Is possible to use Numba/cupy/cython to speed up the preprocessing step of FinRL?

AI4Finance-Foundation / FinRL

FinRL: Financial Reinforcement Learning. 🔥

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Is possible to use Numba/cupy/cython to speed up the preprocessing step of FinRL? #613

Open dev590t opened 2 years ago

dev590t commented 2 years ago

The preprocessing is very slow in FinRL. The Nmba allow GPU parallelization with C speed for process array data. Some high speed trading framework like vectorbt use it.

Is possible to take experience from vectorbt and use Numba to speed up FinRL?

YangletLiu commented 2 years ago

https://github.com/AI4Finance-Foundation/ElegantRL If you check ElegantRL, you will find more advanced ways of using GPU, DGX-2 server and even NVIDIA's SuperPod. Enjoy!

dev590t commented 2 years ago

@XiaoYangLiu-FinRL If I understood the code correctly,ElegantRL is used in DRL step. I mean use Numba in preprocessing data step, like here:

data = DP.clean_data(data)
data = DP.add_technical_indicator(data, INDICATORS)
data = DP.add_vix(data)

Did this part of code use also GPU acceleration?

YangletLiu commented 2 years ago

Yes, ElegantRL is for DRL algorithms. Your suggestions for accelerating data preprocessing are quite interesting. Looking forward to workable codes, would that be possible from your end?

zhumingpassional commented 2 years ago

@XiaoYangLiu-FinRL If I understood the code correctly,ElegantRL is used in DRL step. I mean use Numba in preprocessing data step, like here:
data = DP.clean_data(data)
data = DP.add_technical_indicator(data, INDICATORS)
data = DP.add_vix(data)
Did this part of code use also GPU acceleration?

@dev590t Not yet. The current does not support numba. We may test the acceleration and add numba if it is good.

dev590t commented 2 years ago

Unfortunaly, I'm not a python data scientist programmer, and have never used Numba. I have discover this lib from the trading lib vectorbt.

vectorbt can also preprocess data. I think it is maybe possible to use the dataframe produced by vectorbt and pass it directly to DRL agent, instead of use dataframe produced by the built-in code of FinRL.

This have few advandage:

GPU acceleration in preprocessing
it maybe support more indicator than FinRL. According its doc, it support for 99% indicators in Technical Analysis Library, Pandas TA, and TA-Lib. So it is possible to pass more indicators to DRL agent.
less code to maintain in FinRL

Do you think it is a good direction to explore? Externalize the preprocessing to vectorbt.

zhumingpassional commented 2 years ago

Yes. it is a good direction. Thanks for your suggestions.

zhumingpassional commented 2 years ago

Some engineers said that they tried numba, however there exist errors if the scenario becomes complex. They recommend cupy and cython.

dev590t commented 2 years ago

I think it is better to test in first existing GPU trading indicator librairy before to decide to reimplement or no the preprocessing step with Numba or Cupy. And if existing GPU trading indicator librairy is not suitable, Cupy also could be a interested solution.

zhumingpassional commented 2 years ago

With respect to existing GPU trading indicator librairy, could you give any examples?

dev590t commented 2 years ago

If I understand correctly the interface of Finrl, Finrl can directly use the data from other libraries. During preprocessing step: For classic Talib indicator, Finrl can use the subset of feature of the GPU trading librairy vectorbt: https://vectorbt.dev/ For portfolio management indicator, Finrl maybe can use some indicators of pyportfolioopt: https://pyportfolioopt.readthedocs.io/en/latest/RiskModels.html.