Deep Reinforcement Learning for Quantitative Trading Challenges and Opportunities 量化交易的深度强化学习:挑战与机遇---IEEE 背景 量化交易:量化交易是指借助现代统计学和数学的方法,利用计算机技术来进行交易的证券投资方式。 量化交易从庞大的历史数据中海选能带来超额收益的多种“大概率”事件以制定策略,用数量模型验...
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...
Paper: Deep Reinforcement Learning for Tradinghttps://arxiv.org/pdf/1911.10107.pdf Markov Decision Process Formalization Expected discounted cumulative rewards: State Space- a list of features: Acti…
不一定,Deep Learning并非万能,有其适用场景。目前, 人工精心设计的features + 传统Machine Learning在trading上性能好于Deep Learning Deep Learning是Neural Network的分支,那Neural Network是最强的Machine Learning模型吗?
Deep Learning for Finance 作者:Sofien Kaabar 出版社:O'Reilly Media, Inc. 副标题:Creating Machine & Deep Learning Models for Trading in Python 出版年:2024-1 页数:350 装帧:Paperback ISBN:9781098148393 豆瓣评分 目前无人评价 评价: 写笔记
深Q-Learning代理人接受训练,以使累积的总奖励最大化。对当前的DeepQ-Trading模型进行了改进,采用了Q函数逼近的Deep-Sense体系结构。 Dataset 每分钟比特币系列是通过修改本存储库中提到的程序获得的。对Coinbase交易所的交易进行抽样,以生成比特币价格序列。请参考assets/dataset下载数据集。 Preprocessing 基本预处理...
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 ...
QUANT[22]论文2:Deep Direct Reinforcement Learning for Financial Signal Representation and Trading,程序员大本营,技术文章内容聚合第一站。
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and...
Deep Learning: The Future of TradingMansi Singhal