Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume Curve

Paper: arXiv 2502.13722

Abstract

Volume-Weighted Average Price (VWAP) is arguably the most prevalent benchmark for trade execution as it provides an unbiased standard for comparing performance across market participants. However, achieving VWAP is inherently challenging due to its dependence on two dynamic factors, volumes and prices. Traditional approaches typically focus on forecasting the market’s volume curve, an assumption that may hold true under steady conditions but becomes suboptimal in more volatile environments or markets such as cryptocurrency where prediction error margins are higher. In this study, I propose a deep learning framework that directly optimizes the VWAP execution objective by bypassing the intermediate step of volume curve prediction. Leveraging automatic differentiation and custom loss functions, my method calibrates order allocation to minimize VWAP slippage, thereby fully addressing the complexities of the execution problem. My results demonstrate that this direct optimization approach consistently achieves lower VWAP slippage compared to conventional methods, even when utilizing a naive linear model presented in arXiv:2410.21448. They validate the observation that strategies optimized for VWAP performance tend to diverge from accurate volume curve predictions and thus underscore the advantage of directly modeling the execution objective. This research contributes a more efficient and robust framework for VWAP execution in volatile markets, illustrating the potential of deep learning in complex financial systems where direct objective optimization is crucial. Although my empirical analysis focuses on cryptocurrency markets, the underlying principles of the framework are readily applicable to other asset classes such as equities.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced mathematical modeling with deep learning and custom loss functions, indicating high complexity, but the empirical analysis is based on cryptocurrency markets and lacks the detailed backtest-ready data, statistical metrics, or code implementation typically required for high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Optimize VWAP Execution<br>in Crypto Markets"] --> B["Core Methodology<br>Deep Learning Framework"]
  B --> C["Data & Inputs<br>Market Data &<br>Trade Parameters"]
  C --> D["Computational Process<br>Direct Objective Optimization<br>via Automatic Differentiation"]
  D --> E["Key Finding 1<br>Lower VWAP Slippage<br>vs. Conventional Methods"]
  D --> F["Key Finding 2<br>Strategies Diverge from<br>Volume Curve Prediction"]

Cited References (30)

1. High frequency trading under scrutiny (2009)
2. The Total Cost of Transactions on the NYSE (1988)
3. The implementation shortfall: Paper versus reality (1988)
4. VWAP strategies, Transaction Performance: The Changing Face of Trading Investment Guides Series (2002)
5. Optimal execution of portfolio transactions (2000)
6. Optimal slice of a VWAP trade (2002) — link
7. Optimal VWAP Trading Strategy and Relative Volume (2007) — link
8. Mean Variance Optimal VWAP Trading (2012) — link

3 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.