Starting work on trading algorithm research, Word2Vec (9/11/18)

To the dismay of many, school has officially begun, but I am in a surprisingly good mood right now. I have started work on my computer science independent project, whose focus has been diverted a bit from my original plan. Rather than beginning work on writing my own trading algorithm, I have decided to extend work on an independent project I began over the summer, which involves using Word2Vec word embeddings to create a small-scale search engine. In layman’s terms,  Word2Vec is a machine learning model which creates vectors for all words, creating vectors of similar magnitude for words with similar meanings. This is really interesting, as it shows computers can deduce meanings of words based solely on the context of the data which you provide it. The data which I am currently using is Q&A data from finance forums online, so the results I will be getting are obviously going to be tilted towards financial terms. What I mean by this is the Word2Vec model I will be running won’t be able to define words (or word pairs) such as “banana” or “Puerto Rico”, but should be able to define words such as “dotcom bubble” or “inflation”.
In an ideal world, my queries would yield full passages, examples, or historical examples, so that when the user searches “dotcom bubble”, they could see a map of passages relating to this query, ranked in how closely they associate to the query. One criticism which I’ve received for this is that such a program already exists, and it’s called Google. But, Google does not use Word2Vec or vector mapping as its primary means of search. Google has a specific PageRank algorithm, which has proved tremendously successful, but does have its share of flaws. What I’m trying to do with my small-scale search engine is see how my results compare with search results given by Google. In addition to this, I will learn more about cause/effect relationships in finance along the way, serving as a great segue into my second independent project: writing my own trading algorithm.
In terms of stocks and finance , the SPDR ETF is up slightly (<+1%), continuing the upwards trend we saw in the markets beginning mid-July 2018. At SPDR’s peak on August 29, we saw a rise of +7.60% from July 3. My initial intuition was that this sudden, sharp rise at the end of the summer would be counteracted by a sharp decline in September, as has happened many times before in the past (see: September Effect). There has been some decline, as I expected, but not nearly as much as I anticipated, and definitely not enough to nullify the gains made July-August. In my honest opinion, I expect to see the SPDR maintaining steady losses through September, especially with the impending arrival of winter and category 4.5 Hurricane Florence, which is expected to make landfall sometime this week.  
The reason why I choose to follow the SPDR ETF, as a starting point, is because this fund is designed to track the progress of the S&P 500, perhaps the most important American stock-trading index. The S&P is comprised of the 500 most commonly traded public companies in the U.S, including the likes of Facebook, Amazon, Apple, and Google. The combined market cap of the S&P 500 is estimated around $24 Trillion. 

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