deep learning with pytorch——4 大小、存储偏移量和跨步数 1.为了索引到存储中,张量依赖于一些信息,这些信息连同它们的存储一起,明确地定义了它们:大小、存储偏移量和跨距(图2.5)。size(或者shape,用NumPy术语来说)是一个元组,表示张量的每个维度上有多少个元素。存储偏移量是存储中对应于张量中第一个元素的...
Metabit Trading:量化研究的确是路漫漫其修远兮,那你觉得ML/DL未来在量化私募领域还有哪些值得探索的方向? J同学:目前我们公司在做的事情分以下几类: 我们希望把一些更前沿的Deep Learning模型和方法,应用在金融数据上。近年来,Deep Learning的突破很多,比如NLP领域的Transformer、图像领域的Diffusion Model、和图神经网...
Portfolio OptimizationDeep LearningMachine LearningETFsWe adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimise portfolio weights by updating model parameters. Instead...
Dynamic Portfolio optimization is the process of distribution and rebalancing of a fund into different financial assets such as stocks, cryptocurrencies, etc, in consecutive trading periods to maximize accumulated profits or minimize risks over a time horizon. This field saw huge developments in recent...
Presenting the Case for Deep Learning Trading One of the most challenging and exciting tasks in the financial industry is predicting whetherstock prices will go up or downin the future. Today, we are aware that deep learning algorithms are very good at solving complex tasks, so it is worth ...
Portfolio optimizationCan deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an ...
Deep Learning (488) Python Python
Therefore, it is necessary to incorporate the deep learning architecture into the portfolio optimization and return prediction for developing high efficient model. The major highlights of this paper is pointed out here: • To develop a novel portfolio optimization and return prediction model using ...
The motivation for deep learning techniques begins with a discussion on the broader field of machine learning. Machine learning can be considered as a mechanism for mapping representations of data to outcomes (Goodfellow et al, 2016).In quantitative trading this often manifests itself as hand...
side, the lower the trading price below the reference point, the better the opportunity for buyers. Here, in contrast, sellers are reluctantly to sell the stock because of risk seeking over losses and when there is more demand from buyers and less supply from sellers, trading price goes up....