In 2020, the coronavirus crisis disrupted and dominated the news about the stock market. It was difficult to predict what would happen in the stock market over the short term or long term. However, the market for day trading (buying and selling stocks within the same day) was not significantly affected by each daily news because the risks of day trading were lower than those of other trading methods at that time. Therefore, overall, there were relatively lower risks associated with day trading. This thesis evaluates day-trading algorithms in an attempt to enhance trading methods. I developed five day-trading algorithms, which were evaluated. The algorithms bought 505 stocks from the S&P 500 index, and all stocks were sold before the market closed for the trading day. Value at risk (VaR) and conditional value at risk (CVaR) were used as risk measures. To evaluate performance, the cumulative gross log returns of each algorithm were compared with those of the S&P 500 index fund, SPDR S&P 500 ETF. The experimental results show that model D, based on the last nine days’ data, performed better and had a lower risk than the other methods.