Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information. Recently, machine learning(ML) methods have become increasingly popular and achieved promising
results for price movement forecasting in both academia and industry. Most existing ML solutions formulate the forecasting problem as a classification(to predict the direction) or a regression(to predict the return) problem in the entire set of training data. However, due to the extremely low signal-to-noise ratio and stochastic nature of financial data, good trading opportunities are extremely scarce. As a result, without careful selection of potentially profitable samples, such ML methods are prone to capture the patterns of noises instead of real signals. To address the above issues, we propose a novel framework-LARA(Locality-Aware Attention and Adaptive Refined Labeling), which contains the following three components: 1)Locality-aware attention automatically extracts the potentially profitable samples by attending to their label information in order to construct a more accurate classifier on these selected samples. 2)Adaptive refined labeling further iteratively refines the labels, alleviating the noise of samples. 3)Equipped with metric learning techniques, Locality-aware attention enjoys task-specific distance metrics and distributes attention on potentially profitable samples in a more effective way. To validate our method, we conduct comprehensive experiments on three real-world financial markets: ETFs, the Chinas A-share stock market, and the cryptocurrency market. LARA achieves superior performance compared with the time-series analysis methods and a set of machine learning based competitors on the Qlib platform. Extensive ablation studies and experiments demonstrate that LARA indeed captures more reliable trading opportunities.
We report three new FRBs discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), namely FRB 181017.J0036+11, FRB 181118 and FRB 181130, through the Commensal Radio Astronomy FAST Survey (CRAFTS). Together with FRB 181123 that
was reported earlier, all four FAST-discovered FRBs share the same characteristics of low fluence ($leq$0.2 Jy ms) and high dispersion measure (DM, $>1000$ dmu), consistent with the anti-correlation between DM and fluence of the entire FRB population. FRB 181118 and FRB 181130 exhibit band-limited features. FRB 181130 is prominently scattered ($tau_ssimeq8$ ms) at 1.25 GHz. FRB 181017.J0036+11 has full-bandwidth emission with a fluence of 0.042 Jy ms, which is one of the faintest FRB sources detected so far. CRAFTS starts to built a new sample of FRBs that fills the region for more distant and fainter FRBs in the fluence-$rm DM_E$ diagram, previously out of reach of other surveys. The implied all sky event rate of FRBs is $1.24^{+1.94}_{-0.90} times 10^5$ sky$^{-1}$ day$^{-1}$ at the $95%$ confidence interval above 0.0146 Jy ms. We also demonstrate here that the probability density function of CRAFTS FRB detections is sensitive to the assumed intrinsic FRB luminosity function and cosmological evolution, which may be further constrained with more discoveries.
The digital correlator is a crucial element in a modern radio telescope. In this paper we describe a scalable design of the correlator system for the Tianlai pathfinder array, which is an experiment dedicated to test the key technologies for conducti
ng 21cm intensity mapping survey. The correlator is of the FX design, which firstly performs Fast Fourier Transform (FFT) including Polyphase Filter Bank (PFB) computation using a Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Reconfigurable Open Architecture Computing Hardware-2 (ROACH2) board, then computes cross-correlations using Graphical Processing Units (GPUs). The design has been tested both in laboratory and in actual observation.
