diff --git a/bitmart/回测-三分之一策略.py b/bitmart/回测-三分之一策略.py
new file mode 100644
index 0000000..63d3935
--- /dev/null
+++ b/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 BitMartETH5M
+
+# 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(BitMartETH5M, 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}
{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)
diff --git a/bitmart/回测图表.png b/bitmart/回测图表.png
new file mode 100644
index 0000000..98b6dd8
Binary files /dev/null and b/bitmart/回测图表.png differ
diff --git a/bitmart/回测图表_交互式.html b/bitmart/回测图表_交互式.html
new file mode 100644
index 0000000..7272d3d
--- /dev/null
+++ b/bitmart/回测图表_交互式.html
@@ -0,0 +1,3888 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/models/bitmart_klines.py b/models/bitmart_klines.py
new file mode 100644
index 0000000..d80f162
--- /dev/null
+++ b/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)