Compare Fundamental Data 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 Ideas
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?
Run Theme Ratings Now
Theme RatingsDetermine theme ratings based on digital equity recommendations. Macroaxis theme ratings are based on combination of fundamental analysis and risk-adjusted market performance |
| All Next | Launch Module |
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 USA ETFs module to find actively traded Exchange Traded Funds (ETF) in USA.
Other Complementary Tools
| Alpha Finder Use alpha and beta coefficients to find investment opportunities after accounting for the risk | |
| Portfolio Comparator Compare the composition, asset allocations and performance of any two portfolios in your account | |
| Stock Screener Find equities using a custom stock filter or screen asymmetry in trading patterns, price, volume, or investment outlook. | |
| Stock Tickers Use high-impact, comprehensive, and customizable stock tickers that can be easily integrated to any websites | |
| Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm |