Name: PINAKI BHATTACHARYYA
Type: User
Bio: Graduate Student
Interests: Soft condensed matter physics, Stochastic Processes, Biophysics, Nonequilibrium Statistical Mechanics, Quantitative Finance
Twitter: pinakiclickz
Location: Bangalore, India
PINAKI BHATTACHARYYA's Projects
Notes for the Book Quantitative Trading by Ernie Chan
Quantitative Finance book
Repository of quantitative finance projects.
Quantlib implementation in pure Julia
ipython notebooks from quantopian lectures series
A framework for quantitative finance In python.
Library for the numerical simulation of closed as well as open quantum systems.
Neural Network Many-Body Wavefunction Reconstruction
Tutorials for a mini course in machine learning and quantum physics
Reactive programming primitives for Julia
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Python Implementation of Reinforcement Learning: An Introduction
In this paper, we implement three state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Policy Gradient (PG)in portfolio management.
This is the code for "Reinforcement Learning for Stock Prediction" By Siraj Raval on Youtube
Numeric computation of probabilities relevant in Renewal Processes using Julia
A dynamic strategy that replicates the payoff of a derivative described as a stochastic process
Jupyter notebook tutorials for the QuantBook Lean system project.
Quantitative research and educational materials
RISE: "Live" Reveal.js Jupyter/IPython Slideshow Extension
Matlab scripts to search for Runge-Kutta methods that are optimal in terms of SSP coefficient
Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv.org/abs/1706.10059 (and an openai gym environment)
An environment to high-frequency trading agents under reinforcement learning
R Markdown: The Definitive Guide (published by Chapman & Hall/CRC in July 2018)
Literature and related resources on Rough Volatility Models
A Python implementation of the rough Bergomi model.
Lectures on scientific computing with python, as IPython notebooks.
scikit-learn: machine learning in Python
Source code for the "Learning scikit-learn: Machine Learning in Python"