Datasets:

usamaahmedsh
/

elliott-wave-scorer-training

rule large_string	is_synthetic bool	label int64	ensemble_score float64	fib_score float64	wave_config large_string	geo_0 float64	geo_1 float64	geo_2 float64	geo_3 float64	geo_4 float64	has_geo4 bool
Corrective	true	1	0.989026	0.966227	[14, 11, 14]	0.469023	1.033773	1.803741	1.859671	-1	false
Corrective	true	0	0	0	[13, 14, 10]	0.639399	1.543245	2.760626	3.494088	-1	false
impulse	true	0	0.692308	0.878209	[14, 14, 14, 14, 15]	0.625536	1.393791	0.245389	1.303376	6.623876	true
bearish_impulse	true	1	0.92	0.915166	[14, 14, 6, 10, 14]	0.654295	1.533166	0.341843	0.95263	3.160135	true
bearish_impulse	true	0	0.78	0.899584	[4, 8, 13, 14, 14]	0.785529	1.899584	0.32332	1.27003	4.382615	true
Corrective	true	0	0	0	[11, 7, 14]	0.320885	1.148952	0.950495	3.515704	-1	false
impulse	true	0	0.794872	0.922303	[14, 14, 14, 14, 14]	0.64134	1.194303	0.330728	0.937739	6.586692	true
Corrective	true	1	0.942387	0.991221	[9, 8, 13]	0.547716	0.794779	2.247382	0.817291	-1	false
impulse	true	1	0.948718	0.899231	[12, 14, 14, 14, 14]	0.416821	1.899231	0.327506	0.778186	7.605424	true
bearish_impulse	true	1	0.96	0.916713	[14, 14, 12, 10, 14]	0.553732	1.355287	0.368869	1.032908	4.609447	true
impulse	true	0	0	0	[14, 14, 14, 14, 7]	0.549128	3.414663	0.024123	0.761847	8.141141	true
Corrective	true	1	0.903978	0.975256	[12, 9, 14]	0.496578	1.296744	1.373389	1.594928	-1	false
bearish_impulse	true	1	0.94	0.894342	[13, 14, 6, 4, 14]	0.320807	1.894342	0.265624	0.820323	4.247596	true
Corrective	true	1	0.854595	0.989491	[14, 7, 14]	0.476978	1.010509	0.35665	0.452431	-1	false
impulse	true	1	0.897436	0.870745	[14, 14, 14, 14, 14]	0.608054	1.870745	0.264221	1.310916	9.248728	true
bearish_impulse	true	1	0.84	0.868555	[15, 11, 14, 4, 14]	0.367126	2.131445	0.156758	1.071645	3.954046	true
impulse	true	1	0.974359	0.922889	[14, 14, 14, 14, 14]	0.402386	1.695111	0.28359	1.034647	8.859809	true
impulse	true	1	0.897436	0.915848	[14, 15, 2, 10, 14]	0.483637	1.915848	0.148411	0.886714	6.844015	true
impulse	true	0	0.769231	0.946307	[9, 14, 14, 14, 14]	0.696979	1.671693	0.281458	0.780889	9.019115	true
bearish_impulse	true	0	0	0	[14, 14, 14, 14, 14]	0.401161	2.323669	0.269353	1.378986	3.91485	true
bearish_impulse	true	1	0.84	0.959986	[14, 14, 2, 14, 14]	0.499283	1.231986	0.474543	0.794011	4.626335	true
impulse	true	0	0	0	[14, 14, 14, 14, 15]	0.26707	2.651202	0.032855	0.932323	9.165566	true
bearish_impulse	true	1	0.96	0.995283	[14, 10, 14, 14, 14]	0.613474	1.995283	0.342798	1.317507	4.300785	true
impulse	true	0	0	0	[12, 14, 14, 4, 14]	0.552868	2.171138	0.271504	1.119084	7.917582	true
impulse	true	1	0.948718	0.960587	[14, 0, 14, 5, 14]	0.491542	1.232587	0.405629	1.220254	7.393032	true
impulse	true	1	0.923077	0.943376	[14, 14, 3, 14, 14]	0.440054	1.943376	0.322289	1.39241	7.125426	true
bearish_impulse	true	1	0.94	0.907566	[14, 14, 13, 14, 14]	0.417093	2.092434	0.30689	0.84045	3.93159	true
Corrective	true	1	0.903978	0.970815	[10, 9, 12]	0.55136	1.029185	0.304106	1.356374	-1	false
bearish_impulse	true	1	0.94	0.935605	[13, 14, 12, 14, 14]	0.345275	1.553605	0.311783	0.76177	3.583704	true
Corrective	true	0	0	0	[14, 14, 13]	0.64049	0.449967	1.695747	0.01	-1	false
Corrective	true	1	0.950617	0.908092	[10, 6, 14]	0.462013	0.877908	2.646955	1.488264	-1	false
Corrective	true	0	0	0	[11, 11, 13]	0.410892	0.142256	2.384223	0.220194	-1	false
Corrective	true	0	0	0	[14, 10, 5]	0.545278	1.29926	3.442181	3.628615	-1	false
bearish_impulse	true	0	0	0	[14, 2, 5, 13, 14]	0.38125	1.807681	0.178988	0.685136	3.5313	true
impulse	true	0	0	0	[9, 14, 14, 14, 0]	0.529133	2.8676	0.333837	0.564033	8.580364	true
impulse	true	1	0.846154	0.897438	[14, 12, 14, 14, 6]	0.542194	1.515438	0.183609	0.949961	8.515055	true
Corrective	true	1	0.803841	0.951278	[14, 9, 14]	0.572233	1.048722	0.36675	1.62085	-1	false
Corrective	true	1	0.78738	0.981904	[11, 11, 14]	0.498668	0.804096	2.708843	0.408448	-1	false
Corrective	true	1	0.923182	0.932148	[13, 7, 14]	0.478181	0.853852	0.605291	1.633558	-1	false
bearish_impulse	true	0	0	0	[8, 14, 14, 14, 14]	0.549074	3.227782	0.104801	1.229161	3.883064	true
bearish_impulse	true	0	0	0	[14, 14, 9, 14, 14]	0.489229	2.695489	0.308029	1.464836	3.468973	true
impulse	true	1	0.923077	0.977034	[14, 14, 14, 14, 5]	0.416081	1.977034	0.38751	1.337892	6.458406	true
bearish_impulse	true	1	0.92	0.89469	[14, 14, 13, 14, 14]	0.542489	1.72331	0.4116	0.808246	3.673153	true
bearish_impulse	true	1	0.84	0.942696	[14, 10, 14, 11, 14]	0.458413	2.560696	0.266658	0.736022	3.534269	true
bearish_impulse	true	0	0	0	[14, 14, 14, 14, 5]	0.606298	2.344413	0.352978	1.182093	3.221966	true
impulse	true	0	0	0	[14, 4, 13, 14, 14]	0.611562	2.890408	0.195684	1.310701	8.259433	true
bearish_impulse	true	1	0.92	0.877035	[14, 14, 14, 14, 14]	0.502716	1.495035	0.443225	0.81691	4.228353	true
impulse	true	0	0.794872	0.878482	[14, 14, 0, 14, 14]	0.654128	2.121518	0.231155	0.795388	8.854564	true
Corrective	true	1	0.993141	0.955107	[14, 4, 14]	0.523692	1.044893	1.993091	1.627009	-1	false
impulse	true	0	0	0	[1, 14, 4, 14, 14]	0.689244	2.231527	0.34827	1.49244	6.508354	true
bearish_impulse	true	0	0.28	0.893056	[14, 14, 14, 14, 14]	0.315434	1.106944	0.160533	1.199831	3.386483	true
impulse	true	0	0.589744	0.995378	[14, 9, 14, 14, 4]	0.424289	2.613378	0.396128	1.076127	9.194083	true
impulse	true	0	0	0	[9, 14, 14, 10, 14]	0.273345	3.16714	0.173039	0.975688	7.101284	true
impulse	true	0	0	0	[14, 14, 14, 14, 14]	0.30116	2.483272	0.01	0.233695	9.221254	true
Corrective	true	1	0.893004	0.93172	[14, 5, 13]	0.544397	0.85428	2.531075	0.608238	-1	false
impulse	true	0	0	0	[14, 5, 14, 14, 14]	0.521739	1.845195	0.107182	0.466689	6.25053	true
Corrective	true	1	0.906722	0.949337	[14, 11, 10]	0.475871	1.050663	2.399431	0.758898	-1	false
bearish_impulse	true	0	0	0	[14, 14, 6, 14, 14]	0.140499	3.161326	0.067999	0.560159	3.88927	true
Corrective	true	1	0.781893	0.945912	[6, 11, 12]	0.51155	1.054088	2.398892	0.494104	-1	false
Corrective	true	1	0.759945	0.972179	[9, 12, 13]	0.412984	0.813821	0.473269	1.416633	-1	false
Corrective	true	1	0.893004	0.960663	[10, 5, 13]	0.436458	0.746663	1.903126	1.559221	-1	false
impulse	true	0	0.230769	0.892403	[2, 14, 14, 14, 15]	0.645851	1.510403	0.466996	0.515609	7.076601	true
bearish_impulse	true	0	0	0	[14, 14, 13, 14, 14]	0.469076	1.447051	0.066004	1.705874	3.222292	true
impulse	true	1	0.974359	0.897857	[14, 14, 14, 13, 14]	0.473239	1.515857	0.291778	0.759269	8.692023	true
Corrective	true	1	0.91358	0.920332	[13, 8, 11]	0.548099	0.706332	1.225913	0.958221	-1	false
Corrective	true	0	0	0	[12, 10, 14]	0.721404	1.049335	5.614916	2.417435	-1	false
bearish_impulse	true	0	0.36	0.963971	[13, 14, 14, 14, 14]	0.761291	1.654029	0.443145	0.767254	3.94906	true
impulse	true	0	0.589744	0.918246	[14, 14, 14, 0, 14]	0.21561	1.190246	0.186741	0.563939	6.318871	true
impulse	true	0	0	0	[14, 14, 13, 14, 14]	0.651452	3.441652	0.574982	1.855187	7.011068	true
bearish_impulse	true	0	0	0	[2, 13, 13, 14, 14]	0.311194	2.68143	0.038673	1.207436	3.936641	true
bearish_impulse	true	1	0.96	0.949126	[14, 14, 14, 14, 14]	0.441649	1.949126	0.313302	0.854122	4.292862	true
Corrective	true	1	0.960219	0.928712	[14, 8, 12]	0.48733	1.200712	1.861497	1.98986	-1	false
impulse	true	1	0.974359	0.925051	[0, 14, 14, 14, 3]	0.509914	1.543051	0.284431	0.813437	6.433313	true
impulse	true	1	0.923077	0.87413	[14, 14, 14, 12, 14]	0.340182	1.49213	0.327545	0.692809	6.428103	true
Corrective	true	0	0	0	[3, 9, 14]	0.526571	1.028789	3.489372	2.142419	-1	false
impulse	true	0	0.538462	0.876644	[11, 14, 14, 14, 14]	0.641319	2.741356	0.289963	0.793409	6.555838	true
bearish_impulse	true	1	0.84	0.967309	[1, 4, 14, 14, 14]	0.682823	1.585309	0.289771	1.266805	4.258941	true
Corrective	true	1	0.969822	0.977604	[13, 9, 6]	0.466705	0.977604	0.427029	1.115811	-1	false
bearish_impulse	true	0	0.2	0.882661	[14, 14, 14, 13, 13]	0.544645	1.154661	0.129082	0.291485	3.818233	true
bearish_impulse	true	1	0.86	0.933154	[14, 14, 13, 11, 13]	0.546862	1.684846	0.235494	1.296948	4.087953	true
Corrective	true	1	0.965706	0.941882	[13, 8, 12]	0.462646	0.727882	1.358592	0.920972	-1	false
impulse	true	0	0	0	[2, 12, 14, 14, 11]	0.718364	3.025743	0.325759	1.016742	8.703775	true
impulse	true	1	0.897436	0.870839	[2, 14, 5, 14, 14]	0.500424	1.129161	0.455341	1.042372	8.70826	true
bearish_impulse	true	0	0	0	[14, 7, 14, 14, 13]	0.888077	1.347194	0.228717	0.872451	4.647974	true
impulse	true	1	0.897436	0.900296	[14, 14, 10, 14, 14]	0.290396	1.717704	0.252638	1.036741	6.407699	true
impulse	true	1	0.871795	0.910934	[14, 15, 14, 14, 5]	0.66407	2.089066	0.30995	0.916832	7.290699	true
impulse	true	0	0	0	[15, 14, 14, 14, 14]	0.654847	3.008907	0.444238	1.850884	8.562789	true
bearish_impulse	true	1	0.84	0.850947	[14, 14, 7, 14, 14]	0.653699	2.149053	0.224311	0.683717	4.595243	true
bearish_impulse	true	0	0.12	0.982257	[13, 14, 14, 2, 14]	0.789385	1.289743	0.634611	0.969245	4.400455	true
bearish_impulse	true	1	0.94	0.856547	[1, 14, 9, 4, 14]	0.629752	2.143453	0.2275	0.933452	3.573157	true
impulse	true	0	0	0	[14, 12, 14, 14, 14]	0.460082	2.234213	0.155674	0.330415	9.056964	true
bearish_impulse	true	1	0.84	0.945961	[14, 10, 14, 14, 14]	0.619256	2.672039	0.324876	0.95855	4.374218	true
impulse	true	0	0	0	[14, 10, 10, 13, 14]	0.437936	1.864054	0.139315	0.050627	8.307376	true
bearish_impulse	true	0	0	0	[14, 11, 12, 14, 14]	0.873659	2.839715	0.353472	1.168115	3.633252	true
Corrective	true	1	0.972565	0.870772	[10, 12, 10]	0.530781	1.129228	1.225882	1.073627	-1	false
Corrective	true	1	0.758573	0.927607	[14, 12, 14]	0.453936	0.690393	0.466113	1.097854	-1	false
bearish_impulse	true	1	0.9	0.860381	[14, 14, 14, 14, 14]	0.589213	1.478381	0.440182	0.856825	3.83962	true
impulse	true	1	0.948718	0.852123	[14, 14, 2, 14, 13]	0.519508	1.852123	0.250878	0.730225	6.230826	true
Corrective	true	0	0.42524	0.982399	[5, 10, 14]	0.580168	1.254399	0.667876	2.514025	-1	false
impulse	true	1	0.871795	0.967595	[14, 14, 13, 14, 14]	0.545666	1.304405	0.479106	1.341463	7.822608	true

End of preview. Expand in Data Studio

Elliott Wave Scorer — Training Dataset

Binary-labelled dataset for training a neural network scorer that distinguishes valid Elliott Wave patterns (label=1) from invalid/borderline patterns (label=0).

Dataset Summary

Field	Value
Total rows	30,000,000
Good samples (label=1)	15,000,000
Bad samples (label=0)	15,000,000
Bad / Good ratio	1.00
Wave types	Corrective, bearish_impulse, impulse
Run	`run_20260318_135833_t5000000`

Label Semantics

Label	Meaning	How generated
`1`	Valid Elliott Wave	Passed all 4 generation tiers + diversity filter
`0` (Type A)	Wrong Fibonacci ratios	Passed T1+T1b geometry shape, failed T2 Fibonacci gate
`0` (Type B)	Borderline pattern	Passed T1+T1b+T2 (correct Fib ratios), failed T3 MVN score threshold

Type A = hard negatives (clearly wrong geometry).
Type B = soft negatives (plausible geometry, but low density vs real distribution).

Schema

Column	Description
`rule`	Wave type: `Corrective`, `impulse`, `bearish_impulse`
`wave_config`	Wave config string
`geo_0..geo_4`	Geometry ratios
`ensemble_score`	MVN-based quality score (0.0 for Type A bad, ~0.0–0.79 for Type B bad, ≥0.80 for good)
`fib_score`	Fibonacci proximity score
`is_synthetic`	Always `True` in this dataset
`label`	Target variable: `1` = good, `0` = bad

Recommended Input Features

features = ['geo_0', 'geo_1', 'geo_2', 'geo_3', 'ensemble_score', 'fib_score']
target   = 'label'

Optionally add derived Fibonacci-distance features:

FIBS = [0.236, 0.382, 0.5, 0.618, 0.786, 1.0, 1.272, 1.618, 2.0, 2.618]
for i in range(4):
    df[f'fib_dist_{i}'] = df[f'geo_{i}'].apply(
        lambda v: min(abs(v - f) for f in FIBS))

Training Notes

Dataset is pre-shuffled (50/50 split, random seed)
Stratify by rule when splitting train/val/test
A 3–4 layer MLP or gradient-boosted tree works well given the clear feature separation
ensemble_score alone achieves strong separation; geometry features add complementary signal

Related Dataset

Good samples only (for synthesis evaluation): usamaahmedsh/synthetic-elliott-waves

Downloads last month: 35