400 lines
16 KiB
Python
400 lines
16 KiB
Python
"""
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多信号组合策略回测 — 目标 1000 USDT/月
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5种信号源，同一时间只持一个仓位，每笔100U保证金100x杠杆：
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信号1: EMA金叉死叉 + ATR过滤 + 大趋势方向（已验证盈利）
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信号2: 三分之一策略 — 前K线实体的1/3作为触发价（动量突破）
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信号3: 布林带反弹 — 价格触及上下轨 + RSI确认（均值回归）
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信号4: 吞没形态 — 当前K线完全包裹前K线（反转信号）
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信号5: Pin Bar — 长影线蜡烛（拒绝信号）
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所有信号共用：
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- 大趋势过滤 EMA(120)
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- 最低持仓 200秒 (>3分钟)
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- 各自独立的止盈止损参数
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- 90% 手续费返佣
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"""
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import sys, time, datetime, sqlite3, statistics
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from pathlib import Path
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from collections import defaultdict
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class EMA:
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__slots__ = ('k', 'v')
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def __init__(self, p):
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self.k = 2.0 / (p + 1); self.v = None
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def update(self, x):
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self.v = x if self.v is None else x * self.k + self.v * (1 - self.k)
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return self.v
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def load():
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db = Path(__file__).parent.parent / 'models' / 'database.db'
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s = int(datetime.datetime(2025,1,1).timestamp()) * 1000
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e = int(datetime.datetime(2026,1,1).timestamp()) * 1000
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conn = sqlite3.connect(str(db))
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rows = conn.cursor().execute(
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"SELECT id,open,high,low,close FROM bitmart_eth_1m WHERE id>=? AND id<? ORDER BY id", (s,e)
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).fetchall()
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conn.close()
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return [(datetime.datetime.fromtimestamp(r[0]/1000.0), r[1], r[2], r[3], r[4]) for r in rows]
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def calc_bb(closes, period=20, nstd=2.0):
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if len(closes) < period:
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return None, None, None
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rec = closes[-period:]
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mid = sum(rec) / period
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var = sum((x - mid)**2 for x in rec) / period
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std = var ** 0.5
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return mid + nstd * std, mid, mid - nstd * std
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def calc_rsi(closes, period=14):
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if len(closes) < period + 1:
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return None
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gains = 0; losses = 0
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for i in range(-period, 0):
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d = closes[i] - closes[i-1]
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if d > 0: gains += d
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else: losses -= d
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if losses == 0: return 100.0
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rs = (gains/period) / (losses/period)
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return 100 - 100 / (1 + rs)
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def main():
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print("Loading...", flush=True)
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data = load()
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N = len(data)
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print(f"{N} bars loaded\n", flush=True)
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# ===== 参数 =====
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NOTIONAL = 10000.0 # 100U * 100x
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FEE_RATE = 0.0006
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REB_RATE = 0.90
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MIN_HOLD = 200 # 秒
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FEE_PER_TRADE = NOTIONAL * FEE_RATE * 2 # 12 USDT
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REB_PER_TRADE = FEE_PER_TRADE * REB_RATE # 10.8 USDT
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NET_FEE = FEE_PER_TRADE - REB_PER_TRADE # 1.2 USDT
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# 信号参数
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# 信号1: EMA交叉
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EMA_FAST = 8; EMA_SLOW = 21; EMA_BIG = 120
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ATR_MIN = 0.003; ATR_P = 14
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SL1 = 0.004; HSL1 = 0.006
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# 信号2: 三分之一 (用5分钟聚合K线)
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BODY_MIN = 0.0008 # 最小实体占价格比例 0.08%
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SL2 = 0.003; TP2 = 0.004 # 三分之一策略：小止损高胜率
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# 信号3: 布林带反弹
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BB_P = 20; BB_STD = 2.0; RSI_P = 14
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RSI_LONG = 30; RSI_SHORT = 70
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SL3 = 0.003; TP3 = 0.002 # 均值回归：回到中轨附近
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# 信号4: 吞没形态
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ENGULF_MIN_BODY = 0.001 # 最小吞没实体占比
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SL4 = 0.004; TP4 = 0.005
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# 信号5: Pin Bar
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PIN_SHADOW_RATIO = 2.0 # 影线 >= 2倍实体
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PIN_MIN_SHADOW = 0.001 # 最小影线占价格比例
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SL5 = 0.003; TP5 = 0.004
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MAX_HOLD = 1800 # 所有信号共用最大持仓
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# ===== 状态 =====
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ema_f = EMA(EMA_FAST); ema_s = EMA(EMA_SLOW); ema_b = EMA(EMA_BIG)
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prev_fast = None; prev_slow = None
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closes_buf = []; highs_buf = []; lows_buf = []
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# 5分钟K线聚合
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bar5_open = None; bar5_high = None; bar5_low = None; bar5_close = None
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bar5_count = 0; bars5 = [] # 完成的5分钟K线
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pos = 0; op = 0.0; ot = None; sig_type = ""; sl_pct = 0; tp_pct = 0
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trades = []
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def do_close(price, dt_, reason):
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nonlocal pos, op, ot, sig_type
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pp = (price - op) / op if pos == 1 else (op - price) / op
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pnl = NOTIONAL * pp
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fee = FEE_PER_TRADE; reb = REB_PER_TRADE
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hsec = (dt_ - ot).total_seconds()
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trades.append((sig_type, 'long' if pos==1 else 'short', op, price,
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pnl, fee, reb, hsec, reason, ot, dt_))
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pos = 0; op = 0; ot = None; sig_type = ""
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def do_open(direction, price, dt_, stype, sl, tp):
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nonlocal pos, op, ot, sig_type, sl_pct, tp_pct
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pos = 1 if direction == 'long' else -1
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op = price; ot = dt_; sig_type = stype; sl_pct = sl; tp_pct = tp
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for i in range(N):
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dt, o_, h_, l_, c_ = data[i]
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p = c_
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# 更新缓存
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closes_buf.append(p); highs_buf.append(h_); lows_buf.append(l_)
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if len(closes_buf) > 300:
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closes_buf = closes_buf[-300:]
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highs_buf = highs_buf[-300:]
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lows_buf = lows_buf[-300:]
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# EMA更新
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fast = ema_f.update(p); slow = ema_s.update(p); big = ema_b.update(p)
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# ATR
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atr_pct = 0.0
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if len(highs_buf) > ATR_P + 1:
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s = 0.0
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for j in range(-ATR_P, 0):
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tr = highs_buf[j] - lows_buf[j]
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d1 = abs(highs_buf[j] - closes_buf[j-1])
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d2 = abs(lows_buf[j] - closes_buf[j-1])
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if d1 > tr: tr = d1
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if d2 > tr: tr = d2
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s += tr
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atr_pct = s / (ATR_P * p) if p > 0 else 0
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# EMA交叉
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ema_cross_up = prev_fast is not None and prev_fast <= prev_slow and fast > slow
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ema_cross_dn = prev_fast is not None and prev_fast >= prev_slow and fast < slow
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prev_fast = fast; prev_slow = slow
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# 布林带 & RSI
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bb_upper, bb_mid, bb_lower = calc_bb(closes_buf, BB_P, BB_STD)
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rsi = calc_rsi(closes_buf, RSI_P)
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# 5分钟K线聚合
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if bar5_open is None:
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bar5_open = o_; bar5_high = h_; bar5_low = l_; bar5_close = c_; bar5_count = 1
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else:
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bar5_high = max(bar5_high, h_)
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bar5_low = min(bar5_low, l_)
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bar5_close = c_
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bar5_count += 1
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new_bar5 = None
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if bar5_count >= 5:
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new_bar5 = {'open': bar5_open, 'high': bar5_high, 'low': bar5_low, 'close': bar5_close}
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bars5.append(new_bar5)
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if len(bars5) > 50: bars5 = bars5[-50:]
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bar5_open = None; bar5_count = 0
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# K线形态（用1分钟线）
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prev_bar = data[i-1] if i > 0 else None
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prev2_bar = data[i-2] if i > 1 else None
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# ===== 有持仓：检查平仓 =====
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if pos != 0 and ot is not None:
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pp = (p - op) / op if pos == 1 else (op - p) / op
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hsec = (dt - ot).total_seconds()
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# 硬止损
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hard_sl = max(sl_pct * 1.5, 0.006)
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if -pp >= hard_sl:
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do_close(p, dt, f"硬止损({pp*100:+.2f}%)"); continue
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if hsec >= MIN_HOLD:
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# 止损
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if -pp >= sl_pct:
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do_close(p, dt, f"止损({pp*100:+.2f}%)"); continue
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# 止盈
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if tp_pct > 0 and pp >= tp_pct:
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do_close(p, dt, f"止盈({pp*100:+.2f}%)"); continue
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# 超时
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if hsec >= MAX_HOLD:
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do_close(p, dt, f"超时({hsec:.0f}s)"); continue
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# EMA信号反转平仓（仅EMA信号开的仓）
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if sig_type == "EMA":
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if pos == 1 and ema_cross_dn:
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do_close(p, dt, "EMA反转"); continue
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if pos == -1 and ema_cross_up:
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do_close(p, dt, "EMA反转"); continue
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# BB信号回到中轨平仓
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if sig_type == "BB" and bb_mid is not None:
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if pos == 1 and p >= bb_mid:
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do_close(p, dt, "BB回中轨"); continue
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if pos == -1 and p <= bb_mid:
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do_close(p, dt, "BB回中轨"); continue
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# ===== 无持仓：检查开仓（按优先级） =====
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if pos == 0 and i > 20:
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signal = None; s_sl = 0; s_tp = 0; s_type = ""
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# 优先级1: EMA交叉（最高质量）
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if atr_pct >= ATR_MIN:
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if ema_cross_up and p > big:
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signal = 'long'; s_type = "EMA"; s_sl = SL1; s_tp = 0
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elif ema_cross_dn and p < big:
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signal = 'short'; s_type = "EMA"; s_sl = SL1; s_tp = 0
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# 优先级2: 三分之一策略（5分钟K线动量）
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if signal is None and len(bars5) >= 2 and new_bar5 is not None:
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prev5 = bars5[-2]
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body5 = abs(prev5['close'] - prev5['open'])
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body5_pct = body5 / prev5['close'] if prev5['close'] > 0 else 0
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if body5_pct >= BODY_MIN:
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trigger_up = prev5['close'] + body5 / 3
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trigger_dn = prev5['close'] - body5 / 3
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cur5 = bars5[-1]
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if cur5['high'] >= trigger_up and p > big:
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signal = 'long'; s_type = "1/3"; s_sl = SL2; s_tp = TP2
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elif cur5['low'] <= trigger_dn and p < big:
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signal = 'short'; s_type = "1/3"; s_sl = SL2; s_tp = TP2
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# 优先级3: 吞没形态
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if signal is None and prev_bar is not None:
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pb_o, pb_c = prev_bar[1], prev_bar[4]
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cb_o, cb_c = o_, c_
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pb_body = abs(pb_c - pb_o)
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cb_body = abs(cb_c - cb_o)
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pb_body_pct = pb_body / p if p > 0 else 0
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cb_body_pct = cb_body / p if p > 0 else 0
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if cb_body_pct >= ENGULF_MIN_BODY and cb_body > pb_body * 1.5:
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# 看涨吞没：前阴后阳，当前完全包裹
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if pb_c < pb_o and cb_c > cb_o and cb_c > pb_o and cb_o <= pb_c:
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if p > big and atr_pct >= 0.001:
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signal = 'long'; s_type = "吞没"; s_sl = SL4; s_tp = TP4
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# 看跌吞没：前阳后阴
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elif pb_c > pb_o and cb_c < cb_o and cb_c < pb_o and cb_o >= pb_c:
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if p < big and atr_pct >= 0.001:
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signal = 'short'; s_type = "吞没"; s_sl = SL4; s_tp = TP4
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# 优先级4: Pin Bar（长影线反转）
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if signal is None and prev_bar is not None:
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pb_o, pb_h, pb_l, pb_c = prev_bar[1], prev_bar[2], prev_bar[3], prev_bar[4]
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pb_body = abs(pb_c - pb_o)
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upper_shadow = pb_h - max(pb_o, pb_c)
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lower_shadow = min(pb_o, pb_c) - pb_l
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if pb_body > 0:
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# 看涨Pin Bar：长下影线
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if lower_shadow >= PIN_SHADOW_RATIO * pb_body:
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ls_pct = lower_shadow / p if p > 0 else 0
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if ls_pct >= PIN_MIN_SHADOW and p > big and atr_pct >= 0.001:
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signal = 'long'; s_type = "PinBar"; s_sl = SL5; s_tp = TP5
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# 看跌Pin Bar：长上影线
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if upper_shadow >= PIN_SHADOW_RATIO * pb_body:
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us_pct = upper_shadow / p if p > 0 else 0
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if us_pct >= PIN_MIN_SHADOW and p < big and atr_pct >= 0.001:
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signal = 'short'; s_type = "PinBar"; s_sl = SL5; s_tp = TP5
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# 优先级5: 布林带反弹
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if signal is None and bb_upper is not None and rsi is not None:
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if p <= bb_lower and rsi < RSI_LONG and p > big and atr_pct >= 0.0008:
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signal = 'long'; s_type = "BB"; s_sl = SL3; s_tp = TP3
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elif p >= bb_upper and rsi > RSI_SHORT and p < big and atr_pct >= 0.0008:
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signal = 'short'; s_type = "BB"; s_sl = SL3; s_tp = TP3
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if signal:
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do_open(signal, p, dt, s_type, s_sl, s_tp)
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# 强制平仓
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if pos != 0:
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p = data[-1][4]; dt = data[-1][0]
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do_close(p, dt, "回测结束")
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# ===== 分析结果 =====
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if not trades:
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print("No trades!", flush=True); return
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n = len(trades)
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total_pnl = sum(t[4] for t in trades)
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total_fee = FEE_PER_TRADE * n
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total_reb = REB_PER_TRADE * n
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net = total_pnl - (total_fee - total_reb)
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wins = [t for t in trades if t[4] > 0]
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wr = len(wins) / n * 100
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# 按信号类型统计
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by_type = defaultdict(lambda: {'n':0, 'pnl':0, 'w':0})
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for t in trades:
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by_type[t[0]]['n'] += 1
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by_type[t[0]]['pnl'] += t[4]
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if t[4] > 0: by_type[t[0]]['w'] += 1
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# 月度
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monthly = defaultdict(lambda: {'n':0, 'pnl':0, 'reb':0, 'fee':0, 'w':0})
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for t in trades:
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k = t[10].strftime('%Y-%m')
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monthly[k]['n'] += 1
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monthly[k]['pnl'] += t[4]
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monthly[k]['reb'] += REB_PER_TRADE
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monthly[k]['fee'] += FEE_PER_TRADE
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if t[4] > 0: monthly[k]['w'] += 1
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# 最大回撤
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cum=0; peak=0; dd=0
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for t in trades:
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cum += t[4] - NET_FEE
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if cum > peak: peak = cum
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if peak - cum > dd: dd = peak - cum
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print("=" * 75, flush=True)
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print(" 多信号组合策略回测 | 100U x 100倍 | 目标1000U/月", flush=True)
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print("=" * 75, flush=True)
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print(f"\n --- 核心收益 ---", flush=True)
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print(f" 方向盈亏: {total_pnl:>+12.2f} USDT", flush=True)
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print(f" 返佣(90%): {total_reb:>+12.2f} USDT", flush=True)
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print(f" 净手续费(10%): {total_fee-total_reb:>12.2f} USDT", flush=True)
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print(f" ================================", flush=True)
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print(f" 年净利润: {net:>+12.2f} USDT", flush=True)
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print(f" 月均净利: {net/12:>+12.2f} USDT", flush=True)
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print(f" 最大回撤: {dd:>12.2f} USDT", flush=True)
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print(f"\n --- 交易统计 ---", flush=True)
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print(f" 总交易: {n} 笔 | 胜率: {wr:.1f}% | 月均: {n/12:.0f} 笔", flush=True)
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print(f"\n --- 按信号类型 ---", flush=True)
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print(f" {'类型':<10} {'笔数':>6} {'方向盈亏':>10} {'净利':>10} {'胜率':>6} {'每笔均利':>10}", flush=True)
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print(f" {'-'*56}", flush=True)
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for stype in sorted(by_type.keys()):
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d = by_type[stype]
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net_t = d['pnl'] - NET_FEE * d['n']
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avg = net_t / d['n'] if d['n'] > 0 else 0
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wr_t = d['w'] / d['n'] * 100 if d['n'] > 0 else 0
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mark = " ++" if net_t > 0 else " --"
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print(f" {stype:<10} {d['n']:>6} {d['pnl']:>+10.2f} {net_t:>+10.2f} {wr_t:>5.1f}% {avg:>+10.2f}{mark}", flush=True)
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print(f"\n --- 月度明细 ---", flush=True)
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print(f" {'月份':<8} {'笔数':>5} {'方向盈亏':>10} {'返佣':>8} {'净利润':>10} {'胜率':>6}", flush=True)
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print(f" {'-'*52}", flush=True)
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for m in sorted(monthly.keys()):
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d = monthly[m]
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net_m = d['pnl'] - (d['fee'] - d['reb'])
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wr_m = d['w'] / d['n'] * 100 if d['n'] > 0 else 0
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print(f" {m:<8} {d['n']:>5} {d['pnl']:>+10.2f} {d['reb']:>8.2f} {net_m:>+10.2f} {wr_m:>5.1f}%", flush=True)
|
||
print(f" {'-'*52}", flush=True)
|
||
print(f" {'合计':<8} {n:>5} {total_pnl:>+10.2f} {total_reb:>8.2f} {net:>+10.2f} {wr:>5.1f}%", flush=True)
|
||
|
||
# ===== 测试不同仓位大小达到1000U/月需要多少 =====
|
||
print(f"\n --- 仓位放大测试 ---", flush=True)
|
||
print(f" {'保证金':>8} {'杠杆':>4} {'名义价值':>12} {'年净利':>10} {'月均':>8} {'达标':>4}", flush=True)
|
||
print(f" {'-'*52}", flush=True)
|
||
for margin in [100, 200, 300, 500, 800, 1000]:
|
||
lev = 100
|
||
notional_test = margin * lev
|
||
scale = notional_test / NOTIONAL
|
||
net_scaled = net * scale
|
||
monthly_avg = net_scaled / 12
|
||
ok = "Yes" if monthly_avg >= 1000 else "No"
|
||
print(f" {margin:>7}U {lev:>3}x {notional_test:>11,}U {net_scaled:>+10.0f} {monthly_avg:>+8.0f} {ok:>4}", flush=True)
|
||
|
||
print(f"\n{'='*75}", flush=True)
|
||
|
||
# 保存CSV
|
||
csv = Path(__file__).parent.parent / 'combo_trades.csv'
|
||
with open(csv, 'w', encoding='utf-8-sig') as f:
|
||
f.write("信号,方向,开仓价,平仓价,方向盈亏,手续费,返佣,持仓秒,原因,开仓时间,平仓时间\n")
|
||
for t in trades:
|
||
f.write(f"{t[0]},{t[1]},{t[2]:.2f},{t[3]:.2f},{t[4]:.2f},"
|
||
f"{t[5]:.2f},{t[6]:.2f},{t[7]:.0f},{t[8]},{t[9]},{t[10]}\n")
|
||
print(f"\n Saved: {csv}", flush=True)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|