201 lines
6.8 KiB
Python
201 lines
6.8 KiB
Python
"""
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山寨币策略回测脚本
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对 DB 中所有有完整入场方案（stop_loss/tp1/tp2）的推荐做模拟跟踪。
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"""
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import sys, os, json, sqlite3
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from datetime import datetime, timedelta
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sys.path.insert(0, '/home/ubuntu/quant_monitor/altcoin')
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import ccxt
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import pandas as pd
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import numpy as np
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exchange = ccxt.binance({'enableRateLimit': True})
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DB = '/home/ubuntu/quant_monitor/altcoin/altcoin_monitor.db'
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def fetch_klines_since(symbol, timeframe, since_ms, limit=500):
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"""Fetch K-lines from a specific timestamp."""
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try:
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ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=since_ms, limit=limit)
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if not ohlcv:
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return None
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df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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return df
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except Exception as e:
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print(f" fetch_klines error for {symbol}: {e}")
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return None
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def simulate_trade(rec, klines_df):
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"""Simulate one trade: walk through K-lines, check TP/stop."""
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entry_price = float(rec['entry_price'])
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stop_loss = float(rec['stop_loss'] or 0)
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tp1 = float(rec['tp1'] or 0)
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tp2 = float(rec['tp2'] or 0)
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if stop_loss <= 0 or tp1 <= 0:
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return {'result': 'no_entry_plan', 'exit_price': 0, 'exit_time': '', 'pnl_pct': 0, 'hours': 0}
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result = 'expired'
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exit_price = entry_price
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exit_time = ''
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max_profit_pct = 0
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max_loss_pct = 0
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for _, row in klines_df.iterrows():
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high = float(row['high'])
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low = float(row['low'])
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close = float(row['close'])
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ts = row['timestamp']
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current_pnl = (close / entry_price - 1) * 100
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max_profit_pct = max(max_profit_pct, (high / entry_price - 1) * 100)
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max_loss_pct = min(max_loss_pct, (low / entry_price - 1) * 100)
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# Check TP2 first (higher target)
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if tp2 > 0 and high >= tp2:
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result = 'hit_tp2'
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exit_price = tp2
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exit_time = str(ts)
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break
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# Check TP1
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if tp1 > 0 and high >= tp1:
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result = 'hit_tp1'
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exit_price = tp1
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exit_time = str(ts)
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break
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# Check stop loss
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if stop_loss > 0 and low <= stop_loss:
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result = 'stopped_out'
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exit_price = stop_loss
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exit_time = str(ts)
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break
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pnl_pct = round((exit_price / entry_price - 1) * 100, 2)
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# Calculate holding hours
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if exit_time:
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try:
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et = datetime.fromisoformat(exit_time)
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rt = datetime.fromisoformat(rec['rec_time'])
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hours = round((et - rt).total_seconds() / 3600, 1)
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except:
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hours = 0
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else:
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hours = 0
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return {
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'result': result,
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'exit_price': round(exit_price, 6),
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'exit_time': exit_time,
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'pnl_pct': pnl_pct,
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'max_profit_pct': round(max_profit_pct, 2),
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'max_loss_pct': round(max_loss_pct, 2),
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'hours': hours,
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}
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def main():
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conn = sqlite3.connect(DB)
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conn.row_factory = sqlite3.Row
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# Only backtest "爆发" with full entry plans
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rows = conn.execute("""
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SELECT id, symbol, rec_time, rec_state, rec_score, entry_price,
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stop_loss, tp1, tp2, status, entry_plan_json, signals, sector
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FROM recommendation
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WHERE stop_loss > 0 AND tp1 > 0
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ORDER BY id
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""").fetchall()
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conn.close()
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print(f"回测样本: {len(rows)} 条 (有完整入场方案)\n")
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results = []
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wins = losses = expired_count = 0
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total_pnl = 0
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max_win = -999
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max_loss = 999
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for i, rec in enumerate(rows, 1):
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symbol = rec['symbol']
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rec_time = datetime.fromisoformat(rec['rec_time'])
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since_ms = int(rec_time.timestamp() * 1000)
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print(f"[{i}/{len(rows)}] {symbol} rec_time={rec['rec_time'][:19]} "
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f"entry={rec['entry_price']} stop={rec['stop_loss']} tp1={rec['tp1']} tp2={rec['tp2']}", end='')
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klines = fetch_klines_since(symbol, '15m', since_ms, limit=2000)
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if klines is None or len(klines) < 2:
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print(" → 数据不足，跳过")
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continue
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sim = simulate_trade(rec, klines)
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results.append({**sim, 'symbol': symbol, 'rec_time': rec['rec_time'],
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'entry_price': rec['entry_price'], 'rec_state': rec['rec_state']})
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tag = {'hit_tp1': '🟢', 'hit_tp2': '🟢🟢', 'stopped_out': '🔴', 'expired': '⏰', 'no_entry_plan': '❓'}
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print(f" → {tag.get(sim['result'], '?')} {sim['result']} pnl={sim['pnl_pct']}% "
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f"max_profit={sim['max_profit_pct']}% max_loss={sim['max_loss_pct']}% {sim['hours']}h")
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if sim['result'] in ('hit_tp1', 'hit_tp2'):
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wins += 1
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total_pnl += sim['pnl_pct']
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max_win = max(max_win, sim['pnl_pct'])
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elif sim['result'] == 'stopped_out':
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losses += 1
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total_pnl += sim['pnl_pct']
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max_loss = min(max_loss, sim['pnl_pct'])
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else:
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expired_count += 1
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print(f"\n{'='*60}")
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print(f"回测汇总 (n={len(results)})")
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print(f"{'='*60}")
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print(f"止盈(TP): {wins} 笔")
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print(f"止损: {losses} 笔")
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print(f"未触达: {expired_count} 笔")
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closed = wins + losses
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if closed > 0:
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print(f"胜率: {wins}/{closed} = {round(wins/closed*100,1)}%")
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print(f"平均盈亏: {round(total_pnl/closed, 2)}%")
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print(f"最大盈利: {max_win}%")
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print(f"最大亏损: {max_loss}%")
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print(f"盈亏比: {round(abs(max_win/max_loss) if max_loss != 0 else 99, 1)}")
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print(f"{'='*60}")
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# Detail table
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print(f"\n{'symbol':<14} {'time':<17} {'result':<14} {'pnl':>7} {'max+':>7} {'max-':>7} {'h':>5}")
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print("-" * 70)
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for r in results:
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print(f"{r['symbol']:<14} {r['rec_time'][:16]:<17} {r['result']:<14} "
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f"{r['pnl_pct']:>6.1f}% {r['max_profit_pct']:>6.1f}% {r['max_loss_pct']:>6.1f}% {r['hours']:>5.1f}")
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# Save to JSON for HTML report
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with open('/home/ubuntu/quant_monitor/altcoin/backtest_result.json', 'w') as f:
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json.dump({
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'generated_at': datetime.now().isoformat(),
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'total': len(results),
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'wins': wins,
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'losses': losses,
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'expired': expired_count,
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'win_rate': round(wins/closed*100, 1) if closed > 0 else 0,
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'avg_pnl': round(total_pnl/closed, 2) if closed > 0 else 0,
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'max_win': max_win,
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'max_loss': max_loss,
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'details': [{k: str(v) if isinstance(v, (datetime, pd.Timestamp)) else v
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for k, v in r.items()} for r in results],
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}, f, ensure_ascii=False, indent=2, default=str)
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print(f"\n结果已保存: backtest_result.json")
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if __name__ == '__main__':
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main()
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