mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-08 12:37:44 +00:00
Xisen-Wang
110 lines
3.5 KiB
YAML
110 lines
3.5 KiB
YAML
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
|
|
kwargs:
|
|
signal: <PRED>
|
|
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: GeneralPTNN
|
|
module_path: qlib.contrib.model.pytorch_general_nn
|
|
kwargs:
|
|
n_epochs: 100
|
|
lr: 1e-3
|
|
early_stop: 10
|
|
batch_size: 2000
|
|
metric: loss
|
|
loss: mse
|
|
n_jobs: 20
|
|
GPU: 2
|
|
# loss: mse
|
|
# lr: 0.002
|
|
# optimizer: adam
|
|
# batch_size: 8192
|
|
# GPU: 0
|
|
weight_decay: 0.0001
|
|
# pt_model_uri: "qlib.contrib.model.pytorch_nn.Net"
|
|
# pt_model_uri: "env_tpl.model.Net"
|
|
# pt_model_uri: "./model.py:Net"
|
|
pt_model_uri: "model.model_cls"
|
|
pt_model_kwargs: {
|
|
"num_features": 20,
|
|
{% if num_timesteps %}num_timesteps: {{ num_timesteps }}{% endif %}
|
|
}
|
|
# input_dim: 20
|
|
# How should I use jinja to put step len here conditionally
|
|
dataset:
|
|
class: {{ dataset_cls | default("DatasetH") }}
|
|
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]
|
|
{% if step_len %}step_len: {{ step_len }}{% endif %}
|
|
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
|