diff --git a/requirements.txt b/requirements.txt
index 0706dba5..8eaa8cf4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,3 +17,4 @@ tigeropen
 rqdatac
 ta-lib
 ibapi
+deap
\ No newline at end of file
diff --git a/tests/backtesting/turtle.ipynb b/tests/backtesting/turtle.ipynb
index 9807424b..8a4f75f4 100644
--- a/tests/backtesting/turtle.ipynb
+++ b/tests/backtesting/turtle.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,115 +54,526 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-04-15 22:19:49.696835\t参数：{'atr_length': 22}, 目标：121.19996051999999\n",
-      "2019-04-15 22:19:49.709531\t参数：{'atr_length': 23}, 目标：116.54901966000013\n",
-      "2019-04-15 22:19:49.710507\t参数：{'atr_length': 24}, 目标：113.29820520000014\n"
+      "2019-05-02 22:29:22.289010\t开始运行遗传算法，每代族群总数：100, 优良品种筛选个数：80，迭代次数：300，交叉概率：0.95，突变概率：0.05\n",
+      "2019-05-02 22:29:22.289010\t开始加载历史数据\n",
+      "2019-05-02 22:29:24.103532\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:24.173848\t策略初始化完成\n",
+      "2019-05-02 22:29:24.173848\t开始回放历史数据\n",
+      "2019-05-02 22:29:24.788129\t历史数据回放结束\n",
+      "2019-05-02 22:29:24.789106\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:24.789106\t成交记录为空，无法计算\n",
+      "2019-05-02 22:29:24.789106\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:24.789106\t开始加载历史数据\n",
+      "2019-05-02 22:29:24.789106\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:24.867234\t策略初始化完成\n",
+      "2019-05-02 22:29:24.868210\t开始回放历史数据\n",
+      "2019-05-02 22:29:25.834068\t历史数据回放结束\n",
+      "2019-05-02 22:29:25.835044\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:25.839927\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:25.840904\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:25.856529\t开始加载历史数据\n",
+      "2019-05-02 22:29:25.857506\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:25.939540\t策略初始化完成\n",
+      "2019-05-02 22:29:25.939540\t开始回放历史数据\n",
+      "2019-05-02 22:29:27.055794\t历史数据回放结束\n",
+      "2019-05-02 22:29:27.056771\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:27.062630\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:27.062630\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:27.074350\t开始加载历史数据\n",
+      "2019-05-02 22:29:27.074350\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:27.156384\t策略初始化完成\n",
+      "2019-05-02 22:29:27.156384\t开始回放历史数据\n",
+      "2019-05-02 22:29:28.159352\t历史数据回放结束\n",
+      "2019-05-02 22:29:28.160329\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:28.165212\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:28.165212\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:28.176931\t开始加载历史数据\n",
+      "2019-05-02 22:29:28.176931\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:28.260919\t策略初始化完成\n",
+      "2019-05-02 22:29:28.260919\t开始回放历史数据\n",
+      "2019-05-02 22:29:29.418190\t历史数据回放结束\n",
+      "2019-05-02 22:29:29.418190\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:29.424049\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:29.424049\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:29.436745\t开始加载历史数据\n",
+      "2019-05-02 22:29:29.436745\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:29.521709\t策略初始化完成\n",
+      "2019-05-02 22:29:29.522686\t开始回放历史数据\n",
+      "2019-05-02 22:29:30.513935\t历史数据回放结束\n",
+      "2019-05-02 22:29:30.514911\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:30.519794\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:30.519794\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:30.531514\t开始加载历史数据\n",
+      "2019-05-02 22:29:30.531514\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:30.611595\t策略初始化完成\n",
+      "2019-05-02 22:29:30.611595\t开始回放历史数据\n",
+      "2019-05-02 22:29:31.729802\t历史数据回放结束\n",
+      "2019-05-02 22:29:31.730778\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:31.735661\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:31.735661\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:31.747381\t开始加载历史数据\n",
+      "2019-05-02 22:29:31.747381\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:31.825509\t策略初始化完成\n",
+      "2019-05-02 22:29:31.826485\t开始回放历史数据\n",
+      "2019-05-02 22:29:32.840196\t历史数据回放结束\n",
+      "2019-05-02 22:29:32.840196\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:32.846056\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:32.847032\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:32.858751\t开始加载历史数据\n",
+      "2019-05-02 22:29:32.858751\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:32.936879\t策略初始化完成\n",
+      "2019-05-02 22:29:32.936879\t开始回放历史数据\n",
+      "2019-05-02 22:29:34.065829\t历史数据回放结束\n",
+      "2019-05-02 22:29:34.066806\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:34.071689\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:34.072665\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:34.085361\t开始加载历史数据\n",
+      "2019-05-02 22:29:34.085361\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:34.161536\t策略初始化完成\n",
+      "2019-05-02 22:29:34.162512\t开始回放历史数据\n",
+      "2019-05-02 22:29:35.174270\t历史数据回放结束\n",
+      "2019-05-02 22:29:35.175247\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:35.180130\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:35.180130\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:35.192825\t开始加载历史数据\n",
+      "2019-05-02 22:29:35.192825\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:35.274860\t策略初始化完成\n",
+      "2019-05-02 22:29:35.274860\t开始回放历史数据\n",
+      "2019-05-02 22:29:35.918439\t历史数据回放结束\n",
+      "2019-05-02 22:29:35.918439\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:35.918439\t成交记录为空，无法计算\n",
+      "2019-05-02 22:29:35.918439\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:35.918439\t开始加载历史数据\n",
+      "2019-05-02 22:29:35.918439\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:35.999497\t策略初始化完成\n",
+      "2019-05-02 22:29:35.999497\t开始回放历史数据\n",
+      "2019-05-02 22:29:36.671398\t历史数据回放结束\n",
+      "2019-05-02 22:29:36.671398\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:36.672374\t成交记录为空，无法计算\n",
+      "2019-05-02 22:29:36.672374\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:36.673351\t开始加载历史数据\n",
+      "2019-05-02 22:29:36.673351\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:36.761245\t策略初始化完成\n",
+      "2019-05-02 22:29:36.762222\t开始回放历史数据\n",
+      "2019-05-02 22:29:37.830622\t历史数据回放结束\n",
+      "2019-05-02 22:29:37.831599\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:37.837458\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:37.837458\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:37.849177\t开始加载历史数据\n",
+      "2019-05-02 22:29:37.849177\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:37.926329\t策略初始化完成\n",
+      "2019-05-02 22:29:37.927305\t开始回放历史数据\n",
+      "2019-05-02 22:29:38.877537\t历史数据回放结束\n",
+      "2019-05-02 22:29:38.878514\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:38.884373\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:38.884373\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:38.895116\t开始加载历史数据\n",
+      "2019-05-02 22:29:38.896093\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:38.974221\t策略初始化完成\n",
+      "2019-05-02 22:29:38.974221\t开始回放历史数据\n",
+      "2019-05-02 22:29:40.131492\t历史数据回放结束\n",
+      "2019-05-02 22:29:40.131492\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:40.137351\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:40.137351\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:40.149070\t开始加载历史数据\n",
+      "2019-05-02 22:29:40.149070\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:40.234035\t策略初始化完成\n",
+      "2019-05-02 22:29:40.235011\t开始回放历史数据\n",
+      "2019-05-02 22:29:41.257511\t历史数据回放结束\n",
+      "2019-05-02 22:29:41.258488\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:41.263371\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:41.263371\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:41.275090\t开始加载历史数据\n",
+      "2019-05-02 22:29:41.275090\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:41.353218\t策略初始化完成\n",
+      "2019-05-02 22:29:41.353218\t开始回放历史数据\n",
+      "2019-05-02 22:29:42.819095\t历史数据回放结束\n",
+      "2019-05-02 22:29:42.819095\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:42.823978\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:42.823978\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:42.835697\t开始加载历史数据\n",
+      "2019-05-02 22:29:42.836674\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:42.917731\t策略初始化完成\n",
+      "2019-05-02 22:29:42.917731\t开始回放历史数据\n",
+      "2019-05-02 22:29:43.901168\t历史数据回放结束\n",
+      "2019-05-02 22:29:43.901168\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:43.907027\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:43.908004\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:43.920700\t开始加载历史数据\n",
+      "2019-05-02 22:29:43.921676\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:44.004687\t策略初始化完成\n",
+      "2019-05-02 22:29:44.005664\t开始回放历史数据\n",
+      "2019-05-02 22:29:45.120941\t历史数据回放结束\n",
+      "2019-05-02 22:29:45.121918\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:45.126801\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:45.126801\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:45.137543\t开始加载历史数据\n",
+      "2019-05-02 22:29:45.138520\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:45.216648\t策略初始化完成\n",
+      "2019-05-02 22:29:45.217624\t开始回放历史数据\n",
+      "2019-05-02 22:29:46.272352\t历史数据回放结束\n",
+      "2019-05-02 22:29:46.272352\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:46.277235\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:46.278212\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:46.289931\t开始加载历史数据\n",
+      "2019-05-02 22:29:46.289931\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:46.372942\t策略初始化完成\n",
+      "2019-05-02 22:29:46.372942\t开始回放历史数据\n",
+      "2019-05-02 22:29:47.500915\t历史数据回放结束\n",
+      "2019-05-02 22:29:47.501892\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:47.507751\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:47.507751\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:47.519471\t开始加载历史数据\n",
+      "2019-05-02 22:29:47.519471\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:47.590762\t策略初始化完成\n",
+      "2019-05-02 22:29:47.590762\t开始回放历史数据\n",
+      "2019-05-02 22:29:48.230435\t历史数据回放结束\n",
+      "2019-05-02 22:29:48.231412\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:48.231412\t成交记录为空，无法计算\n",
+      "2019-05-02 22:29:48.231412\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:48.231412\t开始加载历史数据\n",
+      "2019-05-02 22:29:48.231412\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:48.315400\t策略初始化完成\n",
+      "2019-05-02 22:29:48.315400\t开始回放历史数据\n",
+      "2019-05-02 22:29:49.317391\t历史数据回放结束\n",
+      "2019-05-02 22:29:49.317391\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:49.323251\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:49.323251\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:49.335947\t开始加载历史数据\n",
+      "2019-05-02 22:29:49.335947\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:49.416028\t策略初始化完成\n",
+      "2019-05-02 22:29:49.416028\t开始回放历史数据\n",
+      "2019-05-02 22:29:50.513726\t历史数据回放结束\n",
+      "2019-05-02 22:29:50.513726\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:50.519586\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:50.519586\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:50.531305\t开始加载历史数据\n",
+      "2019-05-02 22:29:50.531305\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:50.610410\t策略初始化完成\n",
+      "2019-05-02 22:29:50.611386\t开始回放历史数据\n",
+      "2019-05-02 22:29:51.688576\t历史数据回放结束\n",
+      "2019-05-02 22:29:51.689553\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:51.694436\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:51.695412\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:51.707131\t开始加载历史数据\n",
+      "2019-05-02 22:29:51.708108\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:51.804791\t策略初始化完成\n",
+      "2019-05-02 22:29:51.807721\t开始回放历史数据\n",
+      "2019-05-02 22:29:53.077301\t历史数据回放结束\n",
+      "2019-05-02 22:29:53.078278\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:53.083161\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:53.083161\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:53.094880\t开始加载历史数据\n",
+      "2019-05-02 22:29:53.094880\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:53.182774\t策略初始化完成\n",
+      "2019-05-02 22:29:53.183751\t开始回放历史数据\n",
+      "2019-05-02 22:29:54.420126\t历史数据回放结束\n",
+      "2019-05-02 22:29:54.421103\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:54.425986\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:54.425986\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:54.437705\t开始加载历史数据\n",
+      "2019-05-02 22:29:54.437705\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:54.517786\t策略初始化完成\n",
+      "2019-05-02 22:29:54.517786\t开始回放历史数据\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "[(\"{'atr_length': 22}\",\n",
-       "  121.19996051999999,\n",
-       "  {'start_date': datetime.date(2013, 1, 18),\n",
-       "   'end_date': datetime.date(2019, 4, 11),\n",
-       "   'total_days': 1514,\n",
-       "   'profit_days': 763,\n",
-       "   'loss_days': 750,\n",
-       "   'capital': 1000000,\n",
-       "   'end_balance': 2211999.6052,\n",
-       "   'max_drawdown': -248787.6971999996,\n",
-       "   'max_ddpercent': -12.636908338002794,\n",
-       "   'total_net_pnl': 1211999.6052000003,\n",
-       "   'daily_net_pnl': 800.5281408190227,\n",
-       "   'total_commission': 242400.39479999998,\n",
-       "   'daily_commission': 160.10594108322323,\n",
-       "   'total_slippage': 481860.0,\n",
-       "   'daily_slippage': 318.2694848084544,\n",
-       "   'total_turnover': 8080013160.0,\n",
-       "   'daily_turnover': 5336864.702774108,\n",
-       "   'total_trade_count': 8031,\n",
-       "   'daily_trade_count': 5.30449141347424,\n",
-       "   'total_return': 121.19996051999999,\n",
-       "   'annual_return': 19.212675379656538,\n",
-       "   'daily_return': 0.052348808029058974,\n",
-       "   'return_std': 0.9487639654919149,\n",
-       "   'sharpe_ratio': 0.854779772691872,\n",
-       "   'return_drawdown_ratio': 9.590950355754112}),\n",
-       " (\"{'atr_length': 23}\",\n",
-       "  116.54901966000013,\n",
-       "  {'start_date': datetime.date(2013, 1, 18),\n",
-       "   'end_date': datetime.date(2019, 4, 11),\n",
-       "   'total_days': 1514,\n",
-       "   'profit_days': 759,\n",
-       "   'loss_days': 754,\n",
-       "   'capital': 1000000,\n",
-       "   'end_balance': 2165490.1966000013,\n",
-       "   'max_drawdown': -232904.1239999996,\n",
-       "   'max_ddpercent': -13.536251422505968,\n",
-       "   'total_net_pnl': 1165490.1966000004,\n",
-       "   'daily_net_pnl': 769.8085842800531,\n",
-       "   'total_commission': 242769.80339999998,\n",
-       "   'daily_commission': 160.34993619550858,\n",
-       "   'total_slippage': 482700.0,\n",
-       "   'daily_slippage': 318.82430647291943,\n",
-       "   'total_turnover': 8092326780.0,\n",
-       "   'daily_turnover': 5344997.873183619,\n",
-       "   'total_trade_count': 8045,\n",
-       "   'daily_trade_count': 5.313738441215324,\n",
-       "   'total_return': 116.54901966000013,\n",
-       "   'annual_return': 18.475406022721288,\n",
-       "   'daily_return': 0.0509452313711608,\n",
-       "   'return_std': 0.961380153488665,\n",
-       "   'sharpe_ratio': 0.8209448965768181,\n",
-       "   'return_drawdown_ratio': 8.610139987960078}),\n",
-       " (\"{'atr_length': 24}\",\n",
-       "  113.29820520000014,\n",
-       "  {'start_date': datetime.date(2013, 1, 18),\n",
-       "   'end_date': datetime.date(2019, 4, 11),\n",
-       "   'total_days': 1514,\n",
-       "   'profit_days': 760,\n",
-       "   'loss_days': 753,\n",
-       "   'capital': 1000000,\n",
-       "   'end_balance': 2132982.0520000015,\n",
-       "   'max_drawdown': -236503.9475999996,\n",
-       "   'max_ddpercent': -13.23872340727957,\n",
-       "   'total_net_pnl': 1132982.0520000013,\n",
-       "   'daily_net_pnl': 748.3368903566719,\n",
-       "   'total_commission': 242817.948,\n",
-       "   'daily_commission': 160.3817357992074,\n",
-       "   'total_slippage': 482700.0,\n",
-       "   'daily_slippage': 318.82430647291943,\n",
-       "   'total_turnover': 8093931600.0,\n",
-       "   'daily_turnover': 5346057.85997358,\n",
-       "   'total_trade_count': 8045,\n",
-       "   'daily_trade_count': 5.313738441215324,\n",
-       "   'total_return': 113.29820520000014,\n",
-       "   'annual_return': 17.96008536856013,\n",
-       "   'daily_return': 0.049946173936258026,\n",
-       "   'return_std': 0.959328411709829,\n",
-       "   'sharpe_ratio': 0.8065671672003681,\n",
-       "   'return_drawdown_ratio': 8.558091419728651})]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-05-02 22:29:55.671151\t历史数据回放结束\n",
+      "2019-05-02 22:29:55.672127\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:55.677010\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:55.677987\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:55.689706\t开始加载历史数据\n",
+      "2019-05-02 22:29:55.689706\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:55.767834\t策略初始化完成\n",
+      "2019-05-02 22:29:55.767834\t开始回放历史数据\n",
+      "2019-05-02 22:29:56.790334\t历史数据回放结束\n",
+      "2019-05-02 22:29:56.790334\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:56.796194\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:56.796194\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:56.806937\t开始加载历史数据\n",
+      "2019-05-02 22:29:56.807913\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:56.889948\t策略初始化完成\n",
+      "2019-05-02 22:29:56.890924\t开始回放历史数据\n",
+      "2019-05-02 22:29:57.607749\t历史数据回放结束\n",
+      "2019-05-02 22:29:57.608725\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:57.608725\t成交记录为空，无法计算\n",
+      "2019-05-02 22:29:57.608725\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:57.608725\t开始加载历史数据\n",
+      "2019-05-02 22:29:57.609702\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:57.698572\t策略初始化完成\n",
+      "2019-05-02 22:29:57.699549\t开始回放历史数据\n",
+      "2019-05-02 22:29:58.882212\t历史数据回放结束\n",
+      "2019-05-02 22:29:58.882212\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:29:58.888071\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:29:58.888071\t开始计算策略统计指标\n",
+      "2019-05-02 22:29:58.898814\t开始加载历史数据\n",
+      "2019-05-02 22:29:58.898814\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:29:58.986708\t策略初始化完成\n",
+      "2019-05-02 22:29:58.987684\t开始回放历史数据\n",
+      "2019-05-02 22:30:00.071710\t历史数据回放结束\n",
+      "2019-05-02 22:30:00.072687\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:00.077570\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:00.081476\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:00.092219\t开始加载历史数据\n",
+      "2019-05-02 22:30:00.095149\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:00.173277\t策略初始化完成\n",
+      "2019-05-02 22:30:00.174253\t开始回放历史数据\n",
+      "2019-05-02 22:30:00.906703\t历史数据回放结束\n",
+      "2019-05-02 22:30:00.907680\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:00.907680\t成交记录为空，无法计算\n",
+      "2019-05-02 22:30:00.908657\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:00.908657\t开始加载历史数据\n",
+      "2019-05-02 22:30:00.908657\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:01.018036\t策略初始化完成\n",
+      "2019-05-02 22:30:01.019012\t开始回放历史数据\n",
+      "2019-05-02 22:30:02.128430\t历史数据回放结束\n",
+      "2019-05-02 22:30:02.135266\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:02.141126\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:02.141126\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:02.151868\t开始加载历史数据\n",
+      "2019-05-02 22:30:02.152845\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:02.229020\t策略初始化完成\n",
+      "2019-05-02 22:30:02.229020\t开始回放历史数据\n",
+      "2019-05-02 22:30:03.410706\t历史数据回放结束\n",
+      "2019-05-02 22:30:03.411682\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:03.416565\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:03.416565\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:03.428285\t开始加载历史数据\n",
+      "2019-05-02 22:30:03.429261\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:03.533757\t策略初始化完成\n",
+      "2019-05-02 22:30:03.533757\t开始回放历史数据\n",
+      "2019-05-02 22:30:04.638292\t历史数据回放结束\n",
+      "2019-05-02 22:30:04.638292\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:04.643175\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:04.644152\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:04.654894\t开始加载历史数据\n",
+      "2019-05-02 22:30:04.654894\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:04.738882\t策略初始化完成\n",
+      "2019-05-02 22:30:04.738882\t开始回放历史数据\n",
+      "2019-05-02 22:30:05.721341\t历史数据回放结束\n",
+      "2019-05-02 22:30:05.722318\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:05.728178\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:05.728178\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:05.739897\t开始加载历史数据\n",
+      "2019-05-02 22:30:05.739897\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:05.824861\t策略初始化完成\n",
+      "2019-05-02 22:30:05.825838\t开始回放历史数据\n",
+      "2019-05-02 22:30:06.928419\t历史数据回放结束\n",
+      "2019-05-02 22:30:06.928419\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:06.933302\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:06.934279\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:06.945998\t开始加载历史数据\n",
+      "2019-05-02 22:30:06.945998\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:07.024126\t策略初始化完成\n",
+      "2019-05-02 22:30:07.025102\t开始回放历史数据\n",
+      "2019-05-02 22:30:08.041743\t历史数据回放结束\n",
+      "2019-05-02 22:30:08.042720\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:08.048579\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:08.048579\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:08.062252\t开始加载历史数据\n",
+      "2019-05-02 22:30:08.062252\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:08.145263\t策略初始化完成\n",
+      "2019-05-02 22:30:08.145263\t开始回放历史数据\n",
+      "2019-05-02 22:30:09.292768\t历史数据回放结束\n",
+      "2019-05-02 22:30:09.293744\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:09.298627\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:09.298627\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:09.311323\t开始加载历史数据\n",
+      "2019-05-02 22:30:09.311323\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:09.393357\t策略初始化完成\n",
+      "2019-05-02 22:30:09.393357\t开始回放历史数据\n",
+      "2019-05-02 22:30:10.363121\t历史数据回放结束\n",
+      "2019-05-02 22:30:10.363121\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:10.368981\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:10.369957\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:10.380700\t开始加载历史数据\n",
+      "2019-05-02 22:30:10.381677\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:10.459805\t策略初始化完成\n",
+      "2019-05-02 22:30:10.459805\t开始回放历史数据\n",
+      "2019-05-02 22:30:11.536994\t历史数据回放结束\n",
+      "2019-05-02 22:30:11.536994\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:11.542854\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:11.542854\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:11.554573\t开始加载历史数据\n",
+      "2019-05-02 22:30:11.554573\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:11.638561\t策略初始化完成\n",
+      "2019-05-02 22:30:11.639537\t开始回放历史数据\n",
+      "2019-05-02 22:30:12.632740\t历史数据回放结束\n",
+      "2019-05-02 22:30:12.633716\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:12.638599\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:12.639576\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:12.650318\t开始加载历史数据\n",
+      "2019-05-02 22:30:12.651295\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:12.733329\t策略初始化完成\n",
+      "2019-05-02 22:30:12.734306\t开始回放历史数据\n",
+      "2019-05-02 22:30:13.352494\t历史数据回放结束\n",
+      "2019-05-02 22:30:13.353470\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:13.353470\t成交记录为空，无法计算\n",
+      "2019-05-02 22:30:13.353470\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:13.354447\t开始加载历史数据\n",
+      "2019-05-02 22:30:13.354447\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:13.436481\t策略初始化完成\n",
+      "2019-05-02 22:30:13.436481\t开始回放历史数据\n",
+      "2019-05-02 22:30:14.540039\t历史数据回放结束\n",
+      "2019-05-02 22:30:14.541016\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:14.546876\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:14.546876\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:14.558595\t开始加载历史数据\n",
+      "2019-05-02 22:30:14.558595\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:14.650395\t策略初始化完成\n",
+      "2019-05-02 22:30:14.650395\t开始回放历史数据\n",
+      "2019-05-02 22:30:15.294951\t历史数据回放结束\n",
+      "2019-05-02 22:30:15.294951\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:15.294951\t成交记录为空，无法计算\n",
+      "2019-05-02 22:30:15.294951\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:15.294951\t开始加载历史数据\n",
+      "2019-05-02 22:30:15.294951\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:15.377962\t策略初始化完成\n",
+      "2019-05-02 22:30:15.377962\t开始回放历史数据\n",
+      "2019-05-02 22:30:16.346749\t历史数据回放结束\n",
+      "2019-05-02 22:30:16.346749\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:16.352609\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:16.352609\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:16.365305\t开始加载历史数据\n",
+      "2019-05-02 22:30:16.365305\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:16.445386\t策略初始化完成\n",
+      "2019-05-02 22:30:16.446363\t开始回放历史数据\n",
+      "2019-05-02 22:30:17.544061\t历史数据回放结束\n",
+      "2019-05-02 22:30:17.545038\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:17.550897\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:17.550897\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:17.563593\t开始加载历史数据\n",
+      "2019-05-02 22:30:17.563593\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:17.640744\t策略初始化完成\n",
+      "2019-05-02 22:30:17.640744\t开始回放历史数据\n",
+      "2019-05-02 22:30:18.641759\t历史数据回放结束\n",
+      "2019-05-02 22:30:18.642736\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:18.647619\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:18.647619\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:18.659338\t开始加载历史数据\n",
+      "2019-05-02 22:30:18.660315\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:18.736490\t策略初始化完成\n",
+      "2019-05-02 22:30:18.736490\t开始回放历史数据\n",
+      "2019-05-02 22:30:19.827352\t历史数据回放结束\n",
+      "2019-05-02 22:30:19.828328\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:19.833211\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:19.833211\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:19.844931\t开始加载历史数据\n",
+      "2019-05-02 22:30:19.845907\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:19.927942\t策略初始化完成\n",
+      "2019-05-02 22:30:19.928918\t开始回放历史数据\n",
+      "2019-05-02 22:30:20.916261\t历史数据回放结束\n",
+      "2019-05-02 22:30:20.916261\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:20.922120\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:20.922120\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:20.934816\t开始加载历史数据\n",
+      "2019-05-02 22:30:20.935793\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:21.010991\t策略初始化完成\n",
+      "2019-05-02 22:30:21.011968\t开始回放历史数据\n",
+      "2019-05-02 22:30:22.131151\t历史数据回放结束\n",
+      "2019-05-02 22:30:22.131151\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:22.137011\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:22.137011\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:22.149707\t开始加载历史数据\n",
+      "2019-05-02 22:30:22.149707\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:22.227835\t策略初始化完成\n",
+      "2019-05-02 22:30:22.228811\t开始回放历史数据\n",
+      "2019-05-02 22:30:23.201505\t历史数据回放结束\n",
+      "2019-05-02 22:30:23.202481\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:23.207364\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:23.207364\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:23.219084\t开始加载历史数据\n",
+      "2019-05-02 22:30:23.219084\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:23.295258\t策略初始化完成\n",
+      "2019-05-02 22:30:23.295258\t开始回放历史数据\n",
+      "2019-05-02 22:30:24.418348\t历史数据回放结束\n",
+      "2019-05-02 22:30:24.418348\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:24.423231\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:24.424208\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:24.437880\t开始加载历史数据\n",
+      "2019-05-02 22:30:24.437880\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:24.517962\t策略初始化完成\n",
+      "2019-05-02 22:30:24.517962\t开始回放历史数据\n",
+      "2019-05-02 22:30:25.200605\t历史数据回放结束\n",
+      "2019-05-02 22:30:25.201582\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:25.201582\t成交记录为空，无法计算\n",
+      "2019-05-02 22:30:25.202558\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:25.202558\t开始加载历史数据\n",
+      "2019-05-02 22:30:25.203535\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:25.275803\t策略初始化完成\n",
+      "2019-05-02 22:30:25.276780\t开始回放历史数据\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-05-02 22:30:25.947704\t历史数据回放结束\n",
+      "2019-05-02 22:30:25.948681\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:25.948681\t成交记录为空，无法计算\n",
+      "2019-05-02 22:30:25.949657\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:25.949657\t开始加载历史数据\n",
+      "2019-05-02 22:30:25.949657\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:26.027785\t策略初始化完成\n",
+      "2019-05-02 22:30:26.027785\t开始回放历史数据\n",
+      "2019-05-02 22:30:27.024894\t历史数据回放结束\n",
+      "2019-05-02 22:30:27.025870\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:27.030753\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:27.030753\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:27.042473\t开始加载历史数据\n",
+      "2019-05-02 22:30:27.042473\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:27.127437\t策略初始化完成\n",
+      "2019-05-02 22:30:27.128413\t开始回放历史数据\n",
+      "2019-05-02 22:30:28.317912\t历史数据回放结束\n",
+      "2019-05-02 22:30:28.317912\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:28.323772\t逐日盯市盈亏计算完成\n",
+      "2019-05-02 22:30:28.324748\t开始计算策略统计指标\n",
+      "2019-05-02 22:30:28.335491\t开始加载历史数据\n",
+      "2019-05-02 22:30:28.336468\t历史数据加载完成，数据量：18240\n",
+      "2019-05-02 22:30:28.412642\t策略初始化完成\n",
+      "2019-05-02 22:30:28.413619\t开始回放历史数据\n",
+      "2019-05-02 22:30:29.072824\t历史数据回放结束\n",
+      "2019-05-02 22:30:29.072824\t开始计算逐日盯市盈亏\n",
+      "2019-05-02 22:30:29.072824\t成交记录为空，无法计算\n",
+      "2019-05-02 22:30:29.072824\t开始计算策略统计指标\n",
+      "gen\tnevals\tmean        \tstd         \tmin          \tmax         \n",
+      "0  \t100   \t[1.24452619]\t[2.90495733]\t[-3.24204978]\t[8.88922512]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "empty range for randrange() (1,1, 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-4726e35b67fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\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_length\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m105\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      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\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    602\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    603\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--> 604\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    605\u001b[0m         )    \n\u001b[0;32m    606\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    316\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mgen\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mngen\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m         \u001b[1;31m# Vary the population\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 318\u001b[1;33m         \u001b[0moffspring\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvarOr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtoolbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlambda_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcxpb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutpb\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    319\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\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[1;32mc:\\miniconda3\\lib\\site-packages\\deap\\algorithms.py\u001b[0m in \u001b[0;36mvarOr\u001b[1;34m(population, toolbox, lambda_, cxpb, mutpb)\u001b[0m\n\u001b[0;32m    234\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mop_choice\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mcxpb\u001b[0m\u001b[1;33m:\u001b[0m            \u001b[1;31m# Apply crossover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m             \u001b[0mind1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\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[0mclone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpopulation\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[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 236\u001b[1;33m             \u001b[0mind1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoolbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind2\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    237\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mind1\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m             \u001b[0moffspring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mind1\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\\site-packages\\deap\\tools\\crossover.py\u001b[0m in \u001b[0;36mcxTwoPoint\u001b[1;34m(ind1, ind2)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mind2\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     43\u001b[0m     \u001b[0mcxpoint1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m     \u001b[0mcxpoint2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\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[0m\u001b[0;32m     45\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcxpoint2\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mcxpoint1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[0mcxpoint2\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\miniconda3\\lib\\random.py\u001b[0m in \u001b[0;36mrandint\u001b[1;34m(self, a, b)\u001b[0m\n\u001b[0;32m    220\u001b[0m         \"\"\"\n\u001b[0;32m    221\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\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[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m     def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type,\n",
+      "\u001b[1;32mc:\\miniconda3\\lib\\random.py\u001b[0m in \u001b[0;36mrandrange\u001b[1;34m(self, start, stop, step, _int)\u001b[0m\n\u001b[0;32m    198\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mistart\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_randbelow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstep\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[1;32m--> 200\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"empty range for randrange() (%d,%d, %d)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mistart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mistop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\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    201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m         \u001b[1;31m# Non-unit step argument supplied.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: empty range for randrange() (1,1, 0)"
+     ]
     }
    ],
    "source": [
     "setting = OptimizationSetting()\n",
     "setting.set_target(\"total_return\")\n",
-    "setting.add_parameter(\"atr_length\", 22, 24, 1)\n",
+    "setting.add_parameter(\"atr_length\", 3, 105, 1)\n",
     "\n",
-    "engine.run_optimization(setting)"
+    "engine.run_ga_optimization(setting)"
    ]
   },
   {
diff --git a/vnpy/app/cta_strategy/backtesting.py b/vnpy/app/cta_strategy/backtesting.py
index e4aa25c7..083cb2d0 100644
--- a/vnpy/app/cta_strategy/backtesting.py
+++ b/vnpy/app/cta_strategy/backtesting.py
@@ -3,12 +3,15 @@ from datetime import date, datetime
 from typing import Callable
 from itertools import product
 from functools import lru_cache
+from time import time
 import multiprocessing
+import random
 
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 from pandas import DataFrame
+from deap import creator, base, tools, algorithms
 
 from vnpy.trader.constant import (Direction, Offset, Exchange, 
                                   Interval, Status)
@@ -514,6 +517,101 @@ class BacktestingEngine:
 
         return result_values
 
+    def run_ga_optimization(self, optimization_setting: OptimizationSetting, output=True):
+        """"""
+        # Get optimization setting and target
+        settings = optimization_setting.generate_setting()
+        target_name = optimization_setting.target_name
+
+        if not settings:
+            self.output("优化参数组合为空，请检查")
+            return
+
+        if not target_name:
+            self.output("优化目标未设置，请检查")
+            return
+
+        # Define parameter generation function
+        def generate_parameter():
+            """"""
+            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
+        )
+
+        # 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("select", tools.selNSGA2)       
+
+        # pool = multiprocessing.Pool(multiprocessing.cpu_count())
+        # toolbox.register("map", pool.map)
+        
+        MU = 80         # 设置每一代选择的个体数
+        LAMBDA = 100    # 设置每一代产生的子女数
+        POP = 100       
+        CXPB = 0.95     # 交叉概率    
+        MUTPB = 0.05    # 变异概率
+        NGEN = 300      # 种群代数
+        
+        pop = toolbox.population(POP)               # 设置族群里面的个体数量
+        hof = tools.ParetoFront()                   # 解的集合：帕累托前沿(非占优最优集)
+
+        stats = tools.Statistics(lambda ind: ind.fitness.values)
+        np.set_printoptions(suppress=True)          # 对numpy默认输出的科学计数法转换
+        stats.register("mean", np.mean, axis=0)     # 统计目标优化函数结果的平均值
+        stats.register("std", np.std, axis=0)       # 统计目标优化函数结果的标准差
+        stats.register("min", np.min, axis=0)       # 统计目标优化函数结果的最小值
+        stats.register("max", np.max, axis=0)       # 统计目标优化函数结果的最大值
+        
+        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,MU,NGEN,CXPB,MUTPB)
+        self.output(msg)
+
+        # Run ga optimization
+        # esMuPlusLambda是一种基于(μ+λ)选择策略的多目标优化分段遗传算法
+        start = time()
+
+        algorithms.eaMuPlusLambda(
+            pop, 
+            toolbox, 
+            MU, 
+            LAMBDA, 
+            CXPB, 
+            MUTPB, 
+            NGEN, 
+            stats,
+            halloffame=hof
+        )    
+
+        end = time()
+        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
+
     def update_daily_close(self, price: float):
         """"""
         d = self.datetime.date()
@@ -968,6 +1066,54 @@ 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 _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],)
+
+    def ga_optimize(parameter_values: list):
+        """"""
+        return _optimizae(tuple(parameter_values))
+
+    return ga_optimize
+
+
 @lru_cache(maxsize=10)
 def load_bar_data(
     symbol: str,