Boosting for an intraday strategy

We introduced high-frequency trading (HFT) in Chapter 1, Machine Learning for Trading – From Idea to Execution, as a key trend that accelerated the adoption of algorithmic strategies. There is no objective definition of HFT that pins down the properties of the activities it encompasses, including holding periods, order types (for example, passive versus aggressive), and strategies (momentum or reversion, directional or liquidity provision, and so on). However, most of the more technical treatments of HFT seem to agree that the data driving HFT activity tends to be the most granular available. Typically, this would be microstructure data directly from the exchanges such as the NASDAQ ITCH data that we introduced in Chapter 2, Market and Fundamental Data – Sources and Techniques, to demonstrate how it details every order placed, every execution, and every cancelation, and thus permits the reconstruction of the full limit order book, at...