statistics-timeseries

Analyze historical price data with ARIMA/GARCH models and stationarity tests.

10|2|Updated Mar 8, 2026

Search Tags:#statistics #backtesting #volatility #regime-detection #garch #timeseries #arima

Ready to run

npx skills add https://github.com/mahmoud20138/Tradecraft --skill statistics-timeseries

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistics-timeseries
Source: https://github.com/mahmoud20138/Tradecraft/tree/main/plugins/tradecraft/skills/statistics-timeseries
Command: npx skills add https://github.com/mahmoud20138/Tradecraft --skill statistics-timeseries

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Financial time-series and statistics require rigorous analysis; this skill provides framework and methods to analyze regimes, volatility, and relationships for trading.

Core Features & Use Cases

Descriptive statistics and distribution analysis for returns.
Time-series stationarity checks (ADF, KPSS) and unit-root diagnostics.
ARIMA/GARCH modeling, volatility forecasting, and regression/factor models.
Use cases: regime detection, risk metrics, backtesting baselines for trading strategies.

Quick Start

Analyze a price series to identify regime changes and forecast volatility using the included methods.

Frequently Asked Questions about statistics-timeseries

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect regime changes in financial time-series data for trading?▼

To forecast volatility in time-series data, apply GARCH models to historical price series using Python libraries like arch. This method models volatility dynamics and generates forecasts to support risk assessment and trading strategy development.

How do I check time-series stationarity before fitting ARIMA models in Python?▼

To analyze return distributions for trading, apply descriptive statistics to historical price data to extract statistical features. This distribution analysis reveals risk metrics and baseline characteristics across time horizons and asset classes for strategy development.

Can I use statsmodels and arch Python packages for backtesting trading strategies?▼

Yes, you can use statsmodels and arch Python packages for backtesting trading strategies. The workflow applies ARIMA and GARCH modeling to historical price data, generating reproducible inputs and outputs that establish backtesting baselines for trading strategies.

What is the best way to combine factor models with volatility forecasting for risk assessment?▼

The best way to combine factor models with volatility forecasting for risk assessment is applying regression approaches alongside GARCH models to historical price data. This unified statistical analysis evaluates relationships and volatility dynamics to produce comprehensive risk metrics.