Comparison of Portfolio Optimisation Techniques - Monte Carlo vs SLSQP vs Bayesian
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
Dear Recruiters (Interactive Post)
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.
Reflecting on Two Years of Investing
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.
What I learn from building a HFT bot processing >$10 million a month.
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.
Smart Contract for Hiring
What’s a better way to hire a smart contract developer than to write a smart contract to do it? That’s what my team thought when we decided to add hiring to the lengthy list of problems that smart contracts promised to solve. We developed a technical hiring smart contract.