# -----------------------------------------------------------------------------
# Tune run 3 (NEXT run): 5-day label + clean 10-name universe.
#
# Baseline (exp 1 / run f29f5446):
#   IC 0.071, ICIR 0.17, Rank IC 0.014, Rank ICIR 0.03  ->  ranking ~ coin flip
#   valid l2 best at round 0 and never improved (early-stopped ~50 rounds, overfit)
#   backtest: strategy +4.9% ann (raw) vs equal-weight universe +89.2% ann
#   (benchmark was unset -> qlib used equal-weight), excess w/ cost -94.0% ann,
#   IR -2.23, max DD -18.9%. topk=2, 24 trades/27 days, $11.1k cost (1.1% of $1M),
#   ending book ~97.5% in AAPL+IBIT (two names, both ~49%).
#
# PRIMARY LEVER (change one thing, everything else held at baseline):
#   label: 1-day next return -> 5-day forward return
#     "Ref($close,-6)/Ref($close,-1)-1".
#   Rationale: Rank ICIR 0.03 is the binding constraint - a topk book's return
#   is bounded by ranking quality, and no backtest tuning fixes a non-existent
#   ranking. The retained TA features (rsi_14, macd_hist, ema_20, volume,
#   stoch, aroon) are momentum/mean-reversion proxies that predict multi-day
#   drift, not overnight noise; and the avg holding in the baseline book was
#   several days, so a 1-day label mismatches the holding horizon.
#
# SUPPORTING (kept minimal, flagged for attribution):
#   - universe 17 -> 10: drop leveraged/vol/cash names (VXX, USO, SLV, BIL)
#     and near-duplicate index baskets (GPIQ, QQQE, KTEC). 17 names were really
#     ~8 independent betas (QQQ/QQQE/IVV/SMH/AIQ overlap heavily).
#   - topk 2 -> 5, n_drop 1 -> 2: stop the 2-name lottery, cut per-name turnover.
#   - benchmark: unset -> QQQ (a real index ETF the universe tracks; the
#     "excess return" vs equal-weight of a 17-name universe is misleading).
#   - model: explicitly num_boost_round 1000 + early_stopping_rounds 50 so the
#     round count is actually controlled (baseline's n_estimators: 200 was a
#     no-op, swallowed into lgb params; rounds were the 1000 default).
#     Hyperparameters otherwise identical to baseline (lr 0.05, num_leaves 15,
#     reg 0.01/0.01) for a clean label A/B.
#
# Trigger into a NEW experiment (do not pollute exp 1):
#   rd_run_workflow config_path=tac-qlib/workflows/tune_run3_label5d_clean_universe.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:///{{ LAKE }}/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
            num_boost_round: 1000
            early_stopping_rounds: 50
            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: 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