End-to-end data collection

This research data is utilized in the article 'An End-to-End Direct Reinforcement Learning Approach for Multi-factor Based Portfolio Management.' It consists of stock market data from China and the United States, and includes macroeconomic data, financial indicators of the index, and trading data of the index. The article integrates multi-factor models with direct reinforcement learning to formulate an end-to-end portfolio management algorithm, which diverges from traditional prediction-then-optimize approaches. We employ this algorithm for empirical testing in both the Chinese and U.S. stock markets. The dataset encompasses the original data of asset prices and selected factors selected in the article.