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QQQ
float64
XLK
float64
XLF
float64
XLE
float64
XLV
float64
XLI
float64
XLY
float64
XLP
float64
XLU
float64
XLRE
float64
XLB
float64
GDX
float64
XME
float64
IWM
float64
__index_level_0__
int64
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End of preview. Expand in Data Studio

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 β†’ 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/ β”œβ”€β”€ fixed/ β”‚ β”œβ”€β”€ model.pkl # Trained model β”‚ β”œβ”€β”€ predictions.parquet # Test set predictions β”‚ β”œβ”€β”€ actuals.parquet # Test set actual returns β”‚ └── metrics.json # Performance metrics └── shrinking/ β”œβ”€β”€ model_window_*.pkl # Per-window models β”œβ”€β”€ window_results.parquet # Window metadata β”œβ”€β”€ consensus.parquet # Consensus pick β”œβ”€β”€ 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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