In a series of unfortunate events, you and your friends are left stranded in the middle of the Atlantic ocean. Together, you have managed to salvage some items from the wreck. As a group, your task is to agree on the importance of these items. Your decisions will determine if you get to walk away alive. The problem now is how do you decide on the importance? Quadratic voting can help!

Given a basket of assets, how would you allocation your capital across the different assets to maximize returns and minimize risk? This problem can be seen as a classic optimisation problem in data science. In this experiment, I will attempt to compare the performance of three different techniques, Monte Carlo, SLSQP and Bayesian Optimisation on a simple 3-Fund Portfolio for investors in Singapore

I've received numerous job invitations but realised most of these jobs do not meet my expectations. Rather than reviewing each one manually, I'm automating the process by writing this so I can focus my attention to those jobs that I can give my 101% efforts to without wasting a trip down your office.

For the past two years, I've been putting more than 60% of my income into different assets for investment. For that amount, one would have thought that I had a great investment strategy. Today I shall reveal my strategy for the past two years and share my new strategy moving forward.

December 2017. That was the month I built a High-Frequency Trading (HFT) bot for arbitrage trading. Through building the application, I realised that there are limited literature on HFT and quantitative trading. There were even fewer personal recounts. I've learnt many lessons by building a program that deals with real money and has made many costly mistakes, some of which obliterated huge parts of my profits. Since I've paid my tuition dues, I might as well share my lessons with the ones who would like to embark on the pilgrimage.