Trading since March 2023
I instantly knew it was something I would develop a deep interest in…
In March 2023 my father introduced me to investing and trading, I instantly felt a sense of possibility and excitement that I hadn’t felt for engineering. I became fascinated with trading strategies and began consuming lots of online content to learn as much as possible. I then turned to other resources, books and podcasts, from which I learned about trading psychology, derivatives, and technical analysis.
Since then I have managed a portfolio on Hargreaves Lansdown consisting of equities, index funds, and leveraged commodity ETNs for short term gains, using Trading 212 for its more intricate charting tools. I have created and implemented a mix of discretionary and systematic strategies of various time frames, with some long-term growth positions as well as capitalising on short term market rallies. In August 2025 I began trading Forex pairs on MetaTrader 5, which provides a more technically rigorous and fast-paced trading environment, as opposed to the longer term investment focus of HL.
Quantitative Trading Projects
Finding rule-based, systematic trading strategies that were simple, repeatable, and objective has been a great interest of mine. Whilst human discretion, biases, and emotions can get in the way of successful trading, a data-driven trading strategy can bypass all of these.
It all started when I noticed (by eye) some frequently repeating patterns on a certain commodity ETN, and I wanted to find a way to capitalise on this through a mean-reversion strategy. Just from looking at the charts, I could tell there was the opportunity for massive gains. To create a strategy, I wanted to find out some specific data about the ETN: essentially how many days on average it trends for, how long it takes to revert back to the mean, the percentage gap between the end of the trend and the mean, and the time between successive peaks/troughs. If I could work out the averages of these, then I could devise a strategy that incorporates this data so I have a better chance of entering and exiting at the correct times. For example, on the long side, if it, on average, trends down for 13 days, takes 6 days to revert back to the mean, and bottoms out at 15% below the mean, I could look at a down trend and compare to this data to make an informed assessment of when the trend is likely to end, then project the mean 6 days in advance and set my take profit at this point.
Initially, I collected this information by hand. Going back two years on the charts and putting this information into an Excel spreadsheet was a lengthy and inefficient process, however it worked and I was able to determine the mean, median, and mode of these values in Excel. I didn’t finish collecting all the data, since I was doing this as a side project during my third year at university where my workload was ridiculous, so I parked this for a while until I thought…
There must be a better way of doing this.