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Fix ValueError in dump_bin.py by excluding 'symbol' field from conversion #1865

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Fix ValueError in dump_bin.py by excluding 'symbol' field from conversion #1865

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7 changes: 5 additions & 2 deletions scripts/data_collector/baostock_5min/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
1. download data to csv: `python scripts/data_collector/baostock_5min/collector.py download_data`

This will download the raw data such as date, symbol, open, high, low, close, volume, amount, adjustflag from baostock to a local directory. One file per symbol.

- parameters:
- `source_dir`: save the directory
- `interval`: `5min`
Expand All @@ -39,6 +40,7 @@
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
Expand All @@ -62,7 +64,7 @@
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
Expand All @@ -78,4 +80,5 @@
```bash
# dump 5min cn
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/hs300_5min_nor --qlib_dir ~/.qlib/qlib_data/hs300_5min_bin --freq 5min --exclude_fields date,symbol
```
```
> **Note**: Ensure to run `normalize_data` before using `dump_bin.py` to avoid conversion errors.
4 changes: 3 additions & 1 deletion scripts/dump_bin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def __init__(
limit_nums: int = None,
):
"""

Parameters
----------
csv_path: str
Expand Down Expand Up @@ -223,6 +223,8 @@ def _data_to_bin(self, df: pd.DataFrame, calendar_list: List[pd.Timestamp], feat
# used when creating a bin file
date_index = self.get_datetime_index(_df, calendar_list)
for field in self.get_dump_fields(_df.columns):
if field == self.symbol_field_name:
continue
bin_path = features_dir.joinpath(f"{field.lower()}.{self.freq}{self.DUMP_FILE_SUFFIX}")
if field not in _df.columns:
continue
Expand Down
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