feat: 备份 crypto/ + stocks/ 子目录到 skill 仓库
【备份】cron 已迁到 ~/.hermes/scripts/symlink, 旧副本 ~/.hermes/scripts/crypto/ 和 stocks/ 即将删, 先备份
- crypto-t-monitor/scripts/backtest.py + okx_t_monitor.py
- intraday-trading/scripts/{hk,us}_intraday_cli.py + hk_intraday_cli_runner.sh
- strategy-management/scripts/backtest.py (与 crypto-t-monitor 重复, 备份占位)
【未删本地】等用户确认
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#!/usr/bin/env python3
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"""
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OKX 币圈做T 回测工具 (v2.0.0)
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基于历史 K 线模拟策略, 验证 buy/sell 价位参数
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"""
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import os, json, sys, argparse, datetime
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sys.path.insert(0, os.path.dirname(__file__))
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# 加载凭证
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okx_creds = {}
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with open(os.path.expanduser('~/.bashrc')) as f:
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import re
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for line in f:
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m = re.match(r'export\s+(OKX_\w+)=(.*)', line.strip())
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if m:
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okx_creds[m.group(1)] = m.group(2).strip().strip('"').strip("'")
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def fetch_history_klines(sym, bar='1H', days=30):
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"""拉 OKX 历史 K 线 (OKX 限制单次 100 根, 多页拉)
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用 OKX 的 'after' 参数翻页 (传毫秒时间戳)
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"""
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import subprocess
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import hmac, base64, hashlib
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all_data = []
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# OKX 时间戳 (毫秒)
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cur_ts = int(datetime.datetime.utcnow().timestamp() * 1000)
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# 计算需要多少页 (1H K线, 24 根/天)
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pages = max(1, (days * 24 + 99) // 100)
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for page in range(pages):
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path = f"/api/v5/market/history-candles?instId={sym}-USDT-SWAP&bar={bar}&limit=100&after={cur_ts}"
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msg = datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S.') + \
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f"{datetime.datetime.utcnow().microsecond // 1000:03d}Z" + 'GET' + path
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signature = base64.b64encode(
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hmac.new(okx_creds['OKX_SECRET'].encode(), msg.encode(), hashlib.sha256).digest()
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).decode()
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ts_str = msg[:30] # YYYY-MM-DDTHH:MM:SS.sssZ (但实际上 ms 只有 3 位 + Z)
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# 修正: 用 'Z' 结尾的后 24 字节
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curl_cmd = [
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'curl', '-s', '--proxy', 'http://127.0.0.1:7890',
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'-H', f'OK-ACCESS-KEY: {okx_creds["OKX_API_KEY"]}',
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'-H', f'OK-ACCESS-SIGN: {signature}',
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'-H', f'OK-ACCESS-TIMESTAMP: {ts_str}',
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'-H', f'OK-ACCESS-PASSPHRASE: {okx_creds["OKX_PASSPHRASE"]}',
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f'https://www.okx.com{path}'
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]
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try:
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r = subprocess.run(curl_cmd, capture_output=True, text=True, timeout=20)
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data = json.loads(r.stdout)
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if data.get('code') == '0':
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klines = data.get('data', [])
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if not klines:
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break
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all_data.extend(klines)
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# 翻页: after 是上一个数据最小时间戳 - 1
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cur_ts = int(klines[-1][0]) - 1
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if len(klines) < 100:
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break
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else:
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print(f"⚠️ Page {page} code={data.get('code')} msg={data.get('msg')}")
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break
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except Exception as e:
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print(f"⚠️ Page {page} failed: {e}")
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break
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print(f"📥 拉到 {len(all_data)} 根 K 线")
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return all_data
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def calc_atr(klines, period=14):
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"""ATR 计算"""
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if len(klines) < period + 1:
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return None
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closes = [float(k[4]) for k in klines]
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highs = [float(k[2]) for k in klines]
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lows = [float(k[3]) for k in klines]
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trs = []
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for i in range(1, len(closes)):
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tr = max(highs[i] - lows[i],
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abs(highs[i] - closes[i-1]),
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abs(lows[i] - closes[i-1]))
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trs.append(tr)
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return sum(trs[-period:]) / period
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def simulate_strategy(klines, atr_multiplier=0.5, t_qty=0.05, leverage=25, ct_val=0.1, initial_usdt=1000, threshold=0.003):
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"""基于历史 K 线模拟做T策略
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每小时检查价位:
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- 跌到 buy1/buy2 → 买入
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- 涨到 sell1/sell2 → 卖出
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持仓同步变化 (跟 okx_t_monitor 一致)
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"""
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trades = []
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position = 0
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avg_cost = 0
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last_trade_ts = None
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for i in range(20, len(klines)):
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row = klines[i]
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ts = row[0]
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high = float(row[2])
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low = float(row[3])
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close = float(row[4])
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# 计算过去 14 根 K 线的 ATR
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past = klines[i-20:i]
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atr = calc_atr(past, 14)
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if not atr:
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continue
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buy1 = close - atr * atr_multiplier * 0.5
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buy2 = close - atr * atr_multiplier
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sell1 = close + atr * atr_multiplier * 0.5
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sell2 = close + atr * atr_multiplier
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# 检查是否触及价位 (用 high/low 比对 close)
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if last_trade_ts == ts:
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continue
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# 优先 sell1 > buy1 (趋势方向)
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if position > 0 and (high >= sell2 or (high >= sell1 and position > 0)):
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# 卖出
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sell_price = sell2 if high >= sell2 else sell1
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pnl = (sell_price - avg_cost) * position
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trades.append(('sell', sell_price, position, pnl, ts))
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position = 0
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avg_cost = 0
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last_trade_ts = ts
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elif position == 0 and (low <= buy2 or low <= buy1):
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buy_price = buy2 if low <= buy2 else buy1
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position = t_qty
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avg_cost = buy_price
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trades.append(('buy', buy_price, position, None, ts))
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last_trade_ts = ts
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# 统计
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total_pnl = sum(t[3] for t in trades if t[3] is not None)
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buy_count = sum(1 for t in trades if t[0] == 'buy')
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sell_count = sum(1 for t in trades if t[0] == 'sell')
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win_trades = [t for t in trades if t[3] and t[3] > 0]
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win_rate = len(win_trades) / sell_count * 100 if sell_count > 0 else 0
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return {
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'trades': trades,
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'total_pnl': total_pnl,
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'buy_count': buy_count,
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'sell_count': sell_count,
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'win_rate': win_rate,
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'final_position': position,
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'final_avg_cost': avg_cost,
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}
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def main():
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parser = argparse.ArgumentParser(description='币圈做T回测 (v2.0.0)')
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parser.add_argument('symbol', help='币种 (如 ETH)')
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parser.add_argument('--mode', choices=['short', 'trend'], default='trend',
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help='short=日内(1H,默认) / trend=趋势(4H,默认短期)')
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parser.add_argument('--days', type=int, default=30, help='回测天数 (short=7, trend=30)')
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parser.add_argument('--bar', default=None, help='K 线周期 (覆盖 mode 默认)')
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parser.add_argument('--atr-multiplier', type=float, default=None, help='ATR 倍数')
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parser.add_argument('--t-qty', type=float, default=0.05, help='每笔数量 (默认 0.05)')
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parser.add_argument('--leverage', type=int, default=25, help='杠杆 (默认 25)')
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parser.add_argument('--ct-val', type=float, default=0.1, help='合约面值 (默认 0.1)')
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args = parser.parse_args()
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# Mode-based defaults
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if args.bar is None:
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args.bar = '1H' if args.mode == 'short' else '4H'
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if args.atr_multiplier is None:
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# Trend: 更宽价位 (ATR × 1.5), 避免被洗
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args.atr_multiplier = 0.5 if args.mode == 'short' else 1.5
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if args.days == 30: # 如果用户没指定，按 mode
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args.days = 7 if args.mode == 'short' else 30
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print(f"📊 {args.symbol} {args.bar} 回测 ({args.days} 天, mode={args.mode})")
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print(f" ATR={args.atr_multiplier} t_qty={args.t_qty} lev={args.leverage}x")
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print()
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# 拉数据
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klines = fetch_history_klines(args.symbol, args.bar, args.days)
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if not klines:
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print("❌ 没拉到数据")
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sys.exit(1)
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print(f"✅ 拉到 {len(klines)} 根 K 线")
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print()
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# 模拟
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result = simulate_strategy(klines, args.atr_multiplier, args.t_qty,
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args.leverage, args.ct_val)
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# 报告
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print(f"📈 回测结果:")
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print(f" 买入: {result['buy_count']} 次")
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print(f" 卖出: {result['sell_count']} 次")
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print(f" 胜率: {result['win_rate']:.1f}%")
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print(f" 总盈亏: ${result['total_pnl']:.2f}")
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print(f" 最终仓位: {result['final_position']}张 @ ${result['final_avg_cost']:.2f}" if result['final_position'] > 0 else " 最终仓位: 0 (全平)")
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# Top 5 交易
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closed = [t for t in result['trades'] if t[3] is not None]
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if closed:
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print()
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print(f" Top 5 盈利交易:")
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for t in sorted(closed, key=lambda x: -x[3])[:5]:
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print(f" ${t[1]:.2f} | pnl ${t[3]:.2f} | {t[4]}")
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if __name__ == '__main__':
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main()
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