FinJEPA: Financial Joint-Embedding Predictive Architecture

FinJEPA is a JEPA-based world model for portfolio optimization over a separated action space consisting of:

• Portfolio weights (continuous, simplex-constrained)
• Trading signals (discrete: long/short/flat per asset)
• Hedging signal (binary: on/off)

Architecture (SOTA Blend)

Component	Source	Key Innovation
Time Series Encoder	TS-JEPA (Sennadir 2025)	1D-CNN patch tokenizer + Transformer
Action Conditioning	JEPA-WMs (Terver 2025)	AdaLN + RoPE in predictor
Collapse Prevention	EB-JEPA (Terver 2026)	SIGReg + Inverse Dynamics Model
Multi-step Rollout	EB-JEPA	K-step autoregressive training
Planner	JEPA-WMs + EB-JEPA	CEM L2 cost / MPPI cumulative cost in latent space
TD Branch	TD-JEPA (Bagatella 2025)	Optional separate task encoder for zero-shot RL

Model

Financial Time Series (T, F)
    │
    ▼
[TimeSeriesTokenizer] ── 1D-CNN patches + position encoding
    │
    ├───► [Context Encoder] (student)
    │          │
    │          ▼
    │     [Predictor] ◄─── Action embedding (weights + signals)
    │     (AdaLN + RoPE)       │
    │          │               │
    │          ▼               ▼
    │     Predicted target  [ActionEmbedder]
    │     embeddings           ├── weights (continuous)
    │                            ├── signals (discrete)
    │                            └── hedge (binary)
    │
    └───► [Target Encoder] (teacher, EMA frozen)
              │
              ▼
         Ground truth target embeddings

Usage

python finjepa/run_training_fast.py

Full training on real data:

python finjepa/train.py --data_source hf \
  --dataset_name paperswithbacktest/Stocks-Daily-Price \
  --n_assets 5 --batch_size 128 --epochs 50 --push_to_hub

References

1. TS-JEPA — Sennadir et al. (2025). arxiv:2509.25449
2. JEPA-WMs — Terver et al. (2025). arxiv:2512.24497
3. EB-JEPA — Terver et al. (2026). arxiv:2602.03604
4. V-JEPA 2 — Assran et al. (2025). arxiv:2506.09985
5. TD-JEPA — Bagatella et al. (2025). arxiv:2510.00739

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