# CTA 1-Day Return Prediction - Experiment Configuration

# Data Configuration
data:
  dt_range: ['2020-01-01', '2023-12-31']
  feature_sets:
    - alpha158
    - hffactor
  normalization: dual
  blend_weights: equal  # Options: equal, zscore_heavy, rolling_heavy, cs_heavy, short_term, long_term

# Data Segments (train/valid/test split)
segments:
  train: ['2020-01-01', '2022-06-30']
  valid: ['2022-07-01', '2022-12-31']
  test: ['2023-01-01', '2023-12-31']

# Model Configuration
model:
  type: xgb
  params:
    objective: reg:squarederror
    eval_metric: rmse
    eta: 0.05
    max_depth: 6
    subsample: 0.8
    colsample_bytree: 0.8
    seed: 42
  num_boost_round: 500
  early_stopping_rounds: 50

# Training Configuration
training:
  return_type: o2c_twap1min
  weight_factors:
    positive: 1.0
    negative: 2.0

# Backtest Configuration
backtest:
  num_trades: 4
  signal_dist: normal
  pos_weight: true

# Output Configuration
output:
  base_dir: results/cta_1d
  save_model: true
  save_predictions: true
  save_importance: true