At the forefront of innovation in algo trading
An interview with Larisa Chizhova, enspired's Head of AI
1. How has your professional development shaped your career path? What were the most influential periods in your life?
I started to work as a software developer in 2016 at VisoTech. I had just received my Ph.D. and was looking for opportunities in business and real-world problem solving. By coincidence, I ended up working for energy companies, developing software and discovering the potential of algo trading. I also got a glimpse of financial engineering, risk management, and automated power and gas trading. Eventually, I became interested in machine learning and committed myself to its application in price forecasting.
2. Why did you decide to become a part of the enspired?
I joined enspired because the idea behind the company was to take automated energy trading to a completely new level. It appealed to me that instead of writing trading software for the customers, we would do the trading for them. This gives us full decision power for conducting research and implementing innovation. Soon after joining I knew I was in the right place to move our industry forward.
3. How do artificial intelligence and machine learning impact the energy market?
There are still a lot of opportunities for automated trading in the energy market. Compared to the financial markets, the intraday energy market falls behind on the number of order executions, order execution speed, number of trading participants, number of automated trading solutions, and number of automated trading algorithms utilizing machine learning. In the financial world, there is serious competition between the research groups, quants, and traders to develop models predicting stock prices with high accuracy. Portfolio optimization is another research area based on advances in machine learning. I remember reading about a program called “Star,” which was operating on a financial exchange and learned its stock-picking strategy on its own by scanning a plurality of different price tendencies (see Dark Pools by Scott Patterson).
We are witnessing a transition from manual to completely automated electronic trading in the energy market right now, similar to what happened in the financial sector 10-20 years ago. And the use of machine learning plays a major role in this transition. Finding trading signals in the vastness of available trading data, weather data, and load data is a task for machines to crunch. We, as humans, can draw only very few conclusions from the data we see, and we easily miss important trends. Likewise, we are prone to emotional risks, panics, fatigue, and even something as seemingly trivial as typos in writing. Machines are free of those and can learn on their own.
4. Where do you find inspiration for work, and how do you keep up to date with the latest developments in your field?
I get my inspiration both online and offline, for example from chatting with colleagues. I have the pleasure of working with knowledgeable, curious people who ask difficult questions. Furthermore, I like to read blogs about programming and computer science. Coming from a scientific educational background, it was only natural that I fell into the field of data science. In our modern world, the internet offers infinite resources to grow in this area.
What's it like to work at enspired?
