原文：

New York Stock Exchange

S&P 500 companies historical prices with fundamental data

Context

This dataset is a playground for fundamental and technical analysis. It is said that 30% of traffic on stocks is already generated by machines, can trading be fully automated? If not, there is still a lot to learn from historical data.

Content

Dataset consists of following files:

prices.csv: raw, as-is daily prices. Most of data spans from 2010 to the end 2016, for companies new on stock market date range is shorter. There have been approx. 140 stock splits in that time, this set doesn't account for that.
prices-split-adjusted.csv: same as prices, but there have been added adjustments for splits.
securities.csv: general description of each company with division on sectors
fundamentals.csv: metrics extracted from annual SEC 10K fillings (2012-2016), should be enough to derive most of popular fundamental indicators.

Acknowledgements

Prices were fetched from Yahoo Finance, fundamentals are from Nasdaq Financials, extended by some fields from EDGAR SEC databases.

Inspiration

Here is couple of things one could try out with this data:

One day ahead prediction: Rolling Linear Regression, ARIMA, Neural Networks, LSTM
Momentum/Mean-Reversion Strategies
Security clustering, portfolio construction/hedging

Which company has biggest chance of being bankrupt? Which one is undervalued (how prices behaved afterwards), what is Return on Investment?

译文：

纽约证券交易所

标普500指数成份股基本面历史价格

概述：

这个数据集是基础和技术分析的平台。据说，30%的股票流量已经由机器产生，交易可以完全自动化吗？如果不是这样的话，从历史数据中还是有很多东西要学的。

内容：

数据集由以下文件组成：

● prices.csv：原价，如每日价格。大部分数据都是从2010年到2016年底，对于刚上市的公司来说，数据范围较短。在这段时间里，大约有140只股票被拆分，这一组没有考虑到这一点。

● prices-split-adjusted.csv：与prices相同，但已添加了拆分调整。

● securities.csv：各部门各公司的一般说明

● fundamentals.csv：从美国证券交易委员会（SEC）年度10K指标（2012-2016年）中提取的指标应足以得出大多数流行的基本面指标。

价格来自雅虎金融，基本面来自纳斯达克金融，扩展了EDGAR SEC数据库中的一些字段。

以下是我们可以用这些数据尝试的几件事：

● 提前一天预测：滚动线性回归，ARIMA，神经网络，LSTM

● 动量/均值回归策略

● 证券集群、投资组合构建/对冲

大家可以到官网地址下载数据集，我自己也在百度网盘分享了一份。可关注本人公众号，回复“2021071601”获取下载链接。