We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, small business revenue, mobility, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel benchmarking method that uses a mixed-frequency dynamic factor model to constrain the weekly index to match the monthly MRTS. We use the index to document several interesting features of U.S. retail sales during the Covid-19 pandemic, many of which are not visible in the MRTS. In addition, we show that our index would have more accurately predicted the MRTS in real time during the pandemic when compared to either consensus forecasts available at the time, monthly autoregressive models, or other high-frequency data that attempts to track consumer spending. The gains are substantial, with roughly 60 to 80 percent reductions in mean absolute forecast errors.