Compare Return On Assets Across Equities
You can use any or all of fundamental ratio historical patterns as a complementary method for asset selection as well as a tool for deciding entry and exit points. Many technical investors use fundamentals to limit their universe of possible positions. Check out your portfolio center.
Explore Investing Opportunities
You can quickly originate your optimal portfoio using our predefined set of ideas and optimize them against your very unique investing style. A single investing idea is a collection of funds, stocks, ETFs, or cryptocurrencies that are programmatically selected from a pull of investment themes. After you determine your investment opportunity, you can then find an optimal portfolio that will maximize potential returns on the chosen idea or minimize its exposure to market volatility.Did you try this?
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AI Portfolio ArchitectUse AI to generate optimal portfolios and find profitable investment opportunities |
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Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the AI Portfolio Architect module to use AI to generate optimal portfolios and find profitable investment opportunities.
Other Complementary Tools
| Positions Ratings Determine portfolio positions ratings based on digital equity recommendations. Macroaxis instant position ratings are based on combination of fundamental analysis and risk-adjusted market performance | |
| Portfolio Optimization Compute new portfolio that will generate highest expected return given your specified tolerance for risk | |
| Idea Breakdown Analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes | |
| Odds Of Bankruptcy Get analysis of equity chance of financial distress in the next 2 years | |
| Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm |