How I Enjoyed Fintech Internship at Alpaca, with Deep Learning for Trading Research
By Peter J. Zhang, an internship student at Alpaca
I clicked into Alpaca’s website by chance when I was looking for an internship late last year. I am a Ph.D student in theoretical physics in U.S.. I have a background of math and physics, some programming experiences but not formal training. I was seeking for a rather research-like job as my first internship in the state-of-art areas, namely deep learning, computer vision, AI etc.. So after a couple rejections from big names, I started looking for startups. Everything looks normal until I notice the strange puzzle in the recruiting page. I am exactly the kind of person who would never ignore such an interesting thing – it took me a couple hours to tackle the puzzle, and the moment I got the answer I thought I have to see whoever created this puzzle.
I had a lot of hesitations before actually arrived Tokyo. I don’t speak Japanese, I haven’t been to the country, not to mention work there. I don’t know how to rent a housing place in Japan and I have no idea how to behave in an appropriate polite manner that won’t causing troubles. Luckily the company solved most problems and arranged a comfort apartment for me.
The internship starts with a enthusiastic yet relaxing atmosphere, like the spring weather in Tokyo. After the first week for getting familiar with all the tools, I was asked to re-produce the result from a Natural Language Processing research paper (1) of market risk prediction using public news. As a Ph.D student, it is quite a familiar start for me. With the help from colleagues and the computation power of the server, I was able to finish the task in just a few days. For the first time I realized that an ordinary person is just not that far from “the frontier” – where quants from Wall streets came up with complex strategies to profit in the market. And this is exactly what the product Capitalico is trying to tell people: Bringing the technologies to everyone.
I have seen a lot of naive startups during college. Alpaca is definitely not like any of them. It has a strong tech-background personnel, with experienced developers and professional researchers from graduate schools; it has a clear commercial plan and deep connections with investors. It has a mixture of Japanese style and Silicon valley company culture. Quoted from our esteemed professional law buddy Ian, “Everyone knows what they are doing.”
Thanks to the shared code from Paul, Tomoaki and Jun-ya, the study process of deep learning went smoother than I thought. I remembered how amazed I was when I first saw the paper about “art style transferring” (the popular phone app which applies artistic filters). Now I am not only able to create one running on my own computer, but also extend the idea to other concepts, from generating natural languages to classifying different stock symbols.
During the last week of the internship, I was able to carry out a research on my own. It started with an ambitious idea: Can we predict the stock market, given enough information? I explored some results using neural network and got interesting results.(2)
Every morning on the way to the office, there are always thousands of suited-up people walking in and out from Tokyo station. On the other hand, we are probably the only people in casual clothes among them. There is no fixed working time or location, or strict office manners. Tomo-san always brought coffee and snacks, taking us out for tour bus or go to the bar after work. It feels more like a project group in college. I guess it is a major attracting point for people like me.
The internship granted me so much valuable experience, and also an extraordinary long trip traveling around Japan. I couldn’t have a better way to spend the 10 weeks anywhere else.
- Kogan, Shimon, et al. “Predicting risk from financial reports with regression.” Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2009.