Search

Portfolio optimization is a control problem whose objective is to find the optimal strategy for the process of selecting the proportions of assets that can provide the maximum return. Conventional approaches formulate the problem as a single Markov decision process and apply reinforcement learning methods to provide solutions. However, it is well known that financial markets involve non-stationary processes, leading to violations of this assumption in these methods. In this work, we reformulate the portfolio optimization problem to deal with the non-stationary nature of financial markets. In our approach, we divide a long-term process into multiple short-term processes to adapt to context changes and consider the portfolio optimization problem as a multitask control problem. Thereafter, we propose an evolutionary meta reinforcement learning approach to search for an initial policy that can quickly adapt to the upcoming target tasks. We model the policies as convolutional networks that can score the match of the patterns in market data charts. Finally, we test our approach using real-world cryptographic currency data and show that it adapts well to the changes in the market and leads to better profitability.

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential., 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany

Chart pattern analysis uses knowledge extracted from graphical information of price movements. There are two representative types of problems in chart pattern analysis: the matching problem and the search problem. There have been extensive studies on chart pattern matching. However, chart pattern search has not yet drawn much interest. Instead of automatic search, most studies use chart patterns manually designed by financial experts. In this paper, we suggest an automatic algorithm that searches a rule-based chart pattern. We formulate rule-based chart pattern search as an optimization problem for a genetic algorithm. The suggested genetic algorithm includes a considerable amount of problem-specific manipulation. The algorithm successfully found attractive patterns working on the Korean stock market. We studied the rules used in the found patterns, noting that they are rising-support patterns. In addition, the automated pattern generation uses designs at a higher level of abstraction.