From 4cd84b45a5aa91b69e0e3cb0a83a6bf3ff0c8ccc Mon Sep 17 00:00:00 2001
From: "vn.py" <xiaoyou.chen@foxmail.com>
Date: Fri, 3 May 2019 16:10:11 +0800
Subject: [PATCH] [Mod]add return value of run_ga_optimization

---
 tests/backtesting/GA_Pre_Final.ipynb | 104 +++--------------
 tests/backtesting/run_ga.py          |  27 +++++
 tests/backtesting/turtle.ipynb       |  60 +++++-----
 vnpy/app/cta_strategy/backtesting.py | 164 ++++++++++++++-------------
 4 files changed, 153 insertions(+), 202 deletions(-)
 create mode 100644 tests/backtesting/run_ga.py

diff --git a/tests/backtesting/GA_Pre_Final.ipynb b/tests/backtesting/GA_Pre_Final.ipynb
index 0d99a169..de3b6d46 100644
--- a/tests/backtesting/GA_Pre_Final.ipynb
+++ b/tests/backtesting/GA_Pre_Final.ipynb
@@ -20,17 +20,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "数据总体： 13824\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "setting = OptimizationSetting()\n",
     "#setting.add_parameter('atr_length', 10, 50, 2)\n",
@@ -50,20 +42,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['boll_window', 'cci_window', 'atr_window'])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "setting_names = random.choice(local_setting).keys()\n",
     "setting_names"
@@ -71,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,40 +63,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[48, 6, 26]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "parameter_generate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'boll_window': 16, 'cci_window': 48, 'atr_window': 6}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "setting=dict(zip(setting_names,parameter_generate()))\n",
     "setting"
@@ -123,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,41 +126,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-05-03 15:02:07.528909\t开始加载历史数据\n",
-      "2019-05-03 15:02:34.854177\t历史数据加载完成，数据量：175440\n",
-      "2019-05-03 15:02:34.877616\t策略初始化完成\n",
-      "2019-05-03 15:02:34.877616\t开始回放历史数据\n",
-      "2019-05-03 15:02:37.000744\t历史数据回放结束\n",
-      "2019-05-03 15:02:37.000744\t开始计算逐日盯市盈亏\n",
-      "2019-05-03 15:02:37.012463\t逐日盯市盈亏计算完成\n",
-      "2019-05-03 15:02:37.012463\t开始计算策略统计指标\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.96, 0.38)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "object_func(parameter_generate())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,15 +217,7 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "开始运行遗传算法，每代族群总数：34, 优良品种筛选个数：27，迭代次数：30，交叉概率：0.95，突变概率：0.05\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "optimize()"
    ]
diff --git a/tests/backtesting/run_ga.py b/tests/backtesting/run_ga.py
new file mode 100644
index 00000000..4ce1c6b3
--- /dev/null
+++ b/tests/backtesting/run_ga.py
@@ -0,0 +1,27 @@
+from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting
+from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import (
+    AtrRsiStrategy,
+)
+from datetime import datetime
+
+if __name__ == "__main__":
+    engine = BacktestingEngine()
+    engine.set_parameters(
+        vt_symbol="IF88.CFFEX",
+        interval="1m",
+        start=datetime(2019, 1, 1),
+        end=datetime(2019, 4, 30),
+        rate=0.3 / 10000,
+        slippage=0.2,
+        size=300,
+        pricetick=0.2,
+        capital=1_000_000,
+    )
+    engine.add_strategy(AtrRsiStrategy, {})
+
+    setting = OptimizationSetting()
+    setting.set_target("sharpe_ratio")
+    setting.add_parameter("atr_length", 3, 39, 1)
+    setting.add_parameter("atr_ma_length", 10, 30, 1)
+
+    engine.run_ga_optimization(setting)
diff --git a/tests/backtesting/turtle.ipynb b/tests/backtesting/turtle.ipynb
index fc872235..822614f8 100644
--- a/tests/backtesting/turtle.ipynb
+++ b/tests/backtesting/turtle.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,44 +54,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-05-03 14:58:44.510371\t开始运行遗传算法，每代族群总数：20, 优良品种筛选个数：16，迭代次数：300，交叉概率：0.95，突变概率：0.05\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "Can't pickle local object 'create_ga_optimize.<locals>.ga_optimize'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-3-d83ea019c683>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msetting\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_parameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"atr_ma_length\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m80\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_ga_optimization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msetting\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\Github\\vnpy\\vnpy\\app\\cta_strategy\\backtesting.py\u001b[0m in \u001b[0;36mrun_ga_optimization\u001b[1;34m(self, optimization_setting, output)\u001b[0m\n\u001b[0;32m    601\u001b[0m             \u001b[0mngen\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    602\u001b[0m             \u001b[0mstats\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 603\u001b[1;33m             \u001b[0mhalloffame\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhof\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    604\u001b[0m         )    \n\u001b[0;32m    605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\miniconda3\\lib\\site-packages\\deap\\algorithms.py\u001b[0m in \u001b[0;36meaMuPlusLambda\u001b[1;34m(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats, halloffame, verbose)\u001b[0m\n\u001b[0;32m    301\u001b[0m     \u001b[1;31m# Evaluate the individuals with an invalid fitness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m     \u001b[0minvalid_ind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mind\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpopulation\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalid\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m     \u001b[0mfitnesses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoolbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtoolbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minvalid_ind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfit\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minvalid_ind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfitnesses\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[0mind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mthat\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mreturned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m         '''\n\u001b[1;32m--> 290\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmapstar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    291\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstarmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    681\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 683\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    685\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36m_handle_tasks\u001b[1;34m(taskqueue, put, outqueue, pool, cache)\u001b[0m\n\u001b[0;32m    455\u001b[0m                         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m                     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m                         \u001b[0mput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m                     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m                         \u001b[0mjob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    204\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_writable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ForkingPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrecv_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlength\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\reduction.py\u001b[0m in \u001b[0;36mdumps\u001b[1;34m(cls, obj, protocol)\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[0mbuf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetbuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: Can't pickle local object 'create_ga_optimize.<locals>.ga_optimize'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
     "setting = OptimizationSetting()\n",
     "setting.set_target(\"sharpe_ratio\")\n",
-    "setting.add_parameter(\"atr_length\", 3, 105, 1)\n",
-    "setting.add_parameter(\"atr_ma_length\", 10, 80, 1)\n",
+    "setting.add_parameter(\"atr_length\", 3, 39, 1)\n",
+    "setting.add_parameter(\"atr_ma_length\", 10, 30, 1)\n",
     "\n",
     "engine.run_ga_optimization(setting)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = _"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(result)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/vnpy/app/cta_strategy/backtesting.py b/vnpy/app/cta_strategy/backtesting.py
index 004bdda5..bb670872 100644
--- a/vnpy/app/cta_strategy/backtesting.py
+++ b/vnpy/app/cta_strategy/backtesting.py
@@ -6,6 +6,7 @@ from functools import lru_cache
 from time import time
 import multiprocessing
 import random
+import math
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -29,6 +30,8 @@ from .base import (
 from .template import CtaTemplate
 
 sns.set_style("whitegrid")
+creator.create("FitnessMax", base.Fitness, weights=(1.0,))
+creator.create("Individual", list, fitness=creator.FitnessMax)
 
 
 class OptimizationSetting:
@@ -537,44 +540,52 @@ class BacktestingEngine:
             return list(random.choice(settings).values())
 
         # Create ga object function
-        object_func = create_ga_optimize(
-            target_name,
-            self.strategy_class,
-            settings[0],
-            self.vt_symbol,
-            self.interval,
-            self.start,
-            self.rate,
-            self.slippage,
-            self.size,
-            self.pricetick,
-            self.capital,
-            self.end,
-            self.mode
-        )
+        global ga_target_name
+        global ga_strategy_class
+        global ga_setting
+        global ga_vt_symbol
+        global ga_interval
+        global ga_start
+        global ga_rate
+        global ga_slippage
+        global ga_size
+        global ga_pricetick
+        global ga_capital
+        global ga_end
+        global ga_mode
+
+        ga_target_name = target_name
+        ga_strategy_class = self.strategy_class
+        ga_setting = settings[0]
+        ga_vt_symbol = self.vt_symbol
+        ga_interval = self.interval
+        ga_start = self.start
+        ga_rate = self.rate
+        ga_slippage = self.slippage
+        ga_size = self.size
+        ga_pricetick = self.pricetick
+        ga_capital = self.capital
+        ga_end = self.end
+        ga_mode = self.mode
 
         # Set up genetic algorithem
-        creator.create("FitnessMax", base.Fitness, weights=(1.0,))
-        creator.create("Individual", list, fitness=creator.FitnessMax)
-
         toolbox = base.Toolbox() 
         toolbox.register("individual", tools.initIterate, creator.Individual, generate_parameter)                          
         toolbox.register("population", tools.initRepeat, list, toolbox.individual)                                            
         toolbox.register("mate", tools.cxTwoPoint)                                               
         toolbox.register("mutate", tools.mutUniformInt, low=4, up=40, indpb=1)               
-        toolbox.register("evaluate", object_func)                                                
+        toolbox.register("evaluate", ga_optimize)                                                
         toolbox.register("select", tools.selNSGA2)       
 
-        pool = multiprocessing.Pool(multiprocessing.cpu_count())
-        toolbox.register("map", pool.map)
-        
-        mu = 16         # number of individuals to select for the next generation
-        lambda_ = 20    # number of children to produce at each generation
-        cxpb = 0.95     # probability that an offspring is produced by crossover    
-        mutpb = 0.05    # probability that an offspring is produced by mutation
-        ngen = 300      # number of generation
-        
-        pop_size = 20   # number of individuals in each generation
+        total_size = len(settings)
+        pop_size = int(pow(total_size, 1 / math.e))     # number of individuals in each generation
+        lambda_ = pop_size                              # number of children to produce at each generation
+        mu = int(pop_size * 0.8)                        # number of individuals to select for the next generation
+
+        cxpb = 0.95         # probability that an offspring is produced by crossover    
+        mutpb = 1 - cxpb    # probability that an offspring is produced by mutation
+        ngen = 30           # number of generation
+                
         pop = toolbox.population(pop_size)      
         hof = tools.ParetoFront()               # end result of pareto front
 
@@ -584,11 +595,15 @@ class BacktestingEngine:
         stats.register("std", np.std, axis=0)
         stats.register("min", np.min, axis=0)
         stats.register("max", np.max, axis=0)
-        
-        msg = "开始运行遗传算法，每代族群总数：%s, 优良品种筛选个数：%s，迭代次数：%s，交叉概率：%s，突变概率：%s" %(pop_size, mu, ngen, cxpb, mutpb)
-        self.output(msg)
+
+        # Multiprocessing is not supported yet.
+        # pool = multiprocessing.Pool(multiprocessing.cpu_count())
+        # toolbox.register("map", pool.map)
 
         # Run ga optimization
+        msg = "开始运行遗传算法，每代族群总数：%s, 优良品种筛选个数：%s，迭代次数：%s，交叉概率：%s，突变概率：%s" % (pop_size, mu, ngen, cxpb, mutpb)
+        self.output(msg)
+
         start = time()
 
         algorithms.eaMuPlusLambda(
@@ -607,9 +622,17 @@ class BacktestingEngine:
         cost = int((end - start))
 
         self.output(f"遗传算法优化完成，耗时{cost}秒")
-        self.output("输出帕累托前沿解集：")
         
-        return hof
+        # Return result list
+        results = []
+        parameter_keys = list(ga_setting.keys())
+
+        for parameter_values in hof:
+            setting = dict(zip(parameter_keys, parameter_values))
+            target_value = ga_optimize(parameter_values)[0]
+            results.append((setting, target_value))
+        
+        return results
 
     def update_daily_close(self, price: float):
         """"""
@@ -1065,52 +1088,33 @@ def optimize(
     return (str(setting), target_value, statistics)
 
 
-def create_ga_optimize(
-    target_name: str,
-    strategy_class: CtaTemplate,
-    setting: dict,
-    vt_symbol: str,
-    interval: Interval,
-    start: datetime,
-    rate: float,
-    slippage: float,
-    size: float,
-    pricetick: float,
-    capital: int,
-    end: datetime,
-    mode: BacktestingMode,
-):
-    """
-    Function for running in multiprocessing.pool
-    """
-    parameter_keys = list(setting.keys())
+@lru_cache(maxsize=1000000)
+def _ga_optimizae(parameter_values: tuple):
+    """"""
+    parameter_keys = list(ga_setting.keys())
+    setting = dict(zip(parameter_keys, parameter_values))
 
-    @lru_cache(maxsize=1000000)
-    def _optimizae(parameter_values: tuple):
-        """"""
-        setting = dict(zip(parameter_keys, parameter_values))
-        result = optimize(
-            target_name,
-            strategy_class,
-            setting,
-            vt_symbol,
-            interval,
-            start,
-            rate,
-            slippage,
-            size,
-            pricetick,
-            capital,
-            end,
-            mode
-        )
-        return (result[1],)
+    result = optimize(
+        ga_target_name,
+        ga_strategy_class,
+        setting,
+        ga_vt_symbol,
+        ga_interval,
+        ga_start,
+        ga_rate,
+        ga_slippage,
+        ga_size,
+        ga_pricetick,
+        ga_capital,
+        ga_end,
+        ga_mode
+    )
+    return (result[1],)
 
-    def ga_optimize(parameter_values: list):
-        """"""
-        return _optimizae(tuple(parameter_values))
 
-    return ga_optimize
+def ga_optimize(parameter_values: list):
+    """"""
+    return _ga_optimizae(tuple(parameter_values))
 
 
 @lru_cache(maxsize=10)
@@ -1141,6 +1145,8 @@ def load_tick_data(
 
 
 # GA related global value
+ga_end = None
+ga_mode = None
 ga_target_name = None
 ga_strategy_class = None
 ga_setting = None
@@ -1151,6 +1157,4 @@ ga_rate = None
 ga_slippage = None
 ga_size = None
 ga_pricetick = None
-ga_capital = None
-ga_end = None
-ga_mode = None
\ No newline at end of file
+ga_capital = None
\ No newline at end of file