ml-finance-python

notebook.ipynb

(210719B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#Introduction to the Research Environment\n",
     "\n",
     "The research environment is powered by IPython notebooks, which allow one to perform a great deal of data analysis and statistical validation. We'll demonstrate a few simple techniques here."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Code Cells vs. Text Cells\n",
     "\n",
     "As you can see, each cell can be either code or text. To select between them, choose from the 'Cell Type' dropdown menu on the top left."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Executing a Command\n",
     "\n",
     "A code cell will be evaluated when you press play, or when you press the shortcut, shift-enter. Evaluating a cell evaluates each line of code in sequence, and prints the results of the last line below the cell."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "4"
      },
      "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "2 + 2"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Sometimes there is no result to be printed, as is the case with assignment."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "X = 2"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Remember that only the result from the last line is printed."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "6"
      },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "2 + 2\n",
     "3 + 3"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "However, you can print whichever lines you want using the `print` statement."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "4\n"
      ]
     },
     {
      "data": {
       "text/plain": "6"
      },
      "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "print (2 + 2)\n",
     "3 + 3"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Knowing When a Cell is Running\n",
     "\n",
     "While a cell is running, a `[*]` will display on the left. When a cell has yet to be executed, `[ ]` will display. When it has been run, a number will display indicating the order in which it was run during the execution of the notebook `[5]`. Try on this cell and note it happening."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "49999995000000"
      },
      "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#Take some time to run something\n",
     "c = 0\n",
     "for i in range(10000000):\n",
     "    c = c + i\n",
     "c"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Importing Libraries\n",
     "\n",
     "The vast majority of the time, you'll want to use functions from pre-built libraries. You can't import every library on Quantopian due to security issues, but you can import most of the common scientific ones. Here I import numpy and pandas, the two most common and useful libraries in quant finance. I recommend copying this import statement to every new notebook.\n",
     "\n",
     "Notice that you can rename libraries to whatever you want after importing. The `as` statement allows this. Here we use `np` and `pd` as aliases for `numpy` and `pandas`. This is a very common aliasing and will be found in most code snippets around the web. The point behind this is to allow you to type fewer characters when you are frequently accessing these libraries."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
     "# This is a plotting library for pretty pictures.\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from broker import get_pricing\n",
     "\n",
     "%matplotlib inline\n",
     "%reload_ext autoreload\n",
     "%autoreload 2\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Tab Autocomplete\n",
     "\n",
     "Pressing tab will give you a list of IPython's best guesses for what you might want to type next. This is incredibly valuable and will save you a lot of time. If there is only one possible option for what you could type next, IPython will fill that in for you. Try pressing tab very frequently, it will seldom fill in anything you don't want, as if there is ambiguity a list will be shown. This is a great way to see what functions are available in a library.\n",
     "\n",
     "Try placing your cursor after the `.` and pressing tab."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<function RandomState.random>"
      },
      "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.random.random"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Getting Documentation Help\n",
     "\n",
     "Placing a question mark after a function and executing that line of code will give you the documentation IPython has for that function. It's often best to do this in a new cell, as you avoid re-executing other code and running into bugs."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "np.random.normal?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Sampling\n",
     "\n",
     "We'll sample some random data using a function from `numpy`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Sample 100 points with a mean of 0 and an std of 1. This is a standard normal distribution.\n",
     "X = np.random.normal(0, 1, 100)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Plotting\n",
     "\n",
     "We can use the plotting library we imported as follows."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "[<matplotlib.lines.Line2D at 0x7f26423e4d00>]"
      },
      "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.plot(X)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "###Squelching Line Output\n",
     "\n",
     "You might have noticed the annoying line of the form `[<matplotlib.lines.Line2D at 0x7f72fdbc1710>]` before the plots. This is because the `.plot` function actually produces output. Sometimes we wish not to display output, we can accomplish this with the semi-colon as follows."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.plot(X);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "###Adding Axis Labels\n",
     "\n",
     "No self-respecting quant leaves a graph without labeled axes. Here are some commands to help with that."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "X = np.random.normal(0, 1, 100)\n",
     "X2 = np.random.normal(0, 1, 100)\n",
     "\n",
     "plt.plot(X);\n",
     "plt.plot(X2);\n",
     "plt.xlabel('Time') # The data we generated is unitless, but don't forget units in general.\n",
     "plt.ylabel('Returns')\n",
     "plt.legend(['X', 'X2']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Generating Statistics\n",
     "\n",
     "Let's use `numpy` to take some simple statistics."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "0.05256354010243057"
      },
      "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.mean(X)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "0.9995952494729183"
      },
      "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.std(X)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Getting Real Pricing Data\n",
     "\n",
     "Randomly sampled data can be great for testing ideas, but let's get some real data. We can use `get_pricing` to do that. You can use the `?` syntax as discussed above to get more information on `get_pricing`'s arguments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
     "data = get_pricing('MSFT', start_date='2012-1-1', end_date='2015-6-1')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Our data is now a dataframe. You can see the datetime index and the colums with different pricing data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "                      price\ndate                       \n2012-01-03 14:00:00  26.765\n2012-01-04 14:00:00  27.400\n2012-01-05 14:00:00  27.680\n2012-01-06 14:00:00  28.105\n2012-01-09 14:00:00  27.740\n...                     ...\n2015-05-22 13:00:00  46.900\n2015-05-26 13:00:00  46.590\n2015-05-27 13:00:00  47.610\n2015-05-28 13:00:00  47.450\n2015-05-29 13:00:00  46.860\n\n[856 rows x 1 columns]",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-03 14:00:00</th>\n      <td>26.765</td>\n    </tr>\n    <tr>\n      <th>2012-01-04 14:00:00</th>\n      <td>27.400</td>\n    </tr>\n    <tr>\n      <th>2012-01-05 14:00:00</th>\n      <td>27.680</td>\n    </tr>\n    <tr>\n      <th>2012-01-06 14:00:00</th>\n      <td>28.105</td>\n    </tr>\n    <tr>\n      <th>2012-01-09 14:00:00</th>\n      <td>27.740</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015-05-22 13:00:00</th>\n      <td>46.900</td>\n    </tr>\n    <tr>\n      <th>2015-05-26 13:00:00</th>\n      <td>46.590</td>\n    </tr>\n    <tr>\n      <th>2015-05-27 13:00:00</th>\n      <td>47.610</td>\n    </tr>\n    <tr>\n      <th>2015-05-28 13:00:00</th>\n      <td>47.450</td>\n    </tr>\n    <tr>\n      <th>2015-05-29 13:00:00</th>\n      <td>46.860</td>\n    </tr>\n  </tbody>\n</table>\n<p>856 rows × 1 columns</p>\n</div>"
      },
      "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This is a pandas dataframe, so we can index in to just get price like this. For more info on pandas, please [click here](http://pandas.pydata.org/pandas-docs/stable/10min.html)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "X = data['price']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Because there is now also date information in our data, we provide two series to `.plot`. `X.index` gives us the datetime index, and `X.values` gives us the pricing values. These are used as the X and Y coordinates to make a graph."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.plot(X.index, X.values)\n",
     "plt.ylabel('Price')\n",
     "plt.legend(['MSFT']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can get statistics again on real data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "36.05159007009346"
      },
      "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.mean(X)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "6.691343238397549"
      },
      "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.std(X)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Getting Returns from Prices\n",
     "\n",
     "We can use the `pct_change` function to get returns. Notice how we drop the first element after doing this, as it will be `NaN` (nothing -> something results in a NaN percent change)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "R = X.pct_change()[1:]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can plot the returns distribution as a histogram."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.hist(R, bins=20)\n",
     "plt.xlabel('Return')\n",
     "plt.ylabel('Frequency')\n",
     "plt.legend(['MSFT Returns']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Get statistics again."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "0.0007595065441719578"
      },
      "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.mean(R)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "0.01442666427270917"
      },
      "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.std(R)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now let's go backwards and generate data out of a normal distribution using the statistics we estimated from Microsoft's returns. We'll see that we have good reason to suspect Microsoft's returns may not be normal, as the resulting normal distribution looks far different."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.hist(np.random.normal(np.mean(R), np.std(R), 10000), bins=20)\n",
     "plt.xlabel('Return')\n",
     "plt.ylabel('Frequency')\n",
     "plt.legend(['Normally Distributed Returns']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##Generating a Moving Average\n",
     "\n",
     "`pandas` has some nice tools to allow us to generate rolling statistics. Here's an example. Notice how there's no moving average for the first 60 days, as we don't have 60 days of data on which to generate the statistic."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
       "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Take the average of the last 60 days at each timepoint.\n",
     "MAVG = X.rolling(60).mean()\n",
     "plt.plot(X.index, X.values)\n",
     "plt.plot(MAVG.index, MAVG.values)\n",
     "plt.ylabel('Price')\n",
     "plt.legend(['MSFT', '60-day MAVG']);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances."
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