{
 "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": null,
   "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": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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": []
  }
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