Paper: arXiv 2403.18831

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses a deep learning model but does not present heavy mathematical derivations, focusing more on simulation setup and performance results. Empirical rigor is moderately high due to extensive backtesting in a simulated market with statistical validation against benchmarks.

Research Flowchart

  flowchart TD
  A["<b>Research Goal</b><br/>Can a simple DL model rival<br/>conventional trading strategies?"] --> B["<b>Methodology: DeepTraderX (DTX)</b><br/>Deep Learning Trader trained via RL"]

  B --> C["<b>Input Data</b><br/>Level-2 LOB Data<br/>Historic Market Schedules"]

  C --> D["<b>Market Simulation</b><br/>500+ Trading Days<br/>Multi-threaded Environment"]

  D --> E["<b>Core Process</b><br/>State S<sub>t</sub> &rarr; DTX &rarr; Quote P<sub>t</sub>"]

  E --> F["<b>Key Findings</b><br/>DTX rivals/surpasses<br/>public-domain traders<br/>Simple models succeed in<br/>complex simulations"]

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0 of 46 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.