295 lines
9.3 KiB
Python
295 lines
9.3 KiB
Python
"""布林带均线策略回测（优化版）
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策略逻辑:
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- 阳线 + 碰到布林带均线 → 开多（可选 1m 线过滤：先涨碰到才开）
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- 持多: 碰到上轨 → 止盈（无下轨止损）
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- 阴线 + 碰到布林带均线 → 平多开空（可选 1m：先跌碰到才开）
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- 持空: 碰到下轨 → 止盈（无上轨止损）
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全仓模式 | 200U | 1% 权益/单 | 万五手续费 | 90%返佣次日8点到账 | 100x杠杆
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import List, Optional
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import numpy as np
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import pandas as pd
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from .indicators import bollinger
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@dataclass
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class BBMidlineConfig:
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bb_period: int = 20
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bb_std: float = 2.0
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initial_capital: float = 200.0
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margin_pct: float = 0.01
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leverage: float = 100.0
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cross_margin: bool = True
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fee_rate: float = 0.0005
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rebate_pct: float = 0.90
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rebate_hour_utc: int = 0
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slippage_pct: float = 0.0
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fill_at_close: bool = True
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# 是否用 1m 线判断「先涨碰到」/「先跌碰到」均线
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use_1m_touch_filter: bool = True
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# 主K线周期(分钟)，用于 1m 触及方向时的桶对齐，5/15/30
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kline_step_min: int = 5
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@dataclass
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class BBTrade:
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side: str
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entry_price: float
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exit_price: float
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entry_time: object
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exit_time: object
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margin: float
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leverage: float
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qty: float
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gross_pnl: float
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fee: float
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net_pnl: float
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exit_reason: str
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@dataclass
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class BBMidlineResult:
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equity_curve: pd.DataFrame
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trades: List[BBTrade]
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daily_stats: pd.DataFrame
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total_fee: float
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total_rebate: float
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config: BBMidlineConfig
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def run_bb_midline_backtest(
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df: pd.DataFrame,
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cfg: BBMidlineConfig,
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df_1m: Optional[pd.DataFrame] = None,
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arr_touch_dir_override: Optional[np.ndarray] = None,
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) -> BBMidlineResult:
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close = df["close"].astype(float)
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high = df["high"].astype(float)
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low = df["low"].astype(float)
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open_ = df["open"].astype(float)
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n = len(df)
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bb_mid, bb_upper, bb_lower, _ = bollinger(close, cfg.bb_period, cfg.bb_std)
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arr_mid = bb_mid.values
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# 1m 触及方向：1=先涨碰到, -1=先跌碰到, 0=未碰到
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arr_touch_dir = None
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if arr_touch_dir_override is not None:
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arr_touch_dir = np.asarray(arr_touch_dir_override, dtype=np.int32)
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if len(arr_touch_dir) != n:
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raise ValueError(f"arr_touch_dir_override 长度不匹配: {len(arr_touch_dir)} != {n}")
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elif cfg.use_1m_touch_filter and df_1m is not None and len(df_1m) > 0:
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from .data_loader import get_1m_touch_direction
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arr_touch_dir = get_1m_touch_direction(df, df_1m, arr_mid, kline_step_min=cfg.kline_step_min)
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arr_close = close.values
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arr_high = high.values
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arr_low = low.values
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arr_open = open_.values
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arr_upper = bb_upper.values
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arr_lower = bb_lower.values
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ts_index = df.index
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balance = cfg.initial_capital
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position = 0
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entry_price = 0.0
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entry_time = None
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entry_margin = 0.0
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entry_qty = 0.0
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trades: List[BBTrade] = []
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total_fee = 0.0
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total_rebate = 0.0
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day_pnl = 0.0
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current_day = None
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today_fees = 0.0
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pending_rebate = 0.0
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rebate_applied_today = False
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out_equity = np.full(n, np.nan)
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out_balance = np.full(n, np.nan)
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out_position = np.zeros(n)
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def unrealised(price):
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if position == 0:
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return 0.0
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if position == 1:
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return entry_qty * (price - entry_price)
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return entry_qty * (entry_price - price)
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def close_position(exit_price, exit_idx, reason: str):
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nonlocal balance, position, entry_price, entry_time, entry_margin, entry_qty
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nonlocal total_fee, total_rebate, day_pnl, today_fees
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if position == 0:
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return
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if position == 1:
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exit_price = exit_price * (1 - cfg.slippage_pct)
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else:
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exit_price = exit_price * (1 + cfg.slippage_pct)
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if position == 1:
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gross = entry_qty * (exit_price - entry_price)
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else:
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gross = entry_qty * (entry_price - exit_price)
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exit_notional = entry_qty * exit_price
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fee = exit_notional * cfg.fee_rate
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net = gross - fee
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trades.append(BBTrade(
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side="long" if position == 1 else "short",
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entry_price=entry_price,
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exit_price=exit_price,
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entry_time=entry_time,
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exit_time=ts_index[exit_idx],
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margin=entry_margin,
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leverage=cfg.leverage,
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qty=entry_qty,
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gross_pnl=gross,
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fee=fee,
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net_pnl=net,
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exit_reason=reason,
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))
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balance += net
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total_fee += fee
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today_fees += fee
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day_pnl += net
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position = 0
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entry_price = 0.0
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entry_time = None
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entry_margin = 0.0
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entry_qty = 0.0
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def open_position(side, price, idx):
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nonlocal position, entry_price, entry_time, entry_margin, entry_qty
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nonlocal balance, total_fee, day_pnl, today_fees
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if side == "long":
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price = price * (1 + cfg.slippage_pct)
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else:
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price = price * (1 - cfg.slippage_pct)
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equity = balance + unrealised(price) if position != 0 else balance
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margin = equity * cfg.margin_pct
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margin = min(margin, balance * 0.95)
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if margin <= 0:
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return False
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notional = margin * cfg.leverage
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qty = notional / price
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fee = notional * cfg.fee_rate
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balance -= fee
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total_fee += fee
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today_fees += fee
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day_pnl -= fee
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position = 1 if side == "long" else -1
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entry_price = price
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entry_time = ts_index[idx]
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entry_margin = margin
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entry_qty = qty
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return True
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for i in range(n):
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bar_day = ts_index[i].date() if hasattr(ts_index[i], 'date') else None
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bar_hour = ts_index[i].hour if hasattr(ts_index[i], 'hour') else 0
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if bar_day is not None and bar_day != current_day:
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pending_rebate += today_fees * cfg.rebate_pct
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today_fees = 0.0
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rebate_applied_today = False
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day_pnl = 0.0
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current_day = bar_day
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if cfg.rebate_pct > 0 and not rebate_applied_today and bar_hour >= cfg.rebate_hour_utc and pending_rebate > 0:
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balance += pending_rebate
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total_rebate += pending_rebate
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pending_rebate = 0.0
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rebate_applied_today = True
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if np.isnan(arr_upper[i]) or np.isnan(arr_lower[i]) or np.isnan(arr_mid[i]):
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out_equity[i] = balance + unrealised(arr_close[i])
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out_balance[i] = balance
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out_position[i] = position
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continue
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fill_price = arr_close[i] if cfg.fill_at_close else None
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bullish = arr_close[i] > arr_open[i]
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bearish = arr_close[i] < arr_open[i]
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# 碰到均线：K 线贯穿或触及 mid
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touched_mid = arr_low[i] <= arr_mid[i] <= arr_high[i]
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touched_upper = arr_high[i] >= arr_upper[i]
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touched_lower = arr_low[i] <= arr_lower[i]
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exec_upper = fill_price if fill_price is not None else arr_upper[i]
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exec_lower = fill_price if fill_price is not None else arr_lower[i]
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# 1m 过滤：开多需先涨碰到，开空需先跌碰到
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touch_up_ok = True if arr_touch_dir is None else (arr_touch_dir[i] == 1)
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touch_down_ok = True if arr_touch_dir is None else (arr_touch_dir[i] == -1)
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# 单根 K 线只允许一次操作
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if position == 1 and touched_upper:
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# 持多止盈
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close_position(exec_upper, i, "tp_upper")
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elif position == -1 and touched_lower:
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# 持空止盈
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close_position(exec_lower, i, "tp_lower")
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elif position == 1 and bearish and touched_mid and touch_down_ok:
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# 阴线触中轨: 平多并反手开空
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close_position(arr_close[i], i, "flip_to_short")
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if balance > 0:
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open_position("short", arr_close[i], i)
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elif position == -1 and bullish and touched_mid and touch_up_ok:
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# 阳线触中轨: 平空并反手开多
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close_position(arr_close[i], i, "flip_to_long")
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if balance > 0:
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open_position("long", arr_close[i], i)
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elif position == 0 and bullish and touched_mid and touch_up_ok:
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# 空仓开多
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open_position("long", arr_close[i], i)
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elif position == 0 and bearish and touched_mid and touch_down_ok:
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# 空仓开空
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open_position("short", arr_close[i], i)
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out_equity[i] = balance + unrealised(arr_close[i])
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out_balance[i] = balance
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out_position[i] = position
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if position != 0:
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close_position(arr_close[n - 1], n - 1, "end")
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out_equity[n - 1] = balance
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out_balance[n - 1] = balance
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out_position[n - 1] = 0
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eq_df = pd.DataFrame({
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"equity": out_equity,
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"balance": out_balance,
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"price": arr_close,
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"position": out_position,
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}, index=ts_index)
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daily_eq = eq_df["equity"].resample("1D").last().dropna().to_frame("equity")
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daily_eq["pnl"] = daily_eq["equity"].diff().fillna(0.0)
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return BBMidlineResult(
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equity_curve=eq_df,
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trades=trades,
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daily_stats=daily_eq,
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total_fee=total_fee,
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total_rebate=total_rebate,
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config=cfg,
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)
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