Weiner_Process_10_Names.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wiener Stochastic Process\n",
    "\n",
    "Gaussian Probability Diffusion over Twelve Time Steps\n",
    "\n",
    "Portfolio of 10 Names \n",
    "\n",
    "Replace the multiple array structure of the single name FP model with a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats  import invgauss\n",
    "from random import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize model parameters\n",
    "Note:  feel free to change these parameter values and watch the graph change\n",
    "    \n",
    "* mu is the drift term\n",
    "* dt is the time step\n",
    "*  sigma is the standard deviation\n",
    "*  S, dS, and density are random Gaussian values with paramterers N(0, dt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mu =  0.00500 \n",
    "sigma =  0.00500 \n",
    "dt = 1\n",
    "S0 = 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the data by declaring arrays and filling the first element in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#create a big array of nones to hold our data\n",
    "w,h = 40, 10\n",
    "array = [[None] * w for i in range(h)]\n",
    "\n",
    "#set up the column headings\n",
    "columns = []\n",
    "for i in range(10):\n",
    "    columns.append('Time' + str(i))\n",
    "    columns.append('density' + str(i))\n",
    "    columns.append('dS' + str(i))\n",
    "    columns.append('S' + str(i))\n",
    "\n",
    "#set up the indexes\n",
    "index = []\n",
    "for i  in range (h):\n",
    "    index.append(str(i))\n",
    "\n",
    "#create a dataframe based on the columns and indices\n",
    "df = pd.DataFrame(array, columns=columns, index=index)\n",
    "\n",
    "#intialize first row\n",
    "first_row = [0, 0,0,S0] * 10\n",
    "\n",
    "df.loc[0] = first_row\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the second element in the array containing the initial value of the density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = dt\n",
    "density1 = 0.00500 \n",
    "dS1 = S0 * density1\n",
    "S1 = S0 + dS1\n",
    "\n",
    "#initialize the second row\n",
    "second_row = [t1, density1, dS1, S1] * 10\n",
    "\n",
    "df.loc[1] = second_row\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we loop through the remaining time steps, adding to the arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2, 12):\n",
    "    prev_iter = df.loc[i-1]\n",
    "    #print(prev_iter)\n",
    "    \n",
    "    new_row = []\n",
    "    \n",
    "    for j in range(10):\n",
    "        prev_index = str(j)\n",
    "        time_hash = \"Time\" + prev_index\n",
    "        density_hash = \"density\" + prev_index \n",
    "        dS_hash = \"dS\" + prev_index\n",
    "        S_hash = \"S\" + prev_index\n",
    "    \n",
    "        new_time = prev_iter[time_hash] + dt\n",
    "        randvar = invgauss.rvs(mu=random(), loc=0 ,scale=dt)\n",
    "        walk = mu * dt + sigma * randvar\n",
    "        new_density = walk\n",
    "        new_dS = prev_iter[dS_hash] + new_density\n",
    "        new_S = prev_iter[S_hash] + new_dS\n",
    "        \n",
    "        new_row.append(new_time)\n",
    "        new_row.append(new_density)\n",
    "        new_row.append(new_dS)\n",
    "        new_row.append(new_S)\n",
    "        \n",
    "    df.loc[i] = new_row\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now take a look at the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Time0    density0        dS0       S0 Time1    density1        dS1  \\\n",
      "0   None        None       None     None  None        None       None   \n",
      "1   None        None       None     None  None        None       None   \n",
      "2   None        None       None     None  None        None       None   \n",
      "3   None        None       None     None  None        None       None   \n",
      "4   None        None       None     None  None        None       None   \n",
      "5   None        None       None     None  None        None       None   \n",
      "6   None        None       None     None  None        None       None   \n",
      "7   None        None       None     None  None        None       None   \n",
      "8   None        None       None     None  None        None       None   \n",
      "9   None        None       None     None  None        None       None   \n",
      "0      0           0          0        1     0           0          0   \n",
      "1      1       0.005      0.005    1.005     1       0.005      0.005   \n",
      "2      2  0.00679657  0.0117966   1.0168     2  0.00578243  0.0107824   \n",
      "3      3  0.00846851  0.0202651  1.03706     3  0.00911478  0.0198972   \n",
      "4      4   0.0128099  0.0330749  1.07014     4   0.0059747  0.0258719   \n",
      "5      5  0.00548408   0.038559   1.1087     5  0.00530855  0.0311805   \n",
      "6      6  0.00613397   0.044693  1.15339     6  0.00716211  0.0383426   \n",
      "7      7  0.00587723  0.0505702  1.20396     7  0.00549879  0.0438414   \n",
      "8      8  0.00608586  0.0566561  1.26061     8  0.00525511  0.0490965   \n",
      "9      9  0.00569158  0.0623477  1.32296     9  0.00694383  0.0560403   \n",
      "10    10  0.00728131   0.069629  1.39259    10  0.00758024  0.0636205   \n",
      "11    11  0.00565288  0.0752819  1.46787    11  0.00677741  0.0703979   \n",
      "\n",
      "         S1 Time2    density2   ...           dS7       S7 Time8    density8  \\\n",
      "0      None  None        None   ...          None     None  None        None   \n",
      "1      None  None        None   ...          None     None  None        None   \n",
      "2      None  None        None   ...          None     None  None        None   \n",
      "3      None  None        None   ...          None     None  None        None   \n",
      "4      None  None        None   ...          None     None  None        None   \n",
      "5      None  None        None   ...          None     None  None        None   \n",
      "6      None  None        None   ...          None     None  None        None   \n",
      "7      None  None        None   ...          None     None  None        None   \n",
      "8      None  None        None   ...          None     None  None        None   \n",
      "9      None  None        None   ...          None     None  None        None   \n",
      "0         1     0           0   ...             0        1     0           0   \n",
      "1     1.005     1       0.005   ...         0.005    1.005     1       0.005   \n",
      "2   1.01578     2   0.0199477   ...     0.0149257  1.01993     2  0.00941395   \n",
      "3   1.03568     3  0.00656863   ...     0.0239541  1.04388     3  0.00533945   \n",
      "4   1.06155     4  0.00591755   ...     0.0293873  1.07327     4  0.00630253   \n",
      "5   1.09273     5   0.0055472   ...     0.0354429  1.10871     5  0.00726987   \n",
      "6   1.13107     6  0.00580162   ...     0.0408603  1.14957     6   0.0231972   \n",
      "7   1.17492     7   0.0101411   ...     0.0527244  1.20229     7  0.00885926   \n",
      "8   1.22401     8  0.00583373   ...     0.0582078   1.2605     8  0.00802119   \n",
      "9   1.28005     9  0.00533201   ...     0.0706067  1.33111     9  0.00658677   \n",
      "10  1.34367    10  0.00553841   ...     0.0783933   1.4095    10  0.00673053   \n",
      "11  1.41407    11  0.00936052   ...      0.084814  1.49432    11  0.00702065   \n",
      "\n",
      "          dS8       S8 Time9    density9        dS9       S9  \n",
      "0        None     None  None        None       None     None  \n",
      "1        None     None  None        None       None     None  \n",
      "2        None     None  None        None       None     None  \n",
      "3        None     None  None        None       None     None  \n",
      "4        None     None  None        None       None     None  \n",
      "5        None     None  None        None       None     None  \n",
      "6        None     None  None        None       None     None  \n",
      "7        None     None  None        None       None     None  \n",
      "8        None     None  None        None       None     None  \n",
      "9        None     None  None        None       None     None  \n",
      "0           0        1     0           0          0        1  \n",
      "1       0.005    1.005     1       0.005      0.005    1.005  \n",
      "2   0.0144139  1.01941     2  0.00763453  0.0126345  1.01763  \n",
      "3   0.0197534  1.03917     3   0.0129616  0.0255962  1.04323  \n",
      "4   0.0260559  1.06522     4  0.00610482   0.031701  1.07493  \n",
      "5   0.0333258  1.09855     5   0.0150495  0.0467505  1.12168  \n",
      "6    0.056523  1.15507     6  0.00831067  0.0550612  1.17674  \n",
      "7   0.0653823  1.22045     7  0.00738743  0.0624486  1.23919  \n",
      "8   0.0734034  1.29386     8  0.00551621  0.0679648  1.30716  \n",
      "9   0.0799902  1.37385     9  0.00511775  0.0730826  1.38024  \n",
      "10  0.0867207  1.46057    10  0.00768503  0.0807676  1.46101  \n",
      "11  0.0937414  1.55431    11   0.0100188  0.0907864  1.55179  \n",
      "\n",
      "[22 rows x 40 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally generate the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4pw5IT8xm6ZRd6PSCgU9HUDPAo0iejpwc4h59jJytWwn64H1877mHzIVryNxu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\nBiAV3BVFUQYgFdwVRVEGoP8HqG0d6XoRqlIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xade1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df[\"Time0\"]\n",
    "\n",
    "for i in range(10):\n",
    "    hash = \"S\" + str(i)\n",
    "    plt.plot(x,df[hash], marker='o', linestyle='solid')\n",
    "\n",
    "plt.title(\"Share Price - Wiener Stochastic Process - 10 Names\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}