Synthetic Data & Backtesting Bundle
Simulation, Modelling, and Rigorous Backtesting
in One Complete Framework
About This Bundle
The Synthetic Data & Backtesting Bundle brings together three complementary works covering the core components of a quantitative workflow for scientifically grounded trading-model evaluation. It integrates rigorous backtesting with statistical inference and explainability, synthetic OHLC generation for evaluation via an ARMA–GARCH–Copula framework combined with AI foundation-model forecasting, and DCC–GARCH-based baseline and stressed simulation of OHLC time series, with a brief presentation of its interface to walk-forward backtesting procedures. Designed for quantitative finance practitioners and researchers, the bundle offers both methodological depth and working implementation in R and Python.
What's Included
Synthetic Data in Inferential Backtesting
ARMA–GARCH–Copula and AI Foundation Forecasting Model for OHLC time series simulation.
Synthetic OHLC Simulation via DCC–GARCH
Comprehensive R and Python implementation with a walk-forward backtesting interface.
Scientific Backtesting
Methodological framework for inference and explainability in trading models.