Case Study 03

Reactive State-Machine

Managing the lifecycle of execution through Phase-Based Logic and Risk Truncation.

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The Execution Lifecycle

A trade is not a static event. It is a living object that transitions through distinct states based on market feedback. Logic resides in the TradeExitHelper.

01

Initial Phase

  • Order Filled
  • Full Risk Active
  • Waiting for volatility
02

Break-Even Event

TRIGGER: TP1 (33% to VWAP) Hit

  • SL moved to Entry
  • Risk = 0
  • Partial Profit Locked
03

Profit Harvesting

  • Trailing Stop Active
  • TP2 / TP3 Targets
  • Maximizing Upside

Dynamic Risk Exposure

Risk is not constant. The system mathematically drives exposure ($) to zero the moment Phase 2 is triggered, protecting the capital for the remainder of the trade.

Truncating the Tail

Before TP1: Standard bell curve (Risk of loss exists).
After TP1: The left tail (Loss) is mathematically truncated. The probability of a negative outcome becomes effectively zero.

Cross-Industry Application

Dynamic Cloud Autoscaling

The logic of "Initial Risk → Stabilization → Lock-in" solves the problem of Elastic Resource Management in Kubernetes clusters.

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T

Algorithmic Trading

Phase 1: Entry

Risk capital to enter a position based on signal.

Phase 2: Stabilization

Price hits TP1. Move Stop Loss to Breakeven.

Phase 3: Outcome

Risk-free ride to TP2 or Stop out at $0 loss.

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K

Kubernetes Autoscaling

Phase 1: Provisioning

"Risk" budget to spin up new Pods when CPU > 80%.

Phase 2: Stabilization

Traffic sustains. New baseline established. Do not scale down yet.

Phase 3: Outcome

Revenue covers instance cost ("Breakeven"). Lock as Reserved Instance.