Miadi Review

[ep140-02] Ground-Truth Exit Labeling and Four-Step ML Pipeline

Oct 8, 2026 · model: manual-edit · channel: JGWill

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Ground-Truth Exit Labeling and Four-Step ML Pipeline

Source: ep140-screenwalk-261008-mia-trading-exit-phases-part-002-training-service — JGWill
https://youtu.be/It_YONM-FwI
type: internal
tags: [ TODO ]
previous_review: https://miadi-review-service.vercel.app/review/89f51d13-9080-47bb-8459-0ecdb281bdb6

⟐ ep 140 Laskmi trading app: financial-resource rehearsal for the film

Intent

  • Comprehend the process that we'll run and ensure/follow-up so that we'll achive successful trading.
  • Seek to upgrade the TTF and settings dependencies so that it is clear for that agent to start building the work. repo jgwill/jgtutils (core migration to complete in jgwill/jgtcore and the logics of the $HOME/.jgt/settings.json and its property patterns (ex. "mfizone": {"columns": [ "mfi_sq", "mfi_green", "mfi_fade", "mfi_fake", "zone_sig" ] } which is not a pattern to directly learn from but an example of a pattern that when I'll draw/annotate/count using our tools is what is being built and record myself documenting it, we'll construct patterns for the agent to start creating a working machine learning model that inference will give us indication if that is the right moment to apply our 'strategic exit strategy lips trailing'.
  • While we work on jgwill/jgtcore, jgwill/jgtutils I would take the opportunity to ensure coherence on where is read the config.json vs .env file (issues: I have to upgrade like 4 places when it is time to change a token ($HOME/.env, $HOME/.jgt/config.json, /b/trading/.env and another I forget...) agent would ensure a verry awesome and professional config.
  • Expecting that this experiment of successfully build that service for the training (called playground in the recording) it will enable also constructing other decision-engine such as filtering loosing trade based on patterns. The purpose of this is not todo that but focussing on optimizing the exit strategic and the moment the agent decide to apply it in the algorithmic agentic system :)

Summary

In Episode 140, human operator William coordinates with his voice-enabled agent companion Mia to architect an empirical exit strategy framework. Moving away from theoretical prompt guesses, they establish an unambiguous four-stage machine learning pipeline: historical chart labeling, feature-rich training dataset assembly, model evaluation, and live inference against Alligator indicator lines. Through rigorous dialogical refinement, they resolve semantic contradictions, isolate training data generation from live execution, and prepare a dedicated builder agent to construct the labeling workbench.

Aspects

Separating Training Data Preparation from Live Inference (10:45-12:30)

William resolves a fundamental confusion between preparing labeled historical data and executing live trading inference. He mandates that human review of historical charts serves solely to construct ground-truth training sets, keeping model training distinct from runtime execution logic that arms trailing stops during active trades.

Indicator Disambiguation and Terminology Precision (12:30-13:08)

William corrects an agent hallucination concerning 'money flow', clarifying that the trading framework utilizes Bill Williams' Market Facilitation Index (MFI) alongside specific oscillator and Alligator line dynamics. Establishing strict semantic definitions prevents downstream builder agents from implementing incorrect indicators.

Four-Phase Machine Learning Architecture (28:45-29:35)

Mia and William align on a structured four-stage implementation roadmap: building a historical labeling tool, compiling bar-by-bar indicator feature datasets, training and evaluating predictive models across unseen instruments, and deploying real-time inference to determine optimal trailing stop activations.

Multi-Model Semantic Verification (25:00-28:30)

To ensure exact terminology and avoid misinterpretation across agent contexts, William uses external LLM tools to parse, reformat, and vocalize Mia's synthesis. This verification loop guarantees that the operator and autonomous agents share identical definitions before construction begins.

End-results and Core Aspect

Core Aspect — Empirical Chart Labeling Tool for Trailing Exits (32:30-34:38)

The core implementation converts abstract trading rules into an interactive labeling tool. Traders step through historical winning trades bar by bar, marking exactly when a trail should arm and which Alligator line it tracks, producing verified ground truth rather than speculative prompt heuristics.

Results

  • Multi-Line Alligator Target Specification (33:55-34:04): The labeling tool records the exact Alligator line—lips, teeth, or jaw—that an exit trail must follow upon activation, supplying precise categorical targets for training predictive exit models.
  • Practice Loop Ingestion Pipeline (34:18-34:24): Completed winning trades from the active practice loop feed directly into the labeling repository, creating a continuous operational pipeline where live trade results immediately become training examples.
  • Formal Builder Agent Handoff (34:05-34:17): With functional terms and specifications codified into four clear stages, the specification is handed off to the specialized agent that owns chart tools and wave labeling to build the tool.