AI能在加密货币中赚钱吗？衡量从回测到真实市场的差距
Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets
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中文总结 AI 辅助
本研究提出一个统一基准，通过历史回测、纸面交易和实盘交易三个阶段，量化加密货币市场中AI交易方法从回测到真实市场的性能差距，并提供开源系统支持评估。
中文摘要 AI 辅助
基于AI的交易方法已从机器学习和强化学习迅速发展到大型语言模型（LLMs）和交易智能体，但其性能仍主要通过历史回测来评估。此类评估提供的证据有限，无法证明方法能否泛化到未见过的未来市场，或其在现实交易摩擦（如延迟、滑点、流动性约束和市场冲击）下能否维持回测表现。我们提出了一个统一基准，通过三个逐步更贴近现实的阶段来评估加密货币市场中具有代表性的机器学习、强化学习、基于LLM和基于智能体的交易方法：历史回测、基于交易所的前瞻性纸面交易和真实资金实盘交易。这些阶段共同增加了时间现实性（从历史市场转向未见过的未来市场）和执行现实性（从离线模拟转向实盘交易）。该协议使我们能够量化回测到实现的差距，识别性能开始恶化的阶段，并比较不同主要类别的AI交易方法之间该差距的差异。我们还提供了一个统一的开源系统，支持所有三个评估阶段，以及一个持续更新基准结果的公共平台。代码可在该https URL获取。
英文摘要
AI-based trading methods have rapidly evolved from machine learning and reinforcement learning to large language models (LLMs) and trading agents, yet their performance is still predominantly assessed through historical backtesting. Such evaluations provide limited evidence of whether a method can generalize to unseen future markets or whether its backtested performance can be sustained in realistic trading frictions (e.g., latency, slippage, liquidity constraints, and market impact). We present a unified benchmark that evaluates representative machine learning, reinforcement learning, LLM-based, and agent-based trading methods in cryptocurrency markets through three progressively more realistic stages: historical backtesting, prospective exchange-based paper trading, and real-money live trading. These stages jointly increase temporal realism by moving from historical to unseen future markets, and execution realism by moving from offline simulation toward live trading. This protocol enables us to quantify the backtest-to-realization gap, identify when performance begins to deteriorate, and compare how this gap differs across major classes of AI trading methods. We further provide a unified open-source system supporting all three evaluation stages, together with a public platform that continuously updates benchmark results. Code is available at https://github.com/Starlien95/Awesome-TradingAI.
发表机构
- The Chinese University of Hong Kong（香港中文大学）
- Jilin University（吉林大学）
- RMIT University（皇家墨尔本理工大学）
- Infplane Computing Lab（Infplane 计算实验室）
- Singapore Management University（新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。
