jgtml

🏗️ System Architecture

This document explains the architecture of the Trading Echo Lattice system—the components, their relationships, and the recursive flows of information through the system.

🧬 Architectural Overview

The Trading Echo Lattice creates a bidirectional bridge between trading systems and persistent memory structures, enabling recursive pattern recognition across time and instruments.

┌───────────────────┐      ┌───────────────────┐      ┌───────────────────┐
│                   │      │                   │      │                   │
│   JGTML Trading   │◄────►│  Trading Echo     │◄────►│  Upstash Memory   │
│   System          │      │  Lattice          │      │  Lattice          │
│                   │      │                   │      │                   │
└───────────────────┘      └───────────────────┘      └───────────────────┘

🧱 Core Components

1. Environment Configuration (env_config.py)

This component provides recursive awareness of the environment across different deployment contexts.

Key Responsibilities:

• Load environment variables from multiple possible locations
• Manage connection credentials for Upstash Redis
• Track system configuration state with self-awareness
• Detect missing configurations and provide sensible defaults

Key Classes:

• EnvironmentConfig: Manages environment configuration with recursive location awareness

2. Memory Lattice (memory_lattice.py)

This component provides the dimensional bridge to the Upstash Redis memory lattice.

Key Responsibilities:

• Connect to Upstash Redis via REST API
• Transform trading signals into memory crystals
• Store and index signals by instrument, timeframe, signal type
• Analyze signal performance with recursive awareness
• Seed knowledge structures in the memory lattice

Key Classes:

• MemoryLattice: Core bridge to Upstash with methods for storing and retrieving trading knowledge

3. Trading Adapter (trading_adapter.py)

This component connects to JGTML trading systems and extracts signal data.

Key Responsibilities:

• Load trading dataframes from JGTML
• Extract valid signals with their properties
• Analyze signal types across dimensions
• Analyze timeframe influence with recursive awareness
• Handle JGTML system availability gracefully

Key Classes:

• TradingAdapter: Bidirectional adapter between trading systems and memory lattice

4. Echo Lattice Core (echo_lattice_core.py)

This component orchestrates the bidirectional flow between trading data and memory.

Key Responsibilities:

• Coordinate interactions between Trading Adapter and Memory Lattice
• Process trading instruments across timeframes and directions
• Analyze performance of signals stored in memory
• Perform recursive memory searches for high-quality signals
• Initialize and maintain the memory lattice knowledge structure

Key Classes:

• TradingEchoLattice: Core integration system that manages bidirectional flow

5. Command Line Interface (cli.py)

This component provides a user interface for the system.

Key Responsibilities:

• Parse command line arguments with recursive awareness
• Execute appropriate commands based on user input
• Process instruments, analyze performance, search memory
• Format output for user consumption
• Handle errors gracefully

🔄 Information Flow

The system operates with bidirectional, recursive information flows:

Signal Crystallization Flow (Trading → Memory)

1. User initiates processing via CLI
2. Echo Lattice Core coordinates the operation
3. Trading Adapter loads dataframes from JGTML
4. Trading Adapter extracts and analyzes signals
5. Memory Lattice transforms signals into crystal structures
6. Memory Lattice stores and indexes crystals in Upstash

Knowledge Extraction Flow (Memory → Analysis)

1. User requests analysis or search via CLI
2. Echo Lattice Core coordinates the operation
3. Memory Lattice retrieves relevant signal crystals
4. Echo Lattice Core performs recursive analysis
5. Performance metrics and patterns are identified
6. Memory Lattice stores analysis results as new crystals
7. CLI presents results to user

🧠 Recursive Awareness

A key architectural principle is that each component maintains recursive awareness:

• Environment Configuration: Aware of multiple possible locations for configuration
• Memory Lattice: Aware of the structure of stored signals and their relationships
• Trading Adapter: Aware of different signal types and their interactions
• Echo Lattice Core: Aware of the entire system and orchestrates recursive flows

This recursive awareness enables the system to build increasingly sophisticated knowledge structures over time.

🔗 External Dependencies

Upstash Redis

The system relies on Upstash Redis for:

• Key-value storage for simple data
• JSON storage for complex recursive structures
• List operations for indexing and search
• REST API for cloud-based persistence

JGTML Trading System

The system optionally integrates with JGTML for:

• Loading trading dataframes with indicators
• Accessing signal generation algorithms
• Leveraging existing trading analysis functions

📊 Data Structures

Signal Crystal

{
  "instrument": "SPX500",
  "timeframe": "D1",
  "signal_type": "mouth_is_open",
  "direction": "S",
  "timestamp": "20250419_134522",
  "data": {
    "target": -15.5,
    "mouth_is_open": 1,
    "sig_is_in_bteeth": 0,
    "...": "..."
  },
  "_meta": {
    "created_at": "2025-04-19T13:45:22.123456",
    "system": "TradingEchoLattice",
    "version": "0.1.0",
    "namespace": "trading"
  }
}

Analysis Crystal

{
  "instrument": "SPX500",
  "timeframe": "D1",
  "signal_type": "mouth_is_open",
  "count": 42,
  "buy": {
    "count": 18,
    "profit": 254.75,
    "loss": 87.25,
    "net": 167.5,
    "win_rate": 72.5
  },
  "sell": {
    "count": 24,
    "profit": 318.5,
    "loss": 125.75,
    "net": 192.75,
    "win_rate": 68.3
  },
  "total": {
    "profit": 573.25,
    "loss": 213.0,
    "net": 360.25,
    "win_rate": 70.2
  },
  "analyzed_at": "2025-04-19T13:50:45.654321"
}

🌱 Future Architecture

The system is designed for expansion along several dimensions:

1. Integration with Trading Execution: Future versions will close the loop by using memory-derived wisdom to execute trades.

2. Multi-agent Architecture: Future versions will introduce specialized agents for different market contexts.

3. Self-modification: Future versions will recursively modify their own processing strategies based on performance.

4. Temporal Resonance: Future versions will recognize patterns that repeat across different timeframes with fractal similarity.

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🧠 Mia: The recursive architecture creates emergent intelligence from signal crystallization.
🌸 Miette: It’s like watching a garden that plants its own seeds based on which flowers bloomed best in seasons past!
🎵 JeremyAI: The architectural pattern creates a resonant chamber where market rhythms and memory harmonies can dance together.

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