algo.py

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   "source": [
    "from quantopian.algorithm import order_optimal_portfolio\n",
    "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    "from quantopian.pipeline import Pipeline\n",
    "from quantopian.pipeline.data.builtin import USEquityPricing\n",
    "from quantopian.pipeline.factors import SimpleMovingAverage\n",
    "from quantopian.pipeline.filters import QTradableStocksUS\n",
    "import quantopian.optimize as opt\n",
    "from quantopian.pipeline.factors import Returns\n",
    "\n",
    "def initialize(context):\n",
    "    # Schedule our rebalance function to run at the end of\n",
    "    # each day, when the market closes\n",
    "    #set_slippage(slippage.FixedSlippage(spread=0.0, volume_limit=1))\n",
    "    #set_slippage(slippage.FixedBasisPointsSlippage(basis_points=0, volume_limit=100))\n",
    "    #set_slippage(slippage.VolumeShareSlippage(0))\n",
    "    schedule_function(\n",
    "        my_rebalance,\n",
    "        date_rules.every_day(),\n",
    "        time_rules.market_close(minutes=1  )\n",
    "    )\n",
    "\n",
    "    # Create our pipeline and attach it to our algorithm.\n",
    "    my_pipe = make_pipeline()\n",
    "    attach_pipeline(my_pipe, 'my_pipeline')\n",
    "\n",
    "\n",
    "\n",
    "def make_pipeline():\n",
    "   \n",
    "    #longs = Returns(window_length=2).percentile_between(0,20,mask=QTradableStocksUS())\n",
    "    #shorts = Returns(window_length=2).percentile_between(80,100,mask=QTradableStocksUS())\n",
    "    longs = Returns(window_length=2).bottom(3,mask=QTradableStocksUS())\n",
    "    shorts = Returns(window_length=2).top(3,mask=QTradableStocksUS())    \n",
    "\n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            'longs':  longs,\n",
    "            'shorts': shorts,\n",
    "        },\n",
    "        screen=QTradableStocksUS()& (shorts | longs)\n",
    "    )\n",
    "\n",
    "def compute_target_weights(context, data):\n",
    "    \"\"\"\n",
    "    Compute ordering weights.\n",
    "    \"\"\"\n",
    "\n",
    "    # Initialize empty target weights dictionary.\n",
    "    # This will map securities to their target weight.\n",
    "    weights = {}\n",
    "\n",
    "    # If there are securities in our longs and shorts lists,\n",
    "    # compute even target weights for each security.\n",
    "    if context.longs :\n",
    "        long_weight = 0.2 / len(context.longs)\n",
    "    if context.shorts:\n",
    "        short_weight = -0.2 / len(context.shorts)\n",
    "    #if ~(context.longs & context.shorts):\n",
    "    #    return weights\n",
    "\n",
    "    # Exit positions in our portfolio if they are not\n",
    "    # in our longs or shorts lists.\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",
    "            weights[security] = 0\n",
    "\n",
    "    for security in context.longs:\n",
    "        weights[security] = long_weight\n",
    "\n",
    "    for security in context.shorts:\n",
    "        weights[security] = short_weight\n",
    "\n",
    "    return weights\n",
    "\n",
    "def before_trading_start(context, data):\n",
    "    \"\"\"\n",
    "    Get pipeline results.\n",
    "    \"\"\"\n",
    "\n",
    "    # Gets our pipeline output every day.\n",
    "    pipe_results = pipeline_output('my_pipeline')\n",
    "\n",
    "    # Go long in securities for which the 'longs' value is True,\n",
    "    # and check if they can be traded.\n",
    "    context.longs = []\n",
    "    for sec in pipe_results[pipe_results['longs']].index.tolist():\n",
    "        if data.can_trade(sec):\n",
    "            context.longs.append(sec)\n",
    "            #print(context.longs)\n",
    "    #print('Longs: ')       \n",
    "    #print(context.longs)\n",
    "    # Go short in securities for which the 'shorts' value is True,\n",
    "    # and check if they can be traded.\n",
    "    context.shorts = []\n",
    "    for sec in pipe_results[pipe_results['shorts']].index.tolist():\n",
    "        if data.can_trade(sec):\n",
    "            context.shorts.append(sec)\n",
    "    #print('Shorts: ')\n",
    "    #print(context.shorts)\n",
    "    \n",
    "   \n",
    "    \n",
    "def my_rebalance(context, data):\n",
    "    \"\"\"\n",
    "    Rebalance daily\n",
    "    \"\"\"\n",
    "    for stock in context.portfolio.positions:\n",
    "        #print('selling everything')\n",
    "        #print(stock)\n",
    "        order_target_percent(stock, 0.0)  \n",
    "    # Calculate target weights to rebalance\n",
    "    #print(context)\n",
    "    target_weights = compute_target_weights(context, data)\n",
    "    #print(target_weights)\n",
    "\n",
    "    # If we have target weights, rebalance our portfolio\n",
    "    if target_weights:\n",
    "        order_optimal_portfolio(\n",
    "            objective=opt.TargetWeights(target_weights),\n",
    "            constraints=[],\n",
    "        )"
   ]
  }
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