Why does the skill analyze GARCH fit quality?

GARCH fit quality predicts how accurately a volatility model can forecast future risk. Assets with a high GARCH score allow for more precise position sizing and risk management.

What libraries are required to run these statistical analyses?

The skill is optimized for Python 3.10+ environments using standard data science libraries like numpy, pandas, and scipy, with optional support for arch and hmmlearn.

How does this skill differ from standard stock screening?

Standard screens often focus on simple metrics like volume or price change. This skill evaluates deep statistical properties such as mean-reversion speed, volatility modelability, and the mathematical persistence of market regimes.

What is the importance of the Hurst exponent in symbol selection?

The Hurst exponent identifies whether an asset is mean-reverting or trending. This skill uses a dual-scale approach to find assets that mean-revert in the short term while trending in the long term, offering the best of both worlds.

Statistical Trading Symbol Selection

Name: Statistical Trading Symbol Selection
Author: smith6jt-cop

bysmith6jt-cop

Ciencia de Datos y ML

Analyzes market data using Hurst exponent, GARCH models, and Markov regime detection to identify optimal trading symbols for quantitative strategies.

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This skill provides a sophisticated statistical framework for selecting trading symbols that are compatible with advanced algorithmic strategies like mean-reversion, momentum, and Markov-switching models. It moves beyond basic metrics like Sharpe ratios by implementing Detrended Fluctuation Analysis (DFA) for the Hurst exponent, Ornstein-Uhlenbeck processes for mean-reversion half-life, and GARCH fit quality scoring. It is an essential tool for quantitative researchers and developers who need to ensure their chosen assets possess the specific volatility persistence and regime characteristics required for high-performance trading systems.

Características Principales

Mean-reversion half-life calculation via Ornstein-Uhlenbeck (OU) processes
0 GitHub stars
GARCH(1,1) fit quality scoring and stationarity validation
Multi-scale Hurst Exponent analysis using Detrended Fluctuation Analysis (DFA)
Weighted composite scoring system for multi-factor asset ranking and selection
Hidden Markov Model (HMM) for regime persistence and transition probability analysis

Casos de Uso

Detecting assets with stable regime persistence for Markov-switching trading systems
Identifying symbols with specific mean-reversion speeds suitable for hourly trading windows
Filtering asset universes for compatibility with GARCH-based position sizing models

Acerca de

Características Principales

Mean-reversion half-life calculation via Ornstein-Uhlenbeck (OU) processes
0 GitHub stars
GARCH(1,1) fit quality scoring and stationarity validation
Multi-scale Hurst Exponent analysis using Detrended Fluctuation Analysis (DFA)
Weighted composite scoring system for multi-factor asset ranking and selection
Hidden Markov Model (HMM) for regime persistence and transition probability analysis

Casos de Uso

Detecting assets with stable regime persistence for Markov-switching trading systems
Identifying symbols with specific mean-reversion speeds suitable for hourly trading windows
Filtering asset universes for compatibility with GARCH-based position sizing models