添加本地文件

bitmart/test_api.py

@@ -0,0 +1,29 @@
+"""测试 BitMart API K线获取"""
+import time
+from bitmart.api_contract import APIContract
+
+api_key = "a0fb7b98464fd9bcce67e7c519d58ec10d0c38a8"
+secret_key = "4eaeba78e77aeaab1c2027f846a276d164f264a44c2c1bb1c5f3be50c8de1ca5"
+memo = "数据抓取"
+
+contractAPI = APIContract(api_key, secret_key, memo, timeout=(5, 15))
+
+# 测试获取最近1小时的15分钟K线
+end_time = int(time.time())
+start_time = end_time - 3600  # 1小时前
+
+print(f"当前时间戳: {end_time}")
+print(f"开始时间戳: {start_time}")
+print(f"当前时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time))}")
+print(f"开始时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time))}")
+
+try:
+    response = contractAPI.get_kline(
+        contract_symbol="ETHUSDT",
+        step=15,
+        start_time=start_time,
+        end_time=end_time
+    )
+    print(f"\n响应: {response}")
+except Exception as e:
+    print(f"\n错误: {e}")

bitmart/回测-三分之一策略.py

@@ -0,0 +1,656 @@
+"""
+量化交易回测系统 - 三分之一回归策略（优化版）
+
+========== 策略规则 ==========
+
+1. 开多条件：
+   - 找到实体>=0.1的前一根K线（如果前一根实体<0.1，继续往前找）
+   - 前一根是阴线（close < open）
+   - 当前K线的最高价（包括影线）涨到前一根阴线实体的 1/3 处
+   - 即：当前high >= prev_close + (prev_open - prev_close) / 3
+
+2. 平多/开空条件：
+   - 找到实体>=0.1的前一根K线（如果前一根实体<0.1，继续往前找）
+   - 前一根是阳线（close > open）
+   - 当前K线的最低价（包括影线）跌到前一根阳线实体的 1/3 处
+   - 即：当前low <= prev_close - (prev_close - prev_open) / 3
+
+3. 执行逻辑：
+   - 做多时遇到开空信号 -> 平多并反手开空
+   - 做空时遇到开多信号 -> 平空并反手开多
+
+4. 实体过滤：
+   - 如果前一根K线的实体部分（|open - close|）< 0.1，继续往前查找
+   - 直到找到实体>=0.1的K线，再用那根K线来判断1/3位置
+"""
+
+import datetime
+import calendar
+import os
+from typing import List, Dict, Optional
+from loguru import logger
+import pandas as pd
+import mplfinance as mpf
+import matplotlib.pyplot as plt
+import matplotlib
+try:
+    import plotly.graph_objects as go
+except Exception:
+    go = None
+from models.bitmart_klines import BitMartETH15M
+
+# 配置中文字体
+import matplotlib.font_manager as fm
+import warnings
+
+# 忽略matplotlib的字体警告
+warnings.filterwarnings('ignore', category=UserWarning, module='matplotlib.font_manager')
+warnings.filterwarnings('ignore', message='.*Glyph.*missing.*', category=UserWarning)
+
+# 尝试设置中文字体，按优先级尝试
+chinese_fonts = ['SimHei', 'Microsoft YaHei', 'SimSun', 'KaiTi', 'FangSong', 'STSong', 'STHeiti']
+available_fonts = [f.name for f in fm.fontManager.ttflist]
+
+# 找到第一个可用的中文字体
+font_found = None
+for font_name in chinese_fonts:
+    if font_name in available_fonts:
+        font_found = font_name
+        break
+
+if font_found:
+    plt.rcParams['font.sans-serif'] = [font_found] + ['DejaVu Sans']
+    logger.info(f"使用中文字体: {font_found}")
+else:
+    # 如果没有找到中文字体，尝试使用系统默认字体
+    plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'SimSun', 'Arial Unicode MS', 'DejaVu Sans']
+    logger.warning("未找到中文字体，使用默认配置")
+
+plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+plt.rcParams['font.size'] = 10  # 设置默认字体大小
+
+# 尝试清除字体缓存（如果可能）
+try:
+    # 不强制重建，避免性能问题
+    pass
+except:
+    pass
+
+# 获取当前脚本所在目录
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# ========================= 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 get_body_size(candle):
+    """计算K线实体大小（绝对值）"""
+    return abs(float(candle['open']) - float(candle['close']))
+
+
+def find_valid_prev_bar(all_data: List[Dict], current_idx: int, min_body_size: float = 0.1):
+    """
+    从当前索引往前查找，直到找到实体>=min_body_size的K线
+    返回：(有效K线的索引, K线数据) 或 (None, None)
+    """
+    if current_idx <= 0:
+        return None, None
+    
+    for i in range(current_idx - 1, -1, -1):
+        prev = all_data[i]
+        body_size = get_body_size(prev)
+        if body_size >= min_body_size:
+            return i, prev
+    
+    return None, None
+
+
+def get_one_third_level(prev):
+    """
+    计算前一根K线实体的 1/3 回归位置
+    返回：(触发价格, 方向)
+    - 如果前一根是阴线：返回向上1/3价格，方向为 'long'
+    - 如果前一根是阳线：返回向下1/3价格，方向为 'short'
+    """
+    p_open = float(prev['open'])
+    p_close = float(prev['close'])
+    
+    if is_bearish(prev):  # 阴线，向上回归
+        # 阴线实体 = open - close
+        body = p_open - p_close
+        trigger_price = p_close + body / 3  # 从低点涨 1/3
+        return trigger_price, 'long'
+    
+    elif is_bullish(prev):  # 阳线，向下回归
+        # 阳线实体 = close - open
+        body = p_close - p_open
+        trigger_price = p_close - body / 3  # 从高点跌 1/3
+        return trigger_price, 'short'
+    
+    return None, None
+
+
+def check_trigger(all_data: List[Dict], current_idx: int, min_body_size: float = 0.1):
+    """
+    检查当前K线是否触发了交易信号
+    返回：(方向, 触发价格, 有效前一根K线索引) 或 (None, None, None)
+    规则：考虑影线部分（high/low），因为实际交易中价格会到达影线位置
+    """
+    if current_idx <= 0:
+        return None, None, None
+    
+    curr = all_data[current_idx]
+    
+    # 查找实体>=0.1的前一根K线
+    valid_prev_idx, prev = find_valid_prev_bar(all_data, current_idx, min_body_size)
+    
+    if prev is None:
+        return None, None, None
+    
+    trigger_price, direction = get_one_third_level(prev)
+    
+    if trigger_price is None:
+        return None, None, None
+    
+    # 使用影线部分（high/low）来判断，因为实际交易中价格会到达这些位置
+    c_high = float(curr['high'])
+    c_low = float(curr['low'])
+    
+    # 做多：前一根阴线，当前K线的最高价（包括影线）达到触发价格
+    if direction == 'long' and c_high >= trigger_price:
+        return 'long', trigger_price, valid_prev_idx
+    
+    # 做空：前一根阳线，当前K线的最低价（包括影线）达到触发价格
+    if direction == 'short' and c_low <= trigger_price:
+        return 'short', trigger_price, valid_prev_idx
+    
+    return None, None, None
+
+
+def get_data_by_date(model, date_str: str):
+    """按天获取指定表的数据"""
+    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())
+    data = [{'id': i.id, 'open': i.open, 'high': i.high, 'low': i.low, 'close': i.close} for i in query]
+    
+    if data:
+        data.sort(key=lambda x: x['id'])
+    
+    return data
+
+
+# ========================= 回测逻辑 =========================
+
+def backtest_one_third_strategy(dates: List[str]):
+    """三分之一回归策略回测"""
+    all_data: List[Dict] = []
+    
+    for d in dates:
+        day_data = get_data_by_date(BitMartETH15M, d)
+        all_data.extend(day_data)
+    
+    logger.info(f"总共查询了 {len(dates)} 天，获取到 {len(all_data)} 条K线数据")
+    
+    if not all_data:
+        logger.warning("未获取到任何数据")
+        return [], {'long': {'count': 0, 'wins': 0, 'total_profit': 0.0},
+                   'short': {'count': 0, 'wins': 0, 'total_profit': 0.0}}
+
+    all_data.sort(key=lambda x: x['id'])
+    
+    if len(all_data) > 1:
+        first_time = datetime.datetime.fromtimestamp(all_data[0]['id'] / 1000)
+        last_time = datetime.datetime.fromtimestamp(all_data[-1]['id'] / 1000)
+        logger.info(f"数据范围：{first_time.strftime('%Y-%m-%d %H:%M')} 到 {last_time.strftime('%Y-%m-%d %H:%M')}")
+        
+        # 验证排序：打印前5条数据
+        logger.info("===== 前5条数据（验证排序）=====")
+        for i in range(min(5, len(all_data))):
+            d = all_data[i]
+            t = datetime.datetime.fromtimestamp(d['id'] / 1000).strftime('%Y-%m-%d %H:%M:%S')
+            k_type = "阳线" if is_bullish(d) else ("阴线" if is_bearish(d) else "十字星")
+            logger.info(f"  [{i}] {t} | {k_type} | O={d['open']:.2f} H={d['high']:.2f} L={d['low']:.2f} C={d['close']:.2f}")
+
+    stats = {
+        'long': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做多'},
+        'short': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做空'},
+    }
+
+    trades: List[Dict] = []
+    current_position: Optional[Dict] = None
+    
+    for idx in range(1, len(all_data)):
+        curr = all_data[idx]
+        
+        # 使用新的check_trigger函数，它会自动查找实体>=0.1的前一根K线
+        direction, trigger_price, valid_prev_idx = check_trigger(all_data, idx, min_body_size=0.1)
+        
+        # 获取有效的前一根K线用于日志输出
+        valid_prev = all_data[valid_prev_idx] if valid_prev_idx is not None else None
+        
+        # 空仓时，有信号就开仓
+        if current_position is None:
+            if direction and valid_prev is not None:
+                # 调试：打印开仓时的K线信息
+                prev_time = datetime.datetime.fromtimestamp(valid_prev['id'] / 1000).strftime('%Y-%m-%d %H:%M')
+                curr_time = datetime.datetime.fromtimestamp(curr['id'] / 1000).strftime('%Y-%m-%d %H:%M')
+                prev_type = "阳线" if is_bullish(valid_prev) else ("阴线" if is_bearish(valid_prev) else "十字星")
+                curr_type = "阳线" if is_bullish(curr) else ("阴线" if is_bearish(curr) else "十字星")
+                prev_body = get_body_size(valid_prev)
+                logger.info(f"【开仓】{direction} @ {trigger_price:.2f}")
+                logger.info(f"  有效前一根[{prev_time}]: {prev_type} 实体={prev_body:.2f} O={valid_prev['open']:.2f} H={valid_prev['high']:.2f} L={valid_prev['low']:.2f} C={valid_prev['close']:.2f}")
+                logger.info(f"  当前根[{curr_time}]: {curr_type} O={curr['open']:.2f} H={curr['high']:.2f} L={curr['low']:.2f} C={curr['close']:.2f}")
+                
+                current_position = {
+                    'direction': direction,
+                    'entry_price': trigger_price,
+                    'entry_time': curr['id'],
+                    'entry_bar': idx
+                }
+                stats[direction]['count'] += 1
+            continue
+        
+        # 有仓位时，检查是否有反向信号
+        pos_dir = current_position['direction']
+        
+        if direction and direction != pos_dir and valid_prev is not None:
+            # 平仓
+            exit_price = trigger_price
+            
+            if pos_dir == 'long':
+                diff = exit_price - current_position['entry_price']
+            else:
+                diff = current_position['entry_price'] - exit_price
+            
+            # 调试：打印平仓时的K线信息
+            prev_time = datetime.datetime.fromtimestamp(valid_prev['id'] / 1000).strftime('%Y-%m-%d %H:%M')
+            curr_time = datetime.datetime.fromtimestamp(curr['id'] / 1000).strftime('%Y-%m-%d %H:%M')
+            prev_type = "阳线" if is_bullish(valid_prev) else ("阴线" if is_bearish(valid_prev) else "十字星")
+            curr_type = "阳线" if is_bullish(curr) else ("阴线" if is_bearish(curr) else "十字星")
+            prev_body = get_body_size(valid_prev)
+            logger.info(f"【平仓反手】{pos_dir} -> {direction} @ {exit_price:.2f}, 盈亏: {diff:.2f}")
+            logger.info(f"  有效前一根[{prev_time}]: {prev_type} 实体={prev_body:.2f} O={valid_prev['open']:.2f} H={valid_prev['high']:.2f} L={valid_prev['low']:.2f} C={valid_prev['close']:.2f}")
+            logger.info(f"  当前根[{curr_time}]: {curr_type} O={curr['open']:.2f} H={curr['high']:.2f} L={curr['low']:.2f} C={curr['close']:.2f}")
+            
+            trades.append({
+                'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
+                'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
+                'entry_time_ms': current_position['entry_time'],
+                'exit_time_ms': curr['id'],
+                'direction': '做多' if pos_dir == 'long' else '做空',
+                'entry': current_position['entry_price'],
+                'exit': exit_price,
+                'diff': diff
+            })
+            
+            stats[pos_dir]['total_profit'] += diff
+            if diff > 0:
+                stats[pos_dir]['wins'] += 1
+            
+            # 反手开仓
+            current_position = {
+                'direction': direction,
+                'entry_price': trigger_price,
+                'entry_time': curr['id'],
+                'entry_bar': idx
+            }
+            stats[direction]['count'] += 1
+
+    # 尾仓处理
+    if current_position:
+        last = all_data[-1]
+        exit_price = float(last['close'])
+        pos_dir = current_position['direction']
+        
+        if pos_dir == 'long':
+            diff = exit_price - current_position['entry_price']
+        else:
+            diff = 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),
+            'entry_time_ms': current_position['entry_time'],
+            'exit_time_ms': last['id'],
+            'direction': '做多' if pos_dir == 'long' else '做空',
+            'entry': current_position['entry_price'],
+            'exit': exit_price,
+            'diff': diff
+        })
+        stats[pos_dir]['total_profit'] += diff
+        if diff > 0:
+            stats[pos_dir]['wins'] += 1
+
+    return trades, stats, all_data
+
+
+# ========================= 绘图函数 =========================
+def plot_trades(all_data: List[Dict], trades: List[Dict], save_path: str = None):
+    """
+    绘制K线图并标注交易点位
+    """
+    if not all_data:
+        logger.warning("没有数据可绘制")
+        return
+    
+    # 转换为 DataFrame
+    df = pd.DataFrame(all_data)
+    df['datetime'] = pd.to_datetime(df['id'], unit='ms')
+    df.set_index('datetime', inplace=True)
+    df = df.rename(columns={'open': 'Open', 'high': 'High', 'low': 'Low', 'close': 'Close'})
+    df = df[['Open', 'High', 'Low', 'Close']]
+    
+    # 准备标记点
+    buy_signals = []  # 做多开仓
+    sell_signals = []  # 做空开仓
+    buy_exits = []  # 做多平仓
+    sell_exits = []  # 做空平仓
+    
+    for trade in trades:
+        entry_time = pd.to_datetime(trade['entry_time_ms'], unit='ms')
+        exit_time = pd.to_datetime(trade['exit_time_ms'], unit='ms')
+        direction = trade['direction']
+        entry_price = trade['entry']
+        exit_price = trade['exit']
+        
+        if direction == '做多':
+            buy_signals.append((entry_time, entry_price))
+            buy_exits.append((exit_time, exit_price))
+        else:
+            sell_signals.append((entry_time, entry_price))
+            sell_exits.append((exit_time, exit_price))
+    
+    # 创建标记序列
+    buy_markers = pd.Series(index=df.index, dtype=float)
+    sell_markers = pd.Series(index=df.index, dtype=float)
+    buy_exit_markers = pd.Series(index=df.index, dtype=float)
+    sell_exit_markers = pd.Series(index=df.index, dtype=float)
+    
+    for t, p in buy_signals:
+        if t in buy_markers.index:
+            buy_markers[t] = p
+    for t, p in sell_signals:
+        if t in sell_markers.index:
+            sell_markers[t] = p
+    for t, p in buy_exits:
+        if t in buy_exit_markers.index:
+            buy_exit_markers[t] = p
+    for t, p in sell_exits:
+        if t in sell_exit_markers.index:
+            sell_exit_markers[t] = p
+    
+    # 添加标记
+    add_plots = []
+    
+    if buy_markers.notna().any():
+        add_plots.append(mpf.make_addplot(buy_markers, type='scatter', markersize=100, 
+                                          marker='^', color='green', label='做多开仓'))
+    if sell_markers.notna().any():
+        add_plots.append(mpf.make_addplot(sell_markers, type='scatter', markersize=100, 
+                                          marker='v', color='red', label='做空开仓'))
+    if buy_exit_markers.notna().any():
+        add_plots.append(mpf.make_addplot(buy_exit_markers, type='scatter', markersize=80, 
+                                          marker='x', color='darkgreen', label='做多平仓'))
+    if sell_exit_markers.notna().any():
+        add_plots.append(mpf.make_addplot(sell_exit_markers, type='scatter', markersize=80, 
+                                          marker='x', color='darkred', label='做空平仓'))
+    
+    # 绘制K线图（更接近交易所风格）
+    market_colors = mpf.make_marketcolors(
+        up='#26a69a',       # 常见交易所绿色
+        down='#ef5350',     # 常见交易所红色
+        edge='inherit',
+        wick='inherit',
+        volume='inherit'
+    )
+    style = mpf.make_mpf_style(
+        base_mpf_style='binance',
+        marketcolors=market_colors,
+        gridstyle='-',
+        gridcolor='#e6e6e6'
+    )
+    
+    fig, axes = mpf.plot(
+        df,
+        type='candle',
+        style=style,
+        title='三分之一回归策略回测',
+        ylabel='价格',
+        addplot=add_plots if add_plots else None,
+        figsize=(16, 9),
+        returnfig=True
+    )
+    
+    # 添加图例
+    axes[0].legend(['做多开仓 ▲', '做空开仓 ▼', '做多平仓 ✕', '做空平仓 ✕'], loc='upper left')
+    
+    # 标注开仓细节（方向、价格、时间）
+    if trades:
+        ax = axes[0]
+        max_annotate = 60  # 过多会拥挤，可按需调大/调小
+        annotated = 0
+        for i, trade in enumerate(trades):
+            if annotated >= max_annotate:
+                break
+            entry_time = pd.to_datetime(trade['entry_time_ms'], unit='ms')
+            if entry_time not in df.index:
+                continue
+            entry_price = trade['entry']
+            direction = trade['direction']
+            color = 'green' if direction == '做多' else 'red'
+            text = f"{direction} @ {entry_price:.2f}\n{entry_time.strftime('%m-%d %H:%M')}"
+            y_offset = 20 if (i % 2 == 0) else -30
+            ax.annotate(
+                text,
+                xy=(entry_time, entry_price),
+                xytext=(0, y_offset),
+                textcoords='offset points',
+                ha='center',
+                va='bottom' if y_offset > 0 else 'top',
+                fontsize=8,
+                color=color,
+                arrowprops=dict(arrowstyle='->', color=color, lw=0.6, alpha=0.6)
+            )
+            annotated += 1
+    
+    if save_path:
+        plt.savefig(save_path, dpi=150, bbox_inches='tight')
+        logger.info(f"图表已保存到: {save_path}")
+    
+    plt.show()
+
+
+def plot_trades_interactive(all_data: List[Dict], trades: List[Dict], html_path: str = None):
+    """
+    交互式K线图（TradingView风格：支持缩放、时间区间/价格区间平移缩放）
+    """
+    if not all_data:
+        logger.warning("没有数据可绘制")
+        return
+    if go is None:
+        logger.warning("未安装 plotly，无法绘制交互式图。请先安装：pip install plotly")
+        return
+
+    df = pd.DataFrame(all_data)
+    df['datetime'] = pd.to_datetime(df['id'], unit='ms')
+    df.sort_values('datetime', inplace=True)
+
+    fig = go.Figure(
+        data=[
+            go.Candlestick(
+                x=df['datetime'],
+                open=df['open'],
+                high=df['high'],
+                low=df['low'],
+                close=df['close'],
+                increasing_line_color='#26a69a',
+                decreasing_line_color='#ef5350',
+                name='K线'
+            )
+        ]
+    )
+
+    # 标注开仓点
+    if trades:
+        entry_x = []
+        entry_y = []
+        entry_text = []
+        entry_color = []
+        for t in trades:
+            entry_x.append(pd.to_datetime(t['entry_time_ms'], unit='ms'))
+            entry_y.append(t['entry'])
+            entry_text.append(f"{t['direction']} @ {t['entry']:.2f}<br>{t['entry_time']}")
+            entry_color.append('#26a69a' if t['direction'] == '做多' else '#ef5350')
+
+        fig.add_trace(
+            go.Scatter(
+                x=entry_x,
+                y=entry_y,
+                mode='markers',
+                marker=dict(size=8, color=entry_color),
+                name='开仓',
+                text=entry_text,
+                hoverinfo='text'
+            )
+        )
+
+    # TradingView风格：深色背景 + 交互缩放
+    fig.update_layout(
+        title='三分之一回归策略回测（交互式）',
+        xaxis=dict(
+            rangeslider=dict(visible=True),
+            type='date',
+            showgrid=False
+        ),
+        yaxis=dict(
+            showgrid=False,
+            fixedrange=False
+        ),
+        plot_bgcolor='#0b0e11',
+        paper_bgcolor='#0b0e11',
+        font=dict(color='#d1d4dc'),
+        hovermode='x unified',
+        dragmode='zoom'
+    )
+
+    fig.show()
+    if html_path:
+        fig.write_html(html_path)
+        logger.info(f"交互图已保存到: {html_path}")
+
+
+# ========================= 主程序 =========================
+if __name__ == '__main__':
+    # ==================== 配置参数 ====================
+    START_DATE = "2025-01-01"
+    END_DATE = "2025-12-31"
+    
+    # ==================== 生成日期列表 ====================
+    dates = []
+    if START_DATE and END_DATE:
+        start_dt = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
+        end_dt = datetime.datetime.strptime(END_DATE, '%Y-%m-%d')
+        
+        current_dt = start_dt
+        while current_dt <= end_dt:
+            dates.append(current_dt.strftime('%Y-%m-%d'))
+            current_dt += datetime.timedelta(days=1)
+        
+        logger.info(f"查询日期范围：{START_DATE} 到 {END_DATE}，共 {len(dates)} 天")
+
+    # ==================== 执行回测 ====================
+    trades, stats, all_data = backtest_one_third_strategy(dates)
+
+    # ==================== 输出结果 ====================
+    logger.info("===== 每笔交易详情 =====")
+
+    contract_size = 10000
+    open_fee_fixed = 5
+    close_fee_rate = 0.0005
+
+    total_points_profit = 0
+    total_money_profit = 0
+    total_fee = 0
+
+    for t in trades:
+        entry = t['entry']
+        exit_p = t['exit']
+        direction = t['direction']
+
+        point_diff = t['diff']
+        money_profit = point_diff / entry * contract_size
+        fee = open_fee_fixed + (contract_size / entry * exit_p * close_fee_rate)
+        net_profit = money_profit - fee
+
+        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} "
+            f"入={entry:.2f} 出={exit_p:.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}"
+        )
+
+    # ==================== 汇总统计 ====================
+    total_net_profit = total_money_profit - total_fee
+    
+    print(f"\n{'='*60}")
+    print(f"【三分之一回归策略 回测结果】")
+    print(f"{'='*60}")
+    print(f"交易笔数：{len(trades)}")
+    print(f"总点差：{total_points_profit:.2f}")
+    print(f"总原始盈利：{total_money_profit:.2f}")
+    print(f"总手续费：{total_fee:.2f}")
+    print(f"总净利润：{total_net_profit:.2f}")
+    print(f"{'='*60}")
+
+    print("\n===== 方向统计 =====")
+    for k, v in stats.items():
+        count = v['count']
+        wins = v['wins']
+        total_p = 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"{v['name']}: 次数={count} 胜率={win_rate:.2f}% 总价差={total_p:.2f} 平均价差={avg_p:.2f}")
+
+    # ==================== 绘制图表 ====================
+    if trades and all_data:
+        # 如果数据太多，只绘制最近一部分（比如最近500根K线）
+        max_bars = 500
+        if len(all_data) > max_bars:
+            logger.info(f"数据量较大({len(all_data)}条)，只绘制最近 {max_bars} 根K线")
+            plot_data = all_data[-max_bars:]
+            # 过滤出在这个时间范围内的交易
+            min_time = datetime.datetime.fromtimestamp(plot_data[0]['id'] / 1000)
+            plot_trades_filtered = [t for t in trades if t['entry_time'] >= min_time]
+        else:
+            plot_data = all_data
+            plot_trades_filtered = trades
+        
+        save_path = os.path.join(SCRIPT_DIR, '回测图表.png')
+        plot_trades(plot_data, plot_trades_filtered, save_path=save_path)
+        # 交互式版本（TradingView风格）：支持时间区间/价格缩放
+        html_path = os.path.join(SCRIPT_DIR, '回测图表_交互式.html')
+        plot_trades_interactive(plot_data, plot_trades_filtered, html_path=html_path)

bitmart/抓取多周期K线.py

@@ -0,0 +1,279 @@
+"""
+BitMart 多周期K线数据抓取脚本
+支持同时获取 1分钟、3分钟、5分钟、15分钟、30分钟、1小时 K线数据
+自动创建对应的数据库表
+"""
+
+import time
+import datetime
+from pathlib import Path
+from loguru import logger
+from peewee import *
+from bitmart.api_contract import APIContract
+
+# 数据库配置
+DB_PATH = Path(__file__).parent.parent / 'models' / 'database.db'
+db = SqliteDatabase(str(DB_PATH))
+
+# K线周期配置：step值 -> 表名后缀
+KLINE_CONFIGS = {
+    1: '1m',      # 1分钟
+    3: '3m',      # 3分钟
+    5: '5m',      # 5分钟
+    15: '15m',    # 15分钟
+    30: '30m',    # 30分钟
+    60: '1h',     # 1小时
+}
+
+
+def create_kline_model(step: int):
+    """
+    动态创建K线数据模型
+    :param step: K线周期（分钟）
+    :return: Model类
+    """
+    suffix = KLINE_CONFIGS.get(step, f'{step}m')
+    tbl_name = f'bitmart_eth_{suffix}'
+    
+    # 使用 type() 动态创建类，避免闭包问题
+    attrs = {
+        'id': BigIntegerField(primary_key=True),
+        'open': FloatField(null=True),
+        'high': FloatField(null=True),
+        'low': FloatField(null=True),
+        'close': FloatField(null=True),
+    }
+    
+    # 创建 Meta 类
+    meta_attrs = {
+        'database': db,
+        'table_name': tbl_name,
+    }
+    Meta = type('Meta', (), meta_attrs)
+    attrs['Meta'] = Meta
+    
+    # 动态创建 Model 类
+    model_name = f'BitMartETH{suffix.upper()}'
+    KlineModel = type(model_name, (Model,), attrs)
+    
+    return KlineModel
+
+
+class BitMartMultiKlineCollector:
+    """多周期K线数据抓取器"""
+    
+    def __init__(self):
+        self.api_key = "a0fb7b98464fd9bcce67e7c519d58ec10d0c38a8"
+        self.secret_key = "4eaeba78e77aeaab1c2027f846a276d164f264a44c2c1bb1c5f3be50c8de1ca5"
+        self.memo = "数据抓取"
+        self.contract_symbol = "ETHUSDT"
+        self.contractAPI = APIContract(self.api_key, self.secret_key, self.memo, timeout=(5, 15))
+        
+        # 存储各周期的模型
+        self.models = {}
+        
+        # 初始化数据库连接和表
+        self._init_database()
+    
+    def _init_database(self):
+        """初始化数据库，创建所有周期的表"""
+        db.connect(reuse_if_open=True)
+        
+        for step in KLINE_CONFIGS.keys():
+            model = create_kline_model(step)
+            self.models[step] = model
+            # 创建表（如果不存在）
+            db.create_tables([model], safe=True)
+            logger.info(f"初始化表: {model._meta.table_name}")
+    
+    def get_klines(self, step: int, start_time: int, end_time: int):
+        """
+        获取K线数据
+        :param step: K线周期（分钟）
+        :param start_time: 开始时间戳（秒级）
+        :param end_time: 结束时间戳（秒级）
+        :return: K线数据列表
+        """
+        try:
+            # 确保是整数
+            start_time = int(start_time)
+            end_time = int(end_time)
+            
+            logger.debug(f"API请求: step={step}, start={start_time}, end={end_time}")
+            
+            response = self.contractAPI.get_kline(
+                contract_symbol=self.contract_symbol,
+                step=step,
+                start_time=start_time,
+                end_time=end_time
+            )[0]
+            
+            if response['code'] != 1000:
+                logger.error(f"获取 {step}分钟 K线失败: {response}")
+                return []
+            
+            klines = response.get('data', [])
+            formatted = []
+            for k in klines:
+                timestamp_ms = int(k["timestamp"]) * 1000
+                formatted.append({
+                    'id': timestamp_ms,
+                    'open': float(k["open_price"]),
+                    'high': float(k["high_price"]),
+                    'low': float(k["low_price"]),
+                    'close': float(k["close_price"])
+                })
+            
+            formatted.sort(key=lambda x: x['id'])
+            return formatted
+        except Exception as e:
+            logger.error(f"获取 {step}分钟 K线异常: {e}")
+            return []
+    
+    def save_klines(self, step: int, klines: list):
+        """
+        保存K线数据到数据库
+        :param step: K线周期
+        :param klines: K线数据列表
+        :return: 保存的数量
+        """
+        model = self.models.get(step)
+        if not model:
+            logger.error(f"未找到 {step}分钟 的数据模型")
+            return 0
+        
+        saved_count = 0
+        for kline in klines:
+            try:
+                model.get_or_create(
+                    id=kline['id'],
+                    defaults={
+                        'open': kline['open'],
+                        'high': kline['high'],
+                        'low': kline['low'],
+                        'close': kline['close'],
+                    }
+                )
+                saved_count += 1
+            except Exception as e:
+                logger.error(f"保存 {step}分钟 K线数据失败 {kline['id']}: {e}")
+        
+        return saved_count
+    
+    def collect_single_period(self, step: int, start_date: str = None, days: int = None):
+        """
+        抓取单个周期的历史数据（从当前时间向前抓取，直到遇到API限制）
+        :param step: K线周期（分钟）
+        :param start_date: 起始日期 'YYYY-MM-DD'（目标，可能无法达到）
+        :param days: 抓取天数（目标，可能无法达到）
+        """
+        suffix = KLINE_CONFIGS.get(step, f'{step}m')
+        now = int(time.time())
+        
+        if start_date:
+            start_dt = datetime.datetime.strptime(start_date, '%Y-%m-%d')
+            target_start_time = int(start_dt.timestamp())
+            logger.info(f"开始抓取 {suffix} K线数据: 目标从 {start_date} 开始（从现在向前抓取）")
+        elif days:
+            target_start_time = now - 3600 * 24 * days
+            logger.info(f"开始抓取 {suffix} K线数据: 目标最近 {days} 天")
+        else:
+            target_start_time = now - 3600 * 24 * 30
+            logger.info(f"开始抓取 {suffix} K线数据: 目标最近 30 天")
+        
+        # 根据周期调整批次大小
+        if step <= 5:
+            batch_seconds = 3600 * 6   # 小周期每次6小时
+        elif step <= 30:
+            batch_seconds = 3600 * 24  # 中周期每次1天
+        else:
+            batch_seconds = 3600 * 24 * 3  # 大周期每次3天
+        
+        total_saved = 0
+        fail_count = 0
+        max_fail = 3
+        
+        # 从当前时间向前抓取
+        current_end = now
+        while current_end > target_start_time:
+            current_start = current_end - batch_seconds
+            
+            # 打印时间范围
+            start_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(current_start))
+            end_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(current_end))
+            logger.info(f"[{suffix}] 抓取: {start_str} -> {end_str}")
+            
+            klines = self.get_klines(step, current_start, current_end)
+            if klines:
+                saved = self.save_klines(step, klines)
+                total_saved += saved
+                logger.info(f"[{suffix}] 保存 {saved} 条，累计 {total_saved} 条")
+                fail_count = 0
+            else:
+                fail_count += 1
+                logger.warning(f"[{suffix}] 未获取到数据 (连续失败 {fail_count} 次)")
+                if fail_count >= max_fail:
+                    earliest = time.strftime('%Y-%m-%d', time.localtime(current_end))
+                    logger.warning(f"[{suffix}] 达到API历史数据限制，最早可用数据约 {earliest}")
+                    break
+            
+            current_end = current_start
+            time.sleep(0.3)  # API请求间隔
+        
+        logger.success(f"[{suffix}] 抓取完成，共保存 {total_saved} 条数据")
+        return total_saved
+    
+    def collect_all_periods(self, start_date: str = None, days: int = None, 
+                           periods: list = None):
+        """
+        抓取所有周期的历史数据
+        :param start_date: 起始日期 'YYYY-MM-DD'
+        :param days: 抓取天数
+        :param periods: 要抓取的周期列表，如 [1, 5, 15]，默认全部
+        """
+        if periods is None:
+            periods = list(KLINE_CONFIGS.keys())
+        
+        logger.info(f"开始抓取多周期K线数据，周期: {[KLINE_CONFIGS[p] for p in periods]}")
+        
+        results = {}
+        for step in periods:
+            if step not in KLINE_CONFIGS:
+                logger.warning(f"不支持的周期: {step}分钟，跳过")
+                continue
+            
+            logger.info(f"\n{'='*50}")
+            logger.info(f"开始抓取 {KLINE_CONFIGS[step]} K线")
+            logger.info(f"{'='*50}")
+            
+            saved = self.collect_single_period(step, start_date, days)
+            results[KLINE_CONFIGS[step]] = saved
+            
+            time.sleep(1)  # 不同周期之间间隔
+        
+        logger.info(f"\n{'='*50}")
+        logger.info("所有周期抓取完成！统计：")
+        for period, count in results.items():
+            logger.info(f"  {period}: {count} 条")
+        logger.info(f"{'='*50}")
+        
+        return results
+    
+    def close(self):
+        """关闭数据库连接"""
+        if not db.is_closed():
+            db.close()
+
+
+if __name__ == '__main__':
+    collector = BitMartMultiKlineCollector()
+    
+    try:
+        # 抓取尽可能多的历史数据（从现在向前，直到遇到API限制自动停止）
+        # 目标：2025-01-01，但实际能抓取多少取决于 BitMart API 的历史数据限制
+        collector.collect_all_periods(
+            start_date='2025-01-01',  # 目标起始日期
+            periods=[1, 3, 5, 15, 30, 60]  # 所有周期
+        )
+    finally:
+        collector.close()

models/bitmart_klines.py

@@ -0,0 +1,97 @@
+"""
+BitMart 多周期K线数据模型
+包含 1分钟、3分钟、5分钟、15分钟、30分钟、1小时 K线数据表
+"""
+
+from peewee import *
+from models import db
+
+
+# ==================== 1分钟 K线 ====================
+class BitMartETH1M(Model):
+    id = BigIntegerField(primary_key=True)  # 时间戳（毫秒级）
+    open = FloatField(null=True)
+    high = FloatField(null=True)
+    low = FloatField(null=True)
+    close = FloatField(null=True)
+
+    class Meta:
+        database = db
+        table_name = 'bitmart_eth_1m'
+
+
+# ==================== 3分钟 K线 ====================
+class BitMartETH3M(Model):
+    id = BigIntegerField(primary_key=True)
+    open = FloatField(null=True)
+    high = FloatField(null=True)
+    low = FloatField(null=True)
+    close = FloatField(null=True)
+
+    class Meta:
+        database = db
+        table_name = 'bitmart_eth_3m'
+
+
+# ==================== 5分钟 K线 ====================
+class BitMartETH5M(Model):
+    id = BigIntegerField(primary_key=True)
+    open = FloatField(null=True)
+    high = FloatField(null=True)
+    low = FloatField(null=True)
+    close = FloatField(null=True)
+
+    class Meta:
+        database = db
+        table_name = 'bitmart_eth_5m'
+
+
+# ==================== 15分钟 K线 ====================
+class BitMartETH15M(Model):
+    id = BigIntegerField(primary_key=True)
+    open = FloatField(null=True)
+    high = FloatField(null=True)
+    low = FloatField(null=True)
+    close = FloatField(null=True)
+
+    class Meta:
+        database = db
+        table_name = 'bitmart_eth_15m'
+
+
+# ==================== 30分钟 K线 ====================
+class BitMartETH30M(Model):
+    id = BigIntegerField(primary_key=True)
+    open = FloatField(null=True)
+    high = FloatField(null=True)
+    low = FloatField(null=True)
+    close = FloatField(null=True)
+
+    class Meta:
+        database = db
+        table_name = 'bitmart_eth_30m'
+
+
+# ==================== 1小时 K线 ====================
+class BitMartETH1H(Model):
+    id = BigIntegerField(primary_key=True)
+    open = FloatField(null=True)
+    high = FloatField(null=True)
+    low = FloatField(null=True)
+    close = FloatField(null=True)
+
+    class Meta:
+        database = db
+        table_name = 'bitmart_eth_1h'
+
+
+# 连接数据库并创建表
+db.connect(reuse_if_open=True)
+db.create_tables([
+    BitMartETH1M,
+    BitMartETH3M,
+    BitMartETH5M,
+    BitMartETH15M,
+    BitMartETH30M,
+    BitMartETH1H,
+], safe=True)

交易/bitmart-三分之一策略交易.py

@@ -0,0 +1,557 @@
+"""
+BitMart 三分之一回归策略交易
+使用5分钟K线周期
+
+策略规则：
+1. 开多条件：
+   - 找到实体>=0.1的前一根K线（如果前一根实体<0.1，继续往前找）
+   - 前一根是阴线（close < open）
+   - 当前K线的最高价（包括影线）涨到前一根阴线实体的 1/3 处
+   - 即：当前high >= prev_close + (prev_open - prev_close) / 3
+
+2. 平多/开空条件：
+   - 找到实体>=0.1的前一根K线
+   - 前一根是阳线（close > open）
+   - 当前K线的最低价（包括影线）跌到前一根阳线实体的 1/3 处
+   - 即：当前low <= prev_close - (prev_close - prev_open) / 3
+
+3. 执行逻辑：
+   - 做多时遇到开空信号 -> 平多并反手开空
+   - 做空时遇到开多信号 -> 平空并反手开多
+"""
+
+import time
+import datetime
+
+from tqdm import tqdm
+from loguru import logger
+from bit_tools import openBrowser
+from DrissionPage import ChromiumPage
+from DrissionPage import ChromiumOptions
+
+from bitmart.api_contract import APIContract
+from 交易.tools import send_dingtalk_message
+
+
+class BitmartOneThirdStrategy:
+    def __init__(self, bit_id):
+
+        self.page: ChromiumPage | None = None
+
+        self.api_key = "a0fb7b98464fd9bcce67e7c519d58ec10d0c38a8"
+        self.secret_key = "4eaeba78e77aeaab1c2027f846a276d164f264a44c2c1bb1c5f3be50c8de1ca5"
+        self.memo = "合约交易"
+
+        self.contract_symbol = "ETHUSDT"
+
+        self.contractAPI = APIContract(self.api_key, self.secret_key, self.memo, timeout=(5, 15))
+
+        self.start = 0  # 持仓状态: -1 空, 0 无, 1 多
+        self.direction = None
+
+        self.pbar = tqdm(total=5, desc="等待K线", ncols=80)  # 5分钟周期
+
+        self.last_kline_time = None
+
+        self.leverage = "100"  # 高杠杆（全仓模式下可开更大仓位）
+        self.open_type = "cross"  # 全仓模式
+        self.risk_percent = 0.01  # 每次开仓使用可用余额的 1%
+
+        self.open_avg_price = None  # 开仓价格
+        self.current_amount = None  # 持仓量
+
+        self.bit_id = bit_id
+
+        # 三分之一策略参数
+        self.min_body_size = 0.1  # 最小实体大小
+        self.kline_step = 5  # K线周期（5分钟）
+        self.kline_count = 20  # 获取的K线数量，用于向前查找有效K线
+
+    # ========================= 三分之一策略核心函数 =========================
+
+    def is_bullish(self, c):
+        """判断阳线"""
+        return float(c['close']) > float(c['open'])
+
+    def is_bearish(self, c):
+        """判断阴线"""
+        return float(c['close']) < float(c['open'])
+
+    def get_body_size(self, candle):
+        """计算K线实体大小（绝对值）"""
+        return abs(float(candle['open']) - float(candle['close']))
+
+    def find_valid_prev_bar(self, all_data, current_idx, min_body_size=0.1):
+        """
+        从当前索引往前查找，直到找到实体>=min_body_size的K线
+        返回：(有效K线的索引, K线数据) 或 (None, None)
+        """
+        if current_idx <= 0:
+            return None, None
+
+        for i in range(current_idx - 1, -1, -1):
+            prev = all_data[i]
+            body_size = self.get_body_size(prev)
+            if body_size >= min_body_size:
+                return i, prev
+
+        return None, None
+
+    def get_one_third_level(self, prev):
+        """
+        计算前一根K线实体的 1/3 回归位置
+        返回：(触发价格, 方向)
+        - 如果前一根是阴线：返回向上1/3价格，方向为 'long'
+        - 如果前一根是阳线：返回向下1/3价格，方向为 'short'
+        """
+        p_open = float(prev['open'])
+        p_close = float(prev['close'])
+
+        if self.is_bearish(prev):  # 阴线，向上回归
+            # 阴线实体 = open - close
+            body = p_open - p_close
+            trigger_price = p_close + body / 3  # 从低点涨 1/3
+            return trigger_price, 'long'
+
+        elif self.is_bullish(prev):  # 阳线，向下回归
+            # 阳线实体 = close - open
+            body = p_close - p_open
+            trigger_price = p_close - body / 3  # 从高点跌 1/3
+            return trigger_price, 'short'
+
+        return None, None
+
+    def check_trigger(self, all_data, current_idx):
+        """
+        检查当前K线是否触发了交易信号
+        返回：(方向, 触发价格, 有效前一根K线索引) 或 (None, None, None)
+        规则：考虑影线部分（high/low），因为实际交易中价格会到达影线位置
+        """
+        if current_idx <= 0:
+            return None, None, None
+
+        curr = all_data[current_idx]
+
+        # 查找实体>=min_body_size的前一根K线
+        valid_prev_idx, prev = self.find_valid_prev_bar(all_data, current_idx, self.min_body_size)
+
+        if prev is None:
+            return None, None, None
+
+        trigger_price, direction = self.get_one_third_level(prev)
+
+        if trigger_price is None:
+            return None, None, None
+
+        # 使用影线部分（high/low）来判断
+        c_high = float(curr['high'])
+        c_low = float(curr['low'])
+
+        # 做多：前一根阴线，当前K线的最高价（包括影线）达到触发价格
+        if direction == 'long' and c_high >= trigger_price:
+            return 'long', trigger_price, valid_prev_idx
+
+        # 做空：前一根阳线，当前K线的最低价（包括影线）达到触发价格
+        if direction == 'short' and c_low <= trigger_price:
+            return 'short', trigger_price, valid_prev_idx
+
+        return None, None, None
+
+    # ========================= BitMart API 函数 =========================
+
+    def get_klines(self):
+        """获取最近N根5分钟K线"""
+        try:
+            end_time = int(time.time())
+            # 获取足够多的K线用于向前查找有效K线
+            response = self.contractAPI.get_kline(
+                contract_symbol=self.contract_symbol,
+                step=self.kline_step,  # 5分钟
+                start_time=end_time - 3600 * 3,  # 取最近3小时
+                end_time=end_time
+            )[0]["data"]
+
+            # 每根: [timestamp, open, high, low, close, volume]
+            formatted = []
+            for k in response:
+                formatted.append({
+                    'id': int(k["timestamp"]),
+                    'open': float(k["open_price"]),
+                    'high': float(k["high_price"]),
+                    'low': float(k["low_price"]),
+                    'close': float(k["close_price"])
+                })
+            formatted.sort(key=lambda x: x['id'])
+            return formatted
+        except Exception as e:
+            error_msg = str(e)
+            # 检查是否是429限流错误
+            if "429" in error_msg or "too many requests" in error_msg.lower():
+                logger.warning(f"API限流，等待60秒后重试: {e}")
+                time.sleep(60)
+            else:
+                logger.error(f"获取K线异常: {e}")
+                self.ding(msg="获取K线异常", error=True)
+            return None
+
+    def get_current_price(self):
+        """获取当前最新价格"""
+        try:
+            end_time = int(time.time())
+            response = self.contractAPI.get_kline(
+                contract_symbol=self.contract_symbol,
+                step=1,  # 1分钟
+                start_time=end_time - 3600 * 3,
+                end_time=end_time
+            )[0]
+            if response['code'] == 1000:
+                return float(response['data'][-1]["close_price"])
+            return None
+        except Exception as e:
+            logger.error(f"获取价格异常: {e}")
+            return None
+
+    def get_available_balance(self):
+        """获取合约账户可用USDT余额"""
+        try:
+            response = self.contractAPI.get_assets_detail()[0]
+            if response['code'] == 1000:
+                data = response['data']
+                if isinstance(data, dict):
+                    return float(data.get('available_balance', 0))
+                elif isinstance(data, list):
+                    for asset in data:
+                        if asset.get('currency') == 'USDT':
+                            return float(asset.get('available_balance', 0))
+            return None
+        except Exception as e:
+            logger.error(f"余额查询异常: {e}")
+            return None
+
+    def get_position_status(self):
+        """获取当前持仓方向"""
+        try:
+            response = self.contractAPI.get_position(contract_symbol=self.contract_symbol)[0]
+            if response['code'] == 1000:
+                positions = response['data']
+                if not positions:
+                    self.start = 0
+                    self.open_avg_price = None
+                    self.current_amount = None
+                    self.position_cross = None
+                    return True
+                self.start = 1 if positions[0]['position_type'] == 1 else -1
+                self.open_avg_price = positions[0]['open_avg_price']
+                self.current_amount = positions[0]['current_amount']
+                self.position_cross = positions[0]["position_cross"]
+                return True
+            else:
+                return False
+        except Exception as e:
+            logger.error(f"持仓查询异常: {e}")
+            return False
+
+    def set_leverage(self):
+        """程序启动时设置全仓 + 高杠杆"""
+        try:
+            response = self.contractAPI.post_submit_leverage(
+                contract_symbol=self.contract_symbol,
+                leverage=self.leverage,
+                open_type=self.open_type
+            )[0]
+            if response['code'] == 1000:
+                logger.success(f"全仓模式 + {self.leverage}x 杠杆设置成功")
+                return True
+            else:
+                logger.error(f"杠杆设置失败: {response}")
+                return False
+        except Exception as e:
+            logger.error(f"设置杠杆异常: {e}")
+            return False
+
+    # ========================= 浏览器自动化函数 =========================
+
+    def openBrowser(self):
+        """打开 TGE 对应浏览器实例"""
+        try:
+            bit_port = openBrowser(id=self.bit_id)
+            co = ChromiumOptions()
+            co.set_local_port(port=bit_port)
+            self.page = ChromiumPage(addr_or_opts=co)
+            return True
+        except:
+            return False
+
+    def close_extra_tabs_in_browser(self):
+        """关闭多余 tab"""
+        try:
+            for idx, tab in enumerate(self.page.get_tabs()):
+                if idx > 0:
+                    tab.close()
+            return True
+        except:
+            return False
+
+    def click_safe(self, xpath, sleep=0.5):
+        """安全点击"""
+        try:
+            ele = self.page.ele(xpath)
+            if not ele:
+                return False
+            ele.scroll.to_see(center=True)
+            time.sleep(sleep)
+            ele.click(by_js=True)
+            return True
+        except:
+            return False
+
+    def 平仓(self):
+        """市价平仓"""
+        logger.info("执行平仓操作...")
+        self.click_safe('x://span[normalize-space(text()) ="市价"]')
+        time.sleep(0.5)
+        self.ding(msg="执行平仓操作")
+
+    def 开单(self, marketPriceLongOrder=0, size=None):
+        """
+        市价开单
+        marketPriceLongOrder: 1 做多, -1 做空
+        """
+        if size is None or size <= 0:
+            logger.warning("开单金额无效")
+            return False
+
+        direction_str = "做多" if marketPriceLongOrder == 1 else "做空"
+        logger.info(f"执行{direction_str}操作，金额: {size}")
+
+        try:
+            if marketPriceLongOrder == -1:
+                self.click_safe('x://button[normalize-space(text()) ="市价"]')
+                self.page.ele('x://*[@id="size_0"]').input(size)
+                self.click_safe('x://span[normalize-space(text()) ="卖出/做空"]')
+            elif marketPriceLongOrder == 1:
+                self.click_safe('x://button[normalize-space(text()) ="市价"]')
+                self.page.ele('x://*[@id="size_0"]').input(size)
+                self.click_safe('x://span[normalize-space(text()) ="买入/做多"]')
+
+            self.ding(msg=f"执行{direction_str}操作，金额: {size}")
+            return True
+        except Exception as e:
+            logger.error(f"开单异常: {e}")
+            return False
+
+    def ding(self, msg, error=False):
+        """统一消息格式"""
+        prefix = "❌三分之一策略：" if error else "🔔三分之一策略："
+        if error:
+            logger.error(msg)
+            for i in range(10):
+                send_dingtalk_message(f"{prefix}{msg}")
+        else:
+            logger.info(msg)
+            send_dingtalk_message(f"{prefix}{msg}")
+
+    # ========================= 时间计算函数 =========================
+
+    def get_now_time(self):
+        """获取当前5分钟整点时间戳"""
+        current_timestamp = time.time()
+        current_datetime = datetime.datetime.fromtimestamp(current_timestamp)
+
+        # 计算距离当前时间最近的5分钟整点
+        minute = current_datetime.minute
+        target_minute = (minute // 5) * 5  # 向下取整到5分钟
+        target_datetime = current_datetime.replace(minute=target_minute, second=0, microsecond=0)
+
+        return int(target_datetime.timestamp())
+
+    def get_time_to_next_5min(self):
+        """获取距离下一个5分钟的秒数"""
+        current_timestamp = time.time()
+        current_datetime = datetime.datetime.fromtimestamp(current_timestamp)
+
+        minute = current_datetime.minute
+        next_5min = ((minute // 5) + 1) * 5
+        if next_5min >= 60:
+            next_datetime = current_datetime.replace(minute=0, second=0, microsecond=0) + datetime.timedelta(hours=1)
+        else:
+            next_datetime = current_datetime.replace(minute=next_5min, second=0, microsecond=0)
+
+        return (next_datetime - current_datetime).total_seconds()
+
+    # ========================= 主运行函数 =========================
+
+    def action(self):
+        """主运行逻辑"""
+        # 启动时设置全仓高杠杆
+        if not self.set_leverage():
+            logger.error("杠杆设置失败，程序继续运行但可能下单失败")
+            return
+
+        # 1. 打开浏览器
+        if not self.openBrowser():
+            self.ding("打开浏览器失败！", error=True)
+            return
+        logger.info("浏览器打开成功")
+
+        if self.close_extra_tabs_in_browser():
+            logger.info('关闭多余标签页成功')
+        else:
+            logger.info('关闭多余标签页失败')
+
+        self.page.get("https://derivatives.bitmart.com/zh-CN/futures/ETHUSDT")
+        time.sleep(2)
+
+        self.click_safe('x://button[normalize-space(text()) ="市价"]')
+
+        self.pbar = tqdm(total=5, desc="等待5分钟K线", ncols=80)
+
+        self.time_start = None  # 时间状态，避免同一时段重复处理
+
+        while True:
+            # 更新进度条
+            current_time = time.localtime()
+            current_minute = current_time.tm_min
+            self.pbar.n = current_minute % 5
+            self.pbar.refresh()
+
+            # 检查是否已处理过当前时间段
+            if self.time_start == self.get_now_time():
+                time.sleep(3)
+                continue
+
+            # 获取K线数据
+            kline_data = self.get_klines()
+            if not kline_data:
+                logger.warning("获取K线数据失败")
+                time.sleep(5)
+                continue
+
+            # 检查数据是否是最新的
+            if len(kline_data) < 3:
+                logger.warning("K线数据不足")
+                time.sleep(5)
+                continue
+
+            # 判断最新K线时间
+            latest_kline_time = kline_data[-1]['id']
+            if self.get_now_time() != latest_kline_time:
+                time.sleep(3)
+                continue
+
+            self.time_start = self.get_now_time()
+
+            # 获取持仓状态
+            if not self.get_position_status():
+                logger.warning("获取仓位信息失败")
+                self.ding(msg="获取仓位信息失败！", error=True)
+                continue
+
+            logger.info(f"当前持仓状态: {self.start} (1=多, -1=空, 0=无)")
+
+            # ========== 三分之一策略信号检测 ==========
+            current_idx = len(kline_data) - 1
+            direction, trigger_price, valid_prev_idx = self.check_trigger(kline_data, current_idx)
+
+            if direction:
+                # 获取有效前一根K线用于日志
+                valid_prev = kline_data[valid_prev_idx] if valid_prev_idx is not None else None
+                curr = kline_data[current_idx]
+
+                if valid_prev:
+                    prev_time = datetime.datetime.fromtimestamp(valid_prev['id']).strftime('%H:%M')
+                    curr_time = datetime.datetime.fromtimestamp(curr['id']).strftime('%H:%M')
+                    prev_type = "阳线" if self.is_bullish(valid_prev) else "阴线"
+                    prev_body = self.get_body_size(valid_prev)
+
+                    logger.info(f"检测到{direction}信号，触发价格: {trigger_price:.2f}")
+                    logger.info(f"  有效前一根[{prev_time}]: {prev_type} 实体={prev_body:.2f}")
+                    logger.info(f"  当前根[{curr_time}]: H={curr['high']:.2f} L={curr['low']:.2f}")
+
+            # ========== 执行交易逻辑 ==========
+            balance = self.get_available_balance()
+            if balance is None:
+                balance = 0
+            trade_size = balance * self.risk_percent
+
+            if direction == "long":
+                if self.start == -1:  # 当前空仓，平空开多
+                    logger.info("平空仓，反手开多")
+                    self.平仓()
+                    time.sleep(1)
+                    self.开单(marketPriceLongOrder=1, size=trade_size)
+                elif self.start == 0:  # 当前无仓，直接开多
+                    logger.info("无仓位，开多")
+                    self.开单(marketPriceLongOrder=1, size=trade_size)
+                # 已有多仓则不操作
+
+            elif direction == "short":
+                if self.start == 1:  # 当前多仓，平多开空
+                    logger.info("平多仓，反手开空")
+                    self.平仓()
+                    time.sleep(1)
+                    self.开单(marketPriceLongOrder=-1, size=trade_size)
+                elif self.start == 0:  # 当前无仓，直接开空
+                    logger.info("无仓位，开空")
+                    self.开单(marketPriceLongOrder=-1, size=trade_size)
+                # 已有空仓则不操作
+
+            # ========== 发送持仓信息 ==========
+            self._send_position_message(kline_data[-1])
+
+            self.pbar.reset()
+
+    def _send_position_message(self, latest_kline):
+        """发送持仓信息到钉钉"""
+        current_price = float(latest_kline["close"])
+        balance = self.get_available_balance()
+        self.balance = balance if balance is not None else 0.0
+
+        if self.start != 0:
+            open_avg_price = float(self.open_avg_price) if self.open_avg_price else 0.0
+            current_amount = float(self.current_amount) if self.current_amount else 0.0
+            position_cross = float(self.position_cross) if hasattr(self, 'position_cross') and self.position_cross else 0.0
+
+            # 计算浮动盈亏
+            if self.start == 1:  # 多头
+                unrealized_pnl = current_amount * 0.001 * (current_price - open_avg_price)
+            else:  # 空头
+                unrealized_pnl = current_amount * 0.001 * (open_avg_price - current_price)
+
+            # 计算收益率
+            if open_avg_price > 0:
+                if self.start == 1:
+                    pnl_rate = (current_price - open_avg_price) / open_avg_price * 10000
+                else:
+                    pnl_rate = (open_avg_price - current_price) / open_avg_price * 10000
+                rate_str = f" ({pnl_rate:+.2f}%)"
+            else:
+                rate_str = ""
+
+            direction_str = "空" if self.start == -1 else "多"
+            pnl_str = f"{unrealized_pnl:+.2f} USDT"
+
+            msg = (
+                f"【三分之一策略 {self.contract_symbol} 5分钟】\n"
+                f"当前方向：{direction_str}\n"
+                f"当前现价：{current_price:.2f} USDT\n"
+                f"开仓均价：{open_avg_price:.2f} USDT\n"
+                f"持仓量(eth)：{float(current_amount) / 1000} eth\n"
+                f"持仓量(usdt)：{position_cross} usdt\n"
+                f"浮动盈亏：{pnl_str}{rate_str}\n"
+                f"账户可用余额：{self.balance:.2f} usdt"
+            )
+        else:
+            msg = (
+                f"【三分之一策略 {self.contract_symbol} 5分钟】\n"
+                f"当前方向：无\n"
+                f"当前现价：{current_price:.2f} USDT\n"
+                f"账户可用余额：{self.balance:.2f} usdt"
+            )
+
+        self.ding(msg=msg)
+
+
+if __name__ == '__main__':
+    # 启动三分之一策略交易
+    BitmartOneThirdStrategy(bit_id="f2320f57e24c45529a009e1541e25961").action()