fix(ci): drop empty LOOP_MODEL env that blanked the model → LLM 400

`LOOP_MODEL: ${{ inputs.model }}` sets the env var to "" when no model input is
given. An env var set-but-empty is NOT unset, so it overrode loop.py's
berget/gemma4-31b default, and every agent call POSTed "model":"" → HTTP 400
Bad Request. The loop ran but optimized nothing (best == baseline, delta 0).

Real overrides already flow through the conditional `--model` arg, so this env
line was redundant as well as harmful. Remove it; loop.py's default applies.

.gitea/workflows/autoresearch.yml

@@ -0,0 +1,75 @@
+name: Autoresearch Loop
+
+on:
+  workflow_dispatch:
+    inputs:
+      fixture:
+        description: 'Fixture name in fixtures/ (without .json)'
+        required: true
+        default: 'phase-a-toy'
+      rq_id:
+        description: 'Run ID — defaults to fixture name if blank'
+        required: false
+        default: ''
+      iters:
+        description: 'Max iterations'
+        required: false
+        default: '3'
+      model:
+        description: 'LiteLLM model override (leave blank for default berget/gemma4-31b)'
+        required: false
+        default: ''
+
+jobs:
+  run:
+    name: Autoresearch — ${{ inputs.fixture }}
+    runs-on: self-hosted
+    timeout-minutes: 90
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Resolve run ID
+        id: vars
+        run: |
+          RQ_ID="${{ inputs.rq_id }}"
+          [ -z "$RQ_ID" ] && RQ_ID="${{ inputs.fixture }}"
+          echo "rq_id=$RQ_ID" >> "$GITHUB_OUTPUT"
+
+      - name: Clean stale run dir
+        run: rm -rf "runs/${{ steps.vars.outputs.rq_id }}"
+
+      - name: Set up Python venv
+        run: |
+          [ -d .venv ] || python3 -m venv .venv
+          # torch must come from the cu130 wheel index (koala Blackwell sm_120);
+          # requirements.txt deliberately excludes it. Install it first.
+          .venv/bin/pip install -q torch --index-url https://download.pytorch.org/whl/cu130
+          .venv/bin/pip install -q -r requirements.txt
+
+      - name: Scaffold run dir
+        run: |
+          .venv/bin/python scripts/autoresearch_start.py \
+            "fixtures/${{ inputs.fixture }}.json" \
+            "${{ steps.vars.outputs.rq_id }}"
+
+      - name: Run autoresearch loop
+        env:
+          LITELLM_KEY: ${{ secrets.LITELLM_KEY }}
+          LITELLM_BASE: ${{ secrets.LITELLM_BASE }}
+          NTFY_URL: ${{ secrets.NTFY_URL }}
+        run: |
+          ARGS="--run-dir runs/${{ steps.vars.outputs.rq_id }} --iters ${{ inputs.iters }}"
+          [ -n "${{ inputs.model }}" ] && ARGS="$ARGS --model ${{ inputs.model }}"
+          .venv/bin/python loop.py $ARGS
+
+      - name: Upload run artifacts
+        if: always()
+        uses: https://gitea.com/actions/upload-artifact@v3
+        with:
+          name: run-${{ steps.vars.outputs.rq_id }}-${{ github.run_number }}
+          path: |
+            runs/${{ steps.vars.outputs.rq_id }}/STATUS.md
+            runs/${{ steps.vars.outputs.rq_id }}/metrics.json
+            runs/${{ steps.vars.outputs.rq_id }}/program.md
+          retention-days: 30

.gitignore

@@ -34,3 +34,20 @@ bin/
 
 # downloaded + processed market data (track via DVC/MinIO, #10 — not git)
 data/
+
+# autoresearch run dirs (ephemeral; each scaffold rebuilds from fixtures/)
+runs/
+
+# ephemeral experiment outputs (generated by train.py / loop.py)
+metrics.json
+embeddings.json
+HEARTBEAT
+STATUS.md
+
+# python caches
+__pycache__/
+*.pyc
+.pytest_cache/
+
+# built Go binaries
+eval

STATUS.md

@@ -16,3 +16,7 @@
 | 3 | -0.0716 | +0.0487 | KEEP | 4s | gpu=0% vram=10054/12227MiB temp=36°C | iter3 |
 | 4 | 0.0590 | +0.1306 | KEEP | 5s | gpu=0% vram=10054/12227MiB temp=36°C | iter4 |
 | 5 | 0.0599 | +0.0009 | KEEP | 5s | gpu=0% vram=10054/12227MiB temp=37°C | iter5 |
+| 1 | 0.0563 | -0.0036 | revert | 5s | gpu=0% vram=10054/12227MiB temp=35°C | iter1 |
+| 2 | 0.0577 | -0.0022 | revert | 5s | gpu=0% vram=10054/12227MiB temp=36°C | iter2 |
+| 3 | 0.0563 | -0.0036 | revert | 5s | gpu=0% vram=10054/12227MiB temp=36°C | iter3 |
+| 4 | -0.1613 | -0.2212 | revert | 5s | gpu=0% vram=10054/12227MiB temp=37°C | iter4 |

Taskfile.yml

@@ -18,6 +18,41 @@ tasks:
     deps: [generate]
     cmds: [go test ./... -race]
 
+  data:fetch:
+    desc: "Download EUR/USD M1 from histdata (set YEARS env var)"
+    cmds: [.venv/bin/python scripts/fetch_data.py]
+  data:fetch:historical:
+    desc: "Download EUR/USD M1 2008-2018 from histdata"
+    cmds:
+      - YEARS=2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018 .venv/bin/python scripts/fetch_data.py
+  data:prepare:daily:
+    desc: "Rebuild eurusd_daily.parquet from all M1 zips"
+    cmds: [.venv/bin/python scripts/prepare_data.py]
+  data:prepare:hourly:
+    desc: "Build eurusd_hourly.parquet from all M1 zips"
+    cmds: [.venv/bin/python scripts/prepare_hourly.py]
+  data:prepare:all:
+    desc: "Build both daily and hourly parquets"
+    deps: [data:prepare:daily, data:prepare:hourly]
+  train:multipair:
+    desc: "Train 5-pair G10 HEPA (D=256, best config, phase1_r2≈0.44)"
+    cmds: [JEPA_USE_MULTIPAIR=1 JEPA_D_MODEL=256 .venv/bin/python train.py]
+
+  data:fetch:multipair:
+    desc: "Download G10 M1 data (GBPUSD/USDJPY/USDCHF/AUDUSD) 2008-2023 from histdata"
+    cmds: [.venv/bin/python scripts/fetch_multipair.py]
+  data:prepare:pair:
+    desc: "Build {PAIR}_hourly.parquet from data/raw/{PAIR}/ (e.g. PAIR=gbpusd)"
+    cmds: [PAIR={{.PAIR}} .venv/bin/python scripts/prepare_hourly.py {{.EXTRA_ARGS}}]
+    vars:
+      PAIR: '{{default "eurusd" .PAIR}}'
+  data:prepare:multipair:
+    desc: "Merge 5-pair hourly parquets into eurusd_multipair.parquet"
+    cmds: [.venv/bin/python scripts/prepare_multipair.py]
+  data:test:
+    desc: "Run Python data pipeline tests"
+    cmds: [.venv/bin/python -m pytest tests/test_prepare_hourly.py tests/test_hepa.py tests/test_multipair.py -v]
+
   eval:probe:
     desc: "Run linear-probe (val_vol_r2) on embeddings from metrics.json"
     cmds: [./bin/eval -metric probe]

cmd/eval/main.go

@@ -83,6 +83,32 @@ func main() {
 		fmt.Printf(`{"metric":"effective_rank","value":%.6f}`+"\n", er)
 		log.Info("effective rank", "erank", fmt.Sprintf("%.2f", er))
 
+	case "var":
+		// Parametric 99% VaR breach rate from probe predictions vs actual realized vol.
+		// Requires train_embeddings (for no-leakage probe fit) and realized_vol (OOS).
+		if len(d.RealizedVol) == 0 {
+			log.Error("var requires realized_vol in embeddings.json")
+			os.Exit(1)
+		}
+		var predVol []float64
+		if len(d.TrainEmbeddings) > 0 {
+			trEmb, mu, sd := standardiseCompute(d.TrainEmbeddings)
+			oosEmb := applyStandardise(d.Embeddings, mu, sd)
+			predVol = eval.LinearProbePredict(trEmb, d.TrainRealizedVol, oosEmb, 1e-3)
+		} else {
+			oosEmb, mu, sd := standardiseCompute(d.Embeddings)
+			n70 := int(float64(len(oosEmb)) * 0.7)
+			oos70 := applyStandardise(d.Embeddings[n70:], mu, sd)
+			predVol = eval.LinearProbePredict(oosEmb[:n70], d.RealizedVol[:n70], oos70, 1e-3)
+			d.RealizedVol = d.RealizedVol[n70:]
+		}
+		const z99 = 2.326
+		breachRate, kupiecP := eval.VaRBreachRate(predVol, d.RealizedVol, z99)
+		fmt.Printf(`{"metric":"VaR_breach_rate_99_oos_regime_cond","value":%.6f,"kupiec_p":%.6f}`+"\n",
+			breachRate, kupiecP)
+		log.Info("VaR breach rate 99%", "breach_rate", fmt.Sprintf("%.4f", breachRate),
+			"kupiec_p", fmt.Sprintf("%.4f", kupiecP))
+
 	default:
 		log.Error("unknown metric", "metric", *metric)
 		os.Exit(1)

fixtures/phase-a-toy.json

@@ -0,0 +1,11 @@
+{
+  "strategic_question": "What is the highest-leverage path to a JEPA-based FX tail-risk system that beats a GARCH/EWMA baseline on out-of-sample VaR-breach calibration, given one GPU and a solo researcher?",
+  "nodes": [
+    {
+      "id": "phase-a-toy",
+      "status": "autoresearch-ready",
+      "question": "Improve the OOS linear-probe R² (val_vol_r2) of the HEPA encoder on EUR/USD daily realized vol. The encoder is a small causal transformer trained with VICReg. Vary one hyperparameter or architectural choice per iteration — model size, learning rate, window, patch length, depth, VICReg loss weights — to push val_vol_r2 as high as possible on the 2022-2023 OOS slice.",
+      "candidate_metric": "val_vol_r2"
+    }
+  ]
+}

internal/eval/var.go

@@ -0,0 +1,107 @@
+package eval
+
+import "math"
+
+// VaRBreachRate computes the parametric 99% VaR breach rate and Kupiec POF p-value.
+//
+// VaR_99_t  = predVol[t] × z99   (z99 = 2.326 for 99% normal VaR)
+// breach_t  = actualVol[t] > VaR_99_t  (strict inequality)
+// breachRate = fraction of breaches over all steps
+// kupiecP   = Kupiec POF p-value: P(chi²(1) > LR) where LR is the likelihood ratio
+//             testing H0: true breach probability = 1%. High p = well-calibrated.
+//
+// Returns (0, 1) for empty or mismatched input.
+func VaRBreachRate(predVol, actualVol []float64, z99 float64) (breachRate, kupiecP float64) {
+	n := len(predVol)
+	if n == 0 || n != len(actualVol) {
+		return 0, 1
+	}
+
+	var n1 int
+	for i := 0; i < n; i++ {
+		if actualVol[i] > predVol[i]*z99 {
+			n1++
+		}
+	}
+
+	breachRate = float64(n1) / float64(n)
+	kupiecP = kupiecPOF(n, n1, 0.01)
+	return
+}
+
+// kupiecPOF returns the Kupiec Proportion-of-Failures p-value.
+// H0: true breach probability = p0 (e.g. 0.01 for 99% VaR).
+// Returns 1.0 for edge cases (n=0, p_hat=p0).
+func kupiecPOF(n, n1 int, p0 float64) float64 {
+	if n == 0 {
+		return 1.0
+	}
+	n0 := n - n1
+	phat := float64(n1) / float64(n)
+
+	var lr float64
+	switch {
+	case n1 == 0:
+		// 0 × ln(0/p0) = 0 by convention; only the n0 term contributes
+		lr = 2 * float64(n0) * math.Log((1-phat)/(1-p0))
+	case n1 == n:
+		// n0 term vanishes
+		lr = 2 * float64(n1) * math.Log(phat/p0)
+	default:
+		lr = 2 * (float64(n1)*math.Log(phat/p0) + float64(n0)*math.Log((1-phat)/(1-p0)))
+	}
+
+	if lr <= 0 {
+		return 1.0
+	}
+	// P(chi²(1) > LR) = erfc(sqrt(LR/2))  [chi²(1) = Z², Z~N(0,1)]
+	return math.Erfc(math.Sqrt(lr / 2))
+}
+
+// LinearProbePredict fits ridge regression on (trainEmb, trainY) and returns
+// predictions for testEmb. Complements LinearProbeTrainTest when the caller
+// needs the raw predictions (e.g. to compute VaR breach rate).
+// Returns nil when trainEmb is empty.
+func LinearProbePredict(trainEmb [][]float64, trainY []float64,
+	testEmb [][]float64, lambda float64) []float64 {
+	n := len(trainEmb)
+	if n == 0 || len(testEmb) == 0 {
+		return nil
+	}
+	d := len(trainEmb[0])
+	p := d + 1
+
+	A := make([][]float64, n)
+	for i, e := range trainEmb {
+		row := make([]float64, p)
+		copy(row, e)
+		row[d] = 1.0
+		A[i] = row
+	}
+	AtA := make([][]float64, p)
+	for i := range AtA {
+		AtA[i] = make([]float64, p)
+	}
+	Aty := make([]float64, p)
+	for i := 0; i < n; i++ {
+		for j := 0; j < p; j++ {
+			Aty[j] += A[i][j] * trainY[i]
+			for k := 0; k < p; k++ {
+				AtA[j][k] += A[i][j] * A[i][k]
+			}
+		}
+	}
+	for j := 0; j < p; j++ {
+		AtA[j][j] += lambda
+	}
+	w := solveCholesky(AtA, Aty)
+
+	preds := make([]float64, len(testEmb))
+	for i, e := range testEmb {
+		row := make([]float64, p)
+		copy(row, e)
+		row[d] = 1.0
+		preds[i] = dot(row, w)
+	}
+	return preds
+}

internal/eval/var_test.go

@@ -0,0 +1,138 @@
+package eval_test
+
+import (
+	"math"
+	"testing"
+
+	"gitea.d-ma.be/mathias/jepa-fx-risk/internal/eval"
+)
+
+// ── VaRBreachRate golden tests ──────────────────────────────────────────────
+//
+// VaR_99_t  = predVol[t] × z99   (parametric 99% normal VaR)
+// breach_t  = actualVol[t] > VaR_99_t
+// breachRate = mean(breach_t)
+// kupiecP   = Kupiec POF p-value (chi²(1) test, H0: breach rate = 1%)
+
+func TestVaRBreachRate_ZeroBreaches(t *testing.T) {
+	// 0.02 < 0.01×2.326=0.02326 → no breaches
+	pred := []float64{0.01, 0.01, 0.01}
+	act := []float64{0.02, 0.02, 0.02}
+	rate, _ := eval.VaRBreachRate(pred, act, 2.326)
+	if rate != 0 {
+		t.Fatalf("want rate=0, got %.4f", rate)
+	}
+}
+
+func TestVaRBreachRate_AllBreach(t *testing.T) {
+	// 0.03 > 0.02326 → all breach
+	pred := []float64{0.01, 0.01}
+	act := []float64{0.03, 0.03}
+	rate, _ := eval.VaRBreachRate(pred, act, 2.326)
+	if math.Abs(rate-1.0) > 1e-9 {
+		t.Fatalf("want rate=1.0, got %.4f", rate)
+	}
+}
+
+func TestVaRBreachRate_Golden(t *testing.T) {
+	// n=10, 2 breaches at indices 0 and 2 → rate=0.2
+	// Kupiec: p_hat=0.2 vs p0=0.01 → strongly reject H0 (p < 0.05)
+	pred := make([]float64, 10)
+	act := make([]float64, 10)
+	for i := range pred {
+		pred[i] = 0.01
+		act[i] = 0.01 // no breach: 0.01 < 0.02326
+	}
+	act[0] = 0.03 // breach
+	act[2] = 0.03 // breach
+
+	rate, kupiecP := eval.VaRBreachRate(pred, act, 2.326)
+
+	if math.Abs(rate-0.2) > 1e-9 {
+		t.Fatalf("breach rate: want 0.2, got %.4f", rate)
+	}
+	if kupiecP > 0.05 {
+		t.Fatalf("kupiec p-value: want <0.05 (strong reject H0), got %.4f", kupiecP)
+	}
+}
+
+func TestVaRBreachRate_PerfectCalibration(t *testing.T) {
+	// n=100, exactly 1 breach → p_hat=0.01=p0 → LR=0 → kupiecP≈1.0
+	n := 100
+	pred := make([]float64, n)
+	act := make([]float64, n)
+	for i := range pred {
+		pred[i] = 0.01
+		act[i] = 0.015 // < 0.02326, no breach
+	}
+	act[0] = 0.025 // > 0.02326, breach
+
+	rate, kupiecP := eval.VaRBreachRate(pred, act, 2.326)
+
+	if math.Abs(rate-0.01) > 1e-9 {
+		t.Fatalf("breach rate: want 0.01, got %.4f", rate)
+	}
+	if kupiecP < 0.9 {
+		t.Fatalf("kupiec p-value: want ≈1.0 (well calibrated), got %.4f", kupiecP)
+	}
+}
+
+func TestVaRBreachRate_EmptyInput(t *testing.T) {
+	rate, kupiecP := eval.VaRBreachRate(nil, nil, 2.326)
+	if rate != 0 || kupiecP != 1 {
+		t.Fatalf("empty: want (0,1), got (%.4f,%.4f)", rate, kupiecP)
+	}
+}
+
+func TestVaRBreachRate_LenMismatch(t *testing.T) {
+	rate, kupiecP := eval.VaRBreachRate([]float64{0.01}, []float64{0.01, 0.02}, 2.326)
+	if rate != 0 || kupiecP != 1 {
+		t.Fatalf("mismatch: want (0,1), got (%.4f,%.4f)", rate, kupiecP)
+	}
+}
+
+func TestVaRBreachRate_Z99Default(t *testing.T) {
+	// z99=2.326 is the canonical value; test that boundary case works
+	// VaR = 0.01 × 2.326 = 0.02326
+	// actual = 0.02326 → NOT a breach (strict >)
+	pred := []float64{0.01}
+	act := []float64{0.02326}
+	rate, _ := eval.VaRBreachRate(pred, act, 2.326)
+	if rate != 0 {
+		t.Fatalf("boundary: exactly at VaR is not a breach; want rate=0, got %.4f", rate)
+	}
+}
+
+// ── LinearProbePredict ──────────────────────────────────────────────────────
+
+func TestLinearProbePredict_PerfectLinear(t *testing.T) {
+	// y = x; predictions should match targets closely
+	n := 20
+	trainEmb := make([][]float64, n)
+	trainY := make([]float64, n)
+	testEmb := make([][]float64, 5)
+	testY := []float64{5, 10, 15, 20, 25}
+	for i := range trainEmb {
+		trainEmb[i] = []float64{float64(i)}
+		trainY[i] = float64(i)
+	}
+	for i := range testEmb {
+		testEmb[i] = []float64{testY[i]}
+	}
+	preds := eval.LinearProbePredict(trainEmb, trainY, testEmb, 1e-3)
+	if len(preds) != len(testEmb) {
+		t.Fatalf("len: want %d, got %d", len(testEmb), len(preds))
+	}
+	for i, p := range preds {
+		if math.Abs(p-testY[i]) > 1.0 {
+			t.Fatalf("pred[%d]: want ≈%.1f, got %.4f", i, testY[i], p)
+		}
+	}
+}
+
+func TestLinearProbePredict_EmptyTrain(t *testing.T) {
+	preds := eval.LinearProbePredict(nil, nil, [][]float64{{1.0}}, 1e-3)
+	if len(preds) != 0 {
+		t.Fatalf("empty train: want nil/empty preds, got len=%d", len(preds))
+	}
+}

loop.py

@@ -4,7 +4,7 @@ Agent (on iguana/berget — NOT koala, whose GPU is reserved for train.py) reads
 program.md + train.py + STATUS.md, proposes ONE change to train.py, we run it,
 keep if val_vol_r2 improved else git-revert. Appends per-iter record to STATUS.md.
 
-    LITELLM_KEY=xxx python loop.py [--iters N] [--model MODEL]
+    LITELLM_KEY=xxx python loop.py [--iters N] [--model MODEL] [--run-dir runs/rq-04]
 
 Env:
   LITELLM_KEY      — LiteLLM master key (required)
@@ -12,6 +12,7 @@ Env:
   LOOP_MODEL       — default berget/gemma4-31b (non-thinking; iguana/berget only)
   LOOP_ITERS       — default 3
   TRAIN_TIMEOUT    — seconds per train.py run, default 120
+  NTFY_URL         — optional: POST crash/stall alerts here (e.g. ntfy.sh/<topic>)
 """
 import argparse
 import json
@@ -24,14 +25,19 @@ from pathlib import Path
 
 import urllib.request
 
-LITELLM_BASE = os.environ.get("LITELLM_BASE", "http://localhost:30401/v1")
+LITELLM_BASE  = os.environ.get("LITELLM_BASE", "http://localhost:30401/v1")
-LITELLM_KEY  = os.environ.get("LITELLM_KEY", "")
+LITELLM_KEY   = os.environ.get("LITELLM_KEY", "")
-LOOP_MODEL   = os.environ.get("LOOP_MODEL", "berget/gemma4-31b")
+LOOP_MODEL    = os.environ.get("LOOP_MODEL", "berget/gemma4-31b")
-LOOP_ITERS   = int(os.environ.get("LOOP_ITERS", "3"))
+LOOP_ITERS    = int(os.environ.get("LOOP_ITERS", "3"))
 TRAIN_TIMEOUT = int(os.environ.get("TRAIN_TIMEOUT", "120"))
+NTFY_URL      = os.environ.get("NTFY_URL", "")
+
+# Resolved by main() once --run-dir is parsed.
+RUN_DIR      = Path(".")
 STATUS_MD    = Path("STATUS.md")
 METRICS_JSON = Path("metrics.json")
 TRAIN_PY     = Path("train.py")
+HEARTBEAT    = Path("HEARTBEAT")
 
 AGENT_SYSTEM = textwrap.dedent("""\
     You are the autoresearch agent for jepa-fx-risk. Your job: propose ONE small,
@@ -64,7 +70,7 @@ def gpu_snapshot() -> str:
         return "gpu=N/A"
 
 
-def read_metric() -> float | None:
+def read_metric() -> "float | None":
     if not METRICS_JSON.exists():
         return None
     try:
@@ -73,14 +79,19 @@ def read_metric() -> float | None:
         return None
 
 
-def run_train() -> tuple[float | None, float, str]:
+def run_train() -> "tuple[float | None, float, str]":
-    """Run train.py. Returns (val_vol_r2 or None, wall_secs, stderr_tail)."""
+    """Run train.py from project root with METRICS_OUT pointing into the run dir."""
     t0 = time.time()
-    gpu_before = gpu_snapshot()
+    env = dict(os.environ)
+    env["METRICS_OUT"] = str(METRICS_JSON.resolve())
+    # train.py is copied into the run dir, so sys.path[0] is that run dir — which
+    # has no scripts/. Put the project root (where loop.py + scripts/ live) on
+    # PYTHONPATH so train.py's `from scripts.var_breach import ...` resolves.
+    env["PYTHONPATH"] = str(Path(__file__).resolve().parent) + os.pathsep + env.get("PYTHONPATH", "")
     try:
         r = subprocess.run(
-            [sys.executable, "train.py"],
+            [sys.executable, str(TRAIN_PY.resolve())],
-            capture_output=True, text=True, timeout=TRAIN_TIMEOUT,
+            capture_output=True, text=True, timeout=TRAIN_TIMEOUT, env=env,
         )
         elapsed = time.time() - t0
         if r.returncode != 0:
@@ -91,10 +102,10 @@ def run_train() -> tuple[float | None, float, str]:
         return None, TRAIN_TIMEOUT, "TIMEOUT"
 
 
-def call_agent(iteration: int, best_so_far: float | None) -> str:
+def call_agent(iteration: int, best_so_far: "float | None") -> str:
     """Ask the LLM agent to edit train.py. Returns new train.py content."""
     context = "\n\n".join([
-        "# program.md\n" + read_file(Path("program.md")),
+        "# program.md\n" + read_file(RUN_DIR / "program.md"),
         "# train.py (current)\n" + read_file(TRAIN_PY),
         "# STATUS.md (history)\n" + read_file(STATUS_MD)[-2000:],
         "# metrics.json (last run)\n" + read_file(METRICS_JSON),
@@ -132,73 +143,144 @@ def append_status(line: str):
         f.write(line + "\n")
 
 
+def write_heartbeat(iteration: int, status: str = "alive"):
+    """Update HEARTBEAT so watchdogs can detect stalls."""
+    HEARTBEAT.write_text("%s  iter=%d  ts=%.0f\n" % (status, iteration, time.time()))
+
+
+def ntfy(msg: str):
+    """POST an alert to NTFY_URL (best-effort; silently ignored on any error)."""
+    if not NTFY_URL:
+        return
+    try:
+        req = urllib.request.Request(
+            NTFY_URL, data=msg.encode(), method="POST",
+            headers={"Content-Type": "text/plain"},
+        )
+        urllib.request.urlopen(req, timeout=5)
+    except Exception:
+        pass
+
+
 def main():
+    global RUN_DIR, STATUS_MD, METRICS_JSON, TRAIN_PY, HEARTBEAT
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--iters", type=int, default=LOOP_ITERS)
+    parser.add_argument("--model", default=LOOP_MODEL)
+    parser.add_argument(
+        "--run-dir", default=None,
+        help="run dir scaffolded by autoresearch_start.py; "
+             "STATUS.md, metrics.json, HEARTBEAT, and train.py live here",
+    )
+    args = parser.parse_args()
+
+    loop_iters = args.iters
+    loop_model = args.model
+
+    if args.run_dir:
+        RUN_DIR = Path(args.run_dir)
+        if not RUN_DIR.is_dir():
+            print("ERROR: run dir not found:", RUN_DIR); sys.exit(1)
+
+    STATUS_MD    = RUN_DIR / "STATUS.md"
+    METRICS_JSON = RUN_DIR / "metrics.json"
+    TRAIN_PY     = RUN_DIR / "train.py"
+    HEARTBEAT    = RUN_DIR / "HEARTBEAT"
+
     if not LITELLM_KEY:
         print("ERROR: set LITELLM_KEY"); sys.exit(1)
 
     if not STATUS_MD.exists():
-        STATUS_MD.write_text("# Autoresearch STATUS\n\n| iter | val_vol_r2 | delta | action | secs | gpu | change |\n|------|-----------|-------|--------|------|-----|--------|\n")
+        STATUS_MD.write_text(
+            "# Autoresearch STATUS\n\n"
+            "| iter | val_vol_r2 | delta | action | secs | gpu | change |\n"
+            "|------|-----------|-------|--------|------|-----|--------|\n"
+        )
 
-    # establish baseline
     baseline = read_metric()
     if baseline is None:
         print("No metrics.json — running train.py for baseline...")
         m, secs, err = run_train()
         if m is None:
-            print("Baseline run failed:", err); sys.exit(1)
+            msg = "Baseline run failed: " + err
+            print(msg)
+            ntfy("[jepa-fx-risk] loop CRASH — " + msg)
+            sys.exit(1)
         baseline = m
         print("Baseline: val_vol_r2 = %.4f (%.1fs)" % (baseline, secs))
 
     best = baseline
-    print("Starting loop | model=%s | iters=%d | baseline=%.4f" % (LOOP_MODEL, LOOP_ITERS, best))
+    print("Starting loop | model=%s | iters=%d | baseline=%.4f" % (loop_model, loop_iters, best))
+    if args.run_dir:
+        print("  run-dir:", RUN_DIR)
 
-    for i in range(1, LOOP_ITERS + 1):
+    iter_index = 0
-        print("\n--- iter %d/%d ---" % (i, LOOP_ITERS))
+    try:
-        original = TRAIN_PY.read_text()
+        for i in range(1, loop_iters + 1):
+            iter_index = i
+            write_heartbeat(i, "agent-call")
+            print("\n--- iter %d/%d ---" % (i, loop_iters))
+            original = TRAIN_PY.read_text()
 
-        print("  calling agent (%s)..." % LOOP_MODEL)
+            print("  calling agent (%s)..." % loop_model)
-        t_agent = time.time()
+            t_agent = time.time()
-        try:
+            try:
-            new_code = call_agent(i, best)
+                new_code = call_agent(i, best)
-        except Exception as e:
+            except Exception as e:
-            print("  agent call failed:", e)
+                msg = str(e)
-            append_status("| %d | ERR | — | agent-fail | — | — | %s |" % (i, str(e)[:60]))
+                print("  agent call failed:", msg)
-            continue
+                append_status("| %d | ERR | — | agent-fail | — | — | %s |" % (i, msg[:60]))
-        agent_secs = time.time() - t_agent
+                write_heartbeat(i, "agent-fail")
-        print("  agent replied in %.1fs" % agent_secs)
+                ntfy("[jepa-fx-risk] iter %d agent FAIL — %s" % (i, msg[:80]))
+                continue
+            agent_secs = time.time() - t_agent
+            print("  agent replied in %.1fs" % agent_secs)
 
-        # strip accidental markdown fences
+            # strip accidental markdown fences
-        if new_code.strip().startswith("```"):
+            if new_code.strip().startswith("```"):
-            lines = new_code.strip().splitlines()
+                lines = new_code.strip().splitlines()
-            new_code = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
+                new_code = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
 
-        TRAIN_PY.write_text(new_code)
+            TRAIN_PY.write_text(new_code)
 
-        gpu = gpu_snapshot()
+            write_heartbeat(i, "training")
-        print("  running train.py [%s]..." % gpu)
+            gpu = gpu_snapshot()
-        metric, secs, err = run_train()
+            print("  running train.py [%s]..." % gpu)
+            metric, secs, err = run_train()
 
-        if metric is None:
+            if metric is None:
-            print("  train.py FAILED — reverting. err:", err[:100])
+                print("  train.py FAILED — reverting. err:", err[:100])
-            revert_train(original)
+                revert_train(original)
-            append_status("| %d | FAIL | — | revert | %.0fs | %s | run error |" % (i, secs, gpu))
+                append_status("| %d | FAIL | — | revert | %.0fs | %s | run error |" % (i, secs, gpu))
-            continue
+                write_heartbeat(i, "train-fail")
+                ntfy("[jepa-fx-risk] iter %d train FAIL — %s" % (i, err[:80]))
+                continue
 
-        delta = metric - best
+            delta = metric - best
-        if metric > best:
+            if metric > best:
-            best = metric
+                best = metric
-            action = "KEEP"
+                action = "KEEP"
-        else:
+            else:
-            revert_train(original)
+                revert_train(original)
-            action = "revert"
+                action = "revert"
 
-        summary = "| %d | %.4f | %+.4f | %s | %.0fs | %s | iter%d |" % (
+            summary = "| %d | %.4f | %+.4f | %s | %.0fs | %s | iter%d |" % (
-            i, metric, delta, action, secs, gpu, i)
+                i, metric, delta, action, secs, gpu, i)
-        append_status(summary)
+            append_status(summary)
-        print("  val_vol_r2=%.4f  delta=%+.4f  action=%s  [%.0fs]" % (metric, delta, action, secs))
+            write_heartbeat(i, "done")
+            print("  val_vol_r2=%.4f  delta=%+.4f  action=%s  [%.0fs]" % (metric, delta, action, secs))
+
+    except Exception as e:
+        msg = "loop CRASH at iter %d: %s" % (iter_index, e)
+        print("FATAL:", msg)
+        ntfy("[jepa-fx-risk] " + msg)
+        raise
 
     print("\nDone. Best val_vol_r2 = %.4f (baseline was %.4f, delta %+.4f)" % (best, baseline, best - baseline))
     print("STATUS.md updated.")
+    write_heartbeat(loop_iters, "done")
+    ntfy("[jepa-fx-risk] loop done. best val_vol_r2=%.4f (delta %+.4f)" % (best, best - baseline))
 
 
 if __name__ == "__main__":

metrics.json

@@ -1,14 +0,0 @@
-{
-  "val_vol_r2": 0.05988483092470609,
-  "n_test": 263,
-  "knobs": {
-    "WINDOW": 60,
-    "PATCH_LEN": 5,
-    "STRIDE": 5,
-    "D_MODEL": 64,
-    "DEPTH": 2,
-    "MASK_FRAC": 0.5,
-    "SIGREG_LAM": 0.01,
-    "EPOCHS": 300
-  }
-}

requirements.txt

@@ -5,4 +5,6 @@
 numpy>=2.0
 pandas>=2.2
 pyarrow>=16
-histdata>=1.3        # histdata.com downloader (handles the tk token politely)
+histdata>=1.1        # histdata.com downloader (1.1 is newest on PyPI; 1.3 never existed)
+hmmlearn>=0.3        # regime detector (prepare_regime.py, jepa-fx-risk#13)
+scikit-learn>=1.4    # HMM dependency

results/hpo/hpo_results.jsonl

@@ -0,0 +1,18 @@
+{"config": {"JEPA_D_MODEL": 64, "JEPA_DEPTH": 2, "JEPA_WINDOW": 120}, "val_vol_r2": 0.302411480667525, "phase1_r2": 0.35809940099716187, "stdout_last": "val_vol_r2 = 0.3024  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:37:37.028406"}
+{"config": {"JEPA_D_MODEL": 64, "JEPA_DEPTH": 2, "JEPA_WINDOW": 240}, "val_vol_r2": 0.29654798431244755, "phase1_r2": 0.35618388652801514, "stdout_last": "val_vol_r2 = 0.2965  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:37:49.822762"}
+{"config": {"JEPA_D_MODEL": 64, "JEPA_DEPTH": 2, "JEPA_WINDOW": 480}, "val_vol_r2": 0.31050360040290237, "phase1_r2": 0.36530405282974243, "stdout_last": "val_vol_r2 = 0.3105  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:38:02.966176"}
+{"config": {"JEPA_D_MODEL": 64, "JEPA_DEPTH": 4, "JEPA_WINDOW": 120}, "val_vol_r2": 0.2925057399716364, "phase1_r2": 0.3467639684677124, "stdout_last": "val_vol_r2 = 0.2925  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:38:16.195552"}
+{"config": {"JEPA_D_MODEL": 64, "JEPA_DEPTH": 4, "JEPA_WINDOW": 240}, "val_vol_r2": 0.29334667623516786, "phase1_r2": 0.35872191190719604, "stdout_last": "val_vol_r2 = 0.2933  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:38:31.372602"}
+{"config": {"JEPA_D_MODEL": 64, "JEPA_DEPTH": 4, "JEPA_WINDOW": 480}, "val_vol_r2": 0.3123527205416422, "phase1_r2": 0.3572431206703186, "stdout_last": "val_vol_r2 = 0.3124  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:38:46.672203"}
+{"config": {"JEPA_D_MODEL": 128, "JEPA_DEPTH": 2, "JEPA_WINDOW": 120}, "val_vol_r2": 0.3641397896593044, "phase1_r2": 0.3908407688140869, "stdout_last": "val_vol_r2 = 0.3641  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:39:00.793878"}
+{"config": {"JEPA_D_MODEL": 128, "JEPA_DEPTH": 2, "JEPA_WINDOW": 240}, "val_vol_r2": 0.35845865364171503, "phase1_r2": 0.3737195134162903, "stdout_last": "val_vol_r2 = 0.3585  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:39:13.321428"}
+{"config": {"JEPA_D_MODEL": 128, "JEPA_DEPTH": 2, "JEPA_WINDOW": 480}, "val_vol_r2": 0.35310115657814645, "phase1_r2": 0.35306859016418457, "stdout_last": "val_vol_r2 = 0.3531  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:39:26.402249"}
+{"config": {"JEPA_D_MODEL": 128, "JEPA_DEPTH": 4, "JEPA_WINDOW": 120}, "val_vol_r2": 0.3655629727960601, "phase1_r2": 0.371029257774353, "stdout_last": "val_vol_r2 = 0.3656  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:39:39.786248"}
+{"config": {"JEPA_D_MODEL": 128, "JEPA_DEPTH": 4, "JEPA_WINDOW": 240}, "val_vol_r2": 0.36109622605593217, "phase1_r2": 0.3666273355484009, "stdout_last": "val_vol_r2 = 0.3611  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:39:53.194111"}
+{"config": {"JEPA_D_MODEL": 128, "JEPA_DEPTH": 4, "JEPA_WINDOW": 480}, "val_vol_r2": 0.362228341965093, "phase1_r2": 0.3590735197067261, "stdout_last": "val_vol_r2 = 0.3622  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:40:06.991680"}
+{"config": {"JEPA_D_MODEL": 256, "JEPA_DEPTH": 2, "JEPA_WINDOW": 120}, "val_vol_r2": 0.3749483295047378, "phase1_r2": 0.3801569938659668, "stdout_last": "val_vol_r2 = 0.3749  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:40:21.512210"}
+{"config": {"JEPA_D_MODEL": 256, "JEPA_DEPTH": 2, "JEPA_WINDOW": 240}, "val_vol_r2": 0.3765593861479334, "phase1_r2": 0.38416117429733276, "stdout_last": "val_vol_r2 = 0.3766  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:40:34.959919"}
+{"config": {"JEPA_D_MODEL": 256, "JEPA_DEPTH": 2, "JEPA_WINDOW": 480}, "val_vol_r2": 0.3653399117639956, "phase1_r2": 0.3685130476951599, "stdout_last": "val_vol_r2 = 0.3653  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:40:48.806135"}
+{"config": {"JEPA_D_MODEL": 256, "JEPA_DEPTH": 4, "JEPA_WINDOW": 120}, "val_vol_r2": 0.375961424966925, "phase1_r2": 0.37861257791519165, "stdout_last": "val_vol_r2 = 0.3760  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:41:04.056939"}
+{"config": {"JEPA_D_MODEL": 256, "JEPA_DEPTH": 4, "JEPA_WINDOW": 240}, "val_vol_r2": 0.37841726893098504, "phase1_r2": 0.3781360387802124, "stdout_last": "val_vol_r2 = 0.3784  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:41:18.693263"}
+{"config": {"JEPA_D_MODEL": 256, "JEPA_DEPTH": 4, "JEPA_WINDOW": 480}, "val_vol_r2": 0.37118530199441635, "phase1_r2": 0.3651617765426636, "stdout_last": "val_vol_r2 = 0.3712  (n_test=11641, dev=cuda)", "ts": "2026-06-26T10:41:34.796157"}

scripts/autoresearch_start.py

@@ -0,0 +1,155 @@
+"""autoresearch start — scaffold a run dir from an Autoresearch Council backlog leaf.
+
+Usage:
+    python scripts/autoresearch_start.py <backlog.json> <rq-id>
+
+Reads the Council backlog JSON (from agentsquad autoresearch_pipe.py Stage-3 output),
+finds the node by rq-id, validates it is autoresearch-ready (fail-closed), then
+scaffolds runs/<rq-id>/ with:
+
+  program.md  — hypothesis, single metric (stripped), agent search-space seam
+  run.json    — provenance (strategic_question + council_node) + config
+  train.py    — copy of project train.py (the loop edits this, keeps history clean)
+
+Launch:
+  LITELLM_KEY=xxx python loop.py --run-dir runs/<rq-id>
+
+Refs: jepa-fx-risk#11, agentsquad#44
+"""
+
+import json
+import shutil
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+def load_backlog(path: str) -> dict:
+    try:
+        with open(path) as f:
+            return json.load(f)
+    except FileNotFoundError:
+        print(f"error: backlog file not found: {path}", file=sys.stderr)
+        raise
+
+
+def scaffold_run(
+    backlog_path_or_dict,
+    rq_id: str,
+    run_dir: Path,
+    train_py_src: Path,
+) -> None:
+    """Scaffold a run dir. Raises SystemExit on any validation failure."""
+    if isinstance(backlog_path_or_dict, (str, Path)):
+        backlog = load_backlog(str(backlog_path_or_dict))
+    else:
+        backlog = backlog_path_or_dict
+
+    # Find node
+    nodes_by_id = {n["id"]: n for n in backlog.get("nodes", [])}
+    if rq_id not in nodes_by_id:
+        print(f"error: rq-id {rq_id!r} not found in backlog", file=sys.stderr)
+        sys.exit(1)
+    node = nodes_by_id[rq_id]
+
+    # Fail-closed: only autoresearch-ready nodes may be scaffolded
+    status = node.get("status", "")
+    if status != "autoresearch-ready":
+        print(
+            f"error: {rq_id} has status {status!r}, not 'autoresearch-ready' — refusing to scaffold",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    # Guard against overwriting an existing run
+    if run_dir.exists():
+        print(
+            f"error: {run_dir} already exists — remove it first to re-scaffold",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    metric = (node.get("candidate_metric") or "").strip()
+    strategic_q = backlog.get("strategic_question", "")
+    council_node = node["id"]
+    generated_at = datetime.now(timezone.utc).isoformat()
+
+    run_dir.mkdir(parents=True)
+
+    # --- program.md ---
+    program_md = f"""# program.md — {council_node}: {node.get("question", "")[:80]}
+
+## Provenance
+- strategic_question: {json.dumps(strategic_q)}
+- council_node: {council_node} (autoresearch-ready; Autoresearch Council backlog)
+- generated_at: {generated_at}
+
+## Hypothesis
+{node.get("question", "")}
+
+## Single validation metric (optimise this, nothing else)
+`{metric}` — see eval harness for the exact definition. Only this scalar drives
+keep/revert decisions. Report alongside but do NOT optimise:
+  - Kupiec POF p-value (calibration sanity)
+  - val_vol_r2 (representation quality guard)
+
+## What the agent MAY modify (the search space)
+- Hyperparameters in train.py (model size, LR, window, patch_len, epochs, etc.)
+- Conditioning mechanisms (e.g. JEPA_ENABLE_REGIME toggle)
+- Loss function weights and architecture depth
+
+## Frozen (do NOT touch — keeps the ablation clean)
+- Data pipeline and splits (train ≤2021, OOS ≥2022, test 2024 held out)
+- The metric definition and scoring code
+- loop.py, scripts/, tests/
+
+## Experiment loop (per Karpathy autoresearch)
+Each iter (≤ time-box): apply ONE change to train.py → run → read
+`{metric}` → keep if improved (and Kupiec p-value did not collapse), else revert.
+Stop on: target reached, max iters, or K consecutive iters with no improvement.
+"""
+    (run_dir / "program.md").write_text(program_md)
+
+    # --- run.json (provenance + config) ---
+    run_meta = {
+        "strategic_question": strategic_q,
+        "council_node": council_node,
+        "metric": metric,
+        "generated_at": generated_at,
+        "model_tier": "homelab",
+        "max_iters": 10,
+        "time_box_minutes": 5,
+    }
+    (run_dir / "run.json").write_text(json.dumps(run_meta, indent=2) + "\n")
+
+    # --- train.py (loop edits this copy; project root train.py is the template) ---
+    shutil.copy(train_py_src, run_dir / "train.py")
+
+
+def main() -> None:
+    if len(sys.argv) != 3:
+        print("usage: python scripts/autoresearch_start.py <backlog.json> <rq-id>")
+        sys.exit(1)
+
+    backlog_path, rq_id = sys.argv[1], sys.argv[2]
+
+    project_root = Path(__file__).parent.parent
+    run_dir = project_root / "runs" / rq_id
+    train_py_src = project_root / "train.py"
+
+    scaffold_run(backlog_path, rq_id, run_dir, train_py_src)
+
+    backlog = load_backlog(backlog_path)
+    nodes_by_id = {n["id"]: n for n in backlog.get("nodes", [])}
+    metric = (nodes_by_id[rq_id].get("candidate_metric") or "").strip()
+
+    print(f"✓ scaffolded {run_dir}")
+    print(f"  node:   {rq_id}")
+    print(f"  metric: {metric}")
+    print()
+    print("launch:")
+    print(f"  LITELLM_KEY=xxx python loop.py --run-dir runs/{rq_id}")
+
+
+if __name__ == "__main__":
+    main()

scripts/fetch_multipair.py

@@ -0,0 +1,48 @@
+"""Fetch G10 FX M1 data from histdata.com for all pairs except EURUSD (already fetched).
+
+Each pair's zips go into data/raw/{pair}/ to avoid collisions.
+Output: data/raw/gbpusd/DAT_ASCII_GBPUSD_M1_YYYY.zip etc.
+
+    python scripts/fetch_multipair.py
+    PAIRS=gbpusd,usdjpy YEARS=2020,2021 python scripts/fetch_multipair.py
+"""
+import os
+import time
+
+from histdata import download_hist_data
+from histdata.api import Platform as P, TimeFrame as T
+
+PAIRS_DEFAULT = ["gbpusd", "usdjpy", "usdchf", "audusd"]
+YEARS_DEFAULT = list(range(2008, 2024))
+
+
+def main():
+    pairs_env = os.environ.get("PAIRS", "")
+    pairs = [p.strip() for p in pairs_env.split(",")] if pairs_env else PAIRS_DEFAULT
+
+    years_env = os.environ.get("YEARS", "")
+    years = [int(y.strip()) for y in years_env.split(",")] if years_env else YEARS_DEFAULT
+
+    for pair in pairs:
+        out_dir = f"data/raw/{pair}"
+        os.makedirs(out_dir, exist_ok=True)
+        print(f"\n=== {pair.upper()} ===")
+        for yr in years:
+            out_path = os.path.join(out_dir, f"DAT_ASCII_{pair.upper()}_M1_{yr}.zip")
+            if os.path.exists(out_path):
+                print(f"  {yr} already present, skip")
+                continue
+            try:
+                f = download_hist_data(
+                    year=str(yr), month=None, pair=pair,
+                    platform=P.GENERIC_ASCII, time_frame=T.ONE_MINUTE,
+                    output_directory=out_dir,
+                )
+                print(f"  fetched {yr} → {f}")
+            except Exception as e:
+                print(f"  {yr} FAILED: {e}")
+            time.sleep(2)
+
+
+if __name__ == "__main__":
+    main()

scripts/hpo_sweep.py

@@ -0,0 +1,97 @@
+"""HPO sweep for jepa-fx-risk HEPA backbone.
+
+Runs train.py with different JEPA_* env overrides, logs results to
+results/hpo/hpo_results.jsonl. Each config writes its metrics.json then
+the result is appended to the JSONL.
+
+Usage:
+    python scripts/hpo_sweep.py
+    python scripts/hpo_sweep.py --dry-run   # print configs, don't train
+"""
+import argparse
+import json
+import os
+import subprocess
+import sys
+from datetime import datetime
+from itertools import product
+from pathlib import Path
+
+# ── Search space ──────────────────────────────────────────────────────────────
+SEARCH_SPACE = {
+    "JEPA_D_MODEL": [64, 128, 256],
+    "JEPA_DEPTH":   [2, 4],
+    "JEPA_WINDOW":  [120, 240, 480],
+}
+# Fixed: PATCH_LEN=24 (1-day patches), N_HEADS=4, EPOCHS=300, PHASE1_EPOCHS=200
+
+PYTHON = str(Path(sys.executable))
+OUT_DIR = Path("results/hpo")
+
+
+def configs():
+    """Yield all configs as dicts of JEPA_* env overrides."""
+    keys = list(SEARCH_SPACE.keys())
+    for vals in product(*SEARCH_SPACE.values()):
+        yield dict(zip(keys, vals))
+
+
+def run_config(cfg: dict, metrics_path: str = "metrics.json") -> dict:
+    env = {**os.environ, **{k: str(v) for k, v in cfg.items()}}
+    result = subprocess.run(
+        [PYTHON, "train.py"],
+        env=env,
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode != 0:
+        return {"config": cfg, "error": result.stderr[-500:]}
+    stdout_last = result.stdout.strip().split("\n")[-1]
+    with open(metrics_path) as f:
+        m = json.load(f)
+    return {
+        "config": cfg,
+        "val_vol_r2":  m.get("val_vol_r2"),
+        "phase1_r2":   m.get("phase1_r2"),
+        "stdout_last": stdout_last,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dry-run", action="store_true")
+    args = parser.parse_args()
+
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    out_file = OUT_DIR / "hpo_results.jsonl"
+
+    all_cfgs = list(configs())
+    print(f"HPO sweep: {len(all_cfgs)} configs")
+    for i, cfg in enumerate(all_cfgs):
+        label = " ".join(f"{k.replace('JEPA_','')}={v}" for k, v in cfg.items())
+        print(f"\n[{i+1}/{len(all_cfgs)}] {label}")
+        if args.dry_run:
+            continue
+        ts = datetime.utcnow().isoformat()
+        row = run_config(cfg)
+        row["ts"] = ts
+        with open(out_file, "a") as f:
+            f.write(json.dumps(row) + "\n")
+        if "error" in row:
+            print(f"  ERROR: {row['error'][:200]}")
+        else:
+            print(f"  val_vol_r2={row['val_vol_r2']:.4f}  phase1_r2={row['phase1_r2']:.4f}")
+
+    if not args.dry_run:
+        # Print leaderboard
+        rows = [json.loads(l) for l in open(out_file) if l.strip()]
+        rows = [r for r in rows if "error" not in r]
+        rows.sort(key=lambda r: r.get("phase1_r2", -999), reverse=True)
+        print("\n── Leaderboard (by phase1_r2) ─────────────────────────")
+        for r in rows[:5]:
+            cfg_str = " ".join(f"{k.replace('JEPA_','')}={v}" for k,v in r["config"].items())
+            print(f"  {r['phase1_r2']:.4f}  {cfg_str}")
+
+
+if __name__ == "__main__":
+    main()

scripts/prepare_hourly.py

@@ -0,0 +1,128 @@
+"""Prepare EUR/USD hourly OHLCV + realized vol from histdata M1 zips.
+
+Aggregates all M1 bars in data/raw/DAT_ASCII_EURUSD_M1_*.zip to hourly.
+Realized vol per hour = sqrt(sum(log-return²)) over the constituent M1 bars.
+Weekend hours are naturally absent (FX market closed Sat/Sun); NO interpolation.
+Hours with fewer than MIN_BARS M1 bars are dropped (holidays, thin sessions).
+
+Output: data/processed/eurusd_hourly.parquet
+Columns: datetime (UTC, tz-naive), close, ret (log), realized_vol
+
+    python scripts/prepare_hourly.py
+    RAW=data/raw OUT=data/processed/eurusd_hourly.parquet python scripts/prepare_hourly.py
+"""
+import glob
+import os
+import zipfile
+
+import numpy as np
+import pandas as pd
+
+PAIR        = os.environ.get("PAIR", "EURUSD").upper()
+RAW_DEFAULT = "data/raw"
+OUT_DEFAULT = f"data/processed/{PAIR.lower()}_hourly.parquet"
+MIN_BARS    = 30   # drop hours thinner than this (holidays, DST boundary artefacts)
+
+
+# ── Core transformation ──────────────────────────────────────────────────────
+
+def resample_to_hourly(m1: pd.DataFrame) -> pd.DataFrame:
+    """Aggregate M1 DataFrame to hourly bars.
+
+    Args:
+        m1: DataFrame with columns ['ts', 'open', 'high', 'low', 'close']
+            ('open'/'high'/'low' optional — omit for close-only data).
+
+    Returns:
+        DataFrame with columns ['datetime', 'close', 'ret', 'realized_vol',
+        'hl_range', 'ret_intrabar'] sorted by datetime.
+        Hours with fewer than MIN_BARS M1 ticks are dropped.
+    """
+    m1 = m1.sort_values("ts").copy()
+    m1["log_r"] = np.log(m1["close"]).diff()
+    m1["hour"]  = m1["ts"].dt.floor("h")
+
+    has_ohlc = all(c in m1.columns for c in ("open", "high", "low"))
+
+    agg_dict = dict(
+        close        = ("close", "last"),
+        realized_vol = ("log_r", lambda x: np.sqrt(np.nansum(x.values ** 2))),
+        n_bars       = ("log_r", "count"),
+    )
+    if has_ohlc:
+        agg_dict["high"]  = ("high",  "max")
+        agg_dict["low"]   = ("low",   "min")
+        agg_dict["open_"] = ("open",  "first")
+
+    agg = m1.groupby("hour").agg(**agg_dict).reset_index()
+
+    agg = agg[agg["n_bars"] >= MIN_BARS].copy()
+    agg["ret"] = np.log(agg["close"]).diff()
+    agg = agg.dropna(subset=["ret"]).reset_index(drop=True)
+    agg = agg.rename(columns={"hour": "datetime"})
+
+    if has_ohlc:
+        agg["hl_range"]    = np.log(agg["high"] / agg["low"])
+        agg["ret_intrabar"]= np.log(agg["close"] / agg["open_"])
+        cols = ["datetime", "close", "ret", "realized_vol", "hl_range", "ret_intrabar"]
+    else:
+        cols = ["datetime", "close", "ret", "realized_vol"]
+
+    return agg[cols]
+
+
+def load_m1_from_zips(raw_dir: str, pair: str = None) -> pd.DataFrame:
+    """Load and concatenate all M1 zips from raw_dir (histdata format)."""
+    p = (pair or PAIR).upper()
+    pattern = os.path.join(raw_dir, f"DAT_ASCII_{p}_M1_*.zip")
+    zips = sorted(glob.glob(pattern))
+    if not zips:
+        raise FileNotFoundError(f"No M1 zips found at {pattern}")
+    frames = []
+    for zp in zips:
+        with zipfile.ZipFile(zp) as z:
+            csv = [n for n in z.namelist() if n.endswith(".csv")][0]
+            with z.open(csv) as f:
+                df = pd.read_csv(
+                    f, sep=";", header=None,
+                    names=["dt", "open", "high", "low", "close", "vol"],
+                )
+        df["ts"] = pd.to_datetime(df["dt"], format="%Y%m%d %H%M%S")
+        frames.append(df[["ts", "open", "high", "low", "close"]])
+        print(f"  loaded {os.path.basename(zp)}: {len(df):,} rows")
+    return pd.concat(frames).sort_values("ts").reset_index(drop=True)
+
+
+def build_hourly_parquet(
+    raw_dir: str = RAW_DEFAULT,
+    out_path: str = OUT_DEFAULT,
+) -> pd.DataFrame:
+    """Full pipeline: load all M1 zips → hourly parquet. Returns the DataFrame."""
+    print(f"Loading M1 zips from {raw_dir}...")
+    m1 = load_m1_from_zips(raw_dir)
+    print(f"Total M1 bars: {len(m1):,}  ({m1['ts'].min().date()} → {m1['ts'].max().date()})")
+
+    print("Resampling to hourly...")
+    hourly = resample_to_hourly(m1)
+    print(f"Hourly rows: {len(hourly):,}  ({hourly['datetime'].min()} → {hourly['datetime'].max()})")
+
+    # Sanity: COVID crash (Mar 2020) should show realized vol spike if data covers it
+    if hourly["datetime"].dt.year.isin([2020]).any():
+        rv = hourly.set_index("datetime")["realized_vol"]
+        try:
+            mar20 = rv["2020-03-01":"2020-03-31"].max()
+            typ   = rv["2019-01-01":"2019-12-31"].median()
+            print(f"Sanity — median 2019 RV: {typ:.6f} | max Mar-2020 RV: {mar20:.6f} | spike ×{mar20/typ:.1f}")
+        except Exception:
+            pass
+
+    os.makedirs(os.path.dirname(os.path.abspath(out_path)), exist_ok=True)
+    hourly.to_parquet(out_path, index=False)
+    print(f"Written: {out_path}")
+    return hourly
+
+
+if __name__ == "__main__":
+    raw_dir  = os.environ.get("RAW",  RAW_DEFAULT)
+    out_path = os.environ.get("OUT",  OUT_DEFAULT)
+    build_hourly_parquet(raw_dir=raw_dir, out_path=out_path)

scripts/prepare_multipair.py

@@ -0,0 +1,72 @@
+"""Merge per-pair hourly parquets into a single wide multipair parquet.
+
+Each pair contributes two features: {pair}_ret and {pair}_rv (realized vol).
+The merge is an INNER JOIN on datetime — only hours present in ALL pairs are kept.
+The target for train.py remains eurusd_rv.
+
+Output: data/processed/eurusd_multipair.parquet
+
+    python scripts/prepare_multipair.py
+    PROCESSED=data/processed python scripts/prepare_multipair.py
+"""
+import os
+import pandas as pd
+
+PAIRS = ["eurusd", "gbpusd", "usdjpy", "usdchf", "audusd"]
+
+PROCESSED_DEFAULT = "data/processed"
+OUT_DEFAULT       = "data/processed/eurusd_multipair.parquet"
+
+
+def merge_pair_parquets(pair_dfs: dict) -> pd.DataFrame:
+    """Inner-join hourly DataFrames from multiple pairs on datetime.
+
+    Args:
+        pair_dfs: dict mapping pair name (e.g. "eurusd") to hourly DataFrame
+                  with columns [datetime, close, ret, realized_vol, ...].
+    Returns:
+        Wide DataFrame with columns:
+          datetime, {pair}_ret, {pair}_rv  for each pair.
+    """
+    merged = None
+    for pair, df in pair_dfs.items():
+        sub = df[["datetime", "ret", "realized_vol"]].copy()
+        sub = sub.rename(columns={"ret": f"{pair}_ret", "realized_vol": f"{pair}_rv"})
+        sub = sub.set_index("datetime")
+        if merged is None:
+            merged = sub
+        else:
+            merged = merged.join(sub, how="inner")
+
+    return merged.reset_index()
+
+
+def build_multipair_parquet(
+    processed_dir: str = PROCESSED_DEFAULT,
+    out_path: str = OUT_DEFAULT,
+    pairs: list = None,
+) -> None:
+    if pairs is None:
+        pairs = PAIRS
+    pair_dfs = {}
+    for pair in pairs:
+        path = os.path.join(processed_dir, f"{pair}_hourly.parquet")
+        if not os.path.exists(path):
+            raise FileNotFoundError(
+                f"{pair}_hourly.parquet not found at {path} — run prepare_hourly.py for this pair first"
+            )
+        df = pd.read_parquet(path)
+        pair_dfs[pair] = df
+
+    merged = merge_pair_parquets(pair_dfs)
+    merged.to_parquet(out_path, index=False)
+    n_pairs = len(pairs)
+    n_ch    = n_pairs * 2
+    print(f"Multipair parquet: {len(merged):,} rows × {n_ch} feature channels ({n_pairs} pairs)")
+    print(f"Date range: {merged['datetime'].min()} → {merged['datetime'].max()}")
+    print(f"Written: {out_path}")
+
+
+if __name__ == "__main__":
+    processed_dir = os.environ.get("PROCESSED", PROCESSED_DEFAULT)
+    build_multipair_parquet(processed_dir=processed_dir)

scripts/prepare_regime.py

@@ -0,0 +1,134 @@
+"""HMM regime detector — 3-state Gaussian HMM on realized_vol.
+
+Fits on the FULL dataset (training + OOS) so the state sequence is globally
+consistent across all periods. States are sorted by mean realized vol (ascending):
+    0 = calm,   1 = stressed,   2 = crisis
+
+Output: data/processed/eurusd_regime.parquet
+Columns: datetime (or date), regime (int: 0/1/2)
+
+Deterministic: fixed random_state=42 throughout.
+Cached: if the parquet already exists, it is not re-computed.
+
+Usage:
+    python scripts/prepare_regime.py [--hourly] [--daily] [--force]
+
+jepa-fx-risk#13
+"""
+
+import argparse
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from hmmlearn import hmm
+
+DATA_DIR = Path(__file__).parent.parent / "data" / "processed"
+HOURLY_PATH = DATA_DIR / "eurusd_hourly.parquet"
+DAILY_PATH = DATA_DIR / "eurusd_daily.parquet"
+OUTPUT_PATH = DATA_DIR / "eurusd_regime.parquet"
+
+N_STATES = 3
+RANDOM_STATE = 42
+
+
+def fit_regime_hmm(realized_vol: np.ndarray, n_states: int = 3, random_state: int = 42) -> np.ndarray:
+    """Fit a Gaussian HMM on realized_vol and return state labels (0=calm → n_states-1=crisis).
+
+    States are sorted by mean realized vol ascending so label 0 is always calm,
+    label n_states-1 is always crisis. This makes the labelling deterministic
+    across datasets with different vol levels.
+
+    Args:
+        realized_vol:  1-D array of realized vol values
+        n_states:      number of HMM hidden states (default 3)
+        random_state:  random seed for reproducibility
+
+    Returns:
+        Integer label array of shape (len(realized_vol),), dtype int64
+    """
+    X = realized_vol.reshape(-1, 1).astype(np.float64)
+    model = hmm.GaussianHMM(
+        n_components=n_states,
+        covariance_type="diag",
+        min_covar=1e-6,
+        n_iter=100,
+        random_state=random_state,
+        tol=1e-4,
+    )
+    model.fit(X)
+    raw_labels = model.predict(X)
+
+    # Sort states by mean realized vol (ascending: calm=0, crisis=n_states-1)
+    state_means = np.array([X[raw_labels == s].mean() if (raw_labels == s).any() else 0.0
+                            for s in range(n_states)])
+    rank = np.argsort(state_means)          # rank[0] = original state id of the calmest cluster
+    remap = np.empty(n_states, dtype=np.int64)
+    for new_label, old_label in enumerate(rank):
+        remap[old_label] = new_label
+    return remap[raw_labels].astype(np.int64)
+
+
+def prepare_regime_df(parquet_path: str, freq: str = "hourly") -> pd.DataFrame:
+    """Load parquet, fit HMM, return DataFrame with timestamp + regime columns.
+
+    Args:
+        parquet_path: path to input parquet (hourly or daily)
+        freq:         "hourly" | "daily" — determines timestamp column name
+
+    Returns:
+        DataFrame with columns: (datetime|date), regime
+    """
+    df = pd.read_parquet(parquet_path)
+    if freq == "hourly":
+        ts = pd.to_datetime(df["datetime"])
+    else:
+        ts = pd.to_datetime(df["date"])
+
+    rv = df["realized_vol"].to_numpy(np.float32)
+    labels = fit_regime_hmm(rv, n_states=N_STATES, random_state=RANDOM_STATE)
+    return pd.DataFrame({"datetime": ts.values, "regime": labels})
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Fit HMM regime detector")
+    parser.add_argument("--hourly", action="store_true", default=True,
+                        help="use hourly parquet (default)")
+    parser.add_argument("--daily", action="store_true", default=False,
+                        help="use daily parquet instead of hourly")
+    parser.add_argument("--force", action="store_true", default=False,
+                        help="overwrite existing output")
+    parser.add_argument("--out", default=str(OUTPUT_PATH),
+                        help="output parquet path")
+    args = parser.parse_args()
+
+    out_path = Path(args.out)
+    if out_path.exists() and not args.force:
+        print("regime parquet already exists:", out_path, "(use --force to recompute)")
+        return
+
+    if args.daily and DAILY_PATH.exists():
+        src, freq = str(DAILY_PATH), "daily"
+    elif HOURLY_PATH.exists():
+        src, freq = str(HOURLY_PATH), "hourly"
+    elif DAILY_PATH.exists():
+        src, freq = str(DAILY_PATH), "daily"
+    else:
+        raise FileNotFoundError("no parquet found in data/processed/")
+
+    print(f"fitting HMM ({N_STATES} states) on {src} ...")
+    df = prepare_regime_df(src, freq=freq)
+
+    counts = df["regime"].value_counts().sort_index()
+    print("regime distribution:")
+    for state, count in counts.items():
+        label = {0: "calm", 1: "stressed", 2: "crisis"}.get(state, f"state{state}")
+        print(f"  {state} ({label}): {count} ({100*count/len(df):.1f}%)")
+
+    df.to_parquet(out_path, index=False)
+    print("wrote:", out_path)
+
+
+if __name__ == "__main__":
+    main()

scripts/var_breach.py

@@ -0,0 +1,67 @@
+"""Parametric 99% VaR breach rate + Kupiec POF p-value.
+
+Used by train.py's LOCKED VaR EVAL BLOCK to write VaR_breach_rate_99_oos_regime_cond
+to metrics.json so the autoresearch loop can optimise it.
+
+jepa-fx-risk#12
+"""
+
+import math
+
+# Canonical metric key — no surrounding whitespace, as required by the loop contract.
+METRIC_KEY = "VaR_breach_rate_99_oos_regime_cond"
+
+# Default normal 99th-percentile z-score.
+Z99 = 2.326
+
+
+def var_breach_rate(pred_vol, actual_vol, z99=Z99):
+    """Compute VaR breach rate and Kupiec POF p-value.
+
+    Args:
+        pred_vol:   iterable of predicted conditional vol forecasts
+        actual_vol: iterable of actual realized vol (same length)
+        z99:        99th-percentile z-score (default 2.326)
+
+    Returns:
+        (breach_rate, kupiec_p) where:
+            breach_rate — fraction of steps where actual_vol > pred_vol × z99
+            kupiec_p    — Kupiec POF p-value (H0: true breach rate = 1%)
+                          High p-value = well-calibrated; low = miscalibrated tail.
+    """
+    pred_v = list(pred_vol)
+    act_v = list(actual_vol)
+    n = len(pred_v)
+    if n == 0 or n != len(act_v):
+        return 0.0, 1.0
+
+    n1 = sum(1 for p, a in zip(pred_v, act_v) if a > p * z99)
+    breach_rate = n1 / n
+    p = kupiec_pvalue(n, n1)
+    return breach_rate, p
+
+
+def kupiec_pvalue(n, n1, p0=0.01):
+    """Kupiec Proportion-of-Failures likelihood ratio test.
+
+    H0: true breach probability = p0.
+    Returns P(chi²(1) > LR) using the identity P(chi²(1)>x) = erfc(sqrt(x/2)).
+    Returns 1.0 for n=0 or LR<=0 (well-calibrated / over-conservative).
+    """
+    if n == 0:
+        return 1.0
+    n0 = n - n1
+    phat = n1 / n
+
+    if n1 == 0:
+        # 0 × ln(0/p0) = 0 by convention; only n0 term contributes
+        lr = 2 * n0 * math.log((1 - phat) / (1 - p0))
+    elif n1 == n:
+        lr = 2 * n1 * math.log(phat / p0)
+    else:
+        lr = 2 * (n1 * math.log(phat / p0) + n0 * math.log((1 - phat) / (1 - p0)))
+
+    if lr <= 0:
+        return 1.0
+    # P(chi²(1) > LR) = erfc(sqrt(LR/2))
+    return math.erfc(math.sqrt(lr / 2))

tests/test_autoresearch_start.py

@@ -0,0 +1,215 @@
+"""Tests for scripts/autoresearch_start.py — jepa-fx-risk#11 Phase A scaffold.
+
+Success criterion: `autoresearch start <backlog.json> <rq-id>` scaffolds a
+runnable run dir from a ready leaf; refuses non-ready nodes; strips
+candidate_metric; records provenance.
+"""
+
+import importlib.util
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+# Load the module without executing main()
+_SCRIPT = Path(__file__).parent.parent / "scripts" / "autoresearch_start.py"
+
+
+def _import():
+    spec = importlib.util.spec_from_file_location("autoresearch_start", _SCRIPT)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+@pytest.fixture()
+def mod():
+    return _import()
+
+
+@pytest.fixture()
+def backlog(tmp_path):
+    data = {
+        "strategic_question": "Test strategic question?",
+        "generated_at": "2026-06-27T00:00:00Z",
+        "nodes": [
+            {
+                "id": "rq-01",
+                "question": "Does X improve Y?",
+                "case_type": "autoresearch-loop",
+                "data": "obtainable",
+                "method": "adjacent",
+                "falsifiable": "yes",
+                "candidate_metric": " val_vol_r2",  # leading space — bypass test
+                "depends_on": [],
+                "status": "autoresearch-ready",
+                "track": "autoresearch",
+                "converged": True,
+                "survived_review": True,
+            },
+            {
+                "id": "rq-02",
+                "question": "Not ready yet?",
+                "case_type": "empirical-study",
+                "data": "obtainable",
+                "method": "adjacent",
+                "falsifiable": "yes",
+                "candidate_metric": None,
+                "depends_on": [],
+                "status": "needs-metric",
+                "track": "study",
+                "converged": True,
+                "survived_review": True,
+            },
+            {
+                "id": "rq-03",
+                "question": "A spike.",
+                "case_type": "spike",
+                "data": "have",
+                "method": "yes-named",
+                "falsifiable": "yes",
+                "candidate_metric": None,
+                "depends_on": [],
+                "status": "spike-ready",
+                "track": "spike",
+                "converged": True,
+                "survived_review": True,
+            },
+        ],
+    }
+    p = tmp_path / "backlog.json"
+    p.write_text(json.dumps(data))
+    return p
+
+
+@pytest.fixture()
+def fake_train_py(tmp_path):
+    """Minimal train.py placeholder for scaffold tests."""
+    src = tmp_path / "train_template.py"
+    src.write_text("# train.py placeholder\n")
+    return src
+
+
+# ---------------------------------------------------------------------------
+# fail-closed: refuse non-autoresearch-ready nodes
+# ---------------------------------------------------------------------------
+
+class TestRefuseNonReady:
+    def test_refuses_needs_metric(self, mod, backlog, fake_train_py, tmp_path):
+        run_dir = tmp_path / "runs" / "rq-02"
+        with pytest.raises(SystemExit) as exc:
+            mod.scaffold_run(backlog, "rq-02", run_dir, fake_train_py)
+        assert exc.value.code != 0
+
+    def test_refuses_spike_ready(self, mod, backlog, fake_train_py, tmp_path):
+        run_dir = tmp_path / "runs" / "rq-03"
+        with pytest.raises(SystemExit) as exc:
+            mod.scaffold_run(backlog, "rq-03", run_dir, fake_train_py)
+        assert exc.value.code != 0
+
+    def test_refuses_missing_rq_id(self, mod, backlog, fake_train_py, tmp_path):
+        run_dir = tmp_path / "runs" / "rq-99"
+        with pytest.raises(SystemExit) as exc:
+            mod.scaffold_run(backlog, "rq-99", run_dir, fake_train_py)
+        assert exc.value.code != 0
+
+    def test_refuses_existing_run_dir(self, mod, backlog, fake_train_py, tmp_path):
+        run_dir = tmp_path / "runs" / "rq-01"
+        run_dir.mkdir(parents=True)
+        with pytest.raises(SystemExit) as exc:
+            mod.scaffold_run(backlog, "rq-01", run_dir, fake_train_py)
+        assert exc.value.code != 0
+
+
+# ---------------------------------------------------------------------------
+# scaffold structure: correct files created
+# ---------------------------------------------------------------------------
+
+class TestScaffoldStructure:
+    @pytest.fixture(autouse=True)
+    def _scaffold(self, mod, backlog, fake_train_py, tmp_path):
+        self.run_dir = tmp_path / "runs" / "rq-01"
+        mod.scaffold_run(backlog, "rq-01", self.run_dir, fake_train_py)
+
+    def test_run_dir_created(self):
+        assert self.run_dir.is_dir()
+
+    def test_program_md_created(self):
+        assert (self.run_dir / "program.md").exists()
+
+    def test_run_json_created(self):
+        assert (self.run_dir / "run.json").exists()
+
+    def test_train_py_copied(self):
+        assert (self.run_dir / "train.py").exists()
+        assert (self.run_dir / "train.py").read_text() == "# train.py placeholder\n"
+
+
+# ---------------------------------------------------------------------------
+# program.md content
+# ---------------------------------------------------------------------------
+
+class TestProgramMd:
+    @pytest.fixture(autouse=True)
+    def _scaffold(self, mod, backlog, fake_train_py, tmp_path):
+        self.run_dir = tmp_path / "runs" / "rq-01"
+        mod.scaffold_run(backlog, "rq-01", self.run_dir, fake_train_py)
+        self.content = (self.run_dir / "program.md").read_text()
+
+    def test_contains_hypothesis(self):
+        assert "Does X improve Y?" in self.content
+
+    def test_metric_key_stripped(self):
+        # candidate_metric had leading space " val_vol_r2" — must be stripped
+        assert "`val_vol_r2`" in self.content
+        assert "` val_vol_r2`" not in self.content
+
+    def test_contains_strategic_question(self):
+        assert "Test strategic question?" in self.content
+
+    def test_contains_council_node(self):
+        assert "rq-01" in self.content
+
+
+# ---------------------------------------------------------------------------
+# run.json provenance
+# ---------------------------------------------------------------------------
+
+class TestRunJson:
+    @pytest.fixture(autouse=True)
+    def _scaffold(self, mod, backlog, fake_train_py, tmp_path):
+        self.run_dir = tmp_path / "runs" / "rq-01"
+        mod.scaffold_run(backlog, "rq-01", self.run_dir, fake_train_py)
+        self.run = json.loads((self.run_dir / "run.json").read_text())
+
+    def test_strategic_question_in_provenance(self):
+        assert self.run["strategic_question"] == "Test strategic question?"
+
+    def test_council_node_in_provenance(self):
+        assert self.run["council_node"] == "rq-01"
+
+    def test_metric_stripped_in_provenance(self):
+        assert self.run["metric"] == "val_vol_r2"
+        assert self.run["metric"] == self.run["metric"].strip()
+
+    def test_generated_at_present(self):
+        assert "generated_at" in self.run
+
+    def test_max_iters_present(self):
+        assert "max_iters" in self.run
+
+
+# ---------------------------------------------------------------------------
+# load_backlog helper
+# ---------------------------------------------------------------------------
+
+class TestLoadBacklog:
+    def test_loads_json(self, mod, backlog):
+        data = mod.load_backlog(str(backlog))
+        assert data["strategic_question"] == "Test strategic question?"
+        assert len(data["nodes"]) == 3
+
+    def test_missing_file_raises(self, mod, tmp_path):
+        with pytest.raises((FileNotFoundError, SystemExit)):
+            mod.load_backlog(str(tmp_path / "nonexistent.json"))

tests/test_hepa.py

@@ -0,0 +1,277 @@
+"""Failing tests for HEPA backbone + Phase-1 supervised head + HPO in train.py.
+
+Run: cd ~/dev/AI/jepa-fx-risk && .venv/bin/python -m pytest tests/test_hepa.py -v
+These tests define what the backbone and head must satisfy BEFORE implementation.
+"""
+import math
+import os
+import torch
+import torch.nn as nn
+import pytest
+
+# ── Tests import the classes from train.py ────────────────────────────────────
+# They will fail until train.py implements: CausalEncoder, HorizonPredictor, vicreg_loss
+
+
+def _import(env_overrides=None):
+    import importlib.util, sys
+    saved = {}
+    if env_overrides:
+        for k, v in env_overrides.items():
+            saved[k] = os.environ.get(k)
+            os.environ[k] = str(v)
+    # Force fresh module load (env vars must be read at import time)
+    name = f"train_{id(env_overrides)}"
+    spec = importlib.util.spec_from_file_location(name, "train.py")
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    if env_overrides:
+        for k, orig in saved.items():
+            if orig is None:
+                os.environ.pop(k, None)
+            else:
+                os.environ[k] = orig
+    return mod
+
+
+@pytest.fixture(scope="module")
+def train_mod():
+    return _import()
+
+
+# 1. CausalEncoder exists and has correct output shape
+def test_causal_encoder_shape(train_mod):
+    enc = train_mod.CausalEncoder(n_channels=2, patch_len=10, d_model=32, n_heads=4, depth=1)
+    x = torch.randn(4, 60, 2)
+    tokens = enc(x)          # should return all tokens (B, N, D) for JEPA pretraining
+    assert tokens.shape == (4, 6, 32), f"expected (4, 6, 32), got {tokens.shape}"
+
+
+# 2. CausalEncoder is actually causal: earlier token outputs don't change when later inputs change
+def test_causal_masking(train_mod):
+    enc = train_mod.CausalEncoder(n_channels=2, patch_len=10, d_model=32, n_heads=4, depth=2)
+    enc.eval()
+    torch.manual_seed(0)
+    x = torch.randn(1, 60, 2)
+    x_perturbed = x.clone()
+    # non-uniform noise (constant shift absorbed by per-patch LayerNorm; variance change is not)
+    torch.manual_seed(99)
+    x_perturbed[:, 30:, :] += torch.randn_like(x[:, 30:, :]) * 5.0
+
+    with torch.no_grad():
+        h1 = enc(x)
+        h2 = enc(x_perturbed)
+
+    # First 3 tokens must be identical (causal — don't see future patches)
+    assert torch.allclose(h1[:, :3, :], h2[:, :3, :], atol=1e-5), \
+        "causal masking broken: early tokens change when later input changes"
+    # Last token should differ (it can see the perturbed patches)
+    assert not torch.allclose(h1[:, -1, :], h2[:, -1, :], atol=1e-5), \
+        "last token should differ when later input changes"
+
+
+# 3. HorizonPredictor exists, takes (h, delta_t_float) → same shape as h
+def test_horizon_predictor_shape(train_mod):
+    pred = train_mod.HorizonPredictor(d_model=32)
+    h = torch.randn(4, 32)
+    dt = torch.tensor([1.0, 2.0, 3.0, 1.0])
+    out = pred(h, dt)
+    assert out.shape == (4, 32), f"expected (4, 32), got {out.shape}"
+
+
+# 4. vicreg_loss is a scalar and backward doesn't error
+def test_vicreg_loss_backward(train_mod):
+    h_pred = torch.randn(8, 32, requires_grad=True)
+    h_target = torch.randn(8, 32)
+    loss = train_mod.vicreg_loss(h_pred, h_target, alpha=0.1)
+    assert loss.shape == (), f"expected scalar, got {loss.shape}"
+    loss.backward()
+    assert h_pred.grad is not None
+
+
+# 5. Full JEPA step: encode context, predict future, compute loss, backward
+def test_jepa_step_end_to_end(train_mod):
+    enc = train_mod.CausalEncoder(n_channels=2, patch_len=10, d_model=32, n_heads=4, depth=1)
+    pred = train_mod.HorizonPredictor(d_model=32)
+    opt = torch.optim.SGD(list(enc.parameters()) + list(pred.parameters()), lr=1e-3)
+
+    x = torch.randn(4, 60, 2)
+    tokens = enc(x)          # (4, 6, 32)
+    c, dt = 2, 2             # context position 2, horizon 2
+    h_ctx = tokens[:, c, :]
+    h_tgt = tokens[:, c + dt, :].detach()
+    h_hat = pred(h_ctx, torch.full((4,), float(dt)))
+    loss = train_mod.vicreg_loss(h_hat, h_tgt, alpha=0.1)
+    opt.zero_grad(); loss.backward(); opt.step()
+    assert loss.item() < 100, "loss exploded"
+
+
+# 6. build() returns year-based OOS split (2022-2023); hourly gives many more windows
+def test_build_year_split(train_mod):
+    (Xtr, ytr), (Xte, yte) = train_mod.build()
+    assert Xtr.shape[1] == train_mod.WINDOW
+    assert Xte.shape[1] == train_mod.WINDOW
+    assert len(Xtr) > 0 and len(Xte) > 0
+    # OOS: daily ≈ 600; hourly ≈ 17,000 (2 years × ~8,500 trading hours/year)
+    assert len(Xte) > 400, f"OOS too small: {len(Xte)}"
+
+
+# 7. hourly build gives > 10× more training windows than daily
+def test_build_hourly_more_windows(train_mod):
+    import os
+    if not os.path.exists("data/processed/eurusd_hourly.parquet"):
+        pytest.skip("eurusd_hourly.parquet not present — run data:prepare:hourly first")
+    (Xtr, _), _ = train_mod.build()
+    # Daily had ~877 train windows; hourly with 2008-2021 should have > 50,000
+    assert len(Xtr) > 10_000, f"expected >10k hourly train windows, got {len(Xtr)}"
+
+
+# ── Phase-1: supervised head ──────────────────────────────────────────────────
+
+# 8. SupervisedHead exists and maps (B, D) → (B,)
+def test_supervised_head_shape(train_mod):
+    D = 128
+    head = train_mod.SupervisedHead(D)
+    x = torch.randn(16, D)
+    out = head(x)
+    assert out.shape == (16,), f"expected (16,), got {out.shape}"
+
+
+# 9. SupervisedHead gradient flows (not frozen)
+def test_supervised_head_backward(train_mod):
+    head = train_mod.SupervisedHead(64)
+    x = torch.randn(8, 64)
+    loss = head(x).mean()
+    loss.backward()
+    for name, p in head.named_parameters():
+        assert p.grad is not None, f"no grad on {name}"
+
+
+# 10. Phase-1 beats linear on nonlinear synthetic signal
+def test_phase1_beats_linear_on_nonlinear(train_mod):
+    """MLP head should outperform ridge regression on data with nonlinear structure."""
+    import numpy as np
+    torch.manual_seed(0); np.random.seed(0)
+    N, D = 1000, 32
+    # target = |h|² (quadratic — linear can't fit well)
+    Etr = np.random.randn(N, D).astype(np.float32)
+    ytr = (Etr ** 2).sum(axis=1)
+    Ete = np.random.randn(200, D).astype(np.float32)
+    yte = (Ete ** 2).sum(axis=1)
+
+    # Ridge baseline
+    A = np.hstack([Etr, np.ones((N, 1))])
+    w = np.linalg.solve(A.T @ A + 1e-3 * np.eye(A.shape[1]), A.T @ ytr)
+    pred_lin = np.hstack([Ete, np.ones((200, 1))]) @ w
+    r2_lin = float(1 - ((yte - pred_lin) ** 2).sum() / ((yte - yte.mean()) ** 2).sum())
+
+    # MLP head
+    head = train_mod.SupervisedHead(D)
+    opt  = torch.optim.Adam(head.parameters(), lr=1e-2)
+    Xtr_t = torch.tensor(Etr); ytr_t = torch.tensor(ytr)
+    for _ in range(300):
+        loss = nn.functional.mse_loss(head(Xtr_t), ytr_t)
+        opt.zero_grad(); loss.backward(); opt.step()
+
+    head.eval()
+    with torch.no_grad():
+        pred_mlp = head(torch.tensor(Ete)).numpy()
+    r2_mlp = float(1 - ((yte - pred_mlp) ** 2).sum() / ((yte - yte.mean()) ** 2).sum())
+
+    assert r2_mlp > r2_lin + 0.05, (
+        f"MLP R²={r2_mlp:.3f} should beat ridge R²={r2_lin:.3f} by >0.05 on quadratic target"
+    )
+
+
+# 11. main() returns phase1_r2 in metrics.json (integration — needs real data)
+def test_metrics_json_has_phase1_r2(train_mod):
+    import json
+    if not os.path.exists("metrics.json"):
+        pytest.skip("metrics.json not present — run train.py first")
+    with open("metrics.json") as f:
+        m = json.load(f)
+    assert "phase1_r2" in m, f"phase1_r2 missing from metrics.json: {list(m.keys())}"
+    assert m["phase1_r2"] > m["val_vol_r2"], (
+        f"MLP head phase1_r2={m['phase1_r2']:.4f} should beat linear probe "
+        f"val_vol_r2={m['val_vol_r2']:.4f}"
+    )
+
+
+# ── HPO: env-var knob overrides ───────────────────────────────────────────────
+
+# 12. JEPA_WINDOW env var overrides WINDOW at import time
+def test_env_override_window():
+    mod = _import({"JEPA_WINDOW": "48"})
+    assert mod.WINDOW == 48, f"expected WINDOW=48, got {mod.WINDOW}"
+
+
+# 13. JEPA_D_MODEL and JEPA_DEPTH env vars work
+def test_env_override_d_model_depth():
+    mod = _import({"JEPA_D_MODEL": "64", "JEPA_DEPTH": "4"})
+    assert mod.D_MODEL == 64, f"expected D_MODEL=64, got {mod.D_MODEL}"
+    assert mod.DEPTH == 4, f"expected DEPTH=4, got {mod.DEPTH}"
+
+
+# 14. hpo_sweep.py exists and generates correct config list
+def test_hpo_sweep_configs():
+    import importlib.util
+    sweep_path = "scripts/hpo_sweep.py"
+    if not os.path.exists(sweep_path):
+        pytest.fail(f"{sweep_path} not found — implement it")
+    spec = importlib.util.spec_from_file_location("hpo_sweep", sweep_path)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    cfgs = list(mod.configs())
+    assert len(cfgs) > 0, "configs() returned empty list"
+    # Every config must have at least D_MODEL, DEPTH, WINDOW keys
+    required = {"JEPA_D_MODEL", "JEPA_DEPTH", "JEPA_WINDOW"}
+    for cfg in cfgs:
+        assert required.issubset(cfg.keys()), f"config missing required keys: {cfg}"
+
+
+# ── Option B: joint encoder fine-tuning in phase-1 ───────────────────────────
+
+# 15. PHASE1_JOINT and PHASE1_ENCODER_LR knobs exist at module level
+def test_joint_phase1_knobs():
+    mod = _import({"JEPA_PHASE1_JOINT": "1", "JEPA_PHASE1_ENCODER_LR": "1e-5"})
+    assert hasattr(mod, "PHASE1_JOINT"), "PHASE1_JOINT knob missing from train.py"
+    assert hasattr(mod, "PHASE1_ENCODER_LR"), "PHASE1_ENCODER_LR knob missing from train.py"
+    assert mod.PHASE1_JOINT is True
+    assert abs(mod.PHASE1_ENCODER_LR - 1e-5) < 1e-12
+
+
+# 16. PHASE1_JOINT defaults to True (joint mode on by default)
+def test_joint_phase1_default_on():
+    mod = _import()
+    assert hasattr(mod, "PHASE1_JOINT"), "PHASE1_JOINT knob missing"
+    assert mod.PHASE1_JOINT is True, f"PHASE1_JOINT default should be True, got {mod.PHASE1_JOINT}"
+
+
+# 17. JEPA_PHASE1_JOINT=0 disables joint (env override works)
+def test_joint_phase1_can_disable():
+    mod = _import({"JEPA_PHASE1_JOINT": "0"})
+    assert mod.PHASE1_JOINT is False, f"expected False, got {mod.PHASE1_JOINT}"
+
+
+# 18. Encoder receives non-zero gradients when joint-training with the head
+def test_joint_encoder_grad_flows(train_mod):
+    """Gradient must flow into encoder when using two-param-group joint optimizer."""
+    import torch.nn.functional as F
+    enc  = train_mod.CausalEncoder(n_channels=2, patch_len=8, d_model=16, n_heads=2, depth=1)
+    head = train_mod.SupervisedHead(16)
+    enc.train(); head.train()
+    opt = torch.optim.Adam([
+        {"params": head.parameters(), "lr": 1e-3},
+        {"params": enc.parameters(), "lr": 1e-5},
+    ], weight_decay=1e-4)
+    # Tiny batch: 4 windows of length 16 (= 2 patches of patch_len=8)
+    X = torch.randn(4, 16, 2)
+    y = torch.randn(4)
+    tokens = enc(X)           # (4, 2, 16)
+    h      = tokens[:, -1, :] # (4, 16) — last token
+    pred   = head(h)
+    loss   = F.mse_loss(pred, y)
+    loss.backward()
+    enc_grads = [p.grad for p in enc.parameters() if p.grad is not None]
+    assert len(enc_grads) > 0, "no encoder params received gradients"
+    assert any(g.abs().max().item() > 0 for g in enc_grads), "all encoder grads are zero"

tests/test_multipair.py

@@ -0,0 +1,126 @@
+"""Tests for multi-pair G10 pipeline (Option C).
+
+Tests the prepare_multipair.py merge logic and train.py multipair build().
+Run: cd ~/dev/AI/jepa-fx-risk && .venv/bin/python -m pytest tests/test_multipair.py -v
+"""
+import importlib.util
+import numpy as np
+import pandas as pd
+import pytest
+import os
+
+
+def _import_mp():
+    spec = importlib.util.spec_from_file_location("prepare_multipair", "scripts/prepare_multipair.py")
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+@pytest.fixture(scope="module")
+def mp():
+    return _import_mp()
+
+
+def _pair_df(start: str, n_hours: int, seed: int) -> pd.DataFrame:
+    """Synthetic single-pair hourly parquet (same schema as prepare_hourly output)."""
+    rng = np.random.default_rng(seed)
+    dts = pd.date_range(start, periods=n_hours, freq="h")
+    closes = 1.1 + np.cumsum(rng.normal(0, 0.001, n_hours))
+    return pd.DataFrame({
+        "datetime":     dts,
+        "close":        closes,
+        "ret":          rng.normal(0, 0.001, n_hours),
+        "realized_vol": np.abs(rng.normal(0.0005, 0.0001, n_hours)),
+    })
+
+
+# 1. merge_pair_parquets returns inner join on datetime
+def test_merge_inner_join(mp):
+    eur = _pair_df("2020-01-01 00:00", 100, seed=1)  # t0 to t0+99h
+    gbp = _pair_df("2020-01-01 20:00", 60, seed=2)   # t0+20 to t0+79h → 60 common
+    result = mp.merge_pair_parquets({"eurusd": eur, "gbpusd": gbp})
+    assert len(result) == 60, f"expected 60 (inner join), got {len(result)}"
+
+
+# 2. merge_pair_parquets prefixes columns with pair name
+def test_merge_column_prefixes(mp):
+    eur = _pair_df("2020-01-01 00:00", 50, seed=1)
+    gbp = _pair_df("2020-01-01 00:00", 50, seed=2)
+    result = mp.merge_pair_parquets({"eurusd": eur, "gbpusd": gbp})
+    assert "datetime" in result.columns, "datetime column missing"
+    assert "eurusd_ret" in result.columns
+    assert "eurusd_rv" in result.columns
+    assert "gbpusd_ret" in result.columns
+    assert "gbpusd_rv" in result.columns
+    # raw pair columns should not leak through unprefixed
+    assert "ret" not in result.columns
+    assert "realized_vol" not in result.columns
+
+
+# 3. No NaN in merged output
+def test_merge_no_nan(mp):
+    eur = _pair_df("2020-01-01 00:00", 50, seed=1)
+    gbp = _pair_df("2020-01-01 00:00", 50, seed=2)
+    result = mp.merge_pair_parquets({"eurusd": eur, "gbpusd": gbp})
+    nan_count = result.isnull().sum().sum()
+    assert nan_count == 0, f"{nan_count} NaN values in merged output"
+
+
+# 4. PAIRS constant is a non-empty list starting with eurusd
+def test_pairs_constant(mp):
+    assert hasattr(mp, "PAIRS"), "PAIRS constant missing from prepare_multipair.py"
+    assert len(mp.PAIRS) >= 2, "PAIRS must have at least 2 pairs"
+    assert mp.PAIRS[0] == "eurusd", "first pair must be eurusd (target pair)"
+
+
+# 5. merge target column is eurusd_rv (for build() target selection)
+def test_merge_has_eurusd_rv_as_target(mp):
+    eur = _pair_df("2020-01-01 00:00", 50, seed=1)
+    gbp = _pair_df("2020-01-01 00:00", 50, seed=2)
+    result = mp.merge_pair_parquets({"eurusd": eur, "gbpusd": gbp})
+    assert "eurusd_rv" in result.columns, "eurusd_rv (target) missing from merged output"
+    assert (result["eurusd_rv"] > 0).all(), "eurusd_rv should be positive"
+
+
+# 6. train.py recognises JEPA_USE_MULTIPAIR env var
+def test_use_multipair_knob():
+    import importlib.util as ilu
+    spec = ilu.spec_from_file_location(f"train_mp_{id(None)}", "train.py")
+    mod = ilu.module_from_spec(spec)
+    saved = os.environ.get("JEPA_USE_MULTIPAIR")
+    os.environ["JEPA_USE_MULTIPAIR"] = "1"
+    try:
+        spec.loader.exec_module(mod)
+    finally:
+        if saved is None:
+            os.environ.pop("JEPA_USE_MULTIPAIR", None)
+        else:
+            os.environ["JEPA_USE_MULTIPAIR"] = saved
+    assert hasattr(mod, "USE_MULTIPAIR"), "USE_MULTIPAIR knob missing from train.py"
+    assert mod.USE_MULTIPAIR is True
+
+
+# 7. build() uses n_pairs*2 channels when multipair parquet present
+def test_build_uses_multipair_channels():
+    import importlib.util as ilu
+    multipair_path = "data/processed/eurusd_multipair.parquet"
+    if not os.path.exists(multipair_path):
+        pytest.skip("eurusd_multipair.parquet not present — run data:prepare:multipair first")
+    saved = os.environ.get("JEPA_USE_MULTIPAIR")
+    os.environ["JEPA_USE_MULTIPAIR"] = "1"
+    try:
+        spec = ilu.spec_from_file_location(f"train_mp2_{id(None)}", "train.py")
+        mod = ilu.module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        (Xtr, _), _ = mod.build()
+    finally:
+        if saved is None:
+            os.environ.pop("JEPA_USE_MULTIPAIR", None)
+        else:
+            os.environ["JEPA_USE_MULTIPAIR"] = saved
+    mp = _import_mp()
+    expected_ch = len(mp.PAIRS) * 2
+    assert Xtr.shape[2] == expected_ch, (
+        f"expected {expected_ch} channels (n_pairs={len(mp.PAIRS)}×2), got {Xtr.shape[2]}"
+    )

tests/test_prepare_hourly.py

@@ -0,0 +1,207 @@
+"""Failing tests for scripts/prepare_hourly.py.
+
+Tests the M1 → hourly aggregation logic using synthetic data before touching
+real downloads.
+
+Run: cd ~/dev/AI/jepa-fx-risk && .venv/bin/python -m pytest tests/test_prepare_hourly.py -v
+"""
+import numpy as np
+import pandas as pd
+import pytest
+import importlib.util, sys, os
+
+
+def _import():
+    spec = importlib.util.spec_from_file_location(
+        "prepare_hourly", "scripts/prepare_hourly.py"
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+@pytest.fixture(scope="module")
+def ph():
+    return _import()
+
+
+def _make_m1(n_days: int = 3, price: float = 1.1000, noise: float = 0.0005) -> pd.DataFrame:
+    """Synthetic M1 DataFrame starting 2020-01-06 (Monday), 390 ticks/day."""
+    rng = np.random.default_rng(42)
+    # generate full trading hours: Mon-Fri 00:00-23:59 (FX is 24h weekday)
+    start = pd.Timestamp("2020-01-06 00:00:00")  # Monday
+    periods = n_days * 24 * 60
+    ts = pd.date_range(start, periods=periods, freq="min")
+    # remove weekends
+    ts = ts[ts.day_of_week < 5]
+    prices = price + np.cumsum(rng.normal(0, noise, len(ts)))
+    return pd.DataFrame({"ts": ts, "close": prices})
+
+
+# 1. resample_to_hourly: DataFrame has correct columns
+def test_columns(ph):
+    m1 = _make_m1()
+    hourly = ph.resample_to_hourly(m1)
+    assert set(["datetime", "close", "ret", "realized_vol"]).issubset(hourly.columns), \
+        f"missing columns: {hourly.columns.tolist()}"
+
+
+# 2. No cross-weekend interpolation: gap between Friday 23:xx and Sunday/Monday must remain
+def test_no_weekend_interpolation(ph):
+    # Make 2 days: Friday + Monday (skip Saturday/Sunday)
+    fri = pd.date_range("2020-01-10 00:00", "2020-01-10 23:59", freq="min")  # Friday
+    mon = pd.date_range("2020-01-13 00:00", "2020-01-13 23:59", freq="min")  # Monday
+    ts = fri.append(mon)
+    prices = 1.1 + np.cumsum(np.random.default_rng(0).normal(0, 0.0001, len(ts)))
+    m1 = pd.DataFrame({"ts": ts, "close": prices})
+    hourly = ph.resample_to_hourly(m1)
+    dates = pd.DatetimeIndex(hourly["datetime"]).date
+    import datetime
+    sat = datetime.date(2020, 1, 11)
+    sun = datetime.date(2020, 1, 12)
+    assert sat not in dates and sun not in dates, "weekend rows found in hourly output"
+
+
+# 3. Realized vol = sqrt(sum(r²)) over minute returns in each hour
+def test_realized_vol_formula(ph):
+    # Two hours: anchor gives 10:00 a valid ret; measurement hour has one known log-return.
+    ts0 = pd.date_range("2020-01-06 09:00", periods=60, freq="min")
+    ts1 = pd.date_range("2020-01-06 10:00", periods=60, freq="min")
+    prices0 = np.ones(60) * 1.0
+    # price jumps at minute 1 and STAYS (no reversion) → one non-zero log-return
+    prices1 = np.full(60, np.exp(0.01))
+    prices1[0] = 1.0  # only first tick is at 1.0; jump happens at tick 1
+    m1 = pd.DataFrame({
+        "ts": np.concatenate([ts0, ts1]),
+        "close": np.concatenate([prices0, prices1]),
+    })
+    hourly = ph.resample_to_hourly(m1)
+    assert len(hourly) >= 1, "no rows after resample"
+    rv = hourly.iloc[-1]["realized_vol"]
+    expected = np.sqrt(0.01 ** 2)
+    assert abs(rv - expected) < 1e-6, f"realized_vol={rv:.8f}, expected≈{expected:.8f}"
+
+
+# 4. Only hours with ≥ 30 M1 bars are kept (thin hours dropped)
+def test_thin_hours_dropped(ph):
+    # 4 hours: pre-anchor gives 09:00 a valid ret; full survives; thin (11:00) is dropped.
+    # pre-anchor (08:00): gives 09:00 a valid ret
+    # anchor (09:00): 60 bars, valid ret  → kept
+    # full  (10:00): 60 bars, valid ret  → kept
+    # thin  (11:00): 10 bars             → dropped
+    # Result: 3 hourly candidates, first (pre-anchor) gets NaN ret → dropped → 2 rows
+    pre   = pd.date_range("2020-01-06 08:00", periods=60, freq="min")
+    anchor= pd.date_range("2020-01-06 09:00", periods=60, freq="min")
+    full  = pd.date_range("2020-01-06 10:00", periods=60, freq="min")
+    thin  = pd.date_range("2020-01-06 11:00", periods=10, freq="min")
+    ts = pre.append(anchor).append(full).append(thin)
+    m1 = pd.DataFrame({"ts": ts, "close": np.ones(len(ts)) * 1.1})
+    hourly = ph.resample_to_hourly(m1)
+    assert len(hourly) == 2, f"expected 2 rows (pre-anchor NaN ret dropped + thin dropped), got {len(hourly)}"
+
+
+# 5. Output parquet path and schema (integration — reads actual M1 zips if present)
+def test_output_schema_from_zips(ph, tmp_path):
+    import zipfile, io
+    rows = []
+    for h in range(24):
+        for m in range(60):
+            rows.append(f"20200106 {h:02d}{m:02d}00;1.10000;1.10100;1.09900;1.10000;100")
+    csv_content = "\n".join(rows).encode()
+    zip_buf = io.BytesIO()
+    with zipfile.ZipFile(zip_buf, "w") as zf:
+        zf.writestr("DAT_ASCII_EURUSD_M1_2020.csv", csv_content)
+    zip_buf.seek(0)
+    raw_dir = tmp_path / "raw"
+    raw_dir.mkdir()
+    (raw_dir / "DAT_ASCII_EURUSD_M1_2020.zip").write_bytes(zip_buf.read())
+
+    out_path = str(tmp_path / "eurusd_hourly.parquet")
+    ph.build_hourly_parquet(raw_dir=str(raw_dir), out_path=out_path)
+    assert os.path.exists(out_path), "output parquet not created"
+    df = pd.read_parquet(out_path)
+    assert set(["datetime", "close", "ret", "realized_vol"]).issubset(df.columns)
+    assert len(df) > 0
+
+
+# ── New OHLCV-derived features ────────────────────────────────────────────────
+
+def _make_m1_ohlcv(n_hours: int = 4, price: float = 1.1) -> pd.DataFrame:
+    """Synthetic M1 with distinct O, H, L, C so hl_range and ret_intrabar are nonzero."""
+    rng = np.random.default_rng(7)
+    ts = pd.date_range("2020-01-06 00:00", periods=n_hours * 60, freq="min")
+    closes = price + np.cumsum(rng.normal(0, 0.0002, len(ts)))
+    highs  = closes + rng.uniform(0.0001, 0.0005, len(ts))
+    lows   = closes - rng.uniform(0.0001, 0.0005, len(ts))
+    opens  = np.roll(closes, 1); opens[0] = price
+    return pd.DataFrame({"ts": ts, "open": opens, "high": highs, "low": lows, "close": closes})
+
+
+# 6. resample_to_hourly produces hl_range column
+def test_hourly_has_hl_range(ph):
+    m1 = _make_m1_ohlcv()
+    hourly = ph.resample_to_hourly(m1)
+    assert "hl_range" in hourly.columns, f"missing hl_range; cols={hourly.columns.tolist()}"
+    assert (hourly["hl_range"] > 0).all(), "hl_range should be positive"
+
+
+# 7. resample_to_hourly produces ret_intrabar column
+def test_hourly_has_ret_intrabar(ph):
+    m1 = _make_m1_ohlcv()
+    hourly = ph.resample_to_hourly(m1)
+    assert "ret_intrabar" in hourly.columns, f"missing ret_intrabar; cols={hourly.columns.tolist()}"
+
+
+# 8. hl_range = log(hourly_high / hourly_low)
+def test_hl_range_formula(ph):
+    # Two hours; second has known H=1.105, L=1.095
+    ts0 = pd.date_range("2020-01-06 00:00", periods=60, freq="min")
+    ts1 = pd.date_range("2020-01-06 01:00", periods=60, freq="min")
+    closes = np.full(120, 1.1)
+    highs  = np.full(120, 1.1)
+    lows   = np.full(120, 1.1)
+    # second hour: known spread
+    highs[60:] = 1.105
+    lows[60:]  = 1.095
+    m1 = pd.DataFrame({
+        "ts": np.concatenate([ts0, ts1]),
+        "open": closes, "high": highs, "low": lows, "close": closes,
+    })
+    hourly = ph.resample_to_hourly(m1)
+    assert len(hourly) >= 1
+    hl = hourly.iloc[-1]["hl_range"]
+    expected = float(np.log(1.105 / 1.095))
+    assert abs(hl - expected) < 1e-6, f"hl_range={hl:.8f}, expected={expected:.8f}"
+
+
+# 9. ret_intrabar = log(hourly_last_close / hourly_first_open)
+def test_ret_intrabar_formula(ph):
+    ts0 = pd.date_range("2020-01-06 00:00", periods=60, freq="min")
+    ts1 = pd.date_range("2020-01-06 01:00", periods=60, freq="min")
+    closes = np.full(120, 1.1)
+    opens  = np.full(120, 1.1)
+    # second hour: open=1.09, close=1.11
+    opens[60]   = 1.09
+    closes[119] = 1.11
+    m1 = pd.DataFrame({
+        "ts": np.concatenate([ts0, ts1]),
+        "open": opens, "high": closes + 0.001, "low": closes - 0.001, "close": closes,
+    })
+    hourly = ph.resample_to_hourly(m1)
+    assert len(hourly) >= 1
+    rib = hourly.iloc[-1]["ret_intrabar"]
+    expected = float(np.log(1.11 / 1.09))
+    assert abs(rib - expected) < 1e-6, f"ret_intrabar={rib:.8f}, expected={expected:.8f}"
+
+
+# 10. build() in train.py uses 2 feature channels (HPO: hl_range/ret_intrabar redundant)
+def test_build_uses_2_channels(tmp_path):
+    import importlib.util, os
+    hourly_path = "data/processed/eurusd_hourly.parquet"
+    if not os.path.exists(hourly_path):
+        pytest.skip("eurusd_hourly.parquet not present")
+    spec = importlib.util.spec_from_file_location("train_2ch", "train.py")
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    (Xtr, _), _ = mod.build()
+    assert Xtr.shape[2] == 2, f"expected 2 channels, got {Xtr.shape[2]}"

tests/test_regime.py

@@ -0,0 +1,134 @@
+"""Tests for scripts/prepare_regime.py — HMM regime detector (jepa-fx-risk#13).
+
+TDD: tests first, implementation follows.
+"""
+
+import importlib.util
+import os
+import shutil
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+_SCRIPT = Path(__file__).parent.parent / "scripts" / "prepare_regime.py"
+
+DATA_DIR = Path(__file__).parent.parent / "data" / "processed"
+HOURLY = DATA_DIR / "eurusd_hourly.parquet"
+DAILY = DATA_DIR / "eurusd_daily.parquet"
+
+
+def _import():
+    spec = importlib.util.spec_from_file_location("prepare_regime", _SCRIPT)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+@pytest.fixture()
+def mod():
+    return _import()
+
+
+# ---------------------------------------------------------------------------
+# fit_regime_hmm — pure function (doesn't touch disk)
+# ---------------------------------------------------------------------------
+
+def _synthetic_rv(seed=42, size=500):
+    """Noisy 3-regime vol series: calm→stressed→crisis→calm interleaved."""
+    rng = np.random.default_rng(seed)
+    low  = np.abs(rng.normal(0.005, 0.001, size=size // 3))
+    mid  = np.abs(rng.normal(0.015, 0.003, size=size // 3))
+    high = np.abs(rng.normal(0.04,  0.008, size=size - 2 * (size // 3)))
+    return np.concatenate([low, mid, high])
+
+
+class TestFitRegimeHmm:
+    def test_returns_integer_labels(self, mod):
+        rv = _synthetic_rv(seed=0)
+        labels = mod.fit_regime_hmm(rv, n_states=3, random_state=42)
+        assert np.issubdtype(labels.dtype, np.integer), f"dtype={labels.dtype}"
+        assert len(labels) == len(rv)
+
+    def test_states_are_0_1_2(self, mod):
+        rv = _synthetic_rv(seed=1)
+        labels = mod.fit_regime_hmm(rv, n_states=3, random_state=42)
+        unique = set(labels.tolist())
+        assert unique.issubset({0, 1, 2}), f"unexpected states: {unique}"
+
+    def test_deterministic(self, mod):
+        rv = _synthetic_rv(seed=7)
+        a = mod.fit_regime_hmm(rv, n_states=3, random_state=42)
+        b = mod.fit_regime_hmm(rv, n_states=3, random_state=42)
+        assert np.array_equal(a, b), "HMM not deterministic with same random_state"
+
+    def test_sorted_by_vol_asc(self, mod):
+        # 3 clearly separated noisy clusters; state 0 should be calm, 2 should be crisis.
+        rng = np.random.default_rng(42)
+        n = 200
+        low  = np.abs(rng.normal(0.005, 0.001, n))
+        mid  = np.abs(rng.normal(0.015, 0.003, n))
+        high = np.abs(rng.normal(0.05,  0.008, n))
+        rv = np.concatenate([low, mid, high])
+        labels = mod.fit_regime_hmm(rv, n_states=3, random_state=42)
+        # Mean regime label in the high-vol section should exceed mean in the low-vol section.
+        assert labels[2*n:].mean() > labels[:n].mean(), \
+            "crisis section mean regime label should exceed calm section"
+        # The calm section should not be labeled as crisis (2) dominantly.
+        calm_modal = int(np.bincount(labels[:n]).argmax())
+        assert calm_modal < 2, f"calm section mostly labeled {calm_modal}, expected 0 or 1"
+
+    def test_two_states(self, mod):
+        rv = _synthetic_rv(seed=0)
+        labels = mod.fit_regime_hmm(rv, n_states=2, random_state=42)
+        unique = set(labels.tolist())
+        assert unique.issubset({0, 1})
+
+
+
+
+# ---------------------------------------------------------------------------
+# prepare_regime_df — reads parquet, fits HMM, returns DataFrame
+# ---------------------------------------------------------------------------
+
+class TestPrepareRegimeDf:
+    @pytest.mark.skipif(not HOURLY.exists(), reason="hourly parquet not available")
+    def test_output_columns(self, mod):
+        df = mod.prepare_regime_df(str(HOURLY), freq="hourly")
+        assert "datetime" in df.columns
+        assert "regime" in df.columns
+
+    @pytest.mark.skipif(not HOURLY.exists(), reason="hourly parquet not available")
+    def test_regime_values(self, mod):
+        df = mod.prepare_regime_df(str(HOURLY), freq="hourly")
+        unique = set(df["regime"].tolist())
+        assert unique.issubset({0, 1, 2}), f"unexpected regime values: {unique}"
+
+    @pytest.mark.skipif(not HOURLY.exists(), reason="hourly parquet not available")
+    def test_no_nulls(self, mod):
+        df = mod.prepare_regime_df(str(HOURLY), freq="hourly")
+        assert df["regime"].isna().sum() == 0
+
+    @pytest.mark.skipif(not DAILY.exists(), reason="daily parquet not available")
+    def test_daily_fallback(self, mod):
+        df = mod.prepare_regime_df(str(DAILY), freq="daily")
+        assert "regime" in df.columns
+        assert set(df["regime"].tolist()).issubset({0, 1, 2})
+
+
+# ---------------------------------------------------------------------------
+# Integration: check that train.py REGIME SEAM exists and is togglable
+# ---------------------------------------------------------------------------
+
+class TestTrainPyRegimeSeam:
+    def test_enable_regime_env_var_documented(self):
+        train_py = Path(__file__).parent.parent / "train.py"
+        content = train_py.read_text()
+        assert "JEPA_ENABLE_REGIME" in content, "JEPA_ENABLE_REGIME toggle not found in train.py"
+
+    def test_regime_seam_comment_present(self):
+        train_py = Path(__file__).parent.parent / "train.py"
+        content = train_py.read_text()
+        assert "REGIME" in content and "seam" in content.lower(), \
+            "agent-editable regime seam marker not found in train.py"

tests/test_var_metric.py

@@ -0,0 +1,108 @@
+"""Tests for scripts/var_breach.py — VaR breach rate + Kupiec POF (jepa-fx-risk#12).
+
+Golden tests first: verify the math before wiring it into train.py.
+"""
+
+import importlib.util
+import math
+from pathlib import Path
+
+import pytest
+
+_SCRIPT = Path(__file__).parent.parent / "scripts" / "var_breach.py"
+
+
+def _import():
+    spec = importlib.util.spec_from_file_location("var_breach", _SCRIPT)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+@pytest.fixture()
+def mod():
+    return _import()
+
+
+# ---------------------------------------------------------------------------
+# var_breach_rate
+# ---------------------------------------------------------------------------
+
+class TestVarBreachRate:
+    def test_zero_breaches(self, mod):
+        # 0.02 < 0.01×2.326=0.02326 → no breach
+        rate, _ = mod.var_breach_rate([0.01, 0.01], [0.02, 0.02])
+        assert rate == 0.0
+
+    def test_all_breach(self, mod):
+        # 0.03 > 0.02326 → all breach
+        rate, _ = mod.var_breach_rate([0.01, 0.01], [0.03, 0.03])
+        assert rate == 1.0
+
+    def test_golden_two_of_ten(self, mod):
+        pred = [0.01] * 10
+        actual = [0.01] * 10
+        actual[0] = 0.03   # breach
+        actual[2] = 0.03   # breach
+        rate, kupiec_p = mod.var_breach_rate(pred, actual)
+        assert abs(rate - 0.2) < 1e-9, f"rate={rate}"
+        assert kupiec_p < 0.05, f"kupiec_p={kupiec_p}"  # strong reject
+
+    def test_perfect_calibration(self, mod):
+        # n=100, 1 breach → p_hat=0.01=p0=0.01 → LR=0 → kupiec_p≈1
+        pred = [0.01] * 100
+        actual = [0.015] * 100
+        actual[0] = 0.025  # 0.025 > 0.02326 → breach
+        rate, kupiec_p = mod.var_breach_rate(pred, actual)
+        assert abs(rate - 0.01) < 1e-9
+        assert kupiec_p > 0.9, f"kupiec_p={kupiec_p}"
+
+    def test_boundary_at_var_is_not_breach(self, mod):
+        # exactly at VaR_99 is NOT a breach (strict >)
+        z99 = 2.326
+        var = 0.01 * z99
+        rate, _ = mod.var_breach_rate([0.01], [var], z99=z99)
+        assert rate == 0.0
+
+    def test_empty_returns_zero_one(self, mod):
+        rate, kupiec_p = mod.var_breach_rate([], [])
+        assert rate == 0.0
+        assert kupiec_p == 1.0
+
+    def test_metric_key_no_whitespace(self, mod):
+        key = mod.METRIC_KEY
+        assert key == key.strip(), f"metric key has surrounding whitespace: {key!r}"
+        assert " " not in key, f"metric key contains space: {key!r}"
+
+    def test_metric_key_is_canonical(self, mod):
+        assert mod.METRIC_KEY == "VaR_breach_rate_99_oos_regime_cond"
+
+
+# ---------------------------------------------------------------------------
+# kupiec_pvalue
+# ---------------------------------------------------------------------------
+
+class TestKupiecPValue:
+    def test_perfectly_calibrated(self, mod):
+        # p_hat == p0 → LR=0 → p-value=1
+        p = mod.kupiec_pvalue(100, 1, p0=0.01)
+        assert p > 0.99, f"p={p}"
+
+    def test_strong_reject_high_breach(self, mod):
+        # 20% breach when 1% expected → p << 0.05
+        p = mod.kupiec_pvalue(100, 20, p0=0.01)
+        assert p < 0.001, f"p={p}"
+
+    def test_zero_breaches_not_nan(self, mod):
+        p = mod.kupiec_pvalue(100, 0, p0=0.01)
+        assert not math.isnan(p)
+        assert 0 <= p <= 1.0
+
+    def test_all_breaches_not_nan(self, mod):
+        p = mod.kupiec_pvalue(10, 10, p0=0.01)
+        assert not math.isnan(p)
+        assert p < 0.001  # extremely unlikely
+
+    def test_zero_observations(self, mod):
+        p = mod.kupiec_pvalue(0, 0)
+        assert p == 1.0

train.py

@@ -1,14 +1,13 @@
 """train.py — autoresearch agent file (only this may be edited).
 
-TS-JEPA backbone with SIGReg regularization (Balestriero & LeCun, LeJEPA
+HEPA backbone (Petersen et al., arXiv:2605.11130, ICML 2026 Spotlight):
-arXiv:2511.08544; time-series placement from ChronoJEPA arXiv: 2505.XXXXX).
+Causal Transformer pre-trained via horizon-conditioned JEPA. Predictor
+maps (h_t, Δt) → predicted future embedding; loss = VICReg (L1 alignment
+on L2-normalised reps + variance-covariance regulariser, no stop-gradient).
+Probe: ridge regression on the last-token embedding (true OOS split).
 
-PatchTST-style encoder over windowed daily [return, realized_vol] → FREEZE →
+Agent may tune: encoder depth/width, patch geometry, ALPHA, DELTA_T_MAX,
-linear probe predicts NEXT-day realized vol → val_vol_r2 (OOS R²).
+optimizer, LR. Do NOT touch prepare_data.py, loop.py, or the data pipeline.
-Writes metrics.json — the single scalar the loop reads.
-
-Agent may tune: encoder depth/width, patch geometry, mask strategy, SIGReg
-lambda, optimizer. Do NOT touch prepare_data.py, loop.py, or the data pipeline.
 """
 import json
 import math
@@ -16,19 +15,29 @@ import numpy as np
 import pandas as pd
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
-# --- agent-tunable knobs ---
+# --- agent-tunable knobs (all overridable via JEPA_* env vars for HPO) ---
-WINDOW     = 60     # INCREASED lookback for better volatility persistence capture
+import os as _os
-PATCH_LEN  = 5      # time-patch size (must divide WINDOW)
+USE_HOURLY    = True
-STRIDE     = 5
+WINDOW        = int(_os.environ.get("JEPA_WINDOW",        120))   # HPO winner: 5-day context
-D_MODEL    = 64     # transformer hidden dim - INCREASED for capacity
+PATCH_LEN     = int(_os.environ.get("JEPA_PATCH_LEN",     24))
-DEPTH      = 2      # transformer layers
+D_MODEL       = int(_os.environ.get("JEPA_D_MODEL",       128))
-N_HEADS    = 4
+DEPTH         = int(_os.environ.get("JEPA_DEPTH",         2))
-MASK_FRAC  = 0.50   # INCREASED mask fraction to force the encoder to learn better global representations
+N_HEADS       = int(_os.environ.get("JEPA_N_HEADS",       4))
-SIGREG_LAM = 0.01   # SIGReg weight (λ) - REDUCED to allow more representation capacity
+ALPHA         = float(_os.environ.get("JEPA_ALPHA",       0.1))
-EPOCHS     = 300
+DELTA_T_MAX   = int(_os.environ.get("JEPA_DELTA_T_MAX",   3))
-LR         = 3e-4
+BATCH_SIZE    = int(_os.environ.get("JEPA_BATCH_SIZE",    512))
-SEED       = 0
+EPOCHS        = int(_os.environ.get("JEPA_EPOCHS",        300))
+LR            = float(_os.environ.get("JEPA_LR",          3e-4))
+PHASE1_EPOCHS      = int(_os.environ.get("JEPA_PHASE1_EPOCHS",       200))
+PHASE1_LR          = float(_os.environ.get("JEPA_PHASE1_LR",           1e-3))
+PHASE1_JOINT       = bool(int(_os.environ.get("JEPA_PHASE1_JOINT",     1)))
+PHASE1_JOINT_EPOCHS= int(_os.environ.get("JEPA_PHASE1_JOINT_EPOCHS",  30))
+PHASE1_ENCODER_LR  = float(_os.environ.get("JEPA_PHASE1_ENCODER_LR",  3e-6))
+USE_MULTIPAIR      = bool(int(_os.environ.get("JEPA_USE_MULTIPAIR",    0)))
+JEPA_ENABLE_REGIME = bool(int(_os.environ.get("JEPA_ENABLE_REGIME",    0)))
+SEED          = int(_os.environ.get("JEPA_SEED",          0))
 # ---------------------------
 
 torch.manual_seed(SEED)
@@ -36,77 +45,151 @@ np.random.seed(SEED)
 dev = "cuda" if torch.cuda.is_available() else "cpu"
 
 
-# ── SIGReg (from LeJEPA/ChronoJEPA, token-level placement) ─────────────────
+# ── VICReg pretraining loss ──────────────────────────────────────────────────
 
-def sigreg(tokens: torch.Tensor, knots: int = 17) -> torch.Tensor:
+def vicreg_loss(h_pred: torch.Tensor, h_target: torch.Tensor, alpha: float = 0.1) -> torch.Tensor:
-    """Epps-Pulley test statistic pushes token embeddings toward isotropic Gaussian.
+    """L = (1-α)·L1(normalize(ĥ), normalize(h*)) + α·(L_var + L_cov).
 
-    tokens: (B, T, D) — applied per-token, averaged across B and T.
+    Both encoders receive gradients (joint training — no stop-grad on h_target).
+    Variance-covariance terms prevent embedding collapse.
     """
-    B, T, D = tokens.shape
+    pred_n = F.normalize(h_pred, dim=-1)
-    z = tokens.reshape(B * T, D)           # (N, D)
+    targ_n = F.normalize(h_target, dim=-1)
-    t = torch.linspace(0, 3, knots, device=z.device, dtype=z.float().dtype)
+    l1 = F.l1_loss(pred_n, targ_n)
-    dt = 3.0 / (knots - 1)
+    # variance hinge: push each feature std toward ≥ 1
-    w = torch.full((knots,), 2 * dt, device=z.device, dtype=z.float().dtype)
+    std = h_pred.std(dim=0) + 1e-4
-    w[0] = dt; w[-1] = dt
+    l_var = F.relu(1.0 - std).mean()
-    phi = torch.exp(-t.square() / 2.0)
+    # covariance penalty: decorrelate features
-
+    B, D = h_pred.shape
-    A = torch.randn(D, 256, device=z.device, dtype=z.float().dtype)
+    h_c = h_pred - h_pred.mean(dim=0, keepdim=True)
-    A = A / A.norm(p=2, dim=0)
+    cov = (h_c.t() @ h_c) / max(B - 1, 1)
-    x_t = (z.float() @ A).unsqueeze(-1) * t              # (N, 256, knots)
+    off = cov - torch.diag(torch.diag(cov))
-    err = (x_t.cos().mean(0) - phi).square() + x_t.sin().mean(0).square()
+    l_cov = (off ** 2).sum() / D
-    return ((err @ (w * phi)) * z.shape[0]).mean()
+    return (1 - alpha) * l1 + alpha * (l_var + l_cov)
 
 
-# ── Encoder + Predictor ─────────────────────────────────────────────────────
+# ── CausalEncoder ─────────────────────────────────────────────────────────────
 
-class PatchEncoder(nn.Module):
+class CausalEncoder(nn.Module):
-    """PatchTST-style encoder for univariate windows."""
+    """Non-overlapping patches → per-patch LayerNorm → causal Transformer → all tokens (B, N, D).
-    def __init__(self, in_feats, patch_len, stride, d_model, depth, n_heads):
+
+    Per-patch LayerNorm instead of full-window RevIN: each patch is normalised
+    using only its own timesteps, so no future statistics leak into past tokens.
+    Use [:, -1, :] for probing (last token sees full context).
+    Use [:, c, :] for JEPA pretraining (context-at-c).
+    """
+    def __init__(self, n_channels: int, patch_len: int, d_model: int,
+                 n_heads: int, depth: int):
         super().__init__()
         self.patch_len = patch_len
-        self.stride    = stride
         self.d_model   = d_model
-        self.embed = nn.Linear(patch_len * in_feats, d_model)
+        patch_dim = patch_len * n_channels
+        self.patch_norm = nn.LayerNorm(patch_dim)   # applied per-patch, no future leakage
+        self.embed     = nn.Linear(patch_dim, d_model)
         layer = nn.TransformerEncoderLayer(d_model, n_heads, 2 * d_model,
                                            dropout=0.0, batch_first=True)
-        self.tf = nn.TransformerEncoder(layer, num_layers=depth)
+        self.tf   = nn.TransformerEncoder(layer, num_layers=depth)
-        n_patches = (WINDOW - patch_len) // stride + 1
+        self.norm = nn.LayerNorm(d_model)
-        pos = torch.zeros(n_patches, d_model)
-        for p in range(n_patches):
-            for i in range(0, d_model, 2):
-                pos[p, i]     = math.sin(p / 10000 ** (i / d_model))
-                if i + 1 < d_model:
-                    pos[p, i+1] = math.cos(p / 10000 ** (i / d_model))
-        self.register_buffer("pos", pos)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # x: (B, W, F)  → patches → (B, T, D)
         B, W, F = x.shape
-        n_patches = (W - self.patch_len) // self.stride + 1
+        P = self.patch_len
-        patches = torch.stack([x[:, i*self.stride:i*self.stride+self.patch_len, :]
+        N = W // P
-                                .reshape(B, -1) for i in range(n_patches)], dim=1)
+        tokens = x[:, :N * P, :].reshape(B, N, P * F)
-        tokens = self.embed(patches) + self.pos[:n_patches]
+        tokens = self.embed(self.patch_norm(tokens))
-        return self.tf(tokens)  # (B, T, D)
+        # sinusoidal PE
+        pos = torch.arange(N, device=x.device).float()
+        div = torch.exp(torch.arange(0, self.d_model, 2, device=x.device).float()
+                        * -(math.log(10000.0) / self.d_model))
+        pe = torch.zeros(N, self.d_model, device=x.device)
+        pe[:, 0::2] = torch.sin(pos.unsqueeze(1) * div)
+        pe[:, 1::2] = torch.cos(pos.unsqueeze(1) * div)
+        tokens = tokens + pe
+        # causal mask
+        mask = nn.Transformer.generate_square_subsequent_mask(N, device=x.device)
+        return self.norm(self.tf(tokens, mask=mask, is_causal=True))
 
 
-class Predictor(nn.Module):
+# ── HorizonPredictor ─────────────────────────────────────────────────────────
-    def __init__(self, d_model):
+
+class HorizonPredictor(nn.Module):
+    """MLP(cat(h_t, Δt)) → predicted future embedding."""
+    def __init__(self, d_model: int):
         super().__init__()
-        self.net = nn.Sequential(nn.Linear(d_model, d_model), nn.GELU(),
+        self.net = nn.Sequential(
-                                 nn.Linear(d_model, d_model))
+            nn.Linear(d_model + 1, d_model), nn.GELU(),
-    def forward(self, x):
+            nn.Linear(d_model, d_model),     nn.GELU(),
-        return self.net(x)
+            nn.Linear(d_model, d_model),
+        )
+
+    def forward(self, h: torch.Tensor, delta_t: torch.Tensor) -> torch.Tensor:
+        dt = delta_t.float().unsqueeze(-1)
+        return self.net(torch.cat([h, dt], dim=-1))
 
 
-# ── Data ────────────────────────────────────────────────────────────────────
+# ── Phase-1 supervised head ──────────────────────────────────────────────────
+
+class SupervisedHead(nn.Module):
+    """Small MLP trained on frozen HEPA embeddings to predict next-period realized vol."""
+    def __init__(self, d_model: int):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(d_model, d_model // 2), nn.GELU(),
+            nn.Linear(d_model // 2, 1),
+        )
+
+    def forward(self, h: torch.Tensor) -> torch.Tensor:
+        return self.net(h).squeeze(-1)
+
+
+# ── Data ─────────────────────────────────────────────────────────────────────
 
 def build():
-    """Year-based split: encoder trains on 2019-2021; probe evaluates on 2022-2023 OOS."""
+    """Year-based split: encoder trains on ≤2021; probe evaluates on ≥2022 OOS.
-    df = pd.read_parquet("data/processed/eurusd_daily.parquet").reset_index(drop=True)
+
-    df["date"] = pd.to_datetime(df["date"])
+    Uses eurusd_hourly.parquet when USE_HOURLY=True and the file exists;
-    feats  = df[["ret", "realized_vol"]].to_numpy(np.float32)
+    falls back to eurusd_daily.parquet otherwise.
-    target = df["realized_vol"].to_numpy(np.float32)
+    """
+    import os
+    multipair_path = "data/processed/eurusd_multipair.parquet"
+    hourly_path    = "data/processed/eurusd_hourly.parquet"
+    daily_path     = "data/processed/eurusd_daily.parquet"
+    if USE_MULTIPAIR and os.path.exists(multipair_path):
+        df = pd.read_parquet(multipair_path).reset_index(drop=True)
+        df["date"] = pd.to_datetime(df["datetime"])
+        # All {pair}_ret + {pair}_rv columns as features; eurusd_rv as target
+        feat_cols = [c for c in df.columns if c.endswith("_ret") or c.endswith("_rv")]
+        FEAT_COLS = feat_cols
+        target_col = "eurusd_rv"
+    elif USE_HOURLY and os.path.exists(hourly_path):
+        df = pd.read_parquet(hourly_path).reset_index(drop=True)
+        df["date"] = pd.to_datetime(df["datetime"])
+        # 2-channel default (HPO: adding hl_range+ret_intrabar hurt — correlated with base feats)
+        FEAT_COLS = ["ret", "realized_vol"]
+        target_col = "realized_vol"
+    else:
+        df = pd.read_parquet(daily_path).reset_index(drop=True)
+        df["date"] = pd.to_datetime(df["date"])
+        FEAT_COLS = ["ret", "realized_vol"]
+        target_col = "realized_vol"
+    # ── REGIME CONDITIONING SEAM — agent may vary this mechanism ─────────────
+    # Baseline: concat regime flag as an additional feature channel (0=calm, 2=crisis).
+    # Agent may swap for FiLM conditioning, learned regime embedding, or gating.
+    _regime_path = "data/processed/eurusd_regime.parquet"
+    if JEPA_ENABLE_REGIME and os.path.exists(_regime_path):
+        _rdf = pd.read_parquet(_regime_path)
+        _ts_col = "datetime" if "datetime" in _rdf.columns else "date"
+        _rdf[_ts_col] = pd.to_datetime(_rdf[_ts_col])
+        df = df.copy()
+        df = df.merge(
+            _rdf.rename(columns={_ts_col: "date"})[["date", "regime"]],
+            on="date", how="left",
+        )
+        df["regime"] = df["regime"].fillna(0).astype(np.float32)
+        FEAT_COLS = list(FEAT_COLS) + ["regime"]
+    # ── END REGIME SEAM ───────────────────────────────────────────────────────
+    feats  = df[FEAT_COLS].to_numpy(np.float32)
+    target = df[target_col].to_numpy(np.float32)
     tr_idx = df.index[df["date"].dt.year <= 2021].tolist()
     te_idx = df.index[df["date"].dt.year >= 2022].tolist()
     mu = feats[:tr_idx[-1]+1].mean(0)
@@ -121,92 +204,180 @@ def build():
     return windows(tr_idx), windows(te_idx)
 
 
-# ── Training ─────────────────────────────────────────────────────────────────
+# ── Training ──────────────────────────────────────────────────────────────────
 
 def main():
     (Xtr, ytr), (Xte, yte) = build()
-    n_feats  = Xtr.shape[2]
+    n_feats   = Xtr.shape[2]
-    Xtr_t    = torch.tensor(Xtr, device=dev)
+    n_patches = WINDOW // PATCH_LEN
-    enc      = PatchEncoder(n_feats, PATCH_LEN, STRIDE, D_MODEL, DEPTH, N_HEADS).to(dev)
+    N_tr      = len(Xtr)
-    pred     = Predictor(D_MODEL).to(dev)
+    bs        = min(BATCH_SIZE, N_tr)
-    opt      = torch.optim.AdamW(list(enc.parameters()) + list(pred.parameters()), lr=LR)
 
-    n_patches = (WINDOW - PATCH_LEN) // STRIDE + 1
+    enc  = CausalEncoder(n_feats, PATCH_LEN, D_MODEL, N_HEADS, DEPTH).to(dev)
-    n_mask    = max(1, int(MASK_FRAC * n_patches))
+    pred = HorizonPredictor(D_MODEL).to(dev)
+    opt  = torch.optim.AdamW(list(enc.parameters()) + list(pred.parameters()), lr=LR)
 
     for ep in range(EPOCHS):
-        # JEPA: predict masked-out patch tokens from visible tokens
+        # Random mini-batch (avoids OOM on large hourly dataset)
-        idx_mask = torch.randperm(n_patches)[:n_mask]
+        idx_b = torch.randperm(N_tr)[:bs]
-        ctx_mask = torch.ones(n_patches, dtype=torch.bool, device=dev)
+        Xb    = torch.tensor(Xtr[idx_b.numpy()], device=dev)
-        ctx_mask[idx_mask] = False
 
-        tokens_ctx    = enc(Xtr_t)                 # encode all (B, T, D)
+        # Sample random context position and horizon
-        tokens_target = enc(Xtr_t).detach()        # target (frozen): same input, no grad
+        c  = torch.randint(0, n_patches - 1, ()).item()
-        pred_out = pred(tokens_ctx[:, idx_mask, :])
+        dt = torch.randint(1, max(2, min(DELTA_T_MAX, n_patches - 1 - c) + 1), ()).item()
-        jepa_loss = ((pred_out - tokens_target[:, idx_mask, :]) ** 2).mean()
+
-        reg_loss  = sigreg(tokens_ctx)
+        tokens = enc(Xb)                        # (bs, N, D)
-        loss      = jepa_loss + SIGREG_LAM * reg_loss
+        h_ctx  = tokens[:, c, :]               # context embedding
+        h_tgt  = tokens[:, c + dt, :]          # target embedding (joint training)
+        h_hat  = pred(h_ctx, torch.full((bs,), float(dt), device=dev))
+        loss   = vicreg_loss(h_hat, h_tgt, alpha=ALPHA)
         opt.zero_grad(); loss.backward(); opt.step()
 
     enc.eval()
     with torch.no_grad():
         def embed(X_np):
-            t = torch.tensor(X_np, device=dev)
+            chunks = []
-            return enc(t).mean(1).cpu().numpy()   # pool over time patches
+            for i in range(0, len(X_np), bs):
+                t = torch.tensor(X_np[i:i+bs], device=dev)
+                chunks.append(enc(t)[:, -1, :].cpu().numpy())
+            return np.concatenate(chunks, axis=0)
 
         Etr = embed(Xtr)
         Ete = embed(Xte)
 
-    # ridge linear probe (closed form)
+    # Ridge probe: fit on train, evaluate on OOS (true OOS R²)
-    A  = np.hstack([Etr, np.ones((len(Etr), 1))])
+    mu_e = Etr.mean(0); sd_e = Etr.std(0) + 1e-8
+    Etr_n = (Etr - mu_e) / sd_e
+    Ete_n = (Ete - mu_e) / sd_e
+    A  = np.hstack([Etr_n, np.ones((len(Etr_n), 1))])
     w  = np.linalg.solve(A.T @ A + 1e-3 * np.eye(A.shape[1]), A.T @ ytr)
-    pred_np = np.hstack([Ete, np.ones((len(Ete), 1))]) @ w
+    pred_np = np.hstack([Ete_n, np.ones((len(Ete_n), 1))]) @ w
     ss_res  = ((yte - pred_np) ** 2).sum()
     ss_tot  = ((yte - yte.mean()) ** 2).sum()
     val_vol_r2 = float(1 - ss_res / ss_tot)
 
+    # Phase-1: MLP supervised head — joint or frozen-encoder path
+    ytr_mu = float(ytr.mean()); ytr_sd = float(ytr.std()) + 1e-8
+    ytr_z  = (ytr - ytr_mu) / ytr_sd
+    head   = SupervisedHead(D_MODEL).to(dev)
+    p1_bs  = min(BATCH_SIZE, len(Etr_n))
+
+    # Shared tensors for the frozen-head warmup (used by both paths)
+    Etr_t   = torch.tensor(Etr_n, device=dev)
+    ytr_z_t = torch.tensor(ytr_z, device=dev)
+    Ete_t   = torch.tensor(Ete_n, device=dev)
+    N_tr_h  = len(Etr_t)
+
+    # Phase 1a: warm up head on frozen embeddings (both paths run this)
+    head_opt = torch.optim.Adam(head.parameters(), lr=PHASE1_LR, weight_decay=1e-4)
+    for _ in range(PHASE1_EPOCHS):
+        perm = torch.randperm(N_tr_h, device=dev)
+        for start in range(0, N_tr_h, p1_bs):
+            idx_h = perm[start:start + p1_bs]
+            loss_h = F.mse_loss(head(Etr_t[idx_h]), ytr_z_t[idx_h])
+            head_opt.zero_grad(); loss_h.backward(); head_opt.step()
+
+    if PHASE1_JOINT:
+        # Phase 1b: short joint fine-tuning — encoder nudged with tiny LR.
+        # Normalize live encoder output with FROZEN stats (mu_e, sd_e) so the
+        # head sees the same embedding distribution it was warmed up on.
+        enc.train()
+        mu_e_t = torch.tensor(mu_e, device=dev)
+        sd_e_t = torch.tensor(sd_e, device=dev)
+        Xtr_t  = torch.tensor(Xtr, device=dev)
+        joint_opt = torch.optim.Adam([
+            {"params": head.parameters(), "lr": PHASE1_LR * 0.1},
+            {"params": enc.parameters(),  "lr": PHASE1_ENCODER_LR},
+        ], weight_decay=1e-4)
+        for _ in range(PHASE1_JOINT_EPOCHS):
+            perm = torch.randperm(len(Xtr_t), device=dev)
+            for start in range(0, len(Xtr_t), p1_bs):
+                idx_j = perm[start:start + p1_bs]
+                h_raw = enc(Xtr_t[idx_j])[:, -1, :]
+                h_n   = (h_raw - mu_e_t) / sd_e_t  # frozen-stats normalisation
+                loss_j = F.mse_loss(head(h_n), ytr_z_t[idx_j])
+                joint_opt.zero_grad(); loss_j.backward(); joint_opt.step()
+        enc.eval()
+        # Re-extract test embeddings with fine-tuned encoder, same normalisation
+        with torch.no_grad():
+            chunks = []
+            for i in range(0, len(Xte), p1_bs):
+                t = torch.tensor(Xte[i:i+p1_bs], device=dev)
+                h = enc(t)[:, -1, :]
+                chunks.append(((h - mu_e_t) / sd_e_t).cpu().numpy())
+            Ete_t = torch.tensor(np.concatenate(chunks), device=dev)
+
+    head.eval()
+    with torch.no_grad():
+        pred_h_z = head(Ete_t).cpu().numpy()
+
+    pred_h    = pred_h_z * ytr_sd + ytr_mu          # de-standardise
+    phase1_r2 = float(1 - ((yte - pred_h) ** 2).sum() / ss_tot)
+    print("phase1_r2   = %.4f  (n_test=%d)" % (phase1_r2, len(yte)))
+
+    # ── VaR EVAL BLOCK — do NOT edit (agent boundary) ───────────────────────
+    import sys as _sys
+    _sys.path.insert(0, _os.path.dirname(_os.path.abspath(__file__)))
+    from scripts.var_breach import var_breach_rate as _var_breach_rate, METRIC_KEY as _VAR_KEY
+    _var_rate, _kupiec_p = _var_breach_rate(pred_np.tolist(), yte.tolist())
+    print("%s=%.4f  Kupiec_p=%.4f" % (_VAR_KEY, _var_rate, _kupiec_p))
+    # ── END VaR EVAL BLOCK ───────────────────────────────────────────────────
+
+    _metrics_out = _os.environ.get("METRICS_OUT", "metrics.json")
     json.dump({
-        "val_vol_r2": val_vol_r2, "n_test": len(yte),
+        "val_vol_r2": val_vol_r2, "phase1_r2": phase1_r2, "n_test": len(yte),
-        "knobs": {"WINDOW": WINDOW, "PATCH_LEN": PATCH_LEN, "STRIDE": STRIDE,
+        _VAR_KEY: _var_rate, "kupiec_p": _kupiec_p,
-                  "D_MODEL": D_MODEL, "DEPTH": DEPTH, "MASK_FRAC": MASK_FRAC,
+        "knobs": {"WINDOW": WINDOW, "PATCH_LEN": PATCH_LEN,
-                  "SIGREG_LAM": SIGREG_LAM, "EPOCHS": EPOCHS},
+                  "D_MODEL": D_MODEL, "DEPTH": DEPTH, "ALPHA": ALPHA,
-    }, open("metrics.json", "w"), indent=2)
+                  "DELTA_T_MAX": DELTA_T_MAX, "EPOCHS": EPOCHS},
+    }, open(_metrics_out, "w"), indent=2)
     print("val_vol_r2 = %.4f  (n_test=%d, dev=%s)" % (val_vol_r2, len(yte), dev))
 
     # ── EXPORT BLOCK — do NOT edit (agent boundary) ──────────────────────────
     # Set EXPORT_EMBEDDINGS=1 to write embeddings.json for the Go eval harness.
-    # Uses year-based split (train≤2021, OOS≥2022) regardless of probe split.
     import os
     if os.environ.get("EXPORT_EMBEDDINGS") == "1":
-        df2 = pd.read_parquet("data/processed/eurusd_daily.parquet").reset_index(drop=True)
+        hourly_path2 = "data/processed/eurusd_hourly.parquet"
-        df2["date"] = pd.to_datetime(df2["date"])
+        daily_path2  = "data/processed/eurusd_daily.parquet"
+        if USE_HOURLY and os.path.exists(hourly_path2):
+            df2 = pd.read_parquet(hourly_path2).reset_index(drop=True)
+            df2["date"] = pd.to_datetime(df2["datetime"])
+        else:
+            df2 = pd.read_parquet(daily_path2).reset_index(drop=True)
+            df2["date"] = pd.to_datetime(df2["date"])
         tr_mask = df2["date"].dt.year <= 2021
-        feats2 = df2[["ret", "realized_vol"]].to_numpy(np.float32)
+        base2 = ["ret", "realized_vol"]
+        extra2 = [c for c in ["hl_range", "ret_intrabar"] if c in df2.columns]
+        feats2 = df2[base2 + extra2].to_numpy(np.float32)
         mu2 = feats2[tr_mask].mean(0); sd2 = feats2[tr_mask].std(0) + 1e-8
         fn2 = (feats2 - mu2) / sd2
         def _export_windows(year_mask):
             idx = df2.index[year_mask].tolist()
             Xs, dates, rvs = [], [], []
             for t in idx:
-                if t - WINDOW >= 0:
+                if t - WINDOW >= 0 and t + 1 < len(df2):
                     Xs.append(fn2[t - WINDOW:t])
                     dates.append(str(df2["date"].iloc[t].date()))
-                    rvs.append(float(df2["realized_vol"].iloc[t]))
+                    rvs.append(float(df2["realized_vol"].iloc[t + 1]))
             if not Xs:
                 return [], [], []
+            Xa = np.stack(Xs)
+            chunks = []
             with torch.no_grad():
-                E = enc(torch.tensor(np.stack(Xs), device=dev)).mean(1).cpu().numpy().tolist()
+                for i in range(0, len(Xa), bs):
+                    chunks.append(enc(torch.tensor(Xa[i:i+bs], device=dev))[:, -1, :].cpu().numpy())
+            E = np.concatenate(chunks, axis=0).tolist()
             return E, dates, rvs
-        Etr, dates_tr, rv_tr = _export_windows(tr_mask)
+        Etr2, dates_tr, rv_tr = _export_windows(tr_mask)
         Eoos, dates_oos, rv_oos = _export_windows(df2["date"].dt.year >= 2022)
         hv_thr = float(np.percentile(rv_oos, 67))
         hv_label = [1 if v >= hv_thr else 0 for v in rv_oos]
         json.dump({"embeddings": Eoos, "dates": dates_oos,
                    "realized_vol": rv_oos, "hv_label": hv_label,
-                   "train_embeddings": Etr, "train_realized_vol": rv_tr},
+                   "train_embeddings": Etr2, "train_realized_vol": rv_tr},
                   open("embeddings.json", "w"))
         print("exported embeddings.json  train=%d  oos=%d  HV=%d/%d" % (
-            len(Etr), len(Eoos), sum(hv_label), len(hv_label)))
+            len(Etr2), len(Eoos), sum(hv_label), len(hv_label)))
     # ── END EXPORT BLOCK ─────────────────────────────────────────────────────