Combining finance, mathematics, and programming, this guide offers foundational knowledge essential for entering computational finance. It emphasizes the interconnection of these disciplines, using mathematical concepts as a backdrop to explore financial theories and programming methods. The practical approach equips readers with the fundamental tools needed to understand financial economics effectively.
Focusing on practical applications, this book guides readers through using Python for algorithmic trading, making it accessible to individuals and small organizations. It covers essential topics such as setting up a Python environment, retrieving financial data, and vectorized backtesting with tools like NumPy and pandas. Additionally, it explores machine learning for market predictions and real-time data processing. By providing insights into automated trading strategies using platforms like OANDA and FXCM, the book empowers traders to compete effectively in the financial markets.
This second edition provides a comprehensive introduction to essential libraries and tools for developers and quantitative analysts, focusing on their applications in development and financial analytics. It equips readers with the necessary skills and knowledge to effectively utilize these resources in their work, enhancing their analytical capabilities in the financial sector.
The book explores the transformative impact of AI and machine learning on the financial industry, particularly in algorithmic trading. It offers practical insights into leveraging historical and real-time financial data to identify and exploit statistical inefficiencies in the markets. Readers will gain valuable knowledge on integrating advanced technologies into trading strategies, enhancing their ability to navigate and succeed in a rapidly evolving financial landscape.