新增实盘策略：FisherTrendStrategy(FG)
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import numpy as np
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import math
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from collections import deque
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from typing import Optional, Any, List, Dict
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from src.core_data import Bar, Order
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from src.indicators.base_indicators import Indicator
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from src.strategies.base_strategy import Strategy
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class PragmaticCyberneticStrategy(Strategy):
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"""
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【务实优化版·控制论策略】
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优化核心：
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1. 保持“钝感”：拒绝灵敏的趋势反转平仓，保留“死拿趋势”的盈利特性。
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2. 修复系数：将 Stoch 映射系数从 0.66 提升至 1.98，使 Fisher 值域恢复到 [-3, 3] 可调范围。
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- 现在你可以设置 exit_level = 2.0 来捕捉极端利润，而不是被迫等到换月。
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3. 纯粹逻辑：开仓是开仓，平仓是平仓。互不干扰，消除中间地带的内耗。
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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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min_tick: float = 1.0,
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# --- 核心参数 (保持原策略风格) ---
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trend_period: int = 26, # T: 趋势中轴
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fisher_period: int = 20, # FB: 动能周期 (原策略 46 可能太慢，建议 20-30)
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atr_period: int = 23,
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# --- 阈值参数 (关键调整) ---
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# 1. 止盈阈值：因为系数修复了，现在 FB 能跑到 2.5 以上。
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# 建议设为 2.0 - 2.5。如果设得很高(如 5.0)，效果就等于你原来的“死拿”。
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fisher_exit_level: float = 2.2,
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# 2. 入场阈值：保持在 0.5 左右，只接深回调
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fb_entry_threshold: float = 0.,
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stop_mult: float = 2, # 稍微放宽止损，适应趋势震荡
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limit_offset_mult: float = 0.2, # FV 挂单偏移
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order_direction: Optional[List[str]] = None,
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indicator: Indicator = None,
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):
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super().__init__(context, main_symbol, enable_log)
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self.trade_volume = trade_volume
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self.min_tick = min_tick
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self.t_len = trend_period
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self.f_len = fisher_period
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self.atr_len = atr_period
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self.fisher_exit_level = fisher_exit_level
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self.fb_entry_threshold = fb_entry_threshold
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self.stop_mult = stop_mult
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self.limit_offset_mult = limit_offset_mult
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self.order_direction = order_direction or ['BUY', 'SELL']
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self.indicator = indicator
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# 缓存
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self._buf_len = max(self.t_len, self.f_len, self.atr_len) + 5
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self._highs = deque(maxlen=self._buf_len)
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self._lows = deque(maxlen=self._buf_len)
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self._closes = deque(maxlen=self._buf_len)
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# 记录上一根 Bar 的 FB，仅用于判断拐点
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self._prev_fb = 0.0
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self.order_id_counter = 0
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def round_to_tick(self, price: float) -> float:
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if self.min_tick <= 0: return price
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return round(price / self.min_tick) * self.min_tick
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def _calculate_indicators(self):
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"""
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计算逻辑：保留原策略的简洁性，仅修复 Scaling 系数
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"""
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if len(self._closes) < self._buf_len:
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return None, None, None, None
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# 1. 趋势中轴 T (物理中轴)
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h_trend = list(self._highs)[-self.t_len:]
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l_trend = list(self._lows)[-self.t_len:]
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T = (max(h_trend) + min(l_trend)) / 2.0
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# 2. 公平价 FV (FIR 滤波)
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FV = (self._closes[-1] + 2 * self._closes[-2] + 2 * self._closes[-3] + self._closes[-4]) / 6.0
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# 3. Fisher Transform (非递归版，响应更快)
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h_fisher = list(self._highs)[-self.f_len:]
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l_fisher = list(self._lows)[-self.f_len:]
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max_h, min_l = max(h_fisher), min(l_fisher)
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denom = max_h - min_l if max_h != min_l else self.min_tick
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# --- 关键修正点 ---
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# 你的原代码是 0.66 * (stoc - 0.5)，这导致最大值被锁死。
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# 改为 1.98，使得输入范围从 [-0.33, 0.33] 扩大到 [-0.99, 0.99]。
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# 这样 math.log 就能计算出 -3 到 +3 的值，让止盈逻辑“复活”且可控。
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stoc = (self._closes[-1] - min_l) / denom
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value = max(-0.999, min(0.999, 1.98 * (stoc - 0.5)))
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FB = 0.5 * math.log((1.0 + value) / (1.0 - value))
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# 4. ATR
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tr_list = []
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for i in range(1, self.atr_len + 1):
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h, l, pc = self._highs[-i], self._lows[-i], self._closes[-i - 1]
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tr = max(h - l, abs(h - pc), abs(l - pc))
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tr_list.append(tr)
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ATR = sum(tr_list) / self.atr_len
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return T, FV, FB, ATR
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def on_open_bar(self, open_price: float, symbol: str):
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self.symbol = symbol
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# 每次 Bar 重置挂单，防止挂单“长在”图表上
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self.cancel_all_pending_orders(self.symbol)
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bars = self.get_bar_history()
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if len(bars) < 1: return
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prev_bar = bars[-1]
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self._highs.append(prev_bar.high)
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self._lows.append(prev_bar.low)
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self._closes.append(prev_bar.close)
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T, FV, FB, ATR = self._calculate_indicators()
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if T is None: return
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pos = self.get_current_positions().get(self.symbol, 0)
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entry_price = self.get_average_position_price(self.symbol)
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# 状态定义
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# 这里我们不去定义 "short_signal" 这种会诱发反向平仓的变量
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# 而是只关注眼下的：大势(T) 和 动能(FB)
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trend_up = prev_bar.close > T
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trend_down = prev_bar.close < T
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# ==========================================
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# 1. 持仓逻辑：简单、迟钝、粘性强
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# ==========================================
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if pos != 0:
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stop_dist = max(self.stop_mult * ATR, self.min_tick * 20)
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if pos > 0:
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# A. 动能止盈 (Mean Reversion Exit)
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# 只有当行情极其疯狂(FB > 2.2) 且开始回头时才止盈。
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# 正常波动绝不下车。
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if FB > self.fisher_exit_level and FB < self._prev_fb:
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self.log(f"TAKE PROFIT (Long): FB {FB:.2f} Peak Reached")
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self.send_market_order("CLOSE_LONG", abs(pos), "CLOSE")
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return
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# B. 硬止损 (ATR Stop) - 最后的防线
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if prev_bar.close < entry_price - stop_dist:
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self.send_market_order("CLOSE_LONG", abs(pos), "CLOSE")
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return
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# C. (可选) 极端趋势反转保护
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# 如果你希望策略更像原来的“死拿”，这部分可以注释掉，或者把判定条件设严
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# if prev_bar.close < T - ATR: ...
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elif pos < 0:
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# A. 动能止盈
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if FB < -self.fisher_exit_level and FB > self._prev_fb:
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self.log(f"TAKE PROFIT (Short): FB {FB:.2f} Bottom Reached")
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self.send_market_order("CLOSE_SHORT", abs(pos), "CLOSE")
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return
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# B. 硬止损
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if prev_bar.close > entry_price + stop_dist:
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self.send_market_order("CLOSE_SHORT", abs(pos), "CLOSE")
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return
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# ==========================================
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# 2. 开仓逻辑：严苛、左侧、顺大势
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# ==========================================
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if pos == 0:
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is_met = self.indicator is None or self.indicator.is_condition_met(*self.get_indicator_tuple())
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# 挂单价格优化：
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# 在 FV 基础上再便宜一点点，增加胜率
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long_limit = self.round_to_tick(FV - (self.limit_offset_mult * ATR))
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short_limit = self.round_to_tick(FV + (self.limit_offset_mult * ATR))
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# 开多：趋势向上 + 动能深跌 (FB < -0.5)
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if trend_up and FB < -self.fb_entry_threshold and is_met:
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if "BUY" in self.order_direction:
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self.send_limit_order(long_limit, "BUY", self.trade_volume, "OPEN")
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# 开空：趋势向下 + 动能冲高 (FB > 0.5)
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elif trend_down and FB > self.fb_entry_threshold and is_met:
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if "SELL" in self.order_direction:
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self.send_limit_order(short_limit, "SELL", self.trade_volume, "OPEN")
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# 记录上一根FB，仅用于止盈时的拐点比较
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self._prev_fb = FB
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# --- 辅助 ---
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def send_market_order(self, direction, volume, offset):
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ts = self.get_current_time().strftime('%H%M%S')
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oid = f"{self.symbol}_{direction}_MKT_{ts}_{self.order_id_counter}"
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self.order_id_counter += 1
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self.send_order(Order(id=oid, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
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submitted_time=self.get_current_time(), offset=offset))
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def send_limit_order(self, price, direction, volume, offset):
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ts = self.get_current_time().strftime('%H%M%S')
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oid = f"{self.symbol}_{direction}_LMT_{ts}_{self.order_id_counter}"
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self.order_id_counter += 1
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self.send_order(Order(id=oid, symbol=self.symbol, direction=direction, volume=volume, price_type="LIMIT",
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submitted_time=self.get_current_time(), offset=offset, limit_price=price))
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