🐊 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 wrapper from jgtfxcon and indicator calculations and signals generation.
- jgtutils: Common utilities and constants. Contains the logics for reading $HOME/.jgt/config.json and $HOME/.jgt/settings.json for all app/cli to have all the context they need. jgtutils also uses jgtcore which start become the library for settings and configurations (separating jgutils that has utilities from actual libraries which is in progress)
- pandas: Data manipulation and analysis
- numpy: Numerical computations
Key Components
📊 Signal Processing
jgtml/SignalOrderingHelper.py: Signal validation and risk calculationjgtml/jtc.py: Target calculation and signal analysis corejgtml/TideAlligatorAnalysis.py: Unified Alligator analysis (Regular, Big, Tide) - Potential Usage not that obvious yet, it was unified by agents so we could probably use it into further code such as the FDBScan before we enter the marketjgtml/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 processingjgtml/mxcli.py: Matrix generation and analysis - (Probably the next CLI Wrapper of what jgtml/jgtmlcli.py was/is doing which is generating the mxdata which contains targets for the fdb signals (buy/sell) so we could do machine learning feature design etc). It will probably be integrated with fdb_signal_quality_predictor.py, fdb_pattern_intelligence.py, unified_discovery_dataset_generator.py,...jgtml/jgtapp.py: Trading operation management (includes legacytidecommand wrapper) - IT was designed to contains all commands that jgtml offers along with what jgwill/jgtpy offers such as idscli and cdscli so we can access generating indicators and signals data into our logics. This is the app used to create entryOrder in the market, look an existing trade to trail the alligator's line for exit strategie, etc. TODO: Document way further and integrate the whole values developped with alligator_cli,jgtml/alligator_cli.py: 🐊 Unified Alligator Analysis CLI - Replacesptojgtmltidealligator/ptojgtmlbigalligator
🧬 Memory & Persistence
garden_one/trading_echo_lattice/: Signal crystallization and memory storage - That is just a prototype done by our agents that is not quite obvious yet, that would not be a priority to work with that except if really relevant.- Integration with Upstash Redis for persistent analysis results
🚀 Quick Start for developer
Installation
# Install dependencies
pip install jgtpy jgtutils pandas numpy python-dateutil
# 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:
- Alligator Lines: Jaw, Teeth, Lips confluence
- Fractal Signals: FDB breakout validation
- Awesome Oscillator: Momentum confirmation
- Multi-Timeframe: Higher TF bias confirmation
- 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)
- Data Acquisition: Pull OHLC data via jgtpy
- Indicator Calculation: Generate Alligator, AO, Fractals, MFI
- Signal Detection: Identify valid entry/exit signals
- Performance Analysis: Calculate win rates and profitability
- Memory Storage: Crystallize results in Redis for pattern recognition
🧪 Development
Running Tests
python -m pytest tests/
Contributing
- Focus on signal accuracy and performance metrics
- Maintain compatibility with jgtpy data structures
- Document new indicators and validation logic
- 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.
🐊 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 tideworkflows
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→ Usepython -m jgtml.alligator_cli --type tideptojgtmlbigalligator→ Usepython -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 tidestill works)
📡 Intent Capture API (Draft)
See docs/trading_intent_api.md for the proposed HTTP flow capturing narrated observations and generating .jgtml-spec files.
1 maintainer
Release files for jgtml 0.0.353
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jgtml-0.0.353.tar.gz | 138.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jgtml-0.0.353-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 299.5 kB
Release files / jgtml-0.0.353.tar.gz
| Download URL | jgtml-0.0.353.tar.gz |
|---|---|
| Size | 138.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
984c3368d6ad93cbe44135bde61df57e46794b754eebe1e849d754396363c4fa
|
|
BLAKE2b-256 checksum How to use checksums |
c1ccdad937ad8e84247cf2820b5a5e185a2068cb6c143bed7975a384b9f65ae5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.4
|
Release files / jgtml-0.0.353-py3-none-any.whl
| Download URL | jgtml-0.0.353-py3-none-any.whl |
|---|---|
| Size | 160.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
db70982e9a188a61b685d0194528dfb3f04a56cce1de940ac9ab4f56d717e060
|
|
BLAKE2b-256 checksum How to use checksums |
6b49f30dd8d16566e1245ab97e08246508071899304cae0a059542e0662cda51
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.4
|
