arXiv (All)
open access
Deep Learning of Robust Market Making under Regime-Switching Order Flow
Classical market-making strategies based on stochastic control, such as the Avellaneda-Stoikov and the Guéant-Lehalle-Fernandez-Tapia (GLFT) extension, provide closed-form quoting rules, but rest on assumptions that break down at realistic microstructure timescales. One of them is that order flow is
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DeepSeekMath Meets Order Book: Group-Aware Policy Optimization for High-Frequency Directional Trading#630019
- DeepSeekMath Meets Order Book: Group-Aware Policy Optimization for High-Frequency Directional Trading#628157
- Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization#126535
- Strategy Saturation and Regime Failure in Financial ML: Evaluative Control and the Limits of Optimization#74713
- Strategizing Crude Oil Market Dynamics: Using Deep Learning Predictive Models and the Influence of Parameter Optimization#216053
Record · ID 999130
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive