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Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies
90% of respondents would recommend this to a friend
€ 60
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What Stands Out
Product Details
| Publisher | Packt Publishing |
| Publication date | April 29, 2021 |
| Language | English |
| Print length | 360 pages |
| ISBN-10 | 1838982884 |
| ISBN-13 | 978-1838982881 |
| Item Weight | 1.36 pounds (620 grams) |
| Dimensions | 7.5 x 0.82 x 9.25 inches (19.1 x 2.1 x 23.5 cm) |
Who Should Buy?
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Aspiring Traders
Individuals looking to learn financial trading strategies with practical Python applications for successful trading.
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Quantitative Analysts
Professionals wanting to enhance their skills in using Python for backtesting and creating trading algorithms.
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Python Developers
Software engineers interested in applying their Python skills to the financial markets for algorithmic trading.
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Beginners in Finance
People with no prior knowledge of finance may find the content too technical and challenging to comprehend.
Product Description
Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies
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Mathematical & Statistical Editorial Review
**** "Holding the reins of algorithmic trading in Python is made accessible with 'Hands-on Financial Trading with Python'. This book comes highly recommended for aspiring algo traders, particularly those who are willing to delve into practical applications using Python. Readers commend chapters focused on essential libraries like NumPy, Pandas, and Matplotlib, which are presented with clear, hands-on coding examples that facilitate easy comprehension and implementation. However, navigating the more detailed chapters, especially those involving time series models and statistical backgrounds, may pose challenges for beginners. Some feedback suggests that while the book serves as an excellent introduction to various aspects of trading, it may only skim the surface of more advanced concepts, necessitating further exploration into specialized literature for deeper knowledge. Although much of the information is practical and instructive, there have been critiques regarding the lack of explanations surrounding certain coding values and outcomes, leaving some confusion among readers about the book's suitability for their level of expertise. The inclusion of real-world scenarios demonstrates the author's understanding of the aio trading environment and aligns with the practical needs of readers. Particularly noted is the treatment of zipline, with many readers appreciating the insights on installation and setup amidst a scarcity of reliable resources. The suggestion for a new chapter on zipline-trader indicates that even amidst the positive reception, there is room for expansion and improvement. In summary, 'Hands-on Financial Trading with Python' engages readers with its structured approach to algorithmic trading, aided by practical examples. Yet, potential buyers should bear in mind that while the book serves as a user-friendly entry point, those seeking comprehensive understanding might still need to pursue additional resources to fully grasp the complexities of the field." **
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Pros
- Comprehensive and practical approach to Python libraries like NumPy, Pandas, and Matplotlib.
- Clear coding examples that enhance understanding.
- Gradual build-up of concepts aiding the learning process.
- Helpful resource for those new to algorithmic trading or looking to expand their toolkit.
Cons
- May require some statistical background for deeper comprehension, particularly in advanced chapters.
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