jgtml 0.0.303

JGT Ml - Machine Learning Tools

🐊 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

• jgtml/SignalOrderingHelper.py: Signal validation and risk calculation
• jgtml/jtc.py: Target calculation and signal analysis core
• jgtml/TideAlligatorAnalysis.py: Unified Alligator analysis (Regular, Big, Tide)
• jgtml/alligator_cli.py: 🐊 NEW Unified Alligator CLI with graceful TTF pattern handling
• TODO add TTF (TTF != Time-To-Future but more like feature of multiple timeframe) probably ttfcli.py

🚀 Command Line Tools

• jgtml/jgtmlcli.py: Main CLI for data processing
• jgtml/mxcli.py: Matrix generation and analysis
• jgtml/jgtapp.py: Trading operation management (includes legacy tide command wrapper)
• jgtml/alligator_cli.py: 🐊 Unified Alligator Analysis CLI - Replaces ptojgtmltidealligator/ptojgtmlbigalligator

🧬 Memory & Persistence

• garden_one/trading_echo_lattice/: Signal crystallization and memory storage
• Integration with Upstash Redis for persistent analysis results

🚀 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]

# Unified Alligator Analysis ✨ NEW ✨
python -m jgtml.alligator_cli -i SPX500 -t D1 -d S --type tide    # Single Alligator
python -m jgtml.alligator_cli -i EUR/USD -t H4 -d B --type all    # Multi-Alligator convergence
python -m jgtml.alligator_cli -i GBPUSD -t D1 -d S --generate-spec # Generate .jgtml-spec

# Legacy Support (redirects to unified CLI)
jgtapp tide -i SPX500 -t D1 B  # Legacy wrapper → unified Alligator CLI

# 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.

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🧠 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.

🐊 Unified Alligator Analysis

Multi-Timeframe Convergence System ✨ NEW ✨

The unified Alligator CLI consolidates three powerful analysis frameworks into a single, graceful interface:

🔍 Regular Alligator (5-8-13 periods)

• Purpose: Quick market direction detection and entry signals
• Best For: Day trading, scalping, short-term momentum
• Signals: Immediate price action around Alligator mouth

🌊 Big Alligator (34-55-89 periods)

• Purpose: Intermediate cycle analysis and trend validation
• Best For: Swing trading, weekly positioning
• Signals: Higher timeframe context and cycle turns

🌀 Tide Alligator (144-233-377 periods)

• Purpose: Macro trend identification and major support/resistance
• Best For: Position trading, monthly strategic positioning
• Signals: Long-term trend direction and major reversals

Key Features

• 🔄 Graceful Pattern Handling: Automatically handles missing TTF patterns (zonesq, mfi, ttf)
• 🎯 Intent-Driven Analysis: Generates .jgtml-spec files for agentic integration
• 🌐 Self-Contained: No external bash script dependencies
• ⚡ Multi-Type Convergence: Analyze all three Alligator types simultaneously
• 🔧 Legacy Compatible: Seamless integration with existing jgtapp tide workflows

Usage Examples

# Single Alligator Analysis
python -m jgtml.alligator_cli -i SPX500 -t D1 -d S --type tide

# Multi-Alligator Convergence (recommended)
python -m jgtml.alligator_cli -i EUR/USD -t H4 -d B --type all

# Generate .jgtml-spec for agentic workflows
python -m jgtml.alligator_cli -i GBPUSD -t D1 -d S --type all --generate-spec

# Legacy support (automatically redirects to unified CLI)
jgtapp tide -i SPX500 -t D1 B

🔄 Migration from Legacy Commands

Important: The following legacy commands have been deprecated and replaced by the unified Alligator CLI:

Deprecated Commands ❌

• ptojgtmltidealligator → Use python -m jgtml.alligator_cli --type tide
• ptojgtmlbigalligator → Use python -m jgtml.alligator_cli --type big
• Bash function jgtml_ptojgtmltidealligator_by_instrument_tf_21 → Use unified CLI

Migration Benefits ✅

• Self-contained operation (no bash script dependencies)
• Graceful error handling (TTF pattern failures don't crash analysis)
• Multi-Alligator convergence analysis capability
• Enhanced .jgtml-spec generation for agentic workflows
• Backward compatibility (legacy jgtapp tide still works)