200 lines
7.6 KiB
Python
200 lines
7.6 KiB
Python
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"""Run Bollinger Band mean-reversion backtest on ETH 2023+2024.
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Preloads data once, then sweeps parameters in-memory for speed.
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"""
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import sys, time
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sys.stdout.reconfigure(line_buffering=True)
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sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
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import numpy as np
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import pandas as pd
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from pathlib import Path
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from collections import defaultdict
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from strategy.bb_backtest import BBConfig, run_bb_backtest
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from strategy.data_loader import KlineSource, load_klines
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from datetime import datetime, timezone
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root = Path(__file__).resolve().parents[1]
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src = KlineSource(db_path=root / "models" / "database.db", table_name="bitmart_eth_5m")
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out_dir = root / "strategy" / "results"
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out_dir.mkdir(parents=True, exist_ok=True)
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t0 = time.time()
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# Preload data once
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print("Loading data...")
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df_23 = load_klines(src, datetime(2023,1,1,tzinfo=timezone.utc),
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datetime(2023,12,31,23,59,tzinfo=timezone.utc))
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df_24 = load_klines(src, datetime(2024,1,1,tzinfo=timezone.utc),
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datetime(2024,12,31,23,59,tzinfo=timezone.utc))
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data = {2023: df_23, 2024: df_24}
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print(f"Loaded: 2023={len(df_23)} bars, 2024={len(df_24)} bars ({time.time()-t0:.1f}s)")
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# ================================================================
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# Sweep
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# ================================================================
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print("\n" + "=" * 120)
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print(" Bollinger Band Mean-Reversion — ETH 5min | 1000U capital")
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print(" touch upper BB -> short, touch lower BB -> long (flip)")
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print("=" * 120)
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results = []
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def test(label, cfg):
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"""Run on both years, print summary, store results."""
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row = {"label": label, "cfg": cfg}
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for year in [2023, 2024]:
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r = run_bb_backtest(data[year], cfg)
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d = r.daily_stats
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pnl = d["pnl"].astype(float)
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eq = d["equity"].astype(float)
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dd = float((eq - eq.cummax()).min())
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final = float(eq.iloc[-1])
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nt = len(r.trades)
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wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1) * 100
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nf = r.total_fee - r.total_rebate
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row[f"a{year}"] = float(pnl.mean())
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row[f"d{year}"] = dd
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row[f"r{year}"] = r
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row[f"n{year}"] = nt
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row[f"w{year}"] = wr
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row[f"f{year}"] = nf
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row[f"eq{year}"] = final
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mn = min(row["a2023"], row["a2024"])
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avg = (row["a2023"] + row["a2024"]) / 2
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mark = " <<<" if mn >= 20 else (" **" if mn >= 10 else "")
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print(f" {label:52s} 23:{row['a2023']:+6.1f} 24:{row['a2024']:+6.1f} "
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f"avg:{avg:+5.1f} n23:{row['n2023']:3d} n24:{row['n2024']:3d} "
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f"dd:{min(row['d2023'],row['d2024']):+7.0f}{mark}")
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row["mn"] = mn; row["avg"] = avg
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results.append(row)
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# [1] BB period
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print("\n[1] Period sweep")
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for p in [10, 15, 20, 30, 40]:
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test(f"BB({p},2.0) 80u 100x", BBConfig(bb_period=p, bb_std=2.0, margin_per_trade=80, leverage=100))
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# [2] BB std
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print("\n[2] Std sweep")
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for s in [1.5, 1.8, 2.0, 2.5, 3.0]:
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test(f"BB(20,{s}) 80u 100x", BBConfig(bb_period=20, bb_std=s, margin_per_trade=80, leverage=100))
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# [3] Margin
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print("\n[3] Margin sweep")
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for m in [40, 60, 80, 100, 120]:
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test(f"BB(20,2.0) {m}u 100x", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=m, leverage=100))
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# [4] SL
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print("\n[4] Stop-loss sweep")
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for sl in [0.0, 0.01, 0.02, 0.03, 0.05]:
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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))
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# [5] MDL
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print("\n[5] Max daily loss")
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for mdl in [50, 100, 150, 200]:
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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))
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# [6] Combined fine-tune
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print("\n[6] Fine-tune")
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for p in [15, 20, 30]:
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for s in [1.5, 2.0, 2.5]:
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for m in [80, 100]:
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test(f"BB({p},{s}) {m}u mdl=150",
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BBConfig(bb_period=p, bb_std=s, margin_per_trade=m, leverage=100, max_daily_loss=150))
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# ================================================================
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# Ranking
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# ================================================================
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results.sort(key=lambda x: x["mn"], reverse=True)
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print(f"\n{'='*120}")
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print(f" TOP 10 — ranked by min(daily_avg_2023, daily_avg_2024)")
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print(f"{'='*120}")
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for i, r in enumerate(results[:10]):
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print(f" {i+1:2d}. {r['label']:50s} 23:{r['a2023']:+6.1f} 24:{r['a2024']:+6.1f} "
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f"min:{r['mn']:+6.1f} dd:{min(r['d2023'],r['d2024']):+7.0f} "
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f"wr23:{r['w2023']:.0f}% wr24:{r['w2024']:.0f}%")
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# ================================================================
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# Detailed report for best
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# ================================================================
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best = results[0]
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print(f"\n{'#'*70}")
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print(f" BEST: {best['label']}")
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print(f"{'#'*70}")
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for year in [2023, 2024]:
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r = best[f"r{year}"]
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cfg = best["cfg"]
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d = r.daily_stats
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pnl = d["pnl"].astype(float)
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eq = d["equity"].astype(float)
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dd = (eq - eq.cummax()).min()
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final = float(eq.iloc[-1])
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nt = len(r.trades)
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wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1)
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nf = r.total_fee - r.total_rebate
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loss_streak = max_ls = 0
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for v in pnl.values:
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if v < 0: loss_streak += 1; max_ls = max(max_ls, loss_streak)
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else: loss_streak = 0
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print(f"\n --- {year} ---")
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print(f" Final equity : {final:,.2f} U ({final-cfg.initial_capital:+,.2f}, "
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f"{(final-cfg.initial_capital)/cfg.initial_capital*100:+.1f}%)")
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print(f" Max drawdown : {dd:,.2f} U")
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print(f" Avg daily PnL : {pnl.mean():+,.2f} U")
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print(f" Median daily PnL : {pnl.median():+,.2f} U")
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print(f" Best/worst day : {pnl.max():+,.2f} / {pnl.min():+,.2f}")
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print(f" Profitable days : {(pnl>0).sum()}/{len(pnl)} ({(pnl>0).mean():.1%})")
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print(f" Days >= 20U : {(pnl>=20).sum()}")
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print(f" Max loss streak : {max_ls} days")
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print(f" Trades : {nt} (win rate {wr:.1%})")
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print(f" Net fees : {nf:,.0f} U")
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sharpe = pnl.mean() / max(pnl.std(), 1e-10) * np.sqrt(365)
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print(f" Sharpe (annual) : {sharpe:.2f}")
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# ================================================================
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# Chart
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# ================================================================
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fig, axes = plt.subplots(3, 2, figsize=(18, 12),
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gridspec_kw={"height_ratios": [3, 1.5, 1]})
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for col, year in enumerate([2023, 2024]):
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r = best[f"r{year}"]
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cfg = best["cfg"]
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d = r.daily_stats
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eq = d["equity"].astype(float)
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pnl = d["pnl"].astype(float)
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dd = eq - eq.cummax()
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axes[0, col].plot(eq.index, eq.values, linewidth=1.2, color="#1f77b4")
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axes[0, col].axhline(cfg.initial_capital, color="gray", ls="--", lw=0.5)
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axes[0, col].set_title(f"BB Strategy Equity — {year}\n"
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f"BB({cfg.bb_period},{cfg.bb_std}) {cfg.margin_per_trade}u {cfg.leverage:.0f}x",
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fontsize=11)
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axes[0, col].set_ylabel("Equity (U)")
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axes[0, col].grid(True, alpha=0.3)
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colors = ["#2ca02c" if v >= 0 else "#d62728" for v in pnl.values]
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axes[1, col].bar(pnl.index, pnl.values, color=colors, width=0.8)
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axes[1, col].axhline(20, color="orange", ls="--", lw=1, label="20U target")
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axes[1, col].axhline(0, color="gray", lw=0.5)
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axes[1, col].set_ylabel("Daily PnL (U)")
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axes[1, col].legend(fontsize=8)
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axes[1, col].grid(True, alpha=0.3)
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axes[2, col].fill_between(dd.index, dd.values, 0, color="#d62728", alpha=0.4)
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axes[2, col].set_ylabel("Drawdown (U)")
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axes[2, col].grid(True, alpha=0.3)
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fig.tight_layout()
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fig.savefig(out_dir / "bb_strategy_report.png", dpi=150)
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plt.close(fig)
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print(f"\nChart: {out_dir / 'bb_strategy_report.png'}")
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print(f"Total time: {time.time()-t0:.0f}s")
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