Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning for Active High Frequency Trading   We introduce the first end-to-end Deep Reinforcement Learning (DRL) based
framework for active high frequency trading. 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.

5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess