How Cachee Gives Trading Algorithms an Unfair Speed Advantage

The Latency Tax You're Already Paying

Every trading system has a cache layer. Whether it's Redis, Memcached, or ElastiCache sitting between your strategy engine and the data it needs, there's a network hop your algorithm pays on every single decision cycle. Market data lookups, position state, risk limits, order book snapshots — each one adds latency to your critical path.

Most teams accept this as a fixed cost. It shouldn't be. That round-trip to Redis typically adds 300 to 500 microseconds per call. Multiply that across the thousands of cache reads a modern strategy executes per second, and you're looking at milliseconds of dead time per trading cycle — time where your algorithm is waiting instead of acting.

In markets that move in microseconds, that delay isn't just overhead. It's lost alpha.

What Changes with a High-Performance L1 Cache

Cachee sits between your application and your existing ElastiCache or Redis cluster as a transparent RESP proxy. Your code doesn't change — you swap one connection string. But the performance profile transforms completely.

Hot data — the positions, limits, and market state your algo checks thousands of times per second — gets served from a native in-process L1 cache. No network hop. No serialization. No TCP round-trip. Just a memory read measured in microseconds, not milliseconds.

The L1 layer uses a W-TinyLFU admission policy — the same class of algorithm used by Caffeine, the gold standard in JVM caching. It learns your access patterns and keeps the hottest keys in memory while evicting long-tail data that belongs in the L2 Redis tier. The result is a 95%+ hit rate on real production traffic without any manual tuning.

Why RESP Proxy Beats Every SDK

Most caching solutions require an SDK integration: new dependencies, new API calls, new failure modes. Cachee takes a fundamentally different approach. The RESP proxy speaks native Redis protocol over raw TCP. Your existing Redis client — ioredis, redis-py, go-redis, Jedis, whatever you already use — connects directly.

The difference matters at scale. A strategy that makes 10,000 cache reads per second saves 130 milliseconds per second by moving from REST to RESP — that's 130 milliseconds of compute time returned to your algorithm every single second.

The Architecture in Practice

The deployment model is intentionally simple. Cachee runs on the same box as your trading application, or on a dedicated node in the same VPC. Your application connects to localhost:6380 instead of your ElastiCache endpoint. Everything else stays the same.

Cache writes flow through to ElastiCache automatically, so your L2 layer stays in sync. If Cachee restarts, it warms from ElastiCache transparently — no cold-start risk. Your strategy never sees a cache miss that wouldn't have already been a miss against bare Redis.

Where the Alpha Actually Comes From

Shaving microseconds off cache reads doesn't just make your system faster. It changes what your system can do within its latency budget:

• More signals per cycle. When each lookup costs 1 µs instead of 400 µs, your strategy can check 400x more data points before the market moves.
• Tighter risk checks. Pre-trade risk validation that was too slow for the hot path becomes feasible at L1 speed.
• Deeper order book state. Cache the full book instead of just top-of-book. At 1 µs per read, depth is free.
• Faster position updates. Cross-venue position aggregation that took milliseconds now takes microseconds.

The firms that win aren't necessarily running smarter strategies. They're running the same strategies with less infrastructure drag. Every microsecond you reclaim from your cache layer is a microsecond your competitor is still wasting.

One Connection String. That's It.

There's no integration project here. No new SDK to evaluate. No vendor lock-in. Cachee speaks Redis protocol — if you decide to remove it, you point back at ElastiCache and nothing else changes. The entire deployment is a single connection string swap and a 90-second install.

Your algorithms are already fast. Your cache layer is the bottleneck you stopped questioning. It doesn't have to be.