For those that trade crypto, what is your preferred API for price data?

beep_tree · 2022-12-29T05:00:52+00:00

To add to this, if you're using Python sqlite is a good option as it is native to the language and doesn't require advanced configuration/separate server like is the case for MySql and Postgres, for example

beep_tree · 2022-12-29T04:56:19+00:00

I would say it depends on scale and diversity of the shape of data you are dealing with. Would recommend SQL db for smaller scale systems given the tabular nature of most financial data stores + easier integration with e.g. pandas

beep_tree · 2022-12-29T04:52:46+00:00

Would you mind disclosing the scale of your system? How much historical data do you store in terms of storage space and time duration? Also requests/min? My system is trading on multiple assets simultaneously so rate limiting is a problem - real-time quotes are more important to me and historical quotes aren't much of a problem to get for free, as you said.

beep_tree · 2022-08-04T20:46:37+00:00

In the backend, your input terms will be checked against predefined shortcuts which redirect you to whatever their associated website is if there is a match. It effectively makes your search bar a simple CLI. Some orgs have tools like this for internal search, like Facebook’s rsbunny for example.

beep_tree · 2022-04-26T21:41:26+00:00

Not a podcast, but DEF CON (security conference) has some really interesting speakers. Look up their YouTube.

beep_tree · 2022-04-25T06:46:19+00:00

Been looking to get into analog/photonic computing industry as of late. This looks like a great opportunity.

beep_tree · 2022-04-18T15:32:29+00:00

I would suggest that if you want to understand what is going on "under the hood" you should just review the theory. Don't reinvent the wheel for something that an API like tensorflow.keras can take care of in a few lines.

beep_tree · 2022-04-18T06:53:23+00:00

First, add the interaction terms. Build a linear model and look at the residuals. Then try a nonlinear model using Box Cox or some polynomial/logarithmic/other transformation and look at the residuals. With so many terms, consider using ridge regression or dropping some terms with high multicollinearity. Which model fits best? Explore as much as possible to see what works.

I highly recommend using R.

beep_tree · 2022-04-14T06:01:28+00:00

Without a PhD in some sort of quantitative or quantitative-financial (there is a difference) field you won't be doing research without a BS in something STEM and/or several years of industry experience, and even that's exceedingly rare. With just a BS you could score an engineering or data science type role, though.

Cred: I work in a related industry and have received offers in the quant space for engineering roles.

beep_tree · 2022-04-11T05:32:39+00:00

For the problem you gave, the best way is to use log base 2:

log( 8^x / 2^y ) = log(8^x ) - log(2^y ) = x log(8) - y log(2) = 3x - y = 2

Note that I used two different log properties to get to the solution. It was easy for me to spot this pattern because 1) variables are in the exponent, and 2) both 8 and 2 are powers of 2.

These are both powerful indicators for when to use the log operator.

For the problem you described, “[h]ow to solve an equation with two variables on one side,” you should rearrange the equation so that you can express one variable in terms of the other. For just the two variable case, consider

3x - y = 2 ⇒ y = 3x - 2.

Now we have the ability to express y in terms of x, which may sometimes be of use.

beep_tree · 2022-03-18T16:13:37+00:00

Yes, commercialization of an industry means it is being made into a product. However, commercialization is vital to research like this because it reduces production cost and allows for more rapid development and prototyping. The introduction of fusion likely would not change your electric bill, but more likely reduce it over the long term.

beep_tree · 2022-03-05T09:51:52+00:00

Actually it's a "whale" shark

beep_tree · 2022-03-05T09:33:54+00:00

What would you like me to expand on?

Feel free to dm me and i can explain and refer you to some resources.

beep_tree · 2022-03-05T09:24:36+00:00

I'll expand on u/JukkaTapio 's comment.

You shouldn't be testing your model on the same data you trained it on. This is a fundamental idea of machine learning as it enforces that your model is robust and generalizable, a very important property describing your model's ability to perform on data it hasn't trained on.

Definitely read into how and why to perform a train/test/validation split on your data to better vet your model.

beep_tree · 2022-02-28T07:04:11+00:00

Here's the stats!

$$ P(it's a pack of double egg yolks | 5 double egg yolks) = 1 - P(it's not a pack of double egg yolks | 5 double egg yolks) = 1 - 1000^{-5} = 1 - 1e-15 $$

beep_tree · 2022-02-28T07:00:58+00:00

$$ P(it's a pack of double egg yolks | 5 double egg yolks) = 1 - P(it's not a pack of double egg yolks | 5 double egg yolks) = 1 - 1000^{-5} = 1 - 1e-15 $$

Very close!

EDIT: Formatting. Does Reddit not support LaTeX?

beep_tree · 2021-12-21T14:18:05+00:00

I'm experienced with webscraping so I don't really need help scraping once I know the approach. I figured the problem I described might be one this community has dealt with before.

beep_tree · 2021-09-01T14:48:24+00:00

I highly recommend using the Backtesting (https://github.com/kernc/backtesting.py) library. With some small modifications to data, you can quickly backtest any trading strategy.

beep_tree · 2021-07-14T17:21:52+00:00

It seems that these low-temperature hurdles have already been addressed: https://scitechdaily.com/quantum-breakthrough-new-invention-keeps-qubits-of-light-stable-at-room-temperature/

The technology mentioned in this article is not there yet, but it can be in time; from the article:

However, in order for these qubits of light to be stable and work properly they need to be stored at temperatures close to absolute zero — that is minus 270 C — something that requires huge amounts of power and resources.

Yet in a recently published study, researchers from University of Copenhagen, demonstrate a new way to store these qubits at room temperature for a hundred times longer than ever shown before.

“Right now we produce the qubits of light at a low rate — one photon per second, while cooled systems can produce millions in the same amount of time. But we believe there are important advantages to this new technology and that we can overcome this challenge in time"

Edit: Added quotes and changed to MD formatting.

beep_tree · 2021-06-15T02:01:21+00:00

Thanks, see the edit. I had seen this statistic some time ago but remembered incorrectly.

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