arXiv (All)
open access
Data-Driven Measures of High-Frequency Trading
Public data do not identify high-frequency trading (HFT), and standard proxies do not separate liquidity-supplying from liquidity-demanding strategies. We overcome this measurement challenge by training machine learning models on proprietary Nasdaq data to map observed HFT activity to public intrada
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
- A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs#1076018
- TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification#1069898
- When LLM Signals Hurt: A Coverage-Density Analysis of LLM-Augmented Reinforcement Learning for Stock Trading#181083
- Balancing Expressivity and Overfitting in Quantum Gaussian Process Regression#987159
- A Physics Informed Learning Augmented Framework for Grid-Aware P2P Energy Trading#1049844
Record · ID 821969
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