增加交易策略、交易指标、量化库代码等文件夹

1.交易策略/2.价差策略/1.松鼠SF14_商品股指套利策略/使用文稿/TBQ/VIP14工作区.sws

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1.交易策略/2.价差策略/1.松鼠SF14_商品股指套利策略/使用文稿/VNPY/VIP14.py

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+from typing import List, Dict
+from datetime import datetime
+
+from vnpy_portfoliostrategy import (
+    StrategyEngine,
+    StrategyTemplate,
+    TickData,
+    BarData,
+    TradeData,
+    ArrayManager,
+)
+
+from vnpy.trader.utility import Interval
+from vnpy_portfoliostrategy.utility import PortfolioBarGenerator
+import numpy as np
+import talib as ta
+
+#使用portfoliostrategy模块编写的交易策略，无参数优化，请自行调试优化参数。
+#公众号：松鼠Quant
+#主页：www.quant789.com
+#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
+class VIP14(StrategyTemplate):
+    """"""
+
+    author = "松鼠quant"
+    
+    N = 2
+    M = 5
+    X = 5
+    TRS=5
+    
+    fixed_size=1
+    
+    am_1=0
+    am_2=0
+    current_bar=0
+    
+    parameters = [
+        "N",
+        "M",
+        "X",
+        "fixed_size",
+        "TRS"
+    ]
+    variables = []
+
+    def __init__(
+        self,
+        strategy_engine: StrategyEngine,
+        strategy_name: str,
+        vt_symbols: List[str],
+        setting: dict
+    ):
+        """"""
+        super().__init__(strategy_engine, strategy_name, vt_symbols, setting)
+
+        self.KG=0
+        self.high_after_entry=0
+        self.low_after_entry=0
+        self.dliq_point=0
+        self.kliq_point=0
+        self.curbar_h = np.zeros(100)  # 例如，初始化为长度为 100 的数组
+        self.curbar_l = np.zeros(100)
+        # 初始化 hd 属性
+        self.hd = np.zeros(100)  # 例如，初始化为长度为 100 的数组
+        self.ld = np.zeros(100)  # 例如，初始化为长度为 100 的数组
+        self.targets: Dict[str, int] = {}
+        self.last_tick_time: datetime = None
+
+        # Obtain contract info
+        self.ams: Dict[str, ArrayManager] = {}
+        for vt_symbol in self.vt_symbols:
+            self.ams[vt_symbol] = ArrayManager(100)
+            self.targets[vt_symbol] = 0
+
+        self.pbg = PortfolioBarGenerator(self.on_bars, 2, self.on_2hour_bars, Interval.HOUR)
+
+    def on_init(self):
+        """
+        Callback when strategy is inited.
+        """
+        self.write_log("策略初始化")
+
+        self.load_bars(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.pbg.update_tick(tick)
+
+    def on_bars(self, bars: Dict[str, BarData]):
+        """
+        Callback of new bars data update.
+        """
+        self.pbg.update_bars(bars)
+
+      
+    def on_2hour_bars(self, bars: Dict[str, BarData]):
+        """"""
+        self.cancel_all()
+        # 初始化
+        nx = 0
+
+        # 获取合约的数据
+        for vt_symbol, bar in bars.items():
+            nx += 1
+            # 更新 ArrayManager
+            am: ArrayManager = self.ams[vt_symbol]
+            am.update_bar(bar)
+            #print(vt_symbol)
+            # 如果 ArrayManager 还未初始化完毕，则跳过
+            if not am.inited:
+                return
+            # 将第一个和第二个合约的 ArrayManager 分别保存下来
+            if nx == 1:
+                self.am_1 = am
+            elif nx == 2:
+                self.am_2 = am
+                
+        am_1=self.am_1 
+        am_2=self.am_2
+
+        self.current_bar=self.current_bar+1 #Bar线计数
+        # 计算相关指标
+        c_jb = am_1.close_array / am_2.close_array   # 计算价比
+        copen = am_1.open_array / am_2.open_array    # 计算开比
+        chigh = np.maximum(np.maximum(am_1.high_array / am_2.high_array, copen), c_jb)
+        clow = np.minimum(np.minimum(am_1.low_array / am_2.low_array, copen), c_jb)
+        
+        # 计算多个指标的序列
+        var1 = self.highest(chigh, self.N) - self.lowest(clow, self.N)
+        var2 = self.highest(chigh, self.N) - c_jb[-self.N:]
+        var3 = c_jb[-self.N:] - self.lowest(clow, self.N)
+        var4 = var2 / var1 * 100 - 70
+        var5 = (c_jb[-self.N:] - self.lowest(clow, self.N)) / var1 * 100
+        var6 = (2 * c_jb + chigh + clow) / 4
+        var7 = self.sma(var3 / var1 * 100, 3, 1)
+        var8 = self.lowest(clow, self.M)
+        var9 = self.sma(var7, 3, 1) - self.sma(var4, 9, 1)
+        vara = self.highest(chigh, self.M)
+        bd1 = self.xaverage((var6[-self.M:] - var8) / (vara - var8) * 100, self.M)
+        bd2 = self.xaverage(bd1, self.X)
+        ma1 = self.xaverage(c_jb, self.X)
+        # 检查金叉 (BD1 从小于 BD2 变为大于 BD2)
+        dk_up = bd1[-1] > bd2[-1] and bd1[-2] <= bd2[-1]
+
+        # 检查死叉 (BD1 从大于 BD2 变为小于 BD2)
+        kk_dn = bd1[-1] < bd2[-1] and bd1[-2] >= bd2[-1]
+
+        # 确保 dk_up 和 kk_dn 不为空
+        if dk_up:
+            self.curbar_h = self.current_bar  # 在最后一次金叉发生时更新 curbar_h
+            period = max(self.curbar_h - self.curbar_l, self.M)
+            
+            # 确保 period 不超过 chigh 的长度
+            if len(chigh) >= period:
+                Highup = max(chigh[-period:-1])
+        else:
+            Highup = max(chigh[-self.M:-1])  # 如果 period 超出范围，则取整个序列的最大值
+                
+        if kk_dn:
+            self.curbar_l = self.current_bar  # 在最后一次死叉发生时更新 curbar_l
+            period = max(self.curbar_l - self.curbar_h, self.M)
+            
+            # 确保 period 不超过 clow 的长度
+            if len(clow) >= period:
+                Lowdown = min(clow[-period:-1])
+        else:
+            Lowdown = min(clow[-self.M:-1])  # 如果 period 超出范围，则取整个序列的最小值
+        
+        #print(f'bd1[-1]{bd1[-1] },bd2[-1] {bd2[-1]},chigh[-1]{chigh[-1]},Highup{Highup},c_jb[-1]{c_jb[-1]},ma1[-1]{ma1[-1]}')
+        # 开仓条件
+        cond_dk = (bd1[-1] > bd2[-1]) & (chigh[-1] >= Highup) & (c_jb[-1] > ma1[-1])
+        cond_kk = (bd1[-1] < bd2[-1]) & (clow[-1] <= Lowdown) & (c_jb[-1] < ma1[-1])
+
+        self.KG = np.where(cond_dk, 1, np.where(cond_kk, -1, 0))
+        
+        #print(self.KG)
+        #开仓部分
+        nx=0
+        for vt_symbol, bar in bars.items():
+            nx+=1
+            am: ArrayManager = self.ams[vt_symbol]
+            if not am.inited:
+                return
+            price = bar.close_price
+            current_pos = self.get_pos(vt_symbol)
+            #print(nx,current_pos,self.KG)
+            #数据源1：
+            if nx==1:
+                #开多
+                if self.KG>0 : 
+
+                        if current_pos<0:
+                            self.cover(vt_symbol, price, self.fixed_size)
+                            self.buy(vt_symbol, price, self.fixed_size)
+                            self.low_after_entry = clow[-1]
+                        if current_pos==0:
+                            self.buy(vt_symbol, price, self.fixed_size)
+                            self.low_after_entry = clow[-1]
+                #开空      
+                if self.KG<0  : 
+                        if current_pos > 0:
+                            self.sell(vt_symbol, price, self.fixed_size)
+                            self.short(vt_symbol, price, self.fixed_size)
+                            self.high_after_entry = chigh[-1]
+                        if current_pos == 0:
+                            self.short(vt_symbol, price, self.fixed_size)
+                            self.high_after_entry = chigh[-1]
+            #数据源2
+            elif nx==2:
+                #开多
+                if self.KG>0: 
+                        if current_pos < 0:
+                            self.cover(vt_symbol, price, self.fixed_size)
+                            self.short(vt_symbol, price, self.fixed_size)
+                            self.high_after_entry = chigh[-1]   
+                        if current_pos == 0:
+                            self.short(vt_symbol, price, self.fixed_size)
+                            self.high_after_entry = chigh[-1]                              
+                #开空      
+                if self.KG<0 and current_pos >= 0 :
+                    if current_pos > 0 :
+                        self.sell(vt_symbol, price, self.fixed_size)
+                        self.buy(vt_symbol, price, self.fixed_size)
+                        self.low_after_entry = clow[-1]     
+                    if current_pos == 0 :
+                        self.buy(vt_symbol, price, self.fixed_size)
+                        self.low_after_entry = clow[-1]                      
+        #移动出场
+        if self.KG == 0:
+            self.high_after_entry = chigh[-1]
+            self.low_after_entry = clow[-1]
+        elif self.KG != 0:
+            self.high_after_entry = min(self.high_after_entry, chigh[-1])
+            self.low_after_entry = max(self.low_after_entry, clow[-1])
+
+        # Calculate liquidation points
+        if self.KG > 0:
+            self.dliq_point = self.low_after_entry - (copen[-1] * self.TRS / 1000)
+        elif self.KG < 0:
+            self.kliq_point = self.high_after_entry + (copen[-1] * self.TRS / 1000)
+
+        nx=0
+        # print("多头",c_jb[-1],self.dliq_point)
+        # print("空头",c_jb[-1],self.kliq_point)
+        for vt_symbol, bar in bars.items():
+            nx+=1
+            am: ArrayManager = self.ams[vt_symbol]
+            if not am.inited:
+                return
+            price = bar.close_price
+            current_pos = self.get_pos(vt_symbol)
+            
+            #数据源1
+            if nx==1:
+                
+                if self.KG > 0 and c_jb[-1]<= self.dliq_point and self.dliq_point > 0 and current_pos>0:
+                        self.sell(vt_symbol, price, current_pos)
+                if self.KG < 0 and c_jb[-1] >= self.kliq_point and self.kliq_point > 0 and current_pos<0:
+                        self.cover(vt_symbol, price, current_pos)
+            #数据源2
+            if nx==2:
+                if self.KG > 0 and c_jb[-1]<= self.dliq_point and self.dliq_point > 0 and current_pos<0:
+                        self.cover(vt_symbol, price, current_pos)
+                if self.KG < 0 and c_jb[-1] >= self.kliq_point and self.kliq_point > 0 and current_pos>0:
+                        self.sell(vt_symbol, price, current_pos)
+
+        self.put_event()
+
+
+    def highest(self, data, period):
+        """
+        计算序列最后 period 个元素中的最高值，返回一个序列
+        :param data: 数据序列
+        :param period: 周期
+        :return: 最后 period 个元素中的最高值序列
+        """
+        result = []
+        start_index = max(0, len(data) - period)
+        
+        for i in range(start_index, len(data)):
+            result.append(max(data[max(0, i - period + 1):i + 1]))
+        
+        return np.array(result)
+
+
+    def lowest(self, data, period):
+        """
+        计算序列最后 period 个元素中的最小值，返回一个序列
+        :param data: 数据序列
+        :param period: 周期
+        :return: 最后 period 个元素中的最小值序列
+        """
+        result = []
+        start_index = max(0, len(data) - period)
+        
+        for i in range(start_index, len(data)):
+            result.append(min(data[max(0, i - period + 1):i + 1]))
+        
+        return np.array(result)
+
+
+    def sma(self, data, period, weight=1):
+        """
+        计算简单移动平均（SMA）的序列，仅计算最后 period 个元素
+        :param data: 数据序列
+        :param period: 周期
+        :param weight: 权重
+        :return: SMA序列
+        """
+        if len(data) < period:
+            return np.convolve(data, np.ones((len(data),))/len(data), mode='valid')
+        else:
+            return np.convolve(data[-period:], np.ones((period,))/period, mode='valid')
+
+
+
+
+    def xaverage(self, data, period):
+        """
+        计算加权平均（WMA）的序列
+        :param data: 数据序列
+        :param period: 周期
+        :return: WMA序列
+        """
+        if len(data) < period:
+            weights = np.arange(1, len(data) + 1)
+            return np.convolve(data, weights/weights.sum(), mode='full')[-len(data):]
+        else:
+            weights = np.arange(1, period + 1)
+            return np.convolve(data[-period:], weights/weights.sum(), mode='full')[-period:]
+
+
+
+    def HHV(self, data, period):
+        """
+        计算给定数据序列中最后 period 个元素的最大值
+        :param data: 数据序列
+        :param period: 周期
+        :return: 最后 period 个元素的最大值
+        """
+        if len(data) == 0:
+            return None  # 如果数据为空，返回 None 或者抛出异常
+
+        if period > len(data):
+            period = len(data)  # 如果 period 超出数据长度，则取数据长度
+
+        return max(data[-period:])
+    
+    
+    def LLV(self, data, period):
+        """
+        计算给定数据序列中最后 period 个元素的最小值
+        :param data: 数据序列
+        :param period: 周期
+        :return: 最后 period 个元素的最小值
+        """
+        if len(data) == 0:
+            return None  # 如果数据为空，返回 None 或者抛出异常
+
+        if period > len(data):
+            period = len(data)  # 如果 period 超出数据长度，则取数据长度
+
+        return min(data[-period:])