344 lines
15 KiB
Python
344 lines
15 KiB
Python
"""
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量化交易回测系统 - 仅15分钟K线 & 信号续持/反手/单根反色平仓逻辑（包含逐仓爆仓逻辑）
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"""
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import datetime
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from dataclasses import dataclass
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from typing import List, Dict, Optional
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from loguru import logger
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from models.weex import Weex30 # 替换为你的15分钟K线模型
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# ========================= 工具函数 =========================
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def is_bullish(c): # 阳线
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return float(c['close']) > float(c['open'])
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def is_bearish(c): # 阴线
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return float(c['close']) < float(c['open'])
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def check_signal(prev, curr):
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"""
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包住形态信号判定（仅15分钟K线）：
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- 前跌后涨包住 -> 做多
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- 前涨后跌包住 -> 做空
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"""
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p_open, p_close = float(prev['open']), float(prev['close'])
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c_open, c_close = float(curr['open']), float(curr['close'])
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# 前跌后涨包住 -> 做多
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if is_bullish(curr) and is_bearish(prev) and c_open <= p_close and c_close >= p_open:
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return "long", "bear_bull_engulf"
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# 前涨后跌包住 -> 做空
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if is_bearish(curr) and is_bullish(prev) and c_open >= p_close and c_close <= p_open:
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return "short", "bull_bear_engulf"
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return None, None
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def get_data_by_date(model, date_str: str):
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"""按天获取指定表的数据（15分钟）"""
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try:
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target_date = datetime.datetime.strptime(date_str, '%Y-%m-%d')
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except ValueError:
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logger.error("日期格式不正确，请使用 YYYY-MM-DD 格式。")
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return []
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start_ts = int(target_date.timestamp() * 1000)
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end_ts = int((target_date + datetime.timedelta(days=1)).timestamp() * 1000) - 1
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query = model.select().where(model.id.between(start_ts, end_ts)).order_by(model.id.asc())
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return [{'id': i.id, 'open': i.open, 'high': i.high, 'low': i.low, 'close': i.close} for i in query]
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# ========================= 回测逻辑（加入逐仓爆仓） =========================
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def backtest_15m_trend_optimized(dates: List[str], initial_margin=100.0, leverage=100.0):
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"""
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initial_margin: 每笔开仓的保证金（USD），例如 100
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leverage: 杠杆倍数，例如 100 -> notional = initial_margin * leverage
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说明：
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- 逐仓：每笔保证金单独计，若该仓爆仓只损失该笔保证金（不影响其它仓位/账户余额）
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- 爆仓判断：当价格触及爆仓价（entry_price +/- entry_price/leverage）视为爆仓
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- 爆仓后：设置 waiting_for_next_signal 标记，消费下一个信号但不在该信号开仓（满足“等下一个信号来了再开仓”）
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"""
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notional_per_trade = initial_margin * leverage # 合约名义价值
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# 统计结构（保留你原来的字段语义：total_profit 仍记录价差（price units））
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stats = {
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'bear_bull_engulf': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '涨包跌'},
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'bull_bear_engulf': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '跌包涨'},
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}
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all_data: List[Dict] = []
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for d in dates:
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all_data.extend(get_data_by_date(Weex30, d))
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if not all_data:
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return [], stats
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all_data.sort(key=lambda x: x['id'])
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trades: List[Dict] = []
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current_position: Optional[Dict] = None # 开仓信息
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waiting_for_next_signal = False # 爆仓后等待“下一个信号”才可重新开仓
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idx = 1
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while idx < len(all_data) - 1:
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prev, curr, next_bar = all_data[idx - 1], all_data[idx], all_data[idx + 1]
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direction, signal_key = check_signal(prev, curr)
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# ========== 空仓逻辑 ==========
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if current_position is None and direction:
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# 如果之前发生过爆仓，按照需求：爆仓后“等下一个信号来了再开仓”。
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# 实现策略：爆仓后第一个出现的 signal 会被消费（不打开仓），真正开仓需要后续的信号。
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if waiting_for_next_signal:
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# consume this signal but do not open; reset flag and continue
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waiting_for_next_signal = False
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idx += 1
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continue
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# 正常开仓：使用 next_bar 的开盘价作为入场价（保持和原逻辑一致）
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entry_price = float(next_bar['open'])
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# 计算该仓的爆仓价（逐仓，简化模型）
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# delta_price = initial_margin * entry_price / notional_per_trade = entry_price / leverage
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delta_price = entry_price / leverage
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if direction == 'long':
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liq_price = entry_price - delta_price
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else: # short
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liq_price = entry_price + delta_price
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current_position = {
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'direction': direction,
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'signal': stats[signal_key]['name'],
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'signal_key': signal_key,
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'entry_price': entry_price,
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'entry_time': next_bar['id'],
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'liq_price': liq_price,
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'initial_margin': initial_margin,
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'leverage': leverage,
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'notional': notional_per_trade
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}
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stats[signal_key]['count'] += 1
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idx += 1
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continue
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# ========== 有仓位时的处理 ==========
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if current_position:
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pos_dir = current_position['direction']
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pos_sig_key = current_position['signal_key']
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# 1) 检查在当前（curr）K是否触及爆仓价（使用当根的 high/low 判断）
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liq_price = current_position['liq_price']
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was_liquidated = False
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liquidated_at_price = None
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# 对多仓：若当根 low <= liq_price -> 爆仓
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if pos_dir == 'long' and float(curr['low']) <= liq_price:
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was_liquidated = True
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liquidated_at_price = liq_price
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# 对空仓：若当根 high >= liq_price -> 爆仓
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if pos_dir == 'short' and float(curr['high']) >= liq_price:
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was_liquidated = True
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liquidated_at_price = liq_price
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if was_liquidated:
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# 记录一笔爆仓交易：损失等于 initial_margin 对应的价差（price units）
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# 计算价差（price units），注意方向：对多仓是 exit - entry（通常为负）
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entry_price = current_position['entry_price']
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exit_price = liquidated_at_price
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if pos_dir == 'long':
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diff = exit_price - entry_price
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else:
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diff = entry_price - exit_price
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trades.append({
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'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
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'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
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'signal': current_position['signal'],
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'direction': '做多' if pos_dir == 'long' else '做空',
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'entry': entry_price,
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'exit': exit_price,
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'diff': diff,
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'liquidated': True
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})
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stats[pos_sig_key]['total_profit'] += diff
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if diff > 0:
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stats[pos_sig_key]['wins'] += 1
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# 爆仓后按需求：不立即再开仓，等下一个信号才可开
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current_position = None
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waiting_for_next_signal = True
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idx += 1
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continue
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# 2) 反向信号 -> 下一根开盘平仓 + 同价反手（保持原逻辑）
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if direction and direction != pos_dir:
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# 用 next_bar 的开盘价作为出场价（与你原逻辑保持一致）
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exit_price = float(next_bar['open'])
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diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
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current_position['entry_price'] - exit_price)
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trades.append({
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'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
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'exit_time': datetime.datetime.fromtimestamp(next_bar['id'] / 1000),
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'signal': current_position['signal'],
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'direction': '做多' if pos_dir == 'long' else '做空',
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'entry': current_position['entry_price'],
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'exit': exit_price,
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'diff': diff,
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'liquidated': False
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})
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stats[pos_sig_key]['total_profit'] += diff
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if diff > 0: stats[pos_sig_key]['wins'] += 1
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# 同价反手开仓（保持原逻辑）
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current_position = {
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'direction': direction,
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'signal': stats[signal_key]['name'],
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'signal_key': signal_key,
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'entry_price': exit_price,
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'entry_time': next_bar['id'],
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# recompute liq_price for new position
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'liq_price': (exit_price - exit_price / leverage) if direction == 'long' else (
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exit_price + exit_price / leverage),
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'initial_margin': initial_margin,
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'leverage': leverage,
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'notional': notional_per_trade
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}
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stats[signal_key]['count'] += 1
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idx += 1
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continue
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# 3) 同向信号 -> 续持（不做任何改动）
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if direction and direction == pos_dir:
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idx += 1
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continue
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# 4) 单根反色K线 -> 判断后续是否能组成信号（保留原逻辑）
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curr_is_opposite = (pos_dir == 'long' and is_bearish(curr)) or (pos_dir == 'short' and is_bullish(curr))
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if curr_is_opposite:
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can_peek = idx + 1 < len(all_data)
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if can_peek:
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lookahead_dir, _ = check_signal(curr, all_data[idx + 1])
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if lookahead_dir is not None:
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idx += 1
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continue # 后续可组成信号，等待信号处理
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# 否则按收盘价平仓（与原逻辑一致）
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exit_price = float(next_bar['close'])
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diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
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current_position['entry_price'] - exit_price)
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trades.append({
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'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
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'exit_time': datetime.datetime.fromtimestamp(all_data[idx + 1]['id'] / 1000),
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'signal': current_position['signal'],
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'direction': '做多' if pos_dir == 'long' else '做空',
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'entry': current_position['entry_price'],
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'exit': exit_price,
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'diff': diff,
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'liquidated': False
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})
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stats[pos_sig_key]['total_profit'] += diff
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if diff > 0: stats[pos_sig_key]['wins'] += 1
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current_position = None
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idx += 1
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# ========== 尾仓：最后一根收盘价平仓（如仍有仓位） ==========
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if current_position:
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last = all_data[-1]
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exit_price = float(last['close'])
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pos_dir = current_position['direction']
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diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
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current_position['entry_price'] - exit_price)
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trades.append({
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'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
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'exit_time': datetime.datetime.fromtimestamp(last['id'] / 1000),
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'signal': current_position['signal'],
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'direction': '做多' if pos_dir == 'long' else '做空',
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'entry': current_position['entry_price'],
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'exit': exit_price,
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'diff': diff,
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'liquidated': False
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})
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stats[current_position['signal_key']]['total_profit'] += diff
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if diff > 0: stats[current_position['signal_key']]['wins'] += 1
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return trades, stats
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# ========================= 运行示例（包含爆仓） =========================
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if __name__ == '__main__':
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dates = []
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for m in range(1, 11):
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for d in range(1, 31):
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dates.append(f"2025-{f'0{m}' if len(str(m)) < 2 else m}-{d}")
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# 参数：初始保证金 100 USD，100 倍
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trades, stats = backtest_15m_trend_optimized(dates, initial_margin=100.0, leverage=100.0)
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logger.info("===== 每笔交易详情 =====")
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# === 手续费/合约规模设定（沿用你原有实现） ===
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contract_size = 10000 # 合约规模（1手对应多少基础货币），你原来这里等于 notional_per_trade（100*100）
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open_fee_fixed = 5 # 固定开仓手续费
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close_fee_rate = 0.0005 # 按成交额比例的平仓手续费率
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total_points_profit = 0 # 累计点差
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total_money_profit = 0 # 累计金额盈利
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total_fee = 0 # 累计手续费
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for t in trades:
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entry = t['entry']
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exit = t['exit']
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direction = t['direction']
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# === 1️⃣ 原始价差（点差） ===
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point_diff = (exit - entry) if direction == '做多' else (entry - exit)
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# === 2️⃣ 金额盈利（考虑合约规模） ===
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money_profit = point_diff / entry * contract_size # 利润以基础货币计（例如USD）
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# === 3️⃣ 手续费计算 ===
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fee = open_fee_fixed + (contract_size / entry * exit * close_fee_rate)
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# === 4️⃣ 净利润 ===
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net_profit = money_profit - fee
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# 保存计算结果
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t.update({
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'point_diff': point_diff,
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'raw_profit': money_profit,
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'fee': fee,
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'net_profit': net_profit
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})
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total_points_profit += point_diff
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total_money_profit += money_profit
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total_fee += fee
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if point_diff < -50:
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logger.info(
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f"{t['entry_time']} {direction}({t['signal']}) "
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f"入={entry:.6f} 出={exit:.6f} 差价={point_diff:.6f} "
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f"原始盈利={money_profit:.2f} 手续费={fee:.2f} 净利润={net_profit:.2f} "
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f"{'(LIQ)' if t.get('liquidated') else ''} {t['exit_time']}"
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)
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# === 汇总统计 ===
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total_net_profit = total_money_profit - total_fee
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print(f"\n一共交易笔数：{len(trades)}")
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print(f"总点差：{total_points_profit:.6f}")
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print(f"总原始盈利（未扣费）：{total_money_profit:.2f}")
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print(f"总手续费：{total_fee:.2f}")
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print(f"总净利润：{total_net_profit:.2f}\n")
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print("===== 信号统计 =====")
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for k, v in stats.items():
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name, count, wins, total_p = v['name'], v['count'], v['wins'], v['total_profit']
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win_rate = (wins / count * 100) if count > 0 else 0.0
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avg_p = (total_p / count) if count > 0 else 0.0
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print(f"{name}: 次数={count} 胜率={win_rate:.2f}% 总价差={total_p:.6f} 平均价差={avg_p:.6f}")
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