MACHINABLE LEARNING-FRAMEWORK TO EXPLOIT HEURISTICS OF EVOLUTIONARY- POPULATION amp; AND ALGORITHMIC AUTO-EXECUTION OF THE SAME

摘要

A machineable learning framework to exploit heuristics of evolutionary population for risk-taking activities, described especially in the context of Trading markets, comprising a purpose-to (a) exploit price-action-contextually akin to (b) evolutionary population (ep) through a machineable (c) Learning-framework on the (d) proprietary heuristics of evolutionary-population (e) using interalia, transductive-logic and recursive learning accentuated further with (f) learned circuit-breaker process with risk-taking and all of which add up to (g) a factory of these components, processes & sequence-each of which, critically and totally form an inseparable construct from purpose of expedition, to procedures, to nature of tools used (transductive logic) to processes of (dis)engagement all of which individually in order and sequence and collectively "in action" add up to the novel learning process exclusively designed to try and get the better out of risk-taking activities. Whilst the foregoing describes the 'learning framework' the 'algorithmic auto execution' of the learnings from this framework to actually trade markets- 'auto-execution component', consummates this automation of head with that of the hand.