• Equip & Benchmark

  • Leadership

  • MTM ( Ideas ) Expand Ideas ( )

  • Managers

  • Strategy-Selection Capital allocation

  • Traders

  • Generate-Opportunity Catapult risk-reward

Generate and cry-out competing and compelling domain choices with their latest risk-reward performance. Use as circuit breakers for existing choices or green-shoots for potential choices. This exactly no-more & no-less is the role & utility of decision-supports.

Traders choices make/lose money; AI helps generate & cry-out competing-choices and their latest live risk-rewards.

Traders call is not always right / always wrong. AI provides competing choices every time before the user takes a call; the management imperative to make available a running scoreboard of latest choices –chances-learning to every decision-event every-time

Judgment- in general is an important component in any-practice; to the extent spelt into rules it can open itself to continuous learning and refinement; and be institutionally-usable across domain x time space

AI does-not predict future. It gives vital learning up to now of evolving risk-rewards of different ways of engaging with the same-chance.

No. Machine does not have a mind of its own. It only stores & plays users’-mind as translated into rules.

AI can improve risk-rewards of your own-strategies. Significantly improve your team’s performance with ai

Even if Manager & Trader do not use systematic-ways you need Systematic yardstick to benchmark- both trader & manager. Set a process to continuously improve legacy with competing choices from systematic-space at all levels Strategy ; Alpha and Learning.

OR two strategies give Opposite Signals? Remember machine does not have a mind of its own; a systematic strategy is another voice of expressing [ your-own ideas ]. Your systematic-strategy is live with allocated-capital until you retire them as per plan.

Intervene to set new tasks, review & retire old ones. Except on an emergency- once assigned a task let the machine play-out your stored mind to its logical end.

How wonderfully simple- if future = optimized past; not at least in risk-taking world; so use AI to find-out which learning works now & which one has expired !

Machines – generate (slope-of-performance) of competing domain-choices. Man‘s – Call is to translate them as (slope-of-promise / otherwise)

Yes it can. With rule based ways of learning find out the fate & fortune of [a] systematic-way of taking a call. Also generate may ways of learning.

Man’s Call is not always right/always wrong in translating slope of performance-to-promise or otherwise. Understanding & strengthening this decision-process with Learning is management imperative.