fund management with Coursera

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Murtaza The Promoted Fund Manager "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Functions and Dependencies from External File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All programs and commands in the python file fundanalyze.py may be loaded with the following\n",
    "command:\n",
    "\n",
    "(See [here](http://stackoverflow.com/a/21034906/499167) for a great reference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thomas/anaconda2/lib/python2.7/site-packages/QSTK/qstkutil/qsdateutil.py:36: FutureWarning: TimeSeries is deprecated. Please use Series\n",
      "  return pd.TimeSeries(index=dates, data=dates)\n"
     ]
    }
   ],
   "source": [
    "%run fundanalyze.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read in the file containing the functions into the current cell, use the following command\n",
    "(This is not necessary, but represents an alternative approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load fundanalyze.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " To see all 'magic' commands, execute the following. (\n",
    "If Automagic is on, % prefix is NOT needed. Try it!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Begin Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Let's say I have a fund consisting of four stocks, AAPL, BRCM, TXN, ADI and I want to retrospectively optimize\n",
    "their performance by maximizing the Sharpe Ratio for the years 2000 - 2016,\n",
    "with a view to critically accessing the Sharpe ratio as an indicator of fund performance. \n",
    "\n",
    "* Let's start with 2010, just to get things started.\n",
    "\n",
    "* We can use simulate2() to calculate the overall fund performance giving each stock a weighting \n",
    "factor of 0.25 (ls_allocations = [0.25, 0.25, 0.25, 0.25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Final Results:\n",
      "vol (sqStdDev):  0.0157864029377\n",
      "Avg Daily Return:  0.00131242333752\n",
      "Sharpe Ratio  1.31712690579\n",
      "Cumulative Return  1.34724538783\n",
      "ls_allocations  [0.25, 0.25, 0.25, 0.25]\n"
     ]
    }
   ],
   "source": [
    "formatSimulate(\n",
    "    simulate2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
    "              ['AAPL', 'BRCM', 'TXN', 'ADI'], [0.25, 0.25, 0.25, 0.25], \n",
    "              initial_allocation=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize the Fund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2010, 1, 1, 0, 0),\n",
       " datetime.datetime(2010, 12, 31, 0, 0),\n",
       " ['AAPL', 'BRCM', 'TXN', 'ADI'],\n",
       " [1.0, 0.0, 0.0, 0.0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myoptimized = optimizer2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
    "               ['AAPL', 'BRCM', 'TXN', 'ADI'], datagen(2),quiet = True)\n",
    "myoptimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Final Results:\n",
      "vol (sqStdDev):  0.0168315943362\n",
      "Avg Daily Return:  0.0017905981418\n",
      "Sharpe Ratio  1.68542617642\n",
      "Cumulative Return  1.51234162365\n",
      "ls_allocations  [1.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "formatSimulate(simulate2(*myoptimized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85933151455268364, [0.0, 0.0, 0.0, 1.0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2010, 1, 1, 0, 0),\n",
       " datetime.datetime(2010, 12, 31, 0, 0),\n",
       " ['AAPL', 'BRCM', 'TXN', 'ADI'],\n",
       " [0.0, 0.0, 0.0, 1.0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myWorstCase = deoptimizer(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
    "               ['AAPL', 'BRCM', 'TXN', 'ADI'], datagen(2))\n",
    "myWorstCase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Final Results:\n",
      "vol (sqStdDev):  0.018527929613\n",
      "Avg Daily Return:  0.00100496461527\n",
      "Sharpe Ratio  0.859331514553\n",
      "Cumulative Return  1.23266142808\n",
      "ls_allocations  [0.0, 0.0, 0.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "formatSimulate(simulate2(*myWorstCase))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate the Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fund_stats_guess=fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
    "                            ['AAPL', 'BRCM', 'TXN', 'ADI'], [0.25, 0.25, 0.25, 0.25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fund_stats_optimized=fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
    "                            ['AAPL', 'BRCM', 'TXN', 'ADI'], [1, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fund_stats_worst=fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
    "                            ['AAPL', 'BRCM', 'TXN', 'ADI'], [0, 0, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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E3GzczCGqyYiKAl9fzWFtKH36QMuWsHYt2Nho0VGurtCpEzz3HNx3n2bKGjQI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42b591bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(fund_stats_guess[:,[1]])\n",
    "plt.plot(fund_stats_optimized[:,[1]])\n",
    "plt.plot(fund_stats_worst[:,[1]])\n",
    "plt.legend(['Guess', 'Optimized', 'Worst'], loc=2)\n",
    "plt.ylabel('Normalized Price of Fund (%)')\n",
    "plt.xlabel('Trading Days (2010)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}