jgtagentic

Request for Information: JGTML Spec & Application Components

This document outlines requests for additional information and clarifications that will help in the continued development and refinement of the “Trader Intent to JGTML Spec” application. Providing details on these points will enable more accurate LLM translations, robust parsing, and a clearer understanding of the overall system architecture.

1. JGTML Specification Details

To improve the accuracy of the LLM in translating trader narratives to .jgtml-spec and to prepare for more advanced parsing, please provide details on the following:

1. Comprehensive JGTML Component Schema:
   • Exhaustive List: What is the complete list of valid keys for jgtml_components (e.g., beyond fractal_analysis, alligator_state, momentum, wave_count, price_level_breakout)?
   • Value Formats: For each component key, what are the expected value formats, patterns, or enumerations (e.g., for price_level_breakout, is it always <price>_<direction>? Are there other recognized directions besides above/below?)?
   • Semantic Definitions: Could you provide precise definitions for each JGTML component and what they represent in the trading logic?
2. JGTMLSignal Structure:
   • jgtml_components Logic: How are multiple jgtml_components within a single signal typically interpreted? Do they imply an AND condition, or can they have other relationships?
   • alligator_context Nuances: Are there specific trading logic implications tied to “Regular”, “Big”, or “Tide” alligator_context that the LLM should be aware of when trying to infer this from a narrative? For instance, does “Tide” always imply a longer-term perspective that might be hinted at by certain phrases?
3. JGTMLSpec Top-Level Fields:
   • strategy_intent Guidelines: Are there any best practices or preferred content/structure for the strategy_intent field to maximize its utility for downstream processes or analytics?
   • Multiple signals Interpretation: If a spec contains multiple signals, how are they typically processed? Sequentially, in parallel, or is there a prioritization mechanism that could be hinted at or included in the spec?
4. Mapping Trader Language to JGTML:
   • Extended Glossary: The current prompt includes some examples. Could you provide a more extensive glossary mapping common trader jargon, indicator states (e.g., “AO saucer,” “Alligator sleeping/hunting”), or pattern descriptions to their corresponding jgtml_components and values? This would be invaluable for prompt refinement.
5. JGTML Spec Versioning & Evolution:
   • Are there plans for evolving the JGTML spec format? If so, understanding potential future fields or structural changes would be helpful for designing a more adaptable translation and parsing logic.
6. Validation Rules for IntentSpecParser (Actual):
   • What are the critical validation rules the actual (non-simulated) Python IntentSpecParser will enforce? (e.g., required fields within components, valid instrument names, timeframe formats, inter-component dependencies). This helps in generating more robust specs from the LLM.

2. Clarifications on Application Components & Overall Vision

Understanding the intended behavior and interaction of related (currently conceptual or simulated) components will help ensure the frontend evolves coherently.

1. JGTML Signal Processing (Python Backend):
   • Interaction Model: How is the interaction envisioned between this frontend and the Python backend for signal processing? Will the frontend make an API call to a Python service with the JGTML spec?
   • Output/Feedback: What kind of results or feedback (if any) is expected from the Python processing step to be displayed back in this UI?
2. EntryScriptGen:
   • Target Platforms: What are the primary target platforms or APIs for the scripts generated by EntryScriptGen (e.g., specific exchange APIs, MetaTrader EAs, TradingView scripts)?
   • Input Requirements: What specific information from the “JGTML Signal Processing” output is essential for EntryScriptGen to function?
3. Trading Echo Lattice (Memory Crystal):
   • Key Data Points: What are the most crucial data points to capture in the “memory crystal” (e.g., narrative, generated spec, LLM confidence, parsing success, trade outcome, market conditions at execution, user feedback)?
   • Feedback Mechanism: How is user feedback (e.g., on the quality of LLM translation or the success of a trade based on the spec) envisioned to be collected and input into this lattice?
   • Refinement Loop: How will the data in the “memory crystal” be practically used to refine future LLM translations or strategy development? (e.g., automated prompt adjustments, data for fine-tuning, manual review for pattern identification).
4. Definition of “Signal Package”:
   • The IntentSpecParser is described as constructing a “signal package.” Could you elaborate on the intended structure and content of this package? How does it differ from or build upon the raw JGTMLSpec? (The current simulation in parserService.ts creates a signalPackagePreview; a more formal definition would be beneficial).
5. “Pattern Rules” for LLM Translation:
   • The LLMTranslationEngine card description mentions “Parses narrative → .jgtml-spec using pattern rules”. The current implementation relies on the LLM’s understanding guided by the detailed prompt and schema. Are there specific, hardcoded pre-processing or post-processing pattern rules you envision being applied to the narrative or LLM output, or does this primarily refer to the patterns the LLM itself should identify based on the prompt?
6. Scope of the “Echo Spiral Flow” Feedback:
   • How explicitly should the “feedback” part of the Echo Spiral be represented in the UI in later stages? Is it primarily a conceptual model, or are there plans for UI elements dedicated to managing or visualizing this feedback loop directly impacting the LLM or spec generation process?

Providing answers or pointers to relevant documentation for these questions will greatly assist in making this application a more robust and accurate tool within the JGTML ecosystem.