增加交易策略、交易指标、量化库代码等文件夹

1.交易策略/1.CTA策略/1.松鼠策略/7.松鼠SF16_第三代出场模块_末端加速出场/使用文稿/vnpy/vip16.py

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+from vnpy_ctastrategy import (
+    CtaTemplate,
+    TargetPosTemplate,
+    StopOrder,
+    TickData,
+    BarData,
+    TradeData,
+    OrderData,
+    BarGenerator,
+    ArrayManager,
+)
+import numpy as np
+import talib
+
+class vip16(CtaTemplate):
+    """
+    自适应高斯移动平均策略
+    """
+    author = "Your Name"
+
+    # 策略参数
+    length = 65           # 计算周期
+    ends = 25         # 计算结束点
+    x = 0.9              # 自适应系数
+    trailing_stop_rate = 45  # 跟踪止损率
+    lots = 1             # 交易手数
+
+    # 策略变量
+    volatility_period = 20    # 自适应周期
+    volatility_std = 1.0      # 波动率标准差
+    adaptive = True           # 是否自适应
+    current_agma=[]
+    
+
+    parameters = ["length", "ends", "x", "trailing_stop_rate", "lots"]
+    variables = ["volatility_period", "volatility_std", "adaptive"]
+
+    def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
+        super().__init__(cta_engine, strategy_name, vt_symbol, setting)
+        
+        self.bg = BarGenerator(self.on_bar)
+        self.am = ArrayManager(size=100)  # 确保大小足够存储所需数据
+        
+        # 初始化变量
+        self.agma = 0
+        self.score = 0
+        self.re_score=0
+        self.long_cout = 0
+        self.short_cout = 0
+        self.total_l = 0
+        self.total_s = 0
+        self.loss_long_x = 0.1
+        self.loss_short_x = 0.1
+        self.highest_low_after_entry = 0
+        self.lowest_high_after_entry = 0
+        self.ent_bar = 0
+        self.current_bar = 0
+        self.kg = 0
+        self.Long_jiasu_kg=False
+        self.Short_jiasu_kg=False
+
+    def calculate_agma(self):
+        """计算自适应高斯移动平均"""
+        if not self.am.inited:
+            return 0
+            
+        # 计算标准差
+        if self.adaptive:
+            sigma = self.am.std(self.volatility_period)
+        else:
+            sigma = self.volatility_std
+            
+        sum_weights = 0
+        agma = 0
+        
+        for i in range(self.length):
+            # 计算高斯权重
+            weight = np.exp(-((i - (self.length - 1)) / (2 * sigma)) ** 2 / 2)
+            
+            # 计算最高价和最低价的和
+            high = self.am.high_array[-(i+1)]
+            low = self.am.low_array[-(i+1)]
+            value = high + low
+            
+            agma += value * weight
+            sum_weights += weight
+            
+        return (agma / sum_weights) / 2
+
+    def calculate_score(self):
+        """计算信号得分"""
+        if not self.am.inited:
+            return 0, 0,0,0
+        
+        re_score=self.score  
+        score = 0
+        long_cout = 0
+        short_cout = 0
+        
+        self.current_agma.append(self.calculate_agma())
+        
+        if len(self.current_agma)<self.ends + 1:
+            
+            return score, long_cout, short_cout,re_score
+        
+        for j in range(1, self.ends + 1):
+            # 计算AGMA比较得分
+            if self.current_agma[-1] > self.current_agma[-j]:
+                score += 1
+            else:
+                score -= 1
+                
+            # 计算多空累计得分
+            if self.am.close_array[-j] > self.am.open_array[-j]:
+                long_cout += abs(self.am.close_array[-j] - self.am.open_array[-j])
+            else:
+                short_cout += abs(self.am.open_array[-j] - self.am.close_array[-j])
+        
+        return score, long_cout, short_cout,re_score
+
+    def on_init(self):
+        """
+        Callback when strategy is inited.
+        """
+        self.write_log("策略初始化")
+        self.load_bar(10)
+
+    def on_start(self):
+        """
+        Callback when strategy is started.
+        """
+        self.write_log("策略启动")
+
+    def on_stop(self):
+        """
+        Callback when strategy is stopped.
+        """
+        self.write_log("策略停止")
+
+    def on_tick(self, tick: TickData):
+        """
+        Callback of new tick data update.
+        """
+        self.bg.update_tick(tick)
+
+    def on_bar(self, bar: BarData):
+        """
+        Callback of new bar data update.
+        """
+        self.am.update_bar(bar)
+        if not self.am.inited:
+            return
+
+        # 更新当前bar计数
+        self.current_bar += 1
+        
+        # 计算信号相关指标
+        self.score, self.long_cout, self.short_cout,self.re_score = self.calculate_score()
+        
+        if self.long_cout + self.short_cout > 0:
+            self.total_l = self.long_cout / (self.long_cout + self.short_cout)
+            self.total_s = self.short_cout / (self.long_cout + self.short_cout)
+        
+        # 生成交易信号
+        long_signal = self.score > 20 and self.re_score <= 20  # 上穿20
+        short_signal = self.score < -20 and self.re_score >= -20  # 下穿-20
+
+        if long_signal:
+            self.kg = 1
+        elif short_signal:
+            self.kg = -1
+
+        # 计算过去ends周期的最高最低价
+        hhv = self.am.high_array[-self.ends:].max()
+        llv = self.am.low_array[-self.ends:].min()
+
+        # 入场逻辑
+        if self.kg == 1 and bar.high_price >= hhv and not self.pos:
+            self.buy(max(bar.open_price, hhv), self.lots)
+            self.highest_low_after_entry = max(bar.open_price, hhv)
+            self.ent_bar = self.current_bar
+            
+        elif self.kg == -1 and bar.low_price <= llv and not self.pos:
+            self.short(min(bar.open_price, llv), self.lots)
+            self.lowest_high_after_entry = min(bar.open_price, llv)
+            self.ent_bar = self.current_bar
+
+        # 更新止损系数
+        if not self.pos:
+            self.loss_long_x = 1
+            self.loss_short_x = 1
+            self.Long_jiasu_kg=False
+            self.Short_jiasu_kg=False
+        else:
+            if self.total_l >= self.x:
+                if self.Long_jiasu_kg==False:
+                    self.loss_long_x=self.x
+                    self.Long_jiasu_kg=True
+                else:
+                    self.loss_long_x = max(self.loss_long_x, self.total_l)
+            if self.total_s >= self.x:
+                if self.Short_jiasu_kg==False:
+                    self.loss_short_x=self.x
+                    self.Short_jiasu_kg=True
+                else:
+                    self.loss_short_x = max(self.loss_short_x, self.total_s)
+
+        # 更新跟踪止损价位
+        if self.pos > 0:
+            if self.current_bar > self.ent_bar:
+                self.highest_low_after_entry = max(self.highest_low_after_entry, bar.low_price)
+            dliq_point = self.highest_low_after_entry - (bar.open_price * self.trailing_stop_rate/1000) * 1 if self.loss_long_x==1  else 1 - self.loss_long_x
+            
+            if bar.low_price <= dliq_point:
+                self.sell(min(bar.open_price, dliq_point), abs(self.pos))
+
+        elif self.pos < 0:
+            if self.current_bar > self.ent_bar:
+                self.lowest_high_after_entry = min(self.lowest_high_after_entry, bar.high_price)
+            kliq_point = self.lowest_high_after_entry + (bar.open_price * self.trailing_stop_rate/1000) * 1 if self.loss_short_x==1 else 1 - self.loss_short_x
+            
+            if bar.high_price >= kliq_point:
+                self.cover(max(bar.open_price, kliq_point), abs(self.pos))
+
+        self.put_event()
+