Theano, a Python library
This library is very useful when you implement e.g. machine learning algorithms. I am going to use it. Here are the main points:
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:
- tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
- transparent use of a GPU – Perform data-intensive calculations up to 140x faster than with CPU. (float32 only)
- efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
- speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
- dynamic C code generation – Evaluate expressions faster.
- extensive unit-testing and self-verification – Detect and diagnose many types of mistake.
4:10 am • 25 February 2013
Y combinator application questions
- What is your company going to make?
- If this application is a response to a YC RFS, which one?
- Please tell us in one or two sentences about the most impressive thing other than this startup that each founder has built or achieved.
- Please tell us about the time you most successfully hacked some (non-computer) system to your advantage.
- Please tell us about an interesting project, preferably outside of class or work, that two or more of you created together. Include urls if possible.
- How long have the founders known one another and how did you meet? Have any of the founders not met in person?
- Why did you pick this idea to work on? Do you have domain expertise in this area? How do you know people need what you’re making?
- What’s new about what you’re making? What substitutes do people resort to because it doesn’t exist yet (or they don’t know about it)?
- Who are your competitors, and who might become competitors? Who do you fear most?
- What do you understand about your business that other companies in it just don’t get?
- How do or will you make money? How much could you make? (We realize you can’t know precisely, but give your best estimate.)
- If you’ve already started working on it, how long have you been working and how many lines of code (if applicable) have you written?
- How far along are you? Do you have a beta yet? If not, when will you? Are you launched? If so, how many users do you have? Do you have revenue? If so, how much? If you’re launched, what is your monthly growth rate (in users or revenue or both)?
- If you have an online demo, what’s the url? (Please don’t password protect it; just use an obscure url.)
- How will you get users? If your idea is the type that faces a chicken-and-egg problem in the sense that it won’t be attractive to users till it has a lot of users (e.g. a marketplace, a dating site, an ad network), how will you overcome that?
- If you’re already incorporated, when were you? Who are the shareholders and what percent does each own? If you’ve had funding, how much, at what valuation(s)?
- If you’re not incorporated yet, please list the percent of the company you plan to give each founder, and anyone else you plan to give stock to. (This question is as much for you as us.)
- If we fund you, which of the founders will commit to working exclusively (no school, no other jobs) on this project for the next year?
- For founders who can’t, why not? What level of commitment are they willing to make?
- Do any founders have other commitments between June and August 2013 inclusive?
- Do any founders have commitments in the future (e.g. finishing college, going to grad school), and if so what?
- Where do you live now, and where would the company be based after YC?
- Are any of the founders covered by noncompetes or intellectual property agreements that overlap with your project? Will any be working as employees or consultants for anyone else?
- Was any of your code written by someone who is not one of your founders? If so, how can you safely use it? (Open source is ok of course.)
- Are any of the following true? (a) You are the only founder. (b) You are a student who may return to school when the next term starts. (c) Half or more of your group can’t move to the Bay Area. (d) One or more founders will keep their current jobs. (e) None of the founders are programmers. (Answering yes doesn’t disqualify you. It’s just to remind us to check.)
- If you had any other ideas you considered applying with, please list them. One may be something we’ve been waiting for. Often when we fund people it’s to do something they list here and not in the main application.
- Please tell us something surprising or amusing that one of you has discovered. (The answer need not be related to your project.)
10:15 am • 10 February 2013
Python Data Analysis Library
If you use python and need to do some proper data analysis, you should know about Pandas, or Python Data Analysis Library.
It is supplanting the functionality of R, but with the advantage of using good multipurpose programming language.
The library is already used in some very interesting projects, such as
- Zipline, Pythonic Algorithmic Trading Library, made by Quantopian
- QuantSoftware ToolKit for quantitative analysis in finance, done by people from Georgia Tech
And the author of Pandas, Wes McKinney, wrote a book about the library Python for Data Analysis . I’m currently reading it and it is indeed pretty useful for the job of data crunchers that want to use python.
Thanks to Pandas python is becoming one of the best languages around for data analysis and quant finance. Thanks Wes!
5:17 am • 5 February 2013
learning material for starting investors
First of all, you should always think for yourself. Do not obey authorities or experts. The world is a pretty fascinating place and it is very helpful for you to understand it.
The following are some good resources I have used in the last several months to learn about computational finance, investing and making reasonable decisions.
For a start, Ray Dalio’s Principles are worth reading.
Ray Dalio (of Bridgewater) views the (macro)economy as a machine and creates a big picture view of its workings, see How the Economic Machine Works.
Then it is also worth to learn from Charlie Munger and Warren Buffett. The book Seeking Wisdom by Peter Bevelin is collecting their (and other people) wisdom of avoiding mistakes. The entrepreneur Derek Sivers provides a useful summary.
A really nice textbook of quantitative finance is Paul Wilmott Introduces Quantitative Finance. Gambling included!
Also, I’ve learned quite a lot from Eric Zivot’s online course Introduction to Computational Finance and Financial Econometrics. It is the core of technical analysis done with R. A good book on this topic is Statistics and Data Analysis for Financial Engineers by David Ruppert.
A standard finance MBA textbook is Corporate Finance by Stephen Ross, I do not recommend you to read it all, just learn what the subject is about and then only read what you really need (probably not much).
Now the other interesting stuff. The new and also likely the best book of Nassim Nicholas Taleb. Antifragile: Things That Gain from Disorder. Former trader, Taleb got some interesting insights into financial markets from empirical experiments, that were contradicting most of what he studied for finance MBA. He put his money where his mouth was and bet against the famous economists, gaining hugely from the financial Black swan of 2008. However the best thing is that Taleb is true to his principles and is able to fight against the bullshit of many famous talking-heads. I applaud his courage.
Regarding algorithmic trading, Chris Stucchio is able to explain the main principles and problems quite well on his blog.
This will likely continue later.
5:06 am • 14 January 2013