"""Run Bollinger Band mean-reversion backtest on ETH 2023+2024.

Preloads data once, then sweeps parameters in-memory for speed.
"""
import sys, time
sys.stdout.reconfigure(line_buffering=True)
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))

import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
from collections import defaultdict

from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import KlineSource, load_klines
from datetime import datetime, timezone

root = Path(__file__).resolve().parents[1]
src = KlineSource(db_path=root / "models" / "database.db", table_name="bitmart_eth_5m")
out_dir = root / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)

t0 = time.time()

# Preload data once
print("Loading data...")
df_23 = load_klines(src, datetime(2023,1,1,tzinfo=timezone.utc),
                    datetime(2023,12,31,23,59,tzinfo=timezone.utc))
df_24 = load_klines(src, datetime(2024,1,1,tzinfo=timezone.utc),
                    datetime(2024,12,31,23,59,tzinfo=timezone.utc))
data = {2023: df_23, 2024: df_24}
print(f"Loaded: 2023={len(df_23)} bars, 2024={len(df_24)} bars ({time.time()-t0:.1f}s)")

# ================================================================
# Sweep
# ================================================================
print("\n" + "=" * 120)
print("  Bollinger Band Mean-Reversion — ETH 5min | 1000U capital")
print("  touch upper BB -> short, touch lower BB -> long (flip)")
print("=" * 120)

results = []

def test(label, cfg):
    """Run on both years, print summary, store results."""
    row = {"label": label, "cfg": cfg}
    for year in [2023, 2024]:
        r = run_bb_backtest(data[year], cfg)
        d = r.daily_stats
        pnl = d["pnl"].astype(float)
        eq = d["equity"].astype(float)
        dd = float((eq - eq.cummax()).min())
        final = float(eq.iloc[-1])
        nt = len(r.trades)
        wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1) * 100
        nf = r.total_fee - r.total_rebate
        row[f"a{year}"] = float(pnl.mean())
        row[f"d{year}"] = dd
        row[f"r{year}"] = r
        row[f"n{year}"] = nt
        row[f"w{year}"] = wr
        row[f"f{year}"] = nf
        row[f"eq{year}"] = final
    mn = min(row["a2023"], row["a2024"])
    avg = (row["a2023"] + row["a2024"]) / 2
    mark = " <<<" if mn >= 20 else (" **" if mn >= 10 else "")
    print(f"  {label:52s} 23:{row['a2023']:+6.1f} 24:{row['a2024']:+6.1f} "
          f"avg:{avg:+5.1f} n23:{row['n2023']:3d} n24:{row['n2024']:3d} "
          f"dd:{min(row['d2023'],row['d2024']):+7.0f}{mark}")
    row["mn"] = mn; row["avg"] = avg
    results.append(row)

# [1] BB period
print("\n[1] Period sweep")
for p in [10, 15, 20, 30, 40]:
    test(f"BB({p},2.0) 80u 100x", BBConfig(bb_period=p, bb_std=2.0, margin_per_trade=80, leverage=100))

# [2] BB std
print("\n[2] Std sweep")
for s in [1.5, 1.8, 2.0, 2.5, 3.0]:
    test(f"BB(20,{s}) 80u 100x", BBConfig(bb_period=20, bb_std=s, margin_per_trade=80, leverage=100))

# [3] Margin
print("\n[3] Margin sweep")
for m in [40, 60, 80, 100, 120]:
    test(f"BB(20,2.0) {m}u 100x", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=m, leverage=100))

# [4] SL
print("\n[4] Stop-loss sweep")
for sl in [0.0, 0.01, 0.02, 0.03, 0.05]:
    test(f"BB(20,2.0) 80u SL={sl:.0%}", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=80, leverage=100, stop_loss_pct=sl))

# [5] MDL
print("\n[5] Max daily loss")
for mdl in [50, 100, 150, 200]:
    test(f"BB(20,2.0) 80u mdl={mdl}", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=80, leverage=100, max_daily_loss=mdl))

# [6] Combined fine-tune
print("\n[6] Fine-tune")
for p in [15, 20, 30]:
    for s in [1.5, 2.0, 2.5]:
        for m in [80, 100]:
            test(f"BB({p},{s}) {m}u mdl=150",
                 BBConfig(bb_period=p, bb_std=s, margin_per_trade=m, leverage=100, max_daily_loss=150))

# ================================================================
# Ranking
# ================================================================
results.sort(key=lambda x: x["mn"], reverse=True)
print(f"\n{'='*120}")
print(f"  TOP 10 — ranked by min(daily_avg_2023, daily_avg_2024)")
print(f"{'='*120}")
for i, r in enumerate(results[:10]):
    print(f"  {i+1:2d}. {r['label']:50s} 23:{r['a2023']:+6.1f} 24:{r['a2024']:+6.1f} "
          f"min:{r['mn']:+6.1f} dd:{min(r['d2023'],r['d2024']):+7.0f} "
          f"wr23:{r['w2023']:.0f}% wr24:{r['w2024']:.0f}%")

# ================================================================
# Detailed report for best
# ================================================================
best = results[0]
print(f"\n{'#'*70}")
print(f"  BEST: {best['label']}")
print(f"{'#'*70}")

for year in [2023, 2024]:
    r = best[f"r{year}"]
    cfg = best["cfg"]
    d = r.daily_stats
    pnl = d["pnl"].astype(float)
    eq = d["equity"].astype(float)
    dd = (eq - eq.cummax()).min()
    final = float(eq.iloc[-1])
    nt = len(r.trades)
    wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1)
    nf = r.total_fee - r.total_rebate

    loss_streak = max_ls = 0
    for v in pnl.values:
        if v < 0: loss_streak += 1; max_ls = max(max_ls, loss_streak)
        else: loss_streak = 0

    print(f"\n  --- {year} ---")
    print(f"  Final equity      : {final:,.2f} U ({final-cfg.initial_capital:+,.2f}, "
          f"{(final-cfg.initial_capital)/cfg.initial_capital*100:+.1f}%)")
    print(f"  Max drawdown      : {dd:,.2f} U")
    print(f"  Avg daily PnL     : {pnl.mean():+,.2f} U")
    print(f"  Median daily PnL  : {pnl.median():+,.2f} U")
    print(f"  Best/worst day    : {pnl.max():+,.2f} / {pnl.min():+,.2f}")
    print(f"  Profitable days   : {(pnl>0).sum()}/{len(pnl)} ({(pnl>0).mean():.1%})")
    print(f"  Days >= 20U       : {(pnl>=20).sum()}")
    print(f"  Max loss streak   : {max_ls} days")
    print(f"  Trades            : {nt} (win rate {wr:.1%})")
    print(f"  Net fees          : {nf:,.0f} U")
    sharpe = pnl.mean() / max(pnl.std(), 1e-10) * np.sqrt(365)
    print(f"  Sharpe (annual)   : {sharpe:.2f}")

# ================================================================
# Chart
# ================================================================
fig, axes = plt.subplots(3, 2, figsize=(18, 12),
                         gridspec_kw={"height_ratios": [3, 1.5, 1]})

for col, year in enumerate([2023, 2024]):
    r = best[f"r{year}"]
    cfg = best["cfg"]
    d = r.daily_stats
    eq = d["equity"].astype(float)
    pnl = d["pnl"].astype(float)
    dd = eq - eq.cummax()

    axes[0, col].plot(eq.index, eq.values, linewidth=1.2, color="#1f77b4")
    axes[0, col].axhline(cfg.initial_capital, color="gray", ls="--", lw=0.5)
    axes[0, col].set_title(f"BB Strategy Equity — {year}\n"
                           f"BB({cfg.bb_period},{cfg.bb_std}) {cfg.margin_per_trade}u {cfg.leverage:.0f}x",
                           fontsize=11)
    axes[0, col].set_ylabel("Equity (U)")
    axes[0, col].grid(True, alpha=0.3)

    colors = ["#2ca02c" if v >= 0 else "#d62728" for v in pnl.values]
    axes[1, col].bar(pnl.index, pnl.values, color=colors, width=0.8)
    axes[1, col].axhline(20, color="orange", ls="--", lw=1, label="20U target")
    axes[1, col].axhline(0, color="gray", lw=0.5)
    axes[1, col].set_ylabel("Daily PnL (U)")
    axes[1, col].legend(fontsize=8)
    axes[1, col].grid(True, alpha=0.3)

    axes[2, col].fill_between(dd.index, dd.values, 0, color="#d62728", alpha=0.4)
    axes[2, col].set_ylabel("Drawdown (U)")
    axes[2, col].grid(True, alpha=0.3)

fig.tight_layout()
fig.savefig(out_dir / "bb_strategy_report.png", dpi=150)
plt.close(fig)
print(f"\nChart: {out_dir / 'bb_strategy_report.png'}")
print(f"Total time: {time.time()-t0:.0f}s")