Seasons Of Code
Reinforcement Learning to Finance • Siddesh Pawar
WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Running Projects
- Browser Based PDF manager
- Resume Script Generator
- Physicc : A Simple Physics Engine
- Image Colorization
- Language Model Based Syntax Autocompletion in a Text Editor
- Computer vision based web app
- Cribbit Cribbit (Open for PGs Only)
- Techster Texter
- Language Detection
- Book Tracker
- ResoBin - Not the bin we deserve but the bin we need!
- Moodify
- Agree to disagree
- Unscripted
- Watson (World's smartest assistant in your pocket)
- IITinder
- BriefKing
- Meta Learning - Learning to Learn
- Break free of the matrix, by building one!
- Procedurally Generated Infinite Open World
- Introduction to App Development
- PAC MAN
- Introduction to Web Development
- (Un)Clear
- Goal ICPC
- Traffic congestion modelling and rendering
- PyRated
- Tools for Data Science
- Machine Learning Based Metropolitan Air Pollution Estimation
- Audio controlled drone
- NLPlay with Transformers
- DIY FaceApp
- A Deep Dive into CNNs
- Competitive Coding
- Snake AI
- Facial Recognition App
- Gaming meets AI !!!
- R(ea)L Trader
- Computational Geometry
- Deep reinforcement learning - 2048 AI
- Reinforcement Learning to Finance
- Developing Hybrid ANN-Statistical Model for Robust Stock Market Prediction
- Si-Phy
- Astronomical Data-modelling and Interpretation
- Visual Perception for Self Driving Cars
- Convolutional Neural Networks and Applications
- Quantum Computing Algorithms
- Algorithm Visualizer
- Anime Club IITB Website using Django
- Machine Learning in Browser
Reinforcement Learning to Finance
This project would be dealing with dealing with applications of reinforcement learning algorithms to stock training and portfolio optimization.
No. of mentees: 5(freshies) + 3(sophies and above)
The experiments would be carried using libraries: OpenAI Gym and FinRL. A strong inclination towards mathematics is required(this should reflect in the proposal). The project would include experiments on NASDAQ-100, DJIA, S&P 500, HSI datasets. The tasks would be different for freshies and sophies. The task for freshies would be more focussed on reinforcement learning algorithms with a few experiments on the datasets towards the end. The ones for sophies would be heavier on the implementation side. The project would include contribution to the Note that this would be more of a reinforcement learning project than a core finance project. The final aim of the project is to set baselines for RL algorithms using different datasets.
It is mandatory to read the following blogs and summarize the content in the proposal:
https://medium.com/ai%C2%B3-theory-practice-business/reinforcement-learning-part-1-a-brief-introduction-a53a849771cf
https://blog.floydhub.com/an-introduction-to-q-learning-reinforcement-learning/
https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/
https://analyticsindiamag.com/reinforcement-learning-in-finance-a-newbie-in-portfolio-selection-and-allocation/
Prerequisites:
Freshies: Prior experience in python/C++.
Sophies:
A completed course in Machine learning and a basic course in probability.
Prior exposure to convex optimization would be an add-on(but not necessary).
Sophies should explicitly mention their other commitments during the summers in their proposal.
Tentative Project Timeline
| Week Number | Tasks to be Completed |
|---|---|
| Week 1 | Introduction to basic RL algorithms and their implementation in Open AI Gym. |
| Week 2 | Setting baselines in OpenAI Gym and FinRL. |
| Week 3 | Experiments using basic online learning algorithms and MDPs(markov decision processes) on available datasets. |
| Week 4 | Experiments using Deep RL algorithms and developing a UI. |
| Week 5 | Pushing code to FinRL library. |