• ## ColorPrint: Print Colorfully in Python

This is a simple print function overwritten so that you can specify different colors in Terminal outputs.

• ## C++ Key Points

This is the first post of my ambitious plan trying to enumerate as many key points about the C++ language as I can. These notes are only for personal reviewing purposes and shall definitely be used commercially by anyone interested. Just please comment below for any missing C++ syntax or features. 👍🏻

• ## Time Synchronization on Amazon EC2

My trading bot just ceased this morning from its loyal 24/7 service. It's running on an Amazon EC2 server with Ubuntu 16.04 and I'm sure this time I'm not having an unpaid-bill issue any more. After some time digging I think I finally figure out the cause of this unexpected strike -- asynchronism.

• ## LDA and R-MDA

This is a note of Linear Discriminant Analysis (LDA) and an original Regularized Matrix Discriminant Analysis (R-MDA) method proposed by Jie Su et al, 2018. Both methods are suitable for efficient multiclass classification, while the latter is a state-of-the-art version of the classical LDA method s.t. data in matrix forms can be classified without destroying the original structure.

• ## Literature Review on Optimal Order Execution (5)

This is the fifth post on optimal order execution. Based on Almgren and Chriss (2000), today we attempt to estimate the market impact coefficient $\eta$. Specifically, for high-frequency transaction data, we have the approximation $dS = \eta\cdot dQ$ and thus can easily estimate it by the method of Ordinary Least Squares (OLS), using the message book data provided by LOBSTER.

• ## Literature Review on Optimal Order Execution (4)

Today we implement the order placement strategy in Almgren and Chriss (2000) s.t. for a certain order size $Q$, we can estimate the probability to perform the optimal strategy in the paper within time horizon of $T$.

• ## Parameter Estimation of Brownian motions by Method of Moments

How to estimate the parameters of a geometric Brownian motion (GBM)? It seems rather simple but actually took me quite some time to solve it. The most intuitive way is by using the method of moments.