PYTHON PROGRAMMING: 3 MANUSCRIPTS CRASH COURSE CODING WITH PYTHON DATA SCIENCE. THE STEP BY STEP GUIDE FOR BEGINNERS TO MASTER SOFTWARE PROJECTS, ALGORITHMS, TRICKS AND TIPS
Python Mini Reference 2022: A Quick Guide to the Modern Python Programming Language for Busy Coders (A Hitchhiker's Guide to the Modern Programming Languages Book 3) Harry Yoon
Chapter 9: Natural Language Processing (NLP) for Financial News
Analysis
Python Basics for Finance Guide
Data Handling and Analysis in Python for Finance Guide
Time Series Analysis in Python for Finance Guide
Visualization in Python for Finance Guide
Algorithmic Trading in Python
Additional Resources
PREFACE
Welcome to "Python for Finance: A Crash Course Modern Guide", a comprehensive resource designed for advanced users who are poised to leverage Python's capabilities to analyze financial data, develop trading strategies, and solve complex financial problems. This book assumes a foundational understanding of both Python programming and financial principles, aiming to bridge the gap between advanced theoretical concepts and their practical applications in finance.
The world of finance is both dynamic and complex, characterized by an incessant influx of data and a constant demand for more refined analytical processes. In this environment, Python has emerged as an indispensable tool for financial analysts, traders, and quantitative researchers due to its simplicity, flexibility, and powerful libraries specifically tailored for financial analysis. This book will guide you through the advanced use of Python in finance, covering topics from portfolio optimization and risk management to algorithmic trading and beyond.
Our purpose in writing this book is twofold. First, we aim to provide you with the advanced programming skills needed to execute sophisticated financial analyses and develop robust trading strategies using Python. Second, we endeavor to present these concepts in a manner that bridges theoretical knowledge and practical application, enabling you to tackle realworld financial challenges with confidence and precision.
The target audience for this book is not beginners to programming or finance. Rather, it is written for those who already possess an intermediate
to advanced understanding of Python and a solid foundation in financial concepts. Whether you are a finance professional seeking to enhance your analytical skills, a data scientist aspiring to specialize in financial applications, or a student in a related field aiming to expand your expertise, this book is tailored for you.
Through concise explanations, practical examples, and hands-on exercises, you will learn to apply Python's extensive libraries and tools in finance. Each chapter builds on the knowledge of the preceding ones, structured to facilitate both comprehensive learning and quick reference. By the end of this journey, you will not only have mastered advanced financial analysis with Python but also acquired a toolkit to tackle the evolving challenges of the financial industry with innovation and efficiency.
As you turn these pages, we invite you to immerse yourself in the exploration of Python's vast potential in finance. Let this book be your guide to mastering the complexity of financial analysis with the simplicity and power of Python. Welcome to the intersection of finance and technology, where your journey towards becoming an advanced practitioner begins.
Happy coding and analyzing,
CHAPTER 1: WHY PYTHON FOR FINANCE?
The integration of Python into finance can be traced back to its core attributes: simplicity, flexibility, and a vast ecosystem of libraries. Unlike its contemporaries, Python was designed with readability and straightforward syntax in mind, allowing financial analysts, who may not have a deep background in computer science, to quickly grasp and implement complex financial models and simulations.
Python's versatility is evident in its wide application, ranging from straightforward financial calculations to developing complex trading algorithms. The language's robust libraries, such as NumPy for numerical computing, pandas for data manipulation, and matplotlib for data visualization, have underpinned its utility in handling vast datasets common in finance.
The financial industry thrives on the analysis and interpretation of data. Python excels here, offering tools that streamline the process of data collection, cleaning, and analysis. Through libraries like pandas, financial professionals can manipulate and analyze time-series data, crucial for market trend analysis and economic forecasting.
Moreover, Python's Scikit-learn library has opened avenues for machine learning in finance, enabling predictive models that can assess risk, identify trading opportunities, and automate trading strategies. This application of Python for both traditional financial analysis and the cutting-edge field of
machine learning illustrates its dual role as both a foundational tool and a gateway to innovation.
The rise of Python has also led to a paradigm shift in the skill set required in the finance sector. Financial institutions now seek professionals who are not only adept in financial theories but also proficient in Python. This demand has propelled Python to a core subject in finance-related academic curricula and professional training programs.
Moreover, Python's accessibility and the community's commitment to opensource principles have fostered a collaborative environment. Financial analysts, traders, and researchers share code, develop libraries, and contribute to forums, pushing the boundaries of financial innovation.
One of the most notable applications of Python in finance is in the world of algorithmic trading. Python's capacity to process and analyze large datasets in real-time, coupled with its ability to integrate with trading platforms, makes it an ideal choice for developing automated trading systems. These systems can monitor market movements, execute trades based on predefined criteria, and manage risk, all with minimal human intervention.
Python's role in algorithmic trading is not limited to strategy development. Libraries like backtrader and pyfolio offer tools for backtesting, allowing traders to simulate their strategies on historical data before deploying them in live markets. This capability to rigorously test and refine trading algorithms underscores Python's value in reducing risk and enhancing profitability.
The incursion of Python into the financial industry is not merely a trend but a transformation. From data analysis and risk management to algorithmic trading and machine learning, Python has become the cornerstone of modern financial operations. Its simplicity, power, and the supportive community have democratized financial analysis, enabling professionals and institutions to navigate the complexities of the financial world with unprecedented precision and insight.
As we look towards the future, Python's role in finance is poised to grow, driven by ongoing innovation in financial technology. For those embarking on a journey in finance, proficiency in Python is not just an asset but a necessity, unlocking doors to new opportunities and frontiers in the financial domain.
The Growth of Python in Finance
The ascent of Python within the finance sector is a narrative of evolutionary growth, marked by pivotal developments and the strategic foresight of the financial community. This segment explores the dynamic expansion of Python's role in finance, charting its journey from a supplementary tool to a fundamental driver of financial analysis, innovation, and strategy.
Python's initial incursion into the finance industry was met with skepticism. Traditional tools like Excel, MATLAB, and C++ dominated the landscape, and the introduction of a new, seemingly less powerful language was viewed with caution. However, the early adopters of Python in finance recognized its potential for simplifying tasks that were cumbersome with existing tools. They leveraged Python’s simplicity to automate routine data analysis tasks, laying the groundwork for a broader acceptance.
The turning point came with the exponential growth of the Python community. Developers and financial professionals began contributing to a burgeoning ecosystem of libraries specifically designed for finance. This collaborative effort resulted in tools that could handle time-series data, perform complex calculations, and model financial theories more efficiently than ever before.
Quantitative finance, with its reliance on complex mathematical models and large-scale data analysis, provided fertile ground for Python's expansion. The language's ability to seamlessly manage large datasets and its capacity for rapid prototyping made it an invaluable asset for quants. Python libraries such as NumPy and pandas facilitated operations on numerical data, while matplotlib and seaborn offered advanced visualization capabilities, making the interpretation of financial models and data more intuitive.
The development of libraries like QuantLib, dedicated to quantitative finance, further cemented Python's place in the industry. These tools enabled professionals to model derivatives, perform risk management tasks, and construct financial instruments with unprecedented ease.
Python's accessibility has played a crucial role in democratizing financial analysis. With resources readily available online and a supportive community, individuals and smaller firms could undertake sophisticated financial analyses that were previously the domain of large institutions. This democratization has spurred innovation, with novel financial technologies and strategies emerging from beyond traditional financial powerhouses.
The fintech revolution, characterized by the amalgamation of technology and finance, has been significantly powered by Python. Startups and established financial institutions alike have adopted Python to develop applications ranging from automated trading systems to personal finance management tools. The language's ability to handle vast amounts of data in real-time makes it ideal for applications that require quick, accurate financial insights.
Moreover, Python's role in the rise of machine learning in finance cannot be overstated. Libraries such as TensorFlow and Keras have made it possible to apply complex machine learning algorithms to financial data, enabling predictive models that can forecast market trends, enhance trading strategies, and identify risks with a level of accuracy previously unimaginable.
Today, Python stands as the lingua franca of the financial industry. Its adoption by leading financial institutions, universities, and regulatory bodies worldwide speaks to its unmatched capabilities and the value it brings to the finance sector. As we move forward, the continued evolution of Python and its ecosystem holds the promise of further innovations. From blockchain technologies to artificial intelligence in finance, Python is at the forefront of driving change and shaping the future of the industry.
the growth of Python in finance is a testament to the language's adaptability, power, and the vibrant community that supports it. What began as a tool for simplifying data analysis tasks has transformed into a cornerstone of financial strategy and innovation, demonstrating the profound impact Python has had on the financial landscape.
Comparison with Other Programming Languages
In the financial sector, the choice of programming language can significantly influence the efficiency, scalability, and flexibility of financial models and analyses. Python, with its concise syntax and robust ecosystem, has emerged as a preferred language for many. However, to appreciate its standing, one must consider how it compares to other stalwarts in the field such as R, MATLAB, C++, and Java.
R, like Python, is a language designed with data analysis in mind. It boasts a rich set of libraries for statistical analysis and visualization, making it a favorite among statisticians and data scientists. However, Python edges out R in terms of versatility. Python's syntax is more intuitive and easier for non-statisticians to learn, which democratizes data analysis across different domains, including finance. Furthermore, Python's extensive libraries such as NumPy, pandas, and scikit-learn offer comparable, if not superior, data manipulation and machine learning capabilities, making it a more wellrounded choice for financial analysis that goes beyond mere statistical computations.
MATLAB has been a mainstay in engineering and quantitative finance for decades, prized for its powerful mathematical and numerical computing capabilities. Its toolboxes are highly optimized for performance, and its extensive use in academia means many financial engineers are proficient in it. However, Python provides an open-source alternative with libraries like NumPy and SciPy that match MATLAB’s capabilities. While MATLAB's syntax and debugging tools are tailored for matrix operations and numerical simulations, Python's simplicity and versatility, combined with its free access, have led many to migrate from MATLAB for financial modeling and analysis tasks.
Visit https://ebookmass.com today to explore a vast collection of ebooks across various genres, available in popular formats like PDF, EPUB, and MOBI, fully compatible with all devices. Enjoy a seamless reading experience and effortlessly download highquality materials in just a few simple steps. Plus, don’t miss out on exciting offers that let you access a wealth of knowledge at the best prices!
C++ is renowned for its execution speed and control over system resources, making it ideal for high-frequency trading applications where performance is critical. Python, on the other hand, is an interpreted language and inherently slower in raw execution. However, Python offers greater developer productivity and faster prototyping, which is crucial in rapidly changing financial markets. Python’s ability to bind with C/C++ libraries (using Cython, for example) allows developers to optimize performancecritical parts of their code. This symbiosis enables the rapid development of complex financial models in Python, with critical sections accelerated through C++ integration, offering a balance between speed and development efficiency.
Java holds a strong position in large-scale, enterprise-level financial applications, thanks to its platform independence, robustness, and performance. It is extensively used in banking and financial services for transaction management systems, back-end services, and as the basis for many trading platforms. While Python is used for data analysis and prototyping, Java often becomes the choice for deploying scalable, highperformance financial applications. Yet, Python's simplicity and the rich ecosystem, especially for data analysis and machine learning, make it a preferred language for the exploratory and analytical phases of financial projects.
In the diverse ecosystem of programming languages used in finance, Python stands out for its ability to offer a blend of simplicity, versatility, and a comprehensive suite of libraries tailored for financial analysis. While other languages have their niches—R for statistical analysis, MATLAB for engineering applications, C++ for performance-critical tasks, and Java for enterprise applications—Python's widespread adoption is a testament to its balance of readability, performance, and an active community that continually enhances its capabilities.
Python's growth in finance is not about surpassing other languages in every aspect but about offering a harmonious blend that caters to the dynamic needs of financial analysis, modeling, and algorithmic trading. Its role as a bridge between different programming paradigms and its ability to integrate
with other languages and platforms underscores its central position in the modern financial technology stack.
Case Studies of Successful Python Implementations in Finance
The ascent of Python within the financial industry is best understood through a lens of real-world applications and success stories. Below, we delve into a series of case studies that exemplify Python's transformative role in finance, highlighting its versatility, ease of use, and powerful libraries that have revolutionized financial analysis, modeling, and algorithmic trading.
One of the world's leading hedge funds, Renaissance Technologies, famous for its Medallion Fund, has been a pioneer in adopting Python to distill vast amounts of market data into profitable trading strategies. Their approach combines mathematical models, statistical analysis, and machine learning to predict price movements. Python, with its rich ecosystem of data analysis and machine learning libraries such as pandas, NumPy, and scikit-learn, has been instrumental in processing and analyzing data, backtesting strategies, and executing trades. The flexibility of Python allowed their quants (quantitative analysts) to develop complex algorithms rapidly, test hypotheses, and refine strategies, contributing to the fund's extraordinary returns.
A leading global bank integrated Python into its risk management framework to perform credit risk analysis and real-time market risk assessment. By leveraging Python's pandas library for data manipulation and analysis, along with SciPy for advanced mathematical functions, the bank developed a comprehensive risk assessment toolkit. This toolkit enabled the bank to automate the aggregation of risk metrics, perform sensitivity analysis, and generate detailed risk reports. Python's ability to interface with other languages and technologies meant the bank could seamlessly integrate these tools into its existing systems, enhancing its ability to respond to emerging risks swiftly.
A FinTech startup utilized Python to develop a robo-advisor platform offering automated, algorithm-driven financial planning services with little
to no human supervision. Python's simplicity and the extensive selection of financial libraries allowed the startup to implement sophisticated investment algorithms that consider the client's risk tolerance and financial goals to create personalized investment portfolios. Libraries such as matplotlib and seaborn were used for data visualization, enabling clients to have interactive, intuitive access to their investment data. This implementation showcases Python's capability to power innovative financial products that require complex mathematical computations and user-friendly interfaces.
An asset management firm adopted Python to optimize its operations, from portfolio management to client reporting. By leveraging Python's pandas library for data analysis and Excel integration with the openpyxl library, the firm automated the generation of client reports, significantly reducing manual errors and operational costs. Python scripts were also used to monitor portfolio performance, automate trade orders, and manage assets more efficiently, demonstrating Python's impact on enhancing operational efficiencies in asset management.
A proprietary trading firm developed a suite of algorithmic trading strategies using Python, capitalizing on its ability to process high-frequency data and execute trades at speed. Using event-driven programming and the asyncio library, the firm created algorithms that could analyze market data in real-time and execute trades based on predefined criteria. The flexibility and performance of Python, combined with libraries like NumPy for numerical computations and requests for handling HTTP requests to trading platforms, allowed the firm to stay competitive in the fast-paced world of algorithmic trading.
These case studies underscore Python's pivotal role in financial innovation, offering a glimpse into its application across various domains of finance. From hedge funds leveraging Python for quantitative trading to banks employing it for risk management, and FinTech startups using it to power robo-advisors, Python's adaptability, and powerful libraries have made it an indispensable tool in the financial industry. Through these implementations, Python has demonstrated its ability to simplify complex financial analyses,
enhance operational efficiencies, and drive the development of cutting-edge financial technologies.
Advantages of Using Python in Finance
Python's syntax is celebrated for its readability and simplicity, making it an ideal learning platform for beginners in programming and finance alike. Its straightforward syntax mirrors that of the English language, allowing professionals to focus more on solving financial problems and less on the intricacies of programming. For individuals transitioning from different fields into finance, Python provides a gentle learning curve, facilitating a smoother integration into the complex world of financial analysis without the steep learning curve associated with some other programming languages.
The Python ecosystem boasts an extensive array of libraries tailored for data analysis, numerical computations, and machine learning, which are critical components in financial analysis. Libraries such as NumPy and pandas offer powerful tools for numerical computing and data manipulation, enabling analysts to handle vast datasets with ease. For statistical modeling and machine learning, libraries like SciPy and scikitlearn provide financial professionals with the means to develop predictive models, essential for risk management, algorithmic trading, and asset valuation. Python's rich library ecosystem ensures that financial practitioners can perform complex analyses and develop sophisticated models with relative ease.
Python's interoperability with other languages and technologies amplifies its utility in finance. It can interface seamlessly with C/C++, allowing for the execution of computationally intensive algorithms without sacrificing performance. Additionally, Python's compatibility with various data sources and formats facilitates the integration of diverse datasets, a common requirement in financial analysis. This flexibility enables firms to leverage Python across different areas of their operations, from quantitative analysis and risk management to automatic report generation and trading systems, ensuring a cohesive and efficient workflow.
Being open-source, Python has fostered a vibrant community of developers and financial professionals who continually contribute to the enhancement of its libraries and frameworks. This collaborative environment accelerates the development of innovative financial applications and keeps Python at the cutting edge of financial technology. Moreover, the open-source nature of Python significantly reduces software costs for organizations, making advanced financial analysis more accessible to smaller firms and individual practitioners.
In the world of algorithmic trading, Python's capabilities shine brightly. The language's event-driven programming models, coupled with powerful libraries for real-time data processing, make it an excellent tool for developing automated trading systems. Python enables traders to monitor market movements, analyze financial data in real time, and execute trades at lightning speed, thereby capitalizing on fleeting market opportunities. Through libraries such as pandas and event-driven frameworks like Twisted, Python equips finance professionals with the tools to build sophisticated trading algorithms that can process and react to market events as they occur.
Python's scalability is another merit that makes it suited for finance. Financial institutions deal with an ever-increasing volume of data and require tools that can scale with their growing needs. Python's ability to handle large datasets, coupled with its performance optimization capabilities, ensures that financial applications can grow in complexity and size without a corresponding increase in processing time or resources.
The financial industry's adoption of Python is not a passing trend but a testament to the language's robust capabilities and suitability for addressing the myriad challenges faced by financial practitioners. With its blend of simplicity, power, and flexibility, Python has democratized financial analysis, enabling a broader range of professionals to contribute to the innovation and complexity of financial strategies. As we delve deeper into the era of digital finance, Python's role as a critical tool in the financial toolkit is not only secure but poised for greater expansion.
Ease of Learning and Readability
Python's syntax is often lauded for its clarity and simplicity, closely resembling the natural language we use in daily communication. This design philosophy reduces the cognitive load on programmers, allowing them to grasp the basics of programming in Python rapidly. For financial analysts, who may not have a background in computer science, this means being able to script basic Python code to automate mundane tasks or analyze financial datasets with minimal training. The intuitive nature of Python's syntax also facilitates the debugging process, making it easier to identify and correct errors, which is crucial in a high-stakes environment like finance where accuracy is paramount.
Unlike programming languages that require an understanding of complex concepts from the outset, Python allows newcomers to dive in with basic tasks and gradually progress to more complex operations. The language supports various programming paradigms, including procedural, objectoriented, and functional programming, catering to different levels of expertise and project requirements. This flexibility ensures that individuals in the finance sector can start leveraging Python's capabilities early in their learning journey, progressively enhancing their skills as they tackle more sophisticated financial models and analyses.
In the fast-paced world of finance, where codes must often be reviewed, shared, or modified by team members, Python's emphasis on readability is a significant advantage. Python's syntax encourages the use of white space and discourages overly complex expressions, which means codes are cleaner and more organized. Financial institutions that adopt Python can maintain a codebase that is more understandable for analysts and developers alike, facilitating collaboration and reducing the risk of errors in financial computations or analyses.
A significant factor contributing to Python's ease of learning is the vast array of educational resources available, including comprehensive documentation, forums, online courses, and books tailored to every expertise level. The Python community is known for being welcoming and supportive, offering assistance to both novices and experienced programmers. For financial professionals venturing into programming with Python, this community provides a wealth of knowledge and tools to solve
specific finance-related coding challenges, from complex quantitative models to automated trading algorithms.
Python's approachability has led to its widespread adoption in finance, not just among quants and data scientists but also among traders, investment analysts, and risk managers. Its readability ensures that financial models and analyses are transparent and accessible, enabling better decisionmaking and fostering innovation in financial strategies and products. Moreover, Python scripts can often be easily integrated into existing financial software and systems, enhancing their functionality and efficiency without the need for extensive redevelopment.
The combination of Python's ease of learning, readability, and a supportive community has significantly lowered the barrier to entry for finance professionals looking to harness the power of programming in their work. Whether it's automating reports, analyzing vast datasets, or developing predictive models, Python offers the tools and flexibility to enhance the efficiency and accuracy of financial operations. As the financial industry continues to evolve, Python's role in empowering professionals with the skills to innovate and adapt cannot be overstated, making it a pivotal language in the toolkit of the modern financial analyst.
Robust Libraries and Frameworks Suitable for Financial Analysis
At the heart of Python's scientific computing stack lies NumPy, a library that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these data structures. NumPy is indispensable in financial analysis for its efficiency in numerical computations, which is crucial for tasks such as pricing derivatives, optimizing portfolios, or simulating risk models. Its ability to handle vast datasets with speed and precision makes NumPy a cornerstone for high-performance financial computing.
pandas stands out as Python's premier library for data manipulation and analysis. In the context of finance, pandas excels by offering intuitive structures for storing and manipulating time series data, which is ubiquitous in financial applications. Whether it's adjusting financial time series for
corporate actions, performing rolling window calculations on stock prices, or aggregating transaction data, pandas provides the functionality to make data manipulation tasks both straightforward and efficient. Its DataFrame object, in particular, is a powerful tool for managing financial datasets, allowing for sophisticated indexing, slicing, and pivoting operations that are essential for thorough financial analysis.
Visualization is a critical aspect of financial analysis, providing insights that numbers alone cannot. matplotlib and seaborn, two of Python's leading libraries for data visualization, offer a wide range of functionalities to create compelling and informative financial charts. From plotting historical stock prices to visualizing the correlation matrices of asset returns, these libraries enable analysts to convey complex financial concepts and data patterns in an accessible and visually appealing manner. seaborn, built on top of matplotlib, further simplifies the process of creating statistical graphics, allowing for the creation of rich, informative plots with minimal code.
The advent of machine learning in finance has opened new frontiers for predictive analytics, and scikit-learn stands at the forefront of this revolution. Offering simple and efficient tools for data mining and analysis, scikit-learn is a powerful ally for financial professionals looking to apply machine learning models to predict market movements, identify trading signals, or enhance risk management techniques. With its comprehensive suite of algorithms for classification, regression, clustering, and dimensionality reduction, scikit-learn equips financial analysts with the means to uncover patterns and make predictions based on vast datasets, driving forward the capabilities of financial analysis and decision-making.
QuantLib deserves special mention for its focus on the quantitative finance community. As a comprehensive library for modeling, trading, and risk management in real-life, QuantLib facilitates a wide array of financial calculations and processes, including derivative pricing, interest rate modeling, and portfolio optimization. Its extensive functionality and opensource nature make it particularly valuable for quants and financial engineers engaged in sophisticated financial modeling and analysis tasks.
Python's libraries and frameworks form an ecosystem that is unparalleled in its capacity to support the complex requirements of financial analysis and computational finance. By leveraging these tools, financial professionals can significantly enhance their analytical capabilities, from data manipulation and visualization to predictive modeling and quantitative analysis. As the financial industry continues to evolve amidst rapidly changing markets and increasing data volumes, Python's robust ecosystem remains a critical asset for innovation and efficiency in financial analysis, enabling professionals to deliver deeper insights and drive strategic financial decisions.
Community Support and Resources
The Python community, known for its diversity and inclusivity, is a treasure trove of knowledge and expertise. Financial analysts, quants, and coders from various backgrounds contribute to forums, social media platforms, and special interest groups, making it easier for newcomers to find guidance and for seasoned professionals to stay abreast of the latest developments. Platforms such as Stack Overflow and Reddit are bustling with activity, where practical advice and programming tips are exchanged freely. Moreover, Python's widespread adoption across industries ensures a broad base of knowledge, meaning that financial professionals can often find solutions to domain-specific issues or collaborate on projects that bridge gaps between fields.
For those new to Python or looking to expand their skill set, the array of learning resources available is vast and varied. From online courses offered by platforms like Coursera and Udemy to comprehensive tutorials on developers' websites, there's no shortage of material to get started or dive deeper. Additionally, the Python Software Foundation and other entities regularly host webinars, workshops, and conferences that cater to all levels of expertise, covering fundamental concepts as well as advanced applications in finance and beyond. Python's official documentation itself is an exemplary resource, known for its clarity and thoroughness, guiding users through intricacies with ease.
Python's strength in finance is amplified by its rich ecosystem of opensource libraries, which are continually enhanced by contributions from the community. Libraries such as pandas, NumPy, and QuantLib, central to financial analysis, benefit from the collective effort of countless individuals who contribute code, report bugs, and suggest improvements. This collaborative model ensures that the tools remain at the cutting edge, incorporating the latest research and innovations. It also means that financial analysts and quants have the unique opportunity to contribute back to the projects that facilitate their work, fostering a sense of ownership and community spirit.
Local meetups and special interest groups provide invaluable opportunities for networking and learning within the Python community. These gatherings, often organized through platforms like Meetup.com, enable finance professionals to connect with peers, share experiences, and discuss challenges and solutions in a supportive environment. Additionally, PyCon, PyData, and other specialized conferences offer forums for presenting research, exploring new tools, and engaging with the broader Python and financial analytics communities. These events not only facilitate knowledge sharing but also spark collaborations that can lead to innovative applications and approaches in financial analysis.
The robustness of community support and the wealth of resources available to Python users form a foundation that significantly enhances the language's effectiveness in financial analysis. This ecosystem not only provides the tools and knowledge necessary for sophisticated financial modeling and analysis but also fosters a culture of collaboration and continuous learning. By engaging with the community, leveraging the available resources, and contributing to the collective knowledge base, financial professionals can push the boundaries of what's possible in financial analysis, driving innovation and achieving greater success in their endeavors.
Applications of Python in Finance
At the forefront of Python's applications in finance is the development of automated trading systems. These systems enable the execution of trades at speeds and volumes that are humanly impossible, relying on algorithms that
Visit https://ebookmass.com today to explore a vast collection of ebooks across various genres, available in popular formats like PDF, EPUB, and MOBI, fully compatible with all devices. Enjoy a seamless reading experience and effortlessly download highquality materials in just a few simple steps. Plus, don’t miss out on exciting offers that let you access a wealth of knowledge at the best prices!
Exploring the Variety of Random Documents with Different Content
689.
“The vagrant action of the limbs was suppressed, but the source of irritation in the brain was left out of consideration.” Conolly. 690.
Quoted by Beach, Hunterian Oration, 1891.
691.
W. Massie, A History of England during the Reign of George III., iii. p. 207. London, 1865.
See also J. M. D. Meiklejohn, Hist. Eng. Pt. ii. p. 330. 692. Massie, Hist. p. 208. 693.
Wynter, Insanity, p. 80.
694.
J. H. Jesse, Memoirs of the Life of George III., iii. pp. 95 and 274. Later on he was placed in the better care of Dr. Willis, a clergyman who was much celebrated for his management of mad people; see Jesse, iii. p. 90, etc.
695.
Hunterian Oration, p. 5.
696.
Besant, London in the Eighteenth Century, p. 377. See also Charles Reade’s book, HardCash.
697. See Conolly’s description of the old-time reception of a private patient. Treatmentofthe Insane, p. 138.
698.
D. H. Tuke, Hist. p. 171.
699.
R. Gardner Hill, Lunacy; its Pastandits Present, p. 7. London, 1870.
700.
R. Gardner Hill, p. 6.
701.
J. B. Sharpe, Report and Minutes of Evidence on the Madhouses of England; evidence of G. Higgins, pp. 12 and 13; of R. Fowler, p. 308; and of H. Alabaster, p. 326. London, 1815.
702.
EdinburghReview, xxviii. p. 445. Edinburgh, 1817.
703.
Jonathan Gray, History ofYorkAsylum, p. 12. York, 1815.
704. See Conolly’s amazing denunciation in his Treatmentofthe Insane.
705.
A female patient was got with child by the head keeper; he was subsequently given a piece of plate, and kept a private madhouse of his own; see Sharpe, ReportandMin. ofEv. p. 14.
706.
Gray, chap. iv.; ibid. p. 26; Beach, p. 4.
707. S. W. Nicoll, An Enquiry into the Present State and Visitation of Asylums, p. 10, etc. London, 1828.
708.
Sharpe, p. 12; Gray, p. 23.
709.
Sharpe, ReportandMin. ofEv. pp. 277, 290, 297.
710.
Ibid. p. 46.
711. For instance, at Bethnal Green Asylum.—Beach, p. 12.
712.
As late as 1837.—Tuke, Hist. p. 81.
713.
Sharpe, p. 46.
714.
Ibid. p. 85.
715. Ibid. p. 48.
716. See Besant, London in the Eighteenth Century, where a print is given of this prisoner in his cell at p. 375.
717. Treatmentofthe Insane, p. 28.
718.
Sharpe, ReportandMin. ofEv. p. 120.
719. Ibid. p. 59.
720. Tuke, Hist. p. 153.
721.
Sharpe, ReportandMin. ofEv. p. 68.
722.
For an account of some of these, especially as used in Portugal into later times, see G. A. Tucker, Lunacy in Many Lands, pp. 16, 1346, etc. Sydney, 1887.
723.
John Haslam, Observations on Madness, p. 317. London, 1809.
724.
Besant, London in the Eighteenth Century, p. 377. There is also a reproduction of Hogarth’s “Scene in Bedlam” from the “Rake’s Progress.”
725.
R. Gardner Hill, A Concise History of the Non-Restraint System, p. 139. London, 1857.
726.
W. A. F. Browne, p. 119.
727. One large asylum is said to have made £400 a year from exhibiting lunatics, but this would probably not include the keepers’ tips; see Tuke, Hist. p. 73.
728.
Conolly, p. 33. See also P. Pinel, Traité Médico-philosophique sur l’Aliénation Mentale, p. 65. Paris, An IX.
J. B. Tuke, Ency. Brit. ninth ed. vol. xiii. p. 111.
729. See E. Westermarck, MoralIdeas, i. p. 274.
730.
H. W. Carter, PrincipalHospitals, p. 42. London, 1819.
731.
P. Pinel, Traité, p. 64.
732.
A. Halliday, Lunatic Asylums, p. 76.
733.
M. Esquirol, Mémoires de Charenton, pp. 46, 48.
734.
F. Beach, p. 11.
J. Conolly, p. 10.
R. Gardner Hill, Concise Hist. p. 141.
735.
Animadversions on the Present Government of York Asylum. York, 1788. It deals mainly with the question of finance.
EdinburghReview, vol. xxviii. p. 433.
These produced A Letter from a Subscriber to the York Lunatic Asylum. York, 1788, etc.
736.
He died in 1797, and an inscription was erected to him in Westminster Abbey. See Dict. Nat. Biog., and Jonathan Gray, History of York Asylum, p. 18.
737. Samuel Tuke, Description ofthe Retreat, p. 22. York, 1813.
738.
The Description ofthe Retreat near York, already alluded to.
739.
To the York Herald, dated September 23, 1813. It was signed merely “Evigilator,” but had been written by Dr. Best, the head of the York Asylum.
See J. Gray, Hist. p. 28; also D. H. Tuke, Hist. pp. 129, 148.
740.
EdinburghReview, vol. xxviii. p. 433. Edinburgh, 1817.
741.
S. W. Nicoll, An Enquiry, p. 11; and see Jonathan Gray, Hist. p. 31. 742.
D. H. Tuke, Hist. p. 79.
743. J. Gray, Hist. chap. vi.
744.
D. H. Tuke, p. 161.
745.
D. H. Tuke, p. 157.
746. Nicoll, p. 21.
747. D. H. Tuke, Hist. p. 162.
748. Ibid. p. 173. 749.
J. B. Tuke, Ency. Brit. ninth ed.; D. H. Tuke, Hist. p. 85; R. Gardner Hill, Lunacy, p. 5.
750. See, for instance, Hunterian Oration, 1891, etc.
751. R. Gardner Hill, Lunacy, p. 42.
752. Andrew Wynter, p. 100.
753.
Hill, pp. 87, 88. 754.
Halliday, Lunatic Asylums, p. 2.
755.
F. Willis, ATreatise on MentalDerangement, p. 6. London, 1823. 756.
Lunatic Asylums, p. 2.
757. W. A. F. Browne, p. 4.
758.
BorderlandofInsanity, p. 11. 759.
Alexander Gibson, in Ency. Brit. ninth ed. art. “Insanity (Law).” 760.
“That” (kleptomania) “is one of the diseases I was sent here to cure,” a certain judge is said to have observed; but he did not cure it.
761.
One of these legal tests had been a knowledge of the multiplication table. W. A. F. Browne, p. 3. 762.
The “robust” attitude has been shown by Dickens. “That young Pitcher’s had a fever.” “No!” exclaimed Mr. Squeers. “Damn that boy, he’s always at something of that sort.” “Never was such a boy, I do believe,” said Mrs. Squeers; “whatever he has is always catching too. I say it’s obstinacy, and nothing shall ever convince me that it isn’t. I’d beat it out of him.”—Nicholas Nickleby, chap. vii. 763.
D. H. Tuke, Hist. p. 96. 764.
W. Tallack, Penological and Preventive Principles, pp. 249, 250. London, 1896. 765.
DepartmentalCommittee on Prisons Report, p. 8. London, 1895.
Transcriber’s Notes:
Missing or obscured punctuation was silently corrected.
Typographical errors were silently corrected. Inconsistent spelling and hyphenation were made consistent only when a predominant form was found in this book.
*** END OF THE PROJECT GUTENBERG EBOOK PENAL METHODS OF THE MIDDLE AGES: CRIMINALS, WITCHES, LUNATICS ***
Updated editions will replace the previous one—the old editions will be renamed.
Creating the works from print editions not protected by U.S. copyright law means that no one owns a United States copyright in these works, so the Foundation (and you!) can copy and distribute it in the United States without permission and without paying copyright royalties. Special rules, set forth in the General Terms of Use part of this license, apply to copying and distributing Project Gutenberg™ electronic works to protect the PROJECT GUTENBERG™ concept and trademark. Project Gutenberg is a registered trademark, and may not be used if you charge for an eBook, except by following the terms of the trademark license, including paying royalties for use of the Project Gutenberg trademark. If you do not charge anything for copies of this eBook, complying with the trademark license is very easy. You may use this eBook for nearly any purpose such as creation of derivative works, reports, performances and research. Project Gutenberg eBooks may be modified and printed and given away you may do practically ANYTHING in the United States with eBooks not protected by U.S. copyright law. Redistribution is subject to the trademark license, especially commercial redistribution.
START: FULL LICENSE
THE FULL PROJECT GUTENBERG LICENSE
PLEASE READ THIS BEFORE YOU DISTRIBUTE OR USE THIS WORK
To protect the Project Gutenberg™ mission of promoting the free distribution of electronic works, by using or distributing this work (or any other work associated in any way with the phrase “Project Gutenberg”), you agree to comply with all the terms of the Full Project Gutenberg™ License available with this file or online at www.gutenberg.org/license.
Section 1. General Terms of Use and Redistributing Project Gutenberg™ electronic works
1.A. By reading or using any part of this Project Gutenberg™ electronic work, you indicate that you have read, understand, agree to and accept all the terms of this license and intellectual property (trademark/copyright) agreement. If you do not agree to abide by all the terms of this agreement, you must cease using and return or destroy all copies of Project Gutenberg™ electronic works in your possession. If you paid a fee for obtaining a copy of or access to a Project Gutenberg™ electronic work and you do not agree to be bound by the terms of this agreement, you may obtain a refund from the person or entity to whom you paid the fee as set forth in paragraph 1.E.8.
1.B. “Project Gutenberg” is a registered trademark. It may only be used on or associated in any way with an electronic work by people who agree to be bound by the terms of this agreement. There are a few things that you can do with most Project Gutenberg™ electronic works even without complying with the full terms of this agreement. See paragraph 1.C below. There are a lot of things you can do with Project Gutenberg™ electronic works if you follow the terms of this agreement and help preserve free future access to Project Gutenberg™ electronic works. See paragraph 1.E below.
1.C. The Project Gutenberg Literary Archive Foundation (“the Foundation” or PGLAF), owns a compilation copyright in the collection of Project Gutenberg™ electronic works. Nearly all the individual works in the collection are in the public domain in the United States. If an individual work is unprotected by copyright law in the United States and you are located in the United States, we do not claim a right to prevent you from copying, distributing, performing, displaying or creating derivative works based on the work as long as all references to Project Gutenberg are removed. Of course, we hope that you will support the Project Gutenberg™ mission of promoting free access to electronic works by freely sharing Project Gutenberg™ works in compliance with the terms of this agreement for keeping the Project Gutenberg™ name associated with the work. You can easily comply with the terms of this agreement by keeping this work in the same format with its attached full Project Gutenberg™ License when you share it without charge with others.
1.D. The copyright laws of the place where you are located also govern what you can do with this work. Copyright laws in most countries are in a constant state of change. If you are outside the United States, check the laws of your country in addition to the terms of this agreement before downloading, copying, displaying, performing, distributing or creating derivative works based on this work or any other Project Gutenberg™ work. The Foundation makes no representations concerning the copyright status of any work in any country other than the United States.
1.E. Unless you have removed all references to Project Gutenberg:
1.E.1. The following sentence, with active links to, or other immediate access to, the full Project Gutenberg™ License must appear prominently whenever any copy of a Project Gutenberg™ work (any work on which the phrase “Project
Gutenberg” appears, or with which the phrase “Project Gutenberg” is associated) is accessed, displayed, performed, viewed, copied or distributed:
This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook.
1.E.2. If an individual Project Gutenberg™ electronic work is derived from texts not protected by U.S. copyright law (does not contain a notice indicating that it is posted with permission of the copyright holder), the work can be copied and distributed to anyone in the United States without paying any fees or charges. If you are redistributing or providing access to a work with the phrase “Project Gutenberg” associated with or appearing on the work, you must comply either with the requirements of paragraphs 1.E.1 through 1.E.7 or obtain permission for the use of the work and the Project Gutenberg™ trademark as set forth in paragraphs 1.E.8 or 1.E.9.
1.E.3. If an individual Project Gutenberg™ electronic work is posted with the permission of the copyright holder, your use and distribution must comply with both paragraphs 1.E.1 through 1.E.7 and any additional terms imposed by the copyright holder. Additional terms will be linked to the Project Gutenberg™ License for all works posted with the permission of the copyright holder found at the beginning of this work.
1.E.4. Do not unlink or detach or remove the full Project Gutenberg™ License terms from this work, or any files
containing a part of this work or any other work associated with Project Gutenberg™.
1.E.5. Do not copy, display, perform, distribute or redistribute this electronic work, or any part of this electronic work, without prominently displaying the sentence set forth in paragraph 1.E.1 with active links or immediate access to the full terms of the Project Gutenberg™ License.
1.E.6. You may convert to and distribute this work in any binary, compressed, marked up, nonproprietary or proprietary form, including any word processing or hypertext form. However, if you provide access to or distribute copies of a Project Gutenberg™ work in a format other than “Plain Vanilla ASCII” or other format used in the official version posted on the official Project Gutenberg™ website (www.gutenberg.org), you must, at no additional cost, fee or expense to the user, provide a copy, a means of exporting a copy, or a means of obtaining a copy upon request, of the work in its original “Plain Vanilla ASCII” or other form. Any alternate format must include the full Project Gutenberg™ License as specified in paragraph 1.E.1.
1.E.7. Do not charge a fee for access to, viewing, displaying, performing, copying or distributing any Project Gutenberg™ works unless you comply with paragraph 1.E.8 or 1.E.9.
1.E.8. You may charge a reasonable fee for copies of or providing access to or distributing Project Gutenberg™ electronic works provided that:
• You pay a royalty fee of 20% of the gross profits you derive from the use of Project Gutenberg™ works calculated using the method you already use to calculate your applicable taxes. The fee is owed to the owner of the Project Gutenberg™ trademark, but he has agreed to donate royalties under this paragraph to the Project Gutenberg Literary Archive Foundation. Royalty
payments must be paid within 60 days following each date on which you prepare (or are legally required to prepare) your periodic tax returns. Royalty payments should be clearly marked as such and sent to the Project Gutenberg Literary Archive Foundation at the address specified in Section 4, “Information about donations to the Project Gutenberg Literary Archive Foundation.”
• You provide a full refund of any money paid by a user who notifies you in writing (or by e-mail) within 30 days of receipt that s/he does not agree to the terms of the full Project Gutenberg™ License. You must require such a user to return or destroy all copies of the works possessed in a physical medium and discontinue all use of and all access to other copies of Project Gutenberg™ works.
• You provide, in accordance with paragraph 1.F.3, a full refund of any money paid for a work or a replacement copy, if a defect in the electronic work is discovered and reported to you within 90 days of receipt of the work.
• You comply with all other terms of this agreement for free distribution of Project Gutenberg™ works.
1.E.9. If you wish to charge a fee or distribute a Project Gutenberg™ electronic work or group of works on different terms than are set forth in this agreement, you must obtain permission in writing from the Project Gutenberg Literary Archive Foundation, the manager of the Project Gutenberg™ trademark. Contact the Foundation as set forth in Section 3 below.
1.F.
1.F.1. Project Gutenberg volunteers and employees expend considerable effort to identify, do copyright research on, transcribe and proofread works not protected by U.S. copyright
law in creating the Project Gutenberg™ collection. Despite these efforts, Project Gutenberg™ electronic works, and the medium on which they may be stored, may contain “Defects,” such as, but not limited to, incomplete, inaccurate or corrupt data, transcription errors, a copyright or other intellectual property infringement, a defective or damaged disk or other medium, a computer virus, or computer codes that damage or cannot be read by your equipment.
1.F.2. LIMITED WARRANTY, DISCLAIMER OF DAMAGES - Except for the “Right of Replacement or Refund” described in paragraph 1.F.3, the Project Gutenberg Literary Archive Foundation, the owner of the Project Gutenberg™ trademark, and any other party distributing a Project Gutenberg™ electronic work under this agreement, disclaim all liability to you for damages, costs and expenses, including legal fees. YOU AGREE THAT YOU HAVE NO REMEDIES FOR NEGLIGENCE, STRICT LIABILITY, BREACH OF WARRANTY OR BREACH OF CONTRACT EXCEPT THOSE PROVIDED IN PARAGRAPH 1.F.3. YOU AGREE THAT THE FOUNDATION, THE TRADEMARK OWNER, AND ANY DISTRIBUTOR UNDER THIS AGREEMENT WILL NOT BE LIABLE TO YOU FOR ACTUAL, DIRECT, INDIRECT, CONSEQUENTIAL, PUNITIVE OR INCIDENTAL DAMAGES EVEN IF YOU GIVE NOTICE OF THE POSSIBILITY OF SUCH DAMAGE.
1.F.3. LIMITED RIGHT OF REPLACEMENT OR REFUND - If you discover a defect in this electronic work within 90 days of receiving it, you can receive a refund of the money (if any) you paid for it by sending a written explanation to the person you received the work from. If you received the work on a physical medium, you must return the medium with your written explanation. The person or entity that provided you with the defective work may elect to provide a replacement copy in lieu of a refund. If you received the work electronically, the person or entity providing it to you may choose to give you a second opportunity to receive the work electronically in lieu of a refund.
If the second copy is also defective, you may demand a refund in writing without further opportunities to fix the problem.
1.F.4. Except for the limited right of replacement or refund set forth in paragraph 1.F.3, this work is provided to you ‘AS-IS’, WITH NO OTHER WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY PURPOSE.
1.F.5. Some states do not allow disclaimers of certain implied warranties or the exclusion or limitation of certain types of damages. If any disclaimer or limitation set forth in this agreement violates the law of the state applicable to this agreement, the agreement shall be interpreted to make the maximum disclaimer or limitation permitted by the applicable state law. The invalidity or unenforceability of any provision of this agreement shall not void the remaining provisions.
1.F.6. INDEMNITY - You agree to indemnify and hold the Foundation, the trademark owner, any agent or employee of the Foundation, anyone providing copies of Project Gutenberg™ electronic works in accordance with this agreement, and any volunteers associated with the production, promotion and distribution of Project Gutenberg™ electronic works, harmless from all liability, costs and expenses, including legal fees, that arise directly or indirectly from any of the following which you do or cause to occur: (a) distribution of this or any Project Gutenberg™ work, (b) alteration, modification, or additions or deletions to any Project Gutenberg™ work, and (c) any Defect you cause.
Section 2. Information about the Mission of
Project Gutenberg™ is synonymous with the free distribution of electronic works in formats readable by the widest variety of computers including obsolete, old, middle-aged and new computers. It exists because of the efforts of hundreds of volunteers and donations from people in all walks of life.
Volunteers and financial support to provide volunteers with the assistance they need are critical to reaching Project Gutenberg™’s goals and ensuring that the Project Gutenberg™ collection will remain freely available for generations to come. In 2001, the Project Gutenberg Literary Archive Foundation was created to provide a secure and permanent future for Project Gutenberg™ and future generations. To learn more about the Project Gutenberg Literary Archive Foundation and how your efforts and donations can help, see Sections 3 and 4 and the Foundation information page at www.gutenberg.org.
Section 3. Information about the Project
Gutenberg Literary Archive
Foundation
The Project Gutenberg Literary Archive Foundation is a nonprofit 501(c)(3) educational corporation organized under the laws of the state of Mississippi and granted tax exempt status by the Internal Revenue Service. The Foundation’s EIN or federal tax identification number is 64-6221541. Contributions to the Project Gutenberg Literary Archive Foundation are tax deductible to the full extent permitted by U.S. federal laws and your state’s laws.
The Foundation’s business office is located at 809 North 1500 West, Salt Lake City, UT 84116, (801) 596-1887. Email contact links and up to date contact information can be found at the Foundation’s website and official page at www.gutenberg.org/contact
Section 4. Information about Donations to the Project Gutenberg Literary Archive Foundation
Project Gutenberg™ depends upon and cannot survive without widespread public support and donations to carry out its mission of increasing the number of public domain and licensed works that can be freely distributed in machine-readable form accessible by the widest array of equipment including outdated equipment. Many small donations ($1 to $5,000) are particularly important to maintaining tax exempt status with the IRS.
The Foundation is committed to complying with the laws regulating charities and charitable donations in all 50 states of the United States. Compliance requirements are not uniform and it takes a considerable effort, much paperwork and many fees to meet and keep up with these requirements. We do not solicit donations in locations where we have not received written confirmation of compliance. To SEND DONATIONS or determine the status of compliance for any particular state visit www.gutenberg.org/donate.
While we cannot and do not solicit contributions from states where we have not met the solicitation requirements, we know of no prohibition against accepting unsolicited donations from donors in such states who approach us with offers to donate.
International donations are gratefully accepted, but we cannot make any statements concerning tax treatment of donations received from outside the United States. U.S. laws alone swamp our small staff.
Please check the Project Gutenberg web pages for current donation methods and addresses. Donations are accepted in a number of other ways including checks, online payments and
