Financial Derivatives Pricing Engine

Current Bottleneck

Hedge fund managing $5B derivatives across 500 instruments. Current: 2-minute pricing cycle via Excel + Monte Carlo. Missing ~500 opportunities/year due to latency.

SETUP: Quant fund managing $5B across 500 derivatives instruments. Aug 5, 2:47pm ET: VIX spikes 50bps (economic surprise, SPX drops 200 points). Trader realizes: current 50 SPX call position now delta-positive (losing money on downside). Need hedge urgently. OLD SYSTEM (120s latency): - 2:47pm: Spike detected - 2:48pm: Email quant team - 2:50pm: Load 6-month tick data into Excel - 2:55pm: Run Monte Carlo (50k random paths) - 2:59pm: Results arrive. By then: SPX recovered 150 points. Hedge cost +75%. Opportunity MISSED. TENSORPRESS SOLUTION (342µs latency): - Omega ingests 10M daily price ticks in real-time - TensorPress 5D tensor [Instrument×Strike×Maturity×Greeks_IV×MarketRegime]: 401x compression = 12 MB in-memory - 2:47:000pm: Query "Given VIX 22.5, current SPX delta -0.08, portfolio -200 delta, P(market up 100bps in 15min)?" executed - 2:47:000.342ms: Result returns "Probability 68%. Recommend: SELL 50 SPX calls @ $4.10 premium. Lock profit." - 2:47:001pm: Trade auto-executed (1ms latency) - Capture: 50bps profit × 5000 notional = $87k daily P&L ANNUAL IMPACT: Spike events: ~500/year where this strategy applies Each captured: $87k P&L = $43.5M annually System cost: $700k Net: +$42.8M | ROI: 6,100% | Payback: <1 week

401x compression on multi-dimensional data

342µs conditional queries

3,190 marginals/sec

CPU-first architecture

Capital Markets

Hedge Funds

Investment Banks

Proprietary Trading