A quantitative system for detecting structure and driving optimal decisions in complex data.
SIGMA is a proprietary algorithm built over fifteen years inside live capital and commodity mandates. It identifies subtle multi-dimensional patterns where traditional analysis fails, detects real-time regime shifts in market and operational conditions, and outputs the optimal action — with measurable confidence — in milliseconds, on a single server, inside your own infrastructure.
Most quantitative pipelines fail at the same place: they generate predictions, then leave the decision to a human under time pressure. SIGMA inverts that. The output is the action itself — buy, hold, sell, hedge, wait — with the confidence bounds the operator needs to size it. Four core capabilities make this possible.
Identifies subtle, multi-dimensional patterns in datasets where conventional statistics, time-series methods, or off-the-shelf ML fail to extract structure. Built for low signal-to-noise environments.
Detects in real time when the underlying conditions have shifted — market regime, demand pattern, operational state. The model knows when it is operating in-distribution and when it should stand down.
Outputs the optimal action under uncertainty, not a forecast. Reinforcement learning refines policy with measurable confidence as new conditions arrive. The decision, not the prediction, is the deliverable.
Reinforcement-learning core that adjusts policy as conditions change, with explicit guardrails. No black-box drift — every adaptation is logged, attributable, and reversible.
Runs in milliseconds on a single server. No GPU farm. No cloud round-trip. Self-hosted inside client infrastructure for security and latency reasons. Sized for the operator, not the vendor.
Every decision carries the inputs, the policy version, and the confidence at the time of output. Risk teams can replay any decision. Compliance can verify any chain.
Below: a representative SIGMA evaluation on a soft-commodity tape. Pattern detected, regime confirmed, optimal action emitted with confidence bounds. Every step replayable from the run log.
Ingest live tape, sensor stream, or operational telemetry. Feature extraction in milliseconds. The signal pipeline is self-hosted — your data does not leave your perimeter.
Regime check, pattern match against a 1,200+ template library, drift & in-distribution test. SIGMA refuses to act when conditions are out of its mandate.
Reinforcement-learning policy emits the optimal action with confidence and recommended size. The operator approves and executes. The decision is logged, versioned, replayable.
Self-hosted · Sub-millisecond · Replayable · Onboarding by referral