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Compare models

21 registered · 21 with usable out-of-sample days · scope: val + test

val + test + liveval + testval onlyall (incl. in-sample)

By cell type

cellhorizonmodelsrank IC (mean ± sd)spread across modelsLS decilehit (alpha)
gru+1d11+0.0689 ± 0.0055+0.0585 … +0.0772+1.06%55.1%
gru+7d11+0.0833 ± 0.0079+0.0724 … +0.0956+3.73%59.2%
gru+30d11+0.1040 ± 0.0058+0.0973 … +0.1124+10.80%60.8%
lstm+1d10+0.0605 ± 0.0059+0.0489 … +0.0693+0.93%55.0%
lstm+7d10+0.0746 ± 0.0082+0.0592 … +0.0854+3.16%59.6%
lstm+30d10+0.0935 ± 0.0069+0.0807 … +0.1040+9.10%62.2%
Two-stage aggregation: each model's daily rank-IC series is averaged to one score, then those scores are averaged and spread across models. So ±sd is the model-to-model (seed) spread, not day-to-day noise — that distinction is the whole point of a seeded sweep.

Note this group holds every registered model of that cell type, including any that are not part of the seeded sweep (beta_5y, for instance, which is a gru model but was trained separately). The Paired by seed section below is the one that corresponds to compare_cells.py.

Paired by seed

Same seed = same initialisation stream and the same data, so per-seed differences cancel seed-level noise. Values are lstm − gru in rank IC.

horizonseeds pairedmean Δ± SEtwin raterelativeverdict
+1d10-0.00800.0022-3.5620% lstm-11.6%gru better
+7d10-0.00790.0026-3.0410% lstm-9.6%no measurable difference
+30d10-0.01020.0023-4.340% lstm-9.8%gru better
These figures are val + test. compare_cells.py selects on the validation split only — choose the val only pill above and this table reproduces its output exactly. Verified to 4 decimals against the 10-seed run: gru +0.0745/+0.0920/+0.1190 vs lstm +0.0646/+0.0810/+0.1036, paired Δ −0.0099/−0.0110/−0.0154 with t = −3.94/−4.06/−5.28. Under any other scope the numbers legitimately differ, because they cover more dates. The val-only figure is the one the GRU-over-LSTM decision was made on.

Every model

modelcellparamsdata endIC +1dLS +1dhit +1dIC +7dLS +7dhit +7dIC +30dLS +30dhit +30ddays
beta_5ygru250,7712026-10-21+0.0738+1.05%55.6%+0.0915+3.74%60.1%+0.1074+11.32%61.6%530
gru_s0gru250,7712026-10-21+0.0772+1.12%55.3%+0.0956+4.30%58.4%+0.1101+10.96%60.5%530
gru_s1gru250,7712026-10-21+0.0720+1.15%55.5%+0.0868+3.86%59.8%+0.1051+10.97%61.1%530
gru_s2gru250,7712026-10-21+0.0706+1.20%54.7%+0.0765+3.72%56.4%+0.0984+10.79%58.7%530
gru_s3gru250,7712026-10-21+0.0645+0.99%55.5%+0.0730+3.05%60.2%+0.0988+9.51%60.9%530
gru_s4gru250,7712026-10-21+0.0585+0.96%53.0%+0.0724+3.38%58.0%+0.0979+11.75%57.6%530
gru_s5gru250,7712026-10-21+0.0665+0.97%55.6%+0.0773+3.16%60.8%+0.0973+9.12%62.8%530
gru_s6gru250,7712026-10-21+0.0650+1.00%54.6%+0.0833+3.64%57.4%+0.1121+10.86%61.8%530
gru_s7gru250,7712026-10-21+0.0649+1.05%55.6%+0.0840+3.92%60.6%+0.1013+10.85%62.1%530
gru_s8gru250,7712026-10-21+0.0710+1.04%55.6%+0.0836+3.83%60.3%+0.1035+10.92%62.4%530
gru_s9gru250,7712026-10-21+0.0741+1.09%55.2%+0.0923+4.39%59.1%+0.1124+11.81%59.9%530
lstm_s0lstm316,8192026-10-21+0.0571+0.99%55.4%+0.0758+3.36%60.3%+0.0929+8.98%63.2%530
lstm_s1lstm316,8192026-10-21+0.0630+0.99%55.7%+0.0831+3.89%60.8%+0.1040+10.63%60.2%530
lstm_s2lstm316,8192026-10-21+0.0649+1.06%53.5%+0.0744+3.32%56.6%+0.0918+9.01%59.2%530
lstm_s3lstm316,8192026-10-21+0.0538+0.85%55.4%+0.0645+2.54%60.3%+0.0870+8.25%62.7%530
lstm_s4lstm316,8192026-10-21+0.0606+0.93%52.7%+0.0808+3.27%56.0%+0.0960+9.57%61.3%530
lstm_s5lstm316,8192026-10-21+0.0489+0.82%55.4%+0.0592+2.11%60.7%+0.0807+7.37%63.3%530
lstm_s6lstm316,8192026-10-21+0.0604+0.90%55.5%+0.0694+2.62%60.4%+0.0891+8.51%63.0%530
lstm_s7lstm316,8192026-10-21+0.0654+0.95%55.7%+0.0787+3.76%60.9%+0.0942+10.23%63.4%530
lstm_s8lstm316,8192026-10-21+0.0610+0.90%55.6%+0.0745+3.15%60.5%+0.1006+9.22%63.2%530
lstm_s9lstm316,8192026-10-21+0.0693+0.93%55.3%+0.0854+3.56%59.9%+0.0990+9.24%62.7%530