vnpy/tests/backtesting/turtle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%\n",
    "from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting\n",
    "from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import (\n",
    "    AtrRsiStrategy,\n",
    ")\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%\n",
    "engine = BacktestingEngine()\n",
    "engine.set_parameters(\n",
    "    vt_symbol=\"IF88.CFFEX\",\n",
    "    interval=\"1m\",\n",
    "    start=datetime(2019, 1, 1),\n",
    "    end=datetime(2019, 4, 30),\n",
    "    rate=0.3/10000,\n",
    "    slippage=0.2,\n",
    "    size=300,\n",
    "    pricetick=0.2,\n",
    "    capital=1_000_000,\n",
    ")\n",
    "engine.add_strategy(AtrRsiStrategy, {})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#%%\n",
    "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": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-05-03 16:19:04.193703\t开始运行遗传算法，每代族群总数：11, 优良品种筛选个数：8，迭代次数：30，交叉概率：0.95，突变概率：0.050000000000000044\n",
      "gen\tnevals\tmean        \tstd         \tmin         \tmax        \n",
      "0  \t11    \t[0.58423524]\t[0.30377007]\t[0.13231977]\t[1.2382818]\n",
      "1  \t11    \t[0.90248989]\t[0.15747112]\t[0.68707859]\t[1.2382818]\n",
      "2  \t11    \t[1.09406088]\t[0.18860523]\t[0.86284921]\t[1.46762684]\n",
      "3  \t11    \t[1.21413386]\t[0.12138014]\t[1.02072108]\t[1.46762684]\n",
      "4  \t11    \t[1.29561806]\t[0.09930932]\t[1.2382818] \t[1.46762684]\n",
      "5  \t11    \t[1.41029058]\t[0.09930932]\t[1.2382818] \t[1.46762684]\n",
      "6  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "7  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "8  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "9  \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "10 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "11 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "12 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "13 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "14 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "15 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "16 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "17 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "18 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "19 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "20 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "21 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "22 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "23 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "24 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "25 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "26 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "27 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "28 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "29 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "30 \t11    \t[1.46762684]\t[0.]        \t[1.46762684]\t[1.46762684]\n",
      "2019-05-03 16:19:58.256354\t遗传算法优化完成，耗时54秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[({'atr_length': 38, 'atr_ma_length': 25}, 1.4676268402266743)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setting = OptimizationSetting()\n",
    "setting.set_target(\"sharpe_ratio\")\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,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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