etf_analyzer.ipynb

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    "# Create a Web Application for an ETF Analyzer\n",
    "\n",
    "In this Challenge assignment, you’ll build a financial database and web application by using SQL, Python, and the Voilà library to analyze the performance of a hypothetical fintech ETF.\n",
    "\n",
    "Instructions: \n",
    "\n",
    "Use this notebook to complete your analysis of a fintech ETF that consists of four stocks: GOST, GS, PYPL, and SQ. Each stock has its own table in the `etf.db` database, which the `Starter_Code` folder also contains.\n",
    "\n",
    "Analyze the daily returns of the ETF stocks both individually and as a whole. Then deploy the visualizations to a web application by using the Voilà library.\n",
    "\n",
    "The detailed instructions are divided into the following parts:\n",
    "\n",
    "* Analyze a single asset in the ETF\n",
    "\n",
    "* Optimize data access with Advanced SQL queries\n",
    "\n",
    "* Analyze the ETF portfolio\n",
    "\n",
    "* Deploy the notebook as a web application\n",
    "\n",
    "#### Analyze a Single Asset in the ETF\n",
    "\n",
    "For this part of the assignment, you’ll use SQL queries with Python, Pandas, and hvPlot to analyze the performance of a single asset from the ETF.\n",
    "\n",
    "Complete the following steps:\n",
    "\n",
    "1. Write a SQL `SELECT` statement by using an f-string that reads all the PYPL data from the database. Using the SQL `SELECT` statement, execute a query that reads the PYPL data from the database into a Pandas DataFrame.\n",
    "\n",
    "2. Use the `head` and `tail` functions to review the first five and the last five rows of the DataFrame. Make a note of the beginning and end dates that are available from this dataset. You’ll use this information to complete your analysis.\n",
    "\n",
    "3. Using hvPlot, create an interactive visualization for the PYPL daily returns. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability.\n",
    "\n",
    "4. Using hvPlot, create an interactive visualization for the PYPL cumulative returns. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability.\n",
    "\n",
    "#### Optimize Data Access with Advanced SQL Queries\n",
    "\n",
    "For this part of the assignment, you’ll continue to analyze a single asset (PYPL) from the ETF. You’ll use advanced SQL queries to optimize the efficiency of accessing data from the database.\n",
    "\n",
    "Complete the following steps:\n",
    "\n",
    "1. Access the closing prices for PYPL that are greater than 200 by completing the following steps:\n",
    "\n",
    "    - Write a SQL `SELECT` statement to select the dates where the PYPL closing price was higher than 200.0.\n",
    "\n",
    "    - Using the SQL statement, read the data from the database into a Pandas DataFrame, and then review the resulting DataFrame.\n",
    "\n",
    "    - Select the “time” and “close” columns for those dates where the closing price was higher than 200.0.\n",
    "\n",
    "2. Find the top 10 daily returns for PYPL by completing the following steps:\n",
    "\n",
    "    -  Write a SQL statement to find the top 10 PYPL daily returns. Make sure to do the following:\n",
    "\n",
    "        * Use `SELECT` to select only the “time” and “daily_returns” columns.\n",
    "\n",
    "        * Use `ORDER` to sort the results in descending order by the “daily_returns” column.\n",
    "\n",
    "        * Use `LIMIT` to limit the results to the top 10 daily return values.\n",
    "\n",
    "    - Using the SQL statement, read the data from the database into a Pandas DataFrame, and then review the resulting DataFrame.\n",
    "\n",
    "#### Analyze the ETF Portfolio\n",
    "\n",
    "For this part of the assignment, you’ll build the entire ETF portfolio and then evaluate its performance. To do so, you’ll build the ETF portfolio by using SQL joins to combine all the data for each asset.\n",
    "\n",
    "Complete the following steps:\n",
    "\n",
    "1. Write a SQL query to join each table in the portfolio into a single DataFrame. To do so, complete the following steps:\n",
    "\n",
    "    - Use a SQL inner join to join each table on the “time” column. Access the “time” column in the `GDOT` table via the `GDOT.time` syntax. Access the “time” columns from the other tables via similar syntax.\n",
    "\n",
    "    - Using the SQL query, read the data from the database into a Pandas DataFrame. Review the resulting DataFrame.\n",
    "\n",
    "2. Create a DataFrame that averages the “daily_returns” columns for all four assets. Review the resulting DataFrame.\n",
    "\n",
    "    > **Hint** Assuming that this ETF contains equally weighted returns, you can average the returns for each asset to get the average returns of the portfolio. You can then use the average returns of the portfolio to calculate the annualized returns and the cumulative returns. For the calculation to get the average daily returns for the portfolio, use the following code:\n",
    "    >\n",
    "    > ```python\n",
    "    > etf_portfolio_returns = etf_portfolio['daily_returns'].mean(axis=1)\n",
    "    > ```\n",
    "    >\n",
    "    > You can use the average daily returns of the portfolio the same way that you used the daily returns of a single asset.\n",
    "\n",
    "3. Use the average daily returns in the `etf_portfolio_returns` DataFrame to calculate the annualized returns for the portfolio. Display the annualized return value of the ETF portfolio.\n",
    "\n",
    "> **Hint**  To calculate the annualized returns, multiply the mean of the `etf_portfolio_returns` values by 252.\n",
    ">\n",
    "> To convert the decimal values to percentages, multiply the results by 100.\n",
    "\n",
    "4. Use the average daily returns in the `etf_portfolio_returns` DataFrame to calculate the cumulative returns of the ETF portfolio.\n",
    "\n",
    "5. Using hvPlot, create an interactive line plot that visualizes the cumulative return values of the ETF portfolio. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability.\n",
    "\n",
    "#### Deploy the Notebook as a Web Application\n",
    "\n",
    "For this part of the assignment, complete the following steps:\n",
    "\n",
    "1. Use the Voilà library to deploy your notebook as a web application. You can deploy the web application locally on your computer.\n",
    "\n",
    "2. Take a screen recording or screenshots to show how the web application appears when using Voilà. Include the recording or screenshots in the `README.md` file for your GitHub repository.\n"
   ]
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   "source": [
    "## Review the following code which imports the required libraries, initiates your SQLite database, popluates the database with records from the `etf.db` seed file that was included in your Starter_Code folder, creates the database engine, and confirms that data tables that it now contains."
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GDOT', 'GS', 'PYPL', 'SQ']"
      ]
     },
     "execution_count": 3,
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   "source": [
    "# Importing the required libraries and dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import hvplot.pandas\n",
    "import sqlalchemy\n",
    "import warnings # This is used to ignore random warnings from cells, this will not affect any of the code. \n",
    "warnings.filterwarnings(\"ignore\") \n",
    "\n",
    "# Create a temporary SQLite database and populate the database with content from the etf.db seed file\n",
    "database_connection_string = 'sqlite:///etf.db'\n",
    "\n",
    "# Create an engine to interact with the SQLite database\n",
    "engine = sqlalchemy.create_engine(database_connection_string)\n",
    "\n",
    "# Confirm that table names contained in the SQLite database.\n",
    "engine.table_names()"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Analyze a single asset in the FinTech ETF\n",
    "\n",
    "For this part of the assignment, you’ll use SQL queries with Python, Pandas, and hvPlot to analyze the performance of a single asset from the ETF.\n",
    "\n",
    "Complete the following steps:\n",
    "\n",
    "1. Write a SQL `SELECT` statement by using an f-string that reads all the PYPL data from the database. Using the SQL `SELECT` statement, execute a query that reads the PYPL data from the database into a Pandas DataFrame.\n",
    "\n",
    "2. Use the `head` and `tail` functions to review the first five and the last five rows of the DataFrame. Make a note of the beginning and end dates that are available from this dataset. You’ll use this information to complete your analysis.\n",
    "\n",
    "3. Using hvPlot, create an interactive visualization for the PYPL daily returns. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability.\n",
    "\n",
    "4. Using hvPlot, create an interactive visualization for the PYPL cumulative returns. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Step 1: Write a SQL `SELECT` statement by using an f-string that reads all the PYPL data from the database. Using the SQL `SELECT` statement, execute a query that reads the PYPL data from the database into a Pandas DataFrame."
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Write a SQL query to SELECT all of the data from the PYPL table\n",
    "query = f\"SELECT * FROM 'PYPL';\"\n",
    "\n",
    "# Use the query to read the PYPL data into a Pandas DataFrame\n",
    "pypl_dataframe = pd.read_sql_query(query, con=engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Use the `head` and `tail` functions to review the first five and the last five rows of the DataFrame. Make a note of the beginning and end dates that are available from this dataset. You’ll use this information to complete your analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_returns</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-16 00:00:00.000000</td>\n",
       "      <td>39.90</td>\n",
       "      <td>39.90</td>\n",
       "      <td>39.12</td>\n",
       "      <td>39.32</td>\n",
       "      <td>7298861</td>\n",
       "      <td>-0.005564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-19 00:00:00.000000</td>\n",
       "      <td>39.40</td>\n",
       "      <td>39.80</td>\n",
       "      <td>39.11</td>\n",
       "      <td>39.45</td>\n",
       "      <td>3436478</td>\n",
       "      <td>0.003306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-20 00:00:00.000000</td>\n",
       "      <td>39.61</td>\n",
       "      <td>39.74</td>\n",
       "      <td>39.26</td>\n",
       "      <td>39.74</td>\n",
       "      <td>2940991</td>\n",
       "      <td>0.007351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-21 00:00:00.000000</td>\n",
       "      <td>39.84</td>\n",
       "      <td>40.74</td>\n",
       "      <td>39.82</td>\n",
       "      <td>40.09</td>\n",
       "      <td>5826704</td>\n",
       "      <td>0.008807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-12-22 00:00:00.000000</td>\n",
       "      <td>40.04</td>\n",
       "      <td>40.09</td>\n",
       "      <td>39.54</td>\n",
       "      <td>39.68</td>\n",
       "      <td>4338385</td>\n",
       "      <td>-0.010227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                         time   open   high    low  close   volume  \\\n",
       "0  2016-12-16 00:00:00.000000  39.90  39.90  39.12  39.32  7298861   \n",
       "1  2016-12-19 00:00:00.000000  39.40  39.80  39.11  39.45  3436478   \n",
       "2  2016-12-20 00:00:00.000000  39.61  39.74  39.26  39.74  2940991   \n",
       "3  2016-12-21 00:00:00.000000  39.84  40.74  39.82  40.09  5826704   \n",
       "4  2016-12-22 00:00:00.000000  40.04  40.09  39.54  39.68  4338385   \n",
       "\n",
       "   daily_returns  \n",
       "0      -0.005564  \n",
       "1       0.003306  \n",
       "2       0.007351  \n",
       "3       0.008807  \n",
       "4      -0.010227  "
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   ],
   "source": [
    "# View the first 5 rows of the DataFrame\n",
    "display(pypl_dataframe.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_returns</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2020-11-30 00:00:00.000000</td>\n",
       "      <td>212.51</td>\n",
       "      <td>215.83</td>\n",
       "      <td>207.0900</td>\n",
       "      <td>214.200</td>\n",
       "      <td>8992681</td>\n",
       "      <td>0.013629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2020-12-01 00:00:00.000000</td>\n",
       "      <td>217.15</td>\n",
       "      <td>220.57</td>\n",
       "      <td>214.3401</td>\n",
       "      <td>216.520</td>\n",
       "      <td>9148174</td>\n",
       "      <td>0.010831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2020-12-02 00:00:00.000000</td>\n",
       "      <td>215.60</td>\n",
       "      <td>215.75</td>\n",
       "      <td>210.5000</td>\n",
       "      <td>212.660</td>\n",
       "      <td>6414746</td>\n",
       "      <td>-0.017827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2020-12-03 00:00:00.000000</td>\n",
       "      <td>213.33</td>\n",
       "      <td>216.93</td>\n",
       "      <td>213.1100</td>\n",
       "      <td>214.680</td>\n",
       "      <td>6463339</td>\n",
       "      <td>0.009499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2020-12-04 00:00:00.000000</td>\n",
       "      <td>214.88</td>\n",
       "      <td>217.28</td>\n",
       "      <td>213.0100</td>\n",
       "      <td>217.235</td>\n",
       "      <td>2118319</td>\n",
       "      <td>0.011901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                           time    open    high       low    close   volume  \\\n",
       "994  2020-11-30 00:00:00.000000  212.51  215.83  207.0900  214.200  8992681   \n",
       "995  2020-12-01 00:00:00.000000  217.15  220.57  214.3401  216.520  9148174   \n",
       "996  2020-12-02 00:00:00.000000  215.60  215.75  210.5000  212.660  6414746   \n",
       "997  2020-12-03 00:00:00.000000  213.33  216.93  213.1100  214.680  6463339   \n",
       "998  2020-12-04 00:00:00.000000  214.88  217.28  213.0100  217.235  2118319   \n",
       "\n",
       "     daily_returns  \n",
       "994       0.013629  \n",
       "995       0.010831  \n",
       "996      -0.017827  \n",
       "997       0.009499  \n",
       "998       0.011901  "
      ]
     },
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    }
   ],
   "source": [
    "# View the last 5 rows of the DataFrame.\n",
    "display(pypl_dataframe.tail())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Using hvPlot, create an interactive visualization for the PYPL daily returns. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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      "text/plain": [
       ":Curve   [time]   (daily_returns)"
      ]
     },
     "execution_count": 5,
     "metadata": {
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       "id": "1002"
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   "source": [
    "# Create an interactive visualization with hvplot to plot the daily returns for PYPL.\n",
    "pypl_dataframe.hvplot(x='time', y='daily_returns', kind='line', title='PYPL Daily Returns', xlabel='Date and Time', ylabel='Daily Returns', color='green')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Using hvPlot, create an interactive visualization for the PYPL cumulative returns. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.005564\n",
       "1     -0.002276\n",
       "2      0.005058\n",
       "3      0.013910\n",
       "4      0.003541\n",
       "         ...   \n",
       "994    4.417299\n",
       "995    4.475974\n",
       "996    4.378351\n",
       "997    4.429439\n",
       "998    4.494057\n",
       "Name: daily_returns, Length: 999, dtype: float64"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#⭐️ Calculate the cummulative returns using the `.cumprod()` function. ⭐️\n",
    "cummulative_returns_df = (1 + pypl_dataframe['daily_returns']).cumprod() - 1\n",
    "\n",
    "# Add the cummulative returns to the pypl_dataframe\n",
    "pypl_dataframe['cummulative_returns_df'] = cummulative_returns_df\n",
    "\n",
    "# Call the dataframe\n",
    "cummulative_returns_df"
   ]
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   "execution_count": 7,
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      "text/plain": [
       ":Curve   [time]   (cummulative_returns_df)"
      ]
     },
     "execution_count": 7,
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   "source": [
    "# Create an interactive visaulization with hvplot to plot the cumulative returns for PYPL.\n",
    "pypl_dataframe.hvplot(x='time', y='cummulative_returns_df', kind='line', title='PYPL Cummulative Returns', xlabel='Date and Time', ylabel='Cummulative Returns', color='purple')\n"
   ]
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   "source": [
    "## Optimize the SQL Queries\n",
    "\n",
    "For this part of the assignment, you’ll continue to analyze a single asset (PYPL) from the ETF. You’ll use advanced SQL queries to optimize the efficiency of accessing data from the database.\n",
    "\n",
    "Complete the following steps:\n",
    "\n",
    "1. Access the closing prices for PYPL that are greater than 200 by completing the following steps:\n",
    "\n",
    "1. Access the closing prices for PYPL that are greater than 200 by completing the following steps:\n",
    "\n",
    "    - Write a SQL `SELECT` statement to select the dates where the PYPL closing price was higher than 200.0.\n",
    "\n",
    "    - Select the “time” and “close” columns for those dates where the closing price was higher than 200.0.\n",
    "\n",
    "    - Using the SQL statement, read the data from the database into a Pandas DataFrame, and then review the resulting DataFrame.\n",
    "\n",
    "2. Find the top 10 daily returns for PYPL by completing the following steps:\n",
    "\n",
    "    -  Write a SQL statement to find the top 10 PYPL daily returns. Make sure to do the following:\n",
    "\n",
    "        * Use `SELECT` to select only the “time” and “daily_returns” columns.\n",
    "\n",
    "        * Use `ORDER` to sort the results in descending order by the “daily_returns” column.\n",
    "\n",
    "        * Use `LIMIT` to limit the results to the top 10 daily return values.\n",
    "\n",
    "    - Using the SQL statement, read the data from the database into a Pandas DataFrame, and then review the resulting DataFrame.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Access the closing prices for PYPL that are greater than 200 by completing the following steps:\n",
    "\n",
    "    - Write a SQL `SELECT` statement to select the dates where the PYPL closing price was higher than 200.0.\n",
    "\n",
    "    - Select the “time” and “close” columns for those dates where the closing price was higher than 200.0.\n",
    "\n",
    "    - Using the SQL statement, read the data from the database into a Pandas DataFrame, and then review the resulting DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>close</th>\n",
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       "      <th>0</th>\n",
       "      <td>2016-12-16 00:00:00.000000</td>\n",
       "      <td>39.320</td>\n",
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       "      <th>1</th>\n",
       "      <td>2016-12-19 00:00:00.000000</td>\n",
       "      <td>39.450</td>\n",
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       "      <td>2016-12-20 00:00:00.000000</td>\n",
       "      <td>39.740</td>\n",
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       "      <th>3</th>\n",
       "      <td>2016-12-21 00:00:00.000000</td>\n",
       "      <td>40.090</td>\n",
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       "      <th>4</th>\n",
       "      <td>2016-12-22 00:00:00.000000</td>\n",
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       "      <td>2020-11-30 00:00:00.000000</td>\n",
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       "      <td>2020-12-01 00:00:00.000000</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2020-12-02 00:00:00.000000</td>\n",
       "      <td>212.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2020-12-03 00:00:00.000000</td>\n",
       "      <td>214.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2020-12-04 00:00:00.000000</td>\n",
       "      <td>217.235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time    close\n",
       "0    2016-12-16 00:00:00.000000   39.320\n",
       "1    2016-12-19 00:00:00.000000   39.450\n",
       "2    2016-12-20 00:00:00.000000   39.740\n",
       "3    2016-12-21 00:00:00.000000   40.090\n",
       "4    2016-12-22 00:00:00.000000   39.680\n",
       "..                          ...      ...\n",
       "994  2020-11-30 00:00:00.000000  214.200\n",
       "995  2020-12-01 00:00:00.000000  216.520\n",
       "996  2020-12-02 00:00:00.000000  212.660\n",
       "997  2020-12-03 00:00:00.000000  214.680\n",
       "998  2020-12-04 00:00:00.000000  217.235\n",
       "\n",
       "[999 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write a SQL SELECT statement to select the time and close columns \n",
    "# where the PYPL closing price was higher than 200.0.\n",
    "query = \"\"\"\n",
    "SELECT time, close FROM 'PYPL'\n",
    "WHERE 'PYPL' > 200;\n",
    "\"\"\"\n",
    "\n",
    "# Using the query, read the data from the database into a Pandas DataFrame\n",
    "pypl_higher_than_200 = pd.read_sql_query(query, con=engine)\n",
    "\n",
    "# Review the resulting DataFrame\n",
    "pypl_higher_than_200\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Find the top 10 daily returns for PYPL by completing the following steps:\n",
    "\n",
    "    -  Write a SQL statement to find the top 10 PYPL daily returns. Make sure to do the following:\n",
    "\n",
    "        * Use `SELECT` to select only the “time” and “daily_returns” columns.\n",
    "\n",
    "        * Use `ORDER` to sort the results in descending order by the “daily_returns” column.\n",
    "\n",
    "        * Use `LIMIT` to limit the results to the top 10 daily return values.\n",
    "\n",
    "    - Using the SQL statement, read the data from the database into a Pandas DataFrame, and then review the resulting DataFrame.\n"
   ]
  },
  {
   "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>time</th>\n",
       "      <th>daily_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-24 00:00:00.000000</td>\n",
       "      <td>0.140981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-07 00:00:00.000000</td>\n",
       "      <td>0.140318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-13 00:00:00.000000</td>\n",
       "      <td>0.138700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-06 00:00:00.000000</td>\n",
       "      <td>0.100877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-19 00:00:00.000000</td>\n",
       "      <td>0.093371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-10-24 00:00:00.000000</td>\n",
       "      <td>0.085912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-11-04 00:00:00.000000</td>\n",
       "      <td>0.080986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-03-10 00:00:00.000000</td>\n",
       "      <td>0.080863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-04-22 00:00:00.000000</td>\n",
       "      <td>0.075321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-12-26 00:00:00.000000</td>\n",
       "      <td>0.074656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time  daily_returns\n",
       "0  2020-03-24 00:00:00.000000       0.140981\n",
       "1  2020-05-07 00:00:00.000000       0.140318\n",
       "2  2020-03-13 00:00:00.000000       0.138700\n",
       "3  2020-04-06 00:00:00.000000       0.100877\n",
       "4  2018-10-19 00:00:00.000000       0.093371\n",
       "5  2019-10-24 00:00:00.000000       0.085912\n",
       "6  2020-11-04 00:00:00.000000       0.080986\n",
       "7  2020-03-10 00:00:00.000000       0.080863\n",
       "8  2020-04-22 00:00:00.000000       0.075321\n",
       "9  2018-12-26 00:00:00.000000       0.074656"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write a SQL SELECT statement to select the time and daily_returns columns\n",
    "# Sort the results in descending order and return only the top 10 return values\n",
    "query = \"\"\"\n",
    "SELECT time, daily_returns FROM 'PYPL'\n",
    "ORDER BY daily_returns DESC\n",
    "LIMIT 10;\n",
    "\"\"\"\n",
    "\n",
    "# Using the query, read the data from the database into a Pandas DataFrame\n",
    "pypl_top_10_returns = pd.read_sql_query(query, con=engine)\n",
    "\n",
    "# Review the resulting DataFrame\n",
    "pypl_top_10_returns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the Fintech ETF Portfolio\n",
    "\n",
    "For this part of the assignment, you’ll build the entire ETF portfolio and then evaluate its performance. To do so, you’ll build the ETF portfolio by using SQL joins to combine all the data for each asset.\n",
    "\n",
    "Complete the following steps:\n",
    "\n",
    "1. Write a SQL query to join each table in the portfolio into a single DataFrame. To do so, complete the following steps:\n",
    "\n",
    "    - Use a SQL inner join to join each table on the “time” column. Access the “time” column in the `GDOT` table via the `GDOT.time` syntax. Access the “time” columns from the other tables via similar syntax.\n",
    "\n",
    "    - Using the SQL query, read the data from the database into a Pandas DataFrame. Review the resulting DataFrame.\n",
    "\n",
    "2. Create a DataFrame that averages the “daily_returns” columns for all four assets. Review the resulting DataFrame.\n",
    "\n",
    "    > **Hint** Assuming that this ETF contains equally weighted returns, you can average the returns for each asset to get the average returns of the portfolio. You can then use the average returns of the portfolio to calculate the annualized returns and the cumulative returns. For the calculation to get the average daily returns for the portfolio, use the following code:\n",
    "    >\n",
    "    > ```python\n",
    "    > etf_portfolio_returns = etf_portfolio['daily_returns'].mean(axis=1)\n",
    "    > ```\n",
    "    >\n",
    "    > You can use the average daily returns of the portfolio the same way that you used the daily returns of a single asset.\n",
    "\n",
    "3. Use the average daily returns in the `etf_portfolio_returns` DataFrame to calculate the annualized returns for the portfolio. Display the annualized return value of the ETF portfolio.\n",
    "\n",
    "> **Hint**  To calculate the annualized returns, multiply the mean of the `etf_portfolio_returns` values by 252.\n",
    ">\n",
    "> To convert the decimal values to percentages, multiply the results by 100.\n",
    "\n",
    "4. Use the average daily returns in the `etf_portfolio_returns` DataFrame to calculate the cumulative returns of the ETF portfolio.\n",
    "\n",
    "5. Using hvPlot, create an interactive line plot that visualizes the cumulative return values of the ETF portfolio. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Write a SQL query to join each table in the portfolio into a single DataFrame. To do so, complete the following steps:\n",
    "\n",
    "    - Use a SQL inner join to join each table on the “time” column. Access the “time” column in the `GDOT` table via the `GDOT.time` syntax. Access the “time” columns from the other tables via similar syntax.\n",
    "\n",
    "    - Using the SQL query, read the data from the database into a Pandas DataFrame. Review the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_returns</th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>...</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_returns</th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>daily_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-16 00:00:00.000000</td>\n",
       "      <td>24.41</td>\n",
       "      <td>24.7300</td>\n",
       "      <td>23.9400</td>\n",
       "      <td>23.980</td>\n",
       "      <td>483544</td>\n",
       "      <td>-0.023218</td>\n",
       "      <td>2016-12-16 00:00:00.000000</td>\n",
       "      <td>39.90</td>\n",
       "      <td>39.90</td>\n",
       "      <td>...</td>\n",
       "      <td>238.94</td>\n",
       "      <td>5017963</td>\n",
       "      <td>-0.016708</td>\n",
       "      <td>2016-12-16 00:00:00.000000</td>\n",
       "      <td>14.29</td>\n",
       "      <td>14.4700</td>\n",
       "      <td>14.2300</td>\n",
       "      <td>14.3750</td>\n",
       "      <td>4516341</td>\n",
       "      <td>0.017339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-19 00:00:00.000000</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.0100</td>\n",
       "      <td>23.5500</td>\n",
       "      <td>23.790</td>\n",
       "      <td>288149</td>\n",
       "      <td>-0.007923</td>\n",
       "      <td>2016-12-19 00:00:00.000000</td>\n",
       "      <td>39.40</td>\n",
       "      <td>39.80</td>\n",
       "      <td>...</td>\n",
       "      <td>239.13</td>\n",
       "      <td>2970314</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>2016-12-19 00:00:00.000000</td>\n",
       "      <td>14.34</td>\n",
       "      <td>14.6000</td>\n",
       "      <td>14.3000</td>\n",
       "      <td>14.3600</td>\n",
       "      <td>3944657</td>\n",
       "      <td>-0.001043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-20 00:00:00.000000</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.9400</td>\n",
       "      <td>23.5800</td>\n",
       "      <td>23.820</td>\n",
       "      <td>220341</td>\n",
       "      <td>0.001261</td>\n",
       "      <td>2016-12-20 00:00:00.000000</td>\n",
       "      <td>39.61</td>\n",
       "      <td>39.74</td>\n",
       "      <td>...</td>\n",
       "      <td>243.10</td>\n",
       "      <td>3268700</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>2016-12-20 00:00:00.000000</td>\n",
       "      <td>14.73</td>\n",
       "      <td>14.8200</td>\n",
       "      <td>14.4100</td>\n",
       "      <td>14.4900</td>\n",
       "      <td>5207412</td>\n",
       "      <td>0.009053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-21 00:00:00.000000</td>\n",
       "      <td>23.90</td>\n",
       "      <td>23.9700</td>\n",
       "      <td>23.6900</td>\n",
       "      <td>23.860</td>\n",
       "      <td>249189</td>\n",
       "      <td>0.001679</td>\n",
       "      <td>2016-12-21 00:00:00.000000</td>\n",
       "      <td>39.84</td>\n",
       "      <td>40.74</td>\n",
       "      <td>...</td>\n",
       "      <td>241.42</td>\n",
       "      <td>2604678</td>\n",
       "      <td>-0.006911</td>\n",
       "      <td>2016-12-21 00:00:00.000000</td>\n",
       "      <td>14.45</td>\n",
       "      <td>14.5400</td>\n",
       "      <td>14.2701</td>\n",
       "      <td>14.3800</td>\n",
       "      <td>3901738</td>\n",
       "      <td>-0.007591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-12-22 00:00:00.000000</td>\n",
       "      <td>23.90</td>\n",
       "      <td>24.0100</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>24.005</td>\n",
       "      <td>383139</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>2016-12-22 00:00:00.000000</td>\n",
       "      <td>40.04</td>\n",
       "      <td>40.09</td>\n",
       "      <td>...</td>\n",
       "      <td>240.17</td>\n",
       "      <td>2026506</td>\n",
       "      <td>-0.005178</td>\n",
       "      <td>2016-12-22 00:00:00.000000</td>\n",
       "      <td>14.33</td>\n",
       "      <td>14.3400</td>\n",
       "      <td>13.9301</td>\n",
       "      <td>14.0400</td>\n",
       "      <td>3874004</td>\n",
       "      <td>-0.023644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2020-11-30 00:00:00.000000</td>\n",
       "      <td>55.87</td>\n",
       "      <td>56.0899</td>\n",
       "      <td>53.1100</td>\n",
       "      <td>53.550</td>\n",
       "      <td>361004</td>\n",
       "      <td>-0.043750</td>\n",
       "      <td>2020-11-30 00:00:00.000000</td>\n",
       "      <td>212.51</td>\n",
       "      <td>215.83</td>\n",
       "      <td>...</td>\n",
       "      <td>230.58</td>\n",
       "      <td>2111945</td>\n",
       "      <td>-0.021266</td>\n",
       "      <td>2020-11-30 00:00:00.000000</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.5400</td>\n",
       "      <td>200.2300</td>\n",
       "      <td>210.9800</td>\n",
       "      <td>10485040</td>\n",
       "      <td>-0.007153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2020-12-01 00:00:00.000000</td>\n",
       "      <td>54.00</td>\n",
       "      <td>54.2500</td>\n",
       "      <td>52.0007</td>\n",
       "      <td>53.790</td>\n",
       "      <td>546792</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>2020-12-01 00:00:00.000000</td>\n",
       "      <td>217.15</td>\n",
       "      <td>220.57</td>\n",
       "      <td>...</td>\n",
       "      <td>232.09</td>\n",
       "      <td>1875447</td>\n",
       "      <td>0.006549</td>\n",
       "      <td>2020-12-01 00:00:00.000000</td>\n",
       "      <td>212.56</td>\n",
       "      <td>212.9500</td>\n",
       "      <td>200.5000</td>\n",
       "      <td>203.0000</td>\n",
       "      <td>9911165</td>\n",
       "      <td>-0.037823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2020-12-02 00:00:00.000000</td>\n",
       "      <td>53.20</td>\n",
       "      <td>53.7900</td>\n",
       "      <td>50.9400</td>\n",
       "      <td>52.320</td>\n",
       "      <td>479868</td>\n",
       "      <td>-0.027328</td>\n",
       "      <td>2020-12-02 00:00:00.000000</td>\n",
       "      <td>215.60</td>\n",
       "      <td>215.75</td>\n",
       "      <td>...</td>\n",
       "      <td>237.75</td>\n",
       "      <td>1783965</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>2020-12-02 00:00:00.000000</td>\n",
       "      <td>200.13</td>\n",
       "      <td>204.0596</td>\n",
       "      <td>195.1300</td>\n",
       "      <td>202.1100</td>\n",
       "      <td>9968056</td>\n",
       "      <td>-0.004384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2020-12-03 00:00:00.000000</td>\n",
       "      <td>52.48</td>\n",
       "      <td>54.1600</td>\n",
       "      <td>52.0200</td>\n",
       "      <td>53.760</td>\n",
       "      <td>474175</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>2020-12-03 00:00:00.000000</td>\n",
       "      <td>213.33</td>\n",
       "      <td>216.93</td>\n",
       "      <td>...</td>\n",
       "      <td>235.62</td>\n",
       "      <td>1701862</td>\n",
       "      <td>-0.008959</td>\n",
       "      <td>2020-12-03 00:00:00.000000</td>\n",
       "      <td>207.00</td>\n",
       "      <td>208.6900</td>\n",
       "      <td>204.0500</td>\n",
       "      <td>205.5300</td>\n",
       "      <td>6497836</td>\n",
       "      <td>0.016921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2020-12-04 00:00:00.000000</td>\n",
       "      <td>53.87</td>\n",
       "      <td>54.1900</td>\n",
       "      <td>53.0850</td>\n",
       "      <td>53.860</td>\n",
       "      <td>83596</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>2020-12-04 00:00:00.000000</td>\n",
       "      <td>214.88</td>\n",
       "      <td>217.28</td>\n",
       "      <td>...</td>\n",
       "      <td>238.57</td>\n",
       "      <td>751535</td>\n",
       "      <td>0.012520</td>\n",
       "      <td>2020-12-04 00:00:00.000000</td>\n",
       "      <td>206.29</td>\n",
       "      <td>208.9500</td>\n",
       "      <td>203.5800</td>\n",
       "      <td>207.6164</td>\n",
       "      <td>2820542</td>\n",
       "      <td>0.010151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time   open     high      low   close  volume  \\\n",
       "0    2016-12-16 00:00:00.000000  24.41  24.7300  23.9400  23.980  483544   \n",
       "1    2016-12-19 00:00:00.000000  24.00  24.0100  23.5500  23.790  288149   \n",
       "2    2016-12-20 00:00:00.000000  23.75  23.9400  23.5800  23.820  220341   \n",
       "3    2016-12-21 00:00:00.000000  23.90  23.9700  23.6900  23.860  249189   \n",
       "4    2016-12-22 00:00:00.000000  23.90  24.0100  23.7000  24.005  383139   \n",
       "..                          ...    ...      ...      ...     ...     ...   \n",
       "994  2020-11-30 00:00:00.000000  55.87  56.0899  53.1100  53.550  361004   \n",
       "995  2020-12-01 00:00:00.000000  54.00  54.2500  52.0007  53.790  546792   \n",
       "996  2020-12-02 00:00:00.000000  53.20  53.7900  50.9400  52.320  479868   \n",
       "997  2020-12-03 00:00:00.000000  52.48  54.1600  52.0200  53.760  474175   \n",
       "998  2020-12-04 00:00:00.000000  53.87  54.1900  53.0850  53.860   83596   \n",
       "\n",
       "     daily_returns                        time    open    high  ...   close  \\\n",
       "0        -0.023218  2016-12-16 00:00:00.000000   39.90   39.90  ...  238.94   \n",
       "1        -0.007923  2016-12-19 00:00:00.000000   39.40   39.80  ...  239.13   \n",
       "2         0.001261  2016-12-20 00:00:00.000000   39.61   39.74  ...  243.10   \n",
       "3         0.001679  2016-12-21 00:00:00.000000   39.84   40.74  ...  241.42   \n",
       "4         0.006077  2016-12-22 00:00:00.000000   40.04   40.09  ...  240.17   \n",
       "..             ...                         ...     ...     ...  ...     ...   \n",
       "994      -0.043750  2020-11-30 00:00:00.000000  212.51  215.83  ...  230.58   \n",
       "995       0.004482  2020-12-01 00:00:00.000000  217.15  220.57  ...  232.09   \n",
       "996      -0.027328  2020-12-02 00:00:00.000000  215.60  215.75  ...  237.75   \n",
       "997       0.027523  2020-12-03 00:00:00.000000  213.33  216.93  ...  235.62   \n",
       "998       0.001860  2020-12-04 00:00:00.000000  214.88  217.28  ...  238.57   \n",
       "\n",
       "      volume  daily_returns                        time    open      high  \\\n",
       "0    5017963      -0.016708  2016-12-16 00:00:00.000000   14.29   14.4700   \n",
       "1    2970314       0.000795  2016-12-19 00:00:00.000000   14.34   14.6000   \n",
       "2    3268700       0.016602  2016-12-20 00:00:00.000000   14.73   14.8200   \n",
       "3    2604678      -0.006911  2016-12-21 00:00:00.000000   14.45   14.5400   \n",
       "4    2026506      -0.005178  2016-12-22 00:00:00.000000   14.33   14.3400   \n",
       "..       ...            ...                         ...     ...       ...   \n",
       "994  2111945      -0.021266  2020-11-30 00:00:00.000000  213.00  213.5400   \n",
       "995  1875447       0.006549  2020-12-01 00:00:00.000000  212.56  212.9500   \n",
       "996  1783965       0.024387  2020-12-02 00:00:00.000000  200.13  204.0596   \n",
       "997  1701862      -0.008959  2020-12-03 00:00:00.000000  207.00  208.6900   \n",
       "998   751535       0.012520  2020-12-04 00:00:00.000000  206.29  208.9500   \n",
       "\n",
       "          low     close    volume  daily_returns  \n",
       "0     14.2300   14.3750   4516341       0.017339  \n",
       "1     14.3000   14.3600   3944657      -0.001043  \n",
       "2     14.4100   14.4900   5207412       0.009053  \n",
       "3     14.2701   14.3800   3901738      -0.007591  \n",
       "4     13.9301   14.0400   3874004      -0.023644  \n",
       "..        ...       ...       ...            ...  \n",
       "994  200.2300  210.9800  10485040      -0.007153  \n",
       "995  200.5000  203.0000   9911165      -0.037823  \n",
       "996  195.1300  202.1100   9968056      -0.004384  \n",
       "997  204.0500  205.5300   6497836       0.016921  \n",
       "998  203.5800  207.6164   2820542       0.010151  \n",
       "\n",
       "[999 rows x 28 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wreate a SQL query to join each table in the portfolio into a single DataFrame \n",
    "# Use the time column from each table as the basis for the join\n",
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM GDOT\n",
    "INNER JOIN PYPL ON GDOT.time = PYPL.time\n",
    "INNER JOIN GS ON GDOT.time = GS.time\n",
    "INNER JOIN SQ ON GDOT.time = SQ.time\n",
    "\"\"\"\n",
    "\n",
    "# Using the query, read the data from the database into a Pandas DataFrame\n",
    "etf_portfolio = pd.read_sql_query(query, con=engine)\n",
    "\n",
    "# Review the resulting DataFrame\n",
    "etf_portfolio\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Create a DataFrame that averages the “daily_returns” columns for all four assets. Review the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.007038\n",
       "1     -0.001216\n",
       "2      0.008567\n",
       "3     -0.001004\n",
       "4     -0.008243\n",
       "         ...   \n",
       "994   -0.014635\n",
       "995   -0.003990\n",
       "996   -0.006288\n",
       "997    0.011246\n",
       "998    0.009108\n",
       "Length: 999, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame that displays the mean value of the “daily_returns” columns for all four assets.\n",
    "etf_portfolio_returns = etf_portfolio['daily_returns'].mean(axis=1)\n",
    "# Review the resulting DataFrame\n",
    "etf_portfolio_returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Use the average daily returns in the etf_portfolio_returns DataFrame to calculate the annualized returns for the portfolio. Display the annualized return value of the ETF portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4382727240061359"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the average daily returns provided by the etf_portfolio_returns DataFrame \n",
    "# to calculate the annualized return for the portfolio. \n",
    "annualized_etf_portfolio_returns = etf_portfolio_returns.mean()*252\n",
    "\n",
    "# Display the annualized return value of the ETF portfolio.\n",
    "annualized_etf_portfolio_returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Use the average daily returns in the `etf_portfolio_returns` DataFrame to calculate the cumulative returns of the ETF portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "994    3.374534\n",
       "995    3.357078\n",
       "996    3.329679\n",
       "997    3.378371\n",
       "998    3.418250\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the average daily returns provided by the etf_portfolio_returns DataFrame \n",
    "# to calculate the cumulative returns\n",
    "etf_cumulative_returns = (1 + etf_portfolio_returns).cumprod() - 1\n",
    "\n",
    "# Display the final cumulative return value\n",
    "etf_cumulative_returns.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Using hvPlot, create an interactive line plot that visualizes the cumulative return values of the ETF portfolio. Reflect the “time” column of the DataFrame on the x-axis. Make sure that you professionally style and format your visualization to enhance its readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[999]},\"A_0\":{\"__ndarray__\":\"gJXUqovTfL9AvERo/+KAvwAgTze4bDA/AMCZ3jixSL+AKfOKdmmCv8Bck4lT44S/gC+bm8FFhL/ANi6VS72MvyDTPMCPf5O/AGkjdRQwl78A22oC6fCIvwCPuO9CqH0/QKe6v3LukT/AG8GIv+ChP8CmcHsmG54/wAX/jcWOnD9AohpF8NWfP0DTd1L0EJ4/ICjy9vUUoT/A4j8v9HiVP4BmLg/4IZU/wE+rGCZYkT9Akmn6fg+bP8Bo3E4wYZY/wBhzrJ/Snz8ggxu8rgajP4CoXFPJU6I/wPzM1Ru1mz8AaqZszoeVP4DOKsbTGZM/gKcJo8aChD8AiUuEu/uDP0CYNbDJbpU/wIckOtlzlj/AGQQiM3aWP4B0rGjRtps/oKL2GTGRpD/AYfvuuQGjP6DUJxaxhac/QKNwP+7NqD8AqpxkuYSpP+DzzFz9uKU/AJTqHIywqz9gzbWGfR6wPxCzcpkOS7E/8EvLbtY1wD/I6UBTz2vBP/gz1/RX8cI/qBCcE4kvwD8gpX4k84/BP4i9htHvcME/SGrLcO0pwT+QlQX0AQnBP7Di7ozidcA/OHMTcK84wD+ov2xD0HXBP+BczE8HJ8E/4EgC5Z7owD/g/lEpPcXDP0hCwpgEiMQ/sGDNJbkkxT9AOPmHFhzFP2CDgb0ppsQ/+LjOtamjwD+o4oLBTHXAPzDTo9kwvME/KAFWTr7lwD8Q1TH1PffBP7j53pZUssI/EENzdLZowj9QrDZRZ8/DP2Cb/7F3IsM/+NLcuKcUwj9oC6Mu2B/CP1BYKzMA78A/OE/zmDbdwT/I3DIYaaHAP/iYKf626cE/yCUcC6OWwT8AvOUljaDBP9iDbaFBVMA/8NoKPVEowj8Ah5YAuUPAP/DCYtW/1sA/gNOf6nmJwz+Idmr2p6rCPyjJ77GOgcU/gJVMQuowxz+wSfuDSzvHP7Bbt+MvZsk/MDI/y9pIyT+Ao0CctlTKP3CH1y9ICMw/MOA+X5zhyj+42hJAs63OP/g1wtiHbM4/yKAMy6WgzT/wueJrPQPOP7i1q/K1QdA/nLLSndsE0D+YMuTueyTQP1TwbII6w9A/QFZPbLUk0T8g25N0fADNP1gsW5PHns0/QLpBXchczj/UNZqwJojQP3jaou3jzNE/hMr8zEzz0j8sxX9vRyXTP5g/dbb1WNM/uMqgDIkt0z94x+aa4nHTP6B9Jg1krdQ/QADTFiHe1D/Qp3zRqU7VPzyCYSXTgdY/7PhpVjex1j/oQCuE0yDYP1yQx8KyuNY/pE2WxgHA1j/U5Sm+pLrWPyRwzdTx8NY/VIV1HrlS1z8g7cQvVHvWP7QZl1zyBtg/DBaSETRN1z9UiIhZwcbXPzxsPWYXl9c/GMOENfd+1z8Iui7AP+7WP4ijI8G4wdU/VNzNHTUd2D/8g5fAZavWP4Bo7V7sn9Y/9C0YkElK1j9QpoJemg3YPzxTB3p66tY/tKLAN2Ky1z9sjw1+F3nYP/T9BWOuFdo/uISFwqur2z9sgZ/jJ0ncP6ApRFSUQdw/DD4x19kr3D8ggv0cGIDcP6xv0jSCLtw/VBsePR563D+M81BkTjDcP4xmFHoU89s/iFylsvm23D9cpmh/rHPdPzRgVP4ppdw/9JILs8aj3D+0cVg0KqbcP/xjezg4jt0/vCSr3//L3D+c8Px5cRXbPzQ2I3cK59s/WAkzfoIN3T9I+eg3uoLdP3hYDSYdK+A/kOv3vM1o3j/MrVOgIrrdP7jzcLzgUd8/ZNuFptmP3z+Onta6WgLgP5iEvvQJrN4/UKhkzxvU3j+EWcOZQ3XeP6rBquLnJuA/8pDVVncA4D8QZyPmzD7gP8BHFvWz8t8/pAQi3/bm3z+uffApxQzgP6gJ1d6Hv+A/QvfhKboJ4T8MQ9q+jAbhP4wsBIX0eeA/+k0Xlcd04D+SbdU+CaDgP1T/6SfNr+A/7FEfIR7H4T+MwzmZXdnhP2CO0aIlxuE/zr1tfsUe4j/QBCfhLBXiP1BTvEPnreI/Kv03X68V4z/2NzkfEjXjP24+iAHUNeM/XtpIsYBt4z/wbwe1f1niP8rNZK9+YeI/qDlslPZv4z8S0UmD0LPjPyS15iFA6+M/aDBvCzae5D9qIEvzICDlP8bfYdQ6AeU/LuJGoDVW5j8s9du+Dc7mP2DWH2dF+uY/nMUuSLIT5z/+2VrMuY3nP4hwDzsR8Oc/ZmiUbsi35z9Am5XZX+nnP/b8OezJO+c/evyXkMCs5z8OwBkVL1vnP6qqGMP9qeg/xA2vY39q6D84Ce2yHOToP2ojBd+nN+k/GP+pBDLR6T8KOYVOXFbqP/b2/hvsTeo/jAqLsDht6z+0suBprRbrP7D32NqdP+s/yETiIvKW6z+Aabqo9APsPzLlhpaDLus/APVmD5yl7T/Uq3afNhzuP7g3MPM1Ve4/Miobp5hg7j+sUauxbBXuP+IbQ/Ap9+0/cDyhEfCm7z+W49efvxfwP6pTUYSRYPA/aq86fMEh8T9yQy0sMiDxP9bilgETQ/E/agHUiohR7z8sOXBj+zDwP5DXr757Du4/OteLbO337j8gmrmFs3DuPwjK/Gautew/uuLypDTd7D8CZbvxfF7tP3Apl5viEe4/9PSCK5f87T+sHBz+YTLuP3AXJoARt+4/fvdS/WC+7T9SaDuZ3bDtPzovnk5OUu4/zsRcJfs27z+UW+5j8FzuP0rYPULylu0/YF3RNEwM7j+ABEwZD57tPxBcuFJjdu0/HPsOaSe07T+q0SkTTlDtP7Zo/gKwy+w/vmIjccJe7T+E9MN6vYzuP+QLvFBwM+8/VHj4buOh8D8yG66G14TwP7Inttk0dfA/vMFFowRU8D9wFPxjIMjwP6w0MZewvvA/domA3uM18D+2PEMmFInwP6A2wK2pkfA/UuoHpHhv8T8Oe3BrQC7yP9A2AGaIQPI/3NkCV84I8j8+L/gND2fyP25sOWup1fI/zkSOZwyB8j9gMMGvYVnyP46rn4c/rvI/QNrjwIW68T/Yd8eVs6vwP+zfMy3Gvu4/GFYol3DK7z8W9AxIRqPvP/4ccWDGC+0/fGRVsNzd7T9yTgtnxEbuP4yFTU/XEPA/bOGGgFym8D8Miyn1YVvxPzpI88HNW/E/JHfSg11Y8T/8udzEIgPxP56lcFqODfI/rjn3D8hs8j/EPHzKE+XyPyBiAHAxn/I/ms52rJpa8j9cRvaS+RzyP4AGf0e4PfI/StSI/FAr8z9eZ4ZHbFXzP1aH0cVmj/M/5v9StLn78z9sEVf9y1D0P+hqTPLQk/Q/ZGRMY08L9D9MAS60F7z0P6L5/lMWsvQ/hACV1nbA9D8UlhFyPSL0P0gKddWe+vQ/VAl4YfSw9D8mZcaZ4IvzPwRRUBx7fvI/0tmeJIXr8z9KMlVEanXyP15PzZORffE/6qq798Ei8j9EAp7kcFjxP9YqQx68dPE/5BSSsaOt8T/Iw23NcBPyPyq3E9k/8PA/auTaE88x8T/Qtm7uSQfyP452G93nxfE/1pH+eSl38j/YIUsmjufxPxxnb2jS//E/3usKxrGH8j9Sl3lzLfXyP+g7rA2ur/I/aOpnhfCl8j8MOuHPMkjyP+p7spinyvA/mm4lvBpG8D+GFuQwzPTwPwh/7Bsjy/A/jqjE2/HQ8D+AGKJcV+fwP35becwepvA/DNbbSGbd8D/mdsgJ8gjxP1JW+Mlwp/E/PtbcF3Az8j9aKFjKGbPyPyaDM5AeWfQ/eida/n1I9D+wYMdokEn0P2JZpIyTI/Q/Cq82rIle9D9uVHG/PTL0Pxj9l5vhOvQ/4iWvjADP9D+Y/kJ7VCb0P7i2rWmnT/Q/5tadMSVx9D9g0Yqkilz0P5DMY+RxIvQ/zMgVjOCz9D9QVphlLnL0P9ZDH8jmEfU/yLGBZ4Kx9T+44SebYNz1P5QH5TI2UPY/cJtyJ9Cy9T/UiEKCGCr2PyyEk9xON/Y/fo4sZA+m9j9k9dwQLqj2P9Q9q6Zk5/Y//OXvFt3t9j9ALultJTz3P2IoV7XByPY/Jp/uVclA9z+Iy1UE6xf3P/jfH8OxsPY/Mjnd9klE9T+EIziXunT1PwIw7AglafQ/WCCC9LcI9T8mXtSZKDL1P0JpDZeb0vU/2h4Q1t9m9T/0bFNFqAD2P6Y+Aa1EjfY/vFZjG5AB9z9eKp9eQNL2P2aXXrdS6PY/1vcRisrJ9z/44tdqE2b3P3AH6RShivc/0M99cg769z+Y85+gCT74P9ZAXnt7XPg/fv4ZJhiB+D9guuh+iRL5P3iU3V9TDPk/iH3JKwLF+T8USNS8U2b5P1K5GjQ4Uvg/iln72nop9z/0zT0qOwX3P5xn6zMQj/c/flvRbSe7+D8oBQEVlNX3P4ZLTcTxVfg/jK8aRCGr+D96YRWQMKL4P4D5peBNtvg/KPJqV9u2+D/2GAShTbD4P+rQH28Cb/k/riOzXfa0+D9Q8YRYlyH5P2j+EowZNfk//CQrCYyB+T/+WS0PdNr5PyBZb+qWRvo/wMcIlk5B+j8mVD6SSqn6P3xMt6P///o/3PzeJLJV+z/sqTe+Vif8P8CwfFNaUvw/ztalkQ9y/D+yeVYezs/8P7ztzOIetfs/Gh5S71es+z+4Ryboivr7P5DRVZ2RAfw/PrVCf41t/D+G/DECOTj8P8jEF9StRvw/xGNCl3/T/D88PhEepLT7PwLFnuBJMvw/5Kyr/vOt+z+YOPwFJUT8P+43rSep+fs/SNEE23y8+z+OqG6dPxz9P84UlV6/xPw/asOlkUEP/T+2ZZsLoAD9P1Z7UO64sPw/imOXFZeS/D909QxrvrH8PxInNlKWkPs/OgU097Q++z/EZ4ulu7T5P7D6Z238fPk/Sirphy/l9j+cd4TdunD1P/Ao8F4HofY/yLr25pOL9j+GBxCOpFL4Pxo8K5GDLfg/PBMPvxcu9z8im8l3giL4P3q6k3UARPg/frWy7Eo3+D+EZYKYm0T2P5a0Jm72rPc/dutLyI6+9j+Y8JRiz9H1P1hPK7ZeTvY/IDo5MoLx9z+kVwMOJsL4P1Tv6wjHCPk/OILXvoGY+D+srnE/Z0X5Pw6wYoXfSPs/0M0R0T5H+z/oLMKX7Xr5P2bXG2OeH/c/7GhuiNBF9z/KAXRC3Rn3P6ofAW6iTfg/QCZvhLfT9z+QS5v9KBn1PwryeI7n4vM/9NuZyZm19D9k6iFkdG/0P5h/bq4Ry/U/7L8RiXW99T8eHqYfkHf3P4z+k8ov4fY/qF2JwWsW9z8e7EyZRLj3P7Bd0/d+N/U/6PhMLxS09T9KyPIW92/0P2B01qFqFfU//qKzCgLP9D8kvsa6o3j1P+h2irROV/U/9tPtcMXd9D9EVFglKo/zPwKvLfrcJvQ//hgDpGR48z9IFi+592nyP/J2saYXwfA/sKiaAsEU8D9OP9Ara2byPxZF/vT68/I/2EV5fMSi8j/q26DZeBvzPyaJreJZsfM/KjM8JmUe8j/QAg2GBkv0Pw7zpuqetfQ/GFGFxppB9T+UFS7vIwb2P3QQAEy+S/Y/UAeTmcBg9j+ApPXh8lX2P5qo01F+sPY/mntLHH0M9z/E7pZjrH/3P8hpbg7qVvg/TNpLw3kU9z+0EN/lJnr3P6C4kXDvZ/g/BB3X+FNS+T8yTSjMzsT4P0qJFFlVG/c/0GNzKJ3/9z+4T4fhPyz3P/4i2PooU/c/ZgdTj0id9z/WqBMMbsn3P8xNN9wsh/c/oM/zRJkF9z9YCHtJfzn3PzhVWymBWvc/dHyngp5E+D8yFil4Bj/4P6SHFTawEvg/9pBH1nh3+D9oeszFArj4P77gTnOfjfg/KBNFm4E+9z+YQJ/xTJf3P54DT9ovg/c/cBrQ+hts9z+IvbuyWt33P6D3bOxaE/g/nDl5RFii9z/aAqbkw6D2Pyq0xv1XivY/pt2U40nk9T8Wc4KnU6f1P3YnoIs/xvU/SB5DTCpF9j9KieV4wGH2P+xlujzPnvY/jMjP9tXG9j/MA9b4A+j2P7YzsG7TBfc/wqnyD85p9z9sTfwtbRH3P5Q9b/Up4fc/Bq/EVjpd9j/AUBBBvaf2P+6wvTIylfY//ln8yDFc9j8iRKPIReH2P5LmfHr5DPc/ovMh9THT9z+O3cKKAq73Pxquv0ezDPg/nlOKP+Sr9z/CCIpFBfv3P+YZ0ZlNEfg/UJDKXYDv9z9qPnl5Azr4Pwo+CoRxHPg/mE2QDa2p+D/qvfCeSQf4P2hsJfGg3Pc/ELH4/oYT+D++/5q3Gon3P4KbAMM1ufc/RN34gR7j9z+08KsCm573PzTrUqXiN/g/duUBVxg4+D8U0azYruH4P/ajMGPl0Pg/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       "    var render_items = [{\"docid\":\"f0c67648-f504-4297-a208-476b82cf0f94\",\"root_ids\":[\"1224\"],\"roots\":{\"1224\":\"a9312278-c656-4b8f-b13f-0dd8804e2908\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Curve   [index]   (0)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1224"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using hvplot, create an interactive line plot that visualizes the ETF portfolios cumulative return values.\n",
    "etf_cumulative_returns.hvplot(title='ETF Portfolio Cumulative Returns', color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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