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Quantitative Finance > Trading and Market Microstructure

arXiv:2504.06932 (q-fin)
[Submitted on 9 Apr 2025 (v1), last revised 17 Apr 2025 (this
version, v2)]

Title:Maximizing Battery Storage Profits via High-Frequency Intraday
Trading

Authors:David Schaurecker, David Wozabal, Nils Lohndorf, Thorsten
Staake
View a PDF of the paper titled Maximizing Battery Storage Profits via
High-Frequency Intraday Trading, by David Schaurecker and 3 other
authors
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    Abstract:Maximizing revenue for grid-scale battery energy storage
    systems in continuous intraday electricity markets requires
    strategies that are able to seize trading opportunities as soon
    as new information arrives. This paper introduces and evaluates
    an automated high-frequency trading strategy for battery energy
    storage systems trading on the intraday market for power while
    explicitly considering the dynamics of the limit order book,
    market rules, and technical parameters. The standard rolling
    intrinsic strategy is adapted for continuous intraday electricity
    markets and solved using a dynamic programming approximation that
    is two to three orders of magnitude faster than an exact
    mixed-integer linear programming solution. A detailed backtest
    over a full year of German order book data demonstrates that the
    proposed dynamic programming formulation does not reduce trading
    profits and enables the policy to react to every relevant order
    book update, enabling realistic rapid backtesting. Our results
    show the significant revenue potential of high-frequency trading:
    our policy earns 58% more than when re-optimizing only once every
    hour and 14% more than when re-optimizing once per minute,
    highlighting that profits critically depend on trading speed.
    Furthermore, we leverage the speed of our algorithm to train a
    parametric extension of the rolling intrinsic, increasing yearly
    revenue by 8.4% out of sample.

Subjects: Trading and Market Microstructure (q-fin.TR); Systems and
          Control (eess.SY); Optimization and Control (math.OC)
Cite as:  arXiv:2504.06932 [q-fin.TR]
          (or arXiv:2504.06932v2 [q-fin.TR] for this version)
          https://doi.org/10.48550/arXiv.2504.06932
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: David Schaurecker [view email]
[v1] Wed, 9 Apr 2025 14:38:09 UTC (475 KB)
[v2] Thu, 17 Apr 2025 07:04:13 UTC (488 KB)
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