[mlpack] GSoC-2021

[Gopi Manohar Tatiraju]

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


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


*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


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

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
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