Senior Project - Bitcoin Stock Prediction

  Introduction Markets move in predictable patterns. This is the hypothesis of a realm of stock prediction called technical analysis. As described by Pring (2002) a leading technical analyst: The technical approach to investment is essentially a reflection of the idea that prices move in trends that are determined by the changing attitudes of investors toward a variety of economic, monetary, political, and psychological forces. The art of technical analysis, for it is an art, is to identify a trend reversal at a relatively early stage and ride on that trend until the weight of the evidence shows or proves that the trend has reversed. For my senior project this semester, I tested this hypothesis by analyzing short-term Bitcoin (BTC-USD) stock price trends to see how accurately I could predict 1-hour changes in stock price. The subsequent model was then automated to create real time predictions. Methods and Setup In my readings I read about patterns that have been identifi...

  Introduction In the last blog post, I discussed the possibility of using a recurrent neural network to detect patterns within the stock market. In this blog post I will show my findings from training an RNN on stock data, the practicality with application in the real world, and a new method that could potentially yield higher accuracy. Data Collection and Granularity For this project I am pulling data from Yahoo finance by using the “yfinance” API package. Stock data is retrieved by passing in a ticker symbol, specifying a time frame, and declaring the level of granularity for the data. Data can be retrieved down to the minute, however, at that granularity only 30 days of data can be retrieved. Because RNN’s are data hungry, I wanted to find a granularity and time frame combination that would maximize the amount of data being pulled in while also staying within the scope of analyzing short term market trends. As a result, the data is at a 2-minute granularity for the previous...

  Introduction There are patterns all around us. For example, the I-15 through Salt Lake will always be busiest at 7:00 AM and 5:30 PM. This situation can easily be explained by an external effect: the end of the working day. In many cases there is a simple explanation for why a pattern occurs, but often there are several complex variables at play that make it difficult to predict the direction and confidence of a trend. However, what if there was insight within the trend itself? Patterns in the Stock Market One example of an area that is very difficult/impossible to predict are stock market prices. Several factors go into the price of a stock. These can include company earnings, investor expectations, emotions such as greed and fear, and macro-economic variables. Some of these can be measured, but others are subjective and create a significant amount of volatility and noise. The process of fundamental analysis seeks to measure these variables and predict a stocks fair market...