vnpy/examples/TurtleStrategy/.ipynb_checkpoints/run-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from turtleEngine import BacktestingEngine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = BacktestingEngine()\n",
    "engine.setPeriod(datetime(2015, 1, 1), datetime(2018, 11, 9))\n",
    "engine.initPortfolio('setting.csv', 10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:22:46.074000:000300.XSHG数据加载完成，总数据量：940\n",
      "15:22:46.074000:全部数据加载完成\n"
     ]
    }
   ],
   "source": [
    "engine.loadData()\n",
    "engine.runBacktesting()\n",
    "engine.calculateResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for dt, l in engine.tradeDict.items():\n",
    "    print dt\n",
    "    for trade in l:\n",
    "        print trade.vtSymbol, trade.direction, trade.offset, trade.price, trade.volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for result in engine.resultList:\n",
    "    if result.totalPnl:\n",
    "        print result.date, result.totalPnl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8190eb0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8127530>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = [result.totalPnl for result in engine.resultList]\n",
    "equity = np.cumsum(l)\n",
    "plt.plot(equity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}