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Item #:
26245849
Algorithmic Trading with Python: Quantitative Methods and Strategy Development
87% of respondents would recommend this to a friend
Item #:
26245849
SAR 126
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Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn.
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What Stands Out
Practical Framework
Offers a hands-on approach to algorithmic trading, providing readers with actionable strategies and methods to implement in real-world scenarios, enhancing their trading effectiveness.
Comprehensive Coverage
Covers a wide range of quantitative methods, offering in-depth insights and techniques that cater to both beginners and experienced traders looking to refine their strategies.
Python Integration
Leverages Python for algorithm development, making it accessible for users familiar with programming, while providing powerful tools for data analysis and strategy implementation.
Product Details
- Discusses modern quant trading methods in Python with a focus on pandas, numpy, and scikit-learn
- Covers technical indicators, performance metrics, and the development of a trading simulator, strategy optimizer, and financial machine learning pipeline
- Maintains a high standard of reproducibility with self-contained code and data in a GitHub repo
- Includes hyper-realistic simulated price data and alternative data based on real securities
- Serves as the spiritual successor to Automated Trading with R (2016), covering more content in less time
- Utilizes advances in open-source technologies for quantitative analysis
| Publisher | Independently published |
| Publication date | April 9, 2020 |
| Language | English |
| Print length | 126 pages |
| ISBN-13 | 979-8632784986 |
| Item Weight | 11 ounces (311.85 grams) |
| Dimensions | 8.5 x 0.29 x 11 inches (21.6 x 0.7 x 27.9 cm) |
| Book 1 of 3 | Algorithmic Trading |
Who Should Buy?
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Aspiring Traders
Ideal for beginners looking to understand algorithmic trading concepts and implementation using Python.
-
Data Scientists
Benefits data scientists interested in applying quantitative analysis techniques in financial markets.
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Financial Analysts
Financial analysts can enhance their skills in developing automated trading strategies through practical coding applications.
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Complete Beginners
Not suitable for those without any prior knowledge of programming or finance concepts.
Product Description
Algorithmic Trading with Python: Quantitative Methods and Strategy Development
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Analysis & Strategy Editorial Review
Algorithmic Trading with Python is a well-presented book with a clear expository style and provides valuable material for trading with Python. The book includes high-quality code implemented in GitHub and a link to a GitHub Repository that contains test data and functional programs. It is a wonderful resource with accurate results that demonstrate the effectiveness of various indicators and strategies implemented with Python frameworks. However, buyers need to be aware that it doesn't provide high-quality backtest results. The book is not suitable for intermediate/advanced users who already have experience with algorithmic trading in Python and Scikit Learn. One downfall is that the backtesting code requires signing up for the ActiveState for Python pypl package, which can lead to being encouraged to install Komodo IDE and sell a corporate package. Python is also slower than other languages and can cause latency issues. The book could benefit from an index and a glossary.
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Pros
- Well-presented with clear expository style and valuable material
- High quality code in GitHub with test data and functional programs
- Accurate results demonstrate effectiveness of indicators and strategies
- GitHub repository available for code samples and author encourages discussion
Cons
- Not suitable for intermediate/advanced users with experience in algorithmic trading with Python and Scikit Learn
Product Price History
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Features & Benefits
- Discusses modern quant trading methods in Python
- Heavy focus on pandas, numpy, and scikit-learn
- Develop a trading simulator, strategy optimizer, and financial machine learning pipeline
- Code and data is self-contained in a GitHub repo
- Covers more content than its predecessor due to advances in open-source technologies for quantitative analysis
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