Have you looked at the significance of the signs? Something tells me that
| 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 | |||
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Check out the new article: CFTC Data Mining in Python and Building an AI Model.
Successful trading on the Forex market requires not only technical analysis, but also consideration of fundamental factors. Valuable but often ignored sources are CFTC reports (COT and TFF), which reveal the positions of major market participants and allow us to assess the behavior of institutional investors.
The Forex market is the largest in the world, but its high volatility makes forecasting difficult. COT/TFF reports provide insight into the actions of smart money and help uncover hidden market trends.
The proposed approach combines COT/TFF data and market quotes into a single Python model with automated trading via MetaTrader 5. This allows us to move from analysis to action without delays and human intervention.
Author: Yevgeniy Koshtenko