Research project: apply JEPA-based self-supervised representation learning to FX risk management for a corporate bank with an internal global FX trading desk.

Primary tasks: FX volatility forecasting and VaR/CVaR estimation. Target output: internal PoC for the trading desk.

Architecture decision (see DECISIONS.md ADR-001)

TS-JEPA + SIGReg — TS-JEPA temporal patchwise architecture (Ennadir et al., arXiv:2509.25449) with EMA replaced by Sketched Isotropic Gaussian Regularization (SIGReg, Balestriero & LeCun, arXiv:2511.08544). Single search axis: λ ∈ [0.01, 1.0].

Phase 2 hypothesis: MTS-JEPA multi-resolution objective (arXiv:2602.04643).

Stack

Go (src/): data pipeline (DUKASCopy fetch + hourly processing), evaluation harness (silhouette, linear probe R², collapse diagnostic, Kupiec/Christoffersen), results dashboard (Templ + HTMX + CDN Tailwind).

Python (model/): all training and embedding export only. PyTorch cu130 (Blackwell sm_120 compatible). No evaluation logic in Python.

Infra: koala (Arch Linux, Blackwell GPU 12 GB VRAM) for training. iguana (Mac Studio M2 Ultra) + LiteLLM on piguard for autoresearch agent LLM.

Repository layout

jepa-fx-risk/
├── src/                    # Go — data pipeline + eval harness + dashboard
│   ├── data/               # DUKASCopy fetch, hourly processing, validation
│   └── eval/               # silhouette, linear probe, collapse, backtest
├── model/                  # Python — training only
│   ├── train.py            # TS-JEPA + SIGReg backbone (autoresearch edits this)
│   ├── prepare.py          # LOCKED — data loading, tokenization, export
│   └── requirements.txt
├── specs/                  # Research specs (one per phase/experiment type)
├── experiments/            # Per-run outputs: embeddings, metrics.json, git tag
├── results/summaries/      # Human-readable outcome per experiment
├── program.md              # Autoresearch agenda — researcher edits this
├── DECISIONS.md            # Architecture Decision Records
└── Taskfile.yml            # task data:fetch, task experiment:run, task eval:*

Phase structure

Phase 0 (current): MAE baseline on EUR/USD hourly 2008–2022. Gate: silhouette > 0.20, MAE > PCA, ±10% over 3 reruns.
Phase 1: TS-JEPA + SIGReg autoresearch sweep, 50 experiments. Gate: val_vol_r2 > GARCH baseline AND Kupiec p > 0.05.
Phase 2: MTS-JEPA multi-resolution hypothesis.
Phase 3: Internal bank tick/position data (future, out of current scope).

Data

DUKASCopy hourly OHLCV, 10 G10 pairs, 2008–2022 train / 2023 val / 2024 test. Features: log-return, log rolling-20-period HV, VIX (daily interpolated). Weekend gaps handled explicitly. See ADR-002.

Key conventions

prepare.py is LOCKED — never modified by agents or autoresearch
Evaluation metrics are always computed by the Go harness, never in Python
Every experiment gets a git tag: exp/YYYYMMDD-description
Null results are recorded explicitly in results/summaries/ — do not iterate silently
program.md is the only file the researcher edits to steer autoresearch
CI runs task check (lint + vet + test) only — no GPU, no training

Evaluation metrics

Primary (autoresearch optimizes): val_vol_r2 — linear probe R² on 1-day realized volatility from frozen embeddings, computed by Go harness.

Secondary (logged, not optimized): Kupiec p-value (VaR 99% backtest on EUR/USD). Must co-move with val_vol_r2 — checked from experiment 1.

Diagnostics: silhouette score (regime clustering), PC1/HV correlation (collapse check).

Benchmarks to beat (trading desk comparison)

GARCH(1,1) — volatility forecasting baseline
Historical Simulation VaR (250-day rolling) — Basel default
EWMA RiskMetrics (λ=0.94)

Agent guidance

Read DECISIONS.md before making architecture suggestions. Do not modify prepare.py or src/eval/. Do not suggest changing the Python/Go separation. Training runs are always manual via task experiment:run — never triggered by CI. When implementing, follow Go conventions in .skills/go-patterns/SKILL.md.

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jepa-fx-risk

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