Python提供了数据挖掘和许多机器学习算法的实现。
准备数据
Tickhistory的数据可以精确到每个tick,我就导出一个月的XAU日线OHLC数据,样本如下:
Date |
Open |
High |
Low |
Close |
Volume |
20170927 |
1293.231 |
1296.208 |
1281.451 |
1283.511 |
108.903715221408 |
20170928 |
1283.512 |
1288.741 |
1277.682 |
1286.932 |
105.861770183576 |
20170929 |
1286.931 |
1290.332 |
1275.751 |
1278.502 |
105.976955427706 |
20171001 |
1279.541 |
1280.072 |
1276.771 |
1277.868 |
3.52173000181574 |
20171002 |
1277.861 |
1277.878 |
1269.328 |
1271.092 |
95.848550074621 |
20171003 |
1271.071 |
1274.701 |
1268.298 |
1272.439 |
85.1280900694219 |
20171004 |
1272.439 |
1282.172 |
1270.592 |
1273.959 |
92.5239001664886 |
20171005 |
1273.952 |
1278.962 |
1266.328 |
1269.052 |
92.4760702919739 |
20171006 |
1269.051 |
1276.492 |
1260.581 |
1276.262 |
92.658720124633 |
20171008 |
1276.051 |
1277.722 |
1275.332 |
1276.591 |
2.77864000279806 |
20171009 |
1276.592 |
1285.431 |
1275.251 |
1283.169 |
8 |