OSQF_Frankfurt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python for Quant Finance\n",
    "\n",
    "## how to build an energy trading business from scratch\n",
    "###Dr. Teodora Baeva"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src='https://raw.githubusercontent.com/tpbaeva/OSQF_Frankfurt/master/btg_ataglance.png' width=\"1000\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "% pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What does a quant do in commodities trading?\n",
    "\n",
    "* model development\n",
    "* portfolio optimisation\n",
    "* trading strategies\n",
    "* pricing of complex products (storage, tolling, swing contracts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example: Gas storage\n",
    "\n",
    "* natural gas can be stored\n",
    "* used to \n",
    "  - meet load variations\n",
    "  - speculation (buy low, store, sell high)\n",
    "* subject to physical constraints...\n",
    "  - injection and withdrawal rate depend on time and current amount of gas stored\n",
    "  - minimum and maximum storage capacity can vary with time\n",
    "* ...find an optimal strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "start_date = datetime(2015, 7, 1)\n",
    "end_date = datetime(2016, 7, 1)\n",
    "dates = pd.date_range(start_date, end_date)\n",
    "\n",
    "max_inventory = pd.TimeSeries(3640, index=dates)\n",
    "max_inventory[10:] = 3200\n",
    "min_inventory = pd.TimeSeries(0, index=dates)\n",
    "min_inventory[10:] = 200\n",
    "with_rate = pd.Series([2, 4, 8, 15, 23, 30, 35, 38, 40, 42, 43, 43], \n",
    "                      index=[0, 1300, 1550, 2000, 2150, 2550, 2800, 3100, 3150, 3200, 3350, 3600])\n",
    "inj_rate  = pd.Series([27, 26, 25, 24, 23, 22, 21, 20, 17, 11, 10, 5], \n",
    "                      index=[0, 1300, 1550, 2000, 2150, 2550, 2800, 3100, 3150, 3200, 3350, 3600])\n",
    "wth_rate_constraints = pd.DataFrame({'min': pd.Series(2, index=dates),\n",
    "                                     'max': pd.Series(45, index=dates),\n",
    "                                    })\n",
    "inj_rate_constraints = pd.DataFrame({'min': pd.Series(2, index=dates),\n",
    "                                     'max': pd.Series(26, index=dates),\n",
    "                                    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "storage_params = {'name': 'TestStorage',\n",
    "                  'start_inventory': 2500, \n",
    "                  'max_inventory': max_inventory,\n",
    "                  'min_inventory': min_inventory,\n",
    "                  'with_rate': with_rate,\n",
    "                  'inj_rate': inj_rate,\n",
    "                  'inj_cost': 0,\n",
    "                  'with_cost': 0,\n",
    "                  'end_inventory': 2500,\n",
    "                  'storage_start_date': start_date,\n",
    "                  'storage_end_date': end_date,\n",
    "                  'wth_rate_constraints': wth_rate_constraints,\n",
    "                  'inj_rate_constraints': inj_rate_constraints,\n",
    "                 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import btglib.quant.models.storage as sc\n",
    "\n",
    "storage = sc.Storage(storage_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "RUTCDiGEFhEJO4QQWkQk7BBCaBH/B8nPVZ088E7oAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8e99b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "curve = 20*pd.Series(np.sin(np.linspace(0,10,len(dates))/1.5-0.5), index=dates)+40\n",
    "\n",
    "storage.optimize(curve, curve)\n",
    "storage.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "NfH/vwF3sIC+h8wAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x90b1750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "curve = 20*pd.Series(np.sin(np.linspace(0,10,len(dates))/1.5+1), index=dates)+40\n",
    "\n",
    "storage.optimize(curve, curve)\n",
    "storage.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The optimal strategy depends on the gas price curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "...which we don't know..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "...so we generate scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from btglib.quant.models.simulator import Simulator\n",
    "\n",
    "model_parameters = {'distribution': 'LogNormal',\n",
    "                    'volatility': np.array([0.1]*len(dates))[:,None], \n",
    "                    'correlation': np.eye(1), \n",
    "                    'names': ['gas'],\n",
    "                   }\n",
    "\n",
    "simulator = Simulator('5-Jun-2015', dates, curve[:,None], model_parameters)\n",
    "sims = simulator.generate_paths(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "pJ6QkJCQQ0iknpCQkJBD+P9YE7mjjI2cggAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12236650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sims.ix[0,:,:], color='k', alpha=0.1);\n",
    "plt.plot(curve, color='r', linewidth=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Stochastic optimisation problem\n",
    "\n",
    "* optimal strategy under uncertainty\n",
    "* Monte-Carlo simulation with different distributions\n",
    "* large optimisation problems\n",
    "* price sensitivities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why Python?\n",
    "\n",
    "* ideal for start-up\n",
    "* quick cycle from idea to prototype to tool\n",
    "* \"batteries included\" - scientific libraries, visualisation tools, documentation\n",
    "* python may be slow...but cython (c/c++) is not!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## To trade complex products one first needs to be able to...trade!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "trades = pd.DataFrame({'Product': ['WkEnd 04/07-05/', 'Q315', 'Aug 15'], \n",
    "                       'Market': ['Germany Baseload', 'Germany Peaks', 'Germany Baseload'], \n",
    "                       'Volume': [50, -10, 20]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Building an ETRM (Energy Trading and Risk Management system)\n",
    "  - parse trades\n",
    "  - calculate positions and PnL per trader/book/strategy\n",
    "  - produce custom reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Market</th>\n",
       "      <th>Product</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> Germany Baseload</td>\n",
       "      <td> WkEnd 04/07-05/</td>\n",
       "      <td> 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>    Germany Peaks</td>\n",
       "      <td>            Q315</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> Germany Baseload</td>\n",
       "      <td>          Aug 15</td>\n",
       "      <td> 20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Market          Product  Volume\n",
       "0  Germany Baseload  WkEnd 04/07-05/      50\n",
       "1     Germany Peaks             Q315     -10\n",
       "2  Germany Baseload           Aug 15      20"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "BWNpN1FpUTfH+nRckMx2zL+qn2Mn97zgd9aUtMCqW+M3E5HTROSPuAPT22YwjEFPSlogPT310qfJ\n",
       "d1R1Lq4SInoi1bI5sKa/zpCfgyP7CcaYSEaLuh8CGYir3Djb71umJQBS01MvNXv0RvGIyHqquqTo\n",
       "cTSDlLSU4q8rdKWQJaakBdLTUysW6CNECv6R5GaSkhZIS09KWiA9PX3BAr1hGEbitOrHwQ3DMIxA\n",
       "sEBvGIaROBboDcMwEscCvWEYRuJYoDcMw0gcC/SGYRiJ839WF0erDm6GiAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1269cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from btglib.utils import parse_traded_product\n",
    "\n",
    "schedules = [parse_traded_product(p, m, datetime.now(), v) for p,m,v in trades[['Product','Market','Volume']].values]\n",
    "position = pd.concat(schedules, axis=1).sum(axis=1)\n",
    "position.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ETRM\n",
    "\n",
    "* trade processing with <code>re</code>\n",
    "* positions generation/aggregation with <code>pandas</code>\n",
    "* reporting with <code>reportlab</code>\n",
    "* interface with Excel using <code>pyxll</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## To protect downside risk...need to be able to trade options!\n",
    "### (and to make money too)\n",
    "\n",
    "<br>\n",
    "* model for quick valuation of option portfolios\n",
    "* display portfolio sensitivities\n",
    "* pnl attribution\n",
    "* scenario testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "All of these require an implied volatility surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=http://data.theice.com/MyAccount/Login.aspx?ReturnUrl=~\\ViewData\\Default.aspx width=1060 height=550></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=http://data.theice.com/MyAccount/Login.aspx?ReturnUrl=~\\ViewData\\Default.aspx width=1060 height=550></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Web-scraping with Python\n",
    "\n",
    "* <code>requests</code> for simple web pages\n",
    "* <code>selenium</code> when we need to drive a web browser to navigate to the data\n",
    "  - portable software testing framework for web applications\n",
    "  - compatible with most modern browsers\n",
    "  - bindings for a number of popular programming languages, including Python (and some others like Java, C#, Groovy, Perl, PHP and Ruby)\n",
    "* <code>BeautifulSoup</code> to parse the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=https://plot.ly/~tbaeva/98/vol-surface-25-feb-2015/ width=960 height=700></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=https://plot.ly/~tbaeva/98/vol-surface-25-feb-2015/ width=960 height=700></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Automation, reporting, alerting\n",
    "\n",
    "* python is great for automating tasks to scrape data, process and produce model results\n",
    "* ...but also to produce and send out custom reports\n",
    "* reports can be built with <code>reportlab</code> and saved as pdf\n",
    "* emailing is easy with <code>smtplib</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Automation, reporting, alerting\n",
    "\n",
    "* reading your Outlook to automatically process data (e.g. from brokers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yves Hilpisch\n",
      "RE: OSQF Conference in Frankfurt 05.06.2015\n"
     ]
    }
   ],
   "source": [
    "import win32com.client\n",
    "\n",
    "outlook = win32com.client.Dispatch(\"Outlook.Application\").GetNamespace(\"MAPI\")\n",
    "inbox = outlook.GetDefaultFolder(6)\n",
    "last_message = inbox.Items.GetLast()\n",
    "print last_message.sender()\n",
    "print last_message.subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Deploying models to the desk\n",
    "\n",
    "* traders love Excel\n",
    "* python modules can be used from Excel, e.g. via <code>pyxll</code> (Tony Roberts)\n",
    "* easy to centralise application modules and keep them under version control\n",
    "* however...\n",
    "  - requires python installation on trader's machine\n",
    "  - package updates locally\n",
    "  - ...and traders don't like rebooting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Deploying models to the desk\n",
    "\n",
    "* web interface\n",
    "  - models are triggered from the browser\n",
    "  - models run on a server\n",
    "  - results displayed on browser\n",
    "  - model runs can be logged\n",
    "  - easy to embed interactive plotting!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Web Interface\n",
    "\n",
    "* built using <code>flask</code> and <code>jinja</code>\n",
    "* allows to have interface, documentation, wiki etc in one place\n",
    "* running via Apache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src='https://raw.githubusercontent.com/tpbaeva/OSQF_Frankfurt/master/qpy1.png' width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Web Interface\n",
    "\n",
    "* interactive plotting with <code>bokeh</code>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "<img src='https://raw.githubusercontent.com/tpbaeva/OSQF_Frankfurt/master/qpy2.png' width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Web Interface\n",
    "\n",
    "* embedded pdf report created with <code>reportlab</code>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "<img src='https://raw.githubusercontent.com/tpbaeva/OSQF_Frankfurt/master/qpy3.png' width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## In conclusion\n",
    "\n",
    "* python is traditionally seen as a scientific language (scientific libraries)\n",
    "* due to python's versatility and rich ecosystem it can be used to build a trading business from scratch\n",
    "* full suite from data collection, to modelling, to visualisation, to automation, reporting and alerting, to great user interfaces\n",
    "\n",
    "<br>\n",
    "<img src='https://raw.githubusercontent.com/tpbaeva/OSQF_Frankfurt/master/framework.png' width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### thanks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src='https://raw.githubusercontent.com/tpbaeva/OSQF_Frankfurt/master/btg_disclaimer.png' width=\"1000\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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