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Merge branch 'main' into nested_decision_exe

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you-n-g
2021-07-23 12:38:20 +08:00
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parent 4e862f7d1f 3810a4cd33
commit d445f28e5f
31 changed files with 1738 additions and 39 deletions
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3
examples/benchmarks/Localformer/requirements.txt Normal file
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@@ -0,0 +1,3 @@
numpy==1.17.4
pandas==1.1.2
torch==1.2.0

82
examples/benchmarks/Localformer/workflow_config_localformer_Alpha158.yaml Normal file
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@@ -0,0 +1,82 @@
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: FilterCol
kwargs:
fields_group: feature
col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10",
"ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5",
"RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
]
- 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.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: LocalformerModel
module_path: qlib.contrib.model.pytorch_localformer_ts
kwargs:
seed: 0
n_jobs: 20
dataset:
class: TSDatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha158
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]
step_len: 20
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- 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

73
examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml Normal file
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@@ -0,0 +1,73 @@
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.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: LocalformerModel
module_path: qlib.contrib.model.pytorch_localformer
kwargs:
d_feat: 6
seed: 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: {}
- 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

6
examples/benchmarks/README.md
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@@ -1,6 +1,6 @@
# Benchmarks Performance
Here are the results of each benchmark model running on Qlib's `Alpha360` and `Alpha158` dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 20 runs.
Here are the results of each benchmark model running on Qlib's `Alpha360` and `Alpha158` dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 20 runs with different random seeds.
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.
@@ -23,6 +23,8 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha360 | 0.0407±0.00| 0.3053±0.00 | 0.0490±0.00 | 0.3840±0.00 | 0.0380±0.02 | 0.5000±0.21 | -0.0984±0.02 |
| TabNet (Sercan O. Arik, et al.)| Alpha360 | 0.0192±0.00 | 0.1401±0.00| 0.0291±0.00 | 0.2163±0.00 | -0.0258±0.00 | -0.2961±0.00| -0.1429±0.00 |
| TCTS (Xueqing Wu, et al.)| Alpha360 | 0.0485±0.00 | 0.3689±0.04| 0.0586±0.00 | 0.4669±0.02 | 0.0816±0.02 | 1.1572±0.30| -0.0689±0.02 |
| Transformer (Ashish Vaswani, et al.)| Alpha360 | 0.0141±0.00 | 0.0917±0.02| 0.0331±0.00 | 0.2357±0.03 | -0.0259±0.03 | -0.3323±0.43| -0.1763±0.07 |
| Localformer (Juyong Jiang, et al.)| Alpha360 | 0.0408±0.00 | 0.2988±0.03| 0.0538±0.00 | 0.4105±0.02 | 0.0275±0.03 | 0.3464±0.37| -0.1182±0.03 |
## Alpha158 dataset
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
@@ -39,6 +41,8 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |
| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha158 | 0.0544±0.00 | 0.4338±0.01 | 0.0523±0.00 | 0.4257±0.01 | 0.1253±0.01 | 1.4105±0.14 | -0.0902±0.01 |
| TabNet (Sercan O. Arik, et al.)| Alpha158 | 0.0383±0.00 | 0.3414±0.00| 0.0388±0.00 | 0.3460±0.00 | 0.0226±0.00 | 0.2652±0.00| -0.1072±0.00 |
| Transformer (Ashish Vaswani, et al.)| Alpha158 | 0.0274±0.00 | 0.2166±0.04| 0.0409±0.00 | 0.3342±0.04 | 0.0204±0.03 | 0.2888±0.40| -0.1216±0.04 |
| Localformer (Juyong Jiang, et al.)| Alpha158 | 0.0355±0.00 | 0.2747±0.04| 0.0466±0.00 | 0.3762±0.03 | 0.0506±0.02 | 0.7447±0.34| -0.0875±0.02 |
- The selected 20 features are based on the feature importance of a lightgbm-based model.
- The base model of DoubleEnsemble is LGBM.

3
examples/benchmarks/Transformer/requirements.txt Normal file
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@@ -0,0 +1,3 @@
numpy==1.17.4
pandas==1.1.2
torch==1.2.0

82
examples/benchmarks/Transformer/workflow_config_transformer_Alpha158.yaml Normal file
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@@ -0,0 +1,82 @@
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: FilterCol
kwargs:
fields_group: feature
col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10",
"ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5",
"RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
]
- 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.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: TransformerModel
module_path: qlib.contrib.model.pytorch_transformer_ts
kwargs:
seed: 0
n_jobs: 20
dataset:
class: TSDatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha158
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]
step_len: 20
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- 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

73
examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml Normal file
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@@ -0,0 +1,73 @@
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.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: TransformerModel
module_path: qlib.contrib.model.pytorch_transformer
kwargs:
d_feat: 6
seed: 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: {}
- 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

2
examples/highfreq/highfreq_handler.py
View File

@@ -99,8 +99,6 @@ class HighFreqHandler(DataHandlerLP):
]
names += ["$volume_1"]
fields += ["Cut({0}, 240, None)".format(template_paused.format("Date($close)"))]
names += ["date"]
return fields, names

3
examples/highfreq/highfreq_processor.py
View File

@@ -33,6 +33,9 @@ class HighFreqNorm(Processor):
self.feature_vmin[name] = np.nanmin(part_values)
def __call__(self, df_features):
df_features["date"] = pd.to_datetime(
df_features.index.get_level_values(level="datetime").to_series().dt.date.values
)
df_features.set_index("date", append=True, drop=True, inplace=True)
df_values = df_features.values
names = {

44
examples/run_all_model.py
View File

@@ -23,7 +23,6 @@ from qlib.config import REG_CN
from qlib.workflow import R
from qlib.tests.data import GetData
# init qlib
provider_uri = "~/.qlib/qlib_data/cn_data"
exp_folder_name = "run_all_model_records"
@@ -40,6 +39,7 @@ exp_manager = {
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
qlib.init(provider_uri=provider_uri, region=REG_CN, exp_manager=exp_manager)
# decorator to check the arguments
def only_allow_defined_args(function_to_decorate):
@functools.wraps(function_to_decorate)
@@ -92,7 +92,8 @@ def create_env():
# function to execute the cmd
def execute(cmd):
def execute(cmd, wait_when_err=False):
print("Running CMD:", cmd)
with subprocess.Popen(cmd, stdout=subprocess.PIPE, bufsize=1, universal_newlines=True, shell=True) as p:
for line in p.stdout:
sys.stdout.write(line.split("\b")[0])
@@ -102,6 +103,8 @@ def execute(cmd):
sys.stdout.write("\b" * 10 + "\b".join(line.split("\b")[1:-1]))
if p.returncode != 0:
if wait_when_err:
input("Press Enter to Continue")
return p.stderr
else:
return None
@@ -184,7 +187,15 @@ def gen_and_save_md_table(metrics, dataset):
# function to run the all the models
@only_allow_defined_args
def run(times=1, models=None, dataset="Alpha360", exclude=False):
def run(
times=1,
models=None,
dataset="Alpha360",
exclude=False,
qlib_uri: str = "git+https://github.com/microsoft/qlib#egg=pyqlib",
wait_before_rm_env: bool = False,
wait_when_err: bool = False,
):
"""
Please be aware that this function can only work under Linux. MacOS and Windows will be supported in the future.
Any PR to enhance this method is highly welcomed. Besides, this script doesn't support parrallel running the same model
@@ -200,6 +211,13 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False):
determines whether the model being used is excluded or included.
dataset : str
determines the dataset to be used for each model.
qlib_uri : str
the uri to install qlib with pip
it could be url on the we or local path
wait_before_rm_env : bool
wait before remove environment.
wait_when_err : bool
wait when errors raised when executing commands
Usage:
-------
@@ -240,32 +258,36 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False):
sys.stderr.write("\n")
# install requirements.txt
sys.stderr.write("Installing requirements.txt...\n")
execute(f"{python_path} -m pip install -r {req_path}")
execute(f"{python_path} -m pip install -r {req_path}", wait_when_err=wait_when_err)
sys.stderr.write("\n")
# setup gpu for tft
if fn == "TFT":
execute(
f"conda install -y --prefix {env_path} anaconda cudatoolkit=10.0 && conda install -y --prefix {env_path} cudnn"
f"conda install -y --prefix {env_path} anaconda cudatoolkit=10.0 && conda install -y --prefix {env_path} cudnn",
wait_when_err=wait_when_err,
)
sys.stderr.write("\n")
# install qlib
sys.stderr.write("Installing qlib...\n")
execute(f"{python_path} -m pip install --upgrade pip") # TODO: FIX ME!
execute(f"{python_path} -m pip install --upgrade cython") # TODO: FIX ME!
execute(f"{python_path} -m pip install --upgrade pip", wait_when_err=wait_when_err) # TODO: FIX ME!
execute(f"{python_path} -m pip install --upgrade cython", wait_when_err=wait_when_err) # TODO: FIX ME!
if fn == "TFT":
execute(
f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall --ignore-installed PyYAML -e git+https://github.com/microsoft/qlib#egg=pyqlib"
f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall --ignore-installed PyYAML -e {qlib_uri}",
wait_when_err=wait_when_err,
) # TODO: FIX ME!
else:
execute(
f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft/qlib#egg=pyqlib"
f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e {qlib_uri}",
wait_when_err=wait_when_err,
) # TODO: FIX ME!
sys.stderr.write("\n")
# run workflow_by_config for multiple times
for i in range(times):
sys.stderr.write(f"Running the model: {fn} for iteration {i+1}...\n")
errs = execute(
f"{python_path} {env_path / 'src/pyqlib/qlib/workflow/cli.py'} {yaml_path} {fn} {exp_folder_name}"
f"{python_path} {env_path / 'bin' / 'qrun'} {yaml_path} {fn} {exp_folder_name}",
wait_when_err=wait_when_err,
)
if errs is not None:
_errs = errors.get(fn, {})
@@ -274,6 +296,8 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False):
sys.stderr.write("\n")
# remove env
sys.stderr.write(f"Deleting the environment: {env_path}...\n")
if wait_before_rm_env:
input("Press Enter to Continue")
shutil.rmtree(env_path)
# getting all results
sys.stderr.write(f"Retrieving results...\n")