/kPCA-in-high-frequency-trading

• Partition trading time series data into 30 minutes intervals by picking the mean transaction price and volumes in each interval and compute the log-return (aka ’U sequence’) and write it into a corresponding csv file: JNJ_1004_1015_2010_HFT_30min_.csv • Visualize the high frequency data with PCA by using 2 or 3 PCs: you need to calculate the variance explained ratios for your visualization. • Identify outliers in your PCA analysis • Visualize it by using KPCA and compare its results with those of PCA (you need to at least try two kernels)

Primary LanguageJupyter Notebook

kPCA-in-high-frequency-trading

• Partition trading time series data into 30 minutes intervals by picking the mean transaction price and volumes in each interval and compute the log-return (aka ’U sequence’) and write it into a corresponding csv file

• Visualize the high frequency data with PCA by using 2 or 3 PCs: you need to calculate the variance explained ratios for your visualization.

• Identify outliers in the PCA analysis

• Visualize it by using KPCA and compare its results with those of PCA (you need to at least try two kernels)