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Reformat example data names: use {region}_data for 1-day data, and {region}_data_1min for 1-min data (#781)
* Fix high-freq data name from `yahoo_cn_1min` to `cn_data_1min`
* re-format example data names using `qlib_{region}_{feq}`, e.g. qlib_cn_1d
* re-format example data names using `{region}_{feq}`, e.g. us_1d and cn_1min
* keep using for 1day data, and change 1min data to
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@@ -18,10 +18,10 @@ pip install -r requirements.txt
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```bash
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# download from eastmoney.com
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python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
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python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_data --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
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# normalize
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python collector.py normalize_data --source_dir ~/.qlib/fund_data/source/cn_1d --normalize_dir ~/.qlib/fund_data/source/cn_1d_nor --region CN --interval 1d --date_field_name FSRQ
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python collector.py normalize_data --source_dir ~/.qlib/fund_data/source/cn_data --normalize_dir ~/.qlib/fund_data/source/cn_1d_nor --region CN --interval 1d --date_field_name FSRQ
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# dump data
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cd qlib/scripts
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@@ -279,7 +279,7 @@ class Run(BaseRun):
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Examples
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---------
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# get daily data
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$ python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
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$ python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_data --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
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"""
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super(Run, self).download_data(max_collector_count, delay, start, end, interval, check_data_length, limit_nums)
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@@ -296,7 +296,7 @@ class Run(BaseRun):
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Examples
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---------
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$ python collector.py normalize_data --source_dir ~/.qlib/fund_data/source/cn_1d --normalize_dir ~/.qlib/fund_data/source/cn_1d_nor --region CN --interval 1d --date_field_name FSRQ
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$ python collector.py normalize_data --source_dir ~/.qlib/fund_data/source/cn_data --normalize_dir ~/.qlib/fund_data/source/cn_1d_nor --region CN --interval 1d --date_field_name FSRQ
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"""
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super(Run, self).normalize_data(date_field_name, symbol_field_name)
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