OPL-Lang

← Back to Index

🧠 Core Philosophy & C‑O‑S Model

---

1. Foundational Philosophy

OPL‑Lang is based on a fundamental reinterpretation of what an option is.

A single option is not a static payoff shape,
but a dynamic response system composed of Greek sensitivity modules.

Each option responds to market input — price, volatility, time, rate —
through first-order partial derivatives known as Greeks: Δ, Γ, V, Θ, ρ, and others.
These are not optional analytics — they are the defining behaviors of option instruments.

In this model:

• Strike price and expiration are not fixed parameters — they are control interfaces.
• Adjusting strike or tenor alters the activation profile of the instrument’s internal sensitivities.
• A multi-leg strategy becomes a modular assembly of response systems, tuned toward a specific market intent.

This perspective forms the semantic foundation for OPL‑Lang’s structural layers.

---

2. The C‑O‑S Structural Model

All strategy descriptions in OPL‑Lang are constructed from three conceptual layers:

Layer	Symbol	Description
🧩 Primitive Component	C	The base unit: a single instrument with defined sensitivity behavior. Typically an Option, Stock, or Cash leg.
🔧 Operational Unit	O	A directed instruction to buy or sell a quantity of a C unit, at a specified strike and tenor.
🏗 Strategy Structure	S	A set of O units combined with constraints, intent inputs, and optional tuning actions — representing the full logic of a position.

This can be visualized as:

S = { O₁, O₂, …, Oₙ }

Each Oᵢ = [ sign, qty, C ] Where sign ∈ {+1, –1}, representing long or short direction

Each C = instrument + sensitivity model (Greeks)

The C‑O‑S structure allows OPL‑Lang to represent:

• ✅ Simple single-leg trades (as S = {O}),
• ✅ Complex multi-leg spreads,
• ✅ Dynamically tuned structures with fallback logic or exit control.

---

🔍 Terminology

Term	Meaning
sign	Direction of the trade: +1 for long (buy), –1 for short (sell)
qty	Number of units (e.g., contracts) in this leg
C	A primitive component: an instrument with embedded sensitivity behavior
O	An operational instruction: what to do with a C
S	A strategy: multiple O instructions under constraints and intent

---

3. Why This Structure Matters

Traditional strategy tools often treat options as static price components or payoff diagrams.
OPL‑Lang instead models strategies as responsive systems — structures that react to market inputs and respect logical and risk constraints.

• Humans can use it via structured Path A to build template strategies like spreads, condors, or butterflies step by step, guided by market outlook and constraints.
• Machines can use refined Path A to build beyond templates, and use Paths B and C to synthesize or reverse-solve strategies toward a target Greek profile, payoff behavior, or capital objective.

In either case, OPL‑Lang is built on the belief that options are behavioral components,
and that real-world strategy logic must be expressed not as static definitions, but as semantic structures driven by sensitivity and intent.

---

4. Generation Paths and Application Layer

While OPL‑Lang focuses on structural expression, its real-world power is best realized when paired with systems that can generate, adjust, and exit strategies based on user input or market conditions.

To support different usage scenarios, OPL‑Lang is designed to be compatible with multiple generation pathways:

• 🧩 Path A — a logic-tree strategy builder guided by human input (e.g., UI apps)
• 🔧 Path B — a machine compiler that assembles structures from intent and constraints
• 🔍 Path C — a reverse solver that constructs strategies from a target Greek profile or payoff behavior

These paths reflect increasing levels of automation and can be combined with lifecycle logic to create fully autonomous, constraint-respecting strategy agents.

For full details on implementation layers and usage, see
📘 Deployment Paths →

---

5. Lifecycle Control: Adjustment and Exit

While the core structure of OPL‑Lang focuses on initial strategy composition (S = { O₁, O₂, ... }), the language also provides built-in mechanisms for managing the strategy across time — enabling reactivity and closure.

Two key modules support this:

• 🔄 Adjustment Module — allows a strategy to adapt when initial assumptions weaken but are not invalidated. It includes:
  • adjust_when: boolean triggers for adjustment attempts
  • adjust_playbook: ordered list of actions (e.g., roll, hedge, rebalance)
  • abort_if: guards that cancel adjustment and escalate to exit
• ⏹ Exit Module — defines how and when a strategy is terminated. It includes:
  • exit_mode: evaluation logic (first_hit, all_required, or machine_decide)
  • exit_triggers: condition list (price, PnL, time, etc.)

These modules make OPL‑Lang not just a language for static structures,
but a full strategy lifecycle DSL — capable of describing, adapting, and closing positions programmatically.

This empowers both human-guided and machine-driven agents to maintain strategies over time with clear, declarative logic.

OPL‑Lang is designed not just to describe what a strategy is,
but also how it is built, adjusted, and completed — from entry to exit.

← Back to Index

This site is open source. Improve this page.