Chapter 10 - Natural Language Processing/Case Study 1 - NLP and Sentiments Analysis based Trading Strategy/NLPandSentimentAnalysisBasedTradingStrategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "83708667-4fdc-1563-7b3a-06b6575d2865"
   },
   "source": [
    "# NLP and Sentiment Analysis based Trading Strategy\n",
    "\n",
    "In this case study we use NLP to build a trading strategy combining some of the concepts that we went through in some of the previous chapters.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [1. Problem Statement](#1)\n",
    "* [2. Getting Started - Load Libraries and Dataset](#2)\n",
    "    * [2.1. Load Libraries](#2.1) \n",
    "    * [2.1. Loading the stock data](#2.2)  \n",
    "* [3. Data Preparation](#3)      \n",
    "    * [3.1. Loading and preprocessing the News data](#3.1)\n",
    "    * [3.2. Preparing the combined data](#3.2)\n",
    "    * [3.3. Preparing combined data](#3.3)\n",
    "* [4.Model Evaluation-Sentiment Analysis](#4)    \n",
    "    * [4.1. Predefined model-TextBlob package](#4.1)\n",
    "    * [4.2. Supervised Learning based-Classification algorithms and LSTM](#4.2)\n",
    "    * [4.3. Unsupervised Learning based-based on financial lexicon](#4.3)  \n",
    "    * [4.4. Exploratory Data Analysis and comparison](#4.4)  \n",
    "* [5.Models Evaluation-Building a Trading Strategy ](#5)\n",
    "    * [5.1 Setting up the strategy ](#5.1) \n",
    "    * [5.2.Results by Individual Stocks](#5.2) \n",
    "    * [5.3.Results by Multiple Stocks](#5.3) \n",
    "    * [5.4 Results by Varying Time Period](#5.4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>\n",
    "# 1. Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem statement is to build a trading strategy that uses NLP to extracts the information inside the news headlines, assigns a sentiment to that and use the sentiments and the information inside the news headlines for a trading strategy.\n",
    "\n",
    "The data used for this case study will be from three sources:\n",
    "* **News headlines data compiled from RSS feeds of several news websites**: This news headlines data is complied by different news website and contains the most financially relevant news filtered by human editors. For the purpose of this study, we will only look at the headline, not the details in the story. Another important characteristic of this dataset is that the relevant tickers in the story are extracted. Our dataset contains 82,643 headlines from to 2011-05-02 to 2018-12-28\n",
    "* **Yahoo finance website for the stock return:** (The return data can be obtained from other website such as yahoo finance)a\n",
    "* **kaggle**: labelled data of of news sentiments obtained for a classification based sentiment analysis model. This data may not be authentic and is used only for demonstration purpose in this case study. \n",
    "* Stock market lexicon created based on stock market conversations in microblogging services. The source of this lexicon is *Oliveira, Nuno, Paulo Cortez, and Nelson Areal. \"Stock market sentiment lexicon acquisition using microblogging data and statistical measures.\" Decision Support Systems 85 (2016): 62-73.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>\n",
    "# 2. Getting Started- Loading the data and python packages\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.1'></a>\n",
    "## 2.1. Loading the python packages\n",
    "\n",
    "As a first step we check if the additional packages needed are present, if not install them. These are checked separately as they aren't included in requirement.txt as they aren't used for all case studies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pkg_resources\n",
    "import pip\n",
    "installedPackages = {pkg.key for pkg in pkg_resources.working_set}\n",
    "required = {'nltk', 'spacy', 'textblob', 'backtrader'}\n",
    "missing = required - installedPackages\n",
    "if missing:\n",
    "    !pip install nltk==3.4\n",
    "    !pip install textblob==0.15.3\n",
    "    !pip install -U SpaCy==2.2.0\n",
    "    !python -m spacy download en_core_web_lg\n",
    "    !pip install backtrader==1.9.74.123    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "5d8fee34-f454-2642-8b06-ed719f0317e1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\tatsa\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#NLP libraries\n",
    "from textblob import TextBlob\n",
    "import spacy\n",
    "import nltk\n",
    "import warnings\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "nltk.download('vader_lexicon')\n",
    "import csv\n",
    "import pandas as pd\n",
    "\n",
    "#Run the command python -m spacy download en_core_web_sm to download this\n",
    "#https://spacy.io/models\n",
    "import en_core_web_lg\n",
    "nlp = en_core_web_lg.load()\n",
    "\n",
    "#Libraries for processing the news headlines\n",
    "from lxml import etree\n",
    "import json\n",
    "from io import StringIO\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from pandas.tseries.offsets import BDay\n",
    "from scipy.stats.mstats import winsorize\n",
    "from copy import copy\n",
    "\n",
    "# Libraries for Classification for modeling the sentiments\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "# Keras package for the deep learning model for the sentiment prediction. \n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, LSTM, Dropout, Activation\n",
    "from keras.layers.embeddings import Embedding\n",
    "\n",
    "# Load libraries\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from datetime import date\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "\n",
    "#Additional Libraries \n",
    "import json  \n",
    "import zipfile\n",
    "import os.path\n",
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Diable the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.2'></a>\n",
    "## 2.2. Loading the stock price data\n",
    "\n",
    "The stock price data is loaded in this step from Yahoo Finance in this step. The loaded data is saved in csv for further use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = ['AAPL','MSFT','AMZN','GOOG','FB','WMT','JPM','TSLA','NFLX','ADBE']\n",
    "start = '2010-01-01'\n",
    "end = '2018-12-31'\n",
    "df_ticker_return = pd.DataFrame()\n",
    "for ticker in tickers:    \n",
    "    ticker_yf = yf.Ticker(ticker)\n",
    "    if df_ticker_return.empty:\n",
    "        df_ticker_return = ticker_yf.history(start = start, end = end)\n",
    "        df_ticker_return['ticker']= ticker \n",
    "    else:\n",
    "        data_temp = ticker_yf.history(start = start, end = end)\n",
    "        data_temp['ticker']= ticker \n",
    "        df_ticker_return = df_ticker_return.append(data_temp)\n",
    "df_ticker_return.to_csv(r'Data\\Step2.2_ReturnData.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the details of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>ticker</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>26.40</td>\n",
       "      <td>26.53</td>\n",
       "      <td>26.27</td>\n",
       "      <td>26.47</td>\n",
       "      <td>123432400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>26.54</td>\n",
       "      <td>26.66</td>\n",
       "      <td>26.37</td>\n",
       "      <td>26.51</td>\n",
       "      <td>150476200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Dividends  Stock Splits  \\\n",
       "Date                                                                         \n",
       "2010-01-04  26.40  26.53  26.27  26.47  123432400        0.0           0.0   \n",
       "2010-01-05  26.54  26.66  26.37  26.51  150476200        0.0           0.0   \n",
       "\n",
       "           ticker  \n",
       "Date               \n",
       "2010-01-04   AAPL  \n",
       "2010-01-05   AAPL  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ticker_return.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data contains the tickers and their return. In this next step, we clean the data make sure that the starting point is 2010 and the NAs in the data are dropped. Let us look at the news data now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3'></a>\n",
    "# 3. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We divide the data preparation into couple of steps as follows:\n",
    "* Loading and preprocessing the news data\n",
    "* Preparing the combined data\n",
    "\n",
    "\n",
    "#### <font color='red'>Note : Step 3.1 to 3.2 might be time consuming. Skip to step 3.3 and load the preprocessed data directly in case you want to avoid these steps. You can also start directly with step 4.4 in case you want to skip the model training and used the sentiments directly for the trading strategy.</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3.1'></a>\n",
    "## 3.1 Loading and preprocessing News Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The news data is downloaded from the News RSS feed and the file is downloaded in the json format and the json files for different dates are kept under a zipped folder. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see the content of the json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'li class=\"n-box-item date-title\" data-end=\"1305172799\" data-start=\"1305086400\" data-txt=\"Tuesday, December 17, 2019\">Wednesday, May 11, 2011</li><li class=\"n-box-item sa-box-item\" data-id=\"76179\" data-ts=\"1305149244\"><div class=\"media media-overflow-fix\"><div class=\"media-left\"><a class=\"box-ticker\" href=\"/symbol/CSCO\" target=\"_blank\">CSCO</a></div><div class=\"media-body\"><h4 class=\"media-heading\"><a href=\"/news/76179\" sasource=\"on_the_move_news_fidelity\" target=\"_blank\">Cisco (NASDAQ:CSCO): Pr'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = zipfile.ZipFile(\"Data/Raw Headline Data.zip\", \"r\")\n",
    "testFile=z.namelist()[10]\n",
    "fileData= z.open(testFile).read() \n",
    "fileDataSample = json.loads(fileData)['content'][1:500]  \n",
    "fileDataSample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that the json format is not suitable for the algorithm. We need to get the news from the jsons and the following function is used for it. Regex becomes the vital part of this step. Regex can find a pattern in the raw, messy text and perform actions accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function performs the json parsing given the \n",
    "def jsonParser(json_data): \n",
    "    xml_data = json_data['content']\n",
    "            \n",
    "    tree = etree.parse(StringIO(xml_data), parser=etree.HTMLParser())\n",
    "\n",
    "    headlines = tree.xpath(\"//h4[contains(@class, 'media-heading')]/a/text()\")\n",
    "    assert len(headlines) == json_data['count']\n",
    "\n",
    "    main_tickers = list(map(lambda x: x.replace('/symbol/', ''), tree.xpath(\"//div[contains(@class, 'media-left')]//a/@href\")))\n",
    "    assert len(main_tickers) == json_data['count']\n",
    "    final_headlines = [''.join(f.xpath('.//text()')) for f in tree.xpath(\"//div[contains(@class, 'media-body')]/ul/li[1]\")]\n",
    "    if len(final_headlines) == 0:\n",
    "        final_headlines = [''.join(f.xpath('.//text()')) for f in tree.xpath(\"//div[contains(@class, 'media-body')]\")]\n",
    "        final_headlines = [f.replace(h, '').split('\\xa0')[0].strip() for f,h in zip (final_headlines, headlines)]\n",
    "    return main_tickers, final_headlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Cisco Systems (NASDAQ:CSCO) falls further into the red on FQ4 guidance of $0.37-0.39 vs. $0.42 Street consensus. Sales seen flat to +2% vs. 8% Street view. CSCO recently -2.1%.'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsonParser(json.loads(fileData))[1][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that using the json parser the news headlines are extracted from the complex html format. This format is good enough to be used for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we extract the ticker and the headlines from all the json files and put it in a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = None \n",
    "data_df_news = []\n",
    "ret = []\n",
    "ret_f = []\n",
    "with zipfile.ZipFile(\"Data/Raw Headline Data.zip\", \"r\") as z:\n",
    "    for filename in z.namelist(): \n",
    "        #print(filename)\n",
    "        try:               \n",
    "            #print('Running {}'.format(filename))\n",
    "            with z.open(filename) as f:  \n",
    "                data = f.read()  \n",
    "                json_data = json.loads(data)      \n",
    "            if json_data.get('count', 0)> 10:\n",
    "                #Step 1: Parse the News Jsons \n",
    "                main_tickers, final_headlines = jsonParser(json_data) \n",
    "                if len(final_headlines) != json_data['count']:\n",
    "                    continue\n",
    "                #Step 2: Prepare Future and Event Return and assign Future and Event return for each ticker. \n",
    "                file_date = filename.split('/')[-1].replace('.json', '')\n",
    "                file_date = date(int(file_date[:4]), int(file_date[5:7]), int(file_date[8:]))\n",
    "               #Step 3: Merge all the data in a data frame\n",
    "                df_dict = {'ticker': main_tickers,\n",
    "                           'headline': final_headlines,            \n",
    "                           'date': [file_date] * len(main_tickers)\n",
    "                           }\n",
    "                df_f = pd.DataFrame(df_dict)            \n",
    "                data_df_news.append(df_f)            \n",
    "        except:\n",
    "            pass  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the content of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>headline</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DVA</td>\n",
       "      <td>Davita (NYSE:DVA): EPS of $0.96 beats by $0.01...</td>\n",
       "      <td>2011-05-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CGNX</td>\n",
       "      <td>Cognex (NASDAQ:CGNX): EPS of $0.32 beats by $0...</td>\n",
       "      <td>2011-05-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker                                           headline        date\n",
       "0    DVA  Davita (NYSE:DVA): EPS of $0.96 beats by $0.01...  2011-05-02\n",
       "1   CGNX  Cognex (NASDAQ:CGNX): EPS of $0.32 beats by $0...  2011-05-02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_news=pd.concat(data_df_news)\n",
    "data_df_news.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the data has the ticker, headlines and the date which will be used in the next step for combining with the return."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3.2'></a>\n",
    "## 3.2 Preparing the combined data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step we extract the event return, which is the return that corresponds to\n",
    "the event. We do this because at times the news is reported late and at other times it is\n",
    "reported after market close. Having a slightly wider window ensures that we capture\n",
    "the essence of the event. Event return in defined as follows:\n",
    "$ R_{t-1} + R_t + R_{t+1} $\n",
    "\n",
    "Where, $ R_{t-1} $, $ R_{t+1} $ are the return before and after the news data and $ R_{t} $ is the return on\n",
    "the day of the news (i.e. time t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Computing the return\n",
    "df_ticker_return['ret_curr'] = df_ticker_return['Close'].pct_change()\n",
    "#Computing the event return\n",
    "df_ticker_return['eventRet'] = df_ticker_return['ret_curr'] + df_ticker_return['ret_curr'].shift(-1) + df_ticker_return['ret_curr'].shift(1)\n",
    "df_ticker_return.reset_index(level=0, inplace=True)\n",
    "df_ticker_return['date'] = pd.to_datetime(df_ticker_return['Date']).apply(lambda x: x.date())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all the data in place we will prepare a combined dataframe which will have the news headlines mapped to the date, event Return and stock ticker. This dataframe will be used for further analysis for sentiment analysis model and for building the trading strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "combinedDataFrame = pd.merge(data_df_news, df_ticker_return, how='left', left_on=['date','ticker'], right_on=['date','ticker'])\n",
    "combinedDataFrame = combinedDataFrame[combinedDataFrame['ticker'].isin(tickers)]\n",
    "data_df = combinedDataFrame[['ticker','headline','date','eventRet','Close']]\n",
    "data_df = data_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>headline</th>\n",
       "      <th>date</th>\n",
       "      <th>eventRet</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMZN</td>\n",
       "      <td>Whole Foods (WFMI) -5.2% following a downgrade...</td>\n",
       "      <td>2011-05-02</td>\n",
       "      <td>0.017650</td>\n",
       "      <td>201.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NFLX</td>\n",
       "      <td>Netflix (NFLX +1.1%) shares post early gains a...</td>\n",
       "      <td>2011-05-02</td>\n",
       "      <td>-0.013003</td>\n",
       "      <td>33.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                                           headline        date  \\\n",
       "5    AMZN  Whole Foods (WFMI) -5.2% following a downgrade...  2011-05-02   \n",
       "11   NFLX  Netflix (NFLX +1.1%) shares post early gains a...  2011-05-02   \n",
       "\n",
       "    eventRet   Close  \n",
       "5   0.017650  201.19  \n",
       "11 -0.013003   33.88  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us save the data in a csv file to be used later, so that the data processing step can be skipped everytime we are looking into analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.dropna().to_csv(r'Data\\Step3_NewsAndReturnData.csv', sep='|', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3.3'></a>\n",
    "## 3.3 Loading the preprocessed data\n",
    "#### Start from this step in case you dont want to run the previous preprocessing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.read_csv(r'Data\\Step3_NewsAndReturnData.csv', sep='|')\n",
    "data_df = data_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2759, 5) (10,)\n"
     ]
    }
   ],
   "source": [
    "print(data_df.shape, data_df.ticker.unique().shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, we prepared a clean dataframe that has ticker, headline, event return, return for a given day and future return for 10 unique stock tickers with total 2759 rows of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4'></a>\n",
    "# 4 Evaluate Models for Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will go through following three different approaches of getting the sentiments for the news which we will use for building the trading strategy.\n",
    "\n",
    "* Predefined model-TextBlob package\n",
    "* Tuned Model-Classification algorithms and LSTM\n",
    "* Model based on financial lexicon\n",
    "\n",
    "We will also explore the difference between different ways of performing the sentiment analysis. Let us go through the steps. \n",
    "\n",
    "\n",
    "#### <font color='red'>Note : The steps 4.1-4.3 are time consuming steps. Skip to step 4.5 to load the pretrained sentiments and use them for further analysis and building trading strategy.</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.1'></a>\n",
    "## 4.1 - Predefined model-TextBlob package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The texblob sentiment function is pretrained model based on Naïve-Bayes classification algorithm to convert a sentence to a numerical value of sentiment between -1 to +1 and map adjectives frequently found in movie reviews(source code: https://textblob.readthedocs.io/en/dev/_modules/textblob/en/sentiments.html) to sentiment polarity scores, ranging from -1 to +1 (negative ↔ positive) and a similar subjectivity score (objective ↔ subjective). We apply this on all headline articles. Let us compute the sentiment for all the headlines in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "text1 = \"Bayer (OTCPK:BAYRY) started the week up 3.5% to €74/share in Frankfurt, touching their \\\n",
    "highest level in 14 months, after the U.S. government said a $25M glyphosate decision against the \\\n",
    "company should be reversed.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob(text1).sentiment.polarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sentiment polarity is a number between -1 (Very Negative) and +1 (Very Positive). We apply this on all headline we have in the data processed in the previous step. Let us compute the sentiment for all the headlines in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['sentiment_textblob'] = [TextBlob(s).sentiment.polarity for s in data_df['headline']] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us analyse the scatterplot of the sentiments and the return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_df['sentiment_textblob'],data_df['eventRet'], alpha=0.5)\n",
    "plt.title('Scatter Between Event return and sentiments-all data')\n",
    "plt.ylabel('Event Return')\n",
    "plt.xlabel('Sentiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.042749340813834254\n"
     ]
    }
   ],
   "source": [
    "corrlation = data_df['eventRet'].corr(data_df['sentiment_textblob'])\n",
    "print(corrlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation is positive, which means that news with positive sentiments lead to positive return and is expected. However,the correlation isn't very high.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df_stock  = data_df[data_df['ticker'] == 'AAPL']\n",
    "plt.scatter(data_df_stock['sentiment_textblob'],data_df_stock['eventRet'], alpha=0.5)\n",
    "plt.title('Scatter Between Event return and sentiments-AAPL')\n",
    "plt.ylabel('Event Return')\n",
    "plt.xlabel('Sentiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, from the results we can see that there isn't a strong correlation between the news and the sentiments. Also, there are a lot of sentiments centred around 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.5, subjectivity=0.5, assessments=[(['touching'], 0.5, 0.5, None)])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"Bayer (OTCPK:BAYRY) started the week up 3.5% to €74/share in Frankfurt, touching their highest level in 14 months, after the U.S. government said a $25M glyphosate decision against the company should be reversed.\"\n",
    "TextBlob(text).sentiment_assessments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the statement has a positive sentiment of .5 but looking at the words that give rise to the sentiments, the word \"touching\" and not \"high\" causes positive sentiment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.2'></a>\n",
    "## 4.2 - Supervised Learning-Classification algorithms and LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, we develop customised model for sentiment analysis, based on available labelled data. The label data for this is obtained from kaggle website. Let us look at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiments_data = pd.read_csv(r'Data/LabelledNewsData.csv',encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>headline</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/16/2020 5:25</td>\n",
       "      <td>$MMM fell on hard times but could be set to re...</td>\n",
       "      <td>MMM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime                                           headline ticker  \\\n",
       "0  1/16/2020 5:25  $MMM fell on hard times but could be set to re...    MMM   \n",
       "\n",
       "   sentiment  \n",
       "0          0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiments_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9470 , 30\n"
     ]
    }
   ],
   "source": [
    "print(sentiments_data.shape[0],',', sentiments_data.ticker.unique().shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has headlines for the news across 30 different stocks, with total 9470 rows, and has sentiments lacelled 0 and 1. The headlines are already in almost cleaned. We perform the classification steps that we discussed in chapter 6, using the classification model development python template discussed in that chapter. \n",
    "\n",
    "In order to run a supervised learning model, we first need to convert the news headlines into feature representation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#word-embedding\n",
    "all_vectors = pd.np.array([pd.np.array([token.vector for token in nlp(s) ]).mean(axis=0)*pd.np.ones((300)) \\\n",
    "                           for s in sentiments_data['headline']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have prepared the independent variable we train the classification model in a similar manner as discussed in chapter 6. We first divide the data into training set and test set and futher run the key classification models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split out validation dataset for the end\n",
    "Y= sentiments_data[\"sentiment\"]\n",
    "X = all_vectors\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
    "validation_size = 0.3\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)\n",
    "\n",
    "# test options for classification\n",
    "num_folds = 10\n",
    "seed = 7\n",
    "scoring = 'accuracy'\n",
    "\n",
    "# spot check the algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('SVM', SVC()))\n",
    "#Neural Network\n",
    "models.append(('NN', MLPClassifier()))\n",
    "#Ensable Models \n",
    "models.append(('RF', RandomForestClassifier()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running all the classification models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.881882 (0.008648) 0.896666 0.874340\n",
      "[[1043  166]\n",
      " [ 191 1441]]\n",
      "KNN: 0.786244 (0.025196) 0.868758 0.788103\n",
      "[[ 935  303]\n",
      " [ 299 1304]]\n",
      "CART: 0.693317 (0.016015) 0.999698 0.692714\n",
      "[[ 770  409]\n",
      " [ 464 1198]]\n",
      "SVM: 0.895004 (0.010993) 0.920652 0.897923\n",
      "[[1088  144]\n",
      " [ 146 1463]]\n",
      "NN: 0.925479 (0.009083) 0.999547 0.935938\n",
      "[[1142   90]\n",
      " [  92 1517]]\n",
      "RF: 0.818377 (0.016399) 0.999698 0.811686\n",
      "[[ 852  153]\n",
      " [ 382 1454]]\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "kfold_results = []\n",
    "test_results = []\n",
    "train_results = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    #msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    #print(msg)\n",
    "   # Full Training period\n",
    "    res = model.fit(X_train, Y_train)\n",
    "    train_result = accuracy_score(res.predict(X_train), Y_train)\n",
    "    train_results.append(train_result)\n",
    "    \n",
    "    # Test results\n",
    "    test_result = accuracy_score(res.predict(X_test), Y_test)\n",
    "    test_results.append(test_result)    \n",
    "    \n",
    "    msg = \"%s: %f (%f) %f %f\" % (name, cv_results.mean(), cv_results.std(), train_result, test_result)\n",
    "    print(msg)\n",
    "    print(confusion_matrix(res.predict(X_test), Y_test))\n",
    "    #print(classification_report(res.predict(X_test), Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "from matplotlib import pyplot\n",
    "fig = pyplot.figure()\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the NN model is the best performer with the a training accuracy of 99% and test accuracy of 93%. The performance of Random forest, SVM and Logistic regression are good as well. CART and KNN don't perform as good as other models. CART has higher overfitting as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM based model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code snippet, we used the some of the model function which look at the individual works in the sentence for training. However, for NLP, RNN based model are preferred over other machine learning models as RNN stores the information for current feature as well neighboring features for prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create sequence\n",
    "vocabulary_size = 20000\n",
    "tokenizer = Tokenizer(num_words= vocabulary_size)\n",
    "tokenizer.fit_on_texts(sentiments_data['headline'])\n",
    "sequences = tokenizer.texts_to_sequences(sentiments_data['headline'])\n",
    "X_LSTM = pad_sequences(sequences, maxlen=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividing the data into the training and test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_LSTM = sentiments_data[\"sentiment\"]\n",
    "X_train_LSTM, X_test_LSTM, Y_train_LSTM, Y_test_LSTM = train_test_split(X_LSTM, \\\n",
    "                       Y_LSTM, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following code snippet, we used Keras library to build a neural network classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From D:\\Anaconda\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 3977 samples, validate on 2652 samples\n",
      "Epoch 1/3\n",
      "3977/3977 [==============================] - ETA: 2:40 - loss: 0.6905 - acc: 0.500 - ETA: 1:25 - loss: 0.6900 - acc: 0.562 - ETA: 1:00 - loss: 0.6886 - acc: 0.593 - ETA: 48s - loss: 0.6914 - acc: 0.562 - ETA: 40s - loss: 0.6912 - acc: 0.56 - ETA: 35s - loss: 0.6897 - acc: 0.56 - ETA: 31s - loss: 0.6884 - acc: 0.56 - ETA: 29s - loss: 0.6876 - acc: 0.56 - ETA: 27s - loss: 0.6859 - acc: 0.57 - ETA: 25s - loss: 0.6852 - acc: 0.56 - ETA: 23s - loss: 0.6814 - acc: 0.57 - ETA: 22s - loss: 0.6855 - acc: 0.56 - ETA: 21s - loss: 0.6846 - acc: 0.56 - ETA: 20s - loss: 0.6804 - acc: 0.57 - ETA: 19s - loss: 0.6776 - acc: 0.57 - ETA: 19s - loss: 0.6726 - acc: 0.58 - ETA: 18s - loss: 0.6688 - acc: 0.59 - ETA: 18s - loss: 0.6623 - acc: 0.59 - ETA: 17s - loss: 0.6617 - acc: 0.59 - ETA: 17s - loss: 0.6620 - acc: 0.59 - ETA: 16s - loss: 0.6616 - acc: 0.59 - ETA: 16s - loss: 0.6583 - acc: 0.60 - ETA: 15s - loss: 0.6567 - acc: 0.60 - ETA: 15s - loss: 0.6554 - acc: 0.60 - ETA: 15s - loss: 0.6546 - acc: 0.60 - ETA: 14s - loss: 0.6529 - acc: 0.60 - ETA: 14s - loss: 0.6496 - acc: 0.61 - ETA: 14s - loss: 0.6507 - acc: 0.60 - ETA: 14s - loss: 0.6495 - acc: 0.61 - ETA: 13s - loss: 0.6477 - acc: 0.61 - ETA: 13s - loss: 0.6465 - acc: 0.61 - ETA: 13s - loss: 0.6443 - acc: 0.62 - ETA: 12s - loss: 0.6426 - acc: 0.62 - ETA: 12s - loss: 0.6414 - acc: 0.62 - ETA: 12s - loss: 0.6401 - acc: 0.62 - ETA: 12s - loss: 0.6375 - acc: 0.63 - ETA: 12s - loss: 0.6362 - acc: 0.63 - ETA: 11s - loss: 0.6338 - acc: 0.63 - ETA: 11s - loss: 0.6316 - acc: 0.63 - ETA: 11s - loss: 0.6305 - acc: 0.63 - ETA: 11s - loss: 0.6282 - acc: 0.63 - ETA: 10s - loss: 0.6234 - acc: 0.64 - ETA: 10s - loss: 0.6194 - acc: 0.64 - ETA: 10s - loss: 0.6139 - acc: 0.65 - ETA: 10s - loss: 0.6097 - acc: 0.65 - ETA: 10s - loss: 0.6094 - acc: 0.65 - ETA: 10s - loss: 0.6042 - acc: 0.66 - ETA: 9s - loss: 0.5984 - acc: 0.6680 - ETA: 9s - loss: 0.5965 - acc: 0.670 - ETA: 9s - loss: 0.5931 - acc: 0.673 - ETA: 9s - loss: 0.5857 - acc: 0.678 - ETA: 9s - loss: 0.5792 - acc: 0.683 - ETA: 9s - loss: 0.5752 - acc: 0.686 - ETA: 9s - loss: 0.5697 - acc: 0.692 - ETA: 8s - loss: 0.5651 - acc: 0.695 - ETA: 8s - loss: 0.5588 - acc: 0.700 - ETA: 8s - loss: 0.5553 - acc: 0.702 - ETA: 8s - loss: 0.5502 - acc: 0.706 - ETA: 8s - loss: 0.5463 - acc: 0.709 - ETA: 8s - loss: 0.5410 - acc: 0.713 - ETA: 7s - loss: 0.5371 - acc: 0.715 - ETA: 7s - loss: 0.5322 - acc: 0.718 - ETA: 7s - loss: 0.5283 - acc: 0.721 - ETA: 7s - loss: 0.5250 - acc: 0.723 - ETA: 7s - loss: 0.5223 - acc: 0.725 - ETA: 7s - loss: 0.5178 - acc: 0.728 - ETA: 7s - loss: 0.5139 - acc: 0.729 - ETA: 7s - loss: 0.5124 - acc: 0.731 - ETA: 6s - loss: 0.5092 - acc: 0.733 - ETA: 6s - loss: 0.5049 - acc: 0.736 - ETA: 6s - loss: 0.5007 - acc: 0.739 - ETA: 6s - loss: 0.4956 - acc: 0.741 - ETA: 6s - loss: 0.4905 - acc: 0.744 - ETA: 6s - loss: 0.4869 - acc: 0.747 - ETA: 6s - loss: 0.4822 - acc: 0.750 - ETA: 6s - loss: 0.4799 - acc: 0.752 - ETA: 5s - loss: 0.4754 - acc: 0.755 - ETA: 5s - loss: 0.4723 - acc: 0.757 - ETA: 5s - loss: 0.4694 - acc: 0.759 - ETA: 5s - loss: 0.4657 - acc: 0.761 - ETA: 5s - loss: 0.4631 - acc: 0.763 - ETA: 5s - loss: 0.4608 - acc: 0.765 - ETA: 5s - loss: 0.4583 - acc: 0.766 - ETA: 4s - loss: 0.4552 - acc: 0.768 - ETA: 4s - loss: 0.4529 - acc: 0.770 - ETA: 4s - loss: 0.4506 - acc: 0.772 - ETA: 4s - loss: 0.4464 - acc: 0.774 - ETA: 4s - loss: 0.4481 - acc: 0.774 - ETA: 4s - loss: 0.4448 - acc: 0.776 - ETA: 4s - loss: 0.4423 - acc: 0.777 - ETA: 4s - loss: 0.4403 - acc: 0.779 - ETA: 3s - loss: 0.4392 - acc: 0.780 - ETA: 3s - loss: 0.4357 - acc: 0.782 - ETA: 3s - loss: 0.4324 - acc: 0.784 - ETA: 3s - loss: 0.4295 - acc: 0.785 - ETA: 3s - loss: 0.4263 - acc: 0.787 - ETA: 3s - loss: 0.4247 - acc: 0.788 - ETA: 3s - loss: 0.4211 - acc: 0.789 - ETA: 3s - loss: 0.4191 - acc: 0.790 - ETA: 2s - loss: 0.4165 - acc: 0.792 - ETA: 2s - loss: 0.4141 - acc: 0.793 - ETA: 2s - loss: 0.4112 - acc: 0.795 - ETA: 2s - loss: 0.4094 - acc: 0.796 - ETA: 2s - loss: 0.4063 - acc: 0.798 - ETA: 2s - loss: 0.4044 - acc: 0.799 - ETA: 2s - loss: 0.4027 - acc: 0.800 - ETA: 2s - loss: 0.3997 - acc: 0.801 - ETA: 1s - loss: 0.3972 - acc: 0.802 - ETA: 1s - loss: 0.3945 - acc: 0.804 - ETA: 1s - loss: 0.3916 - acc: 0.806 - ETA: 1s - loss: 0.3902 - acc: 0.806 - ETA: 1s - loss: 0.3894 - acc: 0.807 - ETA: 1s - loss: 0.3871 - acc: 0.808 - ETA: 1s - loss: 0.3849 - acc: 0.809 - ETA: 1s - loss: 0.3843 - acc: 0.810 - ETA: 0s - loss: 0.3821 - acc: 0.811 - ETA: 0s - loss: 0.3807 - acc: 0.812 - ETA: 0s - loss: 0.3781 - acc: 0.814 - ETA: 0s - loss: 0.3755 - acc: 0.815 - ETA: 0s - loss: 0.3735 - acc: 0.816 - ETA: 0s - loss: 0.3725 - acc: 0.816 - ETA: 0s - loss: 0.3708 - acc: 0.818 - ETA: 0s - loss: 0.3692 - acc: 0.819 - ETA: 0s - loss: 0.3671 - acc: 0.820 - 16s 4ms/step - loss: 0.3665 - acc: 0.8207 - val_loss: 0.1225 - val_acc: 0.9585\n",
      "Epoch 2/3\n",
      "3977/3977 [==============================] - ETA: 12s - loss: 0.1263 - acc: 0.96 - ETA: 12s - loss: 0.0756 - acc: 0.98 - ETA: 12s - loss: 0.0782 - acc: 0.98 - ETA: 12s - loss: 0.0687 - acc: 0.99 - ETA: 12s - loss: 0.0766 - acc: 0.98 - ETA: 12s - loss: 0.0700 - acc: 0.98 - ETA: 12s - loss: 0.0876 - acc: 0.97 - ETA: 12s - loss: 0.0815 - acc: 0.98 - ETA: 12s - loss: 0.0858 - acc: 0.97 - ETA: 11s - loss: 0.0796 - acc: 0.98 - ETA: 11s - loss: 0.0828 - acc: 0.97 - ETA: 11s - loss: 0.0780 - acc: 0.97 - ETA: 11s - loss: 0.0737 - acc: 0.98 - ETA: 11s - loss: 0.0695 - acc: 0.98 - ETA: 11s - loss: 0.0677 - acc: 0.98 - ETA: 11s - loss: 0.0663 - acc: 0.98 - ETA: 11s - loss: 0.0629 - acc: 0.98 - ETA: 11s - loss: 0.0688 - acc: 0.97 - ETA: 11s - loss: 0.0685 - acc: 0.98 - ETA: 11s - loss: 0.0684 - acc: 0.97 - ETA: 11s - loss: 0.0668 - acc: 0.98 - ETA: 10s - loss: 0.0670 - acc: 0.98 - ETA: 10s - loss: 0.0683 - acc: 0.97 - ETA: 10s - loss: 0.0665 - acc: 0.98 - ETA: 10s - loss: 0.0654 - acc: 0.98 - ETA: 10s - loss: 0.0648 - acc: 0.98 - ETA: 10s - loss: 0.0636 - acc: 0.98 - ETA: 10s - loss: 0.0637 - acc: 0.98 - ETA: 10s - loss: 0.0629 - acc: 0.98 - ETA: 10s - loss: 0.0615 - acc: 0.98 - ETA: 10s - loss: 0.0618 - acc: 0.98 - ETA: 10s - loss: 0.0605 - acc: 0.98 - ETA: 10s - loss: 0.0591 - acc: 0.98 - ETA: 9s - loss: 0.0581 - acc: 0.9844 - ETA: 9s - loss: 0.0602 - acc: 0.983 - ETA: 9s - loss: 0.0593 - acc: 0.983 - ETA: 9s - loss: 0.0591 - acc: 0.984 - ETA: 9s - loss: 0.0588 - acc: 0.983 - ETA: 9s - loss: 0.0580 - acc: 0.984 - ETA: 9s - loss: 0.0569 - acc: 0.984 - ETA: 9s - loss: 0.0586 - acc: 0.984 - ETA: 9s - loss: 0.0596 - acc: 0.983 - ETA: 8s - loss: 0.0604 - acc: 0.983 - ETA: 8s - loss: 0.0596 - acc: 0.983 - ETA: 8s - loss: 0.0589 - acc: 0.984 - ETA: 8s - loss: 0.0593 - acc: 0.983 - ETA: 8s - loss: 0.0585 - acc: 0.983 - ETA: 8s - loss: 0.0575 - acc: 0.983 - ETA: 8s - loss: 0.0564 - acc: 0.984 - ETA: 8s - loss: 0.0559 - acc: 0.984 - ETA: 8s - loss: 0.0551 - acc: 0.984 - ETA: 8s - loss: 0.0555 - acc: 0.984 - ETA: 7s - loss: 0.0547 - acc: 0.984 - ETA: 7s - loss: 0.0539 - acc: 0.985 - ETA: 7s - loss: 0.0530 - acc: 0.985 - ETA: 7s - loss: 0.0525 - acc: 0.984 - ETA: 7s - loss: 0.0561 - acc: 0.984 - ETA: 7s - loss: 0.0558 - acc: 0.984 - ETA: 7s - loss: 0.0551 - acc: 0.984 - ETA: 7s - loss: 0.0544 - acc: 0.984 - ETA: 7s - loss: 0.0562 - acc: 0.984 - ETA: 6s - loss: 0.0554 - acc: 0.984 - ETA: 6s - loss: 0.0546 - acc: 0.985 - ETA: 6s - loss: 0.0552 - acc: 0.984 - ETA: 6s - loss: 0.0544 - acc: 0.985 - ETA: 6s - loss: 0.0537 - acc: 0.985 - ETA: 6s - loss: 0.0530 - acc: 0.985 - ETA: 6s - loss: 0.0523 - acc: 0.985 - ETA: 6s - loss: 0.0516 - acc: 0.986 - ETA: 6s - loss: 0.0527 - acc: 0.985 - ETA: 5s - loss: 0.0543 - acc: 0.985 - ETA: 5s - loss: 0.0539 - acc: 0.985 - ETA: 5s - loss: 0.0533 - acc: 0.985 - ETA: 5s - loss: 0.0545 - acc: 0.985 - ETA: 5s - loss: 0.0544 - acc: 0.985 - ETA: 5s - loss: 0.0539 - acc: 0.985 - ETA: 5s - loss: 0.0538 - acc: 0.985 - ETA: 5s - loss: 0.0535 - acc: 0.985 - ETA: 5s - loss: 0.0532 - acc: 0.985 - ETA: 4s - loss: 0.0527 - acc: 0.985 - ETA: 4s - loss: 0.0526 - acc: 0.985 - ETA: 4s - loss: 0.0522 - acc: 0.985 - ETA: 4s - loss: 0.0517 - acc: 0.985 - ETA: 4s - loss: 0.0512 - acc: 0.985 - ETA: 4s - loss: 0.0515 - acc: 0.985 - ETA: 4s - loss: 0.0541 - acc: 0.984 - ETA: 4s - loss: 0.0536 - acc: 0.984 - ETA: 4s - loss: 0.0538 - acc: 0.984 - ETA: 3s - loss: 0.0548 - acc: 0.984 - ETA: 3s - loss: 0.0547 - acc: 0.984 - ETA: 3s - loss: 0.0547 - acc: 0.984 - ETA: 3s - loss: 0.0543 - acc: 0.984 - ETA: 3s - loss: 0.0551 - acc: 0.984 - ETA: 3s - loss: 0.0550 - acc: 0.984 - ETA: 3s - loss: 0.0556 - acc: 0.983 - ETA: 3s - loss: 0.0564 - acc: 0.983 - ETA: 3s - loss: 0.0565 - acc: 0.983 - ETA: 2s - loss: 0.0582 - acc: 0.982 - ETA: 2s - loss: 0.0584 - acc: 0.982 - ETA: 2s - loss: 0.0580 - acc: 0.982 - ETA: 2s - loss: 0.0579 - acc: 0.982 - ETA: 2s - loss: 0.0574 - acc: 0.982 - ETA: 2s - loss: 0.0572 - acc: 0.983 - ETA: 2s - loss: 0.0575 - acc: 0.982 - ETA: 2s - loss: 0.0570 - acc: 0.983 - ETA: 2s - loss: 0.0575 - acc: 0.982 - ETA: 1s - loss: 0.0575 - acc: 0.982 - ETA: 1s - loss: 0.0572 - acc: 0.982 - ETA: 1s - loss: 0.0569 - acc: 0.983 - ETA: 1s - loss: 0.0565 - acc: 0.983 - ETA: 1s - loss: 0.0561 - acc: 0.983 - ETA: 1s - loss: 0.0559 - acc: 0.983 - ETA: 1s - loss: 0.0558 - acc: 0.983 - ETA: 1s - loss: 0.0554 - acc: 0.983 - ETA: 1s - loss: 0.0553 - acc: 0.983 - ETA: 0s - loss: 0.0549 - acc: 0.983 - ETA: 0s - loss: 0.0545 - acc: 0.983 - ETA: 0s - loss: 0.0542 - acc: 0.983 - ETA: 0s - loss: 0.0540 - acc: 0.984 - ETA: 0s - loss: 0.0538 - acc: 0.984 - ETA: 0s - loss: 0.0534 - acc: 0.984 - ETA: 0s - loss: 0.0531 - acc: 0.984 - ETA: 0s - loss: 0.0528 - acc: 0.984 - ETA: 0s - loss: 0.0525 - acc: 0.984 - 15s 4ms/step - loss: 0.0525 - acc: 0.9847 - val_loss: 0.0914 - val_acc: 0.9679\n",
      "Epoch 3/3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x23b6a11acc0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "def create_model(input_length=50):\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(20000, 300, input_length=50))\n",
    "    model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])    \n",
    "    return model    \n",
    "model_LSTM = KerasClassifier(build_fn=create_model, epochs=3, verbose=1, validation_split=0.4)\n",
    "model_LSTM.fit(X_train_LSTM, Y_train_LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6629/6629 [==============================] - ETA: 38 - ETA: 9 - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - 4s 562us/step\n",
      "2841/2841 [==============================] - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - 1s 495us/step\n"
     ]
    }
   ],
   "source": [
    "train_result_LSTM = accuracy_score(model_LSTM.predict(X_train_LSTM), Y_train_LSTM)\n",
    "# Test results\n",
    "test_result_LSTM = accuracy_score(model_LSTM.predict(X_test_LSTM), Y_test_LSTM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the accuracy and confusion metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9882335193845225 0.9704329461457233\n"
     ]
    }
   ],
   "source": [
    "print(train_result_LSTM,test_result_LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion_matrix(model_LSTM.predict(X_test_LSTM), Y_test_LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_results.append(train_result_LSTM);test_results.append(test_result_LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "names.append(\"LSTM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "from matplotlib import pyplot\n",
    "fig = pyplot.figure()\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the LSTM model has the best performance in the test set as compared to all other models that we saw before. Let us use LSTM model for the computation of the sentiments in the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences_LSTM = tokenizer.texts_to_sequences(data_df['headline'])\n",
    "X_LSTM = pad_sequences(sequences_LSTM, maxlen=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2759/2759 [==============================] - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - 1s 471us/step\n"
     ]
    }
   ],
   "source": [
    "Y_LSTM = model_LSTM.predict(X_LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['sentiment_LSTM'] = Y_LSTM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10977726955235573\n"
     ]
    }
   ],
   "source": [
    "corrlation = data_df['eventRet'].corr(data_df['sentiment_LSTM'])\n",
    "print(corrlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>headline</th>\n",
       "      <th>date</th>\n",
       "      <th>eventRet</th>\n",
       "      <th>Close</th>\n",
       "      <th>sentiment_textblob</th>\n",
       "      <th>sentiment_LSTM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMZN</td>\n",
       "      <td>Whole Foods (WFMI) -5.2% following a downgrade...</td>\n",
       "      <td>2011-05-02</td>\n",
       "      <td>0.01765</td>\n",
       "      <td>201.19</td>\n",
       "      <td>0.2625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker                                           headline        date  \\\n",
       "0   AMZN  Whole Foods (WFMI) -5.2% following a downgrade...  2011-05-02   \n",
       "\n",
       "   eventRet   Close  sentiment_textblob  sentiment_LSTM  \n",
       "0   0.01765  201.19              0.2625               0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.3'></a>\n",
    "## 4.3 - Unsupervised - Model based on financial lexicon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lexicons are special dictionaries or vocabularies that have been created for analyzing sentiments.\n",
    "VADER (Valence Aware Dictionary for Entiment Reasoning) is a pre-built sentiment analysis model included in the NLTK package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock market lexicon\n",
    "sia = SentimentIntensityAnalyzer()\n",
    "stock_lex = pd.read_csv('Data\\LexiconData.csv')\n",
    "stock_lex['sentiment'] = (stock_lex['Aff_Score'] + stock_lex['Neg_Score'])/2\n",
    "stock_lex = dict(zip(stock_lex.Item, stock_lex.sentiment))\n",
    "stock_lex = {k:v for k,v in stock_lex.items() if len(k.split(' '))==1}\n",
    "stock_lex_scaled = {}\n",
    "for k, v in stock_lex.items():\n",
    "    if v > 0:\n",
    "        stock_lex_scaled[k] = v / max(stock_lex.values()) * 4\n",
    "    else:\n",
    "        stock_lex_scaled[k] = v / min(stock_lex.values()) * -4\n",
    "\n",
    "final_lex = {}\n",
    "final_lex.update(stock_lex_scaled)\n",
    "final_lex.update(sia.lexicon)\n",
    "sia.lexicon = final_lex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of extracting sentiment score for a sentence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"AAPL is trading higher after reporting its October sales rose 12.6% M/M. It has seen a 20%+ jump in orders\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4535"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia.polarity_scores(text)['compound']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we extract the sentiment for the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2759,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vader_sentiments = pd.np.array([sia.polarity_scores(s)['compound'] for s in data_df['headline']])\n",
    "vader_sentiments.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['sentiment_lex'] = vader_sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1047785005800468\n"
     ]
    }
   ],
   "source": [
    "corrlation = data_df['eventRet'].corr(data_df['sentiment_lex'])\n",
    "print(corrlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_df['sentiment_lex'],data_df['eventRet'], alpha=0.5)\n",
    "plt.title('Scatter Between Event return and sentiments-all data')\n",
    "plt.ylabel('Event Return')\n",
    "plt.xlabel('Sentiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don's see many high returns for lower sentiments, but the data may not be very clear. Let us look at the result for one of the stock tickers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df_stock  = data_df[data_df['ticker'] == 'AMZN']\n",
    "plt.scatter(data_df_stock['sentiment_lex'],data_df_stock['eventRet'], alpha=0.5)\n",
    "plt.title('Scatter Between Event return and sentiments-AMZN')\n",
    "plt.ylabel('Event Return')\n",
    "plt.xlabel('Sentiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a positive correlation between the event return and sentiments. We will look deeper into the comparison of different types of the sentiment analysis in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.to_csv(r'Data\\Step4_DataWithSentimentsResults.csv', sep='|', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.4'></a>\n",
    "## 4.4 Exploratory Data Analysis and comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.read_csv(r'Data\\Step4_DataWithSentimentsResults.csv', sep='|')\n",
    "data_df = data_df[data_df['ticker'].isin(tickers)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the sample headlines and the the sentiments from three different methodology, followed by the analysis using visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>headline</th>\n",
       "      <th>sentiment_textblob</th>\n",
       "      <th>sentiment_LSTM</th>\n",
       "      <th>sentiment_lex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NFLX</td>\n",
       "      <td>Netflix (NFLX +1.1%) shares post early gains after Citigroup ups its rating to Buy and lifts its price target to $300 from $245. U.S. revenue growth is sustainable, Citi says, \"with a path to 50M subscribers by 2013,\" adding that NFLX has little competition in price, selection and  convenience; mass market adoption of tablets will help, and the mass-market adoption phase is still to come.</td>\n",
       "      <td>-0.04375</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker  \\\n",
       "1   NFLX   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                  headline  \\\n",
       "1  Netflix (NFLX +1.1%) shares post early gains after Citigroup ups its rating to Buy and lifts its price target to $300 from $245. U.S. revenue growth is sustainable, Citi says, \"with a path to 50M subscribers by 2013,\" adding that NFLX has little competition in price, selection and  convenience; mass market adoption of tablets will help, and the mass-market adoption phase is still to come.   \n",
       "\n",
       "   sentiment_textblob  sentiment_LSTM  sentiment_lex  \n",
       "1            -0.04375               1         0.8575  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_new_df_stock=data_df[data_df['ticker']== 'NFLX'][['ticker','headline','sentiment_textblob','sentiment_LSTM','sentiment_lex']]\n",
    "from pandas import option_context\n",
    "\n",
    "with option_context('display.max_colwidth', 400):\n",
    "    display(data_new_df_stock.head(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at one of the headlines, the sentiment from this sentence is positive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b7b0e35c0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = data_df[['sentiment_textblob','sentiment_LSTM','sentiment_lex','eventRet']].dropna(axis=0).corr()\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(correlation[['eventRet']], vmax=1, annot=True,cmap='cubehelix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the sentiments have positive relationship with the return which is intuitive and expected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_data = []\n",
    "for ticker in data_df['ticker'].unique():\n",
    "    data_new_df_stock=data_df[data_df['ticker']==ticker]\n",
    "    #Only look for the stocks with sufficient data\n",
    "    if data_new_df_stock.shape[0] > 40 : \n",
    "        corr_textblob= data_new_df_stock['eventRet'].corr(data_new_df_stock['sentiment_textblob'])    \n",
    "        corr_LSTM = data_new_df_stock['eventRet'].corr(data_new_df_stock['sentiment_LSTM'])\n",
    "        corr_lex = data_new_df_stock['eventRet'].corr(data_new_df_stock['sentiment_lex'])\n",
    "        corr_data.append([ticker,corr_textblob, corr_LSTM, corr_lex])\n",
    "        #print(ticker,corr_vader, corr_LSTM, corr_textblob)\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>corr_textblob</th>\n",
       "      <th>corr_LSTM</th>\n",
       "      <th>corr_lex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.01983</td>\n",
       "      <td>0.134878</td>\n",
       "      <td>0.138296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        corr_textblob  corr_LSTM  corr_lex\n",
       "ticker                                    \n",
       "AMZN          0.01983   0.134878  0.138296"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df = pd.DataFrame(corr_data, columns =  ['ticker','corr_textblob','corr_LSTM','corr_lex'])  \n",
    "corr_df=corr_df.set_index('ticker')\n",
    "corr_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R2ANY189zJUmS2l4jQtmDwN4RMSYidqY2cX9htzYLgenV46nA3VkbN10InFxdnTkG2Bt4oAE1SZIktZS6r77MzPURcQZwG9ABzM3MZRFxIbA4MxcCVwH/FBHLqfWQnVyduywivgk8DqwH/sYrLyVJ0vao7lAGkJm3Ard22/epLo9fAE7s5dzZwOxG1CFJktSqXNFfkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgrQkHXKJGmgeLNxSe3KnjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkA3pC8jayYc3SzS5AkSVvJnjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkAhjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpQVyiLiKERcUdEPFl937OXdtOrNk9GxPQu+++JiJ9ExJLq6xX11CNJktSq6u0pOxe4KzP3Bu6qtjcREUOBTwNvByYCn+4W3t6TmeOrr1/XWY8kSVJLqjeUHQfMqx7PA47voc0U4I7MXJeZvwHuAI6q83UlSZLaSr2h7JWZ+UuA6ntPw4+vAX7RZXtVtW+Dr1dDlxdERPT2QhExMyIWR8TiNWvW1Fm2JElSWXbsq0FE3An8WQ+Hzu/na/QUtLL6/p7M/LeIGALcCEwDrunpSTLzCuAKgM7OzuypjSRJUqvqM5Rl5hG9HYuIX0XEqzLzlxHxKqCnOWGrgEldtkcC91TP/W/V9+ci4jpqc856DGWSJEntrN7hy4XAhqsppwMLemhzG3BkROxZTfA/ErgtInaMiOEAEbETcAzwWJ31SJIktaR6Q9kc4J0R8STwzmqbiOiMiK8BZOY64DPAg9XXhdW+XaiFs0eBJcC/AVfWWY8kSVJL6nP4cnMycy0wuYf9i4H3d9meC8zt1uZ54K31vL4kSVK7cEV/SZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkAhjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkAhjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkAhjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSpAXaEsIoZGxB0R8WT1fc9e2n03Ip6JiFu67R8TET+qzr8+Inaupx5JkqRWVW9P2bnAXZm5N3BXtd2Ti4BpPez/HPCl6vzfAO+rsx5JkqSWVG8oOw6YVz2eBxzfU6PMvAt4ruu+iAjgcOCGvs6XJElqd/WGsldm5i8Bqu+v2IJzhwHPZOb6ansV8JreGkfEzIhYHBGL16xZs9UFS5IklWjHvhpExJ3An/Vw6Pw6Xzt62Je9Nc7MK4ArADo7O3ttJ0mS1Ir6DGWZeURvxyLiVxHxqsz8ZUS8Cvj1Frz2fwAvj4gdq96ykcDqLThfkiSpbdQ7fLkQmF49ng4s6O+JmZnAvwBTt+Z8SZKkdlJvKJsDvDMingTeWW0TEZ0R8bUNjSLiB8C3gMkRsSoiplSHzgE+GhHLqc0xu6rOeiRJklpSn8OXm5OZa4HJPexfDLy/y/Y7ejn/KWBiPTVIkiS1A1f0lyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkAhjJJkqQCGMokSZIKYCiTJEkqgKFMkiSpAIYySZKkAhjKJEmSCmAokyRJKoChTJIkqQCGMkmSpAIYyiRJkgpgKJMkSSqAoUySJKkAhjJJkqQCGMokSZIKYCiTJEkqwI7NLqBR/vCHP7Bq1SpeeOGFZpeiyqBBgxg5ciQ77bRTs0uRJKl4bRPKVq1axZAhQxg9ejQR0exytnuZydq1a1m1ahVjxoxpdjmSJBWvbYYvX3jhBYYNG2YgK0REMGzYMHsuJUnqp7YJZYCBrDD+e0iS1H9tFcokSZJaVdvMKetu9LnfaejzrZhzdEOfT5IkqSt7ylrEkiVLuPXWW7f6/HvuuYcf/vCHG7dPP/10brjhhh7bHXPMMZt9rlmzZnHxxRdvdS2SJOmlDGVNtn79+s1ub9DoUCZJkspiKGuga665hnHjxvHmN7+ZadOm8fTTTzN58mTGjRvH5MmTWblyJVDrpfroRz/KYYcdxjnnnMOsWbOYOXMmRx55JKeddtpLnvf3v/89n/rUp7j++usZP348119/Pc8//zwzZszgbW97GxMmTGDBggUAfPGLX2TGjBkALF26lDe96U08/vjjXHbZZXzpS19i/Pjx/OAHPwDgzjvv5B3veAf77LMPt9xyy0ted926dRx//PGMGzeOP//zP+fRRx/deOyRRx7h8MMPZ++99+bKK69s+H9LSZK2N207p2ygLVu2jNmzZ3PvvfcyfPhw1q1bx/Tp0znttNOYPn06c+fO5cwzz+Tmm28G4Kc//Sl33nknHR0dzJo1i4ceeohFixYxePDglzz3zjvvzIUXXsjixYv5yle+AsAnPvEJDj/8cObOncszzzzDxIkTOeKIIzjrrLOYNGkSN910E7Nnz+byyy9n//3354Mf/CC77bYbH/vYxwC46qqrWLFiBd/73vf42c9+xmGHHcby5cs3ed1Pf/rTTJgwgZtvvpm7776b0047jSVLlgDw6KOPcv/99/P8888zYcIEjj76aF796ldvy//EkiS1NXvKGuTuu+9m6tSpDB8+HIChQ4dy3333ceqppwIwbdo0Fi1atLH9iSeeSEdHx8btY489tsdA1pvbb7+dOXPmMH78eCZNmsQLL7zAypUr2WGHHbj66quZNm0ahx56KAcddFCvz/Hud7+bHXbYgb333pvXve51/Ou//usmxxctWsS0adMAOPzww1m7di3PPvssAMcddxyDBw9m+PDhHHbYYTzwwAP9rl2SJL2UPWUNkpl9rsvV9fiuu+66ybHu2/15vRtvvJF99933JceefPJJdtttN1avXt3venrazsxez+nrXEmStGXaNpQN9BIWkydP5oQTTuDss89m2LBhrFu3jgMPPJD58+czbdo0rr32Wg4++OCtfv4hQ4bw3HPPbdyeMmUKl156KZdeeikRwcMPP8yECRN49tln+chHPsL3v/99zjjjDG644QamTp3KkCFD+M///M9NnvNb3/oW06dP5+c//zlPPfUU++67L/fff//G44cccgjXXnstF1xwAffccw/Dhw9n9913B2DBggWcd955PP/889xzzz3MmTNnq9+bJElq41A20A444ADOP/98Dj30UDo6OpgwYQKXXHIJM2bM4KKLLmLEiBF8/etf3+rnP+ywwzYOV5533nlccMEFnHXWWYwbN47MZPTo0dxyyy2cffbZfOhDH2Kfffbhqquu4rDDDuOQQw7hXe96F1OnTmXBggVceumlAOy7774ceuih/OpXv+Kyyy5j0KBBm7zmrFmz+Ou//mvGjRvHy172MubNm7fx2MSJEzn66KNZuXIlF1xwgfPJJEmqU/Q0RFW6zs7OXLx48Sb7nnjiCfbbb78mVaTe+O8itb7+LMbtAttS7yLioczs7KudE/0lSZIK4PBlYW677TbOOeecTfaNGTOGm266qUkVSZKkgWAoK8yUKVOYMmVKs8uQJEkDzOFLSZKkAhjKJEmSCmAokyRJKkD7zimbtUeDn+/Zxj6fJElSF3X1lEXE0Ii4IyKerL7v2Uu770bEMxFxS7f9V0fEzyNiSfU1vp562tk999zDMccc85L9t9xyCxMmTODNb34z+++/P5dffjmzZ89m/PjxjB8/no6Ojo2PL7nkEmbNmkVEbHLz8S996UtEBN3XfpMkSQOn3uHLc4G7MnNv4K5quycXAdN6OfbxzBxffS2ps56Ws379+s1ub84f/vAHZs6cybe//W0eeeQRHn74YSZNmsT555/PkiVLWLJkCYMHD974+MwzzwRg7NixzJ8/f+Pz3HDDDey///6NeUOSJGmr1Dt8eRwwqXo8D7gHOKd7o8y8KyImdd/fbq655houvvhiIoJx48bx2c9+lhkzZrBmzZqNt1kaNWoUp59+OkOHDuXhhx/mLW95C0OGDGH16tWsWLGC4cOHc9111/Xr9Z577jnWr1/PsGHDANhll116vEF5d8cffzwLFizgk5/8JE899RR77LEHO+20U13vXZIk1afenrJXZuYvAarvr9iK55gdEY9GxJciYpfeGkXEzIhYHBGL16xZs7X1bjPLli1j9uzZ3H333TzyyCN8+ctf5owzzuC0007j0Ucf5T3vec/GniqAn/70p9x555184QtfAOChhx5iwYIF/Q5kAEOHDuXYY49lr7324pRTTuHaa6/lj3/8Y5/n7b777rz2ta/lscce4xvf+AYnnXTSlr9hSZLUUH2Gsoi4MyIe6+HruAa8/nnAG4G3AUPpoZdtg8y8IjM7M7NzxIgRDXjpxrr77ruZOnUqw4cPB2qB6b777uPUU08FYNq0aSxatGhj+xNPPJGOjo6N28ceeyyDBw/e4tf92te+xl133cXEiRO5+OKLmTFjRr/OO/nkk5k/fz4333wzJ5xwwha/riRJaqw+hy8z84jejkXEryLiVZn5y4h4FfDrLXnxDb1swO8i4uvAx7bk/JJkJhGx2TZdj++6666bHOu+vSXGjh3L2LFjmTZtGmPGjOHqq6/u85x3vetdfPzjH6ezs5Pdd999q19bkiQ1Rr1zyhYC04E51fcFW3Jyl0AXwPHAY3XW8ycDvITF5MmTOeGEEzj77LMZNmwY69at48ADD2T+/PlMmzaNa6+9loMPPrihr/nb3/6WxYsXM2nSJACWLFnCXnvt1a9zBw8ezOc+9zn22WefhtYkSZK2Tr2hbA7wzYh4H7ASOBEgIjqBD2bm+6vtH1AbptwtIlYB78vM24BrI2IEEMAS4IN11tM0BxxwAOeffz6HHnooHR0dTJgwgUsuuYQZM2Zw0UUXbZzoX4+77rqLkSNHbtz+xje+wec//3k+8IEPMHjwYHbdddd+9ZJtcPLJJ9dVjyRJapzIzGbXsMU6Ozuz+5paTzzxBPvtt1+TKlJv/HeRWt/oc7/TZ5sVc44egEqk1hQRD2VmZ1/tvM2SJElSAdr3Nkst6rbbbuOccza9CHXMmDHcdNNNTapIkiQNBENZYaZMmcKUKVOaXYYkSRpgDl9KkiQVwFAmSZJUAIcvJUmb5ZWV0sBo21A2dt7Yhj7f0ulLG/p8kiRJXTl82SLuuecejjnmmGaXIUmSthFDWZOtX79+s9uSJGn70LbDl81wzTXXcPHFFxMRjBs3js9+9rPMmDGDNWvWbLzN0qhRozj99NMZOnQoDz/8MG95y1sYMmQIq1evZsWKFQwfPpzrrrtus6/z/PPP8+EPf5ilS5eyfv16Zs2axXHHHccXv/hFHnvsMebOncvSpUs55ZRTeOCBB3jZy142QP8FJEnS1jKUNciyZcuYPXs29957L8OHD2fdunVMnz6d0047jenTpzN37lzOPPNMbr75ZgB++tOfcuedd9LR0cGsWbN46KGHWLRoEYMHD+7ztWbPns3hhx/O3LlzeeaZZ5g4cSJHHHEEZ511FpMmTeKmm25i9uzZXH755QYySZJahMOXDXL33XczdepUhg8fDsDQoUO57777OPXUUwGYNm0aixYt2tj+xBNPpKOjY+P2scce269ABnD77bczZ84cxo8fz6RJk3jhhRdYuXIlO+ywA1dffTXTpk3j0EMP5aCDDmrgO5QkSduSPWUNkplExGbbdD2+6667bnKs+3Zfr3XjjTey7777vuTYk08+yW677cbq1av7/XySJKnx9pH3AAAREklEQVT52jaUDfQSFpMnT+aEE07g7LPPZtiwYaxbt44DDzyQ+fPnM23aNK699loOPvjghrzWlClTuPTSS7n00kuJCB5++GEmTJjAs88+y0c+8hG+//3vc8YZZ3DDDTcwderUhrymJEnatto2lA20Aw44gPPPP59DDz2Ujo4OJkyYwCWXXMKMGTO46KKLNk70b4QLLriAs846i3HjxpGZjB49mltuuYWzzz6bD33oQ+yzzz5cddVVHHbYYRxyyCG84hWvaMjrSpKkbScys9k1bLHOzs5cvHjxJvueeOIJ9ttvvyZVpN747yJJ2t5FxEOZ2dlXOyf6S5IkFcDhy8LcdtttnHPOOZvsGzNmDDfddFOTKpIkSQOhrUJZf66ALN2UKVOYMmVKs8toiFYcGpckqVnaZvhy0KBBrF271iBQiMxk7dq1DBo0qNmlSJLUEtqmp2zkyJGsWrWKNWvWNLsUVQYNGsTIkSObXYYkSS2hbULZTjvtxJgxY5pdhiRJ0lZpm+FLSZKkVmYokyRJKoChTJIkqQAtuaJ/RKwBnh7AlxwO/McAvt5Aa+f3187vDXx/rc7317ra+b2B76/R9srMEX01aslQNtAiYnF/bo/Qqtr5/bXzewPfX6vz/bWudn5v4PtrFocvJUmSCmAokyRJKoChrH+uaHYB21g7v792fm/g+2t1vr/W1c7vDXx/TeGcMkmSpALYUyZJklQAQ5kkSVIBDGWSJG2hiBgUESc2uw61l7a5Ifm2FhFvz8wfNbuOekXE32fmJ5pdh7Q9iYgzMvMrza5jW4iISzZ3PDPPHKhatrWI6ACOBE4BpgA/AL7V1KIaICJ2yMw/9nLs5Zn5zEDX1CgRsXtm/mcvx0Zl5sqBrmlznOjfTxGxMjNHNbuOekXEjzPzLc2uo9Ei4udA1x/m6LKdmfn6ga9q24qI1wAd1ebqzFzfzHrqFRHDgb8BfgPMBS4C3gH8DPjbzFzexPLq0q6/dwAR8XvgMeCbwGpqv3sbZea8ZtTVSBFxCHAqcDTwAHAQ8LrM/K+mFtYgEfFj4P/t3vEQEe8HPpGZr2tOZfXr+rsXEXdl5uSejpXCnrL+i76btISOiNiTXt5PZq4b4HoapfvKzDsA7wY+Bjw88OU0XkScB+yUmRdWu+4DngF2BuYB/7tZtTXIdcBiYG9qf/i+DnyZWjD7GjCpaZVpc14FnAicBKwHrgduzMzfNLWqBomIVcBK4P8AH8/M5yLi5+0SyCpnAldExAPAOcBewD8Cq4BDmllYA3T9Wzd0M8eKYCjrv3bpUnwj8BA9/zAm0JKfiDJzLdS64YFpwMeBJcDRmfl4M2troBOpBZQN1mbmhGpI5Xu0fih7ZWZ+IiICeDozL6r2/2tE/E0zC2uAcRHR0xBKUOvJ3X2gC2qU6nfvMuCyqvf2FGBZRJyTmf/U3Ooa4kbgeGqh88WIWED7/D0AIDMXRcRbgL+j1jP9W+B9mXl7cytriOzlcU/bTWco6yIivk3P/0gBDBvgcraVxzNzQrOLaLSI2AmYAZwNLAKOy8yfNbeqxsvM57tsfrna92JEDG5SSY30ItQSSkR0v1Fwj/NdWsjSdvy966r6o34K8E7gn6l9+Gt5mfmRiDgLOIza+7sI2D0i3g3cmpm/bWqBjXMitff3f4AjgJOq+0O26ujJBq+IiI9S+zu+4THVdp83CB9ozinrIiIO3dzxzPzeQNWyrUTEw+34x6EaYlgP/AO1oYZNZOb/N+BFNVhE/BQ4IDP/0G3/LsBjmbl3cyprjIh4Bvg+tf9ZvqN6TLV9cGbu2aza6tWuv3cAEfF3wDHAE8B84LutPr9xc6oPgEdRCzBHZubwJpdUt4i4E/hv4MzM/HnVW30GcBbwucwscvX7/oiIT2/ueGb+3UDV0h+Gsm4iYgLwemBZZj7R7HoaLSJmtvIvWG8i4mp674rOzJwxgOVsExHx98CfAWdsmM8SEbsCXwH+PTPPa2Z99WrnD0UR8YnM/Ptm17EtRMQfgaeo/VGHP/0ebhiaHdeUwgZARHwzM9/d7DrqFREnZOZNPez/M+DizHxvE8raLhnKuoiITwHvpdbt/nbgf2fmlc2tqrG6XYlyaWZ+uNk1qX+quWOzgfcDT1P7o/da4Crgk63eO1Hi5emN0s7LRkTEXps7nplPD1QtA61drsrfnIi4PjNPanYdWysiBlGbD/gb4NvU5hsfQm3u3Gcys/tUiaYylHUREcuAt2Xmf0XEMGrd8G9rdl2N1HUYpcTLgbdWRNwHnJ+Zd/dwbJPLoFtdNX/sDdXm8sz87821bxXdPjDcmJl/1eyaGqXdl42IiOOp/Uwuzczbml3PQNlOQllLv8eI+CbwB2BXYE9qv4ffBg4GxmfmMU0s7yWc6L+pFzYMC2Xm2upKvnbTril8FPCViLgVOK/bvKvul0G3rIh4BbW1vA6g9m/5eER8NTN/3dzKGqJrUGnJq4A3o22XjYiIf6T28/hD4DMRMTEzP9PkshqmuoChx0PATgNZi7bK/pn5pojYEViVmRumSXw3Ih5pZmE9MZRt6vURsbB6HN22ycxjm1NWQ70xIh7lT+/v0Wp/q8//+BW1Tz6XAD+KiFMy8yfVsbYIohFxELW1vK4GrqH2b/YW4IGIeE9m3tvE8hphc5eut7Q2XzbiEODN1VXAL6O2yn3bhDLgC5s59q8DVsU21ObB8/cAmbk+IlZ3O/ZiE+rZLEPZpo7rtn1xU6rYtvZrdgHbStXL+f6IOAG4o7ql1GUUuEDgVvoCcHxmdl0Md0FE3ARcTm0eZCt7c7WWVwCDu6zr1fJreW3QpstG/D4zNyxn8l/VlXttIzMPa3YNA6Cdg+fIak5ndHlMtf2a5pXVM+eU9UNEvBY4uctilm2jmjt3CLAyM1v2D0T3+XFVb8TV1K4I26/Vl4sAiIjHM3P/LT2m5mvnZSMi4r+BJ/nTh5/XA8tp/d53ACLibcAvMvPfq+3TgL+idrHNrDZYx6utRcT0zR0vbT6noawX1X34Niym9xrgpsz8WHOrql9E3AKcm5mPRcSrgB9Tu7XN64ErMvMfmlrgVoqI72bmUT3s/zi1K2wGNaGshoqIJ4ADu89DioihwA8z843NqUx9aedlI6qrL3v9Q9LqV9RG7b6QR2TmuqjdA3M+8GFgPLUPfFObWmADVKMKn6gevzMz72h2TdsrQ1kXETEEOIHajWf3AW4CTsrMkU0trIEiYllmHlA9/gTwxsw8rXrv97byH4d2FxEzgf9J7X6eP652vxX4HDA3My9vVm3avIjYcKeJ31C7EmwTrbxsREQ8R++h7HfUlh44PzPvGriqGiciHsnMN1ePvwqsycxZ1faSzBzfzPoaoduVz21zVf4GVW/ZR4B9q11PAJdk5jXNq6pnzinb1K+p3Qj5k8Ci6nYvJzS5pkbr+gdhMnAlQNZustuyt7Jp53WgNsjMK6qJqp+hdrUbwDLgs5n57eZVpn54DbXbYr0ReJTalYr3Ave1+vBXZg7p7Vi1tt6bgGur762oIyJ2rIabJwMzuxzzb2jhquHms4CPUvswu+ECqYsigtKCmT1lXVSfZk+mtp7JddQuW78jM9vm8vyo3d/zdmAVMBcYk5nPVGtfLd7Qi9Zq2n0dKLWHiNgZ6AQOBP6i+nqm3ecDRsQHWrUnt1pUfArwH9SW3nlL9YH9DcC8zDyoqQU2QNRuU/dFav/fPLt6vFFmfrGn81pBRNxPbU74im77RwPzM/PPm1BWr9pxHa6tlplfysy3A8dS++G8GXh1RJwTEfs0t7qGeR+1XpbTqQ3NPlPt/3Pg680qqgFeBVxB7X+e06hdxr0wM+e1UyCLiL+MiO9FxH9ExJrq8f/T7LrUb4OB3YE9qq/VwI+aWtEAaNVAVjke+FtqFw4dnH/qydiB2tyydnAlMATYrcvjrl+tbPfugQyg2lfcFd32lPUhIsZRu8n1Xpn5+mbXo751WQfqo0A7rAMFQET8T+ADwP+idnEG1Hpd5gBfyza8p2m7iIgrqH0Yeo5aCLsfuL8dFo9td+04x2p7EhEPZeZbt/RYszge3ouIGE/tD/tJwApqwazldV0MtyetvkBum64DtcHZ1D6pd52DdHdE/CW1SeSGsnKNAnahtnTEv1GbPvDMZs9QKV4RER/t7WArD+1tUA3R9iZb/A4N+3VZJL2roMA7hxjKuqiGKE+m9kd9LbU5ZZGZk5pZV4P9BfAL4BvUPrG3xUKPPawDdV67rAPVRfQ0Kby6JVgz6lE/ZeZR1aKqB1CbT/a3wJsiYh21yf6fbmqB2pwOasN67fxL9nwP+3alNt1lGK19h4aeFkwPYCTwiQGupU8OX3ZRXX34A+B9mbm82vdUm03076DWi3QKMA74DvCNzFzW1MLq1Mc6UH/ccEl7K4uIHwEzM/ORbvvfDFyZmRObU5m2RESMBA6iFs6OAYZl5subW5V6s70NX1bLI32EWiD7JvCFNrm37oYRsFOBdwM/p3b/2a80t6pN2VO2qb+i1lP2LxHxXWo9Lm316ai6Hcp3qd2MdRdq4eyeiLgwMy9tbnV1GdPDvmI/DW2lvwUWRsTXqQ3LJvA2YDrw3mYWps2LiDOphbCDqC1Lcy9wH7UroJc2sTT1ra3+BvSmWoT6o8B7gHnUrjJt+TmPmxkBK/L2WfaU9SAidqV2xc0pwOHUfkBvyszbm1pYg1Rh7Ghq7280sJDa4qP/1sy6GqUVPg1trYh4JfA3bLpO2Vcz81fNq0p9iYgvUq1Nlpm/bHY96r+IGNrqa8n1JSIuAv4HtXmpX83M3za5pIZptREwQ1kfqk8PJ1JbPuLwZtdTr4iYR20Rx3+mtkbLY00uqSF6+TT0sczcq6mFNVBEHAeMzMyvVtsPACOo9Zj9r8y8oZn1SWpNVXD5HbCeTe/OsOE2YMUtHdFf1QLwJ1Prqd4wAva1zOxpdKXpDGXbmeqXb8Okzrb55Wu1T0NbIyLupbYI4i+q7SXUenJ3A76emZObWZ8klapVRsBcPHY7k5k7ZOaQ6mv3Ll9DWjWQVf4K+Hdq8wGvjIjJtN9ckJ03BLLKosxcV93weddmFSVJpcvM5zPz2sw8htpc4yXAuU0u6yXsKVNbaZVPQ1sjIpZn5ht6OfYzFzeWpNZmT5naSqt8GtpKP6pW9d9ERHwAeKAJ9UiSGsieMqlFRMQrqN2P9XfAj6vdb6W2UvzxXoEpSa3NUCa1mIg4nC5LYmTm3c2sR5LUGIYySZKkAjinTJIkqQCGMkmSpAIYyiS1nYh4eUR8qHr86ojY7N0OImJFRAwfmOokqWeGMknt6OXAhwAyc3VmTt1WLxQRHdvquSVtXwxlktrRHOD1EbEkIr4VEY9BLUBFxMURsTQiHo2ID3c9KSIGR8R3N6wHFxHvjYgHque5fEMAi4jfRsSFEfEj4C8G+s1Jak+GMknt6FzgZ5k5Hvh4l/0zgTHAhMwcB1zb5dhuwLeB6zLzyojYDzgJOKh6nheB91RtdwUey8y3Z+aibfxeJG0ndmx2AZI0gI4ALsvM9QCZua7LsQXA5zNzQ1CbTG1x3gcjAmAw8Ovq2IvAjQNSsaTthqFM0vYkgN4WZ7wX+MuIuC5rCzgGMC8zz+uh7QuZ+eK2KlLS9snhS0nt6DlgSA/7bwc+GBE7AkTE0C7HPgWsBf6x2r4LmFrd3oqIGBoRe227kiVt7wxlktpOZq4F7q0m+F/U5dDXgJXAoxHxCHBqt1PPAgZFxOcz83Hgk8DtEfEocAfwqm1fvaTtlbdZkiRJKoA9ZZIkSQUwlEmSJBXAUCZJklQAQ5kkSVIBDGWSJEkFMJRJkiQVwFAmSZJUgP8fDu8Z3x0en3wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#corr_df.to_csv(\"correlation.csv\")     \n",
    "#tickers = ['AAPL','MSFT','AMZN','GOOG','BABA','FB','WMT','V','JPM','TSLA']\n",
    "corr_df.loc[tickers].plot.bar(figsize = (10,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the chart the corrlation from the lexicon methodology is highest across all the stock tickers, which corraborates the conclusion from the previous analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#tickers = corr_df.nlargest(5,'corr_lex').index\n",
    "for ticker in tickers[2:4]:\n",
    "    data_df_stock  = data_df[data_df['ticker'] == ticker]\n",
    "    fig = plt.figure(figsize=(14, 4), constrained_layout=False)\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.scatter(data_df_stock['sentiment_lex'],data_df_stock['eventRet'], alpha=0.5)\n",
    "    plt.title(ticker + '-Scatter Between Event return and sentiments-lexicon')\n",
    "    plt.ylabel('Event Return')\n",
    "    plt.xlabel('Sentiments-Lexicon')\n",
    "\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.scatter(data_df_stock['sentiment_textblob'],data_df_stock['eventRet'], alpha=0.5)\n",
    "    plt.title(ticker + '-Scatter Between Event return and sentiments-textblob')\n",
    "    plt.ylabel('Event Return')\n",
    "    plt.xlabel('Sentiments-Textblob')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lexicon based sentiments on the left definitely shows a positive relationship between the sentiments and return. We use lexicon based sentiments for the trading strategy in the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5'></a>\n",
    "# 5. Model Evaluation- Building a Trading Strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sentiment data can be used in different ways for the trading strategy. Sentiment scores can be used as a directional signal and ideally create a long-short portfolio, by buying the stocks with positive score and selling the stocks with negative score. The sentiments can also be used as additional features over and above other features(such as correlated stocks, technical indicators) in a supervised learning model to predict the price or come up with a trading strategy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the trading strategy in this case study we buy and sell stock as per the current stock sentiments : \n",
    "* Buy a stock when the change in sentiment score (Current sentiment score - previous sentiment score) is greater than .5 and sell a stock when the change in sentiment score is less than -.5.\n",
    "* Additionally, we check for 15 days moving average while buying and selling and buy or sell in a unit of 100. \n",
    "\n",
    "Obviusly, there can be many ways to create the trading strategy based in sentiments, by varying the threshold, or changing the number of units based on the initial cash available.\n",
    "\n",
    "We use lexicon based sentiments for the trading strategy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5.1'></a>\n",
    "## 5.1. Setting up the strategy \n",
    "Here we use backtrader which is a Python based API for writing and backtesting trading strategy. . Backtrader allows you to focus on writing reusable trading strategies, indicators and analyzers instead of having to spend time building infrastructure. we have a convenient framework to backtest and write our trading strategy. We used the Quickstart code in the docs (i.e. Refer to https://www.backtrader.com/docu/quickstart/quickstart/) as a base and modified it to include the sentiment scores.\n",
    "\n",
    "We imlement a simple strategy to buy if the previous day’s sentiment score increases by 0.5 from the last day and sell if it decreases by 0.5.\n",
    "\n",
    "\n",
    "The following function contains two classes: \n",
    "* Sentiment:\n",
    "* SentimentStrat: The \"next\" function of this class implements the actual trading strategy. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import backtrader as bt\n",
    "import backtrader.indicators as btind\n",
    "import backtrader.analyzers as btanalyzers\n",
    "\n",
    "class Sentiment(bt.Indicator):\n",
    "    lines = ('sentiment',)\n",
    "    plotinfo = dict(\n",
    "        plotymargin=0.5,\n",
    "        plothlines=[0],\n",
    "        plotyticks=[1.0, 0, -1.0])\n",
    "    \n",
    "    def next(self):\n",
    "        self.sentiment = 0.0\n",
    "        self.date = self.data.datetime\n",
    "        date = bt.num2date(self.date[0]).date()\n",
    "        prev_sentiment = self.sentiment        \n",
    "        if date in date_sentiment:\n",
    "            self.sentiment = date_sentiment[date]\n",
    "        self.lines.sentiment[0] = self.sentiment\n",
    "\n",
    "\n",
    "class SentimentStrat(bt.Strategy):\n",
    "    params = (\n",
    "        ('period', 15),\n",
    "        ('printlog', True),\n",
    "    )\n",
    "\n",
    "    def log(self, txt, dt=None, doprint=False):\n",
    "        ''' Logging function for this strategy'''\n",
    "        if self.params.printlog or doprint:\n",
    "            dt = dt or self.datas[0].datetime.date(0)\n",
    "            print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    def __init__(self):\n",
    "        # Keep a reference to the \"close\" line in the data[0] dataseries\n",
    "        self.dataclose = self.datas[0].close\n",
    "        # Keep track of pending orders\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "        self.sma = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=self.params.period)\n",
    "        self.date = self.data.datetime\n",
    "        self.sentiment = None\n",
    "        Sentiment(self.data)\n",
    "        self.plotinfo.plot = False\n",
    "        \n",
    "        \n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n",
    "            return\n",
    "        \n",
    "        # Check if an order has been completed\n",
    "        # Attention: broker could reject order if not enough cash\n",
    "        if order.status in [order.Completed]:\n",
    "            if order.isbuy():\n",
    "                self.log(\n",
    "                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                    (order.executed.price,\n",
    "                     order.executed.value,\n",
    "                     order.executed.comm))\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            else:  # Sell\n",
    "                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                         (order.executed.price,\n",
    "                          order.executed.value,\n",
    "                          order.executed.comm))\n",
    "                \n",
    "            self.bar_executed = len(self)     \n",
    "            \n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "            \n",
    "        # Write down: no pending order\n",
    "        self.order = None\n",
    "        \n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "\n",
    "        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n",
    "                 (trade.pnl, trade.pnlcomm))\n",
    "    \n",
    "    ### Main Strat ###\n",
    "    def next(self):        \n",
    "        date = bt.num2date(self.date[0]).date()\n",
    "        prev_sentiment = self.sentiment\n",
    "        if date in date_sentiment:\n",
    "            self.sentiment = date_sentiment[date]\n",
    "        \n",
    "        # Check if an order is pending. if yes, we cannot send a 2nd one\n",
    "        if self.order:\n",
    "            return       \n",
    "        # If not in the market and previous sentiment not none\n",
    "        if not self.position and prev_sentiment:\n",
    "            # buy if current close more than sma AND sentiment increased by >= 0.5\n",
    "            if self.dataclose[0] > self.sma[0] and self.sentiment - prev_sentiment >= 0.5:\n",
    "                self.log('Previous Sentiment %.2f, New Sentiment %.2f BUY CREATE, %.2f' % (prev_sentiment, self.sentiment, self.dataclose[0]))                \n",
    "                self.order = self.buy()\n",
    "                \n",
    "        # Already in the market and previous sentiment not none\n",
    "        elif prev_sentiment:\n",
    "            # sell if current close less than sma AND sentiment decreased by >= 0.5\n",
    "            if self.dataclose[0] < self.sma[0] and self.sentiment - prev_sentiment <= -0.5:\n",
    "                self.log('Previous Sentiment %.2f, New Sentiment %.2f SELL CREATE, %.2f' % (prev_sentiment, self.sentiment, self.dataclose[0]))                \n",
    "                self.order = self.sell()\n",
    "\n",
    "    def stop(self):\n",
    "        self.log('(MA Period %2d) Ending Value %.2f' %\n",
    "                 (self.params.period, self.broker.getvalue()), doprint=True)   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for running the trading strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we write a generic function to run the strategy for any stock. We specified the “ticker” stock feeds to be pulled from Yahoo Finance, set an initial amount of $100,000, a fixed size of 100 lots per trade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_strategy(ticker, start, end):\n",
    "    print(ticker)    \n",
    "    ticker = yf.Ticker(ticker)\n",
    "    df_ticker = ticker.history(start = start, end = end)\n",
    "    \n",
    "    cerebro = bt.Cerebro()\n",
    "    # Add the data\n",
    "    cerebro.addstrategy(SentimentStrat)        \n",
    "    data = bt.feeds.PandasData(dataname=df_ticker)\n",
    "    cerebro.adddata(data)    \n",
    "    start = 100000.0\n",
    "    cerebro.broker.setcash(start)   \n",
    "    cerebro.addsizer(bt.sizers.FixedSize, stake=100)\n",
    "    print('Starting Portfolio Value: %.2f' % start)    \n",
    "    plt.rcParams['figure.figsize']=[10,6]\n",
    "    plt.rcParams[\"font.size\"]=\"12\"\n",
    "    cerebro.run() \n",
    "    cerebro.plot(volume=False, iplot=True, plotname= ticker)\n",
    "    end = cerebro.broker.getvalue()\n",
    "    print('Start Portfolio value: %.2f\\nFinal Portfolio Value: %.2f\\nProfit: %.2f\\n' \\\n",
    "          % (start, end, end - start))\n",
    "    return float(df_ticker['Close'][0]), (end - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5.2'></a>\n",
    "## 5.2. Results for Individual Stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First running the strategy for google"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GOOG\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-04-12, Previous Sentiment 0.24, New Sentiment 0.80 BUY CREATE, 324.29\n",
      "2012-04-13, BUY EXECUTED, Price: 322.57, Cost: 32257.00, Comm 0.00\n",
      "2012-10-18, Previous Sentiment 0.98, New Sentiment 0.08 SELL CREATE, 346.20\n",
      "2012-10-19, SELL EXECUTED, Price: 351.47, Cost: 32257.00, Comm 0.00\n",
      "2012-10-19, OPERATION PROFIT, GROSS 2890.00, NET 2890.00\n",
      "2013-01-10, Previous Sentiment 0.08, New Sentiment 0.80 BUY CREATE, 369.36\n",
      "2013-01-11, BUY EXECUTED, Price: 369.61, Cost: 36961.00, Comm 0.00\n",
      "2014-07-17, Previous Sentiment 0.73, New Sentiment -0.22 SELL CREATE, 572.16\n",
      "2014-07-18, SELL EXECUTED, Price: 591.38, Cost: 36961.00, Comm 0.00\n",
      "2014-07-18, OPERATION PROFIT, GROSS 22177.00, NET 22177.00\n",
      "2014-07-18, Previous Sentiment -0.22, New Sentiment 0.77 BUY CREATE, 593.45\n",
      "2014-07-21, BUY EXECUTED, Price: 590.13, Cost: 59013.00, Comm 0.00\n",
      "2014-09-12, Previous Sentiment 0.66, New Sentiment -0.05 SELL CREATE, 574.04\n",
      "2014-09-15, SELL EXECUTED, Price: 571.37, Cost: 59013.00, Comm 0.00\n",
      "2014-09-15, OPERATION PROFIT, GROSS -1876.00, NET -1876.00\n",
      "2015-07-17, Previous Sentiment 0.01, New Sentiment 0.90 BUY CREATE, 672.93\n",
      "2015-07-20, BUY EXECUTED, Price: 659.24, Cost: 65924.00, Comm 0.00\n",
      "2015-08-24, Previous Sentiment 0.91, New Sentiment -0.51 SELL CREATE, 589.61\n",
      "2015-08-25, SELL EXECUTED, Price: 614.91, Cost: 65924.00, Comm 0.00\n",
      "2015-08-25, OPERATION PROFIT, GROSS -4433.00, NET -4433.00\n",
      "2015-10-22, Previous Sentiment -0.79, New Sentiment 0.73 BUY CREATE, 651.79\n",
      "2015-10-23, BUY EXECUTED, Price: 727.50, Cost: 72750.00, Comm 0.00\n",
      "2017-09-25, Previous Sentiment 0.69, New Sentiment 0.14 SELL CREATE, 920.97\n",
      "2017-09-26, SELL EXECUTED, Price: 923.72, Cost: 72750.00, Comm 0.00\n",
      "2017-09-26, OPERATION PROFIT, GROSS 19622.00, NET 19622.00\n",
      "2017-10-27, Previous Sentiment 0.01, New Sentiment 0.60 BUY CREATE, 1019.27\n",
      "2017-10-30, BUY EXECUTED, Price: 1014.00, Cost: 101400.00, Comm 0.00\n",
      "2018-04-09, Previous Sentiment 0.54, New Sentiment -0.45 SELL CREATE, 1015.45\n",
      "2018-04-10, SELL EXECUTED, Price: 1026.44, Cost: 101400.00, Comm 0.00\n",
      "2018-04-10, OPERATION PROFIT, GROSS 1244.00, NET 1244.00\n",
      "2018-04-23, Previous Sentiment 0.03, New Sentiment 0.87 BUY CREATE, 1067.45\n",
      "2018-04-24, BUY EXECUTED, Price: 1052.00, Cost: 105200.00, Comm 0.00\n",
      "2018-09-04, Previous Sentiment 0.76, New Sentiment 0.01 SELL CREATE, 1197.00\n",
      "2018-09-05, SELL EXECUTED, Price: 1193.80, Cost: 105200.00, Comm 0.00\n",
      "2018-09-05, OPERATION PROFIT, GROSS 14180.00, NET 14180.00\n",
      "2018-10-01, Previous Sentiment 0.01, New Sentiment 0.88 BUY CREATE, 1195.31\n",
      "2018-10-02, BUY EXECUTED, Price: 1190.96, Cost: 119096.00, Comm 0.00\n",
      "2018-10-05, Previous Sentiment 0.88, New Sentiment 0.31 SELL CREATE, 1157.35\n",
      "2018-10-08, SELL EXECUTED, Price: 1150.11, Cost: 119096.00, Comm 0.00\n",
      "2018-10-08, OPERATION PROFIT, GROSS -4085.00, NET -4085.00\n",
      "2018-12-11, (MA Period 15) Ending Value 149719.00\n"
     ]
    },
    {
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       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 149719.00\n",
      "Profit: 49719.00\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(331.46, 49719.0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ticker = 'GOOG'\n",
    "date_sentiment=data_df[data_df['ticker'].isin([ticker])]\n",
    "date_sentiment=date_sentiment[['date','sentiment_lex']]\n",
    "date_sentiment['date']=pd.to_datetime(date_sentiment['date'], format='%Y-%m-%d').dt.date\n",
    "date_sentiment=date_sentiment.set_index('date')['sentiment_lex']\n",
    "date_sentiment=date_sentiment.to_dict()\n",
    "run_strategy(ticker, start = '2012-01-01', end = '2018-12-12')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show an overall profit of 49719.00. The chart is divided into three panels. \n",
    "\n",
    "* Top Panel : Top panel is the cash value observer which as the name implies keeps track of the Cash and total portolio Value (including cash) during the life of the backtesting run. As we can see that we started with 100000.00 and final value at the end is 149719.00 as shown in this panel.\n",
    "\n",
    "* Second Panel : This panel is Trade Observer which shows, at the end of a trade, the actual Profit and Loss. A trade is defined as opening a position and taking the position back to 0 (directly or crossing over from long to short or short to long). Five out of eight actions of buy and sell are profitable for the strategy. \n",
    "\n",
    "* Third Panel : This panel is Buy Sell observer which plots (on top of the prices) where buy and sell operations have taken place. In general we see that (specially around 2018) the buy action takes place when the stock price is increasing and the sell action takes place when the stock price has started declining.   \n",
    "\n",
    "* Bottom Panel : This panel shows the sentiment score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we choose one of the days on which buy was triggered and we look at the news on that data and previous day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current News: [\"Axiom Securities has upgraded Google (GOOG +13.4%, GOOGL +14.8%) to Buy following the company's Q2 beat and investor-pleasing comments about spending discipline, potential capital returns, and YouTube/mobile growth. MKM has launched coverage at Buy, and plenty of other firms have hiked their targets. Google's market cap is now above $450B.\"] \n",
      "\n",
      " Previous News: [\"While Google's (GOOG, GOOGL) Q2 revenue slightly missed estimates when factoring traffic acquisitions costs (TAC), its ex-TAC revenue of $14.35B was slightly above a $14.3B consensus. The reason: TAC fell to 21% of ad revenue from Q1's 22% and Q2 2014's 23%. That also, of course, helped EPS beat estimates.\", 'Google (NASDAQ:GOOG): Q2 EPS of $6.99 beats by $0.28.']\n"
     ]
    }
   ],
   "source": [
    "GOOG_ticker= data_df[data_df['ticker'].isin([ticker])]\n",
    "New= list(GOOG_ticker[GOOG_ticker['date'] ==  '2015-07-17']['headline'])\n",
    "Old= list(GOOG_ticker[GOOG_ticker['date'] ==  '2015-07-16']['headline'])\n",
    "print(\"Current News:\",New,\"\\n\\n\",\"Previous News:\", Old)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly the News on the current day has positive sentiment as compared to the news at the previous day causing the buy to be triggered. Now, we run the strategy for Facebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FB\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-11-14, Previous Sentiment -0.82, New Sentiment 0.71 BUY CREATE, 22.36\n",
      "2012-11-15, BUY EXECUTED, Price: 22.34, Cost: 2234.00, Comm 0.00\n",
      "2013-03-13, Previous Sentiment 0.95, New Sentiment -0.25 SELL CREATE, 27.08\n",
      "2013-03-14, SELL EXECUTED, Price: 27.10, Cost: 2234.00, Comm 0.00\n",
      "2013-03-14, OPERATION PROFIT, GROSS 476.00, NET 476.00\n",
      "2013-04-10, Previous Sentiment -0.25, New Sentiment 0.94 BUY CREATE, 27.57\n",
      "2013-04-11, BUY EXECUTED, Price: 27.48, Cost: 2748.00, Comm 0.00\n",
      "2013-11-18, Previous Sentiment 0.98, New Sentiment 0.40 SELL CREATE, 45.83\n",
      "2013-11-19, SELL EXECUTED, Price: 46.26, Cost: 2748.00, Comm 0.00\n",
      "2013-11-19, OPERATION PROFIT, GROSS 1878.00, NET 1878.00\n",
      "2014-01-30, Previous Sentiment 0.01, New Sentiment 0.82 BUY CREATE, 61.08\n",
      "2014-01-31, BUY EXECUTED, Price: 60.47, Cost: 6047.00, Comm 0.00\n",
      "2014-12-16, Previous Sentiment 0.68, New Sentiment -0.39 SELL CREATE, 74.69\n",
      "2014-12-17, SELL EXECUTED, Price: 75.01, Cost: 6047.00, Comm 0.00\n",
      "2014-12-17, OPERATION PROFIT, GROSS 1454.00, NET 1454.00\n",
      "2014-12-22, Previous Sentiment -0.39, New Sentiment 0.73 BUY CREATE, 81.45\n",
      "2014-12-23, BUY EXECUTED, Price: 82.02, Cost: 8202.00, Comm 0.00\n",
      "2016-04-27, Previous Sentiment 0.91, New Sentiment 0.01 SELL CREATE, 108.89\n",
      "2016-04-28, SELL EXECUTED, Price: 119.58, Cost: 8202.00, Comm 0.00\n",
      "2016-04-28, OPERATION PROFIT, GROSS 3756.00, NET 3756.00\n",
      "2016-07-06, Previous Sentiment -0.11, New Sentiment 0.81 BUY CREATE, 116.70\n",
      "2016-07-07, BUY EXECUTED, Price: 116.63, Cost: 11663.00, Comm 0.00\n",
      "2016-11-18, Previous Sentiment 0.78, New Sentiment -0.13 SELL CREATE, 117.02\n",
      "2016-11-21, SELL EXECUTED, Price: 118.20, Cost: 11663.00, Comm 0.00\n",
      "2016-11-21, OPERATION PROFIT, GROSS 157.00, NET 157.00\n",
      "2016-11-21, Previous Sentiment -0.13, New Sentiment 0.83 BUY CREATE, 121.77\n",
      "2016-11-22, BUY EXECUTED, Price: 122.40, Cost: 12240.00, Comm 0.00\n",
      "2016-12-01, Previous Sentiment 0.83, New Sentiment -0.01 SELL CREATE, 115.10\n",
      "2016-12-02, SELL EXECUTED, Price: 115.11, Cost: 12240.00, Comm 0.00\n",
      "2016-12-02, OPERATION PROFIT, GROSS -729.00, NET -729.00\n",
      "2018-04-10, Previous Sentiment 0.03, New Sentiment 0.55 BUY CREATE, 165.04\n",
      "2018-04-11, BUY EXECUTED, Price: 165.36, Cost: 16536.00, Comm 0.00\n",
      "2018-08-16, Previous Sentiment 0.80, New Sentiment -0.23 SELL CREATE, 174.70\n",
      "2018-08-17, SELL EXECUTED, Price: 174.50, Cost: 16536.00, Comm 0.00\n",
      "2018-08-17, OPERATION PROFIT, GROSS 914.00, NET 914.00\n",
      "2018-12-03, Previous Sentiment -0.63, New Sentiment 0.47 BUY CREATE, 141.09\n",
      "2018-12-04, BUY EXECUTED, Price: 140.73, Cost: 14073.00, Comm 0.00\n",
      "2018-12-11, (MA Period 15) Ending Value 108041.00\n"
     ]
    },
    {
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       "        var fmt = mpl.extensions[ind];\n",
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       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
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       "\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
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       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
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       "\n",
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       "\n",
       "\n",
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       "    var format_dropdown = fig.format_dropdown;\n",
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       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
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       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
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       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
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       "    fig.rubberband_canvas.style.cursor = cursor;\n",
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       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
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       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
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       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
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       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 108041.00\n",
      "Profit: 8041.00\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(38.23, 8041.0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ticker = 'FB'\n",
    "date_sentiment=data_df[data_df['ticker'].isin([ticker])]\n",
    "date_sentiment=date_sentiment[['date','sentiment_lex']]\n",
    "date_sentiment['date']=pd.to_datetime(date_sentiment['date'], format='%Y-%m-%d').dt.date\n",
    "date_sentiment=date_sentiment.set_index('date')['sentiment_lex']\n",
    "date_sentiment=date_sentiment.to_dict()\n",
    "run_strategy(ticker, start = '2012-01-01', end = '2018-12-12')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top Panel : The CashValue shows overall profit of 8041.00.\n",
    "\n",
    "Second Panel : A Trade Observer shows that six out of 7 actions are profitable. \n",
    "\n",
    "Third Panel : A BuySell observer shows that in general the buy action takes place when the stock price is increasing and the sell action takes place when the stock price has started declining.   \n",
    "\n",
    "Forth Panel : It shows high number of positive sentiment for the FB around 2013 and 2014 period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5.3'></a>\n",
    "## 5.3. Results for Multiple Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "Starting Portfolio Value: 100000.00\n",
      "2013-12-13, Previous Sentiment -0.38, New Sentiment 0.86 BUY CREATE, 70.91\n",
      "2013-12-16, BUY EXECUTED, Price: 70.99, Cost: 7099.00, Comm 0.00\n",
      "2014-09-25, Previous Sentiment 0.78, New Sentiment 0.04 SELL CREATE, 89.08\n",
      "2014-09-26, SELL EXECUTED, Price: 89.68, Cost: 7099.00, Comm 0.00\n",
      "2014-09-26, OPERATION PROFIT, GROSS 1869.00, NET 1869.00\n",
      "2014-10-21, Previous Sentiment 0.14, New Sentiment 0.68 BUY CREATE, 93.27\n",
      "2014-10-22, BUY EXECUTED, Price: 93.61, Cost: 9361.00, Comm 0.00\n",
      "2015-06-11, Previous Sentiment 0.48, New Sentiment -0.42 SELL CREATE, 118.51\n",
      "2015-06-12, SELL EXECUTED, Price: 118.14, Cost: 9361.00, Comm 0.00\n",
      "2015-06-12, OPERATION PROFIT, GROSS 2453.00, NET 2453.00\n",
      "2015-09-14, Previous Sentiment 0.35, New Sentiment 0.95 BUY CREATE, 106.75\n",
      "2015-09-15, BUY EXECUTED, Price: 107.33, Cost: 10733.00, Comm 0.00\n",
      "2015-12-14, Previous Sentiment 0.24, New Sentiment -0.62 SELL CREATE, 104.58\n",
      "2015-12-15, SELL EXECUTED, Price: 104.08, Cost: 10733.00, Comm 0.00\n",
      "2015-12-15, OPERATION PROFIT, GROSS -325.00, NET -325.00\n",
      "2016-03-15, Previous Sentiment 0.09, New Sentiment 0.66 BUY CREATE, 97.76\n",
      "2016-03-16, BUY EXECUTED, Price: 97.79, Cost: 9779.00, Comm 0.00\n",
      "2016-07-26, Previous Sentiment 0.93, New Sentiment 0.14 SELL CREATE, 90.92\n",
      "2016-07-27, SELL EXECUTED, Price: 98.06, Cost: 9779.00, Comm 0.00\n",
      "2016-07-27, OPERATION PROFIT, GROSS 27.00, NET 27.00\n",
      "2016-12-13, Previous Sentiment 0.15, New Sentiment 0.74 BUY CREATE, 109.48\n",
      "2016-12-14, BUY EXECUTED, Price: 109.34, Cost: 10934.00, Comm 0.00\n",
      "2017-09-22, Previous Sentiment 0.58, New Sentiment 0.08 SELL CREATE, 146.16\n",
      "2017-09-25, SELL EXECUTED, Price: 144.33, Cost: 10934.00, Comm 0.00\n",
      "2017-09-25, OPERATION PROFIT, GROSS 3499.00, NET 3499.00\n",
      "2017-11-06, Previous Sentiment 0.14, New Sentiment 0.64 BUY CREATE, 167.67\n",
      "2017-11-07, BUY EXECUTED, Price: 167.35, Cost: 16735.00, Comm 0.00\n",
      "2017-12-08, Previous Sentiment 0.57, New Sentiment -0.02 SELL CREATE, 163.56\n",
      "2017-12-11, SELL EXECUTED, Price: 163.40, Cost: 16735.00, Comm 0.00\n",
      "2017-12-11, OPERATION PROFIT, GROSS -395.00, NET -395.00\n",
      "2017-12-13, Previous Sentiment -0.02, New Sentiment 0.71 BUY CREATE, 166.36\n",
      "2017-12-14, BUY EXECUTED, Price: 166.49, Cost: 16649.00, Comm 0.00\n",
      "2018-02-01, Previous Sentiment 0.66, New Sentiment 0.06 SELL CREATE, 162.03\n",
      "2018-02-02, SELL EXECUTED, Price: 160.31, Cost: 16649.00, Comm 0.00\n",
      "2018-02-02, OPERATION PROFIT, GROSS -618.00, NET -618.00\n",
      "2018-03-01, Previous Sentiment -0.07, New Sentiment 0.85 BUY CREATE, 169.69\n",
      "2018-03-02, BUY EXECUTED, Price: 167.56, Cost: 16756.00, Comm 0.00\n",
      "2018-03-28, Previous Sentiment 0.60, New Sentiment 0.09 SELL CREATE, 161.43\n",
      "2018-03-29, SELL EXECUTED, Price: 162.72, Cost: 16756.00, Comm 0.00\n",
      "2018-03-29, OPERATION PROFIT, GROSS -484.00, NET -484.00\n",
      "2018-04-11, Previous Sentiment -0.23, New Sentiment 0.66 BUY CREATE, 167.21\n",
      "2018-04-12, BUY EXECUTED, Price: 168.15, Cost: 16815.00, Comm 0.00\n",
      "2018-04-24, Previous Sentiment 0.89, New Sentiment -0.23 SELL CREATE, 157.99\n",
      "2018-04-25, SELL EXECUTED, Price: 157.68, Cost: 16815.00, Comm 0.00\n",
      "2018-04-25, OPERATION PROFIT, GROSS -1047.00, NET -1047.00\n",
      "2018-10-01, Previous Sentiment -0.35, New Sentiment 0.75 BUY CREATE, 221.99\n",
      "2018-10-02, BUY EXECUTED, Price: 221.98, Cost: 22198.00, Comm 0.00\n",
      "2018-10-10, Previous Sentiment 0.52, New Sentiment -0.11 SELL CREATE, 211.34\n",
      "2018-10-11, SELL EXECUTED, Price: 209.54, Cost: 22198.00, Comm 0.00\n",
      "2018-10-11, OPERATION PROFIT, GROSS -1244.00, NET -1244.00\n",
      "2018-12-11, (MA Period 15) Ending Value 103735.00\n"
     ]
    },
    {
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       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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     "text": [
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 103735.00\n",
      "Profit: 3735.00\n",
      "\n",
      "MSFT\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-03-08, Previous Sentiment -0.50, New Sentiment 0.92 BUY CREATE, 26.43\n",
      "2012-03-09, BUY EXECUTED, Price: 26.51, Cost: 2651.00, Comm 0.00\n",
      "2012-04-04, Previous Sentiment 0.87, New Sentiment -0.42 SELL CREATE, 25.77\n",
      "2012-04-05, SELL EXECUTED, Price: 25.72, Cost: 2651.00, Comm 0.00\n",
      "2012-04-05, OPERATION PROFIT, GROSS -79.00, NET -79.00\n",
      "2012-06-19, Previous Sentiment 0.15, New Sentiment 0.88 BUY CREATE, 25.52\n",
      "2012-06-20, BUY EXECUTED, Price: 25.71, Cost: 2571.00, Comm 0.00\n",
      "2012-07-12, Previous Sentiment 0.68, New Sentiment -0.65 SELL CREATE, 23.80\n",
      "2012-07-13, SELL EXECUTED, Price: 23.90, Cost: 2571.00, Comm 0.00\n",
      "2012-07-13, OPERATION PROFIT, GROSS -181.00, NET -181.00\n",
      "2012-11-01, Previous Sentiment -0.69, New Sentiment 0.59 BUY CREATE, 24.70\n",
      "2012-11-02, BUY EXECUTED, Price: 24.76, Cost: 2476.00, Comm 0.00\n",
      "2012-11-13, Previous Sentiment 0.96, New Sentiment 0.13 SELL CREATE, 22.85\n",
      "2012-11-14, SELL EXECUTED, Price: 22.98, Cost: 2476.00, Comm 0.00\n",
      "2012-11-14, OPERATION PROFIT, GROSS -178.00, NET -178.00\n",
      "2013-04-16, Previous Sentiment -0.74, New Sentiment 0.90 BUY CREATE, 24.64\n",
      "2013-04-17, BUY EXECUTED, Price: 24.54, Cost: 2454.00, Comm 0.00\n",
      "2013-07-29, Previous Sentiment 0.80, New Sentiment -0.30 SELL CREATE, 27.01\n",
      "2013-07-30, SELL EXECUTED, Price: 27.22, Cost: 2454.00, Comm 0.00\n",
      "2013-07-30, OPERATION PROFIT, GROSS 268.00, NET 268.00\n",
      "2013-09-20, Previous Sentiment 0.25, New Sentiment 0.82 BUY CREATE, 28.28\n",
      "2013-09-23, BUY EXECUTED, Price: 28.07, Cost: 2807.00, Comm 0.00\n",
      "2013-10-24, Previous Sentiment 0.58, New Sentiment 0.01 SELL CREATE, 29.08\n",
      "2013-10-25, SELL EXECUTED, Price: 30.95, Cost: 2807.00, Comm 0.00\n",
      "2013-10-25, OPERATION PROFIT, GROSS 288.00, NET 288.00\n",
      "2013-11-20, Previous Sentiment 0.18, New Sentiment 0.89 BUY CREATE, 32.22\n",
      "2013-11-21, BUY EXECUTED, Price: 32.39, Cost: 3239.00, Comm 0.00\n",
      "2014-01-07, Previous Sentiment 0.49, New Sentiment -0.23 SELL CREATE, 31.64\n",
      "2014-01-08, SELL EXECUTED, Price: 31.29, Cost: 3239.00, Comm 0.00\n",
      "2014-01-08, OPERATION PROFIT, GROSS -110.00, NET -110.00\n",
      "2014-01-16, Previous Sentiment -0.76, New Sentiment 0.45 BUY CREATE, 32.06\n",
      "2014-01-17, BUY EXECUTED, Price: 32.01, Cost: 3201.00, Comm 0.00\n",
      "2014-07-31, Previous Sentiment 0.95, New Sentiment 0.01 SELL CREATE, 38.06\n",
      "2014-08-01, SELL EXECUTED, Price: 38.10, Cost: 3201.00, Comm 0.00\n",
      "2014-08-01, OPERATION PROFIT, GROSS 609.00, NET 609.00\n",
      "2015-04-24, Previous Sentiment 0.01, New Sentiment 0.94 BUY CREATE, 43.04\n",
      "2015-04-27, BUY EXECUTED, Price: 42.47, Cost: 4247.00, Comm 0.00\n",
      "2015-07-27, Previous Sentiment 0.80, New Sentiment -0.44 SELL CREATE, 41.04\n",
      "2015-07-28, SELL EXECUTED, Price: 41.25, Cost: 4247.00, Comm 0.00\n",
      "2015-07-28, OPERATION PROFIT, GROSS -122.00, NET -122.00\n",
      "2015-10-23, Previous Sentiment 0.01, New Sentiment 0.71 BUY CREATE, 48.16\n",
      "2015-10-26, BUY EXECUTED, Price: 47.85, Cost: 4785.00, Comm 0.00\n",
      "2016-02-04, Previous Sentiment 0.58, New Sentiment 0.01 SELL CREATE, 47.69\n",
      "2016-02-05, SELL EXECUTED, Price: 47.64, Cost: 4785.00, Comm 0.00\n",
      "2016-02-05, OPERATION PROFIT, GROSS -21.00, NET -21.00\n",
      "2016-04-21, Previous Sentiment -0.47, New Sentiment 0.90 BUY CREATE, 51.52\n",
      "2016-04-22, BUY EXECUTED, Price: 47.95, Cost: 4795.00, Comm 0.00\n",
      "2016-04-28, Previous Sentiment 0.90, New Sentiment 0.01 SELL CREATE, 46.09\n",
      "2016-04-29, SELL EXECUTED, Price: 45.59, Cost: 4795.00, Comm 0.00\n",
      "2016-04-29, OPERATION PROFIT, GROSS -236.00, NET -236.00\n",
      "2017-04-24, Previous Sentiment -0.08, New Sentiment 0.69 BUY CREATE, 64.02\n",
      "2017-04-25, BUY EXECUTED, Price: 64.37, Cost: 6437.00, Comm 0.00\n",
      "2017-12-05, Previous Sentiment 0.58, New Sentiment -0.19 SELL CREATE, 78.60\n",
      "2017-12-06, SELL EXECUTED, Price: 78.56, Cost: 6437.00, Comm 0.00\n",
      "2017-12-06, OPERATION PROFIT, GROSS 1419.00, NET 1419.00\n",
      "2018-01-04, Previous Sentiment -0.29, New Sentiment 0.37 BUY CREATE, 83.92\n",
      "2018-01-05, BUY EXECUTED, Price: 84.45, Cost: 8445.00, Comm 0.00\n",
      "2018-10-09, Previous Sentiment 0.71, New Sentiment 0.01 SELL CREATE, 109.54\n",
      "2018-10-10, SELL EXECUTED, Price: 108.55, Cost: 8445.00, Comm 0.00\n",
      "2018-10-10, OPERATION PROFIT, GROSS 2410.00, NET 2410.00\n",
      "2018-12-11, (MA Period 15) Ending Value 104067.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 104067.00\n",
      "Profit: 4067.00\n",
      "\n",
      "AMZN\n",
      "Starting Portfolio Value: 100000.00\n",
      "2013-01-30, Previous Sentiment 0.40, New Sentiment 0.95 BUY CREATE, 272.76\n",
      "2013-01-31, BUY EXECUTED, Price: 271.04, Cost: 27104.00, Comm 0.00\n",
      "2013-04-18, Previous Sentiment 0.89, New Sentiment 0.38 SELL CREATE, 259.42\n",
      "2013-04-19, SELL EXECUTED, Price: 258.16, Cost: 27104.00, Comm 0.00\n",
      "2013-04-19, OPERATION PROFIT, GROSS -1288.00, NET -1288.00\n",
      "2013-10-25, Previous Sentiment 0.15, New Sentiment 0.84 BUY CREATE, 363.39\n",
      "2013-10-28, BUY EXECUTED, Price: 359.92, Cost: 35992.00, Comm 0.00\n",
      "2014-02-03, Previous Sentiment 0.56, New Sentiment -0.15 SELL CREATE, 346.15\n",
      "2014-02-04, SELL EXECUTED, Price: 349.59, Cost: 35992.00, Comm 0.00\n",
      "2014-02-04, OPERATION PROFIT, GROSS -1033.00, NET -1033.00\n",
      "2014-05-20, Previous Sentiment -0.59, New Sentiment 0.08 BUY CREATE, 301.19\n",
      "2014-05-21, BUY EXECUTED, Price: 302.21, Cost: 30221.00, Comm 0.00\n",
      "2014-11-05, Previous Sentiment 0.79, New Sentiment 0.02 SELL CREATE, 296.52\n",
      "2014-11-06, SELL EXECUTED, Price: 296.69, Cost: 30221.00, Comm 0.00\n",
      "2014-11-06, OPERATION PROFIT, GROSS -552.00, NET -552.00\n",
      "2015-01-30, Previous Sentiment 0.18, New Sentiment 0.91 BUY CREATE, 354.53\n",
      "2015-02-02, BUY EXECUTED, Price: 350.05, Cost: 35005.00, Comm 0.00\n",
      "2016-01-20, Previous Sentiment 0.51, New Sentiment -0.08 SELL CREATE, 571.77\n",
      "2016-01-21, SELL EXECUTED, Price: 573.58, Cost: 35005.00, Comm 0.00\n",
      "2016-01-21, OPERATION PROFIT, GROSS 22353.00, NET 22353.00\n",
      "2016-01-28, Previous Sentiment -0.08, New Sentiment 0.91 BUY CREATE, 635.35\n",
      "2016-01-29, BUY EXECUTED, Price: 571.98, Cost: 57198.00, Comm 0.00\n",
      "2016-02-10, Previous Sentiment 0.72, New Sentiment 0.02 SELL CREATE, 490.48\n",
      "2016-02-11, SELL EXECUTED, Price: 491.17, Cost: 57198.00, Comm 0.00\n",
      "2016-02-11, OPERATION PROFIT, GROSS -8081.00, NET -8081.00\n",
      "2016-02-29, Previous Sentiment 0.02, New Sentiment 0.88 BUY CREATE, 552.52\n",
      "2016-03-01, BUY EXECUTED, Price: 556.29, Cost: 55629.00, Comm 0.00\n",
      "2016-04-28, Previous Sentiment 0.96, New Sentiment 0.31 SELL CREATE, 602.00\n",
      "2016-04-29, SELL EXECUTED, Price: 666.00, Cost: 55629.00, Comm 0.00\n",
      "2016-04-29, OPERATION PROFIT, GROSS 10971.00, NET 10971.00\n",
      "2016-05-10, Previous Sentiment 0.02, New Sentiment 0.57 BUY CREATE, 703.07\n",
      "2016-05-11, BUY EXECUTED, Price: 705.79, Cost: 70579.00, Comm 0.00\n",
      "2016-06-15, Previous Sentiment 0.71, New Sentiment 0.07 SELL CREATE, 714.26\n",
      "2016-06-16, SELL EXECUTED, Price: 712.05, Cost: 70579.00, Comm 0.00\n",
      "2016-06-16, OPERATION PROFIT, GROSS 626.00, NET 626.00\n",
      "2016-07-13, Previous Sentiment 0.07, New Sentiment 0.60 BUY CREATE, 742.63\n",
      "2016-07-14, BUY EXECUTED, Price: 748.86, Cost: 74886.00, Comm 0.00\n",
      "2016-07-26, Previous Sentiment 0.46, New Sentiment -0.28 SELL CREATE, 735.59\n",
      "2016-07-27, SELL EXECUTED, Price: 737.97, Cost: 74886.00, Comm 0.00\n",
      "2016-07-27, OPERATION PROFIT, GROSS -1089.00, NET -1089.00\n",
      "2016-07-28, Previous Sentiment -0.18, New Sentiment 0.81 BUY CREATE, 752.61\n",
      "2016-07-29, BUY EXECUTED, Price: 765.00, Cost: 76500.00, Comm 0.00\n",
      "2016-11-02, Previous Sentiment 0.79, New Sentiment 0.02 SELL CREATE, 765.56\n",
      "2016-11-03, SELL EXECUTED, Price: 765.05, Cost: 76500.00, Comm 0.00\n",
      "2016-11-03, OPERATION PROFIT, GROSS 5.00, NET 5.00\n",
      "2017-02-01, Previous Sentiment -0.16, New Sentiment 0.52 BUY CREATE, 832.35\n",
      "2017-02-02, BUY EXECUTED, Price: 836.59, Cost: 83659.00, Comm 0.00\n",
      "2018-03-28, Previous Sentiment 0.87, New Sentiment -0.02 SELL CREATE, 1431.42\n",
      "2018-03-29, SELL EXECUTED, Price: 1406.00, Cost: 83659.00, Comm 0.00\n",
      "2018-03-29, OPERATION PROFIT, GROSS 56941.00, NET 56941.00\n",
      "2018-07-12, Previous Sentiment 0.36, New Sentiment 0.87 BUY CREATE, 1796.62\n",
      "2018-07-13, Order Canceled/Margin/Rejected\n",
      "2018-09-04, Previous Sentiment -0.24, New Sentiment 0.58 BUY CREATE, 2039.51\n",
      "2018-09-05, Order Canceled/Margin/Rejected\n",
      "2018-12-10, Previous Sentiment -0.11, New Sentiment 0.51 BUY CREATE, 1641.03\n",
      "2018-12-11, BUY EXECUTED, Price: 1678.00, Cost: 167800.00, Comm 0.00\n",
      "2018-12-11, (MA Period 15) Ending Value 175377.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 175377.00\n",
      "Profit: 75377.00\n",
      "\n",
      "GOOG\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-04-12, Previous Sentiment 0.24, New Sentiment 0.80 BUY CREATE, 324.29\n",
      "2012-04-13, BUY EXECUTED, Price: 322.57, Cost: 32257.00, Comm 0.00\n",
      "2012-10-18, Previous Sentiment 0.98, New Sentiment 0.08 SELL CREATE, 346.20\n",
      "2012-10-19, SELL EXECUTED, Price: 351.47, Cost: 32257.00, Comm 0.00\n",
      "2012-10-19, OPERATION PROFIT, GROSS 2890.00, NET 2890.00\n",
      "2013-01-10, Previous Sentiment 0.08, New Sentiment 0.80 BUY CREATE, 369.36\n",
      "2013-01-11, BUY EXECUTED, Price: 369.61, Cost: 36961.00, Comm 0.00\n",
      "2014-07-17, Previous Sentiment 0.73, New Sentiment -0.22 SELL CREATE, 572.16\n",
      "2014-07-18, SELL EXECUTED, Price: 591.38, Cost: 36961.00, Comm 0.00\n",
      "2014-07-18, OPERATION PROFIT, GROSS 22177.00, NET 22177.00\n",
      "2014-07-18, Previous Sentiment -0.22, New Sentiment 0.77 BUY CREATE, 593.45\n",
      "2014-07-21, BUY EXECUTED, Price: 590.13, Cost: 59013.00, Comm 0.00\n",
      "2014-09-12, Previous Sentiment 0.66, New Sentiment -0.05 SELL CREATE, 574.04\n",
      "2014-09-15, SELL EXECUTED, Price: 571.37, Cost: 59013.00, Comm 0.00\n",
      "2014-09-15, OPERATION PROFIT, GROSS -1876.00, NET -1876.00\n",
      "2015-07-17, Previous Sentiment 0.01, New Sentiment 0.90 BUY CREATE, 672.93\n",
      "2015-07-20, BUY EXECUTED, Price: 659.24, Cost: 65924.00, Comm 0.00\n",
      "2015-08-24, Previous Sentiment 0.91, New Sentiment -0.51 SELL CREATE, 589.61\n",
      "2015-08-25, SELL EXECUTED, Price: 614.91, Cost: 65924.00, Comm 0.00\n",
      "2015-08-25, OPERATION PROFIT, GROSS -4433.00, NET -4433.00\n",
      "2015-10-22, Previous Sentiment -0.79, New Sentiment 0.73 BUY CREATE, 651.79\n",
      "2015-10-23, BUY EXECUTED, Price: 727.50, Cost: 72750.00, Comm 0.00\n",
      "2017-09-25, Previous Sentiment 0.69, New Sentiment 0.14 SELL CREATE, 920.97\n",
      "2017-09-26, SELL EXECUTED, Price: 923.72, Cost: 72750.00, Comm 0.00\n",
      "2017-09-26, OPERATION PROFIT, GROSS 19622.00, NET 19622.00\n",
      "2017-10-27, Previous Sentiment 0.01, New Sentiment 0.60 BUY CREATE, 1019.27\n",
      "2017-10-30, BUY EXECUTED, Price: 1014.00, Cost: 101400.00, Comm 0.00\n",
      "2018-04-09, Previous Sentiment 0.54, New Sentiment -0.45 SELL CREATE, 1015.45\n",
      "2018-04-10, SELL EXECUTED, Price: 1026.44, Cost: 101400.00, Comm 0.00\n",
      "2018-04-10, OPERATION PROFIT, GROSS 1244.00, NET 1244.00\n",
      "2018-04-23, Previous Sentiment 0.03, New Sentiment 0.87 BUY CREATE, 1067.45\n",
      "2018-04-24, BUY EXECUTED, Price: 1052.00, Cost: 105200.00, Comm 0.00\n",
      "2018-09-04, Previous Sentiment 0.76, New Sentiment 0.01 SELL CREATE, 1197.00\n",
      "2018-09-05, SELL EXECUTED, Price: 1193.80, Cost: 105200.00, Comm 0.00\n",
      "2018-09-05, OPERATION PROFIT, GROSS 14180.00, NET 14180.00\n",
      "2018-10-01, Previous Sentiment 0.01, New Sentiment 0.88 BUY CREATE, 1195.31\n",
      "2018-10-02, BUY EXECUTED, Price: 1190.96, Cost: 119096.00, Comm 0.00\n",
      "2018-10-05, Previous Sentiment 0.88, New Sentiment 0.31 SELL CREATE, 1157.35\n",
      "2018-10-08, SELL EXECUTED, Price: 1150.11, Cost: 119096.00, Comm 0.00\n",
      "2018-10-08, OPERATION PROFIT, GROSS -4085.00, NET -4085.00\n",
      "2018-12-11, (MA Period 15) Ending Value 149719.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 149719.00\n",
      "Profit: 49719.00\n",
      "\n",
      "FB\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-11-14, Previous Sentiment -0.82, New Sentiment 0.71 BUY CREATE, 22.36\n",
      "2012-11-15, BUY EXECUTED, Price: 22.34, Cost: 2234.00, Comm 0.00\n",
      "2013-03-13, Previous Sentiment 0.95, New Sentiment -0.25 SELL CREATE, 27.08\n",
      "2013-03-14, SELL EXECUTED, Price: 27.10, Cost: 2234.00, Comm 0.00\n",
      "2013-03-14, OPERATION PROFIT, GROSS 476.00, NET 476.00\n",
      "2013-04-10, Previous Sentiment -0.25, New Sentiment 0.94 BUY CREATE, 27.57\n",
      "2013-04-11, BUY EXECUTED, Price: 27.48, Cost: 2748.00, Comm 0.00\n",
      "2013-11-18, Previous Sentiment 0.98, New Sentiment 0.40 SELL CREATE, 45.83\n",
      "2013-11-19, SELL EXECUTED, Price: 46.26, Cost: 2748.00, Comm 0.00\n",
      "2013-11-19, OPERATION PROFIT, GROSS 1878.00, NET 1878.00\n",
      "2014-01-30, Previous Sentiment 0.01, New Sentiment 0.82 BUY CREATE, 61.08\n",
      "2014-01-31, BUY EXECUTED, Price: 60.47, Cost: 6047.00, Comm 0.00\n",
      "2014-12-16, Previous Sentiment 0.68, New Sentiment -0.39 SELL CREATE, 74.69\n",
      "2014-12-17, SELL EXECUTED, Price: 75.01, Cost: 6047.00, Comm 0.00\n",
      "2014-12-17, OPERATION PROFIT, GROSS 1454.00, NET 1454.00\n",
      "2014-12-22, Previous Sentiment -0.39, New Sentiment 0.73 BUY CREATE, 81.45\n",
      "2014-12-23, BUY EXECUTED, Price: 82.02, Cost: 8202.00, Comm 0.00\n",
      "2016-04-27, Previous Sentiment 0.91, New Sentiment 0.01 SELL CREATE, 108.89\n",
      "2016-04-28, SELL EXECUTED, Price: 119.58, Cost: 8202.00, Comm 0.00\n",
      "2016-04-28, OPERATION PROFIT, GROSS 3756.00, NET 3756.00\n",
      "2016-07-06, Previous Sentiment -0.11, New Sentiment 0.81 BUY CREATE, 116.70\n",
      "2016-07-07, BUY EXECUTED, Price: 116.63, Cost: 11663.00, Comm 0.00\n",
      "2016-11-18, Previous Sentiment 0.78, New Sentiment -0.13 SELL CREATE, 117.02\n",
      "2016-11-21, SELL EXECUTED, Price: 118.20, Cost: 11663.00, Comm 0.00\n",
      "2016-11-21, OPERATION PROFIT, GROSS 157.00, NET 157.00\n",
      "2016-11-21, Previous Sentiment -0.13, New Sentiment 0.83 BUY CREATE, 121.77\n",
      "2016-11-22, BUY EXECUTED, Price: 122.40, Cost: 12240.00, Comm 0.00\n",
      "2016-12-01, Previous Sentiment 0.83, New Sentiment -0.01 SELL CREATE, 115.10\n",
      "2016-12-02, SELL EXECUTED, Price: 115.11, Cost: 12240.00, Comm 0.00\n",
      "2016-12-02, OPERATION PROFIT, GROSS -729.00, NET -729.00\n",
      "2018-04-10, Previous Sentiment 0.03, New Sentiment 0.55 BUY CREATE, 165.04\n",
      "2018-04-11, BUY EXECUTED, Price: 165.36, Cost: 16536.00, Comm 0.00\n",
      "2018-08-16, Previous Sentiment 0.80, New Sentiment -0.23 SELL CREATE, 174.70\n",
      "2018-08-17, SELL EXECUTED, Price: 174.50, Cost: 16536.00, Comm 0.00\n",
      "2018-08-17, OPERATION PROFIT, GROSS 914.00, NET 914.00\n",
      "2018-12-03, Previous Sentiment -0.63, New Sentiment 0.47 BUY CREATE, 141.09\n",
      "2018-12-04, BUY EXECUTED, Price: 140.73, Cost: 14073.00, Comm 0.00\n",
      "2018-12-11, (MA Period 15) Ending Value 108041.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 108041.00\n",
      "Profit: 8041.00\n",
      "\n",
      "WMT\n",
      "Starting Portfolio Value: 100000.00\n",
      "2013-05-16, Previous Sentiment -0.72, New Sentiment 0.70 BUY CREATE, 65.85\n",
      "2013-05-17, BUY EXECUTED, Price: 65.68, Cost: 6568.00, Comm 0.00\n",
      "2013-08-15, Previous Sentiment 0.70, New Sentiment -0.59 SELL CREATE, 62.80\n",
      "2013-08-16, SELL EXECUTED, Price: 62.55, Cost: 6568.00, Comm 0.00\n",
      "2013-08-16, OPERATION PROFIT, GROSS -313.00, NET -313.00\n",
      "2016-01-07, Previous Sentiment 0.01, New Sentiment 0.64 BUY CREATE, 58.16\n",
      "2016-01-08, BUY EXECUTED, Price: 58.20, Cost: 5820.00, Comm 0.00\n",
      "2017-03-07, Previous Sentiment 0.68, New Sentiment -0.03 SELL CREATE, 64.30\n",
      "2017-03-08, SELL EXECUTED, Price: 64.61, Cost: 5820.00, Comm 0.00\n",
      "2017-03-08, OPERATION PROFIT, GROSS 641.00, NET 641.00\n",
      "2017-10-10, Previous Sentiment -0.48, New Sentiment 0.75 BUY CREATE, 79.01\n",
      "2017-10-11, BUY EXECUTED, Price: 79.43, Cost: 7943.00, Comm 0.00\n",
      "2018-05-07, Previous Sentiment 0.52, New Sentiment -0.10 SELL CREATE, 81.60\n",
      "2018-05-08, SELL EXECUTED, Price: 81.65, Cost: 7943.00, Comm 0.00\n",
      "2018-05-08, OPERATION PROFIT, GROSS 222.00, NET 222.00\n",
      "2018-07-26, Previous Sentiment -0.10, New Sentiment 0.51 BUY CREATE, 84.76\n",
      "2018-07-27, BUY EXECUTED, Price: 85.16, Cost: 8516.00, Comm 0.00\n",
      "2018-12-11, (MA Period 15) Ending Value 101152.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 101152.00\n",
      "Profit: 1152.00\n",
      "\n",
      "JPM\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-06-20, Previous Sentiment -0.47, New Sentiment 0.65 BUY CREATE, 28.85\n",
      "2012-06-21, BUY EXECUTED, Price: 28.88, Cost: 2888.00, Comm 0.00\n",
      "2016-01-25, Previous Sentiment 0.70, New Sentiment 0.18 SELL CREATE, 48.85\n",
      "2016-01-26, SELL EXECUTED, Price: 49.02, Cost: 2888.00, Comm 0.00\n",
      "2016-01-26, OPERATION PROFIT, GROSS 2014.00, NET 2014.00\n",
      "2018-12-11, (MA Period 15) Ending Value 102014.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 102014.00\n",
      "Profit: 2014.00\n",
      "\n",
      "TSLA\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-03-26, Previous Sentiment 0.30, New Sentiment 0.90 BUY CREATE, 37.40\n",
      "2012-03-27, BUY EXECUTED, Price: 37.16, Cost: 3716.00, Comm 0.00\n",
      "2012-09-10, Previous Sentiment 0.89, New Sentiment -0.33 SELL CREATE, 27.37\n",
      "2012-09-11, SELL EXECUTED, Price: 27.76, Cost: 3716.00, Comm 0.00\n",
      "2012-09-11, OPERATION PROFIT, GROSS -940.00, NET -940.00\n",
      "2012-12-04, Previous Sentiment 0.22, New Sentiment 0.76 BUY CREATE, 33.90\n",
      "2012-12-05, BUY EXECUTED, Price: 33.82, Cost: 3382.00, Comm 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-02-21, Previous Sentiment 0.87, New Sentiment 0.04 SELL CREATE, 35.16\n",
      "2013-02-22, SELL EXECUTED, Price: 35.72, Cost: 3382.00, Comm 0.00\n",
      "2013-02-22, OPERATION PROFIT, GROSS 190.00, NET 190.00\n",
      "2013-03-25, Previous Sentiment 0.32, New Sentiment 0.87 BUY CREATE, 37.53\n",
      "2013-03-26, BUY EXECUTED, Price: 37.98, Cost: 3798.00, Comm 0.00\n",
      "2013-09-12, Previous Sentiment 0.94, New Sentiment 0.28 SELL CREATE, 164.93\n",
      "2013-09-13, SELL EXECUTED, Price: 162.77, Cost: 3798.00, Comm 0.00\n",
      "2013-09-13, OPERATION PROFIT, GROSS 12479.00, NET 12479.00\n",
      "2014-02-24, Previous Sentiment -0.27, New Sentiment 0.80 BUY CREATE, 217.65\n",
      "2014-02-25, BUY EXECUTED, Price: 230.00, Cost: 23000.00, Comm 0.00\n",
      "2014-03-18, Previous Sentiment 0.80, New Sentiment 0.11 SELL CREATE, 240.04\n",
      "2014-03-19, SELL EXECUTED, Price: 241.39, Cost: 23000.00, Comm 0.00\n",
      "2014-03-19, OPERATION PROFIT, GROSS 1139.00, NET 1139.00\n",
      "2014-06-05, Previous Sentiment -0.08, New Sentiment 0.65 BUY CREATE, 206.90\n",
      "2014-06-06, BUY EXECUTED, Price: 209.75, Cost: 20975.00, Comm 0.00\n",
      "2014-07-07, Previous Sentiment 0.86, New Sentiment -0.77 SELL CREATE, 222.66\n",
      "2014-07-08, SELL EXECUTED, Price: 218.65, Cost: 20975.00, Comm 0.00\n",
      "2014-07-08, OPERATION PROFIT, GROSS 890.00, NET 890.00\n",
      "2014-11-20, Previous Sentiment -0.01, New Sentiment 0.69 BUY CREATE, 248.71\n",
      "2014-11-21, BUY EXECUTED, Price: 252.21, Cost: 25221.00, Comm 0.00\n",
      "2014-12-01, Previous Sentiment 0.69, New Sentiment -0.33 SELL CREATE, 231.64\n",
      "2014-12-02, SELL EXECUTED, Price: 234.57, Cost: 25221.00, Comm 0.00\n",
      "2014-12-02, OPERATION PROFIT, GROSS -1764.00, NET -1764.00\n",
      "2015-02-06, Previous Sentiment -0.49, New Sentiment 0.47 BUY CREATE, 217.36\n",
      "2015-02-09, BUY EXECUTED, Price: 215.38, Cost: 21538.00, Comm 0.00\n",
      "2015-03-06, Previous Sentiment 0.77, New Sentiment 0.27 SELL CREATE, 193.88\n",
      "2015-03-09, SELL EXECUTED, Price: 194.39, Cost: 21538.00, Comm 0.00\n",
      "2015-03-09, OPERATION PROFIT, GROSS -2099.00, NET -2099.00\n",
      "2015-06-16, Previous Sentiment 0.01, New Sentiment 0.67 BUY CREATE, 253.12\n",
      "2015-06-17, BUY EXECUTED, Price: 252.17, Cost: 25217.00, Comm 0.00\n",
      "2015-08-24, Previous Sentiment 0.24, New Sentiment -0.79 SELL CREATE, 218.87\n",
      "2015-08-25, SELL EXECUTED, Price: 230.52, Cost: 25217.00, Comm 0.00\n",
      "2015-08-25, OPERATION PROFIT, GROSS -2165.00, NET -2165.00\n",
      "2015-09-24, Previous Sentiment 0.17, New Sentiment 0.89 BUY CREATE, 263.12\n",
      "2015-09-25, BUY EXECUTED, Price: 266.61, Cost: 26661.00, Comm 0.00\n",
      "2015-10-16, Previous Sentiment 0.89, New Sentiment -0.32 SELL CREATE, 227.01\n",
      "2015-10-19, SELL EXECUTED, Price: 226.50, Cost: 26661.00, Comm 0.00\n",
      "2015-10-19, OPERATION PROFIT, GROSS -4011.00, NET -4011.00\n",
      "2015-11-06, Previous Sentiment -0.22, New Sentiment 0.85 BUY CREATE, 232.36\n",
      "2015-11-09, BUY EXECUTED, Price: 232.99, Cost: 23299.00, Comm 0.00\n",
      "2016-01-11, Previous Sentiment 0.74, New Sentiment 0.09 SELL CREATE, 207.85\n",
      "2016-01-12, SELL EXECUTED, Price: 211.60, Cost: 23299.00, Comm 0.00\n",
      "2016-01-12, OPERATION PROFIT, GROSS -2139.00, NET -2139.00\n",
      "2016-03-03, Previous Sentiment 0.29, New Sentiment 0.83 BUY CREATE, 195.74\n",
      "2016-03-04, BUY EXECUTED, Price: 198.00, Cost: 19800.00, Comm 0.00\n",
      "2016-05-10, Previous Sentiment 0.33, New Sentiment -0.44 SELL CREATE, 208.69\n",
      "2016-05-11, SELL EXECUTED, Price: 207.59, Cost: 19800.00, Comm 0.00\n",
      "2016-05-11, OPERATION PROFIT, GROSS 959.00, NET 959.00\n",
      "2016-07-11, Previous Sentiment -0.85, New Sentiment 0.35 BUY CREATE, 224.78\n",
      "2016-07-12, BUY EXECUTED, Price: 224.10, Cost: 22410.00, Comm 0.00\n",
      "2016-08-03, Previous Sentiment 0.79, New Sentiment -0.22 SELL CREATE, 225.79\n",
      "2016-08-04, SELL EXECUTED, Price: 225.69, Cost: 22410.00, Comm 0.00\n",
      "2016-08-04, OPERATION PROFIT, GROSS 159.00, NET 159.00\n",
      "2016-08-04, Previous Sentiment -0.22, New Sentiment 0.47 BUY CREATE, 230.61\n",
      "2016-08-05, BUY EXECUTED, Price: 230.00, Cost: 23000.00, Comm 0.00\n",
      "2016-08-15, Previous Sentiment 0.47, New Sentiment -0.16 SELL CREATE, 225.59\n",
      "2016-08-16, SELL EXECUTED, Price: 225.49, Cost: 23000.00, Comm 0.00\n",
      "2016-08-16, OPERATION PROFIT, GROSS -451.00, NET -451.00\n",
      "2016-10-24, Previous Sentiment 0.18, New Sentiment 0.79 BUY CREATE, 202.76\n",
      "2016-10-25, BUY EXECUTED, Price: 202.90, Cost: 20290.00, Comm 0.00\n",
      "2016-11-09, Previous Sentiment 0.57, New Sentiment 0.01 SELL CREATE, 190.06\n",
      "2016-11-10, SELL EXECUTED, Price: 191.05, Cost: 20290.00, Comm 0.00\n",
      "2016-11-10, OPERATION PROFIT, GROSS -1185.00, NET -1185.00\n",
      "2017-02-09, Previous Sentiment -0.01, New Sentiment 0.64 BUY CREATE, 269.20\n",
      "2017-02-10, BUY EXECUTED, Price: 269.79, Cost: 26979.00, Comm 0.00\n",
      "2017-08-02, Previous Sentiment 0.77, New Sentiment 0.01 SELL CREATE, 325.89\n",
      "2017-08-03, SELL EXECUTED, Price: 345.33, Cost: 26979.00, Comm 0.00\n",
      "2017-08-03, OPERATION PROFIT, GROSS 7554.00, NET 7554.00\n",
      "2018-03-12, Previous Sentiment -0.36, New Sentiment 0.32 BUY CREATE, 345.51\n",
      "2018-03-13, BUY EXECUTED, Price: 328.61, Cost: 32861.00, Comm 0.00\n",
      "2018-03-19, Previous Sentiment 0.32, New Sentiment -0.40 SELL CREATE, 313.56\n",
      "2018-03-20, SELL EXECUTED, Price: 314.87, Cost: 32861.00, Comm 0.00\n",
      "2018-03-20, OPERATION PROFIT, GROSS -1374.00, NET -1374.00\n",
      "2018-06-01, Previous Sentiment -0.60, New Sentiment 0.43 BUY CREATE, 291.82\n",
      "2018-06-04, BUY EXECUTED, Price: 294.34, Cost: 29434.00, Comm 0.00\n",
      "2018-07-03, Previous Sentiment 0.75, New Sentiment -0.20 SELL CREATE, 310.86\n",
      "2018-07-05, SELL EXECUTED, Price: 313.76, Cost: 29434.00, Comm 0.00\n",
      "2018-07-05, OPERATION PROFIT, GROSS 1942.00, NET 1942.00\n",
      "2018-10-23, Previous Sentiment -0.17, New Sentiment 0.39 BUY CREATE, 294.14\n",
      "2018-10-24, BUY EXECUTED, Price: 301.05, Cost: 30105.00, Comm 0.00\n",
      "2018-12-11, (MA Period 15) Ending Value 115755.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 115755.00\n",
      "Profit: 15755.00\n",
      "\n",
      "NFLX\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-02-08, Previous Sentiment 0.08, New Sentiment 0.62 BUY CREATE, 17.71\n",
      "2012-02-09, BUY EXECUTED, Price: 17.77, Cost: 1777.00, Comm 0.00\n",
      "2012-02-21, Previous Sentiment 0.92, New Sentiment -0.24 SELL CREATE, 16.77\n",
      "2012-02-22, SELL EXECUTED, Price: 16.42, Cost: 1777.00, Comm 0.00\n",
      "2012-02-22, OPERATION PROFIT, GROSS -135.00, NET -135.00\n",
      "2012-03-21, Previous Sentiment 0.10, New Sentiment 0.73 BUY CREATE, 17.16\n",
      "2012-03-22, BUY EXECUTED, Price: 17.15, Cost: 1715.00, Comm 0.00\n",
      "2012-05-31, Previous Sentiment 0.92, New Sentiment 0.11 SELL CREATE, 9.06\n",
      "2012-06-01, SELL EXECUTED, Price: 8.71, Cost: 1715.00, Comm 0.00\n",
      "2012-06-01, OPERATION PROFIT, GROSS -844.00, NET -844.00\n",
      "2012-07-03, Previous Sentiment -0.07, New Sentiment 0.85 BUY CREATE, 10.29\n",
      "2012-07-05, BUY EXECUTED, Price: 10.62, Cost: 1062.00, Comm 0.00\n",
      "2014-10-15, Previous Sentiment 0.93, New Sentiment -0.02 SELL CREATE, 64.08\n",
      "2014-10-16, SELL EXECUTED, Price: 47.53, Cost: 1062.00, Comm 0.00\n",
      "2014-10-16, OPERATION PROFIT, GROSS 3691.00, NET 3691.00\n",
      "2015-07-16, Previous Sentiment -0.22, New Sentiment 0.46 BUY CREATE, 115.81\n",
      "2015-07-17, BUY EXECUTED, Price: 117.34, Cost: 11734.00, Comm 0.00\n",
      "2015-08-20, Previous Sentiment 0.46, New Sentiment -0.08 SELL CREATE, 112.49\n",
      "2015-08-21, SELL EXECUTED, Price: 106.20, Cost: 11734.00, Comm 0.00\n",
      "2015-08-21, OPERATION PROFIT, GROSS -1114.00, NET -1114.00\n",
      "2015-10-08, Previous Sentiment 0.22, New Sentiment 0.80 BUY CREATE, 114.93\n",
      "2015-10-09, BUY EXECUTED, Price: 114.11, Cost: 11411.00, Comm 0.00\n",
      "2016-01-04, Previous Sentiment 0.81, New Sentiment -0.13 SELL CREATE, 109.96\n",
      "2016-01-05, SELL EXECUTED, Price: 110.45, Cost: 11411.00, Comm 0.00\n",
      "2016-01-05, OPERATION PROFIT, GROSS -366.00, NET -366.00\n",
      "2016-01-06, Previous Sentiment -0.13, New Sentiment 0.84 BUY CREATE, 117.68\n",
      "2016-01-07, BUY EXECUTED, Price: 116.36, Cost: 11636.00, Comm 0.00\n",
      "2016-01-13, Previous Sentiment 0.84, New Sentiment -0.48 SELL CREATE, 106.56\n",
      "2016-01-14, SELL EXECUTED, Price: 105.65, Cost: 11636.00, Comm 0.00\n",
      "2016-01-14, OPERATION PROFIT, GROSS -1071.00, NET -1071.00\n",
      "2016-05-26, Previous Sentiment 0.04, New Sentiment 0.58 BUY CREATE, 102.81\n",
      "2016-05-27, BUY EXECUTED, Price: 102.44, Cost: 10244.00, Comm 0.00\n",
      "2016-06-13, Previous Sentiment 0.58, New Sentiment -0.32 SELL CREATE, 93.85\n",
      "2016-06-14, SELL EXECUTED, Price: 94.16, Cost: 10244.00, Comm 0.00\n",
      "2016-06-14, OPERATION PROFIT, GROSS -828.00, NET -828.00\n",
      "2016-07-11, Previous Sentiment -0.54, New Sentiment 0.71 BUY CREATE, 94.67\n",
      "2016-07-12, BUY EXECUTED, Price: 95.07, Cost: 9507.00, Comm 0.00\n",
      "2016-07-19, Previous Sentiment 0.61, New Sentiment -0.25 SELL CREATE, 85.84\n",
      "2016-07-20, SELL EXECUTED, Price: 86.67, Cost: 9507.00, Comm 0.00\n",
      "2016-07-20, OPERATION PROFIT, GROSS -840.00, NET -840.00\n",
      "2016-10-03, Previous Sentiment 0.09, New Sentiment 0.60 BUY CREATE, 102.63\n",
      "2016-10-04, BUY EXECUTED, Price: 103.19, Cost: 10319.00, Comm 0.00\n",
      "2016-11-09, Previous Sentiment 0.66, New Sentiment -0.52 SELL CREATE, 122.19\n",
      "2016-11-10, SELL EXECUTED, Price: 122.76, Cost: 10319.00, Comm 0.00\n",
      "2016-11-10, OPERATION PROFIT, GROSS 1957.00, NET 1957.00\n",
      "2017-01-12, Previous Sentiment -0.20, New Sentiment 0.47 BUY CREATE, 129.18\n",
      "2017-01-13, BUY EXECUTED, Price: 131.15, Cost: 13115.00, Comm 0.00\n",
      "2017-06-12, Previous Sentiment 0.89, New Sentiment 0.21 SELL CREATE, 151.44\n",
      "2017-06-13, SELL EXECUTED, Price: 154.38, Cost: 13115.00, Comm 0.00\n",
      "2017-06-13, OPERATION PROFIT, GROSS 2323.00, NET 2323.00\n",
      "2017-07-18, Previous Sentiment -0.22, New Sentiment 0.73 BUY CREATE, 183.60\n",
      "2017-07-19, BUY EXECUTED, Price: 182.97, Cost: 18297.00, Comm 0.00\n",
      "2018-07-13, Previous Sentiment 0.52, New Sentiment -0.37 SELL CREATE, 395.80\n",
      "2018-07-16, SELL EXECUTED, Price: 398.98, Cost: 18297.00, Comm 0.00\n",
      "2018-07-16, OPERATION PROFIT, GROSS 21601.00, NET 21601.00\n",
      "2018-09-19, Previous Sentiment -0.31, New Sentiment 0.43 BUY CREATE, 366.96\n",
      "2018-09-20, BUY EXECUTED, Price: 370.26, Cost: 37026.00, Comm 0.00\n",
      "2018-10-16, Previous Sentiment 0.60, New Sentiment 0.01 SELL CREATE, 346.40\n",
      "2018-10-17, SELL EXECUTED, Price: 378.33, Cost: 37026.00, Comm 0.00\n",
      "2018-10-17, OPERATION PROFIT, GROSS 807.00, NET 807.00\n",
      "2018-12-11, (MA Period 15) Ending Value 125181.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 125181.00\n",
      "Profit: 25181.00\n",
      "\n",
      "ADBE\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-09-20, Previous Sentiment -0.84, New Sentiment 0.97 BUY CREATE, 34.53\n",
      "2012-09-21, BUY EXECUTED, Price: 34.36, Cost: 3436.00, Comm 0.00\n",
      "2014-09-16, Previous Sentiment 0.81, New Sentiment 0.16 SELL CREATE, 70.73\n",
      "2014-09-17, SELL EXECUTED, Price: 68.03, Cost: 3436.00, Comm 0.00\n",
      "2014-09-17, OPERATION PROFIT, GROSS 3367.00, NET 3367.00\n",
      "2015-09-18, Previous Sentiment -0.01, New Sentiment 0.83 BUY CREATE, 81.25\n",
      "2015-09-21, BUY EXECUTED, Price: 81.42, Cost: 8142.00, Comm 0.00\n",
      "2015-12-10, Previous Sentiment 0.74, New Sentiment -0.01 SELL CREATE, 88.96\n",
      "2015-12-11, SELL EXECUTED, Price: 92.51, Cost: 8142.00, Comm 0.00\n",
      "2015-12-11, OPERATION PROFIT, GROSS 1109.00, NET 1109.00\n",
      "2017-03-17, Previous Sentiment -0.01, New Sentiment 0.76 BUY CREATE, 127.01\n",
      "2017-03-20, BUY EXECUTED, Price: 126.99, Cost: 12699.00, Comm 0.00\n",
      "2018-10-15, Previous Sentiment 0.71, New Sentiment 0.09 SELL CREATE, 238.01\n",
      "2018-10-16, SELL EXECUTED, Price: 252.50, Cost: 12699.00, Comm 0.00\n",
      "2018-10-16, OPERATION PROFIT, GROSS 12551.00, NET 12551.00\n",
      "2018-12-11, (MA Period 15) Ending Value 117027.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 117027.00\n",
      "Profit: 17027.00\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_tickers = {}\n",
    "for ticker in tickers:    \n",
    "    date_sentiment=data_df[data_df['ticker'].isin([ticker])]\n",
    "    date_sentiment=date_sentiment[['date','sentiment_lex']]\n",
    "    date_sentiment['date']=pd.to_datetime(date_sentiment['date'], format='%Y-%m-%d').dt.date\n",
    "    date_sentiment=date_sentiment.set_index('date')['sentiment_lex']\n",
    "    date_sentiment=date_sentiment.to_dict()\n",
    "    results_tickers[ticker] = run_strategy(ticker, start = '2012-01-01', end = '2018-12-12')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>FB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>JPM</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>ADBE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PerUnitStartPrice</th>\n",
       "      <td>50.86</td>\n",
       "      <td>21.96</td>\n",
       "      <td>179.03</td>\n",
       "      <td>331.46</td>\n",
       "      <td>38.23</td>\n",
       "      <td>48.78</td>\n",
       "      <td>27.31</td>\n",
       "      <td>28.08</td>\n",
       "      <td>10.32</td>\n",
       "      <td>28.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StrategyProfit</th>\n",
       "      <td>3735.00</td>\n",
       "      <td>4067.00</td>\n",
       "      <td>75377.00</td>\n",
       "      <td>49719.00</td>\n",
       "      <td>8041.00</td>\n",
       "      <td>1152.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>15755.00</td>\n",
       "      <td>25181.00</td>\n",
       "      <td>17027.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      AAPL     MSFT      AMZN      GOOG       FB      WMT  \\\n",
       "PerUnitStartPrice    50.86    21.96    179.03    331.46    38.23    48.78   \n",
       "StrategyProfit     3735.00  4067.00  75377.00  49719.00  8041.00  1152.00   \n",
       "\n",
       "                       JPM      TSLA      NFLX      ADBE  \n",
       "PerUnitStartPrice    27.31     28.08     10.32     28.57  \n",
       "StrategyProfit     2014.00  15755.00  25181.00  17027.00  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(results_tickers).set_index([pd.Index([\"PerUnitStartPrice\", 'StrategyProfit'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strategy yield an overall profit be for all the stock. Now we run the strategy varying the time period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5.4'></a>\n",
    "## 5.4. Varying the strategy time period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step we run the strategy for differnt time periods and look at the results. We first run it for the time period between 2012 and 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "Starting Portfolio Value: 100000.00\n",
      "2013-12-13, Previous Sentiment -0.38, New Sentiment 0.86 BUY CREATE, 70.91\n",
      "2013-12-16, BUY EXECUTED, Price: 70.99, Cost: 7099.00, Comm 0.00\n",
      "2014-09-25, Previous Sentiment 0.78, New Sentiment 0.04 SELL CREATE, 89.08\n",
      "2014-09-26, SELL EXECUTED, Price: 89.68, Cost: 7099.00, Comm 0.00\n",
      "2014-09-26, OPERATION PROFIT, GROSS 1869.00, NET 1869.00\n",
      "2014-10-21, Previous Sentiment 0.14, New Sentiment 0.68 BUY CREATE, 93.27\n",
      "2014-10-22, BUY EXECUTED, Price: 93.61, Cost: 9361.00, Comm 0.00\n",
      "2014-12-30, (MA Period 15) Ending Value 102794.00\n"
     ]
    },
    {
     "data": {
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
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       "\n",
       "    var canvas = $('<canvas/>');\n",
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       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
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       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
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       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
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       "    // Add hover states to the ui-buttons\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
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       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
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       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
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       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
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       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
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       "\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" 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     "text": [
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 102794.00\n",
      "Profit: 2794.00\n",
      "\n",
      "MSFT\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-03-08, Previous Sentiment -0.50, New Sentiment 0.92 BUY CREATE, 26.43\n",
      "2012-03-09, BUY EXECUTED, Price: 26.51, Cost: 2651.00, Comm 0.00\n",
      "2012-04-04, Previous Sentiment 0.87, New Sentiment -0.42 SELL CREATE, 25.77\n",
      "2012-04-05, SELL EXECUTED, Price: 25.72, Cost: 2651.00, Comm 0.00\n",
      "2012-04-05, OPERATION PROFIT, GROSS -79.00, NET -79.00\n",
      "2012-06-19, Previous Sentiment 0.15, New Sentiment 0.88 BUY CREATE, 25.52\n",
      "2012-06-20, BUY EXECUTED, Price: 25.71, Cost: 2571.00, Comm 0.00\n",
      "2012-07-12, Previous Sentiment 0.68, New Sentiment -0.65 SELL CREATE, 23.80\n",
      "2012-07-13, SELL EXECUTED, Price: 23.90, Cost: 2571.00, Comm 0.00\n",
      "2012-07-13, OPERATION PROFIT, GROSS -181.00, NET -181.00\n",
      "2012-11-01, Previous Sentiment -0.69, New Sentiment 0.59 BUY CREATE, 24.70\n",
      "2012-11-02, BUY EXECUTED, Price: 24.76, Cost: 2476.00, Comm 0.00\n",
      "2012-11-13, Previous Sentiment 0.96, New Sentiment 0.13 SELL CREATE, 22.85\n",
      "2012-11-14, SELL EXECUTED, Price: 22.98, Cost: 2476.00, Comm 0.00\n",
      "2012-11-14, OPERATION PROFIT, GROSS -178.00, NET -178.00\n",
      "2013-04-16, Previous Sentiment -0.74, New Sentiment 0.90 BUY CREATE, 24.64\n",
      "2013-04-17, BUY EXECUTED, Price: 24.54, Cost: 2454.00, Comm 0.00\n",
      "2013-07-29, Previous Sentiment 0.80, New Sentiment -0.30 SELL CREATE, 27.01\n",
      "2013-07-30, SELL EXECUTED, Price: 27.22, Cost: 2454.00, Comm 0.00\n",
      "2013-07-30, OPERATION PROFIT, GROSS 268.00, NET 268.00\n",
      "2013-09-20, Previous Sentiment 0.25, New Sentiment 0.82 BUY CREATE, 28.28\n",
      "2013-09-23, BUY EXECUTED, Price: 28.07, Cost: 2807.00, Comm 0.00\n",
      "2013-10-24, Previous Sentiment 0.58, New Sentiment 0.01 SELL CREATE, 29.08\n",
      "2013-10-25, SELL EXECUTED, Price: 30.95, Cost: 2807.00, Comm 0.00\n",
      "2013-10-25, OPERATION PROFIT, GROSS 288.00, NET 288.00\n",
      "2013-11-20, Previous Sentiment 0.18, New Sentiment 0.89 BUY CREATE, 32.22\n",
      "2013-11-21, BUY EXECUTED, Price: 32.39, Cost: 3239.00, Comm 0.00\n",
      "2014-01-07, Previous Sentiment 0.49, New Sentiment -0.23 SELL CREATE, 31.64\n",
      "2014-01-08, SELL EXECUTED, Price: 31.29, Cost: 3239.00, Comm 0.00\n",
      "2014-01-08, OPERATION PROFIT, GROSS -110.00, NET -110.00\n",
      "2014-01-16, Previous Sentiment -0.76, New Sentiment 0.45 BUY CREATE, 32.06\n",
      "2014-01-17, BUY EXECUTED, Price: 32.01, Cost: 3201.00, Comm 0.00\n",
      "2014-07-31, Previous Sentiment 0.95, New Sentiment 0.01 SELL CREATE, 38.06\n",
      "2014-08-01, SELL EXECUTED, Price: 38.10, Cost: 3201.00, Comm 0.00\n",
      "2014-08-01, OPERATION PROFIT, GROSS 609.00, NET 609.00\n",
      "2014-12-30, (MA Period 15) Ending Value 100617.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 100617.00\n",
      "Profit: 617.00\n",
      "\n",
      "AMZN\n",
      "Starting Portfolio Value: 100000.00\n",
      "2013-01-30, Previous Sentiment 0.40, New Sentiment 0.95 BUY CREATE, 272.76\n",
      "2013-01-31, BUY EXECUTED, Price: 271.04, Cost: 27104.00, Comm 0.00\n",
      "2013-04-18, Previous Sentiment 0.89, New Sentiment 0.38 SELL CREATE, 259.42\n",
      "2013-04-19, SELL EXECUTED, Price: 258.16, Cost: 27104.00, Comm 0.00\n",
      "2013-04-19, OPERATION PROFIT, GROSS -1288.00, NET -1288.00\n",
      "2013-10-25, Previous Sentiment 0.15, New Sentiment 0.84 BUY CREATE, 363.39\n",
      "2013-10-28, BUY EXECUTED, Price: 359.92, Cost: 35992.00, Comm 0.00\n",
      "2014-02-03, Previous Sentiment 0.56, New Sentiment -0.15 SELL CREATE, 346.15\n",
      "2014-02-04, SELL EXECUTED, Price: 349.59, Cost: 35992.00, Comm 0.00\n",
      "2014-02-04, OPERATION PROFIT, GROSS -1033.00, NET -1033.00\n",
      "2014-05-20, Previous Sentiment -0.59, New Sentiment 0.08 BUY CREATE, 301.19\n",
      "2014-05-21, BUY EXECUTED, Price: 302.21, Cost: 30221.00, Comm 0.00\n",
      "2014-11-05, Previous Sentiment 0.79, New Sentiment 0.02 SELL CREATE, 296.52\n",
      "2014-11-06, SELL EXECUTED, Price: 296.69, Cost: 30221.00, Comm 0.00\n",
      "2014-11-06, OPERATION PROFIT, GROSS -552.00, NET -552.00\n",
      "2014-12-30, (MA Period 15) Ending Value 97127.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 97127.00\n",
      "Profit: -2873.00\n",
      "\n",
      "GOOG\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-04-12, Previous Sentiment 0.24, New Sentiment 0.80 BUY CREATE, 324.29\n",
      "2012-04-13, BUY EXECUTED, Price: 322.57, Cost: 32257.00, Comm 0.00\n",
      "2012-10-18, Previous Sentiment 0.98, New Sentiment 0.08 SELL CREATE, 346.20\n",
      "2012-10-19, SELL EXECUTED, Price: 351.47, Cost: 32257.00, Comm 0.00\n",
      "2012-10-19, OPERATION PROFIT, GROSS 2890.00, NET 2890.00\n",
      "2013-01-10, Previous Sentiment 0.08, New Sentiment 0.80 BUY CREATE, 369.36\n",
      "2013-01-11, BUY EXECUTED, Price: 369.61, Cost: 36961.00, Comm 0.00\n",
      "2014-07-17, Previous Sentiment 0.73, New Sentiment -0.22 SELL CREATE, 572.16\n",
      "2014-07-18, SELL EXECUTED, Price: 591.38, Cost: 36961.00, Comm 0.00\n",
      "2014-07-18, OPERATION PROFIT, GROSS 22177.00, NET 22177.00\n",
      "2014-07-18, Previous Sentiment -0.22, New Sentiment 0.77 BUY CREATE, 593.45\n",
      "2014-07-21, BUY EXECUTED, Price: 590.13, Cost: 59013.00, Comm 0.00\n",
      "2014-09-12, Previous Sentiment 0.66, New Sentiment -0.05 SELL CREATE, 574.04\n",
      "2014-09-15, SELL EXECUTED, Price: 571.37, Cost: 59013.00, Comm 0.00\n",
      "2014-09-15, OPERATION PROFIT, GROSS -1876.00, NET -1876.00\n",
      "2014-12-30, (MA Period 15) Ending Value 123191.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 123191.00\n",
      "Profit: 23191.00\n",
      "\n",
      "FB\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-11-14, Previous Sentiment -0.82, New Sentiment 0.71 BUY CREATE, 22.36\n",
      "2012-11-15, BUY EXECUTED, Price: 22.34, Cost: 2234.00, Comm 0.00\n",
      "2013-03-13, Previous Sentiment 0.95, New Sentiment -0.25 SELL CREATE, 27.08\n",
      "2013-03-14, SELL EXECUTED, Price: 27.10, Cost: 2234.00, Comm 0.00\n",
      "2013-03-14, OPERATION PROFIT, GROSS 476.00, NET 476.00\n",
      "2013-04-10, Previous Sentiment -0.25, New Sentiment 0.94 BUY CREATE, 27.57\n",
      "2013-04-11, BUY EXECUTED, Price: 27.48, Cost: 2748.00, Comm 0.00\n",
      "2013-11-18, Previous Sentiment 0.98, New Sentiment 0.40 SELL CREATE, 45.83\n",
      "2013-11-19, SELL EXECUTED, Price: 46.26, Cost: 2748.00, Comm 0.00\n",
      "2013-11-19, OPERATION PROFIT, GROSS 1878.00, NET 1878.00\n",
      "2014-01-30, Previous Sentiment 0.01, New Sentiment 0.82 BUY CREATE, 61.08\n",
      "2014-01-31, BUY EXECUTED, Price: 60.47, Cost: 6047.00, Comm 0.00\n",
      "2014-12-16, Previous Sentiment 0.68, New Sentiment -0.39 SELL CREATE, 74.69\n",
      "2014-12-17, SELL EXECUTED, Price: 75.01, Cost: 6047.00, Comm 0.00\n",
      "2014-12-17, OPERATION PROFIT, GROSS 1454.00, NET 1454.00\n",
      "2014-12-22, Previous Sentiment -0.39, New Sentiment 0.73 BUY CREATE, 81.45\n",
      "2014-12-23, BUY EXECUTED, Price: 82.02, Cost: 8202.00, Comm 0.00\n",
      "2014-12-30, (MA Period 15) Ending Value 103528.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 103528.00\n",
      "Profit: 3528.00\n",
      "\n",
      "WMT\n",
      "Starting Portfolio Value: 100000.00\n",
      "2013-05-16, Previous Sentiment -0.72, New Sentiment 0.70 BUY CREATE, 65.85\n",
      "2013-05-17, BUY EXECUTED, Price: 65.68, Cost: 6568.00, Comm 0.00\n",
      "2013-08-15, Previous Sentiment 0.70, New Sentiment -0.59 SELL CREATE, 62.80\n",
      "2013-08-16, SELL EXECUTED, Price: 62.55, Cost: 6568.00, Comm 0.00\n",
      "2013-08-16, OPERATION PROFIT, GROSS -313.00, NET -313.00\n",
      "2014-12-30, (MA Period 15) Ending Value 99687.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 99687.00\n",
      "Profit: -313.00\n",
      "\n",
      "JPM\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-06-20, Previous Sentiment -0.47, New Sentiment 0.65 BUY CREATE, 28.85\n",
      "2012-06-21, BUY EXECUTED, Price: 28.88, Cost: 2888.00, Comm 0.00\n",
      "2014-12-30, (MA Period 15) Ending Value 102472.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 102472.00\n",
      "Profit: 2472.00\n",
      "\n",
      "TSLA\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-03-26, Previous Sentiment 0.30, New Sentiment 0.90 BUY CREATE, 37.40\n",
      "2012-03-27, BUY EXECUTED, Price: 37.16, Cost: 3716.00, Comm 0.00\n",
      "2012-09-10, Previous Sentiment 0.89, New Sentiment -0.33 SELL CREATE, 27.37\n",
      "2012-09-11, SELL EXECUTED, Price: 27.76, Cost: 3716.00, Comm 0.00\n",
      "2012-09-11, OPERATION PROFIT, GROSS -940.00, NET -940.00\n",
      "2012-12-04, Previous Sentiment 0.22, New Sentiment 0.76 BUY CREATE, 33.90\n",
      "2012-12-05, BUY EXECUTED, Price: 33.82, Cost: 3382.00, Comm 0.00\n",
      "2013-02-21, Previous Sentiment 0.87, New Sentiment 0.04 SELL CREATE, 35.16\n",
      "2013-02-22, SELL EXECUTED, Price: 35.72, Cost: 3382.00, Comm 0.00\n",
      "2013-02-22, OPERATION PROFIT, GROSS 190.00, NET 190.00\n",
      "2013-03-25, Previous Sentiment 0.32, New Sentiment 0.87 BUY CREATE, 37.53\n",
      "2013-03-26, BUY EXECUTED, Price: 37.98, Cost: 3798.00, Comm 0.00\n",
      "2013-09-12, Previous Sentiment 0.94, New Sentiment 0.28 SELL CREATE, 164.93\n",
      "2013-09-13, SELL EXECUTED, Price: 162.77, Cost: 3798.00, Comm 0.00\n",
      "2013-09-13, OPERATION PROFIT, GROSS 12479.00, NET 12479.00\n",
      "2014-02-24, Previous Sentiment -0.27, New Sentiment 0.80 BUY CREATE, 217.65\n",
      "2014-02-25, BUY EXECUTED, Price: 230.00, Cost: 23000.00, Comm 0.00\n",
      "2014-03-18, Previous Sentiment 0.80, New Sentiment 0.11 SELL CREATE, 240.04\n",
      "2014-03-19, SELL EXECUTED, Price: 241.39, Cost: 23000.00, Comm 0.00\n",
      "2014-03-19, OPERATION PROFIT, GROSS 1139.00, NET 1139.00\n",
      "2014-06-05, Previous Sentiment -0.08, New Sentiment 0.65 BUY CREATE, 206.90\n",
      "2014-06-06, BUY EXECUTED, Price: 209.75, Cost: 20975.00, Comm 0.00\n",
      "2014-07-07, Previous Sentiment 0.86, New Sentiment -0.77 SELL CREATE, 222.66\n",
      "2014-07-08, SELL EXECUTED, Price: 218.65, Cost: 20975.00, Comm 0.00\n",
      "2014-07-08, OPERATION PROFIT, GROSS 890.00, NET 890.00\n",
      "2014-11-20, Previous Sentiment -0.01, New Sentiment 0.69 BUY CREATE, 248.71\n",
      "2014-11-21, BUY EXECUTED, Price: 252.21, Cost: 25221.00, Comm 0.00\n",
      "2014-12-01, Previous Sentiment 0.69, New Sentiment -0.33 SELL CREATE, 231.64\n",
      "2014-12-02, SELL EXECUTED, Price: 234.57, Cost: 25221.00, Comm 0.00\n",
      "2014-12-02, OPERATION PROFIT, GROSS -1764.00, NET -1764.00\n",
      "2014-12-30, (MA Period 15) Ending Value 111994.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 111994.00\n",
      "Profit: 11994.00\n",
      "\n",
      "NFLX\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-02-08, Previous Sentiment 0.08, New Sentiment 0.62 BUY CREATE, 17.71\n",
      "2012-02-09, BUY EXECUTED, Price: 17.77, Cost: 1777.00, Comm 0.00\n",
      "2012-02-21, Previous Sentiment 0.92, New Sentiment -0.24 SELL CREATE, 16.77\n",
      "2012-02-22, SELL EXECUTED, Price: 16.42, Cost: 1777.00, Comm 0.00\n",
      "2012-02-22, OPERATION PROFIT, GROSS -135.00, NET -135.00\n",
      "2012-03-21, Previous Sentiment 0.10, New Sentiment 0.73 BUY CREATE, 17.16\n",
      "2012-03-22, BUY EXECUTED, Price: 17.15, Cost: 1715.00, Comm 0.00\n",
      "2012-05-31, Previous Sentiment 0.92, New Sentiment 0.11 SELL CREATE, 9.06\n",
      "2012-06-01, SELL EXECUTED, Price: 8.71, Cost: 1715.00, Comm 0.00\n",
      "2012-06-01, OPERATION PROFIT, GROSS -844.00, NET -844.00\n",
      "2012-07-03, Previous Sentiment -0.07, New Sentiment 0.85 BUY CREATE, 10.29\n",
      "2012-07-05, BUY EXECUTED, Price: 10.62, Cost: 1062.00, Comm 0.00\n",
      "2014-10-15, Previous Sentiment 0.93, New Sentiment -0.02 SELL CREATE, 64.08\n",
      "2014-10-16, SELL EXECUTED, Price: 47.53, Cost: 1062.00, Comm 0.00\n",
      "2014-10-16, OPERATION PROFIT, GROSS 3691.00, NET 3691.00\n",
      "2014-12-30, (MA Period 15) Ending Value 102712.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 102712.00\n",
      "Profit: 2712.00\n",
      "\n",
      "ADBE\n",
      "Starting Portfolio Value: 100000.00\n",
      "2012-09-20, Previous Sentiment -0.84, New Sentiment 0.97 BUY CREATE, 34.53\n",
      "2012-09-21, BUY EXECUTED, Price: 34.36, Cost: 3436.00, Comm 0.00\n",
      "2014-09-16, Previous Sentiment 0.81, New Sentiment 0.16 SELL CREATE, 70.73\n",
      "2014-09-17, SELL EXECUTED, Price: 68.03, Cost: 3436.00, Comm 0.00\n",
      "2014-09-17, OPERATION PROFIT, GROSS 3367.00, NET 3367.00\n",
      "2014-12-30, (MA Period 15) Ending Value 103367.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 103367.00\n",
      "Profit: 3367.00\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_tickers = {}\n",
    "for ticker in tickers:  \n",
    "    date_sentiment=data_df[data_df['ticker'].isin([ticker])]\n",
    "    date_sentiment=date_sentiment[['date','sentiment_lex']]\n",
    "    date_sentiment['date']=pd.to_datetime(date_sentiment['date'], format='%Y-%m-%d').dt.date\n",
    "    date_sentiment=date_sentiment.set_index('date')['sentiment_lex']\n",
    "    date_sentiment=date_sentiment.to_dict()\n",
    "    results_tickers[ticker] = run_strategy(ticker, start = '2012-01-01', end = '2014-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>FB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>JPM</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>ADBE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>StockPriceBeginning</th>\n",
       "      <td>50.86</td>\n",
       "      <td>21.96</td>\n",
       "      <td>179.03</td>\n",
       "      <td>331.46</td>\n",
       "      <td>38.23</td>\n",
       "      <td>48.78</td>\n",
       "      <td>27.31</td>\n",
       "      <td>28.08</td>\n",
       "      <td>10.32</td>\n",
       "      <td>28.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StrategyProfit</th>\n",
       "      <td>2794.00</td>\n",
       "      <td>617.00</td>\n",
       "      <td>-2873.00</td>\n",
       "      <td>23191.00</td>\n",
       "      <td>3528.00</td>\n",
       "      <td>-313.00</td>\n",
       "      <td>2472.00</td>\n",
       "      <td>11994.00</td>\n",
       "      <td>2712.00</td>\n",
       "      <td>3367.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        AAPL    MSFT     AMZN      GOOG       FB     WMT  \\\n",
       "StockPriceBeginning    50.86   21.96   179.03    331.46    38.23   48.78   \n",
       "StrategyProfit       2794.00  617.00 -2873.00  23191.00  3528.00 -313.00   \n",
       "\n",
       "                         JPM      TSLA     NFLX     ADBE  \n",
       "StockPriceBeginning    27.31     28.08    10.32    28.57  \n",
       "StrategyProfit       2472.00  11994.00  2712.00  3367.00  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(results_tickers).set_index([pd.Index([\"StockPriceBeginning\", 'StrategyProfit'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strategy yield an overall profit for all the stocks except two. Now we run the strategy between 2016 and 2018. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-03-15, Previous Sentiment 0.09, New Sentiment 0.66 BUY CREATE, 97.76\n",
      "2016-03-16, BUY EXECUTED, Price: 97.79, Cost: 9779.00, Comm 0.00\n",
      "2016-07-26, Previous Sentiment 0.93, New Sentiment 0.14 SELL CREATE, 90.92\n",
      "2016-07-27, SELL EXECUTED, Price: 98.06, Cost: 9779.00, Comm 0.00\n",
      "2016-07-27, OPERATION PROFIT, GROSS 27.00, NET 27.00\n",
      "2016-12-13, Previous Sentiment 0.15, New Sentiment 0.74 BUY CREATE, 109.48\n",
      "2016-12-14, BUY EXECUTED, Price: 109.34, Cost: 10934.00, Comm 0.00\n",
      "2017-09-22, Previous Sentiment 0.58, New Sentiment 0.08 SELL CREATE, 146.16\n",
      "2017-09-25, SELL EXECUTED, Price: 144.33, Cost: 10934.00, Comm 0.00\n",
      "2017-09-25, OPERATION PROFIT, GROSS 3499.00, NET 3499.00\n",
      "2017-11-06, Previous Sentiment 0.14, New Sentiment 0.64 BUY CREATE, 167.67\n",
      "2017-11-07, BUY EXECUTED, Price: 167.35, Cost: 16735.00, Comm 0.00\n",
      "2017-12-08, Previous Sentiment 0.57, New Sentiment -0.02 SELL CREATE, 163.56\n",
      "2017-12-11, SELL EXECUTED, Price: 163.40, Cost: 16735.00, Comm 0.00\n",
      "2017-12-11, OPERATION PROFIT, GROSS -395.00, NET -395.00\n",
      "2017-12-13, Previous Sentiment -0.02, New Sentiment 0.71 BUY CREATE, 166.36\n",
      "2017-12-14, BUY EXECUTED, Price: 166.49, Cost: 16649.00, Comm 0.00\n",
      "2018-02-01, Previous Sentiment 0.66, New Sentiment 0.06 SELL CREATE, 162.03\n",
      "2018-02-02, SELL EXECUTED, Price: 160.31, Cost: 16649.00, Comm 0.00\n",
      "2018-02-02, OPERATION PROFIT, GROSS -618.00, NET -618.00\n",
      "2018-03-01, Previous Sentiment -0.07, New Sentiment 0.85 BUY CREATE, 169.69\n",
      "2018-03-02, BUY EXECUTED, Price: 167.56, Cost: 16756.00, Comm 0.00\n",
      "2018-03-28, Previous Sentiment 0.60, New Sentiment 0.09 SELL CREATE, 161.43\n",
      "2018-03-29, SELL EXECUTED, Price: 162.72, Cost: 16756.00, Comm 0.00\n",
      "2018-03-29, OPERATION PROFIT, GROSS -484.00, NET -484.00\n",
      "2018-04-11, Previous Sentiment -0.23, New Sentiment 0.66 BUY CREATE, 167.21\n",
      "2018-04-12, BUY EXECUTED, Price: 168.15, Cost: 16815.00, Comm 0.00\n",
      "2018-04-24, Previous Sentiment 0.89, New Sentiment -0.23 SELL CREATE, 157.99\n",
      "2018-04-25, SELL EXECUTED, Price: 157.68, Cost: 16815.00, Comm 0.00\n",
      "2018-04-25, OPERATION PROFIT, GROSS -1047.00, NET -1047.00\n",
      "2018-10-01, Previous Sentiment -0.35, New Sentiment 0.75 BUY CREATE, 221.99\n",
      "2018-10-02, BUY EXECUTED, Price: 221.98, Cost: 22198.00, Comm 0.00\n",
      "2018-10-10, Previous Sentiment 0.52, New Sentiment -0.11 SELL CREATE, 211.34\n",
      "2018-10-11, SELL EXECUTED, Price: 209.54, Cost: 22198.00, Comm 0.00\n",
      "2018-10-11, OPERATION PROFIT, GROSS -1244.00, NET -1244.00\n",
      "2018-12-28, (MA Period 15) Ending Value 99738.00\n"
     ]
    },
    {
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       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
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       "\n",
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       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
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       "\n",
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       "\n",
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       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
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       "\n",
       "\n",
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       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
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       "\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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     "text": [
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 99738.00\n",
      "Profit: -262.00\n",
      "\n",
      "MSFT\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-01-29, Previous Sentiment 0.01, New Sentiment 0.58 BUY CREATE, 50.52\n",
      "2016-02-01, BUY EXECUTED, Price: 50.33, Cost: 5033.00, Comm 0.00\n",
      "2016-02-04, Previous Sentiment 0.58, New Sentiment 0.01 SELL CREATE, 47.69\n",
      "2016-02-05, SELL EXECUTED, Price: 47.64, Cost: 5033.00, Comm 0.00\n",
      "2016-02-05, OPERATION PROFIT, GROSS -269.00, NET -269.00\n",
      "2016-04-21, Previous Sentiment -0.47, New Sentiment 0.90 BUY CREATE, 51.52\n",
      "2016-04-22, BUY EXECUTED, Price: 47.95, Cost: 4795.00, Comm 0.00\n",
      "2016-04-28, Previous Sentiment 0.90, New Sentiment 0.01 SELL CREATE, 46.09\n",
      "2016-04-29, SELL EXECUTED, Price: 45.59, Cost: 4795.00, Comm 0.00\n",
      "2016-04-29, OPERATION PROFIT, GROSS -236.00, NET -236.00\n",
      "2017-04-24, Previous Sentiment -0.08, New Sentiment 0.69 BUY CREATE, 64.02\n",
      "2017-04-25, BUY EXECUTED, Price: 64.37, Cost: 6437.00, Comm 0.00\n",
      "2017-12-05, Previous Sentiment 0.58, New Sentiment -0.19 SELL CREATE, 78.60\n",
      "2017-12-06, SELL EXECUTED, Price: 78.56, Cost: 6437.00, Comm 0.00\n",
      "2017-12-06, OPERATION PROFIT, GROSS 1419.00, NET 1419.00\n",
      "2018-01-04, Previous Sentiment -0.29, New Sentiment 0.37 BUY CREATE, 83.92\n",
      "2018-01-05, BUY EXECUTED, Price: 84.45, Cost: 8445.00, Comm 0.00\n",
      "2018-10-09, Previous Sentiment 0.71, New Sentiment 0.01 SELL CREATE, 109.54\n",
      "2018-10-10, SELL EXECUTED, Price: 108.55, Cost: 8445.00, Comm 0.00\n",
      "2018-10-10, OPERATION PROFIT, GROSS 2410.00, NET 2410.00\n",
      "2018-12-28, (MA Period 15) Ending Value 103324.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 103324.00\n",
      "Profit: 3324.00\n",
      "\n",
      "AMZN\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-02-29, Previous Sentiment 0.02, New Sentiment 0.88 BUY CREATE, 552.52\n",
      "2016-03-01, BUY EXECUTED, Price: 556.29, Cost: 55629.00, Comm 0.00\n",
      "2016-04-28, Previous Sentiment 0.96, New Sentiment 0.31 SELL CREATE, 602.00\n",
      "2016-04-29, SELL EXECUTED, Price: 666.00, Cost: 55629.00, Comm 0.00\n",
      "2016-04-29, OPERATION PROFIT, GROSS 10971.00, NET 10971.00\n",
      "2016-05-10, Previous Sentiment 0.02, New Sentiment 0.57 BUY CREATE, 703.07\n",
      "2016-05-11, BUY EXECUTED, Price: 705.79, Cost: 70579.00, Comm 0.00\n",
      "2016-06-15, Previous Sentiment 0.71, New Sentiment 0.07 SELL CREATE, 714.26\n",
      "2016-06-16, SELL EXECUTED, Price: 712.05, Cost: 70579.00, Comm 0.00\n",
      "2016-06-16, OPERATION PROFIT, GROSS 626.00, NET 626.00\n",
      "2016-07-13, Previous Sentiment 0.07, New Sentiment 0.60 BUY CREATE, 742.63\n",
      "2016-07-14, BUY EXECUTED, Price: 748.86, Cost: 74886.00, Comm 0.00\n",
      "2016-07-26, Previous Sentiment 0.46, New Sentiment -0.28 SELL CREATE, 735.59\n",
      "2016-07-27, SELL EXECUTED, Price: 737.97, Cost: 74886.00, Comm 0.00\n",
      "2016-07-27, OPERATION PROFIT, GROSS -1089.00, NET -1089.00\n",
      "2016-07-28, Previous Sentiment -0.18, New Sentiment 0.81 BUY CREATE, 752.61\n",
      "2016-07-29, BUY EXECUTED, Price: 765.00, Cost: 76500.00, Comm 0.00\n",
      "2016-11-02, Previous Sentiment 0.79, New Sentiment 0.02 SELL CREATE, 765.56\n",
      "2016-11-03, SELL EXECUTED, Price: 765.05, Cost: 76500.00, Comm 0.00\n",
      "2016-11-03, OPERATION PROFIT, GROSS 5.00, NET 5.00\n",
      "2017-02-01, Previous Sentiment -0.16, New Sentiment 0.52 BUY CREATE, 832.35\n",
      "2017-02-02, BUY EXECUTED, Price: 836.59, Cost: 83659.00, Comm 0.00\n",
      "2018-03-28, Previous Sentiment 0.87, New Sentiment -0.02 SELL CREATE, 1431.42\n",
      "2018-03-29, SELL EXECUTED, Price: 1406.00, Cost: 83659.00, Comm 0.00\n",
      "2018-03-29, OPERATION PROFIT, GROSS 56941.00, NET 56941.00\n",
      "2018-07-12, Previous Sentiment 0.36, New Sentiment 0.87 BUY CREATE, 1796.62\n",
      "2018-07-13, Order Canceled/Margin/Rejected\n",
      "2018-09-04, Previous Sentiment -0.24, New Sentiment 0.58 BUY CREATE, 2039.51\n",
      "2018-09-05, Order Canceled/Margin/Rejected\n",
      "2018-12-10, Previous Sentiment -0.11, New Sentiment 0.51 BUY CREATE, 1641.03\n",
      "2018-12-11, Order Canceled/Margin/Rejected\n",
      "2018-12-28, (MA Period 15) Ending Value 167454.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 167454.00\n",
      "Profit: 67454.00\n",
      "\n",
      "GOOG\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-02-03, Previous Sentiment 0.01, New Sentiment 0.73 BUY CREATE, 726.95\n",
      "2016-02-04, BUY EXECUTED, Price: 722.81, Cost: 72281.00, Comm 0.00\n",
      "2017-09-25, Previous Sentiment 0.69, New Sentiment 0.14 SELL CREATE, 920.97\n",
      "2017-09-26, SELL EXECUTED, Price: 923.72, Cost: 72281.00, Comm 0.00\n",
      "2017-09-26, OPERATION PROFIT, GROSS 20091.00, NET 20091.00\n",
      "2017-10-27, Previous Sentiment 0.01, New Sentiment 0.60 BUY CREATE, 1019.27\n",
      "2017-10-30, BUY EXECUTED, Price: 1014.00, Cost: 101400.00, Comm 0.00\n",
      "2018-04-09, Previous Sentiment 0.54, New Sentiment -0.45 SELL CREATE, 1015.45\n",
      "2018-04-10, SELL EXECUTED, Price: 1026.44, Cost: 101400.00, Comm 0.00\n",
      "2018-04-10, OPERATION PROFIT, GROSS 1244.00, NET 1244.00\n",
      "2018-04-23, Previous Sentiment 0.03, New Sentiment 0.87 BUY CREATE, 1067.45\n",
      "2018-04-24, BUY EXECUTED, Price: 1052.00, Cost: 105200.00, Comm 0.00\n",
      "2018-09-04, Previous Sentiment 0.76, New Sentiment 0.01 SELL CREATE, 1197.00\n",
      "2018-09-05, SELL EXECUTED, Price: 1193.80, Cost: 105200.00, Comm 0.00\n",
      "2018-09-05, OPERATION PROFIT, GROSS 14180.00, NET 14180.00\n",
      "2018-10-01, Previous Sentiment 0.01, New Sentiment 0.88 BUY CREATE, 1195.31\n",
      "2018-10-02, BUY EXECUTED, Price: 1190.96, Cost: 119096.00, Comm 0.00\n",
      "2018-10-05, Previous Sentiment 0.88, New Sentiment 0.31 SELL CREATE, 1157.35\n",
      "2018-10-08, SELL EXECUTED, Price: 1150.11, Cost: 119096.00, Comm 0.00\n",
      "2018-10-08, OPERATION PROFIT, GROSS -4085.00, NET -4085.00\n",
      "2018-12-28, (MA Period 15) Ending Value 131430.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 131430.00\n",
      "Profit: 31430.00\n",
      "\n",
      "FB\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-01-28, Previous Sentiment 0.01, New Sentiment 0.86 BUY CREATE, 109.11\n",
      "2016-01-29, BUY EXECUTED, Price: 108.99, Cost: 10899.00, Comm 0.00\n",
      "2016-04-27, Previous Sentiment 0.91, New Sentiment 0.01 SELL CREATE, 108.89\n",
      "2016-04-28, SELL EXECUTED, Price: 119.58, Cost: 10899.00, Comm 0.00\n",
      "2016-04-28, OPERATION PROFIT, GROSS 1059.00, NET 1059.00\n",
      "2016-07-06, Previous Sentiment -0.11, New Sentiment 0.81 BUY CREATE, 116.70\n",
      "2016-07-07, BUY EXECUTED, Price: 116.63, Cost: 11663.00, Comm 0.00\n",
      "2016-11-18, Previous Sentiment 0.78, New Sentiment -0.13 SELL CREATE, 117.02\n",
      "2016-11-21, SELL EXECUTED, Price: 118.20, Cost: 11663.00, Comm 0.00\n",
      "2016-11-21, OPERATION PROFIT, GROSS 157.00, NET 157.00\n",
      "2016-11-21, Previous Sentiment -0.13, New Sentiment 0.83 BUY CREATE, 121.77\n",
      "2016-11-22, BUY EXECUTED, Price: 122.40, Cost: 12240.00, Comm 0.00\n",
      "2016-12-01, Previous Sentiment 0.83, New Sentiment -0.01 SELL CREATE, 115.10\n",
      "2016-12-02, SELL EXECUTED, Price: 115.11, Cost: 12240.00, Comm 0.00\n",
      "2016-12-02, OPERATION PROFIT, GROSS -729.00, NET -729.00\n",
      "2018-04-10, Previous Sentiment 0.03, New Sentiment 0.55 BUY CREATE, 165.04\n",
      "2018-04-11, BUY EXECUTED, Price: 165.36, Cost: 16536.00, Comm 0.00\n",
      "2018-08-16, Previous Sentiment 0.80, New Sentiment -0.23 SELL CREATE, 174.70\n",
      "2018-08-17, SELL EXECUTED, Price: 174.50, Cost: 16536.00, Comm 0.00\n",
      "2018-08-17, OPERATION PROFIT, GROSS 914.00, NET 914.00\n",
      "2018-12-03, Previous Sentiment -0.63, New Sentiment 0.47 BUY CREATE, 141.09\n",
      "2018-12-04, BUY EXECUTED, Price: 140.73, Cost: 14073.00, Comm 0.00\n",
      "2018-12-28, (MA Period 15) Ending Value 100648.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 100648.00\n",
      "Profit: 648.00\n",
      "\n",
      "WMT\n",
      "Starting Portfolio Value: 100000.00\n",
      "2017-10-10, Previous Sentiment -0.48, New Sentiment 0.75 BUY CREATE, 79.01\n",
      "2017-10-11, BUY EXECUTED, Price: 79.43, Cost: 7943.00, Comm 0.00\n",
      "2018-05-07, Previous Sentiment 0.52, New Sentiment -0.10 SELL CREATE, 81.60\n",
      "2018-05-08, SELL EXECUTED, Price: 81.65, Cost: 7943.00, Comm 0.00\n",
      "2018-05-08, OPERATION PROFIT, GROSS 222.00, NET 222.00\n",
      "2018-07-26, Previous Sentiment -0.10, New Sentiment 0.51 BUY CREATE, 84.76\n",
      "2018-07-27, BUY EXECUTED, Price: 85.16, Cost: 8516.00, Comm 0.00\n",
      "2018-12-28, (MA Period 15) Ending Value 100657.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 100657.00\n",
      "Profit: 657.00\n",
      "\n",
      "JPM\n",
      "Starting Portfolio Value: 100000.00\n",
      "2018-12-28, (MA Period 15) Ending Value 100000.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 100000.00\n",
      "Profit: 0.00\n",
      "\n",
      "TSLA\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-03-03, Previous Sentiment 0.29, New Sentiment 0.83 BUY CREATE, 195.74\n",
      "2016-03-04, BUY EXECUTED, Price: 198.00, Cost: 19800.00, Comm 0.00\n",
      "2016-05-10, Previous Sentiment 0.33, New Sentiment -0.44 SELL CREATE, 208.69\n",
      "2016-05-11, SELL EXECUTED, Price: 207.59, Cost: 19800.00, Comm 0.00\n",
      "2016-05-11, OPERATION PROFIT, GROSS 959.00, NET 959.00\n",
      "2016-07-11, Previous Sentiment -0.85, New Sentiment 0.35 BUY CREATE, 224.78\n",
      "2016-07-12, BUY EXECUTED, Price: 224.10, Cost: 22410.00, Comm 0.00\n",
      "2016-08-03, Previous Sentiment 0.79, New Sentiment -0.22 SELL CREATE, 225.79\n",
      "2016-08-04, SELL EXECUTED, Price: 225.69, Cost: 22410.00, Comm 0.00\n",
      "2016-08-04, OPERATION PROFIT, GROSS 159.00, NET 159.00\n",
      "2016-08-04, Previous Sentiment -0.22, New Sentiment 0.47 BUY CREATE, 230.61\n",
      "2016-08-05, BUY EXECUTED, Price: 230.00, Cost: 23000.00, Comm 0.00\n",
      "2016-08-15, Previous Sentiment 0.47, New Sentiment -0.16 SELL CREATE, 225.59\n",
      "2016-08-16, SELL EXECUTED, Price: 225.49, Cost: 23000.00, Comm 0.00\n",
      "2016-08-16, OPERATION PROFIT, GROSS -451.00, NET -451.00\n",
      "2016-10-24, Previous Sentiment 0.18, New Sentiment 0.79 BUY CREATE, 202.76\n",
      "2016-10-25, BUY EXECUTED, Price: 202.90, Cost: 20290.00, Comm 0.00\n",
      "2016-11-09, Previous Sentiment 0.57, New Sentiment 0.01 SELL CREATE, 190.06\n",
      "2016-11-10, SELL EXECUTED, Price: 191.05, Cost: 20290.00, Comm 0.00\n",
      "2016-11-10, OPERATION PROFIT, GROSS -1185.00, NET -1185.00\n",
      "2017-02-09, Previous Sentiment -0.01, New Sentiment 0.64 BUY CREATE, 269.20\n",
      "2017-02-10, BUY EXECUTED, Price: 269.79, Cost: 26979.00, Comm 0.00\n",
      "2017-08-02, Previous Sentiment 0.77, New Sentiment 0.01 SELL CREATE, 325.89\n",
      "2017-08-03, SELL EXECUTED, Price: 345.33, Cost: 26979.00, Comm 0.00\n",
      "2017-08-03, OPERATION PROFIT, GROSS 7554.00, NET 7554.00\n",
      "2018-03-12, Previous Sentiment -0.36, New Sentiment 0.32 BUY CREATE, 345.51\n",
      "2018-03-13, BUY EXECUTED, Price: 328.61, Cost: 32861.00, Comm 0.00\n",
      "2018-03-19, Previous Sentiment 0.32, New Sentiment -0.40 SELL CREATE, 313.56\n",
      "2018-03-20, SELL EXECUTED, Price: 314.87, Cost: 32861.00, Comm 0.00\n",
      "2018-03-20, OPERATION PROFIT, GROSS -1374.00, NET -1374.00\n",
      "2018-06-01, Previous Sentiment -0.60, New Sentiment 0.43 BUY CREATE, 291.82\n",
      "2018-06-04, BUY EXECUTED, Price: 294.34, Cost: 29434.00, Comm 0.00\n",
      "2018-07-03, Previous Sentiment 0.75, New Sentiment -0.20 SELL CREATE, 310.86\n",
      "2018-07-05, SELL EXECUTED, Price: 313.76, Cost: 29434.00, Comm 0.00\n",
      "2018-07-05, OPERATION PROFIT, GROSS 1942.00, NET 1942.00\n",
      "2018-10-23, Previous Sentiment -0.17, New Sentiment 0.39 BUY CREATE, 294.14\n",
      "2018-10-24, BUY EXECUTED, Price: 301.05, Cost: 30105.00, Comm 0.00\n",
      "2018-12-28, (MA Period 15) Ending Value 110886.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 110886.00\n",
      "Profit: 10886.00\n",
      "\n",
      "NFLX\n",
      "Starting Portfolio Value: 100000.00\n",
      "2016-05-26, Previous Sentiment 0.04, New Sentiment 0.58 BUY CREATE, 102.81\n",
      "2016-05-27, BUY EXECUTED, Price: 102.44, Cost: 10244.00, Comm 0.00\n",
      "2016-06-13, Previous Sentiment 0.58, New Sentiment -0.32 SELL CREATE, 93.85\n",
      "2016-06-14, SELL EXECUTED, Price: 94.16, Cost: 10244.00, Comm 0.00\n",
      "2016-06-14, OPERATION PROFIT, GROSS -828.00, NET -828.00\n",
      "2016-07-11, Previous Sentiment -0.54, New Sentiment 0.71 BUY CREATE, 94.67\n",
      "2016-07-12, BUY EXECUTED, Price: 95.07, Cost: 9507.00, Comm 0.00\n",
      "2016-07-19, Previous Sentiment 0.61, New Sentiment -0.25 SELL CREATE, 85.84\n",
      "2016-07-20, SELL EXECUTED, Price: 86.67, Cost: 9507.00, Comm 0.00\n",
      "2016-07-20, OPERATION PROFIT, GROSS -840.00, NET -840.00\n",
      "2016-10-03, Previous Sentiment 0.09, New Sentiment 0.60 BUY CREATE, 102.63\n",
      "2016-10-04, BUY EXECUTED, Price: 103.19, Cost: 10319.00, Comm 0.00\n",
      "2016-11-09, Previous Sentiment 0.66, New Sentiment -0.52 SELL CREATE, 122.19\n",
      "2016-11-10, SELL EXECUTED, Price: 122.76, Cost: 10319.00, Comm 0.00\n",
      "2016-11-10, OPERATION PROFIT, GROSS 1957.00, NET 1957.00\n",
      "2017-01-12, Previous Sentiment -0.20, New Sentiment 0.47 BUY CREATE, 129.18\n",
      "2017-01-13, BUY EXECUTED, Price: 131.15, Cost: 13115.00, Comm 0.00\n",
      "2017-06-12, Previous Sentiment 0.89, New Sentiment 0.21 SELL CREATE, 151.44\n",
      "2017-06-13, SELL EXECUTED, Price: 154.38, Cost: 13115.00, Comm 0.00\n",
      "2017-06-13, OPERATION PROFIT, GROSS 2323.00, NET 2323.00\n",
      "2017-07-18, Previous Sentiment -0.22, New Sentiment 0.73 BUY CREATE, 183.60\n",
      "2017-07-19, BUY EXECUTED, Price: 182.97, Cost: 18297.00, Comm 0.00\n",
      "2018-07-13, Previous Sentiment 0.52, New Sentiment -0.37 SELL CREATE, 395.80\n",
      "2018-07-16, SELL EXECUTED, Price: 398.98, Cost: 18297.00, Comm 0.00\n",
      "2018-07-16, OPERATION PROFIT, GROSS 21601.00, NET 21601.00\n",
      "2018-09-19, Previous Sentiment -0.31, New Sentiment 0.43 BUY CREATE, 366.96\n",
      "2018-09-20, BUY EXECUTED, Price: 370.26, Cost: 37026.00, Comm 0.00\n",
      "2018-10-16, Previous Sentiment 0.60, New Sentiment 0.01 SELL CREATE, 346.40\n",
      "2018-10-17, SELL EXECUTED, Price: 378.33, Cost: 37026.00, Comm 0.00\n",
      "2018-10-17, OPERATION PROFIT, GROSS 807.00, NET 807.00\n",
      "2018-12-28, (MA Period 15) Ending Value 125020.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 125020.00\n",
      "Profit: 25020.00\n",
      "\n",
      "ADBE\n",
      "Starting Portfolio Value: 100000.00\n",
      "2017-03-17, Previous Sentiment -0.01, New Sentiment 0.76 BUY CREATE, 127.01\n",
      "2017-03-20, BUY EXECUTED, Price: 126.99, Cost: 12699.00, Comm 0.00\n",
      "2018-10-15, Previous Sentiment 0.71, New Sentiment 0.09 SELL CREATE, 238.01\n",
      "2018-10-16, SELL EXECUTED, Price: 252.50, Cost: 12699.00, Comm 0.00\n",
      "2018-10-16, OPERATION PROFIT, GROSS 12551.00, NET 12551.00\n",
      "2018-12-28, (MA Period 15) Ending Value 112551.00\n",
      "Start Portfolio value: 100000.00\n",
      "Final Portfolio Value: 112551.00\n",
      "Profit: 12551.00\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_tickers = {}\n",
    "for ticker in tickers: \n",
    "    date_sentiment=data_df[data_df['ticker'].isin([ticker])]\n",
    "    date_sentiment=date_sentiment[['date','sentiment_lex']]\n",
    "    date_sentiment['date']=pd.to_datetime(date_sentiment['date'], format='%Y-%m-%d').dt.date\n",
    "    date_sentiment=date_sentiment.set_index('date')['sentiment_lex']\n",
    "    date_sentiment=date_sentiment.to_dict()\n",
    "    results_tickers[ticker] = run_strategy(ticker, start = '2016-01-01', end = '2018-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>FB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>JPM</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>ADBE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PerUnitStartPrice</th>\n",
       "      <td>97.95</td>\n",
       "      <td>50.26</td>\n",
       "      <td>636.99</td>\n",
       "      <td>741.84</td>\n",
       "      <td>102.22</td>\n",
       "      <td>54.97</td>\n",
       "      <td>55.84</td>\n",
       "      <td>223.41</td>\n",
       "      <td>109.96</td>\n",
       "      <td>91.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StrategyProfit</th>\n",
       "      <td>-262.00</td>\n",
       "      <td>3324.00</td>\n",
       "      <td>67454.00</td>\n",
       "      <td>31430.00</td>\n",
       "      <td>648.00</td>\n",
       "      <td>657.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10886.00</td>\n",
       "      <td>25020.00</td>\n",
       "      <td>12551.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AAPL     MSFT      AMZN      GOOG      FB     WMT    JPM  \\\n",
       "PerUnitStartPrice   97.95    50.26    636.99    741.84  102.22   54.97  55.84   \n",
       "StrategyProfit    -262.00  3324.00  67454.00  31430.00  648.00  657.00   0.00   \n",
       "\n",
       "                       TSLA      NFLX      ADBE  \n",
       "PerUnitStartPrice    223.41    109.96     91.97  \n",
       "StrategyProfit     10886.00  25020.00  12551.00  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(results_tickers).set_index([pd.Index([\"PerUnitStartPrice\", 'StrategyProfit'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a good performance of the sentiment based strategy across all the stocks except AAPL and we can conclude that our sentiment based strategy performs quite well on different time period. The strategy can be further be tweaked to modify the threshold, order size. Additional metrics such as sharpe ratio and maximum drawdown can also be used to understand the performance of the stratefy. The sentiments can also be used along with the other features such as correlated variables and technical indicators for prediction.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    " We performed a comparison of the models and concluded that one of the most important step is training the model for sentiment analysis is training it using the domain-specific vocabulary. \n",
    "\n",
    "We further used the sentiments as signals to develop different trading strategy. This initial result suggests that the model trained on a financial lexicon based sentiments could prove a viable model for a trading strategy. \n",
    "\n",
    "Additional improvements to this can be made by using more complex pre-trained sentiment analysis models such as Bert by google or diffeent pre-trained NLP models available in open source platforms. Existing NLP libraries fill in some of the pre-processing and encoding steps to allow us to focus on the inference step.\n"
   ]
  }
 ],
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