Forschungsdesign für Krypto-Perpetual-Funding-Strategien

Artikel Machine Learning for Trading

Zusammenfassung

Diese Fallstudie skizziert eine Forschungspipeline für Krypto-Perpetual-Futures. Dabei werden Funding-Zahlungen, die bei regelmäßigen Abrechnungen zwischen Long- und Short-Positionen ausgetauscht werden, als mögliche Renditequelle betrachtet. Beschrieben werden Daten- und Modellschritte von der Erstellung der Labels und Finanzmerkmale über statistische und Machine-Learning-Modelle, Kausalanalyse, Backtesting, Portfolioaufteilung, Risiko- und Kostenbewertung. Das Setup verwendet einen Querschnitt von Perpetual-Paaren, Beobachtungen im Acht-Stunden-Takt und Transaktionskostenannahmen; die Validierung stützt sich auf eine begrenzte Zahl zeitlicher Folds.

Das Dokument betont das Timing abgeschlossener Kerzen, offizielle Funding-Zahlungsströme und die Trennung von Modellbelegen und Diagnosen, die auf einem eingefrorenen Träger erneut abgespielt werden. Es hält fest, dass im aktuellen Register Backtests oder Kohorten fehlen und die vollständige Release-Pipeline daher noch keine von Lesern reproduzierbare End-to-End-Strategie ist. Nachgelagerte Notebooks können für ausdrücklich genannte Diagnosezwecke ausgeführt werden; ihre Ausgaben sollten jedoch erst dann zu einer aktuellen Strategie zusammengeführt werden, wenn Register und Veröffentlichungshistorie geklärt sind. Der Pipeline-Überblick ist daher als Forschungsdesign nützlich, nicht als Beleg für profitable Ergebnisse.

Kernaussagen

Funding-Transfers bei Perpetual-Futures werden neben preisabhängigen Signalen als mögliche Renditequelle untersucht.
Die Pipeline erstellt Labels und Merkmale, bevor lineare, Boosting- und Deep-Learning-Modelle getestet werden.
Das Timing abgeschlossener Kerzen, offizielle Funding-Zahlungsströme und Transaktionskosten sind zentrale Bestandteile des Forschungsdesigns.
Die Validierung verwendet nur zwei Folds, was die Aussagekraft der Schlussfolgerungen begrenzt.
Diagnosen auf einem eingefrorenen Träger ergeben keine aktuelle End-to-End-Strategie, solange Backtests und Kohorten im Register fehlen.

Schlagwörter

Krypto Perpetual Futures Futures Maschinelles Lernen Backtesting Orderausführung

Volltext

# Crypto Perpetuals Funding


# Crypto Perpetuals Funding

This case study uses Binance perpetual futures to examine an asset-class-specific return source:
the transfer between long and short positions at each 8-hour funding settlement. Nineteen
perpetuals create the book's smallest cross-section and highest non-intraday decision frequency.
The pipeline therefore emphasizes completed-bar timing, official funding cash flows, transaction
costs, and uncertainty from only two validation folds.

## Dataset Profile

| Property | Value |
|---|---|
| Asset class | Crypto perpetual futures |
| Frequency | 8-hourly, aligned to funding settlements |
| Universe | 19 perpetual pairs |
| History | 2020-2025 |
| Primary label | `fwd_ret_8h` |
| Validation design | 2 folds, 2-year train and 1-year validation |
| Cost model | 2 bps maker and 4 bps taker |

## Pipeline

| Stage | Notebook | Chapter | Description | Writes |
|---|---|---|---|---|
| Feasibility | [`01_feasibility_analysis`](01_feasibility_analysis.ipynb) | Ch6 | Checks universe breadth at the funding timestamp, move scale against the fee, premium persistence, and the walk-forward folds. | Nothing; the contract list is fixed in `setup.yaml` |
| Labels | [`02_labels`](02_labels.ipynb) | Ch7 | Builds forward returns and class labels without admitting holdout-ending observations; folds are derived from `setup.yaml` and the label timeline, not written here | One parquet per label in `labels/` (`fwd_ret_8h` plus the `fwd_ret_24h`, `fwd_dir_8h`, `fwd_dir_8h_3c` variants), each with a `.digest.json` sidecar |
| Financial features | [`03_financial_features`](03_financial_features.ipynb) | Ch8 | Produces 39 premium, funding, momentum, volatility, and liquidity features. | `features/financial.parquet` |
| Model-based features | [`04_model_based_features`](04_model_based_features.ipynb) | Ch9 | Adds five fold-specific volatility and regime features fit on prior data. | `features/model_based.parquet` |
| Evaluation | [`05_evaluation`](05_evaluation.ipynb) | Ch7-9 | Evaluates the exact 44-feature training frame on the canonical label clock. | `evaluation/triage_ledger.parquet`, `evaluation/ic_timeseries.parquet` |
| Linear models | [`06_linear`](06_linear.ipynb) | Ch11 | Fits complete Ridge, Lasso, and ElasticNet validation surfaces. | Training runs and prediction sets in `run_log/registry.db`; coefficients under `run_log/training/{hash}/`, scores under `run_log/predictions/{hash}/` |
| Gradient boosting | [`07_gbm`](07_gbm.ipynb) | Ch12 | Trains the CUDA LightGBM grid and preserves physical boosters and predictions. | Training runs and prediction sets; boosters, `learning_curves.parquet`, and `fold_metrics.parquet` under `run_log/training/{hash}/` |
| Tabular deep learning | [`08_tabular_dl`](08_tabular_dl.ipynb) | Ch12 | Trains TabM checkpoints on the same fingerprinted frame. | Training runs and prediction sets; checkpoints under `run_log/training/tabular_dl/` |
| LSTM | [`09_dl_lstm`](09_dl_lstm.ipynb) | Ch13 | Evaluates causal 60-bar recurrent sequences on CUDA. | Training runs and prediction sets; checkpoints under `run_log/training/deep_learning/` |
| TCN | [`10_dl_tcn`](10_dl_tcn.ipynb) | Ch13 | Evaluates dilated causal convolutions on the same sequence contract. | Training runs and prediction sets; checkpoints under `run_log/training/deep_learning/` |
| Causal DML | [`11_causal_dml`](11_causal_dml.ipynb) | Ch15 | Tests whether the basis premium has a causal interpretation after adjustment. | A row in the registry's `causal_runs` |
| Model analysis | [`12_model_analysis`](12_model_analysis.ipynb) | Ch12-15 | Compares four current family leaders on one physical validation panel. | Nothing - it reads the registry |
| Backtest | [`13_backtest`](13_backtest.ipynb) | Ch16 | Replays a frozen carrier with completed-bar prices and official funding. | Nothing - it replays a frozen carrier with `register=False` |
| Portfolio | [`14_portfolio_management`](14_portfolio_management.ipynb) | Ch17 | Compares corrected point-in-time allocation methods on that carrier. | Nothing - it replays a frozen carrier with `register=False` |
| Risk | [`15_risk_management`](15_risk_management.ipynb) | Ch19 | Evaluates fixed and pre-validation-calibrated position-risk rules. | Nothing - it replays a frozen carrier with `register=False` |
| Costs | [`16_costs`](16_costs.ipynb) | Ch18 | Measures cost sensitivity and price-only versus funding-inclusive breakevens, on the configuration risk management selected. | Nothing - it replays a frozen carrier with `register=False` |
| Synthesis | [`19_strategy_analysis`](19_strategy_analysis.ipynb) | Ch20 | Keeps current model evidence separate from frozen carrier diagnostics. | Nothing - it reads the registry |

## Running

Run notebooks from the repository root. Notebooks 07-10 require CUDA; the other notebooks use CPU.
The complete release pipeline is not yet supported because the current model registry has no
backtests or cohorts, while notebooks 13-16 preserve a frozen carrier for diagnostic replay. The
publication lineage must be chosen before the downstream producer sequence can be documented as a
reader-reproducible run.

The signed current-model sequence is:

```bash
uv run python case_studies/crypto_perps_funding/01_feasibility_analysis.py
uv run python case_studies/crypto_perps_funding/02_labels.py
uv run python case_studies/crypto_perps_funding/03_financial_features.py
uv run python case_studies/crypto_perps_funding/04_model_based_features.py
uv run python case_studies/crypto_perps_funding/05_evaluation.py
uv run python case_studies/crypto_perps_funding/06_linear.py
uv run python case_studies/crypto_perps_funding/07_gbm.py
uv run python case_studies/crypto_perps_funding/08_tabular_dl.py
uv run python case_studies/crypto_perps_funding/09_dl_lstm.py
uv run python case_studies/crypto_perps_funding/10_dl_tcn.py
uv run python case_studies/crypto_perps_funding/11_causal_dml.py
uv run python case_studies/crypto_perps_funding/12_model_analysis.py
```

Notebooks 13-17 are signed for their declared frozen-versus-current boundaries. They are not a
current end-to-end strategy and should not be combined into one until the release registry is fixed.

Vollständig mit Quellenangabe unter der Lizenz der Quelle angezeigt. Lizenz: MIT

Diese Zusammenfassung wurde vom Research-Agenten von Stratmill anhand des Originals verfasst; sie ist keine Kopie der Quelle.