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Add the HIST and IGMTF model on Alpha360 (#1040)

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* Commit the code of HIST and IGMTF on Alpha360

* add stock index

* Update README.md

* delete useless code

* fix the bug of code format with black

* fix pylint bugs

* fix the bugs of pylint

* fix pylint bugs

* fix flake8
This commit is contained in:
Wentao Xu
2022-04-14 01:45:49 +08:00
committed by GitHub
parent 7bfc7e1797
commit 87926513cb
11 changed files with 1149 additions and 0 deletions
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examples/benchmarks/HIST/README.md Normal file
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# HIST
* Code: [https://github.com/Wentao-Xu/HIST](https://github.com/Wentao-Xu/HIST)
* Paper: [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared InformationAdaRNN: Adaptive Learning and Forecasting for Time Series](https://arxiv.org/abs/2110.13716).

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examples/benchmarks/HIST/qlib_csi300_stock_index.npy Normal file
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examples/benchmarks/HIST/requirements.txt Normal file
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pandas==1.1.2
numpy==1.21.0
scikit_learn==0.23.2
torch==1.7.0

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examples/benchmarks/HIST/workflow_config_hist_Alpha360.yaml Normal file
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy
kwargs:
signal:
- <MODEL>
- <DATASET>
topk: 50
n_drop: 5
backtest:
start_time: 2017-01-01
end_time: 2020-08-01
account: 100000000
benchmark: *benchmark
exchange_kwargs:
limit_threshold: 0.095
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: HIST
module_path: qlib.contrib.model.pytorch_hist
kwargs:
d_feat: 6
hidden_size: 64
num_layers: 2
dropout: 0
n_epochs: 200
lr: 1e-4
early_stop: 20
metric: ic
loss: mse
base_model: LSTM
model_path: "benchmarks/LSTM/model_lstm_csi300.pkl"
stock2concept: "benchmarks/HIST/qlib_csi300_stock2concept.npy"
stock_index: "benchmarks/HIST/qlib_csi300_stock_index.npy"
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs:
model: <MODEL>
dataset: <DATASET>
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

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examples/benchmarks/IGMTF/README.md Normal file
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# IGMTF
* Code: [https://github.com/Wentao-Xu/IGMTF](https://github.com/Wentao-Xu/IGMTF)
* Paper: [IGMTF: An Instance-wise Graph-based Framework for
Multivariate Time Series Forecasting](https://arxiv.org/abs/2109.06489).

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examples/benchmarks/IGMTF/requirements.txt Normal file
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pandas==1.1.2
numpy==1.21.0
scikit_learn==0.23.2
torch==1.7.0

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examples/benchmarks/IGMTF/workflow_config_igmtf_Alpha360.yaml Normal file
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy
kwargs:
model: <MODEL>
dataset: <DATASET>
topk: 50
n_drop: 5
backtest:
start_time: 2017-01-01
end_time: 2020-08-01
account: 100000000
benchmark: *benchmark
exchange_kwargs:
limit_threshold: 0.095
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: IGMTF
module_path: qlib.contrib.model.pytorch_igmtf
kwargs:
d_feat: 6
hidden_size: 64
num_layers: 2
dropout: 0
n_epochs: 200
lr: 1e-4
early_stop: 20
metric: ic
loss: mse
base_model: LSTM
model_path: "benchmarks/LSTM/model_lstm_csi300.pkl"
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs:
model: <MODEL>
dataset: <DATASET>
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

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examples/benchmarks/README.md
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@@ -65,6 +65,9 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
| GATs (Petar Velickovic, et al.) | Alpha360 | 0.0476±0.00 | 0.3508±0.02 | 0.0598±0.00 | 0.4604±0.01 | 0.0824±0.02 | 1.1079±0.26 | -0.0894±0.03 |
| TCTS(Xueqing Wu, et al.) | Alpha360 | 0.0508±0.00 | 0.3931±0.04 | 0.0599±0.00 | 0.4756±0.03 | 0.0893±0.03 | 1.2256±0.36 | -0.0857±0.02 |
| TRA(Hengxu Lin, et al.) | Alpha360 | 0.0485±0.00 | 0.3787±0.03 | 0.0587±0.00 | 0.4756±0.03 | 0.0920±0.03 | 1.2789±0.42 | -0.0834±0.02 |
| IGMTF(Wentao Xu, et al.) | Alpha360 | 0.0480±0.00 | 0.3589±0.02 | 0.0606±0.00 | 0.4773±0.01 | 0.0946±0.02 | 1.3509±0.25 | -0.0716±0.02 |
| HIST(Wentao Xu, et al.) | Alpha360 | 0.0522±0.00 | 0.3530±0.01 | 0.0667±0.00 | 0.4576±0.01 | 0.0987±0.02 | 1.3726±0.27 | -0.0681±0.01 |
- The selected 20 features are based on the feature importance of a lightgbm-based model.
- The base model of DoubleEnsemble is LGBM.