  We introduce the first end-to-end Deep Reinforcement Learning (DRL) based
framework for active high frequency trading in the stock market. We train DRL
agents to trade one unit of Intel Corporation stock by employing the Proximal
Policy Optimization algorithm. The training is performed on three contiguous
months of high frequency Limit Order Book data, of which the last month
constitutes the validation data. In order to maximise the signal to noise ratio
in the training data, we compose the latter by only selecting training samples
with largest price changes. The test is then carried out on the following month
of data. Hyperparameters are tuned using the Sequential Model Based
Optimization technique. We consider three different state characterizations,
which differ in their LOB-based meta-features. Analysing the agents'
performances on test data, we argue that the agents are able to create a
dynamic representation of the underlying environment. They identify occasional
regularities present in the data and exploit them to create long-term
profitable trading strategies. Indeed, agents learn trading strategies able to
produce stable positive returns in spite of the highly stochastic and
non-stationary environment.
