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Forex Historical Data 30Min OHLCV CSV datasets (17 pairs) (2007-2022)
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17 forex pairs: USDJPY, USDCHF, USDCAD, NZDUSD, GBPUSD, GBPCAD, GBPAUD, GBPJPY, EURCAD, EURAUD, EURJPY, EURGBP, EURCHF, EURCAD, CHFJPY, AUDUSD, AUDJPY.
Data Granularity: 30-minute time frame
Time Zone: GMT
Start Date – End Date: January 2007 – January 2022
File Format: CSV
Description
17 forex pairs: USDJPY, USDCHF, USDCAD, NZDUSD, GBPUSD, GBPCAD, GBPAUD, GBPJPY, EURCAD, EURAUD, EURJPY, EURGBP, EURCHF, EURCAD, CHFJPY, AUDUSD, AUDJPY. Data Granularity: 30-minute time frame Time Zone: GMT Start Date – End Date: January 2007 – January 2022 File Format: CSVSample
| Time | Open | High | Low | Close | Volume |
| 2007-01-01 0:00:00 | 93.801 | 93.93 | 93.721 | 93.756 | 5747 |
| 2007-01-01 0:30:00 | 93.758 | 93.803 | 93.721 | 93.767 | 5874 |
| 2007-01-01 1:00:00 | 93.784 | 93.818 | 93.738 | 93.804 | 5737 |
| 2007-01-01 1:30:00 | 93.8 | 93.824 | 93.752 | 93.815 | 5554 |
| 2007-01-01 2:00:00 | 93.807 | 94.042 | 93.728 | 93.891 | 5501 |
| 2007-01-01 2:30:00 | 93.963 | 94.022 | 93.729 | 93.77 | 5753 |
| 2007-01-01 3:00:00 | 93.763 | 93.785 | 93.724 | 93.758 | 5626 |
| 2007-01-01 3:30:00 | 93.758 | 93.787 | 93.725 | 93.746 | 5015 |
| 2007-01-01 4:00:00 | 93.744 | 93.985 | 93.736 | 93.79 | 5668 |
| 2007-01-01 4:30:00 | 93.857 | 93.99 | 93.738 | 93.759 | 5539 |
| 2007-01-01 5:00:00 | 93.747 | 93.784 | 93.719 | 93.774 | 5749 |
| 2007-01-01 5:30:00 | 93.75 | 93.781 | 93.712 | 93.756 | 5457 |
| 2007-01-01 6:00:00 | 93.762 | 93.966 | 93.718 | 93.945 | 5689 |
| 2007-01-01 6:30:00 | 93.959 | 93.979 | 93.732 | 93.893 | 5787 |
| 2007-01-01 7:00:00 | 93.878 | 93.923 | 93.864 | 93.915 | 5098 |
| 2007-01-01 7:30:00 | 93.896 | 93.919 | 93.867 | 93.893 | 5975 |
| 2007-01-01 8:00:00 | 93.878 | 93.977 | 93.877 | 93.921 | 5350 |
Example of Machine Learning models developed using these datasets
Generating Forex Intraday Trading Signals using Machine Learning Ensemble and NLP Sentiment Analysis Models – Part 1
Generating Forex Intraday Trading Signals using Machine Learning Ensemble and NLP Sentiment Analysis Models – Part 2
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