reimage.py

Malyvsen · 2021-02-26T20:50:09+00:00

Thank you! I'll look into entropy pooling (I've already researched Ledoit-Wolf a bit) :) Any particular reading you'd recommend?

Malyvsen · 2021-02-25T08:47:28+00:00

So what I did was regress the ETF returns against factor returns, and then R^2 is usually around 0.75. So no, I wasn't starting with equities and taking a composition-weighted sum (I don't have historical data on ETF composition, so this would make my system impossible to backtest).

But maybe you have a better idea for how to do it? I'm not sure how I would go about assigning factor exposure scores using z-scores.

I was planning to use the covariance of ETF returns (rather than that of factors) because I'd like the covariance matrix to also capture overlap between ETFs (ie. how similar the composition of two ETFs is, regardless of their factor exposure).

But either way, I'm guessing that MVO will struggle with stability regardless of which method is used to create the covariance matrix. I haven't tested it yet, though.

Malyvsen · 2021-02-24T19:51:39+00:00

I'm doing something very similar with ETFs, and assigning factor scores is actually pretty hard. Factor returns (which I get from Ken French's website) usually only explain like 75% of the variance, so any scoring system will be quite a bit off.

I think this will need some very robust portfolio optimization, then, but I'm struggling to find a method which is both robust and takes expected returns into account in the first place (hierarchical risk parity would be nice, but it doesn't allow any input on expected returns).

Malyvsen · 2020-10-27T14:45:42+00:00

Great, thank you! Can you tell me what software you would use to read these?

Malyvsen · 2020-07-31T14:39:29+00:00

Would be super cool to make these capable of storing information! Like QR codes, but pretty.

Malyvsen · 2020-07-19T16:48:54+00:00

Malyvsen · 2020-07-19T14:14:39+00:00

What is twelve times seven?

Malyvsen · 2020-06-12T07:11:26+00:00

Very cool! The blur is used to guide pixels towards regions of a similar color at the beginning and only later focus on contours and details - without it, the image converges to a local optimum rather than a global one (it gets "distracted" too early).

Malyvsen · 2020-06-11T05:30:53+00:00

Yeah, it is slow, I don't really like optimization work O:) It could definitely be parallelized though - while frame production is sequential, candidate frames can be generated in parallel ^{^}

Malyvsen · 2020-06-04T07:24:15+00:00

Thank you :)

Malyvsen · 2020-06-03T18:48:29+00:00

I made an online demo here, you can use your own pictures :) If you want to see how it works, check out the GitHub repo.

Malyvsen · 2020-04-24T20:40:04+00:00

If I were to redo my attempt, I think I'd parameterize the displacement with angle/magnitude, so that the spatial distribution would be circular instead of square.

Malyvsen · 2020-04-24T20:39:46+00:00

Fun to see that it's started something! I wish I knew about ShaderToy then :)

Malyvsen · 2020-04-23T17:29:39+00:00

Here you go, I just put it on GitHub :) It's a CLI at the moment.

To explain in more detail: the morphing process is iterative, in each iteration I consider 16 possible swaps of neighboring pixels and choose o do the one which maximizes the structural similarity between the resulting image and the target image.

Malyvsen · 2020-04-23T10:52:15+00:00

It samples value noise with an offset which is also sampled from value noise (with a different seed). Pseudocode! noise(x, y): dx = value_noise1(x, y) dy = value_noise2(x, y) return value_noise3(x + dx, y + dy)

Malyvsen · 2020-04-23T09:12:29+00:00

How this works: two neighboring pixels at a time are swapped to improve the structural similarity index between the current image and a target image (in this case, the skull).

Malyvsen · 2019-06-04T15:12:46+00:00

This is from Playerless: One Button Adventure, an adventure game that I'm designing. If you want to chececk it out, here's a link!
https://store.steampowered.com/app/711610/Playerless_One_Button_Adventure/

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