bessiechen Goto Github PK

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

academic-project-on-the-impact-of-earning-report-based-on-object-oriented-programming-in-cpp

Chose 498 S&P 500 component stocks and categorized them according to the spread between their estimated and actual EPS, then calculated abnormal average returns of each group and plotted their graph individually by establishing a user-friendly menu

algo

algorithm-visualization

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).

algorithms

bessie-fresh

fresh market

bessie-recruit

B2B2C

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.github.io

blog

没事写写文章，喜欢的话请点star，想订阅点watch，千万别fork！

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-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.

coursera-algorithms-on-graphs

This course covers several ways to traverse graphs and how to do useful things while traversing the graph in some order. It also talks about shortest paths algorithms.— from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services.

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.

coursera-data-structures

In this course I learn the common data structures that are used in various computational problems, how these data structures are implemented in different programming languages and practice implementing them in our programming assignments. I understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. I also learn typical use cases for these data structures.

coursera-genome-assembly-programming-challenge

Assembled Phi-X174 genome using Overlap Graph, Kmer Composition and De-Bruijn Graph.

coursera_deep_learning

cpython

The Python programming language

data-structure

deep_learning_cases_with_tensorflow

design_pattern

dl-implementation

edtech

fat32_filesystem

goapplet

high_frequency_trading_platform

highly_concurrent_server

leetcode