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.
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Use Investing Themes to Complement your positions

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.

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Run Corona Opportunity Thematic Idea Now

Corona Opportunity
Corona Opportunity Theme
Firms that are involved in some capacity in making products or providing services to the public to buttle the virus directly or indirectly. This also includes some defencive and contrarian stocks and ETFs that are managing to avoid the big market correction coused by coronavirus. The Corona Opportunity theme has 46 constituents at this time.
You can either use a buy-and-hold strategy to lock in the entire theme or actively trade it to take advantage of the short-term price volatility of individual constituents. Macroaxis can help you discover thousands of investment opportunities in different asset classes. In addition, you can partner with us for reliable portfolio optimization as you plan to utilize Corona Opportunity Theme or any other thematic opportunities.
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.

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