# -----------------------------------------------------------------------------
# Run 94736d89 (exp-4 tac-rd-tune2) follow-up -- single lever: WIDER UNIVERSE.
#
# Baseline (run 94736d89): 10 correlated tech/growth names -> weak cross-section
# (IC 0.038 / ICIR 0.10), topk=5 book all-correlated, 295 trades / 152d and
# $58k cost drag (5.8% of $1M) -> excess ann -18.8% vs QQQ.
#
# This run holds EVERYTHING else fixed (windows, 5-day label, LGB hyperparams,
# topk=5/n_drop=2, benchmark QQQ) and only widens the universe 10 -> 17 with the
# full lake set, adding genuinely uncorrelated assets (BIL cash, USO oil, SLV
# silver, VXX vol, KTEC/QQQE/GPIQ factor sleeves) to de-correlate the cross-section,
# stabilize the top-5 ranking and cut the churn/cost drag.
# -----------------------------------------------------------------------------
{%- 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-tune3"

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,BIL,GPIQ,KTEC,QQQE,SLV,USO,VXX
                    start_time: 2000-01-03
                    end_time: 2026-08-01
                    fit_start_time: 2024-06-03
                    fit_end_time: 2025-11-28
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
                    label: "Ref($close,-6)/Ref($close,-1)-1"
            segments:
                train: [2024-06-03, 2025-11-28]
                valid: [2025-12-01, 2025-12-31]
                test: [2026-01-01, 2026-08-01]

    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: 2026-01-01
                      end_time: 2026-08-01
                      account: 1000000
                      benchmark: QQQ
                      exchange_kwargs:
                          codes: AAPL,MSFT,TSLA,QQQ,IVV,SMH,TLT,IBIT,MCHI,AIQ,BIL,GPIQ,KTEC,QQQE,SLV,USO,VXX
                          deal_price: $close
                          freq: day
                          open_cost: 0.0005
                          close_cost: 0.0015
                          min_cost: 5.0
              risk_analysis_freq: 1d