Using ROOT to analyse High-Frequency Finance Data

The analysis of high-frequency financial trading data faces similar problems as High Energy Physics (HEP) analysis. The data is noisy, irregular in shape, and large in size. Recent research on the intra-day behaviour of financial markets shows a lack of tools specialized for finance data, and describes this problem as a computational burden. In contrary to HEP data, finance data consists of time series. Each time series spans multiple hours from the start to the end of a trading session, and is related to others (i.e., multiple financial products
are traded in parallel at an exchange). This presentation shows how ROOT can be used in high-frequency finance analysis, which extensions are required to process time series data, and what the advantages are with regard to high-frequency finance data. We provide implementations for data synchronisation (i.e., zipping multiple files together), iterating over the data sequentially with a mutable state (i.e., each entry updates the state of a financial product), generating snapshots (i.e., resampling data based on the timestamps of the entries), and visualisation. These transformations make it possible to fold time series data into high-dimensional data points, where each data point contains an aggregation of recent time steps. This new dataset removes the need to process data serially as a time series, and instead allows the use of parallelised tools in ROOT, like RDataFrame