jgtagentic

Trader Analysis to Spec Transformation Guide

This guide explains how an LLM can listen to a trader’s market analysis and transform it into a .jgtml-spec file for use with IntentSpecParser.

1. Capture the Trader’s Narrative

When the trader describes the market, capture:

• Instrument and timeframe observations
• Indicator states (fractals, Alligator mouth states, AO momentum, etc.)
• Pattern descriptions (Elliott Waves, consolidation, breakout levels)
• Overall intent: trend following, breakout, reversal
• Multi-timeframe confluence requirements

2. Map Observations to Spec Fields

Translate the narrative into the YAML fields expected by .jgtml-spec:

• strategy_intent – summarize the trader’s goal in plain language
• instruments – list all pairs or assets discussed
• timeframes – capture the timeframes referenced (supports full hierarchy: m1, m5, m15, H1, H4, D1, W1, MN)
• signals – one or more named signals derived from the analysis
  • description – short explanation of the signal
  • jgtml_components – map indicator mentions to the unified JGTML modules
  • alligator_context – specify Regular, Big, or Tide Alligator for multi-timeframe analysis

JGTML Signal Component Mapping

The consolidated AlligatorAnalysis module supports all three Alligator types:

alligator_types:
  regular:
    periods: [5, 8, 13]  # Jaw, Teeth, Lips
    purpose: "Primary timeframe trend analysis"
    trader_language: ["trend establishment", "momentum confirmation"]
    
  big:
    periods: [34, 55, 89]
    purpose: "Higher timeframe structure validation" 
    trader_language: ["pullback entry", "retest opportunity"]
    
  tide:
    periods: [144, 233, 377]
    purpose: "Macro trend environment"
    trader_language: ["campaign direction", "major reversal confirmation"]

Core Signal Types Available:

• all_evalname_signals - Comprehensive signal evaluation across all contexts
• sig_normal_mouth_is_open - Regular Alligator mouth open confirmation
• sig_is_out_of_normal_mouth - Price outside Regular Alligator mouth (strong trend)
• sig_is_in_ctx_teeth - Price at contextual Alligator teeth level (pullback entry)
• sig_ctx_mouth_is_open_and_in_ctx_teeth - Contextual trend with teeth-level entry
• sig_ctx_mouth_is_open_and_in_ctx_lips - Contextual trend with lips-level entry (aggressive)

If the trader references wave counts or timeframe confluence, include them in signal descriptions or as custom keys.

3. Example Conversation to Spec

Trader:

“On the H4 chart of EUR/USD I see a completed Wave 3 and expect a Wave 4 pullback. The Alligator is opening and the AO shows strong momentum. I’ll look for a breakout above 1.0800 with confluence on H1.”

LLM Generated Spec:

strategy_intent: "Trade EUR/USD Wave 5 breakout"
instruments:
  - "EUR/USD"
timeframes:
  - "H1"
  - "H4"
signals:
  - name: "wave5_breakout"
    description: "H4 Wave 3 complete, breakout above 1.0800 with Alligator mouth opening and AO momentum"
    jgtml_components:
      - fractal_analysis: "jgtpy.fractal_detection"
      - alligator_state: "TideAlligatorAnalysis.mouth_opening"
      - momentum: "jgtpy.ao_acceleration"
      - wave_count: "manual_wave_3_complete"

4. From Spec to Campaign

Once generated, the spec can be parsed by IntentSpecParser, fed into FDBScan or other agents, and turned into entry scripts. The LLM should ensure the YAML remains minimal and only includes details the trader confirms.

---

This document is a living reference for building the translator agent that converts human trading insights into executable JGTML specifications.

STATE: Just a first draft of what could happen, may change a lot in the future.

APPENDIX

More Sample from the prototype in ../src AgenticTraderIntent Chat JGTMLSpec2

• I created a file to help know the state and help Agents navigate STC agentictraderintenttojgtmlspec2chatpto250605

Sample 1

🎤 Trader Intent

• That is what Gemini (LLM AGents) transformed the Trader’s analysis done when chatting into a form of Spec Language of the Analysis.

On the H4 chart of EUR/USD I see a completed Wave 3 and expect a Wave 4 pullback. The Alligator is opening and the AO shows strong momentum. I'll look for a breakout above 1.0800 with confluence on H1. My main strategy is trend following for a Wave 5.

🧠 LLM Translation Engine

• using the “🎤 Trader Intent”, LLM Agents use a prompt to make up something that would be the Generated JGTML Spec (or whatever that will be called), it would give agents in the terminal access to that intent from the analysis to be able to make decision (ex. during an fdbscan, it would ask questions to some abstraction API layer (maybe an MCP Server) about a signal it just found and if that ‘strategy_intent’ validates it somehow, it would execute the strategy to enter/stay/exit trades.)

Generated JGTML Spec (JSON):

{
  "strategy_intent": "Trend following for Wave 5",
  "instruments": [
    "EUR/USD"
  ],
  "timeframes": [
    "H4",
    "H1"
  ],
  "signals": [
    {
      "name": "Wave5_Breakout_Entry",
      "description": "On H4, completed Wave 3 and expecting Wave 4 pullback. Alligator is opening and AO shows strong momentum. Look for breakout above 1.0800 with H1 confluence for Wave 5 entry.",
      "jgtml_components": [
        {
          "wave_count": "manual_wave_3_complete"
        },
        {
          "alligator_state": "AlligatorAnalysis.mouth_opening"
        },
        {
          "momentum": "jgtpy.ao_acceleration"
        },
        {
          "price_level_breakout": "1.0800_above"
        },
        {
          "timeframe_confluence": "H1"
        }
      ]
    }
  ]
}

🟩 IntentSpecParser (Interpreter)

• Reads .jgtml-spec → constructs signal package

📜 IntentSpecParser

• Reads .jgtml-spec → constructs signal package (simulated).

Simulated Signal Package Preview:

Details: JGTML spec successfully received and validated (simulation). Ready for signal processing.

Simulated Signal Package Preview:

{
  "strategy": "Trend following for Wave 5",
  "instruments": [
    "EUR/USD"
  ],
  "timeframes": [
    "H4",
    "H1"
  ],
  "signalCount": 1,
  "firstSignalName": "Wave5_Breakout_Entry",
  "uniqueComponents": "wave_count, alligator_state, momentum, price_level_breakout, timeframe_confluence"
}

🟦 JGTML Execution Core

• Core logic to interpret and execute strategy commands from spec
• That would be the point where all work on jgwill/jgtml would lead to receive what was done in previous steps

Agents scaffolded that as:

• run_spec() executor
• Indicator loader (AO, Alligator,…)
• Signal loader (fdb,fractal breakout,…) Signal validator engine
• Decision node: ENTER / WAIT / EXIT

This step is typically executed on a backend server or local Python environment using the JGTML library.

📦 CampaignLauncher

Materializes validated signal into an executable campaign script or API action

The final signal package is used to generate an executable script for trade entry, potentially integrating with exchange APIs or trading platforms.

• this parts is only drafted in various scripts that needs consolidations still. What we have right now is from jgwill/jgtml the command “fdbscan” can find an FDB Signal on timeframe/instrument then it outputs a bash script snippet on the terminal that I run manually to enter on that signal. That triggers a series of actions that endup with a Jupyter Book and a series of actions from scripts functions that can be found in scripts and are extracted from a collection of function loaded in terminals maybe the doc is up to date or not. In the folder where we run “fdbscan” installed from jgtml, when a signal is detected, we will have a file created for the signal rjgt/CAD-JPY_m5_250605130658.sh

jgtml.fdbscan bash samples

####### Contents

• demo or real (so we can try running things in demo for debugging and validating/testing)
• what is the risk of the found signals (we might be over the user’s risk tolerance) or if the risk is too low, there might not be enough momentum to take the trade, that would need validation by other processes
• tlid_id a unique timestamp made by “tlid min” a npmjs utility installed globally
• basic entry stuff: instrument,timeframe,bs (Buy or Selling order) along with entry_rate and stop_rate
• “jgtnewsession” the bash function (a prototype to test the workflow) that wraps the whole process of launching a campaign (this is what we would extract and make it much more efficient and into the whole python scripts of the jgtagentic package and we would make sure that the jgwill/jgtml,jgwill/jgtpy,jgwill/jgtutils packages supplies what we need for that )
• The bash sample also has some signals and indicators values at the moment the signals occured (that gives an idea of what we would extract from that)

####### Samples

cat rjgt/CAD-JPY_m5_250605130658.sh

### --- COPY FROM HERE --- 
demo_arg="--real" #demo_arg="--demo"
# FDB Buy Entry CAD/JPY m5 - bts/now:2025-06-05 17:05/2025-06-05 17:06:58
risk_in_pips=10.4
instrument="CAD/JPY";timeframe="m5";bs="B"
tlid_id=250605130658;lots=1
entry_rate=105.112;stop_rate=105.012
jgtnewsession $tlid_id $instrument $timeframe $entry_rate $stop_rate $bs $lots $demo_arg
fade2=0;squat2=0;b4zlc2=8;fade1=0;squat1=0;b4zlc1=61;zone=S-B-S-N-B-S-S
### ---- COPY TO HERE ---

cat rjgt/SPX500_m5_250605130601.sh

### --- COPY FROM HERE --- 
demo_arg="--real" #demo_arg="--demo"
# FDB Buy Entry SPX500 m5 - bts/now:2025-06-05 17:05/2025-06-05 17:06:01
risk_in_pips=103.4
instrument="SPX500";timeframe="m5";bs="B"
tlid_id=250605130601;lots=1
entry_rate=5963.88;stop_rate=5953.58
jgtnewsession $tlid_id $instrument $timeframe $entry_rate $stop_rate $bs $lots $demo_arg
fade2=0;squat2=0;b4zlc2=0;fade1=0;squat1=1;b4zlc1=7;zone=S-B-N-N-B-S-S
### ---- COPY TO HERE ---

RESULTS from running bash above from 📦 CampaignLauncher

🗃️ Trading Echo Lattice (Memory Crystallization)

Records outcome + feedback to memory crystal

Outcomes, market conditions, and any manual feedback are stored. This data refines future LLM translations and strategy adaptations, completing the Echo Spiral.

• Really just a sketch of a step where we would end up doing simple work with what happens when a campaign is ended.
• It could become really complex and the first iteration of that (because nothing exist much in here) would requires the most simplest core feature required because we can get lost into complexiti in here, be warned….