large-scale-spreadsheet.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "QuantLET example: Infinite Spreadsheets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://quantlet.net\n",
      "\n",
      "This example shows the use of QuantLET reactives for on-demand realtime computations. Reactives can be connected to streams to consume or generate signals.\n",
      "\n",
      "This example shows the calculation of portfolio values based on changes of prices or quantities of assets, applied to stocks. \n",
      "\n",
      "This is a very simple and unrealistic example of quantlet, an on-going research work. \n",
      "\n",
      "(c) 2014 Jorge M. Faleiro Jr"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Initialization"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First things first, import what we will need from QuantLET and set configuration of underlying libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from quantlet.reactive import R, describe, sum_all\n",
      "from quantlet import QLet, figsize"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['figsize']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Simple Reactive Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's define the reactive model: first the primitives, STOCKS and PRICE..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "STOCKS = R(0,'STOCKS')\n",
      "PRICE = R(0.0, 'PRICE')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "... and the definition of a reactive POSITION, given by STOCKS*PRICE"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "POSITION = STOCKS * PRICE"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's it, done. How does the reactive model we just created look like?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "describe(POSITION)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L7IFiKFn1BCbzAAqD8AV2UzGWrHoCk3kA9iF8gd0UDAblcDiKtmTVE7qazMPj8aiioqLQ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\n2IzwBQDAZoQvAAA2I3wBALDZ/wd7ZNh0vA9obwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaecb1ac>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pretty clear... we have a reactive POSITION (given by STOCKS*PRICE) and a dependency from POSITION to reactive primitives STOCKS and PRICE, i.e. whenever PRICE or STOCKS change, so does POSITION."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's play with this model and do something useful with it. \n",
      "\n",
      "Just for the sake of ilustration (and debugging), we will add a listener to reactive POSITION, printing our overall position value whenever it changes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "POSITION.add_listener(lambda x,y: print('position changed: %s' % x) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can change either STOCKS or PRICE..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PRICE.val = 1.0\n",
      "STOCKS.val = 10\n",
      "PRICE.val = 0.0\n",
      "STOCKS.val = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "position changed: [STOCKS*PRICE](0.0)\n",
        "position changed: [STOCKS*PRICE](10.0)\n",
        "position changed: [STOCKS*PRICE](0.0)\n",
        "position changed: [STOCKS*PRICE](0.0)\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...our position changes, and our listener reacts to the change showing the latest position value"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we will show how to bind this reactive model to a quantlet stream. \n",
      "\n",
      "We will do that by a QLet called 'tick' we create here inline: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@QLet\n",
      "def tick(iterator):\n",
      "    for i in iterator:\n",
      "        if 'q' in i:\n",
      "            STOCKS.val = i['q']\n",
      "        if 'p' in i:\n",
      "            PRICE.val = i['p']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now define a sequence of quantity and price changes (given for q quantity and p price) and stream it through 'tick' we just created, and watch the model tick through each change, we use the usual synchronous stream '>>' for that:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "( \\\n",
      " dict(q=10,p=9.97), \\\n",
      " dict(p=9.12), \\\n",
      " dict(p=8.00), \\\n",
      " dict(q=9), \\\n",
      ") \\\n",
      ">> tick"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "position changed: [STOCKS*PRICE](0.0)\n",
        "position changed: [STOCKS*PRICE](99.7)\n",
        "position changed: [STOCKS*PRICE](91.2)\n",
        "position changed: [STOCKS*PRICE](80.0)\n",
        "position changed: [STOCKS*PRICE](72.0)\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's it for a simple model."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "A More Elaborated Reactive Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, a bit more sophisticated example showing a model of objects Position and Portfolio.\n",
      "\n",
      "In this model a Portfolio has a number of Positions associated to it, as well as a reactive PORTFOLIO, guaranteed to always hold the sum of all VALUE reactives across all Positions \n",
      "\n",
      "Each Position has a symbol (a string), a quantity, a price (reactive primitives QUANTITY and PRICE) and a value (reactive given by QUANTITY * PRICE)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Position:\n",
      "    def __init__(self, symbol):\n",
      "        self.QUANTITY = R(0, '%s[QUANTITY]' % symbol)\n",
      "        self.PRICE = R(0.0, '%s[PRICE]' % symbol)\n",
      "        self.VALUE = self.QUANTITY * self.PRICE\n",
      "        self.VALUE.label = symbol\n",
      "        \n",
      "class Portfolio:\n",
      "    def __init__(self, *args):\n",
      "        self.positions = dict((k.VALUE.label,k) for k in args)\n",
      "        self.PORTFOLIO = sum_all([x.VALUE for x in args])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Structuring a portfolio is straighforward. In this case, we create a portfolio and 3 positions of GOOG, IBM ans MS."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "IBM = Position('IBM')\n",
      "MS = Position('MS')\n",
      "GOOG = Position('GOOG')\n",
      "\n",
      "portfolio = Portfolio(IBM, MS, GOOG)\n",
      "portfolio.PORTFOLIO.label = 'portfolio'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And here's how the graph for this model looks like. Since this model has a bit more nodes, we will first increase the size of the figure to be displayed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(15.0, 12.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "describe(portfolio.PORTFOLIO)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4Gg6HiqJInucpDEPV63XV63Xl8/n0bpn5vIMJAAAA84s4AwAL1O7du/Xtb39b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\nUEicAQAAACgkzgAAAAAUEmcAAAAACv0Tlh0zpfpsm9QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xae98b4c>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Same as before, let's add a listener to portfolio, for illustration and debugging purposes only..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "portfolio.PORTFOLIO.add_listener(lambda x,y: print(\"portfolio changed: %s\" % x) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...and define a QLet to feed data in our model..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@QLet\n",
      "def my_positions(iterator):\n",
      "    for i in iterator:\n",
      "        position = portfolio.positions[i['s']]\n",
      "        if 'q' in i:\n",
      "            position.QUANTITY.val = i['q']\n",
      "        if 'p' in i:\n",
      "            position.PRICE.val = i['p']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The same way as before, we can define some test data and flow through our model. Our listener will show modifications to the portfolio."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_data = ( \\\n",
      "dict(s='GOOG', q=10, p=9.97),\n",
      "dict(s='IBM', q=9, p=8.88),\n",
      "dict(s='MS', p=9.12),\n",
      "dict(s='MS', q=1, p=8.00),\n",
      "dict(s='MS', q=9) \\\n",
      ")\n",
      "\n",
      "test_data >> my_positions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "portfolio changed: portfolio(0.0)\n",
        "portfolio changed: portfolio(99.7)\n",
        "portfolio changed: portfolio(99.7)\n",
        "portfolio changed: portfolio(179.62)\n",
        "portfolio changed: portfolio(179.62)\n",
        "portfolio changed: portfolio(188.74)\n",
        "portfolio changed: portfolio(187.62)\n",
        "portfolio changed: portfolio(251.62)\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also add listeners to any reactives, for example we can add listeners for every position in the portfolio:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for position in portfolio.positions.values():\n",
      "    position.VALUE.add_listener(lambda x,y: print(\"position changed: %s\" % x))\n",
      "\n",
      "# and flow the same test data through our qlet:\n",
      "test_data >> my_positions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "portfolio changed: portfolio(251.62)\n",
        "position changed: GOOG(99.7)\n",
        "portfolio changed: portfolio(251.62)\n",
        "position changed: GOOG(99.7)\n",
        "portfolio changed: portfolio(251.62)\n",
        "position changed: IBM(79.92)\n",
        "portfolio changed: portfolio(251.62)\n",
        "position changed: IBM(79.92)\n",
        "portfolio changed: portfolio(261.7)\n",
        "position changed: MS(82.08)\n",
        "portfolio changed: portfolio(188.74)\n",
        "position changed: MS(9.12)\n",
        "portfolio changed: portfolio(187.62)\n",
        "position changed: MS(8.0)\n",
        "portfolio changed: portfolio(251.62)\n",
        "position changed: MS(72.0)\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Additional Features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's it. As a final reminder, reactives can be also bound to regular, more elaborate, QuantLET streams. For example..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## fixed quantities for each position\n",
      "GOOG.QUANTITY.val = 10\n",
      "IBM.QUANTITY.val = 15\n",
      "MS.QUANTITY.val = 12\n",
      "\n",
      "## each price to follow a different random walk, where s0=price at t0; mu=drift; sigma=volatility\n",
      "random_walk(so=10.0, mu=0.1, sigma=0.25) >> GOOG.PRICE.val\n",
      "random_walk(so=1.1, mu=-0.2, sigma=0.01) >> IBM.PRICE.val\n",
      "random_walk(so=1.3, mu=-0.3, sigma=0.02) >> MS.PRICE.val\n",
      "\n",
      "portfolio.PORTFOLIO >> plot"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...will show a plot of changes of the portfolio value over time"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}