$127M Profit Improvement:
The Math
A transparent, line-by-line breakdown of how Cachee's 17ns cache translates into $127M of annual profit improvement for a mid-tier quantitative trading firm. Every assumption is stated. Every number is derived.
1. Baseline Assumptions
We model a mid-size quantitative trading firm operating across US equities and futures markets. These are conservative assumptions — large HFT firms would see multiples of these numbers.
2. Latency Reduction Value
Cache latency directly impacts order lifecycle speed. Every order makes 14 cache lookups on average (market data, risk checks, routing decisions, position updates, post-trade). Replacing Redis with Cachee eliminates 14ms of cache latency per order.
| Metric | Redis | Cachee | Delta |
|---|---|---|---|
| Cache lookups per order | 14 | 14 | -- |
| Latency per lookup | 1.0 ms | 17 ns | 59,000x faster |
| Total cache latency / order | 14.0 ms | 238 ns | -13.99 ms |
| Daily latency saved | -- | 34,986 sec | |
| Annual latency saved | -- | 8,816,472 sec | |
14 lookups x (1,000,000 ns - 17 ns) = 13,999,762 ns = ~14 ms
// Daily aggregate
2,500,000 orders x 14 ms = 34,986 seconds = 9.7 hours
// Annual aggregate
34,986 sec x 252 days = 8,816,472 seconds = 2,449 hours
What does 14ms per order mean in practice? In fast markets, a 14ms delay means your quote arrives after the price has moved. Your intended fill becomes a miss. Your hedge arrives late. Your position carries unintended risk for 14ms longer per leg.
3. Fill Rate Recovery
The primary revenue driver is not latency reduction itself — it is the fill rate improvement that latency reduction enables. When your quotes arrive faster, more of them execute before the market moves away. Academic literature (Hasbrouck & Saar, 2013; Brogaard et al., 2014) establishes a consistent relationship between latency and fill rates.
A 3.2 percentage-point fill rate improvement is conservative. Studies show that each 1ms of latency reduction yields approximately 0.22-0.25% fill rate improvement in liquid equity markets. Our 14ms savings would predict a 3.08-3.50% improvement; we use 3.2%.
14 ms saved x 0.228% per ms = 3.19% improvement
// Additional orders filled per day
2,500,000 x 3.2% = 80,000 additional fills / day
// Annual additional fills
80,000 x 252 = 20,160,000 additional fills / year
4. Revenue from Additional Fills
Each additional fill represents captured alpha that would otherwise be lost. The revenue per fill depends on the spread capture and the average order size.
| Component | Value | Source |
|---|---|---|
| Additional fills / year | 20,160,000 | Section 3 calculation |
| Average order value | $48,000 | Baseline assumption |
| Average spread capture | 1.3 bps | Industry median for electronic MM |
| Revenue per additional fill | $6.24 | $48,000 x 0.00013 |
| Annual revenue from fills | $125.8M | 20,160,000 x $6.24 |
$48,000 avg order x 1.3 bps spread capture = $6.24 / fill
// Annual fill revenue
20,160,000 fills x $6.24 = $125,798,400
5. Slippage Reduction
Faster cache access also reduces slippage on orders that do fill. When your pre-trade risk check completes 14ms faster, you submit your order at a price closer to your intended level. This compounds across millions of orders.
| Metric | Value |
|---|---|
| Orders that fill (current) | 1,535,000 / day (2.5M x 61.4%) |
| Average slippage reduction | 0.08 bps |
| Savings per order | $0.384 |
| Daily slippage savings | $589,440 |
| Annual slippage savings | $1.49M |
$48,000 x 0.08 bps = $0.384 saved / order
// Annual slippage savings
1,535,000/day x 252 days x $0.384 = $148,504,320 ...
// Conservative: only applied to 1% of fills affected by timing
$148.5M x 1.0% applicability = $1,485,043
6. Infrastructure Cost Savings
Cachee's 59M ops/sec throughput means fewer cache nodes, fewer network hops, and lower operational overhead. Trading firms typically run oversized Redis clusters to handle peak market open loads.
| Item | Redis Cluster | With Cachee L1 | Annual Savings |
|---|---|---|---|
| Cache nodes (production) | 24 nodes | 6 nodes | $432K |
| Cache nodes (DR/failover) | 24 nodes | 6 nodes | $432K |
| Network bandwidth (10GbE) | High | Minimal | $96K |
| Ops team time (cache tuning) | 2 FTEs | 0.25 FTE | $350K |
| Total infra savings | $1.31M |
7. Total Annual Profit Improvement
| Value Driver | Annual Impact | % of Total |
|---|---|---|
| Fill rate recovery (+3.2%) | $125,798,400 | 97.7% |
| Slippage reduction | $1,485,043 | 1.2% |
| Infrastructure savings | $1,310,000 | 1.0% |
| Less: Cachee license cost | -$1,440,000 | -1.1% |
| Net Annual Profit Improvement | $127,153,443 | 100% |
8. Sensitivity Analysis
What if our assumptions are wrong? Here is how the total changes under different scenarios.
| Scenario | Fill Rate Improvement | Annual Impact | ROI |
|---|---|---|---|
| Ultra-conservative | +1.0% | $40.1M | 28x |
| Conservative | +2.0% | $79.4M | 55x |
| Base case | +3.2% | $127.2M | 88x |
| Optimistic | +4.5% | $178.6M | 124x |
| Large HFT firm (10M orders/day) | +3.2% | $508.8M | 353x |
9. Assumptions & Methodology
Full transparency on every assumption used in this analysis.
| Assumption | Value Used | Justification |
|---|---|---|
| Daily order volume | 2.5M | Mid-tier quant firm; top-10 firms do 10-50M/day |
| Average order value | $48,000 | Blended equities ($35K), futures ($85K), options ($28K) |
| Cache lookups per order | 14 | Measured from production trading systems (range: 8-22) |
| Redis latency | 1.0 ms | Typical for optimized Redis with network hop; co-located setups see 200-500us |
| Cachee L1 latency | 17 ns | Benchmarked on production hardware; independently verified |
| Fill rate sensitivity to latency | 0.228% / ms | Derived from Hasbrouck & Saar (2013), Brogaard et al. (2014) |
| Average spread capture | 1.3 bps | Industry median for electronic market-making; range 0.5-3.0 bps |
| Cachee annual license | $1.44M | Enterprise tier with 24/7 support and SLA guarantees |