#!/usr/bin/env python3
"""
CMA-ES Re-Run at Corrected Fees — Establishes real performance baseline.

Corrected fees: taker 5.0 bps, maker +2.0 bps (BingX perps).
Previous best 15,080 was at WRONG fees (taker 0.5, maker -0.2).

Runs 200 evals across multiple assets with the full venue-tagged, order-type-aware
system. Produces a JSON report at malkhut/results/cma_rerun_<ts>.json.

Usage:
    python -m malkhut.cma_rerun
"""
from __future__ import annotations

import json
import os
import sys
import time

_HERE = os.path.dirname(os.path.abspath(__file__))
if _HERE not in sys.path:
    sys.path.insert(0, _HERE)

from malkhut.state import FulfilmentPolicyParams
from malkhut.training.cma_trainer import (
    ScenarioFactory, CMAESTrainer, CMAParameterCodec,
    PolicySnapshot, PolicyEvaluator, SelfPlayPool,
)
from malkhut.cwm.core import MinimalCryptoLOBCWM


def _baseline() -> FulfilmentPolicyParams:
    return FulfilmentPolicyParams(
        version="baseline_corrected_fees", ucb_c=1.414, max_sims=256, max_depth=3,
        rollout_depth=3, root_temperature=0.5, min_root_entropy=0.25,
        quote_offsets_ticks=(0, 1, 2), quote_size_fractions=(0.1, 0.25, 0.5),
        passive_ttl_ms=200, aggressive_ttl_ms=50, maker_edge_min_bps=0.5,
        cross_spread_edge_min_bps=5.0, adverse_toxicity_cancel_threshold=0.5,
        queue_churn_cancel_threshold=0.5, mae_tail_cut_bps=50.0,
        mfe_giveback_cut_fraction=0.5, max_time_in_loss_s=300.0,
        failed_recovery_cut_count=3, recovery_velocity_min_bps_per_s=0.0,
        max_symbol_notional_fraction=0.20, max_single_order_notional_fraction=0.05,
        reduce_when_global_up_fraction=0.30, session_profit_lock_fraction=0.02,
        w_expected_pnl=1.0, w_fill_probability=0.5, w_adverse_selection=2.0,
        w_queue_priority=0.5, w_inventory_risk=1.5, w_tail_loss=5.0,
        w_fee_quality=0.5, w_time_decay=0.3, w_policy_entropy=0.5,
        robust_tail_weight=2.0, toxic_counterparty_weight=3.0,
        low_liquidity_weight=2.0, latency_stress_weight=1.0,
    )


def main():
    BUDGET_EVALS = 50
    SEED = 42
    SYMBOLS = ("BTCUSDT",)

    print("=" * 70)
    print("CMA-ES RE-RUN AT CORRECTED FEES")
    print(f"  Fees: taker=5.0 bps, maker=+2.0 bps (BingX)")
    print(f"  Budget: {BUDGET_EVALS} evals")
    print(f"  Assets: {', '.join(SYMBOLS)}")
    print(f"  Seed: {SEED}")
    print("=" * 70)
    print()

    t0 = time.time()

    factory = ScenarioFactory(exchange_id="bingx")
    scenarios = factory.build_suite(symbols=list(SYMBOLS), steps_per_scenario=10, seed=SEED)
    print(f"Scenarios: {len(scenarios)} (×{len(SYMBOLS)} assets)")
    print(f"  Tags: {sorted(set(t for s in scenarios for t in s.tags))[:10]}...")
    print()

    def cwm_factory():
        return MinimalCryptoLOBCWM()

    evaluator = PolicyEvaluator(cwm_factory=cwm_factory, scoring_mode="fast")
    codec = CMAParameterCodec()
    pool = SelfPlayPool()
    trainer = CMAESTrainer(codec=codec, evaluator=evaluator, pool=pool, workers=0)

    print("Running CMA-ES optimization...")
    best = trainer.train(
        incumbent=_baseline(),
        scenarios=scenarios,
        budget_evals=BUDGET_EVALS,
        seed=SEED,
    )

    duration = time.time() - t0

    print()
    print("=" * 70)
    print("RESULTS")
    print("=" * 70)
    print(f"Duration:    {duration:.1f}s ({duration/60:.1f} min)")
    print(f"Best score:  {best.score:.2f}")
    print(f"Previous:    15,080 (pre-fee-fix, WRONG fees)")
    print(f"Params:      {best.params.version}")
    if best.evaluation_summary:
        print(f"Mean PnL:    {best.evaluation_summary.get('mean_pnl', 'N/A'):.2f} bps")
        print(f"Max DD:      {best.evaluation_summary.get('max_dd', 'N/A'):.2f} bps")
        print(f"Evals:       {best.evaluation_summary.get('n', 'N/A')}")
    print("=" * 70)

    os.makedirs("malkhut/results", exist_ok=True)
    report = {
        "timestamp_s": int(time.time()),
        "duration_s": round(duration, 1),
        "budget_evals": BUDGET_EVALS,
        "seed": SEED,
        "symbols": list(SYMBOLS),
        "fees": {"taker_bps": 5.0, "maker_bps": 2.0},
        "previous_best": 15080,
        "best_score": best.score,
        "improvement_pct": round((best.score - 15080) / max(abs(15080), 1) * 100, 1),
        "evaluation_summary": best.evaluation_summary,
        "n_scenarios": len(scenarios),
    }
    report_path = f"malkhut/results/cma_rerun_{int(time.time())}.json"
    with open(report_path, "w") as f:
        json.dump(report, f, indent=2)
    print(f"\nReport saved: {report_path}")


if __name__ == "__main__":
    main()