FinancialAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link to website: https://danellsworth.github.io/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Milestone 1 - Financial Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dan Ellsworth and Alden Pratt\n",
    "\n",
    "> For our Final Tutorial Project, we will be examining the effectiveness of using sentiment analysis based on Twitter posts relating to publicly traded companies as a leading or lagging indicator of the stock price.  Fluctuations in stock prices can be largely attributed to changes in the attitude of its buyers and holders. As such, platforms in which people are allowed to freely voice their attitude toward a company, such as Twitter, can be influential for a broad audience of traders.  If a particular authority voices optimism regarding the future of a company’s stock, and enough people are convinced into buying, its stock price will increase to meet the increased demand.  Conversely, authority figures voicing pessimism generally results in a lower price.  We will be using company sentiment data from Twitter to see if there is a significant correlation between community sentiment and the change in the stock price. \n",
    "> Through studying this issue, several questions arise.  We are curious to know if sentiment generally follows stock prices or if stock prices follow sentiment. In other words, should we be looking at sentiment as a leading or lagging indicator of stock prices?  Who is influential?  Are some companies intrinsically more or less resistant to changes in sentiment?  Can our findings be used in developing a complete and profitable trading strategy?  \n",
    "    \n",
    "Below are a couple of data sets containing sentiment analysis for various companies:\n",
    "   >  https://www.kaggle.com/vivekrathi055/sentiment-analysis-on-financial-tweets \n",
    "   >  https://www.kaggle.com/yash612/stockmarket-sentiment-dataset\n",
    "    \n",
    "> To determine the effects that Twitter posts have on stock prices, we plan on combining one of these data sets with historical stock price data that can be pulled from Yahoo Finance.  Yahoo Finance provides data for stock price fluctuations for every company and every day.  We expect that the changes in public sentiment over time will somewhat mirror the changes in the stock price as provided by Yahoo Finance.    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaboration Plan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We have and will continue to meet via Zoom biweekly, and possibly more frequently as needed to satisfy approaching deadlines. We acknowledge that this will likely change depending on what phase of the project we are in.\n",
    "\n",
    "### Technologies:\n",
    "- Github to coordinate code\n",
    "- Shared Google Docs for write ups\n",
    "- Twitter API\n",
    "- RapidAPI (For Yahoo Finance data)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup up and import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "import yfinance as yf\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>symbols</th>\n",
       "      <th>company_names</th>\n",
       "      <th>url</th>\n",
       "      <th>verified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VIDEO: “I was in my office. I was minding my o...</td>\n",
       "      <td>Wed Jul 18 21:33:26 +0000 2018</td>\n",
       "      <td>GoldmanSachs</td>\n",
       "      <td>GS</td>\n",
       "      <td>The Goldman Sachs</td>\n",
       "      <td>https://twitter.com/i/web/status/1019696670777...</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The price of lumber $LB_F is down 22% since hi...</td>\n",
       "      <td>Wed Jul 18 22:22:47 +0000 2018</td>\n",
       "      <td>StockTwits</td>\n",
       "      <td>M</td>\n",
       "      <td>Macy's</td>\n",
       "      <td>https://twitter.com/i/web/status/1019709091038...</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Who says the American Dream is dead? https://t...</td>\n",
       "      <td>Wed Jul 18 22:32:01 +0000 2018</td>\n",
       "      <td>TheStreet</td>\n",
       "      <td>AIG</td>\n",
       "      <td>American</td>\n",
       "      <td>https://buff.ly/2L3kmc4</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barry Silbert is extremely optimistic on bitco...</td>\n",
       "      <td>Wed Jul 18 22:52:52 +0000 2018</td>\n",
       "      <td>MarketWatch</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>https://twitter.com/i/web/status/1019716662587...</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How satellites avoid attacks and space junk wh...</td>\n",
       "      <td>Wed Jul 18 23:00:01 +0000 2018</td>\n",
       "      <td>Forbes</td>\n",
       "      <td>ORCL</td>\n",
       "      <td>Oracle</td>\n",
       "      <td>http://on.forbes.com/6013DqDDU</td>\n",
       "      <td>TRUE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                                text  \\\n",
       "0  VIDEO: “I was in my office. I was minding my o...   \n",
       "1  The price of lumber $LB_F is down 22% since hi...   \n",
       "2  Who says the American Dream is dead? https://t...   \n",
       "3  Barry Silbert is extremely optimistic on bitco...   \n",
       "4  How satellites avoid attacks and space junk wh...   \n",
       "\n",
       "                        timestamp        source symbols      company_names  \\\n",
       "0  Wed Jul 18 21:33:26 +0000 2018  GoldmanSachs      GS  The Goldman Sachs   \n",
       "1  Wed Jul 18 22:22:47 +0000 2018    StockTwits       M             Macy's   \n",
       "2  Wed Jul 18 22:32:01 +0000 2018     TheStreet     AIG           American   \n",
       "3  Wed Jul 18 22:52:52 +0000 2018   MarketWatch     BTC            Bitcoin   \n",
       "4  Wed Jul 18 23:00:01 +0000 2018        Forbes    ORCL             Oracle   \n",
       "\n",
       "                                                 url verified  \n",
       "0  https://twitter.com/i/web/status/1019696670777...     TRUE  \n",
       "1  https://twitter.com/i/web/status/1019709091038...     TRUE  \n",
       "2                            https://buff.ly/2L3kmc4     TRUE  \n",
       "3  https://twitter.com/i/web/status/1019716662587...     TRUE  \n",
       "4                     http://on.forbes.com/6013DqDDU     TRUE  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the data from csv files\n",
    "StockDF = pd.read_csv('TwitterStockData.csv')\n",
    "SentimentDF = pd.read_csv('tweet_sentiment.csv')\n",
    "\n",
    "# declare start and end date for financial data\n",
    "start = datetime.datetime(2018,7,17)\n",
    "end = datetime.datetime(2018,10,25)\n",
    "\n",
    "# read S&P500 data for declared dates\n",
    "SPY500 = web.DataReader('SPY', 'yahoo', start, end)\n",
    "BTC = web.DataReader('BTC-USD', 'yahoo', start, end)\n",
    "\n",
    "StockDF.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cleaned_tweets</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>video offic mind busi david solomon tell gs in...</td>\n",
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       "      <th>1</th>\n",
       "      <td>price lumber lb f sinc hit ytd high maci turna...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>say american dream dead</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>barri silbert extrem optimist bitcoin predict ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>satellit avoid attack space junk circl earth paid</td>\n",
       "      <td>-1</td>\n",
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      ],
      "text/plain": [
       "                                      cleaned_tweets  sentiment\n",
       "0  video offic mind busi david solomon tell gs in...          0\n",
       "1  price lumber lb f sinc hit ytd high maci turna...          0\n",
       "2                            say american dream dead         -1\n",
       "3  barri silbert extrem optimist bitcoin predict ...          1\n",
       "4  satellit avoid attack space junk circl earth paid         -1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
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   ],
   "source": [
    "SentimentDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-17</th>\n",
       "      <td>280.910004</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>278.470001</td>\n",
       "      <td>280.470001</td>\n",
       "      <td>52315500.0</td>\n",
       "      <td>268.572174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>281.179993</td>\n",
       "      <td>280.059998</td>\n",
       "      <td>280.559998</td>\n",
       "      <td>281.059998</td>\n",
       "      <td>44593500.0</td>\n",
       "      <td>269.137207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19</th>\n",
       "      <td>280.739990</td>\n",
       "      <td>279.459991</td>\n",
       "      <td>280.309998</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>61412100.0</td>\n",
       "      <td>268.122101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20</th>\n",
       "      <td>280.480011</td>\n",
       "      <td>279.500000</td>\n",
       "      <td>279.769989</td>\n",
       "      <td>279.679993</td>\n",
       "      <td>82337700.0</td>\n",
       "      <td>267.815704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23</th>\n",
       "      <td>280.429993</td>\n",
       "      <td>279.059998</td>\n",
       "      <td>279.450012</td>\n",
       "      <td>280.200012</td>\n",
       "      <td>47047600.0</td>\n",
       "      <td>268.313629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2018-07-17  280.910004  278.410004  278.470001  280.470001  52315500.0   \n",
       "2018-07-18  281.179993  280.059998  280.559998  281.059998  44593500.0   \n",
       "2018-07-19  280.739990  279.459991  280.309998  280.000000  61412100.0   \n",
       "2018-07-20  280.480011  279.500000  279.769989  279.679993  82337700.0   \n",
       "2018-07-23  280.429993  279.059998  279.450012  280.200012  47047600.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2018-07-17  268.572174  \n",
       "2018-07-18  269.137207  \n",
       "2018-07-19  268.122101  \n",
       "2018-07-20  267.815704  \n",
       "2018-07-23  268.313629  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SPY500.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidying Data\n",
    "\n",
    "> The data we have used here was conveniently formatted in a way that did not require much altering. The data from Kaggle contained two separate CSV files. One for tweet information, and another for sentiment scores for cleaned tweets (a cleaned tweet omits unimportant words). Since sentiment scores are different from tweet information, this is exactly how we hoped the tables would be formatted. We can combine the two tables based on the indexes, as we do below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge the two data frames (tweet info and tweet sentiment) - inner join based on index\n",
    "StockSentimentDF = StockDF.merge(SentimentDF, how='inner', left_index = True, right_index=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> A few columns are added to our dataframe that will be useful when doing our exploratory data analysis.  These useful columns are the tweet's date as a datetime type, how much the stock changed on that day, how much the stock changed on the next day, and the stock's sector.  This information is gathered using the Python Yahoo! Finance library.  In this example, we only consider the first 100 entities because the calls to the Yahoo! Finance library take a considerable amount of time.  However, the data ought to be run with the full data set in the first deliverable.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FNSR: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FNSR: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531112400, endDate = 1531198800\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FNSR: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- BTC: No data found for this date range, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- HRS: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- BTC: No data found for this date range, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- TWX: No data found for this date range, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- TICKER: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- CBS: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FB: No data found for this date range, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FNSR: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FNSR: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- ARRY: Data doesn't exist for startDate = 1531198800, endDate = 1531285200\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FNSR: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- JEC: No data found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "StockSentimentDF = StockSentimentDF.head(100)\n",
    "\n",
    "def getStockData(symbol, date):\n",
    "        if not \"-\" in symbol:\n",
    "            t = yf.download(symbol, start=date, end=date + datetime.timedelta(days=1))\n",
    "            if t.size > 0:\n",
    "                perc_change = (t[\"Open\"].iloc[0] - t[\"Close\"].iloc[0] ) / t[\"Open\"].iloc[0]  * 100\n",
    "                return perc_change\n",
    "            return None\n",
    "        else:\n",
    "            return None\n",
    "        \n",
    "def getSector(symbol):\n",
    "    try:\n",
    "        ticker = yf.Ticker(symbol)\n",
    "        c = ticker.info['sector']\n",
    "        return c\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "def tweetDate(timestamp):\n",
    "    t = datetime.datetime.strptime(timestamp, \"%a %b %d %H:%M:%S +0000 %Y\")\n",
    "    return t.date()\n",
    "\n",
    "StockSentimentDF[\"date\"] = StockSentimentDF.apply(lambda row: tweetDate(row[\"timestamp\"]), axis = 1)\n",
    "\n",
    "StockSentimentDF[\"perc_change\"] = StockSentimentDF.apply(lambda row: getStockData(row[\"symbols\"], row[\"date\"]), axis = 1)\n",
    "StockSentimentDF[\"NextDay_perc_change\"] = StockSentimentDF.apply(lambda row: getStockData(row[\"symbols\"], row[\"date\"] + datetime.timedelta(days=1)), axis = 1)\n",
    "StockSentimentDF[\"sector\"] = StockSentimentDF.apply(lambda row: getSector(row[\"symbols\"]), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tweet Dates\n",
    "> First, we wanted to know the date distribution of our tweets.  What timeframe does our data focus on? To do this, we created a density plot.  Using code from [here](https://stackoverflow.com/questions/31348737/how-to-plot-kernel-density-plot-of-dates-in-pandas), we found that most of our data are from the summer of 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(736650.0, 0, '2017-11-17'),\n",
       " Text(736750.0, 0, '2018-02-25'),\n",
       " Text(736850.0, 0, '2018-06-05'),\n",
       " Text(736950.0, 0, '2018-09-13')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "a = StockSentimentDF.groupby(['date']).count()\n",
    "StockSentimentDF['ordinal'] = [x.toordinal() for x in StockSentimentDF[\"date\"]]\n",
    "\n",
    "ax = StockSentimentDF['ordinal'].plot(kind='kde')\n",
    "x_ticks = ax.get_xticks()\n",
    "ax.set_xticks(x_ticks[::2])\n",
    "xlabels = [datetime.datetime.fromordinal(int(x)).strftime('%Y-%m-%d') for x in x_ticks[::2]]\n",
    "ax.set_xticklabels(xlabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Sectors\n",
    "> Next, we looked at the sector of stocks mentioned in our twitter data.  Our distribution contains several different sectors, which means that users on twitter do not only talk about one or two types of stocks, but rather at interested in a very diverse set of stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sector'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "e = StockSentimentDF.groupby(['sector']).count()\n",
    "e[\"text\"].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of stock tweets per day\n",
    "> Next, we were interested in the number of tweets any given stock gets in one day.  Our set is rather limited, and the most popular symbol-date pairings are shown below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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></th>\n",
       "      <th>text</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>company_names</th>\n",
       "      <th>url</th>\n",
       "      <th>verified</th>\n",
       "      <th>cleaned_tweets</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>perc_change</th>\n",
       "      <th>NextDay_perc_change</th>\n",
       "      <th>sector</th>\n",
       "      <th>ordinal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbols</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FNSR</th>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ODP</th>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DLTR</th>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEC</th>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARRY</th>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    text  timestamp  source  company_names  url  verified  \\\n",
       "symbols date                                                                \n",
       "FNSR    2018-07-09     3          3       3              3    3         3   \n",
       "ODP     2018-07-09     3          3       3              3    2         3   \n",
       "DLTR    2018-07-09     4          4       4              4    2         4   \n",
       "JEC     2018-07-09     8          8       8              8    2         8   \n",
       "ARRY    2018-07-09     9          9       9              9    3         9   \n",
       "\n",
       "                    cleaned_tweets  sentiment  perc_change  \\\n",
       "symbols date                                                 \n",
       "FNSR    2018-07-09               3          3            0   \n",
       "ODP     2018-07-09               3          3            3   \n",
       "DLTR    2018-07-09               4          4            4   \n",
       "JEC     2018-07-09               8          8            0   \n",
       "ARRY    2018-07-09               9          9            0   \n",
       "\n",
       "                    NextDay_perc_change  sector  ordinal  \n",
       "symbols date                                              \n",
       "FNSR    2018-07-09                    0       0        3  \n",
       "ODP     2018-07-09                    3       3        3  \n",
       "DLTR    2018-07-09                    4       4        4  \n",
       "JEC     2018-07-09                    0       0        8  \n",
       "ARRY    2018-07-09                    0       9        9  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = StockSentimentDF.groupby(['symbols', 'date']).count()\n",
    "b.sort_values(\"text\").tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Price Percent Change vs. Average Sentiment\n",
    "\n",
    "> Here, we take the average sentiment from Twitter regarding a particular stock on a particular date, and plot it against the percent change on that day.  We expect to see a positive correlation between the two because if a stock performs well on a given day, we expect general optimism throughout Tweets on that day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation:  -0.1617680688320239\n"
     ]
    },
    {
     "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": [
    "m = StockSentimentDF.groupby(['symbols', 'date']).mean()\n",
    "t = StockSentimentDF.merge(m, on=[\"symbols\", \"perc_change\"])\n",
    "t.plot.scatter(x=\"perc_change\", y=\"sentiment_y\")\n",
    "print(\"Correlation: \", t[\"perc_change\"].corr(t[\"sentiment_y\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Next-Day Price Percent Change vs. Average Sentiment\n",
    "> This is similar to the plot above.  However, instead of looking at the price percent change of the day of the tweet, we are now looking at the price percent change of the the next day.  In essence, a positive correlation means that optimistic tweets might have optimistic effects on a stock on the following day.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation:  0.13078438787074123\n"
     ]
    },
    {
     "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": [
    "m = StockSentimentDF.groupby(['symbols', 'date']).mean()\n",
    "t = StockSentimentDF.merge(m, on=[\"symbols\", \"NextDay_perc_change\"])\n",
    "t.plot.scatter(x=\"NextDay_perc_change\", y=\"sentiment_y\")\n",
    "print(\"Correlation: \", t[\"NextDay_perc_change\"].corr(t[\"sentiment_y\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Market Sentiment vs Bitcoin Sentiment\n",
    "Just for fun!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitcoin Tweet Sentiment'}>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# create a bar graph displaying the number of neutral, positive and negative BTC tweets \n",
    "colors = ['grey','g','r']\n",
    "StockSentimentDF[StockSentimentDF.symbols == 'BTC'].sentiment.value_counts().plot.bar(color = colors, title = \"Bitcoin Tweet Sentiment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Overall Market Tweet Sentiment'}>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# create a bar graph displaying the number of neutral, positive and negative non-BTC tweets (Overall Market)\n",
    "StockSentimentDF[StockSentimentDF.symbols != 'BTC'].sentiment.value_counts().plot.bar(color = colors, title = \"Overall Market Tweet Sentiment\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate daily percent change in new column 'returns'\n",
    "SPY500['returns'] = SPY500['Close'].pct_change(1)\n",
    "BTC['returns'] = BTC['Close'].pct_change(1)\n",
    "\n",
    "# plot the daily returns for BTC and SPY500\n",
    "SPY500['returns'].plot(kind = 'line', title = \"Daily Returns BTC vs S&P500\", label = 'SPY500', figsize = (15,6))\n",
    "BTC['returns'].plot(kind = 'line', title = \"Daily Returns BTC vs S&P500\", label = 'BTC-USD', figsize = (15,6))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate cumulative returns in a new column ''daily_cumulative_return'\n",
    "SPY500['daily_cumulative_return'] = ( 1 + SPY500['returns'] ).cumprod()\n",
    "BTC['daily_cumulative_return'] = ( 1 + BTC['returns'] ).cumprod()\n",
    "\n",
    "# plot the cumulative returns for BTC and SPY500\n",
    "SPY500['daily_cumulative_return'].plot(label='SPY500',figsize= (16,8))\n",
    "BTC['daily_cumulative_return'].plot(label = 'BTC-USD', title = \"Cumulative Returns BTC vs S&P500\")\n",
    "plt.legend();\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}