# -----------------------------------------------------------------------------
# Improved RankIC workflow: 300+ stock universe, proven RankICLGBModel params,
# extended 12-month validation, full SP feature set (40 features).
#
# Changes from repro run:
#   1. Single RankICLGBModel (not ensemble) — proven config from skill
#   2. num_leaves=15 (not 31) — the verified value
#   3. Universe expanded from 50 ETFs to 300+ single stocks + ETFs
#   4. Validation extended to 12 months (2025-01 to 2026-01)
#   5. Full 40 SP features (no leakage confirmed)
#   6. Early stopping still at 200 (proven)
#
# Run:
#   rd_run_workflow config_path=tac-qlib/workflows/workflow_lgb_300sp_rankic.yaml \
#       experiment_name=tac-rd-300sp-rankic
# -----------------------------------------------------------------------------
{%- 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-300sp-rankic" }

task:
    model:
        # Single RankICLGBModel — proven config from tac-qlib-custom skill.
        # Per-day query groups + feval=rankic + metric='None' so early-stopping
        # tracks mean per-day Spearman instead of l2.
        class: RankICLGBModel
        module_path: tac_qlib.contrib.model.rank_gbdt
        kwargs:
            loss: mse
            learning_rate: 0.02
            num_leaves: 15
            num_boost_round: 3000
            early_stopping_rounds: 200
            min_data_in_leaf: 20
            lambda_l1: 0.0
            lambda_l2: 0.5
            colsample_bytree: 0.8
            subsample: 0.8
            subsample_freq: 1
            seed: 2026

    dataset:
        class: DatasetH
        module_path: qlib.data.dataset
        kwargs:
            handler:
                class: TACHandler
                module_path: tac_qlib.contrib.data.handler
                kwargs:
                    # Expanded universe: all lake symbols (instruments: "all" = every symbol with bars in the lake)
                    instruments: "all"
                    start_time: "2015-01-03"
                    end_time: "2026-08-14"
                    fit_start_time: "2016-01-04"
                    fit_end_time: "2025-01-01"
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
                    label: "Ref($close,-6)/Ref($close,-1)-1"
                    # Full 40 SP features + 6 OHLCV = 46 features
                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_logp,sp_hurst_exponent,sp_ou_half_life,sp_ou_revert,sp_ou_zscore,sp_hmm_state,sp_hmm_p_regime1,sp_jump_flag,sp_jump_ratio,sp_jump_tail,sp_max_move,sp_max_up,sp_max_down,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5"
                    infer_processors:
                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-01-01" } }
                        - { class: ProcessInf, kwargs: {} }
                        - { class: CSRankNorm, kwargs: {} }
                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-01-01" } }
                        - { class: Fillna, kwargs: {} }
            segments:
                train: ["2016-01-04", "2024-12-31"]
                valid: ["2025-01-02", "2026-01-02"]
                test:  ["2026-01-04", "2026-08-14"]

    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: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
                  backtest:
                      start_time: "2026-01-04"
                      end_time: "2026-08-14"
                      account: 1000000
                      benchmark: SPY
                      exchange_kwargs:
                          codes: ""
                          deal_price: $close
                          freq: day
                          open_cost: 0.0005
                          close_cost: 0.0015
                          min_cost: 5.0
              risk_analysis_freq: 1d