Stock Trend Prediction

(1) provide financial news for each specific stock. (2) provide various stock technical factors and fundamental factors for each stock.

The EDT dataset is designed for corporate event detection and text-based stock prediction (trading strategy) benchmark. 1. Corporate Event Detection It includes 9721​ news articles with token-level event labels. Including 11 event types: Acquisitions, Clinical Trials, Guidance Changes, New Contracts, Stock Repurchases, Stock Split, Reverse Stock Split/ADS Ratio Change, Regular Dividend, Special Dividend, Dividend Cut, Dividend Increase 2. Text-Based Stock Prediction Benchmark It includes 303893​ first-hand news articles from high-quality sources. Each news article is assigned a minute-level timestamp and comprehensive stock price labels. Please see this Github Link and paper for more details.

A daily emerging stock market dataset (Chinese CSI 300 dataset) including 300 stocks and 5,088 time steps from the CSMAR database. We construct our stock dataset using a pool of stocks from the CSI 300 index for the last 21 years, from 01/02/2000 to 12/31/2020. Instead of all stocks in the market, we select the stocks that used to belong to the major market index CSI 300, and filter out stocks that have missing price data over the period. For each trading day, we use the fundamental price features as the features of stocks, including open price, close price, and volume. Additionally, we normalize price features such as open price and close price with logarithm. The dataset randomly splits stocks into five non-overlapping sub-datasets. For each subset, the first 90% of trading days are used as train data, the following 5% as validation data, and the rest 5% as test data.

TRADES-LOB comprises simulated TRADES market data for Tesla and Intel, for 29/01 and 30/01. Specifically, the dataset is structured into four CSV files, each containing 50 columns. The initial six columns delineate the order features, followed by 40 columns that represent a snapshot of the LOB across the top 10 levels. The concluding four columns provide key financial metrics: mid-price, spread, order volume imbalance, and Volume-Weighted Average Price (VWAP), which can be useful for downstream financial tasks, such as stock price prediction. In total, the dataset is composed of 265,986 rows and 13,299,300 cells, which is similar in size to the benchmark FI-2010 dataset.