Merge pull request #1523 from 1122455801/genetic_algo

[Add]遗传算法模块
2019-04-05 15:22:42 +08:00 · 2019-04-05 15:22:42 +08:00 · d4a70b8a9b
commit d4a70b8a9b
parent f16a879905 ce64ede969
3 changed files with 222 additions and 26 deletions
--- a/tests/backtesting/genetic_algorithm.ipynb
+++ b/tests/backtesting/genetic_algorithm.ipynb
@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import multiprocessing\n",
+    "import numpy as np\n",
+    "from deap import creator, base, tools, algorithms\n",
+    "from vnpy.app.cta_strategy.backtesting import BacktestingEngine\n",
+    "from boll_channel_strategy import BollChannelStrategy\n",
+    "from datetime import datetime\n",
+    "import multiprocessing           #多进程\n",
+    "from scoop import futures        #多进程"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parameter_generate():\n",
+    "    '''\n",
+    "    根据设置的起始值，终止值和步进，随机生成待优化的策略参数\n",
+    "    '''\n",
+    "    parameter_list = []\n",
+    "    p1 = random.randrange(4,50,2)      #布林带窗口\n",
+    "    p2 = random.randrange(4,50,2)      #布林带通道阈值\n",
+    "    p3 = random.randrange(4,50,2)      #CCI窗口\n",
+    "    p4 = random.randrange(18,40,2)     #ATR窗口 \n",
+    "\n",
+    "    parameter_list.append(p1)\n",
+    "    parameter_list.append(p2)\n",
+    "    parameter_list.append(p3)\n",
+    "    parameter_list.append(p4)\n",
+    "\n",
+    "    return parameter_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def object_func(strategy_avg):\n",
+    "    \"\"\"\n",
+    "    本函数为优化目标函数，根据随机生成的策略参数，运行回测后自动返回2个结果指标：收益回撤比和夏普比率\n",
+    "    \"\"\"\n",
+    "    # 创建回测引擎对象\n",
+    "    engine = BacktestingEngine()\n",
+    "    engine.set_parameters(\n",
+    "        vt_symbol=\"IF88.CFFEX\",\n",
+    "        interval=\"1m\",\n",
+    "        start=datetime(2018, 9, 1),\n",
+    "        end=datetime(2019, 1,1),\n",
+    "        rate=0,\n",
+    "        slippage=0,\n",
+    "        size=300,\n",
+    "        pricetick=0.2,\n",
+    "        capital=1_000_000,\n",
+    "    )\n",
+    "\n",
+    "    setting = {'boll_window': strategy_avg[0],       #布林带窗口\n",
+    "               'boll_dev': strategy_avg[1],        #布林带通道阈值\n",
+    "               'cci_window': strategy_avg[2],         #CCI窗口\n",
+    "               'atr_window': strategy_avg[3],}    #ATR窗口               \n",
+    "\n",
+    "    #加载策略          \n",
+    "    #engine.initStrategy(TurtleTradingStrategy, setting)\n",
+    "    engine.add_strategy(BollChannelStrategy, setting)\n",
+    "    engine.load_data()\n",
+    "    engine.run_backtesting()\n",
+    "    engine.calculate_result()\n",
+    "    result = engine.calculate_statistics(Output=False)\n",
+    "\n",
+    "    return_drawdown_ratio = round(result['return_drawdown_ratio'],2)  #收益回撤比\n",
+    "    sharpe_ratio= round(result['sharpe_ratio'],2)                   #夏普比率\n",
+    "    return return_drawdown_ratio , sharpe_ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#设置优化方向：最大化收益回撤比，最大化夏普比率\n",
+    "creator.create(\"FitnessMulti\", base.Fitness, weights=(1.0, 1.0)) # 1.0 求最大值；-1.0 求最小值\n",
+    "creator.create(\"Individual\", list, fitness=creator.FitnessMulti)\n",
+    "\n",
+    "def optimize():\n",
+    "    \"\"\"\"\"\"   \n",
+    "    toolbox = base.Toolbox()  #Toolbox是deap库内置的工具箱，里面包含遗传算法中所用到的各种函数\n",
+    "\n",
+    "    # 初始化     \n",
+    "    toolbox.register(\"individual\", tools.initIterate, creator.Individual,parameter_generate) # 注册个体：随机生成的策略参数parameter_generate()                                          \n",
+    "    toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)               #注册种群：个体形成种群                                    \n",
+    "    toolbox.register(\"mate\", tools.cxTwoPoint)                                               #注册交叉：两点交叉  \n",
+    "    toolbox.register(\"mutate\", tools.mutUniformInt,low = 4,up = 40,indpb=0.6)                #注册变异：随机生成一定区间内的整数\n",
+    "    toolbox.register(\"evaluate\", object_func)                                                #注册评估：优化目标函数object_func()    \n",
+    "    toolbox.register(\"select\", tools.selNSGA2)       #注册选择:NSGA-II(带精英策略的非支配排序的遗传算法)\n",
+    "    #pool = multiprocessing.Pool()\n",
+    "    #toolbox.register(\"map\", pool.map)\n",
+    "    #toolbox.register(\"map\", futures.map)\n",
+    "\n",
+    "    #遗传算法参数设置\n",
+    "    MU = 40                                  #设置每一代选择的个体数\n",
+    "    LAMBDA = 160                             #设置每一代产生的子女数\n",
+    "    pop = toolbox.population(400)            #设置族群里面的个体数量\n",
+    "    CXPB, MUTPB, NGEN = 0.5, 0.35,40        #分别为种群内部个体的交叉概率、变异概率、产生种群代数\n",
+    "    hof = tools.ParetoFront()                #解的集合：帕累托前沿(非占优最优集)\n",
+    "\n",
+    "    #解的集合的描述统计信息\n",
+    "    #集合内平均值，标准差，最小值，最大值可以体现集合的收敛程度\n",
+    "    #收敛程度低可以增加算法的迭代次数\n",
+    "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
+    "    np.set_printoptions(suppress=True)            #对numpy默认输出的科学计数法转换\n",
+    "    stats.register(\"mean\", np.mean, axis=0)       #统计目标优化函数结果的平均值\n",
+    "    stats.register(\"std\", np.std, axis=0)         #统计目标优化函数结果的标准差\n",
+    "    stats.register(\"min\", np.min, axis=0)         #统计目标优化函数结果的最小值\n",
+    "    stats.register(\"max\", np.max, axis=0)         #统计目标优化函数结果的最大值\n",
+    "\n",
+    "    #运行算法\n",
+    "    algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n",
+    "                              halloffame=hof)     #esMuPlusLambda是一种基于(μ+λ)选择策略的多目标优化分段遗传算法\n",
+    "\n",
+    "    return pop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "optimize()"
+   ]
+  },
+  {
+   "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": "3.7.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/vnpy/app/cta_strategy/backtesting.py
+++ b/vnpy/app/cta_strategy/backtesting.py
@ -293,7 +293,7 @@ class BacktestingEngine:
        self.output("逐日盯市盈亏计算完成")
        return self.daily_df

-    def calculate_statistics(self, df: DataFrame = None):
+    def calculate_statistics(self, df: DataFrame = None, Output=True):
        """"""
        self.output("开始计算策略统计指标")

@ -325,6 +325,7 @@ class BacktestingEngine:
            daily_return = 0
            return_std = 0
            sharpe_ratio = 0
+            return_drawdown_ratio = 0
        else:
            # Calculate balance related time series data
            df["balance"] = df["net_pnl"].cumsum() + self.capital
@ -373,38 +374,42 @@ class BacktestingEngine:
            else:
                sharpe_ratio = 0

+            return_drawdown_ratio = -total_return / max_ddpercent
+
        # Output
-        self.output("-" * 30)
-        self.output(f"首个交易日：\t{start_date}")
-        self.output(f"最后交易日：\t{end_date}")
+        if Output:
+            self.output("-" * 30)
+            self.output(f"首个交易日：\t{start_date}")
+            self.output(f"最后交易日：\t{end_date}")

-        self.output(f"总交易日：\t{total_days}")
-        self.output(f"盈利交易日：\t{profit_days}")
-        self.output(f"亏损交易日：\t{loss_days}")
+            self.output(f"总交易日：\t{total_days}")
+            self.output(f"盈利交易日：\t{profit_days}")
+            self.output(f"亏损交易日：\t{loss_days}")

-        self.output(f"起始资金：\t{self.capital:,.2f}")
-        self.output(f"结束资金：\t{end_balance:,.2f}")
+            self.output(f"起始资金：\t{self.capital:,.2f}")
+            self.output(f"结束资金：\t{end_balance:,.2f}")

-        self.output(f"总收益率：\t{total_return:,.2f}%")
-        self.output(f"年化收益：\t{annual_return:,.2f}%")
-        self.output(f"最大回撤: \t{max_drawdown:,.2f}")
-        self.output(f"百分比最大回撤: {max_ddpercent:,.2f}%")
+            self.output(f"总收益率：\t{total_return:,.2f}%")
+            self.output(f"年化收益：\t{annual_return:,.2f}%")
+            self.output(f"最大回撤: \t{max_drawdown:,.2f}")
+            self.output(f"百分比最大回撤: {max_ddpercent:,.2f}%")

-        self.output(f"总盈亏：\t{total_net_pnl:,.2f}")
-        self.output(f"总手续费：\t{total_commission:,.2f}")
-        self.output(f"总滑点：\t{total_slippage:,.2f}")
-        self.output(f"总成交金额：\t{total_turnover:,.2f}")
-        self.output(f"总成交笔数：\t{total_trade_count}")
+            self.output(f"总盈亏：\t{total_net_pnl:,.2f}")
+            self.output(f"总手续费：\t{total_commission:,.2f}")
+            self.output(f"总滑点：\t{total_slippage:,.2f}")
+            self.output(f"总成交金额：\t{total_turnover:,.2f}")
+            self.output(f"总成交笔数：\t{total_trade_count}")

-        self.output(f"日均盈亏：\t{daily_net_pnl:,.2f}")
-        self.output(f"日均手续费：\t{daily_commission:,.2f}")
-        self.output(f"日均滑点：\t{daily_slippage:,.2f}")
-        self.output(f"日均成交金额：\t{daily_turnover:,.2f}")
-        self.output(f"日均成交笔数：\t{daily_trade_count}")
+            self.output(f"日均盈亏：\t{daily_net_pnl:,.2f}")
+            self.output(f"日均手续费：\t{daily_commission:,.2f}")
+            self.output(f"日均滑点：\t{daily_slippage:,.2f}")
+            self.output(f"日均成交金额：\t{daily_turnover:,.2f}")
+            self.output(f"日均成交笔数：\t{daily_trade_count}")

-        self.output(f"日均收益率：\t{daily_return:,.2f}%")
-        self.output(f"收益标准差：\t{return_std:,.2f}%")
-        self.output(f"Sharpe Ratio：\t{sharpe_ratio:,.2f}")
+            self.output(f"日均收益率：\t{daily_return:,.2f}%")
+            self.output(f"收益标准差：\t{return_std:,.2f}%")
+            self.output(f"Sharpe Ratio：\t{sharpe_ratio:,.2f}")
+            self.output(f"收益回撤比：\t{return_drawdown_ratio:,.2f}")

        statistics = {
            "start_date": start_date,
@ -430,6 +435,7 @@ class BacktestingEngine:
            "daily_return": daily_return,
            "return_std": return_std,
            "sharpe_ratio": sharpe_ratio,
+            "return_drawdown_ratio": return_drawdown_ratio,
        }

        return statistics
--- a/vnpy/gateway/tiger/tiger_gateway.py
+++ b/vnpy/gateway/tiger/tiger_gateway.py
@ -1,5 +1,6 @@
 # encoding: UTF-8
 """
+Author: KeKe
 Please install tiger-api before use.
 pip install tigeropen
 """