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mirror of https://github.com/microsoft/qlib.git synced 2026-07-07 21:11:50 +08:00

solve the conflict

This commit is contained in:
bxdd
2021-04-30 23:23:56 +08:00
69 changed files with 2289 additions and 317 deletions

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@@ -25,4 +25,11 @@ The example is given in `workflow.py`, users can run the code as follows.
Run the example by running the following command:
```bash
python workflow.py dump_and_load_dataset
```
```
## Benchmarks Performance
### Signal Test
Here are the results of signal test for benchmark models. We will keep updating benchmark models in future.
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Long precision| Short Precision | Long-Short Average Return | Long-Short Average Sharpe |
|---|---|---|---|---|---|---|---|---|---|
| LightGBM | Alpha158 | 0.3042±0.00 | 1.5372±0.00| 0.3117±0.00 | 1.6258±0.00 | 0.6720±0.00 | 0.6870±0.00 | 0.000769±0.00 | 1.0190±0.00 |

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@@ -27,12 +27,11 @@ from qlib.tests.data import GetData
from highfreq_ops import get_calendar_day, DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut
class HighfreqWorkflow(object):
class HighfreqWorkflow:
SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut], "expression_cache": None}
MARKET = "all"
BENCHMARK = "SH000300"
start_time = "2020-09-15 00:00:00"
end_time = "2021-01-18 16:00:00"
@@ -146,35 +145,40 @@ class HighfreqWorkflow(object):
self._prepare_calender_cache()
##=============reinit dataset=============
dataset.init(
dataset.config(
handler_kwargs={
"start_time": "2021-01-19 00:00:00",
"end_time": "2021-01-25 16:00:00",
},
segments={
"test": (
"2021-01-19 00:00:00",
"2021-01-25 16:00:00",
),
},
)
dataset.setup_data(
handler_kwargs={
"init_type": DataHandlerLP.IT_LS,
"start_time": "2021-01-19 00:00:00",
"end_time": "2021-01-25 16:00:00",
},
segment_kwargs={
"test": (
"2021-01-19 00:00:00",
"2021-01-25 16:00:00",
),
},
)
dataset_backtest.init(
dataset_backtest.config(
handler_kwargs={
"start_time": "2021-01-19 00:00:00",
"end_time": "2021-01-25 16:00:00",
},
segment_kwargs={
segments={
"test": (
"2021-01-19 00:00:00",
"2021-01-25 16:00:00",
),
},
)
dataset_backtest.setup_data(handler_kwargs={})
##=============get data=============
xtest = dataset.prepare(["test"])
backtest_test = dataset_backtest.prepare(["test"])
xtest = dataset.prepare("test")
backtest_test = dataset_backtest.prepare("test")
print(xtest, backtest_test)
return

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@@ -0,0 +1,65 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data_1min"
region: cn
market: &market 'csi300'
start_time: &start_time "2020-09-15 00:00:00"
end_time: &end_time "2021-01-18 16:00:00"
train_end_time: &train_end_time "2020-11-15 16:00:00"
valid_start_time: &valid_start_time "2020-11-16 00:00:00"
valid_end_time: &valid_end_time "2020-11-30 16:00:00"
test_start_time: &test_start_time "2020-12-01 00:00:00"
data_handler_config: &data_handler_config
start_time: *start_time
end_time: *end_time
fit_start_time: *start_time
fit_end_time: *train_end_time
instruments: *market
freq: '1min'
infer_processors:
- class: 'RobustZScoreNorm'
kwargs:
fields_group: 'feature'
clip_outlier: false
- class: "Fillna"
kwargs:
fields_group: 'feature'
learn_processors:
- class: 'DropnaLabel'
- class: 'CSRankNorm'
kwargs:
fields_group: 'label'
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
task:
model:
class: "HFLGBModel"
module_path: "qlib.contrib.model.highfreq_gdbt_model"
kwargs:
objective: 'binary'
metric: ['binary_logloss','auc']
verbosity: -1
learning_rate: 0.01
max_depth: 8
num_leaves: 150
lambda_l1: 1.5
lambda_l2: 1
num_threads: 20
dataset:
class: "DatasetH"
module_path: "qlib.data.dataset"
kwargs:
handler:
class: "Alpha158"
module_path: "qlib.contrib.data.handler"
kwargs: *data_handler_config
segments:
train: [*start_time, *train_end_time]
valid: [*train_end_time, *valid_end_time]
test: [*test_start_time, *end_time]
record:
- class: "SignalRecord"
module_path: "qlib.workflow.record_temp"
kwargs: {}
- class: "HFSignalRecord"
module_path: "qlib.workflow.record_temp"
kwargs: {}