ACE-Trader: Adversarial Co-Evolution for Generator-Critic Trading Systems
In this work, we propose ACE-Trader, an adversarial co-evolution trading framework.
ProofPaper ↗
Key points
- Generator--critic trading systems pair an LLM-based trader that makes trading decisions with a red team agent that evaluates these decisions and provides feedback for revision.
- However, improving individual trading decisions does not ensure that both agents learn from experience and improve together over time.
- This raises an important question: how can the trader and red-Team select useful experience and learn from each other's evolving behavior?
- We further design a co-evolution mechanism that links role-specific memories through shared trading records.
Sources (1)
- [1]ACE-Trader: Adversarial Co-Evolution for Generator-Critic Trading SystemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:04 AM
In this work, we propose ACE-Trader, an adversarial co-evolution trading framework.
Generator--critic trading systems pair an LLM-based trader that makes trading decisions with a red team agent that evaluates these decisions and provides feedback for revision.
Extractive summary: sentences quoted from the sources.
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