alpha_lab/cta_1d

CTA 1-Day Return Prediction

Experiments for predicting CTA (Commodity Trading Advisor) futures 1-day returns.

Data

• Features: alpha158, hffactor
• Labels: Return indicators (o2c_twap1min, o2o_twap1min, etc.)
• Normalization: dual (blend of zscore, cs_zscore, rolling_20, rolling_60)

Notebooks

Notebook	Purpose
01_data_check.ipynb	Load and validate CTA data
02_label_analysis.ipynb	Explore label distributions and blending
03_baseline_xgb.ipynb	Train baseline XGBoost model
04_blend_comparison.ipynb	Compare different normalization blends

Blend Configurations

The label blending combines 4 normalization methods:

• zscore: Fit-time mean/std normalization
• cs_zscore: Cross-sectional z-score per datetime
• rolling_20: 20-day rolling window normalization
• rolling_60: 60-day rolling window normalization

Predefined weights (from qshare.config.research.cta.labels):

• equal: [0.25, 0.25, 0.25, 0.25]
• zscore_heavy: [0.5, 0.2, 0.15, 0.15]
• rolling_heavy: [0.1, 0.1, 0.3, 0.5]
• cs_heavy: [0.2, 0.5, 0.15, 0.15]
• short_term: [0.1, 0.1, 0.4, 0.4]
• long_term: [0.4, 0.2, 0.2, 0.2]

Default: [0.2, 0.1, 0.3, 0.4]

Processors Module

The cta_1d.src.processors module provides Polars-based data processors that replicate Qlib's preprocessing pipeline:

Available Processors

Processor	Description
DiffProcessor	Adds diff features with configurable period
FlagMarketInjector	Adds market_0, market_1 columns based on instrument codes
FlagSTInjector	Creates IsST column from ST flags
ColumnRemover	Removes specified columns
FlagToOnehot	Converts one-hot industry flags to single index column
IndusNtrlInjector	Industry neutralization per datetime
RobustZScoreNorm	Robust z-score normalization using median/MAD
Fillna	Fills NaN values with specified value

RobustZScoreNorm with Pre-fitted Parameters

The RobustZScoreNorm processor supports loading pre-fitted parameters from Qlib's proc_list.proc:

from cta_1d.src.processors import RobustZScoreNorm

# Method 1: Load from saved version (recommended)
processor = RobustZScoreNorm.from_version("csiallx_feature2_ntrla_flag_pnlnorm")

# Method 2: Load with direct parameters
processor = RobustZScoreNorm(
    feature_cols=['KMID', 'KLEN', ...],
    use_qlib_params=True,
    qlib_mean=mean_array,
    qlib_std=std_array
)

# Apply normalization
df = processor.process(df)

Parameter Extraction

Extract parameters from Qlib's proc_list.proc:

python stock_1d/d033/alpha158_beta/scripts/extract_qlib_params.py \
    --proc-list /path/to/proc_list.proc \
    --version my_version

Output structure:

data/robust_zscore_params/{version}/
├── mean_train.npy    # Pre-fitted mean (330,)
├── std_train.npy     # Pre-fitted std (330,)
└── metadata.json     # Feature columns and metadata

Pipeline Helper Functions

from cta_1d.src.processors import create_processor_pipeline, get_final_feature_columns

# Create pipeline from processor configs
pipeline = create_processor_pipeline([
    {'type': 'Diff', 'columns': ['turnover', 'free_turnover']},
    {'type': 'RobustZScoreNorm', 'feature_cols': feature_cols},
    {'type': 'Fillna', 'value': 0},
])

# Get final feature columns after industry neutralization
final_cols = get_final_feature_columns(
    alpha158_cols=ALPHA158_COLS,
    market_ext_cols=MARKET_EXT_COLS,
)