Financial-Time-series-analysis-for-High-Frequency-Trading

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the field of High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this project, we have implemented a shallow-architecture methodology for the forecasting of financial time-series data, which gives state-of-the-art results. This architecture has been trained and tested on the benchmark Limit Order Book(LOB) FI-2010 dataset, and the corresponding results are compared and analyzed using a variety of measures.