As of now, the most pressing matter for my Capstone Project is to set-up interviews for the year. The purpose of these interviews is to get insights into topics pertaining to trading algorithms/Word2Vec-type programming. I have organized my potential interviewees into three main themes, each theme being a different perspective on the work I plan on doing. Below is the list of the first group of people I have found:
1. People with close experience to actual trading, who can give a perspective into this field. Ex-stock traders, financiers,
- Christian Daniel Griset, former employee at KCG Holdings (American global finance service which specialized in high-frequency trading, https://www.forbes.com/sites/quora/2017/04/25/is-the-success-of-a-high-frequency-trading-operation-driven-by-math-prowess-or-tech-infrastructure/#4868c7d918d7)
- Tom Groves, ex-hedge fund partner. Quit because of dislike of the job, feel like it would be an interesting interview. Also mentions that he could make money easier through risky, “not smart” decisions as opposed to the logical, “right” moves. (https://www.quora.com/profile/Tom-Groves)
2. Upper-management level investors, ex-fund retirement fund managers, people who oversee portfolios, angel investors, investment bankers.
- Jim Seidman, retired investment adviser, software executive.
- Federico M Dominguez Garcia Diego, founded his own AI-based hedge fund in London, has been working with automated trading since 1998. Recommends that hedge funds are better off without human traders, as AI is more precise, fast, efficient, and only improves over time. (https://www.linkedin.com/in/fededominguez?trk=author_mini-profile_title)
- Lauren Bernut, https://www.quora.com/profile/Laurent-Bernut. Ex-Hedge Fund analyst, current algorithmic trader, also shares the belief that machines are much preferable over human traders.
3. People with a more technical perspective, quants, people who have written/have experience with trading algorithms, programmers, mathematicians.
- Borislav Agapiev, specialist in Word2Vec, computer science, search engines.
- Michael Halls-Moore, ex-quantitative researcher, now runs blog about research findings, advice, tutorials, etc…
- Nikola Bozinovic, tech entrepreneur from Serbia who founded Frame, a cloud software which was recently acquired. He was responsible for most of the coding/framework in creating his company, so this would be an ideal person to speak with.
Of course, this list is not exhaustive, and my goal is to get at least 20-25 people on this list (organized by theme) by the end of the week (9/30/18), and have all my outreach to them done by then as well.
I have been exploring potential questions for the interviews, as well.
- Human traders vs. trading algorithms? Do you think human traders will have a place in the future, or are algorithms and technologies too far superior?
- Mathematical prowess vs. technological advantages? Which is most important in trading, writing trading algorithms?
- How do you personally judge the value of assets, positions? What are the most important factors you look at when analyzing?
- What advice would you give to a younger person who wants to work in this field? Best/worst decisions? Possible setbacks to avoid?
I will have to alter these questions in some way from person to person, and will add in some more specific questions based on who agrees to participate.
On the computer science end of things, I have continued work and research on my Word2Vec search engine. On a larger scale, I have written-out a general plan of action for my independent computer science work this year, which goes as follows:
- Write Word2Vec search
- Improve data by adding more datasets
- Test capability for noticing trends
- Study trading algorithms
- Implement Word2Vec → algos
- See how non-Word2Vec trading algorithms compare to standard trading algorithms.
I will go into more detail on numbers 5 & 6 in my next post, but as can be seen at the top of my list, my biggest priority right now (aside from scheduling interviews) is creating a solid Word2Vec word embedding dataset. Once this is done and tested, I can move into the trading algorithm portion of my work, where I will bring everything (my interview findings, my stock research, my newfound knowledge about financial instruments, and discoveries from the books I am reading) together.