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YAML Metadata Warning:The task_categories "quantitative-finance" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
P2 ETF Rough Path Forecaster Results
This dataset contains the output from the ROUGH-PATH-FORECASTER engine.
Engine Description
Uses signature kernel methods and Log-ODE for ETF return forecasting.
- Signature Kernel: Neumann series expansion with dynamic truncation
- Log-ODE: Neural controlled differential equations on log-signature space
- Ensemble: Weighted combination of depths 2, 3, and 4
Universes
Fixed Income / Commodities
- Benchmark: AGG
- Tickers (7): TLT, LQD, HYG, VNQ, GLD, SLV, VCIT
Equity
- Benchmark: SPY
- Tickers (14): QQQ, XLK, XLF, XLE, XLV, XLI, XLY, XLP, XLU, XLRE, XLB, GDX, XME, IWM
Training Modes
Fixed Dataset
- Period: 2008 → 2026 YTD
- Split: 80% train, 10% validation, 10% test
- Single model trained on all available data
Shrinking Windows (17 windows)
- Start years: 2008 through 2024
- End year: 2026 YTD (all windows)
- Each window: independent model
- Consensus scoring across windows
Consensus Weights
- 60% Annualized Return
- 20% Sharpe Ratio
- 20% (-)Max Drawdown
Output Structure
fi/ ├── fixed/ │ ├── model.pkl # Trained model │ ├── predictions.parquet # Test set predictions │ ├── actuals.parquet # Test set actual returns │ └── metrics.json # Performance metrics └── shrinking/ ├── model_window_*.pkl # Per-window models ├── window_results.parquet # Window metadata ├── consensus.parquet # Consensus pick ├── window_picks.parquet # Per-window picks └── window_metrics.parquet # Per-window performance
equity/ └── (same structure as fi/)
metadata.json
Performance Metrics
| Metric | Description |
|---|---|
| annualized_return_pct | Annualized return percentage |
| annualized_vol_pct | Annualized volatility percentage |
| sharpe_ratio | Risk-adjusted return |
| max_drawdown_pct | Maximum peak-to-trough decline |
| hit_rate_pct | Percentage of positive days |
| alpha_vs_benchmark_pct | Excess return over benchmark |
Last Updated
2026-06-23T03:29:25.425342
License
MIT
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