Development of an Adaptive Algorithmic Trading Strategy Using Smart Money Concepts, the Adaptive Market Hypothesis, and Non-Linear Predictive Analytics: A Framework for Market Prediction and Performance Enhancement

Registration ID: IJNRD_301623

Published ID: IJNRD2411009

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Maalav Whorra, Ananya Chandra, Dr. Shalini Lamba

Algorithmic Trading, Smart Money Concepts (SMC), Adaptive Market Hypothesis (AMH), Non-linear Predictive Analytics, Financial Markets, Institutional Investor Behavior, Market Efficiency, Machine Learning Models, Market Analysis, Empirical Testing, Python-based Algorithm, Quantitative Finance, Predictive Accuracy, Trading Performance, Market Microstructure, Backtesting, Adaptive Strategies, Asset Classes, Market Regimes, Financial Prediction

This comprehensive research investigates the potential of optimizing algorithmic trading strategies by integrating Smart Money Concepts (SMC), the Adaptive Market Hypothesis (AMH), and non-linear predictive analytics. Traditional indicators and linear models often fall short in capturing the nuanced dynamics of modern financial markets, which are characterized by adaptive behaviors and complex, non-linear relationships. By contrast, this study proposes a novel framework that combines the insights from institutional investor behavior (SMC), the evolutionary perspective of market efficiency (AMH), and the flexibility of non-linear machine learning models to create a more sophisticated approach to market analysis and prediction. Through rigorous analysis, simulations, and empirical testing, we develop an innovative Python-based algorithm that incorporates these advanced concepts. Our research aims to bridge the gap between theoretical frameworks and practical, implementable trading solutions, offering tangible insights for both academic researchers and industry practitioners in the field of quantitative finance. The study demonstrates how this integrated approach can lead to more accurate predictions, enhanced trading performance, and a deeper understanding of market microstructure across various asset classes and market conditions. Key findings include the superior predictive accuracy of our SMC-enhanced, AMH-aligned non-linear models compared to traditional approaches, significant outperformance in backtesting scenarios, and the ability to adapt to changing market regimes. This research not only contributes to the growing body of knowledge in quantitative finance but also provides a practical roadmap for developing more effective and adaptive algorithmic trading strategies in the complex landscape of modern financial markets.

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Maalav Whorra, Ananya Chandra, & Dr. Shalini Lamba (November-2024). Development of an Adaptive Algorithmic Trading Strategy Using Smart Money Concepts, the Adaptive Market Hypothesis, and Non-Linear Predictive Analytics: A Framework for Market Prediction and Performance Enhancement. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(11), a93-a108. https://ijnrd.org/papers/IJNRD2411009.pdf

Volume 9 Issue 11, November-2024

Pages : a93-a108

Paper Reg. ID: IJNRD_301623

Published Paper Id: IJNRD2411009

Research Area: Science and Technology

Author Type: Indian Author

Country: Lucknow, Uttar Pradesh, India

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