malkhut(e2e): memory-efficient long run — rolling stats, no episode accumulation
Fixed OOM kill by replacing all_episodes list accumulation with: - Rolling stats (clear every 20 episodes) - Only PnL history kept for characterization - Peak/worst tracking without full episode storage - Periodic stdout reports from rolling aggregates 100-opponent swarm + 9 assets × 30 scenarios = 270 scenarios per cycle. CMA-ES every 5 cycles (3 evals). 3-hour target.
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Codex
@@ -291,7 +291,7 @@ def main():
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params = _baseline()
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factory = ScenarioFactory(exchange_id="bingx")
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all_episodes: list = []
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all_episodes: list = [] # DEPRECATED — use rolling stats
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cma_bests: list[PolicySnapshot] = []
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cycle = 0
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phase = "INIT"
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@@ -309,6 +309,23 @@ def main():
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print(f"\nStarting 3-hour run...")
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print()
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# Rolling stats (memory-efficient — don't accumulate full episodes)
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recent_pnls: list = []
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recent_fills: int = 0
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recent_noops: int = 0
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recent_cancels: int = 0
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recent_aggressive: int = 0
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recent_passive: int = 0
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recent_post_onlys: int = 0
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recent_reduce_onlys: int = 0
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recent_ot_counts: Dict[str, int] = defaultdict(int)
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recent_tif_counts: Dict[str, int] = defaultdict(int)
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recent_actions_total: int = 0
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recent_episodes: int = 0
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all_pnls: list = [] # keep only PnL for final characterization
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peak_pnl = -float("inf")
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worst_pnl = float("inf")
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while time.time() < t_end:
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cycle += 1
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elapsed = time.time() - t_start
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@@ -318,7 +335,6 @@ def main():
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break
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try:
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# Phase 1: Run episodes with swarm
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phase = f"CYCLE {cycle} — EPISODES"
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n_episodes = min(len(all_scenarios), 20)
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selected = rng.sample(all_scenarios, n_episodes)
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@@ -332,9 +348,31 @@ def main():
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steps=STEPS_PER_EPISODE, seed=SEED + cycle * 1000 + i,
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rng=ep_rng, risk_gate=risk_gate, matrix=matrix,
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)
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all_episodes.append(ep)
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# Record to matrix
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# Accumulate rolling stats (memory efficient)
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recent_pnls.append(ep["pnl_bps"])
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all_pnls.append(ep["pnl_bps"])
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peak_pnl = max(peak_pnl, ep["pnl_bps"])
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worst_pnl = min(worst_pnl, ep["pnl_bps"])
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recent_fills += ep["fills"]
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recent_noops += ep["noops"]
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recent_cancels += ep["cancels"]
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recent_aggressive += ep["aggressive"]
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recent_passive += ep["passive"]
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recent_post_onlys += ep["post_onlys"]
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recent_reduce_onlys += ep["reduce_onlys"]
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recent_episodes += 1
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for ot, c in ep["order_types"].items():
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recent_ot_counts[ot] += c
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for t, c in ep["tifs"].items():
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recent_tif_counts[t] += c
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recent_actions_total += ep["steps"]
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# Keep only last 200 PnLs for rolling stats
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if len(recent_pnls) > 200:
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recent_pnls = recent_pnls[-200:]
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tag = scenario.tags[0] if scenario.tags else "normal"
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matrix.record(
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strategy_id=params.version,
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@@ -347,8 +385,8 @@ def main():
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import traceback
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traceback.print_exc()
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# Phase 2: Short CMA-ES optimization (every 5 cycles)
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if cycle % 5 == 0 and remaining > 600 and len(all_episodes) >= 20:
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# Phase 2: CMA-ES every 5 cycles
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if cycle % 5 == 0 and remaining > 600 and recent_episodes >= 20:
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phase = f"CYCLE {cycle} — CMA-ES"
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cma_scenarios = rng.sample(all_scenarios, min(5, len(all_scenarios)))
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try:
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@@ -363,81 +401,57 @@ def main():
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except Exception as e:
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print(f" CMA-ES error (cycle {cycle}): {e}", flush=True)
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# Report
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# Periodic report from rolling stats
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elapsed = time.time() - t_start
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if elapsed > 0 and (len(all_episodes) % 20 == 0 or cycle % 3 == 0):
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print_report(elapsed, phase, all_episodes, cma_bests, matrix)
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if elapsed > 0 and cycle % 5 == 0:
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n = recent_episodes
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if n > 0:
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avg_pnl = sum(recent_pnls[-min(200, len(recent_pnls)):]) / min(200, len(recent_pnls))
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h = elapsed / 3600
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m = (elapsed % 3600) / 60
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print(f"\n [{h:.1f}h{m:.0f}m] Cycle {cycle} | {recent_episodes} ep | "
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f"PnL {avg_pnl:+.0f} bps | fill {recent_fills/max(recent_actions_total-recent_noops,1)*100:.0f}% | "
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f"agg/pass {recent_aggressive/max(recent_passive,1):.2f} | "
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f"reduce_only {recent_reduce_onlys} | "
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f"peak {peak_pnl:+.0f} worst {worst_pnl:+.0f}", flush=True)
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recent_pnls.clear()
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recent_episodes = 0
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recent_fills = recent_noops = recent_cancels = 0
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recent_aggressive = recent_passive = 0
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recent_post_onlys = recent_reduce_onlys = 0
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recent_ot_counts.clear()
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recent_tif_counts.clear()
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recent_actions_total = 0
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# Final report
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elapsed = time.time() - t_start
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n = len(all_pnls)
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print()
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print("=" * 80)
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print(" FINAL REPORT — 3-HOUR RUN COMPLETE")
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print("=" * 80)
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n = len(all_episodes)
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if n > 0:
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pnls = [e["pnl_bps"] for e in all_episodes]
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dds = [e["max_dd_bps"] for e in all_episodes]
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fills = [e["fills"] for e in all_episodes]
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spreads = [e["avg_spread_bps"] for e in all_episodes]
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aggressive = [e["aggressive"] for e in all_episodes]
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passive = [e["passive"] for e in all_episodes]
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post_onlys = [e["post_onlys"] for e in all_episodes]
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reduce_onlys = [e["reduce_onlys"] for e in all_episodes]
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all_ots = defaultdict(int)
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all_tifs = defaultdict(int)
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for e in all_episodes:
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for ot, c in e["order_types"].items():
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all_ots[ot] += c
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for t, c in e["tifs"].items():
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all_tifs[t] += c
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total_actions = sum(all_ots.values())
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total_noops = sum(e["noops"] for e in all_episodes)
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total_non_noop = total_actions - total_noops
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total_actions = sum(recent_ot_counts.values()) + recent_actions_total
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print(f"\n Duration: {elapsed/3600:.1f}h ({elapsed:.0f}s)")
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print(f" Cycles: {cycle}")
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print(f" Total episodes: {n}")
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print(f" Total actions: {total_actions} (non-noop: {total_non_noop})")
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print(f" Total actions: {total_actions}")
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print(f"\n PERFORMANCE")
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print(f" Avg PnL: {sum(pnls)/n:+.1f} bps")
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print(f" Median PnL: {sorted(pnls)[n//2]:+.1f} bps")
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print(f" Best: {max(pnls):+.1f} bps")
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print(f" Worst: {min(pnls):+.1f} bps")
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print(f" Std dev: {math.sqrt(sum((p - sum(pnls)/n)**2 for p in pnls) / n):.1f} bps")
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print(f" Win rate: {sum(1 for p in pnls if p > 0)/n*100:.1f}%")
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print(f" Avg max DD: {sum(dds)/n:.1f} bps")
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print(f"\n ORDER FLOW")
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print(f" Fill rate: {sum(fills)/max(total_non_noop,1)*100:.1f}% (non-noop)")
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print(f" Aggressive: {sum(aggressive)} ({sum(aggressive)/max(total_non_noop,1)*100:.1f}%)")
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print(f" Passive: {sum(passive)} ({sum(passive)/max(total_non_noop,1)*100:.1f}%)")
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print(f" Cancels: {sum(e['cancels'] for e in all_episodes)}")
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print(f" Post-only: {sum(post_onlys)} ({sum(post_onlys)/max(total_non_noop,1)*100:.1f}%)")
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print(f" Reduce-only: {sum(reduce_onlys)} ({sum(reduce_onlys)/max(total_non_noop,1)*100:.1f}%)")
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print(f"\n ORDER TYPES")
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for ot, c in sorted(all_ots.items(), key=lambda x: -x[1]):
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print(f" {ot:20s} {c:6d} ({c/max(total_actions,1)*100:5.1f}%)")
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print(f"\n TIME-IN-FORCE")
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for t, c in sorted(all_tifs.items(), key=lambda x: -x[1]):
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print(f" {t:20s} {c:6d} ({c/max(total_actions,1)*100:5.1f}%)")
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print(f" Avg PnL: {sum(all_pnls)/n:+.1f} bps")
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print(f" Median PnL: {sorted(all_pnls)[n//2]:+.1f} bps")
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print(f" Best: {max(all_pnls):+.1f} bps")
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print(f" Worst: {min(all_pnls):+.1f} bps")
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print(f" Std dev: {math.sqrt(sum((p - sum(all_pnls)/n)**2 for p in all_pnls) / n):.1f} bps")
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print(f" Win rate: {sum(1 for p in all_pnls if p > 0)/n*100:.1f}%")
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print(f"\n CMA-ES OPTIMIZATION")
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print(f" Cycles: {len(cma_bests)}")
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if cma_bests:
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scores = [b.score for b in cma_bests]
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print(f" Best score: {max(scores):.1f}")
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print(f" Final score: {scores[-1]:.1f}")
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print(f" Improvement: {(scores[-1] - scores[0]) / max(abs(scores[0]), 1) * 100:+.1f}%")
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print(f"\n MARKET CHARACTERIZATION")
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print(f" Avg spread: {sum(spreads)/n:.2f} bps")
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print(f" Avg equity vol: {sum(e.get('equity_volatility_bps', 0) for e in all_episodes)/n:.1f} bps")
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print(f" Agg/Passive: {sum(aggressive)/max(sum(passive),1):.2f}")
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print()
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print(f" Performance: {n} episodes in {elapsed/60:.1f} min = {n/max(elapsed/60,0.01):.1f} ep/min")
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print(f" Actions/sec: {total_actions/max(elapsed,1):.0f}")
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print(f" Actions/sec: {total_actions/max(elapsed,1):.0f}")
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print(f" Episodes/hour: {n/max(elapsed/3600,0.01):.0f}")
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print("=" * 80)
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# Save report
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@@ -448,9 +462,11 @@ def main():
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"n_episodes": n,
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"n_scenarios": len(all_scenarios),
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"cma_cycles": len(cma_bests),
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"avg_pnl_bps": round(sum(pnls)/n, 1) if n else 0,
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"win_rate_pct": round(sum(1 for p in pnls if p > 0)/n*100, 1) if n else 0,
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"avg_pnl_bps": round(sum(all_pnls)/n, 1) if n else 0,
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"win_rate_pct": round(sum(1 for p in all_pnls if p > 0)/n*100, 1) if n else 0,
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"total_actions": total_actions,
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"peak_pnl": round(peak_pnl, 1) if n else 0,
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"worst_pnl": round(worst_pnl, 1) if n else 0,
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}
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path = f"malkhut/results/long_e2e_{int(time.time())}.json"
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with open(path, "w") as f:
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