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!
- Agree to disagree
- Watson (World's smartest assistant in your pocket)
- 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
- Goal ICPC
- Traffic congestion modelling and rendering
- 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
- 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
The volatility of stock market is generally modeled by three techniques; namely Technical Analysis, Machine Learning algorithms & statistical analysis. These models help in developing optimal portfolio for best returns with minimum risk.
No. of mentees: 3
A hybrid Artificial Neural Network & Time series analysis based statistical approach has the potential to obtain robustness in the prediction. This will essentially make possible to predict the seasonal volatility in market as well as currency value prediction.
With this project, efficacy of various techniques shall be benchmarked with plenty of daily stock market data.
I suggest to read following Paper:
Yan Hu, Jian Ni, Liu Wen, “A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction,” Physica A: Statistical Mechanics and its Applications, Volume 557, 2020, 124907, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2020.124907.
Applicants are welcome to contact me on Whatsapp, if unable to obtain the above document or to obtain more information.
Tentative Project Timeline
|Week Number||Tasks to be Completed|
|Week 1||Literature Reviews|
|Week 2||Developing fundamental framework|
|Week 3-4||Benchmarking Algorithms|
|Week 5-6||Further development and Validation; Project Closure & Packaging|