193 lines
7.4 KiB
Python
193 lines
7.4 KiB
Python
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import numpy as np
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from typing import Optional, Any, List
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from src.core_data import Bar, Order
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from src.strategies.base_strategy import Strategy
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class SemiVarianceAsymmetryStrategy(Strategy):
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"""
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已实现半方差不对称策略 (RSVA)
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核心原理：
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放弃"阈值计数"，改用"波动能量占比"。
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因子 = (上行波动能量 - 下行波动能量) / 总波动能量
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优势：
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1. 自适应：自动适应2021的高波动和2023的低波动，无需调整阈值。
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2. 灵敏：能捕捉到没有大阳线但持续上涨的"蠕动趋势"。
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3. 稳健：使用平方项(Variance)而非三次方(Skewness)，对异常值更鲁棒。
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"""
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def __init__(
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self,
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context: Any,
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main_symbol: str,
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enable_log: bool,
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trade_volume: int,
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# --- 窗口参数 ---
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season_days: int = 20, # 计算日内季节性基准的回溯天数
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calc_window: int = 120, # 计算不对称因子的窗口 (约5天)
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cycle_length: int = 23, # 固定周期 (每天23根Bar)
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# --- 信号阈值 ---
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# RSVA 范围是 [-1, 1]。
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# 0.2 表示上涨能量比下跌能量多20% (即 60% vs 40%)，是一个显著的失衡信号。
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entry_threshold: float = 0.2,
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exit_threshold: float = 0.05,
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order_direction: Optional[List[str]] = None,
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):
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super().__init__(context, main_symbol, enable_log)
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if order_direction is None:
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order_direction = ['BUY', 'SELL']
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self.trade_volume = trade_volume
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self.season_days = season_days
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self.calc_window = calc_window
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self.cycle_length = cycle_length
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self.entry_threshold = entry_threshold
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self.exit_threshold = exit_threshold
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self.order_direction = order_direction
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# 计算最小历史需求
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# 我们需要: calc_window 个标准化数据
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# 每个标准化数据需要回溯: season_days * cycle_length
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self.min_history = self.calc_window + (self.season_days * self.cycle_length)
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# 缓冲区设大一点，避免频繁触发边界检查
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self.calc_buffer_size = self.min_history + 100
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self.log(f"RSVA Strategy Init: Window={calc_window}, Thresh={entry_threshold}")
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def on_open_bar(self, open_price: float, symbol: str):
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self.cancel_all_pending_orders(symbol)
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# 1. 获取历史数据 (切片优化)
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all_history = self.get_bar_history()
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total_len = len(all_history)
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if total_len < self.min_history:
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return
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# 只取计算所需的最后一段数据，保证计算复杂度恒定
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start_idx = max(0, total_len - self.calc_buffer_size)
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relevant_bars = all_history[start_idx:]
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# 转为 numpy array
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closes = np.array([b.close for b in relevant_bars])
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# 2. 计算对数收益率 (Log Returns)
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# 对数收益率消除了价格水平(Price Level)的影响
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log_rets = np.diff(np.log(closes))
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current_idx = len(log_rets) - 1
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# 3. 标准化收益率计算 (De-seasonalization)
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# 这一步至关重要：剔除日内季节性(早盘波动大、午盘波动小)的干扰
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std_rets = []
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# 循环计算过去 calc_window 个点的标准化值
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for i in range(self.calc_window):
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target_idx = current_idx - i
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# 高效切片：利用 stride=cycle_length 提取同一时间槽的历史
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# slot_history 包含 [t, t-23, t-46, ...]
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slot_history = log_rets[target_idx::-self.cycle_length]
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# 截取 season_days
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if len(slot_history) > self.season_days:
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slot_history = slot_history[:self.season_days]
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# 计算该时刻的基准波动率
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if len(slot_history) < 5:
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# 降级处理：样本不足时用近期全局波动率
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slot_vol = np.std(log_rets[-self.cycle_length:]) + 1e-9
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else:
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slot_vol = np.std(slot_history) + 1e-9
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# 标准化 (Z-Score)
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std_ret = log_rets[target_idx] / slot_vol
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std_rets.append(std_ret)
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# 转为数组 (注意：std_rets 是倒序的，但这不影响平方和计算)
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std_rets_arr = np.array(std_rets)
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# 4. 【核心】计算已实现半方差不对称性 (RSVA)
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# 分离正收益和负收益
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pos_rets = std_rets_arr[std_rets_arr > 0]
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neg_rets = std_rets_arr[std_rets_arr < 0]
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# 计算上行能量 (Upside Variance) 和 下行能量 (Downside Variance)
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rv_pos = np.sum(pos_rets ** 2)
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rv_neg = np.sum(neg_rets ** 2)
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total_rv = rv_pos + rv_neg + 1e-9 # 防止除零
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# 计算因子: [-1, 1]
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# > 0 说明上涨更有力(或更频繁)，< 0 说明下跌主导
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rsva_factor = (rv_pos - rv_neg) / total_rv
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# 5. 交易逻辑
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current_pos = self.get_current_positions().get(symbol, 0)
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self.log_status(rsva_factor, rv_pos, rv_neg, current_pos)
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if current_pos == 0:
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self.evaluate_entry(rsva_factor)
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else:
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self.evaluate_exit(current_pos, rsva_factor)
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def evaluate_entry(self, factor: float):
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direction = None
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# 因子 > 0.2: 哪怕没有极端K线，只要累计的上涨能量显著压过下跌能量，就开仓
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if factor > self.entry_threshold:
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if "BUY" in self.order_direction:
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direction = "BUY"
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elif factor < -self.entry_threshold:
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if "SELL" in self.order_direction:
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direction = "SELL"
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if direction:
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self.log(f"ENTRY: {direction} | RSVA={factor:.4f}")
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self.send_market_order(direction, self.trade_volume, "OPEN")
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def evaluate_exit(self, volume: int, factor: float):
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do_exit = False
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reason = ""
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# 当多空能量趋于平衡 (因子回到 0 附近)，说明趋势动能耗尽，平仓
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# 这种离场方式对震荡市非常友好：一旦陷入震荡，rv_pos 和 rv_neg 会迅速接近，因子归零
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if volume > 0 and factor < self.exit_threshold:
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do_exit = True
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reason = f"Bull Energy Fade (RSVA={factor:.4f})"
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elif volume < 0 and factor > -self.exit_threshold:
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do_exit = True
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reason = f"Bear Energy Fade (RSVA={factor:.4f})"
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if do_exit:
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direction = "CLOSE_LONG" if volume > 0 else "CLOSE_SHORT"
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self.log(f"EXIT: {reason}")
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self.send_market_order(direction, abs(volume), "CLOSE")
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def send_market_order(self, direction: str, volume: int, offset: str):
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# 严格遵守要求：使用 get_current_time()
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current_time = self.get_current_time()
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order = Order(
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id=f"{self.main_symbol}_{direction}_{current_time.timestamp()}",
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symbol=self.symbol,
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direction=direction,
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volume=volume,
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price_type="MARKET",
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submitted_time=current_time,
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offset=offset
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)
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self.send_order(order)
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def log_status(self, factor: float, pos_e: float, neg_e: float, current_pos: int):
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if self.enable_log:
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# 仅在有持仓或信号明显时打印
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if current_pos != 0 or abs(factor) > self.entry_threshold * 0.8:
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self.log(f"Status: Pos={current_pos} | RSVA={factor:.4f} | Energy(+/-)={pos_e:.1f}/{neg_e:.1f}")
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