# -----------------------------------------------------------------------------
# Tune run 2: same-day signal, strongly regularized model, 3x rotating book.
#
# Baseline (exp 1 / run f29f5446): IC 0.071 / ICIR 0.17, Rank IC ~0.014;
# excess return w/ cost -0.94 ann, IR -2.23. The 1-day signal was noisy
# (Rank IC ~ 0) and the topk=2 book turned over 24 times in 27 days, paying
# ~1.1% of the $1M account in costs.
#
# Changes (isolates model/backtest effects; universe + label same as baseline):
#   - model: stronger regularization (reg_alpha 0.5, reg_lambda 5.0,
#     subsample 0.7, colsample 0.6) to combat the unstable Rank IC.
#   - topk 2 -> 3, n_drop 1 -> 2: rotate out losers faster (lower cost drag,
#     higher turnover on only the worst names).
#   - benchmark AAPL -> QQQ.
#   - universe: drop leveraged/duplicate names (VXX, USO, SLV, BIL, GPIQ,
#     QQQE, KTEC) for a cleaner cross-section; keeps baseline 1-day label.
#
# Trigger:
#   rd_run_workflow config_path=tac-qlib/workflows/tune_run2_regularized.yaml \
#       experiment_name=tac-rd-tune
# -----------------------------------------------------------------------------
{%- 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-tune"

task:
    model:
        class: LGBModel
        module_path: qlib.contrib.model.gbdt
        kwargs:
            loss: mse
            learning_rate: 0.05
            num_leaves: 15
            n_estimators: 250
            colsample_bytree: 0.6
            subsample: 0.7
            subsample_freq: 1
            reg_alpha: 0.5
            reg_lambda: 5.0
            seed: 2026

    dataset:
        class: DatasetH
        module_path: qlib.data.dataset
        kwargs:
            handler:
                class: TACHandler
                module_path: tac_qlib.contrib.data.handler
                kwargs:
                    instruments: AAPL,MSFT,TSLA,QQQ,IVV,SMH,TLT,IBIT,MCHI,AIQ
                    start_time: 2000-01-03
                    end_time: 2026-08-06
                    fit_start_time: 2026-03-01
                    fit_end_time: 2026-05-31
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
            segments:
                train: [2026-03-01, 2026-05-31]
                valid: [2026-06-01, 2026-06-30]
                test: [2026-07-01, 2026-08-06]

    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: 3
                          n_drop: 2
                          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,TSLA,QQQ,IVV,SMH,TLT,IBIT,MCHI,AIQ
                          deal_price: $close
                          freq: day
                          open_cost: 0.0005
                          close_cost: 0.0015
                          min_cost: 5.0
              risk_analysis_freq: 1d