ASSET ALLOCATION OPTIMISATION
Building a portfolio that protects and multiplies invested capital is a creative process that ends up with a piece of financial art. Today, quantitative techniques are a necessary part of every stage of portfolio management: from assets selection and allocation size to trade timing for each of the chosen assets.

For our customer, a hedge fund, we have developed a quantitative "upgrade" over already existing manual strategy that allowed optimisation for custom goals and risks, which helped us to reduce maximal drawdown by more than 5% alongside growing the Sharpe ratio from 1.5 to 1.85.

Python / Mathematical Modelling / Numerical Optimization / Machine learning
PORTFOLIOS BEYOND MARKOWITZ
THE CHALLENGE
Classical mathematical routines as Markowitz portfolio or Black-Litterman portfolio are must-have instruments for a portfolio manager who relies on quantitative analysis. However, these tools are not flexible enough when it comes to custom target selection, accounting for risks or constraints that are hard to optimize for with well-known tools. They aim to maximise fixed target tied to a fixed risk source (as Sharpe ratio) with fixed market beliefs (assets covariance and some predictive factors). And all that an investor can do - just click the "optimise" button and analyse the backtest without having any real possibility to influence the observed drawbacks.
THE SOLUTION
THE RESULT
We offered to our client a more generalised view on the portfolio optimisation and asset allocation routine. We gathered the requirements about desired goals: Sharpe, Sortino, Calmar ratios, associated risks: Maximal Drawdown, CVaR, and necessary constraints: market neutrality, L2 regularisation on the weights, etc. Also, we have added an option to rebalance the portfolio instead of finding one single set of weights for the allocation of the capital on the assets. Then, we have developed an optimisation model based on the evolutionary algorithms that could find the optimal rebalance scenario and associated allocation weight for each of the assets. This procedure can be repeated with different options for obtaining another optimal portfolio.
There are two direct outcomes of our work that has boosted our clients' business:

  • From the strategy performance point of view, we could reduce maximal drawdown from 15% to 9% which directly increased the Sharpe ratio from 1.5 to 1.85;
  • Also, we have added flexibility to the quantitative analysis routine and gave the portfolio managers new tool for daily routines and market research.
REQUEST
AI AT WORK
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