Portfolio optimization software has seen increased use in the recent decades. Portfolio optimization is based on modern portfolio theory which basically says that expected return for a given financial asset and its risk are related. The higher the risk of given asset, the higher should be expected return. William Sharpe got the Nobel Prize for its work on modern portfolio theory.

There are however other approaches on how to determine optimal portfolio composition or in specific case, asset allocation.

One is Black-Litterman approach. Black is another Nobel Prize winner, co-inventor of the Black-Scholes formula for valuation of options. Litterman was working in Goldman Sachs Asset Management and wrote several interesting papers about asset allocation.

You can read more about Black-Litterman approach here:

https://www.stat.berkeley.edu/~nolan/vigre/reports/Black-Litterman.pdf

Portfolio optimization software most commonly uses mean variance approach where the risk metric is the variance of returns.

However, there are other possible options for risk metric, one is conditional value at risk or CVaR. It is expected value of value-at-risk or VaR below given confidence threshold.

In this case, the corresponding portfolio optimization approach is known as mean cvar portfolio optimization. Portfolio optimization software that supports both mean variance and mean cvar methods is for example alpha quantum portfolio optimiser.

Here is a screenshot from their website, showing how the optimal portfolio weights change with target return:

Or another one showing interactive comparison of current and optimal portfolio:

You can do many sensitivity analysis on various parameters, example from software:

Backtesting is an important of building quant strategies. It involves testing how your strategy would perform in some past historical period. There are many ratios which allow you to evaluate the strategy:

- sharpe ratio
- sortino ratio
- Jensen’s alpha
- Treynor metric