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EBX01000000000000001207900 |
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00000000000000.0 |
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cr||||||||||||||||||||| |
| 008 |
220922 ||| eng |
| 050 |
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4 |
|a QA76.73.P98
|
| 100 |
1 |
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|a Macarty, Matt
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| 245 |
0 |
0 |
|a Hands-on Python for finance
|c Matt Macarty
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| 260 |
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|a [Place of publication not identified]
|b Packt Publishing
|c 2019
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| 300 |
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|a 1 streaming video file (5 hr., 25 min., 24 sec.)
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| 653 |
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|a Finance / Data processing / http://id.loc.gov/authorities/subjects/sh2020000036
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| 653 |
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|a Finances / Informatique
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
|
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|a Object-oriented programming (Computer science) / http://id.loc.gov/authorities/subjects/sh87007503
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| 653 |
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|a Finance / Data processing / fast / (OCoLC)fst00924370
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| 653 |
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|a Visualisation de l'information
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| 653 |
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|a Information visualization / fast / (OCoLC)fst00973185
|
| 653 |
|
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|a Programmation orientée objet (Informatique)
|
| 653 |
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|a Information visualization / http://id.loc.gov/authorities/subjects/sh2002000243
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| 653 |
|
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|a Object-oriented programming (Computer science) / fast / (OCoLC)fst01042804
|
| 653 |
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|a Python (Langage de programmation)
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| 653 |
|
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|a Python (Computer program language) / fast / (OCoLC)fst01084736
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Title from resource description page (Safari, viewed April 11, 2019)
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/9781789800975/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 500
|
| 082 |
0 |
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|a 332
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| 082 |
0 |
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|a 005.13/3
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| 520 |
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|a "This hands-on course helps both developers and quantitative analysts to get started with Python, and guides you through the most important aspects of using Python for quantitative finance. You will begin with a primer to Python and its various data structures. Then you will dive into third party libraries. You will work with Python libraries and tools designed specifically for analytical and visualization purposes. Then you will get an overview of cash flow across the timeline. You will also learn concepts like Time Series Evaluation, Forecasting, Linear Regression and also look at crucial aspects like Linear Models, Correlation and portfolio construction. Finally, you will compute Value at Risk (VaR) and simulate portfolio values using Monte Carlo Simulation which is a broader class of computational algorithms. With numerous practical examples through the course, you will develop a full-fledged framework for Monte Carlo, which is a class of computational algorithms and simulation-based derivatives and risk analytics."--Resource description page
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