preparedata.ipynb

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       "<IPython.core.display.Javascript object>"
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   "source": [
    "%%javascript\n",
    "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"tocheading\">Table of Contents</h1>\n",
    "<div id=\"toc\"></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I recently published a post about applying [deep learning to the stock market](https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02#.e33ao345g). This Notebook is an initial sketch of the implementation. People approach the market to make money, and I am no exception, so it may come as some suprise that I am giving this away. In fact, I don't think this implementation does make money. But it does illustrate a variety of concepts that are important for data science, deep learning and the application of both to finance. I learnt almost everything I know from tutorials like this one on the internet, and I hope someone will learn a thing or two from this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's in this thing?\n",
    "\n",
    "This notebook goes through the full the process of implementing the ideas I layed out in the post. That is it covers\n",
    "* Preparing the data\n",
    "* Building a baseline\n",
    "* Implementing a DL model\n",
    "\n",
    "## A (more or less) standard data science work flow\n",
    "\n",
    "### Defining the problem you want to solve\n",
    "I think all three of those stages are important, and in many respects they are ordered by both importance and the order in which I approach them. My first step in this process was writing that post (4.5K words) explaining in relatively simple terms what I want to do and why. \n",
    "\n",
    "### Collecting data\n",
    "The next stage was collecting data, this actually took me a while and I ended up going with a freely availble dataset you can download [here](https://quantquote.com/historical-stock-data). \n",
    "\n",
    "### Reconciling the problem and the data\n",
    "Once I had an idea of what problem I wanted to solve and I had some data I went about getting the data into a shape  I could work with. \n",
    "\n",
    "### Making a baseline\n",
    "Next I built a baseline model. A baseline doesn't need to be good, but it needs to be something you can compare your actual model to, so that you can know if all the fancy complicated things you did are any better than something simple. This is also a good point to choose how to measure your models performance.\n",
    "\n",
    "### Testing the idea\n",
    "I ended up bulding to baselines. One is a simple logistic regression and the other is a 3 layer feed forward network, or if you like big words, a multi layer perceptron. I made the first because I wanted a baseline and the second because I wanted to see if the additional complexity and computation cost of an RNN added benefit over a simpler network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Prepaparation\n",
    "Maybe its different for the more fortunate but in my experience 80% of the work in data science is data engineering. If I had to break that down even more it would be 20% \"science\", 20% engineering and 60% finding the right, clean data. \n",
    "For the project at hand I wanted a lot of data for many stocks. My \"production\" version uses slightly different data but for the POC using 10 years of the S&P was enough to prover my point.\n",
    "\n",
    "In case you missed it, the S&P500 is an index of 500 stocks that is updated once every 3 months. For POC purposes using daily data is sufficient and I ended up finding a free data set [here](https://quantquote.com/historical-stock-data). You can download it and point the *datapath* variable to point at it and runs this notebook yourself\n",
    "This notebook explores the data, joins all the stocks and creates targets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## preliminaries\n",
    "So first we need to import some libraries and define some helper functions\n",
    "* **get_ticker** applies a regex to the filename we are looking at and extracts the ticker from it\n",
    "* **ret calculates** to log return between two points. Returns should be observed in log space because\n",
    "    * Returns are log-normally distributed so log returns are follow a normal distribution\n",
    "    * You can some log returns instead of taking products which makes life easier\n",
    "* **zscore** maps a pandas series to it's zscore. In other words, it makes it have mean 0 and variance 1\n",
    "    * It's good to have variablised normalized like this as it makes all of the dimensions of your data behave the same\n",
    "    * It doesn't make sense to apply a z-score to distributions that don't (more or less) folllow the normal distribution. So check your variables first and if they don't follow a normal distribution transform them so that they do are use other scaling methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "datapath = './daily'\n",
    "filepath = os.path.join(datapath,os.listdir('./daily')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./daily/table_tel.csv tel\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "ticker_regex = re.compile('.+_(?P<ticker>.+)\\.csv')\n",
    "get_ticker =lambda x :ticker_regex.match(x).groupdict()['ticker']\n",
    "print(filepath,get_ticker(filepath))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret = lambda x,y: log(y/x) #Log return \n",
    "zscore = lambda x:(x -x.mean())/x.std() # zscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First peak at the data\n",
    "I blah blahed alot without actually looking at the data. lets load it. I use the pandas library to rad a single CSV. Since the data had no column headers I specified them. I don't know what the first column is so I labeled it UNK.\n",
    "Notice that the index, which is the real first column in the original data, is a date but in a string pandas didn't understand. We're going to parse it to a datetime object later so that pandas preserves the right order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "D = pd.read_csv(filepath,header=None,names=['UNK','o','h','l','c','v']) #Load the dataframe with headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <th></th>\n",
       "      <th>UNK</th>\n",
       "      <th>o</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>c</th>\n",
       "      <th>v</th>\n",
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       "      <th>20070702</th>\n",
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       "      <td>4021547.844</td>\n",
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       "      <th>20070709</th>\n",
       "      <td>0</td>\n",
       "      <td>34.4493</td>\n",
       "      <td>35.1462</td>\n",
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       "          UNK        o        h        l        c            v\n",
       "20070702    0  34.1445  35.3901  33.9529  34.8065  1834373.873\n",
       "20070703    0  34.8239  35.3726  34.0400  34.5452  4143385.114\n",
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       "20070709    0  34.4493  35.1462  33.5173  35.0329  5388421.001"
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     "execution_count": 6,
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   "source": [
    "D.head() #Lets peack at it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Relevant Data\n",
    "We don't care about prices, just about their fluctations. So we'll give the log return of the various prices, and take a zscore so everything is nice. \n",
    "Also, extract the ticker from the file name and add it to the df as a column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "def make_inputs(filepath):\n",
    "    D = pd.read_csv(filepath,header=None,names=['UNK','o','h','l','c','v']) #Load the dataframe with headers\n",
    "    D.index = pd.to_datetime(D.index,format='%Y%m%d') # Set the indix to a datetime\n",
    "    Res = pd.DataFrame()\n",
    "    ticker = get_ticker(filepath)\n",
    "\n",
    "    Res['c_2_o'] = zscore(ret(D.o,D.c))\n",
    "    Res['h_2_o'] = zscore(ret(D.o,D.h))\n",
    "    Res['l_2_o'] = zscore(ret(D.o,D.l))\n",
    "    Res['c_2_h'] = zscore(ret(D.h,D.c))\n",
    "    Res['h_2_l'] = zscore(ret(D.h,D.l))\n",
    "    Res['c1_c0'] = ret(D.c,D.c.shift(-1)).fillna(0) #Tommorows return \n",
    "    Res['vol'] = zscore(D.v)\n",
    "    Res['ticker'] = ticker\n",
    "    return Res\n",
    "Res = make_inputs(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_2_o</th>\n",
       "      <th>h_2_o</th>\n",
       "      <th>l_2_o</th>\n",
       "      <th>c_2_h</th>\n",
       "      <th>h_2_l</th>\n",
       "      <th>c1_c0</th>\n",
       "      <th>vol</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-07-02</th>\n",
       "      <td>0.801355</td>\n",
       "      <td>1.210723</td>\n",
       "      <td>0.538772</td>\n",
       "      <td>-0.151655</td>\n",
       "      <td>-0.534522</td>\n",
       "      <td>-0.007536</td>\n",
       "      <td>-0.423611</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-03</th>\n",
       "      <td>-0.424752</td>\n",
       "      <td>-0.005068</td>\n",
       "      <td>-0.519118</td>\n",
       "      <td>-0.613751</td>\n",
       "      <td>-0.390993</td>\n",
       "      <td>-0.000252</td>\n",
       "      <td>0.698563</td>\n",
       "      <td>tel</td>\n",
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       "    <tr>\n",
       "      <th>2007-07-05</th>\n",
       "      <td>-0.006181</td>\n",
       "      <td>-0.341182</td>\n",
       "      <td>0.322381</td>\n",
       "      <td>0.362967</td>\n",
       "      <td>0.511406</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>0.882284</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-06</th>\n",
       "      <td>0.357732</td>\n",
       "      <td>-0.156944</td>\n",
       "      <td>-0.060999</td>\n",
       "      <td>0.692674</td>\n",
       "      <td>0.076016</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.639351</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-09</th>\n",
       "      <td>0.693154</td>\n",
       "      <td>0.259451</td>\n",
       "      <td>-0.806687</td>\n",
       "      <td>0.727729</td>\n",
       "      <td>-0.816114</td>\n",
       "      <td>-0.031057</td>\n",
       "      <td>1.303648</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               c_2_o     h_2_o     l_2_o     c_2_h     h_2_l     c1_c0  \\\n",
       "2007-07-02  0.801355  1.210723  0.538772 -0.151655 -0.534522 -0.007536   \n",
       "2007-07-03 -0.424752 -0.005068 -0.519118 -0.613751 -0.390993 -0.000252   \n",
       "2007-07-05 -0.006181 -0.341182  0.322381  0.362967  0.511406  0.003021   \n",
       "2007-07-06  0.357732 -0.156944 -0.060999  0.692674  0.076016  0.011250   \n",
       "2007-07-09  0.693154  0.259451 -0.806687  0.727729 -0.816114 -0.031057   \n",
       "\n",
       "                 vol ticker  \n",
       "2007-07-02 -0.423611    tel  \n",
       "2007-07-03  0.698563    tel  \n",
       "2007-07-05  0.882284    tel  \n",
       "2007-07-06  0.639351    tel  \n",
       "2007-07-09  1.303648    tel  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.head() # Lets look at what we got"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_2_o</th>\n",
       "      <th>h_2_o</th>\n",
       "      <th>l_2_o</th>\n",
       "      <th>c_2_h</th>\n",
       "      <th>h_2_l</th>\n",
       "      <th>c1_c0</th>\n",
       "      <th>vol</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c_2_o</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727980</td>\n",
       "      <td>0.646456</td>\n",
       "      <td>0.664281</td>\n",
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       "    <tr>\n",
       "      <th>h_2_o</th>\n",
       "      <td>0.727980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157799</td>\n",
       "      <td>-0.028889</td>\n",
       "      <td>-0.659993</td>\n",
       "      <td>0.007359</td>\n",
       "      <td>0.300114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_2_o</th>\n",
       "      <td>0.646456</td>\n",
       "      <td>0.157799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.770473</td>\n",
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       "      <td>0.010860</td>\n",
       "      <td>-0.321847</td>\n",
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       "    <tr>\n",
       "      <th>c_2_h</th>\n",
       "      <td>0.664281</td>\n",
       "      <td>-0.028889</td>\n",
       "      <td>0.770473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.608713</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>-0.309896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h_2_l</th>\n",
       "      <td>-0.076033</td>\n",
       "      <td>-0.659993</td>\n",
       "      <td>0.637713</td>\n",
       "      <td>0.608713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002522</td>\n",
       "      <td>-0.478965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1_c0</th>\n",
       "      <td>0.008279</td>\n",
       "      <td>0.007359</td>\n",
       "      <td>0.010860</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>0.002522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>0.011871</td>\n",
       "      <td>0.300114</td>\n",
       "      <td>-0.321847</td>\n",
       "      <td>-0.309896</td>\n",
       "      <td>-0.478965</td>\n",
       "      <td>-0.017168</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "          c_2_o     h_2_o     l_2_o     c_2_h     h_2_l     c1_c0       vol\n",
       "c_2_o  1.000000  0.727980  0.646456  0.664281 -0.076033  0.008279  0.011871\n",
       "h_2_o  0.727980  1.000000  0.157799 -0.028889 -0.659993  0.007359  0.300114\n",
       "l_2_o  0.646456  0.157799  1.000000  0.770473  0.637713  0.010860 -0.321847\n",
       "c_2_h  0.664281 -0.028889  0.770473  1.000000  0.608713  0.004047 -0.309896\n",
       "h_2_l -0.076033 -0.659993  0.637713  0.608713  1.000000  0.002522 -0.478965\n",
       "c1_c0  0.008279  0.007359  0.010860  0.004047  0.002522  1.000000 -0.017168\n",
       "vol    0.011871  0.300114 -0.321847 -0.309896 -0.478965 -0.017168  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.corr() #Quick check to see we didn't mess it up. All values should be different, otherwise we repeated a variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the full data set\n",
    "I'll iterate over each file, run the above and concat to a final df. Then we'll pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Final = pd.DataFrame()\n",
    "for f in os.listdir(datapath):\n",
    "    filepath = os.path.join(datapath,f)\n",
    "    if filepath.endswith('.csv'):\n",
    "        Res = make_inputs(filepath)\n",
    "        Final = Final.append(Res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_2_o</th>\n",
       "      <th>h_2_o</th>\n",
       "      <th>l_2_o</th>\n",
       "      <th>c_2_h</th>\n",
       "      <th>h_2_l</th>\n",
       "      <th>c1_c0</th>\n",
       "      <th>vol</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-07-02</th>\n",
       "      <td>0.801355</td>\n",
       "      <td>1.210723</td>\n",
       "      <td>0.538772</td>\n",
       "      <td>-0.151655</td>\n",
       "      <td>-0.534522</td>\n",
       "      <td>-0.007536</td>\n",
       "      <td>-0.423611</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-03</th>\n",
       "      <td>-0.424752</td>\n",
       "      <td>-0.005068</td>\n",
       "      <td>-0.519118</td>\n",
       "      <td>-0.613751</td>\n",
       "      <td>-0.390993</td>\n",
       "      <td>-0.000252</td>\n",
       "      <td>0.698563</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-05</th>\n",
       "      <td>-0.006181</td>\n",
       "      <td>-0.341182</td>\n",
       "      <td>0.322381</td>\n",
       "      <td>0.362967</td>\n",
       "      <td>0.511406</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>0.882284</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-06</th>\n",
       "      <td>0.357732</td>\n",
       "      <td>-0.156944</td>\n",
       "      <td>-0.060999</td>\n",
       "      <td>0.692674</td>\n",
       "      <td>0.076016</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.639351</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-09</th>\n",
       "      <td>0.693154</td>\n",
       "      <td>0.259451</td>\n",
       "      <td>-0.806687</td>\n",
       "      <td>0.727729</td>\n",
       "      <td>-0.816114</td>\n",
       "      <td>-0.031057</td>\n",
       "      <td>1.303648</td>\n",
       "      <td>tel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               c_2_o     h_2_o     l_2_o     c_2_h     h_2_l     c1_c0  \\\n",
       "2007-07-02  0.801355  1.210723  0.538772 -0.151655 -0.534522 -0.007536   \n",
       "2007-07-03 -0.424752 -0.005068 -0.519118 -0.613751 -0.390993 -0.000252   \n",
       "2007-07-05 -0.006181 -0.341182  0.322381  0.362967  0.511406  0.003021   \n",
       "2007-07-06  0.357732 -0.156944 -0.060999  0.692674  0.076016  0.011250   \n",
       "2007-07-09  0.693154  0.259451 -0.806687  0.727729 -0.816114 -0.031057   \n",
       "\n",
       "                 vol ticker  \n",
       "2007-07-02 -0.423611    tel  \n",
       "2007-07-03  0.698563    tel  \n",
       "2007-07-05  0.882284    tel  \n",
       "2007-07-06  0.639351    tel  \n",
       "2007-07-09  1.303648    tel  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pivot_columns = Final.columns[:-1]\n",
    "P = Final.pivot_table(index=Final.index,columns='ticker',values=pivot_columns) # Make a pivot table from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th colspan=\"10\" halign=\"left\">c_2_o</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">vol</th>\n",
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       "    <tr>\n",
       "      <th>ticker</th>\n",
       "      <th>a</th>\n",
       "      <th>aa</th>\n",
       "      <th>aapl</th>\n",
       "      <th>abbv</th>\n",
       "      <th>abc</th>\n",
       "      <th>abt</th>\n",
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       "      <th>xrx</th>\n",
       "      <th>xyl</th>\n",
       "      <th>yhoo</th>\n",
       "      <th>yum</th>\n",
       "      <th>zion</th>\n",
       "      <th>zmh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017649</td>\n",
       "      <td>6.991394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047250</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-0.368713</td>\n",
       "      <td>-0.678763</td>\n",
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       "    <tr>\n",
       "      <th>1998-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.373488</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-0.160945</td>\n",
       "      <td>0.031659</td>\n",
       "      <td>-0.234577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.090086</td>\n",
       "      <td>0.791331</td>\n",
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       "      <td>-0.621025</td>\n",
       "      <td>1.019607</td>\n",
       "      <td>-0.615370</td>\n",
       "      <td>-0.556684</td>\n",
       "      <td>-0.757660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.064841</td>\n",
       "      <td>0.980283</td>\n",
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       "      <th>1998-01-06</th>\n",
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       "      <td>-0.718569</td>\n",
       "      <td>6.857266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.102853</td>\n",
       "      <td>-0.473164</td>\n",
       "      <td>0.452262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.239961</td>\n",
       "      <td>1.451409</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.634169</td>\n",
       "      <td>0.020195</td>\n",
       "      <td>-0.527923</td>\n",
       "      <td>-1.131583</td>\n",
       "      <td>-0.629732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.150071</td>\n",
       "      <td>1.697887</td>\n",
       "      <td>-0.683202</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980669</td>\n",
       "      <td>-3.007557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.553079</td>\n",
       "      <td>0.746779</td>\n",
       "      <td>0.137372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.878292</td>\n",
       "      <td>-0.534781</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.616242</td>\n",
       "      <td>1.023705</td>\n",
       "      <td>-0.134130</td>\n",
       "      <td>-0.329006</td>\n",
       "      <td>-0.504232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.463651</td>\n",
       "      <td>0.515268</td>\n",
       "      <td>-0.635858</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.828950</td>\n",
       "      <td>1.388888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.488170</td>\n",
       "      <td>-0.221620</td>\n",
       "      <td>-0.484264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.300492</td>\n",
       "      <td>0.538778</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.655003</td>\n",
       "      <td>0.645285</td>\n",
       "      <td>-0.907375</td>\n",
       "      <td>-1.066107</td>\n",
       "      <td>-0.560549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.157777</td>\n",
       "      <td>0.235907</td>\n",
       "      <td>-0.605092</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 3500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           c_2_o                                                             \\\n",
       "ticker         a        aa      aapl abbv       abc       abt       ace acn   \n",
       "1998-01-02   NaN -0.017649  6.991394  NaN  0.047250  1.845516 -0.601609 NaN   \n",
       "1998-01-05   NaN  0.373488 -1.224062  NaN -0.160945  0.031659 -0.234577 NaN   \n",
       "1998-01-06   NaN -0.718569  6.857266  NaN -1.102853 -0.473164  0.452262 NaN   \n",
       "1998-01-07   NaN  0.980669 -3.007557  NaN  0.553079  0.746779  0.137372 NaN   \n",
       "1998-01-08   NaN -1.828950  1.388888  NaN  0.488170 -0.221620 -0.484264 NaN   \n",
       "\n",
       "                               ...       vol                                \\\n",
       "ticker           act      adbe ...        xl      xlnx       xom      xray   \n",
       "1998-01-02 -0.911471 -0.375461 ... -0.676802  1.391497 -1.045626 -1.147952   \n",
       "1998-01-05  2.090086  0.791331 ... -0.621025  1.019607 -0.615370 -0.556684   \n",
       "1998-01-06 -1.239961  1.451409 ... -0.634169  0.020195 -0.527923 -1.131583   \n",
       "1998-01-07 -0.878292 -0.534781 ... -0.616242  1.023705 -0.134130 -0.329006   \n",
       "1998-01-08  1.300492  0.538778 ... -0.655003  0.645285 -0.907375 -1.066107   \n",
       "\n",
       "                                                            \n",
       "ticker           xrx xyl      yhoo       yum      zion zmh  \n",
       "1998-01-02 -0.817355 NaN -0.368713 -0.678763 -0.724482 NaN  \n",
       "1998-01-05 -0.757660 NaN  0.064841  0.980283 -0.735275 NaN  \n",
       "1998-01-06 -0.629732 NaN  0.150071  1.697887 -0.683202 NaN  \n",
       "1998-01-07 -0.504232 NaN -0.463651  0.515268 -0.635858 NaN  \n",
       "1998-01-08 -0.560549 NaN -0.157777  0.235907 -0.605092 NaN  \n",
       "\n",
       "[5 rows x 3500 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flattening the pivot\n",
    "source http://stackoverflow.com/questions/14507794/python-pandas-how-to-flatten-a-hierarchical-index-in-columns\n",
    "At the end of this P is a flattened dataframe of all the entries for each stock, one day per row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mi = P.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_ind = pd.Index(e[1] +'_' + e[0] for e in mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:2: FutureWarning: sort(....) is deprecated, use sort_index(.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "P.columns = new_ind\n",
    "P = P.sort(axis=1) # Sort by columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1998-01-06</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-1.264514</td>\n",
       "      <td>-0.683202</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1998-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.042290</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1998-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-1.828950</td>\n",
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       "      <td>-0.202411</td>\n",
       "      <td>-2.278200</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            a_c1_c0  a_c_2_h  a_c_2_o  a_h_2_l  a_h_2_o  a_l_2_o  a_vol  \\\n",
       "1998-01-02      NaN      NaN      NaN      NaN      NaN      NaN    NaN   \n",
       "1998-01-05      NaN      NaN      NaN      NaN      NaN      NaN    NaN   \n",
       "1998-01-06      NaN      NaN      NaN      NaN      NaN      NaN    NaN   \n",
       "1998-01-07      NaN      NaN      NaN      NaN      NaN      NaN    NaN   \n",
       "1998-01-08      NaN      NaN      NaN      NaN      NaN      NaN    NaN   \n",
       "\n",
       "            aa_c1_c0  aa_c_2_h  aa_c_2_o   ...     zion_h_2_o  zion_l_2_o  \\\n",
       "1998-01-02  0.017390  0.670259 -0.017649   ...       0.246401    0.710284   \n",
       "1998-01-05 -0.022601  0.462520  0.373488   ...       0.127875    0.710284   \n",
       "1998-01-06  0.001693 -0.088471 -0.718569   ...      -0.677287   -1.264514   \n",
       "1998-01-07 -0.042290  1.002099  0.980669   ...      -0.677287   -1.175835   \n",
       "1998-01-08 -0.024094 -2.074267 -1.828950   ...      -0.202411   -2.278200   \n",
       "\n",
       "            zion_vol  zmh_c1_c0  zmh_c_2_h  zmh_c_2_o  zmh_h_2_l  zmh_h_2_o  \\\n",
       "1998-01-02 -0.724482        NaN        NaN        NaN        NaN        NaN   \n",
       "1998-01-05 -0.735275        NaN        NaN        NaN        NaN        NaN   \n",
       "1998-01-06 -0.683202        NaN        NaN        NaN        NaN        NaN   \n",
       "1998-01-07 -0.635858        NaN        NaN        NaN        NaN        NaN   \n",
       "1998-01-08 -0.605092        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            zmh_l_2_o  zmh_vol  \n",
       "1998-01-02        NaN      NaN  \n",
       "1998-01-05        NaN      NaN  \n",
       "1998-01-06        NaN      NaN  \n",
       "1998-01-07        NaN      NaN  \n",
       "1998-01-08        NaN      NaN  \n",
       "\n",
       "[5 rows x 3500 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clean_and_flat = P.dropna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "target_cols = list(filter(lambda x: 'c1_c0' in x, clean_and_flat.columns.values))\n",
    "input_cols  = list(filter(lambda x: 'c1_c0' not in x, clean_and_flat.columns.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "InputDF = clean_and_flat[input_cols][:3900]\n",
    "TargetDF = clean_and_flat[target_cols][:3900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "corrs = TargetDF.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Targets\n",
    "We now have an our inputs and targets, kind of. \n",
    "InputsDF has all the inputs we want to predict. Targets DF has the return of each stock each day. \n",
    "For starters, lets give a simpler target to predict than the reuturn of each stock, since we don't have much data. \n",
    "\n",
    "\n",
    "We're going to label the targets as either up (1) down (-1) or flat (0) days.\n",
    "The top chart shows what would happen if we bought 1 dollar of ewach stock each day\n",
    "The bottom chart shows what would happen if we longed the whole basket on (1) days, shorted it on down days (-1) and ignored it on  (0) days. \n",
    "You can see that this is a valuable target to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_stocks = len(TargetDF.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TotalReturn = ((1-exp(TargetDF)).sum(1))/num_stocks # If i put one dollar in each stock at the close, this is how much I'd get back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def labeler(x):\n",
    "    if x>0.0029:\n",
    "        return 1\n",
    "    if x<-0.00462:\n",
    "        return -1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Labeled = pd.DataFrame()\n",
    "Labeled['return'] = TotalReturn\n",
    "Labeled['class'] = TotalReturn.apply(labeler,1)\n",
    "Labeled['multi_class'] = pd.qcut(TotalReturn,11,labels=range(11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.00301, 0.00119], (0.00741, 0.0999], (0.00119, 0.00741], [-0.108, -0.00877], (-0.00877, -0.00301]]\n",
       "Categories (5, object): [[-0.108, -0.00877] < (-0.00877, -0.00301] < (-0.00301, 0.00119] < (0.00119, 0.00741] < (0.00741, 0.0999]]"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(TotalReturn,5).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.0249, 0.0167], (0.0167, 0.0583], (0.0583, 0.0999], (-0.0665, -0.0249], (-0.108, -0.0665]]\n",
       "Categories (5, object): [(-0.108, -0.0665] < (-0.0665, -0.0249] < (-0.0249, 0.0167] < (0.0167, 0.0583] < (0.0583, 0.0999]]"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def labeler_multi(x):\n",
    "    if x>0.0029:\n",
    "        return 1\n",
    "    if x<-0.00462:\n",
    "        return -1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    1301\n",
       " 0    1301\n",
       "-1    1298\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Labeled['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Labeled['act_return'] = Labeled['class'] * Labeled['return']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fefaad46080>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fefaae84320>], dtype=object)"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MH8esmrGWBpbNAoCn+rbDU33bQa1W4/mP9gAASsoVSMm4DX9vJ6jUaqhU6mp7RVUqtWYr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefaade8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Labeled[['return','act_return']].cumsum().plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making a baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "logreg = linear_model.LogisticRegression(C=1e5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_size=600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res = logreg.fit(InputDF[:-test_size],Labeled['multi_class'][:-test_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.18      0.04      0.06        52\n",
      "          1       0.07      0.06      0.06        50\n",
      "          2       0.08      0.15      0.10        60\n",
      "          3       0.15      0.13      0.14        62\n",
      "          4       0.08      0.06      0.07        50\n",
      "          5       0.06      0.12      0.08        51\n",
      "          6       0.13      0.20      0.16        60\n",
      "          7       0.05      0.04      0.04        53\n",
      "          8       0.06      0.06      0.06        54\n",
      "          9       0.11      0.07      0.09        56\n",
      "         10       0.00      0.00      0.00        52\n",
      "\n",
      "avg / total       0.09      0.09      0.08       600\n",
      "\n",
      "[[ 2  3 11  6  1  7  7  3  3  4  5]\n",
      " [ 2  3  8  7  8  7  6  2  2  2  3]\n",
      " [ 1  4  9  5  2 13  5 11  5  3  2]\n",
      " [ 1  6  7  8  5 15  6  1  7  4  2]\n",
      " [ 1  3  6  2  3  9 10  5  7  4  0]\n",
      " [ 0  5 11  3  3  6  6  4  5  6  2]\n",
      " [ 1  3 15  3  3 12 12  1  4  2  4]\n",
      " [ 0  4  8  3  3  8 12  2 10  3  0]\n",
      " [ 0  2 16  6  3  9 12  2  3  0  1]\n",
      " [ 0  7 11  5  2  7 10  4  4  4  2]\n",
      " [ 3  3 10  6  6  9  6  4  1  4  0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(classification_report(Labeled['multi_class'][-test_size:],res.predict(InputDF[-test_size:])))\n",
    "print(confusion_matrix(Labeled['multi_class'][-test_size:],res.predict(InputDF[-test_size:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[91 37 75]\n",
      " [76 35 89]\n",
      " [92 30 75]]\n"
     ]
    }
   ],
   "source": [
    "Labeled['predicted_action'] = list(map(lambda x: -1 if x <5 else 0 if x==5 else 1,res.predict(InputDF)))\n",
    "print(confusion_matrix(Labeled['class'][-test_size:],Labeled['predicted_action'][-test_size:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Labeled['pred_return'] = Labeled['predicted_action'] * Labeled['return']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefbf909a20>"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xgxiWOISchGxiwjq/HrdIoFGoFRERCSCt3jbWVWzk69I1bKvOx+SbC3QTIuIZ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\nik204rkSMLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefbf72f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Res = Labeled[-test_size:][['return','act_return','pred_return']].cumsum()\n",
    "Res[0] =0\n",
    "Res.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Training a basic feed forward network\n",
    "Here I'll use the tensorflow contrib.learn to quickly train a feed forward network. More of a benchmark than something I plan on using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from  tensorflow.contrib.learn.python.learn.estimators.dnn  import DNNClassifier\n",
    "from tensorflow.contrib.layers import real_valued_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Labeled['tf_class'] = Labeled['multi_class']\n",
    "num_features = len(InputDF.columns)\n",
    "dropout=0.2\n",
    "hidden_1_size = 1000\n",
    "hidden_2_size = 250\n",
    "num_classes = Labeled.tf_class.nunique()\n",
    "NUM_EPOCHS=100\n",
    "BATCH_SIZE=50\n",
    "lr=0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = (InputDF[:-test_size].values,Labeled.tf_class[:-test_size].values)\n",
    "val = (InputDF[-test_size:].values,Labeled.tf_class[-test_size:].values)\n",
    "NUM_TRAIN_BATCHES = int(len(train[0])/BATCH_SIZE)\n",
    "NUM_VAL_BATCHES = int(len(val[1])/BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3900"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(InputDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Model():\n",
    "    def __init__(self):\n",
    "        global_step = tf.contrib.framework.get_or_create_global_step()\n",
    "        self.input_data = tf.placeholder(dtype=tf.float32,shape=[None,num_features])\n",
    "        self.target_data = tf.placeholder(dtype=tf.int32,shape=[None])\n",
    "        self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])\n",
    "        with tf.variable_scope(\"ff\"):\n",
    "            droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)\n",
    "            \n",
    "            layer_1 = tf.contrib.layers.fully_connected(\n",
    "                num_outputs=hidden_1_size,\n",
    "                inputs=droped_input,\n",
    "            )\n",
    "            layer_2 = tf.contrib.layers.fully_connected(\n",
    "                num_outputs=hidden_2_size,\n",
    "                inputs=layer_1,\n",
    "            )\n",
    "            self.logits = tf.contrib.layers.fully_connected(\n",
    "                num_outputs=num_classes,\n",
    "                activation_fn =None,\n",
    "                inputs=layer_2,\n",
    "            )\n",
    "        with tf.variable_scope(\"loss\"):\n",
    "            \n",
    "            self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits,self.target_data)\n",
    "            mask = (1-tf.sign(1-self.target_data)) #Don't give credit for flat days\n",
    "            mask = tf.cast(mask,tf.float32)\n",
    "            self.loss = tf.reduce_sum(self.losses)\n",
    "        \n",
    "        with tf.name_scope(\"train\"):\n",
    "          opt = tf.train.AdamOptimizer(lr)\n",
    "          gvs = opt.compute_gradients(self.loss)\n",
    "          self.train_op = opt.apply_gradients(gvs, global_step=global_step)\n",
    "        \n",
    "        with tf.name_scope(\"predictions\"):\n",
    "            self.probs = tf.nn.softmax(self.logits)\n",
    "            self.predictions = tf.argmax(self.probs, 1)\n",
    "            correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)\n",
    "            self.accuracy = tf.reduce_mean(correct_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-664-27e7559e3218>:6 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "step - 66 loss - 8447.131065368652 acc - 0.12\n",
      "step - 132 loss - 6890.02379989624 acc - 0.28\n",
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      "step - 264 loss - 5001.0221824646 acc - 0.54\n",
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      "step - 396 loss - 3751.737133026123 acc - 0.78\n",
      "step - 462 loss - 3260.896957397461 acc - 0.76\n",
      "step - 528 loss - 2523.7949352264404 acc - 0.78\n",
      "step - 594 loss - 2012.8276433944702 acc - 0.88\n",
      "step - 660 loss - 1719.1777667999268 acc - 0.88\n",
      "step - 726 loss - 1422.7981615066528 acc - 0.86\n",
      "step - 792 loss - 1197.4740686416626 acc - 0.88\n",
      "step - 858 loss - 1133.8116784095764 acc - 0.88\n",
      "step - 924 loss - 971.3043849468231 acc - 0.88\n",
      "step - 990 loss - 1035.503452539444 acc - 0.92\n",
      "step - 1056 loss - 946.5315816402435 acc - 0.8\n",
      "step - 1122 loss - 960.7589373588562 acc - 0.96\n",
      "step - 1188 loss - 843.3934515714645 acc - 0.94\n",
      "step - 1254 loss - 750.8844127655029 acc - 0.86\n",
      "step - 1320 loss - 577.3744535446167 acc - 0.94\n",
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      "step - 1452 loss - 293.17400139570236 acc - 0.94\n",
      "step - 1518 loss - 252.8774927854538 acc - 0.98\n",
      "step - 1584 loss - 227.9954394698143 acc - 0.96\n",
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      "step - 1716 loss - 167.47538980841637 acc - 1.0\n",
      "step - 1782 loss - 172.1363340318203 acc - 0.98\n",
      "step - 1848 loss - 159.17900252342224 acc - 1.0\n",
      "step - 1914 loss - 139.79964539408684 acc - 0.96\n",
      "step - 1980 loss - 127.83966934680939 acc - 0.96\n",
      "step - 2046 loss - 122.7044115960598 acc - 0.96\n",
      "step - 2112 loss - 109.09442016482353 acc - 1.0\n",
      "step - 2178 loss - 108.29805994033813 acc - 0.98\n",
      "step - 2244 loss - 89.04672286659479 acc - 1.0\n",
      "step - 2310 loss - 77.11995178461075 acc - 0.98\n",
      "step - 2376 loss - 65.97096748650074 acc - 0.98\n",
      "step - 2442 loss - 58.03815923631191 acc - 1.0\n",
      "step - 2508 loss - 54.71065887063742 acc - 1.0\n",
      "step - 2574 loss - 52.697464659810066 acc - 1.0\n",
      "step - 2640 loss - 48.56147729605436 acc - 0.98\n",
      "step - 2706 loss - 51.280679762363434 acc - 0.98\n",
      "step - 2772 loss - 63.46054144203663 acc - 1.0\n",
      "step - 2838 loss - 52.819958999753 acc - 1.0\n",
      "step - 2904 loss - 146.99085077643394 acc - 1.0\n",
      "step - 2970 loss - 150.77954614162445 acc - 1.0\n",
      "step - 3036 loss - 208.99775820970535 acc - 0.98\n",
      "step - 3102 loss - 334.6918689906597 acc - 1.0\n",
      "step - 3168 loss - 256.0220812559128 acc - 1.0\n",
      "step - 3234 loss - 172.50200808048248 acc - 1.0\n",
      "step - 3300 loss - 176.80959203839302 acc - 0.98\n",
      "step - 3366 loss - 162.36830121278763 acc - 0.94\n",
      "step - 3432 loss - 232.8863918185234 acc - 0.98\n",
      "step - 3498 loss - 151.8986648619175 acc - 1.0\n",
      "step - 3564 loss - 80.97092315554619 acc - 0.98\n",
      "step - 3630 loss - 45.175595223903656 acc - 1.0\n",
      "step - 3696 loss - 31.07171132415533 acc - 1.0\n",
      "step - 3762 loss - 23.805237226188183 acc - 0.98\n",
      "step - 3828 loss - 18.659388177096844 acc - 1.0\n",
      "step - 3894 loss - 17.505414922721684 acc - 1.0\n",
      "step - 3960 loss - 15.338336419314146 acc - 1.0\n",
      "step - 4026 loss - 13.726457159034908 acc - 1.0\n",
      "step - 4092 loss - 12.529930191114545 acc - 1.0\n",
      "step - 4158 loss - 12.875888019800186 acc - 1.0\n",
      "step - 4224 loss - 11.709485536441207 acc - 1.0\n",
      "step - 4290 loss - 12.39748506899923 acc - 1.0\n",
      "step - 4356 loss - 10.142329946160316 acc - 1.0\n",
      "step - 4422 loss - 12.359895068220794 acc - 1.0\n",
      "step - 4488 loss - 9.553100385703146 acc - 1.0\n",
      "step - 4554 loss - 10.08048278838396 acc - 1.0\n",
      "step - 4620 loss - 9.791451989673078 acc - 1.0\n",
      "step - 4686 loss - 8.63899156684056 acc - 1.0\n",
      "step - 4752 loss - 8.411272457800806 acc - 1.0\n",
      "step - 4818 loss - 7.749729743227363 acc - 1.0\n",
      "step - 4884 loss - 7.433622751850635 acc - 1.0\n",
      "step - 4950 loss - 7.990060992538929 acc - 1.0\n",
      "step - 5016 loss - 7.397974960505962 acc - 1.0\n",
      "step - 5082 loss - 7.048397799488157 acc - 1.0\n",
      "step - 5148 loss - 6.151308918371797 acc - 1.0\n",
      "step - 5214 loss - 6.154656197410077 acc - 1.0\n",
      "step - 5280 loss - 6.255449664779007 acc - 1.0\n",
      "step - 5346 loss - 6.112679478246719 acc - 1.0\n",
      "step - 5412 loss - 5.859058992937207 acc - 1.0\n",
      "step - 5478 loss - 6.458761698566377 acc - 1.0\n",
      "step - 5544 loss - 6.051256897859275 acc - 1.0\n",
      "step - 5610 loss - 5.625357799232006 acc - 1.0\n",
      "step - 5676 loss - 5.570352027192712 acc - 1.0\n",
      "step - 5742 loss - 6.261496446561068 acc - 1.0\n",
      "step - 5808 loss - 6.633889449760318 acc - 1.0\n",
      "step - 5874 loss - 6.511529266834259 acc - 1.0\n",
      "step - 5940 loss - 5.500934490235522 acc - 1.0\n",
      "step - 6006 loss - 4.715749520808458 acc - 1.0\n",
      "step - 6072 loss - 4.403032652102411 acc - 1.0\n",
      "step - 6138 loss - 4.604925341205671 acc - 1.0\n",
      "step - 6204 loss - 4.500160810537636 acc - 1.0\n",
      "step - 6270 loss - 4.204684946220368 acc - 1.0\n",
      "step - 6336 loss - 3.436353671364486 acc - 1.0\n",
      "step - 6402 loss - 4.853738834615797 acc - 1.0\n",
      "step - 6468 loss - 7.298051157500595 acc - 1.0\n",
      "step - 6534 loss - 4.585601169615984 acc - 1.0\n",
      "step - 6600 loss - 4.205870944540948 acc - 1.0\n",
      "done training\n",
      "0.12\n",
      "0.02\n",
      "0.18\n",
      "0.12\n",
      "0.08\n",
      "0.14\n",
      "0.08\n",
      "0.06\n",
      "0.06\n",
      "0.14\n",
      "0.12\n",
      "0.06\n"
     ]
    }
   ],
   "source": [
    "with tf.Graph().as_default():\n",
    "    model = Model()\n",
    "    input_ = train[0]\n",
    "    target = train[1]\n",
    "    with tf.Session() as sess:\n",
    "        init = tf.initialize_all_variables()\n",
    "        sess.run([init])\n",
    "        epoch_loss =0\n",
    "        for e in range(NUM_EPOCHS):\n",
    "            if epoch_loss >0 and epoch_loss <1:\n",
    "                break\n",
    "            epoch_loss =0\n",
    "            for batch in range(0,NUM_TRAIN_BATCHES):\n",
    "                \n",
    "                start = batch*BATCH_SIZE\n",
    "                end = start + BATCH_SIZE \n",
    "                feed = {\n",
    "                    model.input_data:input_[start:end],\n",
    "                    model.target_data:target[start:end],\n",
    "                    model.dropout_prob:0.9\n",
    "                            }\n",
    "                \n",
    "                _,loss,acc = sess.run(\n",
    "                    [\n",
    "                        model.train_op,\n",
    "                        model.loss,\n",
    "                        model.accuracy,\n",
    "                    ]\n",
    "                    ,feed_dict=feed\n",
    "                )\n",
    "                epoch_loss+=loss\n",
    "            print('step - {0} loss - {1} acc - {2}'.format((1+batch+NUM_TRAIN_BATCHES*e),epoch_loss,acc))\n",
    "                \n",
    "        \n",
    "        print('done training')\n",
    "        final_preds =np.array([])\n",
    "        final_probs =None\n",
    "        for batch in range(0,NUM_VAL_BATCHES):\n",
    "            \n",
    "                start = batch*BATCH_SIZE\n",
    "                end = start + BATCH_SIZE \n",
    "                feed = {\n",
    "                    model.input_data:val[0][start:end],\n",
    "                    model.target_data:val[1][start:end],\n",
    "                    model.dropout_prob:1\n",
    "                            }\n",
    "                \n",
    "                acc,preds,probs = sess.run(\n",
    "                    [\n",
    "                        model.accuracy,\n",
    "                        model.predictions,\n",
    "                        model.probs\n",
    "                    ]\n",
    "                    ,feed_dict=feed\n",
    "                )\n",
    "                print(acc)\n",
    "                final_preds = np.concatenate((final_preds,preds),axis=0)\n",
    "                if final_probs is None:\n",
    "                    final_probs = probs\n",
    "                else:\n",
    "                    final_probs = np.concatenate((final_probs,probs),axis=0)\n",
    "        prediction_conf = final_probs[np.argmax(final_probs,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Result = Labeled[-test_size:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Result['nn_pred'] = final_preds\n",
    "Result['mod_nn_prod'] = list(map(lambda x: -1 if x <5 else 0 if x==5 else 1,final_preds))\n",
    "Result['nn_ret'] = Result.mod_nn_prod*Result['return']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fef9b186550>"
      ]
     },
     "execution_count": 669,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L8JHacUNaOqNXPEblnx7Cu2d3p9dQwrHfL1N+/knNNR53SE3LPHfoGeh1Jy/a+yNCZAsG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\nCIIgCHWMdLIFQRAEQRAEoY75P7mBFGzz6c4aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefb246f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Res = Result[-test_size:][['return','act_return','pred_return','nn_ret']].cumsum()\n",
    "Res = (1+Result[-test_size:][['return','act_return','nn_ret','pred_return']]).cumprod()\n",
    "Res[0] =0\n",
    "Res.plot(secondary_y='act_return')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[86 32 85]\n",
      " [71 39 90]\n",
      " [88 19 90]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.35      0.42      0.38       203\n",
      "          0       0.43      0.20      0.27       200\n",
      "          1       0.34      0.46      0.39       197\n",
      "\n",
      "avg / total       0.37      0.36      0.35       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Result['class'],Result['mod_nn_prod']))\n",
    "print(classification_report(Result['class'],Result['mod_nn_prod']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefaa3e17f0>"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1vCusRUTE8j/Mi513T/u4oHOqrq6KmpptSj2NDpfrxw8+rlevXrHbbrvF0qVL23zb+vr1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\nohYAgOSJWgAAkvf/AOKvaJ+IxrjaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefaa3e8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = pd.DataFrame(confusion_matrix(Result['multi_class'],Result['nn_pred']))\n",
    "#sns.heatmap(cm.div(cm.sum(1)))\n",
    "Result[Result.multi_class==6]['return'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.29      0.19      0.23        52\n",
      "          1       0.08      0.10      0.09        50\n",
      "          2       0.11      0.17      0.13        60\n",
      "          3       0.00      0.00      0.00        62\n",
      "          4       0.15      0.08      0.11        50\n",
      "          5       0.09      0.18      0.12        51\n",
      "          6       0.09      0.17      0.12        60\n",
      "          7       0.08      0.02      0.03        53\n",
      "          8       0.11      0.15      0.12        54\n",
      "          9       0.13      0.07      0.09        56\n",
      "         10       0.17      0.13      0.15        52\n",
      "\n",
      "avg / total       0.12      0.11      0.11       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Result['multi_class'],Result['nn_pred']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fefab1ee1d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fefab05d5c0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fefab02a4e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fefaafe4668>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fefaafabda0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fefaaf63f60>]], dtype=object)"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ytX9t9Wpr9fUyrPk9EBkZZcdkRERERJ2XzQrb3Nxc7NixA1qtFqGhofjDH/6A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefab0985c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Result.hist(by='multi_class',column='return',sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main Event - RNN\n",
    "In this section we'll make an rnn model that learns to take the past into account as well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining an rnn Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.contrib.layers.python.layers.initializers import xavier_initializer\n",
    "RNN_HIDDEN_SIZE=100\n",
    "FIRST_LAYER_SIZE=1000\n",
    "SECOND_LAYER_SIZE=250\n",
    "NUM_LAYERS=2\n",
    "BATCH_SIZE=50\n",
    "NUM_EPOCHS=200\n",
    "lr=0.0003\n",
    "NUM_TRAIN_BATCHES = int(len(train[0])/BATCH_SIZE)\n",
    "NUM_VAL_BATCHES = int(len(val[1])/BATCH_SIZE)\n",
    "ATTN_LENGTH=30\n",
    "beta=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class RNNModel():\n",
    "    def __init__(self):\n",
    "        global_step = tf.contrib.framework.get_or_create_global_step()\n",
    "        self.input_data = tf.placeholder(dtype=tf.float32,shape=[BATCH_SIZE,num_features])\n",
    "        self.target_data = tf.placeholder(dtype=tf.int32,shape=[BATCH_SIZE])\n",
    "        self.dropout_prob = tf.placeholder(dtype=tf.float32,shape=[])\n",
    "        \n",
    "        def makeGRUCells():\n",
    "            base_cell = tf.nn.rnn_cell.GRUCell(num_units=RNN_HIDDEN_SIZE,) \n",
    "            layered_cell = tf.nn.rnn_cell.MultiRNNCell([base_cell] * NUM_LAYERS,state_is_tuple=False) \n",
    "            attn_cell =tf.contrib.rnn.AttentionCellWrapper(cell=layered_cell,attn_length=ATTN_LENGTH,state_is_tuple=False)\n",
    "            return attn_cell\n",
    "        \n",
    "        self.gru_cell = makeGRUCells()\n",
    "        self.zero_state = self.gru_cell.zero_state(1, tf.float32)\n",
    "        \n",
    "        self.start_state = tf.placeholder(dtype=tf.float32,shape=[1,self.gru_cell.state_size])\n",
    "        \n",
    "        \n",
    "\n",
    "        with tf.variable_scope(\"ff\",initializer=xavier_initializer(uniform=False)):\n",
    "            droped_input = tf.nn.dropout(self.input_data,keep_prob=self.dropout_prob)\n",
    "            \n",
    "            layer_1 = tf.contrib.layers.fully_connected(\n",
    "                num_outputs=FIRST_LAYER_SIZE,\n",
    "                inputs=droped_input,\n",
    "                \n",
    "            )\n",
    "            layer_2 = tf.contrib.layers.fully_connected(\n",
    "                num_outputs=RNN_HIDDEN_SIZE,\n",
    "                inputs=layer_1,\n",
    "                \n",
    "            )\n",
    "            \n",
    "        \n",
    "        split_inputs = tf.reshape(droped_input,shape=[1,BATCH_SIZE,num_features],name=\"reshape_l1\") # Each item in the batch is a time step, iterate through them\n",
    "        split_inputs = tf.unpack(split_inputs,axis=1,name=\"unpack_l1\")\n",
    "        states =[]\n",
    "        outputs =[]\n",
    "        with tf.variable_scope(\"rnn\",initializer=xavier_initializer(uniform=False)) as scope:\n",
    "            state = self.start_state\n",
    "            for i, inp in enumerate(split_inputs):\n",
    "                if i >0:\n",
    "                    scope.reuse_variables()\n",
    "                \n",
    "                output, state = self.gru_cell(inp, state)\n",
    "                states.append(state)\n",
    "                outputs.append(output)\n",
    "        self.end_state = states[-1]\n",
    "        outputs = tf.pack(outputs,axis=1) # Pack them back into a single tensor\n",
    "        outputs = tf.reshape(outputs,shape=[BATCH_SIZE,RNN_HIDDEN_SIZE])\n",
    "        self.logits = tf.contrib.layers.fully_connected(\n",
    "            num_outputs=num_classes,\n",
    "            inputs=outputs,\n",
    "            activation_fn=None\n",
    "        )\n",
    "\n",
    "            \n",
    "        with tf.variable_scope(\"loss\"):\n",
    "            self.penalties =    tf.reduce_sum([beta*tf.nn.l2_loss(var) for var in tf.trainable_variables()])\n",
    "\n",
    "            \n",
    "            self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits,self.target_data)\n",
    "            self.loss = tf.reduce_sum(self.losses + beta*self.penalties)\n",
    "        \n",
    "        with tf.name_scope(\"train_step\"):\n",
    "          opt = tf.train.AdamOptimizer(lr)\n",
    "          gvs = opt.compute_gradients(self.loss)\n",
    "          self.train_op = opt.apply_gradients(gvs, global_step=global_step)\n",
    "        \n",
    "        with tf.name_scope(\"predictions\"):\n",
    "            probs = tf.nn.softmax(self.logits)\n",
    "            self.predictions = tf.argmax(probs, 1)\n",
    "            correct_pred = tf.cast(tf.equal(self.predictions, tf.cast(self.target_data,tf.int64)),tf.float64)\n",
    "            self.accuracy = tf.reduce_mean(correct_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:<tensorflow.contrib.rnn.python.ops.rnn_cell.AttentionCellWrapper object at 0x7fefaeb8d400>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:From <ipython-input-688-7185bf47d07b>:6 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "step - 0 loss - 8122.736946105957 acc - 0.14\n",
      "step - 1 loss - 7704.171836853027 acc - 0.14\n",
      "step - 2 loss - 7474.963249206543 acc - 0.14\n",
      "step - 3 loss - 7301.8679122924805 acc - 0.16\n",
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      "step - 177 loss - 1296.4326467514038 acc - 0.72\n",
      "step - 178 loss - 1285.1394968032837 acc - 0.78\n",
      "step - 179 loss - 1269.8633303642273 acc - 0.84\n",
      "step - 180 loss - 1308.072467327118 acc - 0.82\n",
      "step - 181 loss - 1282.4272556304932 acc - 0.84\n",
      "step - 182 loss - 1343.8119797706604 acc - 0.9\n",
      "step - 183 loss - 1302.6647219657898 acc - 0.76\n",
      "step - 184 loss - 1332.2255835533142 acc - 0.82\n",
      "step - 185 loss - 1305.2200365066528 acc - 0.78\n",
      "step - 186 loss - 1515.8871870040894 acc - 0.76\n",
      "step - 187 loss - 1414.5942478179932 acc - 0.94\n",
      "step - 188 loss - 1292.6907148361206 acc - 0.84\n",
      "step - 189 loss - 1233.2327451705933 acc - 0.88\n",
      "step - 190 loss - 1219.228615283966 acc - 0.8\n",
      "step - 191 loss - 1262.4607768058777 acc - 0.88\n",
      "step - 192 loss - 1256.4314246177673 acc - 0.9\n",
      "step - 193 loss - 1176.2278003692627 acc - 0.92\n",
      "step - 194 loss - 1151.0619759559631 acc - 0.92\n",
      "step - 195 loss - 1149.1116552352905 acc - 0.88\n",
      "step - 196 loss - 1173.5241236686707 acc - 0.88\n",
      "step - 197 loss - 1192.9216446876526 acc - 0.78\n",
      "step - 198 loss - 1200.6485486030579 acc - 0.86\n",
      "step - 199 loss - 1203.2499680519104 acc - 0.92\n",
      "0.14\n",
      "0.08\n",
      "0.18\n",
      "0.1\n",
      "0.08\n",
      "0.16\n",
      "0.14\n",
      "0.1\n",
      "0.12\n",
      "0.06\n",
      "0.04\n",
      "0.06\n"
     ]
    }
   ],
   "source": [
    "with tf.Graph().as_default():\n",
    "    model = RNNModel()\n",
    "    input_ = train[0]\n",
    "    target = train[1]\n",
    "    with tf.Session() as sess:\n",
    "        init = tf.initialize_all_variables()\n",
    "        sess.run([init])\n",
    "        loss = 2000\n",
    "        \n",
    "        for e in range(NUM_EPOCHS):\n",
    "            state = sess.run(model.zero_state)\n",
    "            epoch_loss =0\n",
    "            for batch in range(0,NUM_TRAIN_BATCHES):\n",
    "                start = batch*BATCH_SIZE\n",
    "                end = start + BATCH_SIZE \n",
    "                feed = {\n",
    "                    model.input_data:input_[start:end],\n",
    "                    model.target_data:target[start:end],\n",
    "                    model.dropout_prob:0.5,\n",
    "                    model.start_state:state\n",
    "                            }\n",
    "                _,loss,acc,state = sess.run(\n",
    "                    [\n",
    "                        model.train_op,\n",
    "                        model.loss,\n",
    "                        model.accuracy,\n",
    "                        model.end_state\n",
    "                    ]\n",
    "                    ,feed_dict=feed\n",
    "                )\n",
    "                epoch_loss+=loss\n",
    "                \n",
    "            print('step - {0} loss - {1} acc - {2}'.format((e),epoch_loss,acc))\n",
    "        final_preds =np.array([])\n",
    "        for batch in range(0,NUM_VAL_BATCHES):\n",
    "                start = batch*BATCH_SIZE\n",
    "                end = start + BATCH_SIZE \n",
    "                feed = {\n",
    "                    model.input_data:val[0][start:end],\n",
    "                    model.target_data:val[1][start:end],\n",
    "                    model.dropout_prob:1,\n",
    "                    model.start_state:state\n",
    "                            }\n",
    "                acc,preds,state = sess.run(\n",
    "                    [\n",
    "                        model.accuracy,\n",
    "                        model.predictions,\n",
    "                        model.end_state\n",
    "                    ]\n",
    "                    ,feed_dict=feed\n",
    "                )\n",
    "                print(acc)\n",
    "                assert len(preds) == BATCH_SIZE\n",
    "                final_preds = np.concatenate((final_preds,preds),axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## RNN Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Result['rnn_pred'] = final_preds\n",
    "Result['mod_rnn_prod'] = list(map(lambda x: -1 if x <5 else 0 if x==5 else 1,final_preds))\n",
    "Result['rnn_ret'] = Result.mod_rnn_prod*Result['return']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10 10  2  2  2  6  0  5  5  2  8]\n",
      " [ 6  1  8  4  5 10  2  5  2  3  4]\n",
      " [ 6  3  5  5 13  6  6  4  2  5  5]\n",
      " [ 5  4  6  4  7  6  2 10 10  4  4]\n",
      " [ 3  2  2  3  8 10  3  8  5  3  3]\n",
      " [ 2  3  9  2  7  7  4  7  3  5  2]\n",
      " [ 5  2  5  5 10 12  4  5  5  4  3]\n",
      " [ 0  5  4  7  6 12  0 10  4  3  2]\n",
      " [ 4  5  5  0  6  7  6  7  3  8  3]\n",
      " [ 5  3  8  3  3  9  2  7  6  3  7]\n",
      " [ 4  4  8  1  4  3  4  6  5  5  8]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.37      0.47      0.41       203\n",
      "          0       0.42      0.18      0.26       200\n",
      "          1       0.37      0.48      0.42       197\n",
      "\n",
      "avg / total       0.39      0.38      0.36       600\n",
      "\n",
      "[[96 25 82]\n",
      " [88 37 75]\n",
      " [77 26 94]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Result['multi_class'],Result['rnn_pred']))\n",
    "print(classification_report(Result['class'],Result['mod_rnn_prod']))\n",
    "print(confusion_matrix(Result['class'],Result['mod_rnn_prod']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5445167225179053"
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(96/(96+82) + 94/(77+94))/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefaa61ea58>"
      ]
     },
     "execution_count": 695,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6cspCdLrSt9/Et20rEa+X9Cuu2ud4NRSi/MP3qVr4bbSnxx5aKldj6T8Ac5++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\n7bmmr2oxv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CGEEEIIIQRqZYLFYE6q6PCH/VorpTL72AV3ep2eb+z8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\nATq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/UgtIgGHnzH9jfBFlW5PWlNID3h6c+n0ymnCapEE0iQkZ6XCaNHbaE5/dq1enthnR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FBQUFC4obllplY6mw1NZJCw2kBHm4PcifPMvvICAIJWS8E3vklG80YKx9vo8MkD14zsuQML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\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VVKKAWFzCW6f78cX/7wheONiBlw7L9wLFic8/sbiEFw52ApAzFo16uan891+6iLEF7N0Y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\nIiICqGgiIiIiIiIiIiIiIiICwH8BZ5uaF51Qv2YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefaa57bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Res = (Result[-test_size:][['return','nn_ret','rnn_ret','pred_return']]).cumsum()\n",
    "Res[0] =0\n",
    "Res.plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fef913041d0>"
      ]
     },
     "execution_count": 700,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oUYKUlJRc6ytVqhTnzp0jMTExx9km5trl5uaGnZ1dtt8Fa2trs2/pZ3ddffv2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VRo/fzQBQXhyM7CXqOgiSvTxKN1a7x79sZ88gQBY64qewytlvA77iJlzQfYszIJuWlatY8hhBBC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\nBTduklX67mwAAAAAAGAFIDQBgGUgkRuajOSEJsEC2nMFJitL5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TMp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\nx7U2WXfV3srN1btmfa2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x/vPniPm8uN55m4FfPKk+Z806LNVzf3AdGVZbTZmKijL+zBLt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oqKioqKioqKioqKioqKioqKisonghVX/mRW486pobjdbker1U6oEhkaGiJ1ip4Q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\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58RD4GKko/sdwrNIEAOwW74oMTczRShMAcIX4r0eFvhRWnx0bTXWQidOzeBBrzwVAmC0S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\n0ERERERERERERERERARQ0ERERERERERERERERARQ0ERERERER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rHr3ytWrGDmzJkAvPvu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefa824b278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Res.columns =['Market Baseline','Simple Neural Newtwork','My Algo','Logistic Regression (simple ML)','Do Nothing(0)']\n",
    "Res.plot(figsize=(20,10),title=\"Performance of MarketVectors algo over 27 months compared with baselines\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 4 elements, new values have 5 elements",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-619-74cc8fe0f2f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mRes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mRes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'baseline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'logistic_regression'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'feed_forward_net'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rnn_net'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'do_nothing'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mRes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   2755\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2756\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2757\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2758\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2759\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/src/properties.pyx\u001b[0m in \u001b[0;36mpandas.lib.AxisProperty.__set__ (pandas/lib.c:46241)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m   2800\u001b[0m             raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[1;32m   2801\u001b[0m                              \u001b[0;34m'new values have %d elements'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2802\u001b[0;31m                              (old_len, new_len))\n\u001b[0m\u001b[1;32m   2803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2804\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 4 elements, new values have 5 elements"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "Res.columns\n",
    "Res.columns =['baseline','logistic_regression','feed_forward_net','rnn_net','do_nothing']\n",
    "Res.plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.ops.rnn_cell import BasicLSTMCell, GRUCell, MultiRNNCell, DropoutWrapper\n",
    "cell = tf.nn.rnn_cell.GRUCell(num_units=RNN_HIDDEN_SIZE)\n",
    "cell = MultiRNNCell(cells=[cell]*NUM_LAYERS,state_is_tuple=True)\n",
    "attn_cell =tf.contrib.rnn.AttentionCellWrapper(cell=cell,attn_length=ATTN_LENGTH,state_is_tuple=True)\n",
    "print(attn_cell.zero_state(batch_size=1,dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.start_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = ([1,2,3,4],())\n",
    "y = sum([1,2,3],())\n",
    "type(())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Labeled.hist(column='return',by='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Result['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "g = sns.FacetGrid(Result, row=\"class\", col=\"rnn_pred\", margin_titles=True)\n",
    "g.map(sns.distplot, \"return\",);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Result.hist(by=['class','nn_pred'],column='return',sharex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['nn_prediction_conf'] not in index\"",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-694-4915aeb5bfab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mResult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zreturn'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'return'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mResult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'day'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdayofweek\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlmplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mResult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'zreturn'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nn_prediction_conf'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'day'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nn_pred'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfit_reg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mlmplot\u001b[0;34m(x, y, data, hue, col, row, palette, col_wrap, size, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws)\u001b[0m\n\u001b[1;32m    539\u001b[0m     \u001b[0mneed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_partial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneed_cols\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m     \u001b[0;31m# Initialize the grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2052\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2053\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2054\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2055\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2095\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2096\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2097\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2098\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2099\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1227\u001b[0m                 \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1228\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s not in index'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['nn_prediction_conf'] not in index\""
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "Result['zreturn'] = zscore(Result['return'])\n",
    "Result['day'] = Result.index.dayofweek\n",
    "sns.lmplot(data=Result,y='zreturn',x='nn_prediction_conf',hue='day',col='class',row='nn_pred',fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Result.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.115179332359851"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.rnn_ret.mean()/Res.rnn_ret.std()"
   ]
  }
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