Explore greater return performance delivered by cutting-edge AI-driven portfolio selection and investment.

In-depth performance analysis of AI-driven stock scores and investment or trading buy-sell signals are conducted through statistical validation methods as well as by benchmarking the returns of AI investment portfolios constructed from the AI-generated buy-sell signals against S&P 500, 400, 600, and Russell 200, 1000 and 2000 indices. The AI-selected portfolios are created for individual sectors, and by market caps across all sectors.

Return performance analysis of AI-selected portfolios by sector

AI investment long and short-portfolios are constructed for each sector using AI-assigned buy–sell signals within the sector. Portfolio return performance is measured against the volume-weighted average return of each sector, as well as key market benchmarks, including the S&P 500 as the large-cap index, S&P 400 for mid-cap, and Russell 2000 for small-cap stocks.

Performance analysis of AI-selected portfolio per market caps

AI investment long and short-portfolios are built using AI-assigned buy-sell signals across small, medium, large and mega cap stocks. Portfolio return performance is measured against market benchmarks including the S&P 500 and Russell 200 as mega-cap index, S&P 500 and Russell 1000 for large-cap, S&P 400 and Russell mid-cap, and S&P 600 and Russell 2000 for small-cap stocks.

Portfolio backtesting & performance analysis toolbox

To empower investors to build and thoroughly analyse portfolios that best match their desired risk-return trade-off, we provide a comprehensive suite of portfolio performance metrics within a robust backtesting toolbox to test any optimal AI-generated portfolios by our platform or user-uploaded portfolio.

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CFTC Rule 4.41 – simulated, or hypothetical performance results have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because such trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight.

No representation is being made that any account will or is likely to achieve profits or losses similar to those shown in any simulation. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk of actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses is a material point which can also adversely affect trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results, and all of these can adversely affect actual trading results. By using our site, you acknowledge you have accepted our: