From Data to Models and Backtesting
ASRQuant 1.2.0 is an open-source quantitative research framework designed to support the full path from market data and hypothesis discovery to statistical validation and backtesting. The release brings together data handling, quantitative diagnostics, portfolio analysis, derivatives and interest-rate modelling, machine learning, and research-oriented backtesting within a consistent Python interface. The paper presents the architecture behind this research workflow and explains how ASRQuant can be used to structure quantitative investigations while reducing fragmentation between data analysis, modelling, testing, and evaluation. Particular attention is given to hypothesis discovery, statistical robustness, reproducible experiments, and the separation between research signals and their subsequent backtesting. ASRQuant is intended for students, researchers, and quantitative practitioners who need a transparent and extensible environment for developing, testing, and comparing quantitative methods.
Abstract
ASRQuant 1.2.0 introduces an integrated Python framework for quantitative finance research, covering the workflow from data acquisition and hypothesis discovery to statistical validation, modelling, portfolio analysis, and backtesting. The framework is designed to reduce the fragmentation commonly encountered when quantitative studies rely on independent tools for data processing, statistical testing, model estimation, and strategy evaluation. The release provides unified components for market-data processing, hypothesis generation and assessment, descriptive and inferential statistics, time-series analysis, portfolio and risk analytics, derivatives and interest-rate modelling, machine-learning workflows, and backtesting. A common interface and structured result objects are used to improve consistency across research stages and facilitate the comparison, extension, and reproduction of experiments. This paper describes the research architecture underlying ASRQuant 1.2.0, its principal modules, and the design principles used to connect exploratory analysis with formal quantitative testing. Particular emphasis is placed on maintaining a clear distinction between hypothesis discovery, statistical evidence, model construction, and out-of-sample evaluation. The resulting architecture provides a practical foundation for reproducible quantitative research while remaining sufficiently modular for educational, experimental, and applied financial modelling workflows.
Cite this work
Alpha Kabinet TOURE. From Data to Models and Backtesting. Alpha Stochastic Research, 2026. DOI: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7217798.