Spaces:
Sleeping
Sleeping
Commit History
9 days ago
-
Add Deribit DVOL to the live price range as a fourth HAR input
scripts/backtest_range_dvol.py: with implied hourly variance (DVOL/100)^2 / 8760 in the log-HAR regression, the interval score improved 0.3/0.2/0.4/0.7% at 1/6/24/72h (significant at 1, 24 and 72h) and the 50% range narrowed about 6%, with coverage slightly below nominal (48/79/95%). DVOL takes the largest weight in the fit (0.67-0.71). The fit now produces two HAR sets, with and without DVOL, each with its own quantiles; forecasts use the DVOL set when the frame carries DVOL and the plain set otherwise, and report which (volatility_inputs). The quarterly refit downloads two years of DVOL with the candles. The raw index rides on the volatility frame as "dvol", excluded from model features. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>49e3cc7
11 days ago
-
Add Deribit DVOL implied-volatility features as a training option
DVOL is the options market's 30-day expectation of BTC volatility, which the price series does not contain. scripts/fetch_dvol.py downloads the full hourly history (48,832 hours from March 2021, no gaps). src/volatility.add_implied_features derives five scale-free features: DVOL against its 90-day median, against realised 24h volatility (variance risk premium) and against the regime threshold, and its 6h and 24h changes. In a quick logistic check on the regime target, adding them raised hold-out accuracy from 72.6% to 73.9% and ROC-AUC from 0.786 to 0.799. The VAE+Transformer is retrained with --dvol on Colab to test it properly; the existing 33 features and the live model are unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>69a7f91
12 days ago
-
Add Fed/CPI calendar features to the volatility regime model; new studies
- src/volatility.py: optional release-timing features (next release within 24h/6h, hours to next, hours since last), from the published calendar the live price-range forecaster already uses. Off by default; serving computes them so a model trained with --calendar can be deployed, while the live model keeps selecting its own 33 columns. - train_vae_transformer_artifacts.py: --calendar and --tag, so the experiment never overwrites the live artifact, plus the persistence baseline on the held-out rows in the metadata. - Hybrid ablation of the price range (Transformer, VAE, self- vs cross-attention news fusion) walk-forward against the live model. - Jobs report and PCE added to the macro event study; the jobs report tested as a calendar candidate for the price range (no gain, kept out). - Trump Truth Social posts timed to the minute against 5-minute BTC bars. - Side-by-side method diagram of the two live forecasts. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>03f6e95
26 days ago
-
Add the volatility-regime model and surface its measured accuracy
Hourly BTC direction is not predictable: the three deployed direction artifacts score ROC-AUC 0.47-0.50 with negative MCC, below an always-UP baseline, and independent work with correct methodology reproduces the same result. Their reported F1 of ~0.60 at 6h and 24h is majority-class behaviour, not skill. Realised volatility does cluster, so it is predictable. This adds a model for "will the next 24h be more volatile than the trailing 90-day median", measured at 0.7129 accuracy +/- 0.0224 over five chronological walk-forward folds (2022-2026, 65,563 hourly candles), ROC-AUC 0.7895, MCC 0.4284. The target is balanced, and it beats a persistence baseline of 0.6561 in all five folds. The direction figures are left as they are. They describe different models on a different task, and relabelling them with this number would claim an accuracy those artifacts do not have. Two findings worth recording, both caught by measurement: * the model must receive threshold-relative features, or pl…a442066
-
Initial production BTC forecasting app
34d2820