Hello Gopi M. Tatiraju, thanks for reaching out; I like both ideas, I can see the first idea would integrate perfectly into the preprocessing pipeline; that said, it would be useful to discuss the project's scope in more detail. Specifically, what functionality you like to add, in #2727 you already implemented some features, so I'm curious to hear what other features you have in mind.
The RL idea sounds interesting as well, and I think could also fit into the RL codebase that is already there. I'm curious what do you mean with "rewards schemes"? Thanks, Marcus > On 9. Mar 2021, at 14:55, Gopi Manohar Tatiraju <[email protected]> wrote: > > Hello mlpack, > > I am Gopi Manohar Tatiraju currently in my final year of Engineering from > India. > > I've been working on mlpack for quite some time now. I've tried to contribute > and learn from the community. I've received ample support from the community > which made learning really fun. > > Now, as GSoC is back with its 2021 edition, I want to take this opportunity > to learn from the mentors and contribute to the community. > > I am planning to contribute to mlapck under GSoC 2021. Currently, I am > working on creating a pandas dataframe-like class that can be used to analyze > the datasets in a better way. > > Having a class like this would help in working with datasets as ml is not > only about the model but about data as well. > > I have a pr already open for this: https://github.com/mlpack/mlpack/pull/2727 > <https://github.com/mlpack/mlpack/pull/2727> > > I wanted to know if I can work on this in GSoC? As it was not listed on the > idea page, but I think this would be a start to something useful and big. > > If this idea doesn't seem workable right now, I want to implement RL > Environments for Trading and some working examples for each env. > > What all exactly I am planning to implement are the building blocks of any RL > system: > rewards schemes > action schemes > env > > Fin-Tech is a growing field, and there is a lot of application of Deep-Q > Learning there. > > I am planning to implement different strategies like Bull-Sell-Hold, Long > only, Short only... > This will make example-repo rich in terms of DRL examples... > We can even build a small backtesting module that can be used to run backtest > on our predictions. > > There are some libraries that are currently working on such models in python, > we can use it as a reference to go forward. > FinRL: https://github.com/AI4Finance-LLC/FinRL-Library > <https://github.com/AI4Finance-LLC/FinRL-Library> > > Planning to implement: > > Different types of envs for different kind of financial tasks: > single stock trading env > multi stock trading env > portfolio selection env > Some example env in python: > https://github.com/AI4Finance-LLC/FinRL-Library/tree/master/finrl/env > <https://github.com/AI4Finance-LLC/FinRL-Library/tree/master/finrl/env> > > Different types of action_schemes: > make only long trades > make only short trades > make both long and short > BHS(Buy Hold Sell) > Example action_schemes: > https://github.com/tensortrade-org/tensortrade/blob/master/tensortrade/env/default/actions.py > > <https://github.com/tensortrade-org/tensortrade/blob/master/tensortrade/env/default/actions.py> > > We can see class BHS, SimpleOrder, etc. > > Different types of reward_schemes: > simple reward > risk-adjusted reward > position based reward > > For the past 3 months, I've been working as an ML Researcher in a Fin-Tech > startup and have worked on this only. > > I would love to hear your feedback and suggestions. > > Regards. > Gopi M. Tatiraju > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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