Can't Async/Await yahoo_fin api calls

I am using FastAPI and it would seem I can’t directly await the API Calls because I receive this error:

object numpy.float64 can't be used in 'await' expression

It would be very helpful to be able to await these:

            price = await si.get_live_price(symbol)
            chain = await options.get_options_chain(symbol, expiration)[optionType]

[mpaccione]

Hey, it was incredibly fast. In fact too fast lol. I remember I was using asyncio and making these calls but I believe I was hitting some rate limiting on yahoo. Basically the pages wouldn’t all finish loading.

I decided to keep it how it was sync but then use rabbitmq and batch the requests out to the queue. I this way I was able to get better performance while still having the request content complete to be able to be parsed.

On Sun, Sep 18, 2022, 9:41 AM msingh00 @.***> wrote:

how much faster is it? was thinking of also making some modifications to use modin and ray to see if can make things faster.

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[alpha-insite]

I am building and MVP so I just went with a queue infrastructure and am relying heavily on redis caching and database for short and long term data. However, I do have computations that can only have 15 minutes max old data so I am a bit hamstrung onto this queue for now. I suppose I could use serverless functions in the future for those but queue is cheap for ideation and I don’t have to fuss around with AWS or DO.

The data is for about 20 years of daily data usually.

I compared performance of:

1. Simple Python + pandas + parallel processing (concurrent.futures)
2. Modin (with ray built-in)
3. Ray + Python

I have settled on the last as its more stable, but yields better performance than plain python multi-processing. I do development on windows (though my servers are hosted linux)…Though it’s great for dealing with large data sets, modin has some stability issues on windows right now - so doing development on windows became a deal breaker. Ray allowed me to get better scaling and performance than standard python + multi-processing. It achieves this i believe by doing something you seem to be doing…using a centralized cache to deal with performance issues related to cross process serialization. At one point they were using redis, but right now i believe they have gotten rid of redis and implemented a custom GCS with optional backing storage (which could still use redis if you like).

Its a very interesting project that i hope gains traction and support.