243 lines
10 KiB
Python
243 lines
10 KiB
Python
"""OCZ策略（One Candle Zone突破回踩策略）。
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策略逻辑：
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1. 寻找关键阻力位（最近N日最高价）
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2. 等待大阳线突破（实体占比>B%，涨幅>R%，放量>V1倍）
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3. 首次回踩到阻力位（±TOL容错）且缩量时买入
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4. 持有期间：
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- 止损：跌破阻力位立即卖出
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- 止盈：达到盈亏比1:2的目标价位立即卖出
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- 或持有指定天数后卖出
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特殊风控说明：
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- 止损位 = 突破的阻力位（买入时记录）
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- 止盈位 = 买入价 + (买入价 - 止损位) * 2
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- 例如：阻力位100元，买入价105元，则止损100元（-5元），止盈115元（+10元）
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参数说明：
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- N: 阻力回望长度（默认30日）
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- B: 实体占比门槛（默认60%）
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- V1: 放量倍数（默认1.5倍）
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- TOL: 回踩容错百分比（默认1.5%）
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- R: 收盘涨幅门槛（默认6%）
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- hold_days: 持有天数（默认5天）
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- volatility_min: 最小波动率（默认2.5%）
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- volatility_max: 最大波动率（默认8.0%）
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"""
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from typing import Dict, List, Optional
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import pandas as pd
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from strategies.base_strategy import BaseStrategy
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from utils.logger import setup_logger
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logger = setup_logger(__name__)
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class OczStrategy(BaseStrategy):
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"""OCZ突破回踩策略。"""
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def __init__(
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self,
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initial_cash: float,
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N: int = 30, # 阻力回望长度
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B: float = 60.0, # 实体占比门槛（%）
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V1: float = 1.5, # 放量倍数
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TOL: float = 1.5, # 回踩容错（%）
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R: float = 4.0, # 收盘涨幅门槛（%）
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hold_days: int = 5, # 持有天数
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volatility_min: float = 2.5, # 最小波动率（%）
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volatility_max: float = 8.0, # 最大波动率（%）
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max_positions: int = 2,
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position_pct_per_stock: float = 0.2,
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stop_loss: Optional[object] = None,
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take_profit: Optional[object] = None,
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position_sizer: Optional[object] = None,
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date_index_dict: Optional[Dict[str, Dict[str, int]]] = None,
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buy_signal_index: Optional[Dict[str, List[str]]] = None,
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):
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"""初始化OCZ策略。"""
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super().__init__(
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initial_cash=initial_cash,
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max_positions=max_positions,
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stop_loss=stop_loss,
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take_profit=take_profit,
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position_sizer=position_sizer,
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date_index_dict=date_index_dict,
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buy_signal_index=buy_signal_index,
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)
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self.N = int(N)
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self.B = float(B)
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self.V1 = float(V1)
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self.TOL = float(TOL)
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self.R = float(R)
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self.hold_days = int(hold_days)
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self.volatility_min = float(volatility_min)
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self.volatility_max = float(volatility_max)
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self.position_pct_per_stock = float(position_pct_per_stock)
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# 记录每只股票的突破信息（用于特殊止损止盈）
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# 格式: {ts_code: {"resistance": 阻力位, "buy_price": 买入价, "stop_loss": 止损价, "take_profit": 止盈价}}
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self.position_risk_info: Dict[str, Dict[str, float]] = {}
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logger.info(
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f"OczStrategy 初始化参数: N={self.N}, B={self.B}, V1={self.V1}, "
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f"TOL={self.TOL}, R={self.R}, hold_days={self.hold_days}, "
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f"max_positions={max_positions}, position_pct_per_stock={self.position_pct_per_stock}"
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)
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def on_bar(self, current_date: str, data_dict: Dict[str, pd.DataFrame]) -> None:
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"""每个交易日的交易决策逻辑。"""
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# 1. 先处理卖出逻辑（止损/止盈/持有到期）
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for ts_code in list(self.positions.keys()):
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df = data_dict.get(ts_code)
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if df is None or df.empty:
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continue
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# 使用预计算的日期索引
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date_index = self.date_index_dict.get(ts_code)
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if date_index is None or current_date not in date_index:
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continue
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idx = date_index[current_date]
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row = df.iloc[idx]
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close = float(row["close"])
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low = float(row["low"]) # 用于检查是否跌破阻力位
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pos = self.positions[ts_code]
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risk_info = self.position_risk_info.get(ts_code, {})
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should_sell = False
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sell_reason = ""
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# 检查特殊止损：跌破阻力位
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if risk_info and "stop_loss" in risk_info:
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stop_loss_price = risk_info["stop_loss"]
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if low <= stop_loss_price: # 最低价跌破止损位
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should_sell = True
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sell_reason = f"跌破阻力位止损({stop_loss_price:.2f})"
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# 检查特殊止盈：达到盈亏比1:2目标
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if not should_sell and risk_info and "take_profit" in risk_info:
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take_profit_price = risk_info["take_profit"]
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if close >= take_profit_price: # 收盘价达到止盈位
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should_sell = True
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sell_reason = f"达到止盈位({take_profit_price:.2f})"
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# 检查持有到期
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if not should_sell and pos.days_held >= self.hold_days:
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should_sell = True
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sell_reason = f"持有到期({self.hold_days}天)"
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# 执行卖出
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if should_sell:
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quantity = pos.quantity
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if quantity > 0:
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self.sell(ts_code, close, quantity)
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# 清理风控信息
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if ts_code in self.position_risk_info:
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del self.position_risk_info[ts_code]
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# logger.info(f"{current_date} 卖出 {ts_code} 原因: {sell_reason}") # 已移除：交易日志
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# 2. 处理买入逻辑（回踩信号）
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if len(self.positions) >= self.max_positions:
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return
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# 优化：如果现金过少（不足初始资金1%），直接返回
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if self.cash < self.initial_cash * 0.01:
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return
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# 使用买入信号索引获取今天有信号的股票列表
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signal_stocks = self.buy_signal_index.get(current_date, [])
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if not signal_stocks:
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return # 今天没有任何买入信号
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candidates = [] # 候选股票列表：[(ts_code, price), ...]
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# 只遍历有买入信号的股票
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for ts_code in signal_stocks:
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if ts_code in self.positions:
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continue
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df = data_dict.get(ts_code)
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if df is None or df.empty:
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continue
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# 使用预计算的日期索引
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date_index = self.date_index_dict.get(ts_code)
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if date_index is None or current_date not in date_index:
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continue
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idx = date_index[current_date]
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# 检查是否有足够的历史数据
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if idx < self.N + 2:
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continue
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row = df.iloc[idx]
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# 确认买入信号存在并获取阻力位
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if "pullback_signal" in df.columns and row["pullback_signal"]:
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price = float(row["close"])
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# 获取突破的阻力位（从预计算的resistance列中读取）
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resistance = float(row["resistance"]) if "resistance" in df.columns else price * 0.95
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candidates.append((ts_code, price, resistance))
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if not candidates:
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return
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# 按价格排序（可选，这里简单按顺序买入）
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remain_slots = self.max_positions - len(self.positions)
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selected = candidates[:remain_slots]
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# 依次买入选中的股票
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for item in selected:
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ts_code, price, resistance = item # 解包包含阻力位的元组
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# 使用仓位管理器计算买入数量（如果有的话）
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if self.position_sizer is not None:
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df = data_dict.get(ts_code)
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quantity = self.position_sizer.calc_shares(
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ts_code=ts_code,
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cash=self.cash,
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price=price,
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remain_slots=remain_slots,
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df=df,
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)
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else:
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# 默认：按配置的比例分配仓位
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target_cash = self.initial_cash * self.position_pct_per_stock
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actual_cash = min(target_cash, self.cash)
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# 计算能买多少股（向下取整到100的整数倍）
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quantity = int(actual_cash // price)
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quantity = (quantity // 100) * 100
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if quantity <= 0:
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continue
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# 买入前记录当前现金和仓位
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before_cash = self.cash
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before_positions = len(self.positions)
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self.buy(ts_code, price, quantity)
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# 买入成功（现金减少且仓位增加）
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if self.cash < before_cash and len(self.positions) > before_positions:
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# 计算并记录止损止盈价位
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stop_loss_price = resistance # 止损位 = 阻力位
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risk_distance = price - resistance # 风险距离 = 买入价 - 阻力位
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take_profit_price = price + risk_distance * 2 # 止盈位 = 买入价 + 风险距离 * 2 (盈亏比1:2)
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# 保存风控信息
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self.position_risk_info[ts_code] = {
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"resistance": resistance,
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"buy_price": price,
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"stop_loss": stop_loss_price,
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"take_profit": take_profit_price,
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}
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# logger.info(
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# f"{current_date} 买入 {ts_code} 价格:{price:.2f} 阻力位:{resistance:.2f} "
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# f"止损:{stop_loss_price:.2f}(-{risk_distance:.2f}) 止盈:{take_profit_price:.2f}(+{risk_distance*2:.2f})"
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# ) # 已移除：交易日志
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