Minimum spanning tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build minimum spanning tree  \n",
    "1. Calculate the correlation matrix of price change\n",
    "2. Obtain the edge list by Prim's algorithm of minimum spanning tree\n",
    "3. Creat the Graph object by networkingx  \n",
    "\n",
    "### Schematising of Minimum Spanning Tree:\n",
    "1. rank a couple of vertices(stocks) from the nearest to the farthest  \n",
    "2. draw the first edge from this rank  \n",
    "3. continue in the rank  \n",
    "4. if the new edge does not close a cycle draw it  \n",
    "5. go to point 3  \n",
    "6. stop when all the vertices have been drawn  \n",
    "\n",
    "Reference:  \n",
    "https://mktstk.com/2015/03/04/stock-market-visualization-minimum-spanning-trees/  \n",
    "http://peekaboo-vision.blogspot.com/2012/02/simplistic-minimum-spanning-tree-in.html  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prim's algotithm to get edge list by minimum spanning trees\n",
    "def minimum_spanning_tree(X, copy_X=True):\n",
    "    \"\"\"X are edge weights of fully connected graph\"\"\"\n",
    "    if copy_X:\n",
    "        X = X.copy()\n",
    " \n",
    "    if X.shape[0] != X.shape[1]:\n",
    "        raise ValueError(\"X needs to be square matrix of edge weights\")\n",
    "    n_vertices = X.shape[0]\n",
    "    spanning_edges = []\n",
    "     \n",
    "    # initialize with node 0:                                                                                         \n",
    "    visited_vertices = [0]                                                                                            \n",
    "    num_visited = 1\n",
    "    # exclude self connections:\n",
    "    diag_indices = np.arange(n_vertices)\n",
    "    X[diag_indices, diag_indices] = np.inf\n",
    "     \n",
    "    while num_visited != n_vertices:\n",
    "        new_edge = np.argmin(X[visited_vertices], axis=None)\n",
    "        # 2d encoding of new_edge from flat, get correct indices                                                      \n",
    "        new_edge = divmod(new_edge, n_vertices)\n",
    "        new_edge = [visited_vertices[new_edge[0]], new_edge[1]]                                                       \n",
    "        # add edge to tree\n",
    "        spanning_edges.append(new_edge)\n",
    "        visited_vertices.append(new_edge[1])\n",
    "        # remove all edges inside current tree\n",
    "        X[visited_vertices, new_edge[1]] = np.inf\n",
    "        X[new_edge[1], visited_vertices] = np.inf                                                                     \n",
    "        num_visited += 1\n",
    "    return np.vstack(spanning_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years = [str(y) for y in range(2008, 2017)]\n",
    "for year in years:\n",
    "    # Read in correlation matrix\n",
    "    corr = pd.read_csv('corr_' + year +'.csv', index_col=0)\n",
    "    # Calculate the distance between pairwise stocks\n",
    "    # distance[i, j] = sqrt(2.0 * ( 1 – correlation[i, j] ) )\n",
    "    dist = np.sqrt(2*(1- corr)) \n",
    "    #dist.to_csv('dist_2016.csv')\n",
    "    # Get edges\n",
    "    dist = np.array(dist)\n",
    "    edge_list_raw = minimum_spanning_tree(dist)\n",
    "    edge_list = []\n",
    "    for i, j in edge_list_raw:\n",
    "        edge = (corr.columns.values[i], corr.columns.values[j])\n",
    "        edge_list.append(edge)\n",
    "\n",
    "    # Export edges\n",
    "    #edges = pd.DataFrame(edge_list)\n",
    "    #edges.to_csv('edges_2016.csv')\n",
    "    \n",
    "    # Create graph object by adding edges\n",
    "    mst = nx.Graph()\n",
    "    for i, j in edge_list:\n",
    "        mst.add_edge(i,j)\n",
    "    \n",
    "    # Export network for further use in Gephi\n",
    "    nx.write_gexf(mst, 'mst_' + year + '.gexf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Portfolio Selection Based on MST \n",
    "#### Measurements of centrality and peripherality\n",
    "\n",
    "(I) Degree K, the number of neighbor nodes connected to a node. The larger the K is, the more the edges that are associated with this node.\n",
    "\n",
    "(II) Betweenness centrality C, reflecting the contribution of a node to the connectivity of the network. Denote V as the set of nodes in the network.  \n",
    "\n",
    "Distance refers to the shortest length from a node to the central node of the network. Here, three types of definitions of a central node are introduced to reduce the error caused by a single method. Therefore three types of distances are described here.  \n",
    "\n",
    "(III) Distance on degree criterion Ddegree, a central node is the node that has the largest degree.\n",
    "\n",
    "(IV) Distance on correlation criterion Dcorrelation, a central node is the node with the highest value of the sum of correlation coefficients with its neighbors;\n",
    "\n",
    "(V) Distance on distance criterion Ddistance, a central node is the node that gives the smallest value for the mean distance.  \n",
    "\n",
    "Then we have two simple index to measure centrality and peripherality.  \n",
    "$Centrality\\ index = 0.5*Degree + 0.5*Betweenness\\ centrality$  \n",
    "$Distance\\ index = \\frac{Ddegree + Dcorrelation + Ddistance}{3}$\n",
    "\n",
    "\n",
    "#### Portfolio Selection  \n",
    "\n",
    "Central portfolio: stocks with the first 15 largest centrality index   \n",
    "Peripheral portfolio: stocks with the first 15 largest distance index  \n",
    "\n",
    "\n",
    "The central portfolios and the peripheral portfolios represent two opposite sides of correlation and agglomeration. Generally speaking, central stocks play a vital role in the market and impose strong influence on other stocks, whereas the correlations between peripheral stocks are weak and contain more noise than central stocks.\n",
    "\n",
    "Reference:  \n",
    "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169299"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "corr = pd.read_csv('corr_2015.csv', index_col = 0)\n",
    "g = nx.read_gexf('mst_2015.gexf')\n",
    "d = nx.degree(g)\n",
    "b_c = nx.betweenness_centrality(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Distance on degree criterion\n",
    "D_dg = {}\n",
    "\n",
    "# central node with the largest degree\n",
    "c1 = max(d, key=d.get)\n",
    "for n in g.nodes():\n",
    "    D_dg[n] = nx.shortest_path_length(g, n, c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Distance on correlation criterion\n",
    "D_c = {}\n",
    "sum_C = {}\n",
    "\n",
    "# central node with the highest value of the sum of correlation coefficients with its neighbors\n",
    "for n in g.nodes():\n",
    "    nbs = nx.neighbors(g, n)\n",
    "    sum_C[n] = sum(corr[n][nb] for nb in nbs)\n",
    "c2 = max(sum_C, key=sum_C.get)\n",
    "\n",
    "for n in g.nodes():\n",
    "    D_c[n] = nx.shortest_path_length(g, n, c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Distance on distance criterion\n",
    "D_d = {}\n",
    "m_d = {}\n",
    "\n",
    "# central node with the smallest mean distance\n",
    "for n in g.nodes():\n",
    "    nodes = g.nodes()\n",
    "    nodes.remove(n) \n",
    "    dd = [nx.shortest_path_length(g, n, ns) for ns in nodes]\n",
    "    m_d[n] = np.mean(dd)\n",
    "\n",
    "c3 = min(m_d, key=m_d.get)\n",
    "\n",
    "for n in g.nodes():\n",
    "    D_d[n] = nx.shortest_path_length(g, n, c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Degree</th>\n",
       "      <th>Betweenness</th>\n",
       "      <th>Centrality</th>\n",
       "      <th>Ddegree</th>\n",
       "      <th>Dcorr</th>\n",
       "      <th>Ddis</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGN</th>\n",
       "      <td>3</td>\n",
       "      <td>0.007976</td>\n",
       "      <td>1.503988</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EOG</th>\n",
       "      <td>3</td>\n",
       "      <td>0.019832</td>\n",
       "      <td>1.509916</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>10.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>2</td>\n",
       "      <td>0.058204</td>\n",
       "      <td>1.029102</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVHC</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDXX</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Degree  Betweenness  Centrality  Ddegree  Dcorr  Ddis   Distance\n",
       "AGN        3     0.007976    1.503988        4      4     6   4.666667\n",
       "EOG        3     0.019832    1.509916       11     11     9  10.333333\n",
       "CPB        2     0.058204    1.029102        6      6     4   5.333333\n",
       "EVHC       1     0.000000    0.500000        8      8     6   7.333333\n",
       "IDXX       1     0.000000    0.500000        6      6     4   5.333333"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_stats = pd.DataFrame.from_dict(d, orient='index')\n",
    "nodes_stats.rename(columns={0:'Degree'}, inplace=True)\n",
    "nodes_stats['Betweenness'] = b_c.values()\n",
    "nodes_stats['Centrality'] = .5*(nodes_stats['Degree'] + nodes_stats['Betweenness'])\n",
    "nodes_stats['Ddegree'] = D_dg.values()\n",
    "nodes_stats['Dcorr'] = D_c.values()\n",
    "nodes_stats['Ddis'] = D_d.values()\n",
    "nodes_stats['Distance'] = (nodes_stats['Ddegree'] + nodes_stats['Dcorr'] + nodes_stats['Ddis'])/3\n",
    "nodes_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FISV', 'ITW', 'MMC', 'HD', 'DTE', 'TMO', 'HON', 'ABT', 'LLTC', 'AMGN', 'BRK-B', 'CINF', 'HSIC', 'COP', 'REG']\n"
     ]
    }
   ],
   "source": [
    "# Central Portfolio\n",
    "c = nodes_stats.sort_values(by='Centrality', ascending = False).head(15)\n",
    "portfolio_c = [name for name in c.index.values]\n",
    "print portfolio_c\n",
    "for n in portfolio_c:\n",
    "    g.node[n]['portfolio'] = 'C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['COG', 'EQT', 'RRC', 'CSRA', 'TSO', 'VLO', 'SWN', 'NFX', 'PXD', 'FTI', 'APC', 'CXO', 'CHK', 'APA', 'RIG']\n"
     ]
    }
   ],
   "source": [
    "# Peripheral Portfolio\n",
    "p = nodes_stats.sort_values(by='Distance', ascending = False).head(15)\n",
    "portfolio_p = [name for name in p.index.values]\n",
    "print portfolio_p\n",
    "for n in portfolio_p:\n",
    "    g.node[n]['portfolio'] = 'P'\n",
    "    \n",
    "nx.write_gexf(g, 'mst_2015_portfolio.gexf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "return_2016 = pd.read_csv('change_2016.csv', index_col = 0)\n",
    "price_2016 = pd.read_csv('price_2016.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True, True, True, True, True, True, True, True, True, True, True, True, True, True, False]\n",
      "[True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]\n"
     ]
    }
   ],
   "source": [
    "# check data completeness\n",
    "print [s in return_2016.columns.values for s in portfolio_c]\n",
    "print [s in return_2016.columns.values for s in portfolio_p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from yahoo_finance import Share\n",
    "reg = Share('REG')\n",
    "df_reg = pd.DataFrame(reg.get_historical('2016-1-1', '2016-12-31'))\n",
    "df_reg.index = df_reg['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "close_reg = df_reg['Adj_Close']\n",
    "price_reg = [float(p) for p in close_reg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def portfolio_value(amount, portfolio_list):\n",
    "    \"\"\"\n",
    "    Return the value of portfolio invested.\n",
    "    Inputs:\n",
    "    amount(number): investment amount\n",
    "    portfolio_list(list): components of portfolio\"\"\"\n",
    "    portfolio_price = pd.DataFrame()\n",
    "    for c in portfolio_list:\n",
    "        try:\n",
    "            portfolio_price[c] = price_2016[c]\n",
    "        except KeyError:\n",
    "            portfolio_price[c] = price_reg\n",
    "    portfolio_unit = portfolio_price.sum(axis=1)[0]\n",
    "    share = amount/portfolio_unit\n",
    "    portfolio_value = portfolio_price.sum(axis=1)*share\n",
    "    return portfolio_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sp500_2016 = pd.read_csv('sp500_2016.csv')\n",
    "price_sp500 = sp500_2016['Adjusted Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Assume invest $100000 on the portfolio on 2016-01-01 and keep the portfolio for 1 year\n",
    "central_portfolio_value = portfolio_value(100000, portfolio_c)\n",
    "peripheral_portfolio_value = portfolio_value(100000, portfolio_p)\n",
    "# compare with marcket performance\n",
    "sp500_value = price_sp500 * (100000/price_sp500[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w+8qaSvw8/FiTvoZJvSfx/NbnGRgykGuHXtto2a9PfM3qtNVE+UW1+f5dqccF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\nmqqK57HlefjvaPG4+7+Q00gd35Ev4c0pkNuKlHF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rzwifga+LL9+d+46DxoPE5sUyL3Ie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UAg+CkjnLZ+4AvlWaKUhWEIkQI8tGwMQf5a/rY2s8P6mkOl3PhGGpGBej78xRx+Njue+WNCuWNB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\niQAKbSIiIiIRQKFNREREJAL8P0VmsEG79XMzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e801d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(252)\n",
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "line1, = ax.plot(x, sp500_value, label='S&P500')\n",
    "line2, = ax.plot(x, central_portfolio_value, label='Central Portfolio')\n",
    "line3, = ax.plot(x, peripheral_portfolio_value, label = 'Peripheral Portfolio' )\n",
    "ax.set_title('Simulated Performance of Portfolios in 2016')\n",
    "ax.legend(loc='upper left')\n",
    "plt.savefig('performance of portfolio.png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}