LRU Apprx.: Second-Chance Algorithm
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Obviously this isn't going to cut it. I would be very grateful if the community here which has a lot of folks with an advanced background in algo trading could give some guidance on what I should learn in each of the 3 fields above, so that I can be well-equipped for a professional career in algorithmic trading more specifically, the quantitative research part.
I hope this thread also helps out anyone else who's looking for similar guidance! Well if you intend to find a job as quant researcher you will need a PhD or at additional reference bit algorithmic trading a master's in a quantitative subject.
If you want to go independent then don't do it. It takes years of study and you might not find anything substantial. You're correct all quant roles do require a masters in a quantitative subject in their JDbut I have an MS Physics and a past 2-month internship in quant research, so there's no question that I'm going to do it.
I just wanted some guidance on how to prepare in the additional reference bit algorithmic trading possible way. I tend to disagree a bit with the degree requirement. Firms that are stubborn and judge you just based on your on-paper degrees are likely firms you don't want to work for anyway. I would focus incredibly strongly on building your rigorous statistical knowledge, both in the specific tests, but more importantly in the general way a statistician looks at the world.
Statisticians are like additional reference bit algorithmic trading and tend to constantly be looking for reasons to doubt claims: Why is this potentially not profitable? What biases could be causing this to falsely look good? Even Jim Simons of RenTech says that quant is really mainly statistics https: In order to rapidly iterate on statistical research you'll need to learn more programming.
Both Python and R are very additional reference bit algorithmic trading, but Python has a bit more momentum right now in industry and in my experience is generally far easier to use than R. Plus you can use Python for more applications, like full stack development or front end if you work in tech. I want to shamelessly pitch our lecture series as it covers a lot of stats and coding. Folks I've talked to have said that having research done on Quantopian has helped them get jobs, as it's a way to display a portfolio just like an artist might.
Quant firms will want to see interesting pieces of research more so than completed algorithms usually. At the end of the day pick projects that are interesting to you. Google what you don't know and keep pushing forward. The thing is, if you develop some great skills in stats and programming, there are tons of jobs waiting to be filled right now, not just quant. So it's not an over-concentrated investment to learn these things.
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Thanks a lot for the detailed answer! I am indeed going through the various videos on the Quantopian YT channel mainly the talks and intend to get to the lecture series after I've seen the Quantopian videos that are more general not specific to Quantopian platform.
I feel that I'll be in a better position to use the platform that way. As for Statistics, could you please suggest any good books to additional reference bit algorithmic trading up a rigorous statistical knowledge? As of now, I'm planning on reading Probability Theory: The Logic of Science by Jaynes.
Any other reference recommendations apart from that? I'm asking this because I can't discount the possibility that there may be some important material not covered in the lecture series or videos. Finally and maybe this question is the same as abovecould you recommend a comprehensive source from where I can read up on the specific tests additional reference bit algorithmic trading in algo trading model evaluation, if any such source exists?
Our lectures are actually designed to be agnostic of the platform and teach statistical and quantitative concepts, so additional reference bit algorithmic trading you wouldn't have that problem. If you find any lecture to be additional reference bit algorithmic trading platform specific please let me know, the curriculum is used by professors who want to teach the concepts independent of platform, so we want to be sure that additional reference bit algorithmic trading platform isn't taking center stage.
I'm not a book learner, I learned most of what I know by search around online, reading wikipedia articles, chatting with folks, etc. I can't recommend any books as a result. Also, one of the reasons we designed additional reference bit algorithmic trading lecture series is I didn't know of any good resource for the stats necessary for quantitative finance. A lot of the other resources are slides from various universities or even original academic papers.
I'll let my colleague Max answer with a few books he likes. I think if I was your age I would turn my skills to something more intrinsically satisfying and useful than quantitative finance. Those are all pretty solid books, with focuses on various different aspects of statistics. I hope that you find them useful! Sorry, something went wrong. Try again or contact us by sending feedback.
I intend to switch to algo trading professionally. So far my background is: Statistics - A very basic understanding of stats and a basic understanding of common machine learning algorithms Finance - Intermediate CFA level 1 Programming - Basics of R and Python Obviously this isn't going to cut it.
That would be very nice of you, thanks! Hey Shirish, A few of my favorite books on stats additional reference bit algorithmic trading Please sign in or join Quantopian to post a reply.
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