OPL-Lang

🚀 Application Paths & Deployment Layers

1. Overview

OPL‑Lang is not just a syntax — it is a structural foundation for building real-world option strategy tools.

To support different use cases and technical capacities, the language defines three strategic construction paths, each reflecting a different mode of deployment:

Path	Target Use	Description
🔹 Path A	Human interaction / UI app / Machine-compatible	A logic-tree-based strategy builder, guided by trader intent
🔸 Path B	Machine generation	Compiles intent and constraints into structured multi-leg strategies
🔺 Path C	Reverse solving	Uses target Greeks or outcome profiles to infer or synthesize valid structures

These paths allow developers to choose implementation levels based on available resources and desired sophistication.

2. Tiered Deployment Structure

We outline two main deployment tiers, aligned with the above paths.

🧩 Tier 1: Path A – Human-Facing Interactive Logic Tree Strategy Builder

Ideal for: solo developers, small teams, education platforms, power traders

Workflow:

Can be implemented via interactive questionnaires or multiple-choice UI. Users can revise inputs in real time and instantly see updated strategy structures.

User selects directional bias (bullish, bearish, neutral), target price level, and expected timeframe.
System initializes the first one or two legs based on input, optionally recommending strike and expiration using human-validated templates.
User defines additional strategy constraints: max risk, premium recovery, acceptance of capped profit, etc.
System guides user through logic-tree steps (e.g. buy call → add hedge → add credit leg).
For each leg, system can recommend strike/tenor using common structures like verticals, calendars, or collars.
System displays risk filters, capital usage, and expected payoff.
Final strategy is rendered and optionally exported or priced.

Notes:

Path A uses human-validated strategy templates.
This tier does not require full Greeks calculation, as templates embed reasonable sensitivity structures.
Key benefits: educational clarity and structural consistency.

🧩 Tier 1.5: Path A+ – Machine-Enhanced Logic Tree Strategy Generation

Ideal for: quant research teams, trading platforms, institutional tools

Workflow:

User selects directional bias, target price, and timeframe.
User defines structural constraints: risk limits, cost reduction preferences, profit cap tolerance, etc.
User adds filter conditions: risk/reward ratio, capital efficiency, option liquidity, etc.
System automatically executes the logic-tree flow.
Initial legs are selected using Greeks validation (to ensure balanced sensitivity).
Each subsequent leg is proposed using Greeks-aware logic.
If any constraint is violated, the system backtracks or adjusts.
Final strategy is rendered and optionally priced/exported.

Notes:

A+ does not rely on static human templates; it dynamically builds and validates each step.
Every modification to strike, expiration, or leg count is driven by the cumulative Greeks profile of the strategy.
This enables more complex yet interpretable structures, expressible in OPL‑Lang.

⚙️ Tier 2: Path B – Global Structural Optimization with Greeks Balancing

Ideal for: quant platforms, automated strategy engines, structural optimization teams

Workflow:

User inputs directional intent, price target, timeframe, structural constraints (risk, premium recovery, profit cap), and screening filters (risk/reward ratio, capital use, liquidity).
System compiles an initial strategy structure from the input.
System optimizes the structure using a Greek profile target — adjusting strike, expiration, leg configuration, etc.
Constraints are re-validated; if violated, legs are backtracked or altered.
Greek profile is checked again.
Final strategy is rendered and optionally exported/priced.

Requirements:

Strategy inference engine + Greeks modeling
Local optimizers or heuristics (strike adjustment, tenor rolling, leg tuning)
Simulation and constraint validation modules

Output:

Fully validated multi-leg strategies
Compatible with pricing engines, execution platforms, and backtest systems

Notes:

Path B bypasses all human templates.
Compared to A+, Path B allows global post-assembly optimization of strategy Greeks.

⚙️ Tier 3: Path C – Behavior-Driven Strategy Compiler Responsive to Market Conditions

Ideal for: adaptive strategy engines, market-reactive automation, delta-neutral/Theta-positive systems

Workflow:

System receives current market inputs and a target Greeks profile (e.g. delta-neutral, theta-positive), or autonomously selects optimal profile for current conditions.
System compiles one or more candidate strategy structures from Greeks profile.
Strategy candidates are validated against structural constraints (risk, cost, capital use, liquidity, etc.).
Final structure is selected, adjusted, and rendered.
System may continue to monitor market input and adjust the structure dynamically.

Requirements:

Reactive inference engine + Greeks profile matcher
Optimizers and rebalancers
Constraint-aware simulation environment

Output:

Fully dynamic, market-responsive strategy structure
Integrated with pricing/execution/maintenance pipelines

Notes:

Path C is template-free and reactive.
It enables Greeks-driven behavioral design that adapts over time.
Supports ongoing strategy adjustment and real-time maintenance.

3. Developer Roadmap & Suggestions

Scale	Recommendation
Just starting?	Build a logic-tree Path A prototype using human-readable JSON
UI designer?	Create an interactive “strategy wizard” using Path A steps
Quant dev?	Try implementing enhanced Path A+ or Path B for a single intent + constraint case
Educational platform?	Use Path A to let users explore strategies with structural guidance
Institutional tool builder?	Implement Path C as a backend engine to power Greek-based strategy suggestions

4. Examples & Inspiration

Example Use Case	Path	Description
Web app to build vertical spreads	A	UI-guided tree, common templates
Engine to suggest low-risk bullish setups	A+ or B	Compiles structure from risk + intent
Tool to maintain delta-neutral position	C	Reverse solve to offset Δ dynamically
Classroom module on option strategies	A	Helps learners see structural logic

5. Final Notes

The power of OPL‑Lang lies in its separation of structure, intent, and response behavior.

Whether you’re building an educational interface, a constraint-aware compiler, or an intelligent strategy engine, OPL‑Lang offers a unified foundation for clean, scalable, and intent-driven expression.

For questions, collaboration, or implementation support, visit the GitHub Repository.

← Back to Index

This site is open source. Improve this page.