By Yves Hilpisch
The monetary has followed Python at an immense expense lately, with a few of the biggest funding banks and hedge cash utilizing it to construct middle buying and selling and threat administration platforms. This hands-on advisor is helping either builders and quantitative analysts start with Python, and publications you thru an important elements of utilizing Python for quantitative finance.
Using sensible examples in the course of the e-book, writer Yves Hilpisch additionally exhibits you ways to advance a full-fledged framework for Monte Carlo simulation-based derivatives and danger analytics, in line with a wide, life like case research. a lot of the e-book makes use of interactive IPython Notebooks, with subject matters that include:
basics: Python info buildings, NumPy array dealing with, time sequence research with pandas, visualization with matplotlib, excessive functionality I/O operations with PyTables, date/time details dealing with, and chosen top practices
monetary themes: mathematical thoughts with NumPy, SciPy and SymPy comparable to regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; facts for normality checks, mean-variance portfolio optimization, vital part research (PCA), and Bayesian regression
distinct issues: functionality Python for monetary algorithms, akin to vectorization and parallelization, integrating Python with Excel, and construction monetary purposes in accordance with internet applied sciences
Read or Download Python for Finance: Analyze Big Financial Data PDF
Similar python books
Fundamentals of Python: From First Programs through Data Structures
In basics OF PYTHON: FROM FIRST courses via info buildings, Washington and Lee collage professor Kenneth A. Lambert offers the entire very important issues in CS1 and CS2 in a single quantity. This reasonably priced layout presents teachers with a constant method of instructing introductory programming and knowledge buildings over a customary two-term path series.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Python for info research is anxious with the nuts and bolts of manipulating, processing, cleansing, and crunching facts in Python. it's also a realistic, sleek creation to medical computing in Python, adapted for data-intensive functions. this can be a e-book concerning the components of the Python language and libraries you'll have to successfully resolve a vast set of information research difficulties.
In the event you intend to take advantage of Amazon net providers (AWS) for distant computing and garage, Python is a perfect programming language for constructing purposes and controlling your cloud-based infrastructure. This cookbook will get you began with greater than dozen recipes for utilizing Python with AWS, in keeping with the author’s boto library.
Artificial Intelligence with Python
Construct real-world man made Intelligence functions with Python to intelligently have interaction with the area round you approximately This publication Step into the fantastic international of clever apps utilizing this finished advisor input the area of synthetic Intelligence, discover it, and create your personal purposes paintings via easy but insightful examples that might get you up and working with synthetic Intelligence very quickly Who This ebook Is For This ebook is for Python builders who are looking to construct real-world man made Intelligence purposes.
Additional resources for Python for Finance: Analyze Big Financial Data
Example text
In a large organization with many computers), and costly to support and maintain; making Python available via a web browser makes deployment much more efficient in certain scenarios. Use of (better) remote hardware When it comes to complex, compute- and memory-intensive analytics tasks, a local computer might not be able to perform such tasks; the use of (multiple) shared servers with multiple cores, larger memories, and maybe GPGPUs makes such tasks possible and more efficient. Collaboration Working, for example, with a team on a single or multiple servers makes collabo‐ ration simpler and also increases efficiency: data is not moved to every local ma‐ chine, nor, after the analytics tasks are finished, are the results moved back to some central storage unit and/or distributed among the team members.
Later chapters illustrate all three techniques in detail. For the moment, we want to stick to a simple, but still realistic, example that touches upon all three techniques. A quite common task in financial analytics is to evaluate complex mathematical ex‐ pressions on large arrays of numbers. To this end, Python itself provides everything needed: In [1]: loops = 25000000 from math import * a = range(1, loops) def f(x): return 3 * log(x) + cos(x) ** 2 %timeit r = [f(x) for x in a] Out[1]: 1 loops, best of 3: 15 s per loop The Python interpreter needs 15 seconds in this case to evaluate the function f 25,000,000 times.
HTML page Generates a single HTML page from a single IPython Notebook file. js framework). LaTeX/PDF Such a file can also be converted to a LaTeX file, which then can be converted into a PDF document. rst) is used, for example, by the SPHINX documentation package for Python projects. Analytics and Publishing Platform A major advantage of IPython Notebook is that you can easily publish and share your complete Notebook with others. Once your analytics project with IPython is finished, you can publish it as an HTML page or a PDF, or use the content for a slide presentation.