Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Reinforcement Learning for Active High Frequency Trading
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2101.07107.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:00:48.919609Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T07:09:46.487078Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 384e56e8-3c14-4c91-ab96-edb67c8c648c · inbound
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning cites this paper.
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning Deep Reinforcement Learning for Active High Frequency Trading
Reference 88
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T05:12:11.711553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9695ba5c-87d5-4bcc-b69e-585d062ab0d6 · inbound
FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions cites this paper.
FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions Deep Reinforcement Learning for Active High Frequency Trading
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:00:48.919609Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d3cf46b7-6f1a-492b-9f22-d260faf2b4ad · inbound
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization cites this paper.
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization Deep Reinforcement Learning for Active High Frequency Trading
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:30:47.826795Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e18f7011-50c9-4c5c-a035-9cb088ecb4d3 · inbound
"So, Tell Me About Your Policy...": Distillation of interpretable policies from Deep Reinforcement Learning agents cites this paper.
"So, Tell Me About Your Policy...": Distillation of interpretable policies from Deep Reinforcement Learning agents Deep Reinforcement Learning for Active High Frequency Trading
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:36:40.897742Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1ccfceb6-7877-4685-9c48-0f8eb6682592 · inbound
Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution cites this paper.
Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution Deep Reinforcement Learning for Active High Frequency Trading
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:09:46.488849Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.