This database offers key risk and performance metrics such as Sharpe ratio, value-at-risk, ETL, and many others for more than 8,000 financial instruments, including stocks, stock indices, and ETFs traded on major US exchanges. It is updated daily with detailed history going back to 2012.
PortfolioEffect is a cloud service powered by Snowfall Systems, a New York based company that brings innovative data solutions to quantitative hedge funds, algorithmic trading firms and independent portfolio managers. The company's key objective is to make traditional price metrics more relevant intraday by pushing the data frequency boundary.
To access individual datasets in this database programmatically via our API or libraries, you will need to know the Quandl code for the dataset(s) you're interested in.
The Quandl codes for all datasets in the PM database follow the format
symbol is the symbol of the instrument you're interested in (stock, index, ETF)
metric can be any one of the metrics listed in the table below.
Each dataset has 3 columns that represent different rolling window lengths used to compute the metrics:
|| 1 day window
|| 1 week (5 trading days) window
|| 1 month (22 trading days) window
The longer windows use more price observations and produce a more long-term version of the metric. The shorter windows rely on fewer observations, but reflect the latest market dynamics faster.
The list of all available symbols and corresponding company names and exchanges can be found here.
The full database is searchable from the Data tab on this page.
For a dynamic list of all datasets in this database, click on the Metadata tab.
All datasets in this database can be accessed using our free API and our libraries for R, Python, Excel, and more. Click on the API tab to learn more.
The metrics are computed based on a time series of high frequency logarithmic returns of an instrument in a rolling window of N days (see the Column Headers section). All metrics are then adjusted to a 1 day time scale.
PortfolioEffect service features a next-generation “smart” model pipeline for high frequency data. Returns are processed with a series of auto-calibrating models for market microstructure noise, price jumps and outliers, fat distribution tails, long memory (fractality) and intraday risk factors.
Using high frequency data dramatically improves precision of statistical estimates due to the so-called bias-variance trade-off. HF data provides many more recent/fresh data points, thus decreasing the variance of estimates, without using stale data points that would increase the estimation bias.
To learn more about PortfolioEffect methodology, please visit https://www.portfolioeffect.com.
Please see this white paper for an example of how the PortfolioEffect data can be used to create a trading strategy.
Subscribers can download the entire database at any time at:
https://www.quandl.com/api/v3/databases/PM/data?api_key=<YOUR API KEY>
The above request will re-direct you to a temporary URL referencing a ZIP file. Download the zip file which will contain a CSV representation of the entire database.
Bulk download is ideal for maintaining a local version of this database or for screening and/or filtering requirements.
For a download of only the latest data, append
https://www.quandl.com/api/v3/databases/PM/data?api_key=<YOUR API KEY>&download_type=partial
The format of the csv is slightly different from Quandl's csv API. It has an added first column for the Quandl code of that row's data.
Please note that the bulk download file is a large zip file. To unzip it, you need an unzipper that supports the zip64 format, e.g. 7zip or unzip version 6.0 or higher.
API AND LIBRARIES
The full PE database is accessible via the Quandl API. The database is also available via Quandl's free libraries for R, Python, Matlab, Excel and other tools.
For complete API documentation, see quandl.com/docs/api API examples specific to this database follow.
To get Apple's Sharpe ratio for the past 10 days in JSON:
|This truncates the result to include only the first ten rows (days) of data|
|This ensures the result includes most recent days|
API and Library Helpers
To quickly generate API calls or library calls, you can visit any PE data page (PM/AAPL_SHARPE_RATIO for example). On the right side of the screen are buttons that help you build API calls based on what you are looking at on the screen.
For more information:
- General Quandl API Documentation
- Quandl Libraries
Premium support is available for this database: firstname.lastname@example.org