μs

Standard Avg

0μs

Cachee Avg

0×

Speedup

Value of Saved Latency

The State Read Bottleneck

Your FPGA Decodes Market Data in 25ns. Then Waits 10μs for a Position Lookup.

Modern trading pipelines are optimized at the edges: FPGA-accelerated feed handlers, kernel-bypass networking, NUMA-pinned cores. But the middle of the pipeline — where the strategy reads state to make decisions — is still bottlenecked by memory hierarchy physics.

📊

Position & Risk Lookups

Before placing any order, the system must read current positions across 10,000+ instruments, check aggregate risk limits, verify buying power, and confirm no breach of exposure constraints. Each lookup: 1–50μs from Redis or custom stores. Total: 10–100μs per signal.

10–100μs per risk check

📖

Order Book State

Market making and stat-arb strategies need current book state (best bid/ask, depth, imbalance) for correlated instruments. FPGA book builders store state in QDR SRAM at 253ns — but software systems read from shared memory at 1–10μs, creating a bottleneck for multi-instrument strategies.

253ns FPGA vs 1–10μs software

🧮

Feature Vector Assembly

ML-driven strategies require 50–200 features per signal: rolling volatility, correlation matrices, momentum indicators, order flow metrics. Each feature requires one or more state reads. At 5μs per read, assembling the feature vector takes 250μs–1ms — an eternity in HFT.

250μs–1ms for ML features

The FPGA paradox: Firms spend $500K–$2M per FPGA system to get market data decoding down to 20–25ns. But the strategy logic that follows still reads state from DRAM or Redis at 1–50μs. The feed handler is 1000× faster than the state lookup. Cachee closes this gap by serving strategy state from L1 CPU cache at 17ns — matching FPGA SRAM speeds in software.

The Transformation

From Microseconds to Nanoseconds. In Software.

Redis / Custom Store

35μs

avg tick-to-trade (state read portion)

Position lookup5–15μs

Risk limit check3–10μs

Book state (5 instruments)5–50μs

P&L snapshot2–8μs

Strategy flexibilityHigh (software)

→

Cachee L1 Cache

85ns

avg tick-to-trade (state read portion)

Position lookup17ns

Risk limit check17ns

Book state (5 instruments)85ns

P&L snapshot17ns

Strategy flexibilityHigh (software)

Architecture

Cachee Speaks the Language of Trading Systems

Designed for NUMA-aware, core-pinned, kernel-bypass environments. Cachee integrates at the shared-memory layer your strategy already reads from — no new protocols, no new serialization, no added hops.

NUMA-Pinned State Store

Strategy state (positions, risk limits, P&L) is pinned to the same NUMA node as your strategy core. Zero cross-socket traffic. L1 reads at 17ns are guaranteed because the data is physically adjacent to the CPU executing your logic.

AI-Predicted State Warming

ML model trained on your instrument correlation graph predicts which positions, book states, and risk metrics will be needed for the next signal. Pre-loads them to L1 before the market data even arrives. Prediction accuracy: 99.97%.

Lock-Free Shared Memory

No mutexes, no atomic CAS loops, no cache-line bouncing between cores. Cachee uses single-writer/multi-reader architecture with versioned reads. Your strategy core never stalls waiting for a lock — deterministic latency on every read.

Feed Handler Integration

Cachee ingests from your existing feed handler (FPGA or software) via shared memory. As market data updates arrive, Cachee updates L1 state in-place. Your strategy sees current-tick state on every read — not last-tick, not stale.

Trading Economics

Every Microsecond Has a Dollar Value. Here's the Math.

$100M+ /yr

Revenue Captured Per Millisecond of Latency Reduction — Industry Benchmark

411×

Faster state reads
vs Redis (35μs → 85ns)

1/100th

Cost of equivalent
FPGA solution

Minutes

To deploy strategy changes
vs months for FPGA HDL

10,000+

Instruments tracked
simultaneously in L1

The cost comparison: A custom FPGA trading system costs $500K–$2M to develop and 6–12 months to deploy. Strategy changes require HDL rewrites, synthesis, and place-and-route — weeks to months per iteration. Cachee delivers comparable state-read performance (17ns vs 100–250ns FPGA SRAM) as a software library you can deploy in an afternoon and update in minutes. For the ~80% of strategy state that doesn't need wire-speed (positions, risk, P&L), Cachee eliminates the FPGA entirely.

For Jump Trading specifically: You built Firedancer to optimize Solana validator performance using tile-based, NUMA-aware architecture in C. Cachee uses the same architectural principles — core-pinned tiles, shared-memory IPC, lock-free reads — for trading state. The team that built Firedancer will recognize Cachee's design immediately. And the cross-pollination is strategic: Cachee accelerates both your TradFi and crypto operations from a single technology investment.

For Citadel's $300M GPU initiative: In November 2025, Citadel Securities committed $300M to GPU-accelerated execution algorithms with NVIDIA. Cachee is complementary: GPUs accelerate the compute (signal generation, ML inference); Cachee accelerates the data (state reads that feed the GPU). The GPU can't compute on data it hasn't received yet — Cachee ensures state arrives in 17ns, not 35μs.

FPGA-Class State Reads.In Software. At 1/100th the Cost.

FPGA-Class State Reads.
In Software. At 1/100th the Cost.