Využití datové analýzy v ekonomickém modelu sběratelské karetní hry

The thesis deals with the application of data analytics in an economic model of a collectible card game Magic: The Gathering whose cards are actively traded on secondary markets, often for considerable amounts. The main goal of this thesis is to create a prototype data-analytics solution in this area, to inform players, traders and collectors about the evolution and composition of their collection and to support their decision-making process in trading cards with data. After mapping the business requirements of the users of the planned solution, a production database has been created to record individual cards and their characteristics, to collect daily market data, to record transactions affecting the state and composition of the collection and to calculate the realized profit/loss. Based on the market data, trading signals are identified using short and medium-term price trends or through open local price arbitrage opportunities. In addition, a data warehouse has been created for the daily valuation of the collection and its storage. Last but not least, machine learning has been used to create a regression model to predict the future price of newly issued cards.