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# 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