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Alpha Quantum Portfolio Optimiser is a state of the art portfolio optimization software solution, used in mutual funds, wealth managers, insurance companies, pension funds. Our Portfolio Optimiser can also serve as a portfolio optimization solution for robo advisors.

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Applications of software

  • asset allocation

  • funds of funds allocation

  • management of stock portfolios

  • fixed income portfolios

  • sophisticated quantitative strategies for hedge funds

  • ALM for insurance companies (optimal for new Solvency II framework)

  • investment advisory tailored to individual risk profile of investors (for wealth managers, RIA and private investors)

  • solutions tailored for CTAs (trend following and others)

  • sophisticated quantitative investment products (constant volatility, momentum, smart beta and many others)

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    Multiperiod framework

    Powerful multiperiod portfolio optimization framework for backtesting and research of strategies

    Advanced risk metrics

    Various risk metrics: mean variance, mean cvar and other

    Deep Learning Quant Strategies

    Use of deep learning neural nets in backtesting of strategies

    Versatile reporting

    Powerful reporting and document generation capabilities

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    MEAN CVAR, MEAN VARIANCE optimization

    Mean variance, mean cvar, mean cdar optimization in our portfolio optimization tool

    Different formulations: target risk, target return, risk aversion formulation

    Support for many different constraints: weights of securities, asset classes, tracking error, etc.

    Efficient Frontiers for mean variance, mean cvar optimizations


    Powerful multiperiod portfolio optimization framework for backtesting and research of strategies

    Detailed statistics for backtesting results: NAV, drawdown, dynamic structure curves, many different performance and risk quantities (sharpe, sortino, etc.)

    Detailed return attribution for multiperiod investment strategies

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    Backtesting for a wide multidimensional grid of investment strategy parameters. Jobs can be defined and saved in bulk for processing in multithread environment

    Optimal weights of all active portfolios can be calculated with batch requests on intraday, daily basis or other periodic intervals

    Continuously optimized portfolios can be easily exported via API to broker solutions


    Use of deep learning for automating search for optimal parameters in backtesting results with multidimensional manifolds

    Our AI solutions and tehnologies (face detection, voice recognition, etc.) enable us to build digital assistants that can be specialised also also to asset management industry

    AiDA is our specialised digital asistant for portfolio optimization

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    Alpha Quantum Portfolio Optimiser has powerful reporting capabilities. Reports can be generated in various formats, including PDF, Excel, Word, XML and many others. Examples of reports for portfolio optimization, multiperiod backtesting report and report on active portfolios and strategies, respectively:

    Portfolio Optimization

    Portfolio optimization is a methodology that seeks to identify portfolios with the highest expected return for a given risk or portfolios with the lowest risk for a given target return. In determining optimized portfolios, additional constraints on assets may be taken into account, such as minimum and maximum weightings for individual securities, investment and other classes.

    In calculating optimal portfolios, the software may select analysts' expectations, historical averages or the results of more complex models of expected returns on stocks or bonds for the expected returns on securities.

    Since 1952 and Harry Markowitz's Nobel Prize-winning paper, portfolio optimization has been based on the so-called Mean Variance methodology, which assumes the standard deviation of returns as a measure of risk.

    Over the last ten years, however, classical portfolio optimization has gradually been replaced by the more advanced Mean CVaR methodology, where the risk measure is CVaR. Morningstar has described the Mean CVaR methodology as the future of portfolio optimization. Unlike standard deviation, CVaR takes into account higher moments of the return distribution and thus describes portfolio risks much better, especially during periods of extreme market events.

    Mean CVaR methodology

    In the Mean CVaR methodology, the measure of portfolio risk is the CVaR or Conditional Value at Risk, which is the weighted average sum of losses that equal or exceed the VaR at a given confidence level. While VaR only tells us the minimum loss we will suffer at a given confidence level, CVaR is more telling as it gives us an indication of how much we will lose on average when extreme, so-called tail events occur.

    The sensitivity of the Mean CVaR method to extreme events in the markets makes the software solution suitable for asset management in any situation in the capital markets.


    Portfolio optimization can be carried out under three formulations, determining the portfolio that has (1) the maximum return for a given risk, (2) the minimum risk for a given target return, or (3) the highest risk-adjusted return among all portfolios.

    The first formulation is, for example, appropriate for insurance companies, and will be particularly so after the introduction of Solvency II, which will define an insurance company's capital as VaR. In mutual fund asset management, it can be used to determine the optimal composition of funds that maximise return for the same risk as the benchmark fund.

    The search for a portfolio with minimum risk for a given return is, among other things, suitable for pension companies that need to achieve a minimum return for their policyholders. The risk-adjusted return formula allows for optimal investment advice to clients while taking into account their individual risk appetite.


    Alpha Quantum Portfolio Optimiser allows for a variety of constraints to be considered, including the setting of weight limits for individual securities, investment, liquidity, credit and other classes, and more complex constraints such as the inclusion of a target value for the tracking error of the portfolio. The scheme allows for all the constraints of internal investment policies, governance rules, fund prospectuses to be respected and is in line with the UCITS Directives.

    Expected returns

    When calculating optimal portfolios in our software, you can either choose your own analysts' expectations for expected security returns or assume historical averages, where you can choose between arithmetic and exponentially weighted averages. More sophisticated models can also be used, including the ARIMA model and vector autoregression models. In addition to the above options, the software includes the calculation of equity and debt returns based on complex models that also incorporate corporate financial statement data.

    Dynamic asset allocation

    Asset allocation plays the most important role in the asset management process, explaining almost 94% of the variability in returns over time between different pension funds, according to the well-known study by Brinson, Hood and Beebow (1986). Strategic allocation is the most common form of allocation, but its weakness is that it ignores changes in correlations and transitions between different return regimes. The use of a dynamic allocation that adapts to market conditions, together with a Mean CVaR methodology that is sensitive to extreme events, helps to reduce losses during periods of major market turbulence and allows higher returns to be achieved while better controlling risks.

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    Do you need consultation, are interested in purchasing one of our products or have a project in mind? We would love to hear from you!

    Get in Touch

    • Contact
        Alpha Quantum
        80801 Munich
        Phone: +49 (0)89 2153 68 219
        E-mail: [email protected]
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