Simba's backtesting spreadsheet

 describes a spreadsheet originally developed by forum member Simba for the purpose of acting as a reference for historical returns, and analyzing a portfolio based on such historical data. The spreadsheet is no longer maintained by Simba, but other forum members continue to support it and to expand functionality, as a Bogleheads community project.

Backtesting
Backtesting refers to testing a model using historical data to predict future performance.

Backtesting seeks to estimate the performance of a strategy if it had been employed during a past period. This requires simulating past conditions with sufficient detail, making one limitation of backtesting the need for detailed historical data. A second limitation is the inability to model strategies that would affect historic prices.

Finally, backtesting, like other modeling, is limited by potential overfitting. That is, it is often possible to find a strategy that would have worked well in the past, but will not work well in the future.

Despite these limitations, backtesting provides valuable information not available when models and strategies are tested on synthetic data.

Simba's spreadsheet
The spreadsheet is discussed in this.

The latest version and download instructions are in [ this post], which links to Google Drive. To download:
 * Hover your mouse near the top of the page and click on the Arrow-Download-4-icon.png icon to download the file.
 * Ignore the "Whoops! There was a problem loading more pages." message if it appears.

Detailed instructions and revision history are in the "README" tab. Here is a brief overview of the individual worksheets:


 * Analyze_Portfolio is a simple way to change the allocations of various funds for a single portfolio. It provides the CAGR (Compound Annual Growth Rate), Sharpe ratio, Sortino ratio, etc. for a given time period. It also compares your portfolio to a benchmark portfolio like the 60/40 classic allocation and draws charts to compare growth, drawdowns, etc.
 * Compare_Portfolios allows to compare two sets of 5 different portfolios for a given time period (e.g. 1970+ or 1985+) with the ability to change the starting and ending years.
 * Lazy_Portfolios compares more than 20 lazy portfolios and shows corresponding charts and statistics.
 * Data_TR_USD includes the returns of ALL the funds being tracked, as well as some historical data series (e.g. from Prof. Shiller)
 * Data_Sources documents the sources used for the various historical returns.
 * Data_Misc provides some additional data series, including long-lived funds, Canadian funds, etc.

The spreadsheet also includes statistics and charts for a portfolio that is rebalanced annually (default) and one that is not rebalanced (un-rebalanced).

Historical Returns
The spreadsheet provides an extensive set of historical returns for various types of index (and non-index) funds. Most funds are from Vanguard, and all corresponding historical returns have been validated with Vanguard. All returns are expressed as total returns, i.e. including dividends. The perspective of a U.S. investor is assumed, with returns expressed in USD.

When actual fund returns are not available (e.g. early years), attempts were made to provide credible numbers to extend the fund's history, using returns from corresponding indices, and in some cases, using a synthetic model.

In general, fairly good quality data is available starting in 1927 for U.S. stocks and bonds. Most International returns are available starting in 1970. Most sector returns aren't available before 1985.

The Data_TR_USD worksheet is the main repository for such historical data, and can be easily copied in another spreadsheet, allowing to perform other types of backtesting analysis than the fairly basic tools provided by the Simba spreadsheet.

Compatibility
The spreadsheet is maintained using Microsoft Excel 2011, and provided in XLSX format. It should work fairly well with recent versions of LibreOffice, with some minor limitations.

There is some level of compatibility with Google Sheets, the calculations and statistics appear to work well, while the charts do not.