Applied Causal AI for Algorithmic Trading and Quantitative Strategy Design
Language: English
Published by Independently Published Okt 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
Neuware - Reactive PublishingOvercome the limit of traditional machine learning in financial markets by moving from pattern recognition to true cause-and-effect modeling.Standard statistical and machine learning models excel at detecting correlations in historical market data. However, when market regimes shift, correlation-based strategies frequently break down, leading to catastrophic overfitting and severe drawdown. Applied Causal AI for Algorithmic Trading and Quantitative Strategy Design introduces a rigorous framework for building trading algorithms grounded in structural causal models (SCMs).By modeling the structural mechanics driving price action rather than relying on surface-level correlations, quantitative researchers and algorithmic traders can build strategies that remain resilient across changing market conditions.Inside this book, you will discover: - Foundations of Causal Inference in Finance: Understand directed acyclic graphs (DAGs), structural causal models, and do-calculus applied specifically to high-frequency and daily market data.- Refining Feature Engineering: Identify true causal drivers of asset returns while eliminating spurious correlations and confounders that lead to backtest overfitting.- Counterfactual Backtesting: Test trading strategies against alternative market scenarios and stress-tests using structural interventions rather than simple historical replay.- Regime Shift Resilience: Build adaptive risk management and position-sizing algorithms designed to detect and adjust to structural breaks in real time.- Practical Implementation: Implement end-to-end Python workflows integrating causal discovery algorithms with standard quantitative finance libraries.Whether you are a quantitative portfolio manager, financial engineer, or independent systematic trader, this guide provides the theoretical foundations and practical tools required to deploy robust, cause-driven algorithmic trading strategies.…
Seller Inventory # 9798178409459
- Title
- Applied Causal AI for Algorithmic Trading and Quantitative Strategy Design
- Author
- Oliver J. Thatch
- Publisher
- Independently Published Okt 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798178409459
- Item weight
- 474 grams
- Dimensions
- 229x152x25 mm
Overcome the limit of traditional machine learning in financial markets by moving from pattern recognition to true cause-and-effect modeling.
Standard statistical and machine learning models excel at detecting correlations in historical market data. However, when market regimes shift, correlation-based strategies frequently break down, leading to catastrophic overfitting and severe drawdown. Applied Causal AI for Algorithmic Trading and Quantitative Strategy Design introduces a rigorous framework for building trading algorithms grounded in structural causal models (SCMs).
By modeling the structural mechanics driving price action rather than relying on surface-level correlations, quantitative researchers and algorithmic traders can build strategies that remain resilient across changing market conditions.
Inside this book, you will discover:
-
Foundations of Causal Inference in Finance: Understand directed acyclic graphs (DAGs), structural causal models, and do-calculus applied specifically to high-frequency and daily market data.
-
Refining Feature Engineering: Identify true causal drivers of asset returns while eliminating spurious correlations and confounders that lead to backtest overfitting.
-
Counterfactual Backtesting: Test trading strategies against alternative market scenarios and stress-tests using structural interventions rather than simple historical replay.
-
Regime Shift Resilience: Build adaptive risk management and position-sizing algorithms designed to detect and adjust to structural breaks in real time.
-
Practical Implementation: Implement end-to-end Python workflows integrating causal discovery algorithms with standard quantitative finance libraries.
Whether you are a quantitative portfolio manager, financial engineer, or independent systematic trader, this guide provides the theoretical foundations and practical tools required to deploy robust, cause-driven algorithmic trading strategies.
"Synopsis" may belong to another edition of this title.
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