Haven't seen this. But I'll look into it
Rithmic API for Python
- Thread starter IAS_LLC
- Start date
Haven't seen this. But I'll look into it
MattZ
Sponsor
As far as I know, they always had C# and C++. The new protocol can work with most APIs.
Haven't tried it, but I see no reason it wouldn't. I've used the threading module with success, but I prefer to manage the threading on the c++ side.
What do you mean "most APIs"? Most languages? If its simply some sort of socket protocol, that would make sense. If that is the case, I wonder if it supports all of the features. Pushing full depth of market over a websocket sounds expensive. Probably good enough for 99% of people though.
MattZ
Sponsor
Yes, most languages.
@IAS_LLC curious about what you are seeing?
What kind of latency are you seeing with python analytics engine? I am running a python analytic engine in parallel to my c++ trading system (Rapi based) to generate trading decisions. Everytime my C++ app observes certain conditions, it issues a http request to the python analytics app, which in turn does predictions and responds if its an "enter trade" or "ignore".
I am seeing a latency of about a few ms per every call. I am trying to figure out whats the best to reduce the latency. Its easy to setup a ML workflow in python/pandas/juypter and train models, but I find it extremely error prone to even migrate the data prep part to C++ once I finalize my ML model in python. Any suggestions on how you are mitigating this latency issue...
What kind of latency are you seeing with python analytics engine? I am running a python analytic engine in parallel to my c++ trading system (Rapi based) to generate trading decisions. Everytime my C++ app observes certain conditions, it issues a http request to the python analytics app, which in turn does predictions and responds if its an "enter trade" or "ignore".
I am seeing a latency of about a few ms per every call. I am trying to figure out whats the best to reduce the latency. Its easy to setup a ML workflow in python/pandas/juypter and train models, but I find it extremely error prone to even migrate the data prep part to C++ once I finalize my ML model in python. Any suggestions on how you are mitigating this latency issue...
I can only measure so well, using my system clock. Ask anyone in TRUE HFT, and they will tell you timing is a challenging science. The way I"m measuring things is far from scientific...so take this for what its worth:
Using std::chrono::system_clock my c++ to Python back to c++ time is less than 1 microsecond, which is the resolution of the clock I'm using. Obviously, this will increase if something expensive is done on the python side.
My c++ and python application are running in the same process space, which is why I'm running so much faster than you. Inter-process/in-memory communication will ALWAYS be significantly faster than any tcp based scheme (like http).
I'm no tcp expert, but I suspect http is relatively slow compared to other forms of tcp. That may be a place to start.
Also...If your doing something where a few ms of latency makes or breaks your success, you need to be really careful with how you're using python.
Using std::chrono::system_clock my c++ to Python back to c++ time is less than 1 microsecond, which is the resolution of the clock I'm using. Obviously, this will increase if something expensive is done on the python side.
My c++ and python application are running in the same process space, which is why I'm running so much faster than you. Inter-process/in-memory communication will ALWAYS be significantly faster than any tcp based scheme (like http).
I'm no tcp expert, but I suspect http is relatively slow compared to other forms of tcp. That may be a place to start.
Also...If your doing something where a few ms of latency makes or breaks your success, you need to be really careful with how you're using python.
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aah embedded python... how did I miss that. Thanks for the pointer.
If I may ask, do you use boost or pybind11 for this? I am assuming that one should be able to import sklearn modules and pass numpy or csv arrays to fire off the inferencing engines?
>> Using std::chrono::system_clock my c++ to Python back to c++ time is less than 1 microsecond,
I use Poco library (https://pocoproject.org/). They have a bunch of time related APIs that are relatively reliable for this type of work: https://pocoproject.org/docs/Poco.Stopwatch.html
If I may ask, do you use boost or pybind11 for this? I am assuming that one should be able to import sklearn modules and pass numpy or csv arrays to fire off the inferencing engines?
>> Using std::chrono::system_clock my c++ to Python back to c++ time is less than 1 microsecond,
I use Poco library (https://pocoproject.org/). They have a bunch of time related APIs that are relatively reliable for this type of work: https://pocoproject.org/docs/Poco.Stopwatch.html