  In this paper, we introduce DeepTraderX (DTX), a simple Deep Learning-based
trader, and present results that demonstrate its performance in a
multi-threaded market simulation. In a total of about 500 simulated market
days, DTX has learned solely by watching the prices that other strategies
produce. By doing this, it has successfully created a mapping from market data
to quotes, either bid or ask orders, to place for an asset. Trained on
historical Level-2 market data, i.e., the Limit Order Book (LOB) for specific
tradable assets, DTX processes the market state $S$ at each timestep $T$ to
determine a price $P$ for market orders. The market data used in both training
and testing was generated from unique market schedules based on real historic
stock market data. DTX was tested extensively against the best strategies in
the literature, with its results validated by statistical analysis. Our
findings underscore DTX's capability to rival, and in many instances, surpass,
the performance of public-domain traders, including those that outclass human
traders, emphasising the efficiency of simple models, as this is required to
succeed in intricate multi-threaded simulations. This highlights the potential
of leveraging "black-box" Deep Learning systems to create more efficient
financial markets.
