Designing, Implementing and Testing an Automated Trading Strategy Based on Dynamic Bayesian Networks, the Limit Order Book Information, and the Random Entry Protocol

This paper evaluates, using the Random Entry Protocol technique, a high-frequency trading strategy based on a Dynamic Bayesian Network (DBN) that can identify predictive trend patterns in foreign exchange orden-driven markets. The proposed DNB allows simultaneously to represent expert knowledge of skilled traders in a model structure and to learn computationally from data information that reflects relevant market sentiment dynamics. The DBN is derived from a Hierarchical Hidden Markov Model (HHMM) that incorporates expert knowledge in its design and learns the trend patterns present in the market data. The wavelet representation is used to produce compact representations of the LOB liquidity dynamics that simultaneously reduces the time complexity of the computational learning and improves its precision. In previous works, this trading strategy has been shown to be competitive when compared with conventional techniques. However, these works failed to control for unwanted dependencies in the return series used for training and testing that may have skewed performance results to the positive side. This paper constructs key trading strategy estimators based on the Random Entry Protocol over the USD-COP data. This technique eliminates unwanted dependencies on returns and order flow while keeps the natural autocorrelation structure of the Limit Order Book (LOB). It is still concluded that the HHMM-based model results are competitive with a positive, statistically significant P/L and a well-understood risk profile. Buy-and-Hold results calculated over the testing period are provided for comparison reasons.