Portfolio of automated trading systems: complexity and learning set size issues

Multitrend Conditional Value at Risk for Portfolio Optimization

Trend representation has been attracting more and more attention recently in portfolio optimization (PO) via machine learning methods. It adopts concepts and phenomena from the field of empirical and behavioral finance when little prior knowledge is obtained or strict statistical assumptions cannot be guaranteed. It is used mostly in estimating the expected asset returns, but hardly in measuring risk. To fill this gap, we propose a novel multitrend conditional value at risk (MT-CVaR), which embeds multiple trends and their influences in CVaR. Besides, we propose a novel PO model with this MT-CVaR as the risk metric and then design a solving algorithm based on the interior point method to compute the portfolio. Extensive experiments on six benchmark datasets from diverse financial markets with different frequencies show that MT-CVaR achieves the state-of-the-art investing performance and risk management.

Immunology-Based Sustainable Portfolio Management

Immunological principles can be used to build a sustainable investment portfolio. The theory of immunology states that information about recognized pathogens is stored in the memory of the immune system. Information about previous illnesses can be helpful when the pathogen re-enters the body. Real-time analysis of 11 automated financial trading datasets confirmed this phenomenon in financial time series. Therefore, in order to increase the sustainability of the portfolio, we propose to train the portfolio with the most similar segments of historical data. The segment size and offset may vary depending on the data set and time.

A Peak Price Tracking-Based Learning System for Portfolio Selection

We propose a novel linear learning system based on the peak price tracking (PPT) strategy for portfolio selection (PS). Recently, the topic of tracking control attracts intensive attention and some novel models are proposed based on backstepping methods, such that the system output tracks a desired trajectory. The proposed system has a similar evolution with a transform function that aggressively tracks the increasing power of different assets. As a result, the better performing assets will receive more investment. The proposed PPT objective can be formulated as a fast backpropagation algorithm, which is suitable for large-scale and time-limited applications, such as high-frequency trading. Extensive experiments on several benchmark data sets from diverse real financial markets show that PPT outperforms other state-of-the-art systems in computational time, cumulative wealth, and risk-adjusted metrics. It suggests that PPT is effective and even more robust than some defensive systems in PS.