book: scaffold + ch00 (execution trail as spine) — evidence exp 8-31, round 3

2026-08-18 22:35:23 +00:00
commit c93424e76c
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+# -----------------------------------------------------------------------------
+# MINIMAL workflow — the canonical "run a backtest" template for the skill.
+#
+# Every traced backtest runs through a workflow YAML like this one via
+# rd_run_workflow, so the `record` blocks write MLflow artifacts to disk
+# (<lake>/mlruns/<exp_id>/<run_id>). The traced experiment's ref id IS the
+# mlflow run id returned by rd_run_workflow.
+#
+# Trigger:
+#   rd_run_workflow config_path=examples/workflow_minimal.yaml \
+#       experiment_name=tac-rd-minimal
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-minimal" }
+
+task:
+    model:
+        class: LGBModel
+        module_path: qlib.contrib.model.gbdt
+        kwargs:
+            loss: mse
+            learning_rate: 0.05
+            num_leaves: 15
+            n_estimators: 200
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.01
+            reg_lambda: 0.01
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: AAPL,MSFT,QQQ,IVV,SMH,TLT
+                    start_time: 2026-03-01
+                    end_time: 2026-08-06
+                    fit_start_time: 2026-03-01
+                    fit_end_time: 2026-05-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+            segments:
+                train: [2026-03-01, 2026-05-31]
+                valid: [2026-06-01, 2026-06-30]
+                test:  [2026-07-01, 2026-08-06]
+
+    # Record block — REQUIRED. Each entry writes one artifact family to mlruns:
+    #   SignalRecord    pred.pkl + label.pkl
+    #   SigAnaRecord    IC / Rank IC series + long-short group returns
+    #   PortAnaRecord   backtest report / positions / risk
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 2
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-07-01
+                      end_time: 2026-08-06
+                      account: 1000000
+                      benchmark: QQQ
+                      exchange_kwargs:
+                          codes: AAPL,MSFT,QQQ,IVV,SMH,TLT
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d