Analyzing Financial Time-Series Data via the Julia Language and QuestDB

QuestDB is a state-of-the-art database designed for market data, boasting high ingestion throughput and advanced SQL analytics, making it highly efficient for handling tick data. The tutorial explores high-frequency finance, which focuses on granular, often overlooked data beneath traditional daily stock prices, and has become more accessible with the rise of free crypto exchange data. It delves into the intricacies of high-frequency data, using examples from Coinbase to highlight the differences between bid, ask, and mid-prices, and the significance of understanding these in financial analysis. The text explains the transformation of mid-prices into stationary time series through returns and log-returns, which exhibit a heavy-tailed distribution, indicative of extreme financial events. It further explores autocorrelation and volatility in log-returns, revealing patterns and regimes in the data. High-frequency trades, categorized by size, are analyzed for their impact on future prices, demonstrating that larger trades have a more significant effect. Overall, the text underscores the value of high-frequency data analysis and the utility of tools like QuestDB in streamlining this process for researchers.