加入 weex
This commit is contained in:
ddrwode
@@ -1,745 +0,0 @@
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"""
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BitMart 五分之一回归策略回测（精准版）
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使用3分钟K线周期计算触发价格，1分钟K线判断触发顺序
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========== 策略规则 ==========
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1. 触发价格计算（基于有效的前一根K线，实体>=0.1）：
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- 做多触发价格 = 收盘价 + 实体/5（从收盘价往上涨1/5）
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- 做空触发价格 = 收盘价 - 实体/5（从收盘价往下跌1/5）
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2. 信号触发条件：
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- 当前K线最高价 >= 做多触发价格 → 做多信号
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- 当前K线最低价 <= 做空触发价格 → 做空信号
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3. 执行逻辑：
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- 做多时遇到做空信号 -> 平多并反手开空
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- 做空时遇到做多信号 -> 平空并反手开多
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- 同一根3分钟K线内只交易一次
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4. 精准判断（使用1分钟K线）：
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- 当一根3分钟K线同时触及做多和做空价格时
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- 使用该3分钟K线对应的3根1分钟K线来判断哪个方向先被触发
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- 这样可以更精准地还原真实交易场景
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"""
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import datetime
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import calendar
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from pathlib import Path
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from typing import List, Dict, Optional
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from loguru import logger
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from peewee import *
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# 数据库配置
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DB_PATH = Path(__file__).parent.parent / 'models' / 'database.db'
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db = SqliteDatabase(str(DB_PATH))
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# ========================= 数据库模型 =========================
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class BitMartETH1m(Model):
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"""1分钟K线模型"""
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id = BigIntegerField(primary_key=True) # 时间戳（毫秒级）
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open = FloatField(null=True)
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high = FloatField(null=True)
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low = FloatField(null=True)
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close = FloatField(null=True)
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class Meta:
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database = db
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table_name = 'bitmart_eth_1m'
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class BitMartETH3m(Model):
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"""3分钟K线模型"""
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id = BigIntegerField(primary_key=True) # 时间戳（毫秒级）
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open = FloatField(null=True)
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high = FloatField(null=True)
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low = FloatField(null=True)
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close = FloatField(null=True)
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class Meta:
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database = db
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table_name = 'bitmart_eth_3m'
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# 连接数据库
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db.connect(reuse_if_open=True)
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# ========================= 工具函数 =========================
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def is_bullish(c):
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"""判断阳线"""
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return float(c['close']) > float(c['open'])
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def is_bearish(c):
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"""判断阴线"""
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return float(c['close']) < float(c['open'])
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def get_body_size(candle):
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"""计算K线实体大小（绝对值）"""
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return abs(float(candle['open']) - float(candle['close']))
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def find_valid_prev_bar(all_data, current_idx, min_body_size=0.1):
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"""
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从当前索引往前查找，直到找到实体>=min_body_size的K线
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返回：(有效K线的索引, K线数据) 或 (None, None)
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"""
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if current_idx <= 0:
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return None, None
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for i in range(current_idx - 1, -1, -1):
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prev = all_data[i]
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body_size = get_body_size(prev)
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if body_size >= min_body_size:
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return i, prev
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return None, None
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def get_one_fifth_levels(prev):
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"""
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计算前一根K线实体的 1/5 双向触发价格
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返回：(做多触发价格, 做空触发价格)
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基于收盘价计算（无论阴线阳线）：
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- 做多触发价格 = 收盘价 + 实体/5（从收盘价往上涨1/5实体）
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- 做空触发价格 = 收盘价 - 实体/5（从收盘价往下跌1/5实体）
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"""
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p_open = float(prev['open'])
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p_close = float(prev['close'])
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body = abs(p_open - p_close)
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if body < 0.001: # 十字星，忽略
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return None, None
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# 基于收盘价的双向触发价格
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long_trigger = p_close + body / 5
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short_trigger = p_close - body / 5
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return long_trigger, short_trigger
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def get_3m_data_by_date(date_str: str) -> List[Dict]:
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"""按天获取3分钟K线数据"""
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try:
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target_date = datetime.datetime.strptime(date_str, '%Y-%m-%d')
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except ValueError:
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logger.error("日期格式不正确，请使用 YYYY-MM-DD 格式。")
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return []
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start_ts = int(target_date.timestamp() * 1000)
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end_ts = int((target_date + datetime.timedelta(days=1)).timestamp() * 1000) - 1
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query = BitMartETH3m.select().where(
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BitMartETH3m.id.between(start_ts, end_ts)
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).order_by(BitMartETH3m.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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return data
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def get_1m_data_by_range(start_ts: int, end_ts: int) -> List[Dict]:
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"""
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获取指定时间范围内的1分钟K线数据
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:param start_ts: 开始时间戳（毫秒）
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:param end_ts: 结束时间戳（毫秒）
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:return: 1分钟K线数据列表
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"""
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query = BitMartETH1m.select().where(
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BitMartETH1m.id.between(start_ts, end_ts - 1)
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).order_by(BitMartETH1m.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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return data
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def get_1m_data_for_3m_bar(bar_3m: Dict) -> List[Dict]:
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"""
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获取3分钟K线对应的3根1分钟K线
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:param bar_3m: 3分钟K线数据
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:return: 对应的1分钟K线数据列表（最多3根）
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"""
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start_ts = bar_3m['id']
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end_ts = start_ts + 3 * 60 * 1000 # 3分钟后
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return get_1m_data_by_range(start_ts, end_ts)
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def determine_trigger_order_by_1m(
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bars_1m: List[Dict],
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long_trigger: float,
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short_trigger: float
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) -> str:
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"""
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使用1分钟K线精确判断在3分钟周期内，是先触发做多还是做空
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:param bars_1m: 3根1分钟K线数据
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:param long_trigger: 做多触发价格
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:param short_trigger: 做空触发价格
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:return: 'long', 'short', 或 None
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"""
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if not bars_1m:
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return None
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for bar in bars_1m:
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high = float(bar['high'])
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low = float(bar['low'])
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open_price = float(bar['open'])
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long_triggered = high >= long_trigger
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short_triggered = low <= short_trigger
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# 如果只触发了一个方向
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if long_triggered and not short_triggered:
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return 'long'
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if short_triggered and not long_triggered:
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return 'short'
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# 如果两个方向都触发了（在同一根1分钟K线内）
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if long_triggered and short_triggered:
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# 根据开盘价判断：
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# 如果开盘价更接近做空触发价，说明先往下走，先触发做空
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# 如果开盘价更接近做多触发价，说明先往上走，先触发做多
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dist_to_long = abs(long_trigger - open_price)
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dist_to_short = abs(short_trigger - open_price)
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if dist_to_short < dist_to_long:
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return 'short'
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else:
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return 'long'
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return None
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def check_reverse_signal_in_first_minute(
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bars_1m: List[Dict],
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long_trigger: float,
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short_trigger: float,
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current_direction: str
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) -> bool:
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"""
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检查反手信号是否在第一分钟触发
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:param bars_1m: 3根1分钟K线数据
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:param long_trigger: 做多触发价格
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:param short_trigger: 做空触发价格
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:param current_direction: 当前持仓方向 ('long' 或 'short')
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:return: True 表示反手信号在第一分钟触发，False 表示不是
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"""
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if not bars_1m:
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return False
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# 只检查第一分钟K线
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first_bar = bars_1m[0]
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high = float(first_bar['high'])
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low = float(first_bar['low'])
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# 如果当前是多仓，检查空信号是否在第一分钟触发
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if current_direction == 'long':
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return low <= short_trigger
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# 如果当前是空仓，检查多信号是否在第一分钟触发
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if current_direction == 'short':
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return high >= long_trigger
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return False
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def get_body_percent(candle) -> float:
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"""
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计算K线实体占价格的百分比
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:param candle: K线数据
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:return: 实体百分比（如0.1表示0.1%）
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"""
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body = abs(float(candle['open']) - float(candle['close']))
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price = (float(candle['open']) + float(candle['close'])) / 2
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if price == 0:
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return 0
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return (body / price) * 100
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def check_breakout_reverse_signal(
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all_data_3m: List[Dict],
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current_idx: int,
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current_position_direction: str,
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min_body_percent: float = 0.1
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) -> tuple:
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"""
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检查"突破上一根K线高低点"的反手信号
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规则：
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- 持多单时：当前K线跌破上一根K线最低点 → 反手开空
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条件：上一根K线是阴线且实体>0.1%波动
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- 持空单时：当前K线涨破上一根K线最高点 → 反手开多
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条件：上一根K线是阳线且实体>0.1%波动
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:param all_data_3m: 3分钟K线数据
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:param current_idx: 当前K线索引
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:param current_position_direction: 当前持仓方向 ('long' 或 'short')
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:param min_body_percent: 最小实体百分比（默认0.1%）
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:return: (方向, 触发价格, 信号类型) 或 (None, None, None)
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"""
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if current_idx <= 0 or current_position_direction is None:
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return None, None, None
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curr = all_data_3m[current_idx]
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prev = all_data_3m[current_idx - 1]
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c_high = float(curr['high'])
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c_low = float(curr['low'])
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prev_high = float(prev['high'])
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prev_low = float(prev['low'])
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# 计算上一根K线的实体百分比
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body_percent = get_body_percent(prev)
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# 持多单时：检查是否跌破上一根K线最低点
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if current_position_direction == 'long':
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# 条件：上一根K线是阴线且实体>min_body_percent%
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if is_bearish(prev) and body_percent >= min_body_percent:
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if c_low < prev_low:
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# 触发反手开空信号
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logger.debug(f"突破反手信号：持多单，当前K线低点{c_low:.2f}跌破上一根阴线低点{prev_low:.2f}，实体{body_percent:.3f}%")
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return 'short', prev_low, 'breakout'
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# 持空单时：检查是否涨破上一根K线最高点
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elif current_position_direction == 'short':
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# 条件：上一根K线是阳线且实体>min_body_percent%
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if is_bullish(prev) and body_percent >= min_body_percent:
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if c_high > prev_high:
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# 触发反手开多信号
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logger.debug(f"突破反手信号：持空单，当前K线高点{c_high:.2f}涨破上一根阳线高点{prev_high:.2f}，实体{body_percent:.3f}%")
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return 'long', prev_high, 'breakout'
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return None, None, None
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def check_trigger_with_1m(
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all_data_3m: List[Dict],
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current_idx: int,
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min_body_size: float = 0.1,
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current_position_direction: str = None,
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first_minute_only: bool = True
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) -> tuple:
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"""
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检查当前3分钟K线是否触发了交易信号
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如果同时触发两个方向，使用1分钟K线精确判断顺序
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新增逻辑：如果有持仓且 first_minute_only=True，反手信号必须在第一分钟触发才有效
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:param all_data_3m: 3分钟K线数据
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:param current_idx: 当前K线索引
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:param min_body_size: 最小实体大小
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:param current_position_direction: 当前持仓方向 ('long', 'short', 或 None)
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:param first_minute_only: 是否只在第一分钟触发反手信号才有效
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返回：(方向, 触发价格, 有效前一根K线索引, 1分钟数据是否使用)
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"""
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if current_idx <= 0:
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return None, None, None, False
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curr = all_data_3m[current_idx]
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# 查找实体>=min_body_size的前一根K线
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valid_prev_idx, prev = find_valid_prev_bar(all_data_3m, current_idx, min_body_size)
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if prev is None:
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return None, None, None, False
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long_trigger, short_trigger = get_one_fifth_levels(prev)
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if long_trigger is None:
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return None, None, None, False
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c_high = float(curr['high'])
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c_low = float(curr['low'])
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# 检测是否触发
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long_triggered = c_high >= long_trigger
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short_triggered = c_low <= short_trigger
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# 如果两个方向都触发，使用1分钟K线精确判断
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if long_triggered and short_triggered:
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bars_1m = get_1m_data_for_3m_bar(curr)
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if bars_1m:
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direction = determine_trigger_order_by_1m(bars_1m, long_trigger, short_trigger)
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if direction:
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trigger_price = long_trigger if direction == 'long' else short_trigger
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# 检查反手信号是否需要在第一分钟触发
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if first_minute_only and current_position_direction and direction != current_position_direction:
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# 这是一个反手信号，检查是否在第一分钟触发
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if not check_reverse_signal_in_first_minute(bars_1m, long_trigger, short_trigger, current_position_direction):
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# 反手信号不是在第一分钟触发，忽略
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logger.debug(f"反手信号 {direction} 不在第一分钟触发，忽略")
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return None, None, None, False
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return direction, trigger_price, valid_prev_idx, True
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# 如果没有1分钟数据，使用开盘价距离判断
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c_open = float(curr['open'])
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dist_to_long = abs(long_trigger - c_open)
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dist_to_short = abs(short_trigger - c_open)
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if dist_to_short <= dist_to_long:
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return 'short', short_trigger, valid_prev_idx, False
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else:
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return 'long', long_trigger, valid_prev_idx, False
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if short_triggered:
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# 检查是否是反手信号且需要第一分钟触发
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if first_minute_only and current_position_direction == 'long':
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bars_1m = get_1m_data_for_3m_bar(curr)
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if bars_1m and not check_reverse_signal_in_first_minute(bars_1m, long_trigger, short_trigger, 'long'):
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logger.debug(f"空信号不在第一分钟触发，忽略（当前持多仓）")
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return None, None, None, False
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return 'short', short_trigger, valid_prev_idx, False
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if long_triggered:
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# 检查是否是反手信号且需要第一分钟触发
|
||||
if first_minute_only and current_position_direction == 'short':
|
||||
bars_1m = get_1m_data_for_3m_bar(curr)
|
||||
if bars_1m and not check_reverse_signal_in_first_minute(bars_1m, long_trigger, short_trigger, 'short'):
|
||||
logger.debug(f"多信号不在第一分钟触发，忽略（当前持空仓）")
|
||||
return None, None, None, False
|
||||
return 'long', long_trigger, valid_prev_idx, False
|
||||
|
||||
return None, None, None, False
|
||||
|
||||
|
||||
# ========================= 回测逻辑 =========================
|
||||
|
||||
def backtest_one_third_strategy(
|
||||
dates: List[str],
|
||||
min_body_size: float = 0.1,
|
||||
first_minute_only: bool = True,
|
||||
enable_breakout_reverse: bool = True,
|
||||
breakout_min_body_percent: float = 0.1
|
||||
):
|
||||
"""
|
||||
三分之一策略回测（精准版）
|
||||
|
||||
:param dates: 日期列表
|
||||
:param min_body_size: 最小实体大小（绝对值）
|
||||
:param first_minute_only: 是否只在第一分钟触发反手信号才有效（默认True）
|
||||
:param enable_breakout_reverse: 是否启用"突破上一根K线高低点"反手信号（默认True）
|
||||
:param breakout_min_body_percent: 突破反手信号的最小实体百分比（默认0.1%）
|
||||
:return: (trades, stats)
|
||||
"""
|
||||
# 获取所有3分钟K线数据
|
||||
all_data: List[Dict] = []
|
||||
total_queried = 0
|
||||
for d in dates:
|
||||
day_data = get_3m_data_by_date(d)
|
||||
all_data.extend(day_data)
|
||||
if day_data:
|
||||
total_queried += len(day_data)
|
||||
|
||||
logger.info(f"总共查询了 {len(dates)} 天，获取到 {total_queried} 条3分钟K线数据")
|
||||
logger.info(f"反手信号仅第一分钟有效: {first_minute_only}")
|
||||
logger.info(f"突破反手信号启用: {enable_breakout_reverse}，最小实体百分比: {breakout_min_body_percent}%")
|
||||
|
||||
if not all_data:
|
||||
logger.warning("未获取到任何数据，请检查数据库")
|
||||
return [], {'long': {'count': 0, 'wins': 0, 'total_profit': 0.0},
|
||||
'short': {'count': 0, 'wins': 0, 'total_profit': 0.0}}
|
||||
|
||||
# 按时间戳排序
|
||||
all_data.sort(key=lambda x: x['id'])
|
||||
|
||||
# 验证排序结果
|
||||
if len(all_data) > 1:
|
||||
first_ts = all_data[0]['id']
|
||||
last_ts = all_data[-1]['id']
|
||||
first_time = datetime.datetime.fromtimestamp(first_ts / 1000)
|
||||
last_time = datetime.datetime.fromtimestamp(last_ts / 1000)
|
||||
logger.info(f"数据范围：{first_time.strftime('%Y-%m-%d %H:%M:%S')} 到 {last_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
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
|
||||
|
||||
# 统计使用1分钟数据精准判断的次数
|
||||
precise_count = 0
|
||||
fallback_count = 0
|
||||
# 统计突破反手信号触发次数
|
||||
breakout_reverse_count = 0
|
||||
# 记录每根K线是否已经执行过突破反手（当前K线只执行一次）
|
||||
last_breakout_bar_id = None
|
||||
|
||||
idx = 1
|
||||
while idx < len(all_data):
|
||||
curr = all_data[idx]
|
||||
curr_bar_id = curr['id']
|
||||
|
||||
# 获取当前持仓方向（用于判断反手信号是否在第一分钟触发）
|
||||
current_pos_dir = current_position['direction'] if current_position else None
|
||||
|
||||
# 检测信号（使用1分钟K线精准判断，并考虑反手信号第一分钟限制）
|
||||
direction, trigger_price, valid_prev_idx, used_1m = check_trigger_with_1m(
|
||||
all_data, idx, min_body_size,
|
||||
current_position_direction=current_pos_dir,
|
||||
first_minute_only=first_minute_only
|
||||
)
|
||||
|
||||
# 如果没有五分之一信号，且有持仓，检查突破反手信号
|
||||
signal_type = 'one_fifth' # 信号类型：one_fifth（五分之一）或 breakout（突破）
|
||||
if direction is None and current_position is not None and enable_breakout_reverse:
|
||||
# 检查当前K线是否已经执行过突破反手
|
||||
if last_breakout_bar_id != curr_bar_id:
|
||||
breakout_dir, breakout_price, breakout_type = check_breakout_reverse_signal(
|
||||
all_data, idx, current_pos_dir, breakout_min_body_percent
|
||||
)
|
||||
if breakout_dir:
|
||||
direction = breakout_dir
|
||||
trigger_price = breakout_price
|
||||
signal_type = 'breakout'
|
||||
|
||||
if used_1m:
|
||||
precise_count += 1
|
||||
elif direction and signal_type == 'one_fifth':
|
||||
fallback_count += 1
|
||||
|
||||
# 无持仓 -> 开仓（只用五分之一信号开仓，突破信号不用于开仓）
|
||||
if current_position is None:
|
||||
if direction and signal_type == 'one_fifth':
|
||||
current_position = {
|
||||
'direction': direction,
|
||||
'entry_price': trigger_price,
|
||||
'entry_time': curr['id'],
|
||||
'entry_bar_idx': idx
|
||||
}
|
||||
stats[direction]['count'] += 1
|
||||
|
||||
time_str = datetime.datetime.fromtimestamp(curr['id'] / 1000).strftime('%Y-%m-%d %H:%M')
|
||||
logger.debug(f"[{time_str}] 开仓{direction} @ {trigger_price:.2f}")
|
||||
|
||||
idx += 1
|
||||
continue
|
||||
|
||||
# 有持仓 -> 检查是否需要反向
|
||||
pos_dir = current_position['direction']
|
||||
|
||||
if direction and direction != pos_dir:
|
||||
# 反向信号，平仓并反手
|
||||
exit_price = trigger_price
|
||||
|
||||
if pos_dir == 'long':
|
||||
diff = exit_price - current_position['entry_price']
|
||||
else:
|
||||
diff = current_position['entry_price'] - exit_price
|
||||
|
||||
# 记录信号类型
|
||||
signal_type_str = '突破' if signal_type == 'breakout' else '五分之一'
|
||||
|
||||
trades.append({
|
||||
'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
|
||||
'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
|
||||
'direction': '做多' if pos_dir == 'long' else '做空',
|
||||
'entry': current_position['entry_price'],
|
||||
'exit': exit_price,
|
||||
'diff': diff,
|
||||
'signal_type': signal_type_str
|
||||
})
|
||||
|
||||
stats[pos_dir]['total_profit'] += diff
|
||||
if diff > 0:
|
||||
stats[pos_dir]['wins'] += 1
|
||||
|
||||
# 如果是突破反手信号，记录当前K线ID，防止重复执行
|
||||
if signal_type == 'breakout':
|
||||
last_breakout_bar_id = curr_bar_id
|
||||
breakout_reverse_count += 1
|
||||
|
||||
# 反手开仓
|
||||
current_position = {
|
||||
'direction': direction,
|
||||
'entry_price': trigger_price,
|
||||
'entry_time': curr['id'],
|
||||
'entry_bar_idx': idx
|
||||
}
|
||||
stats[direction]['count'] += 1
|
||||
|
||||
time_str = datetime.datetime.fromtimestamp(curr['id'] / 1000).strftime('%Y-%m-%d %H:%M')
|
||||
logger.debug(f"[{time_str}] 平{pos_dir}反手{direction}({signal_type_str}) @ {trigger_price:.2f} 盈亏={diff:.2f}")
|
||||
|
||||
idx += 1
|
||||
|
||||
# 尾仓处理：最后一根K线收盘价平仓
|
||||
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),
|
||||
'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
|
||||
|
||||
logger.info(f"回测完成：使用1分钟精准判断 {precise_count} 次，使用开盘价距离判断 {fallback_count} 次，突破反手信号 {breakout_reverse_count} 次")
|
||||
|
||||
return trades, stats
|
||||
|
||||
|
||||
# ========================= 运行回测 =========================
|
||||
|
||||
if __name__ == '__main__':
|
||||
# ==================== 配置参数 ====================
|
||||
START_DATE = "2025-01-01"
|
||||
END_DATE = "2025-12-31"
|
||||
MIN_BODY_SIZE = 0.1 # 最小实体大小（绝对值）
|
||||
FIRST_MINUTE_ONLY = True # 反手信号仅在3分钟K线的第一分钟触发才有效
|
||||
|
||||
# 突破反手信号配置
|
||||
ENABLE_BREAKOUT_REVERSE = True # 是否启用"突破上一根K线高低点"反手信号
|
||||
BREAKOUT_MIN_BODY_PERCENT = 0.1 # 突破反手信号的最小实体百分比（0.1表示0.1%）
|
||||
|
||||
# ==================== 生成查询日期列表 ====================
|
||||
dates = []
|
||||
|
||||
try:
|
||||
start_dt = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
|
||||
end_dt = datetime.datetime.strptime(END_DATE, '%Y-%m-%d')
|
||||
|
||||
if start_dt > end_dt:
|
||||
logger.error(f"开始日期 {START_DATE} 不能晚于结束日期 {END_DATE}")
|
||||
exit(1)
|
||||
|
||||
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"回测日期范围：{START_DATE} 到 {END_DATE}，共 {len(dates)} 天")
|
||||
except ValueError as e:
|
||||
logger.error(f"日期格式错误：{e}")
|
||||
exit(1)
|
||||
|
||||
# ==================== 执行回测 ====================
|
||||
trades, stats = backtest_one_third_strategy(
|
||||
dates,
|
||||
MIN_BODY_SIZE,
|
||||
FIRST_MINUTE_ONLY,
|
||||
ENABLE_BREAKOUT_REVERSE,
|
||||
BREAKOUT_MIN_BODY_PERCENT
|
||||
)
|
||||
|
||||
# ==================== 输出交易详情 ====================
|
||||
logger.info("===== 每笔交易详情 =====")
|
||||
|
||||
# 参数设定
|
||||
contract_size = 10000 # 合约规模
|
||||
open_fee_fixed = 5 # 固定开仓手续费
|
||||
close_fee_rate = 0.0005 # 平仓手续费率
|
||||
|
||||
total_points_profit = 0
|
||||
total_money_profit = 0
|
||||
total_fee = 0
|
||||
|
||||
for t in trades:
|
||||
entry = t['entry']
|
||||
exit_price = t['exit']
|
||||
direction = t['direction']
|
||||
|
||||
# 原始价差
|
||||
point_diff = t['diff']
|
||||
|
||||
# 金额盈利
|
||||
money_profit = point_diff / entry * contract_size
|
||||
|
||||
# 手续费
|
||||
fee = open_fee_fixed + (contract_size / entry * exit_price * close_fee_rate)
|
||||
|
||||
# 净利润
|
||||
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_price:.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{'='*60}")
|
||||
print(f"【BitMart 五分之一策略回测结果（3分钟K线 + 1分钟精准判断）】")
|
||||
print(f"{'='*60}")
|
||||
print(f"回测周期：{START_DATE} 到 {END_DATE}")
|
||||
print(f"最小实体要求：{MIN_BODY_SIZE}")
|
||||
print(f"反手信号仅第一分钟有效：{'是' if FIRST_MINUTE_ONLY else '否'}")
|
||||
print(f"突破反手信号：{'启用' if ENABLE_BREAKOUT_REVERSE else '禁用'}（最小实体{BREAKOUT_MIN_BODY_PERCENT}%）")
|
||||
print(f"{'='*60}")
|
||||
print(f"总交易笔数：{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}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
print("\n===== 方向统计 =====")
|
||||
for k, v in stats.items():
|
||||
name = v['name']
|
||||
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"{name}: 次数={count} 胜率={win_rate:.2f}% 总价差={total_p:.2f} 平均价差={avg_p:.2f}")
|
||||
|
||||
# 保存交易记录到CSV
|
||||
if trades:
|
||||
import csv
|
||||
csv_path = Path(__file__).parent / 'backtest_one_third_trades.csv'
|
||||
with open(csv_path, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.DictWriter(f, fieldnames=[
|
||||
'entry_time', 'exit_time', 'direction', 'entry', 'exit',
|
||||
'point_diff', 'raw_profit', 'fee', 'net_profit', 'signal_type'
|
||||
])
|
||||
writer.writeheader()
|
||||
for t in trades:
|
||||
writer.writerow({
|
||||
'entry_time': t['entry_time'],
|
||||
'exit_time': t['exit_time'],
|
||||
'direction': t['direction'],
|
||||
'entry': t['entry'],
|
||||
'exit': t['exit'],
|
||||
'point_diff': t['point_diff'],
|
||||
'raw_profit': t['raw_profit'],
|
||||
'fee': t['fee'],
|
||||
'net_profit': t['net_profit'],
|
||||
'signal_type': t.get('signal_type', '五分之一')
|
||||
})
|
||||
print(f"\n交易记录已保存到：{csv_path}")
|
||||
@@ -1,764 +0,0 @@
|
||||
"""
|
||||
量化交易回测系统 - 三分之一回归策略（双向触发版）
|
||||
|
||||
========== 策略规则 ==========
|
||||
|
||||
1. 触发价格计算（基于有效的前一根K线，实体>=0.1）：
|
||||
- 做多触发价格 = 收盘价 + 实体/3（从收盘价往上涨1/3）
|
||||
- 做空触发价格 = 收盘价 - 实体/3（从收盘价往下跌1/3）
|
||||
|
||||
2. 信号触发条件：
|
||||
- 当前K线最高价 >= 做多触发价格 → 做多信号
|
||||
- 当前K线最低价 <= 做空触发价格 → 做空信号
|
||||
|
||||
3. 执行逻辑：
|
||||
- 做多时遇到做空信号 -> 平多并反手开空
|
||||
- 做空时遇到做多信号 -> 平空并反手开多
|
||||
- 同一根K线内只交易一次，防止频繁反手
|
||||
|
||||
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线来计算触发价格
|
||||
|
||||
示例1（阳线）：
|
||||
前一根K线：开盘3000，收盘3100（阳线，实体=100）
|
||||
- 做多触发价格 = 3100 + 33 = 3133（继续上涨做多）
|
||||
- 做空触发价格 = 3100 - 33 = 3067（回调做空）
|
||||
|
||||
示例2（阴线）：
|
||||
前一根K线：开盘3100，收盘3000（阴线，实体=100）
|
||||
- 做多触发价格 = 3000 + 33 = 3033（反弹做多）
|
||||
- 做空触发价格 = 3000 - 33 = 2967（继续下跌做空）
|
||||
"""
|
||||
|
||||
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_levels(prev):
|
||||
"""
|
||||
计算前一根K线实体的 1/3 双向触发价格
|
||||
返回：(做多触发价格, 做空触发价格)
|
||||
|
||||
基于收盘价计算（无论阴线阳线）：
|
||||
- 做多触发价格 = 收盘价 + 实体/3（从收盘价往上涨1/3实体）
|
||||
- 做空触发价格 = 收盘价 - 实体/3（从收盘价往下跌1/3实体）
|
||||
|
||||
示例：
|
||||
阳线 open=3000, close=3100, 实体=100
|
||||
- 做多触发 = 3100 + 33 = 3133（继续涨）
|
||||
- 做空触发 = 3100 - 33 = 3067（回调）
|
||||
|
||||
阴线 open=3100, close=3000, 实体=100
|
||||
- 做多触发 = 3000 + 33 = 3033（反弹）
|
||||
- 做空触发 = 3000 - 33 = 2967（继续跌）
|
||||
"""
|
||||
p_open = float(prev['open'])
|
||||
p_close = float(prev['close'])
|
||||
|
||||
body = abs(p_open - p_close)
|
||||
|
||||
if body < 0.001: # 十字星，忽略
|
||||
return None, None
|
||||
|
||||
# 基于收盘价的双向触发价格
|
||||
long_trigger = p_close + body / 3 # 从收盘价往上涨1/3触发做多
|
||||
short_trigger = p_close - body / 3 # 从收盘价往下跌1/3触发做空
|
||||
|
||||
return long_trigger, short_trigger
|
||||
|
||||
|
||||
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（双向检测）
|
||||
返回：(方向, 触发价格, 有效前一根K线索引) 或 (None, None, None)
|
||||
|
||||
规则：
|
||||
- 当前K线高点 >= 做多触发价格 → 做多信号
|
||||
- 当前K线低点 <= 做空触发价格 → 做空信号
|
||||
- 如果同时触发两个方向，以先触发的为准（这里简化为优先做空，因为下跌更快）
|
||||
"""
|
||||
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 = find_valid_prev_bar(all_data, current_idx, min_body_size)
|
||||
|
||||
if prev is None:
|
||||
return None, None, None
|
||||
|
||||
long_trigger, short_trigger = get_one_third_levels(prev)
|
||||
|
||||
if long_trigger 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'])
|
||||
|
||||
# 检测是否触发
|
||||
long_triggered = c_high >= long_trigger
|
||||
short_triggered = c_low <= short_trigger
|
||||
|
||||
# 如果两个方向都触发，需要判断哪个先触发
|
||||
# 简化处理：比较触发价格距离开盘价的远近，更近的先触发
|
||||
if long_triggered and short_triggered:
|
||||
c_open = float(curr['open'])
|
||||
dist_to_long = abs(long_trigger - c_open)
|
||||
dist_to_short = abs(short_trigger - c_open)
|
||||
if dist_to_short <= dist_to_long:
|
||||
return 'short', short_trigger, valid_prev_idx
|
||||
else:
|
||||
return 'long', long_trigger, valid_prev_idx
|
||||
|
||||
if short_triggered:
|
||||
return 'short', short_trigger, valid_prev_idx
|
||||
|
||||
if long_triggered:
|
||||
return 'long', long_trigger, 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（优化版）"""
|
||||
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'},
|
||||
}
|
||||
|
||||
# 额外统计信息
|
||||
extra_stats = {
|
||||
'same_dir_ignored': 0, # 同向信号被忽略次数
|
||||
'no_signal_bars': 0, # 无信号K线数
|
||||
'total_bars': len(all_data) - 1, # 总K线数（排除第一根）
|
||||
}
|
||||
|
||||
trades: List[Dict] = []
|
||||
current_position: Optional[Dict] = None
|
||||
last_trade_bar: Optional[int] = None # 记录上次交易的K线索引，防止同一K线重复交易
|
||||
|
||||
for idx in range(1, len(all_data)):
|
||||
curr = all_data[idx]
|
||||
|
||||
# 使用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
|
||||
|
||||
# 无信号时跳过
|
||||
if direction is None:
|
||||
extra_stats['no_signal_bars'] += 1
|
||||
continue
|
||||
|
||||
# 同一K线内已交易，跳过（与交易代码逻辑一致）
|
||||
if last_trade_bar == idx:
|
||||
continue
|
||||
|
||||
# 空仓时，有信号就开仓
|
||||
if current_position is None:
|
||||
if valid_prev is not None:
|
||||
# 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
|
||||
last_trade_bar = idx # 记录交易K线
|
||||
continue
|
||||
|
||||
# 有仓位时，检查信号
|
||||
pos_dir = current_position['direction']
|
||||
|
||||
# 同向信号，忽略（与交易代码逻辑一致）
|
||||
if direction == pos_dir:
|
||||
extra_stats['same_dir_ignored'] += 1
|
||||
continue
|
||||
|
||||
# 反向信号，平仓反手
|
||||
if valid_prev is not None:
|
||||
exit_price = trigger_price
|
||||
|
||||
if pos_dir == 'long':
|
||||
diff = exit_price - current_position['entry_price']
|
||||
else:
|
||||
diff = current_position['entry_price'] - exit_price
|
||||
|
||||
# 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,
|
||||
'hold_bars': idx - current_position['entry_bar'] # 持仓K线数
|
||||
})
|
||||
|
||||
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
|
||||
last_trade_bar = idx # 记录交易K线
|
||||
|
||||
# 尾仓处理
|
||||
if current_position:
|
||||
last = all_data[-1]
|
||||
last_idx = len(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,
|
||||
'hold_bars': last_idx - current_position['entry_bar'], # 持仓K线数
|
||||
'is_tail': True # 标记为尾仓平仓
|
||||
})
|
||||
stats[pos_dir]['total_profit'] += diff
|
||||
if diff > 0:
|
||||
stats[pos_dir]['wins'] += 1
|
||||
|
||||
logger.info(f"【尾仓平仓】{pos_dir} @ {exit_price:.2f}, 盈亏: {diff:.2f}")
|
||||
|
||||
# 打印额外统计信息
|
||||
logger.info(f"\n===== 信号统计 =====")
|
||||
logger.info(f"总K线数: {extra_stats['total_bars']}")
|
||||
logger.info(f"无信号K线: {extra_stats['no_signal_bars']} ({extra_stats['no_signal_bars']/extra_stats['total_bars']*100:.1f}%)")
|
||||
logger.info(f"同向信号忽略: {extra_stats['same_dir_ignored']}")
|
||||
|
||||
return trades, stats, all_data, extra_stats
|
||||
|
||||
|
||||
# ========================= 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, extra_stats = 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
|
||||
|
||||
# 计算额外统计
|
||||
win_count = len([t for t in trades if t['diff'] > 0])
|
||||
lose_count = len([t for t in trades if t['diff'] <= 0])
|
||||
total_win_rate = (win_count / len(trades) * 100) if trades else 0
|
||||
|
||||
# 计算平均持仓K线数
|
||||
hold_bars_list = [t.get('hold_bars', 0) for t in trades if 'hold_bars' in t]
|
||||
avg_hold_bars = sum(hold_bars_list) / len(hold_bars_list) if hold_bars_list else 0
|
||||
|
||||
# 计算最大连续亏损
|
||||
max_consecutive_loss = 0
|
||||
current_consecutive_loss = 0
|
||||
for t in trades:
|
||||
if t['diff'] <= 0:
|
||||
current_consecutive_loss += 1
|
||||
max_consecutive_loss = max(max_consecutive_loss, current_consecutive_loss)
|
||||
else:
|
||||
current_consecutive_loss = 0
|
||||
|
||||
# 计算最大回撤
|
||||
cumulative_profit = 0
|
||||
peak = 0
|
||||
max_drawdown = 0
|
||||
for t in trades:
|
||||
cumulative_profit += t.get('net_profit', t['diff'])
|
||||
peak = max(peak, cumulative_profit)
|
||||
drawdown = peak - cumulative_profit
|
||||
max_drawdown = max(max_drawdown, drawdown)
|
||||
|
||||
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"盈利笔数：{win_count} 亏损笔数：{lose_count}")
|
||||
print(f"总胜率：{total_win_rate:.2f}%")
|
||||
print(f"平均持仓K线数：{avg_hold_bars:.1f}")
|
||||
print(f"最大连续亏损：{max_consecutive_loss} 笔")
|
||||
print(f"{'='*60}")
|
||||
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"最大回撤：{max_drawdown:.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}")
|
||||
|
||||
print("\n===== 信号统计 =====")
|
||||
print(f"总K线数: {extra_stats['total_bars']}")
|
||||
print(f"无信号K线: {extra_stats['no_signal_bars']} ({extra_stats['no_signal_bars']/extra_stats['total_bars']*100:.1f}%)")
|
||||
print(f"同向信号忽略: {extra_stats['same_dir_ignored']}")
|
||||
|
||||
# ==================== 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)
|
||||
@@ -1,418 +0,0 @@
|
||||
"""
|
||||
量化交易回测系统 - 30分钟K线策略回测（BitMart数据源）
|
||||
|
||||
========== 策略规则 ==========
|
||||
重要：所有开仓和平仓操作都在下一根K线的开盘价执行
|
||||
|
||||
1. 开仓条件（信号出现时，下一根K线开盘价开仓）：
|
||||
- 阳包阴（涨包跌）：前一根是跌（阴线），后一根是涨（阳线），且涨的收盘价 > 跌的开盘价
|
||||
-> 下一根K线开盘价开多
|
||||
- 阴包阳（跌包涨）：前一根是涨（阳线），后一根是跌（阴线），且跌的收盘价 < 涨的开盘价
|
||||
-> 下一根K线开盘价开空
|
||||
|
||||
2. 平仓条件（所有平仓都在下一根K线开盘价执行）：
|
||||
- 持有多单时：遇到两根连续的阴线 -> 下一根K线开盘价平仓
|
||||
- 持有空单时：遇到两根连续的阳线 -> 下一根K线开盘价平仓
|
||||
- 遇到反向信号：下一根K线开盘价平仓并反手开仓
|
||||
|
||||
3. 续持条件：
|
||||
- 遇到同向信号：续持
|
||||
- 未满足平仓条件：续持
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import calendar
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Dict, Optional
|
||||
from loguru import logger
|
||||
from models.bitmart import BitMart30
|
||||
|
||||
|
||||
# ========================= 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（优化版）：
|
||||
只看两种信号，严格按照收盘价与开盘价的比较：
|
||||
|
||||
1. 跌包涨（前涨后跌）-> 做空：
|
||||
- 前一根是涨（阳线：close > open）
|
||||
- 后一根是跌（阴线：close < open）
|
||||
- 且：跌的收盘价 < 涨的开盘价（curr['close'] < prev['open']）
|
||||
|
||||
2. 涨包跌（前跌后涨）-> 做多：
|
||||
- 前一根是跌（阴线：close < open）
|
||||
- 后一根是涨（阳线：close > open）
|
||||
- 且：涨的收盘价 > 跌的开盘价（curr['close'] > prev['open']）
|
||||
"""
|
||||
p_open = float(prev['open'])
|
||||
c_close = float(curr['close'])
|
||||
|
||||
# 跌包涨（前涨后跌） -> 做空：跌的收盘价 < 涨的开盘价
|
||||
if is_bullish(prev) and is_bearish(curr) and c_close < p_open:
|
||||
return "short", "bull_bear_engulf"
|
||||
|
||||
# 涨包跌（前跌后涨） -> 做多：涨的收盘价 > 跌的开盘价
|
||||
if is_bearish(prev) and is_bullish(curr) and c_close > p_open:
|
||||
return "long", "bear_bull_engulf"
|
||||
|
||||
return None, None
|
||||
|
||||
|
||||
def get_data_by_date(model, date_str: str):
|
||||
"""
|
||||
按天获取指定表的数据（30分钟K线）
|
||||
数据格式：时间戳(毫秒级) 开盘价 最高价 最低价 收盘价
|
||||
例如：1767461400000 3106.68 3109.1 3106.22 3107.22
|
||||
|
||||
注意：返回的数据已按时间戳（id）升序排序
|
||||
"""
|
||||
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 []
|
||||
|
||||
# 将日期转换为毫秒级时间戳进行查询
|
||||
start_ts = int(target_date.timestamp() * 1000)
|
||||
end_ts = int((target_date + datetime.timedelta(days=1)).timestamp() * 1000) - 1
|
||||
|
||||
# 查询时按时间戳升序排序
|
||||
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]
|
||||
|
||||
# 确保数据已排序
|
||||
if data:
|
||||
data.sort(key=lambda x: x['id'])
|
||||
|
||||
return data
|
||||
|
||||
|
||||
# ========================= 回测逻辑 =========================
|
||||
|
||||
def backtest_15m_trend_optimized(dates: List[str]):
|
||||
all_data: List[Dict] = []
|
||||
total_queried = 0
|
||||
for d in dates:
|
||||
day_data = get_data_by_date(BitMart30, d)
|
||||
all_data.extend(day_data)
|
||||
if day_data:
|
||||
total_queried += len(day_data)
|
||||
|
||||
logger.info(f"总共查询了 {len(dates)} 天，获取到 {total_queried} 条K线数据")
|
||||
|
||||
if not all_data:
|
||||
logger.warning("未获取到任何数据，请检查：")
|
||||
logger.warning("1. 数据库连接是否正常")
|
||||
logger.warning("2. 查询的日期范围是否在数据范围内")
|
||||
logger.warning("3. 时间戳格式是否正确（毫秒级）")
|
||||
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'},
|
||||
}
|
||||
|
||||
# 重要：合并所有数据后，必须先按时间戳（id）排序
|
||||
all_data.sort(key=lambda x: x['id'])
|
||||
|
||||
# 验证排序结果
|
||||
if len(all_data) > 1:
|
||||
first_ts = all_data[0]['id']
|
||||
last_ts = all_data[-1]['id']
|
||||
first_time = datetime.datetime.fromtimestamp(first_ts / 1000)
|
||||
last_time = datetime.datetime.fromtimestamp(last_ts / 1000)
|
||||
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')}")
|
||||
|
||||
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 # 开仓信息
|
||||
consecutive_opposite_count = 0 # 连续反色K线计数
|
||||
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（下一根K线开盘价）
|
||||
if current_position is None:
|
||||
if 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']
|
||||
}
|
||||
consecutive_opposite_count = 0 # 重置连续反色计数
|
||||
stats[signal_key]['count'] += 1
|
||||
idx += 1
|
||||
continue
|
||||
|
||||
# 有仓位状态：检查平仓条件
|
||||
pos_dir = current_position['direction']
|
||||
pos_sig_key = current_position['signal_key']
|
||||
|
||||
# 1. 反向信号 -> 下一根K线开盘价平仓并反手开仓
|
||||
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']
|
||||
}
|
||||
consecutive_opposite_count = 0 # 重置连续反色计数
|
||||
stats[signal_key]['count'] += 1
|
||||
idx += 1
|
||||
continue
|
||||
|
||||
# 2. 检查连续反色K线平仓条件（下一根K线开盘价平仓）
|
||||
# 持有多单：检查是否连续两根阴线
|
||||
if pos_dir == 'long' and is_bearish(curr):
|
||||
consecutive_opposite_count += 1
|
||||
# 如果已经连续两根阴线，下一根K线开盘价平仓
|
||||
if consecutive_opposite_count >= 2:
|
||||
exit_price = float(next_bar['open'])
|
||||
diff = exit_price - current_position['entry_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',
|
||||
'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
|
||||
consecutive_opposite_count = 0
|
||||
idx += 1
|
||||
continue
|
||||
else:
|
||||
# 只有一根阴线，续持
|
||||
idx += 1
|
||||
continue
|
||||
|
||||
# 持有空单：检查是否连续两根阳线
|
||||
elif pos_dir == 'short' and is_bullish(curr):
|
||||
consecutive_opposite_count += 1
|
||||
# 如果已经连续两根阳线，下一根K线开盘价平仓
|
||||
if consecutive_opposite_count >= 2:
|
||||
exit_price = float(next_bar['open'])
|
||||
diff = 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
|
||||
consecutive_opposite_count = 0
|
||||
idx += 1
|
||||
continue
|
||||
else:
|
||||
# 只有一根阳线，续持
|
||||
idx += 1
|
||||
continue
|
||||
|
||||
# 3. 同向K线或同向信号 -> 续持，重置连续反色计数
|
||||
if (pos_dir == 'long' and is_bullish(curr)) or (pos_dir == 'short' and is_bearish(curr)):
|
||||
consecutive_opposite_count = 0 # 重置连续反色计数
|
||||
|
||||
# 同向信号 -> 续持
|
||||
if direction and direction == pos_dir:
|
||||
consecutive_opposite_count = 0 # 重置连续反色计数
|
||||
idx += 1
|
||||
continue
|
||||
|
||||
idx += 1
|
||||
|
||||
# 尾仓：最后一根收盘价平仓
|
||||
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（优化版盈利计算） =========================
|
||||
if __name__ == '__main__':
|
||||
# ==================== 配置参数：指定查询时间范围 ====================
|
||||
# 方式1：指定开始和结束日期（推荐）
|
||||
START_DATE = "2025-01-01" # 开始日期，格式：YYYY-MM-DD
|
||||
END_DATE = "2025-12-31" # 结束日期，格式：YYYY-MM-DD
|
||||
|
||||
# 方式2：如果上面两个为空，则使用年份和月份范围
|
||||
START_YEAR = None # 开始年份，例如：2025
|
||||
START_MONTH = None # 开始月份，例如：1
|
||||
END_YEAR = None # 结束年份，例如：2025
|
||||
END_MONTH = None # 结束月份，例如：12
|
||||
|
||||
# ==================== 生成查询日期列表 ====================
|
||||
dates = []
|
||||
|
||||
# 优先使用指定的日期范围
|
||||
if START_DATE and END_DATE:
|
||||
try:
|
||||
start_dt = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
|
||||
end_dt = datetime.datetime.strptime(END_DATE, '%Y-%m-%d')
|
||||
|
||||
if start_dt > end_dt:
|
||||
logger.error(f"开始日期 {START_DATE} 不能晚于结束日期 {END_DATE}")
|
||||
exit(1)
|
||||
|
||||
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"使用指定日期范围：{START_DATE} 到 {END_DATE}，共 {len(dates)} 天")
|
||||
except ValueError as e:
|
||||
logger.error(f"日期格式错误：{e}，请使用 YYYY-MM-DD 格式")
|
||||
exit(1)
|
||||
|
||||
# 如果未指定日期范围，使用年份和月份范围
|
||||
elif START_YEAR and END_YEAR:
|
||||
start_m = START_MONTH if START_MONTH else 1
|
||||
end_m = END_MONTH if END_MONTH else 12
|
||||
|
||||
for year in range(START_YEAR, END_YEAR + 1):
|
||||
month_start = start_m if year == START_YEAR else 1
|
||||
month_end = end_m if year == END_YEAR else 12
|
||||
|
||||
for month in range(month_start, month_end + 1):
|
||||
days_in_month = calendar.monthrange(year, month)[1]
|
||||
for day in range(1, days_in_month + 1):
|
||||
dates.append(f"{year}-{month:02d}-{day:02d}")
|
||||
|
||||
logger.info(f"使用年份月份范围：{START_YEAR}年{start_m}月 到 {END_YEAR}年{end_m}月，共 {len(dates)} 天")
|
||||
|
||||
# 如果都没有指定，使用默认范围
|
||||
else:
|
||||
logger.warning("未指定日期范围，使用默认：2025年1-12月")
|
||||
for month in range(1, 13):
|
||||
days_in_month = calendar.monthrange(2025, month)[1]
|
||||
for day in range(1, days_in_month + 1):
|
||||
dates.append(f"2025-{month:02d}-{day:02d}")
|
||||
|
||||
if dates:
|
||||
logger.info(f"准备查询 {len(dates)} 天的数据，日期范围：{dates[0]} 到 {dates[-1]}")
|
||||
else:
|
||||
logger.error("未生成任何查询日期，请检查配置参数")
|
||||
exit(1)
|
||||
|
||||
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）
|
||||
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 ===
|
||||
# 开仓 + 平仓手续费（按比例计算 + 固定）
|
||||
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
|
||||
|
||||
# if net_profit < -400:
|
||||
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【BitMart 回测结果】")
|
||||
print(f"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}")
|
||||
@@ -1,418 +0,0 @@
|
||||
"""
|
||||
量化交易回测系统 - 三分之一策略（5分钟K线 + 1分钟精准判断）
|
||||
|
||||
========== 策略规则（与 交易/bitmart-三分之一策略交易.py 一致）==========
|
||||
1. 触发价格计算（基于有效的前一根5分钟K线，实体>=0.1）：
|
||||
- 做多触发价格 = 收盘价 + 实体/3（从收盘价往上涨1/3）
|
||||
- 做空触发价格 = 收盘价 - 实体/3（从收盘价往下跌1/3）
|
||||
|
||||
2. 信号触发条件：
|
||||
- 当前5分钟K线最高价 >= 做多触发价格 → 做多信号
|
||||
- 当前5分钟K线最低价 <= 做空触发价格 → 做空信号
|
||||
|
||||
3. 执行逻辑：
|
||||
- 做多时遇到做空信号 -> 平多并反手开空
|
||||
- 做空时遇到做多信号 -> 平空并反手开多
|
||||
- 同一根5分钟K线内只交易一次
|
||||
|
||||
4. 精准判断（使用1分钟K线）：
|
||||
- 当一根5分钟K线同时触及做多和做空价格时
|
||||
- 使用该5分钟K线对应的5根1分钟K线来判断哪个方向先被触发
|
||||
- 使回测更贴近真实成交顺序
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import calendar
|
||||
from typing import List, Dict, Optional
|
||||
from loguru import logger
|
||||
from models.bitmart_klines import BitMartETH5M, BitMartETH1M
|
||||
|
||||
|
||||
# ========================= 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):
|
||||
"""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]
|
||||
if get_body_size(prev) >= min_body_size:
|
||||
return i, prev
|
||||
return None, None
|
||||
|
||||
|
||||
def get_one_third_levels(prev: Dict):
|
||||
"""
|
||||
计算前一根K线实体的 1/3 双向触发价格
|
||||
返回：(做多触发价格, 做空触发价格)
|
||||
"""
|
||||
p_open = float(prev['open'])
|
||||
p_close = float(prev['close'])
|
||||
body = abs(p_open - p_close)
|
||||
if body < 0.001:
|
||||
return None, None
|
||||
long_trigger = p_close + body / 3
|
||||
short_trigger = p_close - body / 3
|
||||
return long_trigger, short_trigger
|
||||
|
||||
|
||||
def get_1m_data_by_range(start_ts_ms: int, end_ts_ms: int) -> List[Dict]:
|
||||
"""
|
||||
获取指定时间范围内的1分钟K线数据（毫秒时间戳）
|
||||
"""
|
||||
query = BitMartETH1M.select().where(
|
||||
BitMartETH1M.id.between(start_ts_ms, end_ts_ms - 1)
|
||||
).order_by(BitMartETH1M.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
|
||||
|
||||
|
||||
def get_1m_data_for_5m_bar(bar_5m: Dict) -> List[Dict]:
|
||||
"""获取一根5分钟K线对应的5根1分钟K线"""
|
||||
start_ts = bar_5m['id']
|
||||
end_ts = start_ts + 5 * 60 * 1000
|
||||
return get_1m_data_by_range(start_ts, end_ts)
|
||||
|
||||
|
||||
def determine_trigger_order_by_1m(
|
||||
bars_1m: List[Dict],
|
||||
long_trigger: float,
|
||||
short_trigger: float
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
使用1分钟K线判断在一根5分钟周期内，先触发做多还是做空。
|
||||
按时间顺序遍历每根1分钟K线，先触及哪个方向则返回该方向；
|
||||
若同一根1分钟K线内两个方向都触及，用开盘价距离判断。
|
||||
"""
|
||||
if not bars_1m:
|
||||
return None
|
||||
for bar in bars_1m:
|
||||
high = float(bar['high'])
|
||||
low = float(bar['low'])
|
||||
open_price = float(bar['open'])
|
||||
long_triggered = high >= long_trigger
|
||||
short_triggered = low <= short_trigger
|
||||
if long_triggered and not short_triggered:
|
||||
return 'long'
|
||||
if short_triggered and not long_triggered:
|
||||
return 'short'
|
||||
if long_triggered and short_triggered:
|
||||
dist_to_long = abs(long_trigger - open_price)
|
||||
dist_to_short = abs(short_trigger - open_price)
|
||||
return 'short' if dist_to_short <= dist_to_long else 'long'
|
||||
return None
|
||||
|
||||
|
||||
def check_trigger_with_1m(
|
||||
all_data_5m: List[Dict],
|
||||
current_idx: int,
|
||||
min_body_size: float = 0.1
|
||||
) -> tuple:
|
||||
"""
|
||||
检查当前5分钟K线是否触发交易信号。
|
||||
若同时触发多空，则用该5分钟内的1分钟K线判断先后顺序。
|
||||
返回：(方向, 触发价格, 有效前一根K线索引, 是否使用了1分钟精准判断, 是否双触)
|
||||
"""
|
||||
if current_idx <= 0:
|
||||
return None, None, None, False, False
|
||||
|
||||
curr = all_data_5m[current_idx]
|
||||
valid_prev_idx, prev = find_valid_prev_bar(all_data_5m, current_idx, min_body_size)
|
||||
if prev is None:
|
||||
return None, None, None, False, False
|
||||
|
||||
long_trigger, short_trigger = get_one_third_levels(prev)
|
||||
if long_trigger is None:
|
||||
return None, None, None, False, False
|
||||
|
||||
c_high = float(curr['high'])
|
||||
c_low = float(curr['low'])
|
||||
long_triggered = c_high >= long_trigger
|
||||
short_triggered = c_low <= short_trigger
|
||||
both_triggered = long_triggered and short_triggered
|
||||
|
||||
if both_triggered:
|
||||
bars_1m = get_1m_data_for_5m_bar(curr)
|
||||
if bars_1m:
|
||||
direction = determine_trigger_order_by_1m(bars_1m, long_trigger, short_trigger)
|
||||
if direction:
|
||||
trigger_price = long_trigger if direction == 'long' else short_trigger
|
||||
return direction, trigger_price, valid_prev_idx, True, True
|
||||
c_open = float(curr['open'])
|
||||
dist_to_long = abs(long_trigger - c_open)
|
||||
dist_to_short = abs(short_trigger - c_open)
|
||||
if dist_to_short <= dist_to_long:
|
||||
return 'short', short_trigger, valid_prev_idx, False, True
|
||||
return 'long', long_trigger, valid_prev_idx, False, True
|
||||
|
||||
if short_triggered:
|
||||
return 'short', short_trigger, valid_prev_idx, False, False
|
||||
if long_triggered:
|
||||
return 'long', long_trigger, valid_prev_idx, False, False
|
||||
return None, None, None, False, False
|
||||
|
||||
|
||||
def get_data_by_date(model, date_str: str) -> List[Dict]:
|
||||
"""
|
||||
按天获取指定表的K线数据（毫秒时间戳）。
|
||||
返回格式：id(ms), open, high, low, close，已按 id 升序。
|
||||
"""
|
||||
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_precise(dates: List[str], min_body_size: float = 0.1):
|
||||
"""
|
||||
三分之一策略回测（5分钟K线 + 1分钟精准判断）。
|
||||
风格与 回测数据-30分钟版 一致：按日期拉取、合并排序、统计与输出。
|
||||
"""
|
||||
all_data: List[Dict] = []
|
||||
total_queried = 0
|
||||
for d in dates:
|
||||
day_data = get_data_by_date(BitMartETH5M, d)
|
||||
all_data.extend(day_data)
|
||||
if day_data:
|
||||
total_queried += len(day_data)
|
||||
|
||||
logger.info(f"总共查询了 {len(dates)} 天，获取到 {total_queried} 条5分钟K线数据")
|
||||
|
||||
if not all_data:
|
||||
logger.warning("未获取到任何数据，请检查：")
|
||||
logger.warning("1. 数据库连接与 bitmart_eth_5m / bitmart_eth_1m 表是否存在")
|
||||
logger.warning("2. 是否已用 抓取多周期K线.py 抓取过 1 分钟和 5 分钟数据")
|
||||
return [], {
|
||||
'long': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做多'},
|
||||
'short': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做空'},
|
||||
}, {'precise_1m_count': 0, 'fallback_count': 0}
|
||||
|
||||
all_data.sort(key=lambda x: x['id'])
|
||||
if len(all_data) > 1:
|
||||
first_ts = all_data[0]['id']
|
||||
last_ts = all_data[-1]['id']
|
||||
first_time = datetime.datetime.fromtimestamp(first_ts / 1000)
|
||||
last_time = datetime.datetime.fromtimestamp(last_ts / 1000)
|
||||
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')}")
|
||||
|
||||
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'},
|
||||
}
|
||||
extra = {'precise_1m_count': 0, 'fallback_count': 0}
|
||||
|
||||
trades: List[Dict] = []
|
||||
current_position: Optional[Dict] = None
|
||||
last_trade_bar: Optional[int] = None
|
||||
|
||||
for idx in range(1, len(all_data)):
|
||||
curr = all_data[idx]
|
||||
direction, trigger_price, valid_prev_idx, used_1m, both_triggered = check_trigger_with_1m(all_data, idx, min_body_size)
|
||||
valid_prev = all_data[valid_prev_idx] if valid_prev_idx is not None else None
|
||||
|
||||
if used_1m:
|
||||
extra['precise_1m_count'] += 1
|
||||
elif both_triggered and direction:
|
||||
extra['fallback_count'] += 1
|
||||
|
||||
if direction is None:
|
||||
continue
|
||||
if last_trade_bar == idx:
|
||||
continue
|
||||
|
||||
if current_position is None:
|
||||
current_position = {
|
||||
'direction': direction,
|
||||
'entry_price': trigger_price,
|
||||
'entry_time': curr['id'],
|
||||
'entry_bar': idx,
|
||||
}
|
||||
stats[direction]['count'] += 1
|
||||
last_trade_bar = idx
|
||||
continue
|
||||
|
||||
pos_dir = current_position['direction']
|
||||
if direction == pos_dir:
|
||||
continue
|
||||
|
||||
exit_price = trigger_price
|
||||
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(curr['id'] / 1000),
|
||||
'entry_time_ms': current_position['entry_time'],
|
||||
'exit_time_ms': curr['id'],
|
||||
'signal': '三分之一',
|
||||
'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
|
||||
|
||||
current_position = {
|
||||
'direction': direction,
|
||||
'entry_price': trigger_price,
|
||||
'entry_time': curr['id'],
|
||||
'entry_bar': idx,
|
||||
}
|
||||
stats[direction]['count'] += 1
|
||||
last_trade_bar = idx
|
||||
|
||||
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),
|
||||
'entry_time_ms': current_position['entry_time'],
|
||||
'exit_time_ms': last['id'],
|
||||
'signal': '三分之一',
|
||||
'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
|
||||
|
||||
logger.info(f"回测完成：使用1分钟精准判断 {extra['precise_1m_count']} 次，使用开盘价距离判断 {extra['fallback_count']} 次")
|
||||
return trades, stats, extra
|
||||
|
||||
|
||||
# ========================= 运行示例（与 回测数据-30分钟版 风格一致）=========================
|
||||
if __name__ == '__main__':
|
||||
START_DATE = "2025-01-01"
|
||||
END_DATE = "2025-12-31"
|
||||
START_YEAR = None
|
||||
START_MONTH = None
|
||||
END_YEAR = None
|
||||
END_MONTH = None
|
||||
|
||||
dates = []
|
||||
if START_DATE and END_DATE:
|
||||
try:
|
||||
start_dt = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
|
||||
end_dt = datetime.datetime.strptime(END_DATE, '%Y-%m-%d')
|
||||
if start_dt > end_dt:
|
||||
logger.error(f"开始日期 {START_DATE} 不能晚于结束日期 {END_DATE}")
|
||||
exit(1)
|
||||
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"使用指定日期范围：{START_DATE} 到 {END_DATE}，共 {len(dates)} 天")
|
||||
except ValueError as e:
|
||||
logger.error(f"日期格式错误：{e}，请使用 YYYY-MM-DD 格式")
|
||||
exit(1)
|
||||
elif START_YEAR and END_YEAR:
|
||||
start_m = START_MONTH if START_MONTH else 1
|
||||
end_m = END_MONTH if END_MONTH else 12
|
||||
for year in range(START_YEAR, END_YEAR + 1):
|
||||
month_start = start_m if year == START_YEAR else 1
|
||||
month_end = end_m if year == END_YEAR else 12
|
||||
for month in range(month_start, month_end + 1):
|
||||
days_in_month = calendar.monthrange(year, month)[1]
|
||||
for day in range(1, days_in_month + 1):
|
||||
dates.append(f"{year}-{month:02d}-{day:02d}")
|
||||
logger.info(f"使用年份月份范围：{START_YEAR}年{start_m}月 到 {END_YEAR}年{end_m}月，共 {len(dates)} 天")
|
||||
else:
|
||||
logger.warning("未指定日期范围，使用默认：2025年1-12月")
|
||||
for month in range(1, 13):
|
||||
days_in_month = calendar.monthrange(2025, month)[1]
|
||||
for day in range(1, days_in_month + 1):
|
||||
dates.append(f"2025-{month:02d}-{day:02d}")
|
||||
|
||||
if not dates:
|
||||
logger.error("未生成任何查询日期，请检查配置参数")
|
||||
exit(1)
|
||||
|
||||
trades, stats, extra = backtest_one_third_precise(dates, min_body_size=0.1)
|
||||
|
||||
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 = (exit_p - entry) if direction == '做多' else (entry - exit_p)
|
||||
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}({t['signal']}) "
|
||||
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} {t['exit_time']}"
|
||||
)
|
||||
|
||||
total_net_profit = total_money_profit - total_fee
|
||||
print("\n【BitMart 三分之一策略回测结果（5分钟K线 + 1分钟精准判断）】")
|
||||
print(f"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}")
|
||||
print(f"使用1分钟K线精准判断（双触）次数: {extra['precise_1m_count']}")
|
||||
print(f"使用开盘价距离判断次数: {extra['fallback_count']}")
|
||||
@@ -1,418 +0,0 @@
|
||||
"""
|
||||
量化交易回测系统 - 五分之一策略（3分钟K线 + 1分钟精准判断）
|
||||
|
||||
========== 策略规则 ==========
|
||||
1. 触发价格计算（基于有效的前一根3分钟K线，实体>=0.1）：
|
||||
- 做多触发价格 = 收盘价 + 实体/5（从收盘价往上涨1/5）
|
||||
- 做空触发价格 = 收盘价 - 实体/5（从收盘价往下跌1/5）
|
||||
|
||||
2. 信号触发条件：
|
||||
- 当前3分钟K线最高价 >= 做多触发价格 → 做多信号
|
||||
- 当前3分钟K线最低价 <= 做空触发价格 → 做空信号
|
||||
|
||||
3. 执行逻辑：
|
||||
- 做多时遇到做空信号 -> 平多并反手开空
|
||||
- 做空时遇到做多信号 -> 平空并反手开多
|
||||
- 同一根3分钟K线内只交易一次
|
||||
|
||||
4. 精准判断（使用1分钟K线）：
|
||||
- 当一根3分钟K线同时触及做多和做空价格时
|
||||
- 使用该3分钟K线对应的3根1分钟K线来判断哪个方向先被触发
|
||||
- 使回测更贴近真实成交顺序
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import calendar
|
||||
from typing import List, Dict, Optional
|
||||
from loguru import logger
|
||||
from models.bitmart_klines import BitMartETH3M, BitMartETH1M
|
||||
|
||||
|
||||
# ========================= 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):
|
||||
"""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]
|
||||
if get_body_size(prev) >= min_body_size:
|
||||
return i, prev
|
||||
return None, None
|
||||
|
||||
|
||||
def get_one_fifth_levels(prev: Dict):
|
||||
"""
|
||||
计算前一根K线实体的 1/5 双向触发价格
|
||||
返回：(做多触发价格, 做空触发价格)
|
||||
"""
|
||||
p_open = float(prev['open'])
|
||||
p_close = float(prev['close'])
|
||||
body = abs(p_open - p_close)
|
||||
if body < 0.001:
|
||||
return None, None
|
||||
long_trigger = p_close + body / 5
|
||||
short_trigger = p_close - body / 5
|
||||
return long_trigger, short_trigger
|
||||
|
||||
|
||||
def get_1m_data_by_range(start_ts_ms: int, end_ts_ms: int) -> List[Dict]:
|
||||
"""
|
||||
获取指定时间范围内的1分钟K线数据（毫秒时间戳）
|
||||
"""
|
||||
query = BitMartETH1M.select().where(
|
||||
BitMartETH1M.id.between(start_ts_ms, end_ts_ms - 1)
|
||||
).order_by(BitMartETH1M.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
|
||||
|
||||
|
||||
def get_1m_data_for_3m_bar(bar_3m: Dict) -> List[Dict]:
|
||||
"""获取一根3分钟K线对应的3根1分钟K线"""
|
||||
start_ts = bar_3m['id']
|
||||
end_ts = start_ts + 3 * 60 * 1000
|
||||
return get_1m_data_by_range(start_ts, end_ts)
|
||||
|
||||
|
||||
def determine_trigger_order_by_1m(
|
||||
bars_1m: List[Dict],
|
||||
long_trigger: float,
|
||||
short_trigger: float
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
使用1分钟K线判断在一根3分钟周期内，先触发做多还是做空。
|
||||
按时间顺序遍历每根1分钟K线，先触及哪个方向则返回该方向；
|
||||
若同一根1分钟K线内两个方向都触及，用开盘价距离判断。
|
||||
"""
|
||||
if not bars_1m:
|
||||
return None
|
||||
for bar in bars_1m:
|
||||
high = float(bar['high'])
|
||||
low = float(bar['low'])
|
||||
open_price = float(bar['open'])
|
||||
long_triggered = high >= long_trigger
|
||||
short_triggered = low <= short_trigger
|
||||
if long_triggered and not short_triggered:
|
||||
return 'long'
|
||||
if short_triggered and not long_triggered:
|
||||
return 'short'
|
||||
if long_triggered and short_triggered:
|
||||
dist_to_long = abs(long_trigger - open_price)
|
||||
dist_to_short = abs(short_trigger - open_price)
|
||||
return 'short' if dist_to_short <= dist_to_long else 'long'
|
||||
return None
|
||||
|
||||
|
||||
def check_trigger_with_1m(
|
||||
all_data_3m: List[Dict],
|
||||
current_idx: int,
|
||||
min_body_size: float = 0.1
|
||||
) -> tuple:
|
||||
"""
|
||||
检查当前3分钟K线是否触发交易信号。
|
||||
若同时触发多空，则用该3分钟内的1分钟K线判断先后顺序。
|
||||
返回：(方向, 触发价格, 有效前一根K线索引, 是否使用了1分钟精准判断, 是否双触)
|
||||
"""
|
||||
if current_idx <= 0:
|
||||
return None, None, None, False, False
|
||||
|
||||
curr = all_data_3m[current_idx]
|
||||
valid_prev_idx, prev = find_valid_prev_bar(all_data_3m, current_idx, min_body_size)
|
||||
if prev is None:
|
||||
return None, None, None, False, False
|
||||
|
||||
long_trigger, short_trigger = get_one_fifth_levels(prev)
|
||||
if long_trigger is None:
|
||||
return None, None, None, False, False
|
||||
|
||||
c_high = float(curr['high'])
|
||||
c_low = float(curr['low'])
|
||||
long_triggered = c_high >= long_trigger
|
||||
short_triggered = c_low <= short_trigger
|
||||
both_triggered = long_triggered and short_triggered
|
||||
|
||||
if both_triggered:
|
||||
bars_1m = get_1m_data_for_3m_bar(curr)
|
||||
if bars_1m:
|
||||
direction = determine_trigger_order_by_1m(bars_1m, long_trigger, short_trigger)
|
||||
if direction:
|
||||
trigger_price = long_trigger if direction == 'long' else short_trigger
|
||||
return direction, trigger_price, valid_prev_idx, True, True
|
||||
c_open = float(curr['open'])
|
||||
dist_to_long = abs(long_trigger - c_open)
|
||||
dist_to_short = abs(short_trigger - c_open)
|
||||
if dist_to_short <= dist_to_long:
|
||||
return 'short', short_trigger, valid_prev_idx, False, True
|
||||
return 'long', long_trigger, valid_prev_idx, False, True
|
||||
|
||||
if short_triggered:
|
||||
return 'short', short_trigger, valid_prev_idx, False, False
|
||||
if long_triggered:
|
||||
return 'long', long_trigger, valid_prev_idx, False, False
|
||||
return None, None, None, False, False
|
||||
|
||||
|
||||
def get_data_by_date(model, date_str: str) -> List[Dict]:
|
||||
"""
|
||||
按天获取指定表的K线数据（毫秒时间戳）。
|
||||
返回格式：id(ms), open, high, low, close，已按 id 升序。
|
||||
"""
|
||||
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_fifth_precise(dates: List[str], min_body_size: float = 0.1):
|
||||
"""
|
||||
五分之一策略回测（3分钟K线 + 1分钟精准判断）。
|
||||
风格与 回测数据-30分钟版 一致：按日期拉取、合并排序、统计与输出。
|
||||
"""
|
||||
all_data: List[Dict] = []
|
||||
total_queried = 0
|
||||
for d in dates:
|
||||
day_data = get_data_by_date(BitMartETH3M, d)
|
||||
all_data.extend(day_data)
|
||||
if day_data:
|
||||
total_queried += len(day_data)
|
||||
|
||||
logger.info(f"总共查询了 {len(dates)} 天，获取到 {total_queried} 条3分钟K线数据")
|
||||
|
||||
if not all_data:
|
||||
logger.warning("未获取到任何数据，请检查：")
|
||||
logger.warning("1. 数据库连接与 bitmart_eth_3m / bitmart_eth_1m 表是否存在")
|
||||
logger.warning("2. 是否已用 抓取多周期K线.py 抓取过 1 分钟和 3 分钟数据")
|
||||
return [], {
|
||||
'long': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做多'},
|
||||
'short': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做空'},
|
||||
}, {'precise_1m_count': 0, 'fallback_count': 0}
|
||||
|
||||
all_data.sort(key=lambda x: x['id'])
|
||||
if len(all_data) > 1:
|
||||
first_ts = all_data[0]['id']
|
||||
last_ts = all_data[-1]['id']
|
||||
first_time = datetime.datetime.fromtimestamp(first_ts / 1000)
|
||||
last_time = datetime.datetime.fromtimestamp(last_ts / 1000)
|
||||
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')}")
|
||||
|
||||
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'},
|
||||
}
|
||||
extra = {'precise_1m_count': 0, 'fallback_count': 0}
|
||||
|
||||
trades: List[Dict] = []
|
||||
current_position: Optional[Dict] = None
|
||||
last_trade_bar: Optional[int] = None
|
||||
|
||||
for idx in range(1, len(all_data)):
|
||||
curr = all_data[idx]
|
||||
direction, trigger_price, valid_prev_idx, used_1m, both_triggered = check_trigger_with_1m(all_data, idx, min_body_size)
|
||||
valid_prev = all_data[valid_prev_idx] if valid_prev_idx is not None else None
|
||||
|
||||
if used_1m:
|
||||
extra['precise_1m_count'] += 1
|
||||
elif both_triggered and direction:
|
||||
extra['fallback_count'] += 1
|
||||
|
||||
if direction is None:
|
||||
continue
|
||||
if last_trade_bar == idx:
|
||||
continue
|
||||
|
||||
if current_position is None:
|
||||
current_position = {
|
||||
'direction': direction,
|
||||
'entry_price': trigger_price,
|
||||
'entry_time': curr['id'],
|
||||
'entry_bar': idx,
|
||||
}
|
||||
stats[direction]['count'] += 1
|
||||
last_trade_bar = idx
|
||||
continue
|
||||
|
||||
pos_dir = current_position['direction']
|
||||
if direction == pos_dir:
|
||||
continue
|
||||
|
||||
exit_price = trigger_price
|
||||
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(curr['id'] / 1000),
|
||||
'entry_time_ms': current_position['entry_time'],
|
||||
'exit_time_ms': curr['id'],
|
||||
'signal': '五分之一',
|
||||
'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
|
||||
|
||||
current_position = {
|
||||
'direction': direction,
|
||||
'entry_price': trigger_price,
|
||||
'entry_time': curr['id'],
|
||||
'entry_bar': idx,
|
||||
}
|
||||
stats[direction]['count'] += 1
|
||||
last_trade_bar = idx
|
||||
|
||||
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),
|
||||
'entry_time_ms': current_position['entry_time'],
|
||||
'exit_time_ms': last['id'],
|
||||
'signal': '五分之一',
|
||||
'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
|
||||
|
||||
logger.info(f"回测完成：使用1分钟精准判断 {extra['precise_1m_count']} 次，使用开盘价距离判断 {extra['fallback_count']} 次")
|
||||
return trades, stats, extra
|
||||
|
||||
|
||||
# ========================= 运行示例（与 回测数据-30分钟版 风格一致）=========================
|
||||
if __name__ == '__main__':
|
||||
START_DATE = "2025-01-01"
|
||||
END_DATE = "2025-12-31"
|
||||
START_YEAR = None
|
||||
START_MONTH = None
|
||||
END_YEAR = None
|
||||
END_MONTH = None
|
||||
|
||||
dates = []
|
||||
if START_DATE and END_DATE:
|
||||
try:
|
||||
start_dt = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
|
||||
end_dt = datetime.datetime.strptime(END_DATE, '%Y-%m-%d')
|
||||
if start_dt > end_dt:
|
||||
logger.error(f"开始日期 {START_DATE} 不能晚于结束日期 {END_DATE}")
|
||||
exit(1)
|
||||
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"使用指定日期范围：{START_DATE} 到 {END_DATE}，共 {len(dates)} 天")
|
||||
except ValueError as e:
|
||||
logger.error(f"日期格式错误：{e}，请使用 YYYY-MM-DD 格式")
|
||||
exit(1)
|
||||
elif START_YEAR and END_YEAR:
|
||||
start_m = START_MONTH if START_MONTH else 1
|
||||
end_m = END_MONTH if END_MONTH else 12
|
||||
for year in range(START_YEAR, END_YEAR + 1):
|
||||
month_start = start_m if year == START_YEAR else 1
|
||||
month_end = end_m if year == END_YEAR else 12
|
||||
for month in range(month_start, month_end + 1):
|
||||
days_in_month = calendar.monthrange(year, month)[1]
|
||||
for day in range(1, days_in_month + 1):
|
||||
dates.append(f"{year}-{month:02d}-{day:02d}")
|
||||
logger.info(f"使用年份月份范围：{START_YEAR}年{start_m}月 到 {END_YEAR}年{end_m}月，共 {len(dates)} 天")
|
||||
else:
|
||||
logger.warning("未指定日期范围，使用默认：2025年1-12月")
|
||||
for month in range(1, 13):
|
||||
days_in_month = calendar.monthrange(2025, month)[1]
|
||||
for day in range(1, days_in_month + 1):
|
||||
dates.append(f"2025-{month:02d}-{day:02d}")
|
||||
|
||||
if not dates:
|
||||
logger.error("未生成任何查询日期，请检查配置参数")
|
||||
exit(1)
|
||||
|
||||
trades, stats, extra = backtest_one_fifth_precise(dates, min_body_size=0.1)
|
||||
|
||||
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 = (exit_p - entry) if direction == '做多' else (entry - exit_p)
|
||||
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}({t['signal']}) "
|
||||
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} {t['exit_time']}"
|
||||
)
|
||||
|
||||
total_net_profit = total_money_profit - total_fee
|
||||
print("\n【BitMart 五分之一策略回测结果（3分钟K线 + 1分钟精准判断）】")
|
||||
print(f"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}")
|
||||
print(f"使用1分钟K线精准判断（双触）次数: {extra['precise_1m_count']}")
|
||||
print(f"使用开盘价距离判断次数: {extra['fallback_count']}")
|
||||
382
bitmart/趋势策略.py
382
bitmart/趋势策略.py
@@ -1,382 +0,0 @@
|
||||
import time
|
||||
import datetime
|
||||
import openBrowser
|
||||
|
||||
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
|
||||
|
||||
|
||||
class BitmartFuturesTransaction:
|
||||
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=30, desc="等待K线", ncols=80)
|
||||
|
||||
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（你的“成本开仓”需求）
|
||||
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
|
||||
|
||||
def get_klines(self):
|
||||
"""获取最近3根30分钟K线（step=30）"""
|
||||
try:
|
||||
end_time = int(time.time())
|
||||
# 获取足够多的条目确保有最新3根
|
||||
response = self.contractAPI.get_kline(
|
||||
contract_symbol=self.contract_symbol,
|
||||
step=30, # 30分钟
|
||||
start_time=end_time - 3600 * 10, # 取最近10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 # 最近3根: kline_1 (最老), kline_2, kline_3 (最新)
|
||||
except Exception as e:
|
||||
logger.error(f"获取K线异常: {e}")
|
||||
self.ding(error=True, msg="获取K线异常")
|
||||
return None
|
||||
|
||||
def get_current_price(self):
|
||||
"""获取当前最新价格，用于计算张数"""
|
||||
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, # 取最近10小时
|
||||
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
|
||||
|
||||
# 获取当前持仓方向
|
||||
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
|
||||
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
|
||||
|
||||
# 设置杠杆和全仓
|
||||
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
|
||||
|
||||
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 take_over_browser(self):
|
||||
"""接管浏览器"""
|
||||
try:
|
||||
co = ChromiumOptions()
|
||||
co.set_local_port(self.tge_port)
|
||||
self.page = ChromiumPage(addr_or_opts=co)
|
||||
self.page.set.window.max()
|
||||
return True
|
||||
except:
|
||||
return False
|
||||
|
||||
def close_extra_tabs(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()
|
||||
return True
|
||||
except:
|
||||
return False
|
||||
|
||||
def 平仓(self):
|
||||
self.click_safe('x://span[normalize-space(text()) ="市价"]')
|
||||
|
||||
def 开单(self, marketPriceLongOrder=0, limitPriceShortOrder=0, size=None, price=None):
|
||||
"""
|
||||
marketPriceLongOrder 市价最多或者做空，1是做多，-1是做空
|
||||
limitPriceShortOrder 限价最多或者做空
|
||||
"""
|
||||
|
||||
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"]')
|
||||
|
||||
if limitPriceShortOrder == -1:
|
||||
self.click_safe('x://button[normalize-space(text()) ="限价"]')
|
||||
self.page.ele('x://*[@id="price_0"]').input(vals=price, clear=True)
|
||||
time.sleep(1)
|
||||
self.page.ele('x://*[@id="size_0"]').input(1)
|
||||
self.click_safe('x://span[normalize-space(text()) ="卖出/做空"]')
|
||||
elif limitPriceShortOrder == 1:
|
||||
self.click_safe('x://button[normalize-space(text()) ="限价"]')
|
||||
self.page.ele('x://*[@id="price_0"]').input(vals=price, clear=True)
|
||||
time.sleep(1)
|
||||
self.page.ele('x://*[@id="size_0"]').input(1)
|
||||
self.click_safe('x://span[normalize-space(text()) ="买入/做多"]')
|
||||
|
||||
def ding(self, text, error=False):
|
||||
logger.info(text)
|
||||
|
||||
def close_extra_tabs_in_browser(self):
|
||||
|
||||
try:
|
||||
for _, i in enumerate(self.page.get_tabs()):
|
||||
if _ == 0:
|
||||
continue
|
||||
|
||||
i.close()
|
||||
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
|
||||
return False
|
||||
|
||||
def get_now_time(self):
|
||||
# 获取当前时间戳
|
||||
current_timestamp = time.time()
|
||||
# 将当前时间戳转换为 datetime 对象
|
||||
current_datetime = datetime.datetime.fromtimestamp(current_timestamp)
|
||||
|
||||
# 计算距离当前时间最近的整点或 30 分时刻
|
||||
if current_datetime.minute < 30:
|
||||
target_datetime = current_datetime.replace(minute=0, second=0, microsecond=0)
|
||||
else:
|
||||
target_datetime = current_datetime.replace(minute=30, second=0, microsecond=0)
|
||||
|
||||
# 将目标 datetime 对象转换为时间戳
|
||||
target_timestamp = target_datetime.timestamp()
|
||||
|
||||
return int(target_timestamp)
|
||||
|
||||
def is_bullish(self, c): # 阳线
|
||||
return float(c['close']) > float(c['open'])
|
||||
|
||||
def is_bearish(self, c): # 阴线
|
||||
return float(c['close']) < float(c['open'])
|
||||
|
||||
def check_signal(self, prev, curr):
|
||||
"""
|
||||
包住形态信号判定（优化版）：
|
||||
只看两种信号，严格按照收盘价与开盘价的比较：
|
||||
|
||||
1. 阳包阴（涨包跌，前跌后涨）-> 做多：
|
||||
- 前一根是跌（阴线：close < open）
|
||||
- 后一根是涨（阳线：close > open）
|
||||
- 且：涨的收盘价 > 跌的开盘价（curr['close'] > prev['open']）
|
||||
|
||||
2. 阴包阳（跌包涨，前涨后跌）-> 做空：
|
||||
- 前一根是涨（阳线：close > open）
|
||||
- 后一根是跌（阴线：close < open）
|
||||
- 且：跌的收盘价 < 涨的开盘价（curr['close'] < prev['open']）
|
||||
"""
|
||||
p_open = float(prev['open'])
|
||||
c_close = float(curr['close'])
|
||||
|
||||
# 阳包阴（涨包跌，前跌后涨） -> 做多：涨的收盘价 > 跌的开盘价
|
||||
if self.is_bearish(prev) and self.is_bullish(curr) and c_close > p_open:
|
||||
return "long", "bear_bull_engulf"
|
||||
|
||||
# 阴包阳（跌包涨，前涨后跌） -> 做空：跌的收盘价 < 涨的开盘价
|
||||
if self.is_bullish(prev) and self.is_bearish(curr) and c_close < p_open:
|
||||
return "short", "bull_bear_engulf"
|
||||
|
||||
return None, None
|
||||
|
||||
def action(self):
|
||||
# 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 TGE 失败！", error=True)
|
||||
return
|
||||
logger.info("TGE 端口获取成功")
|
||||
|
||||
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")
|
||||
|
||||
self.click_safe('x://button[normalize-space(text()) ="市价"]')
|
||||
|
||||
self.pbar = tqdm(total=30, desc="等待时间中", ncols=80) # desc：进度条说明，ncols：长度
|
||||
|
||||
self.time_start = None # 时间状态 避免同一个时段，发生太多消息
|
||||
while True:
|
||||
|
||||
# 获取当前时间
|
||||
current_time = time.localtime()
|
||||
current_minute = current_time.tm_min
|
||||
|
||||
if current_minute < 30:
|
||||
self.pbar.n = current_minute
|
||||
self.pbar.refresh()
|
||||
else:
|
||||
self.pbar.n = current_minute - 30
|
||||
self.pbar.refresh()
|
||||
|
||||
if self.time_start == self.get_now_time():
|
||||
time.sleep(5)
|
||||
continue
|
||||
|
||||
new_price_datas = self.get_klines()
|
||||
if not new_price_datas:
|
||||
logger.info("获取最新价格有问题！！！")
|
||||
|
||||
new_price_datas1 = sorted(new_price_datas, key=lambda x: x["id"])
|
||||
self.kline_1, self.kline_2, self.kline_3 = new_price_datas1[-3:]
|
||||
|
||||
# 判断抓取的数据是否正确
|
||||
if self.get_now_time() != self.kline_3["id"]:
|
||||
continue
|
||||
|
||||
self.time_start = self.get_now_time()
|
||||
|
||||
if self.get_position_status():
|
||||
logger.info("获取仓位信息成功!!!")
|
||||
else:
|
||||
logger.info("获取仓位信息失败！！！")
|
||||
|
||||
self.send_dingtalk_message(message_content=f"获取仓位信息失败！！！", type=0)
|
||||
continue
|
||||
|
||||
if self.start == 1:
|
||||
if self.is_bearish(self.kline_1) and self.is_bearish(self.kline_2):
|
||||
self.平仓()
|
||||
elif self.start == -1:
|
||||
if self.is_bullish(self.kline_1) and self.is_bullish(self.kline_2):
|
||||
self.平仓()
|
||||
|
||||
self.direction, signal_key = self.check_signal(prev=self.kline_1, curr=self.kline_2) # 判断信号
|
||||
|
||||
if self.direction == "long":
|
||||
if self.start == -1:
|
||||
self.平仓()
|
||||
self.开单(marketPriceLongOrder=1, size=self.get_available_balance() * self.risk_percent)
|
||||
elif self.start == 0:
|
||||
self.开单(marketPriceLongOrder=1, size=self.get_available_balance() * self.risk_percent)
|
||||
|
||||
if self.direction == "short":
|
||||
if self.start == 1:
|
||||
self.平仓()
|
||||
self.开单(marketPriceLongOrder=-1, size=self.get_available_balance() * self.risk_percent)
|
||||
elif self.start == 0:
|
||||
self.开单(marketPriceLongOrder=-1, size=self.get_available_balance() * self.risk_percent)
|
||||
|
||||
self.pbar.reset() # 重置进度条
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
BitmartFuturesTransaction(bit_id="f2320f57e24c45529a009e1541e25961").action()
|
||||
Reference in New Issue
Block a user