Luis MunozRivero - Corporate Insider
Generate Optimal Portfolios
The classical approach to portfolio optimization is known as Modern Portfolio Theory (MPT). It involves categorizing the investment universe based on risk (standard deviation) and return, and then choosing the mix of investments that achieves the desired risk-versus-return tradeoff. Portfolio optimization can also be thought of as a risk-management strategy as every type of equity has a distinct return and risk characteristics as well as different systemic risks, which describes how they respond to the market at large. Macroaxis enables investors to optimize portfolios that have a mix of equities (such as stocks, funds, or ETFs) and cryptocurrencies (such as Bitcoin, Ethereum or Monero)
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 Price Exposure Probability module to analyze equity upside and downside potential for a given time horizon across multiple markets.
Other Complementary Tools
Crypto Correlations Use cryptocurrency correlation module to diversify your cryptocurrency portfolio across multiple coins | |
Portfolio Optimization Compute new portfolio that will generate highest expected return given your specified tolerance for risk | |
Aroon Oscillator Analyze current equity momentum using Aroon Oscillator and other momentum ratios | |
Transaction History View history of all your transactions and understand their impact on performance | |
Technical Analysis Check basic technical indicators and analysis based on most latest market data | |
Financial Widgets Easily integrated Macroaxis content with over 30 different plug-and-play financial widgets | |
Bond Analysis Evaluate and analyze corporate bonds as a potential investment for your portfolios. | |
Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm |