{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zipline beginner tutorial\n",
    "=========================\n",
    "\n",
    "Basics\n",
    "------\n",
    "\n",
    "Zipline is an open-source algorithmic trading simulator written in Python.\n",
    "\n",
    "The source can be found at: https://github.com/quantopian/zipline\n",
    "\n",
    "Some benefits include:\n",
    "\n",
    "* Realistic: slippage, transaction costs, order delays.\n",
    "* Stream-based: Process each event individually, avoids look-ahead bias.\n",
    "* Batteries included: Common transforms (moving average) as well as common risk calculations (Sharpe).\n",
    "* Developed and continuously updated by [Quantopian](https://www.quantopian.com) which provides an easy-to-use web-interface to Zipline, 10 years of minute-resolution historical US stock data, and live-trading capabilities. This tutorial is directed at users wishing to use Zipline without using Quantopian. If you instead want to get started on Quantopian, see [here](https://www.quantopian.com/faq#get-started).\n",
    "\n",
    "This tutorial assumes that you have zipline correctly installed, see the [installation instructions](https://github.com/quantopian/zipline#installation) if you haven't set up zipline yet.\n",
    "\n",
    "Every `zipline` algorithm consists of two functions you have to define:\n",
    "* `initialize(context)`\n",
    "* `handle_data(context, data)`\n",
    "\n",
    "Before the start of the algorithm, `zipline` calls the `initialize()` function and passes in a `context` variable. `context` is a persistent namespace for you to store variables you need to access from one algorithm iteration to the next.\n",
    "\n",
    "After the algorithm has been initialized, `zipline` calls the `handle_data()` function once for each event. At every call, it passes the same `context` variable and an event-frame called `data` containing the current trading bar with open, high, low, and close (OHLC) prices as well as volume for each stock in your universe. For more information on these functions, see the [relevant part of the Quantopian docs](https://www.quantopian.com/help#api-toplevel)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My first algorithm\n",
    "----------------------\n",
    "\n",
    "Lets take a look at a very simple algorithm from the `examples` directory, `buyapple.py`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    # Load price data from yahoo.\r\n",
      "    data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,\r\n",
      "                           end=end)\r\n",
      "\r\n",
      "    # Create and run the algorithm.\r\n",
      "    algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data,\r\n",
      "                            identifiers=['AAPL'])\r\n",
      "    results = algo.run(data)\r\n",
      "\r\n",
      "    analyze(results=results)\r\n"
     ]
    }
   ],
   "source": [
    "!tail ../../zipline/examples/buyapple.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we first have to import some functions we would like to use. All functions commonly used in your algorithm can be found in `zipline.api`. Here we are using `order()` which takes two arguments -- a security object, and a number specifying how many stocks you would like to order (if negative, `order()` will sell/short stocks). In this case we want to order 10 shares of Apple at each iteration. For more documentation on `order()`, see the [Quantopian docs](https://www.quantopian.com/help#api-order).\n",
    "\n",
    "You don't have to use the `symbol()` function and could just pass in `AAPL` directly but it is good practice as this way your code will be Quantopian compatible.\n",
    "\n",
    "Finally, the `record()` function allows you to save the value of a variable at each iteration. You provide it with a name for the variable together with the variable itself: `varname=var`. After the algorithm finished running you will have access to each variable value you tracked with `record()` under the name you provided (we will see this further below). You also see how we can access the current price data of the AAPL stock in the `data` event frame (for more information see [here](https://www.quantopian.com/help#api-event-properties)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the algorithm\n",
    "\n",
    "To now test this algorithm on financial data, `zipline` provides two interfaces. A command-line interface and an `IPython Notebook` interface.\n",
    "\n",
    "### Command line interface\n",
    "After you installed zipline you should be able to execute the following from your command line (e.g. `cmd.exe` on Windows, or the Terminal app on OSX):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: run_algo.py [-h] [-c FILE] [--algofile ALGOFILE] [--data-frequency {minute,daily}] [--start START] [--end END]\r\n",
      "                   [--capital_base CAPITAL_BASE] [--source {yahoo}] [--source_time_column SOURCE_TIME_COLUMN] [--symbols SYMBOLS]\r\n",
      "                   [--output OUTPUT] [--metadata_path METADATA_PATH] [--metadata_index METADATA_INDEX] [--print-algo] [--no-print-algo]\r\n",
      "\r\n",
      "Zipline version 0.8.0rc1.\r\n",
      "\r\n",
      "optional arguments:\r\n",
      "  -h, --help            show this help message and exit\r\n",
      "  -c FILE, --conf_file FILE\r\n",
      "                        Specify config file\r\n",
      "  --algofile ALGOFILE, -f ALGOFILE\r\n",
      "  --data-frequency {minute,daily}\r\n",
      "  --start START, -s START\r\n",
      "  --end END, -e END\r\n",
      "  --capital_base CAPITAL_BASE\r\n",
      "  --source {yahoo}, -d {yahoo}\r\n",
      "  --source_time_column SOURCE_TIME_COLUMN, -t SOURCE_TIME_COLUMN\r\n",
      "  --symbols SYMBOLS\r\n",
      "  --output OUTPUT, -o OUTPUT\r\n",
      "  --metadata_path METADATA_PATH, -m METADATA_PATH\r\n",
      "  --metadata_index METADATA_INDEX, -x METADATA_INDEX\r\n",
      "  --print-algo, -p\r\n",
      "  --no-print-algo, -q\r\n"
     ]
    }
   ],
   "source": [
    "!run_algo.py --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you have to omit the preceding '!' when you call `run_algo.py`, this is only required by the IPython Notebook in which this tutorial was written.\n",
    "\n",
    "As you can see there are a couple of flags that specify where to find your algorithm (`-f`) as well as parameters specifying which stock data to load from Yahoo! finance (`--symbols`) and the time-range (`--start` and `--end`). Finally, you'll want to save the performance metrics of your algorithm so that you can analyze how it performed. This is done via the `--output` flag and will cause it to write the performance `DataFrame` in the pickle Python file format. Note that you can also define a configuration file with these parameters that you can then conveniently pass to the `-c` option so that you don't have to supply the command line args all the time (see the .conf files in the examples directory).\n",
    "\n",
    "Thus, to execute our algorithm from above and save the results to `buyapple_out.pickle` we would call `run_algo.py` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "[2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.\n",
      "[2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00\n",
      "[2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 21:00:00+00:00\n"
     ]
    }
   ],
   "source": [
    "!run_algo.py -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o buyapple_out.pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`run_algo.py` first outputs the algorithm contents. It then fetches historical price and volume data of Apple from Yahoo! finance in the desired time range, calls the `initialize()` function, and then streams the historical stock price day-by-day through `handle_data()`. After each call to `handle_data()` we instruct `zipline` to order 10 stocks of AAPL. After the call of the `order()` function, `zipline` enters the ordered stock and amount in the order book. After the `handle_data()` function has finished, `zipline` looks for any open orders and tries to fill them. If the trading volume is high enough for this stock, the order is executed after adding the commission and applying the slippage model which models the influence of your order on the stock price, so your algorithm will be charged more than just the stock price * 10. (Note, that you can also change the commission and slippage model that `zipline` uses, see the [Quantopian docs](https://www.quantopian.com/help#ide-slippage) for more information).\n",
    "\n",
    "Note that there is also an `analyze()` function printed. `run_algo.py` will try and look for a file with the ending with `_analyze.py` and the same name of the algorithm (so `buyapple_analyze.py`) or an `analyze()` function directly in the script. If an `analyze()` function is found it will be called *after* the simulation has finished and passed in the performance `DataFrame`. (The reason for allowing specification of an `analyze()` function in a separate file is that this way `buyapple.py` remains a valid Quantopian algorithm that you can copy&paste to the platform).\n",
    "\n",
    "Lets take a quick look at the performance `DataFrame`. For this, we use `pandas` from inside the IPython Notebook and print the first ten rows. Note that `zipline` makes heavy usage of `pandas`, especially for data input and outputting so it's worth spending some time to learn it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>algo_volatility</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "      <th>alpha</th>\n",
       "      <th>benchmark_period_return</th>\n",
       "      <th>benchmark_volatility</th>\n",
       "      <th>beta</th>\n",
       "      <th>capital_used</th>\n",
       "      <th>ending_cash</th>\n",
       "      <th>ending_exposure</th>\n",
       "      <th>...</th>\n",
       "      <th>short_exposure</th>\n",
       "      <th>short_value</th>\n",
       "      <th>shorts_count</th>\n",
       "      <th>sortino</th>\n",
       "      <th>starting_cash</th>\n",
       "      <th>starting_exposure</th>\n",
       "      <th>starting_value</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>transactions</th>\n",
       "      <th>treasury_period_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 21:00:00</th>\n",
       "      <td>3.738314</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.065800</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10000000.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000000.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 21:00:00</th>\n",
       "      <td>3.423135</td>\n",
       "      <td>3.367492e-07</td>\n",
       "      <td>-3.000000e-08</td>\n",
       "      <td>-0.064897</td>\n",
       "      <td>-0.047528</td>\n",
       "      <td>0.323229</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-34.53135</td>\n",
       "      <td>9999965.46865</td>\n",
       "      <td>34.23135</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000000.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>[{u'order_id': u'513357725cb64a539e3dd02b47da7...</td>\n",
       "      <td>0.0649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 21:00:00</th>\n",
       "      <td>3.473229</td>\n",
       "      <td>4.001918e-07</td>\n",
       "      <td>-9.906000e-09</td>\n",
       "      <td>-0.066196</td>\n",
       "      <td>-0.045697</td>\n",
       "      <td>0.329321</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-35.03229</td>\n",
       "      <td>9999930.43636</td>\n",
       "      <td>69.46458</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9999965.46865</td>\n",
       "      <td>34.23135</td>\n",
       "      <td>34.23135</td>\n",
       "      <td>3</td>\n",
       "      <td>[{u'order_id': u'd7d4ad03cfec4d578c0d817dc3829...</td>\n",
       "      <td>0.0662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 21:00:00</th>\n",
       "      <td>3.172661</td>\n",
       "      <td>4.993979e-06</td>\n",
       "      <td>-6.410420e-07</td>\n",
       "      <td>-0.065758</td>\n",
       "      <td>-0.044785</td>\n",
       "      <td>0.298325</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-32.02661</td>\n",
       "      <td>9999898.40975</td>\n",
       "      <td>95.17983</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12731.780516</td>\n",
       "      <td>9999930.43636</td>\n",
       "      <td>69.46458</td>\n",
       "      <td>69.46458</td>\n",
       "      <td>4</td>\n",
       "      <td>[{u'order_id': u'1fbf5e9bfd7c4d9cb2e8383e1085e...</td>\n",
       "      <td>0.0657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 21:00:00</th>\n",
       "      <td>3.322945</td>\n",
       "      <td>5.977002e-06</td>\n",
       "      <td>-2.201900e-07</td>\n",
       "      <td>-0.065206</td>\n",
       "      <td>-0.018908</td>\n",
       "      <td>0.375301</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-33.52945</td>\n",
       "      <td>9999864.88030</td>\n",
       "      <td>132.91780</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>-12629.274583</td>\n",
       "      <td>9999898.40975</td>\n",
       "      <td>95.17983</td>\n",
       "      <td>95.17983</td>\n",
       "      <td>5</td>\n",
       "      <td>[{u'order_id': u'9ea6b142ff09466b9113331a37437...</td>\n",
       "      <td>0.0652</td>\n",
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       "<p>5 rows × 39 columns</p>\n",
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      "text/plain": [
       "                         AAPL  algo_volatility  algorithm_period_return  \\\n",
       "2000-01-03 21:00:00  3.738314     0.000000e+00             0.000000e+00   \n",
       "2000-01-04 21:00:00  3.423135     3.367492e-07            -3.000000e-08   \n",
       "2000-01-05 21:00:00  3.473229     4.001918e-07            -9.906000e-09   \n",
       "2000-01-06 21:00:00  3.172661     4.993979e-06            -6.410420e-07   \n",
       "2000-01-07 21:00:00  3.322945     5.977002e-06            -2.201900e-07   \n",
       "\n",
       "                        alpha  benchmark_period_return  benchmark_volatility  \\\n",
       "2000-01-03 21:00:00 -0.065800                -0.009549              0.000000   \n",
       "2000-01-04 21:00:00 -0.064897                -0.047528              0.323229   \n",
       "2000-01-05 21:00:00 -0.066196                -0.045697              0.329321   \n",
       "2000-01-06 21:00:00 -0.065758                -0.044785              0.298325   \n",
       "2000-01-07 21:00:00 -0.065206                -0.018908              0.375301   \n",
       "\n",
       "                         beta  capital_used     ending_cash  ending_exposure  \\\n",
       "2000-01-03 21:00:00  0.000000       0.00000  10000000.00000          0.00000   \n",
       "2000-01-04 21:00:00  0.000001     -34.53135   9999965.46865         34.23135   \n",
       "2000-01-05 21:00:00  0.000001     -35.03229   9999930.43636         69.46458   \n",
       "2000-01-06 21:00:00 -0.000006     -32.02661   9999898.40975         95.17983   \n",
       "2000-01-07 21:00:00  0.000005     -33.52945   9999864.88030        132.91780   \n",
       "\n",
       "                              ...            short_exposure  short_value  \\\n",
       "2000-01-03 21:00:00           ...                         0            0   \n",
       "2000-01-04 21:00:00           ...                         0            0   \n",
       "2000-01-05 21:00:00           ...                         0            0   \n",
       "2000-01-06 21:00:00           ...                         0            0   \n",
       "2000-01-07 21:00:00           ...                         0            0   \n",
       "\n",
       "                     shorts_count       sortino   starting_cash  \\\n",
       "2000-01-03 21:00:00             0      0.000000  10000000.00000   \n",
       "2000-01-04 21:00:00             0      0.000000  10000000.00000   \n",
       "2000-01-05 21:00:00             0      0.000000   9999965.46865   \n",
       "2000-01-06 21:00:00             0 -12731.780516   9999930.43636   \n",
       "2000-01-07 21:00:00             0 -12629.274583   9999898.40975   \n",
       "\n",
       "                     starting_exposure  starting_value  trading_days  \\\n",
       "2000-01-03 21:00:00            0.00000         0.00000             1   \n",
       "2000-01-04 21:00:00            0.00000         0.00000             2   \n",
       "2000-01-05 21:00:00           34.23135        34.23135             3   \n",
       "2000-01-06 21:00:00           69.46458        69.46458             4   \n",
       "2000-01-07 21:00:00           95.17983        95.17983             5   \n",
       "\n",
       "                                                          transactions  \\\n",
       "2000-01-03 21:00:00                                                 []   \n",
       "2000-01-04 21:00:00  [{u'order_id': u'513357725cb64a539e3dd02b47da7...   \n",
       "2000-01-05 21:00:00  [{u'order_id': u'd7d4ad03cfec4d578c0d817dc3829...   \n",
       "2000-01-06 21:00:00  [{u'order_id': u'1fbf5e9bfd7c4d9cb2e8383e1085e...   \n",
       "2000-01-07 21:00:00  [{u'order_id': u'9ea6b142ff09466b9113331a37437...   \n",
       "\n",
       "                     treasury_period_return  \n",
       "2000-01-03 21:00:00                  0.0658  \n",
       "2000-01-04 21:00:00                  0.0649  \n",
       "2000-01-05 21:00:00                  0.0662  \n",
       "2000-01-06 21:00:00                  0.0657  \n",
       "2000-01-07 21:00:00                  0.0652  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame\n",
    "perf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there is a row for each trading day, starting on the first business day of 2000. In the columns you can find various information about the state of your algorithm. The very first column `AAPL` was placed there by the `record()` function mentioned earlier and allows us to plot the price of apple. For example, we could easily examine now how our portfolio value changed over time compared to the AAPL stock price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff5c6147f90>"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SFmljDN26+aemffeF+fOhV6+6rxs0yIfK2357uOQSrydvaQ31TF/TYq0QERER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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5c84696d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "figsize(12, 12)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax1 = plt.subplot(211)\n",
    "perf.portfolio_value.plot(ax=ax1)\n",
    "ax1.set_ylabel('portfolio value')\n",
    "ax2 = plt.subplot(212, sharex=ax1)\n",
    "perf.AAPL.plot(ax=ax2)\n",
    "ax2.set_ylabel('AAPL stock price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our algorithm performance as assessed by the `portfolio_value` closely matches that of the AAPL stock price. This is not surprising as our algorithm only bought AAPL every chance it got."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPython Notebook\n",
    "\n",
    "The [IPython Notebook](http://ipython.org/notebook.html) is a very powerful browser-based interface to a Python interpreter (this tutorial was written in it). As it is already the de-facto interface for most quantitative researchers `zipline` provides an easy way to run your algorithm inside the Notebook without requiring you to use the CLI. \n",
    "\n",
    "To use it you have to write your algorithm in a cell and let `zipline` know that it is supposed to run this algorithm. This is done via the `%%zipline` IPython magic command that is available after you `import zipline` from within the IPython Notebook. This magic takes the same arguments as the command line interface described above. Thus to run the algorithm from above with the same parameters we just have to execute the following cell after importing `zipline` to register the magic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import zipline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n"
     ]
    }
   ],
   "source": [
    "%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o perf_ipython\n",
    "\n",
    "from zipline.api import symbol, order, record\n",
    "\n",
    "def initialize(context):\n",
    "    pass\n",
    "\n",
    "def handle_data(context, data):\n",
    "    order(symbol('AAPL'), 10)\n",
    "    record(AAPL=data[symbol('AAPL')].price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we did not have to specify an input file as above since the magic will use the contents of the cell and look for your algorithm functions there. Also, instead of defining an output file we are specifying a variable name with `-o` that will be created in the name space and contain the performance `DataFrame` we looked at above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>algo_volatility</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "      <th>alpha</th>\n",
       "      <th>benchmark_period_return</th>\n",
       "      <th>benchmark_volatility</th>\n",
       "      <th>beta</th>\n",
       "      <th>capital_used</th>\n",
       "      <th>ending_cash</th>\n",
       "      <th>ending_exposure</th>\n",
       "      <th>...</th>\n",
       "      <th>short_exposure</th>\n",
       "      <th>short_value</th>\n",
       "      <th>shorts_count</th>\n",
       "      <th>sortino</th>\n",
       "      <th>starting_cash</th>\n",
       "      <th>starting_exposure</th>\n",
       "      <th>starting_value</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>transactions</th>\n",
       "      <th>treasury_period_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 21:00:00</th>\n",
       "      <td>3.738314</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.065800</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10000000.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000000.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 21:00:00</th>\n",
       "      <td>3.423135</td>\n",
       "      <td>3.367492e-07</td>\n",
       "      <td>-3.000000e-08</td>\n",
       "      <td>-0.064897</td>\n",
       "      <td>-0.047528</td>\n",
       "      <td>0.323229</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-34.53135</td>\n",
       "      <td>9999965.46865</td>\n",
       "      <td>34.23135</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000000.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2</td>\n",
       "      <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>\n",
       "      <td>0.0649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 21:00:00</th>\n",
       "      <td>3.473229</td>\n",
       "      <td>4.001918e-07</td>\n",
       "      <td>-9.906000e-09</td>\n",
       "      <td>-0.066196</td>\n",
       "      <td>-0.045697</td>\n",
       "      <td>0.329321</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-35.03229</td>\n",
       "      <td>9999930.43636</td>\n",
       "      <td>69.46458</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9999965.46865</td>\n",
       "      <td>34.23135</td>\n",
       "      <td>34.23135</td>\n",
       "      <td>3</td>\n",
       "      <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>\n",
       "      <td>0.0662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 21:00:00</th>\n",
       "      <td>3.172661</td>\n",
       "      <td>4.993979e-06</td>\n",
       "      <td>-6.410420e-07</td>\n",
       "      <td>-0.065758</td>\n",
       "      <td>-0.044785</td>\n",
       "      <td>0.298325</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-32.02661</td>\n",
       "      <td>9999898.40975</td>\n",
       "      <td>95.17983</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12731.780516</td>\n",
       "      <td>9999930.43636</td>\n",
       "      <td>69.46458</td>\n",
       "      <td>69.46458</td>\n",
       "      <td>4</td>\n",
       "      <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>\n",
       "      <td>0.0657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 21:00:00</th>\n",
       "      <td>3.322945</td>\n",
       "      <td>5.977002e-06</td>\n",
       "      <td>-2.201900e-07</td>\n",
       "      <td>-0.065206</td>\n",
       "      <td>-0.018908</td>\n",
       "      <td>0.375301</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-33.52945</td>\n",
       "      <td>9999864.88030</td>\n",
       "      <td>132.91780</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12629.274583</td>\n",
       "      <td>9999898.40975</td>\n",
       "      <td>95.17983</td>\n",
       "      <td>95.17983</td>\n",
       "      <td>5</td>\n",
       "      <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>\n",
       "      <td>0.0652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         AAPL  algo_volatility  algorithm_period_return  \\\n",
       "2000-01-03 21:00:00  3.738314     0.000000e+00             0.000000e+00   \n",
       "2000-01-04 21:00:00  3.423135     3.367492e-07            -3.000000e-08   \n",
       "2000-01-05 21:00:00  3.473229     4.001918e-07            -9.906000e-09   \n",
       "2000-01-06 21:00:00  3.172661     4.993979e-06            -6.410420e-07   \n",
       "2000-01-07 21:00:00  3.322945     5.977002e-06            -2.201900e-07   \n",
       "\n",
       "                        alpha  benchmark_period_return  benchmark_volatility  \\\n",
       "2000-01-03 21:00:00 -0.065800                -0.009549              0.000000   \n",
       "2000-01-04 21:00:00 -0.064897                -0.047528              0.323229   \n",
       "2000-01-05 21:00:00 -0.066196                -0.045697              0.329321   \n",
       "2000-01-06 21:00:00 -0.065758                -0.044785              0.298325   \n",
       "2000-01-07 21:00:00 -0.065206                -0.018908              0.375301   \n",
       "\n",
       "                         beta  capital_used     ending_cash  ending_exposure  \\\n",
       "2000-01-03 21:00:00  0.000000       0.00000  10000000.00000          0.00000   \n",
       "2000-01-04 21:00:00  0.000001     -34.53135   9999965.46865         34.23135   \n",
       "2000-01-05 21:00:00  0.000001     -35.03229   9999930.43636         69.46458   \n",
       "2000-01-06 21:00:00 -0.000006     -32.02661   9999898.40975         95.17983   \n",
       "2000-01-07 21:00:00  0.000005     -33.52945   9999864.88030        132.91780   \n",
       "\n",
       "                              ...            short_exposure  short_value  \\\n",
       "2000-01-03 21:00:00           ...                         0            0   \n",
       "2000-01-04 21:00:00           ...                         0            0   \n",
       "2000-01-05 21:00:00           ...                         0            0   \n",
       "2000-01-06 21:00:00           ...                         0            0   \n",
       "2000-01-07 21:00:00           ...                         0            0   \n",
       "\n",
       "                     shorts_count       sortino   starting_cash  \\\n",
       "2000-01-03 21:00:00             0      0.000000  10000000.00000   \n",
       "2000-01-04 21:00:00             0      0.000000  10000000.00000   \n",
       "2000-01-05 21:00:00             0      0.000000   9999965.46865   \n",
       "2000-01-06 21:00:00             0 -12731.780516   9999930.43636   \n",
       "2000-01-07 21:00:00             0 -12629.274583   9999898.40975   \n",
       "\n",
       "                     starting_exposure  starting_value  trading_days  \\\n",
       "2000-01-03 21:00:00            0.00000         0.00000             1   \n",
       "2000-01-04 21:00:00            0.00000         0.00000             2   \n",
       "2000-01-05 21:00:00           34.23135        34.23135             3   \n",
       "2000-01-06 21:00:00           69.46458        69.46458             4   \n",
       "2000-01-07 21:00:00           95.17983        95.17983             5   \n",
       "\n",
       "                                                          transactions  \\\n",
       "2000-01-03 21:00:00                                                 []   \n",
       "2000-01-04 21:00:00  [{u'commission': 0.3, u'amount': 10, u'sid': 0...   \n",
       "2000-01-05 21:00:00  [{u'commission': 0.3, u'amount': 10, u'sid': 0...   \n",
       "2000-01-06 21:00:00  [{u'commission': 0.3, u'amount': 10, u'sid': 0...   \n",
       "2000-01-07 21:00:00  [{u'commission': 0.3, u'amount': 10, u'sid': 0...   \n",
       "\n",
       "                     treasury_period_return  \n",
       "2000-01-03 21:00:00                  0.0658  \n",
       "2000-01-04 21:00:00                  0.0649  \n",
       "2000-01-05 21:00:00                  0.0662  \n",
       "2000-01-06 21:00:00                  0.0657  \n",
       "2000-01-07 21:00:00                  0.0652  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_ipython.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual (advanced)\n",
    "\n",
    "If you are happy with either way above you can safely skip this passage. To provide a closer look at how `zipline` actually works it is instructive to see how we run an algorithm without any of the interfaces demonstrated above which hide the actual `zipline` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n"
     ]
    }
   ],
   "source": [
    "import pytz\n",
    "from datetime import datetime\n",
    "\n",
    "from zipline.algorithm import TradingAlgorithm\n",
    "from zipline.utils.factory import load_bars_from_yahoo\n",
    "\n",
    "# Load data manually from Yahoo! finance\n",
    "start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc)\n",
    "end = datetime(2012, 1, 1, 0, 0, 0, 0, pytz.utc)\n",
    "data = load_bars_from_yahoo(stocks=['AAPL'], start=start,\n",
    "                            end=end)\n",
    "\n",
    "# Define algorithm\n",
    "def initialize(context):\n",
    "    pass\n",
    "\n",
    "def handle_data(context, data):\n",
    "    order(symbol('AAPL'), 10)\n",
    "    record(AAPL=data[symbol('AAPL')].price)\n",
    "\n",
    "# Create algorithm object passing in initialize and\n",
    "# handle_data functions\n",
    "algo_obj = TradingAlgorithm(initialize=initialize, \n",
    "                            handle_data=handle_data)\n",
    "\n",
    "# Run algorithm\n",
    "perf_manual = algo_obj.run(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we again define the functions as above but we manually pass them to the `TradingAlgorithm` class which is the main `zipline` class for running algorithms. We also manually load the data using `load_bars_from_yahoo()` and pass it to the `TradingAlgorithm.run()` method which kicks off the backtest simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Access to previous prices using `history`\n",
    "\n",
    "###Working example: Dual Moving Average Cross-Over\n",
    "\n",
    "The Dual Moving Average (DMA) is a classic momentum strategy. It's probably not used by any serious trader anymore but is still very instructive. The basic idea is that we compute two rolling or moving averages (mavg) -- one with a longer window that is supposed to capture long-term trends and one shorter window that is supposed to capture short-term trends. Once the short-mavg crosses the long-mavg from below we assume that the stock price has upwards momentum and long the stock. If the short-mavg crosses from above we exit the positions as we assume the stock to go down further.\n",
    "\n",
    "As we need to have access to previous prices to implement this strategy we need a new concept: History\n",
    "\n",
    "`history()` is a convenience function that keeps a rolling window of data for you. The first argument is the number of bars you want to collect, the second argument is the unit (either `'1d'` for `'1m'` but note that you need to have minute-level data for using `1m`). For a more detailed description `history()`'s features, see the [Quantopian docs](https://www.quantopian.com/help#ide-history). While you can directly use the `history()` function on Quantopian, in `zipline` you have to register each history container you want to use with `add_history()` and pass it the same arguments as the history function below. Lets look at the strategy which should make this clear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n"
     ]
    },
    {
     "data": {
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RySHllZfEHpBctw5WrSp9Lrb0eyrk5/syls6d/WqWZvH5u+s7Jd2SVqrtC0exD0vxD0vx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/nqkrFzp7Xt2llbo4bsWKk8iJxeXqKUUir3iIuD9etl5sYEVapAaGjSDnK3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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5c3d642d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o perf_dma\n",
    "\n",
    "\n",
    "from zipline.api import order_target, record, symbol, history, add_history\n",
    "import numpy as np\n",
    "\n",
    "def initialize(context):\n",
    "    # Register 2 histories that track daily prices,\n",
    "    # one with a 100 window and one with a 300 day window\n",
    "    add_history(100, '1d', 'price')\n",
    "    add_history(300, '1d', 'price')\n",
    "\n",
    "    context.i = 0\n",
    "\n",
    "\n",
    "def handle_data(context, data):\n",
    "    # Skip first 300 days to get full windows\n",
    "    context.i += 1\n",
    "    if context.i < 300:\n",
    "        return\n",
    "\n",
    "    # Compute averages\n",
    "    # history() has to be called with the same params\n",
    "    # from above and returns a pandas dataframe.\n",
    "    short_mavg = history(100, '1d', 'price').mean()\n",
    "    long_mavg = history(300, '1d', 'price').mean()\n",
    "\n",
    "    # Trading logic\n",
    "    if short_mavg[0] > long_mavg[0]:\n",
    "        # order_target orders as many shares as needed to\n",
    "        # achieve the desired number of shares.\n",
    "        order_target(symbol('AAPL'), 100)\n",
    "    elif short_mavg[0] < long_mavg[0]:\n",
    "        order_target(symbol('AAPL'), 0)\n",
    "\n",
    "    # Save values for later inspection\n",
    "    record(AAPL=data[symbol('AAPL')].price,\n",
    "           short_mavg=short_mavg[0],\n",
    "           long_mavg=long_mavg[0])\n",
    "    \n",
    "    \n",
    "def analyze(context, perf):\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(211)\n",
    "    perf.portfolio_value.plot(ax=ax1)\n",
    "    ax1.set_ylabel('portfolio value in $')\n",
    "\n",
    "    ax2 = fig.add_subplot(212)\n",
    "    perf['AAPL'].plot(ax=ax2)\n",
    "    perf[['short_mavg', 'long_mavg']].plot(ax=ax2)\n",
    "\n",
    "    perf_trans = perf.ix[[t != [] for t in perf.transactions]]\n",
    "    buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]\n",
    "    sells = perf_trans.ix[\n",
    "        [t[0]['amount'] < 0 for t in perf_trans.transactions]]\n",
    "    ax2.plot(buys.index, perf.short_mavg.ix[buys.index],\n",
    "             '^', markersize=10, color='m')\n",
    "    ax2.plot(sells.index, perf.short_mavg.ix[sells.index],\n",
    "             'v', markersize=10, color='k')\n",
    "    ax2.set_ylabel('price in $')\n",
    "    plt.legend(loc=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are explicitly defining an `analyze()` function that gets automatically called once the backtest is done (this is not possible on Quantopian currently).\n",
    "\n",
    "Although it might not be directly apparent, the power of `history()` (pun intended) can not be under-estimated as most algorithms make use of prior market developments in one form or another. You could easily devise a strategy that trains a classifier with [`scikit-learn`](http://scikit-learn.org/stable/) which tries to predict future market movements based on past prices (note, that most of the `scikit-learn` functions require `numpy.ndarray`s rather than `pandas.DataFrame`s, so you can simply pass the underlying `ndarray` of a `DataFrame` via `.values`).\n",
    "\n",
    "We also used the `order_target()` function above. This and other functions like it can make order management and portfolio rebalancing much easier. See the [Quantopian documentation on order functions](https://www.quantopian.com/help#api-order-methods) fore more details.\n",
    "\n",
    "# Conclusions\n",
    "\n",
    "We hope that this tutorial gave you a little insight into the architecture, API, and features of `zipline`. For next steps, check out some of the [examples](https://github.com/quantopian/zipline/tree/master/zipline/examples).\n",
    "\n",
    "Feel free to ask questions on [our mailing list](https://groups.google.com/forum/#!forum/zipline), report problems on our [GitHub issue tracker](https://github.com/quantopian/zipline/issues?state=open), [get involved](https://github.com/quantopian/zipline/wiki/Contribution-Requests), and [checkout Quantopian](https://quantopian.com)."
   ]
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