coinbasepro-python VS py-spy

I'm trying to set up algo trading through the Blankly Python library. I've generated my Coinbase Pro API keys, but when I try to enter them it always tells me that it failed to connect, and to check to make sure my keys are correct. I'm quite certain that they are, and I made a new set of API keys just in case something was wrong with the first set. I also cannot create an "authenticated client" as described on their readme because the cbpro library is out of date and currently incompatible with blankly.

which uses the coinbasepro-python library to perform trades https://github.com/danpaquin/coinbasepro-python

My question is, when does the order expire, if ever? The documentation is pretty sparse -- https://github.com/danpaquin/coinbasepro-python .

This is pretty trivial if you don't mind getting your feet wet scripting with cbpro.

The cbpro library for Python, for instance, makes interacting with the API very simple, then all you have to do is loop through your accounts grabbing whatever data you need.

To start, I'd say coinbase has a well documented api, (coinbase pro, the exchange, not the other one). This wrapper package was one of the first I used, I assume it's still supported: https://github.com/danpaquin/coinbasepro-python. To knock out 1 and 2 would look something like (psuedo code, don't quote this):

Yeah it does. You can set it up to deposit money it seems. Under deposit/withdraw here: https://github.com/danpaquin/coinbasepro-python it seems to suggest this. To be honest I was already thinking of doing this before I saw your comment, but if someone else would use it too then that's extra motivation. I understand where you're coming from though. I would personally prefer to set it up for myself so it deposits some money in my account every day, buys algorand, and then transfers it to my algorand wallet after a few days, without me having to think about it at all. Just out of curiosity what do you do for employment? I'm a data scientist so I'm a bit of an evangelist for learning python and if it's something that you would be interested in at all I can recommend some resources.

Not sure what language or library you're using, but try the example here: https://github.com/danpaquin/coinbasepro-python#websocketclient-methods

I’m using a Coinbase Pro API for Python to access data from the WebSocket feed. Here’s a first cut that has some debugging code left in place (along with two ways to generate the VWAP, one inefficient (the _vwap method) and one more efficient). Let’s see if py-spy reveals how much time this code uses.

If not, you can find it here: https://github.com/danpaquin/coinbasepro-python

For CPU cycles, py-spy[0] is getting more and more used. For RAM, I would like to known too...

[0] -- https://github.com/benfred/py-spy

Theres also Py Spy, a profiling tool that can generate flame charts containing a mix of python and C (or C++) calls.

https://github.com/benfred/py-spy

It's worked really well for my needs

You should profile your consumer to check the bottlenecks. You can use the excellent py-spy(written in Rust). IMO a few usage of Numba there and there should solve your performance issues.

So as a first step you'll want to profile your program to figure out where it's slow, and hopefully that'll also tell you why it's slow. I'm the (biased) author of the Sciagraph profiler which is designed for this sort of application (https://sciagraph.com) but you can also try py-spy, which isn't as well designed for data processing/analysis applications (e.g. it won't visualize parallelism at all) but can still be informative (https://github.com/benfred/py-spy). Both are written in Rust ;)

Any advantages/disadvantages compared to py-spy [1]?

[1]: https://github.com/benfred/py-spy

Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.

A good profiler would be https://github.com/benfred/py-spy . If you run your app/benchmark with it, it should be able to draw a flamegraph telling you where the majority of time is spent. The info here is quite fine grained so it would already tell you where the bottleneck is. Without a full-fledged profiler you can also measure the timings in various parts of the code to understand where the bottleneck is.

Might be worth raising an issue on py-spy (a python profiler written in rust which "supports profiling native python extensions written in languages like C/C++ or Cython" to see if that can close the loop.