An Electronic Market-Maker

This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from real-time market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building market-making algorithms.

Room-temperature continuous-wave operation of GaInNAs/GaAs quantum dot laser with GaAsN barrier grown by solid source molecular beam epitaxy

We present the results of GaInNAs/GaAs quantum dot structures with GaAsN barrier layers grown by solid source molecular beam epitaxy. Extension of the emission wavelength of GaInNAs quantum dots by ~170nm was observed in samples with GaAsN barriers in place of GaAs. However, optimization of the GaAsN barrier layer thickness is necessary to avoid degradation in luminescence intensity and structural property of the GaInNAs dots. Lasers with GaInNAs quantum dots as active layer were fabricated and room-temperature continuous-wave lasing was observed for the first time. Lasing occurs via the ground state at ~1.2μm, with threshold current density of 2.1kA/cm[superscript 2] and maximum output power of 16mW. These results are significantly better than previously reported values for this quantum-dot system.