{
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  {
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   "source": [
    "# Stock Sentiment Analysis\n",
    "\n",
    "Check out the video for full details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Code\n",
    "Here is the final code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This section is only importable in the backtester\n",
    "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    "\n",
    "# General pipeline imports\n",
    "from quantopian.pipeline import Pipeline\n",
    "from quantopian.pipeline.factors import AverageDollarVolume\n",
    "\n",
    "\n",
    "# Using the free sample in your pipeline algo\n",
    "from quantopian.pipeline.data.accern import alphaone_free\n",
    "\n",
    "\n",
    "def initialize(context):\n",
    "    # Schedule our rebalance function to run at the start of each week.\n",
    "    schedule_function(my_rebalance, date_rules.every_day())\n",
    "\n",
    "    \n",
    "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    "\n",
    "def make_pipeline():\n",
    "\n",
    "    \n",
    "    # Screen out penny stocks and low liquidity securities.\n",
    "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    " \n",
    "    # Add pipeline factors\n",
    "    impact = alphaone_free.impact_score.latest\n",
    "    sentiment = alphaone_free.article_sentiment.latest\n",
    "\n",
    "    return Pipeline(columns={\n",
    "            'impact': impact,\n",
    "            'sentiment':sentiment,\n",
    "            },\n",
    "            screen = is_liquid)\n",
    "            \n",
    "\n",
    "\n",
    "    \n",
    "def before_trading_start(context, data):\n",
    "    port = pipeline_output('pipeline')\n",
    "    \n",
    "    # Grab stocks with 100 impact and >0.5 sentiment and go long.\n",
    "    context.longs = port[(port['impact']==100) & (port['sentiment']>0.75)].index.tolist()\n",
    "    \n",
    "    # Grab stocks with 100 impact and <-0.5 sentiment and go long.\n",
    "    context.shorts = port[(port['impact']==100) & (port['sentiment']< -0.75)].index.tolist()\n",
    "\n",
    "    context.long_weight, context.short_weight = my_compute_weights(context)\n",
    "\n",
    "def my_compute_weights(context):\n",
    "\n",
    "    # Compute even target weights for our long positions and short positions.\n",
    "    long_weight = 0.5 / len(context.longs)\n",
    "    short_weight = -0.5 / len(context.shorts)\n",
    "\n",
    "    return long_weight, short_weight\n",
    "\n",
    "def my_rebalance(context, data):\n",
    "\n",
    "    for security in context.portfolio.positions:\n",
    "        if security not in context.longs and security not in context.shorts and data.can_trade(security):\n",
    "            order_target_percent(security, 0)\n",
    "\n",
    "    for security in context.longs:\n",
    "        if data.can_trade(security):\n",
    "            order_target_percent(security, context.long_weight)\n",
    "\n",
    "    for security in context.shorts:\n",
    "        if data.can_trade(security):\n",
    "            order_target_percent(security, context.short_weight)\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    \n"
   ]
  }
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