415 lines
17 KiB
Python
415 lines
17 KiB
Python
"""
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量化交易回测系统 - 30分钟K线策略回测（Weex数据源）
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========== 策略规则 ==========
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重要：所有开仓和平仓操作都在下一根K线的开盘价执行
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【策略流程】
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1. 开仓条件（信号出现时，下一根K线开盘价开仓）：
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- 阳包阴（涨包跌）信号 -> 开多
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* 前一根是跌（阴线），后一根是涨（阳线）
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* 且：涨的收盘价 > 跌的开盘价
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- 阴包阳（跌包涨）信号 -> 开空
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* 前一根是涨（阳线），后一根是跌（阴线）
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* 且：跌的收盘价 < 涨的开盘价
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2. 平仓条件（所有平仓都在下一根K线开盘价执行）：
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- 持有多单时：遇到两根连续的阴线 -> 下一根K线开盘价平仓
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- 持有空单时：遇到两根连续的阳线 -> 下一根K线开盘价平仓
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- 遇到反向信号：下一根K线开盘价平仓并反手开仓
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* 例如：持有多单时遇到阴包阳信号 -> 平多开空
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3. 续持条件：
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- 遇到同向信号：续持
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- 未满足平仓条件：续持
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【数据处理流程】
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1. 从数据库读取数据
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2. 数据排序（按时间戳升序）
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3. 开始回测
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"""
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import datetime
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import calendar
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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.mexc import Mexc30
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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（优化版）：
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只看两种信号，严格按照收盘价与开盘价的比较：
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1. 阳包阴（涨包跌，前跌后涨）-> 做多：
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- 前一根是跌（阴线：close < open）
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- 后一根是涨（阳线：close > open）
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- 且：涨的收盘价 > 跌的开盘价（curr['close'] > prev['open']）
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2. 阴包阳（跌包涨，前涨后跌）-> 做空：
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- 前一根是涨（阳线：close > open）
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- 后一根是跌（阴线：close < open）
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- 且：跌的收盘价 < 涨的开盘价（curr['close'] < prev['open']）
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"""
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p_open = float(prev['open'])
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c_close = float(curr['close'])
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# 阳包阴（涨包跌，前跌后涨） -> 做多：涨的收盘价 > 跌的开盘价
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if is_bearish(prev) and is_bullish(curr) and c_close > p_open:
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return "long", "bear_bull_engulf"
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# 阴包阳（跌包涨，前涨后跌） -> 做空：跌的收盘价 < 涨的开盘价
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if is_bullish(prev) and is_bearish(curr) 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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"""
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按天获取指定表的数据（30分钟K线）
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数据格式：时间戳(毫秒级) 开盘价 最高价 最低价 收盘价
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例如：1767461400000 3106.68 3109.1 3106.22 3107.22
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注意：返回的数据已按时间戳（id）升序排序
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"""
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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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# 将日期转换为毫秒级时间戳进行查询
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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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# 查询时按时间戳升序排序
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query = model.select().where(model.id.between(start_ts, end_ts)).order_by(model.id.asc())
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data = [{'id': i.id, 'open': i.open, 'high': i.high, 'low': i.low, 'close': i.close} for i in query]
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# 确保数据已排序
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if data:
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data.sort(key=lambda x: x['id'])
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return data
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# ========================= 回测逻辑 =========================
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def backtest_15m_trend_optimized(dates: List[str]):
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"""
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回测策略逻辑：
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1. 开仓条件（信号出现时，下一根K线开盘价开仓）：
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- 阳包阴（涨包跌）信号 -> 开多
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- 阴包阳（跌包涨）信号 -> 开空
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2. 平仓条件（所有平仓都在下一根K线开盘价执行）：
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- 持有多单时：遇到两根连续的阴线 -> 下一根K线开盘价平仓
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- 持有空单时：遇到两根连续的阳线 -> 下一根K线开盘价平仓
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- 遇到反向信号：下一根K线开盘价平仓并反手开仓
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（例如：持有多单时遇到阴包阳信号 -> 平多开空）
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3. 续持条件：
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- 遇到同向信号：续持
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- 未满足平仓条件：续持
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"""
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# ==================== 步骤1：从数据库读取数据 ====================
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all_data: List[Dict] = []
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total_queried = 0
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for d in dates:
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day_data = get_data_by_date(Mexc30, d)
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all_data.extend(day_data)
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if day_data:
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total_queried += len(day_data)
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logger.info(f"总共查询了 {len(dates)} 天，获取到 {total_queried} 条K线数据")
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if not all_data:
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logger.warning("未获取到任何数据，请检查数据库连接和日期范围")
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return [], {
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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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# ==================== 步骤2：数据去重 + 排序（重要！必须先处理再回测） ====================
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# 以时间戳(id)为唯一键去重，保留最先出现的一条
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unique_by_id: Dict[int, Dict] = {}
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for item in all_data:
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if item['id'] not in unique_by_id:
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unique_by_id[item['id']] = item
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all_data = list(unique_by_id.values())
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all_data.sort(key=lambda x: x['id'])
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# 验证排序结果
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if len(all_data) > 1:
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first_ts = all_data[0]['id']
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last_ts = all_data[-1]['id']
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first_time = datetime.datetime.fromtimestamp(first_ts / 1000)
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last_time = datetime.datetime.fromtimestamp(last_ts / 1000)
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logger.info(f"数据已按时间排序：{first_time.strftime('%Y-%m-%d %H:%M:%S')} 到 {last_time.strftime('%Y-%m-%d %H:%M:%S')}")
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# ==================== 步骤3：开始回测 ====================
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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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trades: List[Dict] = []
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current_position: Optional[Dict] = None # 开仓信息
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consecutive_opposite_count = 0 # 连续反色K线计数
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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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# ==================== 空仓状态：遇到信号则开仓 ====================
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# 策略：阳包阴信号 -> 开多，阴包阳信号 -> 开空
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if current_position is None:
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if direction:
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# 信号出现（prev和curr形成信号），在下一根K线（next_bar）的开盘价开仓
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entry_price = float(next_bar['open'])
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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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}
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consecutive_opposite_count = 0 # 重置连续反色计数
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stats[signal_key]['count'] += 1
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logger.debug(f"开仓: {stats[signal_key]['name']} {'做多' if direction == 'long' else '做空'} @ {entry_price:.2f}")
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idx += 1
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continue
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# ==================== 有仓位状态：检查平仓条件 ====================
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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. 反向信号 -> 下一根K线开盘价平仓并反手开仓
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# 策略：遇到反向信号（如持有多单时遇到阴包阳），平仓并反手开仓
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if direction and direction != pos_dir:
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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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})
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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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# 反手开仓（下一根K线开盘价）
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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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}
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consecutive_opposite_count = 0 # 重置连续反色计数
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stats[signal_key]['count'] += 1
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logger.debug(f"反向信号反手: 平{'做多' if pos_dir == 'long' else '做空'} @ {exit_price:.2f}, 开{'做多' if direction == 'long' else '做空'}")
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idx += 1
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continue
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# 2. 检查连续反色K线平仓条件（下一根K线开盘价平仓）
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# 策略：持有多单时，遇到两根连续的阴线 -> 平仓
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# 持有空单时，遇到两根连续的阳线 -> 平仓
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if pos_dir == 'long' and is_bearish(curr):
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consecutive_opposite_count += 1
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# 如果已经连续两根阴线，下一根K线开盘价平仓
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if consecutive_opposite_count >= 2:
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logger.debug(f"平仓: 做多遇到连续两根阴线")
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exit_price = float(next_bar['open'])
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diff = exit_price - current_position['entry_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',
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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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})
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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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consecutive_opposite_count = 0
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idx += 1
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continue
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else:
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# 只有一根阴线，续持
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idx += 1
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continue
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# 持有空单：检查是否连续两根阳线
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elif pos_dir == 'short' and is_bullish(curr):
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consecutive_opposite_count += 1
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# 如果已经连续两根阳线，下一根K线开盘价平仓
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if consecutive_opposite_count >= 2:
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logger.debug(f"平仓: 做空遇到连续两根阳线")
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exit_price = float(next_bar['open'])
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diff = 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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})
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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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consecutive_opposite_count = 0
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idx += 1
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continue
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else:
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# 只有一根阳线，续持
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idx += 1
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continue
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# 3. 同向K线或同向信号 -> 续持，重置连续反色计数
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if (pos_dir == 'long' and is_bullish(curr)) or (pos_dir == 'short' and is_bearish(curr)):
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consecutive_opposite_count = 0 # 重置连续反色计数
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# 同向信号 -> 续持
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if direction and direction == pos_dir:
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consecutive_opposite_count = 0 # 重置连续反色计数
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idx += 1
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continue
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idx += 1
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# 尾仓：最后一根收盘价平仓
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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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})
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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（优化版盈利计算） =========================
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if __name__ == '__main__':
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dates = []
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# 获取当前日期
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today = datetime.datetime.now()
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target_year = 2025
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for month in range(1, 13):
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# 获取该月的实际天数
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days_in_month = calendar.monthrange(target_year, month)[1]
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for day in range(1, days_in_month + 1):
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# 只添加今天及之前的日期
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date_str = f"{target_year}-{month:02d}-{day:02d}"
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date_obj = datetime.datetime.strptime(date_str, '%Y-%m-%d')
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# 如果日期在今天之后，跳过
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if date_obj > today:
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break
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dates.append(date_str)
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print(dates)
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# dates = [f"2025-09-{i}" for i in range(1, 32)]
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trades, stats = backtest_15m_trend_optimized(dates)
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logger.info("===== 每笔交易详情 =====")
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# === 参数设定 ===
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contract_size = 10000 # 合约规模（1手对应多少基础货币）
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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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# 开仓 + 平仓手续费（按比例计算 + 固定）
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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 net_profit < -400:
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logger.info(
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f"{t['entry_time']} {direction}({t['signal']}) "
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f"入={entry:.2f} 出={exit:.2f} 差价={point_diff:.2f} "
|
||
f"原始盈利={money_profit:.2f} 手续费={fee:.2f} 净利润={net_profit:.2f} {t['exit_time']}"
|
||
)
|
||
|
||
# === 汇总统计 ===
|
||
total_net_profit = total_money_profit - total_fee
|
||
print(f"\n一共交易笔数：{len(trades)}")
|
||
print(f"总点差：{total_points_profit:.2f}")
|
||
print(f"总原始盈利（未扣费）：{total_money_profit:.2f}")
|
||
print(f"总手续费：{total_fee:.2f}")
|
||
print(f"总净利润：{total_net_profit:.2f}\n")
|
||
|
||
print(total_money_profit - total_fee * 0.1)
|
||
|
||
print("===== 信号统计 =====")
|
||
for k, v in stats.items():
|
||
name, count, wins, total_p = v['name'], v['count'], v['wins'], v['total_profit']
|
||
win_rate = (wins / count * 100) if count > 0 else 0.0
|
||
avg_p = (total_p / count) if count > 0 else 0.0
|
||
print(f"{name}: 次数={count} 胜率={win_rate:.2f}% 总价差={total_p:.2f} 平均价差={avg_p:.2f}")
|