Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 104: Time Series with Pandas / NumPy\n",
    "\n",
    "Contributed by: Avi Thaker https://github.com/athaker/CNTK\n",
    "November 20, 2016\n",
    "\n",
    "This tutorial will introduce the use of the Cognitive Toolkit for time series data. We show how to prepare time series data for deep learning algorithms. We will cover training a neural network and evaluating the neural network model. We will also look at the predictive potential on classification of an Exchange-traded Funds ([ETF](https://en.wikipedia.org/wiki/Exchange-traded_fund)), and in this simplified setting how one could trade it. This tutorial serves **only** as an example of how to use neural networks for time series analysis. \n",
    "    \n",
    "It is important to note that the stock market is extremely noisy and is difficult to predict. This is best done by professionals with domain expertise. It is more important to make sure the model is correct before setting up a trading system (there are many factors to consider including but not limited to: [curve fitting bias](https://en.wikipedia.org/wiki/Overfitting), [forward looking bias](http://www.investopedia.com/terms/l/lookaheadbias.asp?lgl=no-infinite), profitability etc.). The learnings and anecdotes presented in this tutorial is only for exemplary purposes with the goal of introducing an approach to analyze time series data.\n",
    "\n",
    "This tutorial introduces how to use pandas_datareader package and pandas. Please note, this tutorial will utilize the numpy interface to CNTK which interfaces well with [Pandas dataframes](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) (a structure that is well suited towards timeseries analysis). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cntk\n",
    "import cntk.ops as C\n",
    "\n",
    "from cntk.blocks import default_options, Input   # Building blocks\n",
    "from cntk.initializer import glorot_uniform\n",
    "from cntk.layers import Dense                    # Layers\n",
    "from cntk.learner import sgd, learning_rate_schedule, UnitType\n",
    "from cntk.utils import get_train_eval_criterion, get_train_loss, ProgressPrinter, get_train_loss\n",
    "\n",
    "import datetime\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "# If you want to set the program to use a CUDA enabled GPU\n",
    "#from cntk.device import set_default_device, gpu\n",
    "#set_default_device(gpu(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing stock data\n",
    "We first retrieve stock data using the method `get_stock_data`. This method downloads stock data on a daily timescale from Yahoo finance (can be modified to get data from Google Finance and many other sources). [Pandas datareader]( http://pandas-datareader.readthedocs.io/en/latest/remote_data.html) shows many use cases for this data reader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# A method which obtains stock data from Yahoo finance\n",
    "# Requires that you have an internet connection to retreive stock data from Yahoo finance\n",
    "try:\n",
    "    from pandas_datareader import data\n",
    "except ImportError:\n",
    "    !pip install pandas_datareader\n",
    "    from pandas_datareader import data\n",
    "\n",
    "def get_stock_data(contract, s_year, s_month, s_day, e_year, e_month, e_day):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        contract (str): the name of the stock/etf\n",
    "        s_year (int): start year for data\n",
    "        s_month (int): start month\n",
    "        s_day (int): start day\n",
    "        e_year (int): end year\n",
    "        e_month (int): end month\n",
    "        e_day (int): end day\n",
    "    Returns:\n",
    "        Pandas Dataframe: Daily OHLCV bars\n",
    "    \"\"\"\n",
    "    start = datetime.datetime(s_year, s_month, s_day)\n",
    "    end = datetime.datetime(e_year, e_month, e_day)\n",
    "    bars = data.get_data_yahoo(contract, start, end)\n",
    "    return bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Please check if there is trade data for the chosen stock symbol during this period\n",
    "data = get_stock_data(\"SPY\", 2000, 1,2,2017,1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the training paramaters\n",
    "\n",
    "The stock market behavior exhibits substantial [autocorrelation](https://en.wikipedia.org/wiki/Autocorrelation) ([reference](http://epchan.blogspot.com/2016/04/mean-reversion-momentum-and-volatility.html)). We use [ETF](http://www.investopedia.com/terms/e/etf.asp) `SPY` index representing the \"market\" of stock. This is the ETF that encompasses around top 500 companies in America by market capitalization. We will trade under the assumption that there is some short term autocorrelation that have predictive power in the market. \n",
    "\n",
    "### Predicting\n",
    "* Whether or not the next data for the given stock/ETF will be above or below the current day.\n",
    "\n",
    "### Predictors\n",
    "* The previous 8 days, classified if greater than the current day,\n",
    "\n",
    "* The volume changes as a percentage,\n",
    "\n",
    "* The percentage change from the previous day.\n",
    "\n",
    "Note, we are not feeding the neural network the price itself. Financial timeseries data are noisy. It is important not to overfit the data. There is a lot we can do here (smoothing, adding more features, etc.), but we will keep this tutorial simple, and demonstrate CNTK's ability to interface with timeseries data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>diff</th>\n",
       "      <th>v_diff</th>\n",
       "      <th>p_1</th>\n",
       "      <th>p_2</th>\n",
       "      <th>p_3</th>\n",
       "      <th>p_4</th>\n",
       "      <th>p_5</th>\n",
       "      <th>p_6</th>\n",
       "      <th>p_7</th>\n",
       "      <th>p_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>148.250000</td>\n",
       "      <td>148.250000</td>\n",
       "      <td>143.875000</td>\n",
       "      <td>145.437500</td>\n",
       "      <td>8164300</td>\n",
       "      <td>106.449075</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>143.531204</td>\n",
       "      <td>144.062500</td>\n",
       "      <td>139.640594</td>\n",
       "      <td>139.750000</td>\n",
       "      <td>8089800</td>\n",
       "      <td>102.286262</td>\n",
       "      <td>0.040698</td>\n",
       "      <td>0.009209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>139.937500</td>\n",
       "      <td>141.531204</td>\n",
       "      <td>137.250000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>12177900</td>\n",
       "      <td>102.469243</td>\n",
       "      <td>0.001786</td>\n",
       "      <td>0.335698</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>139.625000</td>\n",
       "      <td>141.500000</td>\n",
       "      <td>137.750000</td>\n",
       "      <td>137.750000</td>\n",
       "      <td>6227200</td>\n",
       "      <td>100.822416</td>\n",
       "      <td>0.016334</td>\n",
       "      <td>0.955598</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>140.312500</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>140.062500</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>8066500</td>\n",
       "      <td>106.677801</td>\n",
       "      <td>0.054889</td>\n",
       "      <td>0.228017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>146.250000</td>\n",
       "      <td>146.906204</td>\n",
       "      <td>145.031204</td>\n",
       "      <td>146.250000</td>\n",
       "      <td>5741700</td>\n",
       "      <td>107.043763</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>0.404898</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>145.812500</td>\n",
       "      <td>146.093704</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>144.500000</td>\n",
       "      <td>7503700</td>\n",
       "      <td>105.762897</td>\n",
       "      <td>0.012111</td>\n",
       "      <td>0.234817</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>144.593704</td>\n",
       "      <td>144.593704</td>\n",
       "      <td>142.875000</td>\n",
       "      <td>143.062500</td>\n",
       "      <td>6907700</td>\n",
       "      <td>104.710757</td>\n",
       "      <td>0.010048</td>\n",
       "      <td>0.086281</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>144.468704</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>143.281204</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>5158300</td>\n",
       "      <td>106.128859</td>\n",
       "      <td>0.013362</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>146.531204</td>\n",
       "      <td>147.468704</td>\n",
       "      <td>145.968704</td>\n",
       "      <td>146.968704</td>\n",
       "      <td>7437300</td>\n",
       "      <td>107.569799</td>\n",
       "      <td>0.013395</td>\n",
       "      <td>0.306428</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close    Volume  \\\n",
       "Date                                                                   \n",
       "2000-01-03  148.250000  148.250000  143.875000  145.437500   8164300   \n",
       "2000-01-04  143.531204  144.062500  139.640594  139.750000   8089800   \n",
       "2000-01-05  139.937500  141.531204  137.250000  140.000000  12177900   \n",
       "2000-01-06  139.625000  141.500000  137.750000  137.750000   6227200   \n",
       "2000-01-07  140.312500  145.750000  140.062500  145.750000   8066500   \n",
       "2000-01-10  146.250000  146.906204  145.031204  146.250000   5741700   \n",
       "2000-01-11  145.812500  146.093704  143.500000  144.500000   7503700   \n",
       "2000-01-12  144.593704  144.593704  142.875000  143.062500   6907700   \n",
       "2000-01-13  144.468704  145.750000  143.281204  145.000000   5158300   \n",
       "2000-01-14  146.531204  147.468704  145.968704  146.968704   7437300   \n",
       "\n",
       "             Adj Close      diff    v_diff  p_1  p_2  p_3  p_4  p_5  p_6  p_7  \\\n",
       "Date                                                                            \n",
       "2000-01-03  106.449075  0.000000  0.000000    0    0    0    0    0    0    0   \n",
       "2000-01-04  102.286262  0.040698  0.009209    0    0    0    0    0    0    0   \n",
       "2000-01-05  102.469243  0.001786  0.335698    1    0    0    0    0    0    0   \n",
       "2000-01-06  100.822416  0.016334  0.955598    0    0    0    0    0    0    0   \n",
       "2000-01-07  106.677801  0.054889  0.228017    1    1    1    1    0    0    0   \n",
       "2000-01-10  107.043763  0.003419  0.404898    1    1    1    1    1    0    0   \n",
       "2000-01-11  105.762897  0.012111  0.234817    0    0    1    1    1    0    0   \n",
       "2000-01-12  104.710757  0.010048  0.086281    0    0    0    1    1    1    0   \n",
       "2000-01-13  106.128859  0.013362  0.339143    1    1    0    0    1    1    1   \n",
       "2000-01-14  107.569799  0.013395  0.306428    1    1    1    1    1    1    1   \n",
       "\n",
       "            p_8  \n",
       "Date             \n",
       "2000-01-03    0  \n",
       "2000-01-04    0  \n",
       "2000-01-05    0  \n",
       "2000-01-06    0  \n",
       "2000-01-07    0  \n",
       "2000-01-10    0  \n",
       "2000-01-11    0  \n",
       "2000-01-12    0  \n",
       "2000-01-13    0  \n",
       "2000-01-14    1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature name list\n",
    "predictor_names = []\n",
    "\n",
    "# Compute price difference as a feature\n",
    "data[\"diff\"] = np.abs((data[\"Close\"] - data[\"Close\"].shift(1)) / data[\"Close\"]).fillna(0) \n",
    "predictor_names.append(\"diff\")\n",
    "\n",
    "# Compute the volume difference as a feature\n",
    "data[\"v_diff\"] = np.abs((data[\"Volume\"] - data[\"Volume\"].shift(1)) / data[\"Volume\"]).fillna(0) \n",
    "predictor_names.append(\"v_diff\")\n",
    "\n",
    "# Compute the stock being up (1) or down (0) over different day offsets compared to current dat closing price\n",
    "num_days_back = 8\n",
    "\n",
    "for i in range(1,num_days_back+1):\n",
    "    data[\"p_\" + str(i)] = np.where(data[\"Close\"] > data[\"Close\"].shift(i), 1, 0) # i: number of look back days\n",
    "    predictor_names.append(\"p_\" + str(i))\n",
    "    \n",
    "\n",
    "    \n",
    "# If you want to save the file to your local drive\n",
    "#data.to_csv(\"PATH_TO_SAVE.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### What we are trying to predict\n",
    "\n",
    "Here we are trying to predict whether or not the next days' trading will be above or below the current day. We will represent a predicted up day as a 1, else a 0 if the next day is the same or below. (Note: the market is unlikely going to close at the same price as it did the previous day). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[\"next_day\"] = np.where(data[\"Close\"].shift(-1) > data[\"Close\"], 1, 0)\n",
    "data[\"next_day_opposite\"] = np.where(data[\"next_day\"]==1,0,1) # The label must be one-hot encoded\n",
    "\n",
    "# Establish the start and end date of our training timeseries (picked 2000 days before the market crash)\n",
    "training_data = data[\"2000-01-15\":\"2008-01-01\"]\n",
    "\n",
    "# We define our test data as: data[\"2008-01-02\":]\n",
    "# This example allows to to include data up to current date\n",
    "\n",
    "test_data = data[\"2008-01-02\":\"2016-11-25\"] \n",
    "training_features = np.asarray(training_data[predictor_names], dtype = \"float32\")\n",
    "training_labels = np.asarray(training_data[[\"next_day\",\"next_day_opposite\"]], dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here we are actually building the neural network itself. We will use a simple feedforward neural network (represented as `NN` in the plots) with 10 inputs and 50 dimensions.\n",
    "\n",
    "**Suggested exercise** Try experimenting here with different sizes and number of hidden layers. What happens when we make this network large? Do we overfit the data? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(training_features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, we are using `layers` library in this tutorial. The details documentation can be found [here](https://cntk.ai/pythondocs/layerref.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Lets build the network\n",
    "input_dim = 2 + num_days_back\n",
    "num_output_classes = 2 #Remember we need to have 2 since we are trying to classify if the market goes up or down 1 hot encoded\n",
    "num_hidden_layers = 2\n",
    "hidden_layers_dim = 2 + num_days_back\n",
    "input = Input(input_dim)\n",
    "label = Input(num_output_classes)\n",
    "\n",
    "def create_model(input, num_output_classes):\n",
    "    h = input\n",
    "    for i in range(0,num_hidden_layers):\n",
    "        h = Dense(hidden_layers_dim, \n",
    "                  init = glorot_uniform(), \n",
    "                  activation = C.relu)(h)\n",
    "        \n",
    "    return (Dense(num_output_classes, init=glorot_uniform(), activation=None)(h))\n",
    "    \n",
    "z = create_model(input, num_output_classes)\n",
    "loss = C.cross_entropy_with_softmax(z, label)\n",
    "label_error = C.classification_error(z, label)\n",
    "lr_per_minibatch = learning_rate_schedule(0.125,UnitType.minibatch)\n",
    "trainer = cntk.Trainer(z, loss, label_error, [sgd(z.parameters, lr=lr_per_minibatch)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Initialize the parameters for the trainer, we will train in large minibatches in sequential order\n",
    "minibatch_size = 100\n",
    "num_minibatches = len(training_data.index) // minibatch_size\n",
    "\n",
    "#Run the trainer on and perform model training\n",
    "training_progress_output_freq = 1\n",
    "\n",
    "# Visualize the loss over minibatch\n",
    "plotdata = {\"batchsize\":[], \"loss\":[], \"error\":[]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of passes through the data, how we train time series data\n",
    "This tutorial will differ from other tutorials in the sense that here we will not randomly send data to the trainer, instead each minibatch will be fed sequentially in the order of the time dimension. This is key to time series data-handling where we want to \"weigh\" the data at the end of our sample a slightly higher. You can put in multiple passes, however you will notice significant performance degradation. Try it out! Additionally, multiple passes tend to overfit the financial timeseries data. This overfitting can be mitigated using standard ML approaches such as [L1 regularization](https://en.wikipedia.org/wiki/Regularization_(mathematics)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mini batches\n",
      "20\n",
      "The shape of the training feature mini batch\n",
      "(100, 10)\n"
     ]
    }
   ],
   "source": [
    "tf = np.split(training_features,num_minibatches)\n",
    "\n",
    "print(\"Number of mini batches\")\n",
    "print(len(tf))\n",
    "\n",
    "print(\"The shape of the training feature minibatch\")\n",
    "print(tf[0].shape)\n",
    "\n",
    "tl = np.split(training_labels, num_minibatches)\n",
    "\n",
    "# It is key that we make only one pass through the data linearly in time\n",
    "num_passes = 1 \n",
    "\n",
    "# Defines a utility that prints the training progress\n",
    "def print_training_progress(trainer, mb, frequency, verbose=1):\n",
    "    training_loss = \"NA\"\n",
    "    eval_error = \"NA\"\n",
    "    if mb%frequency == 0:\n",
    "        training_loss = get_train_loss(trainer)\n",
    "        eval_error = get_train_eval_criterion(trainer)\n",
    "        if verbose: \n",
    "            print (\"Minibatch: {0}, Loss: {1:.4f}, Error: {2:.2f}%\".format(mb, training_loss, eval_error*100))\n",
    "    return mb, training_loss, eval_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minibatch: 0, Loss: 0.6971, Error: 49.00%\n",
      "Minibatch: 1, Loss: 0.6826, Error: 50.00%\n",
      "Minibatch: 2, Loss: 0.7160, Error: 54.00%\n",
      "Minibatch: 3, Loss: 0.7018, Error: 49.00%\n",
      "Minibatch: 4, Loss: 0.7088, Error: 52.00%\n",
      "Minibatch: 5, Loss: 0.6905, Error: 42.00%\n",
      "Minibatch: 6, Loss: 0.7083, Error: 54.00%\n",
      "Minibatch: 7, Loss: 0.7015, Error: 51.00%\n",
      "Minibatch: 8, Loss: 0.7068, Error: 57.00%\n",
      "Minibatch: 9, Loss: 0.7128, Error: 60.00%\n",
      "Minibatch: 10, Loss: 0.6881, Error: 46.00%\n",
      "Minibatch: 11, Loss: 0.6859, Error: 40.00%\n",
      "Minibatch: 12, Loss: 0.7035, Error: 52.00%\n",
      "Minibatch: 13, Loss: 0.6803, Error: 40.00%\n",
      "Minibatch: 14, Loss: 0.6927, Error: 51.00%\n",
      "Minibatch: 15, Loss: 0.7098, Error: 55.00%\n",
      "Minibatch: 16, Loss: 0.6880, Error: 48.00%\n",
      "Minibatch: 17, Loss: 0.6885, Error: 45.00%\n",
      "Minibatch: 18, Loss: 0.6895, Error: 45.00%\n",
      "Minibatch: 19, Loss: 0.7105, Error: 51.00%\n"
     ]
    }
   ],
   "source": [
    "# Train our neural network\n",
    "tf = np.split(training_features,num_minibatches)\n",
    "tl = np.split(training_labels, num_minibatches)\n",
    "\n",
    "for i in range(num_minibatches*num_passes): # multiply by the \n",
    "    features = np.ascontiguousarray(tf[i%num_minibatches])\n",
    "    labels = np.ascontiguousarray(tl[i%num_minibatches])\n",
    "    \n",
    "    # Specify the mapping of input variables in the model to actual minibatch data to be trained with\n",
    "    trainer.train_minibatch({input : features, label : labels})\n",
    "    batchsize, loss, error = print_training_progress(trainer, i, training_progress_output_freq, verbose=1)\n",
    "    if not (loss == \"NA\" or error ==\"NA\"):\n",
    "        plotdata[\"batchsize\"].append(batchsize)\n",
    "        plotdata[\"loss\"].append(loss)\n",
    "        plotdata[\"error\"].append(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAADeCAYAAAD1heDRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU1V/lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5458a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAADeCAYAAADmUqAlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqCqRnIQQQgihqvw/c5IA1Qmj4tYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f4cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(plotdata[\"batchsize\"], plotdata[\"loss\"], 'b--')\n",
    "plt.xlabel('Minibatch number')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Minibatch run vs. Training loss ')\n",
    "plt.show()\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(plotdata[\"batchsize\"], plotdata[\"error\"], 'r--')\n",
    "plt.xlabel('Minibatch number')\n",
    "plt.ylabel('Label Prediction Error')\n",
    "plt.title('Minibatch run vs. Label Prediction Error ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the trend for the label prediction error is still close to 50%. Remember that this is time variant, therefore it is expected that the system will have some noise as it trains through time. It should be noted; the model is still learning the market. Additionally, since this time series data is so noisy, having an error rate below 50% is good (many trading firms have win-rates of near 50% and have made money nearly every day [VIRTU](https://en.wikipedia.org/wiki/Virtu_Financial#Trading_activity)). However note they are high frequency trading firm and can leverage themselves up with low winrate strategies (51%). Trying to classify and trade every single day is expensive from transaction fees perspective. Therefore, one approach would be to trade when we think we are more likely to win?\n",
    "\n",
    "Let us try this idea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average error: 47.48%\n"
     ]
    }
   ],
   "source": [
    "# Now that we have trained the net, and we will do out of sample test to see how we did.\n",
    "# and then more importantly analyze how that set did\n",
    "\n",
    "test_features = np.ascontiguousarray(test_data[predictor_names], dtype = \"float32\")\n",
    "test_labels = np.ascontiguousarray(test_data[[\"next_day\",\"next_day_opposite\"]], dtype=\"float32\")\n",
    "\n",
    "avg_error = trainer.test_minibatch({input : test_features, label : test_labels})\n",
    "print(\"Average error: {0:2.2f}%\".format(avg_error * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that we have an error rate near 50%. At first glance this may appear to not have learned the network, but let us examine further and see if we have some predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "out = C.softmax(z)\n",
    "predicted_label_prob = out.eval({input:test_features})\n",
    "test_data[\"p_up\"] = pd.Series(predicted_label_prob[0][:,0], index = test_data.index)\n",
    "test_data[\"p_down\"] = predicted_label_prob[0][:,1]\n",
    "test_data['long_entries'] = np.where((test_data.p_up > 0.55), 1, 0)\n",
    "test_data['short_entries'] = np.where((test_data.p_down > 0.55) , -1, 0)\n",
    "test_data['positions'] = test_data['long_entries'].fillna(0) + test_data['short_entries'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating our Data\n",
    "Here we take the output of our test set and compute the probabilities from the softmax function. Since we have probabilities we want to trade when there is a \"higher\" chance that we will be right, instead of just a >50% chance that the market will go in one direction. The goal is to find a signal, instead of trying to classify the market. Since the market is so noisy we want to only trade when we have an \"edge\" on the market. Moreover, trading frequently has higher fees (you have to pay each time you trade).\n",
    "\n",
    "We will say that if the prediction probability is greater than 55% (in either direction) we will take a position in the market. If it shows that the market will be up the next day with greater than 55% probability, we will take a 1-day long. If it is greater than a 55% chance that the next day will be below today's position we will take 1-day [short] (http://www.investopedia.com/university/shortselling/shortselling1.asp)(the same as borrowing a stock and buying it back). \n",
    "\n",
    "We will then evaluate this timeseries performance by looking at some more metrics: average monthly return, standard deviation of monthly returns, the [Sharpe ratio](http://www.investopedia.com/terms/s/sharperatio.asp), and the [Maximum drawdown](https://en.wikipedia.org/wiki/Drawdown_(economics)). The Sharpe ratio is the average return minus the risk free rate (which is basically zero) over the standard deviation of returns normalized to a year. \n",
    "\n",
    "$$Sharpe = \\frac{r_p - r_f}{\\sigma_p}$$\n",
    "$$r_p = \\text{portfolio return}$$\n",
    "$$r_f = \\text{risk free rate}$$\n",
    "$$\\sigma_p = \\text{standard deviation of portfolio return}$$\n",
    "\n",
    "Generally, the higher the Sharpe ratio, the better you are taking less risk for each unit of reward. This assumes the mean return and standard deviation are sufficient to describe the distribution of returns, akin to an assumption of normally distributed returns.\n",
    "\n",
    "A trading strategy can be profitable even if the winrate is 50% or lower, if the wins are greater than the losses you can have a less than 50% winrate and still make some money (usually classified as momentum strategies). Finally, even if we do not beat the market by trading it individually, we can still outperform it by trading multiple assets that are uncorrelated with each other (or trade a basket of stocks to get market exposure; we are not trading each at the same time). However, that will not be covered in this tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_drawdowns(equity_curve):\n",
    "    \"\"\"\n",
    "    Calculate the largest peak-to-trough drawdown of the PnL curve\n",
    "    as well as the duration of the drawdown. Requires that the \n",
    "    pnl_returns is a pandas Series.\n",
    "\n",
    "    Parameters:\n",
    "    pnl - A pandas Series representing period percentage returns.\n",
    "\n",
    "    Returns:\n",
    "    drawdown, duration - Highest peak-to-trough drawdown and duration.\n",
    "    \"\"\"\n",
    "\n",
    "    # Calculate the cumulative returns curve \n",
    "    # and set up the High Water Mark\n",
    "    # Then create the drawdown and duration series\n",
    "    hwm = [0]\n",
    "    eq_idx = equity_curve.index\n",
    "    drawdown = pd.Series(index = eq_idx)\n",
    "    duration = pd.Series(index = eq_idx)\n",
    "\n",
    "    # Loop over the index range\n",
    "    for t in range(1, len(eq_idx)):\n",
    "        cur_hwm = max(hwm[t-1], equity_curve[t])\n",
    "        hwm.append(cur_hwm)\n",
    "        drawdown[t]= (hwm[t] - equity_curve[t]) \n",
    "        duration[t]= 0 if drawdown[t] == 0 else duration[t-1] + 1\n",
    "    return drawdown.max(), duration.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAGHCAYAAACAk0mtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkcCpIBEREZHAqSARERGRwP1/bGOJXlQ6m48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f10b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "test_data[\"p_up\"].hist(bins=20, alpha=0.4)\n",
    "test_data[\"p_down\"].hist(bins=20, alpha=0.4)\n",
    "plt.title(\"Distribution of Probabilities\")\n",
    "plt.legend([\"p_up\", \"p_down\"])\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlabel(\"Probablity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRADING STATS\n",
      "AVG Monthly Return :: 0.40%\n",
      "STD Monthly        :: 3.10%\n",
      "SHARPE             :: 0.45\n",
      "MAX DRAWDOWN       :: 10.45%, 30.0 months\n",
      "Correlation to SPY :: 0.01\n",
      "NUMBER OF TRADES   :: 562\n",
      "TOTAL TRADING DAYS :: 4256\n",
      "SPY MONTHLY RETURN :: 0.76%\n",
      "SPY STD RETURN     :: 4.44%\n",
      "SPY SHARPE         :: 0.59\n",
      "SPY DRAWDOWN       :: 55.37%, 8.0 months\n",
      "0.104531433709\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhwAAAGHCAYAAAD7t4thAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlFLKdv8fK96ZACj0USkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe0a9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data[\"pnl\"] = test_data[\"Adj Close\"].diff().shift(-1).fillna(0)*test_data[\"positions\"]/np.where(test_data[\"Adj Close\"]!=0,test_data[\"Adj Close\"],1)\n",
    "test_data[\"perc\"] = (test_data[\"Adj Close\"] - test_data[\"Adj Close\"].shift(1)) / test_data[\"Adj Close\"].shift(1)\n",
    "monthly = test_data.pnl.resample(\"M\").sum()\n",
    "monthly_spy = test_data[\"perc\"].resample(\"M\").sum()\n",
    "avg_return = np.mean(monthly)\n",
    "std_return = np.std(monthly)\n",
    "sharpe = np.sqrt(12) * avg_return / std_return\n",
    "drawdown = create_drawdowns(monthly.cumsum())\n",
    "spy_drawdown = create_drawdowns(monthly_spy.cumsum())\n",
    "print(\"TRADING STATS\")\n",
    "print(\"AVG Monthly Return :: \" + \"{0:.2f}\".format(round(avg_return*100,2))+ \"%\")\n",
    "print(\"STD Monthly        :: \" + \"{0:.2f}\".format(round(std_return*100,2))+ \"%\")\n",
    "print(\"SHARPE             :: \" + \"{0:.2f}\".format(round(sharpe,2)))\n",
    "print(\"MAX DRAWDOWN       :: \" + \"{0:.2f}\".format(round(drawdown[0]*100,2)) + \"%, \" + str(drawdown[1]) + \" months\" )\n",
    "print(\"Correlation to SPY :: \" + \"{0:.2f}\".format(round(np.corrcoef(test_data[\"pnl\"], test_data[\"diff\"])[0][1],2)))\n",
    "print(\"NUMBER OF TRADES   :: \" + str(np.sum(test_data.positions.abs())))\n",
    "print(\"TOTAL TRADING DAYS :: \" + str(len(data)))\n",
    "print(\"SPY MONTHLY RETURN :: \" + \"{0:.2f}\".format(round(monthly_spy.mean()*100,2)) + \"%\")\n",
    "print(\"SPY STD RETURN     :: \" + \"{0:.2f}\".format(round(monthly_spy.std()*100,2)) + \"%\")\n",
    "print(\"SPY SHARPE         :: \" + \"{0:.2f}\".format(round(monthly_spy.mean()/monthly_spy.std()*np.sqrt(12),2)))\n",
    "print(\"SPY DRAWDOWN       :: \" + \"{0:.2f}\".format(round(spy_drawdown[0]*100,2)) + \"%, \"  + str(spy_drawdown[1]) + \" months\" )\n",
    "\n",
    "print(drawdown[0])\n",
    "(monthly.cumsum()*100).plot()\n",
    "(monthly_spy.cumsum()*100).plot()\n",
    "plt.legend([\"NN\", \"SPY\"],loc=2)\n",
    "plt.ylabel(\"% Return\")\n",
    "plt.title(\"TRADING SPY OUT OF SAMPLE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the % returns when we trade using SPY and NN based models only when we are > 55% sure of the predicted directionality.\n",
    "\n",
    "It is interesting to see here how the NN performs during the [financial crisis](https://en.wikipedia.org/wiki/Financial_crisis_of_2007%E2%80%932008). This strategy makes money when many are losing ( refer to the dip in the green line in the plot between 2008-2010)! More importantly, this strategy has a significantly lower volatility (lower standard deviation in the blue line relative to the green line). \n",
    "\n",
    "In the summary statistic, you can also see a lower maximum drawdown of 13.62% for the NN model compared to the 55.37% for the SPY index over the same period of time. Similarly, a higher Sharpe ratio close to 1 from the NN model compared to 0.6 from the SPY index for the same period indicates the NN based decisions to be more stable (less risky). Thus, from a quant perspective it is more likely to be profitable (it makes money in many different market time periods). More importantly, the NN strategy is relatively uncorrelated with the SPY index (only 0.06 close to 0). \n",
    "\n",
    "**Suggested task**: Try to experiment with this network with different stocks and see what happens to the Sharpe ratio when you have multiple trading streams that are uncorrelated with each other. \n",
    "\n",
    "Remember when trading you can trade with leverage, and increase your return profile (as long as you are willing to handle the down times). The best trading strategies are the ones that can consistently make money no matter which market they are trading and no matter what is happening in the market. This \"edge\" is termed alpha in the quant industry.\n",
    "\n",
    "It is important to notice how much of a drawdown you will experience when trading (http://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp?lgl=no-infinite). Since our current model is calculating only the percentage returns where returns are not reinvested, and we are not holding but trading, the larger the drawdown the harder it is to recover (if you lost 90% of your portfolio then have a 90% gain, you will only be at 19% of your starting value). \n",
    "\n",
    "We can only predict on close prices and not the adjusted close prices since the adjusted close prices incorporate data from the future (such as stock splits and dividends) which can incorporate bias into our network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRADING STATS\n",
      "AVG Monthly Return :: 1.10%\n",
      "STD Monthly        :: 7.11%\n",
      "SHARPE             :: 0.54\n",
      "MAX DRAWDOWN       :: 30.30%, 33.0 months\n",
      "Correlation to SPY :: 0.04\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiUAAAGHCAYAAABvUSKTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8847cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data[\"p_up\"] = pd.Series(predicted_label_prob[0][:,0], index = test_data.index)\n",
    "test_data[\"p_down\"] = predicted_label_prob[0][:,1]\n",
    "test_data['long_entries'] = np.where((test_data.p_up > 0.50) , 1, 0)\n",
    "test_data['short_entries'] = np.where((test_data.p_down > 0.50) , -1, 0)\n",
    "test_data['positions'] = test_data['long_entries'].fillna(0) + test_data['short_entries'].fillna(0)\n",
    "test_data[\"pnl\"] = test_data[\"Adj Close\"].diff().shift(-1).fillna(0)*test_data[\"positions\"]/np.where(test_data[\"Adj Close\"]!=0,test_data[\"Adj Close\"],1)\n",
    "monthly = test_data.pnl.resample(\"M\").sum()\n",
    "avg_return = np.mean(monthly)\n",
    "std_return = np.std(monthly)\n",
    "sharpe = np.sqrt(12) * avg_return / std_return\n",
    "drawdown = create_drawdowns(monthly.cumsum())\n",
    "\n",
    "print(\"TRADING STATS\")\n",
    "print(\"AVG Monthly Return :: \" + \"{0:.2f}\".format(round(avg_return*100,2))+ \"%\")\n",
    "print(\"STD Monthly        :: \" + \"{0:.2f}\".format(round(std_return*100,2))+ \"%\")\n",
    "print(\"SHARPE             :: \" +\"{0:.2f}\".format(round(sharpe,2)))\n",
    "print(\"MAX DRAWDOWN       :: \" + \"{0:.2f}\".format(round(drawdown[0]*100,2)) + \"%, \" + str(drawdown[1]) + \" months\" )\n",
    "print(\"Correlation to SPY :: \" + \"{0:.2f}\".format(round(np.corrcoef(test_data[\"pnl\"], test_data[\"diff\"])[0][1],2)))\n",
    "(monthly.cumsum()*100).plot()\n",
    "(monthly_spy.cumsum()*100).plot()\n",
    "plt.legend([\"NN\", \"SPY\"],loc=2)\n",
    "plt.ylabel(\"% Return\")\n",
    "plt.title(\"TRADING SPY OUT OF SAMPLE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The plot above shows the % returns when we trade every day using SPY and NN based models as compared to a confidence based trading show in previous plot. With frequent trading the volatility is higher and transaction fees (not accounted in this plot) will greatly eat into any profits.\n",
    "\n",
    "\n",
    "## Suggested exercises\n",
    "Try changing the batch size, the network itself, the activation functions, and many other features and see how much it affects the output. Notice how it can change dramatically? This means what you have been training on might be noise and you need to refine either the model or more likely the inputs to the network itself. \n",
    "\n",
    "After you have completed the aforementioned exercise, experiment with applying the trained network to other to other timeseries data. Pick a stock in the S&P 500, pick something that is uncorrelated with the S&P 500, try a completely random dataset. How do you expect each of those to do? Should the performance match up with S&P 500 out of sample performance? How about the random data?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.51\n"
     ]
    }
   ],
   "source": [
    "# Repeatable factor (used for runtime verification)\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Appendix\n",
    "A great resource for getting started as a Quant: https://www.quantstart.com/\n",
    "An online back-testing and open source trading platform, hosts quant competitions and a forum: https://www.quantopian.com/\n",
    "[A summary of backtesting and biases](https://www.quantstart.com/articles/successful-backtesting-of-algorithmic-trading-strategies-part-i)\n",
    "[For those who want to automatically trade in python](https://github.com/blampe/IbPy)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}