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45 lines
1.7 KiB
45 lines
1.7 KiB
3 weeks ago
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# CTA 1D Parquet Dataset
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This directory contains requirements for CTA (Commodity Trading Advisor) futures
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Parquet datasets used by alpha_lab.
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## Tables
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### cta_alpha158_1d
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Alpha158 features for CTA futures.
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- **Source**: `dfs://daily_stock_run.stg_1day_tinysoft_cta_alpha159_0_7_beta`
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- **Output**: `/data/parquet/dataset/cta_alpha158_1d/`
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- **Columns**: ~163 feature columns + code, m_nDate
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### cta_hffactor_1d
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High-frequency factor features (8 columns).
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- **Source**: `dfs://daily_stock_run.stg_1day_tinysoft_cta_hffactor`
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- **Output**: `/data/parquet/dataset/cta_hffactor_1d/`
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- **Transformation**: Pivot from long to wide format
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- Input columns: code, m_nDate, factor_name, value
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- Output columns: code, m_nDate, vol_1min, skew_1min, ... (8 features)
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- **Filter**: Only include factor_name in [vol_1min, skew_1min, volp_1min,
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volp_ratio_1min, voln_ratio_1min, trend_strength_1min, pv_corr_1min,
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flowin_ratio_1min]
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### cta_dom_1d
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Dominant contract mapping for continuous contracts.
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- **Source**: `dfs://daily_stock_run.dwm_1day_cta_dom`
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- **Output**: `/data/parquet/dataset/cta_dom_1d/`
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- **Filter**: version = 'vp_csmax_roll2_cummax'
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- **Aggregation**: GROUP BY m_nDate, code_init; SELECT first(code) as code
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### cta_labels_1d
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Return labels for different return types.
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- **Source**: `dfs://daily_stock_run.stg_1day_tinysoft_cta_hfvalue`
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- **Output**: `/data/parquet/dataset/cta_labels_1d/`
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- **Filter**: indicator in [twap_open1m@1_twap_close1m@1, twap_open1m@1_twap_open1m@2]
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- **Columns**: code, m_nDate, indicator, value
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## Consumer
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Used by: `alpha_lab/cta_1d/src/loader_parquet.py`
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The alpha_lab project will create a parallel loader that reads from these
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Parquet tables instead of DolphinDB.
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