{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "import quantlab\n",
    "\n",
    "from pulp import *\n",
    "\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['font.serif'] = 'Ubuntu'\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 6\n",
    "plt.rcParams['axes.labelsize'] = 6\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['xtick.labelsize'] = 6\n",
    "plt.rcParams['ytick.labelsize'] = 6\n",
    "plt.rcParams['legend.fontsize'] = 6\n",
    "plt.rcParams['figure.titlesize'] = 8\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "import scipy.optimize\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trade Optimization\n",
    "\n",
    "\n",
    "- In practice, portfolio managers must account for the real-world implementation costs – both explicit (e.g. commission) and implicit (e.g. bid/ask spread and impact) associated with trading portfolios.\n",
    "\n",
    "- Managers often implement trade paring constraints that may limit the number of allowed securities, the number of executed trades, the size of a trade, or the size of holdings.  These constraints can turn a well-formed convex optimization into a discrete problem.\n",
    "\n",
    "- In this technical note, we explore how to formulate trade optimization as a Mixed-Integer Linear Programming problem and implement an example in Python.\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In the context of portfolio construction, trade optimization is the process of managing the transactions necessary to move from one set of portfolio weights to another.  These optimizations can play an important role both in the cases of rebalancing as well as in the case of a cash infusion or withdrawal.\n",
    "\n",
    "Explicit (e.g. commission) and implicit (e.g. bid/ask spread and impact) costs associated with trading.  \n",
    "\n",
    "Two approaches are often taken to trade optimization:\n",
    "\n",
    "1. Trading costs and constraints are explicitly considered within portfolio construction.  For example, a portfolio optimization that seeks to maximize exposure to some alpha source may incorporate explicit measures of transaction costs or constrain the number of trades that are allowed to occur at any given rebalance.\n",
    "\n",
    "2. Portfolio construction and trade optimization occur in a two step process.  For example, a portfolio optimization may take place that creates the \"ideal\" portfolio, ignoring consideration of trading constraints and costs.  Trade optimization would then occur as a second step, seeking to identify the trades that would move the current portfolio \"as close as possible\" to the target portfolio while minimizing costs or respecting trade constraints.\n",
    "\n",
    "These two approaches will not necessarily arrive at the same result.  At Newfound, we prefer the latter approach, as we believe it creates more transparency in portfolio construction.  Furthermore, it allows us to target the same model portfolio across all strategy implementations, but vary when and how different portfolios trade depending upon account size and costs.\n",
    "\n",
    "For example, a highly tactical strategy implemented as a pooled vehicle with a large asset base and penny-per-share commissions can likely afford to execute a much higher number of trades than an investor trying to implement the same strategy with \\\\$250,000 and \\\\$7.99 ticket charges.  While implicit and explicit trading costs will create a fixed drag upon strategy returns, failing to implement each trade as dictated by a hypothetical model will create tracking error.  \n",
    "\n",
    "Ultimately, the goal is to minimize the fixed costs while staying within an acceptable distance (e.g. turnover distance or tracking error) of our target portfolio.  Often, this goal is expressed by a portfolio manager with a number of semi-ad-hoc constraints or optimization targets.  For example:\n",
    "\n",
    "- **Asset Paring.** A constraint that specifies the minimum or maximum number of securities that can be held by the portfolio.\n",
    "\n",
    "- **Trade Paring.** A constraint that specifies the minimum or maximum number of trades that may be executed.\n",
    "\n",
    "- **Level Paring.** A constraint that establishes a minimum level threshold for securities (e.g. securities must be at least 1% of the portfolio) or trades (e.g. all trades must be larger than 0.5%).\n",
    "\n",
    "Unfortunately, these constraints often turn the portfolio optimization problem discrete.\n",
    "\n",
    "\n",
    "## Illustrating the problem.\n",
    "\n",
    "Consider the following simplified scenario.  Given our current, drifted portfolio weights $w_{old}$ and a new set of target model weights $w_{target}$, we want to minimize the number of trades we need to execute to bring our portfolio within some acceptable turnover threshold level, $\\theta$.  We can define this as the optimization problem:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}  & \\sum\\limits_{i} 1_{|t_i| \\gt 0}  &\\\\\n",
    "\\text{subject to}& \\sum\\limits_{i} |w_{target, i} - (w_{old, i} + t_i)| \\le 2 * \\theta & \\\\\n",
    "                 & \\sum\\limits_{i} t_i = 0 & \\\\\n",
    "\\text{and}       & t_i \\ge -w_{old,i} &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "Unfortunately, simply trying to throw this problem into an off-the-shelf convex optimizer, as is, will lead to some potentially odd results.  And we have not even introduced any complex paring constraints!\n",
    "\n",
    "The example below demonstrates this numerical issue.  We establish example portfolio and target weights and then run a naive optimization that seeks to minimize the number of trades necessary to bring our holdings within a 5% turnover threshold from the target weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pie(data, title):\n",
    "    n = len(data)\n",
    "    cmap = colors.LinearSegmentedColormap.from_list('custom_blues', [\"#1A2E5C\",\"#284489\",\"#355BB7\",\"#809ADA\",\"#AABCE6\",\"#D5DDF3\",\"#FFFFFF\"][::-1], N=100)\n",
    "\n",
    "    c = [cmap(i / float(n)) for i in range(n)]\n",
    "    \n",
    "    indices = filter(lambda x: x != None, [i if data.ix[i] > 1e-8 else None for i in range(n)])\n",
    "    \n",
    "    data = data.ix[indices]\n",
    "    c = numpy.array(c)[indices]\n",
    "    \n",
    "    fig = pyplot.figure(figsize=(4, 4), dpi = 200)\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    patches, texts, autotexts = ax.pie(data.values, labels = data.index, autopct='%1.1f%%', startangle=90, shadow=False, colors = c, labeldistance = 1.05)\n",
    "    \n",
    "    for pie_wedge in patches:\n",
    "        pie_wedge.set_edgecolor('white')\n",
    "        \n",
    "    for i, autotext in enumerate(autotexts):\n",
    "        # is the background color light or dark?\n",
    "        bg_color = c[i]\n",
    "\n",
    "        r, g, b, a = bg_color\n",
    "\n",
    "        a = 1 - (0.299 * r + 0.587 * g + 0.114 * b)\n",
    "        if a < 0.5:\n",
    "            autotext.set_color('black')\n",
    "        else:\n",
    "            autotext.set_color('white')\n",
    "            \n",
    "    ax.set_title(title, fontname=\"Arial\", fontweight=\"bold\", fontsize=12)\n",
    "    ax.tick_params(axis='both', which='major', pad=8)\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = \"amj bkln bwx cwb emlc hyg idv lqd pbp pcy pff rem shy tlt vnq vnqi vym\".split()\n",
    "w_target = pandas.Series([float(x) for x in \"0.04095391 0.206519656 0 0.061190655 0.049414401 0.105442705 0.038080766 0.07004622 0.045115708 0.08508047 0.115974239 0.076953702 0 0.005797291 0.008955226 0.050530852 0.0399442\".split()], index = tickers)\n",
    "w_old = pandas.Series([float(x) for x in \"0.058788745 0.25 0 0.098132817 0 0.134293993 0.06144967 0.102295438 0.074200473 0 0 0.118318536 0 0 0.04774768 0 0.054772649\".split()], index = tickers)\n",
    "n = len(tickers)\n",
    "\n",
    "w_diff = w_target - w_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a77cf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c272210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Current Weights\n",
    "fig = plot_pie(w_old, \"Current Weights\")\n",
    "pyplot.show(); pyplot.clf(); pyplot.close(fig);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c182310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a77cf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Target Weights\n",
    "fig = plot_pie(w_target, \"Target Weights\")\n",
    "pyplot.show(); pyplot.clf(); pyplot.close(fig);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zgd1LUdxnpLHsNxTO3MOI+qC/mFR/mFmv5NP5iP48xN2/XPrOkwlD6+aErnhXQv+4ZUmPOcuiNF02yO7AWEeggUaRrGIY2JAQzgGOrjGwZr5O4SHz7y2u97my8hzA3X9HeC9nTikrz9M5LzA23eCbGE0+65W6XGnB9RmKe7itmb261UXMbCXCm3ZJYmG2fQ9qUhxaMbBmjiAMUBB1DoeRjvolHcvP+FttfBrajdPrH4i6tlBJGZy8qvL9HJOm1t3vpfA0m59C4f51imiaI6ttHlRSNMSH09sbgf29UvvT3f/JWE/WXSgiihdkvEF1HUKx8SKFcbsubXM2sl6WFnN1nFkxsOY2nU14UkMYKQcumtXMjKLd59QYWAFw98OJ2lAAuyeP8jqmY474VHp9Gnh/2cCarj0b2I/it7hLB9fudw6opodP/++XS7vWrBy/hiIacoeGtEPvJZS1UPLQtEhfnlOa3wLs55XaV+5+P+H8oDRp49mdoub2QXVKOHc/BvhJi2uU00s1GZKUKnhibE3UyIGoYTxuoezu36C1c9mWFHLzdyoG1nyNcxmvEBlFDqkojvJ4dQDwp7RrtyQTNNELOboX7RgmOr4fXchtJEVSHuNWtah5OIYkI783vf2puz9bPWdE0bM79zi8ziiaHNhzPft7GW9gBcDdf004JUI4PW3Z4rtOKhtY0+f/SSmLCFH2aUx2o0RZeTzM+hUrbdfNs08TTmy3EzqSpt/AVyoG1syRxDoCxsvN91E44r6zbg2UnN+zAUNRrGJucg7hgAPN64LtCJ0SNK8Lvl+dXwDc/VQiKw7Am8p6r7SG3C+9vRfYyytpst39ZiKKb5hRH/QXk+qPHsunNwNfKe9IMsWJpV0vEXVjn6mc9wsikh+GbF6XkVUMA5cTRplNaZGCMf2wH0hv62pAQgxUTV4UZU/BpnPKivdhSdfYCY/SkL4mKamzsWZeYLOmi6RUQBcTaedeBHZ29wubzu+Asxva9gyxsIOifugwMdl+yfd8AcYb+HKa2kuJWp4w3sC3KcU8U7dY/AFFhNImFjVisgB1E/UL7UFlM4p7cVzVaJNJitWDCUPF0YShOytAqimD8/sbiag6CIP4HIOSmS1L1DyA5lTBEFExTRxT2t6qxXn9yg4Uqa3GeflVyMcXIp7fKnN9jjCz5SmUGuclB4VxJOXLJ4nf0N515wwgz9JwL5N3eDY2v6rmlGzQXprCi7NMNkQ/yVgD06ZE9gSIheDzDd//Z8amVRfB5un1GSI9YBPfbTrg7rcRNRChOWVwNrL+2d3/VHNcBOUx+/gW5x3Z4tjmpe1W1/gexXw1ivyLhnVImvPzsXkolBdVeiFH96Idw8Rk78dk5bZMu5RsO1Iov06rOT6K6NmdezwH/K7h2LpE9CmEE+Y4A2uJslzdysh6ZsP+sux8QdWRITEq+pVyzbvPmtm42oLu/hN3X8Xd3+vu11WPJ2pr3CVHW09v6+TmPGbNR73SPsvNs5GRVcxFkkNGdhJsSo+a1wWPUAQKVKnVBSZuKm2X9YEbEZnXAE5tclh39/MpHHyHDvVBf9Flf/RKPj2rzlGLsfP6je5+d805UMztQzWvK12wGHjSouuu9DeOZLBbk0g5l1OwNNX4+qs3p8J8vLTdlOKxPOC3qiM2rFzV4v7B2Jpp61Cf7mcxYhJ4fXr/a3c/p+a8TnmyyTiRyP1bW2h9wJlsv1xMGLnnJVIGXwJgZgsQBeYhnByeJVKZrmdm85U8y3IE5b0tlOB7EALVIkTtRYg0Iru2afOgUY6Qblk70N3HeISZ2Y3AW4g+KJONrJdTpKp9GZFyK6fVfjeFgXFMNHGJfxIRyU3tcTP7B2GsWqdV2/uUdUvbK1vr2rJlz+21GOthD9MzR3Ty7DSl9xpU7qh6PlZ4gliE1dWDPo1IgTODWEDMef5TWvKt09tzK4u1t5e2q+nPq/yWwXQ8mEreml7/5K1TvV9LzB0LNhyfSTz741IGJ6/bVdJbRbG2Jqdg/Lu7397ivGsIh4NFa47lPn3Im7PF4O73mtmdwOsm1dLB56o2z/yVpe23Uq+k7oUc3Yt2DBOTvR+TltvSvivT72FlYCczO7CijMqp2O4Grmh1/RFCz+7c484WY01Zbl68jdwMER25MMV8U4c37Jd+peCnwBcIufUQ4AOpbMuFRNmdiWRPecpbp6h9Ir3Wyc3nEI48ixJy8xyHh2TwzUbWK1sozYWYKmZSGH/GpEdNkddZT3J2i7HtLy2uXx6LyvrA8rjWLsPaJbTOmDjoqA/6i0n1Rw/l027mdSjm9qGa1xXJKoYKM1vdzA40sx+b2bXJMPAgIZx+niI6ZZxnYOKRCX5VU8rNUfbghwZDd4myQP5vDeesSRiUMu82s7oopE55vM3xvHBpejYGmbvaHK/tF48aqtl4VzbwrUv8ll4iJt6sfFqEsemHci2fxmgvj1oyX6jsPszr0zoPMsuWtjtdmOb7t3YyDmVD93pp/2WEgjxHWZQjirOh+7YWCvZ7GrzQyuQ2N/1u+5nXlLa/R0QtNv2VI3qXYTzTMUd08+wMOo+1OZ7vZ523/90UtVi3qaQ835Yio0XVQ7OcgrPd/R61uustSamWs5Hub63OTcrCVvd3FsWYVo2oKKcKrjpCiLHk8eOeVielOeC2hsP5Gi37NDHKv4l23vPlPli24Zy72lxjInJ0L9oxTEz2fvRi7s3zy/LABnmnma1Qej9zAjLYqKBnd+7RSr4qy8170VpuPpdCv1InN2cmIj83RSeNxO8jrX0PLu0y4CBCln3YzM4ys51qSviUmajcXPf9T1FEma2fxqnMBkT9PVDkvZgeLiSyfcD4dcH2FIaaVs6XrfSBZSeGsp2kPNe0lKVpNjoNC+qD/qKb/uiFfDrpeT0xlHO7jKxiKDCzZc3sl0StosMJ74t1KOqCPUSkrWs3EKi+Wne0GkSrx+s8KMuUI7KONbN257djlPu2m37JBr61zGyxtJ0jKG9KhtgrqRj4zOxNwHJpX6s0tRDpbssMY1+VU3i0SrtVR+6D+Yga1BAe/AsTwskVKWXI9elY7oMZFGlSW/VBu+ejfE63v8PpYPH2p9SyWM2+6Xg2u3l2Bp3aVL0dkOeRRRkbcZo9NO+nqEGVWbq03e5+T+S3M0qUUztN5Fn9Z9MBd3+AInq1mjL4/en1quSoI5rJ499E+uOJhv25X7vq0xGg3f9eHi+a5qVeyNG9aMcwMdn70Yu5tykl2y4UzkGKxizQszv3aFUDuJdyMzDHsUq0wd2/SpStyNmkMksQSvQzgHvMbKeGS/RKbp4BlL8jy83PUtTzE2KukSLxfpreVtOjZufLeykcbOuuMZlxqDwetpMFmuTooUB90F902R+9kE81r9cgI6sYeMxsESKSK9cmepzwwvsvotjz69x9WXffHSlEp5p2BphyGrpWXkyHuPtuFF43rwe+2E3DRpxu+qVs4MsRktnIehmAuz8B3JD2ZSNgjqB8jqJe6DiS8fzEyu5DzczatHnQKI89rTyQ67iawjM5RxSXDd3ZeSSnNl3fzOYhIsKzV3lTqmCYmOE0PyPtIsL7kTH33t1nTPBv22lr8Vi6eXZGnZ9QLMZ2AjCzV1DU2z29pv5XefG2SJvrN9V3H1UeLW1P5N40pQrOZBkgpwzGzNYGVqocF81kZUM3/ZH7tRd9Osy0+9/LstbDDef0Qo7uRTuGicnej67n3pReO2d72bHkLJIVWn9w91ap80YNPbv9QfnZ36wDuXmpxiuKCePuv3b39xB1U3clFN0PlE55BXC6mW1e9/ku+S1F1oosN88H7JD2/dzd20XLCjFVZLk/p0fFzF5FoaM6s6GuczeUjXbt5OBRkIHVB/3FpPpD8unUISOrGAb2J9KpAJwPLOfuO7j7V9393Er9qCXHf1z0kFe3Ob5yabupPuq17v7ltP0Zikn1c2b2hm4aN8J00y/XE5HgAJukNLW5PlvZKyob+DZIEZTZyHp5m/pKh1LUTfh5el0I+GG6zrDwUGl7+VYnmtmaZvZmM1sc5tSd/lU6nI2rWXCq64Mlgf9B0Qf/onVtyZbPR+qHbNRoVde4Xynf+0FMd9zJs7Oima2XapGPPCnC+7z09t1mtiCwBUX6nDoPzfIzvmKbr3htVw0cMtz9aQrDz8qtzk20m5vOAXJN3u3Sa45ifQE4q6MGjiZZWbriBM5t6o/70+tKDccnco1RYLk2x8v3776Gc3ohR/eiHcPEZO/HpOW2Cjla4BXAemb2WmD1tE9RrGPRs9sfDLrcPBS4+8Pufpq7f5iYG95G4fw8gylwQE+pIfO49BYzWx5YnyLLi8YsMZ1cTiH75HXB9hR2jalwviyXymgnB4+CDKw+6C+66Q/Jp1OAjKxiGNiytP1Jd3+y7iQzex2FB+wwGW/6ibXbHN+4tH1twzlZoYq7308Y4QAWAI6fbMNGnEn3S1psXZTevoMoPJ8jLcoGvkvT68vTOTmitTFNrZmtRRjSIeow7UBRX2994BNt2j1I/KG0vV7jWcFxRGRwuYZqXlS/JRWyXze9LxtPryDq5ELc/1wT95JysfsaljazVgaRNxNpqqD5d9vP/K60vXGrE81sEzObZWaHm9kGrc6di3Ty7OwLXAU8mFJ2i2IBsQgxhuW0wTc31H6+prS9fptr98sz0k9cmV5XM7Olm04ys1UZm45zHClLwgXp7Rbpdfv0eom7/72bho4IV6fXl5vZ6k0nmdlyFPXWquQ+XcrM1mhxjcWB1SbVyuFgvTbOYRuWtpscn3ohR/eiHcPEZO9Ht3Jb5iyKtc1WFHPQi6imdBU9u/1BJ3Lz0mZ2rpl9x8w+2Opc0YyZzTCzHc3sEDM7oHrc3We7+9XA+ygcDBrn9C4pp5HckmLMeoTWmZGEmFJSRF6eN9+R6hPnKOtb3f2G+k92xdWl7Y3bnNtu3TjwqA/6iy77Q/LpFCAjqxgGykq8WgNroiywzj9FbRl1VjCzLeoOpLSw+6W3T1EY7tpxNHBT2t7IzD7aXRNHkm77JRv41gC2Ttu3VBTcV1IY+L5IkVqtdjFmZvMDJ1NElO3r7s8Cn6KIhPqama3Y8D8NGhdR3J89m5RIZvZ6CiVruVZk7pd5gP9JkcZ0jqHb3R+nqG+7GeHxDBNbEO/b4tinS9tnT+Ba/cbPStv7p2eviUOJSLkDCa++fsCBO9L2NsnIPo6UzmvH9PY+4Ja50LZB4FcUkXjbUzgfnNZw/gUUC469K7VA55C8PbeuOzbinJte56O1o8x+LY6VyR64K5rZLihVcKeUa6eNU9yWaNUf55a2W11jD8IhblRZjrGOn3NI806eZ/9FcxmFXsjRvWjHMDHZ+9Gt3AZASq2ZnUW2KrXlYnd/cIL/w6igZ7c/uJIwqEGkEWyVtWN/IkXh/rR3EhENJKfmw4HDgINT5qi6856nqF3cSu/VTVtuBa5Lb8tj1qw2TruimDOgM137ZD83imT5PxuTNqzs7zXXAXel7V2bHEhTxr1N6o4NIeqD/mJS/SH5dGrQAC6GgbtK23vWnWBmn2asAmkiNQjF5DjRzFYp70jK/xOJ2qoAJ060nkcqjv6fpV3fSDX1RGd00y8XE8L/PBR9MaaAevrcH9PbbdLrHe7+3w3tOYhSOgp3/026zgNEPWUIQ2K1XutA4u53USiq3wp8uXpOigL6IUWk/bGlz99PYUDNhotb3b2c0gsKz/7NKZTdEzGyDLSixwAAC99JREFUHmBm76tp0z5A9ky/kQFUarn77ygirdcgfgvjDK1mdhhFGubbKITOaSUpXr6T3i4GzDSzMfVIUg3eIygMUMenz408Kd12XmTsRtzDl2hYeKSxLI87awBHpfs7BzNbEpiFZIk6Tgf+mrYPMrOtqieY2fZM3Mj6S4qaoN9Ir08z1vAnGkhza47O3t3M9qqeY2abUWSVqOM3FF7ku6V5oXqNDYAvddncYeD4GllrXmI+zyUvjnT3Z8Z9sqAXcnQv2jFMdHw/upXbKuTIsDcA70zbSsVWj57daSal/v9eerswcJ6ZLVs9z8zeA3w+vX2h9BkxObJT1FLAd+ocO8xsG4oyWZdXj/eQPGa9q/R9GrPaU65n3EmN4sl+buRIWYiyI/HXKRz2p8TAlyIFj0hvlwbOrFmHL01E/o2EfUV90F902R+ST3vMfNPdACF6wKkUkSlfM7M1CUPAk0Ttop2AatrExcxsnikoyj3q/J2o3XKdmf2ASJ31SuCjhLIa4FY6rCHi7leY2WnAh4mJ9VuEslxMjK76xd0fMbNriXRpi6XddWm6LgXeUnpfmyrYzFYrfdejjI2UhFCk7E6kHd7UzPZw95Ma/rdB4hOEZ9kywBfN7B2EMPgwUZd2L4o6Eie7+xWVz19IpO5t1wflSKM/uXu7OqqPEanUzzGz04nIv3mBbSnShjwF7DrAhruPEKn/Xk6MHeuZ2SnAncRvYweK9DIvAB9Lxrl+4ViijW8nFB63mNlJhDF4OeADFBEEtwBHTkcj+5gfEeNMlnsvbfO7OISIUn0t8btdL81BDwCrAnsTv9UXKRYy/fS8TBvu/nQywv2CcPT4mZmdQUSFzSDS/u4IPE/IaUs0XStd7zkzO5uIknxN2n1BU2kIUcuuhBf4EsAJZrY1Ue/2WWI8+XCrD7v7bDPbmzC0LgIcl4znZ6drvJ0YY+cj5I3aaPsRYDYxLvzBzI4n5pxXEvdmzXTOLRSKojp6IUf3oh3DRDf3o1u5LXMRUedyGeJ38iRFvXBRoGe3f/gakRVnXWLt8Rcz+yFwPbEO2ZiQPbNC+2B3v3Ma2jlMfIuQdZYmorbfamazgLsJw9sGhF4LIuPKV6ewLWcA36bI/naHu181hd83LJTXFkeZ2TEA7n5Wm8/9g3AgXJhwZruZWJ9f7e73TElLB5vTga9QrAt+5+53tDi/W04govy2ADYFbjazEwin0n8nghBeRRGUMAqoD/qLyfaH5NMeM4oPnxg+zgBOSdszCOXdCcBMwus4G1iPA76ftuej8IYVveN0Qpm9BPDZ9P67FIqhG4B3uPu/JnHtz1Kkkd3VzDburqkjRS/65cLK+zoDX3XfuAjK5I1+MkWU5YHVaMzk/LAPReqcb5rZaxhwUjTqRhQ1u9YjvL5PJ9LUZkXdTOL/rzKRPijXZYWJRbHeTUSVzQY+RDiunExhYH0YeI+731T/8f4nRaRsSBglIYTxrwFnAkdRGFgfB7ZvoSidFtJvYiuK/nwtMb+dCXyTwsB6A9FXivAo4e5/ooi0hzYemilC7D0UmTL+gxgzZxGp3F4N/J4iwhjC2CQAd78Y2I5QGM0AdiHmoFOJdNwvEQrEdg4gmZmV90oV3AHufhtRjzjXcduCiL77MYXDWssa6Gn8fycxH0BkS8jX2JOQqw9lbC2/UeP/EmPEYoSsdSZR8iIbh64GNmozPvdCXutFO4aJSd+PHsht+TovMLa+1Tnu/lTT+SOMnt0+IaWlfTfF2mMpIuPB6YSeZWdCl/gS8CV3P3w62jlMpDI8W1HMs28hUgifQThb7kLc88eA9yfZdqra8g/GriGrcpio51cUqbY3JMazM81skeaPzMlalA2xiwHHE+NfbfkAMW4dMKXrgtQ/O1Bkt1iJ+G3OItbjrwJ+C/x0KtvRZ6gP+otJ9Yfk094jI6sYeNx9trt/hPDsu4TwBHsBeAK4GTgJWNvd92OssPj+ud3WUcDddyOiEK8lPGGeIFLVfRxYJyksJnPdB4GDS7uON7MFu2vt6NCDfikb+G5vOP8KwlAHEfl4ac05B1AYhK4ifp917f09Rdq1JYjFxsDj7g6sRtz3S4nx6nngQaJ26Obu/qGk3KhyNYWjAdSkiXL3RylqGMPEjKy4+4mEV/rPU5ueAf5CpBxZ1d2vnMh1+hl3v4W493sD/4eISsy1jf5AeP+t6u7nT1sjW+Du/3T3LYD3EVFo9wHPEcqWK0he7xOIXB5VsvHnaSZQW7j0W/0C8Xw8ToxrNxJRsW9jbHqvR6vXGGXc/QLgjYRz253Eb+3vxDi3kbuf3MHlLqcwyD7KeIcT0QZ3v55wLjyUkI2fIsb684D13b0pzWn5GtcSfXo4EU35HNEfvwa2dPfDpqTxg8OL7r4TYbi+lqgb+SRR33Av4rn/R7uL9EBe60k7hoiu7keXcluZsgNCU03wUUfPbh/h7k+4++aEU80ZhFPmM4Qc9d/AD4C13P1/TV8rhwt3v5qYqw8m1n2PEHqtRwjnvkOJtcrcKGlSHrOUPnICpPHpHcQ68zGi7+4Hlp/Ax/clHH//Rswxj6PUwbW4+18JPRJEJqFZc+E7n3H37YDtif7N0cc3EPLBu9L7kUB90F902R+ST3vIjNmzBzXznxCDhZn9iCIl2wru/rfpbI8QQggxCqQI+nsJL9cz3H2XHl33eMJoPxtYVJ6fYpAxs7wovMzdN57OtgwS/XLf+qUd/UK/3Q8z+wkRhfH/iHWgStYk+q2vhBBgZtcRmVyudve3TXd7hOh3UiminCFmpZRJS8xF1AedI/m0t6gmqxBzj8VK25NJlyuEEEKIztmMMLBCpMJuxMzeQETR3wmc0pQ62szmT9cFuFUGViGEEHWY2SsoSjCcIgWWEKKfMbPVCAMrtJGbhRBCDCaST3uP0gULMfdYLb0+jdIKCiGEEFOOmf0bcGR6exuR2rQVDwAbAB8Fvm1mCzec9y1ghbR9RsM5QgghRphU2uRYYEEihduJ09siIYRoxsyWoCjZ8ziScYUQYuiQfDo1KJJViCnEzHYDVgbWAl6fdl+bCncLIYQQoseY2Y7AF4FngdWBhdKhw9rNv+7+qJmdBexCePHfYmanAbcTC5DXAjtTOE79GfhGz/8JIYQQA0ly7rkceAgwYOl06H+7+93T1jAhhKjBzNYlIlafAN5EkYHtSHdXBjYhhBgCJJ9OPTKyCjG17AmsX3o/G/jmNLVFCCGEGAUeANao7DvH3WdO8PP/CbwGeDuwInBww3lXAB9w92cn00ghhBBDyYPE3PH60r47gM9NS2uEEKI19wFvrOy7BjkRCiHEMCH5dIpRumAhppZ5gKeIFMG/B7Z3919Mb5OEEEKIoeZ24GYikvVu4MvAThP9sLs/BmwC7ACcna7xLDGf3wacA2wDbOLu9/e05UIIIQaaVNPql8Sc8TBwGrChu6tcjBCiH7kPuA54BrgfOAZ4t7s/P62tEkII0TMkn049M2bPVtZSIYQQQgghhBBCCCGEEEIIIYSYKIpkFUIIIYQQQgghhBBCCCGEEEKIDpCRVQghhBBCCCGEEEIIIYQQQgghOkBGViGEEEIIIYQQQgghhBBCCCGE6AAZWYUQQgghhBBCCCGEEEIIIYQQogNkZBVCCCGEEEIIIYQQQgghhBBCiA6QkVUIIYQQQgghhBBCCCGEEEIIITpARlYhhBBCCCGEEEIIIYQQQgghhOgAGVmFEEIIIYQQQgghhBBCCCGEEKIDZGQVQgghhBBCCCGEEEIIIYQQQogOkJFVCCGEEEIIIYQQQgghhBBCCCE6QEZWIYQQQgghhBBCCCGEEEIIIYToABlZhRBCCCGEEEIIIYQQQgghhBCiA2RkFUIIIYQQQgghhBBCCCGEEEKIDpCRVQghhBBCCCGEEEIIIYQQQgghOkBGViGEEEIIIYQQQgghhBBCCCGE6AAZWYUQQgghhBBCCCGEEEIIIYQQogNkZBVCCCGEEEIIIYQQQgghhBBCiA6QkVUIIYQQQgghhBBCCCGEEEIIITpARlYhhBBCCCGEEEIIIYQQQgghhOiA/w/oAEbqlkV5GAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2e2ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130d0090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the difference\n",
    "fig = pyplot.figure(figsize=(11, 8), dpi = 200)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "w_diff.plot(ax = ax, color = \"#4472C4\", kind=\"bar\", rot = 0, legend = None,)\n",
    "ax.set_title(\"Difference Between Target and Current Weights\", fontname=\"Arial\", fontweight=\"bold\")\n",
    "ttl = ax.title\n",
    "ttl.set_position([.5, 1.025])\n",
    "\n",
    "ax.set_ylim(bottom = w_diff.min() - 0.01, top = w_diff.max() + 0.01)\n",
    "ax.set_yticklabels([\"{:,.2%}\".format(y) for y in ax.get_yticks()]);\n",
    "\n",
    "pyplot.show(); pyplot.clf(); pyplot.close(fig);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of trades: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a59bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf8bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = 0.05\n",
    "theta_hat = theta + w_diff.abs().sum() / 2.\n",
    "\n",
    "def _fmin(t):\n",
    "    return numpy.sum(numpy.abs(t) > 1e-8)\n",
    "\n",
    "def _distance_constraint(t):\n",
    "    return theta_hat - numpy.sum(numpy.abs(t)) / 2.\n",
    "\n",
    "def _sums_to_zero(t):\n",
    "    return numpy.sum(numpy.square(t))\n",
    "\n",
    "t0 = w_diff.copy()\n",
    "\n",
    "bounds = [(-w_old[i], 1) for i in range(0, n)]\n",
    "\n",
    "result = scipy.optimize.fmin_slsqp(_fmin, t0, bounds = bounds, eqcons = [_sums_to_zero], ieqcons = [_distance_constraint], disp = -1)\n",
    "\n",
    "result =  pandas.Series(result, index = tickers)\n",
    "\n",
    "print \"\\nNumber of trades: \" + str(_fmin(result))\n",
    "\n",
    "results = pandas.DataFrame({'Difference': w_diff, 'Trades': result})\n",
    "\n",
    "fig = pyplot.figure(figsize=(11, 8), dpi = 200)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "cmap = colors.ListedColormap([\"#4472C4\", \"#ED7D31\"], name='custom_mapping')\n",
    "\n",
    "results.plot(ax = ax, cmap = cmap , kind=\"bar\", rot = 0, legend = \"best\")\n",
    "ax.set_title(\"Difference Between Target and Current Weights & Recommended Trades\", fontname=\"Arial\", fontweight=\"bold\")\n",
    "ttl = ax.title\n",
    "ttl.set_position([.5, 1.025])\n",
    "\n",
    "ax.set_ylim(bottom = results.min().min() - 0.01, top = results.max().max() + 0.01)\n",
    "ax.set_yticklabels([\"{:,.2%}\".format(y) for y in ax.get_yticks()]);\n",
    "\n",
    "pyplot.show(); pyplot.clf(); pyplot.close(fig);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the trades we received are simply $w_{target} - w_{old}$, which was our input to the optimization.\n",
    "\n",
    "What's going on?  Well, many off-the-shelf optimizers, such as Sequential Least Squares Programming (SLSQP), will attempt to solve this problem by first estimating the first derivative of the problem.  To achieve this numerically, small perturbations are made to the input vector and their influence on the resulting output is calculated.\n",
    "\n",
    "In this case, small changes are unlikely to create an influence in the problem we are trying to minimize.  Whether the trade is 5% or 5.0001% will have no influence on the *number* of trades executed.  So the first derivative will appear to be zero and the optimizer will exit.\n",
    "\n",
    "Fortunately, with a bit of elbow grease, we can turn this problem into a mixed integer linear programming problem (\"MILP\"), which have their own set of efficient optimization tools (in this article, we will use the PuLP library for the Python programming language).  A MILP is a category of optimization problems that take the standard form:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & c^{T}x + h^{T}y &\\\\\n",
    "\\text{subject to}  & Ax + Gy \\le b, &\\\\\n",
    "\\text{and}         & x \\in \\mathbb{Z}^{n} &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "Here $b$ is a vector and $A$ and $G$ are matrices.  Don't worry too much about the form.  \n",
    "\n",
    "The important takeaway is that we need: (1) to express our minimization problem as a linear function and (2) express our constraints as a set of linear functions.\n",
    "\n",
    "But first, for us to take advantage of linear programming tools, we need to eliminate our absolute values and indicator functions and somehow transform them into linear constraints.\n",
    "\n",
    "## Turning Absolute Values into a Linear Problem\n",
    "\n",
    "Consider an optimization of the form:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & \\sum\\limits_{i} |x_i| &\\\\\n",
    "\\text{subject to}  & ... &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "To get rid of the absolute value function, we can rewrite the function as a minimization of a new variable, $\\psi$.\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & \\sum\\limits_{i} \\psi_i &\\\\\n",
    "\\text{subject to}  & \\psi_i \\ge x_i &\\\\\n",
    "                   & \\psi_i \\ge -x_i &\\\\\n",
    "\\text{and}         & ... &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "The combination of constraints makes it such that $\\psi_i \\ge |x_i|$.  When $x_i$ is positive, $\\psi_i$ is constrained by the first constraint and when $x_i$ is negative, it is constrained by the latter.  Since the optimization seeks to minimize the sum of each $\\psi_i$, and we know $\\psi_i$ will be positive, the optimizer will will reduce $\\psi_i$ to equal $x_i$, which is it's minimum possible value.\n",
    "\n",
    "Below is an example of this trick in action.  Our goal is to minimize the absolute value of some variables $x_i$.  We apply randomly generated bounds on each $x_i$ to allow the problem to converge on a solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bounds for x: \n",
      "   Left  Right\n",
      "0   -10     -2\n",
      "1    -4      7\n",
      "2    -7      7\n",
      "3    -9      2\n",
      "4    -2      0\n",
      "\n",
      "x variables\n",
      "0   -2.0\n",
      "1    0.0\n",
      "2    0.0\n",
      "3    0.0\n",
      "4    0.0\n",
      "dtype: float64\n",
      "\n",
      "psi Variables (|x|):\n",
      "0    2.0\n",
      "1    0.0\n",
      "2    0.0\n",
      "3    0.0\n",
      "4    0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "lp_problem = LpProblem(\"Absolute Values\", LpMinimize)\n",
    "\n",
    "x_vars = []\n",
    "psi_vars = []\n",
    "\n",
    "bounds = []\n",
    "# generate some arbitrary constraints for our x variables\n",
    "for i in range(5):\n",
    "    left_bound = numpy.random.randint(-10, 10)\n",
    "    right_bound = numpy.random.randint(-10, 10)\n",
    "    bounds.append([min(left_bound, right_bound), max(left_bound, right_bound)])\n",
    "\n",
    "print \"Bounds for x: \"\n",
    "print pandas.DataFrame(bounds, columns = [\"Left\", \"Right\"])\n",
    "\n",
    "for i in range(5):\n",
    "    x_i = LpVariable(\"x_\" + str(i), None, None)\n",
    "    x_vars.append(x_i)\n",
    "    \n",
    "    psi_i = LpVariable(\"psi_i\" + str(i), None, None)\n",
    "    psi_vars.append(psi_i)\n",
    "    \n",
    "lp_problem += lpSum(psi_vars), \"Objective\"\n",
    "\n",
    "for i in range(5):\n",
    "    lp_problem += psi_vars[i] >= -x_vars[i]\n",
    "    lp_problem += psi_vars[i] >= x_vars[i]\n",
    "    \n",
    "    lp_problem += x_vars[i] >= bounds[i][0]\n",
    "    lp_problem += x_vars[i] <= bounds[i][1]\n",
    "    \n",
    "lp_problem.solve()\n",
    "\n",
    "print \"\\nx variables\"\n",
    "print pandas.Series([x_i.value() for x_i in x_vars])\n",
    "\n",
    "print \"\\npsi Variables (|x|):\"\n",
    "print pandas.Series([psi_i.value() for psi_i in psi_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turning the Indicator Function into a Linear Problem\n",
    "\n",
    "Consider an optimization problem of the form:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & \\sum\\limits_{i} 1_{x_i \\ge 0} &\\\\\n",
    "\\text{subject to}  & ... &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "We can re-write this problem by introducing a new variable, $y_i$, and adding a set of linear constraints:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & \\sum\\limits_{i} y_i &\\\\\n",
    "\\text{subject to}  & x_i \\le A*y_i & \\\\\n",
    "                   & y_i \\ge 0 & \\\\\n",
    "                   & y_i \\le 1 & \\\\\n",
    "                   & y_i \\in \\mathbb{Z} & \\\\\n",
    "\\text{and}         & ... &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "Note that the last three constraints, when taken together, tell us that $y_i \\in \\{0, 1\\}$.  The new variable $A$ should be a large constant, bigger than any value of $x_i$.  Let's assume $A = max(x) + 1$.\n",
    "\n",
    "Let's first consider what happens when $x_i \\le 0$.  In such a case, $y_i$ can be set to zero without violating any constraints.  When $x_i$ is positive, however, for $x_i \\le A*y_i$ to be true, it must be the case that $y_i = 1$.\n",
    "\n",
    "What prevennts $y_i$ from equally 1 in the case where $x_i \\le 0$ is the goal of minimizing the sum of $y_i$, which will force $y_i$ to be 0 whenever possible.\n",
    "\n",
    "Below is a sample problem demonstrating this trick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bounds for x: \n",
      "   Left  Right\n",
      "0    -2      6\n",
      "1     4      5\n",
      "2    -8      4\n",
      "3    -4      9\n",
      "4     7      7\n",
      "\n",
      "x variables\n",
      "0   -2.0\n",
      "1    4.0\n",
      "2   -8.0\n",
      "3   -4.0\n",
      "4    7.0\n",
      "dtype: float64\n",
      "\n",
      "y Variables (Indicator):\n",
      "0    0.0\n",
      "1    1.0\n",
      "2    0.0\n",
      "3    0.0\n",
      "4    1.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "lp_problem = LpProblem(\"Absolute Values\", LpMinimize)\n",
    "\n",
    "x_vars = []\n",
    "y_vars = []\n",
    "\n",
    "bounds = []\n",
    "# generate some arbitrary constraints for our x variables\n",
    "for i in range(5):\n",
    "    left_bound = numpy.random.randint(-10, 10)\n",
    "    right_bound = numpy.random.randint(-10, 10)\n",
    "    bounds.append([min(left_bound, right_bound), max(left_bound, right_bound)])\n",
    "\n",
    "A = 11    \n",
    "\n",
    "print \"Bounds for x: \"\n",
    "print pandas.DataFrame(bounds, columns = [\"Left\", \"Right\"])\n",
    "\n",
    "for i in range(5):\n",
    "    x_i = LpVariable(\"x_\" + str(i), None, None)\n",
    "    x_vars.append(x_i)\n",
    "    \n",
    "    y_i = LpVariable(\"ind_\" + str(i), 0, 1, LpInteger)\n",
    "    y_vars.append(y_i)\n",
    "    \n",
    "lp_problem += lpSum(y_vars), \"Objective\"\n",
    "\n",
    "for i in range(5):\n",
    "    lp_problem += x_vars[i] >= bounds[i][0]\n",
    "    lp_problem += x_vars[i] <= bounds[i][1]\n",
    "    \n",
    "    lp_problem += x_vars[i] <= A * y_vars[i]\n",
    "    \n",
    "lp_problem.solve()\n",
    "\n",
    "print \"\\nx variables\"\n",
    "print pandas.Series([x_i.value() for x_i in x_vars])\n",
    "\n",
    "print \"\\ny Variables (Indicator):\"\n",
    "print pandas.Series([y_i.value() for y_i in y_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tying the Tricks Together\n",
    "\n",
    "A problem arises when we try to tie these two tricks together, as both tricks rely upon the minimization function itself.  The $\\psi_i$ are dragged to the absolute value of $x_i$ because we minimize over then.  Similarly, $y_i$ is dragged to zero when \n",
    "the indicator should be off because we are minimizing over it.\n",
    "\n",
    "What happens, however, if we want to solve a problem of the form:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & \\sum\\limits_{i} 1_{|x_i| \\gt 0} &\\\\\n",
    "\\text{subject to}  & ... &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "One way of trying to solve this problem is by using our tricks and then combining the objectives into a single sum.\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}    & \\sum\\limits_{i} (y_i + \\psi_i) &\\\\\n",
    "\\text{subject to}  & \\psi_i \\ge x_i &\\\\\n",
    "                   & \\psi_i \\ge -x_i &\\\\\n",
    "                   & \\psi_i \\le A*y_i & \\\\\n",
    "                   & y_i \\ge 0 & \\\\\n",
    "                   & y_i \\le 1 & \\\\\n",
    "                   & y_i \\in \\mathbb{Z} & \\\\\n",
    "\\text{and}         & ... &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "By minimizing over the sum of both variables, $\\psi_i$ is forced towards $|x_i|$ and $y_i$ is forced to zero when $\\psi_i = 0$.  \n",
    "\n",
    "Below is an example demonstrating this solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bounds for x: \n",
      "   Left  Right\n",
      "0   -10     -7\n",
      "1    -8      2\n",
      "2    -6     -5\n",
      "3   -10      2\n",
      "4    -7     -3\n",
      "\n",
      "x variables\n",
      "0   -7.0\n",
      "1    0.0\n",
      "2   -5.0\n",
      "3    0.0\n",
      "4   -3.0\n",
      "dtype: float64\n",
      "\n",
      "psi Variables (|x|):\n",
      "0    7.0\n",
      "1    0.0\n",
      "2    5.0\n",
      "3    0.0\n",
      "4    3.0\n",
      "dtype: float64\n",
      "\n",
      "y Variables (Indicator):\n",
      "0    1.0\n",
      "1    0.0\n",
      "2    1.0\n",
      "3    0.0\n",
      "4    1.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "lp_problem = LpProblem(\"Absolute Values\", LpMinimize)\n",
    "\n",
    "x_vars = []\n",
    "psi_vars = []\n",
    "y_vars = []\n",
    "\n",
    "bounds = []\n",
    "# generate some arbitrary constraints for our x variables\n",
    "for i in range(5):\n",
    "    left_bound = numpy.random.randint(-10, 10)\n",
    "    right_bound = numpy.random.randint(-10, 10)\n",
    "    bounds.append([min(left_bound, right_bound), max(left_bound, right_bound)])\n",
    "\n",
    "A = 11    \n",
    "\n",
    "print \"Bounds for x: \"\n",
    "print pandas.DataFrame(bounds, columns = [\"Left\", \"Right\"])\n",
    "\n",
    "for i in range(5):\n",
    "    x_i = LpVariable(\"x_\" + str(i), None, None)\n",
    "    x_vars.append(x_i)\n",
    "    \n",
    "    psi_i = LpVariable(\"psi_i\" + str(i), None, None)\n",
    "    psi_vars.append(psi_i)\n",
    "    \n",
    "    y_i = LpVariable(\"ind_\" + str(i), 0, 1, LpInteger)\n",
    "    y_vars.append(y_i)\n",
    "    \n",
    "    \n",
    "lp_problem += lpSum(y_vars) + lpSum(psi_vars), \"Objective\"\n",
    "\n",
    "for i in range(5):\n",
    "    lp_problem += x_vars[i] >= bounds[i][0]\n",
    "    lp_problem += x_vars[i] <= bounds[i][1]\n",
    "    \n",
    "for i in range(5):\n",
    "    lp_problem += psi_vars[i] >= -x_vars[i]\n",
    "    lp_problem += psi_vars[i] >= x_vars[i]\n",
    "    \n",
    "    lp_problem += psi_vars[i] <= A * y_vars[i]\n",
    "    \n",
    "lp_problem.solve()\n",
    "\n",
    "print \"\\nx variables\"\n",
    "print pandas.Series([x_i.value() for x_i in x_vars])\n",
    "\n",
    "print \"\\npsi Variables (|x|):\"\n",
    "print pandas.Series([psi_i.value() for psi_i in psi_vars])\n",
    "\n",
    "print \"\\ny Variables (Indicator):\"\n",
    "print pandas.Series([y_i.value() for y_i in y_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Trade Minimization Model\n",
    "\n",
    "Returning to our original problem,\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}  & \\sum\\limits_{i} 1_{|t_i| \\gt 0}  &\\\\\n",
    "\\text{subject to}& \\sum\\limits_{i} |w_{target, i} - (w_{old, i} + t_i)| \\le 2 * \\theta & \\\\\n",
    "                 & \\sum\\limits_{i} t_i = 0 & \\\\\n",
    "\\text{and}       & t_i \\ge -w_{old,i} &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "We can now use the tricks we have established above to re-write this problem as:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}  & \\sum\\limits_{i} (\\phi_i + \\psi_i + y_i)  &\\\\\n",
    "\\text{subject to}& \\psi_i \\ge -t_i & \\\\\n",
    "                 & \\psi_i \\ge t_i & \\\\\n",
    "                 & \\psi_i \\le A * y_i & \\\\\n",
    "                 & \\phi_i \\ge (w_{target,i} - (w_{old,i} + t_i)) & \\\\\n",
    "                 & \\phi_i \\ge -(w_{target,i} - (w_{old,i} + t_i)) & \\\\\n",
    "                 & \\sum\\limits_{i} \\phi_i \\le 2 * \\theta & \\\\\n",
    "                 & \\sum\\limits_{i} t_i = 0 & \\\\\n",
    "\\text{and}       & t_i \\ge -w_{old,i} &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "While there are a large number of constraints present, in reality there are just a few key steps going on.  First, our key variable in question is $t_i$.  We then use our absolute value trick to create $\\psi_i = |t_i|$.  Next, we use the indicator function trick to create $y_i$, which tells us whether each position is traded or not.  Ultimately, this is the variable we are trying to minimize.\n",
    "\n",
    "Next, we have to deal with our turnover constraint.  Again, we invoke the absolute value trick to create $\\phi_i$, and replace our turnover constraint as a sum of $\\phi$'s.  \n",
    "\n",
    "Et voila?\n",
    "\n",
    "As it turns out, not quite.\n",
    "\n",
    "Consider a simple two-asset portfolio.  The current weights are [0.25, 0.75] and we want to get these weights within 0.05 of [0.5, 0.5] (using the L^1 norm – i.e. the sum of absolute values – as our definition of \"distance\").\n",
    "\n",
    "Let's consider the solution [0.475, 0.525].  At this point, $\\phi = [0.025, 0.025]$ and $\\psi = [0.225, 0.225]$.  Is this solution \"better\" than [0.5, 0.5]?  At [0.5, 0.5], $\\phi = [0.0, 0.0]$ and $\\psi = [0.25, 0.25]$.  From the optimizer's viewpoint, these are equivalent solutions.  Within this region, there are infinite possible solutions.\n",
    "\n",
    "Yet if we are willing to let some of our tricks \"fail,\" we can find a solution.  If we want to get as close as possible, we effectively want to minimize the sum of $\\psi$'s.  The infinite solutions problem arises when we simultaneously try to minimize the sum of $\\psi$'s and $\\phi$'s, which offset each other.\n",
    "\n",
    "Do we actually need the values of $\\psi$ to be correct? As it turns out: no.  All we really need is that $\\psi_i$ is positive when $t_i$ is non-zero, which will then force $y_i$ to be 1.  By minimizing on $y_i$, $\\psi_i$ will still be forced to 0 when $t_i = 0$.\n",
    "\n",
    "So if we simply remove $\\psi_i$ from the minimization, we will end up reducing the number of trades as far as possible and then reducing the distance to the target model as much as possible given that trade level.\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll@{}ll}\n",
    "\\text{minimize}  & \\sum\\limits_{i} (\\phi_i + y_i)  &\\\\\n",
    "\\text{subject to}& \\psi_i \\ge -t_i & \\\\\n",
    "                 & \\psi_i \\ge t_i & \\\\\n",
    "                 & \\psi_i \\le A * y_i & \\\\\n",
    "                 & \\phi_i \\ge (w_{target,i} - (w_{old,i} + t_i)) & \\\\\n",
    "                 & \\phi_i \\ge -(w_{target,i} - (w_{old,i} + t_i)) & \\\\\n",
    "                 & \\sum\\limits_{i} \\phi_i \\le 2 * \\theta & \\\\\n",
    "                 & \\sum\\limits_{i} t_i = 0 & \\\\\n",
    "\\text{and}       & t_i \\ge -w_{old,i} &\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "As a side note, because the sum of $\\phi$'s will at most equal 2 and the sum of $y$'s can equal the number of assets in the portfolio, the optimizer will get more minimization bang for its buck by focusing on reducing the number of trades first before reducing the distance to the target model.  This priority can be adjusted by mulitplying $\\phi_i$ by a sufficiently large scaler in our objective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trades: 12.0\n",
      "Turnover distance: 0.032663284500000014\n",
      "Sum of y, psi, and phi: 36.065326568 (12.0, 24.0, 0.065326568)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c20ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113097990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = 0.05\n",
    "\n",
    "trading_model = LpProblem(\"Trade Minimization Problem\", LpMinimize)\n",
    "\n",
    "t_vars = []\n",
    "psi_vars = []\n",
    "phi_vars = []\n",
    "y_vars = []\n",
    "\n",
    "A = 2\n",
    "    \n",
    "for i in range(n):\n",
    "    t = LpVariable(\"t_\" + str(i), -w_old[i], 1 - w_old[i]) \n",
    "    t_vars.append(t)\n",
    "    \n",
    "    psi = LpVariable(\"psi_\" + str(i), None, None)\n",
    "    psi_vars.append(psi)\n",
    "\n",
    "    phi = LpVariable(\"phi_\" + str(i), None, None)\n",
    "    phi_vars.append(phi)\n",
    "    \n",
    "    y = LpVariable(\"y_\" + str(i), 0, 1, LpInteger) #set y in {0, 1}\n",
    "    y_vars.append(y)\n",
    "\n",
    "    \n",
    "# add our objective to minimize y, which is the number of trades\n",
    "trading_model += lpSum(phi_vars) + lpSum(y_vars), \"Objective\"\n",
    "            \n",
    "for i in range(n):\n",
    "    trading_model += psi_vars[i] >= -t_vars[i]\n",
    "    trading_model += psi_vars[i] >= t_vars[i]\n",
    "    trading_model += psi_vars[i] <= A * y_vars[i]\n",
    "    \n",
    "for i in range(n):\n",
    "    trading_model += phi_vars[i] >= -(w_diff[i] - t_vars[i])\n",
    "    trading_model += phi_vars[i] >= (w_diff[i] - t_vars[i])\n",
    "    \n",
    "# Make sure our trades sum to zero\n",
    "trading_model += (lpSum(t_vars) == 0)\n",
    "\n",
    "# Set our trade bounds\n",
    "trading_model += (lpSum(phi_vars) / 2. <= theta)\n",
    "\n",
    "trading_model.solve()\n",
    "\n",
    "results = pandas.Series([t_i.value() for t_i in t_vars], index = tickers)\n",
    "\n",
    "print \"Number of trades: \" + str(sum([y_i.value() for y_i in y_vars]))\n",
    "\n",
    "print \"Turnover distance: \" + str((w_target - (w_old + results)).abs().sum() / 2.)\n",
    "\n",
    "psi = pandas.Series([psi_i.value() for psi_i in psi_vars], index = tickers)\n",
    "y = pandas.Series([y_i.value() for y_i in y_vars], index = tickers)\n",
    "phi = pandas.Series([phi_i.value() for phi_i in phi_vars], index = tickers)\n",
    "\n",
    "print \"Sum of y, psi, and phi: \" + str(psi.sum() + y.sum() + phi.sum()) + \\\n",
    "                              \" (\" + \", \".join(map(str, [y.sum(), psi.sum(), phi.sum()])) + \")\"\n",
    "\n",
    "results = pandas.DataFrame({'Difference': w_diff, 'Trades': results})\n",
    "\n",
    "fig = pyplot.figure(figsize=(11, 8), dpi = 200)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "cmap = colors.ListedColormap([\"#4472C4\", \"#ED7D31\"], name='custom_mapping')\n",
    "\n",
    "results.plot(ax = ax, cmap = cmap , kind=\"bar\", rot = 0, legend = \"best\")\n",
    "ax.set_title(\"Difference Between Target and Current Weights & Recommended Trades\", fontname=\"Arial\", fontweight=\"bold\")\n",
    "ttl = ax.title\n",
    "ttl.set_position([.5, 1.025])\n",
    "\n",
    "ax.set_ylim(bottom = results.min().min() - 0.01, top = results.max().max() + 0.01)\n",
    "ax.set_yticklabels([\"{:,.2%}\".format(y) for y in ax.get_yticks()]);\n",
    "\n",
    "pyplot.show(); pyplot.clf(); pyplot.close(fig);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Possible Extensions & Limitations\n",
    "\n",
    "There are a number of extensions that can be made to this model, including:\n",
    "\n",
    "- **Accounting for trading costs.**  Instead of minimizing the number of trades, we could minimize the total cost of trading by multiplying each trade against an estimate of cost (including bid/ask spread, commission, and impact).  \n",
    "\n",
    "- **Forcing accuracy.** There may be positions for which more greater drift can be permitted and others where drift is less desired.  This can be achieved by adding specific constraints to our $\\phi_i$ variables.\n",
    "\n",
    "Unfortunately, there are also a number of limitations.  The first set is due to the fact we are formulating our optimization as a linear program.  This means that quadratic constraints or objectives, such as tracking error constraints, are forbidden.  The second set is due to the complexity of the optimization problem.  While the problem may be technincally solvable, problems containing a large number of securities and constraints may be time infeasible.\n",
    "\n",
    "### Non-Linear Constraints\n",
    "In the former case, we can choose to move to a mixed integer quadratic programming framework.  Or, we can also employ multi-step heuristic methods to find feasible, though potentially non-optimal, solutions.\n",
    "\n",
    "For example, consider the case where we wish our optimized portfolio to fall within a certain tracking error constraint of our target portfolio.  Prior to optimization, the marginal contribution to tracking error can be calculated for each asset and the total current tracking error can be calculated.  A constraint can then be added such that the current tracking error minus the sum of weighted marginal contributions must be less than the tracking error target.  After the optimization is complete, we can determine whether our solution meets the tracking error constraint.\n",
    "\n",
    "If it does not, we can use our solution as our new $w_{old}$, re-calculate our tracking error and marginal contribution figures, and re-optimize.  This iterative approach approximates a gradient descent approach.\n",
    "\n",
    "In the example below, we introduce a covariance matrix and seek to target a solution whose tracking error is less than 0.25%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "covariance_matrix = [[ 3.62767735e-02,  2.18757921e-03,  2.88389154e-05,\n",
    "         7.34489308e-03,  1.96701876e-03,  4.42465667e-03,\n",
    "         1.12579361e-02,  1.65860525e-03,  5.64030644e-03,\n",
    "         2.76645571e-03,  3.63015800e-04,  3.74241173e-03,\n",
    "        -1.35199744e-04, -2.19000672e-03,  6.80914121e-03,\n",
    "         8.41701096e-03,  1.07504229e-02],\n",
    "       [ 2.18757921e-03,  5.40346050e-04,  5.52196510e-04,\n",
    "         9.03853792e-04,  1.26047511e-03,  6.54178355e-04,\n",
    "         1.72005989e-03,  3.60920296e-04,  4.32241813e-04,\n",
    "         6.55664695e-04,  1.60990263e-04,  6.64729334e-04,\n",
    "        -1.34505970e-05, -3.61651337e-04,  6.56663689e-04,\n",
    "         1.55184724e-03,  1.06451898e-03],\n",
    "       [ 2.88389154e-05,  5.52196510e-04,  4.73857357e-03,\n",
    "         1.55701811e-03,  6.22138578e-03,  8.13498400e-04,\n",
    "         3.36654245e-03,  1.54941008e-03,  6.19861236e-05,\n",
    "         2.93028853e-03,  8.70115005e-04,  4.90113403e-04,\n",
    "         1.22200026e-04,  2.34074752e-03,  1.39606650e-03,\n",
    "         5.31970717e-03,  8.86435533e-04],\n",
    "       [ 7.34489308e-03,  9.03853792e-04,  1.55701811e-03,\n",
    "         4.70643696e-03,  2.36059044e-03,  1.45119740e-03,\n",
    "         4.46141908e-03,  8.06488179e-04,  2.09341490e-03,\n",
    "         1.54107719e-03,  6.99000273e-04,  1.31596059e-03,\n",
    "        -2.52039718e-05, -5.18390335e-04,  2.41334278e-03,\n",
    "         5.14806453e-03,  3.76769305e-03],\n",
    "       [ 1.96701876e-03,  1.26047511e-03,  6.22138578e-03,\n",
    "         2.36059044e-03,  1.26644146e-02,  2.00358907e-03,\n",
    "         8.04023724e-03,  2.30076077e-03,  5.70077091e-04,\n",
    "         5.65049374e-03,  9.76571021e-04,  1.85279450e-03,\n",
    "         2.56652171e-05,  1.19266940e-03,  5.84713900e-04,\n",
    "         9.29778319e-03,  2.84300900e-03],\n",
    "       [ 4.42465667e-03,  6.54178355e-04,  8.13498400e-04,\n",
    "         1.45119740e-03,  2.00358907e-03,  1.52522064e-03,\n",
    "         2.91651452e-03,  8.70569737e-04,  1.09752760e-03,\n",
    "         1.66762294e-03,  5.36854007e-04,  1.75343988e-03,\n",
    "         1.29714019e-05,  9.11071171e-05,  1.68043070e-03,\n",
    "         2.42628131e-03,  1.90713194e-03],\n",
    "       [ 1.12579361e-02,  1.72005989e-03,  3.36654245e-03,\n",
    "         4.46141908e-03,  8.04023724e-03,  2.91651452e-03,\n",
    "         1.19931947e-02,  1.61222907e-03,  2.75699780e-03,\n",
    "         4.16113427e-03,  6.25609018e-04,  2.91008175e-03,\n",
    "        -1.92908806e-04, -1.57151126e-03,  3.25855486e-03,\n",
    "         1.06990068e-02,  6.05007409e-03],\n",
    "       [ 1.65860525e-03,  3.60920296e-04,  1.54941008e-03,\n",
    "         8.06488179e-04,  2.30076077e-03,  8.70569737e-04,\n",
    "         1.61222907e-03,  1.90797844e-03,  6.04486114e-04,\n",
    "         2.47501106e-03,  8.57227194e-04,  2.42587888e-03,\n",
    "         1.85623409e-04,  2.91479004e-03,  3.33754926e-03,\n",
    "         2.61280946e-03,  1.16461350e-03],\n",
    "       [ 5.64030644e-03,  4.32241813e-04,  6.19861236e-05,\n",
    "         2.09341490e-03,  5.70077091e-04,  1.09752760e-03,\n",
    "         2.75699780e-03,  6.04486114e-04,  2.53455649e-03,\n",
    "         9.66091919e-04,  3.91053383e-04,  1.83120456e-03,\n",
    "        -4.91230334e-05, -5.60316891e-04,  2.28627416e-03,\n",
    "         2.40776877e-03,  3.15907037e-03],\n",
    "       [ 2.76645571e-03,  6.55664695e-04,  2.93028853e-03,\n",
    "         1.54107719e-03,  5.65049374e-03,  1.66762294e-03,\n",
    "         4.16113427e-03,  2.47501106e-03,  9.66091919e-04,\n",
    "         4.81734656e-03,  1.14396535e-03,  3.23711266e-03,\n",
    "         1.69157413e-04,  3.03445975e-03,  3.09323955e-03,\n",
    "         5.27456576e-03,  2.11317800e-03],\n",
    "       [ 3.63015800e-04,  1.60990263e-04,  8.70115005e-04,\n",
    "         6.99000273e-04,  9.76571021e-04,  5.36854007e-04,\n",
    "         6.25609018e-04,  8.57227194e-04,  3.91053383e-04,\n",
    "         1.14396535e-03,  1.39905835e-03,  2.01826986e-03,\n",
    "         1.04811491e-04,  1.67653296e-03,  2.59598793e-03,\n",
    "         1.01532651e-03,  2.60716967e-04],\n",
    "       [ 3.74241173e-03,  6.64729334e-04,  4.90113403e-04,\n",
    "         1.31596059e-03,  1.85279450e-03,  1.75343988e-03,\n",
    "         2.91008175e-03,  2.42587888e-03,  1.83120456e-03,\n",
    "         3.23711266e-03,  2.01826986e-03,  1.16861730e-02,\n",
    "         2.24795908e-04,  3.46679680e-03,  8.38606091e-03,\n",
    "         3.65575720e-03,  1.80220367e-03],\n",
    "       [-1.35199744e-04, -1.34505970e-05,  1.22200026e-04,\n",
    "        -2.52039718e-05,  2.56652171e-05,  1.29714019e-05,\n",
    "        -1.92908806e-04,  1.85623409e-04, -4.91230334e-05,\n",
    "         1.69157413e-04,  1.04811491e-04,  2.24795908e-04,\n",
    "         5.49990619e-05,  5.01897963e-04,  3.74856789e-04,\n",
    "        -8.63113243e-06, -1.51400879e-04],\n",
    "       [-2.19000672e-03, -3.61651337e-04,  2.34074752e-03,\n",
    "        -5.18390335e-04,  1.19266940e-03,  9.11071171e-05,\n",
    "        -1.57151126e-03,  2.91479004e-03, -5.60316891e-04,\n",
    "         3.03445975e-03,  1.67653296e-03,  3.46679680e-03,\n",
    "         5.01897963e-04,  8.74709395e-03,  6.37760454e-03,\n",
    "         1.74349274e-03, -1.26348683e-03],\n",
    "       [ 6.80914121e-03,  6.56663689e-04,  1.39606650e-03,\n",
    "         2.41334278e-03,  5.84713900e-04,  1.68043070e-03,\n",
    "         3.25855486e-03,  3.33754926e-03,  2.28627416e-03,\n",
    "         3.09323955e-03,  2.59598793e-03,  8.38606091e-03,\n",
    "         3.74856789e-04,  6.37760454e-03,  1.55034038e-02,\n",
    "         5.20888498e-03,  4.17926704e-03],\n",
    "       [ 8.41701096e-03,  1.55184724e-03,  5.31970717e-03,\n",
    "         5.14806453e-03,  9.29778319e-03,  2.42628131e-03,\n",
    "         1.06990068e-02,  2.61280946e-03,  2.40776877e-03,\n",
    "         5.27456576e-03,  1.01532651e-03,  3.65575720e-03,\n",
    "        -8.63113243e-06,  1.74349274e-03,  5.20888498e-03,\n",
    "         1.35424275e-02,  5.49882762e-03],\n",
    "       [ 1.07504229e-02,  1.06451898e-03,  8.86435533e-04,\n",
    "         3.76769305e-03,  2.84300900e-03,  1.90713194e-03,\n",
    "         6.05007409e-03,  1.16461350e-03,  3.15907037e-03,\n",
    "         2.11317800e-03,  2.60716967e-04,  1.80220367e-03,\n",
    "        -1.51400879e-04, -1.26348683e-03,  4.17926704e-03,\n",
    "         5.49882762e-03,  7.08734925e-03]]\n",
    "\n",
    "covariance_matrix = pandas.DataFrame(covariance_matrix, index = tickers, columns = tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracking error: 0.0016583319880074485\n",
      "Number of trades: 13\n",
      "Turnover distance: 0.01624453350000001\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf27e90>"
      ]
     },
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    },
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     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1071d1810>"
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   ],
   "source": [
    "theta = 0.05\n",
    "target_te = 0.0025\n",
    "\n",
    "w_old_prime = w_old.copy()\n",
    "\n",
    "# calculate the difference from the target portfolio\n",
    "# and use this difference to estimate tracking error \n",
    "# and marginal contribution to tracking error (\"mcte\")\n",
    "z = (w_old_prime - w_target)\n",
    "te = numpy.sqrt(z.dot(covariance_matrix).dot(z))\n",
    "mcte = (z.dot(covariance_matrix)) / te\n",
    "\n",
    "while True:\n",
    "    w_diff_prime = w_target - w_old_prime\n",
    "\n",
    "    trading_model = LpProblem(\"Trade Minimization Problem\", LpMinimize)\n",
    "\n",
    "    t_vars = []\n",
    "    psi_vars = []\n",
    "    phi_vars = []\n",
    "    y_vars = []\n",
    "\n",
    "    A = 2\n",
    "\n",
    "    for i in range(n):\n",
    "        t = LpVariable(\"t_\" + str(i), -w_old_prime[i], 1 - w_old_prime[i]) \n",
    "        t_vars.append(t)\n",
    "\n",
    "        psi = LpVariable(\"psi_\" + str(i), None, None)\n",
    "        psi_vars.append(psi)\n",
    "\n",
    "        phi = LpVariable(\"phi_\" + str(i), None, None)\n",
    "        phi_vars.append(phi)\n",
    "\n",
    "        y = LpVariable(\"y_\" + str(i), 0, 1, LpInteger) #set y in {0, 1}\n",
    "        y_vars.append(y)\n",
    "\n",
    "\n",
    "    # add our objective to minimize y, which is the number of trades\n",
    "    trading_model += lpSum(phi_vars) + lpSum(y_vars), \"Objective\"\n",
    "\n",
    "    for i in range(n):\n",
    "        trading_model += psi_vars[i] >= -t_vars[i]\n",
    "        trading_model += psi_vars[i] >= t_vars[i]\n",
    "        trading_model += psi_vars[i] <= A * y_vars[i]\n",
    "\n",
    "    for i in range(n):\n",
    "        trading_model += phi_vars[i] >= -(w_diff_prime[i] - t_vars[i])\n",
    "        trading_model += phi_vars[i] >= (w_diff_prime[i] - t_vars[i])\n",
    "\n",
    "    # Make sure our trades sum to zero\n",
    "    trading_model += (lpSum(t_vars) == 0)\n",
    "    \n",
    "    # Set tracking error limit\n",
    "    #    delta(te) = mcte * delta(z) \n",
    "    #              = mcte * ((w_old_prime + t - w_target) - (w_old_prime - w_target)) \n",
    "    #              = mcte * t\n",
    "    #    te + delta(te) <= target_te\n",
    "    #    ==> delta(te) <= target_te - te\n",
    "    trading_model += (lpSum([mcte.ix[i] * t_vars[i] for i in range(n)]) \\\n",
    "                              <= (target_te - te))\n",
    "\n",
    "    # Set our trade bounds\n",
    "    trading_model += (lpSum(phi_vars) / 2. <= theta)\n",
    "\n",
    "    trading_model.solve()\n",
    "    \n",
    "    # update our w_old' with the current trades\n",
    "    results = pandas.Series([t_i.value() for t_i in t_vars], index = tickers)\n",
    "    w_old_prime = (w_old_prime + results)\n",
    "    \n",
    "    z = (w_old_prime - w_target)\n",
    "    te = numpy.sqrt(z.dot(covariance_matrix).dot(z))\n",
    "    mcte = (z.dot(covariance_matrix)) / te\n",
    "    \n",
    "    if te < target_te:\n",
    "        break\n",
    "        \n",
    "print \"Tracking error: \" + str(te) \n",
    "\n",
    "# since w_old' is an iterative update,\n",
    "# the current trades only reflect the updates from\n",
    "# the prior w_old'.  Thus, we need to calculate\n",
    "# the trades by hand\n",
    "results = (w_old_prime - w_old)\n",
    "n_trades = (results.abs() > 1e-8).astype(int).sum()\n",
    "\n",
    "print \"Number of trades: \" + str(n_trades)\n",
    "\n",
    "print \"Turnover distance: \" + str((w_target - (w_old + results)).abs().sum() / 2.)\n",
    "\n",
    "results = pandas.DataFrame({'Difference': w_diff, 'Trades': results})\n",
    "\n",
    "fig = pyplot.figure(figsize=(11, 8), dpi = 200)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "cmap = colors.ListedColormap([\"#4472C4\", \"#ED7D31\"], name='custom_mapping')\n",
    "\n",
    "results.plot(ax = ax, cmap = cmap , kind=\"bar\", rot = 0, legend = \"best\")\n",
    "ax.set_title(\"Difference Between Target and Current Weights & Recommended Trades\", fontname=\"Arial\", fontweight=\"bold\")\n",
    "ttl = ax.title\n",
    "ttl.set_position([.5, 1.025])\n",
    "\n",
    "ax.set_ylim(bottom = results.min().min() - 0.01, top = results.max().max() + 0.01)\n",
    "ax.set_yticklabels([\"{:,.2%}\".format(y) for y in ax.get_yticks()]);\n",
    "\n",
    "pyplot.show(); pyplot.clf(); pyplot.close(fig);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Constraints\n",
    "\n",
    "For time feasibility, heuristic approaches can be employed in effort to rapidly converge upon a \"close enough\" solution.  For example, [Rong and Liu (2011)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1823627) discuss \"build-up\" and \"pare-down\" heuristics.  \n",
    "\n",
    "The basic algorithm of \"pare-down\" is:\n",
    "\n",
    "1. Start with a trade list that includes every security\n",
    "2. Solve the optimization problem in its unconstrained format, allowing trades to occur only for securities in the trade list.\n",
    "2. If the solution meets the necessary constraints (e.g. maximum number of trades, trade size thresholds, tracking error constraints, etc), terminate the optimization.\n",
    "3. Eliminate from the trade list a subset of securities based upon some measure of trade utility (e.g. violation of constraints, contribution to tracking error, etc).\n",
    "4. Go to step 2.\n",
    "\n",
    "The basic algorithm of \"build-up\" is:\n",
    "\n",
    "1. Start with an empty trade list\n",
    "2. Add a subset of securities to the trade list based upon some measure of trade utility\n",
    "3. Solve the optimization problem in its unconstrained format, allowing trades to occur only for securities in the trade list.\n",
    "4. If the solution meets the necessary constraints (e.g. maximum number of trades, trade size thresholds, tracking error constraints, etc), terminate the optimization.\n",
    "5. Go to step 2.\n",
    "\n",
    "These two heuristics can even be combined in an integrated fashion.  For example, a binary search approach can be employed, where the initial trade list list is filled with 50% of the tradable securities.  Depending upon success or failure of the resulting optimization, a pare-down or build-up approach can be taken to either prude or expand the trade list.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n"
   ]
  }
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   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}