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/2.松鼠SF09_基于成交量的阶梯均线过滤震荡行情/使用文稿/vnpy/vip09.ipynb

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'vnpy_ctastrategy.strategies.vip09'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrader\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OptimizationSetting\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy_ctastrategy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbacktesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BacktestingEngine\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy_ctastrategy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstrategies\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvip09\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vip09\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# from vip09 import vip09\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'vnpy_ctastrategy.strategies.vip09'"
+     ]
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "from vnpy.trader.optimize import OptimizationSetting\n",
+    "from vnpy_ctastrategy.backtesting import BacktestingEngine\n",
+    "# from vnpy_ctastrategy.strategies.vip09 import vip09\n",
+    "from vip09 import vip09"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "engine = BacktestingEngine()\n",
+    "engine.set_parameters(\n",
+    "    vt_symbol=\"rb00.SHFE\",\n",
+    "    interval=\"1m\",\n",
+    "    start=datetime(2020, 1, 1),\n",
+    "    end=datetime(2024, 3, 21),\n",
+    "    rate=1.5/10000,\n",
+    "    slippage=1,\n",
+    "    size=10,\n",
+    "    pricetick=1,\n",
+    "    capital=1_000_00,\n",
+    ")\n",
+    "engine.add_strategy(vip09, {})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "engine.load_data()\n",
+    "engine.run_backtesting()\n",
+    "df = engine.calculate_result()\n",
+    "engine.calculate_statistics()\n",
+    "engine.show_chart()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "'''用于参数优化'''\n",
+    "# setting = OptimizationSetting()\n",
+    "# setting.set_target(\"sharpe_ratio\")\n",
+    "# setting.add_parameter(\"Length\", 5, 100, 5)\n",
+    "# setting.add_parameter(\"N\", 5, 100, 5)\n",
+    "# setting.add_parameter(\"X\", 1, 20, 1)\n",
+    "# setting.add_parameter(\"TS\", 5, 100, 5)\n",
+    "# from multiprocessing import cpu_count\n",
+    "# # 获取 CPU 核心数量\n",
+    "# num_cores = cpu_count()\n",
+    "# print(f\"获取 CPU 核心数量:\",round(num_cores/2))\n",
+    "# engine.run_ga_optimization(setting, max_workers=round(num_cores/2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#engine.run_bf_optimization(setting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1b43cb0bd93d5abbadd54afed8252f711d4681fe6223ad6b67ffaee289648f85"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

1.交易策略/1.CTA策略/1.松鼠策略/2.松鼠SF09_基于成交量的阶梯均线过滤震荡行情/使用文稿/vnpy/vip09.py

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+from vnpy_ctastrategy import (
+    CtaTemplate,
+    TargetPosTemplate,
+    StopOrder,
+    TickData,
+    BarData,
+    TradeData,
+    OrderData,
+    BarGenerator,
+    ArrayManager,
+)
+import pandas as pd
+
+class vip09(TargetPosTemplate):
+    """"""
+    author = "松鼠Quant"
+    
+    Length = 30
+    N=5
+    X=10
+    TS = 45
+    lots=1
+
+    current_bar = 0
+
+    Highup=0
+    Lowdown=0
+
+    HigherAfterEntry = float('inf')
+    LowerAfterEntry = -float('inf')
+    liQKA = 0
+    DliqPoint = 0
+    KliqPoint = 0
+    OBVValue=[0]
+    MAOBV=[0]
+    M2=[0]
+    
+    cond1=[0]*2
+    cond2=[0]*2
+    cond3=[0]*2
+    cond4=[0]*2
+    cond5=[0]*2
+    
+    kong_cond1=[0]*2
+    kong_cond2=[0]*2
+    kong_cond3=[0]*2
+    kong_cond4=[0]*2
+    kong_cond5=[0]*2
+    
+    MA1=0
+    MA2=0
+    MA3=0
+    
+    
+
+    parameters = ["Length",'N','X',"TS",'lots']
+    variables = ["HigherAfterEntry","LowerAfterEntry","liQKA","DliqPoint","KliqPoint"]
+
+    def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
+        """"""
+        super().__init__(cta_engine, strategy_name, vt_symbol, setting)
+
+        #df: pd.DataFrame = pd.read_csv(r"index_contract.csv")
+        #self.contracts_sizes = {row.vt_symbol.split('.')[0]: row.contract_size for _, row in df.iterrows()}
+
+        self.bg = BarGenerator(self.on_bar)
+        self.am = ArrayManager(200)
+        self.pos=0
+
+    def on_init(self):
+        """
+        Callback when strategy is inited.
+        """
+        self.write_log("策略初始化")
+        self.load_bar(1)
+
+    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.
+        """
+        
+        am = self.am
+        am.update_bar(bar)
+        if not am.inited:
+            return
+        # 如果历史数据还没有初始化完毕，则直接返回
+        # 注意：am 是交易系统中的一个数据管理器，可能用于管理历史数据
+        # bar 是当前的K线数据，其中包含了开盘价、最高价、最低价、收盘价和成交量等信息
+
+        if self.current_bar == 0:
+            self.OBVValue = [0]
+        else:
+            if am.close_array[-1] > am.close_array[-2]:
+                self.OBVValue += [self.OBVValue[-1] + am.volume_array[-1]]
+            elif am.close_array[-1] < am.close_array[-2]:
+                self.OBVValue += [self.OBVValue[-1] - am.volume_array[-1]]
+
+        # 根据收盘价的涨跌更新 OBV 值
+        # 如果当前收盘价大于昨日收盘价，则 OBV 值加上当前成交量
+        # 如果当前收盘价小于昨日收盘价，则 OBV 值减去当前成交量
+        # 注意：OBV 是一种基于成交量变化来反映买卖力量的技术指标，用于判断市场的买卖压力
+
+        self.current_bar=self.current_bar+1 #Bar线计数
+        #print(bar.datetime,"current_bar:",self.current_bar)
+
+        # 记录当前Bar的计数器值，并打印当前Bar的时间和计数器值
+
+        if(self.current_bar<self.Length*3):
+            return
+
+        # 如果当前Bar的计数器值小于三倍的Length，则直接返回
+        # 这可能是因为需要一定数量的历史数据才能计算出所需的指标值
+
+        # print(f'{self.OBVValue},OBVValue')
+        self.MA1=am.ema(self.Length,array=True)
+        self.MA2=am.ema(self.Length*2,array=True)
+        self.MA3=am.ema(self.Length*3,array=True)
+
+        # 计算三条指定长度的指数移动平均线（EMA）
+        # 分别记为MA1、MA2和MA3
+
+        self.MAOBV += [(sum(self.OBVValue[-self.Length:]) / self.Length)]
+        self.M2 += [(sum(self.OBVValue[-self.X:]) / self.X)]
+
+        # 计算 OBV 和 M2 的移动平均值
+
+        self.cond1=self.MA1 if am.close_array[-1] > self.MA1[-2] else self.cond1
+        self.cond2=self.MA1 if self.M2[-1] > self.M2[-2] else self.cond2
+        self.cond3=self.MA2 if am.close_array[-1] > self.MA2[-1] and self.MAOBV[-1] > self.MAOBV[-2] else self.cond3
+        self.cond4=self.MA2 if am.close_array[-1] > self.MA2[-1] else self.cond4
+        self.cond5=self.MA3 if am.close_array[-1] > self.MA3[-1] and self.MAOBV[-1] > self.MAOBV[-2] else self.cond5
+
+        # 根据条件更新多头条件指标cond1到cond5
+        # 根据条件更新空头条件指标kong_cond1到kong_cond5
+
+        self.kong_cond1=self.MA1 if am.close_array[-1] < self.MA1[-2] else self.kong_cond1
+        self.kong_cond2=self.MA1 if self.M2[-1] < self.M2[-2] else self.kong_cond2
+        self.kong_cond3=self.MA2 if am.close_array[-1] < self.MA2[-1] and self.MAOBV[-1] < self.MAOBV[-2] else self.kong_cond3
+        self.kong_cond4=self.MA2 if am.close_array[-1] < self.MA2[-1] else self.kong_cond4
+        self.kong_cond5=self.MA3 if am.close_array[-1] < self.MA3[-1] and self.MAOBV[-1] < self.MAOBV[-2] else self.kong_cond5
+
+        # 更新空头条件指标kong_cond1到kong_cond5
+
+        self.Highup=max(am.high_array[-self.N:-1]) 
+        self.Lowdown=min(am.low_array[-self.N:-1])
+
+        # 计算最近N个Bar的最高价和最低价，分别记为Highup和Lowdown
+
+        Long_line=am.close_array[-1]>self.MA3[-1] and self.MAOBV[-1]>self.MAOBV[-2]
+        short_line=am.close_array[-1]<self.MA3[-1] and self.MAOBV[-1]<self.MAOBV[-2]
+
+        #print(f'{self.MAOBV[-1]},MAOBV')
+        #print(f'{Long_line},Long_line')
+        #print(f'{short_line},short_line')
+        if Long_line:
+           if self.cond4[-1]>self.cond5[-2] and self.cond5[-1]>self.cond5[-2] and self.cond4[-1]>self.cond4[-2] and self.cond3[-1]>self.cond3[-2] and self.cond2[-1]>self.cond2[-2] and self.cond1[-1]>self.cond1[-2] :
+                if self.pos!=1 and bar.high_price>=self.Highup :
+                    if self.pos == 0:
+                        self.buy(bar.close_price, self.lots)
+                    elif self.pos < 0:
+                        self.cover(bar.close_price, self.lots)
+                        self.buy(bar.close_price, self.lots)
+                    self.LowerAfterEntry = bar.close_price
+
+        if short_line:
+            if self.kong_cond4[-1]<self.kong_cond5[-1] and self.kong_cond5[-1]<self.kong_cond5[-2] and self.kong_cond4[-1]<self.kong_cond4[-2] and self.kong_cond3[-1]<self.kong_cond3[-2] and self.kong_cond2[-1]<self.kong_cond2[-2] and self.kong_cond1[-1]<self.kong_cond1[-2]:
+                if self.pos!=-1 and bar.low_price<=self.Lowdown :
+                    if self.pos == 0:
+                        self.short(bar.close_price, self.lots)
+                    elif self.pos > 0:
+                        self.sell(bar.close_price, self.lots)
+                        self.short(bar.close_price, self.lots)
+                        
+                    self.HigherAfterEntry = bar.close_price
+
+
+        #记录入场后的最高价和最低价
+        if self.pos>0:
+            #self.HigherAfterEntry = HigherAfterEntry
+            self.LowerAfterEntry = max(self.LowerAfterEntry, bar.low_price)
+        elif self.pos<0:
+            self.HigherAfterEntry = min(self.HigherAfterEntry, bar.high_price)	
+            #self.LowerAfterEntry = LowerAfterEntry
+
+        if self.pos == 0:
+            self.liQKA = 1
+        else:
+            self.liQKA = self.liQKA - 0.1
+            self.liQKA = max(self.liQKA, 0.5)
+        
+        if self.pos>0:
+            DliqPoint = self.LowerAfterEntry - ((bar.open_price)*self.TS/1000)*self.liQKA
+            #print(bar.datetime,"DliqPoint",DliqPoint,"low_price",bar.low_price,"self.LowerAfterEntry",self.LowerAfterEntry)
+        if self.pos<0:
+            KliqPoint = self.HigherAfterEntry + ((bar.open_price)*self.TS/1000)*self.liQKA
+            #print(bar.datetime,"KliqPoint",KliqPoint,"high_price",bar.high_price,"self.HigherAfterEntry",self.HigherAfterEntry)
+        
+        if (self.pos>0 and bar.low_price <= DliqPoint and DliqPoint>0):
+            #多头出场
+            self.sell(bar.close_price, self.lots)
+        elif (self.pos<0 and bar.high_price > KliqPoint and KliqPoint>0):
+            #空头出场
+            self.cover(bar.close_price, self.lots)
+
+        self.current_bar += 1
+        self.put_event()
+
+
+    def on_trade(self, trade: TradeData):
+        """
+        Callback of new trade data update.
+        """
+        self.put_event()
+        
+
+    def on_stop_order(self, stop_order: StopOrder):
+        """
+        Callback of stop order update.
+        """
+        pass