An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used epsilon-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.

The pound plummeted in Asian trading early on Friday. And no-one really seems to know why. Did a trader make a mistake? Did the computers go haywire? And where will the pound go next?

What if five years from now robo-advisors are a thing of the past? That would seem counter-intuitive given the current trend and the enthusiasm for it, with technology accelerating as quickly as the many startups competing in FinTech and related verticals, and as the industry for robo-advisors is still in the very early stages of development. How then, could the future of automated financial advice be further transformed, taking on a different dimension that makes the current approach obsolete? By 2020, the market for machine learning will reach 40 billion, according to market research firm IDC. Combine that with the potential for more than 20% of financial services companies to be at risk of losing business to FinTech firms by 2020, according to a recent PricewaterhouseCoopers (PWC) report from earlier this month, and a change in the landscape may be underway, accelerating as approaches to technology cause industries to converge.