run:
  name: baseline_feature_target_model_registry
  seed: 42
  output_dir: artifacts/runs

# ------------------------------------------------------------
# DATA
# ------------------------------------------------------------
data:
  path: bars_bybit_1min_2026-03-01_2026-05-11.parquet
  time_col: time
  symbol_col: symbol
  symbol_candle: INDEX
  candle_rule: 1min
  min_coverage: 0.98
  max_symbols: 120
  required_cols:
    - open
    - high
    - low
    - close
    - buy_volume
    - sell_volume

# ------------------------------------------------------------
# SPLIT
# ------------------------------------------------------------
split:
  train_size: 0.70
  test_size: 0.15
  valid_size: 0.15

# ------------------------------------------------------------
# BACKTEST
# ------------------------------------------------------------
backtest:
  fee_rate: 0.0005
  initial_state: 1

# ------------------------------------------------------------
# FEATURE SETS
# ------------------------------------------------------------
feature_sets:
  A_market_basic:
    description: "returns, range, ATR, volatility, volume imbalance"
    builders:
      - name: market_basic
        params:
          return_lags: [1, 3, 5, 15, 30, 60, 120]
          range_windows: [14, 30, 60, 120]
          atr_windows: [14, 60, 120]
          vol_windows: [30, 60, 120, 360]
          volume_windows: [30, 60, 120]

  B_primitive_pressure:
    description: "primitive continuous pressure parameters from OHLCV"
    builders:
      - name: primitive_pressure
        params:
          price_lags: [13, 34, 55, 144, 233]
          force_lag: 34
          volume_norm_win: 1440

  C_mode_values:
    description: "continuous mode values: level, speed, accel, cycle, persistence"
    builders:
      - name: mode_values
        params:
          speed_ema: 45
          speed_lag: 5
          cycle_win: 360
          persist_win: 120

  D_state_features:
    description: "discrete state features derived from mode values"
    builders:
      - name: state_features
        params:
          deadband: 0.05
          cycle_deadband: 0.15
          persist_deadband: 0.20

  E_agreement_features:
    description: "agreement features derived from state features"
    builders:
      - name: agreement_features
        params:
          state:
            deadband: 0.05
            cycle_deadband: 0.15
            persist_deadband: 0.20

  F_long_context_features:
    description: "long-context features derived from primitive pressures"
    builders:
      - name: long_context
        params:
          ema_spans: [120, 240, 360, 720, 1440]
          trend_lags: [120, 240, 360, 720]
          cycle_wins: [360, 720, 1440]
          persist_wins: [240, 480, 720, 1440]
          fast_span: 45

# ------------------------------------------------------------
# TARGET SETS
# ------------------------------------------------------------
target_sets:
  zz_long:
    builders:
      - name: zigzag
        params:
          pct_list: [0.018, 0.022, 0.026, 0.030, 0.035, 0.040]
          atr_mult_list: [1.5, 2.0, 2.5]
          min_bars_list: [180, 240, 360, 480, 720]
          atr_window: 60
          initial_state: 1

  future_mean:
    builders:
      - name: future_mean
        params:
          horizon_list: [360, 720, 1080, 1440]
          min_move_pct_list: [0.004, 0.0065, 0.010]
          atr_mult_list: [0.75, 1.25, 1.75]
          fee_rate: 0.0005
          fee_safety_rate: 0.00075
          atr_window: 60
          initial_state: 1

  future_return:
    builders:
      - name: future_return
        params:
          horizon_list: [240, 360, 720, 1080, 1440]
          min_move_pct_list: [0.005, 0.008, 0.012]
          atr_mult_list: [1.0, 1.5, 2.0]
          fee_rate: 0.0005
          fee_safety_rate: 0.00075
          atr_window: 60
          initial_state: 1

# ------------------------------------------------------------
# MODELS
# ------------------------------------------------------------
models:
  logreg:
    name: sklearn_logreg
    params:
      C: 0.5
      max_iter: 2000
      class_weight: balanced

  extra_trees:
    name: sklearn_extra_trees
    params:
      n_estimators: 400
      max_depth: 12
      min_samples_leaf: 60
      max_features: sqrt
      n_jobs: -1
      random_state: 42

  hist_gb:
    name: sklearn_hist_gradient_boosting
    params:
      max_iter: 250
      learning_rate: 0.05
      max_leaf_nodes: 31
      l2_regularization: 0.01
      random_state: 42

# ------------------------------------------------------------
# EXPERIMENT MATRIX
# ------------------------------------------------------------
experiment_matrix:
  feature_sets: [A_market_basic, B_primitive_pressure, C_mode_values, D_state_features, E_agreement_features, F_long_context_features]
  target_sets: [zz_long, future_mean, future_return]
  models: [logreg, extra_trees, hist_gb]

training:
  max_targets_per_set: null
  save_predictions: true
  save_models: true

ensemble:
  enabled: true
  methods:
    - majority_vote
    - score_average