增加交易策略、交易指标、量化库代码等文件夹

2025-04-27 15:54:09 +08:00
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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()
+
@@ -0,0 +1,7 @@
+通过网盘分享的文件：VIP15参数文件.rar
+链接: https://pan.baidu.com/s/1liwdmvmGJ7wjHWVewZUUWA?pwd=7777 提取码: 7777 
+--来自百度网盘超级会员v7的分享
+
+如果过期了不能用了，联系小松鼠微信：viquant01
+
+quant789.com可以使用参数选取工具
@@ -0,0 +1,65 @@
+### 自适应高斯移动平均策略 (VIP16) 介绍
+
+#### 策略概述
+
+自适应高斯移动平均策略 (VIP16) 是一种基于市场波动性和价格行为的量化交易策略。该策略通过计算自适应高斯移动平均 (AGMA) 来识别市场的趋势，并结合信号得分 (Score) 来生成交易信号。策略的核心思想是通过动态调整标准差 (Sigma) 来适应市场的波动性，从而提高策略的适应性和稳定性。
+
+#### 策略原理
+
+1. **自适应高斯移动平均 (AGMA) 计算**：
+   - 使用自适应的标准差 Sigma 来计算高斯移动平均。标准差可以根据市场波动性自适应调整，或者使用固定的标准差。
+   - 通过循环计算每个周期的权重和加权平均值，得到自适应高斯移动平均 AGMA。
+
+2. **信号得分 (Score) 计算**：
+   - 通过比较当前 AGMA 与过去 Ends 个周期的 AGMA 值，计算信号得分 Score。得分越高表示多头信号越强，得分越低表示空头信号越强。
+   - 同时计算多头和空头的累计得分 LongCout 和 ShortCout，用于后续的入场和出场逻辑。
+
+3. **交易信号生成**：
+   - 当 Score 上穿 20 时，生成多头信号 LongSignal。
+   - 当 Score 下穿 -20 时，生成空头信号 ShortSignal。
+
+4. **入场条件**：
+   - 当多头信号 LongSignal 为真且当前价格高于过去 Ends 个周期的最高价 HHV，并且当前没有多头持仓时，买入。
+   - 当空头信号 ShortSignal 为真且当前价格低于过去 Ends 个周期的最低价 LLV，并且当前没有空头持仓时，卖出。
+
+5. **跟踪止损逻辑**：
+   - 根据持仓状态和市场波动性，动态调整跟踪止损点 DliqPoint 和 KliqPoint。
+   - 当多头持仓时，如果价格低于跟踪止损点 DliqPoint，则卖出平仓。
+   - 当空头持仓时，如果价格高于跟踪止损点 KliqPoint，则买入平仓。
+
+6. **图形绘制**：
+   - 使用 plt0 和 plt1 绘制多头和空头的跟踪止损点。
+   - 使用 plt2 绘制信号得分 Score。
+
+#### 策略参数
+
+- **Length**：计算周期，用于计算自适应高斯移动平均。
+- **Ends**：计算结束点，用于计算信号得分。
+- **X**：自适应系数，用于调整标准差。
+- **TrailingStopRate**：跟踪止损率，用于计算跟踪止损点。
+- **Fund**：投入保证金，用于计算交易手数。
+
+#### 策略优势
+
+- **自适应性**：通过动态调整标准差 Sigma，策略能够适应不同市场环境下的波动性。
+- **稳定性**：结合信号得分和跟踪止损逻辑，策略能够在不同市场条件下保持稳定的盈利能力。
+- **灵活性**：策略支持多头和空头交易，能够在不同市场趋势下进行交易。
+
+#### 策略应用
+
+自适应高斯移动平均策略 (VIP16) 适用于多种交易品种和时间周期，特别适合追求稳定盈利的量化交易者。通过合理的参数设置和风险管理，该策略能够在不同市场环境下实现稳定的收益。
+
+#### 风险提示
+
+量化交易策略存在一定的风险，包括但不限于市场风险、模型风险和执行风险。建议用户在使用该策略前进行充分的历史回测和模拟交易，并根据自身风险承受能力进行调整。
+
+#### 变量介绍
+
+- **losslongX**：多头止损系数，用于动态调整多头持仓的跟踪止损点。该系数根据多头累计得分 totalL 进行更新，确保在多头信号较强时，止损点能够更紧密地跟随价格。
+- **lossShortX**：空头止损系数，用于动态调整空头持仓的跟踪止损点。该系数根据空头累计得分 totalS 进行更新，确保在空头信号较强时，止损点能够更紧密地跟随价格。
+
+这两个变量的引入使得策略能够根据市场信号的强弱动态调整止损点，从而更好地控制风险和保护利润。
+
+---
+
+由 Ai 生成的内容仅作为学习参考,不能保证正确性，不构成任何投资意见，风险自负。
				`@@ -0,0 +1 @@`
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truncated