Python for Finance

What Hacker News says

1. UnknomadApr 2022
   I would recommend creating one from your own intuition. That's an awesome practice i think. You would be amazed on how simple they can be.

   Also some books i would recommend are: - Hands on Machine Learning for Algorithmic Trading, Stefan Jansen, Packt - Python for Finance Cookbook, Eryk Lewinson, Packt

   Cheers!

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2. westurnerMar 2019
   Thanks for the wealth of resources in this post. Here are a few more:

   "Python for Finance: Analyze Big Financial Data" (2014, 2018) https://g.co/kgs/qkY8J6 ... https://pyalgo.tpq.io also includes the "Finance with Python" course and this book as a PDF and Jupyter notebooks.

   Quantopian put out a call for the best Value Investing algos (implemented in quantopian/zipline) awhile back. This post links to those and other value investing resources: https://westurner.github.io/hnlog/#comment-19181453 (Ctrl-F "econo")

   "Lectures in Quantitative Economics as Python and Julia Notebooks" https://news.ycombinator.com/item?id=19083479 links to these excellent lectures and a number of tools for working with actual data from FRED, ECB, Eurostat, ILO, IMF, OECD, UNSD, UNESCO, World Bank, Quandl.

   One thing that many finance majors, courses, and resources often fail to identify is the role that startup and small businesses play in economic growth and actual value creation: jobs, GDP, return on direct capital investment. Most do not succeed, but it is possible to do better than index funds and have far more impact in terms of sustainable investment than as an owner of a nearly-sure-bet index fund that owns some shares and takes a hands-off approach to business management, research, product development, and operations.

   Is it possible to possess a comprehensive understanding of finance and economics but still not have personal finance down? Personal finance: r/personalfinance/wiki, "Consumer science (a.k.a. home economics) as a college major" https://news.ycombinator.com/item?id=17894632

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3. jackbrianMar 2017
   As a CS student, I'd really make sure your stats knowledge is solid. Perhaps take a class that covers stochastic finance (Black-Scholes, etc.) if available.

   I learned the hard way that it is quite difficult to break into finance as a non-student, so do everything you can now to land that first gig. Good luck!

   Some starting resources:

   -Ernie Chan's books and blog (https://epchan.blogspot.com/)

   -QuantStart has great starter material and a new book, although I haven't read it (https://www.quantstart.com/)

   -"Inside the Black Box" (Narang) I've seen referenced a good bit but felt as though it leaned toward order execution and rather boring

   -"Dark Pools" (Scott Patterson) a great story about the rise of algorithmic trading

   -"Flash Boys' (Michael Lewis) offers a nice follow up (HFT), but considered a bit sensationalist

   EDIT: If you're planning on using Python (a solid bet)...

   -Python for Data Analysis (Wes McKinney) - Great, quick book for Pandas by former AQR (and now Two Sigma?) guy.

   -Yves Hilpisch books: "Python for Finance" is introductory while "Derivative Analytics in Python" is quite math heavy.

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4. big_youthMay 2014
   This book is written by Yves J. Hilpisch a premier figure in the big data python world and great teacher. He has another more advanced book, which I loved, called Derivative Analytics with Python[0] and has given many well known and received talks.

   Note, I am Continuum Analytics[1] alumni, of which is Yves J. Hilpisch is the European director and whose CEO was the lead contributer to NumPy for many years.

   So my endorsement is not against Packt's Python for Finance but rather for O'Reillys Python for Finance based on previous work and experience of the Author.

   [0] http://www.python-for-finance.com/ [1] http://continuum.io/

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5. westurnerMar 2024
   SHMT (2024) has a probably faster blackscholes_2d implementation: https://github.com/jk78346/SHMT/blob/main/src/kernels/functi... https://news.ycombinator.com/item?id=39515038

   "Python for Finance", 2nd Edition (py4fi2nd) has a chapter on BSM with notebooks and a module: https://github.com/yhilpisch/py4fi2nd/tree/master/code/ch12 https://home.tpq.io/books/py4fi/

   GH topic: black-scholes: https://github.com/topics/black-scholes

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6. hailerisDec 2019
   I feel like I'm way behind on fundamentals so mostly textbooks. I'm focused on CS, maths, and finance mainly, not sure I'll achieve this in a year, kind of my perpetual read this within 10 years list. I'm also interested in literature but prefer reading to enrich my knowledge and skills as opposed to reading for leisure :

   Maths:

   James Stewart's Precalculus

   Spivak's Calculus

   How to Solve It

   Godel, Escher, Bach: An Eternal Golden Braid

   I Am a Strange Loop

   Introduction to Linear Algebra

   Euclid's Elements

   The Principia: The Authoritative Translation and Guide: Mathematical Principles of Natural Philosophy

   CS:

   The Algorithm Design Manual

   Finish SICP

   Computer Networking: A Top Down Approach

   Computer Systems: A Programmer's Persepctive

   C Programming: A Modern Approach, 2nd Edition

   Operating Systems: 3 Easy pieces

   Advanced Programming in the UNIX Environment

   Hacking: The Art of Exploitation

   The Elements of Computing Systems: Building a Modern Computer from First Principles

   Compilers: Principles, Techniques, and Tools

   Lions' Commentary on Unix

   TCP/IP Illustrated

   Finance/Econ/Business:

   Liar's Poker

   Investor Z (Manga)

   Trading & Exchanges

   Dynamic Hedging: Managing Vanilla and Exotic Options

   Python for Finance: Mastering Data-Driven Finance

   Never Split the Difference: Negotiating as if Your Life Depended on it

   Alpha Masters

   Fooling Some of the People All of Time

   Dark Pools

   When Genius Failed

   Advances in Financial Machine Learning

   Algorithmic Trading

   Other:

   Getting Things Done: The Art of Stress-Free productivity

   Mythology: Timeless Tales of Gods and Heroes

   Chaos: Making a New Science

   Networks, Crowds, and Markets: Reasoning about a Highly Connected World

   Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World

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