Leading-edge AI-selected investment and trading portfolios deliver greater returns.

SapienTrade Hybrid Investment & Trading AI generates buy-sell signals and equally importantly a ranking within each class. AI portfolios built on top ranked stocks within each signal class yield much greater return than the sector and benchmark indices

AI-selected buy-sell signals

Specialized hybrid supervised and unsupervised AI models, specifically configured for each sector and industry, generate these outputs for each stock across multiple trading and investment horizons, such as daily, monthly, quarterly, biannual, and annual:
- AI Score from 0 to 9 indicating the health and relative value of a stock based on its fundamental and financial data.
- AI Probabilistic price forecasts providing target price ranges from 1 to 99% confidence levels.
- AI (Strong) buy–sell & neutral investment or trading signals.
- AI proprietary investment ranking of stocks based on the signals in (c) for specific holding horizons (e.g., monthly to yearly). Strong-buy and buy stocks are ranked in descending order of expected return, while strong-sell stocks are ranked in ascending order. These rankings determine the top 10, 20, 30, etc., pool of stocks used in AI portfolio construction.
We do not publish the future predictions of AI proprietary investment ranking (d) as part of our AI Investment and AI Trading solutions. These can be licensed separately to qualified partners. However, we publish the historic predictions of (d) to enable users to create AI-predicted portfolios and backtest their performance.
We publish future predictions of (a), (b) and (c) as part of our AI Investment and AI Trading solutions. Users can use these indicators together with other inputs to create their own investment or trading models.

AI-generated investment or trading portfolios

All investment or trading portfolios created in our platform are built on AI-generated buy-sell signals to ensure superior performance. The AI Portfolio toolbox provides different methods to create such portfolios.

AI-built Portfolios.
- To build AI-investment portfolios our solution offers a range of portfolio rules described below. These however, exclusively select stocks from the pool of top N (10, 20, 30, 40, 50) AI-ranked stock signals, be it buy or strong-buy top ranked stocks for long portfolios or strong-sell and sell stocks for short portfolios.
- For example, monthly long AI investment portfolios are constructed each month by selecting from the top N (10, 20, 30, 40, 50) ranked strong-buy and buy stocks. Portfolio positions are refreshed monthly by replacing the existing holdings with newly top-ranked stocks, thereby replicating a synthetic, AI-driven monthly investment strategy based on leading AI-selected stocks.
- The pool of stocks can be from a basket of sectors, diversifying the portfolio, or from a specific market-cap.
Portfolio Parameters. The basic portfolio parameters consists of the listing the stock sectors from which securities are selected, the percentage or ratio of capital allocated to each sector, and the minimum and maximum number of stocks per sector, as well as additional take-profit and stop-loss levels.
Portfolio Allocation Method. A method is required to select the stocks and their weight allocation for each sector in accordance with the above Portfolio Parameters, from the pool of AI-ranked stocks per sector. The following options are available in the portfolio builder toolbox:
- Uniform Equal-weight AI Portfolio allocation by sectors consists of dividing the total capital across sectors according to specified allocation ratios and then distributing each sector's capital equally among the top N (10, 20, 30, 40, or 50) AI-ranked strong-buy and buy stocks within that sector for long portfolios.
- Various portfolio optimization methods are available for stock allocation. Read More
Uniform Equal-weight AI Portfolio allocation for a specific sector, as described in (a), can be created using the portfolio toolbox by allocating 100% of capital to a single sector and selecting the top 10, 20, 30, etc AI-ranked stocks within that sector. The performance of these AI-selected portfolios is analyzed and presented in the performance pages. Read More

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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: