WatchSignal v4.0 — Multi-Horizon 5-Seed Ensemble
BiLSTM + Transformer hybrid with 4 parallel classification heads.
Architecture
- Input:
[batch, 30, 55] float32 (30-candle window, 55 v3.1 features)
- Output:
[batch, 4, 3] float32 (4 horizons × 3 classes: SELL/HOLD/BUY)
- Backbone: BiLSTM(55→256 bidirectional) + 2-layer Transformer (4 heads, pre-LN)
- Pooling: cat(last_step, mean_pool) → Linear(1024→512) → GELU
- Heads: 4 independent classifiers (512→3), one per horizon
Horizons
| Horizon | Days | Buy Threshold | Sell Threshold |
|---|
| Short | 5 | +0.8% | -0.8% |
| Medium | 10 | +1.5% | -1.5% |
| Long | 21 | +3.0% | -3.0% |
| Macro | 63 | +5.0% | -5.0% |
Ensemble
- Seeds: 42, 1, 7, 99, 2025
- Mean weighted accuracy: 0.5690144980676382% ± 0.008819085091941169%
- Per-horizon temperature calibration per seed (20 values total)
Out-of-Sample Backtest (2026-01-09 → 2026-04-10)
(see v4/mh_v4_backtest_report.json for full results)
Inference
- ONNX Runtime Mobile compatible (opset 13)
- Apply per-horizon temperature scaling from
mh_v4_seed{X}_temperatures.json
- Ensemble: average softmax probabilities across 5 seeds, then argmax
Files (v4/ directory)
mh_v4_seed{42,1,7,99,2025}.onnx — ONNX models (~27.7 MB each)mh_v4_seed{42,1,7,99,2025}_temperatures.json — per-horizon temperaturesmh_v4_ensemble.json — ensemble manifestmh_v4_backtest_report.json — out-of-sample backtest resultssha256_manifest.json — SHA-256 hashes for integrity verification
v3.1 Models (root directory)
Previous single-horizon models remain available in the repo root.
