文章 "基于Python的CFTC数据挖掘与AI预测模型构建"
你研究过这些标志的意义吗？直觉告诉我
| Net_NonComm,0.0,0.0。 | |||
| Net_Comm,0.0,0.0. | |||
| Net_Lev_Money,0.0,0.0. | |||
| Net_Asset_Mgr,0.0,0.0 | |||
| Net_NonComm_lag1,0.0,0.0 | |||
| Net_NonComm_change,0.0,0.0 | |||
| Net_Comm_lag1,0.0,0.0 | |||
| Net_Comm_change,0.0,0.0 | |||
| Net_Lev_Money_lag1,0.0,0.0 | |||
| Net_Lev_Money_change,0.0,0.0 | |||
| Net_Asset_Mgr_lag1,0.0,0.0 | |||
| Net_Asset_Mgr_change,0.0,0.0 | |||
新文章 基于Python的CFTC数据挖掘与AI预测模型构建已发布：
想要在外汇市场中实现稳定交易表现，不仅需要技术分析，还必须结合基本面因素。有一类极具价值却常被忽视的数据来源，就是CFTC报告（COT和TFF），能够披露市场主力持仓结构，帮助我们研判机构投资者的交易行为。
外汇市场是全球规模最大的金融市场，但极高的波动率让行情预测充满挑战。COT/TFF报告能够揭示“聪明钱”的动向，帮助发现隐藏的市场趋势。
本文介绍的方法，会将COT/TFF持仓数据与市场行情整合到统一的Python模型中，并通过MetaTrader 5实现自动化交易。这样我们能够从分析直接转化为交易执行，无需延迟，也无需人工干预。
作者：Yevgeniy Koshtenko