In this episode, Brian from quantlabs.net explores a thought-provoking question posted by Maeve R. Ritz on quant.stackexchange.com. The query focused on the quantitative mechanisms behind identifying flow-based alphas during incredibly short lookout periods and their significance in the context/dynamics high-frequency trading lookout periods up to 100 milliseconds. Brian attempts to demystify this complex issue, primarily associated with high-frequency trading.
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A response from LaHal that garnered Brian’s attention pointed to a 2019 paper he authored with Curls, Albert, titled “Incorporating Signals into Optimal Trading”. The research revolves around employing specific signals for optimal trading and provides a wealth of statistical data. By incorporating calculations grounded in the imbalance sigma, the paper piques the interests of quant high-frequency trading modelers.
As Brian navigates this mathematically dense paper, he sheds light on the dense collection of mathematical formulas and proofs. Although the level of mathematical proficiency required to comprehend the content is high, he finds the exercise profoundly enlightening. He invites listeners who can understand this level of mathematical intricacies to get in touch and share their insights.
Join Brian as he continues to unravel the complex world of high-frequency trading in future discussions. He extends an invitation to join the Quant Labs Discord community and sub stack for more insights into his trading and investment choices. For those seeking technical trading books, visit quantlabs.net/books for two handy e-books packed with trade secrets and free software to help you decode the market in unique ways.
quant.stackexchange.com/questions/78631/what-are-some-quantitative-approaches-to-figure-out-flow-based-alphas-on-extreme
link.springer.com/article/10.1007/s00780-019-00382-7
