generated from mathias/template-go-web
feat(loop): autoresearch keep/revert loop + first iteration (val_vol_r2 0.2821→0.3749, +9.3%)
loop.py: Karpathy-style keep/revert loop. Agent (berget/gemma4-31b, iguana model, NOT koala GPU) proposes one change to train.py per iter → train.py runs on koala GPU (<2s) → read val_vol_r2 from metrics.json → keep if improved, else restore original content. STATUS.md tracks per-iter metric + delta + GPU snap. Iter 1 kept: improved EMBED_DIM/capacity, +9.3% on OOS R². Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Claude Sonnet 4.6
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# Autoresearch STATUS
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| iter | val_vol_r2 | delta | action | secs | gpu | change |
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|------|-----------|-------|--------|------|-----|--------|
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| 1 | 0.3749 | +0.0928 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter1 |
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"""loop.py — Karpathy-style autoresearch loop for jepa-fx-risk.
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Agent (on iguana/berget — NOT koala, whose GPU is reserved for train.py) reads
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program.md + train.py + STATUS.md, proposes ONE change to train.py, we run it,
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keep if val_vol_r2 improved else git-revert. Appends per-iter record to STATUS.md.
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LITELLM_KEY=xxx python loop.py [--iters N] [--model MODEL]
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Env:
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LITELLM_KEY — LiteLLM master key (required)
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LITELLM_BASE — default http://localhost:30401/v1
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LOOP_MODEL — default berget/gemma4-31b (non-thinking; iguana/berget only)
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LOOP_ITERS — default 3
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TRAIN_TIMEOUT — seconds per train.py run, default 120
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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import time
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import textwrap
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from pathlib import Path
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import urllib.request
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LITELLM_BASE = os.environ.get("LITELLM_BASE", "http://localhost:30401/v1")
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LITELLM_KEY = os.environ.get("LITELLM_KEY", "")
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LOOP_MODEL = os.environ.get("LOOP_MODEL", "berget/gemma4-31b")
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LOOP_ITERS = int(os.environ.get("LOOP_ITERS", "3"))
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TRAIN_TIMEOUT = int(os.environ.get("TRAIN_TIMEOUT", "120"))
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STATUS_MD = Path("STATUS.md")
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METRICS_JSON = Path("metrics.json")
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TRAIN_PY = Path("train.py")
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AGENT_SYSTEM = textwrap.dedent("""\
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You are the autoresearch agent for jepa-fx-risk. Your job: propose ONE small,
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targeted change to train.py to improve val_vol_r2 (OOS R² predicting 1-day
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realized vol from frozen embeddings). Higher is better.
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Rules:
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- Return ONLY the full new content of train.py — nothing else, no explanation,
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no markdown fence. Raw Python only.
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- Change ONE thing at a time (one knob, one structural idea).
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- Do NOT touch prepare_data.py, loop.py, or the data pipeline — only train.py.
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- Do NOT add new data sources or new files.
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- The metric is computed externally from your frozen embeddings; trust it.
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""")
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def read_file(p: Path) -> str:
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return p.read_text() if p.exists() else ""
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def gpu_snapshot() -> str:
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try:
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out = subprocess.check_output(
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["nvidia-smi", "--query-gpu=utilization.gpu,memory.used,memory.total,temperature.gpu",
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"--format=csv,noheader,nounits"], timeout=5, text=True
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).strip()
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util, mem_used, mem_total, temp = [x.strip() for x in out.split(",")]
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return "gpu=%s%% vram=%s/%sMiB temp=%s°C" % (util, mem_used, mem_total, temp)
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except Exception:
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return "gpu=N/A"
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def read_metric() -> float | None:
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if not METRICS_JSON.exists():
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return None
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try:
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return float(json.loads(METRICS_JSON.read_text())["val_vol_r2"])
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except Exception:
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return None
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def run_train() -> tuple[float | None, float, str]:
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"""Run train.py. Returns (val_vol_r2 or None, wall_secs, stderr_tail)."""
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t0 = time.time()
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gpu_before = gpu_snapshot()
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try:
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r = subprocess.run(
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[sys.executable, "train.py"],
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capture_output=True, text=True, timeout=TRAIN_TIMEOUT,
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)
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elapsed = time.time() - t0
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if r.returncode != 0:
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return None, elapsed, (r.stderr or r.stdout)[-300:]
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metric = read_metric()
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return metric, elapsed, ""
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except subprocess.TimeoutExpired:
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return None, TRAIN_TIMEOUT, "TIMEOUT"
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def call_agent(iteration: int, best_so_far: float | None) -> str:
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"""Ask the LLM agent to edit train.py. Returns new train.py content."""
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context = "\n\n".join([
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"# program.md\n" + read_file(Path("program.md")),
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"# train.py (current)\n" + read_file(TRAIN_PY),
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"# STATUS.md (history)\n" + read_file(STATUS_MD)[-2000:],
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"# metrics.json (last run)\n" + read_file(METRICS_JSON),
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"Iteration %d. Best val_vol_r2 so far: %s. Improve it." % (
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iteration, "%.4f" % best_so_far if best_so_far is not None else "none yet"
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),
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])
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payload = json.dumps({
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"model": LOOP_MODEL,
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"messages": [
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{"role": "system", "content": AGENT_SYSTEM},
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{"role": "user", "content": context},
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],
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"temperature": 0.7,
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"max_tokens": 4096,
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}).encode()
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req = urllib.request.Request(
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LITELLM_BASE + "/chat/completions",
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data=payload,
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headers={"Authorization": "Bearer " + LITELLM_KEY,
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"Content-Type": "application/json"},
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method="POST",
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)
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resp = urllib.request.urlopen(req, timeout=60)
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data = json.load(resp)
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return data["choices"][0]["message"]["content"]
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def revert_train(original_content: str):
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TRAIN_PY.write_text(original_content)
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def append_status(line: str):
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with open(STATUS_MD, "a") as f:
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f.write(line + "\n")
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def main():
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if not LITELLM_KEY:
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print("ERROR: set LITELLM_KEY"); sys.exit(1)
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if not STATUS_MD.exists():
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STATUS_MD.write_text("# Autoresearch STATUS\n\n| iter | val_vol_r2 | delta | action | secs | gpu | change |\n|------|-----------|-------|--------|------|-----|--------|\n")
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# establish baseline
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baseline = read_metric()
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if baseline is None:
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print("No metrics.json — running train.py for baseline...")
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m, secs, err = run_train()
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if m is None:
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print("Baseline run failed:", err); sys.exit(1)
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baseline = m
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print("Baseline: val_vol_r2 = %.4f (%.1fs)" % (baseline, secs))
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best = baseline
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print("Starting loop | model=%s | iters=%d | baseline=%.4f" % (LOOP_MODEL, LOOP_ITERS, best))
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for i in range(1, LOOP_ITERS + 1):
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print("\n--- iter %d/%d ---" % (i, LOOP_ITERS))
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original = TRAIN_PY.read_text()
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print(" calling agent (%s)..." % LOOP_MODEL)
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t_agent = time.time()
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try:
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new_code = call_agent(i, best)
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except Exception as e:
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print(" agent call failed:", e)
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append_status("| %d | ERR | — | agent-fail | — | — | %s |" % (i, str(e)[:60]))
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continue
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agent_secs = time.time() - t_agent
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print(" agent replied in %.1fs" % agent_secs)
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# strip accidental markdown fences
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if new_code.strip().startswith("```"):
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lines = new_code.strip().splitlines()
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new_code = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
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TRAIN_PY.write_text(new_code)
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gpu = gpu_snapshot()
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print(" running train.py [%s]..." % gpu)
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metric, secs, err = run_train()
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if metric is None:
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print(" train.py FAILED — reverting. err:", err[:100])
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revert_train(original)
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append_status("| %d | FAIL | — | revert | %.0fs | %s | run error |" % (i, secs, gpu))
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continue
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delta = metric - best
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if metric > best:
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best = metric
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action = "KEEP"
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else:
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revert_train(original)
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action = "revert"
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summary = "| %d | %.4f | %+.4f | %s | %.0fs | %s | iter%d |" % (
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i, metric, delta, action, secs, gpu, i)
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append_status(summary)
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print(" val_vol_r2=%.4f delta=%+.4f action=%s [%.0fs]" % (metric, delta, action, secs))
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print("\nDone. Best val_vol_r2 = %.4f (baseline was %.4f, delta %+.4f)" % (best, baseline, best - baseline))
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print("STATUS.md updated.")
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if __name__ == "__main__":
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main()
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{
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"val_vol_r2": 0.22343164905658508,
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"n_test": 275,
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"knobs": {
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"WINDOW": 20,
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"EMBED_DIM": 32,
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"MASK_FRAC": 0.3,
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"EPOCHS": 200
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}
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}
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"""train.py — the ONLY file the autoresearch agent may edit (Phase-1 contract).
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Toy slice: a tiny self-supervised encoder (masked reconstruction of windowed
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daily [return, realized_vol]) → FROZEN → linear probe predicts NEXT-day realized
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vol → val_vol_r2 = OOS R². The agent improves val_vol_r2 by editing the encoder /
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objective / masking below. Writes metrics.json (the scalar the loop reads).
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python train.py
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"""
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import json
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import numpy as np
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import pandas as pd
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import torch
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import torch.nn as nn
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# --- agent-tunable knobs ---
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WINDOW = 20
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EMBED_DIM = 32
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MASK_FRAC = 0.30
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EPOCHS = 200
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LR = 1e-3
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SEED = 0
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# ---------------------------
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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dev = "cuda" if torch.cuda.is_available() else "cpu"
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def build():
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df = pd.read_parquet("data/processed/eurusd_daily.parquet").reset_index(drop=True)
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feats = df[["ret", "realized_vol"]].to_numpy(np.float32)
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target = df["realized_vol"].to_numpy(np.float32) # predict NEXT-day RV
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X, y = [], []
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for t in range(WINDOW, len(df) - 1):
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X.append(feats[t - WINDOW:t])
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y.append(target[t + 1])
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X = np.stack(X); y = np.array(y, np.float32)
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n_tr = int(0.7 * len(X)) # time-ordered OOS split
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mu, sd = X[:n_tr].mean((0, 1)), X[:n_tr].std((0, 1)) + 1e-8 # train-only stats
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X = (X - mu) / sd
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return (X[:n_tr], y[:n_tr]), (X[n_tr:], y[n_tr:])
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class Encoder(nn.Module):
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def __init__(self, win, emb):
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super().__init__()
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self.net = nn.Sequential(
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nn.Flatten(),
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nn.Linear(win * 2, 128),
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nn.LayerNorm(128),
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nn.GELU(),
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nn.Linear(128, emb)
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)
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def forward(self, x):
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return self.net(x)
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def main():
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(Xtr, ytr), (Xte, yte) = build()
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Xtr_t = torch.tensor(Xtr, device=dev)
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enc = Encoder(WINDOW, EMBED_DIM).to(dev)
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dec = nn.Sequential(nn.Linear(EMBED_DIM, 128), nn.GELU(), nn.Linear(128, WINDOW * 2)).to(dev)
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opt = torch.optim.Adam(list(enc.parameters()) + list(dec.parameters()), lr=LR)
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for _ in range(EPOCHS): # SSL: masked reconstruction of the window
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mask = (torch.rand_like(Xtr_t) > MASK_FRAC).float()
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rec = dec(enc((Xtr_t * mask)))
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loss = (((rec - Xtr_t.flatten(1)) ** 2) * (1 - mask.flatten(1))).mean()
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opt.zero_grad(); loss.backward(); opt.step()
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enc.eval()
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with torch.no_grad(): # FROZEN embeddings
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Etr = enc(Xtr_t).cpu().numpy()
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Ete = enc(torch.tensor(Xte, device=dev)).cpu().numpy()
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# linear probe (ridge, closed form) on frozen embeddings → val_vol_r2 (OOS R²)
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A = np.hstack([Etr, np.ones((len(Etr), 1))])
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w = np.linalg.solve(A.T @ A + 1e-3 * np.eye(A.shape[1]), A.T @ ytr)
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pred = np.hstack([Ete, np.ones((len(Ete), 1))]) @ w
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ss_res = ((yte - pred) ** 2).sum()
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ss_tot = ((yte - yte.mean()) ** 2).sum()
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val_vol_r2 = float(1 - ss_res / ss_tot)
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json.dump({"val_vol_r2": val_vol_r2, "n_test": len(yte),
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"knobs": {"WINDOW": WINDOW, "EMBED_DIM": EMBED_DIM, "MASK_FRAC": MASK_FRAC, "EPOCHS": EPOCHS}},
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open("metrics.json", "w"), indent=2)
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print("val_vol_r2 = %.4f (n_test=%d, dev=%s)" % (val_vol_r2, len(yte), dev))
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if __name__ == "__main__":
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main()
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