installation/zipline_installation.ipynb

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   "source": [
    "## zipline installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please follow latest instructions [here](https://www.zipline.io/install.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is recommended to install Zipline in an isolated conda environment. Installing Zipline in conda environments will not interfere your default Python deployment or site-packages, which will prevent any possible conflict with your global libraries."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `environment.yml`in this directory to install `zipline` and dependencies in a separate `conda` environment."
   ]
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   "source": [
    "```\n",
    "conda env create -f environment.yml\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment has been created on Ubuntu; if your receive errors while creating this environment on Mac OS or Windows, just delete the problematic packages. We'll put up multiple enviroment files in due course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To circumvent a bug that causes `zipline` to request S&P500 benchmark data from a deprecated API, replace the `benchmarks.py` in your `zipline` installation in the `env_zipline` environment just created with the version in this directory. This will use `pandas-datareader` to access the benchmark data via the FRED data portal provided by the Federal Reserve as described in this chapter."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To identify the location of your `zipline` installation, run \n",
    "```\n",
    "import zipline\n",
    "zipline.__file__\n",
    "```\n",
    "and then got to the `data` folder where you'll find the `benchmarks.py` file."
   ]
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   "source": [
    "## Ingest Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get QUANDL API key and follow instructions to download zipline bundles [here](http://www.zipline.io/bundles.html)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "# !conda install -c conda-forge wrapt -n env_zipline -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2018-10-29T19:46:52.638706Z"
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from zipline.api import order_target, record, symbol\n",
    "from zipline.examples import buyapple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-29T19:44:10.705896Z",
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   "source": [
    "%load_ext zipline\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## zipline Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zipline is the algorithmic trading library that powers the Quantopian backtesting and live-trading platform. It is also available offline to develop a strategy using a limited number of free data bundles that can be ingested and used to test the performance of trading ideas before porting the result to the online Quantopian platform for paper and live trading."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data access using zipline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `zipline` [docs](http://www.zipline.io/bundles.html) on the download and management of data bundles used to simulate backtests. \n",
    "\n",
    "The following commandline instruction lists the available bundles (store per default in `~/.zipline`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !zipline bundles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code illustrates how zipline permits us to access daily stock data for a range of companies. You can run zipline scripts in the Jupyter Notebook using the magic function of the same name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you need to initialize the context with the desired security symbols. We'll also use a counter variable. Then zipline calls handle_data, where we use the `data.history()` method to look back a single period and append the data for the last day to a .csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !zipline ingest"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See "
   ]
  }
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