Crawler/ Machine Learning project aiming at deciding the key influence factors for XAUUSD, then use those factors to train machine to predict the gold price. We found that the factors chosen are sufficient to predict a somewhat accurate pricing comparing to the last 3 year's history XAUUSD prices.
Built with:
Python/ sklearn - DecisionTreeRegressor/ graphviz
Acknowledgments:
idea from: "LI-CHUN, LO (2017) - GOLD PRICING MODEL AFTER 1792: EVIDENCE FROM CHANGE OF DEMAND AND MACRO CONDITION"