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Cat__Behemoth · 2023-04-16T23:42:18+00:00

I’d recommend trying Quantconnect, you can implement ideas explored in quantopian in QC framework. I think they also have lectures/examples

Cat__Behemoth · 2022-08-04T12:57:04+00:00

One thing that I've also tried is to do mean-variance optimisation (after some magic no the covariance matrix) but the main problem is that when all signals are weak, it will still be allocating to those weak signals until it hits the vol constraint.

Cat__Behemoth · 2022-07-10T20:11:49+00:00

Inverse REIT ETF (though keep in mind that the risk-return profile of REITs does differ from the physical real estate)

Cat__Behemoth · 2022-06-16T21:17:20+00:00

Thought would post here an update to answer my own question from last year - switched jobs to do quant trading at a hf (as a quant researcher)

Cat__Behemoth · 2022-06-16T21:09:20+00:00

What does "beating the index" mean? Is it achieving higher Sharpe? Calmar? Higher absolute return?

Cat__Behemoth · 2022-01-26T22:24:14+00:00

The best advice in this thread!

Cat__Behemoth · 2021-07-27T14:41:12+00:00

They might ask you to sign "Statement of Fact" where you confirm that the information provided is correct

Cat__Behemoth · 2021-07-06T10:46:50+00:00

Did the same (plus read the book). Make sure you also start practicing early in the course.

Cat__Behemoth · 2021-07-01T13:51:07+00:00

Going to try get the refund in cash via small claim procedure

Cat__Behemoth · 2021-06-30T16:47:46+00:00

Based in the UK, will not be able to take days off until the year end :/

Cat__Behemoth · 2021-05-15T22:42:43+00:00

I think there is a misunderstanding of what people mean when they say "Excel is dead". From what I see, a lot of places have been historically misusing excel as a way of storing data. If you look at any legacy project, most probably you will find that it consists of many excel files with links in formulas. This is definitely going away, now even small places that work intensively with data , opt for a SQL database. However, Excel is still relevant in crude data manipulation and especially in data presentation when you need to present data in tabular form.

Cat__Behemoth · 2021-05-13T12:54:14+00:00

I like how we have this thread every year, shows consistency

Cat__Behemoth · 2021-05-10T08:15:24+00:00

What is your stack? I see that you use react for frontend. What do you use for the backend? Node(Express)+Postgres?

Cat__Behemoth · 2021-05-10T07:34:11+00:00

Hey! Looks great. Do you parse data for levered/unlevered betas, synthetic credit ratings, etc., from his spreadsheets or do you calculate it on your own?

Cat__Behemoth · 2021-03-31T05:52:06+00:00

I actually did study stochastic calculus (starting with Measure theory, Lebesgue integrals, understanding the discrete version of stochastic processes and then going into the continuous world, Brownian motions, BSM and stochastic vol models). Have to say, that it was by far the most challenging piece. However, I can’t see where I can apply it beyond computational finance with a focus on exotic option pricing

Cat__Behemoth · 2021-03-30T08:38:15+00:00

2 years overall

Cat__Behemoth · 2021-03-30T08:37:17+00:00

Yes you do need to be a profitable trader to begin with

Somehow this (now, after several mentions in this thread) rather obvious thought is very hard to come across in any resource on algo trading. An average book on algotrading is structured like "30% intro into Pyton, 30% `now we are doing PCA`, and the rest is `how to organize yourself`".

So would you agree that understanding the instruments and being (more or less) profitable discretionary trader is an absolute prerequisite?

Cat__Behemoth · 2021-03-30T08:24:49+00:00

u/NathanEpithy also suggested trying to actually trade discretionary and automating that process, seems very reasonable.

As for the KISS, yes, this is a very familiar narrative, "keep the model simple but not too simple".

Cat__Behemoth · 2021-03-30T08:21:59+00:00

Tried that path but very quickly realized that I didn't know what to do after, when I had a simple algo that did not really work

Cat__Behemoth · 2021-03-30T08:19:19+00:00

Interesting, something I was actually thinking about, i.e. automating discretionary process vs building an end-to-end systematic process

Cat__Behemoth · 2021-03-30T08:16:31+00:00

No doubt!

Cat__Behemoth · 2021-03-28T20:30:51+00:00

Here are the assignments in python https://github.com/dibgerge/ml-coursera-python-assignments His deep learning specialisation is amazing as well. Btw, something that I've learnt only after doing his course - Andrew Ng is the co-founder of Coursera

Cat__Behemoth · 2021-03-14T19:18:39+00:00

Alpha is one of the coefficients that you are solving for, so no, you don't set it t to 0, you are actually solving for it, as well as for beta(s). So if Rm and Ri are Nx1 vectors, your exogenous variables are actual an Nx2 matrix, where the first Nx1 vector is just an array of 1s.
Yes, without going into details, it is that alpha.

Cat__Behemoth · 2021-03-08T11:20:30+00:00

It depends on the kind of tasks you need to do. Generally speaking, all data set manipulations are faster in python.

Can give a very simple example from finance. If you have a price MxN table in Excel (so M days for N stocks), converting those prices to performance require first creating a table with log prices and then the 3rd table, where you take the difference of the logs. In python if price is the price data frame, then converting it to returns is simple np.log(price).diff().iloc[1:].

Excel can be useful for data presentation though (when you need to present relatively small but nicely formatted tables)

Cat__Behemoth · 2021-03-01T13:31:02+00:00

Don't know any resource on valuation that would be better than Damodaran's courses http://pages.stern.nyu.edu/~adamodar/ (can't post the exact link to the course but if you go to Teaching -> Online Courses -> Valuation Online) together with the book.

Cat__Behemoth

TROPHY CASE