代码结构优化
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ddrwode
418
bitmart/回测数据-三分之一策略-5分钟精准版.py
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418
bitmart/回测数据-三分之一策略-5分钟精准版.py
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"""
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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）
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========== 策略规则（与 交易/bitmart-三分之一策略交易.py 一致）==========
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1. 触发价格计算（基于有效的前一根5分钟K线，实体>=0.1）：
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- 做多触发价格 = 收盘价 + 实体/3（从收盘价往上涨1/3）
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- 做空触发价格 = 收盘价 - 实体/3（从收盘价往下跌1/3）
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2. 信号触发条件：
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- 当前5分钟K线最高价 >= 做多触发价格 → 做多信号
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- 当前5分钟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5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5分钟K线同时触及做多和做空价格时
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- 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
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- 使回测更贴近真实成交顺序
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"""
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import datetime
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import calendar
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from typing import List, Dict, Optional
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from loguru import logger
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from models.bitmart_klines import BitMartETH5M, BitMartETH1M
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# ========================= 工具函数 =========================
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def is_bullish(c):
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"""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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"""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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"""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: List[Dict], current_idx: int, min_body_size: float = 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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if get_body_size(prev) >= min_body_size:
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return i, prev
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return None, None
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def get_one_third_levels(prev: Dict):
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"""
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计算前一根K线实体的 1/3 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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"""
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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:
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return None, None
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long_trigger = p_close + body / 3
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short_trigger = p_close - body / 3
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return long_trigger, short_trigger
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def get_1m_data_by_range(start_ts_ms: int, end_ts_ms: 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（毫秒时间戳）
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"""
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query = BitMartETH1M.select().where(
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BitMartETH1M.id.between(start_ts_ms, end_ts_ms - 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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if data:
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data.sort(key=lambda x: x['id'])
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return data
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def get_1m_data_for_5m_bar(bar_5m: Dict) -> List[Dict]:
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"""获取一根5分钟K线对应的5根1分钟K线"""
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start_ts = bar_5m['id']
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end_ts = start_ts + 5 * 60 * 1000
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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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) -> Optional[str]:
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"""
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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。
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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；
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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。
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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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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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if long_triggered and short_triggered:
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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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return 'short' if dist_to_short <= dist_to_long else 'long'
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return None
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def check_trigger_with_1m(
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all_data_5m: List[Dict],
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current_idx: int,
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min_body_size: float = 0.1
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) -> tuple:
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"""
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检查当前5分钟K线是否触发交易信号。
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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。
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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)
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"""
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if current_idx <= 0:
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return None, None, None, False, False
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curr = all_data_5m[current_idx]
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valid_prev_idx, prev = find_valid_prev_bar(all_data_5m, current_idx, min_body_size)
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if prev is None:
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return None, None, None, False, False
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long_trigger, short_trigger = get_one_third_levels(prev)
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if long_trigger is None:
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return None, None, None, False, False
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c_high = float(curr['high'])
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c_low = float(curr['low'])
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long_triggered = c_high >= long_trigger
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short_triggered = c_low <= short_trigger
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both_triggered = long_triggered and short_triggered
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if both_triggered:
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bars_1m = get_1m_data_for_5m_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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return direction, trigger_price, valid_prev_idx, True, True
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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, True
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return 'long', long_trigger, valid_prev_idx, False, True
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if short_triggered:
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return 'short', short_trigger, valid_prev_idx, False, False
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if long_triggered:
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return 'long', long_trigger, valid_prev_idx, False, False
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return None, None, None, False, False
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def get_data_by_date(model, date_str: str) -> List[Dict]:
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"""
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按天获取指定表的K线数据（毫秒时间戳）。
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返回格式：id(ms), open, high, low, close，已按 id 升序。
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"""
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try:
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target_date = datetime.datetime.strptime(date_str, '%Y-%m-%d')
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except ValueError:
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logger.error("日期格式不正确，请使用 YYYY-MM-DD 格式。")
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return []
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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 = model.select().where(model.id.between(start_ts, end_ts)).order_by(model.id.asc())
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data = [{'id': i.id, 'open': i.open, 'high': i.high, 'low': i.low, 'close': i.close} for i in query]
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if data:
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data.sort(key=lambda x: x['id'])
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return data
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# ========================= 回测逻辑 =========================
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def backtest_one_third_precise(dates: List[str], min_body_size: float = 0.1):
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"""
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三分之一策略回测（5分钟K线 + 1分钟精准判断）。
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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。
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"""
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all_data: List[Dict] = []
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total_queried = 0
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for d in dates:
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day_data = get_data_by_date(BitMartETH5M, d)
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all_data.extend(day_data)
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if day_data:
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total_queried += len(day_data)
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logger.info(f"总共查询了 {len(dates)} 天，获取到 {total_queried} 条5分钟K线数据")
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if not all_data:
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logger.warning("未获取到任何数据，请检查：")
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logger.warning("1. 数据库连接与 bitmart_eth_5m / bitmart_eth_1m 表是否存在")
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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")
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return [], {
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'long': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做多'},
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'short': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做空'},
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}, {'precise_1m_count': 0, 'fallback_count': 0}
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all_data.sort(key=lambda x: x['id'])
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if len(all_data) > 1:
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first_ts = all_data[0]['id']
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last_ts = all_data[-1]['id']
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first_time = datetime.datetime.fromtimestamp(first_ts / 1000)
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last_time = datetime.datetime.fromtimestamp(last_ts / 1000)
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logger.info(f"数据已按时间排序：{first_time.strftime('%Y-%m-%d %H:%M:%S')} 到 {last_time.strftime('%Y-%m-%d %H:%M:%S')}")
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stats = {
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'long': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做多'},
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'short': {'count': 0, 'wins': 0, 'total_profit': 0.0, 'name': '做空'},
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}
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extra = {'precise_1m_count': 0, 'fallback_count': 0}
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trades: List[Dict] = []
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current_position: Optional[Dict] = None
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last_trade_bar: Optional[int] = None
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for idx in range(1, len(all_data)):
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curr = all_data[idx]
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direction, trigger_price, valid_prev_idx, used_1m, both_triggered = check_trigger_with_1m(all_data, idx, min_body_size)
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valid_prev = all_data[valid_prev_idx] if valid_prev_idx is not None else None
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if used_1m:
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extra['precise_1m_count'] += 1
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elif both_triggered and direction:
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extra['fallback_count'] += 1
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if direction is None:
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continue
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if last_trade_bar == idx:
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continue
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if current_position is None:
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current_position = {
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'direction': direction,
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'entry_price': trigger_price,
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'entry_time': curr['id'],
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'entry_bar': idx,
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}
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stats[direction]['count'] += 1
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last_trade_bar = idx
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continue
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pos_dir = current_position['direction']
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if direction == pos_dir:
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continue
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exit_price = trigger_price
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if pos_dir == 'long':
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diff = exit_price - current_position['entry_price']
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else:
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diff = current_position['entry_price'] - exit_price
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trades.append({
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'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
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'exit_time': datetime.datetime.fromtimestamp(curr['id'] / 1000),
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'entry_time_ms': current_position['entry_time'],
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'exit_time_ms': curr['id'],
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'signal': '三分之一',
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'direction': '做多' if pos_dir == 'long' else '做空',
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'entry': current_position['entry_price'],
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'exit': exit_price,
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'diff': diff,
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})
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stats[pos_dir]['total_profit'] += diff
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if diff > 0:
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stats[pos_dir]['wins'] += 1
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current_position = {
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'direction': direction,
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'entry_price': trigger_price,
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'entry_time': curr['id'],
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'entry_bar': idx,
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}
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stats[direction]['count'] += 1
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last_trade_bar = idx
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if current_position:
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last = all_data[-1]
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exit_price = float(last['close'])
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pos_dir = current_position['direction']
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diff = (exit_price - current_position['entry_price']) if pos_dir == 'long' else (
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current_position['entry_price'] - exit_price)
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trades.append({
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'entry_time': datetime.datetime.fromtimestamp(current_position['entry_time'] / 1000),
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'exit_time': datetime.datetime.fromtimestamp(last['id'] / 1000),
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'entry_time_ms': current_position['entry_time'],
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'exit_time_ms': last['id'],
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'signal': '三分之一',
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'direction': '做多' if pos_dir == 'long' else '做空',
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'entry': current_position['entry_price'],
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'exit': exit_price,
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'diff': diff,
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})
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stats[pos_dir]['total_profit'] += diff
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if diff > 0:
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stats[pos_dir]['wins'] += 1
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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")
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return trades, stats, extra
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# ========================= 运行示例（与 回测数据-30分钟版 风格一致）=========================
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if __name__ == '__main__':
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START_DATE = "2025-01-01"
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END_DATE = "2025-12-31"
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START_YEAR = None
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START_MONTH = None
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END_YEAR = None
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END_MONTH = None
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dates = []
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if START_DATE and END_DATE:
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try:
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start_dt = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
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end_dt = datetime.datetime.strptime(END_DATE, '%Y-%m-%d')
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if start_dt > end_dt:
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logger.error(f"开始日期 {START_DATE} 不能晚于结束日期 {END_DATE}")
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exit(1)
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current_dt = start_dt
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while current_dt <= end_dt:
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dates.append(current_dt.strftime('%Y-%m-%d'))
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current_dt += datetime.timedelta(days=1)
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logger.info(f"使用指定日期范围：{START_DATE} 到 {END_DATE}，共 {len(dates)} 天")
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except ValueError as e:
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logger.error(f"日期格式错误：{e}，请使用 YYYY-MM-DD 格式")
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exit(1)
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elif START_YEAR and END_YEAR:
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start_m = START_MONTH if START_MONTH else 1
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end_m = END_MONTH if END_MONTH else 12
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for year in range(START_YEAR, END_YEAR + 1):
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month_start = start_m if year == START_YEAR else 1
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month_end = end_m if year == END_YEAR else 12
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for month in range(month_start, month_end + 1):
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days_in_month = calendar.monthrange(year, month)[1]
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for day in range(1, days_in_month + 1):
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dates.append(f"{year}-{month:02d}-{day:02d}")
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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")
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else:
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logger.warning("未指定日期范围，使用默认：2025年1-12月")
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for month in range(1, 13):
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days_in_month = calendar.monthrange(2025, month)[1]
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for day in range(1, days_in_month + 1):
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dates.append(f"2025-{month:02d}-{day:02d}")
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if not dates:
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logger.error("未生成任何查询日期，请检查配置参数")
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exit(1)
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trades, stats, extra = backtest_one_third_precise(dates, min_body_size=0.1)
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logger.info("===== 每笔交易详情 =====")
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contract_size = 10000
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open_fee_fixed = 5
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close_fee_rate = 0.0005
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total_points_profit = 0
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total_money_profit = 0
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total_fee = 0
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for t in trades:
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entry = t['entry']
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exit_p = t['exit']
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direction = t['direction']
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point_diff = (exit_p - entry) if direction == '做多' else (entry - exit_p)
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money_profit = point_diff / entry * contract_size
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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']}")
|
||||
418
bitmart/回测数据-五分之一策略-3分钟精准版.py
Normal file
418
bitmart/回测数据-五分之一策略-3分钟精准版.py
Normal file
@@ -0,0 +1,418 @@
|
||||
"""
|
||||
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']}")
|
||||
Reference in New Issue
Block a user