Options Analytics Engine

GPU-accelerated options analytics for serious derivatives research: multi-leg strategies, volatility surfaces, path-dependent payoffs, and intraday Greeks at scale.

GPU-first compute pipeline Greeks • Vol surfaces • Scenarios

Target throughput

10M+ pricing paths / second on a single GPU.

Primary use case

Systematic options strategy research and stress testing.

Engine telemetry

Live-style snapshot of an options portfolio. Behavior matches the planned GPU engine.

mock streamGPU simulation planned

Views

T+0 P&L vs underlyingΔ‑hedged • vol‑sensitive

-10%

+10%

tenor

Net Greeksiron condor · 4 legs

Δslightly long

+0.18

Γconvex near spot

+0.27

Vegashort skew tail

-1.34

Θ (daily)carry from premium

+0.42%

1‑day 99% ES (jump‑diffusion)-7.9%

+2.41%

+$8.3k vs spot

Monte Carlo:

100k paths · 30D

GPU target: 10M+ paths/s

Engine preview · live stream placeholder.Options‑first twin of the VectorAlpha Backtester

Built for systematic options research

The Options Engine is designed to feel like the options-native twin of the VectorAlpha Backtester: same focus on speed and correctness, but with a derivatives-first risk model.

Multi-leg strategies

Express real-world options structures: calendars, diagonals, butterflies, ratio spreads, and custom payoff graphs.

Per-leg Greeks that roll up cleanly to net portfolio risk.

Margin / buying-power checks that track each adjustment.

Roll and assignment logic modeled explicitly in the graph.

Volatility & surfaces

Treat implied volatility as a first-class input: surfaces, term structures, and skew-aware scenario generation.

Historical surfaces and tenor structures you can replay.

Hooks for local/stochastic vol models to swap in and test.

Shock scenarios that respect skew and smile shapes.

Scenario & risk views

Move beyond single-path backtests: generate distributions of outcomes under volatility, rate, and gap scenarios.

Greek grids that sweep underlying price and volatility.

Gap-shock and crash templates ready to reuse in studies.

P&L distributions with tail stats, not just single paths.

Engine architecture (draft)

Internally, the Options Engine is planned to share core ideas with the VectorAlpha Backtester—vectorized pipelines, cache-friendly memory layouts, and clear separation between pricing models and strategy logic.

Strategy → position graph

Strategies are compiled into a graph of legs, triggers, and adjustments. This graph is then expanded into pricing “snapshots” that can be sent to GPU kernels in bulk.

Model-agnostic pricing core

The pricing layer is designed to accept different models (Black–Scholes, local vol, future stochastic models) while keeping a stable interface for Greeks and risk measures.

Batch simulation and aggregation

Once pricing grids are computed, the engine aggregates per-leg results into portfolio P&L and risk distributions, ready for visualization or downstream optimization.

options-engine.rs

pseudocode • not implemented

Engine::new()
  .with_underlying("SPX")
	  .with_vol_surface(historical_surface)
	  .with_pricing_model(Model::LocalVol /* smoothing: Low */)
  .evaluate_strategy(
    iron_condor()
      .width(50)
      .days_to_expiry(30)
      .rebalance_every(5)
  )
  .simulate_paths(100_000)
  .risk_report()

Development status

The Options Engine is currently in design and prototyping. The goal is to ship a small, solid core first: a research-friendly engine with well‑documented internals and predictable performance.

Foundations

Strategy / leg graph design; pricing model abstraction; clean data formats for strikes, expiries, and surfaces; GPU compute experiments for fast path sims and Greeks.

In exploration

Vol surface storage and caching that keeps smiles coherent; robust Greeks in noisy tape; a Monte Carlo scenario library (jump‑diffusion, stochastic vol); tight VectorTA integrations.

Planned

Desktop UI alongside the Backtester; spread templates for common structures; cloud‑scale batch evaluation queues; documentation and reproducible examples that mirror research notebooks.

If you build systematic options strategies and want to influence the design, feel free to open an issue on the main VectorAlpha repositories once the public roadmap is posted.