SMA and LMA.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext zipline\n",
    "%load_ext watermark\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import zipline\n",
    "from yahoofinancials import YahooFinancials\n",
    "import warnings\n",
    "import pandas_datareader as pdr\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [32, 20]\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.panel.Panel'>\n",
      "Dimensions: 1 (items) x 503 (major_axis) x 5 (minor_axis)\n",
      "Items axis: NVDA to NVDA\n",
      "Major_axis axis: 2016-01-04 00:00:00+00:00 to 2017-12-29 00:00:00+00:00\n",
      "Minor_axis axis: open to volume\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\envs\\env_zipline\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n",
      "Panel is deprecated and will be removed in a future version.\n",
      "The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method\n",
      "Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.\n",
      "Pandas provides a `.to_xarray()` method to help automate this conversion.\n",
      "\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "from scripts.get_from_pdr_to_panel import get_from_pdr_to_panel\n",
    "\n",
    "panel = get_from_pdr_to_panel() #nvidia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipline.api import order, record, symbol, set_benchmark\n",
    "import zipline\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "def initialize(context):\n",
    "    pass\n",
    "\n",
    "\n",
    "def handle_data(context, data):\n",
    "    todays_pr= data.current(symbol('NVDA'),'price')\n",
    "    diff= context.portfolio.cash - todays_pr \n",
    "\n",
    "    hist = data.history(symbol('NVDA'),'price', 35, '1d')\n",
    "    sma_35= hist.mean()\n",
    "    sma_5= hist[-5:].mean()\n",
    "    #print(hist)\n",
    "    record(sma_35=sma_35)\n",
    "    record(sma_5=sma_5)\n",
    "    record(NVDA=todays_pr)\n",
    "\n",
    "    if diff>0:\n",
    "        # Trading logic\n",
    "        if sma_5 > sma_35:\n",
    "            # order_target orders as many shares as needed to\n",
    "            # achieve the desired number of shares.\n",
    "            order(symbol(\"NVDA\"), 2)\n",
    "        elif short_mavg < long_mavg:\n",
    "            order(symbol(\"NVDA\"), -2)\n",
    "            \n",
    "    \n",
    "perf = zipline.run_algorithm(start=datetime(2016, 2, 24, 0, 0, 0, 0, pytz.utc),\n",
    "                      end=datetime(2017, 12, 29, 0, 0, 0, 0, pytz.utc),\n",
    "                      initialize=initialize,\n",
    "                      capital_base=100,\n",
    "                      handle_data=handle_data,\n",
    "                      data=panel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NVDA', 'algo_volatility', 'algorithm_period_return', 'alpha',\n",
       "       'benchmark_period_return', 'benchmark_volatility', 'beta',\n",
       "       'capital_used', 'ending_cash', 'ending_exposure', 'ending_value',\n",
       "       'excess_return', 'gross_leverage', 'long_exposure', 'long_value',\n",
       "       'longs_count', 'max_drawdown', 'max_leverage', 'net_leverage', 'orders',\n",
       "       'period_close', 'period_label', 'period_open', 'pnl', 'portfolio_value',\n",
       "       'positions', 'returns', 'sharpe', 'short_exposure', 'short_value',\n",
       "       'shorts_count', 'sma_35', 'sma_5', 'sortino', 'starting_cash',\n",
       "       'starting_exposure', 'starting_value', 'trading_days', 'transactions',\n",
       "       'treasury_period_return'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        "plotlyServerURL": "https://plot.ly"
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\"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('52f55373-bbc8-4e5b-9d04-7afea69d8682');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "fig= go.Figure()\n",
    "fig.add_scatter(x=perf.index, y=perf.portfolio_value,mode='lines', name=\"sma_35\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "lines",
         "name": "sma_35",
         "type": "scatter",
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('23687822-7b9a-4f9d-9bc3-d8265c694118');\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig= go.Figure()\n",
    "fig.add_scatter(x=perf.index, y=perf.sma_35,mode='lines', name=\"sma_35\")\n",
    "fig.add_scatter(x=perf.index, y=perf.sma_5,mode='lines', name=\"sma_5\")\n",
    "fig.add_scatter(x=perf.index, y=perf.NVDA,mode='lines', name=\"NVDA\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'amount': 2,\n",
       "  'commission': None,\n",
       "  'dt': Timestamp('2016-02-26 21:00:00+0000', tz='UTC'),\n",
       "  'order_id': '4fb3cb6249ff43879bd1e23bba2f4e75',\n",
       "  'price': 31.695840305328367,\n",
       "  'sid': Equity(0 [NVDA])}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf[['NVDA', 'starting_cash', 'transactions', 'algorithm_period_return']]\n",
    "\n",
    "perf.transactions[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NVDA</th>\n",
       "      <th>starting_cash</th>\n",
       "      <th>transactions</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-24 21:00:00+00:00</th>\n",
       "      <td>31.820000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-25 21:00:00+00:00</th>\n",
       "      <td>31.889999</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>[{'price': 31.90594438934326, 'order_id': 'c0c...</td>\n",
       "      <td>-0.000339</td>\n",
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       "    <tr>\n",
       "      <th>2016-02-26 21:00:00+00:00</th>\n",
       "      <td>31.680000</td>\n",
       "      <td>36.186111</td>\n",
       "      <td>[{'price': 31.695840305328367, 'order_id': '4f...</td>\n",
       "      <td>-0.004876</td>\n",
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       "    <tr>\n",
       "      <th>2016-02-29 21:00:00+00:00</th>\n",
       "      <td>31.360001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>-0.017676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01 21:00:00+00:00</th>\n",
       "      <td>32.750000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.037924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 21:00:00+00:00</th>\n",
       "      <td>32.939999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.045524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03 21:00:00+00:00</th>\n",
       "      <td>32.650002</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.033924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-04 21:00:00+00:00</th>\n",
       "      <td>32.549999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.029924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-07 21:00:00+00:00</th>\n",
       "      <td>32.340000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.021524</td>\n",
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       "    <tr>\n",
       "      <th>2016-03-08 21:00:00+00:00</th>\n",
       "      <td>31.750000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>-0.002076</td>\n",
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       "    <tr>\n",
       "      <th>2016-03-09 21:00:00+00:00</th>\n",
       "      <td>31.730000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>-0.002876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-10 21:00:00+00:00</th>\n",
       "      <td>31.680000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>-0.004876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-11 21:00:00+00:00</th>\n",
       "      <td>32.220001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.016724</td>\n",
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       "    <tr>\n",
       "      <th>2016-03-14 20:00:00+00:00</th>\n",
       "      <td>32.299999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.019924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-15 20:00:00+00:00</th>\n",
       "      <td>32.139999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.013524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-16 20:00:00+00:00</th>\n",
       "      <td>33.099998</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.051924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-17 20:00:00+00:00</th>\n",
       "      <td>32.820000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.040724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-18 20:00:00+00:00</th>\n",
       "      <td>33.810001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.080324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-21 20:00:00+00:00</th>\n",
       "      <td>33.910000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.084324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-22 20:00:00+00:00</th>\n",
       "      <td>33.849998</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.081924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-23 20:00:00+00:00</th>\n",
       "      <td>34.430000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.105124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-24 20:00:00+00:00</th>\n",
       "      <td>34.480000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.107124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-28 20:00:00+00:00</th>\n",
       "      <td>34.830002</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.121124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-29 20:00:00+00:00</th>\n",
       "      <td>35.389999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.143524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-30 20:00:00+00:00</th>\n",
       "      <td>35.759998</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.158324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31 20:00:00+00:00</th>\n",
       "      <td>35.630001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.153124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01 20:00:00+00:00</th>\n",
       "      <td>36.150002</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.173924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-04 20:00:00+00:00</th>\n",
       "      <td>35.799999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.159924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-05 20:00:00+00:00</th>\n",
       "      <td>35.750000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.157924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-06 20:00:00+00:00</th>\n",
       "      <td>35.799999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.159924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-16 21:00:00+00:00</th>\n",
       "      <td>211.610001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.192324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:00:00+00:00</th>\n",
       "      <td>211.360001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.182324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-20 21:00:00+00:00</th>\n",
       "      <td>214.080002</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.291124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21 21:00:00+00:00</th>\n",
       "      <td>216.050003</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.369924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 21:00:00+00:00</th>\n",
       "      <td>214.929993</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.325124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 18:00:00+00:00</th>\n",
       "      <td>216.960007</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.406325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27 21:00:00+00:00</th>\n",
       "      <td>214.139999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.293524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28 21:00:00+00:00</th>\n",
       "      <td>210.710007</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>7.156325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29 21:00:00+00:00</th>\n",
       "      <td>196.419998</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.584724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 21:00:00+00:00</th>\n",
       "      <td>200.710007</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.756325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 21:00:00+00:00</th>\n",
       "      <td>197.679993</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.635124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04 21:00:00+00:00</th>\n",
       "      <td>186.660004</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.194324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05 21:00:00+00:00</th>\n",
       "      <td>187.740005</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.237525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06 21:00:00+00:00</th>\n",
       "      <td>189.259995</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.298324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07 21:00:00+00:00</th>\n",
       "      <td>191.990005</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.407525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08 21:00:00+00:00</th>\n",
       "      <td>191.490005</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.387525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11 21:00:00+00:00</th>\n",
       "      <td>194.660004</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.514324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12 21:00:00+00:00</th>\n",
       "      <td>190.839996</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.361524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13 21:00:00+00:00</th>\n",
       "      <td>186.179993</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.175124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14 21:00:00+00:00</th>\n",
       "      <td>186.470001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.186724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15 21:00:00+00:00</th>\n",
       "      <td>191.559998</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.390324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18 21:00:00+00:00</th>\n",
       "      <td>197.899994</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.643924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19 21:00:00+00:00</th>\n",
       "      <td>196.110001</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.572324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20 21:00:00+00:00</th>\n",
       "      <td>196.800003</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.599924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21 21:00:00+00:00</th>\n",
       "      <td>195.889999</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.563524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22 21:00:00+00:00</th>\n",
       "      <td>195.270004</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.538724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26 21:00:00+00:00</th>\n",
       "      <td>197.440002</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.625524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27 21:00:00+00:00</th>\n",
       "      <td>197.169998</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.614724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28 21:00:00+00:00</th>\n",
       "      <td>197.399994</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.623924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29 21:00:00+00:00</th>\n",
       "      <td>193.500000</td>\n",
       "      <td>-27.207569</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.467924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>468 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 NVDA  starting_cash  \\\n",
       "2016-02-24 21:00:00+00:00   31.820000     100.000000   \n",
       "2016-02-25 21:00:00+00:00   31.889999     100.000000   \n",
       "2016-02-26 21:00:00+00:00   31.680000      36.186111   \n",
       "2016-02-29 21:00:00+00:00   31.360001     -27.207569   \n",
       "2016-03-01 21:00:00+00:00   32.750000     -27.207569   \n",
       "2016-03-02 21:00:00+00:00   32.939999     -27.207569   \n",
       "2016-03-03 21:00:00+00:00   32.650002     -27.207569   \n",
       "2016-03-04 21:00:00+00:00   32.549999     -27.207569   \n",
       "2016-03-07 21:00:00+00:00   32.340000     -27.207569   \n",
       "2016-03-08 21:00:00+00:00   31.750000     -27.207569   \n",
       "2016-03-09 21:00:00+00:00   31.730000     -27.207569   \n",
       "2016-03-10 21:00:00+00:00   31.680000     -27.207569   \n",
       "2016-03-11 21:00:00+00:00   32.220001     -27.207569   \n",
       "2016-03-14 20:00:00+00:00   32.299999     -27.207569   \n",
       "2016-03-15 20:00:00+00:00   32.139999     -27.207569   \n",
       "2016-03-16 20:00:00+00:00   33.099998     -27.207569   \n",
       "2016-03-17 20:00:00+00:00   32.820000     -27.207569   \n",
       "2016-03-18 20:00:00+00:00   33.810001     -27.207569   \n",
       "2016-03-21 20:00:00+00:00   33.910000     -27.207569   \n",
       "2016-03-22 20:00:00+00:00   33.849998     -27.207569   \n",
       "2016-03-23 20:00:00+00:00   34.430000     -27.207569   \n",
       "2016-03-24 20:00:00+00:00   34.480000     -27.207569   \n",
       "2016-03-28 20:00:00+00:00   34.830002     -27.207569   \n",
       "2016-03-29 20:00:00+00:00   35.389999     -27.207569   \n",
       "2016-03-30 20:00:00+00:00   35.759998     -27.207569   \n",
       "2016-03-31 20:00:00+00:00   35.630001     -27.207569   \n",
       "2016-04-01 20:00:00+00:00   36.150002     -27.207569   \n",
       "2016-04-04 20:00:00+00:00   35.799999     -27.207569   \n",
       "2016-04-05 20:00:00+00:00   35.750000     -27.207569   \n",
       "2016-04-06 20:00:00+00:00   35.799999     -27.207569   \n",
       "...                               ...            ...   \n",
       "2017-11-16 21:00:00+00:00  211.610001     -27.207569   \n",
       "2017-11-17 21:00:00+00:00  211.360001     -27.207569   \n",
       "2017-11-20 21:00:00+00:00  214.080002     -27.207569   \n",
       "2017-11-21 21:00:00+00:00  216.050003     -27.207569   \n",
       "2017-11-22 21:00:00+00:00  214.929993     -27.207569   \n",
       "2017-11-24 18:00:00+00:00  216.960007     -27.207569   \n",
       "2017-11-27 21:00:00+00:00  214.139999     -27.207569   \n",
       "2017-11-28 21:00:00+00:00  210.710007     -27.207569   \n",
       "2017-11-29 21:00:00+00:00  196.419998     -27.207569   \n",
       "2017-11-30 21:00:00+00:00  200.710007     -27.207569   \n",
       "2017-12-01 21:00:00+00:00  197.679993     -27.207569   \n",
       "2017-12-04 21:00:00+00:00  186.660004     -27.207569   \n",
       "2017-12-05 21:00:00+00:00  187.740005     -27.207569   \n",
       "2017-12-06 21:00:00+00:00  189.259995     -27.207569   \n",
       "2017-12-07 21:00:00+00:00  191.990005     -27.207569   \n",
       "2017-12-08 21:00:00+00:00  191.490005     -27.207569   \n",
       "2017-12-11 21:00:00+00:00  194.660004     -27.207569   \n",
       "2017-12-12 21:00:00+00:00  190.839996     -27.207569   \n",
       "2017-12-13 21:00:00+00:00  186.179993     -27.207569   \n",
       "2017-12-14 21:00:00+00:00  186.470001     -27.207569   \n",
       "2017-12-15 21:00:00+00:00  191.559998     -27.207569   \n",
       "2017-12-18 21:00:00+00:00  197.899994     -27.207569   \n",
       "2017-12-19 21:00:00+00:00  196.110001     -27.207569   \n",
       "2017-12-20 21:00:00+00:00  196.800003     -27.207569   \n",
       "2017-12-21 21:00:00+00:00  195.889999     -27.207569   \n",
       "2017-12-22 21:00:00+00:00  195.270004     -27.207569   \n",
       "2017-12-26 21:00:00+00:00  197.440002     -27.207569   \n",
       "2017-12-27 21:00:00+00:00  197.169998     -27.207569   \n",
       "2017-12-28 21:00:00+00:00  197.399994     -27.207569   \n",
       "2017-12-29 21:00:00+00:00  193.500000     -27.207569   \n",
       "\n",
       "                                                                transactions  \\\n",
       "2016-02-24 21:00:00+00:00                                                 []   \n",
       "2016-02-25 21:00:00+00:00  [{'price': 31.90594438934326, 'order_id': 'c0c...   \n",
       "2016-02-26 21:00:00+00:00  [{'price': 31.695840305328367, 'order_id': '4f...   \n",
       "2016-02-29 21:00:00+00:00                                                 []   \n",
       "2016-03-01 21:00:00+00:00                                                 []   \n",
       "2016-03-02 21:00:00+00:00                                                 []   \n",
       "2016-03-03 21:00:00+00:00                                                 []   \n",
       "2016-03-04 21:00:00+00:00                                                 []   \n",
       "2016-03-07 21:00:00+00:00                                                 []   \n",
       "2016-03-08 21:00:00+00:00                                                 []   \n",
       "2016-03-09 21:00:00+00:00                                                 []   \n",
       "2016-03-10 21:00:00+00:00                                                 []   \n",
       "2016-03-11 21:00:00+00:00                                                 []   \n",
       "2016-03-14 20:00:00+00:00                                                 []   \n",
       "2016-03-15 20:00:00+00:00                                                 []   \n",
       "2016-03-16 20:00:00+00:00                                                 []   \n",
       "2016-03-17 20:00:00+00:00                                                 []   \n",
       "2016-03-18 20:00:00+00:00                                                 []   \n",
       "2016-03-21 20:00:00+00:00                                                 []   \n",
       "2016-03-22 20:00:00+00:00                                                 []   \n",
       "2016-03-23 20:00:00+00:00                                                 []   \n",
       "2016-03-24 20:00:00+00:00                                                 []   \n",
       "2016-03-28 20:00:00+00:00                                                 []   \n",
       "2016-03-29 20:00:00+00:00                                                 []   \n",
       "2016-03-30 20:00:00+00:00                                                 []   \n",
       "2016-03-31 20:00:00+00:00                                                 []   \n",
       "2016-04-01 20:00:00+00:00                                                 []   \n",
       "2016-04-04 20:00:00+00:00                                                 []   \n",
       "2016-04-05 20:00:00+00:00                                                 []   \n",
       "2016-04-06 20:00:00+00:00                                                 []   \n",
       "...                                                                      ...   \n",
       "2017-11-16 21:00:00+00:00                                                 []   \n",
       "2017-11-17 21:00:00+00:00                                                 []   \n",
       "2017-11-20 21:00:00+00:00                                                 []   \n",
       "2017-11-21 21:00:00+00:00                                                 []   \n",
       "2017-11-22 21:00:00+00:00                                                 []   \n",
       "2017-11-24 18:00:00+00:00                                                 []   \n",
       "2017-11-27 21:00:00+00:00                                                 []   \n",
       "2017-11-28 21:00:00+00:00                                                 []   \n",
       "2017-11-29 21:00:00+00:00                                                 []   \n",
       "2017-11-30 21:00:00+00:00                                                 []   \n",
       "2017-12-01 21:00:00+00:00                                                 []   \n",
       "2017-12-04 21:00:00+00:00                                                 []   \n",
       "2017-12-05 21:00:00+00:00                                                 []   \n",
       "2017-12-06 21:00:00+00:00                                                 []   \n",
       "2017-12-07 21:00:00+00:00                                                 []   \n",
       "2017-12-08 21:00:00+00:00                                                 []   \n",
       "2017-12-11 21:00:00+00:00                                                 []   \n",
       "2017-12-12 21:00:00+00:00                                                 []   \n",
       "2017-12-13 21:00:00+00:00                                                 []   \n",
       "2017-12-14 21:00:00+00:00                                                 []   \n",
       "2017-12-15 21:00:00+00:00                                                 []   \n",
       "2017-12-18 21:00:00+00:00                                                 []   \n",
       "2017-12-19 21:00:00+00:00                                                 []   \n",
       "2017-12-20 21:00:00+00:00                                                 []   \n",
       "2017-12-21 21:00:00+00:00                                                 []   \n",
       "2017-12-22 21:00:00+00:00                                                 []   \n",
       "2017-12-26 21:00:00+00:00                                                 []   \n",
       "2017-12-27 21:00:00+00:00                                                 []   \n",
       "2017-12-28 21:00:00+00:00                                                 []   \n",
       "2017-12-29 21:00:00+00:00                                                 []   \n",
       "\n",
       "                           algorithm_period_return  \n",
       "2016-02-24 21:00:00+00:00                 0.000000  \n",
       "2016-02-25 21:00:00+00:00                -0.000339  \n",
       "2016-02-26 21:00:00+00:00                -0.004876  \n",
       "2016-02-29 21:00:00+00:00                -0.017676  \n",
       "2016-03-01 21:00:00+00:00                 0.037924  \n",
       "2016-03-02 21:00:00+00:00                 0.045524  \n",
       "2016-03-03 21:00:00+00:00                 0.033924  \n",
       "2016-03-04 21:00:00+00:00                 0.029924  \n",
       "2016-03-07 21:00:00+00:00                 0.021524  \n",
       "2016-03-08 21:00:00+00:00                -0.002076  \n",
       "2016-03-09 21:00:00+00:00                -0.002876  \n",
       "2016-03-10 21:00:00+00:00                -0.004876  \n",
       "2016-03-11 21:00:00+00:00                 0.016724  \n",
       "2016-03-14 20:00:00+00:00                 0.019924  \n",
       "2016-03-15 20:00:00+00:00                 0.013524  \n",
       "2016-03-16 20:00:00+00:00                 0.051924  \n",
       "2016-03-17 20:00:00+00:00                 0.040724  \n",
       "2016-03-18 20:00:00+00:00                 0.080324  \n",
       "2016-03-21 20:00:00+00:00                 0.084324  \n",
       "2016-03-22 20:00:00+00:00                 0.081924  \n",
       "2016-03-23 20:00:00+00:00                 0.105124  \n",
       "2016-03-24 20:00:00+00:00                 0.107124  \n",
       "2016-03-28 20:00:00+00:00                 0.121124  \n",
       "2016-03-29 20:00:00+00:00                 0.143524  \n",
       "2016-03-30 20:00:00+00:00                 0.158324  \n",
       "2016-03-31 20:00:00+00:00                 0.153124  \n",
       "2016-04-01 20:00:00+00:00                 0.173924  \n",
       "2016-04-04 20:00:00+00:00                 0.159924  \n",
       "2016-04-05 20:00:00+00:00                 0.157924  \n",
       "2016-04-06 20:00:00+00:00                 0.159924  \n",
       "...                                            ...  \n",
       "2017-11-16 21:00:00+00:00                 7.192324  \n",
       "2017-11-17 21:00:00+00:00                 7.182324  \n",
       "2017-11-20 21:00:00+00:00                 7.291124  \n",
       "2017-11-21 21:00:00+00:00                 7.369924  \n",
       "2017-11-22 21:00:00+00:00                 7.325124  \n",
       "2017-11-24 18:00:00+00:00                 7.406325  \n",
       "2017-11-27 21:00:00+00:00                 7.293524  \n",
       "2017-11-28 21:00:00+00:00                 7.156325  \n",
       "2017-11-29 21:00:00+00:00                 6.584724  \n",
       "2017-11-30 21:00:00+00:00                 6.756325  \n",
       "2017-12-01 21:00:00+00:00                 6.635124  \n",
       "2017-12-04 21:00:00+00:00                 6.194324  \n",
       "2017-12-05 21:00:00+00:00                 6.237525  \n",
       "2017-12-06 21:00:00+00:00                 6.298324  \n",
       "2017-12-07 21:00:00+00:00                 6.407525  \n",
       "2017-12-08 21:00:00+00:00                 6.387525  \n",
       "2017-12-11 21:00:00+00:00                 6.514324  \n",
       "2017-12-12 21:00:00+00:00                 6.361524  \n",
       "2017-12-13 21:00:00+00:00                 6.175124  \n",
       "2017-12-14 21:00:00+00:00                 6.186724  \n",
       "2017-12-15 21:00:00+00:00                 6.390324  \n",
       "2017-12-18 21:00:00+00:00                 6.643924  \n",
       "2017-12-19 21:00:00+00:00                 6.572324  \n",
       "2017-12-20 21:00:00+00:00                 6.599924  \n",
       "2017-12-21 21:00:00+00:00                 6.563524  \n",
       "2017-12-22 21:00:00+00:00                 6.538724  \n",
       "2017-12-26 21:00:00+00:00                 6.625524  \n",
       "2017-12-27 21:00:00+00:00                 6.614724  \n",
       "2017-12-28 21:00:00+00:00                 6.623924  \n",
       "2017-12-29 21:00:00+00:00                 6.467924  \n",
       "\n",
       "[468 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf[['NVDA', 'starting_cash', 'transactions', 'algorithm_period_return']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf.to_csv(\"temp.csv\")"
   ]
  }
 ],
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