- [Collector Data](#collector-data) - [Get Qlib data](#get-qlib-databin-file) - [Collector *YahooFinance* data to qlib](#collector-yahoofinance-data-to-qlib) - [Automatic update of daily frequency data](#automatic-update-of-daily-frequency-datafrom-yahoo-finance) - [Using qlib data](#using-qlib-data) # Collect Data From Yahoo Finance > *Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup) and the data might not be perfect. We recommend users to prepare their own data if they have high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)* **NOTE**: Yahoo! Finance has blocked the access from China. Please change your network if you want to use the Yahoo data crawler. > **Examples of abnormal data** - [SH000661](https://finance.yahoo.com/quote/000661.SZ/history?period1=1558310400&period2=1590796800&interval=1d&filter=history&frequency=1d) - [SZ300144](https://finance.yahoo.com/quote/300144.SZ/history?period1=1557446400&period2=1589932800&interval=1d&filter=history&frequency=1d) We have considered **STOCK PRICE ADJUSTMENT**, but some price series seem still very abnormal. ## Requirements ```bash pip install -r requirements.txt ``` ## Collector Data ### Get Qlib data(`bin file`) > `qlib-data` from *YahooFinance*, is the data that has been dumped and can be used directly in `qlib`. > This ready-made qlib-data is not updated regularly. If users want the latest data, please follow [these steps](#collector-yahoofinance-data-to-qlib) download the latest data. - get data: `python scripts/get_data.py qlib_data` - parameters: - `target_dir`: save dir, by default *~/.qlib/qlib_data/cn_data* - `version`: dataset version, value from [`v1`, `v2`], by default `v1` - `v2` end date is *2021-06*, `v1` end date is *2020-09* - If users want to incrementally update data, they need to use yahoo collector to [collect data from scratch](#collector-yahoofinance-data-to-qlib). - **the [benchmarks](https://github.com/microsoft/qlib/tree/main/examples/benchmarks) for qlib use `v1`**, *due to the unstable access to historical data by YahooFinance, there are some differences between `v2` and `v1`* - `interval`: `1d` or `1min`, by default `1d` - `region`: `cn` or `us` or `in`, by default `cn` - `delete_old`: delete existing data from `target_dir`(*features, calendars, instruments, dataset_cache, features_cache*), value from [`True`, `False`], by default `True` - `exists_skip`: traget_dir data already exists, skip `get_data`, value from [`True`, `False`], by default `False` - examples: ```bash # cn 1d python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn # cn 1min python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min # us 1d python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/us_data --region us --interval 1d # us 1min python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/us_data_1min --region us --interval 1min # in 1d python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/in_data --region in --interval 1d # in 1min python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/in_data_1min --region in --interval 1min ``` ### Collector *YahooFinance* data to qlib > collector *YahooFinance* data and *dump* into `qlib` format. > If the above ready-made data can't meet users' requirements, users can follow this section to crawl the latest data and convert it to qlib-data. 1. download data to csv: `python scripts/data_collector/yahoo/collector.py download_data` This will download the raw data such as high, low, open, close, adjclose price from yahoo to a local directory. One file per symbol. - parameters: - `source_dir`: save the directory - `interval`: `1d` or `1min`, by default `1d` > **due to the limitation of the *YahooFinance API*, only the last month's data is available in `1min`** - `region`: `CN` or `US` or `IN` or `BR`, by default `CN` - `delay`: `time.sleep(delay)`, by default *0.5* - `start`: start datetime, by default *"2000-01-01"*; *closed interval(including start)* - `end`: end datetime, by default `pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))`; *open interval(excluding end)* - `max_workers`: get the number of concurrent symbols, it is not recommended to change this parameter in order to maintain the integrity of the symbol data, by default *1* - `check_data_length`: check the number of rows per *symbol*, by default `None` > if `len(symbol_df) < check_data_length`, it will be re-fetched, with the number of re-fetches coming from the `max_collector_count` parameter - `max_collector_count`: number of *"failed"* symbol retries, by default 2 - examples: ```bash # cn 1d data python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_data --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region CN # cn 1min data python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_data_1min --delay 1 --interval 1min --region CN # us 1d data python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_data --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region US # us 1min data python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_data_1min --delay 1 --interval 1min --region US # in 1d data python collector.py download_data --source_dir ~/.qlib/stock_data/source/in_data --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region IN # in 1min data python collector.py download_data --source_dir ~/.qlib/stock_data/source/in_data_1min --delay 1 --interval 1min --region IN # br 1d data python collector.py download_data --source_dir ~/.qlib/stock_data/source/br_data --start 2003-01-03 --end 2022-03-01 --delay 1 --interval 1d --region BR # br 1min data python collector.py download_data --source_dir ~/.qlib/stock_data/source/br_data_1min --delay 1 --interval 1min --region BR ``` 2. normalize data: `python scripts/data_collector/yahoo/collector.py normalize_data` This will: 1. Normalize high, low, close, open price using adjclose. 2. Normalize the high, low, close, open price so that the first valid trading date's close price is 1. - parameters: - `source_dir`: csv directory - `normalize_dir`: result directory - `max_workers`: number of concurrent, by default *1* - `interval`: `1d` or `1min`, by default `1d` > if **`interval == 1min`**, `qlib_data_1d_dir` cannot be `None` - `region`: `CN` or `US` or `IN`, by default `CN` - `date_field_name`: column *name* identifying time in csv files, by default `date` - `symbol_field_name`: column *name* identifying symbol in csv files, by default `symbol` - `end_date`: if not `None`, normalize the last date saved (*including end_date*); if `None`, it will ignore this parameter; by default `None` - `qlib_data_1d_dir`: qlib directory(1d data) ``` if interval==1min, qlib_data_1d_dir cannot be None, normalize 1min needs to use 1d data; qlib_data_1d can be obtained like this: $ python scripts/get_data.py qlib_data --target_dir --interval 1d $ python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir --end_date or: download 1d data from YahooFinance ``` - examples: ```bash # normalize 1d cn python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_data --normalize_dir ~/.qlib/stock_data/source/cn_1d_nor --region CN --interval 1d # normalize 1min cn python collector.py normalize_data --qlib_data_1d_dir ~/.qlib/qlib_data/cn_data --source_dir ~/.qlib/stock_data/source/cn_data_1min --normalize_dir ~/.qlib/stock_data/source/cn_1min_nor --region CN --interval 1min # normalize 1d br python scripts/data_collector/yahoo/collector.py normalize_data --source_dir ~/.qlib/stock_data/source/br_data --normalize_dir ~/.qlib/stock_data/source/br_1d_nor --region BR --interval 1d # normalize 1min br python collector.py normalize_data --qlib_data_1d_dir ~/.qlib/qlib_data/br_data --source_dir ~/.qlib/stock_data/source/br_data_1min --normalize_dir ~/.qlib/stock_data/source/br_1min_nor --region BR --interval 1min ``` 3. dump data: `python scripts/dump_bin.py dump_all` This will convert the normalized csv in `feature` directory as numpy array and store the normalized data one file per column and one symbol per directory. - parameters: - `csv_path`: stock data path or directory, **normalize result(normalize_dir)** - `qlib_dir`: qlib(dump) data director - `freq`: transaction frequency, by default `day` > `freq_map = {1d:day, 1mih: 1min}` - `max_workers`: number of threads, by default *16* - `include_fields`: dump fields, by default `""` - `exclude_fields`: fields not dumped, by default `""" > dump_fields = `include_fields if include_fields else set(symbol_df.columns) - set(exclude_fields) exclude_fields else symbol_df.columns` - `symbol_field_name`: column *name* identifying symbol in csv files, by default `symbol` - `date_field_name`: column *name* identifying time in csv files, by default `date` - examples: ```bash # dump 1d cn python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/cn_data --freq day --exclude_fields date,symbol # dump 1min cn python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1min_nor --qlib_dir ~/.qlib/qlib_data/cn_data_1min --freq 1min --exclude_fields date,symbol ``` ### Automatic update of daily frequency data(from yahoo finance) > It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically. > > **NOTE**: Users can't incrementally update data based on the offline data provided by Qlib(some fields are removed to reduce the data size). Users should use [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance) to download Yahoo data from scratch and then incrementally update it. > * Automatic update of data to the "qlib" directory each trading day(Linux) * use *crontab*: `crontab -e` * set up timed tasks: ``` * * * * 1-5 python