# -----------------------------------------------------------------------------
# Tune run 1: wider, longer-horizon, de-duplicated universe.
#
# Baseline (exp 1 / run f29f5446): IC 0.071 / ICIR 0.17, Rank IC ~0.014;
# strategy +4.9% ann (raw) vs benchmark ~+89% ann; excess return w/ cost
# -0.94 ann, IR -2.23, excess max drawdown -18.9%. topk=2 with 24 trades over
# 27 days on a universe of correlated ETFs + leveraged hedges (VXX/USO/SLV)
# produced high turnover and a portfolio that trailed AAPL badly.
#
# Changes:
#   - universe: drop leveraged/noisy names (VXX, USO, SLV, BIL) and near-
#     duplicate index baskets (GPIQ, QQQE, KTEC); keep 10 liquid core names.
#   - label: 5-day forward return (Ref($close,-6)/Ref($close,-1)-1) to cut
#     single-day noise and match the intended holding horizon.
#   - topk 2 -> 5, n_drop 1: more diversification, lower turnover per name.
#   - benchmark AAPL -> QQQ (a real index ETF the universe tracks).
#   - model: learning_rate 0.03, 300 estimators (slower, deeper fit).
#
# Trigger:
#   rd_run_workflow config_path=tac-qlib/workflows/tune_run1_wider_5d.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: 0.03
            num_leaves: 15
            n_estimators: 300
            colsample_bytree: 0.8
            subsample: 0.8
            subsample_freq: 1
            reg_alpha: 0.01
            reg_lambda: 0.01
            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
                    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:
        - 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: 5
                          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,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