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12 days ago
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Ablation ladder: validation-tuned cut-offs and a significance test
Accuracy and MCC were read at a fixed 0.5, which penalised a model whose probabilities rank well but sit off-centre: the VAE+Transformer had the best ROC-AUC (0.762) and one of the lowest accuracies. Every rung now takes the cut-off with the best MCC on its own validation block (the last fifth of training, never the test fold); the 0.5 figures are kept beside them. Test predictions are pooled across folds and the VAE+Transformer's ROC-AUC is compared with every other rung by a moving-block bootstrap (one-week blocks), giving a 95% interval and p-value per comparison. Per-fold scores are saved. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>60df76e -
Ablation ladder on the deployed regime target, with HAR and GARCH rungs
--target regime asks the live question (next 24h above the trailing 90-day median), with persistence as the current regime and two econometric rungs on the same purged folds: HAR (Corsi 2009) and GARCH(1,1) by maximum likelihood, each turned into a probability by logistic calibration on the training block. GARCH forecasts mean variance against a median threshold, so an uncalibrated cut scored 43% where the calibrated one scores 61%. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>2f405cf
24 days ago
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Complete the ablation ladder against the thesis methodology
Five rungs were missing. The ladder now covers every model named in sections 3.1.5 and 3.1.9: added CNN, LSTM baseline 4 added gradient boosting baseline 3, previously quoted from a separate screen rather than measured on these folds added CNN+Transformer ablation (b) added CNN+VAE+Transformer ablation (d), baseline 6 The last is the architecture Contribution 1 claims as novel. No rung in this study contained a CNN at all, so the proposed model had never been measured -- the thesis claimed an architecture the code does not implement and the ablation could not speak to. The three CNN rungs share one CNNEncoder, so they differ only in what follows the convolution, which is what isolating the CNN's contribution requires. Gradient boosting skips with a note where scikit-learn is unavailable -- Windows Application Control blocks it…7b0fceb -
Add the ablation, HAR benchmark, figures and three related-work comparisons
Fifteen studies in three groups -- volatility benchmarking, architecture ablation, historic versus sentiment -- kept separate because a paper benchmarking against HAR-RV is not evidence about whether a VAE earns its place, and mixing them is how a related-work chapter ends up comparing numbers that were never comparable. run_har_benchmark.py regresses log realised volatility against HAR-RV on identical folds and reports QLIKE as well as R2, which is what makes the comparison like-for-like. Nine cells, three assets, three horizons: the neural network wins two, both on BTC, and loses QLIKE in both. On ETH and ADA ordinary least squares on the same 33 features wins both metrics at every horizon. Improvement over HAR is 1.1-1.4x against the 5.4x claimed by arXiv 2511.20105, whose HAR scores 0.128 where this one scores 0.32-0.36. It also corrects an earlier figure in sota_volatility_comparison.md: the 2.1x reported there used a weak HAR specification taken from stored metadata. Fitted pro…58c9c49