jepa-fx-risk/train.py

"""train.py — autoresearch agent file (only this may be edited).

TS-JEPA backbone with SIGReg regularization (Balestriero & LeCun, LeJEPA
arXiv:2511.08544; time-series placement from ChronoJEPA arXiv: 2505.XXXXX).

PatchTST-style encoder over windowed daily [return, realized_vol] → FREEZE →
linear probe predicts NEXT-day realized vol → val_vol_r2 (OOS R²).
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
import numpy as np
import pandas as pd
import torch
import torch.nn as nn

# --- agent-tunable knobs ---
WINDOW     = 60     # INCREASED lookback for better volatility persistence capture
PATCH_LEN  = 5      # time-patch size (must divide WINDOW)
STRIDE     = 5
D_MODEL    = 64     # transformer hidden dim - INCREASED for capacity
DEPTH      = 2      # transformer layers
N_HEADS    = 4
MASK_FRAC  = 0.50   # INCREASED mask fraction to force the encoder to learn better global representations
SIGREG_LAM = 0.01   # SIGReg weight (λ) - REDUCED to allow more representation capacity
EPOCHS     = 300
LR         = 3e-4
SEED       = 0
# ---------------------------

torch.manual_seed(SEED)
np.random.seed(SEED)
dev = "cuda" if torch.cuda.is_available() else "cpu"


# ── SIGReg (from LeJEPA/ChronoJEPA, token-level placement) ─────────────────

def sigreg(tokens: torch.Tensor, knots: int = 17) -> torch.Tensor:
    """Epps-Pulley test statistic pushes token embeddings toward isotropic Gaussian.

    tokens: (B, T, D) — applied per-token, averaged across B and T.
    """
    B, T, D = tokens.shape
    z = tokens.reshape(B * T, D)           # (N, D)
    t = torch.linspace(0, 3, knots, device=z.device, dtype=z.float().dtype)
    dt = 3.0 / (knots - 1)
    w = torch.full((knots,), 2 * dt, device=z.device, dtype=z.float().dtype)
    w[0] = dt; w[-1] = dt
    phi = torch.exp(-t.square() / 2.0)

    A = torch.randn(D, 256, device=z.device, dtype=z.float().dtype)
    A = A / A.norm(p=2, dim=0)
    x_t = (z.float() @ A).unsqueeze(-1) * t              # (N, 256, knots)
    err = (x_t.cos().mean(0) - phi).square() + x_t.sin().mean(0).square()
    return ((err @ (w * phi)) * z.shape[0]).mean()


# ── Encoder + Predictor ─────────────────────────────────────────────────────

class PatchEncoder(nn.Module):
    """PatchTST-style encoder for univariate windows."""
    def __init__(self, in_feats, patch_len, stride, d_model, depth, n_heads):
        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)
        layer = nn.TransformerEncoderLayer(d_model, n_heads, 2 * d_model,
                                           dropout=0.0, batch_first=True)
        self.tf = nn.TransformerEncoder(layer, num_layers=depth)
        n_patches = (WINDOW - patch_len) // stride + 1
        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
        patches = torch.stack([x[:, i*self.stride:i*self.stride+self.patch_len, :]
                                .reshape(B, -1) for i in range(n_patches)], dim=1)
        tokens = self.embed(patches) + self.pos[:n_patches]
        return self.tf(tokens)  # (B, T, D)


class Predictor(nn.Module):
    def __init__(self, d_model):
        super().__init__()
        self.net = nn.Sequential(nn.Linear(d_model, d_model), nn.GELU(),
                                 nn.Linear(d_model, d_model))
    def forward(self, x):
        return self.net(x)


# ── Data ────────────────────────────────────────────────────────────────────

def build():
    """Year-based split: encoder trains on 2019-2021; probe evaluates on 2022-2023 OOS."""
    df = pd.read_parquet("data/processed/eurusd_daily.parquet").reset_index(drop=True)
    df["date"] = pd.to_datetime(df["date"])
    feats  = df[["ret", "realized_vol"]].to_numpy(np.float32)
    target = df["realized_vol"].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)
    sd = feats[:tr_idx[-1]+1].std(0) + 1e-8
    fn = (feats - mu) / sd
    def windows(idx):
        X, y = [], []
        for t in idx:
            if t - WINDOW >= 0 and t + 1 < len(df):
                X.append(fn[t - WINDOW:t]); y.append(target[t + 1])
        return np.stack(X).astype(np.float32), np.array(y, np.float32)
    return windows(tr_idx), windows(te_idx)


# ── Training ─────────────────────────────────────────────────────────────────

def main():
    (Xtr, ytr), (Xte, yte) = build()
    n_feats  = Xtr.shape[2]
    Xtr_t    = torch.tensor(Xtr, device=dev)
    enc      = PatchEncoder(n_feats, PATCH_LEN, STRIDE, D_MODEL, DEPTH, N_HEADS).to(dev)
    pred     = Predictor(D_MODEL).to(dev)
    opt      = torch.optim.AdamW(list(enc.parameters()) + list(pred.parameters()), lr=LR)

    n_patches = (WINDOW - PATCH_LEN) // STRIDE + 1
    n_mask    = max(1, int(MASK_FRAC * n_patches))

    for ep in range(EPOCHS):
        # JEPA: predict masked-out patch tokens from visible tokens
        idx_mask = torch.randperm(n_patches)[:n_mask]
        ctx_mask = torch.ones(n_patches, dtype=torch.bool, device=dev)
        ctx_mask[idx_mask] = False

        tokens_ctx    = enc(Xtr_t)                 # encode all (B, T, D)
        tokens_target = enc(Xtr_t).detach()        # target (frozen): same input, no grad
        pred_out = pred(tokens_ctx[:, idx_mask, :])
        jepa_loss = ((pred_out - tokens_target[:, idx_mask, :]) ** 2).mean()
        reg_loss  = sigreg(tokens_ctx)
        loss      = jepa_loss + SIGREG_LAM * reg_loss
        opt.zero_grad(); loss.backward(); opt.step()

    enc.eval()
    with torch.no_grad():
        def embed(X_np):
            t = torch.tensor(X_np, device=dev)
            return enc(t).mean(1).cpu().numpy()   # pool over time patches

        Etr = embed(Xtr)
        Ete = embed(Xte)

    # ridge linear probe (closed form)
    A  = np.hstack([Etr, np.ones((len(Etr), 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
    ss_res  = ((yte - pred_np) ** 2).sum()
    ss_tot  = ((yte - yte.mean()) ** 2).sum()
    val_vol_r2 = float(1 - ss_res / ss_tot)

    json.dump({
        "val_vol_r2": val_vol_r2, "n_test": len(yte),
        "knobs": {"WINDOW": WINDOW, "PATCH_LEN": PATCH_LEN, "STRIDE": STRIDE,
                  "D_MODEL": D_MODEL, "DEPTH": DEPTH, "MASK_FRAC": MASK_FRAC,
                  "SIGREG_LAM": SIGREG_LAM, "EPOCHS": EPOCHS},
    }, open("metrics.json", "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)
        df2["date"] = pd.to_datetime(df2["date"])
        tr_mask = df2["date"].dt.year <= 2021
        feats2 = df2[["ret", "realized_vol"]].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:
                    Xs.append(fn2[t - WINDOW:t])
                    dates.append(str(df2["date"].iloc[t].date()))
                    rvs.append(float(df2["realized_vol"].iloc[t]))
            if not Xs:
                return [], [], []
            with torch.no_grad():
                E = enc(torch.tensor(np.stack(Xs), device=dev)).mean(1).cpu().numpy().tolist()
            return E, dates, rvs
        Etr, 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},
                  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)))
    # ── END EXPORT BLOCK ─────────────────────────────────────────────────────


if __name__ == "__main__":
    main()