dededdew

weex/读取数据库数据-1小时版.py

@@ -103,7 +103,7 @@ def backtest_15m_trend_optimized(dates: List[str]):
             if direction and direction != pos_dir:
                 exit_price = float(next_bar['open'])
                 diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
-                            current_position['entry_price'] - exit_price)
+                        current_position['entry_price'] - exit_price)
                 trades.append({
                     'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
                     'exit_time': datetime.datetime.fromtimestamp(next_bar['id'] / 1000),
@@ -143,9 +143,9 @@ def backtest_15m_trend_optimized(dates: List[str]):
                         continue  # 后续可组成信号，等待信号处理
 
                 # 否则按收盘价平仓
-                exit_price = float(curr['close'])
+                exit_price = float(next_bar['close'])
                 diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
-                            current_position['entry_price'] - exit_price)
+                        current_position['entry_price'] - exit_price)
                 trades.append({
                     'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
                     'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
@@ -167,7 +167,7 @@ def backtest_15m_trend_optimized(dates: List[str]):
         exit_price = float(last['close'])
         pos_dir = current_position['direction']
         diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
-                    current_position['entry_price'] - exit_price)
+                current_position['entry_price'] - exit_price)
         trades.append({
             'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
             'exit_time': datetime.datetime.fromtimestamp(last['id'] / 1000),
@@ -185,19 +185,26 @@ def backtest_15m_trend_optimized(dates: List[str]):
 
 # ========================= 运行示例（优化版盈利计算） =========================
 if __name__ == '__main__':
-    dates = [f"2025-09-{i}" for i in range(1, 31)]
+    dates = []
+    for i in range(1, 11):
+        for i1 in range(1, 31):
+            dates.append(f"2025-{f'0{i}' if len(str(i)) < 2 else i}-{i1}")
+    #
+    # print(dates)
+
+    # dates = [f"2025-10-{i}" for i in range(1, 31)]
     trades, stats = backtest_15m_trend_optimized(dates)
 
     logger.info("===== 每笔交易详情 =====")
 
     # === 参数设定 ===
-    contract_size = 10000       # 合约规模（1手对应多少基础货币）
+    contract_size = 10000  # 合约规模（1手对应多少基础货币）
-    open_fee_fixed = 5          # 固定开仓手续费
+    open_fee_fixed = 5  # 固定开仓手续费
-    close_fee_rate = 0.0005     # 按成交额比例的平仓手续费率
+    close_fee_rate = 0.0005  # 按成交额比例的平仓手续费率
 
-    total_points_profit = 0     # 累计点差
+    total_points_profit = 0  # 累计点差
-    total_money_profit = 0      # 累计金额盈利
+    total_money_profit = 0  # 累计金额盈利
-    total_fee = 0               # 累计手续费
+    total_fee = 0  # 累计手续费
 
     for t in trades:
         entry = t['entry']
@@ -229,10 +236,11 @@ if __name__ == '__main__':
         total_money_profit += money_profit
         total_fee += fee
 
+        # if net_profit > 500 or net_profit < -500:
         logger.info(
             f"{t['entry_time']} {direction}({t['signal']}) "
             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}"
+            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 ===

weex/读取数据库数据-1小时版，不判断信号.py

@@ -0,0 +1,265 @@
+"""
+量化交易回测系统 - 仅15分钟K线 & 反向K线即时平仓逻辑（完整版）
+"""
+
+import datetime
+from dataclasses import dataclass
+from typing import List, Dict, Optional
+from loguru import logger
+from models.weex import Weex1Hour  # 替换为你的15分钟K线模型
+
+
+# ========================= 工具函数 =========================
+
+def is_bullish(c):  # 阳线
+    return float(c['close']) > float(c['open'])
+
+
+def is_bearish(c):  # 阴线
+    return float(c['close']) < float(c['open'])
+
+
+def check_signal(prev, curr):
+    """
+    包住形态信号判定：
+    - 前跌后涨包住 -> 做多
+    - 前涨后跌包住 -> 做空
+    """
+    p_open, p_close = float(prev['open']), float(prev['close'])
+    c_open, c_close = float(curr['open']), float(curr['close'])
+
+    if is_bullish(curr) and is_bearish(prev) and c_open <= p_close and c_close >= p_open:
+        return "long", "bear_bull_engulf"
+    if is_bearish(curr) and is_bullish(prev) and c_open >= p_close and c_close <= p_open:
+        return "short", "bull_bear_engulf"
+
+    return None, None
+
+
+def get_data_by_date(model, date_str: str):
+    """按天获取指定表的数据（15分钟K线）"""
+    try:
+        target_date = datetime.datetime.strptime(date_str, '%Y-%m-%d')
+    except ValueError:
+        logger.error("日期格式不正确，请使用 YYYY-MM-DD 格式。")
+        return []
+
+    start_ts = int(target_date.timestamp() * 1000)
+    end_ts = int((target_date + datetime.timedelta(days=1)).timestamp() * 1000) - 1
+
+    query = model.select().where(model.id.between(start_ts, end_ts)).order_by(model.id.asc())
+    return [{'id': i.id, 'open': i.open, 'high': i.high, 'low': i.low, 'close': i.close} for i in query]
+
+
+# ========================= 回测逻辑 =========================
+
+def backtest_15m_trend_simplified(dates: List[str]):
+    all_data: List[Dict] = []
+    for d in dates:
+        all_data.extend(get_data_by_date(Weex1Hour, d))
+    if not all_data:
+        return [], {
+            'bear_bull_engulf': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '涨包跌'},
+            'bull_bear_engulf': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '跌包涨'},
+        }
+
+    all_data.sort(key=lambda x: x['id'])
+
+    stats = {
+        'bear_bull_engulf': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '涨包跌'},
+        'bull_bear_engulf': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '跌包涨'},
+    }
+
+    trades: List[Dict] = []
+    current_position: Optional[Dict] = None
+    idx = 1
+
+    while idx < len(all_data) - 1:
+        prev, curr, next_bar = all_data[idx - 1], all_data[idx], all_data[idx + 1]
+        direction, signal_key = check_signal(prev, curr)
+
+        # 空仓 -> 碰到信号则开仓
+        if current_position is None and direction:
+            entry_price = float(next_bar['open'])
+            current_position = {
+                'direction': direction,
+                'signal': stats[signal_key]['name'],
+                'signal_key': signal_key,
+                'entry_price': entry_price,
+                'entry_time': next_bar['id']
+            }
+            stats[signal_key]['count'] += 1
+            idx += 1
+            continue
+
+        if current_position:
+            pos_dir = current_position['direction']
+            pos_sig_key = current_position['signal_key']
+
+            # 反向信号 -> 平仓 + 反手
+            if direction and direction != pos_dir:
+                exit_price = float(next_bar['open'])
+                diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
+                    current_position['entry_price'] - exit_price)
+                trades.append({
+                    'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
+                    'exit_time': datetime.datetime.fromtimestamp(next_bar['id'] / 1000),
+                    'signal': current_position['signal'],
+                    'direction': '做多' if pos_dir == 'long' else '做空',
+                    'entry': current_position['entry_price'],
+                    'exit': exit_price,
+                    'diff': diff
+                })
+                stats[pos_sig_key]['total_profit'] += diff
+                if diff > 0:
+                    stats[pos_sig_key]['wins'] += 1
+
+                # 反手开仓
+                current_position = {
+                    'direction': direction,
+                    'signal': stats[signal_key]['name'],
+                    'signal_key': signal_key,
+                    'entry_price': exit_price,
+                    'entry_time': next_bar['id']
+                }
+                stats[signal_key]['count'] += 1
+                idx += 1
+                continue
+
+            # === 新逻辑：遇到反向K线立即平仓 ===
+            if pos_dir == 'long' and is_bearish(curr):
+                exit_price = float(curr['close'])
+                diff = exit_price - current_position['entry_price']
+                trades.append({
+                    'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
+                    'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
+                    'signal': current_position['signal'],
+                    'direction': '做多',
+                    'entry': current_position['entry_price'],
+                    'exit': exit_price,
+                    'diff': diff
+                })
+                stats[pos_sig_key]['total_profit'] += diff
+                if diff > 0:
+                    stats[pos_sig_key]['wins'] += 1
+                current_position = None
+                idx += 1
+                continue
+
+            if pos_dir == 'short' and is_bullish(curr):
+                exit_price = float(curr['close'])
+                diff = current_position['entry_price'] - exit_price
+                trades.append({
+                    'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
+                    'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
+                    'signal': current_position['signal'],
+                    'direction': '做空',
+                    'entry': current_position['entry_price'],
+                    'exit': exit_price,
+                    'diff': diff
+                })
+                stats[pos_sig_key]['total_profit'] += diff
+                if diff > 0:
+                    stats[pos_sig_key]['wins'] += 1
+                current_position = None
+                idx += 1
+                continue
+
+        idx += 1
+
+    # 尾仓平仓
+    if current_position:
+        last = all_data[-1]
+        exit_price = float(last['close'])
+        pos_dir = current_position['direction']
+        diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
+                current_position['entry_price'] - exit_price)
+        trades.append({
+            'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
+            'exit_time': datetime.datetime.fromtimestamp(last['id'] / 1000),
+            'signal': current_position['signal'],
+            'direction': '做多' if pos_dir == 'long' else '做空',
+            'entry': current_position['entry_price'],
+            'exit': exit_price,
+            'diff': diff
+        })
+        stats[current_position['signal_key']]['total_profit'] += diff
+        if diff > 0:
+            stats[current_position['signal_key']]['wins'] += 1
+
+    return trades, stats
+
+
+# ========================= 运行示例 =========================
+if __name__ == '__main__':
+    # dates = [f"2025-06-{i}" for i in range(1, 31)]
+
+    dates = []
+    for i in range(1, 11):
+        for i1 in range(1, 31):
+            dates.append(f"2025-{f'0{i}' if len(str(i)) < 2 else i}-{f'0{i1}' if len(str(i1)) < 2 else i1}")
+
+
+
+    trades, stats = backtest_15m_trend_simplified(dates)
+
+    logger.info("===== 每笔交易详情 =====")
+
+    # === 参数设定 ===
+    contract_size = 10000       # 合约规模（1手对应多少基础货币）
+    open_fee_fixed = 5          # 固定开仓手续费
+    close_fee_rate = 0.0005     # 按成交额比例的平仓手续费率
+
+    total_points_profit = 0     # 累计点差
+    total_money_profit = 0      # 累计金额盈利
+    total_fee = 0               # 累计手续费
+
+    for t in trades:
+        entry = t['entry']
+        exit = t['exit']
+        direction = t['direction']
+
+        # === 1️⃣ 原始价差（点差） ===
+        point_diff = (exit - entry) if direction == '做多' else (entry - exit)
+
+        # === 2️⃣ 金额盈利（考虑合约规模） ===
+        money_profit = point_diff / entry * contract_size  # 利润以基础货币计（例如USD）
+
+        # === 3️⃣ 手续费计算 ===
+        fee = open_fee_fixed + (contract_size / entry * exit * close_fee_rate)
+
+        # === 4️⃣ 净利润 ===
+        net_profit = money_profit - fee
+
+        # 保存计算结果
+        t.update({
+            'point_diff': point_diff,
+            'raw_profit': money_profit,
+            'fee': fee,
+            'net_profit': net_profit
+        })
+
+        total_points_profit += point_diff
+        total_money_profit += money_profit
+        total_fee += fee
+
+        logger.info(
+            f"{t['entry_time']} {direction}({t['signal']}) "
+            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("===== 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}")