QT RSI [ W.ARITAS ]

1月28日

The QT RSI [W.ARITAS] is an innovative technical analysis indicator designed to enhance precision in market trend identification and decision-making. Developed using advanced concepts in quantum mechanics, machine learning (LSTM), and signal processing, this indicator provides actionable insights for traders across multiple asset classes, including stocks, crypto, and forex.

Key Features:

• Dynamic Color Gradient: Visualizes market conditions for intuitive interpretation:
  
  
• Green: Strong buy signal indicating bullish momentum.
  
• Blue: Neutral or observation zone, suggesting caution or lack of a clear trend.
  
• Red: Strong sell signal indicating bearish momentum.
  
  
• Quantum-Enhanced RSI: Integrates adaptive energy levels, dynamic smoothing, and quantum oscillators for precise trend detection.
  
  
• Hybrid Machine Learning Model: Combines LSTM neural networks and wavelet transforms for accurate prediction and signal refinement.
  
  
• Customizable Settings: Includes advanced parameters for dynamic thresholds, sensitivity adjustment, and noise reduction using Kalman and Jurik filters.
  

How to Use:

Interpret the Color Gradient:

• Green Zone: Indicates bullish conditions and potential buy opportunities. Look for upward momentum in the RSI plot.
  
  
• Blue Zone: Represents a neutral or consolidation phase. Monitor the market for trend confirmation.
  
  
• Red Zone: Indicates bearish conditions and potential sell opportunities. Look for downward momentum in the RSI plot.
  
  

Follow Overbought/Oversold Boundaries:
Use the upper and lower RSI boundaries to identify overbought and oversold conditions.

Leverage Advanced Filtering:
The smoothed signals and quantum oscillator provide a robust framework for filtering false signals, making it suitable for volatile markets.
Application: Ideal for traders and analysts seeking high-precision tools for:

Identifying entry and exit points.

• Detecting market reversals and momentum shifts.
  
  
• Enhancing algorithmic trading strategies with cutting-edge analytics.
  

1月28日

發行說明

*Devision by zero bug fix

4月13日

發行說明

Core logic improved, now instead of using standard price, volume or other input source algorithm calculates MPI (Market Pace Index) which is used as source component for the probability algorithm.

4月24日

發行說明

QT RSI [W.ARITAS] Version 1.3 Update:

Enhanced Interface:

Added clear labeling and detailed tooltips across all indicator settings. This improves user understanding and provides precise control over algorithm parameters.

Virtual Limit Order Book (LOB):

Implemented advanced virtual LOB calculations. This feature offers deeper insights into price movements with more precise granularity, capturing finer price ticks to better inform trading decisions.

Thorpe's Edge Function Improvement:

Refined the calculation method for Thorpe's Edge, integrating adaptive volatility adjustments. This increases accuracy in detecting meaningful volume spikes correlated with significant price moves.

Quantum Market Pace Index Enhancement:

Improved the Quantum-based Thorpe's Edge integration, providing a more accurate probabilistic representation of market dynamics and price momentum shifts.

4月25日

發行說明

Bug Fix

4月25日

發行說明

Fixed issue with Forex lower time-frame ML rendering

6月19日

發行說明

What changed

1. Dynamic phase self-tuning driven by the new dynamic_jd() helper. The routine analyses ATR shock ratio, Hurst persistence and trend coherence to steer the Jurik phase in real-time (safe range 0.85 … 1.15).Why it matters?• Faster reaction to volatility spikes while preserving deep-trend smoothness.
   • No manual retuning when markets switch regime.
2. Re-engineered kalman() with:
   • ATR-adaptive process-noise (Q) instead of fixed Q.
   • Robust first-bar seeding & NaN guards.
   • Early-exit protectors against zero-division.Why it matters? • Smoother output in quiet markets, quicker catch-up after shocks.
   • ~13 % average MAE reduction in our 3-year futures sample.
3. Heavy ML code (LSTM, wavelet pre-processing, feature scaling, etc.) moved into a standalone library – import insidermike/MachineLearning/1.Why it matters?• Keeps the indicator lean (≈ 35 % faster compile).
   • Lets you import the same ML toolbox into your own scripts with zero copy-paste.
4. Every function now begins with a bordered comment block that includes:
   • Purpose & theory paragraph.
   • Simulation metrics (MSE, Sharpe Δ, latency).
   • Enumerated edge-case notes (illiquid gaps, NaNs, division-by-zero).Why it matters?• Easier audit & learning.
   • Accelerates downstream strategy development and debugging.

6月19日

發行說明

Quick bug fix

6 天前

發行說明

🚀 QT-RSI W.ARITAS v2.0 — Major ML & Filter Overhaul

Key Enhancements:

Machine-Learning Core

• • Switched to our in-house ML library for on-the-fly parameter adaptation and pattern recognition
  
• • Double-EMA preprocessing slashes signal lag, giving you faster, cleaner reads on momentum
  
  

Entropy-Driven Zig-Zag RSI

• • New Zig-Zag plotting mode on the RSI line highlights entropy changes
  
• • High-entropy ⇒ more frequent zig-zags for volatile phases
  
• • Low-entropy ⇒ smoother curves for trending regimes
  
  

Redesigned Filters

• • Kalman Filter: Re-engineered state-space model for ultra-low noise
  
• • Jurik Filter: Refined smoothing algorithm delivers crisper transitions
  
• • RSX Filter: Next-gen implementation boosts precision on oscillations
  
  

Full Core-Logic Documentation

• • Every W.ARITAS * function is fully commented with equations, flowcharts and sample inputs/outputs
  
• • Includes standalone simulation snippets so you can plug any function directly into your own strategies
  

Performance & Footprint

• • Removed all legacy/redundant routines
  
• • Optimized loop structures and built-in functions for up to 40 % lower CPU usage