Updates

configs/qlib/workflow_config_lightgbm_Alpha360.yaml

@@ -10,7 +10,7 @@ data_handler_config: &data_handler_config
     fit_end_time: 2014-12-31
     instruments: *market
     infer_processors:
-	- class: RobustZScoreNorm
+        - class: RobustZScoreNorm
           kwargs:
               fields_group: feature
               clip_outlier: true

configs/qlib/workflow_config_transformer_Alpha360.yaml

@@ -0,0 +1,78 @@
+qlib_init:
+    provider_uri: "~/.qlib/qlib_data/cn_data"
+    region: cn
+market: &market all
+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: QuantTransformer
+        module_path: trade_models.quant_transformer
+        kwargs:
+            net_config:
+            opt_config:
+            loss: mse
+            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: {}
+        - class: SignalMseRecord
+          module_path: qlib.contrib.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

configs/qlib/workflow_config_xgboost_Alpha360.yaml

@@ -10,7 +10,7 @@ data_handler_config: &data_handler_config
     fit_end_time: 2014-12-31
     instruments: *market
     infer_processors:
-	- class: RobustZScoreNorm
+        - class: RobustZScoreNorm
           kwargs:
               fields_group: feature
               clip_outlier: true

exps/trading/baselines.py

@@ -56,6 +56,7 @@ def retrieve_configs():
     alg2names["TabNet"] = "workflow_config_TabNet_Alpha360.yaml"
     alg2names["NAIVE-V1"] = "workflow_config_naive_v1_Alpha360.yaml"
     alg2names["NAIVE-V2"] = "workflow_config_naive_v2_Alpha360.yaml"
+    alg2names["Transformer"] = "workflow_config_transformer_Alpha360.yaml"
 
     # find the yaml paths
     alg2paths = OrderedDict()

lib/trade_models/transformers.py

@@ -17,15 +17,15 @@ import layers as xlayers
 
 DEFAULT_NET_CONFIG = dict(
     d_feat=6,
-    embed_dim=48,
+    embed_dim=64,
     depth=5,
     num_heads=4,
     mlp_ratio=4.0,
     qkv_bias=True,
-    pos_drop=0.1,
-    mlp_drop_rate=0.1,
-    attn_drop_rate=0.1,
-    drop_path_rate=0.1,
+    pos_drop=0.0,
+    mlp_drop_rate=0.0,
+    attn_drop_rate=0.0,
+    drop_path_rate=0.0,
 )