AI审计发现，基于蜡烛图的加密货币交易模型无法在扣除成本后产生正收益。
Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models
- 用脚本化模拟与人工监督AI验证多对币种的交易策略表现
- 最强策略在19个周期内亏损6.72%，胜率仅3胜16负
- 所有模型均未跑赢持有策略，最终结论为不交易
本文审计了基于蜡烛图的机器学习模型是否能在假设交易成本下，将加密货币极值或短期预测转化为Binance现货市场的正向纸面策略。实验数据来自固定种子的模型运行和确定性模拟器；人类监督的AI代理通过文献检索、独立批判、结果校验、文档整理与源码打包支持了2024年7月的证据完整性审查，但未参与任何交易决策。最有力的后期证据（基于广泛前期搜索）为负面：一个固定的十币种每日必选策略，在19个周期内以31基点/周期的假设成本亏损6.72%，仅3次盈利，16次亏损。在具体模型评估中，验证选择的局部最小值策略回报-1.79%，而局部最大值卖出换现再入场策略比持续持有差2.80%；其毛平均优势分别为11.11和12.21基点，低于21基点的应力阈值。一种受Gurgul启发的仅使用OHLCV的每日适应模型，实现最小/最大值的ROC AUC为0.874/0.896，但平均精度仅为0.134/0.116，七周期累计亏损44.30%，高于买入持有策略的-41.20%。一次法医审计还质疑了早期的One4All「30日保留」策略：其时间范围影响了先前架构研究，四小时预测窗口未在分割边界清除，使用同价位入场，且原始结果目录缺失。在所有测试的探索性协议中，事件排序表现未能建立可执行的正向策略价值。因此，所有操作决策仍为NO_TRADE。
原文摘要 · Abstract (English)
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-integrity revision through literature retrieval, separately tasked critique, artifact reconciliation, documentation, and source packaging, not trading decisions. The strongest later-period evidence, conditional on extensive predecessor search, is negative: an unchanged ten-pair mandatory-daily selector lost 6.72\% over 19 July cycles at an assumed 31-bps completed-cycle cost, with 3 wins and 16 losses. In short model-specific July evaluations, the validation-selected local-minimum policy returned -1.79\%, while the local-maximum sell-to-cash/re-entry policy underperformed continuous holding by 2.80\%; their gross mean advantages of 11.11 and 12.21 bps were below even the 21-bps stress. A Gurgul-inspired, OHLCV-only daily adaptation attained minimum/maximum ROC AUC of 0.874/0.896 but average precision of only 0.134/0.116 and lost 44.30\% over seven cycles, versus -41.20\% for buy-and-hold. A forensic audit also downgraded an earlier One4All "30-day holdout": its dates had influenced prior architecture work, its four-hour outcome horizon was not purged at split boundaries, it used same-close entry, and its raw result directories were absent. Across the tested, mostly exploratory protocols, event-ranking performance did not establish positive executable policy value. Every operational decision remains NO\_TRADE.
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