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fix YahooNormalize1min && update docs

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zhupr
2021-06-23 16:13:26 +08:00
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commit bab50e8837
4 changed files with 149 additions and 123 deletions
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2
README.md
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@@ -162,7 +162,7 @@ We recommend users to prepare their own data if they have a high-quality dataset
### Automatic update of daily frequency data(from yahoo finance) ### 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. > It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.
> For more information refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#Automatic-update-of-daily-frequency-data) > For more information refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
* Automatic update of data to the "qlib" directory each trading day(Linux) * Automatic update of data to the "qlib" directory each trading day(Linux)
* use *crontab*: `crontab -e` * use *crontab*: `crontab -e`

4
examples/benchmarks/README.md
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@@ -4,6 +4,10 @@ Here are the results of each benchmark model running on Qlib's `Alpha360` and `A
The numbers shown below demonstrate the performance of the entire `workflow` of each model. We will update the `workflow` as well as models in the near future for better results. The numbers shown below demonstrate the performance of the entire `workflow` of each model. We will update the `workflow` as well as models in the near future for better results.
> If you need to reproduce the results below, please use the **v1** dataset: `python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn --version v1`
>
> In the new version of qlib, the default dataset is **v2**. Since the data is collected from the YahooFinance API (which is not very stable), the results of *v2* and *v1* may differ
## Alpha360 dataset ## Alpha360 dataset
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown | | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|---|---|---|---|---|---|---|---|---| |---|---|---|---|---|---|---|---|---|

250
scripts/data_collector/yahoo/README.md
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@@ -1,17 +1,9 @@
- [Collector Data](#collector-data) - [Collector Data](#collector-data)
- [Automatic update data](#automatic-update-of-daily-frequency-data(from-yahoo-finance)) - [Get Qlib data](#get-qlib-databin-file)
- [CN Data](#CN-Data) - [Collector *YahooFinance* data to qlib](#collector-yahoofinance-data-to-qlib)
- [1d from yahoo](#1d-from-yahoocn) - [Automatic update of daily frequency data](#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
- [1d from qlib](#1d-from-qlibcn) - [Using qlib data](#using-qlib-data)
- [using data(1d)](#using-data1d-cn)
- [1min from yahoo](#1min-from-yahoocn)
- [1min from qlib](#1min-from-qlibcn)
- [using data(1min)](#using-data1min-cn)
- [US Data](#CN-Data)
- [1d from yahoo](#1d-from-yahoous)
- [1d from qlib](#1d-from-qlibus)
- [using data(1d)](#using-data1d-us)
# Collect Data From Yahoo Finance # Collect Data From Yahoo Finance
@@ -34,6 +26,110 @@ pip install -r requirements.txt
## Collector Data ## 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`
- 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*
- user can append data to `v2`: [automatic update of daily frequency data](#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
- **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`, 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/qlib_cn_1d --region cn
# cn 1min
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1min --region cn --interval 1min
# us 1d
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_us_1d --region us --interval 1d
# us 1min
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_us_1min --region us --interval 1min
```
### Collector *YahooFinance* data to qlib
> collector *YahooFinance* data and *dump* into `qlib` format
1. download data to csv: `python scripts/data_collector/yahoo/collector.py download_data`
- 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`, 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_1d --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region US
# cn 1min data
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1min --delay 1 --interval 1min --region CN
# us 1d data
python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_1d --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_1min --delay 1 --interval 1min --region US
```
2. normalize data: `python scripts/data_collector/yahoo/collector.py normalize_data`
- 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`, 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 qlilb_data --target_dir <qlib_data_1d_dir> --interval 1d
$ python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <qlib_data_1d_dir> --trading_date 2021-06-01
or:
download 1d data from YahooFinance
```
- examples:
```bash
# normalize 1d cn
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_1d --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/qlib_cn_1d --source_dir ~/.qlib/stock_data/source/cn_1min --normalize_dir ~/.qlib/stock_data/source/cn_1min_nor --region CN --interval 1min
```
3. dump data: `python scripts/dump_bin.py dump_all`
- 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/qlib_cn_1d --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/qlib_cn_1min --freq 1min --exclude_fields date,symbol
```
### Automatic update of daily frequency data(from yahoo finance) ### 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. > It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.
@@ -62,112 +158,36 @@ pip install -r requirements.txt
* *region*: region, value from ["CN", "US"], default "CN" * *region*: region, value from ["CN", "US"], default "CN"
### CN Data ## Using qlib data
#### 1d from yahoo(CN) ```python
import qlib
from qlib.data import D
```bash # 1d data cn
# freq=day, freq default day
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1d", region="cn")
df = D.features(D.instruments("all"), ["$close"], freq="day")
# download from yahoo finance # 1min data cn
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d # freq=1min
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1min", region="cn")
inst = D.list_instruments(D.instruments("all"), freq="1min", as_list=True)
# get 100 symbols
df = D.features(inst[:100], ["$close"], freq="1min")
# get all symbol data
# df = D.features(D.instruments("all"), ["$close"], freq="1min")
# normalize # 1d data us
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_1d --normalize_dir ~/.qlib/stock_data/source/cn_1d_nor --region CN --interval 1d qlib.init(provider_uri="~/.qlib/qlib_data/qlib_us_1d", region="us")
df = D.features(D.instruments("all"), ["$close"], freq="day")
# dump data # 1min data us
cd qlib/scripts qlib.init(provider_uri="~/.qlib/qlib_data/qlib_us_1min", region="cn")
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol inst = D.list_instruments(D.instruments("all"), freq="1min", as_list=True)
# get 100 symbols
df = D.features(inst[:100], ["$close"], freq="1min")
# get all symbol data
# df = D.features(D.instruments("all"), ["$close"], freq="1min")
```
```
### 1d from qlib(CN)
```bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn
```
### using data(1d CN)
```python
import qlib
from qlib.data import D
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1d", region="cn")
df = D.features(D.instruments("all"), ["$close"], freq="day")
```
#### 1min from yahoo(CN)
```bash
# download from yahoo finance
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1min --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1min
# normalize
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_1min --normalize_dir ~/.qlib/stock_data/source/cn_1min_nor --region CN --interval 1min
# dump data
cd qlib/scripts
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1min_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1min --freq 1min --exclude_fields date,adjclose,dividends,splits,symbol
```
### 1min from qlib(CN)
```bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1min --interval 1min --region cn
```
### using data(1min CN)
```python
import qlib
from qlib.data import D
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1min", region="cn")
df = D.features(D.instruments("all"), ["$close"], freq="1min")
```
### US Data
#### 1d from yahoo(US)
```bash
# download from yahoo finance
python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_1d --region US --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
# normalize
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/us_1d --normalize_dir ~/.qlib/stock_data/source/us_1d_nor --region US --interval 1d
# dump data
cd qlib/scripts
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/us_1d_nor --qlib_dir ~/.qlib/stock_data/source/qlib_us_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol
```
#### 1d from qlib(US)
```bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_us_1d --region us
```
### using data(1d US)
```python
# using
import qlib
from qlib.data import D
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_us_1d", region="us")
df = D.features(D.instruments("all"), ["$close"], freq="day")
```
### Help
```bash
pythono collector.py collector_data --help
```
## Parameters
- interval: 1min or 1d
- region: CN or US

16
scripts/data_collector/yahoo/collector.py
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@@ -242,7 +242,10 @@ class YahooCollectorCN1d(YahooCollectorCN):
class YahooCollectorCN1min(YahooCollectorCN): class YahooCollectorCN1min(YahooCollectorCN):
def get_instrument_list(self): def get_instrument_list(self):
symbols = super(YahooCollectorCN1min, self).get_instrument_list() symbols = super(YahooCollectorCN1min, self).get_instrument_list()
return symbols + ["000300.ss", "000905.ss", "00903.ss"] return symbols + ["000300.ss", "000905.ss", "000903.ss"]
def download_index_data(self):
pass
class YahooCollectorUS(YahooCollector, ABC): class YahooCollectorUS(YahooCollector, ABC):
@@ -461,7 +464,7 @@ class YahooNormalize1dExtend(YahooNormalize1d):
_si = df["close"].first_valid_index() _si = df["close"].first_valid_index()
if _si > df.index[0]: if _si > df.index[0]:
logger.warning( logger.warning(
f"{df.loc[_si][self._symbol_field_name]} missing data: {df.loc[:_si-1][self._date_field_name].to_list()}" f"{df.loc[_si][self._symbol_field_name]} missing data: {df.loc[:_si - 1][self._date_field_name].to_list()}"
) )
# normalize # normalize
df = self.normalize_yahoo( df = self.normalize_yahoo(
@@ -524,7 +527,7 @@ class YahooNormalize1min(YahooNormalize, ABC):
data_1d: pd.DataFrame = self.get_1d_data(symbol, _start, _end) data_1d: pd.DataFrame = self.get_1d_data(symbol, _start, _end)
data_1d = data_1d.copy() data_1d = data_1d.copy()
if data_1d is None or data_1d.empty: if data_1d is None or data_1d.empty:
df["factor"] = 1 / df.loc[df["close"].first_valid_index()] df["factor"] = 1 / df.loc[df["close"].first_valid_index()]["close"]
# TODO: np.nan or 1 or 0 # TODO: np.nan or 1 or 0
df["paused"] = np.nan df["paused"] = np.nan
else: else:
@@ -770,7 +773,7 @@ class Run(BaseRun):
def download_data( def download_data(
self, self,
max_collector_count=2, max_collector_count=2,
delay=0, delay=0.5,
start=None, start=None,
end=None, end=None,
check_data_length=None, check_data_length=None,
@@ -783,7 +786,7 @@ class Run(BaseRun):
max_collector_count: int max_collector_count: int
default 2 default 2
delay: float delay: float
time.sleep(delay), default 0 time.sleep(delay), default 0.5
start: str start: str
start datetime, default "2000-01-01"; closed interval(including start) start datetime, default "2000-01-01"; closed interval(including start)
end: str end: str
@@ -844,9 +847,8 @@ class Run(BaseRun):
""" """
if self.interval.lower() == "1min": if self.interval.lower() == "1min":
if qlib_data_1d_dir is None or not Path(qlib_data_1d_dir).expanduser().exists(): if qlib_data_1d_dir is None or not Path(qlib_data_1d_dir).expanduser().exists():
# TODO: add reference url
raise ValueError( raise ValueError(
"If normalize 1min, the qlib_data_1d_dir parameter must be set: --qlib_data_1d_dir <user qlib 1d data >, Reference: " "If normalize 1min, the qlib_data_1d_dir parameter must be set: --qlib_data_1d_dir <user qlib 1d data >, Reference: https://github.com/zhupr/qlib/tree/support_extend_data/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance"
) )
super(Run, self).normalize_data( super(Run, self).normalize_data(
date_field_name, symbol_field_name, end_date=end_date, qlib_data_1d_dir=qlib_data_1d_dir date_field_name, symbol_field_name, end_date=end_date, qlib_data_1d_dir=qlib_data_1d_dir