Speaker
Description
Novel algorithms are developed for algorithmic trading on financial time series by using quantization and volatility information to achieve High Frequency Trading (HFT). The proposed methods are estimation based and trading actions are carried out after estimating the Forward Conditional Probability Distribution (FCPD) on the quantized return values. For estimating FCPD, a FeedForward Neural Network (FFNN) will be used, which can provide a good estimation if the return levels are quantized properly and a special encoding scheme is applied. The performance analysis of our method tested on historical time series (NASDAQ/NYSE stocks) has demonstrated that the algorithm is profitable. As far as high frequency trading is concerned, the algorithm lends itself to GPU implementation, which can considerably increase its performance when time frames become shorter and the computational time tends to be the critical aspect of the algorithm.
