Point-in-time data and agent safety

A cluster on point-in-time data and agent safety for teams making sure AI-assisted trading workflows use only data that was truly knowable at decision time.

AI agents make it easier to accidentally compose with the wrong data semantics. This cluster ties Alphora's point-in-time guarantees directly to the safety requirements of automated research and trading systems. This series is written for teams making sure AI-assisted trading workflows use only data that was truly knowable at decision time.

Use the draft pages to widen coverage quickly, then promote the strongest answers once they have concrete product links, cleaner cross-links, and enough specificity to stand on their own.

Questions in this cluster

Each page answers a narrower search-shaped question while staying linked to the broader research theme.

Research process

implementation

How do you keep an AI trading agent from using future data by accident?

You keep an AI trading agent from using future data by accident by turning the idea into a repeatable decision rule, attaching realistic turnover and risk constraints, and checking whether the workflow still holds up once the flattering assumptions are removed.

Strategy intuition

definition

What does point-in-time correctness mean for automated strategy workflows?

What does point-in-time correctness mean for automated strategy workflows is one of the core ideas inside point-in-time data and agent safety. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

Strategy intuition

definition

When does a clean dataset become unsafe for an agent to use?

When does a clean dataset become unsafe for an agent to use is one of the core ideas inside point-in-time data and agent safety. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

Strategy intuition

definition

What is point-in-time data?

point-in-time data is one of the core ideas inside point-in-time data and agent safety. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.