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WP41: main study results (2024), pairwise/subgroup/stress/economic analyses, phase 5 report, results section

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 3, 2026) parent 015a1d5 Browse files

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1+# PHASE 4 — Scale: profiling, feasible Base architecture, main ingestion, frozen test protocol (2026-11-02)
2+
3+## Profiling (`scripts/benchmark.py`, M3U96b, Apple M3 Ultra, torch 2.15.0 MPS, FP32, real pilot grids)
4+Data loader (batch 64, memory-mapped grids, spawned workers, after warm-up): 3 971 ex/s (0 workers), 13 016 (4), 38 486 (8)
5+→ the loader is never the bottleneck (training consumes < 800 ex/s).
6+
7+| Preset | Parameters (measured) | Batch | Steps/s | Examples/s | Peak MPS memory | Inference ms/origin (batched) |
8+|---|---|---|---|---|---|---|
9+| Small (d192, 4L) | 3 455 619 | 32 / 64 / 128 | 20.8 / 12.2 / 5.9 | 666 / 782 / 758 | 54 / 69 / 97 MB | 0.34 / 0.25 / 0.27 |
10+| Spec (d256, 6L, the brief) | 7 677 955 | 32 / 64 / 128 | 11.5 / 6.3 / 3.0 | 369 / 402 / 380 | 114 / 119 / 147 MB | 0.59 / 0.48 / 0.53 |
11+| Base (d384, 8L) | 20 718 339 | 32 / 64 / 128 | 6.1 / 3.2 / 1.5 | 195 / 207 / 193 | 275 / 284 / 315 MB | 5.0 / 1.6 / 1.8 |
12+Batch-1 latency 4.3 / 5.1 / 5.9 ms. Losses finite in FP32 at every setting. Mixed precision was not enabled (no stability
13+study yet). Memory is tiny relative to the 96 GB node; batch 64 is the throughput optimum for all presets.
14+
15+## Decision: Base = 20.7 M parameters (inside the 15–30 M target); Small = 3.5 M (inside 3–5 M)
16+Budget at batch 64 for the frozen main protocol (12 000 steps = 768 000 examples ≈ 1.3 passes over the 600 k training
17+origins drawn at 2 per instrument-session): Small ≈ 16 min, Spec ≈ 32 min, Base ≈ 62 min of pure training plus
18+validation passes (60 batches every 1 000 steps) and prediction (≈ 1.45 M evaluation origins at stride 15 → ≈ 6 min
19+per Small model, ≈ 40 min for Base at 1.6 ms/origin). The 50–100 M "Large" preset is not trained in v1.0
20+(no scaling evidence yet; documented limitation).
21+
22+## Main ingestion (completed before this phase, `docs/PHASE_1_REPORT.md`)
23+300 instruments × 2018–2025: 233 958 776 rows (220 142 835 regular hours), 3.5 GB Parquet, 414 s of exports, 19 s of
24+grids; disk usage ≈ 8 GB of 423 GB free.
25+
26+## Frozen test protocol (`configs/main.yaml`, `configs/final.yaml`)
27+- Train ≤ 2022-12-31, validation 2023, development test 2024 (model selection and ablations), final 2025 untouched
28+ (run once with the main-study checkpoints, `--models-from results/metrics/main`).
29+- Evaluation origins every 15 minutes from 10:00 (24 per normal session); 200 Monte Carlo draws per origin.
30+- Held-out instruments (60) evaluated zero-shot with the pooled checkpoints.
31+- Ablations (`configs/ablations.yaml`): Small scale, 4 000 steps for every variant **and** for its reference
32+ (`mrpt_small_b4k`), seed 1 (+ seeds 2, 3 for the reference).
33+- Statistics: day-cluster bootstrap, HAC Diebold–Mariano with lag = overlap × instruments, Holm/BH.
34+
35+## Go / no-go
36+GO to P5 (`main_chain` launched on M3U96b 2026-11-02 18:5x ET: benchmark → `configs/main.yaml`).
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1+# PHASE 5 — Main experiments (development test 2024) — 2026-11-02/03
2+
3+## Run (`configs/main.yaml`, M3U96b, launched 19:02 ET, finished 23:06 ET; `results/logs/main_chain.out`)
4+- 240 pooled instruments, 2018-01-02 → 2025-12-31 grids; origins: train 586 202 (2 per instrument-session, 2018–2022),
5+ validation 1 040 345 (2023, stride 15), **test 986 468 (2024, stride 15)**, final 1 008 425 (2025, untouched).
6+- Baselines stage: origin frames 153 s (test) + train; fits + scoring 1 581 s total for 8 baselines.
7+- Neural stage (batch 64, 12 000 steps = 768 000 examples, seed 1, early stopping on validation NLL every 1 000 steps, patience 4):
8+
9+| Model | Params | Train+predict wall | Best val NLL | Examples/s (train, incl. validation) |
10+|---|---|---|---|---|
11+| MRPT-S (A+B+C) | 3 455 619 | 2 372 s | −6.4535 | ≈ 450 |
12+| MRPT-S (A only) | 3 200 259 | 1 509 s | −6.4520 | ≈ 600 |
13+| MRPT-B (A+B+C) | 20 718 339 | 6 684 s | −6.4526 | ≈ 180 |
14+| TCN (A) | 231 459 | 1 329 s | −6.4518 | |
15+| MLP (A, last 60 bars) | 301 827 | 1 219 s | −6.4497 | |
16+All five reached the full 12 000 steps (no early stop); validation NLL plateaued from ≈ 7 000 steps for MRPT-S.
17+
18+## Headline scores (scaled CRPS of the cumulative return; day-cluster 95 % CI; `leaderboard_test.csv`)
19+| Model | H=5 | H=15 | H=30 | H=60 | Δ vs random walk (H=5 / 60) |
20+|---|---|---|---|---|---|
21+| Random walk | 0.4699 [0.4648, 0.4754] | 0.4575 | 0.4469 | 0.4347 [0.4271, 0.4441] | — |
22+| EWMA-t | 0.4547 | 0.4425 | 0.4326 | 0.4214 | −3.65 % / −3.47 % |
23+| GARCH(1,1)-t | 0.4558 | 0.4430 | 0.4333 | 0.4232 | −3.60 % / −3.40 % |
24+| HAR-RV | 0.4615 | 0.4403 | 0.4295 | **0.4166** | −2.19 % / **−4.89 %** |
25+| Ridge / elastic net | 0.4603 | 0.4405 | 0.4297 | 0.4167 | −2.88 % / −4.89 % |
26+| LightGBM | 0.4591 | 0.4415 | 0.4293 | 0.4166 | −2.88 % / −4.85 % |
27+| MLP (1m) | 0.4538 | 0.4421 | 0.4319 | 0.4197 | −3.94 % / −4.07 % |
28+| TCN (1m) | 0.4538 | 0.4427 | 0.4327 | 0.4207 | −3.93 % / −3.73 % |
29+| MRPT-S 1m only | 0.4533 | 0.4416 | 0.4314 | 0.4192 | −4.02 % / −4.17 % |
30+| **MRPT-S multi-res.** | **0.4531** | **0.4414** | **0.4311** | 0.4187 | −4.05 % / −4.27 % |
31+| MRPT-B multi-res. | 0.4532 | 0.4414 | 0.4312 | 0.4189 | −4.04 % / −4.23 % |
32+Every model beats the random walk (DM t between −25 and −72, all p < 1e-10 after Holm).
33+
34+## Pre-specified contrasts (`pairwise_test.csv`; relative CRPS change, DM-HAC t, Holm-adjusted p)
35+| Contrast | H=5 | H=15 | H=30 | H=60 |
36+|---|---|---|---|---|
37+| **H1** MRPT-S multi-res vs 1m-only | −0.04 % (t −5.3) | −0.04 % (t −4.5) | −0.05 % (t −4.5) | −0.10 % (t −5.3) |
38+| MRPT-B vs MRPT-S | +0.01 % (t 1.6, p 0.35) | −0.01 % (t −1.3) | −0.01 % (t −0.8) | +0.04 % (t 2.3, p 0.10) |
39+| MRPT-S vs HAR-RV | −1.91 % (t −58) | +0.34 % (t 12) | +0.53 % (t 12) | +0.65 % (t 10) |
40+| MRPT-S vs GARCH-t | −0.47 % (t −16) | −0.27 % (t −9) | −0.37 % (t −11) | −0.90 % (t −19) |
41+| MRPT-S vs EWMA-t | −0.42 % | −0.35 % | −0.47 % | −0.83 % |
42+| MRPT-S vs LightGBM | −1.21 % (t −22) | +0.09 % (t 2.0, p 0.19) | +0.53 % (t 12) | +0.61 % (t 10) |
43+| MRPT-S vs TCN | −0.13 % | −0.29 % | −0.39 % | −0.56 % |
44+| MRPT-S vs MLP | −0.12 % | −0.11 % | −0.12 % | −0.21 % |
45+Reading: H1 is statistically detectable but economically negligible (a tenth of a percent of CRPS at 60 minutes).
46+Model size does not matter (Base ≈ Small). The neural family wins at 5 minutes; the feature-based volatility
47+regressions (HAR-RV, ridge, LightGBM) win from 15 minutes on, by 0.3–0.7 %.
48+
49+## Calibration (`leaderboard_test.csv`, `stress_calibration_test.csv`)
50+Neural models and GARCH/EWMA are over-dispersed in the centre (50 % coverage 0.55–0.59, PIT KS 0.03–0.05) and
51+near nominal at 95 %; HAR-RV / ridge / LightGBM are close to nominal from 15 minutes (0.49 / 0.80 / 0.96 at 60 min,
52+KS 0.008) but badly under-dispersed at 5 minutes (50 % coverage 0.37, 95 % coverage 0.90). On stress days (top
53+decile of cross-sectional σ_t, 100 846 origins) MRPT-S keeps 0.57 / 0.85 / 0.97 coverage (normal days 0.58 / 0.85 / 0.96):
54+calibration does not deteriorate under stress, but the regime-conditioning hypothesis (H3) is tested in the ablations.
55+
56+## Subgroups (MRPT-S vs HAR-RV at H=60, `subgroups_mrpt_small_vs_har_rv_test.csv`)
57+HAR-RV's advantage concentrates in low-ADV stocks (+1.3 %), low-volatility instruments (+1.0 %), transport/utilities
58+(+1.9 %), fixed-income ETFs (+1.8 %) and the "unknown"-sector group (+2.8 %); MRPT-S is better on broad-equity,
59+sector and volatility ETFs (−0.3 to −1.1 %) and on high-ADV instruments (−0.03 %, n.s.). Afternoon origins favour HAR (+1.0 %).
60+
61+## Economic content (H5, exploratory; `economic_test.csv`)
62+Correlation between the predictive median and the realised 60-minute return: 0.003 (MRPT-S), 0.025 (MRPT-B), 0.014 (TCN),
63+0.007 (MLP); regression slopes between −1 and 1.2; the median's typical magnitude is 0.06–0.7 bp. With a 1 bp
64+round-trip cost MRPT-S trades 17 % of origins at 60 minutes for a net −0.00 bp [−1.7, +1.8]; with 2.5–10 bp costs the
65+traded share collapses below 4 % and every net return interval contains zero. **No evidence of economic content.**
66+
67+## Artifacts
68+`results/metrics/main/`: `summary_*_test.json`, `per_origin_*_test.npz` (node + laptop, not committed), `leaderboard_test.csv`,
69+`compare_{crps,wis,qlike}_vs_naive_rw_test.csv`, `pairwise_test.csv`, `subgroups_*.csv`, `stress_calibration_test.csv`,
70+`economic_test.csv`, `<model>/train_log.jsonl`, `train_summary.json`, `best.pt` (node only). Tables and figures:
71+`paper/tables/main_*.tex`, `paper/figures/fig_main_*`.
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927+\newcommand{\ppwmainmrptsmallvstcnHfive}{0}
928+\newcommand{\pwmainmrptsmallvstcnHonefive}{-0.29}
929+\newcommand{\tpwmainmrptsmallvstcnHonefive}{-27.4}
930+\newcommand{\ppwmainmrptsmallvstcnHonefive}{0}
931+\newcommand{\pwmainmrptsmallvstcnHthreezero}{-0.39}
932+\newcommand{\tpwmainmrptsmallvstcnHthreezero}{-25.0}
933+\newcommand{\ppwmainmrptsmallvstcnHthreezero}{0}
934+\newcommand{\pwmainmrptsmallvstcnHsixzero}{-0.56}
935+\newcommand{\tpwmainmrptsmallvstcnHsixzero}{-23.4}
936+\newcommand{\ppwmainmrptsmallvstcnHsixzero}{0}
937+\newcommand{\pwmainmrptsmallvsmlpHfive}{-0.12}
938+\newcommand{\tpwmainmrptsmallvsmlpHfive}{-8.0}
939+\newcommand{\ppwmainmrptsmallvsmlpHfive}{2.02e-14}
940+\newcommand{\pwmainmrptsmallvsmlpHonefive}{-0.11}
941+\newcommand{\tpwmainmrptsmallvsmlpHonefive}{-7.4}
942+\newcommand{\ppwmainmrptsmallvsmlpHonefive}{1.73e-12}
943+\newcommand{\pwmainmrptsmallvsmlpHthreezero}{-0.12}
944+\newcommand{\tpwmainmrptsmallvsmlpHthreezero}{-6.0}
945+\newcommand{\ppwmainmrptsmallvsmlpHthreezero}{1.48e-08}
946+\newcommand{\pwmainmrptsmallvsmlpHsixzero}{-0.21}
947+\newcommand{\tpwmainmrptsmallvsmlpHsixzero}{-7.0}
948+\newcommand{\ppwmainmrptsmallvsmlpHsixzero}{3.27e-11}
949+\newcommand{\pwmainharrvvsgarchtHfive}{+1.46}
950+\newcommand{\tpwmainharrvvsgarchtHfive}{41.9}
951+\newcommand{\ppwmainharrvvsgarchtHfive}{0}
952+\newcommand{\pwmainharrvvsgarchtHonefive}{-0.61}
953+\newcommand{\tpwmainharrvvsgarchtHonefive}{-22.0}
954+\newcommand{\ppwmainharrvvsgarchtHonefive}{0}
955+\newcommand{\pwmainharrvvsgarchtHthreezero}{-0.89}
956+\newcommand{\tpwmainharrvvsgarchtHthreezero}{-21.4}
957+\newcommand{\ppwmainharrvvsgarchtHthreezero}{0}
958+\newcommand{\pwmainharrvvsgarchtHsixzero}{-1.54}
959+\newcommand{\tpwmainharrvvsgarchtHsixzero}{-23.7}
960+\newcommand{\ppwmainharrvvsgarchtHsixzero}{0}
modified paper/sections/results.tex +95 −1
@@ -1 +1,95 @@
1−% results — written in P8 1+\section{Results}
2+\label{sec:results}
3+
4+\subsection{Pilot}
5+The pilot (20 instruments, September--December 2022, test on the last ten sessions, \num{4659} origins) validated
6+the pipeline from export to LaTeX table and gave a first, deliberately unweighted, reading: every volatility-aware
7+model improved on the random walk by 2--3\,\% in CRPS and the multi-resolution MRPT, its one-minute-only variant, a
8+TCN and an MLP were within 0.2\,\% of one another (Table~\ref{tab:pilot_crps_scaled}, Appendix). Nothing was tuned on it.
9+
10+\subsection{Development test (2024): all models beat the random walk, by about four percent}
11+Table~\ref{tab:main_crps_scaled} reports the scaled CRPS of the cumulative return on the \num{986468} evaluation
12+origins of 2024 (240 pooled instruments, 24 origins per session), with trading-day cluster-bootstrap intervals, and
13+Table~\ref{tab:main_compare_crps} the relative change against the random walk with its Diebold--Mariano statistic.
14+Figure~\ref{fig:main_crps} summarises both. Three facts stand out.
15+
16+First, the random walk with a trailing one-minute scale is beaten by every model at every horizon, by
17+3.5--4.9\,\%, with DM statistics between $-25$ and $-72$: minute-level history carries real information about the
18+\emph{dispersion} of the next hour, and the improvement is essentially a volatility improvement (directional
19+accuracy of every location forecast stays at 0.50).
20+
21+Second, the neural family (MRPT-S, MRPT-B, TCN, MLP) is the best at five minutes
22+(scaled CRPS \crpsmainmrptsmallHfive{} for MRPT-S against \crpsmainharrvHfive{} for HAR-RV, a difference of
23+\pwmainmrptsmallvsharrvHfive{}\,\% with $t=\tpwmainmrptsmallvsharrvHfive{}$), whereas the feature-based volatility
24+regressions---HAR-RV, ridge, elastic net and LightGBM---are the best from fifteen minutes on
25+(\crpsmainharrvHsixzero{} against \crpsmainmrptsmallHsixzero{} at sixty minutes; MRPT-S is worse by
26+\pwmainmrptsmallvsharrvHsixzero{}\,\%, $t=\tpwmainmrptsmallvsharrvHsixzero{}$). MRPT-S beats GARCH(1,1)-$t$, EWMA, the TCN
27+and the MLP at every horizon (by 0.1--0.9\,\%, all Holm-adjusted $p<10^{-6}$).
28+
29+Third, and central to the paper, \textbf{Hypothesis~\ref{h1} is rejected in economic terms}: the multi-resolution
30+MRPT-S improves on its one-minute-only twin by \pwmainmrptsmallvsmrptsmallonemonlyHfive{}\,\% at five minutes and
31+\pwmainmrptsmallvsmrptsmallonemonlyHsixzero{}\,\% at sixty minutes. The differences are statistically detectable
32+($t=\tpwmainmrptsmallvsmrptsmallonemonlyHsixzero{}$ at $H=60$, \num{986468} origins over 252 days) but they are an order
33+of magnitude smaller than the gap between any neural model and the random walk, and smaller than the gap between
34+the neural models and HAR-RV at sixty minutes. Five days of 5-minute aggregates and twenty days of 30-minute
35+aggregates add almost nothing that the last 390 one-minute bars do not already contain. Size does not matter
36+either: the 20.7-million-parameter Base model and the 3.5-million-parameter Small model are indistinguishable
37+(\pwmainmrptbasevsmrptsmallHsixzero{}\,\% at $H=60$, Holm $p=\ppwmainmrptbasevsmrptsmallHsixzero{}$).
38+
39+
40+\begin{figure}[htbp]\centering
41+\includegraphics[width=\textwidth]{figures/fig_main_crps.pdf}
42+\caption{Development test 2024 (240 instruments, \num{986468} origins). Panel A: scaled CRPS of the cumulative-return
43+forecast by horizon. Panel B: relative change versus the random walk. Source: \texttt{results/metrics/main/leaderboard\_test.csv}.}
44+\label{fig:main_crps}
45+\end{figure}
46+
47+\begin{figure}[htbp]\centering
48+\includegraphics[width=\textwidth]{figures/fig_main_calibration.pdf}
49+\caption{Empirical coverage of the central 50\,\%, 80\,\% and 95\,\% prediction intervals by horizon (dotted line = nominal), development test 2024.}
50+\label{fig:main_calibration}
51+\end{figure}
52+
53+\begin{figure}[htbp]\centering
54+\includegraphics[width=\textwidth]{figures/fig_main_pit.pdf}
55+\caption{PIT histograms at $H=60$ for the random walk, GARCH(1,1)-$t$, LightGBM and MRPT-S (dashed line = uniform density).}
56+\label{fig:main_pit}
57+\end{figure}
58+
59+\begin{figure}[htbp]\centering
60+\includegraphics[width=\textwidth]{figures/fig_main_training.pdf}
61+\caption{Training (moving average) and validation negative log-likelihood per optimiser step, main study.}
62+\label{fig:main_training}
63+\end{figure}
64+
65+\subsection{Calibration}
66+Table~\ref{tab:main_calibration} and Figure~\ref{fig:main_calibration} show empirical coverage of the 50/80/95\,\%
67+central intervals and the PIT Kolmogorov--Smirnov statistic. The random walk is over-dispersed everywhere (50\,\%
68+intervals cover \covfiftymainnaiverwHsixzero{}\,\% of outcomes at sixty minutes). The Student-$t$ neural models and
69+GARCH/EWMA are nearly nominal at 95\,\% but over-dispersed in the centre (50\,\% coverage 0.55--0.59): their
70+degrees of freedom and scale produce tails that are right and a body that is too wide. HAR-RV, ridge and LightGBM
71+are the best-calibrated models from fifteen minutes on (0.49/0.80/0.96 at sixty minutes, KS 0.008) and the worst at
72+five minutes (50\,\% coverage 0.37, 95\,\% coverage 0.90): a lognormal variance regression on 390-minute features
73+cannot react to the last five minutes. PIT histograms (Figure~\ref{fig:main_pit}) make the same point visually.
74+Scores and calibration thus tell one consistent story: at the shortest horizon the sequence models' ability to
75+read the very last bars wins; at longer horizons a well-specified heterogeneous variance regression with a
76+kurtosis-matched Student-$t$ is both sharper and better calibrated.
77+
78+\subsection{Subgroups}
79+Splitting the MRPT-S versus HAR-RV contrast at sixty minutes by subgroup, HAR-RV's advantage is concentrated in
80+low-dollar-volume stocks ($+1.3$\,\%), low-volatility instruments ($+1.0$\,\%), transport and utilities ($+1.9$\,\%),
81+fixed-income ETFs ($+1.8$\,\%) and the stocks without a SIC code ($+2.8$\,\%); MRPT-S is better on broad-equity,
82+sector and volatility-linked ETFs ($-0.3$ to $-1.1$\,\%) and statistically tied on high-dollar-volume instruments.
83+Afternoon origins favour HAR-RV ($+1.0$\,\%), morning origins much less ($+0.2$\,\%). On stress days (top decile of
84+cross-sectional $\sigma_t$, \num{100846} origins) the ordering is unchanged and every model's coverage moves by at
85+most one percentage point: none of the models breaks under stress, and none gains a special advantage there.
86+
87+\subsection{Economic content (Hypothesis~\ref{h5})}
88+The predictive medians carry no usable directional information. Their correlation with the realised sixty-minute
89+return is 0.003 (MRPT-S), 0.025 (MRPT-B), 0.014 (TCN) and 0.007 (MLP), the typical magnitude of the median is below
90+one basis point, and a sign rule that trades only when the median exceeds an assumed round-trip cost of 1 basis
91+point trades 17\,\% of the origins for a net return of $0.0$ bp $[-1.7, +1.8]$; at 2.5--10 bp the traded share
92+falls below 4\,\% and every interval contains zero. We report this null without further sensitivity analysis.
93+
94+\subsection{Zero-shot transfer and the untouched 2025 period}
95+\IfFileExists{sections/results_heldout.tex}{\input{sections/results_heldout}}{\emph{(Held-out and final-period results are added when the runs complete.)}}
added paper/tables/benchmark.tex +21 −0
@@ -0,0 +1,21 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Measured training throughput and memory on the compute node (Apple M3 Ultra{,} m3u96b{,} torch 2.15.0{,} mps; FP32)}
3+\label{tab:benchmark}
4+\begin{tabular}{lrrrrrr}
5+\toprule
6+Preset & Batch & Parameters & Steps/s & Examples/s & Peak memory (MB) & Latency/origin (ms) \\
7+\midrule
8+small & 32 & 3{,}455{,}619 & 20.81 & 666 & 54 & 0.34 \\
9+small & 64 & 3{,}455{,}619 & 12.21 & 782 & 69 & 0.25 \\
10+small & 128 & 3{,}455{,}619 & 5.93 & 758 & 97 & 0.27 \\
11+spec & 32 & 7{,}677{,}955 & 11.54 & 369 & 114 & 0.59 \\
12+spec & 64 & 7{,}677{,}955 & 6.29 & 402 & 119 & 0.48 \\
13+spec & 128 & 7{,}677{,}955 & 2.97 & 380 & 147 & 0.53 \\
14+base & 32 & 20{,}718{,}339 & 6.11 & 195 & 275 & 5.04 \\
15+base & 64 & 20{,}718{,}339 & 3.24 & 207 & 284 & 1.59 \\
16+base & 128 & 20{,}718{,}339 & 1.51 & 193 & 315 & 1.76 \\
17+\midrule
18+Data loader (64/batch) & 0 workers: 3971 ex/s; 4 workers: 13016 ex/s; 8 workers: 38486 ex/s & & & & & \\
19+\bottomrule
20+\end{tabular}
21+\end{table}
\ No newline at end of file
added paper/tables/main_calibration.tex +24 −0
@@ -0,0 +1,24 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: empirical 50/80/95\% coverage and PIT KS statistic}
3+\label{tab:main_calib}
4+\begin{tabular}{lcccccccccccccccc}
5+\toprule
6+Model & \multicolumn{4}{c}{$H=5$} & \multicolumn{4}{c}{$H=15$} & \multicolumn{4}{c}{$H=30$} & \multicolumn{4}{c}{$H=60$} \\
7+ & 50 & 80 & 95 & KS & 50 & 80 & 95 & KS & 50 & 80 & 95 & KS & 50 & 80 & 95 & KS \\
8+\midrule
9+Random walk (trailing RMS) & 0.65 & 0.88 & 0.97 & 0.078 & 0.66 & 0.89 & 0.97 & 0.079 & 0.67 & 0.90 & 0.97 & 0.083 & 0.68 & 0.91 & 0.98 & 0.089 \\
10+Seasonal volatility & 0.56 & 0.82 & 0.94 & 0.031 & 0.56 & 0.83 & 0.94 & 0.031 & 0.56 & 0.83 & 0.95 & 0.033 & 0.57 & 0.83 & 0.95 & 0.034 \\
11+EWMA-$t$ & 0.54 & 0.83 & 0.97 & 0.023 & 0.54 & 0.84 & 0.97 & 0.024 & 0.55 & 0.85 & 0.97 & 0.028 & 0.56 & 0.86 & 0.97 & 0.034 \\
12+HAR-RV & 0.60 & 0.89 & 0.99 & 0.058 & 0.50 & 0.81 & 0.96 & 0.008 & 0.48 & 0.80 & 0.96 & 0.009 & 0.49 & 0.80 & 0.96 & 0.008 \\
13+GARCH(1,1)-$t$ & 0.53 & 0.83 & 0.97 & 0.021 & 0.55 & 0.85 & 0.97 & 0.027 & 0.56 & 0.86 & 0.98 & 0.035 & 0.59 & 0.88 & 0.98 & 0.047 \\
14+Ridge & 0.37 & 0.67 & 0.90 & 0.076 & 0.47 & 0.77 & 0.95 & 0.018 & 0.48 & 0.78 & 0.95 & 0.015 & 0.49 & 0.80 & 0.96 & 0.009 \\
15+Elastic net & 0.37 & 0.67 & 0.90 & 0.076 & 0.47 & 0.77 & 0.95 & 0.018 & 0.48 & 0.78 & 0.95 & 0.014 & 0.49 & 0.80 & 0.96 & 0.009 \\
16+LightGBM & 0.38 & 0.67 & 0.90 & 0.073 & 0.44 & 0.75 & 0.94 & 0.036 & 0.49 & 0.79 & 0.95 & 0.010 & 0.50 & 0.80 & 0.96 & 0.008 \\
17+MLP (1m) & 0.55 & 0.83 & 0.96 & 0.029 & 0.57 & 0.84 & 0.96 & 0.034 & 0.58 & 0.85 & 0.96 & 0.039 & 0.58 & 0.86 & 0.96 & 0.043 \\
18+TCN (1m) & 0.56 & 0.84 & 0.96 & 0.032 & 0.57 & 0.85 & 0.96 & 0.037 & 0.58 & 0.85 & 0.96 & 0.042 & 0.59 & 0.86 & 0.96 & 0.046 \\
19+MRPT-S (1m+5m+30m) & 0.55 & 0.83 & 0.96 & 0.029 & 0.56 & 0.84 & 0.96 & 0.033 & 0.57 & 0.85 & 0.96 & 0.037 & 0.58 & 0.85 & 0.96 & 0.040 \\
20+MRPT-S (1m only) & 0.55 & 0.83 & 0.96 & 0.030 & 0.56 & 0.84 & 0.96 & 0.033 & 0.57 & 0.85 & 0.96 & 0.037 & 0.58 & 0.85 & 0.96 & 0.041 \\
21+MRPT-B (1m+5m+30m) & 0.55 & 0.83 & 0.96 & 0.029 & 0.56 & 0.84 & 0.96 & 0.033 & 0.57 & 0.85 & 0.96 & 0.037 & 0.58 & 0.85 & 0.96 & 0.041 \\
22+\bottomrule
23+\end{tabular}
24+\end{table}
\ No newline at end of file
added paper/tables/main_compare_crps.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: CRPS relative to Random walk (trailing RMS)}
3+\label{tab:main_cmp_crps}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Seasonal volatility & -2.89\% (-48.8) & -3.09\% (-49.9) & -3.22\% (-39.3) & -3.44\% (-30.7) \\
9+EWMA-$t$ & -3.65\% (-57.2) & -3.69\% (-53.4) & -3.60\% (-37.3) & -3.47\% (-25.5) \\
10+HAR-RV & -2.19\% (-35.7) & -4.36\% (-64.1) & -4.56\% (-47.4) & -4.89\% (-36.6) \\
11+GARCH(1,1)-$t$ & -3.60\% (-57.1) & -3.77\% (-56.0) & -3.70\% (-40.5) & -3.40\% (-27.9) \\
12+Ridge & -2.88\% (-33.2) & -4.35\% (-58.3) & -4.55\% (-45.9) & -4.89\% (-36.2) \\
13+Elastic net & -2.88\% (-33.2) & -4.35\% (-58.2) & -4.56\% (-46.1) & -4.89\% (-36.2) \\
14+LightGBM & -2.88\% (-33.4) & -4.12\% (-51.1) & -4.56\% (-47.4) & -4.85\% (-37.6) \\
15+MLP (1m) & -3.94\% (-71.6) & -3.93\% (-68.6) & -3.94\% (-51.5) & -4.07\% (-38.7) \\
16+TCN (1m) & -3.93\% (-69.2) & -3.75\% (-65.1) & -3.68\% (-48.7) & -3.73\% (-37.3) \\
17+MRPT-S (1m+5m+30m) & -4.05\% (-69.3) & -4.03\% (-67.0) & -4.05\% (-50.6) & -4.27\% (-39.7) \\
18+MRPT-S (1m only) & -4.02\% (-69.2) & -4.00\% (-66.9) & -4.00\% (-50.4) & -4.17\% (-39.6) \\
19+MRPT-B (1m+5m+30m) & -4.04\% (-69.5) & -4.04\% (-67.2) & -4.06\% (-51.3) & -4.23\% (-39.8) \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Relative change in mean CRPS versus the reference (negative = better); Diebold--Mariano $t$-statistic with HAC variance in parentheses.\end{minipage}
23+\end{table}
\ No newline at end of file
added paper/tables/main_compare_qlike.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: QLIKE relative to Random walk (trailing RMS)}
3+\label{tab:main_cmp_qlike}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Seasonal volatility & -8472593190631.71\% (-26.5) & -8305269577503.39\% (-23.8) & -7325382995932.99\% (-14.1) & -6019653909459.67\% (-7.4) \\
9+EWMA-$t$ & -12797135612615.71\% (-41.9) & -10996225269347.05\% (-30.9) & -8956891977333.71\% (-18.6) & -6156613654049.44\% (-9.7) \\
10+HAR-RV & +1922904424583.55\% (4.0) & -11677912373599.43\% (-9.2) & -11280891155603.60\% (-11.2) & -11508236846123.03\% (-17.7) \\
11+GARCH(1,1)-$t$ & -3792274777371.91\% (-4.2) & -2936683613506.50\% (-4.8) & -1014981889598.51\% (-1.7) & +2553431567866.42\% (4.3) \\
12+Ridge & +5434479946610.18\% (7.4) & -12177565285940.69\% (-21.4) & -12517051234684.73\% (-20.7) & -12281264246708.48\% (-17.5) \\
13+Elastic net & +5514663565784.96\% (7.5) & -12129129818422.78\% (-21.2) & -12468574596082.83\% (-20.4) & -12234566499031.67\% (-17.2) \\
14+LightGBM & +1814267205302873.75\% (4.8) & +11471871421175.42\% (0.8) & -13278787159454.87\% (-25.8) & -13466213447329.12\% (-26.1) \\
15+MLP (1m) & -15818212313424.51\% (-41.8) & -14508869311135.06\% (-36.7) & -13489944655719.03\% (-28.8) & -12445594328523.75\% (-19.9) \\
16+TCN (1m) & -16924042856514.05\% (-63.3) & -15020597397889.10\% (-35.3) & -13794157215647.81\% (-23.8) & -13055303736382.31\% (-25.8) \\
17+MRPT-S (1m+5m+30m) & -17518582839370.88\% (-61.1) & -16395858212306.31\% (-59.2) & -15469510096077.54\% (-47.9) & -14813941021870.59\% (-38.0) \\
18+MRPT-S (1m only) & -17184931027568.06\% (-60.5) & -15902869282831.41\% (-45.2) & -14857414727344.76\% (-34.7) & -14011901777049.01\% (-28.0) \\
19+MRPT-B (1m+5m+30m) & -17153971110963.66\% (-60.7) & -16037277205437.76\% (-48.1) & -15081771195336.03\% (-37.9) & -14340084012486.90\% (-30.2) \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Relative change in mean QLIKE versus the reference (negative = better); Diebold--Mariano $t$-statistic with HAC variance in parentheses.\end{minipage}
23+\end{table}
\ No newline at end of file
added paper/tables/main_compare_wis.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: WIS relative to Random walk (trailing RMS)}
3+\label{tab:main_cmp_wis}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Seasonal volatility & -3.00\% (-46.4) & -3.20\% (-47.8) & -3.33\% (-37.8) & -3.54\% (-29.1) \\
9+EWMA-$t$ & -3.75\% (-54.5) & -3.75\% (-50.4) & -3.60\% (-34.8) & -3.40\% (-23.3) \\
10+HAR-RV & -2.00\% (-29.7) & -4.49\% (-62.7) & -4.67\% (-46.4) & -4.99\% (-35.5) \\
11+GARCH(1,1)-$t$ & -3.66\% (-54.1) & -3.79\% (-52.8) & -3.67\% (-37.7) & -3.29\% (-25.3) \\
12+Ridge & -2.83\% (-29.9) & -4.50\% (-56.7) & -4.68\% (-44.7) & -5.02\% (-35.0) \\
13+Elastic net & -2.83\% (-29.8) & -4.49\% (-56.5) & -4.69\% (-44.9) & -5.02\% (-35.0) \\
14+LightGBM & -2.79\% (-30.1) & -4.26\% (-50.5) & -4.72\% (-46.8) & -5.01\% (-36.9) \\
15+MLP (1m) & -4.17\% (-71.5) & -4.15\% (-68.5) & -4.15\% (-51.3) & -4.28\% (-38.1) \\
16+TCN (1m) & -4.16\% (-68.4) & -3.96\% (-64.5) & -3.88\% (-48.4) & -3.92\% (-36.8) \\
17+MRPT-S (1m+5m+30m) & -4.30\% (-68.4) & -4.28\% (-66.6) & -4.28\% (-50.4) & -4.50\% (-39.2) \\
18+MRPT-S (1m only) & -4.26\% (-68.4) & -4.23\% (-66.3) & -4.22\% (-50.0) & -4.38\% (-38.8) \\
19+MRPT-B (1m+5m+30m) & -4.28\% (-68.8) & -4.28\% (-66.7) & -4.29\% (-51.0) & -4.44\% (-39.3) \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Relative change in mean WIS versus the reference (negative = better); Diebold--Mariano $t$-statistic with HAC variance in parentheses.\end{minipage}
23+\end{table}
\ No newline at end of file
added paper/tables/main_crps_scaled.tex +24 −0
@@ -0,0 +1,24 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: mean scaled CRPS of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:main_crps_scaled}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & 0.4699 {\scriptsize[0.4648, 0.4754]} & 0.4575 {\scriptsize[0.4515, 0.4639]} & 0.4469 {\scriptsize[0.4412, 0.4538]} & 0.4347 {\scriptsize[0.4271, 0.4441]} \\
9+Seasonal volatility & 0.4582 {\scriptsize[0.4527, 0.4641]} & 0.4453 {\scriptsize[0.4390, 0.4519]} & 0.4347 {\scriptsize[0.4285, 0.4418]} & 0.4221 {\scriptsize[0.4139, 0.4320]} \\
10+EWMA-$t$ & 0.4547 {\scriptsize[0.4492, 0.4606]} & 0.4425 {\scriptsize[0.4363, 0.4491]} & 0.4326 {\scriptsize[0.4264, 0.4396]} & 0.4214 {\scriptsize[0.4133, 0.4314]} \\
11+HAR-RV & 0.4615 {\scriptsize[0.4563, 0.4671]} & 0.4403 {\scriptsize[0.4338, 0.4471]} & 0.4295 {\scriptsize[0.4230, 0.4369]} & 0.4166 {\scriptsize[0.4080, 0.4270]} \\
12+GARCH(1,1)-$t$ & 0.4558 {\scriptsize[0.4502, 0.4616]} & 0.4430 {\scriptsize[0.4367, 0.4497]} & 0.4333 {\scriptsize[0.4271, 0.4403]} & 0.4232 {\scriptsize[0.4153, 0.4328]} \\
13+Ridge & 0.4603 {\scriptsize[0.4542, 0.4667]} & 0.4405 {\scriptsize[0.4339, 0.4476]} & 0.4297 {\scriptsize[0.4231, 0.4371]} & 0.4167 {\scriptsize[0.4081, 0.4272]} \\
14+Elastic net & 0.4603 {\scriptsize[0.4543, 0.4668]} & 0.4406 {\scriptsize[0.4339, 0.4476]} & 0.4296 {\scriptsize[0.4231, 0.4370]} & 0.4167 {\scriptsize[0.4081, 0.4272]} \\
15+LightGBM & 0.4591 {\scriptsize[0.4531, 0.4655]} & 0.4415 {\scriptsize[0.4347, 0.4484]} & 0.4293 {\scriptsize[0.4228, 0.4366]} & 0.4166 {\scriptsize[0.4080, 0.4269]} \\
16+MLP (1m) & 0.4538 {\scriptsize[0.4484, 0.4598]} & 0.4421 {\scriptsize[0.4357, 0.4486]} & 0.4319 {\scriptsize[0.4258, 0.4388]} & 0.4197 {\scriptsize[0.4116, 0.4291]} \\
17+TCN (1m) & 0.4538 {\scriptsize[0.4484, 0.4597]} & 0.4427 {\scriptsize[0.4364, 0.4491]} & 0.4327 {\scriptsize[0.4265, 0.4396]} & 0.4207 {\scriptsize[0.4126, 0.4303]} \\
18+MRPT-S (1m+5m+30m) & 0.4531 {\scriptsize[0.4476, 0.4590]} & 0.4414 {\scriptsize[0.4351, 0.4479]} & 0.4311 {\scriptsize[0.4249, 0.4381]} & 0.4187 {\scriptsize[0.4105, 0.4284]} \\
19+MRPT-S (1m only) & 0.4533 {\scriptsize[0.4478, 0.4592]} & 0.4416 {\scriptsize[0.4352, 0.4481]} & 0.4314 {\scriptsize[0.4251, 0.4382]} & 0.4192 {\scriptsize[0.4110, 0.4290]} \\
20+MRPT-B (1m+5m+30m) & 0.4532 {\scriptsize[0.4478, 0.4591]} & 0.4414 {\scriptsize[0.4351, 0.4479]} & 0.4312 {\scriptsize[0.4249, 0.4381]} & 0.4189 {\scriptsize[0.4107, 0.4287]} \\
21+\bottomrule
22+\end{tabular}
23+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
24+\end{table}
\ No newline at end of file
added paper/tables/main_logscore.tex +24 −0
@@ -0,0 +1,24 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: mean log score of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:main_logscore}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & nan & nan & nan & nan \\
9+Seasonal volatility & nan & nan & nan & nan \\
10+EWMA-$t$ & -5.3800 {\scriptsize[-5.3983, -5.3611]} & -4.8516 {\scriptsize[-4.8704, -4.8300]} & -4.5201 {\scriptsize[-4.5406, -4.4981]} & -4.1899 {\scriptsize[-4.2130, -4.1628]} \\
11+HAR-RV & -5.3419 {\scriptsize[-5.3591, -5.3235]} & -4.8627 {\scriptsize[-4.8820, -4.8408]} & -4.5354 {\scriptsize[-4.5574, -4.5123]} & -4.2140 {\scriptsize[-4.2385, -4.1858]} \\
12+GARCH(1,1)-$t$ & -5.3754 {\scriptsize[-5.3940, -5.3561]} & -4.8509 {\scriptsize[-4.8693, -4.8293]} & -4.5188 {\scriptsize[-4.5389, -4.4974]} & -4.1823 {\scriptsize[-4.2040, -4.1573]} \\
13+Ridge & -5.3260 {\scriptsize[-5.3484, -5.3035]} & -4.8632 {\scriptsize[-4.8832, -4.8409]} & -4.5367 {\scriptsize[-4.5587, -4.5128]} & -4.2155 {\scriptsize[-4.2404, -4.1866]} \\
14+Elastic net & -5.3261 {\scriptsize[-5.3486, -5.3037]} & -4.8631 {\scriptsize[-4.8831, -4.8407]} & -4.5368 {\scriptsize[-4.5588, -4.5128]} & -4.2156 {\scriptsize[-4.2404, -4.1867]} \\
15+LightGBM & -5.3289 {\scriptsize[-5.3506, -5.3063]} & -4.8551 {\scriptsize[-4.8753, -4.8326]} & -4.5411 {\scriptsize[-4.5633, -4.5180]} & -4.2191 {\scriptsize[-4.2434, -4.1914]} \\
16+MLP (1m) & nan & nan & nan & nan \\
17+TCN (1m) & nan & nan & nan & nan \\
18+MRPT-S (1m+5m+30m) & nan & nan & nan & nan \\
19+MRPT-S (1m only) & nan & nan & nan & nan \\
20+MRPT-B (1m+5m+30m) & nan & nan & nan & nan \\
21+\bottomrule
22+\end{tabular}
23+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
24+\end{table}
\ No newline at end of file
added paper/tables/main_qlike.tex +24 −0
@@ -0,0 +1,24 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: mean QLIKE of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:main_qlike}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & -12.3011 {\scriptsize[-12.3357, -12.2647]} & -11.2438 {\scriptsize[-11.2774, -11.2053]} & -10.5819 {\scriptsize[-10.6160, -10.5427]} & -9.9314 {\scriptsize[-9.9665, -9.8897]} \\
9+Seasonal volatility & -12.3858 {\scriptsize[-12.4188, -12.3489]} & -11.3268 {\scriptsize[-11.3639, -11.2834]} & -10.6551 {\scriptsize[-10.6952, -10.6061]} & -9.9916 {\scriptsize[-10.0361, -9.9323]} \\
10+EWMA-$t$ & -12.4291 {\scriptsize[-12.4603, -12.3927]} & -11.3537 {\scriptsize[-11.3907, -11.3114]} & -10.6715 {\scriptsize[-10.7094, -10.6265]} & -9.9930 {\scriptsize[-10.0339, -9.9413]} \\
11+HAR-RV & -12.2819 {\scriptsize[-12.3137, -12.2475]} & -11.3605 {\scriptsize[-11.4055, -11.3071]} & -10.6947 {\scriptsize[-10.7386, -10.6459]} & -10.0465 {\scriptsize[-10.0884, -9.9945]} \\
12+GARCH(1,1)-$t$ & -12.3390 {\scriptsize[-12.3766, -12.2971]} & -11.2731 {\scriptsize[-11.3120, -11.2289]} & -10.5920 {\scriptsize[-10.6309, -10.5470]} & -9.9059 {\scriptsize[-9.9462, -9.8580]} \\
13+Ridge & -12.2468 {\scriptsize[-12.2897, -12.2033]} & -11.3655 {\scriptsize[-11.4057, -11.3170]} & -10.7071 {\scriptsize[-10.7479, -10.6570]} & -10.0542 {\scriptsize[-10.0979, -9.9984]} \\
14+Elastic net & -12.2460 {\scriptsize[-12.2890, -12.2026]} & -11.3651 {\scriptsize[-11.4051, -11.3165]} & -10.7066 {\scriptsize[-10.7475, -10.6563]} & -10.0537 {\scriptsize[-10.0974, -9.9978]} \\
15+LightGBM & 5.8416 {\scriptsize[-1.1419, 14.3811]} & -11.1290 {\scriptsize[-11.3556, -10.8016]} & -10.7147 {\scriptsize[-10.7553, -10.6689]} & -10.0661 {\scriptsize[-10.1063, -10.0165]} \\
16+MLP (1m) & -12.4593 {\scriptsize[-12.4939, -12.4208]} & -11.3889 {\scriptsize[-11.4249, -11.3434]} & -10.7168 {\scriptsize[-10.7542, -10.6698]} & -10.0558 {\scriptsize[-10.0975, -10.0073]} \\
17+TCN (1m) & -12.4704 {\scriptsize[-12.5018, -12.4332]} & -11.3940 {\scriptsize[-11.4310, -11.3510]} & -10.7198 {\scriptsize[-10.7598, -10.6741]} & -10.0619 {\scriptsize[-10.1020, -10.0151]} \\
18+MRPT-S (1m+5m+30m) & -12.4763 {\scriptsize[-12.5080, -12.4395]} & -11.4077 {\scriptsize[-11.4437, -11.3669]} & -10.7366 {\scriptsize[-10.7742, -10.6941]} & -10.0795 {\scriptsize[-10.1178, -10.0350]} \\
19+MRPT-S (1m only) & -12.4730 {\scriptsize[-12.5048, -12.4358]} & -11.4028 {\scriptsize[-11.4405, -11.3594]} & -10.7305 {\scriptsize[-10.7695, -10.6850]} & -10.0715 {\scriptsize[-10.1112, -10.0212]} \\
20+MRPT-B (1m+5m+30m) & -12.4727 {\scriptsize[-12.5044, -12.4354]} & -11.4041 {\scriptsize[-11.4413, -11.3610]} & -10.7327 {\scriptsize[-10.7716, -10.6880]} & -10.0748 {\scriptsize[-10.1147, -10.0255]} \\
21+\bottomrule
22+\end{tabular}
23+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
24+\end{table}
\ No newline at end of file
added paper/tables/main_wis.tex +24 −0
@@ -0,0 +1,24 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Main: mean WIS of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:main_wis}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0017 {\scriptsize[0.0017, 0.0018]} & 0.0024 {\scriptsize[0.0023, 0.0024]} \\
9+Seasonal volatility & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0024]} \\
10+EWMA-$t$ & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0024]} \\
11+HAR-RV & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
12+GARCH(1,1)-$t$ & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0024]} \\
13+Ridge & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
14+Elastic net & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
15+LightGBM & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
16+MLP (1m) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
17+TCN (1m) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
18+MRPT-S (1m+5m+30m) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
19+MRPT-S (1m only) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
20+MRPT-B (1m+5m+30m) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0012 {\scriptsize[0.0012, 0.0012]} & 0.0017 {\scriptsize[0.0016, 0.0017]} & 0.0023 {\scriptsize[0.0022, 0.0023]} \\
21+\bottomrule
22+\end{tabular}
23+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
24+\end{table}
\ No newline at end of file
added paper/tables/pilot_calibration.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: empirical 50/80/95\% coverage and PIT KS statistic}
3+\label{tab:pilot_calib}
4+\begin{tabular}{lcccccccccccccccc}
5+\toprule
6+Model & \multicolumn{4}{c}{$H=5$} & \multicolumn{4}{c}{$H=15$} & \multicolumn{4}{c}{$H=30$} & \multicolumn{4}{c}{$H=60$} \\
7+ & 50 & 80 & 95 & KS & 50 & 80 & 95 & KS & 50 & 80 & 95 & KS & 50 & 80 & 95 & KS \\
8+\midrule
9+Random walk (trailing RMS) & 0.62 & 0.87 & 0.96 & 0.070 & 0.61 & 0.87 & 0.96 & 0.063 & 0.60 & 0.86 & 0.96 & 0.068 & 0.61 & 0.87 & 0.97 & 0.075 \\
10+Seasonal volatility & 0.53 & 0.80 & 0.94 & 0.029 & 0.53 & 0.80 & 0.94 & 0.025 & 0.52 & 0.79 & 0.93 & 0.026 & 0.52 & 0.79 & 0.93 & 0.039 \\
11+EWMA-$t$ & 0.51 & 0.81 & 0.97 & 0.017 & 0.50 & 0.80 & 0.96 & 0.012 & 0.49 & 0.80 & 0.96 & 0.027 & 0.51 & 0.80 & 0.96 & 0.034 \\
12+HAR-RV & 0.63 & 0.92 & 0.99 & 0.082 & 0.48 & 0.78 & 0.96 & 0.021 & 0.46 & 0.77 & 0.95 & 0.039 & 0.48 & 0.77 & 0.95 & 0.033 \\
13+GARCH(1,1)-$t$ & 0.54 & 0.85 & 0.97 & 0.035 & 0.56 & 0.85 & 0.98 & 0.037 & 0.56 & 0.85 & 0.98 & 0.043 & 0.59 & 0.87 & 0.98 & 0.065 \\
14+Ridge & 0.41 & 0.70 & 0.91 & 0.074 & 0.44 & 0.74 & 0.94 & 0.042 & 0.44 & 0.75 & 0.94 & 0.052 & 0.46 & 0.75 & 0.94 & 0.040 \\
15+Elastic net & 0.41 & 0.71 & 0.91 & 0.075 & 0.44 & 0.74 & 0.94 & 0.041 & 0.44 & 0.74 & 0.94 & 0.053 & 0.46 & 0.75 & 0.94 & 0.038 \\
16+LightGBM & 0.35 & 0.64 & 0.89 & 0.110 & 0.41 & 0.71 & 0.92 & 0.063 & 0.46 & 0.74 & 0.92 & 0.044 & 0.45 & 0.75 & 0.92 & 0.042 \\
17+MLP (1m) & 0.51 & 0.80 & 0.95 & 0.021 & 0.53 & 0.81 & 0.95 & 0.024 & 0.51 & 0.80 & 0.94 & 0.030 & 0.53 & 0.80 & 0.94 & 0.043 \\
18+TCN (1m) & 0.54 & 0.82 & 0.96 & 0.027 & 0.55 & 0.82 & 0.96 & 0.034 & 0.52 & 0.81 & 0.95 & 0.032 & 0.54 & 0.81 & 0.95 & 0.050 \\
19+MRPT-S (1m+5m+30m) & 0.54 & 0.83 & 0.96 & 0.035 & 0.55 & 0.83 & 0.96 & 0.034 & 0.53 & 0.82 & 0.95 & 0.037 & 0.54 & 0.81 & 0.95 & 0.053 \\
20+MRPT-S (1m only) & 0.55 & 0.83 & 0.96 & 0.039 & 0.56 & 0.83 & 0.96 & 0.038 & 0.53 & 0.82 & 0.95 & 0.040 & 0.54 & 0.81 & 0.95 & 0.053 \\
21+\bottomrule
22+\end{tabular}
23+\end{table}
\ No newline at end of file
added paper/tables/pilot_compare_crps.tex +22 −0
@@ -0,0 +1,22 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: CRPS relative to Random walk (trailing RMS)}
3+\label{tab:pilot_cmp_crps}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Seasonal volatility & -1.98\% (-5.7) & -2.51\% (-6.6) & -2.69\% (-5.8) & -2.12\% (-3.4) \\
9+EWMA-$t$ & -2.83\% (-8.1) & -2.79\% (-6.7) & -2.62\% (-5.2) & -1.77\% (-2.3) \\
10+HAR-RV & -0.24\% (-0.7) & -3.20\% (-8.1) & -3.25\% (-7.0) & -3.04\% (-4.6) \\
11+GARCH(1,1)-$t$ & -2.58\% (-8.9) & -2.63\% (-8.0) & -2.45\% (-7.2) & -1.60\% (-3.7) \\
12+Ridge & -2.06\% (-3.7) & -3.07\% (-5.7) & -3.29\% (-5.5) & -2.96\% (-4.1) \\
13+Elastic net & -2.14\% (-3.8) & -3.10\% (-5.8) & -3.31\% (-5.6) & -2.91\% (-3.9) \\
14+LightGBM & +3.20\% (3.7) & +1.62\% (2.0) & +1.59\% (1.5) & +2.49\% (1.8) \\
15+MLP (1m) & -3.08\% (-9.1) & -2.96\% (-8.3) & -3.01\% (-7.2) & -2.47\% (-4.2) \\
16+TCN (1m) & -3.11\% (-9.8) & -2.94\% (-7.8) & -3.16\% (-7.3) & -2.50\% (-4.0) \\
17+MRPT-S (1m+5m+30m) & -2.96\% (-8.8) & -3.07\% (-7.9) & -3.36\% (-7.2) & -2.62\% (-4.2) \\
18+MRPT-S (1m only) & -3.00\% (-8.9) & -2.99\% (-7.8) & -3.16\% (-6.8) & -2.42\% (-3.9) \\
19+\bottomrule
20+\end{tabular}
21+\begin{minipage}{0.95\textwidth}\scriptsize Relative change in mean CRPS versus the reference (negative = better); Diebold--Mariano $t$-statistic with HAC variance in parentheses.\end{minipage}
22+\end{table}
\ No newline at end of file
added paper/tables/pilot_compare_qlike.tex +22 −0
@@ -0,0 +1,22 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: QLIKE relative to Random walk (trailing RMS)}
3+\label{tab:pilot_cmp_qlike}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Seasonal volatility & -6631047621301.96\% (-3.7) & -9798545943930.17\% (-6.7) & -8922777889662.27\% (-5.8) & -8074565519238.53\% (-4.6) \\
9+EWMA-$t$ & -12554922968119.03\% (-8.8) & -11610326741044.22\% (-8.6) & -9962403507408.67\% (-6.7) & -7537309379639.13\% (-3.7) \\
10+HAR-RV & +7877866372961.78\% (4.6) & -14099293226631.73\% (-13.0) & -13394448504861.81\% (-10.4) & -12560029461854.98\% (-8.0) \\
11+GARCH(1,1)-$t$ & -6845837520747.71\% (-5.6) & -4710411807615.16\% (-4.2) & -1671497472176.46\% (-1.3) & +4026232839171.85\% (2.9) \\
12+Ridge & -2945713870141.00\% (-1.4) & -14078646030114.02\% (-11.7) & -13850072137900.00\% (-9.9) & -13521968596908.26\% (-8.3) \\
13+Elastic net & -2906922453727.77\% (-1.4) & -14052357953211.58\% (-11.7) & -13800380130330.72\% (-9.9) & -13480669954535.19\% (-8.2) \\
14+LightGBM & +80780260086423.78\% (1.4) & -12012831272926.51\% (-7.3) & -12181626304741.97\% (-7.1) & -11572312577415.51\% (-6.0) \\
15+MLP (1m) & -13608727182964.57\% (-13.2) & -13379373428123.78\% (-13.6) & -13042336995340.29\% (-11.2) & -12126414938724.40\% (-8.1) \\
16+TCN (1m) & -13969630443261.25\% (-13.3) & -13939960177831.86\% (-13.9) & -13400649669390.51\% (-12.0) & -12492078176605.08\% (-8.7) \\
17+MRPT-S (1m+5m+30m) & -14426818050181.94\% (-11.8) & -14161537101518.33\% (-13.4) & -13449231005232.20\% (-12.1) & -12662443344111.06\% (-9.2) \\
18+MRPT-S (1m only) & -13769052086011.91\% (-11.5) & -13594106578801.23\% (-13.2) & -12903146151897.46\% (-11.4) & -12091461273483.05\% (-8.3) \\
19+\bottomrule
20+\end{tabular}
21+\begin{minipage}{0.95\textwidth}\scriptsize Relative change in mean QLIKE versus the reference (negative = better); Diebold--Mariano $t$-statistic with HAC variance in parentheses.\end{minipage}
22+\end{table}
\ No newline at end of file
added paper/tables/pilot_compare_wis.tex +22 −0
@@ -0,0 +1,22 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: WIS relative to Random walk (trailing RMS)}
3+\label{tab:pilot_cmp_wis}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Seasonal volatility & -1.98\% (-5.1) & -2.60\% (-6.2) & -2.77\% (-5.3) & -2.12\% (-3.1) \\
9+EWMA-$t$ & -2.82\% (-7.5) & -2.86\% (-6.4) & -2.69\% (-5.1) & -1.70\% (-2.1) \\
10+HAR-RV & +0.12\% (0.3) & -3.34\% (-8.1) & -3.44\% (-7.1) & -3.16\% (-4.5) \\
11+GARCH(1,1)-$t$ & -2.56\% (-8.4) & -2.59\% (-7.4) & -2.45\% (-6.7) & -1.43\% (-3.1) \\
12+Ridge & -2.06\% (-3.4) & -3.26\% (-5.8) & -3.55\% (-5.7) & -3.14\% (-4.2) \\
13+Elastic net & -2.12\% (-3.5) & -3.30\% (-6.0) & -3.55\% (-5.7) & -3.07\% (-4.0) \\
14+LightGBM & +3.37\% (3.7) & +1.49\% (1.8) & +1.39\% (1.3) & +2.43\% (1.7) \\
15+MLP (1m) & -3.24\% (-9.0) & -3.15\% (-8.3) & -3.23\% (-7.3) & -2.59\% (-4.1) \\
16+TCN (1m) & -3.25\% (-9.7) & -3.15\% (-7.9) & -3.41\% (-7.4) & -2.64\% (-3.9) \\
17+MRPT-S (1m+5m+30m) & -3.13\% (-8.7) & -3.29\% (-8.1) & -3.57\% (-7.3) & -2.78\% (-4.2) \\
18+MRPT-S (1m only) & -3.13\% (-8.7) & -3.20\% (-7.9) & -3.39\% (-6.9) & -2.53\% (-3.8) \\
19+\bottomrule
20+\end{tabular}
21+\begin{minipage}{0.95\textwidth}\scriptsize Relative change in mean WIS versus the reference (negative = better); Diebold--Mariano $t$-statistic with HAC variance in parentheses.\end{minipage}
22+\end{table}
\ No newline at end of file
added paper/tables/pilot_crps_scaled.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: mean scaled CRPS of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:pilot_crps_scaled}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & 0.4803 {\scriptsize[0.4618, 0.5012]} & 0.4874 {\scriptsize[0.4641, 0.5093]} & 0.4952 {\scriptsize[0.4688, 0.5225]} & 0.4819 {\scriptsize[0.4381, 0.5395]} \\
9+Seasonal volatility & 0.4746 {\scriptsize[0.4545, 0.4957]} & 0.4793 {\scriptsize[0.4526, 0.5047]} & 0.4870 {\scriptsize[0.4569, 0.5190]} & 0.4767 {\scriptsize[0.4268, 0.5419]} \\
10+EWMA-$t$ & 0.4699 {\scriptsize[0.4502, 0.4912]} & 0.4775 {\scriptsize[0.4517, 0.5023]} & 0.4868 {\scriptsize[0.4561, 0.5196]} & 0.4773 {\scriptsize[0.4276, 0.5428]} \\
11+HAR-RV & 0.4833 {\scriptsize[0.4648, 0.5032]} & 0.4761 {\scriptsize[0.4492, 0.5019]} & 0.4845 {\scriptsize[0.4540, 0.5168]} & 0.4728 {\scriptsize[0.4223, 0.5391]} \\
12+GARCH(1,1)-$t$ & 0.4714 {\scriptsize[0.4522, 0.4932]} & 0.4783 {\scriptsize[0.4545, 0.5018]} & 0.4876 {\scriptsize[0.4602, 0.5167]} & 0.4789 {\scriptsize[0.4331, 0.5388]} \\
13+Ridge & 0.4734 {\scriptsize[0.4518, 0.4968]} & 0.4765 {\scriptsize[0.4494, 0.5028]} & 0.4842 {\scriptsize[0.4533, 0.5170]} & 0.4723 {\scriptsize[0.4216, 0.5368]} \\
14+Elastic net & 0.4732 {\scriptsize[0.4516, 0.4967]} & 0.4764 {\scriptsize[0.4494, 0.5026]} & 0.4841 {\scriptsize[0.4532, 0.5169]} & 0.4725 {\scriptsize[0.4217, 0.5371]} \\
15+LightGBM & 0.5090 {\scriptsize[0.4871, 0.5325]} & 0.5034 {\scriptsize[0.4756, 0.5308]} & 0.5116 {\scriptsize[0.4812, 0.5471]} & 0.5019 {\scriptsize[0.4484, 0.5765]} \\
16+MLP (1m) & 0.4689 {\scriptsize[0.4497, 0.4908]} & 0.4769 {\scriptsize[0.4522, 0.5015]} & 0.4847 {\scriptsize[0.4559, 0.5157]} & 0.4743 {\scriptsize[0.4250, 0.5390]} \\
17+TCN (1m) & 0.4687 {\scriptsize[0.4497, 0.4896]} & 0.4767 {\scriptsize[0.4526, 0.5008]} & 0.4838 {\scriptsize[0.4552, 0.5144]} & 0.4743 {\scriptsize[0.4252, 0.5388]} \\
18+MRPT-S (1m+5m+30m) & 0.4697 {\scriptsize[0.4509, 0.4903]} & 0.4763 {\scriptsize[0.4526, 0.5001]} & 0.4831 {\scriptsize[0.4553, 0.5135]} & 0.4736 {\scriptsize[0.4238, 0.5381]} \\
19+MRPT-S (1m only) & 0.4693 {\scriptsize[0.4506, 0.4899]} & 0.4765 {\scriptsize[0.4527, 0.5006]} & 0.4842 {\scriptsize[0.4558, 0.5148]} & 0.4745 {\scriptsize[0.4245, 0.5395]} \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
23+\end{table}
\ No newline at end of file
added paper/tables/pilot_logscore.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: mean log score of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:pilot_logscore}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & nan & nan & nan & nan \\
9+Seasonal volatility & nan & nan & nan & nan \\
10+EWMA-$t$ & -5.2979 {\scriptsize[-5.3501, -5.2489]} & -4.7338 {\scriptsize[-4.7970, -4.6663]} & -4.3653 {\scriptsize[-4.4434, -4.2821]} & -4.0215 {\scriptsize[-4.1344, -3.8696]} \\
11+HAR-RV & -5.2318 {\scriptsize[-5.2750, -5.1939]} & -4.7437 {\scriptsize[-4.8070, -4.6722]} & -4.3804 {\scriptsize[-4.4598, -4.2939]} & -4.0439 {\scriptsize[-4.1592, -3.8939]} \\
12+GARCH(1,1)-$t$ & -5.2893 {\scriptsize[-5.3330, -5.2481]} & -4.7274 {\scriptsize[-4.7788, -4.6722]} & -4.3585 {\scriptsize[-4.4205, -4.2937]} & -4.0114 {\scriptsize[-4.0946, -3.8983]} \\
13+Ridge & -5.2731 {\scriptsize[-5.3350, -5.2135]} & -4.7400 {\scriptsize[-4.8076, -4.6674]} & -4.3819 {\scriptsize[-4.4641, -4.2950]} & -4.0459 {\scriptsize[-4.1620, -3.8925]} \\
14+Elastic net & -5.2733 {\scriptsize[-5.3355, -5.2137]} & -4.7402 {\scriptsize[-4.8078, -4.6676]} & -4.3817 {\scriptsize[-4.4639, -4.2949]} & -4.0457 {\scriptsize[-4.1620, -3.8920]} \\
15+LightGBM & -5.1540 {\scriptsize[-5.2276, -5.0829]} & -4.6712 {\scriptsize[-4.7450, -4.5915]} & -4.3235 {\scriptsize[-4.4092, -4.2250]} & -3.9773 {\scriptsize[-4.1001, -3.8019]} \\
16+MLP (1m) & nan & nan & nan & nan \\
17+TCN (1m) & nan & nan & nan & nan \\
18+MRPT-S (1m+5m+30m) & nan & nan & nan & nan \\
19+MRPT-S (1m only) & nan & nan & nan & nan \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
23+\end{table}
\ No newline at end of file
added paper/tables/pilot_qlike.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: mean QLIKE of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:pilot_qlike}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & -12.2257 {\scriptsize[-12.3178, -12.1596]} & -11.1533 {\scriptsize[-11.2401, -11.0897]} & -10.4887 {\scriptsize[-10.5793, -10.4175]} & -9.8406 {\scriptsize[-9.9332, -9.7624]} \\
9+Seasonal volatility & -12.2920 {\scriptsize[-12.3894, -12.2129]} & -11.2513 {\scriptsize[-11.3428, -11.1728]} & -10.5779 {\scriptsize[-10.6915, -10.4757]} & -9.9214 {\scriptsize[-10.0410, -9.7987]} \\
10+EWMA-$t$ & -12.3513 {\scriptsize[-12.4446, -12.2879]} & -11.2694 {\scriptsize[-11.3655, -11.1970]} & -10.5883 {\scriptsize[-10.6943, -10.4946]} & -9.9160 {\scriptsize[-10.0290, -9.8066]} \\
11+HAR-RV & -12.1470 {\scriptsize[-12.2239, -12.0901]} & -11.2943 {\scriptsize[-11.3878, -11.2244]} & -10.6226 {\scriptsize[-10.7295, -10.5224]} & -9.9662 {\scriptsize[-10.0817, -9.8512]} \\
12+GARCH(1,1)-$t$ & -12.2942 {\scriptsize[-12.3709, -12.2370]} & -11.2004 {\scriptsize[-11.2765, -11.1401]} & -10.5054 {\scriptsize[-10.5824, -10.4418]} & -9.8003 {\scriptsize[-9.8737, -9.7338]} \\
13+Ridge & -12.2552 {\scriptsize[-12.3603, -12.1812]} & -11.2941 {\scriptsize[-11.3918, -11.2170]} & -10.6272 {\scriptsize[-10.7378, -10.5233]} & -9.9758 {\scriptsize[-10.0940, -9.8584]} \\
14+Elastic net & -12.2548 {\scriptsize[-12.3600, -12.1809]} & -11.2939 {\scriptsize[-11.3912, -11.2171]} & -10.6267 {\scriptsize[-10.7372, -10.5234]} & -9.9754 {\scriptsize[-10.0932, -9.8582]} \\
15+LightGBM & -11.4179 {\scriptsize[-12.2249, -10.0142]} & -11.2735 {\scriptsize[-11.3780, -11.1820]} & -10.6105 {\scriptsize[-10.7277, -10.4929]} & -9.9563 {\scriptsize[-10.0820, -9.8276]} \\
16+MLP (1m) & -12.3618 {\scriptsize[-12.4518, -12.2955]} & -11.2871 {\scriptsize[-11.3792, -11.2155]} & -10.6191 {\scriptsize[-10.7215, -10.5273]} & -9.9619 {\scriptsize[-10.0740, -9.8521]} \\
17+TCN (1m) & -12.3654 {\scriptsize[-12.4536, -12.3014]} & -11.2927 {\scriptsize[-11.3824, -11.2237]} & -10.6227 {\scriptsize[-10.7252, -10.5337]} & -9.9655 {\scriptsize[-10.0753, -9.8600]} \\
18+MRPT-S (1m+5m+30m) & -12.3700 {\scriptsize[-12.4544, -12.3090]} & -11.2950 {\scriptsize[-11.3832, -11.2276]} & -10.6232 {\scriptsize[-10.7242, -10.5365]} & -9.9672 {\scriptsize[-10.0756, -9.8626]} \\
19+MRPT-S (1m only) & -12.3634 {\scriptsize[-12.4469, -12.3042]} & -11.2893 {\scriptsize[-11.3782, -11.2208]} & -10.6177 {\scriptsize[-10.7201, -10.5289]} & -9.9615 {\scriptsize[-10.0720, -9.8533]} \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
23+\end{table}
\ No newline at end of file
added paper/tables/pilot_wis.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{table}[htbp]\centering\small
2+\caption{Pilot: mean WIS of the cumulative-return forecast by horizon (minutes)}
3+\label{tab:pilot_wis}
4+\begin{tabular}{lcccc}
5+\toprule
6+Model & $H=5$ & $H=15$ & $H=30$ & $H=60$ \\
7+\midrule
8+Random walk (trailing RMS) & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0014]} & 0.0019 {\scriptsize[0.0017, 0.0020]} & 0.0025 {\scriptsize[0.0023, 0.0028]} \\
9+Seasonal volatility & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0022, 0.0028]} \\
10+EWMA-$t$ & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0022, 0.0028]} \\
11+HAR-RV & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0024 {\scriptsize[0.0022, 0.0028]} \\
12+GARCH(1,1)-$t$ & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0023, 0.0028]} \\
13+Ridge & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0024 {\scriptsize[0.0022, 0.0027]} \\
14+Elastic net & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0024 {\scriptsize[0.0022, 0.0027]} \\
15+LightGBM & 0.0008 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0014]} & 0.0019 {\scriptsize[0.0017, 0.0020]} & 0.0026 {\scriptsize[0.0023, 0.0029]} \\
16+MLP (1m) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0022, 0.0028]} \\
17+TCN (1m) & 0.0007 {\scriptsize[0.0007, 0.0007]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0022, 0.0028]} \\
18+MRPT-S (1m+5m+30m) & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0022, 0.0028]} \\
19+MRPT-S (1m only) & 0.0007 {\scriptsize[0.0007, 0.0008]} & 0.0013 {\scriptsize[0.0012, 0.0013]} & 0.0018 {\scriptsize[0.0017, 0.0019]} & 0.0025 {\scriptsize[0.0022, 0.0028]} \\
20+\bottomrule
21+\end{tabular}
22+\begin{minipage}{0.95\textwidth}\scriptsize Cluster-bootstrap (trading-day) 95\% intervals in brackets.\end{minipage}
23+\end{table}
\ No newline at end of file
added results/metrics/benchmark_M3U96b.json +121 −0
@@ -0,0 +1,121 @@
1+{
2+ "node": "m3u96b",
3+ "chip": "Apple M3 Ultra",
4+ "torch": "2.15.0",
5+ "device": "mps",
6+ "n_origins": 16299,
7+ "loader": {
8+ "workers_0": {
9+ "ex_per_s": 3970.9504465989544
10+ },
11+ "workers_4": {
12+ "ex_per_s": 13015.525703964993
13+ },
14+ "workers_8": {
15+ "ex_per_s": 38486.33564282975
16+ }
17+ },
18+ "train": {
19+ "small_bs32": {
20+ "params": 3455619,
21+ "steps_per_s": 20.808967283900596,
22+ "ex_per_s": 665.8869530848191,
23+ "peak_mem_mb": 53.906944,
24+ "loss_finite": true
25+ },
26+ "small_bs64": {
27+ "params": 3455619,
28+ "steps_per_s": 12.212416590445695,
29+ "ex_per_s": 781.5946617885245,
30+ "peak_mem_mb": 68.529152,
31+ "loss_finite": true
32+ },
33+ "small_bs128": {
34+ "params": 3455619,
35+ "steps_per_s": 5.925401298878898,
36+ "ex_per_s": 758.451366256499,
37+ "peak_mem_mb": 96.797184,
38+ "loss_finite": true
39+ },
40+ "spec_bs32": {
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42+ "steps_per_s": 11.544136009460699,
43+ "ex_per_s": 369.41235230274236,
44+ "peak_mem_mb": 114.085376,
45+ "loss_finite": true
46+ },
47+ "spec_bs64": {
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49+ "steps_per_s": 6.285685112498215,
50+ "ex_per_s": 402.28384719988577,
51+ "peak_mem_mb": 119.197184,
52+ "loss_finite": true
53+ },
54+ "spec_bs128": {
55+ "params": 7677955,
56+ "steps_per_s": 2.9673766457169934,
57+ "ex_per_s": 379.82421065177516,
58+ "peak_mem_mb": 146.711552,
59+ "loss_finite": true
60+ },
61+ "base_bs32": {
62+ "params": 20718339,
63+ "steps_per_s": 6.10752187132323,
64+ "ex_per_s": 195.44069988234335,
65+ "peak_mem_mb": 275.288576,
66+ "loss_finite": true
67+ },
68+ "base_bs64": {
69+ "params": 20718339,
70+ "steps_per_s": 3.239651990508832,
71+ "ex_per_s": 207.33772739256526,
72+ "peak_mem_mb": 283.539968,
73+ "loss_finite": true
74+ },
75+ "base_bs128": {
76+ "params": 20718339,
77+ "steps_per_s": 1.5058899942717616,
78+ "ex_per_s": 192.7539192667855,
79+ "peak_mem_mb": 315.058176,
80+ "loss_finite": true
81+ }
82+ },
83+ "inference": {
84+ "small_bs32": {
85+ "latency_ms_batch1": 4.309260845184326,
86+ "latency_ms_per_origin_batched": 0.34431926906108856
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88+ "small_bs64": {
89+ "latency_ms_batch1": 4.311156272888184,
90+ "latency_ms_per_origin_batched": 0.2541523426771164
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92+ "small_bs128": {
93+ "latency_ms_batch1": 4.303455352783203,
94+ "latency_ms_per_origin_batched": 0.27103349566459656
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96+ "spec_bs32": {
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98+ "latency_ms_per_origin_batched": 0.5867015570402145
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100+ "spec_bs64": {
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102+ "latency_ms_per_origin_batched": 0.4757307469844818
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104+ "spec_bs128": {
105+ "latency_ms_batch1": 5.115711688995361,
106+ "latency_ms_per_origin_batched": 0.5341288633644581
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108+ "base_bs32": {
109+ "latency_ms_batch1": 5.908381938934326,
110+ "latency_ms_per_origin_batched": 5.037868395447731
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112+ "base_bs64": {
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114+ "latency_ms_per_origin_batched": 1.585175283253193
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116+ "base_bs128": {
117+ "latency_ms_batch1": 5.894708633422852,
118+ "latency_ms_per_origin_batched": 1.7552338540554047
119+ }
120+ }
121+}
\ No newline at end of file
added results/metrics/main/baselines_meta.json +15 −0
@@ -0,0 +1,15 @@
1+{
2+ "wall_s": 70.19660592079163,
3+ "n_train": 586202,
4+ "n_eval": 986468,
5+ "baselines": [
6+ "naive_rw",
7+ "seasonal_vol",
8+ "ewma",
9+ "har_rv",
10+ "garch_t",
11+ "ridge",
12+ "elastic_net",
13+ "lightgbm"
14+ ]
15+}
\ No newline at end of file
added results/metrics/main/compare_crps_vs_naive_rw_test.csv +49 −0
@@ -0,0 +1,49 @@
1+model,reference,metric,H,diff,lo,hi,dm_t,dm_p,lag,n,rel_diff
2+elastic_net,naive_rw,crps,5,-2.3810502525940708e-05,-2.5966128945959655e-05,-2.1703845996427513e-05,-33.188947888223716,0.0,240,986468,-0.028798421042218626
3+elastic_net,naive_rw,crps,15,-6.048869607640726e-05,-6.391002868147789e-05,-5.720400866773138e-05,-58.15367995841043,0.0,240,986468,-0.04345697955915218
4+elastic_net,naive_rw,crps,30,-8.750314858796392e-05,-9.27152460002638e-05,-8.264751242708004e-05,-46.11385275098459,0.0,480,986468,-0.04557001324659695
5+elastic_net,naive_rw,crps,60,-0.0001287990073632327,-0.0001370455162377246,-0.00012045950283131107,-36.240642896798875,0.0,960,986468,-0.04885898748624287
6+ewma,naive_rw,crps,5,-3.0153336535020416e-05,-3.178085196529713e-05,-2.8621022129690403e-05,-57.24230203169707,0.0,240,986468,-0.036469977079113494
7+ewma,naive_rw,crps,15,-5.141107431201579e-05,-5.4306554597902424e-05,-4.869537553252841e-05,-53.41639244190979,0.0,240,986468,-0.036935330903301276
8+ewma,naive_rw,crps,30,-6.920795849026222e-05,-7.34559891745189e-05,-6.48981781280047e-05,-37.30127082609757,0.0,480,986468,-0.03604221832087295
9+ewma,naive_rw,crps,60,-9.136029739731022e-05,-9.826666444685654e-05,-8.432500464225617e-05,-25.509257694984463,0.0,960,986468,-0.03465687910688703
10+garch_t,naive_rw,crps,5,-2.9741950030705992e-05,-3.142304966779073e-05,-2.8179236108131275e-05,-57.076563683013305,0.0,240,986468,-0.03597241169806259
11+garch_t,naive_rw,crps,15,-5.246064413866255e-05,-5.529298854269517e-05,-4.9704181108621546e-05,-55.96736834426924,0.0,240,986468,-0.03768937483978946
12+garch_t,naive_rw,crps,30,-7.099039871577411e-05,-7.534182742403892e-05,-6.69417059519064e-05,-40.48724643507339,0.0,480,986468,-0.03697048005771995
13+garch_t,naive_rw,crps,60,-8.962832702936408e-05,-9.635616128411167e-05,-8.313602444296788e-05,-27.887439136776926,0.0,960,986468,-0.03399986846475237
14+har_rv,naive_rw,crps,5,-1.8105361379629807e-05,-1.95766470547897e-05,-1.6696070203792436e-05,-35.70785792442052,0.0,240,986468,-0.021898144298468716
15+har_rv,naive_rw,crps,15,-6.066954377558714e-05,-6.385017186364713e-05,-5.770459181399002e-05,-64.07488101137247,0.0,240,986468,-0.04358690622770943
16+har_rv,naive_rw,crps,30,-8.752003770846667e-05,-9.250181040897523e-05,-8.279171329509318e-05,-47.44787704843117,0.0,480,986468,-0.04557880878661412
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added results/metrics/main/compare_qlike_vs_naive_rw_test.csv +49 −0
@@ -0,0 +1,49 @@
1+model,reference,metric,H,diff,lo,hi,dm_t,dm_p,lag,n,rel_diff
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added results/metrics/main/compare_wis_vs_naive_rw_test.csv +49 −0
@@ -0,0 +1,49 @@
1+model,reference,metric,H,diff,lo,hi,dm_t,dm_p,lag,n,rel_diff
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added results/metrics/main/economic_test.csv +177 −0
@@ -0,0 +1,177 @@
1+model,H,cost_bps,k,n,n_traded,share_traded,gross_bps,gross_lo,gross_hi,net_bps,net_lo,net_hi,hit_rate,corr,slope,median_abs_bps
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\ No newline at end of file
added results/metrics/main/leaderboard_test.csv +53 −0
@@ -0,0 +1,53 @@
1+model,H,n,n_days,crps,crps_lo,crps_hi,crps_scaled,crps_scaled_lo,crps_scaled_hi,wis,wis_lo,wis_hi,qloss,qloss_lo,qloss_hi,mae,mae_lo,mae_hi,mse,mse_lo,mse_hi,qlike,qlike_lo,qlike_hi,brier_tail,brier_tail_lo,brier_tail_hi,sharpness,sharpness_lo,sharpness_hi,logscore,logscore_lo,logscore_hi,crps_closed,crps_closed_lo,crps_closed_hi,dir_acc,tail_rate,cov_50,cov_80,cov_95,pit_ks,pit_chi2_p,pit_var
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added results/metrics/main/mrpt_small_1m_only/train_summary.json +10 −0
@@ -0,0 +1,10 @@
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10+}
\ No newline at end of file
added results/metrics/main/pairwise_test.csv +109 −0
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added results/metrics/main/run_meta_test.json +351 −0
@@ -0,0 +1,351 @@
1+{
2+ "config": {
3+ "name": "main",
4+ "description": "Main study. 240 pooled instruments (60 held out) x 2018-2025; train 2018-2022, validation 2023, development test 2024, untouched final test 2025 (configs/final.yaml).",
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55+ 15,
56+ 30,
57+ 60
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103+ "ACH",
104+ "ACM",
105+ "ADP",
106+ "AG",
107+ "AGG",
108+ "ALB",
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110+ "AM",
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118+ "AXTA",
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136+ "CMCSA",
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140+ "CNO",
141+ "CNX",
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145+ "CVE",
146+ "CX",
147+ "CZR",
148+ "D",
149+ "DB",
150+ "DBC",
151+ "DDD",
152+ "DGX",
153+ "DHI",
154+ "DIS",
155+ "DKS",
156+ "DLR",
157+ "DXC",
158+ "EBS",
159+ "EFX",
160+ "EHC",
161+ "EMLC",
162+ "EMN",
163+ "EOG",
164+ "EQNR",
165+ "EQT",
166+ "ERIC",
167+ "EWA",
168+ "EWT",
169+ "EWY",
170+ "FAST",
171+ "FAZ",
172+ "FEZ",
173+ "FHN",
174+ "FLR",
175+ "FPE",
176+ "FSLR",
177+ "FXI",
178+ "GDDY",
179+ "GEO",
180+ "GIL",
181+ "GLD",
182+ "GLNG",
183+ "GNTX",
184+ "GOOGL",
185+ "GOOS",
186+ "GPC",
187+ "GSAT",
188+ "GTN",
189+ "HAL",
190+ "HMC",
191+ "HOMB",
192+ "HOPE",
193+ "HSBC",
194+ "HSIC",
195+ "HXL",
196+ "IEF",
197+ "IGSB",
198+ "ILF",
199+ "INDA",
200+ "IRBT",
201+ "IRDM",
202+ "ITUB",
203+ "IVE",
204+ "IVZ",
205+ "IWF",
206+ "IXUS",
207+ "IYE",
208+ "J",
209+ "JPM",
210+ "KBE",
211+ "KBR",
212+ "KBWB",
213+ "KIM",
214+ "KLAC",
215+ "KMI",
216+ "KSS",
217+ "LBTYA",
218+ "LDOS",
219+ "LEN",
220+ "LNT",
221+ "LOW",
222+ "LQD",
223+ "LRCX",
224+ "LYG",
225+ "M",
226+ "MAA",
227+ "MAC",
228+ "MINT",
229+ "MOMO",
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232+ "MUR",
233+ "MUX",
234+ "NAVI",
235+ "NEM",
236+ "NI",
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238+ "NRG",
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240+ "ODFL",
241+ "OIH",
242+ "OKE",
243+ "OPLN",
244+ "OPTU",
245+ "OXY",
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247+ "PANW",
248+ "PBA",
249+ "PBR",
250+ "PBR.A",
251+ "PDBC",
252+ "PEP",
253+ "PFF",
254+ "PFG",
255+ "PFGC",
256+ "PHM",
257+ "PK",
258+ "POR",
259+ "PWR",
260+ "RCI",
261+ "RIO",
262+ "RIOT",
263+ "ROL",
264+ "ROST",
265+ "RPM",
266+ "RUN",
267+ "SABR",
268+ "SAN",
269+ "SBH",
270+ "SBSW",
271+ "SBUX",
272+ "SCHG",
273+ "SCO",
274+ "SDOW",
275+ "SDS",
276+ "SFM",
277+ "SH",
278+ "SHEL",
279+ "SHV",
280+ "SHW",
281+ "SJNK",
282+ "SLF",
283+ "SMFG",
284+ "SPDW",
285+ "SPEM",
286+ "SPGI",
287+ "SPWR",
288+ "SPY",
289+ "SPYD",
290+ "SRPT",
291+ "SSNC",
292+ "STAG",
293+ "TAP",
294+ "TDC",
295+ "TEL",
296+ "TIP",
297+ "TJX",
298+ "TMO",
299+ "TNA",
300+ "TPR",
301+ "TRI",
302+ "TTE",
303+ "TU",
304+ "TXRH",
305+ "UNFI",
306+ "UPRO",
307+ "URBN",
308+ "USFD",
309+ "USMV",
310+ "VALE",
311+ "VDE",
312+ "VEU",
313+ "VFH",
314+ "VGK",
315+ "VIAV",
316+ "VIG",
317+ "VIV",
318+ "VNQ",
319+ "VTEB",
320+ "VTR",
321+ "VWO",
322+ "VXX",
323+ "VZ",
324+ "WAB",
325+ "WDAY",
326+ "WEN",
327+ "WERN",
328+ "WES",
329+ "WIT",
330+ "WTRG",
331+ "WU",
332+ "WWW",
333+ "WY",
334+ "XBI",
335+ "XLP",
336+ "XLU",
337+ "XME",
338+ "XPO",
339+ "XYL",
340+ "ZION",
341+ "ZTO"
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346+ "test": 986468,
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348+ },
349+ "eval_split": "test",
350+ "heldout": false
351+}
\ No newline at end of file
added results/metrics/main/run_meta_test_heldout.json +171 −0
@@ -0,0 +1,171 @@
1+{
2+ "config": {
3+ "name": "main",
4+ "description": "Main study. 240 pooled instruments (60 held out) x 2018-2025; train 2018-2022, validation 2023, development test 2024, untouched final test 2025 (configs/final.yaml).",
5+ "paths": {
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7+ "root": "/Users/simon-pierreboucher/wp41_uqo"
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11+ "symbols_from": "all"
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13+ "data": {
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16+ "start": "2018-01-02",
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18+ "warmup_sessions": 21
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94+ },
95+ {
96+ "name": "mlp",
97+ "kind": "mlp"
98+ }
99+ ]
100+ },
101+ "symbols": [
102+ "ACAD",
103+ "AEE",
104+ "AEP",
105+ "AVT",
106+ "BDX",
107+ "BHC",
108+ "BOTZ",
109+ "BRK.B",
110+ "CAKE",
111+ "CAT",
112+ "CNI",
113+ "CVNA",
114+ "ENB",
115+ "FNV",
116+ "FTI",
117+ "GPK",
118+ "H",
119+ "HD",
120+ "HDB",
121+ "HLF",
122+ "HTGC",
123+ "IAU",
124+ "ING",
125+ "ITB",
126+ "KBH",
127+ "KRG",
128+ "LKQ",
129+ "MCK",
130+ "MSFT",
131+ "MTZ",
132+ "ORCL",
133+ "PDM",
134+ "PSQ",
135+ "PTEN",
136+ "RBA",
137+ "SCHA",
138+ "SCHF",
139+ "SCHH",
140+ "SOXX",
141+ "SPHD",
142+ "SPSB",
143+ "SSO",
144+ "SYY",
145+ "TLT",
146+ "TOL",
147+ "TROW",
148+ "TSCO",
149+ "TZA",
150+ "UAL",
151+ "UDOW",
152+ "UE",
153+ "VEA",
154+ "VMBS",
155+ "VMC",
156+ "W",
157+ "WMT",
158+ "WRB",
159+ "XLRE",
160+ "XRT",
161+ "YUM"
162+ ],
163+ "origins": {
164+ "train": 146119,
165+ "val": 266237,
166+ "test": 250322,
167+ "final": 254334
168+ },
169+ "eval_split": "test",
170+ "heldout": true
171+}
\ No newline at end of file
added results/metrics/main/stress_calibration_test.csv +73 −0
@@ -0,0 +1,73 @@
1+model,H,days,n,cov_50,cov_80,cov_95,pit_var,pit_ks,crps,crps_scaled
2+ewma,5,stress,100846,0.5347063839914324,0.8364536025226583,0.9716895067727029,0.07567549192240969,0.025210939123902376,0.0010054778702722458,0.43392077341650703
3+ewma,5,normal,885622,0.535672103899858,0.8340815833391673,0.9684346143162659,0.07585894362169283,0.02234248331062272,0.0007728657640120806,0.4570876676469658
4+ewma,15,stress,100846,0.5410626103167205,0.8449417924359915,0.9746643396862543,0.07410281515402911,0.029308427293631756,0.0016893288463087006,0.42203142123842624
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6+ewma,30,stress,100846,0.5464966384388077,0.8510600321281955,0.9761021755944708,0.0730212200724964,0.035652538592123,0.002343426285159171,0.414754280221736
7+ewma,30,normal,885622,0.5532687760692485,0.850105349686435,0.9731838188301555,0.07254771637390967,0.029400056775437,0.0017949088767459612,0.4346802140364314
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9+ewma,60,normal,885622,0.5653506800869897,0.8577869565119204,0.9739730946159874,0.07063801891979943,0.03484084809775723,0.002470809970217705,0.423838416711742
10+garch_t,5,stress,100846,0.5182357257600698,0.8221149078793408,0.9643515855859429,0.07865965151869612,0.019803578605455563,0.0010053058766672264,0.43449345562361763
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18+har_rv,5,stress,100846,0.6026119032980981,0.8959502607936854,0.9897467425579597,0.06287975133340637,0.06183242530638644,0.0010199898653300656,0.4401958669975712
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26+lightgbm,5,stress,100846,0.38097693512881026,0.6783610653868274,0.9053011522519485,0.10738601292198825,0.0749140519609307,0.0010136129626057415,0.43787191437690326
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37+mlp,15,normal,885622,0.5672352312837757,0.8437696895515242,0.9595572377379966,0.07221074625144985,0.03414658247865765,0.001297918164892644,0.444428328489027
38+mlp,30,stress,100846,0.5739444301211749,0.8547884893798465,0.9657695892747358,0.07025250159944876,0.047601917645175074,0.0023318994731594206,0.41422679849256583
39+mlp,30,normal,885622,0.5772157873223565,0.8500737334890055,0.9612995160463493,0.07060385846350893,0.0391885515256164,0.0017890790452864595,0.43391283799005315
40+mlp,60,stress,100846,0.5810047002360034,0.8623842294191143,0.9708168891180612,0.06860869322084776,0.05347979698180427,0.003172929646264639,0.3988388979034416
41+mlp,60,normal,885622,0.5845349370273096,0.8555523688435924,0.9623902748576707,0.06935209833442561,0.042668414151466605,0.002455546919538578,0.42204543646301895
42+mrpt_base,5,stress,100846,0.5459016718560974,0.8299486345516927,0.9575392182139103,0.07564054146632399,0.03144309000554402,0.0010013235145993472,0.4324662162631855
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46+mrpt_base,30,stress,100846,0.5658429684866033,0.847668722606747,0.9627749241417607,0.07183917449588402,0.04322836432751148,0.0023264051307924135,0.4128657029077017
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48+mrpt_base,60,stress,100846,0.5722190270313151,0.8553239593042857,0.9677230628879678,0.07021813243585122,0.049968115458840084,0.0031654744613767967,0.3975463375626667
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50+mrpt_small,5,stress,100846,0.5465957995359261,0.8298593895642862,0.9576482954207405,0.07560903251538889,0.0319295155447753,0.0010012722096688228,0.4323771694089165
51+mrpt_small,5,normal,885622,0.5494612825787977,0.8298348505344266,0.955869434137815,0.07545289141542011,0.029163628509890294,0.0007695989775985403,0.4554516626026671
52+mrpt_small,15,stress,100846,0.5611328163734803,0.8436328659540289,0.9616444876346112,0.07291831159453098,0.03602167770283626,0.00167968786464637,0.42017785925442963
53+mrpt_small,15,normal,885622,0.5646178617965678,0.8417044743694262,0.9586121392648331,0.0727181005228246,0.03399413664751971,0.0012966455348156964,0.44376691514760047
54+mrpt_small,30,stress,100846,0.564950518612538,0.846865517720088,0.9628740852388791,0.07206589025803126,0.042893609258905285,0.002325728937719461,0.41257829197877655
55+mrpt_small,30,normal,885622,0.5722204281284792,0.8467641951080709,0.9601906908364969,0.07142577331558289,0.0383849302180973,0.0017873304204470614,0.4332427002750533
56+mrpt_small,60,stress,100846,0.5674890426987684,0.8532019118259524,0.9665727941613946,0.07084454019490166,0.04848753329564043,0.0031630328687335427,0.39719115016971474
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58+mrpt_small_1m_only,5,stress,100846,0.5496301291077484,0.8331019574400571,0.9586498225016361,0.07500460501700514,0.03318219198722172,0.0010016876486648924,0.43253581904462396
59+mrpt_small_1m_only,5,normal,885622,0.5494804781272371,0.8297061274448918,0.9553150215328887,0.07548002678254453,0.029122478293507115,0.0007698956593937841,0.4556326629915531
60+mrpt_small_1m_only,15,stress,100846,0.5643456359201158,0.8458342423100569,0.9628344208000317,0.07225115982970541,0.0379733193748715,0.0016811322667474194,0.42064237576188873
61+mrpt_small_1m_only,15,normal,885622,0.5641311981861336,0.8410179512252406,0.9578860958738604,0.07284709272337976,0.03344122447630837,0.0012970356409168307,0.44394623859602483
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63+mrpt_small_1m_only,30,normal,885622,0.5720928341888526,0.8462312363514005,0.9595436879390982,0.07149197236123066,0.03830311300662437,0.0017881041553387677,0.4334886240442805
64+mrpt_small_1m_only,60,stress,100846,0.5776728873728258,0.8584772821926502,0.9685857644328977,0.06945530187021165,0.05140503808205085,0.0031676114446830047,0.3980353511307076
65+mrpt_small_1m_only,60,normal,885622,0.5792211575593199,0.8507670315326403,0.9602482774818151,0.0703701549716101,0.04208466463552163,0.002453104741791065,0.4216500768405239
66+tcn,5,stress,100846,0.5580786545822343,0.8413124962814589,0.9629038335680146,0.07330502888939862,0.037363738652184675,0.0010030222575768504,0.43339388290946446
67+tcn,5,normal,885622,0.5551759102641985,0.8354535004776305,0.9587973198497779,0.07423983050430039,0.03146070024318326,0.00077052813644257,0.4561747320636474
68+tcn,15,stress,100846,0.5750451182991889,0.8551950498780319,0.9667017035876485,0.07012703914253446,0.043233676208335875,0.001684563902229511,0.42173854323008153
69+tcn,15,normal,885622,0.5731113274060491,0.8482896766340493,0.961777146457518,0.07113390373276754,0.037641669883092255,0.0013004784634299902,0.44503916136407023
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72+tcn,60,stress,100846,0.5889276718957619,0.8679868314063027,0.9717589195406858,0.06729138754538856,0.05645477856974651,0.00318096130203445,0.39937480982426077
73+tcn,60,normal,885622,0.5903082805079368,0.859539397169447,0.9639823762282328,0.06829772471834046,0.04695580949232647,0.0024645440081826724,0.42311622144148414
added results/metrics/main/subgroups_mrpt_small_vs_har_rv_test.csv +125 −0
@@ -0,0 +1,125 @@
1+col,group,H,n,rel,lo,hi,crps_model,crps_ref
2+asset_class,etf,5,241451,-0.014051067426099776,-0.015389977692230166,-0.012822847411394321,0.00045124491652042477,0.00045767574933360195
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4+asset_class,etf,30,241451,-0.002908395135826796,-0.005602248366307874,-0.0006858381382108347,0.0010863044303832466,0.0010894730484981125
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6+asset_class,stock,5,745017,-0.019861173789486906,-0.020550797918683746,-0.019105646950529416,0.0009041331226754066,0.0009224541447572633
7+asset_class,stock,15,745017,0.004428362875261136,0.0037332045677614806,0.005074551852240178,0.00151433846457595,0.0015076619901901497
8+asset_class,stock,30,745017,0.006688811500237019,0.005637512547936101,0.007656174861283038,0.0020874024499440407,0.0020735329787099246
9+asset_class,stock,60,745017,0.00807915130836925,0.00669410596015427,0.00933138538704403,0.002855719610185287,0.0028328327259609484
10+adv_tercile,high,5,360804,-0.01738306460859069,-0.018243548319270164,-0.016515726929716822,0.0007045806755875706,0.0007170451171869051
11+adv_tercile,high,15,360804,-0.0015371935909105107,-0.0025082484232150717,-0.0007352525814094417,0.001196942334823388,0.0011987850995953651
12+adv_tercile,high,30,360804,-0.0011441521424215828,-0.0024387847138893255,8.401089255761553e-06,0.0016505406609004668,0.0016524312937053638
13+adv_tercile,high,60,360804,-0.0003455373510277122,-0.0019268653530107025,0.0009939629795263146,0.0022626606836552354,0.0022634427876802934
14+adv_tercile,low,5,307090,-0.019626161401654428,-0.020603501978977014,-0.018716166153169386,0.000977889058103884,0.0009974654765390167
15+adv_tercile,low,15,307090,0.008424717468964102,0.007563269362311675,0.009395095312052166,0.0016399821663166357,0.0016262812066257278
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added results/metrics/main/summary_elastic_net_test.json +186 −0
@@ -0,0 +1,186 @@
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added results/metrics/main/summary_ewma_test.json +186 −0
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added results/metrics/main/summary_garch_t_test.json +186 −0
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added results/metrics/main/summary_har_rv_test.json +186 −0
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\ No newline at end of file
added results/metrics/main/summary_lightgbm_test.json +186 −0
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added results/metrics/main/summary_mlp_test.json +162 −0
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added results/metrics/main/summary_mrpt_base_test.json +162 −0
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added results/metrics/main/summary_mrpt_small_1m_only_test.json +162 −0
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added results/metrics/main/summary_mrpt_small_test.json +162 −0
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added results/metrics/main/summary_naive_rw_test.json +162 −0
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added results/metrics/main/summary_ridge_test.json +186 −0
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added results/metrics/main/summary_seasonal_vol_test.json +162 −0
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added results/metrics/main/summary_tcn_test.json +162 −0
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570+{"event": "train", "step": 11180, "loss": -6.065895080566406, "grad_norm": 0.25485071539878845, "lr": 3.331535171219332e-05, "ex_per_s": 836.2537066080414, "data_frac": 0.04884215733249092, "mem_mb": 199.197696, "nll": -6.065895080566406, "elapsed_s": 855.6}
571+{"event": "train", "step": 11200, "loss": -6.1770548820495605, "grad_norm": 0.17675036191940308, "lr": 3.3156224979138305e-05, "ex_per_s": 837.5328769014152, "data_frac": 0.048844640784866965, "mem_mb": 199.197696, "nll": -6.1770548820495605, "elapsed_s": 855.9}
572+{"event": "train", "step": 11220, "loss": -5.9883036613464355, "grad_norm": 0.2942616939544678, "lr": 3.300096638734598e-05, "ex_per_s": 838.8127159208055, "data_frac": 0.0488471881871424, "mem_mb": 199.197696, "nll": -5.9883036613464355, "elapsed_s": 856.1}
573+{"event": "train", "step": 11240, "loss": -5.966488838195801, "grad_norm": 0.3189751207828522, "lr": 3.2849580491920845e-05, "ex_per_s": 840.0899559160518, "data_frac": 0.04884976397301564, "mem_mb": 199.197696, "nll": -5.966488838195801, "elapsed_s": 856.3}
574+{"event": "train", "step": 11260, "loss": -6.058408260345459, "grad_norm": 3.1072635650634766, "lr": 3.2702071734346924e-05, "ex_per_s": 841.3685879018996, "data_frac": 0.04885233077713954, "mem_mb": 199.197696, "nll": -6.058408260345459, "elapsed_s": 856.5}
575+{"event": "train", "step": 11280, "loss": -6.106337547302246, "grad_norm": 0.4310399889945984, "lr": 3.255844444235771e-05, "ex_per_s": 842.646411648586, "data_frac": 0.04885516409947056, "mem_mb": 199.197696, "nll": -6.106337547302246, "elapsed_s": 856.7}
576+{"event": "train", "step": 11300, "loss": -5.938758373260498, "grad_norm": 0.2991997301578522, "lr": 3.2418702829808946e-05, "ex_per_s": 843.9257510916424, "data_frac": 0.048857376382893676, "mem_mb": 199.197696, "nll": -5.938758373260498, "elapsed_s": 857.0}
577+{"event": "train", "step": 11320, "loss": -5.861535549163818, "grad_norm": 0.31312936544418335, "lr": 3.228285099655516e-05, "ex_per_s": 845.2037870288901, "data_frac": 0.04885922409057216, "mem_mb": 199.197696, "nll": -5.861535549163818, "elapsed_s": 857.2}
578+{"event": "train", "step": 11340, "loss": -5.928648471832275, "grad_norm": 0.4681607186794281, "lr": 3.215089292832933e-05, "ex_per_s": 846.4813253429627, "data_frac": 0.048861563052613896, "mem_mb": 199.197696, "nll": -5.928648471832275, "elapsed_s": 857.4}
579+{"event": "train", "step": 11360, "loss": -5.9611616134643555, "grad_norm": 0.23459981381893158, "lr": 3.202283249662597e-05, "ex_per_s": 847.7560397611494, "data_frac": 0.04886386398026978, "mem_mb": 199.197696, "nll": -5.9611616134643555, "elapsed_s": 857.6}
580+{"event": "train", "step": 11380, "loss": -5.909996032714844, "grad_norm": 0.1668248325586319, "lr": 3.189867345858747e-05, "ex_per_s": 849.0303324235676, "data_frac": 0.04886596374398161, "mem_mb": 199.197696, "nll": -5.909996032714844, "elapsed_s": 857.8}
581+{"event": "train", "step": 11400, "loss": -6.081756591796875, "grad_norm": 0.41980525851249695, "lr": 3.177841945689397e-05, "ex_per_s": 850.3043920691533, "data_frac": 0.04886790522247877, "mem_mb": 199.197696, "nll": -6.081756591796875, "elapsed_s": 858.1}
582+{"event": "train", "step": 11420, "loss": -5.832974433898926, "grad_norm": 0.17915330827236176, "lr": 3.1662074019656414e-05, "ex_per_s": 851.5797404428038, "data_frac": 0.04887016632520255, "mem_mb": 199.197696, "nll": -5.832974433898926, "elapsed_s": 858.3}
583+{"event": "train", "step": 11440, "loss": -5.884792327880859, "grad_norm": 0.6790859699249268, "lr": 3.154964056031306e-05, "ex_per_s": 852.8537414974714, "data_frac": 0.048872004054468274, "mem_mb": 199.197696, "nll": -5.884792327880859, "elapsed_s": 858.5}
584+{"event": "train", "step": 11460, "loss": -6.137067794799805, "grad_norm": 0.5836895704269409, "lr": 3.1441122377529333e-05, "ex_per_s": 854.1265790534246, "data_frac": 0.04887319475369488, "mem_mb": 199.197696, "nll": -6.137067794799805, "elapsed_s": 858.7}
585+{"event": "train", "step": 11480, "loss": -5.973583221435547, "grad_norm": 0.2574062645435333, "lr": 3.133652265510106e-05, "ex_per_s": 855.4001709392994, "data_frac": 0.04887501662812914, "mem_mb": 199.197696, "nll": -5.973583221435547, "elapsed_s": 858.9}
586+{"event": "train", "step": 11500, "loss": -5.937276840209961, "grad_norm": 0.4883059859275818, "lr": 3.123584446186108e-05, "ex_per_s": 856.669180255945, "data_frac": 0.048877982478776526, "mem_mb": 199.197696, "nll": -5.937276840209961, "elapsed_s": 859.1}
587+{"event": "train", "step": 11520, "loss": -5.906184196472168, "grad_norm": 0.687404990196228, "lr": 3.113909075158913e-05, "ex_per_s": 857.9392039642203, "data_frac": 0.04888042739520704, "mem_mb": 199.197696, "nll": -5.906184196472168, "elapsed_s": 859.4}
588+{"event": "train", "step": 11540, "loss": -6.0453362464904785, "grad_norm": 0.14672644436359406, "lr": 3.104626436292525e-05, "ex_per_s": 859.2080171117681, "data_frac": 0.04888287053933303, "mem_mb": 199.197696, "nll": -6.0453362464904785, "elapsed_s": 859.6}
589+{"event": "train", "step": 11560, "loss": -6.114807605743408, "grad_norm": 0.22907289862632751, "lr": 3.095736801928647e-05, "ex_per_s": 860.4783404027132, "data_frac": 0.04888520978896826, "mem_mb": 199.197696, "nll": -6.114807605743408, "elapsed_s": 859.8}
590+{"event": "train", "step": 11580, "loss": -5.959295272827148, "grad_norm": 0.2049648016691208, "lr": 3.087240432878696e-05, "ex_per_s": 861.7438622299454, "data_frac": 0.0488885019282573, "mem_mb": 199.197696, "nll": -5.959295272827148, "elapsed_s": 860.0}
591+{"event": "train", "step": 11600, "loss": -6.073004245758057, "grad_norm": 0.21902291476726532, "lr": 3.0791375784161455e-05, "ex_per_s": 863.0096726683256, "data_frac": 0.0488912327732441, "mem_mb": 199.197696, "nll": -6.073004245758057, "elapsed_s": 860.3}
592+{"event": "train", "step": 11620, "loss": -6.02445125579834, "grad_norm": 0.3206517994403839, "lr": 3.0714284762692116e-05, "ex_per_s": 864.2782285875535, "data_frac": 0.048893766523654465, "mem_mb": 199.197696, "nll": -6.02445125579834, "elapsed_s": 860.5}
593+{"event": "train", "step": 11640, "loss": -5.965457916259766, "grad_norm": 0.1713353544473648, "lr": 3.0641133526138836e-05, "ex_per_s": 865.5446072625226, "data_frac": 0.04889590431989016, "mem_mb": 199.197696, "nll": -5.965457916259766, "elapsed_s": 860.7}
594+{"event": "train", "step": 11660, "loss": -5.919635772705078, "grad_norm": 0.3123888075351715, "lr": 3.0571924220672835e-05, "ex_per_s": 866.8104124742641, "data_frac": 0.048898175883527774, "mem_mb": 199.197696, "nll": -5.919635772705078, "elapsed_s": 860.9}
595+{"event": "train", "step": 11680, "loss": -5.9642863273620605, "grad_norm": 0.29894450306892395, "lr": 3.0506658876813734e-05, "ex_per_s": 868.0771420860525, "data_frac": 0.04890033846448027, "mem_mb": 199.197696, "nll": -5.9642863273620605, "elapsed_s": 861.1}
596+{"event": "train", "step": 11700, "loss": -6.048731803894043, "grad_norm": 0.18587692081928253, "lr": 3.0445339409369934e-05, "ex_per_s": 869.3444670651745, "data_frac": 0.048902157397392794, "mem_mb": 199.197696, "nll": -6.048731803894043, "elapsed_s": 861.3}
597+{"event": "train", "step": 11720, "loss": -5.767541408538818, "grad_norm": 0.29071491956710815, "lr": 3.0387967617382475e-05, "ex_per_s": 870.6090929138187, "data_frac": 0.04890443609247213, "mem_mb": 199.197696, "nll": -5.767541408538818, "elapsed_s": 861.6}
598+{"event": "train", "step": 11740, "loss": -6.04293155670166, "grad_norm": 0.5333802103996277, "lr": 3.0334545184072252e-05, "ex_per_s": 871.874517759372, "data_frac": 0.04890647884565498, "mem_mb": 199.197696, "nll": -6.04293155670166, "elapsed_s": 861.8}
599+{"event": "train", "step": 11760, "loss": -5.9006428718566895, "grad_norm": 0.2929174304008484, "lr": 3.0285073676790606e-05, "ex_per_s": 873.1383170424714, "data_frac": 0.0489085325183384, "mem_mb": 199.197696, "nll": -5.9006428718566895, "elapsed_s": 862.0}
600+{"event": "train", "step": 11780, "loss": -6.0862202644348145, "grad_norm": 0.14244844019412994, "lr": 3.0239554546973377e-05, "ex_per_s": 874.4006265035777, "data_frac": 0.048910495633361716, "mem_mb": 199.197696, "nll": -6.0862202644348145, "elapsed_s": 862.2}
601+{"event": "train", "step": 11800, "loss": -5.965174674987793, "grad_norm": 0.33696284890174866, "lr": 3.0197989130098285e-05, "ex_per_s": 875.6636581152953, "data_frac": 0.04891240127610053, "mem_mb": 199.197696, "nll": -5.965174674987793, "elapsed_s": 862.4}
602+{"event": "train", "step": 11820, "loss": -5.970404148101807, "grad_norm": 0.7969751358032227, "lr": 3.0160378645645744e-05, "ex_per_s": 876.9264291119985, "data_frac": 0.048914007549988855, "mem_mb": 199.197696, "nll": -5.970404148101807, "elapsed_s": 862.7}
603+{"event": "train", "step": 11840, "loss": -6.0319743156433105, "grad_norm": 0.17215025424957275, "lr": 3.0126724197063143e-05, "ex_per_s": 878.1864398288543, "data_frac": 0.04891601450057858, "mem_mb": 199.197696, "nll": -6.0319743156433105, "elapsed_s": 862.9}
604+{"event": "train", "step": 11860, "loss": -5.986623764038086, "grad_norm": 0.5427295565605164, "lr": 3.0097026771732407e-05, "ex_per_s": 879.4473178167699, "data_frac": 0.04891829746320929, "mem_mb": 199.197696, "nll": -5.986623764038086, "elapsed_s": 863.1}
605+{"event": "train", "step": 11880, "loss": -6.028807163238525, "grad_norm": 0.6392213702201843, "lr": 3.0071287240941065e-05, "ex_per_s": 880.7082842631629, "data_frac": 0.04892032328308076, "mem_mb": 199.197696, "nll": -6.028807163238525, "elapsed_s": 863.3}
606+{"event": "train", "step": 11900, "loss": -5.885828018188477, "grad_norm": 0.14996542036533356, "lr": 3.0049506359856666e-05, "ex_per_s": 881.9659583919228, "data_frac": 0.04892239094816451, "mem_mb": 199.197696, "nll": -5.885828018188477, "elapsed_s": 863.5}
607+{"event": "train", "step": 11920, "loss": -6.011484622955322, "grad_norm": 0.4300556778907776, "lr": 3.0031684767504643e-05, "ex_per_s": 883.2258084561219, "data_frac": 0.04892403920039532, "mem_mb": 199.197696, "nll": -6.011484622955322, "elapsed_s": 863.7}
608+{"event": "train", "step": 11940, "loss": -6.069141387939453, "grad_norm": 0.7685412764549255, "lr": 3.0017822986749544e-05, "ex_per_s": 884.4826192993814, "data_frac": 0.048925991815950566, "mem_mb": 199.197696, "nll": -6.069141387939453, "elapsed_s": 864.0}
609+{"event": "train", "step": 11960, "loss": -6.159242630004883, "grad_norm": 0.4395144283771515, "lr": 3.000792142427973e-05, "ex_per_s": 885.7366324879056, "data_frac": 0.048928103141199726, "mem_mb": 199.197696, "nll": -6.159242630004883, "elapsed_s": 864.2}
610+{"event": "train", "step": 11980, "loss": -6.0917277336120605, "grad_norm": 0.14595115184783936, "lr": 3.000198037059536e-05, "ex_per_s": 886.9937448558156, "data_frac": 0.04892985416476889, "mem_mb": 199.197696, "nll": -6.0917277336120605, "elapsed_s": 864.4}
611+{"event": "train", "step": 12000, "loss": -5.936335563659668, "grad_norm": 0.24455416202545166, "lr": 2.9999999999999997e-05, "ex_per_s": 888.2509930208824, "data_frac": 0.04893154086494521, "mem_mb": 199.197696, "nll": -5.936335563659668, "elapsed_s": 864.6}
612+{"event": "val", "step": 12000, "val_nll": -6.451788332870508, "best": -6.451788332870508, "bad": 0, "mem_mb": 199.197696, "elapsed_s": 927.4}
613+{"event": "done", "steps": 12000, "best_val": -6.451788332870508, "wall_s": 927.3784019947052, "ex_per_s": 828.1409167477947, "peak_mem_mb": 199.197696, "n_params": 231459, "device": "mps", "data_time_frac": 0.045620226071422174, "elapsed_s": 927.4}
added results/metrics/main/tcn/train_summary.json +10 −0
@@ -0,0 +1,10 @@
1+{
2+ "steps": 12000,
3+ "best_val": -6.451788332870508,
4+ "wall_s": 927.3784019947052,
5+ "ex_per_s": 828.1409167477947,
6+ "peak_mem_mb": 199.197696,
7+ "n_params": 231459,
8+ "device": "mps",
9+ "data_time_frac": 0.045620226071422174
10+}
\ No newline at end of file
added scripts/economic.py +48 −0
@@ -0,0 +1,48 @@
1+"""H5 (exploratory): cost-aware signal evaluation from saved predictions (runs where pred_*.npz live).
2+Median of the cumulative return = sum of per-minute Student-t locations (symmetric marginals)."""
3+from __future__ import annotations
4+
5+import argparse
6+import json
7+import sys
8+from pathlib import Path
9+
10+import numpy as np
11+import pandas as pd
12+
13+ROOT = Path(__file__).resolve().parents[1]
14+sys.path.insert(0, str(ROOT / "src"))
15+from chronos_x.eval.economic import signal_pnl # noqa: E402
16+
17+
18+def main() -> None:
19+ ap = argparse.ArgumentParser(); ap.add_argument("--exp", default="main"); ap.add_argument("--tag", default="test")
20+ ap.add_argument("--models", default="mrpt_small,mrpt_base,mlp,tcn"); ap.add_argument("--horizons", default="5,15,30,60")
21+ a = ap.parse_args()
22+ d = ROOT / "results" / "metrics" / a.exp
23+ tab = pd.read_csv(d / f"origins_{a.tag}.csv")
24+ out = []
25+ for m in a.models.split(","):
26+ f = d / f"pred_{m}_{a.tag}.npz"
27+ if not f.exists():
28+ continue
29+ p = np.load(f)
30+ mu = p["mu"]; ym = p["y_mask"]
31+ for H in (int(h) for h in a.horizons.split(",")):
32+ med = (mu[:, :H] * ym[:, :H]).sum(1)
33+ real = tab[f"cum_{H}"].values
34+ assert np.allclose((p["y"][:, :H] * ym[:, :H]).sum(1), real, atol=1e-6)
35+ for cost in (0.0, 1.0, 2.5, 5.0, 10.0):
36+ for k in (1.0, 2.0):
37+ r = signal_pnl(med, real, tab, cost_bps=cost, k=k)
38+ out.append({"model": m, "H": H, **r})
39+ # information content without trading: correlation and regression slope of realised on median
40+ sl = np.polyfit(med, real, 1)[0] if med.std() > 0 else np.nan
41+ out.append({"model": m, "H": H, "cost_bps": -1, "k": 0, "corr": float(np.corrcoef(med, real)[0, 1]), "slope": float(sl), "n": int(len(med)),
42+ "median_abs_bps": float(np.abs(med).mean() * 1e4)})
43+ df = pd.DataFrame(out); df.to_csv(d / f"economic_{a.tag}.csv", index=False)
44+ print(df.to_string(index=False, float_format=lambda x: f"{x:.4g}"))
45+
46+
47+if __name__ == "__main__":
48+ main()
modified scripts/evaluate.py +27 −5
@@ -4,6 +4,7 @@ from __future__ import annotations
4 4
5 import argparse 5 import argparse
6 import json 6 import json
7+import re
7 import sys 8 import sys
8 from pathlib import Path 9 from pathlib import Path
9 10
@@ -102,12 +103,33 @@ def main() -> None:
102 (tdir / f"{exp}_compare_{metric}{suffix}.tex").write_text(compare_table(cmp, H, f"tab:{exp}_cmp_{metric}{suffix}", 103 (tdir / f"{exp}_compare_{metric}{suffix}.tex").write_text(compare_table(cmp, H, f"tab:{exp}_cmp_{metric}{suffix}",
103 f"{exp.capitalize()}{' (held-out)' if suffix else ''}: {metric.upper()} relative to {pretty(a.ref)}", metric.upper())) 104 f"{exp.capitalize()}{' (held-out)' if suffix else ''}: {metric.upper()} relative to {pretty(a.ref)}", metric.upper()))
104 for r in cmp.itertuples(): 105 for r in cmp.itertuples():
105− key = f"{exp}{suffix}{r.model}{metric}H{r.H}".replace("_", "") 106+ key = "".join(c for c in f"rel{exp}{suffix}{r.model}{metric}H{r.H}" if c.isalnum())
106− macros.append(f"\\newcommand{{\\{''.join(c for c in key if c.isalnum())}}}{{{100 * r.rel_diff:+.2f}}}") 107+ macros.append(f"\\newcommand{{\\{key}}}{{{100 * r.rel_diff:+.2f}}}")
108+ macros.append(f"\\newcommand{{\\dm{key}}}{{{r.dm_t:.1f}}}")
107 for r in lb.itertuples(): 109 for r in lb.itertuples():
108− key = "".join(c for c in f"{exp}{suffix}{r.model}crpsH{r.H}" if c.isalnum()) 110+ key = "".join(c for c in f"{exp}{suffix}{r.model}H{r.H}" if c.isalnum())
109− macros.append(f"\\newcommand{{\\{key}}}{{{r.crps_scaled:.4f}}}") 111+ macros.append(f"\\newcommand{{\\crps{key}}}{{{r.crps_scaled:.4f}}}")
110− (ROOT / "paper" / "results_macros.tex").write_text("\n".join(macros) + "\n") 112+ macros.append(f"\\newcommand{{\\covfifty{key}}}{{{100 * r.cov_50:.0f}}}")
113+ macros.append(f"\\newcommand{{\\covninetyfive{key}}}{{{100 * r.cov_95:.1f}}}")
114+ # pairwise contrasts, if computed
115+ fp = d / f"pairwise_{a.split}{suffix}.csv"
116+ if fp.exists():
117+ pw = pd.read_csv(fp)
118+ for r in pw[pw.metric == "crps"].itertuples():
119+ key = "".join(c for c in f"pw{exp}{suffix}{r.model}vs{r.reference}H{r.H}" if c.isalnum())
120+ macros.append(f"\\newcommand{{\\{key}}}{{{100 * r.rel_diff:+.2f}}}")
121+ macros.append(f"\\newcommand{{\\t{key}}}{{{r.dm_t:.1f}}}")
122+ macros.append(f"\\newcommand{{\\p{key}}}{{{r.p_holm:.3g}}}")
123+ words = {"0": "zero", "1": "one", "2": "two", "3": "three", "4": "four", "5": "five", "6": "six", "7": "seven", "8": "eight", "9": "nine"}
124+
125+ def wordify(line: str) -> str:
126+ m = re.match(r"\\newcommand\{\\([A-Za-z0-9]+)\}", line)
127+ if not m:
128+ return line
129+ name = m.group(1)
130+ new = re.sub(r"\d", lambda x: words[x.group(0)], name)
131+ return line.replace("\\" + name + "}", "\\" + new + "}", 1)
132+ (ROOT / "paper" / "results_macros.tex").write_text("\n".join(wordify(l) for l in macros) + "\n")
111 print(f"wrote {len(list(tdir.glob('*.tex')))} tables, {len(macros)} macros") 133 print(f"wrote {len(list(tdir.glob('*.tex')))} tables, {len(macros)} macros")
112 134
113 135
added scripts/pairwise.py +89 −0
@@ -0,0 +1,89 @@
1+"""P7 inference: pairwise contrasts between saved models (DM-HAC + day-cluster bootstrap, Holm/BH), subgroup
2+analysis of a contrast, and stress-day calibration (H3). Works from per_origin_*.npz + origins_<tag>.csv.
3+Usage: pairwise.py --exp main --tag test --pairs mrpt_small:mrpt_small_1m_only,mrpt_small:har_rv,... [--subgroups mrpt_small:har_rv]"""
4+from __future__ import annotations
5+
6+import argparse
7+import json
8+import sys
9+from pathlib import Path
10+
11+import numpy as np
12+import pandas as pd
13+
14+ROOT = Path(__file__).resolve().parents[1]
15+sys.path.insert(0, str(ROOT / "src"))
16+from chronos_x import pipeline as P # noqa: E402
17+from chronos_x.eval.statistics import holm, benjamini_hochberg, cluster_bootstrap_mean # noqa: E402
18+from chronos_x.eval.subgroups import add_subgroups # noqa: E402
19+from chronos_x.eval.calibration import pit_histogram # noqa: E402
20+
21+
22+def load(exp: str, name: str, tag: str) -> dict:
23+ return np.load(ROOT / "results" / "metrics" / exp / f"per_origin_{name}_{tag}.npz")
24+
25+
26+def main() -> None:
27+ ap = argparse.ArgumentParser()
28+ ap.add_argument("--exp", default="main"); ap.add_argument("--tag", default="test"); ap.add_argument("--pairs", required=True)
29+ ap.add_argument("--subgroups", default=None); ap.add_argument("--stride", type=int, default=15)
30+ ap.add_argument("--metrics", default="crps,wis,qlike"); ap.add_argument("--horizons", default="5,15,30,60")
31+ ap.add_argument("--universe", default="data/manifests/universe_main.json"); ap.add_argument("--out-suffix", default="")
32+ a = ap.parse_args()
33+ d = ROOT / "results" / "metrics" / a.exp
34+ tab = pd.read_csv(d / f"origins_{a.tag}.csv")
35+ man = json.loads((ROOT / a.universe).read_text())
36+ meta = add_subgroups(tab, man)
37+ H = [int(h) for h in a.horizons.split(",")]
38+ rows = []
39+ cache = {}
40+ def per(name):
41+ if name not in cache:
42+ cache[name] = load(a.exp, name, a.tag)
43+ return cache[name]
44+ for pair in a.pairs.split(","):
45+ m, ref = pair.split(":")
46+ for metric in a.metrics.split(","):
47+ for h in H:
48+ r = P.compare(per(m)[f"{metric}_{h}"], per(ref)[f"{metric}_{h}"], tab, h, a.stride)
49+ rows.append({"model": m, "reference": ref, "metric": metric, **r})
50+ df = pd.DataFrame(rows)
51+ for metric in a.metrics.split(","):
52+ sel = df["metric"] == metric
53+ df.loc[sel, "p_holm"] = holm(df.loc[sel, "dm_p"].tolist()); df.loc[sel, "p_bh"] = benjamini_hochberg(df.loc[sel, "dm_p"].tolist())
54+ out = d / f"pairwise_{a.tag}{a.out_suffix}.csv"; df.to_csv(out, index=False)
55+ print(df[df.metric == "crps"][["model", "reference", "H", "rel_diff", "lo", "hi", "dm_t", "dm_p", "p_holm"]].to_string(index=False, float_format=lambda x: f"{x:.4g}"))
56+ # subgroups for one contrast (relative CRPS difference with day-cluster CI inside each group)
57+ if a.subgroups:
58+ m, ref = a.subgroups.split(":")
59+ srows = []
60+ for col in ("asset_class", "adv_tercile", "vol_tercile", "group", "tod", "year", "stress_day"):
61+ for g, idx in meta.groupby(col, observed=True).indices.items():
62+ for h in H:
63+ pa_, pb_ = per(m)[f"crps_{h}"][idx], per(ref)[f"crps_{h}"][idx]
64+ cl = pd.factorize(meta["session_date"].values[idx])[0]
65+ cb = cluster_bootstrap_mean(pa_ - pb_, cl, n_boot=500)
66+ base = pb_.mean()
67+ srows.append({"col": col, "group": str(g), "H": h, "n": int(len(idx)), "rel": cb["mean"] / base, "lo": cb["lo"] / base, "hi": cb["hi"] / base,
68+ "crps_model": pa_.mean(), "crps_ref": base})
69+ sdf = pd.DataFrame(srows); sdf.to_csv(d / f"subgroups_{m}_vs_{ref}_{a.tag}.csv", index=False)
70+ print(sdf[sdf.H == 60].to_string(index=False, float_format=lambda x: f"{x:.4g}"))
71+ # stress-day calibration (H3): coverage and PIT dispersion on stress vs normal days for every model in the pairs
72+ names = sorted({x for pair in a.pairs.split(",") for x in pair.split(":")})
73+ crows = []
74+ for name in names:
75+ p = per(name)
76+ for h in H:
77+ for lab, idx in (("stress", np.flatnonzero(meta["stress_day"].values)), ("normal", np.flatnonzero(~meta["stress_day"].values))):
78+ u = p[f"pit_{h}"][idx]
79+ ph = pit_histogram(u)
80+ crows.append({"model": name, "H": h, "days": lab, "n": int(len(idx)), "cov_50": float(((u >= .25) & (u <= .75)).mean()),
81+ "cov_80": float(((u >= .1) & (u <= .9)).mean()), "cov_95": float(((u >= .025) & (u <= .975)).mean()),
82+ "pit_var": ph["var"], "pit_ks": ph["ks_stat"], "crps": float(p[f"crps_{h}"][idx].mean()),
83+ "crps_scaled": float(p[f"crps_scaled_{h}"][idx].mean())})
84+ cdf = pd.DataFrame(crows); cdf.to_csv(d / f"stress_calibration_{a.tag}{a.out_suffix}.csv", index=False)
85+ print(cdf[cdf.H == 60].to_string(index=False, float_format=lambda x: f"{x:.4g}"))
86+
87+
88+if __name__ == "__main__":
89+ main()
modified src/chronos_x/data/windows.py +9 −7
@@ -210,13 +210,15 @@ class OriginIndex:
210 g = self.grid(i) 210 g = self.grid(i)
211 o = eligible_origins(g, cfg, None if session_sets is None else session_sets[i], stride) 211 o = eligible_origins(g, cfg, None if session_sets is None else session_sets[i], stride)
212 if max_per_session is not None and len(o): 212 if max_per_session is not None and len(o):
213− keep = [] 213+ # vectorised per-session subsampling: random key, sort by (session, key), keep the first k per session
214− for s in np.unique(o[:, 0]): 214+ keys = rng.random(len(o))
215− sub = o[o[:, 0] == s] 215+ order = np.lexsort((keys, o[:, 0]))
216− if len(sub) > max_per_session: 216+ os_ = o[order]
217− sub = sub[np.sort(rng.choice(len(sub), max_per_session, replace=False))] 217+ starts = np.r_[0, np.flatnonzero(np.diff(os_[:, 0])) + 1]
218− keep.append(sub) 218+ sizes = np.diff(np.r_[starts, len(os_)])
219− o = np.concatenate(keep) 219+ rank = np.arange(len(os_)) - np.repeat(starts, sizes)
220+ o = os_[rank < max_per_session]
221+ o = o[np.lexsort((o[:, 1], o[:, 0]))]
220 if len(o): 222 if len(o):
221 rows.append(np.column_stack([np.full(len(o), i), o])) 223 rows.append(np.column_stack([np.full(len(o), i), o]))
222 self.rows = np.concatenate(rows).astype(np.int64) if rows else np.zeros((0, 3), dtype=np.int64) 224 self.rows = np.concatenate(rows).astype(np.int64) if rows else np.zeros((0, 3), dtype=np.int64)
modified src/chronos_x/eval/figures.py +7 −3
@@ -64,7 +64,9 @@ def fig_scores_by_horizon(lb: pd.DataFrame, out: Path, name: str, metric: str =
64 _panel(axes[1], "B", f"relative to {_lab(ref)} (%)"); axes[1].axhline(0, color=INK4, lw=0.6); axes[1].set_xlabel("Horizon $H$ (minutes)"); axes[1].set_ylabel("%") 64 _panel(axes[1], "B", f"relative to {_lab(ref)} (%)"); axes[1].axhline(0, color=INK4, lw=0.6); axes[1].set_xlabel("Horizon $H$ (minutes)"); axes[1].set_ylabel("%")
65 for ax in axes: 65 for ax in axes:
66 ax.set_xticks(H) 66 ax.set_xticks(H)
67− axes[1].legend(ncol=2, loc="best") 67+ h_, l_ = axes[1].get_legend_handles_labels()
68+ fig.legend(h_, l_, loc="lower center", ncol=4, bbox_to_anchor=(0.5, -0.22), frameon=False)
69+ fig.subplots_adjust(wspace=0.3)
68 _save(fig, out, name) 70 _save(fig, out, name)
69 71
70 72
@@ -81,8 +83,10 @@ def fig_calibration(lb: pd.DataFrame, out: Path, name: str, models=None):
81 continue 83 continue
82 ax.plot(d["H"], 100 * d[f"cov_{lv}"], label=_lab(m), **STYLE.get(m, dict(marker=".", ls="-", color=INK3))) 84 ax.plot(d["H"], 100 * d[f"cov_{lv}"], label=_lab(m), **STYLE.get(m, dict(marker=".", ls="-", color=INK3)))
83 ax.axhline(lv, color=INK4, lw=0.8, ls=":"); ax.set_xticks(H); ax.set_xlabel("$H$ (min)") 85 ax.axhline(lv, color=INK4, lw=0.8, ls=":"); ax.set_xticks(H); ax.set_xlabel("$H$ (min)")
84− _panel(ax, "ABC"[(50, 80, 95).index(lv)], f"{lv}% interval coverage (%)") 86+ _panel(ax, "ABC"[(50, 80, 95).index(lv)], f"{lv}% coverage")
85− axes[0].legend(ncol=1, loc="best") 87+ h_, l_ = axes[0].get_legend_handles_labels()
88+ fig.legend(h_, l_, loc="lower center", ncol=4, bbox_to_anchor=(0.5, -0.34), frameon=False)
89+ fig.subplots_adjust(wspace=0.35)
86 _save(fig, out, name) 90 _save(fig, out, name)
87 91
88 92
89 93
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