Pinned Repositories
Academic-Project-on-Options-Pricing-based-on-object-oriented-programming-in-Cpp
Calculated the implied volatility under dynamic price of options based on Newton-Raphson method, utilized Monte Carlo techniques for path-dependent derivative securities pricing
Algorithmic-Portfolio-Management-in-R-programming-language
The course, authored by Prof. Jerzy in NYU, applies the R programming language to momentum trading, statistical arbitrage (pairs trading), and other active portfolio management strategies. The course implements volatility and price forecasting models, asset pricing and factor models, and portfolio optimization. The course will apply machine learning techniques, such as backtesting (cross-validation) and parameter regularization (shrinkage).
bessie_foodie_shop
The goal of this project is to build a fully functional e-commerce website platform using a monolithic architecture design, implementing functions including address management, order processing and user center.
Competition-from-International-Association-of-Quantitative-Finance
Our team won the competition hold by IAQF in 2018. We submitted a solution titled, Implementing Momentum Strategy with Options: Dynamic Scaling and Optimization.
Coursera-Advanced-Algorithms-and-Complexity
This course talks about networks flows which are used in more obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. It then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications and many others. Next discussing inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice.
Coursera-Algorithms-on-Strings
This course covers suffix trees, suffix arrays, and other brilliant algorithmic ideas that help doctors to find differences between genomes and power lighting fast internet searches.
Mechine-Learning-Projects-for-Personal-Interest
Projects just for fun, eg. Apply Lasso Technique to Forecast Stock Movement, Apply Random Forest Technique to Forecast Stock Movement
Python-for-Financial-Analysis-and-Algorithmic-Trading
Including packages that frequently used in quantitative finance field and how to implement classic financial model in Quantopian.
R-in-Finance
This course, taught by Prof.Jerzy in NYU, applies the R programming language to momentum trading, statistical arbitrage (pairs trading), and other active portfolio management strategies. The course implements volatility and price forecasting models, asset pricing and factor models, and portfolio optimization. The course applies machine learning techniques, such as backtesting (cross-validation) and parameter regularization (shrinkage).
Reinforcement-Learning-and-Desicion-Making
This project contains three experiments on reinforcement learning. For instance, it demonstrates how to apply the DQN to the Lunar Lander environment and how DDQN can obtain better performance.
BessieChen's Repositories
BessieChen/Coursera-Algorithmic-Toolbox
It covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming.
BessieChen/High_Frequency_Trading_Platform
BessieChen/bessie_foodie_shop
The goal of this project is to build a fully functional e-commerce website platform using a monolithic architecture design, implementing functions including address management, order processing and user center.
BessieChen/FAT32_FileSystem
BessieChen/Reinforcement-Learning-and-Desicion-Making
This project contains three experiments on reinforcement learning. For instance, it demonstrates how to apply the DQN to the Lunar Lander environment and how DDQN can obtain better performance.
BessieChen/Algo
BessieChen/Algorithm-Visualization
BessieChen/bessie-fresh
fresh market
BessieChen/bessie-recruit
B2B2C
BessieChen/BessieChen.github.io
BessieChen/Deep_Learning_Cases_With_TensorFlow
BessieChen/Design_Pattern
BessieChen/DL-Implementation
BessieChen/edtech
BessieChen/goApplet
BessieChen/highly_concurrent_server
BessieChen/LeetCode
BessieChen/Linear_Algebra_Package_Implementation
BessieChen/MiniGames
BessieChen/ML-Implementation
BessieChen/my_compiler
BessieChen/Nginx_Network_Programming
BessieChen/PAT
BessieChen/Play-with-Tensorflow-and-Keras
BessieChen/ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
BessieChen/recruit-dev
B2B2C
BessieChen/RTOS
BessieChen/TCPIP
BessieChen/xdb
BessieChen/xdb_2021