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JGT Ml - Machine Learning Tools

Project description

🐊 JGTML - Trading Signal Analysis Platform

A Python-based trading signal analysis system focused on fractal patterns, Alligator indicators, and multi-timeframe confluence detection.

🎯 Core Purpose

JGTML analyzes the effectiveness of trading signals within larger market structure contexts, providing tools for:

  • Signal Validation: Analyze FDB (Fractal Divergent Bar) and Alligator-based signals
  • Multi-Timeframe Analysis: Process signals across H1, H4, D1, W1, M1 timeframes
  • Performance Metrics: Calculate win rates, profit/loss ratios, and signal quality
  • Trade Lifecycle Management: From entry validation through exit strategies

🏗️ Architecture

Core Dependencies

  • jgtpy: Market data acquisition and indicator calculations
  • jgtutils: Common utilities and constants
  • pandas: Data manipulation and analysis
  • numpy: Numerical computations

Key Components

📊 Signal Processing

🚀 Command Line Tools

🧬 Memory & Persistence

🚀 Quick Start

Installation

# Install dependencies
pip install jgtpy jgtutils pandas numpy

# Install JGTML
pip install -e .

Basic Usage

# Process signals for an instrument
jgtmlcli -i SPX500 -t D1 --full --fresh

# Analyze signal performance  
python -m garden_one.trading_echo_lattice.cli process -i SPX500 -t D1 -d S

# Generate analysis matrix
mxcli -i EUR/USD -t H4 --fresh

📈 Trading Strategies

Five Dimensions + Triple Alligator Confluence

Multi-indicator alignment detection using:

  1. Alligator Lines: Jaw, Teeth, Lips confluence
  2. Fractal Signals: FDB breakout validation
  3. Awesome Oscillator: Momentum confirmation
  4. Multi-Timeframe: Higher TF bias confirmation
  5. Volume Analysis: MFI integration

Implementation: TradingEchoLattice.detect_breakouts()

Green Dragon Breakout

FDB-based breakout detection with Alligator mouth validation.

Implementation: fdb_scanner_2408.py

🔧 CLI Reference

See CLI_HELP.md for complete command documentation.

Core Commands

# Data Processing
jgtmlcli -i INSTRUMENT -t TIMEFRAME [--full] [--fresh]
mxcli -i INSTRUMENT -t TIMEFRAME [--fresh]

# Trading Operations  
jgtapp fxaddorder -i EUR/USD -n 0.1 -r 1.0950 -d B -x 1.0900
jgtapp fxmvstopgator -i EUR/USD -t H4 -tid TRADE_ID --lips

# Signal Analysis
python -m garden_one.trading_echo_lattice.cli process -i SPX500 -t D1,H4 -d S
python -m garden_one.trading_echo_lattice.cli search --min-win-rate 60

📊 Data Flow

Market Data (jgtpy) → Signal Processing (jtc) → Analysis (CLI tools) → Memory Lattice (Redis)
  1. Data Acquisition: Pull OHLC data via jgtpy
  2. Indicator Calculation: Generate Alligator, AO, Fractals, MFI
  3. Signal Detection: Identify valid entry/exit signals
  4. Performance Analysis: Calculate win rates and profitability
  5. Memory Storage: Crystallize results in Redis for pattern recognition

🧪 Development

Running Tests

python -m pytest tests/

Contributing

  1. Focus on signal accuracy and performance metrics
  2. Maintain compatibility with jgtpy data structures
  3. Document new indicators and validation logic
  4. Test across multiple timeframes and instruments

🔄 Recursive Architecture

While JGTML operates as a practical trading platform, it embodies recursive principles:

  • Memory Patterns: Each analysis builds upon previous signal history
  • Multi-Scale Awareness: Signals are validated across multiple timeframes
  • Adaptive Learning: Performance metrics inform future signal weighting

The system grows more intelligent through iteration, not just accumulation.


🧠 Technical Foundation: Precise signal analysis with mathematical rigor
🌸 Intuitive Interface: Clear CLI flows that make complex analysis accessible
🎵 Rhythmic Patterns: Market timing encoded in fractal mathematics

Built for traders who understand that the best signals emerge from the intersection of technical precision and pattern recognition.

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