# Stock 15-Minute Return Prediction - Experiment Configuration

# Data Configuration
data:
  dt_range: ['2020-01-01', '2023-12-31']
  feature_path: /data/parquet/stock_1min_alpha158
  kline_path: /data/parquet/stock_1min_kline
  industry_path: /data/parquet/stock_industry  # Optional
  normalization_mode: dual  # Options: industry, cs_zscore, dual

# 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:
  positive_factor: 1.0  # Weight multiplier for positive returns
  negative_factor: 2.0  # Weight multiplier for negative returns

# Output Configuration
output:
  base_dir: results/stock_15m
  save_model: true
  save_predictions: true