Shanghai/Shenzhen stock exchanges, close to 800 tickers, All sectors
product_id kscore_cn · 700+ stocks · daily refresh · since 2013-01-04 · 5 data columns · API/CSV
curl "https://www.kavout.com/api?product_id=kscore_cn&api_key=YourAPIKey"
symbol / trade_date / field to filter · docs →GET · JSON · CSVTo test the performance of the K Score, we constructed two portfolios – Top Picks and Bottom Picks. The Top Picks portfolio is comprised of stocks on the CSI 800 index that are assigned with a K Score of 9, while the Bottom Picks portfolio have stocks in K Score of 1.
K Score is a predictive equity rating powered by machine learning (ML) technologies. Kavout takes in vast and diverse datasets including fundamentals, pricing, technical indicators, and alternative data, and then uses a combination of statistical analysis and ML techniques with ranking algorithms to deliver an actionable equity rating score with values ranging from 1-to-9.
A higher K Score (7-9) assigned to a stock indicates a higher probability of outperformance, whereas a lower K Score (1-3) indicates a lower probability of outperformance in the next month.
The China A-share stock market has two exchanges – Shanghai stock exchange (SSE) and Shenzhen stock exchange (SZSE). The SSE and the SZSE are the world’s 5th largest and 8th largest stock market by market capitalization respectively.
The most important Indices in China A-share are CSI 300, CSI 500 and CSI 800.
CSI 300 Index consists of the 300 largest and most liquid A-share stocks, similar to the largest 500 stocks by market cap in the US.
CSI 500 Index consists of the largest remaining 500 A-Share stocks after excluding the CSI 300 Index, similar to the largest 2,000 US stocks by market cap. CSI 500 Index reflects the overall performance of small-mid cap A-shares.
CSI 800 Index consists of all the constituents of the CSI 300 Index and CSI 500 Index, similar to the largest 3,000 US stocks by market cap.
K Score covers component stocks on the most important indices in China A-share, namely CSI 300, CSI 500 and CSI 800.
On average close to 800 tickers are rated and ranked on all the trading days across all major industry sectors. Investors can look at the historic trends of a company, and evaluate the strength of a company relative to their sector.
Below are the top 10 sector coverage for the China A-share market.
| Sector | Count | Type | Start date |
|---|---|---|---|
| Medical Biotechnology | 75 | Equities | 2013-01-04 |
| Electronic Technology | 52 | Equities | 2013-01-04 |
| Chemical Industry | 47 | Equities | 2013-01-04 |
| Non Bank Financial | 47 | Equities | 2013-01-04 |
| Real Estate | 46 | Equities | 2013-01-04 |
| Media | 43 | Equities | 2013-01-04 |
| Transportation | 40 | Equities | 2013-01-04 |
| Computer | 38 | Equities | 2013-01-04 |
| Nonferrous Metals | 38 | Equities | 2013-01-04 |
| Electrical Equipment | 34 | Equities | 2013-01-04 |
K Scores are calculated daily for thousands of stocks around the globe.
Data is updated on trading days, and available 3-5 hours before the market opens on the next day.
This data is available via API and CSV download.
Harnessing years of historical data and a variety of data sources, Kavout takes in structured and unstructured data including fundamental information, pricing and trading volumes, technical signals, and alternative data, to deliver an actionable and easy-to-use equity rating score from 1 to 9.
The development of K Scores apply the best-of-breed learning algorithms, and encompass methods such as regression, classification, model selection, deep learning and more to produce a rating to rank stocks. Our state-of-the-art machine learning (ML) models identify the intrinsic relationships, uncover the dependency structure and calculates predictive analytics for future performance.
Pairing fundamental research with quantitative analysis, our ML model evaluates over 200 factors and signals. Some factor groups include but are not limited to
On any given day, Kavout processes millions of diverse data sets, and runs models encompassing many traditional and advanced financial engineering methods such as regression, classification, deep learning, and reinforcement learning to produce a predictive rating to rank stocks.