programming

Personal update in 2022 Note: This is not affiliated with any organization or entity. Whatever opinions I expressed here is of my own. What a year it has been! On the personal portfolio front, I kind of shifted my personal investment philosophy to a complete market neutrality at start of 2021. This works wonder in a bearish regime where the alpha outperformance is simply not losing money! Last year was a great one with performance of ~ +65.

Setting up ens domain The reason why I wrote this post is to just document the steps on how I broadly set up my eth domain here, https://jironghuang.eth.link Steps First and foremost, I already have static pages that reside in this repo, https://github.com/jironghuang/jironghuang.github.io . The pages are served in my personal blog jironghuang.github.io . Second, register for ENS domain by following up the steps listed here, https://ens.domains/ . I registered for my domain jironghuang.

Developing a performance monitoring system for my algorithmic trading system It’s one thing to backtest your signals and forecasts on historical data but it’s a completely different animal in terms of execution. I have written about this here. Another important component of setting up an algorithmic trading system is performance monitoring. Some important questions I had in mind while developing this component, How do I measure my slippage? Commissions paid?

Designing, Building and Deploying a Fully Automated Algorithmic Trading System As I developed several inter-day trading/ portfolio management algorithms, I also embarked on a journey in parallel to develop a fully automated execution framework that could satisfy my requirements. Previously orders were executed manually after signals are generated automatically. Requirements A relatively slow trading system triggered by an hourly task scheduler during trading hours. I’m using Linux cron jobs for this.

Volatility targeting could potentially increase risk-adjusted returns for quantitative strategies Note: Man AHL’s framework in this paper is used in the write-up here. Let’s say you researched and managed to find a quantitative strategy that suits your risk-reward preferences. How do you further improve your strategy while managing your risk? A common way in the trend-following and risk parity space is to target risk - also known as volatility targeting.

Volatility targeting Currently, I’ve a suite of toolkits integrated into my Jarvis that advises me on the investing decisions that I’ve to make on a daily basis. On the latest feature I cobbled together on a Saturday evening, 2 weeks ago, I’ve decided to measure the volatility of my portfolio formally. Why I’m doing this is because managing risks in the form of volatility is easier than targeting returns.

Mapreduce using java I haven’t coded in java in eons. The assignment (Mapreduce, Pig and Spark) I worked on over last 3 weeks is a good way to jolt me out from my comfort zone. Java is something I need to brush up on before taking the Software Development Process module which requires me to write an android app. Argh! Back to Mapreduce. It’s a useful framework if you’ve to summarise huge datasets (gigabytes, terabytes).

Regime detection through hidden markov model It’s rumoured that in the early days of Renaissance Technologies - according to the book ‘The Man Who Solved the Market’ - hidden markov models are used for regime detection. Here I am, a couple of decades later - employing this strategy. This will be integrated into my ‘Jarvis’ - a series of Algorithmic toolkits that advises me in all situations. Hidden markov mode is a statistical unsupervised learning model used to model states.