This article aims to develop a quantitative trading strategy that maximizes profits while finding the best balance of risk and return. We built a high-frequency trading strategy model to maximize profits. We first used the Apriori algorithm to find frequency item sets in historical data before fitting the best daily dynamic position adjustment functions for gold and bitcoin using mathematical statistics and other methods based on price movements. Then we can trade to increase and decrease positions in gold and bitcoin based on the positions suggested by the dynamic position adjustment function. We also simulated three investors with different risk preferences trading using this high-frequency trading model for up to five years and obtained return of 266.05 %, 152.51 %, and 33.29 %, respectively.
Synthetic aperture radar (SAR) images are often affected by speckle noise, which can hinder accurate interpretation and subsequent use of the images in applications such as target detection and segmentation. To address this issue, we propose a denoising algorithm based on a multi-scale attention cascade convolutional neural network (MSAC-Net). Our algorithm employs multi-scale asymmetric convolution to extract image features and an attention mechanism to integrate these features. Additionally, we designed a multi-layer deep cascade convolutional network to enhance the generalization ability of the model features. Experimental results show that our proposed MSAD-Net model significantly outperforms state-of-the-art SAR image denoising algorithms. Specifically, it achieves a significant improvement in peak signal-to-noise ratio (PSNR), with an increase of about 0.81~13.97dB, and structural similarity index (SSIM), with an increase of about 0.01~0.14. Overall, our study presents a novel denoising algorithm for SAR images that greatly improves the accuracy of subsequent image applications.
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