# -----------------------------------------------------------------------------
# Tune run 4 (NEXT run): fix the universe bug + extend the train window.
#
# Previous (exp 3 / run 1e170e7f): IC -0.025 / ICIR -0.071 / RankIC -0.022 /
# RankICIR -0.073 (noise), Long-Short -27% ann; excess +15.8% ann w/ cost
# (IR 1.35) vs QQQ; $1M -> $971.7k (-2.8%); 65 trades/27d, $12.1k cost.
# l2.train 0.35 vs l2.valid 0.95 -> gross overfit (valid best at round 0,
# early-stopped at 16 trees).
#
# CRITICAL BUG in that run: the 10-name universe was silently IGNORED.
# TACHandler passes `instruments` as a comma-separated STRING; qlib wraps it
# as {"market": "<comma string>", "filter_pipe": []}; LakeInstrumentProvider
# ._resolve_symbols() only handles list/tuple/ndarray and falls through to
# load_symbols() = the ENTIRE 17-symbol lake. So the model trained/traded on
# VXX, USO, SLV, BIL, GPIQ, QQQE, KTEC too - exactly the leveraged/hedge
# names the "clean 10-name universe" hypothesis meant to drop. The universe
# A/B is UNTESTED.
#   Fix (providers.py:100 _resolve_symbols): split comma-separated strings.
#
# PRIMARY LEVER (this run, ONE hypothesis):
#   universe = the intended 10-name dedup pool (AAPL,MSFT,TSLA,QQQ,IVV,SMH,
#   TLT,IBIT,MCHI,AIQ), now actually enforced, + train window 3 months -> 2
#   years. The 3-month window (~1000 rows for a 21-feature GBDT) is the hard
#   ceiling on signal; features span 2000-2026 so more data is free.
#   Everything else held at run-1e170e7f for a clean A/B: 5-day label,
#   LGB baseline hyperparams, topk 5 / n_drop 2, benchmark QQQ.
#
# SUPPORTING (flagged, NOT changed this run to keep attribution clean):
#   - if valid loss still rises monotonically after 2y of data, next step is
#     regularization (reg_alpha/lambda 0.01 -> ~0.5, num_leaves 15 -> 10,
#     lr 0.05 -> 0.02) rather than label/topk changes.
#
# Trigger into a NEW experiment (do not pollute exp 1/3):
#   rd_run_workflow config_path=tac-qlib/workflows/tune_run4_fix_universe_longtrain.yaml \
#       experiment_name=tac-rd-tune2
# -----------------------------------------------------------------------------
{%- 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-tune2"

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: 2024-06-03
                    fit_end_time: 2026-05-31
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
                    label: "Ref($close,-6)/Ref($close,-1)-1"
            segments:
                train: [2024-06-03, 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