Rendu Big Data - Aurélien Visentin _ Eliot Leclair.ipynb

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    "Aurélien Visentin - 023501\n",
    "\n",
    "Eliot Leclair - 023484"
   ],
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   "source":[
    "## Imports and spark application setup"
   ],
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    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "from pyspark.ml.stat import Correlation\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import col\n",
    "import pyspark.sql.functions as f\n",
    "from pyspark.sql.functions import mean\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.window import Window\n",
    "from pyspark.sql.dataframe import DataFrame\n",
    "from pyspark.ml import Pipeline, Estimator\n",
    "from pyspark.ml.regression import DecisionTreeRegressor, RandomForestRegressor, LinearRegression\n",
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "import requests\n",
    "import seaborn as sns"
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    "def create_spark_application(application_name: str) -> SparkSession:\n",
    "    \"\"\"\n",
    "    Creates a spark application.\n",
    "    \"\"\"\n",
    "    return SparkSession.builder.appName(application_name).getOrCreate()"
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   "source":[
    "if __name__ == \"__main__\":\n",
    "    spark = create_spark_application(\"Spark_stocks_analysis\")"
   ],
   "execution_count":3,
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   "cell_type":"markdown",
   "source":[
    "## Exploration"
   ],
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    "### Summary of all the functions for the Exploration\n",
    "\n",
    "**Functions for loading datasets:**\n",
    "\n",
    "* read_paths(data_paths: dict, stocks_schema: StructType) -> dict:\n",
    "    * Read all paths situated in the dictionary with format { stock_name: path }\n",
    "    * Returns a `dict` with format { stock_name: DataFrame }\n",
    "\n",
    "* load_stocks(root_data_folder_path=\".\/stocks_data\/\") -> dict:\n",
    "    * Reads the default provided stocks into a dict\n",
    "    * Return a dict with a custom StructType\n",
    "\n",
    "**Functions that show simple information about the dataset:**\n",
    "\n",
    "* print_first_last_elements(df, df_name, elements_count=40) -> None:\n",
    "    * Prints the first and last `elements_count` elements of the dataframe\n",
    "\n",
    "* print_number_observations(df, df_name) -> None:\n",
    "    * Prints the number of observations of the dataframe.\n",
    "\n",
    "* get_date_difference(df, colname=\"Date\") -> DataFrame:\n",
    "    * Returns a dataframe that corresponds to the difference in days between each consecutive date.\n",
    "\n",
    "* print_period(df, df_name, colname=\"Date\") -> None:\n",
    "    * Prints the period of the dataframe.\n",
    "\n",
    "* print_dataset_description(df, df_name) -> None:\n",
    "    * Prints descriptive statistics for each column of the `df` dataset\n",
    "\n",
    "* print_missing_values_count(df, df_name) -> None:\n",
    "    * Prints the missing values for each column of the `df` dataset.\n",
    "\n",
    "**Functions that shows advanced information about the dataset:**\n",
    "\n",
    "* compute_corr_matrix(df, numeric_cols: list):\n",
    "    * Compute the correlation matrix of the `df` dataset using the `numeric_cols` columns. \n",
    "\n",
    "* print_corr_matrix(df, df_name, numeric_cols: list) -> None:\n",
    "    * Prints the correlation matrix of the `df` dataset using the `numeric_cols` columns.\n",
    "    \n",
    "* plot_corr_matrixes(dataframes: dict, numeric_cols: list) -> None:\n",
    "    * Creates a plot of the correlation between each dataframe in `dataframes`. \n",
    "    * Use `plt.show()` to display the plot after calling the function.\n",
    "    \n",
    "* get_daily_return_df(df, colname=\"daily_return\") -> DataFrame:\n",
    "    * Returns a copy of the `df` dataframe with a `colname` column corresponding to the daily return of the stocks.\n",
    "    \n",
    "* get_stocks_returns(stocks: dict, colname=\"daily_return\", inplace=False) -> dict:\n",
    "    * Sets the daily returns for each stocks in the `stocks` dictionary in a `colname` column.\n",
    "    \n",
    "* get_column_stats(df, colname) -> DataFrame:\n",
    "    * Creates a dataframe corresponding to the statistics of a numeric column\n",
    "    \n",
    "* print_column_stats(df, df_name, colname) -> None:\n",
    "    * Prints the statistics of a column in the `df` dataset.\n",
    "    \n",
    "* get_moving_average(df, colname=\"High\", day_period=7) -> DataFrame:\n",
    "    * Gets the moving average of the `df` dataset over `period days`\n",
    "    \n",
    "* print_moving_average(df, df_name, colname=\"High\", day_period=7, show_count=5) -> None:\n",
    "    * Prints the moving average of the dataframe.\n",
    "\n",
    "* join_stocks(df1, df2, suffix1, suffix2):\n",
    "    * Join two stocks by the `Date` column with an added suffix for the columns of each dataframe.\n",
    "\n",
    "* correlate_stocks(df1, df2, suffix1, suffix2, cols_to_correlate) -> DataFrame:\n",
    "    * Returns a Dataframe of the correlation between each column of the stocks `df1` and `df2`\n",
    "\n",
    "* print_correlate_stocks(stock_dict, name1, i) -> None:\n",
    "    * Prints the correlation of each matrix between them\n",
    "\n",
    "* get_return_rate_by_period(df, date: str, period:str='week') -> int:\n",
    "    * Gets the return rate on a set date for a defined period (week, month or year).\n",
    "    \n",
    "* get_best_stock_return_rate(stocks: dict, date: str, period:str =\"month\") -> tuple:\n",
    "    * Gets the best return rate on a set date for a defined period (week, month or year). \n",
    "    * Returns the key of the `stocks` dict and its return rate.\n",
    "    \n",
    "* print_return_rate_by_period(df, df_name: str, date: str, period: str=\"month\") -> None:\n",
    "    * Prints the return rate on a set date for a defined period (week, month or year).\n",
    "* print_best_return_rate_by_period(stocks: dict, date: str, period: str) -> None:\n",
    "    * Prints the best return rate on a set date for a defined period (week, month or year)."
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    "### Functions for loading datasets"
   ],
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    "def read_paths(data_paths: dict, stocks_schema: StructType) -> dict:\n",
    "    \"\"\"\n",
    "    Read all paths situated in the dictionary with format { stock_name : path }\n",
    "    :returns a dict with format { stock_name : DataFrame }\n",
    "    \"\"\"\n",
    "    stocks = {}\n",
    "    for k, v in data_paths.items():\n",
    "        current_df = spark.read.csv(v, header=True, schema=stocks_schema)\n",
    "        stocks[k] = current_df\n",
    "\n",
    "    return stocks\n",
    "\n",
    "\n",
    "def load_stocks(root_data_folder_path=\".\/stocks_data\/\") -> dict:\n",
    "    \"\"\"\n",
    "    Reads the default provided stocks into a dict.\n",
    "    \"\"\"\n",
    "\n",
    "    data_paths = {\n",
    "        \"amazon\": root_data_folder_path + \"AMAZON.csv\",\n",
    "        \"apple\": root_data_folder_path + \"APPLE.csv\",\n",
    "        \"facebook\": root_data_folder_path + \"FACEBOOK.csv\",\n",
    "        \"google\": root_data_folder_path + \"GOOGLE.csv\",\n",
    "        \"microsoft\": root_data_folder_path + \"MICROSOFT.csv\",\n",
    "        \"tesla\": root_data_folder_path + \"TESLA.csv\",\n",
    "        \"zoom\": root_data_folder_path + \"ZOOM.csv\"\n",
    "    }\n",
    "\n",
    "    stocks_schema = StructType([\n",
    "        StructField(\"Date\", DateType()),\n",
    "        StructField(\"High\", DoubleType()),\n",
    "        StructField(\"Low\", DoubleType()),\n",
    "        StructField(\"Open\", DoubleType()),\n",
    "        StructField(\"Close\", DoubleType()),\n",
    "        StructField(\"Volume\", DecimalType()),\n",
    "        StructField(\"Adj Close\", DoubleType()),\n",
    "        StructField(\"company_name\", StringType()),\n",
    "    ])\n",
    "\n",
    "    return read_paths(data_paths, stocks_schema)"
   ],
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    "### Functions that show simple information about the dataset"
   ],
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    "def print_first_last_elements(df, df_name, elements_count=40) -> None:\n",
    "    \"\"\"\n",
    "    Prints the first and last `elements_count` elements of the dataframe.\n",
    "    \"\"\"\n",
    "    print(f\"Showing the first and last {elements_count} rows of the {df_name.capitalize()} dataframe:\")\n",
    "    df.show(elements_count)\n",
    "    spark.createDataFrame(df.tail(elements_count)).show(elements_count)\n",
    "\n",
    "def print_number_observations(df, df_name) -> None:\n",
    "    \"\"\"\n",
    "    Prints the number of observations of the dataframe.\n",
    "    \"\"\"\n",
    "    print(f\"The {df_name.capitalize()} dataframe contains {df.count()} observations.\")\n",
    "    print()\n",
    "\n",
    "def get_date_difference(df, colname=\"Date\") -> DataFrame:\n",
    "    \"\"\"\n",
    "    Returns a dataframe that corresponds to the difference in days between each consecutive date.\n",
    "    \"\"\"\n",
    "    window = Window.partitionBy(f.lit(0)).orderBy(f.col(colname).asc())\n",
    "\n",
    "    return df.withColumn(colname, f.datediff(f.col(colname), f.lag(f.col(colname), 1).over(window)))\n",
    "\n",
    "def print_period(df, df_name, colname=\"Date\") -> None:\n",
    "    \"\"\"\n",
    "    Prints the period of the dataframe.\n",
    "    \"\"\"\n",
    "    date_diff_df = get_date_difference(df, colname)\n",
    "    period_mat = date_diff_df.agg(\n",
    "        f.percentile_approx(colname, 0.5).alias(\"Median period\"),\n",
    "        f.mean(colname).alias(\"Mean period\"), \n",
    "    ).collect()\n",
    "\n",
    "    print(f\"The period between points of the {df_name.capitalize()} dataset is:\")\n",
    "    print(f\"\\t{period_mat[0][0]} days (median)\")\n",
    "    print(f\"\\t{period_mat[0][1]} days (on average)\")\n",
    "    print()\n",
    "\n",
    "def print_dataset_description(df, df_name) -> None:\n",
    "    \"\"\"\n",
    "    Prints descriptive statistics for each column of the `df` dataset \n",
    "    \"\"\"\n",
    "    print(f\"Statistics for {df_name.capitalize()} dataframe:\")\n",
    "    df.describe().show()\n",
    "\n",
    "def print_missing_values_count(df, df_name) -> None:\n",
    "    \"\"\"\n",
    "    Prints the missing values for each column of the `df` dataset. \n",
    "    \"\"\"\n",
    "    print(f\"Missing values for {df_name.capitalize()}:\")\n",
    "    df.select(*[\n",
    "        (\n",
    "            f.count(f.when((f.isnan(c) | f.col(c).isNull()), c)) if t not in (\"timestamp\", \"date\")\n",
    "            else f.count(f.when(f.col(c).isNull(), c))\n",
    "        ).alias(c)\n",
    "        for c, t in df.dtypes if c in df.columns\n",
    "    ]).show()"
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    "### Functions that shows advanced information about the dataset."
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    "\n",
    "def compute_corr_matrix(df, numeric_cols: list):\n",
    "    \"\"\"\n",
    "    Compute the correlation matrix of the `df` dataset using the `numeric_cols` columns. \n",
    "    \"\"\"\n",
    "    # convert to vector column first\n",
    "    vector_col = \"corr_features\"\n",
    "    assembler = VectorAssembler(inputCols=numeric_cols, outputCol=vector_col)\n",
    "    df_vector = assembler.transform(df).select(vector_col)\n",
    "\n",
    "    # get correlation matrix\n",
    "    matrix = Correlation.corr(df_vector, vector_col)\n",
    "    cor_np = matrix.collect()[0][matrix.columns[0]].toArray()\n",
    "    return cor_np\n",
    "\n",
    "def print_corr_matrix(df, df_name, numeric_cols: list) -> None:\n",
    "    \"\"\"\n",
    "    Prints the correlation matrix of the `df` dataset using the `numeric_cols` columns.\n",
    "    \"\"\"\n",
    "    mat = compute_corr_matrix(df, numeric_cols)\n",
    "    print(f\"Correlation matrix for {df_name.capitalize()}\")\n",
    "    print(f\"Columns are: {numeric_cols}\")\n",
    "    print(mat)\n",
    "    print()\n",
    "\n",
    "def plot_corr_matrixes(dataframes: dict, numeric_cols: list) -> None:\n",
    "    \"\"\"\n",
    "    Creates a plot of the correlation between each dataframe in `dataframes`. \n",
    "    Use `plt.show()` to display the plot after calling the function.\n",
    "    \"\"\"\n",
    "    fig, axs = plt.subplots(len(dataframes), 1, figsize=(5, 15), layout='constrained')\n",
    "\n",
    "    for i, df_name in enumerate(dataframes):\n",
    "        mat = compute_corr_matrix(dataframes[df_name], numeric_cols)\n",
    "        upp_mat = np.triu(mat)\n",
    "        sns.heatmap(mat, vmin = -1, vmax = +1, annot = True, cmap = 'Blues', mask = upp_mat, ax=axs[i], label=df_name)\n",
    "        axs[i].set_title(df_name.capitalize(), size = 15)\n",
    "\n",
    "\n",
    "def get_daily_return_df(df, colname=\"daily_return\") -> DataFrame:\n",
    "    \"\"\"\n",
    "    Returns a copy of the `df` dataframe with a `colname` column corresponding to the daily return of the stocks.\n",
    "    \"\"\"\n",
    "    return df.withColumn(colname, df[\"Close\"] - df[\"Open\"])\n",
    "\n",
    "def get_stocks_returns(stocks: dict, colname=\"daily_return\", inplace=False) -> dict:\n",
    "    \"\"\"\n",
    "    Sets the daily returns for each stocks in the `stocks` dictionary in a `colname` column.\n",
    "    \"\"\"\n",
    "    res = stocks\n",
    "    if not inplace:\n",
    "        res = dict()\n",
    "    for name in stocks:\n",
    "        res[name] = get_daily_return_df(stocks[name], colname)\n",
    "    return res\n",
    "\n",
    "def get_column_stats(df, colname) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Creates a dataframe corresponding to the statistics of a numeric column\n",
    "    \"\"\"\n",
    "    return df.agg(*[\n",
    "            f.first(\"company_name\").alias(\"company_name\"),\n",
    "            f.mean(colname).alias(f\"mean({colname})\"),\n",
    "            f.percentile_approx(colname, 0.5).alias(f\"median({colname})\"),\n",
    "            f.stddev(colname).alias(f\"stddev({colname})\"),\n",
    "        ])\n",
    "\n",
    "def print_column_stats(df, df_name, colname) -> None:\n",
    "    \"\"\"\n",
    "    Prints the statistics of a column in the `df` dataset.\n",
    "    \"\"\"\n",
    "    print(f\"The statistics of the {colname} column in the {df_name.capitalize()} dataset are:\")\n",
    "    get_column_stats(df, colname).show()"
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    "### Moving average"
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    "DAY_IN_SECONDS = 86400 # 24 * 60 * 60\n",
    "\n",
    "def get_moving_average(df, colname=\"High\", day_period=7) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Gets the moving average of the `df` dataset over `period days`\n",
    "    \"\"\"\n",
    "    # window = Window.orderBy(col('Date').cast('timestamp').cast('long'))\n",
    "    window = Window.partitionBy(f.lit(0)).orderBy(col('Date').cast('timestamp').cast('long'))\n",
    "    window.rangeBetween(-(day_period * DAY_IN_SECONDS) - 1, 0)\n",
    "    df_ma = df.select('*')\n",
    "    return df_ma.select(col('*'), mean(colname).over(window).alias('Moving_average'))\n",
    "\n",
    "def print_moving_average(df, df_name, colname=\"High\", day_period=7, show_count=5) -> None:\n",
    "    \"\"\"\n",
    "    Prints the moving average of the dataframe.\n",
    "    \"\"\"\n",
    "    moving_average_df = get_moving_average(df, colname, day_period)\n",
    "    print(f\"The moving average of the {df_name} dataframe on {day_period} days is:\")\n",
    "    moving_average_df.show(show_count)"
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    "### Correlation"
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   "source":[
    "def join_stocks(df1, df2, suffix1, suffix2) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Join two stocks by the `Date` column with an added suffix for the columns of each dataframe.\n",
    "    \"\"\"\n",
    "    df1_r = df1.select(*(col(x).alias(x + suffix1) for x in df1.columns))\n",
    "    df2_r = df2.select(*(col(x).alias(x + suffix2) for x in df2.columns))\n",
    "    joined_df = df1_r.join(df2_r, on=df1_r[\"Date\" + suffix1] == df2_r[\"Date\" + suffix2])\n",
    "    # Cleaning memory\n",
    "    df1_r.unpersist(False)\n",
    "    df2_r.unpersist(False)\n",
    "\n",
    "    return joined_df\n",
    "\n",
    "def correlate_stocks(df1, df2, suffix1, suffix2, cols_to_correlate) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Returns a Dataframe of the correlation between each column of the stocks `df1` and `df2`\n",
    "    \"\"\"\n",
    "    joined_df = join_stocks(df1, df2, suffix1, suffix2)\n",
    "    res = []\n",
    "    cols1 = [f\"{c}{suffix1}\" for c in cols_to_correlate]\n",
    "    cols2 = [f\"{c}{suffix2}\" for c in cols_to_correlate]\n",
    "    for c1 in cols1:\n",
    "        current = [c1] + [joined_df.corr(c1, c2) for c2 in cols2]\n",
    "        res.append(current)\n",
    "    # Cleaning memory\n",
    "    joined_df.unpersist(False)\n",
    "    return spark.createDataFrame(res, [\"Col\" + suffix1] + cols2)\n",
    "\n",
    "def print_correlate_stocks(stocks_dict, name, idx, numeric_cols) -> None:\n",
    "    \"\"\"\n",
    "    Prints the correlation of each matrix between them\n",
    "    \"\"\"\n",
    "    for name2 in list(stocks_dict)[idx + 1:]:\n",
    "        print(f\"========= Correlation between {name.capitalize()} and {name2.capitalize()} =========\")\n",
    "        tmp = correlate_stocks(stocks_dict[f\"{name}\"], stocks_dict[f\"{name2}\"], f\"_{name}\", f\"_{name2}\", numeric_cols)\n",
    "        tmp.show()\n",
    "        tmp.unpersist(False)"
   ],
   "execution_count":8,
   "outputs":[
    
   ],
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    "datalore":{
     "type":"CODE",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
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  },
  {
   "cell_type":"markdown",
   "source":[
    "###  Return rates"
   ],
   "attachments":{
    
   },
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   "cell_type":"code",
   "source":[
    "def get_return_rate_by_period(df, date: str, period:str='week') -> int:\n",
    "    \"\"\"\n",
    "    Gets the return rate on a set date for a defined period (week, month or year).\n",
    "    \"\"\"\n",
    "    periods = {'week': 7, 'month': 30, 'year': 365}\n",
    "    if period not in periods:\n",
    "        raise ValueError(f\"Invalid period: {period}\")\n",
    "\n",
    "    date_col = f.lit(date)\n",
    "\n",
    "    reduced_df = df.where(f.col(\"Date\") >= date_col).where(f.col(\"Date\") < f.date_add(date_col, periods[period]))\n",
    "\n",
    "    return reduced_df.select(f.last(\"Close\") - f.first(\"Close\")).collect()[0][0]\n",
    "\n",
    "\n",
    "def get_best_stock_return_rate(stocks: dict, date: str, period:str =\"month\") -> tuple:\n",
    "    \"\"\"\n",
    "    Gets the best return rate on a set date for a defined period (week, month or year). \n",
    "    Returns the key of the `stocks` dict and its return rate.\n",
    "    \"\"\"\n",
    "    rates = {\n",
    "        name: get_return_rate_by_period(stock, date, period) for name, stock in stocks.items()\n",
    "    }\n",
    "    max_key = max(rates, key=rates.get)\n",
    "    return max_key, rates[max_key]\n",
    "\n",
    "\n",
    "def print_return_rate_by_period(df, df_name: str, date: str, period: str=\"month\") -> None:\n",
    "    \"\"\"\n",
    "    Prints the return rate on a set date for a defined period (week, month or year).\n",
    "    \"\"\"\n",
    "    rate = get_return_rate_by_period(df, date, period)\n",
    "    print(f\"The return rate of the {df_name} dataframe from the {date} on {period} days is: {rate}.\")\n",
    "    print()\n",
    "\n",
    "\n",
    "def print_best_return_rate_by_period(stocks: dict, date: str, period: str) -> None:\n",
    "    \"\"\"\n",
    "    Prints the best return rate on a set date for a defined period (week, month or year). \n",
    "    \"\"\"\n",
    "    stock_name, rate = get_best_stock_return_rate(stocks, date, period)\n",
    "    print(f\"The best return rate from the {date} with a '{period}' period is: {stock_name} with a rate of {rate}.\")\n",
    "    print()"
   ],
   "execution_count":9,
   "outputs":[
    
   ],
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    "datalore":{
     "type":"CODE",
     "hide_input_from_viewers":false,
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  },
  {
   "cell_type":"markdown",
   "source":[
    "### Insights\n",
    "\n",
    "#### Modern Portfolio Theory (MPT):\n",
    "Method to select investments in order to maximize their returns with a low level of risks.\n",
    "\n",
    "#### Risk Parity\n",
    "Find the portfolio that equalizes the risk contribution of portfolio assets\n",
    "\n",
    "#### REST API Call\n",
    "The functions defined below allow us to make a call to a REST API to load data stored online and to be used in spark.\n",
    "\n",
    "#### Bollinger Band:\n",
    "This method helps determine whether prices are high or low on a relative basis.\n",
    "\n",
    "#### Autoregressive Integrated Moving Average (ARIMA):\n",
    "Model using the precedently implemented moving average in stock prices in order to perform regression and predict future evolutions.\n",
    "\n",
    "#### Moving Average Convergence Divergence (MACD):\n",
    "MACD is a trend-following momentum indicator that shows the relationship between two moving averages, a short term and a long term.\n",
    "\n",
    "#### Value at Risk (VaR):\n",
    "A statistic that quantifies the extent of possible financial losses within a firm.\n",
    "\n",
    "#### Aroon oscillator:\n",
    "Describes the general tendency of the stock price evolution, whether it is going up or down."
   ],
   "attachments":{
    
   },
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     "type":"MD",
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  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Modern Portfolio Theory\n",
    "\n",
    "The modern portfolio theory, made by Harry Markowitz, is a method to select investments in order to maximize their returns with a low level of risks.\n",
    "\n",
    "It works by making a weighted sum of the portfolio's investment proportions with their expected returns:\n",
    "\n",
    "`expected_return = investment_ratio_A * expected_returns_A + investment_ratio_B * expected_returns_B + ...`\n",
    "\n",
    "We do not have a portfolio for the moment, but the function below can be used for fake ones.\n",
    "\n",
    "For example, the following code: \n",
    "\n",
    "```python\n",
    "portfolio = spark.createDataFrame(\n",
    "    [\n",
    "        (\"apple\", 10., 0.04),\n",
    "        (\"facebook\", 10., 0.06),\n",
    "        (\"amazon\", 10., 0.1),\n",
    "        (\"microsoft\", 10., 0.14)\n",
    "    ],\n",
    "    schema=portfolio_schema\n",
    ")\n",
    "portfolio.show()\n",
    "\n",
    "returns_ratio = modern_portfolio_theory_expected_returns(portfolio)\n",
    "\n",
    "print(f\"We expect {returns_ratio * 100}% of returns\")\n",
    "```\n",
    "Will output:\n",
    "```\n",
    "+------------+-----------------+---------------+\n",
    "|company_name|investment_amount|expected_return|\n",
    "+------------+-----------------+---------------+\n",
    "|       apple|             10.0|           0.04|\n",
    "|    facebook|             10.0|           0.06|\n",
    "|      amazon|             10.0|            0.1|\n",
    "|   microsoft|             10.0|           0.14|\n",
    "+------------+-----------------+---------------+\n",
    "\n",
    "We expect 8.5% of returns\n",
    "```"
   ],
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   },
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     "type":"MD",
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     "hide_output_from_viewers":false
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  {
   "cell_type":"code",
   "source":[
    "portfolio_schema = StructType([\n",
    "    StructField(\"company_name\", StringType()),\n",
    "    StructField(\"investment_amount\", DoubleType()),\n",
    "    StructField(\"expected_return\", DoubleType()),\n",
    "])\n",
    "\n",
    "def modern_portfolio_theory_expected_returns(portfolio):\n",
    "    \"\"\"\n",
    "    The portfolio must have a schema of portfolio_schema.\n",
    "    \"\"\"\n",
    "    df = portfolio.withColumn(\n",
    "        \"investment_ratio\", \n",
    "        f.col(\"investment_amount\") \/ f.sum(\"investment_amount\").over(Window.partitionBy(f.lit(0)))\n",
    "    )\n",
    "    ratio_df = df.select((f.col(\"investment_ratio\") * col(\"expected_return\")).alias(\"product\")).select(f.sum(\"product\"))\n",
    "    return ratio_df.collect()[0][0] # Only one value"
   ],
   "execution_count":10,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "type":"CODE",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
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   }
  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Risk Parity\n",
    "\n",
    "Risk parity is a portfolio allocation strategy that uses risk to determine allocations across various components of an investment portfolio. The risk parity strategy modifies the modern portfolio theory (MPT) approach to investing through the use of leverage.\n",
    "\n",
    "Risk parity is an advanced portfolio technique often used by hedge funds and sophisticated investors. It requires a complex quantitative methodology, which makes its allocations more advanced than simplified allocation strategies. The goal of risk parity investing is to earn the optimal level of return at the targeted risk level."
   ],
   "attachments":{
    
   },
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    "datalore":{
     "type":"MD",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
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  },
  {
   "cell_type":"markdown",
   "source":[
    "#### REST API Call\n",
    "The functions defined below allow us to make a call to a REST API. It allows us to load data stored online and make queries to endpoints if needed.\n",
    "\n",
    "To use these, we need to create a schema of what the API will return. For example:\n",
    "\n",
    "```python\n",
    "schema = StructType([\n",
    "  StructField(\"message\", StringType(), True),\n",
    "  StructField(\"result\", ArrayType(\n",
    "    StructType([\n",
    "      StructField(\"Column1\", IntegerType()),\n",
    "      StructField(\"Column2\", StringType())\n",
    "    ])\n",
    "  ))\n",
    "])\n",
    "```\n",
    "\n",
    "Next, we call the `create_response_dataframe_from_rest(url, schema, headers=None, body=None)` function with the URL of the endpoint and the schema we created. We can also pass HTTP headers or a JSON Body to make specific requests.\n",
    "\n",
    "The resulting dataframe will be composed of the following columns: `(\"verb\", \"url\", \"headers\", \"body\", \"result\")`\n",
    "\n",
    "The result column will be a structure following our schema. In addition, the call to the API will only be made when the dataset is computed, hence not utilizing resources from the master if the dataset is big. \n",
    "\n",
    "Stocks can contain lots of data, it might not be a good idea to load and store the datasets on the disk and it is sometimes easier to just call an endpoint at times."
   ],
   "attachments":{
    
   },
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   "cell_type":"code",
   "source":[
    "def call_rest_api(verb, url, headers=None, body=None):\n",
    "    \"\"\"\n",
    "    Execute a REST API call to an URL.\n",
    "    Function type and parameters are defined by pyspark.\n",
    "    \"\"\"\n",
    "    if headers is None:\n",
    "        headers = { 'content-type': \"application\/json\" }\n",
    "    if body is None:\n",
    "        body = json.dumps({})\n",
    "\n",
    "    try:\n",
    "        if verb.lower() == \"get\":\n",
    "            res = requests.get(url, data=body, headers=headers)\n",
    "        else:\n",
    "            res = requests.post(url, data=body, headers=headers)\n",
    "            \n",
    "        if res is None or res.status_code != 200:\n",
    "            return None\n",
    "        \n",
    "        return json.loads(res.text)\n",
    "    except Exception as e:\n",
    "        return e\n",
    "\n",
    "\n",
    "def create_response_dataframe_from_rest(url, schema, headers=None, body=None):\n",
    "    \"\"\"\n",
    "    Create a dataframe from a rest API.\n",
    "    It will have the following columns (\"verb\", \"url\", \"headers\", \"body\", \"result\")\n",
    "    The result column will follow the schema passed as a parameter.\n",
    "    The API call will be made only when it is used (lazy, we use a Spark User Defined Function - udf).\n",
    "    \"\"\"\n",
    "    if headers is None:\n",
    "        headers = { 'content-type': \"application\/json\" }\n",
    "    if body is None:\n",
    "        body = json.dumps({})\n",
    "\n",
    "    call_rest_api_udf = f.udf(call_rest_api, schema)\n",
    "\n",
    "    request_api_rows = Row(\"verb\", \"url\", \"headers\", \"body\")\n",
    "\n",
    "    request_df = spark.createDataFrame([\n",
    "            request_api_rows(\"get\", url, headers, body)\n",
    "          ])\n",
    "    \n",
    "    result_df = request_df.withColumn(\n",
    "        \"result\", \n",
    "        call_rest_api_udf(col(\"verb\"), col(\"url\"), col(\"headers\"), col(\"body\"))\n",
    "    )\n",
    "    return result_df"
   ],
   "execution_count":11,
   "outputs":[
    
   ],
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    "datalore":{
     "type":"CODE",
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     "hide_output_from_viewers":false
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  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Bollinger Bands \n",
    "Bollinger Bands are a type of price envelope developed by John BollingerOpens in a new window.   \n",
    "Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price.   \n",
    "This method helps determine whether prices are high or low on a relative basis."
   ],
   "attachments":{
    
   },
   "metadata":{
    "datalore":{
     "type":"MD",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
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   }
  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Arima\n",
    "An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. \n",
    "\n",
    "A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock's future prices based on its past performance or forecast a company's earnings based on past periods.\n",
    "\n",
    "```python\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "stocks = load_stocks()\n",
    "arima_model = ARIMA(dataframe)\n",
    "model = arima_model.fit()\n",
    "print(model.summary())\n",
    "```"
   ],
   "attachments":{
    
   },
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    "datalore":{
     "type":"MD",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
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  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Moving Average Convergence Divergence\n",
    "MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.\n",
    "\n",
    "The result of that calculation is the MACD line. A nine-day EMA of the MACD called the \"signal line,\" is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence (MACD) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises\/falls."
   ],
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   },
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     "type":"MD",
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     "hide_output_from_viewers":false
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  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Value at Risk\n",
    "VaR modeling determines the potential for loss in the entity being assessed and the probability that the defined loss will occur. One measures VaR by assessing the amount of `potential loss`, the probability of occurrence for the amount of loss, and the timeframe."
   ],
   "attachments":{
    
   },
   "metadata":{
    "datalore":{
     "type":"MD",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
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  },
  {
   "cell_type":"markdown",
   "source":[
    "#### Aroon Oscillator\n",
    "The Aroon Oscillator is a trend-following indicator that uses aspects of the Aroon Indicator to gauge the strength of a current trend and the likelihood that it will continue.\n",
    "\n",
    "Aroon oscillator readings above zero indicate that an uptrend is present, while readings below zero indicate that a downtrend is present. Traders watch for zero line crossovers to signal potential trend changes. They also watch for big moves, above 50 or below -50 to signal strong price moves."
   ],
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  },
  {
   "cell_type":"markdown",
   "source":[
    "### Calling main code"
   ],
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   "cell_type":"code",
   "source":[
    "def main_exploration():\n",
    "    stocks = load_stocks()\n",
    "    numeric_cols = [\"High\",\"Low\",\"Open\",\"Close\",\"Volume\",\"Adj Close\"]\n",
    "    \n",
    "\n",
    "    get_stocks_returns(stocks, \"daily_return\", inplace=True) # We compute the daily returns and add it to a new column\n",
    "    \n",
    "    for index, (name, stock) in enumerate(stocks.items()):\n",
    "        print(f\"------------ EXPLORATION OF THE <{name.upper()}> DATAFRAME ------------\")\n",
    "        print_first_last_elements(stock, name)\n",
    "        print_number_observations(stock, name)\n",
    "        print_period(stock, name)\n",
    "        print_dataset_description(stock, name)\n",
    "        print_missing_values_count(stock, name)\n",
    "        print_corr_matrix(stock, name, numeric_cols)\n",
    "        print_column_stats(stock, name, \"daily_return\")\n",
    "        print_moving_average(stock, name, day_period=31)\n",
    "        print_correlate_stocks(stocks, name, index, numeric_cols)\n",
    "        print_return_rate_by_period(stock, name, \"2020-01-01\", \"month\")\n",
    "    \n",
    "    print(f\"------------ EXPLORATION STATS OF ALL DATAFRAMES ------------\")\n",
    "    print_best_return_rate_by_period(stocks, \"2020-01-01\", \"month\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main_exploration()"
   ],
   "execution_count":12,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "------------ EXPLORATION OF THE <AMAZON> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Amazon dataframe:\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+--------+-----------------+------------+-----------------+\n",
      "|      Date|             High|              Low|             Open|            Close|  Volume|        Adj Close|company_name|     daily_return|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+--------+-----------------+------------+-----------------+\n",
      "|2017-01-03| 758.760009765625|747.7000122070312|757.9199829101562|753.6699829101562| 3521100|753.6699829101562|      AMAZON|            -4.25|\n",
      "|2017-01-04|759.6799926757812|754.2000122070312|758.3900146484375|757.1799926757812| 2510500|757.1799926757812|      AMAZON|-1.21002197265625|\n",
      "|2017-01-05|782.4000244140625| 760.260009765625|761.5499877929688|780.4500122070312| 5830100|780.4500122070312|      AMAZON| 18.9000244140625|\n",
      "|2017-01-06|799.4400024414062|  778.47998046875|782.3599853515625| 795.989990234375| 5986200| 795.989990234375|      AMAZON| 13.6300048828125|\n",
      "|2017-01-09|  801.77001953125|  791.77001953125|            798.0|796.9199829101562| 3446100|796.9199829101562|      AMAZON|-1.08001708984375|\n",
      "|2017-01-10|            798.0|789.5399780273438|796.5999755859375|795.9000244140625| 2558400|795.9000244140625|      AMAZON|  -0.699951171875|\n",
      "|2017-01-11|            799.5| 789.510009765625|793.6599731445312|  799.02001953125| 2992800|  799.02001953125|      AMAZON| 5.36004638671875|\n",
      "|2017-01-12|814.1300048828125|            799.5|800.3099975585938|813.6400146484375| 4873900|813.6400146484375|      AMAZON|13.33001708984375|\n",
      "|2017-01-13|821.6500244140625|811.4000244140625|814.3200073242188|817.1400146484375| 3791900|817.1400146484375|      AMAZON| 2.82000732421875|\n",
      "|2017-01-17|            816.0|803.4400024414062|815.7000122070312| 809.719970703125| 3670500| 809.719970703125|      AMAZON|-5.98004150390625|\n",
      "|2017-01-18|  811.72998046875|  804.27001953125|            809.5|  807.47998046875| 2354200|  807.47998046875|      AMAZON|   -2.02001953125|\n",
      "|2017-01-19| 813.510009765625|807.3200073242188|            810.0|809.0399780273438| 2540800|809.0399780273438|      AMAZON|-0.96002197265625|\n",
      "|2017-01-20|  816.02001953125| 806.260009765625| 815.280029296875|808.3300170898438| 3376200|808.3300170898438|      AMAZON|-6.95001220703125|\n",
      "|2017-01-23|            818.5|805.0800170898438|806.7999877929688|817.8800048828125| 2797500|817.8800048828125|      AMAZON|11.08001708984375|\n",
      "|2017-01-24| 823.989990234375|            814.5|            822.0|822.4400024414062| 2971700|822.4400024414062|      AMAZON| 0.44000244140625|\n",
      "|2017-01-25|837.4199829101562|825.2899780273438|825.7899780273438|  836.52001953125| 3922600|  836.52001953125|      AMAZON|10.73004150390625|\n",
      "|2017-01-26|843.8400268554688|            833.0| 835.530029296875|839.1500244140625| 3586300|839.1500244140625|      AMAZON|  3.6199951171875|\n",
      "|2017-01-27|839.7000122070312|829.4400024414062|            839.0|  835.77001953125| 2998700|  835.77001953125|      AMAZON|   -3.22998046875|\n",
      "|2017-01-30|            833.5|816.3800048828125|            833.0|830.3800048828125| 3747300|830.3800048828125|      AMAZON| -2.6199951171875|\n",
      "|2017-01-31| 826.989990234375|819.5599975585938|           823.75|  823.47998046875| 3137200|  823.47998046875|      AMAZON|   -0.27001953125|\n",
      "|2017-02-01| 833.780029296875|824.9400024414062|829.2100219726562|832.3499755859375| 3850200|832.3499755859375|      AMAZON| 3.13995361328125|\n",
      "|2017-02-02| 842.489990234375| 828.260009765625|836.5900268554688|839.9500122070312| 7350500|839.9500122070312|      AMAZON|  3.3599853515625|\n",
      "|2017-02-03|818.2999877929688|            804.0| 806.719970703125|810.2000122070312|10868800|810.2000122070312|      AMAZON| 3.48004150390625|\n",
      "|2017-02-06| 810.719970703125|            803.0|809.7999877929688|807.6400146484375| 3897300|807.6400146484375|      AMAZON|-2.15997314453125|\n",
      "|2017-02-07|816.1599731445312|            807.5|809.3099975585938|            812.5| 3466100|            812.5|      AMAZON| 3.19000244140625|\n",
      "|2017-02-08|  821.47998046875|            812.5|812.6900024414062|819.7100219726562| 2858000|819.7100219726562|      AMAZON|    7.02001953125|\n",
      "|2017-02-09|            825.0|819.7100219726562|821.5999755859375|821.3599853515625| 2484900|821.3599853515625|      AMAZON|  -0.239990234375|\n",
      "|2017-02-10|            828.0|822.8499755859375|823.8200073242188|827.4600219726562| 2429600|827.4600219726562|      AMAZON|  3.6400146484375|\n",
      "|2017-02-13|            843.0|828.5499877929688|831.6199951171875| 836.530029296875| 4172600| 836.530029296875|      AMAZON|  4.9100341796875|\n",
      "|2017-02-14|838.3099975585938|831.4500122070312|            837.0|836.3900146484375| 2792400|836.3900146484375|      AMAZON| -0.6099853515625|\n",
      "|2017-02-15|842.8099975585938|832.8200073242188|            834.0|842.7000122070312| 2968900|842.7000122070312|      AMAZON| 8.70001220703125|\n",
      "|2017-02-16|            845.0|839.3800048828125|841.8400268554688|844.1400146484375| 2714700|844.1400146484375|      AMAZON| 2.29998779296875|\n",
      "|2017-02-17|  847.27001953125|  840.72998046875|            842.0|845.0700073242188| 3112300|845.0700073242188|      AMAZON| 3.07000732421875|\n",
      "|2017-02-21|  857.97998046875|           847.25|848.8400268554688|856.4400024414062| 3507700|856.4400024414062|      AMAZON|  7.5999755859375|\n",
      "|2017-02-22|858.4299926757812|852.1799926757812|856.9500122070312|855.6099853515625| 2617000|855.6099853515625|      AMAZON|-1.34002685546875|\n",
      "|2017-02-23|860.8599853515625|            848.0|857.5700073242188|852.1900024414062| 3462000|852.1900024414062|      AMAZON| -5.3800048828125|\n",
      "|2017-02-24|845.8099975585938|           837.75|844.6900024414062| 845.239990234375| 3688000| 845.239990234375|      AMAZON| 0.54998779296875|\n",
      "|2017-02-27|            852.5|839.6699829101562|842.3800048828125|848.6400146484375| 2713600|848.6400146484375|      AMAZON|   6.260009765625|\n",
      "|2017-02-28|854.0900268554688|842.0499877929688|851.4500122070312|845.0399780273438| 2793700|845.0399780273438|      AMAZON| -6.4100341796875|\n",
      "|2017-03-01|854.8300170898438| 849.010009765625|853.0499877929688|853.0800170898438| 2760100|853.0800170898438|      AMAZON|   0.030029296875|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+--------+-----------------+------------+-----------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+--------------------+-----------------+------------+----------------+\n",
      "|      Date|             High|              Low|             Open|            Close|              Volume|        Adj Close|company_name|    daily_return|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+--------------------+-----------------+------------+----------------+\n",
      "|2020-10-07|           3200.0|3132.389892578125|           3135.0| 3195.68994140625|4309400.000000000...| 3195.68994140625|      AMAZON|  60.68994140625|\n",
      "|2020-10-08|  3233.2900390625|3174.989990234375|3224.989990234375|3190.550048828125|3174100.000000000...|3190.550048828125|      AMAZON| -34.43994140625|\n",
      "|2020-10-09|3288.989990234375|   3197.830078125|           3210.0| 3286.64990234375|4907900.000000000...| 3286.64990234375|      AMAZON|  76.64990234375|\n",
      "|2020-10-12|3496.239990234375|3339.550048828125| 3349.93994140625|3442.929931640625|8364200.000000000...|3442.929931640625|      AMAZON| 92.989990234375|\n",
      "|2020-10-13|  3492.3798828125|3424.219970703125|3467.989990234375|  3443.6298828125|5744700.000000000...|  3443.6298828125|      AMAZON|-24.360107421875|\n",
      "|2020-10-14|  3464.8798828125|           3340.0|           3447.0|  3363.7099609375|5828900.000000000...|  3363.7099609375|      AMAZON|  -83.2900390625|\n",
      "|2020-10-15|  3355.8798828125|           3280.0|3292.010009765625| 3338.64990234375|5223400.000000000...| 3338.64990234375|      AMAZON| 46.639892578125|\n",
      "|2020-10-16|3399.659912109375|           3160.0| 3363.22998046875|  3272.7099609375|6474400.000000000...|  3272.7099609375|      AMAZON| -90.52001953125|\n",
      "|2020-10-19|           3329.0|3192.739990234375|3299.610107421875|  3207.2099609375|5223600.000000000...|  3207.2099609375|      AMAZON|-92.400146484375|\n",
      "|2020-10-20|           3266.0|3192.010009765625|3222.280029296875|3217.010009765625|4509700.000000000...|3217.010009765625|      AMAZON|  -5.27001953125|\n",
      "|2020-10-21|  3233.8798828125|           3160.0|           3212.5| 3184.93994140625|4592700.000000000...| 3184.93994140625|      AMAZON| -27.56005859375|\n",
      "|2020-10-22|          3198.75| 3121.93994140625|  3189.8701171875| 3176.39990234375|4212000.000000000...| 3176.39990234375|      AMAZON| -13.47021484375|\n",
      "|2020-10-23|   3205.330078125|           3140.0|           3191.0| 3204.39990234375|3466700.000000000...| 3204.39990234375|      AMAZON|  13.39990234375|\n",
      "|2020-10-26| 3282.97998046875|3153.300048828125|3198.739990234375|  3207.0400390625|5901200.000000000...|  3207.0400390625|      AMAZON|  8.300048828125|\n",
      "|2020-10-27|3291.659912109375|3211.300048828125| 3224.93994140625|   3286.330078125|4291000.000000000...|   3286.330078125|      AMAZON|  61.39013671875|\n",
      "|2020-10-28| 3264.02001953125|3162.469970703125|3249.300048828125|3162.780029296875|5588300.000000000...|3162.780029296875|      AMAZON| -86.52001953125|\n",
      "|2020-10-29|          3257.25|           3164.0| 3201.27001953125|3211.010009765625|6596500.000000000...|3211.010009765625|      AMAZON|  9.739990234375|\n",
      "|2020-10-30|           3167.0|           3019.0|          3157.75| 3036.14990234375|8386400.000000000...| 3036.14990234375|      AMAZON|-121.60009765625|\n",
      "|2020-11-02|  3079.7099609375|  2950.1201171875|3061.739990234375| 3004.47998046875|7257400.000000000...| 3004.47998046875|      AMAZON|-57.260009765625|\n",
      "|2020-11-03| 3074.89990234375| 2980.97998046875|3018.530029296875|3048.409912109375|4897900.000000000...|3048.409912109375|      AMAZON|   29.8798828125|\n",
      "|2020-11-04| 3244.85009765625| 3139.72998046875|3159.989990234375|3241.159912109375|6839000.000000000...|3241.159912109375|      AMAZON|    81.169921875|\n",
      "|2020-11-05|3366.800048828125|  3288.8798828125|3319.969970703125|           3322.0|5789300.000000000...|           3322.0|      AMAZON|  2.030029296875|\n",
      "|2020-11-06|           3322.0|           3232.0|3304.639892578125|  3311.3701171875|4647300.000000000...|  3311.3701171875|      AMAZON|  6.730224609375|\n",
      "|2020-11-09|           3289.0|3112.110107421875|3231.030029296875|3143.739990234375|7190400.000000000...|3143.739990234375|      AMAZON|  -87.2900390625|\n",
      "|2020-11-10|           3114.0| 3019.47998046875| 3095.02001953125| 3035.02001953125|6591000.000000000...| 3035.02001953125|      AMAZON|           -60.0|\n",
      "|2020-11-11| 3139.14990234375|           3050.0|3061.780029296875|3137.389892578125|4366900.000000000...|3137.389892578125|      AMAZON|  75.60986328125|\n",
      "|2020-11-12|  3175.8798828125|3086.050048828125|3159.949951171875|3110.280029296875|4362000.000000000...|3110.280029296875|      AMAZON|   -49.669921875|\n",
      "|2020-11-13|3141.719970703125|3085.389892578125|           3122.0| 3128.81005859375|3756200.000000000...| 3128.81005859375|      AMAZON|   6.81005859375|\n",
      "|2020-11-16|3142.699951171875| 3072.68994140625|3093.199951171875| 3131.06005859375|3808700.000000000...| 3131.06005859375|      AMAZON| 37.860107421875|\n",
      "|2020-11-17|          3189.25|3135.260009765625|  3183.5400390625|3135.659912109375|3444700.000000000...|3135.659912109375|      AMAZON|-47.880126953125|\n",
      "|2020-11-18|           3140.0| 3105.10009765625|           3134.0|  3105.4599609375|2916800.000000000...|  3105.4599609375|      AMAZON|  -28.5400390625|\n",
      "|2020-11-19|           3125.0|   3080.919921875| 3105.31005859375| 3117.02001953125|3010300.000000000...| 3117.02001953125|      AMAZON|   11.7099609375|\n",
      "|2020-11-20|3132.889892578125|3098.050048828125| 3117.02001953125| 3099.39990234375|3374400.000000000...| 3099.39990234375|      AMAZON|  -17.6201171875|\n",
      "|2020-11-23|          3139.75|  3065.4599609375|3116.699951171875|3098.389892578125|4708900.000000000...|3098.389892578125|      AMAZON| -18.31005859375|\n",
      "|2020-11-24|          3134.25|3086.260009765625|           3100.5| 3118.06005859375|3602100.000000000...| 3118.06005859375|      AMAZON|  17.56005859375|\n",
      "|2020-11-25|           3198.0|3140.260009765625|  3141.8701171875|3185.070068359375|3790400.000000000...|3185.070068359375|      AMAZON| 43.199951171875|\n",
      "|2020-11-27| 3216.18994140625|3190.050048828125|3211.260009765625|3195.340087890625|2392900.000000000...|3195.340087890625|      AMAZON|   -15.919921875|\n",
      "|2020-11-30|3228.389892578125|3125.550048828125| 3208.47998046875|  3168.0400390625|4063900.000000000...|  3168.0400390625|      AMAZON| -40.43994140625|\n",
      "|2020-12-01|3248.949951171875|3157.179931640625|           3188.5|   3220.080078125|4537000.000000000...|   3220.080078125|      AMAZON|    31.580078125|\n",
      "|2020-12-02|3223.760009765625|3173.260009765625| 3221.64990234375|  3213.4384765625|1119593.000000000...|  3213.4384765625|      AMAZON|  -8.21142578125|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+--------------------+-----------------+------------+----------------+\n",
      "\n",
      "The Amazon dataframe contains 987 observations.\n",
      "\n",
      "The period between points of the Amazon dataset is:\n",
      "\t1 days (median)\n",
      "\t1.449290060851927 days (on average)\n",
      "\n",
      "Statistics for Amazon dataframe:\n",
      "+-------+------------------+------------------+-----------------+------------------+-----------------+------------------+------------+-------------------+\n",
      "|summary|              High|               Low|             Open|             Close|           Volume|         Adj Close|company_name|       daily_return|\n",
      "+-------+------------------+------------------+-----------------+------------------+-----------------+------------------+------------+-------------------+\n",
      "|  count|               987|               987|              987|               987|              987|               987|         987|                987|\n",
      "|   mean|1762.0071216958152|1722.1011452099956|1743.433881363487|1742.9566644206718|     4509728.0578|1742.9566644206718|        null|-0.4772169428151912|\n",
      "| stddev| 667.2385315752688| 644.7988093382758|657.1153070927137| 655.9576061129322|2179817.628631287| 655.9576061129322|        null|  31.47287478528743|\n",
      "|    min|  758.760009765625| 747.7000122070312|757.9199829101562| 753.6699829101562|           881300| 753.6699829101562|      AMAZON|   -147.06005859375|\n",
      "|    max|           3552.25|  3486.68994140625|           3547.0| 3531.449951171875|         16565000| 3531.449951171875|      AMAZON|    196.64013671875|\n",
      "+-------+------------------+------------------+-----------------+------------------+-----------------+------------------+------------+-------------------+\n",
      "\n",
      "Missing values for Amazon:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Amazon\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[1.         0.99919608 0.99952481 0.99943308 0.16053643 0.99943308]\n",
      " [0.99919608 1.         0.99934439 0.99947694 0.13293678 0.99947694]\n",
      " [0.99952481 0.99934439 1.         0.99885254 0.14904749 0.99885254]\n",
      " [0.99943308 0.99947694 0.99885254 1.         0.14617949 1.        ]\n",
      " [0.16053643 0.13293678 0.14904749 0.14617949 1.         0.14617949]\n",
      " [0.99943308 0.99947694 0.99885254 1.         0.14617949 1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Amazon dataset are:\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|company_name| mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|      AMAZON|-0.4772169428151912|       0.02001953125|   31.47287478528743|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the amazon dataframe on 31 days is:\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+-----------------+-----------------+\n",
      "|      Date|             High|              Low|             Open|            Close| Volume|        Adj Close|company_name|     daily_return|   Moving_average|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+-----------------+-----------------+\n",
      "|2017-01-03| 758.760009765625|747.7000122070312|757.9199829101562|753.6699829101562|3521100|753.6699829101562|      AMAZON|            -4.25| 758.760009765625|\n",
      "|2017-01-04|759.6799926757812|754.2000122070312|758.3900146484375|757.1799926757812|2510500|757.1799926757812|      AMAZON|-1.21002197265625|759.2200012207031|\n",
      "|2017-01-05|782.4000244140625| 760.260009765625|761.5499877929688|780.4500122070312|5830100|780.4500122070312|      AMAZON| 18.9000244140625|766.9466756184896|\n",
      "|2017-01-06|799.4400024414062|  778.47998046875|782.3599853515625| 795.989990234375|5986200| 795.989990234375|      AMAZON| 13.6300048828125|775.0700073242188|\n",
      "|2017-01-09|  801.77001953125|  791.77001953125|            798.0|796.9199829101562|3446100|796.9199829101562|      AMAZON|-1.08001708984375| 780.410009765625|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+-----------------+-----------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "========= Correlation between Amazon and Apple =========\n",
      "+----------------+-------------------+--------------------+-------------------+--------------------+-------------------+-------------------+\n",
      "|      Col_amazon|         High_apple|           Low_apple|         Open_apple|         Close_apple|       Volume_apple|    Adj Close_apple|\n",
      "+----------------+-------------------+--------------------+-------------------+--------------------+-------------------+-------------------+\n",
      "|     High_amazon| 0.9297874259390847|  0.9288404935316446|  0.929224429531747|  0.9289226688309318| 0.1614264488200611|  0.931117345769924|\n",
      "|      Low_amazon| 0.9294814062048777|  0.9294547100635051| 0.9292104679328616|   0.929201231245745|0.14280092623787832| 0.9313750153531389|\n",
      "|     Open_amazon| 0.9295864367635821|  0.9288996867205574|  0.929432373427345|  0.9286513439934997|0.15477270907314372| 0.9308299253600888|\n",
      "|    Close_amazon| 0.9293612105453593|   0.928970764552425| 0.9287589351197002|  0.9292860084204524|0.15015419902613378| 0.9314678774869076|\n",
      "|   Volume_amazon|0.07251513094870253|0.054936587225105346|0.06570150518170476|0.061806672508895474| 0.5340081166420992|0.06255731232524933|\n",
      "|Adj Close_amazon| 0.9293612105453593|   0.928970764552425| 0.9287589351197002|  0.9292860084204524|0.15015419902613378| 0.9314678774869076|\n",
      "+----------------+-------------------+--------------------+-------------------+--------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Amazon and Facebook =========\n",
      "+----------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+\n",
      "|      Col_amazon|       High_facebook|        Low_facebook|       Open_facebook|      Close_facebook|    Volume_facebook|  Adj Close_facebook|\n",
      "+----------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+\n",
      "|     High_amazon|  0.8682214375390286|  0.8521342717486707|  0.8596860804262029|  0.8593512117596962|0.10869617223461035|  0.8593512117596962|\n",
      "|      Low_amazon|  0.8730637282809224|   0.858656263465311|  0.8650159179981645|  0.8652412915221078|0.09537680729198893|  0.8652412915221078|\n",
      "|     Open_amazon|  0.8708008918544238|  0.8553451785562635|  0.8630665010422292|  0.8618216939919258|  0.103750334486037|  0.8618216939919258|\n",
      "|    Close_amazon|  0.8702646988722662|  0.8551202384034641|   0.861574960359263|  0.8628112282541691| 0.1013356846819061|  0.8628112282541691|\n",
      "|   Volume_amazon|-0.05017977519841001|-0.08967822761793803|-0.06426826189635965|-0.07327665664467768| 0.5107653647271243|-0.07327665664467768|\n",
      "|Adj Close_amazon|  0.8702646988722662|  0.8551202384034641|   0.861574960359263|  0.8628112282541691| 0.1013356846819061|  0.8628112282541691|\n",
      "+----------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+\n",
      "\n",
      "========= Correlation between Amazon and Google =========\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_amazon|        High_google|         Low_google|        Open_google|       Close_google|      Volume_google|   Adj Close_google|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_amazon| 0.9279218467592136| 0.9194242953616691| 0.9241871122254385| 0.9230110958463997|0.11796529925645338| 0.9230110958463997|\n",
      "|      Low_amazon| 0.9305628215649896| 0.9238630174063731| 0.9274281074928534| 0.9267777234199257|0.09927249712678549| 0.9267777234199257|\n",
      "|     Open_amazon| 0.9291536961814433| 0.9212800279653777| 0.9262870255493186| 0.9241533215788641|0.11125737704678548| 0.9241533215788641|\n",
      "|    Close_amazon| 0.9289882442849928| 0.9215032070781104| 0.9250821607700972| 0.9255061932293708|0.10750037048538427| 0.9255061932293708|\n",
      "|   Volume_amazon|0.08164538877085967|0.04131092663427266|0.06562796667352563|0.05834004784221119| 0.6104197582530982|0.05834004784221119|\n",
      "|Adj Close_amazon| 0.9289882442849928| 0.9215032070781104| 0.9250821607700972| 0.9255061932293708|0.10750037048538427| 0.9255061932293708|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Amazon and Microsoft =========\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_amazon|     High_microsoft|      Low_microsoft|     Open_microsoft|    Close_microsoft|   Volume_microsoft|Adj Close_microsoft|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_amazon|  0.951284096151614| 0.9509699650639561| 0.9514071287814633| 0.9507393797804062|0.23303714609668455| 0.9500105665455327|\n",
      "|      Low_amazon| 0.9517271852121106| 0.9524913662368054| 0.9522215120881572| 0.9518481851486837|0.21117057991753432| 0.9510908649882712|\n",
      "|     Open_amazon| 0.9508229283842947| 0.9508967161448483| 0.9514272822500527| 0.9502859468869901|0.22398620019533952| 0.9495469490145519|\n",
      "|    Close_amazon| 0.9516431796297432| 0.9519500480632727| 0.9516888358830374| 0.9519391984487741|0.22039041170867088| 0.9511824060153518|\n",
      "|   Volume_amazon|0.09460572904763022|0.07349126801915358|0.08623998541362296|0.08237583421140575|  0.677395251556155|0.08238521584496564|\n",
      "|Adj Close_amazon| 0.9516431796297432| 0.9519500480632727| 0.9516888358830374| 0.9519391984487741|0.22039041170867088| 0.9511824060153518|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Amazon and Tesla =========\n",
      "+----------------+------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_amazon|        High_tesla|          Low_tesla|        Open_tesla|        Close_tesla|       Volume_tesla|    Adj Close_tesla|\n",
      "+----------------+------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|     High_amazon|0.8123427553679604| 0.8094171345381344|0.8107131492452718|  0.810721850580651| 0.3111894881508564|  0.810721850580651|\n",
      "|      Low_amazon|0.8092923820500767| 0.8069472186412819|0.8078084799167959| 0.8081020481284472|0.30513414662606353| 0.8081020481284472|\n",
      "|     Open_amazon|0.8114381148237654| 0.8086864227847395|0.8100728829691062| 0.8097674790573092|0.30837780666809483| 0.8097674790573092|\n",
      "|    Close_amazon|0.8104713067265208| 0.8079287865010962|0.8088450608173325| 0.8094073996359907| 0.3072944628744015| 0.8094073996359907|\n",
      "|   Volume_amazon|0.0874504519313541|0.07952760147374181|0.0852046295431226|0.08224178706639836| 0.1948057517760406|0.08224178706639836|\n",
      "|Adj Close_amazon|0.8104713067265208| 0.8079287865010962|0.8088450608173325| 0.8094073996359907| 0.3072944628744015| 0.8094073996359907|\n",
      "+----------------+------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Amazon and Zoom =========\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_amazon|          High_zoom|           Low_zoom|          Open_zoom|         Close_zoom|        Volume_zoom|     Adj Close_zoom|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_amazon| 0.9133082712469004| 0.9148470047701486| 0.9137901865339186| 0.9154265704410016|0.38261112993962754| 0.9154265704410016|\n",
      "|      Low_amazon| 0.9117776725401895| 0.9140200107429356| 0.9124904485476167| 0.9143795751580468| 0.3709141483690033| 0.9143795751580468|\n",
      "|     Open_amazon|  0.913502465789868| 0.9152921496981538| 0.9144061918476822| 0.9155167267689149| 0.3743023560371588| 0.9155167267689149|\n",
      "|    Close_amazon| 0.9119043345949713| 0.9137873855510473| 0.9123193878984318|  0.914744271428987|0.37848451050530507|  0.914744271428987|\n",
      "|   Volume_amazon|0.23968743744705032|0.22900644266109332|0.23537821734606137|0.23375717592860515|0.34893526694199006|0.23375717592860515|\n",
      "|Adj Close_amazon| 0.9119043345949713| 0.9137873855510473| 0.9123193878984318|  0.914744271428987|0.37848451050530507|  0.914744271428987|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "The return rate of the amazon dataframe from the 2020-01-01 on month days is: -27.3299560546875.\n",
      "\n",
      "------------ EXPLORATION OF THE <APPLE> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Apple dataframe:\n",
      "+----------+------------------+------------------+------------------+------------------+---------+------------------+------------+--------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|   Volume|         Adj Close|company_name|        daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+---------+------------------+------------+--------------------+\n",
      "|2017-01-03|29.082500457763672|28.690000534057617|28.950000762939453|29.037500381469727|115127600|27.277639389038086|       APPLE| 0.08749961853027344|\n",
      "|2017-01-04|29.127500534057617|           28.9375|28.962499618530273|  29.0049991607666| 84472400|27.247108459472656|       APPLE|0.042499542236328125|\n",
      "|2017-01-05| 29.21500015258789|28.952499389648438|28.979999542236328| 29.15250015258789| 88774400| 27.38566780090332|       APPLE|  0.1725006103515625|\n",
      "|2017-01-06|29.540000915527344| 29.11750030517578| 29.19499969482422|29.477500915527344|127007600| 27.69097137451172|       APPLE|   0.282501220703125|\n",
      "|2017-01-09|29.857500076293945|29.485000610351562|29.487499237060547|29.747499465942383|134247600|27.944602966308594|       APPLE| 0.26000022888183594|\n",
      "|2017-01-10|29.844999313354492|29.575000762939453|  29.6924991607666| 29.77750015258789| 97848400| 27.97278594970703|       APPLE| 0.08500099182128906|\n",
      "|2017-01-11|29.982500076293945|29.649999618530273|29.684999465942383|           29.9375|110354400|28.123088836669922|       APPLE|  0.2525005340576172|\n",
      "|2017-01-12|29.825000762939453|29.552499771118164|29.725000381469727|           29.8125|108344800|28.005664825439453|       APPLE| 0.08749961853027344|\n",
      "|2017-01-13|29.905000686645508|29.702499389648438| 29.77750015258789|29.760000228881836|104447600|27.956350326538086|       APPLE|-0.01749992370605...|\n",
      "|2017-01-17|30.059999465942383| 29.55500030517578|29.584999084472656|              30.0|137759200|28.181800842285156|       APPLE| 0.41500091552734375|\n",
      "|2017-01-18|            30.125|29.927499771118164|              30.0|29.997499465942383| 94852000| 28.17945671081543|       APPLE|-0.00250053405761...|\n",
      "|2017-01-19|30.022499084472656|29.842500686645508|29.850000381469727| 29.94499969482422|102389200|  28.1301326751709|       APPLE| 0.09499931335449219|\n",
      "|2017-01-20|30.112499237060547|  29.9325008392334|30.112499237060547|              30.0|130391600|28.181800842285156|       APPLE|-0.11249923706054688|\n",
      "|2017-01-23|30.202499389648438|  29.9424991607666|              30.0|30.020000457763672| 88200800|28.200590133666992|       APPLE|0.020000457763671875|\n",
      "|2017-01-24|30.024999618530273|            29.875|29.887500762939453| 29.99250030517578| 92844000| 28.17475700378418|       APPLE| 0.10499954223632812|\n",
      "|2017-01-25|30.524999618530273| 30.06999969482422|30.104999542236328|30.469999313354492|129510400|28.623313903808594|       APPLE| 0.36499977111816406|\n",
      "|2017-01-26|30.610000610351562|30.399999618530273|30.417499542236328|30.485000610351562|105350400|28.637407302856445|       APPLE| 0.06750106811523438|\n",
      "|2017-01-27|30.587499618530273|30.399999618530273| 30.53499984741211|30.487499237060547| 82251600|28.639755249023438|       APPLE| -0.0475006103515625|\n",
      "|2017-01-30|30.407499313354492|30.165000915527344|30.232500076293945|30.407499313354492|121510000|28.564605712890625|       APPLE| 0.17499923706054688|\n",
      "|2017-01-31| 30.34749984741211|30.155000686645508|30.287500381469727|30.337499618530273|196804000|28.498844146728516|       APPLE|0.049999237060546875|\n",
      "|2017-02-01|32.622501373291016|31.752500534057617| 31.75749969482422|           32.1875|447940000|30.236719131469727|       APPLE| 0.43000030517578125|\n",
      "|2017-02-02| 32.34749984741211| 31.94499969482422|  31.9950008392334| 32.13249969482422|134841600|30.185060501098633|       APPLE|  0.1374988555908203|\n",
      "|2017-02-03| 32.29750061035156|32.040000915527344| 32.07749938964844| 32.27000045776367| 98029200| 30.31422996520996|       APPLE| 0.19250106811523438|\n",
      "|2017-02-06|            32.625|32.224998474121094|32.282501220703125|  32.5724983215332|107383600|30.598392486572266|       APPLE|  0.2899971008300781|\n",
      "|2017-02-07|33.022499084472656| 32.61249923706055|  32.6349983215332| 32.88249969482422|152735200| 30.88960075378418|       APPLE| 0.24750137329101562|\n",
      "|2017-02-08| 33.05500030517578| 32.80500030517578|32.837501525878906|  33.0099983215332| 92016400| 31.00937271118164|       APPLE| 0.17249679565429688|\n",
      "|2017-02-09| 33.11249923706055|32.779998779296875|32.912498474121094| 33.10499954223633|113399600| 31.23344612121582|       APPLE| 0.19250106811523438|\n",
      "|2017-02-10| 33.23500061035156| 33.01250076293945|  33.1150016784668|33.029998779296875| 80262000|31.162689208984375|       APPLE|-0.08500289916992188|\n",
      "|2017-02-13| 33.45500183105469|           33.1875| 33.27000045776367|  33.3224983215332| 92141600| 31.43865203857422|       APPLE| 0.05249786376953125|\n",
      "|2017-02-14|33.772499084472656|           33.3125| 33.36750030517578|33.755001068115234|132904800|31.846702575683594|       APPLE|  0.3875007629394531|\n",
      "|2017-02-15|34.067501068115234|33.654998779296875|33.880001068115234|33.877498626708984|142492400|31.962276458740234|       APPLE|   -0.00250244140625|\n",
      "|2017-02-16|33.974998474121094|33.709999084472656| 33.91749954223633|33.837501525878906| 90338400| 31.92454719543457|       APPLE|-0.07999801635742188|\n",
      "|2017-02-17| 33.95750045776367|33.775001525878906|33.775001525878906| 33.93000030517578| 88792800|32.011810302734375|       APPLE|   0.154998779296875|\n",
      "|2017-02-21|           34.1875|33.994998931884766|34.057498931884766| 34.17499923706055| 98028800|32.242958068847656|       APPLE| 0.11750030517578125|\n",
      "|2017-02-22|34.279998779296875| 34.02750015258789| 34.10749816894531| 34.27750015258789| 83347600|32.339664459228516|       APPLE| 0.17000198364257812|\n",
      "|2017-02-23|34.369998931884766| 34.07500076293945|34.345001220703125| 34.13249969482422| 83152800|  32.2028694152832|       APPLE|-0.21250152587890625|\n",
      "|2017-02-24|34.165000915527344| 33.81999969482422|33.977500915527344|34.165000915527344| 87106400|32.233524322509766|       APPLE|              0.1875|\n",
      "|2017-02-27| 34.36000061035156| 34.06999969482422| 34.28499984741211| 34.23249816894531| 81029600|32.297210693359375|       APPLE|-0.05250167846679...|\n",
      "|2017-02-28| 34.36000061035156| 34.17499923706055| 34.27000045776367|34.247501373291016| 93931600| 32.31135940551758|       APPLE|-0.02249908447265625|\n",
      "|2017-03-01|35.037498474121094|34.400001525878906| 34.47249984741211|  34.9474983215332|145658400|32.971778869628906|       APPLE| 0.47499847412109375|\n",
      "+----------+------------------+------------------+------------------+------------------+---------+------------------+------------+--------------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+--------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|        daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+--------------------+\n",
      "|2020-10-07|115.55000305175781|114.12999725341797|114.62000274658203|115.08000183105469|96849000.00000000...|114.88180541992188|       APPLE| 0.45999908447265625|\n",
      "|2020-10-08| 116.4000015258789|114.58999633789062|            116.25|114.97000122070312|83477200.00000000...|114.77198791503906|       APPLE|  -1.279998779296875|\n",
      "|2020-10-09|             117.0|114.91999816894531|115.27999877929688|116.97000122070312|100506900.0000000...|116.76854705810547|       APPLE|    1.69000244140625|\n",
      "|2020-10-12|125.18000030517578|119.27999877929688|120.05999755859375| 124.4000015258789|240226800.0000000...|124.18575286865234|       APPLE|   4.340003967285156|\n",
      "|2020-10-13|125.38999938964844| 119.6500015258789| 125.2699966430664| 121.0999984741211|262330500.0000000...|120.89143371582031|       APPLE| -4.1699981689453125|\n",
      "|2020-10-14|123.02999877929688|119.62000274658203|             121.0|121.19000244140625|151062300.0000000...|120.98127746582031|       APPLE|    0.19000244140625|\n",
      "|2020-10-15|121.19999694824219| 118.1500015258789|118.72000122070312|120.70999908447266|112559200.0000000...|120.50210571289062|       APPLE|  1.9899978637695312|\n",
      "|2020-10-16|121.55000305175781|118.80999755859375|121.27999877929688| 119.0199966430664|115393800.0000000...|118.81501007080078|       APPLE| -2.2600021362304688|\n",
      "|2020-10-19|120.41999816894531|115.66000366210938|119.95999908447266| 115.9800033569336|120639300.0000000...| 115.7802505493164|       APPLE| -3.9799957275390625|\n",
      "|2020-10-20| 118.9800033569336|115.62999725341797|116.19999694824219|117.51000213623047|124423700.0000000...|    117.3076171875|       APPLE|  1.3100051879882812|\n",
      "|2020-10-21|118.70999908447266|116.44999694824219|116.66999816894531|116.87000274658203|89946000.00000000...| 116.6687240600586|       APPLE| 0.20000457763671875|\n",
      "|2020-10-22|118.04000091552734|114.58999633789062|117.44999694824219|            115.75|101988000.0000000...|115.55064392089844|       APPLE| -1.6999969482421875|\n",
      "|2020-10-23|116.55000305175781|114.27999877929688|116.38999938964844|115.04000091552734|82572600.00000000...|114.84187316894531|       APPLE| -1.3499984741210938|\n",
      "|2020-10-26|116.55000305175781|112.87999725341797|114.01000213623047|115.05000305175781|111850700.0000000...|114.85185241699219|       APPLE|  1.0400009155273438|\n",
      "|2020-10-27|117.27999877929688|114.54000091552734|115.48999786376953| 116.5999984741211|92276800.00000000...|116.39917755126953|       APPLE|  1.1100006103515625|\n",
      "|2020-10-28|115.43000030517578| 111.0999984741211|115.05000305175781|111.19999694824219|143937800.0000000...|111.00847625732422|       APPLE|  -3.850006103515625|\n",
      "|2020-10-29|116.93000030517578|112.19999694824219|112.37000274658203|115.31999969482422|146129200.0000000...|115.12138366699219|       APPLE|  2.9499969482421875|\n",
      "|2020-10-30|111.98999786376953|107.72000122070312|111.05999755859375|108.86000061035156|190272600.0000000...|108.67251586914062|       APPLE| -2.1999969482421875|\n",
      "|2020-11-02|110.68000030517578|107.31999969482422|109.11000061035156| 108.7699966430664|122866900.0000000...| 108.5826644897461|       APPLE|-0.34000396728515625|\n",
      "|2020-11-03|111.48999786376953| 108.7300033569336|109.66000366210938|110.44000244140625|107624400.0000000...|110.24979400634766|       APPLE|   0.779998779296875|\n",
      "|2020-11-04|115.58999633789062| 112.3499984741211|114.13999938964844|114.94999694824219|138235500.0000000...|114.75202178955078|       APPLE|    0.80999755859375|\n",
      "|2020-11-05|119.62000274658203|116.87000274658203|117.94999694824219|119.02999877929688|126387100.0000000...|118.82499694824219|       APPLE|  1.0800018310546875|\n",
      "|2020-11-06|119.19999694824219|116.12999725341797|118.31999969482422|118.69000244140625|114457900.0000000...|118.69000244140625|       APPLE| 0.37000274658203125|\n",
      "|2020-11-09|121.98999786376953|116.05000305175781|             120.5|116.31999969482422|154515300.0000000...|116.31999969482422|       APPLE|  -4.180000305175781|\n",
      "|2020-11-10|117.58999633789062|114.12999725341797|115.55000305175781|115.97000122070312|138023400.0000000...|115.97000122070312|       APPLE|  0.4199981689453125|\n",
      "|2020-11-11|119.62999725341797|116.44000244140625|117.19000244140625|119.48999786376953|112295000.0000000...|119.48999786376953|       APPLE|  2.2999954223632812|\n",
      "|2020-11-12|120.52999877929688|118.56999969482422|119.62000274658203|119.20999908447266|103162300.0000000...|119.20999908447266|       APPLE|  -0.410003662109375|\n",
      "|2020-11-13|119.66999816894531|117.87000274658203|119.44000244140625|119.26000213623047|81581900.00000000...|119.26000213623047|       APPLE|-0.18000030517578125|\n",
      "|2020-11-16|120.98999786376953| 118.1500015258789|118.91999816894531|120.30000305175781|91183000.00000000...|120.30000305175781|       APPLE|     1.3800048828125|\n",
      "|2020-11-17|120.66999816894531|118.95999908447266|119.55000305175781|119.38999938964844|74271000.00000000...|119.38999938964844|       APPLE|  -0.160003662109375|\n",
      "|2020-11-18|119.81999969482422|             118.0|118.61000061035156|118.02999877929688|76322100.00000000...|118.02999877929688|       APPLE| -0.5800018310546875|\n",
      "|2020-11-19|119.05999755859375|116.80999755859375|117.58999633789062|118.63999938964844|74113000.00000000...|118.63999938964844|       APPLE|  1.0500030517578125|\n",
      "|2020-11-20| 118.7699966430664|117.29000091552734|118.63999938964844|117.33999633789062|73391400.00000000...|117.33999633789062|       APPLE| -1.3000030517578125|\n",
      "|2020-11-23|117.62000274658203|            113.75|117.18000030517578| 113.8499984741211|127959300.0000000...| 113.8499984741211|       APPLE| -3.3300018310546875|\n",
      "|2020-11-24| 115.8499984741211|112.58999633789062|113.91000366210938|115.16999816894531|113874200.0000000...|115.16999816894531|       APPLE|  1.2599945068359375|\n",
      "|2020-11-25|            116.75|115.16999816894531|115.55000305175781|116.02999877929688|76499200.00000000...|116.02999877929688|       APPLE|  0.4799957275390625|\n",
      "|2020-11-27|117.48999786376953|116.22000122070312|116.56999969482422|116.58999633789062|46691300.00000000...|116.58999633789062|       APPLE| 0.01999664306640625|\n",
      "|2020-11-30|120.97000122070312|116.80999755859375|116.97000122070312|119.05000305175781|169410200.0000000...|119.05000305175781|       APPLE|  2.0800018310546875|\n",
      "|2020-12-01|123.47000122070312|120.01000213623047|121.01000213623047|122.72000122070312|127728200.0000000...|122.72000122070312|       APPLE|  1.7099990844726562|\n",
      "|2020-12-02|122.62000274658203|120.88999938964844| 122.0199966430664| 122.1593017578125|26330019.00000000...| 122.1593017578125|       APPLE| 0.13930511474609375|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+--------------------+\n",
      "\n",
      "The Apple dataframe contains 987 observations.\n",
      "\n",
      "The period between points of the Apple dataset is:\n",
      "\t1 days (median)\n",
      "\t1.449290060851927 days (on average)\n",
      "\n",
      "Statistics for Apple dataframe:\n",
      "+-------+------------------+------------------+------------------+------------------+-------------------+------------------+------------+-------------------+\n",
      "|summary|              High|               Low|              Open|             Close|             Volume|         Adj Close|company_name|       daily_return|\n",
      "+-------+------------------+------------------+------------------+------------------+-------------------+------------------+------------+-------------------+\n",
      "|  count|               987|               987|               987|               987|                987|               987|         987|                987|\n",
      "|   mean|57.322335375599344| 56.08516212123264| 56.68830042165584|56.735163877075564|     126770016.9392|  55.3555844972682|        null|0.04686345541972399|\n",
      "| stddev|23.909537521895153| 23.04004228077875| 23.50911719644306|23.487454843800442|6.043945164169236E7| 24.00281865091007|        null| 1.0288224907842407|\n",
      "|    min|29.082500457763672|28.690000534057617|28.950000762939453|  29.0049991607666|           20197800|27.247108459472656|       APPLE| -6.870002746582031|\n",
      "|    max|137.97999572753906|130.52999877929688|137.58999633789062|134.17999267578125|          447940000| 133.9488983154297|       APPLE|  5.540000915527344|\n",
      "+-------+------------------+------------------+------------------+------------------+-------------------+------------------+------------+-------------------+\n",
      "\n",
      "Missing values for Apple:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Apple\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[1.         0.99929077 0.9996024  0.99948856 0.16714814 0.99944437]\n",
      " [0.99929077 1.         0.99941351 0.99952778 0.14293344 0.99943611]\n",
      " [0.9996024  0.99941351 1.         0.99904195 0.15680482 0.99897057]\n",
      " [0.99948856 0.99952778 0.99904195 1.         0.15405974 0.99991719]\n",
      " [0.16714814 0.14293344 0.15680482 0.15405974 1.         0.15437409]\n",
      " [0.99944437 0.99943611 0.99897057 0.99991719 0.15437409 1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Apple dataset are:\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|company_name| mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|       APPLE|0.04686345541972399| 0.05500030517578125|  1.0288224907842407|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the apple dataframe on 31 days is:\n",
      "+----------+------------------+------------------+------------------+------------------+---------+------------------+------------+--------------------+------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|   Volume|         Adj Close|company_name|        daily_return|    Moving_average|\n",
      "+----------+------------------+------------------+------------------+------------------+---------+------------------+------------+--------------------+------------------+\n",
      "|2017-01-03|29.082500457763672|28.690000534057617|28.950000762939453|29.037500381469727|115127600|27.277639389038086|       APPLE| 0.08749961853027344|29.082500457763672|\n",
      "|2017-01-04|29.127500534057617|           28.9375|28.962499618530273|  29.0049991607666| 84472400|27.247108459472656|       APPLE|0.042499542236328125|29.105000495910645|\n",
      "|2017-01-05| 29.21500015258789|28.952499389648438|28.979999542236328| 29.15250015258789| 88774400| 27.38566780090332|       APPLE|  0.1725006103515625|29.141667048136394|\n",
      "|2017-01-06|29.540000915527344| 29.11750030517578| 29.19499969482422|29.477500915527344|127007600| 27.69097137451172|       APPLE|   0.282501220703125| 29.24125051498413|\n",
      "|2017-01-09|29.857500076293945|29.485000610351562|29.487499237060547|29.747499465942383|134247600|27.944602966308594|       APPLE| 0.26000022888183594|29.364500427246092|\n",
      "+----------+------------------+------------------+------------------+------------------+---------+------------------+------------+--------------------+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "========= Correlation between Apple and Facebook =========\n",
      "+---------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n",
      "|      Col_apple|      High_facebook|        Low_facebook|       Open_facebook|      Close_facebook|     Volume_facebook|  Adj Close_facebook|\n",
      "+---------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n",
      "|     High_apple|  0.912895378181455|   0.901530797196117|  0.9069799428264861|  0.9067125389014303|0.043064423507730154|  0.9067125389014303|\n",
      "|      Low_apple| 0.9161304364039503|  0.9061836311978357|  0.9105857776438508|  0.9108094534799227| 0.03399154438063118|  0.9108094534799227|\n",
      "|     Open_apple| 0.9146824493861402|  0.9038440194466187|  0.9093706647109683|  0.9084492110830377|  0.0396729408803401|  0.9084492110830377|\n",
      "|    Close_apple| 0.9141211444758756|    0.90363022975835|  0.9081660399414679|   0.909143831170847| 0.03735250886701052|   0.909143831170847|\n",
      "|   Volume_apple|0.02686971256560911|-0.00645885255801...|0.015466086384036484|0.006426621889215457|  0.2985121680724332|0.006426621889215457|\n",
      "|Adj Close_apple| 0.9137488121117533|   0.903046340383374|  0.9076931143397897|  0.9086533367686319|0.037047906839466016|  0.9086533367686319|\n",
      "+---------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n",
      "\n",
      "========= Correlation between Apple and Google =========\n",
      "+---------------+-------------------+------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_apple|        High_google|        Low_google|        Open_google|       Close_google|      Volume_google|   Adj Close_google|\n",
      "+---------------+-------------------+------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_apple| 0.9250570139204963|0.9191814990610254| 0.9220465323554154| 0.9216400976231097|  0.107549306569833| 0.9216400976231097|\n",
      "|      Low_apple| 0.9271805396534684|0.9229422544755259| 0.9246925103923574| 0.9247381505527162|0.09114619238602828| 0.9247381505527162|\n",
      "|     Open_apple| 0.9255982495701384|0.9203505791750032| 0.9232986324073348| 0.9221905687080784|0.10067419767149861| 0.9221905687080784|\n",
      "|    Close_apple| 0.9259794273100581|0.9210632744412091| 0.9229139276251296| 0.9238202964706318|0.09763729038741001| 0.9238202964706318|\n",
      "|   Volume_apple|0.11140197279219025| 0.077009785091057|0.10026008250963374|0.09058643788194837| 0.5506635318061569|0.09058643788194837|\n",
      "|Adj Close_apple| 0.9269217065330005|0.9218899341737181| 0.9238055166735989| 0.9246956687641503|0.09834464092915338| 0.9246956687641503|\n",
      "+---------------+-------------------+------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Apple and Microsoft =========\n",
      "+---------------+-------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_apple|     High_microsoft|      Low_microsoft|    Open_microsoft|    Close_microsoft|   Volume_microsoft|Adj Close_microsoft|\n",
      "+---------------+-------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|     High_apple| 0.9412485224711806| 0.9410209464009509| 0.941079952237636| 0.9407725640703325|0.19760986029162322| 0.9402886243164973|\n",
      "|      Low_apple| 0.9408651745062625| 0.9417320000174492|0.9411433645839435| 0.9410107023198092|0.17667969514416906| 0.9404707962648428|\n",
      "|     Open_apple| 0.9402189069692057|  0.940443853765014|    0.940544899449| 0.9397788297680214| 0.1879406810554132| 0.9392693708200555|\n",
      "|    Close_apple| 0.9410980982120714| 0.9415150509287524|0.9409265617982884| 0.9414663222485514|0.18511624408090305| 0.9409394494072064|\n",
      "|   Volume_apple|0.16034818430776565|0.13809927173960976|0.1504963841886969|0.14770573114551214| 0.6676172470834141|0.14852826062899832|\n",
      "|Adj Close_apple| 0.9441345809464304| 0.9445006654088703| 0.943943037778481| 0.9444658761286495|0.18739980759540675| 0.9439920309680077|\n",
      "+---------------+-------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Apple and Tesla =========\n",
      "+---------------+------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_apple|        High_tesla|          Low_tesla|         Open_tesla|        Close_tesla|       Volume_tesla|    Adj Close_tesla|\n",
      "+---------------+------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_apple|0.9113585843845249| 0.9094178058505018|  0.910182466258854|  0.910354143823789|0.32656197631101624|  0.910354143823789|\n",
      "|      Low_apple|0.9094763149771291| 0.9083134679329079| 0.9083804760380145| 0.9089449180604628|0.31951286918535565| 0.9089449180604628|\n",
      "|     Open_apple|0.9110675700462075| 0.9093805057111825| 0.9101124008032263| 0.9101169506961259|0.32295137631254495| 0.9101169506961259|\n",
      "|    Close_apple|0.9099221025297364| 0.9084309110315691| 0.9087391531251808| 0.9094872749905819| 0.3228110675281686| 0.9094872749905819|\n",
      "|   Volume_apple|0.1456180237031512|0.13551973676265994|0.14368731099655865|0.13906321603962646|0.26500728043262195|0.13906321603962646|\n",
      "|Adj Close_apple|0.9093865761225393| 0.9078874790410435| 0.9082049388159446|  0.908939847211644| 0.3218678123011214|  0.908939847211644|\n",
      "+---------------+------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Apple and Zoom =========\n",
      "+---------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_apple|          High_zoom|           Low_zoom|          Open_zoom|         Close_zoom|        Volume_zoom|     Adj Close_zoom|\n",
      "+---------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_apple| 0.8808886369902045| 0.8816649038884656| 0.8807767021033249| 0.8829093599315536| 0.3791613665021639| 0.8829093599315536|\n",
      "|      Low_apple| 0.8767067194069705| 0.8784167261492821|  0.876801770293797| 0.8793149256125086|0.36344246562546506| 0.8793149256125086|\n",
      "|     Open_apple| 0.8792896036722005| 0.8804330811335669| 0.8794186794999088| 0.8814833759841614| 0.3706986626645329| 0.8814833759841614|\n",
      "|    Close_apple| 0.8772494184151639| 0.8785044638261572| 0.8771849971479735| 0.8798818350347076| 0.3715381733652491| 0.8798818350347076|\n",
      "|   Volume_apple|0.11622172294270718|0.10589893801805458|0.11301829354616512|0.11099403352321677|0.29710004965727693|0.11099403352321677|\n",
      "|Adj Close_apple| 0.8795810981350387| 0.8808313149467092|  0.879510318889593| 0.8822159457571348| 0.3733449486149949| 0.8822159457571348|\n",
      "+---------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "The return rate of the apple dataframe from the 2020-01-01 on month days is: 5.879997253417969.\n",
      "\n",
      "------------ EXPLORATION OF THE <FACEBOOK> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Facebook dataframe:\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|  Volume|         Adj Close|company_name|        daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|2017-01-03|117.83999633789062|115.51000213623047|116.02999877929688|116.86000061035156|20663900|116.86000061035156|    FACEBOOK|  0.8300018310546875|\n",
      "|2017-01-04|119.66000366210938|117.29000091552734|117.55000305175781|118.69000244140625|19630900|118.69000244140625|    FACEBOOK|  1.1399993896484375|\n",
      "|2017-01-05|120.94999694824219|118.31999969482422|118.86000061035156|120.66999816894531|19492200|120.66999816894531|    FACEBOOK|    1.80999755859375|\n",
      "|2017-01-06|123.87999725341797|120.02999877929688| 120.9800033569336|123.41000366210938|28545300|123.41000366210938|    FACEBOOK|  2.4300003051757812|\n",
      "|2017-01-09|125.43000030517578|123.04000091552734|123.55000305175781| 124.9000015258789|22880400| 124.9000015258789|    FACEBOOK|  1.3499984741210938|\n",
      "|2017-01-10|             125.5|124.27999877929688|124.81999969482422| 124.3499984741211|17324600| 124.3499984741211|    FACEBOOK|  -0.470001220703125|\n",
      "|2017-01-11|126.12000274658203|124.05999755859375| 124.3499984741211|126.08999633789062|18356500|126.08999633789062|    FACEBOOK|  1.7399978637695312|\n",
      "|2017-01-12| 126.7300033569336|124.80000305175781|125.61000061035156|126.62000274658203|18653900|126.62000274658203|    FACEBOOK|  1.0100021362304688|\n",
      "|2017-01-13|129.27000427246094|127.37000274658203|127.48999786376953|128.33999633789062|24884300|128.33999633789062|    FACEBOOK|  0.8499984741210938|\n",
      "|2017-01-17|128.33999633789062| 127.4000015258789| 128.0399932861328|127.87000274658203|15294500|127.87000274658203|    FACEBOOK|-0.16999053955078125|\n",
      "|2017-01-18|128.42999267578125|126.83999633789062|128.41000366210938|127.91999816894531|13145900|127.91999816894531|    FACEBOOK| -0.4900054931640625|\n",
      "|2017-01-19|128.35000610351562|127.44999694824219|128.22999572753906|127.55000305175781|12195500|127.55000305175781|    FACEBOOK|   -0.67999267578125|\n",
      "|2017-01-20|128.47999572753906|126.77999877929688|128.10000610351562|127.04000091552734|19097200|127.04000091552734|    FACEBOOK| -1.0600051879882812|\n",
      "|2017-01-23|            129.25|126.94999694824219|127.30999755859375|128.92999267578125|16593600|128.92999267578125|    FACEBOOK|     1.6199951171875|\n",
      "|2017-01-24|129.89999389648438| 128.3800048828125| 129.3800048828125| 129.3699951171875|15162700| 129.3699951171875|    FACEBOOK|     -0.010009765625|\n",
      "|2017-01-25|131.74000549316406|129.77000427246094|             130.0|131.47999572753906|18731300|131.47999572753906|    FACEBOOK|  1.4799957275390625|\n",
      "|2017-01-26|133.13999938964844|131.44000244140625| 131.6300048828125|132.77999877929688|20020100|132.77999877929688|    FACEBOOK|   1.149993896484375|\n",
      "|2017-01-27| 132.9499969482422| 131.0800018310547|132.67999267578125|132.17999267578125|19539500|132.17999267578125|    FACEBOOK|                -0.5|\n",
      "|2017-01-30| 131.5800018310547|129.60000610351562| 131.5800018310547|130.97999572753906|18956100|130.97999572753906|    FACEBOOK|  -0.600006103515625|\n",
      "|2017-01-31|130.66000366210938|129.52000427246094| 130.1699981689453|130.32000732421875|19790500|130.32000732421875|    FACEBOOK|  0.1500091552734375|\n",
      "|2017-02-01|133.49000549316406|130.67999267578125|            132.25|133.22999572753906|50139800|133.22999572753906|    FACEBOOK|  0.9799957275390625|\n",
      "|2017-02-02|135.49000549316406|130.39999389648438|133.22000122070312|130.83999633789062|54366400|130.83999633789062|    FACEBOOK|    -2.3800048828125|\n",
      "|2017-02-03|132.85000610351562|130.75999450683594|131.24000549316406|130.97999572753906|24804900|130.97999572753906|    FACEBOOK|     -0.260009765625|\n",
      "|2017-02-06|132.05999755859375| 130.3000030517578|130.97999572753906|132.05999755859375|17058500|132.05999755859375|    FACEBOOK|  1.0800018310546875|\n",
      "|2017-02-07|             133.0|131.66000366210938|132.24000549316406|131.83999633789062|14596400|131.83999633789062|    FACEBOOK| -0.4000091552734375|\n",
      "|2017-02-08|134.44000244140625|132.44000244140625|132.60000610351562| 134.1999969482422|22390600| 134.1999969482422|    FACEBOOK|  1.5999908447265625|\n",
      "|2017-02-09|             134.5|133.30999755859375|134.49000549316406|134.13999938964844|16470600|134.13999938964844|    FACEBOOK|  -0.350006103515625|\n",
      "|2017-02-10|134.94000244140625|133.67999267578125|134.10000610351562|134.19000244140625|15061900|134.19000244140625|    FACEBOOK|   0.089996337890625|\n",
      "|2017-02-13| 134.6999969482422| 133.6999969482422| 134.6999969482422| 134.0500030517578|13526200| 134.0500030517578|    FACEBOOK|  -0.649993896484375|\n",
      "|2017-02-14|134.22999572753906| 132.5500030517578|134.10000610351562|133.85000610351562|14364900|133.85000610351562|    FACEBOOK|               -0.25|\n",
      "|2017-02-15| 133.6999969482422|132.66000366210938| 133.4499969482422|133.44000244140625|13226500|133.44000244140625|    FACEBOOK| -0.0099945068359375|\n",
      "|2017-02-16| 133.8699951171875|133.02000427246094|133.07000732421875|133.83999633789062|12831100|133.83999633789062|    FACEBOOK|   0.769989013671875|\n",
      "|2017-02-17|134.08999633789062| 133.1699981689453|             133.5|133.52999877929688|12276500|133.52999877929688|    FACEBOOK|   0.029998779296875|\n",
      "|2017-02-21|133.91000366210938|132.89999389648438|             133.5|133.72000122070312|14759100|133.72000122070312|    FACEBOOK|   0.220001220703125|\n",
      "|2017-02-22| 136.7899932861328| 133.4600067138672|133.60000610351562| 136.1199951171875|27360100| 136.1199951171875|    FACEBOOK|   2.519989013671875|\n",
      "|2017-02-23| 136.1199951171875| 134.3300018310547|135.88999938964844|135.36000061035156|18422500|135.36000061035156|    FACEBOOK|  -0.529998779296875|\n",
      "|2017-02-24| 135.6199951171875|134.16000366210938|134.16000366210938|135.44000244140625|12625700|135.44000244140625|    FACEBOOK|   1.279998779296875|\n",
      "|2017-02-27|137.17999267578125|135.02000427246094|135.25999450683594|136.41000366210938|14306700|136.41000366210938|    FACEBOOK|  1.1500091552734375|\n",
      "|2017-02-28|136.80999755859375|            134.75| 136.7899932861328| 135.5399932861328|16112100| 135.5399932861328|    FACEBOOK|               -1.25|\n",
      "|2017-03-01|137.47999572753906| 136.3000030517578|136.47000122070312| 137.4199981689453|16257000| 137.4199981689453|    FACEBOOK|  0.9499969482421875|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|       daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|2020-10-07|260.17999267578125|254.82000732421875| 259.2099914550781| 258.1199951171875|23133400.00000000...| 258.1199951171875|    FACEBOOK| -1.089996337890625|\n",
      "|2020-10-08| 264.6199951171875| 259.1499938964844|            259.75|  263.760009765625|16312800.00000000...|  263.760009765625|    FACEBOOK|     4.010009765625|\n",
      "|2020-10-09|            264.75| 262.1700134277344| 264.5199890136719|264.45001220703125|14107800.00000000...|264.45001220703125|    FACEBOOK| -0.069976806640625|\n",
      "|2020-10-12|280.17999267578125| 267.8699951171875|270.20001220703125|            275.75|31019300.00000000...|            275.75|    FACEBOOK|   5.54998779296875|\n",
      "|2020-10-13| 279.1000061035156| 273.3900146484375| 277.5799865722656| 276.1400146484375|18063300.00000000...| 276.1400146484375|    FACEBOOK| -1.439971923828125|\n",
      "|2020-10-14|            278.75|             271.5| 277.6199951171875|271.82000732421875|15601200.00000000...|271.82000732421875|    FACEBOOK|  -5.79998779296875|\n",
      "|2020-10-15| 269.0400085449219| 263.6700134277344| 267.6000061035156| 266.7200012207031|15416100.00000000...| 266.7200012207031|    FACEBOOK|   -0.8800048828125|\n",
      "|2020-10-16| 271.3699951171875|265.29998779296875| 267.3800048828125|265.92999267578125|16622700.00000000...|265.92999267578125|    FACEBOOK|  -1.45001220703125|\n",
      "|2020-10-19|268.54998779296875| 259.8800048828125| 265.5299987792969| 261.3999938964844|13587000.00000000...| 261.3999938964844|    FACEBOOK|   -4.1300048828125|\n",
      "|2020-10-20|269.70001220703125| 262.8800048828125|263.05999755859375|267.55999755859375|18763200.00000000...|267.55999755859375|    FACEBOOK|                4.5|\n",
      "|2020-10-21|283.04998779296875| 276.3699951171875|279.55999755859375| 278.7300109863281|28998600.00000000...| 278.7300109863281|    FACEBOOK| -0.829986572265625|\n",
      "|2020-10-22|282.45001220703125| 275.0400085449219| 279.8699951171875| 278.1199951171875|16720000.00000000...| 278.1199951171875|    FACEBOOK|              -1.75|\n",
      "|2020-10-23|  285.239990234375|276.82000732421875|278.79998779296875| 284.7900085449219|17535200.00000000...| 284.7900085449219|    FACEBOOK|  5.990020751953125|\n",
      "|2020-10-26| 285.2300109863281|  274.260009765625| 283.1600036621094| 277.1099853515625|21322900.00000000...| 277.1099853515625|    FACEBOOK| -6.050018310546875|\n",
      "|2020-10-27|  284.510009765625|276.30999755859375|  278.760009765625| 283.2900085449219|16287200.00000000...| 283.2900085449219|    FACEBOOK|  4.529998779296875|\n",
      "|2020-10-28| 278.7900085449219|  267.260009765625| 278.7900085449219| 267.6700134277344|23121800.00000000...| 267.6700134277344|    FACEBOOK|  -11.1199951171875|\n",
      "|2020-10-29| 283.6000061035156| 273.7799987792969|276.54998779296875| 280.8299865722656|32368100.00000000...| 280.8299865722656|    FACEBOOK|  4.279998779296875|\n",
      "|2020-10-30|276.70001220703125| 259.1000061035156|             274.5| 263.1099853515625|47247800.00000000...| 263.1099853515625|    FACEBOOK|  -11.3900146484375|\n",
      "|2020-11-02| 270.4700012207031| 257.3399963378906| 264.6000061035156| 261.3599853515625|27165700.00000000...| 261.3599853515625|    FACEBOOK| -3.240020751953125|\n",
      "|2020-11-03|270.04998779296875| 260.8800048828125| 263.1600036621094|265.29998779296875|17961900.00000000...|265.29998779296875|    FACEBOOK|  2.139984130859375|\n",
      "|2020-11-04|288.94000244140625| 278.6199951171875|             281.0| 287.3800048828125|35364400.00000000...| 287.3800048828125|    FACEBOOK|    6.3800048828125|\n",
      "|2020-11-05| 297.3800048828125|288.95001220703125| 291.8999938964844|294.67999267578125|23823600.00000000...|294.67999267578125|    FACEBOOK|  2.779998779296875|\n",
      "|2020-11-06| 294.6000061035156|288.05999755859375|293.95001220703125| 293.4100036621094|13891000.00000000...| 293.4100036621094|    FACEBOOK| -0.540008544921875|\n",
      "|2020-11-09| 292.5799865722656| 278.5299987792969| 289.8699951171875| 278.7699890136719|25117700.00000000...| 278.7699890136719|    FACEBOOK|-11.100006103515625|\n",
      "|2020-11-10|             274.0|             264.0| 273.1000061035156|272.42999267578125|29067700.00000000...|272.42999267578125|    FACEBOOK| -0.670013427734375|\n",
      "|2020-11-11| 278.3999938964844| 272.4800109863281| 273.4700012207031| 276.4800109863281|14957000.00000000...| 276.4800109863281|    FACEBOOK|     3.010009765625|\n",
      "|2020-11-12|            279.75|274.42999267578125|277.17999267578125| 275.0799865722656|12899500.00000000...| 275.0799865722656|    FACEBOOK| -2.100006103515625|\n",
      "|2020-11-13|  277.760009765625| 273.1000061035156| 277.7200012207031|276.95001220703125|10391200.00000000...|276.95001220703125|    FACEBOOK| -0.769989013671875|\n",
      "|2020-11-16| 279.4100036621094| 274.6300048828125|275.04998779296875| 278.9599914550781|12970400.00000000...| 278.9599914550781|    FACEBOOK|  3.910003662109375|\n",
      "|2020-11-17|277.67999267578125| 273.2900085449219|277.67999267578125|             275.0|15040300.00000000...|             275.0|    FACEBOOK|  -2.67999267578125|\n",
      "|2020-11-18| 277.3900146484375| 271.8399963378906| 274.5199890136719| 271.9700012207031|12152900.00000000...| 271.9700012207031|    FACEBOOK|  -2.54998779296875|\n",
      "|2020-11-19| 273.4700012207031|269.19000244140625| 271.0199890136719|272.94000244140625|12963700.00000000...|272.94000244140625|    FACEBOOK|  1.920013427734375|\n",
      "|2020-11-20|             273.0| 269.4100036621094|272.55999755859375|269.70001220703125|18114700.00000000...|269.70001220703125|    FACEBOOK|   -2.8599853515625|\n",
      "|2020-11-23|270.95001220703125| 264.5299987792969| 270.8900146484375|268.42999267578125|20990800.00000000...|268.42999267578125|    FACEBOOK|  -2.46002197265625|\n",
      "|2020-11-24|277.82000732421875| 267.8699951171875|  268.489990234375| 276.9200134277344|16930400.00000000...| 276.9200134277344|    FACEBOOK|  8.430023193359375|\n",
      "|2020-11-25|280.17999267578125| 272.6499938964844| 278.1400146484375| 275.5899963378906|12467000.00000000...| 275.5899963378906|    FACEBOOK| -2.550018310546875|\n",
      "|2020-11-27| 279.1300048828125|274.82000732421875| 277.3900146484375|277.80999755859375|7808400.000000000...|277.80999755859375|    FACEBOOK|   0.41998291015625|\n",
      "|2020-11-30|277.70001220703125|  271.010009765625| 276.0299987792969| 276.9700012207031|16693300.00000000...| 276.9700012207031|    FACEBOOK|   0.94000244140625|\n",
      "|2020-12-01|289.29998779296875| 278.9599914550781| 279.1600036621094|286.54998779296875|20738900.00000000...|286.54998779296875|    FACEBOOK|  7.389984130859375|\n",
      "|2020-12-02|285.95001220703125|280.82501220703125| 285.3599853515625| 285.6499938964844|4113594.000000000...| 285.6499938964844|    FACEBOOK|  0.290008544921875|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "\n",
      "The Facebook dataframe contains 987 observations.\n",
      "\n",
      "The period between points of the Facebook dataset is:\n",
      "\t1 days (median)\n",
      "\t1.449290060851927 days (on average)\n",
      "\n",
      "Statistics for Facebook dataframe:\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|summary|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|       daily_return|\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|  count|               987|               987|               987|               987|                 987|               987|         987|                987|\n",
      "|   mean| 186.3984500224105| 182.0770063931698|184.23939196461964|184.34894618968713|       20852387.1266|184.34894618968713|        null|0.10955422506747879|\n",
      "| stddev|37.254783636139116| 35.96107958363421| 36.62328370702378| 36.62017415511841|1.3239300733826615E7| 36.62017415511841|        null| 2.9798805779120956|\n",
      "|    min|117.83999633789062|115.51000213623047|116.02999877929688|116.86000061035156|             4113594|116.86000061035156|    FACEBOOK| -16.55999755859375|\n",
      "|    max| 304.6700134277344|293.04998779296875| 300.1600036621094| 303.9100036621094|           169803700| 303.9100036621094|    FACEBOOK| 19.910003662109375|\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "\n",
      "Missing values for Facebook:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Facebook\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[ 1.          0.99763976  0.99842827  0.99829194 -0.00189788  0.99829194]\n",
      " [ 0.99763976  1.          0.99818802  0.99841692 -0.0446164   0.99841692]\n",
      " [ 0.99842827  0.99818802  1.          0.99668953 -0.0214664   0.99668953]\n",
      " [ 0.99829194  0.99841692  0.99668953  1.         -0.0235399   1.        ]\n",
      " [-0.00189788 -0.0446164  -0.0214664  -0.0235399   1.         -0.0235399 ]\n",
      " [ 0.99829194  0.99841692  0.99668953  1.         -0.0235399   1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Facebook dataset are:\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|company_name| mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|    FACEBOOK|0.10955422506747879|    0.07000732421875|  2.9798805779120956|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the facebook dataframe on 31 days is:\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+------------------+------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|  Volume|         Adj Close|company_name|      daily_return|    Moving_average|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+------------------+------------------+\n",
      "|2017-01-03|117.83999633789062|115.51000213623047|116.02999877929688|116.86000061035156|20663900|116.86000061035156|    FACEBOOK|0.8300018310546875|117.83999633789062|\n",
      "|2017-01-04|119.66000366210938|117.29000091552734|117.55000305175781|118.69000244140625|19630900|118.69000244140625|    FACEBOOK|1.1399993896484375|            118.75|\n",
      "|2017-01-05|120.94999694824219|118.31999969482422|118.86000061035156|120.66999816894531|19492200|120.66999816894531|    FACEBOOK|  1.80999755859375|119.48333231608073|\n",
      "|2017-01-06|123.87999725341797|120.02999877929688| 120.9800033569336|123.41000366210938|28545300|123.41000366210938|    FACEBOOK|2.4300003051757812|120.58249855041504|\n",
      "|2017-01-09|125.43000030517578|123.04000091552734|123.55000305175781| 124.9000015258789|22880400| 124.9000015258789|    FACEBOOK|1.3499984741210938|121.55199890136718|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+------------------+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "========= Correlation between Facebook and Google =========\n",
      "+------------------+-------------------+--------------------+-------------------+-------------------+--------------------+-------------------+\n",
      "|      Col_facebook|        High_google|          Low_google|        Open_google|       Close_google|       Volume_google|   Adj Close_google|\n",
      "+------------------+-------------------+--------------------+-------------------+-------------------+--------------------+-------------------+\n",
      "|     High_facebook| 0.9013204763144368|  0.9043275027184999| 0.9019668677505956|  0.902354687095601|0.019407080490464086|  0.902354687095601|\n",
      "|      Low_facebook| 0.8922644226959026|  0.8985141699549771| 0.8941047384535051| 0.8951531261002412|-0.01871475820396747| 0.8951531261002412|\n",
      "|     Open_facebook| 0.8970687443417756|  0.9012691601068266| 0.8993882249308792| 0.8979889258247148|0.006386186703783089| 0.8979889258247148|\n",
      "|    Close_facebook| 0.8965810309542903|  0.9013854766243498| 0.8970057541941482| 0.9000839548151618|-0.00154729629134...| 0.9000839548151618|\n",
      "|   Volume_facebook|0.04632919959042133|0.020722659984863605|0.03706274421212798|0.03303840410567292| 0.46201571804233715|0.03303840410567292|\n",
      "|Adj Close_facebook| 0.8965810309542903|  0.9013854766243498| 0.8970057541941482| 0.9000839548151618|-0.00154729629134...| 0.9000839548151618|\n",
      "+------------------+-------------------+--------------------+-------------------+-------------------+--------------------+-------------------+\n",
      "\n",
      "========= Correlation between Facebook and Microsoft =========\n",
      "+------------------+-------------------+--------------------+--------------------+-------------------+--------------------+--------------------+\n",
      "|      Col_facebook|     High_microsoft|       Low_microsoft|      Open_microsoft|    Close_microsoft|    Volume_microsoft| Adj Close_microsoft|\n",
      "+------------------+-------------------+--------------------+--------------------+-------------------+--------------------+--------------------+\n",
      "|     High_facebook| 0.8611344337622792|  0.8661798034668426|  0.8636461285559801|  0.863191860714493| 0.04316509048003523|  0.8618358690806437|\n",
      "|      Low_facebook| 0.8463832234958614|   0.853161152499042|  0.8495968009331035| 0.8494924284555815|4.061103150091626E-4|  0.8480410053768578|\n",
      "|     Open_facebook| 0.8535195020797276|  0.8591224523625867|  0.8567486831660741| 0.8556101964290249| 0.02719051425667169|  0.8542218460507391|\n",
      "|    Close_facebook| 0.8536359526940288|  0.8597059461905258|  0.8561121160577472| 0.8570760230066189| 0.01870183159763725|  0.8556526821290147|\n",
      "|   Volume_facebook|0.04014155283752632|0.028289868788162358|0.035074736795187904|0.03324664114065179| 0.42488250877289696|0.032374299609202094|\n",
      "|Adj Close_facebook| 0.8536359526940288|  0.8597059461905258|  0.8561121160577472| 0.8570760230066189| 0.01870183159763725|  0.8556526821290147|\n",
      "+------------------+-------------------+--------------------+--------------------+-------------------+--------------------+--------------------+\n",
      "\n",
      "========= Correlation between Facebook and Tesla =========\n",
      "+------------------+-------------------+-------------------+-------------------+--------------------+-------------------+--------------------+\n",
      "|      Col_facebook|         High_tesla|          Low_tesla|         Open_tesla|         Close_tesla|       Volume_tesla|     Adj Close_tesla|\n",
      "+------------------+-------------------+-------------------+-------------------+--------------------+-------------------+--------------------+\n",
      "|     High_facebook| 0.8331028488431108| 0.8332877194450948| 0.8329530532319506|  0.8331551926070123| 0.2532437691785577|  0.8331551926070123|\n",
      "|      Low_facebook| 0.8236776678441254| 0.8246657984737641| 0.8237218519464116|  0.8242516160326917|0.24031693731267956|  0.8242516160326917|\n",
      "|     Open_facebook| 0.8285050224871878|  0.828907107502155| 0.8286301094216965|  0.8285065560249492|0.24793008867245908|  0.8285065560249492|\n",
      "|    Close_facebook| 0.8280364700420986| 0.8287412199163757| 0.8278941533918055|  0.8288057479037657|0.24541874758021925|  0.8288057479037657|\n",
      "|   Volume_facebook|0.04797996520904673|0.04430007446824312|0.04748010212056688|0.045451761618572964|0.07437985166882162|0.045451761618572964|\n",
      "|Adj Close_facebook| 0.8280364700420986| 0.8287412199163757| 0.8278941533918055|  0.8288057479037657|0.24541874758021925|  0.8288057479037657|\n",
      "+------------------+-------------------+-------------------+-------------------+--------------------+-------------------+--------------------+\n",
      "\n",
      "========= Correlation between Facebook and Zoom =========\n",
      "+------------------+------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_facebook|         High_zoom|           Low_zoom|         Open_zoom|         Close_zoom|        Volume_zoom|     Adj Close_zoom|\n",
      "+------------------+------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|     High_facebook|0.8549963510605557| 0.8583130338158583|0.8572654487690667| 0.8577987066608805| 0.2240668082078146| 0.8577987066608805|\n",
      "|      Low_facebook|0.8398542703710332| 0.8442544993637039|  0.84233554484553| 0.8435466455920638|0.19346451842988494| 0.8435466455920638|\n",
      "|     Open_facebook|0.8493477910418018|   0.85288198567795| 0.851767510593323| 0.8523112490818678|0.21043062931652975| 0.8523112490818678|\n",
      "|    Close_facebook|0.8463902408767595| 0.8502550723540513|0.8488440426366688| 0.8500061058765412|0.21095494832948944| 0.8500061058765412|\n",
      "|   Volume_facebook|0.2079446186536198|0.20250627110073144|0.2085692505670957|0.20323497924323136| 0.2910013298017762|0.20323497924323136|\n",
      "|Adj Close_facebook|0.8463902408767595| 0.8502550723540513|0.8488440426366688| 0.8500061058765412|0.21095494832948944| 0.8500061058765412|\n",
      "+------------------+------------------+-------------------+------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "The return rate of the facebook dataframe from the 2020-01-01 on month days is: -0.25.\n",
      "\n",
      "------------ EXPLORATION OF THE <GOOGLE> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Google dataframe:\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+-----------------+\n",
      "|      Date|             High|              Low|             Open|            Close| Volume|        Adj Close|company_name|     daily_return|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+-----------------+\n",
      "|2017-01-03|789.6300048828125|775.7999877929688|778.8099975585938|786.1400146484375|1657300|786.1400146484375|      GOOGLE| 7.33001708984375|\n",
      "|2017-01-04|791.3400268554688|783.1599731445312|788.3599853515625|786.9000244140625|1073000|786.9000244140625|      GOOGLE|    -1.4599609375|\n",
      "|2017-01-05|  794.47998046875|  785.02001953125|786.0800170898438|  794.02001953125|1335200|  794.02001953125|      GOOGLE| 7.94000244140625|\n",
      "|2017-01-06|807.9000244140625|792.2039794921875| 795.260009765625|806.1500244140625|1640200|806.1500244140625|      GOOGLE| 10.8900146484375|\n",
      "|2017-01-09|809.9660034179688|802.8300170898438|806.4000244140625|806.6500244140625|1274600|806.6500244140625|      GOOGLE|             0.25|\n",
      "|2017-01-10|809.1300048828125| 803.510009765625|807.8599853515625|804.7899780273438|1176800|804.7899780273438|      GOOGLE|-3.07000732421875|\n",
      "|2017-01-11|808.1500244140625|801.3699951171875|            805.0|807.9099731445312|1065900|807.9099731445312|      GOOGLE| 2.90997314453125|\n",
      "|2017-01-12|807.3900146484375|799.1699829101562|807.1400146484375|806.3599853515625|1353100|806.3599853515625|      GOOGLE|  -0.780029296875|\n",
      "|2017-01-13|811.2239990234375|806.6900024414062|  807.47998046875|807.8800048828125|1099200|807.8800048828125|      GOOGLE|  0.4000244140625|\n",
      "|2017-01-17|807.1400146484375|800.3699951171875|807.0800170898438|804.6099853515625|1362100|804.6099853515625|      GOOGLE|-2.47003173828125|\n",
      "|2017-01-18|806.2050170898438| 800.989990234375|805.8099975585938|806.0700073242188|1294400|806.0700073242188|      GOOGLE|   0.260009765625|\n",
      "|2017-01-19|  809.47998046875|801.7999877929688|805.1199951171875|802.1749877929688| 919300|802.1749877929688|      GOOGLE|-2.94500732421875|\n",
      "|2017-01-20|806.9099731445312|801.6900024414062|806.9099731445312|  805.02001953125|1670000|  805.02001953125|      GOOGLE|-1.88995361328125|\n",
      "|2017-01-23|820.8699951171875| 803.739990234375|           807.25|819.3099975585938|1963600|819.3099975585938|      GOOGLE|12.05999755859375|\n",
      "|2017-01-24|825.9000244140625|817.8209838867188|822.2999877929688|823.8699951171875|1474000|823.8699951171875|      GOOGLE| 1.57000732421875|\n",
      "|2017-01-25|  835.77001953125|825.0599975585938|829.6199951171875|835.6699829101562|1494500|835.6699829101562|      GOOGLE| 6.04998779296875|\n",
      "|2017-01-26|            838.0| 827.010009765625|837.8099975585938|832.1500244140625|2973900|832.1500244140625|      GOOGLE|-5.65997314453125|\n",
      "|2017-01-27|841.9500122070312|820.4400024414062|834.7100219726562|823.3099975585938|2965800|823.3099975585938|      GOOGLE|-11.4000244140625|\n",
      "|2017-01-30|815.8400268554688|799.7999877929688|814.6599731445312|802.3200073242188|3246600|802.3200073242188|      GOOGLE|-12.3399658203125|\n",
      "|2017-01-31|           801.25|  790.52001953125|796.8599853515625|796.7899780273438|2160600|796.7899780273438|      GOOGLE|-0.07000732421875|\n",
      "|2017-02-01|801.1900024414062|791.1900024414062|799.6799926757812|795.6950073242188|2029700|795.6950073242188|      GOOGLE| -3.9849853515625|\n",
      "|2017-02-02|802.7000122070312|            792.0|793.7999877929688| 798.530029296875|1532100| 798.530029296875|      GOOGLE| 4.73004150390625|\n",
      "|2017-02-03|            806.0|800.3699951171875| 802.989990234375| 801.489990234375|1463400| 801.489990234375|      GOOGLE|             -1.5|\n",
      "|2017-02-06|801.6699829101562|           795.25|799.7000122070312|801.3400268554688|1184500|801.3400268554688|      GOOGLE|  1.6400146484375|\n",
      "|2017-02-07|            810.5| 801.780029296875| 803.989990234375| 806.969970703125|1241200| 806.969970703125|      GOOGLE|    2.97998046875|\n",
      "|2017-02-08|811.8400268554688|803.1900024414062|            807.0|808.3800048828125|1155300|808.3800048828125|      GOOGLE|  1.3800048828125|\n",
      "|2017-02-09|810.6599731445312|804.5399780273438| 809.510009765625|809.5599975585938| 989700|809.5599975585938|      GOOGLE| 0.04998779296875|\n",
      "|2017-02-10|           815.25| 809.780029296875|811.7000122070312|813.6699829101562|1135000|813.6699829101562|      GOOGLE|   1.969970703125|\n",
      "|2017-02-13|    820.958984375| 815.489990234375|            816.0| 819.239990234375|1213300| 819.239990234375|      GOOGLE|   3.239990234375|\n",
      "|2017-02-14|            823.0|            816.0|            819.0|820.4500122070312|1054700|820.4500122070312|      GOOGLE| 1.45001220703125|\n",
      "|2017-02-15|            823.0| 818.469970703125|819.3599853515625|  818.97998046875|1313600|  818.97998046875|      GOOGLE| -0.3800048828125|\n",
      "|2017-02-16|824.4000244140625|  818.97998046875|819.9299926757812|824.1599731445312|1287600|824.1599731445312|      GOOGLE|    4.22998046875|\n",
      "|2017-02-17|828.0700073242188| 821.655029296875|  823.02001953125|828.0700073242188|1611000|828.0700073242188|      GOOGLE| 5.04998779296875|\n",
      "|2017-02-21|833.4500122070312|828.3499755859375|828.6599731445312|831.6599731445312|1262300|831.6599731445312|      GOOGLE|              3.0|\n",
      "|2017-02-22|           833.25|828.6400146484375|828.6599731445312| 830.760009765625| 982900| 830.760009765625|      GOOGLE| 2.10003662109375|\n",
      "|2017-02-23|832.4600219726562|822.8800048828125|830.1199951171875|831.3300170898438|1472800|831.3300170898438|      GOOGLE| 1.21002197265625|\n",
      "|2017-02-24|            829.0|824.2000122070312|  827.72998046875|828.6400146484375|1392200|828.6400146484375|      GOOGLE|  0.9100341796875|\n",
      "|2017-02-27|            830.5|            824.0|824.5499877929688| 829.280029296875|1101500| 829.280029296875|      GOOGLE| 4.73004150390625|\n",
      "|2017-02-28|828.5399780273438|820.2000122070312|825.6099853515625|823.2100219726562|2260800|823.2100219726562|      GOOGLE|-2.39996337890625|\n",
      "|2017-03-01|836.2550048828125| 827.260009765625|828.8499755859375| 835.239990234375|1496500| 835.239990234375|      GOOGLE|  6.3900146484375|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+-----------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-----------------+\n",
      "|      Date|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|     daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-----------------+\n",
      "|2020-10-07|   1468.9599609375|            1436.0|   1464.2900390625|   1460.2900390625|1746200.000000000...|   1460.2900390625|      GOOGLE|             -4.0|\n",
      "|2020-10-08|            1490.0|1465.0899658203125|1465.0899658203125|1485.9300537109375|1187800.000000000...|1485.9300537109375|      GOOGLE|  20.840087890625|\n",
      "|2020-10-09|  1516.52001953125| 1489.449951171875| 1494.699951171875| 1515.219970703125|1435300.000000000...| 1515.219970703125|      GOOGLE|   20.52001953125|\n",
      "|2020-10-12|1593.8599853515625|1532.5699462890625|            1543.0|1569.1500244140625|2482600.000000000...|1569.1500244140625|      GOOGLE| 26.1500244140625|\n",
      "|2020-10-13|            1590.0| 1563.199951171875|  1583.72998046875|1571.6800537109375|1601000.000000000...|1571.6800537109375|      GOOGLE|-12.0499267578125|\n",
      "|2020-10-14|1587.6839599609375| 1550.530029296875|1578.5899658203125|1568.0799560546875|1931000.000000000...|1568.0799560546875|      GOOGLE| -10.510009765625|\n",
      "|2020-10-15|  1575.10498046875| 1545.030029296875|1547.1500244140625|1559.1300048828125|1540000.000000000...|1559.1300048828125|      GOOGLE|   11.97998046875|\n",
      "|2020-10-16|1581.1300048828125|            1563.0|1565.8499755859375| 1573.010009765625|1434700.000000000...| 1573.010009765625|      GOOGLE|  7.1600341796875|\n",
      "|2020-10-19|1588.1500244140625|            1528.0|   1580.4599609375|1534.6099853515625|1607100.000000000...|1534.6099853515625|      GOOGLE|-45.8499755859375|\n",
      "|2020-10-20|            1577.5|1525.6700439453125| 1527.050048828125|1555.9300537109375|2241700.000000000...|1555.9300537109375|      GOOGLE| 28.8800048828125|\n",
      "|2020-10-21|  1618.72998046875|1571.6300048828125|1573.3299560546875|  1593.31005859375|2568300.000000000...|  1593.31005859375|      GOOGLE| 19.9801025390625|\n",
      "|2020-10-22| 1621.989990234375|            1585.0| 1593.050048828125|1615.3299560546875|1433600.000000000...|1615.3299560546875|      GOOGLE| 22.2799072265625|\n",
      "|2020-10-23|1642.3599853515625| 1620.510009765625|1626.0699462890625|            1641.0|1375800.000000000...|            1641.0|      GOOGLE| 14.9300537109375|\n",
      "|2020-10-26| 1638.239990234375|            1576.5| 1625.010009765625| 1590.449951171875|1853300.000000000...| 1590.449951171875|      GOOGLE|  -34.56005859375|\n",
      "|2020-10-27| 1606.844970703125| 1582.780029296875|1595.6700439453125| 1604.260009765625|1229000.000000000...| 1604.260009765625|      GOOGLE|  8.5899658203125|\n",
      "|2020-10-28|1561.3499755859375|1514.6199951171875| 1559.739990234375|1516.6199951171875|1834000.000000000...|1516.6199951171875|      GOOGLE|-43.1199951171875|\n",
      "|2020-10-29|   1593.7099609375| 1522.239990234375|1522.3599853515625| 1567.239990234375|2003100.000000000...| 1567.239990234375|      GOOGLE| 44.8800048828125|\n",
      "|2020-10-30|            1687.0|   1604.4599609375|1672.1099853515625| 1621.010009765625|4329100.000000000...| 1621.010009765625|      GOOGLE|-51.0999755859375|\n",
      "|2020-11-02|  1660.77001953125| 1616.030029296875|1628.1600341796875| 1626.030029296875|2535400.000000000...| 1626.030029296875|      GOOGLE| -2.1300048828125|\n",
      "|2020-11-03| 1661.699951171875|1616.6199951171875| 1631.780029296875|   1650.2099609375|1661700.000000000...|   1650.2099609375|      GOOGLE|  18.429931640625|\n",
      "|2020-11-04| 1771.364990234375| 1706.030029296875| 1710.280029296875|1749.1300048828125|3570900.000000000...|1749.1300048828125|      GOOGLE| 38.8499755859375|\n",
      "|2020-11-05|1793.6400146484375| 1750.510009765625|            1781.0|1763.3699951171875|2065800.000000000...|1763.3699951171875|      GOOGLE|-17.6300048828125|\n",
      "|2020-11-06|1772.4300537109375|1740.3499755859375| 1753.949951171875|           1761.75|1660900.000000000...|           1761.75|      GOOGLE|   7.800048828125|\n",
      "|2020-11-09|  1818.06005859375|  1760.02001953125|1790.9000244140625|            1763.0|2268300.000000000...|            1763.0|      GOOGLE|-27.9000244140625|\n",
      "|2020-11-10|            1763.0| 1717.300048828125|1731.0899658203125|1740.3900146484375|2636100.000000000...|1740.3900146484375|      GOOGLE|   9.300048828125|\n",
      "|2020-11-11| 1764.219970703125| 1747.364990234375|            1750.0|   1752.7099609375|1264000.000000000...|   1752.7099609375|      GOOGLE|     2.7099609375|\n",
      "|2020-11-12|  1768.27001953125|1745.5999755859375|1747.6300048828125|1749.8399658203125|1247500.000000000...|1749.8399658203125|      GOOGLE|     2.2099609375|\n",
      "|2020-11-13|   1781.0400390625| 1744.550048828125|1757.6300048828125|  1777.02001953125|1499900.000000000...|  1777.02001953125|      GOOGLE| 19.3900146484375|\n",
      "|2020-11-16|1799.0699462890625|  1767.68994140625| 1771.699951171875|1781.3800048828125|1246800.000000000...|1781.3800048828125|      GOOGLE|  9.6800537109375|\n",
      "|2020-11-17|            1785.0|            1767.0|  1776.93994140625|1770.1500244140625|1147100.000000000...|1770.1500244140625|      GOOGLE| -6.7899169921875|\n",
      "|2020-11-18| 1773.469970703125|1746.1400146484375|  1765.22998046875| 1746.780029296875|1173500.000000000...| 1746.780029296875|      GOOGLE| -18.449951171875|\n",
      "|2020-11-19|1769.5899658203125|1737.0050048828125|1738.3800048828125|1763.9200439453125|1249900.000000000...|1763.9200439453125|      GOOGLE|    25.5400390625|\n",
      "|2020-11-20|            1774.0|1741.8599853515625|   1765.2099609375|  1742.18994140625|2313500.000000000...|  1742.18994140625|      GOOGLE|  -23.02001953125|\n",
      "|2020-11-23|1753.9000244140625| 1717.719970703125|1749.5999755859375|1734.8599853515625|2161600.000000000...|1734.8599853515625|      GOOGLE| -14.739990234375|\n",
      "|2020-11-24|1771.5999755859375|  1727.68994140625|            1730.5|1768.8800048828125|1578000.000000000...|1768.8800048828125|      GOOGLE| 38.3800048828125|\n",
      "|2020-11-25|   1778.5400390625|   1756.5400390625|1772.8900146484375|1771.4300537109375|1045800.000000000...|1771.4300537109375|      GOOGLE|    -1.4599609375|\n",
      "|2020-11-27|            1804.0|  1772.43994140625|1773.0899658203125|  1793.18994140625|884900.0000000000...|  1793.18994140625|      GOOGLE| 20.0999755859375|\n",
      "|2020-11-30|  1788.06494140625|            1755.0|1781.1839599609375| 1760.739990234375|1823800.000000000...| 1760.739990234375|      GOOGLE|-20.4439697265625|\n",
      "|2020-12-01|1824.8299560546875|1769.3699951171875|1774.3699951171875|1798.0999755859375|1736900.000000000...|1798.0999755859375|      GOOGLE|   23.72998046875|\n",
      "|2020-12-02|            1809.0|1789.4725341796875|1798.0999755859375|1807.1199951171875|285821.0000000000...|1807.1199951171875|      GOOGLE|    9.02001953125|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-----------------+\n",
      "\n",
      "The Google dataframe contains 987 observations.\n",
      "\n",
      "The period between points of the Google dataset is:\n",
      "\t1 days (median)\n",
      "\t1.449290060851927 days (on average)\n",
      "\n",
      "Statistics for Google dataframe:\n",
      "+-------+------------------+------------------+------------------+------------------+-----------------+------------------+------------+------------------+\n",
      "|summary|              High|               Low|              Open|             Close|           Volume|         Adj Close|company_name|      daily_return|\n",
      "+-------+------------------+------------------+------------------+------------------+-----------------+------------------+------------+------------------+\n",
      "|  count|               987|               987|               987|               987|              987|               987|         987|               987|\n",
      "|   mean|1175.6790605137292|1152.9178970166856|1163.8854444324065|1164.8144006854736|     1634631.7335|1164.8144006854736|        null|0.9289562530672176|\n",
      "| stddev| 218.8235632110295| 212.2207173542823|214.97779187990264|215.44599608390612|716425.6760312195|215.44599608390612|        null|16.191562223763537|\n",
      "|    min| 789.6300048828125| 775.7999877929688| 778.8099975585938| 786.1400146484375|           285821| 786.1400146484375|      GOOGLE|  -71.489990234375|\n",
      "|    max|1824.8299560546875|1789.4725341796875|1798.0999755859375|1807.1199951171875|          6207000|1807.1199951171875|      GOOGLE|  61.8299560546875|\n",
      "+-------+------------------+------------------+------------------+------------------+-----------------+------------------+------------+------------------+\n",
      "\n",
      "Missing values for Google:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Google\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[1.         0.99797685 0.99876601 0.99852863 0.07899549 0.99852863]\n",
      " [0.99797685 1.         0.99839345 0.99871947 0.03681076 0.99871947]\n",
      " [0.99876601 0.99839345 1.         0.99717218 0.06258244 0.99717218]\n",
      " [0.99852863 0.99871947 0.99717218 1.         0.05524262 1.        ]\n",
      " [0.07899549 0.03681076 0.06258244 0.05524262 1.         0.05524262]\n",
      " [0.99852863 0.99871947 0.99717218 1.         0.05524262 1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Google dataset are:\n",
      "+------------+------------------+--------------------+--------------------+\n",
      "|company_name|mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+------------------+--------------------+--------------------+\n",
      "|      GOOGLE|0.9289562530672176|     0.9100341796875|  16.191562223763537|\n",
      "+------------+------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the google dataframe on 31 days is:\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+----------------+-----------------+\n",
      "|      Date|             High|              Low|             Open|            Close| Volume|        Adj Close|company_name|    daily_return|   Moving_average|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+----------------+-----------------+\n",
      "|2017-01-03|789.6300048828125|775.7999877929688|778.8099975585938|786.1400146484375|1657300|786.1400146484375|      GOOGLE|7.33001708984375|789.6300048828125|\n",
      "|2017-01-04|791.3400268554688|783.1599731445312|788.3599853515625|786.9000244140625|1073000|786.9000244140625|      GOOGLE|   -1.4599609375|790.4850158691406|\n",
      "|2017-01-05|  794.47998046875|  785.02001953125|786.0800170898438|  794.02001953125|1335200|  794.02001953125|      GOOGLE|7.94000244140625|791.8166707356771|\n",
      "|2017-01-06|807.9000244140625|792.2039794921875| 795.260009765625|806.1500244140625|1640200|806.1500244140625|      GOOGLE|10.8900146484375|795.8375091552734|\n",
      "|2017-01-09|809.9660034179688|802.8300170898438|806.4000244140625|806.6500244140625|1274600|806.6500244140625|      GOOGLE|            0.25|798.6632080078125|\n",
      "+----------+-----------------+-----------------+-----------------+-----------------+-------+-----------------+------------+----------------+-----------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "========= Correlation between Google and Microsoft =========\n",
      "+----------------+-------------------+-------------------+-------------------+------------------+-------------------+-------------------+\n",
      "|      Col_google|     High_microsoft|      Low_microsoft|     Open_microsoft|   Close_microsoft|   Volume_microsoft|Adj Close_microsoft|\n",
      "+----------------+-------------------+-------------------+-------------------+------------------+-------------------+-------------------+\n",
      "|     High_google| 0.9453856481181697| 0.9477791326027368| 0.9468536427056968|0.9463410363795931| 0.1836333420881196| 0.9455145184023076|\n",
      "|      Low_google| 0.9382953789067113|  0.942653299169456|  0.940596013050256|0.9403783177298006|0.14041539535348677| 0.9394613590659522|\n",
      "|     Open_google| 0.9418785633244727| 0.9449623090151296| 0.9442185369519542|0.9428452275342947| 0.1676415263501774|  0.941986211501077|\n",
      "|    Close_google| 0.9418832876563558| 0.9453821420675187|  0.943309111722693|0.9442882610519647| 0.1589328118982861| 0.9433944144632486|\n",
      "|   Volume_google|0.12103193474791615|0.10039811863824825|0.11122052932713425|0.1094208711669826| 0.6745911011527568|0.10996316387723008|\n",
      "|Adj Close_google| 0.9418832876563558| 0.9453821420675187|  0.943309111722693|0.9442882610519647| 0.1589328118982861| 0.9433944144632486|\n",
      "+----------------+-------------------+-------------------+-------------------+------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Google and Tesla =========\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+------------------+-------------------+\n",
      "|      Col_google|         High_tesla|          Low_tesla|         Open_tesla|        Close_tesla|      Volume_tesla|    Adj Close_tesla|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+------------------+-------------------+\n",
      "|     High_google| 0.7712390089726987| 0.7692851772790691| 0.7699958027007977| 0.7701732001037066| 0.348710095911716| 0.7701732001037066|\n",
      "|      Low_google| 0.7642354623469032| 0.7631472893662992| 0.7631642668171206| 0.7637690893457404|0.3359670198095236| 0.7637690893457404|\n",
      "|     Open_google| 0.7677109280432608| 0.7660309429837131| 0.7666815667279554| 0.7666678700532288| 0.343562894665466| 0.7666678700532288|\n",
      "|    Close_google| 0.7669227028537191| 0.7654578848653917|  0.765657514442698| 0.7665494283405445|0.3428293246793065| 0.7665494283405445|\n",
      "|   Volume_google|0.10696878462146078|0.09946822539924896|0.10545730915370195|0.10180575690091083|0.2072985321200584|0.10180575690091083|\n",
      "|Adj Close_google| 0.7669227028537191| 0.7654578848653917|  0.765657514442698| 0.7665494283405445|0.3428293246793065| 0.7665494283405445|\n",
      "+----------------+-------------------+-------------------+-------------------+-------------------+------------------+-------------------+\n",
      "\n",
      "========= Correlation between Google and Zoom =========\n",
      "+----------------+-------------------+-------------------+------------------+------------------+-------------------+------------------+\n",
      "|      Col_google|          High_zoom|           Low_zoom|         Open_zoom|        Close_zoom|        Volume_zoom|    Adj Close_zoom|\n",
      "+----------------+-------------------+-------------------+------------------+------------------+-------------------+------------------+\n",
      "|     High_google| 0.7498826496251751| 0.7514198064284143|0.7511706055536816|0.7525215806958431| 0.2768536599950969|0.7525215806958431|\n",
      "|      Low_google| 0.7282067849014708| 0.7311612333480673|0.7298420298412479| 0.731949917181583|0.23768750398312113| 0.731949917181583|\n",
      "|     Open_google| 0.7404192759014541| 0.7425982651370591|0.7419145392050145|0.7434767987691766|0.26056446450260723|0.7434767987691766|\n",
      "|    Close_google|  0.736943193970417|  0.739205159440163| 0.738408297539611|0.7405503876943942| 0.2588574672178055|0.7405503876943942|\n",
      "|   Volume_google|0.10990858021173726|0.09750744823876521|0.1051981784144731|0.1024465920839466|0.36087726939600523|0.1024465920839466|\n",
      "|Adj Close_google|  0.736943193970417|  0.739205159440163| 0.738408297539611|0.7405503876943942| 0.2588574672178055|0.7405503876943942|\n",
      "+----------------+-------------------+-------------------+------------------+------------------+-------------------+------------------+\n",
      "\n",
      "The return rate of the google dataframe from the 2020-01-01 on month days is: 88.469970703125.\n",
      "\n",
      "------------ EXPLORATION OF THE <MICROSOFT> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Microsoft dataframe:\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|  Volume|         Adj Close|company_name|        daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|2017-01-03| 62.84000015258789|62.130001068115234|62.790000915527344| 62.58000183105469|20694100| 58.67324447631836|   MICROSOFT|-0.20999908447265625|\n",
      "|2017-01-04|             62.75|62.119998931884766| 62.47999954223633| 62.29999923706055|21340000| 58.41072463989258|   MICROSOFT|-0.18000030517578125|\n",
      "|2017-01-05| 62.65999984741211|62.029998779296875|62.189998626708984| 62.29999923706055|24876000| 58.41072463989258|   MICROSOFT|  0.1100006103515625|\n",
      "|2017-01-06|63.150001525878906|62.040000915527344| 62.29999923706055| 62.84000015258789|19922900|58.917015075683594|   MICROSOFT|  0.5400009155273438|\n",
      "|2017-01-09| 63.08000183105469|62.540000915527344|  62.7599983215332| 62.63999938964844|20382700|58.729496002197266|   MICROSOFT|-0.11999893188476562|\n",
      "|2017-01-10| 63.06999969482422|62.279998779296875| 62.72999954223633|62.619998931884766|18593000| 58.71074676513672|   MICROSOFT| -0.1100006103515625|\n",
      "|2017-01-11| 63.22999954223633| 62.43000030517578| 62.61000061035156|63.189998626708984|21517300| 59.24515914916992|   MICROSOFT|  0.5799980163574219|\n",
      "|2017-01-12|63.400001525878906| 61.95000076293945|63.060001373291016| 62.61000061035156|20968200| 58.70137023925781|   MICROSOFT| -0.4500007629394531|\n",
      "|2017-01-13|62.869998931884766|62.349998474121094|62.619998931884766| 62.70000076293945|19422300|  58.7857551574707|   MICROSOFT|  0.0800018310546875|\n",
      "|2017-01-17| 62.70000076293945|62.029998779296875| 62.68000030517578|62.529998779296875|20664000| 58.62636947631836|   MICROSOFT|-0.15000152587890625|\n",
      "|2017-01-18| 62.70000076293945|62.119998931884766| 62.66999816894531|              62.5|19670100| 58.59824752807617|   MICROSOFT| -0.1699981689453125|\n",
      "|2017-01-19| 62.97999954223633| 62.20000076293945|  62.2400016784668| 62.29999923706055|18451700| 58.41072463989258|   MICROSOFT|    0.05999755859375|\n",
      "|2017-01-20| 62.81999969482422|62.369998931884766| 62.66999816894531|  62.7400016784668|30213500| 58.82326126098633|   MICROSOFT| 0.07000350952148438|\n",
      "|2017-01-23|63.119998931884766| 62.56999969482422| 62.70000076293945|62.959999084472656|23097600| 59.02952575683594|   MICROSOFT|  0.2599983215332031|\n",
      "|2017-01-24|  63.7400016784668|62.939998626708984| 63.20000076293945| 63.52000045776367|24672900|59.554569244384766|   MICROSOFT| 0.31999969482421875|\n",
      "|2017-01-25|  64.0999984741211| 63.45000076293945| 63.95000076293945| 63.68000030517578|23672700| 59.70457458496094|   MICROSOFT| -0.2700004577636719|\n",
      "|2017-01-26| 64.54000091552734| 63.54999923706055| 64.12000274658203|  64.2699966430664|43554600| 60.25773620605469|   MICROSOFT|   0.149993896484375|\n",
      "|2017-01-27| 65.91000366210938| 64.88999938964844| 65.38999938964844| 65.77999877929688|44818000| 61.67348098754883|   MICROSOFT|  0.3899993896484375|\n",
      "|2017-01-30| 65.79000091552734| 64.80000305175781| 65.69000244140625| 65.12999725341797|31651400| 61.06404495239258|   MICROSOFT| -0.5600051879882812|\n",
      "|2017-01-31|  65.1500015258789| 64.26000213623047| 64.86000061035156|  64.6500015258789|25270500| 60.61402130126953|   MICROSOFT|-0.20999908447265625|\n",
      "|2017-02-01| 64.62000274658203|63.470001220703125| 64.36000061035156| 63.58000183105469|39671500| 59.61082458496094|   MICROSOFT|  -0.779998779296875|\n",
      "|2017-02-02| 63.40999984741211|             62.75|             63.25| 63.16999816894531|45827000| 59.22640609741211|   MICROSOFT| -0.0800018310546875|\n",
      "|2017-02-03| 63.70000076293945| 63.06999969482422|              63.5| 63.68000030517578|30301800| 59.70457458496094|   MICROSOFT| 0.18000030517578125|\n",
      "|2017-02-06|63.650001525878906| 63.13999938964844|              63.5| 63.63999938964844|19796400| 59.66706848144531|   MICROSOFT|  0.1399993896484375|\n",
      "|2017-02-07|63.779998779296875| 63.22999954223633|  63.7400016784668| 63.43000030517578|20277200| 59.47018051147461|   MICROSOFT| -0.3100013732910156|\n",
      "|2017-02-08|63.810001373291016|63.220001220703125| 63.56999969482422| 63.34000015258789|18096400|59.385799407958984|   MICROSOFT|-0.22999954223632812|\n",
      "|2017-02-09| 64.44000244140625| 63.31999969482422| 63.52000045776367| 64.05999755859375|22644400|60.060848236083984|   MICROSOFT|  0.5399971008300781|\n",
      "|2017-02-10| 64.30000305175781| 63.97999954223633|             64.25|              64.0|18170700| 60.00460433959961|   MICROSOFT|               -0.25|\n",
      "|2017-02-13| 64.86000061035156| 64.12999725341797| 64.23999786376953| 64.72000122070312|22920100| 60.67965316772461|   MICROSOFT| 0.48000335693359375|\n",
      "|2017-02-14| 64.72000122070312|  64.0199966430664| 64.41000366210938| 64.56999969482422|23108400|60.906044006347656|   MICROSOFT| 0.15999603271484375|\n",
      "|2017-02-15| 64.56999969482422| 64.16000366210938|              64.5| 64.52999877929688|17005200|  60.8682975769043|   MICROSOFT|   0.029998779296875|\n",
      "|2017-02-16| 65.23999786376953| 64.44000244140625| 64.73999786376953|  64.5199966430664|20546300| 60.85886001586914|   MICROSOFT|  -0.220001220703125|\n",
      "|2017-02-17| 64.69000244140625| 64.30000305175781| 64.47000122070312| 64.62000274658203|21248800| 60.95319747924805|   MICROSOFT| 0.15000152587890625|\n",
      "|2017-02-21| 64.94999694824219| 64.44999694824219| 64.61000061035156| 64.48999786376953|20655900|60.830570220947266|   MICROSOFT|-0.12000274658203125|\n",
      "|2017-02-22| 64.38999938964844| 64.05000305175781| 64.33000183105469| 64.36000061035156|19292700| 60.70794677734375|   MICROSOFT|   0.029998779296875|\n",
      "|2017-02-23|  64.7300033569336| 64.19000244140625| 64.41999816894531| 64.62000274658203|20273100| 60.95319747924805|   MICROSOFT| 0.20000457763671875|\n",
      "|2017-02-24| 64.80000305175781| 64.13999938964844| 64.52999877929688| 64.62000274658203|21796800| 60.95319747924805|   MICROSOFT| 0.09000396728515625|\n",
      "|2017-02-27| 64.54000091552734| 64.05000305175781| 64.54000091552734|  64.2300033569336|15871500|60.585330963134766|   MICROSOFT|   -0.30999755859375|\n",
      "|2017-02-28| 64.19999694824219|  63.7599983215332| 64.08000183105469| 63.97999954223633|23239800| 60.34950256347656|   MICROSOFT|-0.10000228881835938|\n",
      "|2017-03-01| 64.98999786376953|  64.0199966430664| 64.12999725341797| 64.94000244140625|26937500| 61.25505065917969|   MICROSOFT|  0.8100051879882812|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|       daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|2020-10-07|210.11000061035156|206.72000122070312|207.05999755859375| 209.8300018310547|25681100.00000000...|209.28208923339844|   MICROSOFT| 2.7700042724609375|\n",
      "|2020-10-08|211.19000244140625|208.32000732421875|210.50999450683594| 210.5800018310547|19925800.00000000...|210.03013610839844|   MICROSOFT|   0.07000732421875|\n",
      "|2020-10-09|215.86000061035156|211.22999572753906|211.22999572753906|215.80999755859375|26458000.00000000...|215.24647521972656|   MICROSOFT| 4.5800018310546875|\n",
      "|2020-10-12|223.86000061035156|216.80999755859375| 218.7899932861328|221.39999389648438|40461400.00000000...|220.82186889648438|   MICROSOFT| 2.6100006103515625|\n",
      "|2020-10-13| 225.2100067138672|220.42999267578125|222.72000122070312|222.86000061035156|28950800.00000000...|222.27806091308594|   MICROSOFT| 0.1399993896484375|\n",
      "|2020-10-14|224.22000122070312| 219.1300048828125|             223.0|220.86000061035156|23451700.00000000...| 220.2832794189453|   MICROSOFT|-2.1399993896484375|\n",
      "|2020-10-15|220.36000061035156|216.00999450683594|217.10000610351562|219.66000366210938|22733100.00000000...|   219.08642578125|   MICROSOFT|   2.55999755859375|\n",
      "|2020-10-16| 222.2899932861328|219.32000732421875|220.14999389648438|219.66000366210938|26057900.00000000...|   219.08642578125|   MICROSOFT|    -0.489990234375|\n",
      "|2020-10-19| 222.3000030517578|213.72000122070312| 220.4199981689453|214.22000122070312|27625800.00000000...|213.66062927246094|   MICROSOFT|-6.1999969482421875|\n",
      "|2020-10-20| 217.3699951171875|213.08999633789062| 215.8000030517578|214.64999389648438|22753500.00000000...|214.08949279785156|   MICROSOFT|-1.1500091552734375|\n",
      "|2020-10-21| 216.9199981689453| 213.1199951171875| 213.1199951171875| 214.8000030517578|22724900.00000000...|214.23912048339844|   MICROSOFT| 1.6800079345703125|\n",
      "|2020-10-22|216.05999755859375| 211.6999969482422|213.92999267578125|214.88999938964844|22351500.00000000...|214.32887268066406|   MICROSOFT| 0.9600067138671875|\n",
      "|2020-10-23|216.27999877929688|213.16000366210938|215.02999877929688|216.22999572753906|18879600.00000000...|215.66537475585938|   MICROSOFT| 1.1999969482421875|\n",
      "|2020-10-26|216.33999633789062|208.10000610351562|213.85000610351562| 210.0800018310547|37111600.00000000...|209.53143310546875|   MICROSOFT|-3.7700042724609375|\n",
      "|2020-10-27| 214.6699981689453| 210.3300018310547|211.58999633789062|            213.25|36700300.00000000...| 212.6931610107422|   MICROSOFT|  1.660003662109375|\n",
      "|2020-10-28|208.83999633789062|202.10000610351562| 207.6699981689453|202.67999267578125|51195600.00000000...| 202.1507568359375|   MICROSOFT|-4.9900054931640625|\n",
      "|2020-10-29|207.36000061035156| 203.3699951171875|204.07000732421875|204.72000122070312|31432600.00000000...| 204.1854248046875|   MICROSOFT|  0.649993896484375|\n",
      "|2020-10-30| 204.2899932861328| 199.6199951171875|             203.5|202.47000122070312|36953700.00000000...|201.94131469726562|   MICROSOFT| -1.029998779296875|\n",
      "|2020-11-02|205.27999877929688| 200.1199951171875| 204.2899932861328| 202.3300018310547|30842200.00000000...|201.80166625976562|   MICROSOFT| -1.959991455078125|\n",
      "|2020-11-03| 208.1199951171875| 203.1199951171875|203.88999938964844|206.42999267578125|27512000.00000000...|205.89096069335938|   MICROSOFT| 2.5399932861328125|\n",
      "|2020-11-04|218.32000732421875| 212.4199981689453|214.02000427246094|216.38999938964844|42311800.00000000...|  215.824951171875|   MICROSOFT|    2.3699951171875|\n",
      "|2020-11-05| 224.1199951171875|221.14999389648438| 222.0399932861328| 223.2899932861328|36080100.00000000...|222.70693969726562|   MICROSOFT|               1.25|\n",
      "|2020-11-06|224.36000061035156|218.02999877929688|222.25999450683594|223.72000122070312|25231900.00000000...| 223.1358184814453|   MICROSOFT| 1.4600067138671875|\n",
      "|2020-11-09| 228.1199951171875| 217.8800048828125|224.44000244140625|218.38999938964844|44395000.00000000...|217.81973266601562|   MICROSOFT|-6.0500030517578125|\n",
      "|2020-11-10|             216.5|209.72000122070312|             214.5|211.00999450683594|44045100.00000000...|210.45899963378906|   MICROSOFT|-3.4900054931640625|\n",
      "|2020-11-11| 218.0399932861328| 212.1999969482422|212.38999938964844| 216.5500030517578|29440800.00000000...| 215.9845428466797|   MICROSOFT|  4.160003662109375|\n",
      "|2020-11-12|219.11000061035156| 214.4600067138672| 217.2100067138672|215.44000244140625|21593900.00000000...|   214.87744140625|   MICROSOFT|-1.7700042724609375|\n",
      "|2020-11-13| 217.4199981689453|214.16000366210938|216.36000061035156|216.50999450683594|18621100.00000000...|215.94464111328125|   MICROSOFT|  0.149993896484375|\n",
      "|2020-11-16|217.74000549316406|214.52000427246094| 214.8699951171875|217.22999572753906|24953300.00000000...| 216.6627655029297|   MICROSOFT| 2.3600006103515625|\n",
      "|2020-11-17|217.67999267578125| 214.0800018310547|216.10000610351562| 214.4600067138672|24154100.00000000...|213.90000915527344|   MICROSOFT|-1.6399993896484375|\n",
      "|2020-11-18| 215.1699981689453|210.92999267578125|213.64999389648438| 211.0800018310547|28372800.00000000...| 211.0800018310547|   MICROSOFT|-2.5699920654296875|\n",
      "|2020-11-19|213.02999877929688|209.92999267578125| 211.3800048828125| 212.4199981689453|24792700.00000000...| 212.4199981689453|   MICROSOFT| 1.0399932861328125|\n",
      "|2020-11-20| 213.2899932861328|             210.0| 212.1999969482422|210.38999938964844|22829100.00000000...|210.38999938964844|   MICROSOFT|  -1.80999755859375|\n",
      "|2020-11-23| 212.2899932861328|208.16000366210938| 210.9499969482422|210.11000061035156|25683500.00000000...|210.11000061035156|   MICROSOFT| -0.839996337890625|\n",
      "|2020-11-24|            214.25|208.86000061035156|209.58999633789062|213.86000061035156|33979700.00000000...|213.86000061035156|   MICROSOFT| 4.2700042724609375|\n",
      "|2020-11-25| 215.2899932861328| 212.4600067138672|215.11000061035156| 213.8699951171875|21012900.00000000...| 213.8699951171875|   MICROSOFT|-1.2400054931640625|\n",
      "|2020-11-27|216.27000427246094| 214.0399932861328|214.85000610351562|215.22999572753906|14512200.00000000...|215.22999572753906|   MICROSOFT| 0.3799896240234375|\n",
      "|2020-11-30|214.75999450683594|210.83999633789062|214.10000610351562|214.07000732421875|33064800.00000000...|214.07000732421875|   MICROSOFT| -0.029998779296875|\n",
      "|2020-12-01|217.32000732421875|213.35000610351562|214.50999450683594| 216.2100067138672|30904500.00000000...| 216.2100067138672|   MICROSOFT|   1.70001220703125|\n",
      "|2020-12-02| 215.0850067138672| 212.8000030517578| 214.8800048828125| 214.1269989013672|6486678.000000000...| 214.1269989013672|   MICROSOFT|-0.7530059814453125|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "\n",
      "The Microsoft dataframe contains 987 observations.\n",
      "\n",
      "The period between points of the Microsoft dataset is:\n",
      "\t1 days (median)\n",
      "\t1.449290060851927 days (on average)\n",
      "\n",
      "Statistics for Microsoft dataframe:\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+--------------------+\n",
      "|summary|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|        daily_return|\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+--------------------+\n",
      "|  count|               987|               987|               987|               987|                 987|               987|         987|                 987|\n",
      "|   mean|123.52858665334901|121.02308006634466|122.32611970601957|122.34503256924367|       29104952.5613|119.77032003045203|        null|0.018912863224110704|\n",
      "| stddev| 46.40416482581066|45.038031013567725| 45.73307377448831| 45.74600811823214|1.3627194702253582E7| 46.76978863281242|        null|  1.9500676000297985|\n",
      "|    min| 62.65999984741211| 61.95000076293945|62.189998626708984| 62.29999923706055|             6486678| 58.41072463989258|   MICROSOFT| -11.970001220703125|\n",
      "|    max|232.86000061035156|227.35000610351562|229.27000427246094|231.64999389648438|           111242100|231.04510498046875|   MICROSOFT|  11.330001831054688|\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+--------------------+\n",
      "\n",
      "Missing values for Microsoft:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Microsoft\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[1.         0.99921751 0.99956925 0.99944407 0.26261179 0.99947039]\n",
      " [0.99921751 1.         0.99950716 0.99953128 0.23309821 0.99949715]\n",
      " [0.99956925 0.99950716 1.         0.9990912  0.24904375 0.99909304]\n",
      " [0.99944407 0.99953128 0.9990912  1.         0.2456798  0.99997632]\n",
      " [0.26261179 0.23309821 0.24904375 0.2456798  1.         0.24681487]\n",
      " [0.99947039 0.99949715 0.99909304 0.99997632 0.24681487 1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Microsoft dataset are:\n",
      "+------------+--------------------+--------------------+--------------------+\n",
      "|company_name|  mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+--------------------+--------------------+--------------------+\n",
      "|   MICROSOFT|0.018912863224110704| 0.09000396728515625|  1.9500676000297985|\n",
      "+------------+--------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the microsoft dataframe on 31 days is:\n",
      "+----------+------------------+------------------+------------------+-----------------+--------+------------------+------------+--------------------+------------------+\n",
      "|      Date|              High|               Low|              Open|            Close|  Volume|         Adj Close|company_name|        daily_return|    Moving_average|\n",
      "+----------+------------------+------------------+------------------+-----------------+--------+------------------+------------+--------------------+------------------+\n",
      "|2017-01-03| 62.84000015258789|62.130001068115234|62.790000915527344|62.58000183105469|20694100| 58.67324447631836|   MICROSOFT|-0.20999908447265625| 62.84000015258789|\n",
      "|2017-01-04|             62.75|62.119998931884766| 62.47999954223633|62.29999923706055|21340000| 58.41072463989258|   MICROSOFT|-0.18000030517578125|62.795000076293945|\n",
      "|2017-01-05| 62.65999984741211|62.029998779296875|62.189998626708984|62.29999923706055|24876000| 58.41072463989258|   MICROSOFT|  0.1100006103515625|             62.75|\n",
      "|2017-01-06|63.150001525878906|62.040000915527344| 62.29999923706055|62.84000015258789|19922900|58.917015075683594|   MICROSOFT|  0.5400009155273438| 62.85000038146973|\n",
      "|2017-01-09| 63.08000183105469|62.540000915527344|  62.7599983215332|62.63999938964844|20382700|58.729496002197266|   MICROSOFT|-0.11999893188476562|62.896000671386716|\n",
      "+----------+------------------+------------------+------------------+-----------------+--------+------------------+------------+--------------------+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "========= Correlation between Microsoft and Tesla =========\n",
      "+-------------------+-------------------+------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_microsoft|         High_tesla|         Low_tesla|        Open_tesla|        Close_tesla|       Volume_tesla|    Adj Close_tesla|\n",
      "+-------------------+-------------------+------------------+------------------+-------------------+-------------------+-------------------+\n",
      "|     High_microsoft| 0.7743821540247339|0.7711597019679031|0.7724093858735275|  0.772679602138547| 0.3488375230924025|  0.772679602138547|\n",
      "|      Low_microsoft| 0.7729891784803539|0.7703609856919504|0.7711873196646272| 0.7716918875972685|  0.340479382749019| 0.7716918875972685|\n",
      "|     Open_microsoft| 0.7736662919169182|0.7707100648879894|0.7719231288541617| 0.7719539322367913|0.34374428717613087| 0.7719539322367913|\n",
      "|    Close_microsoft| 0.7732515822973471|0.7703686051761256|0.7713189629699578| 0.7720378958676984|0.34450391563608457| 0.7720378958676984|\n",
      "|   Volume_microsoft|0.15584343749542198|  0.14536702342298|0.1530393304450703|0.14848073591109304| 0.2585140694430466|0.14848073591109304|\n",
      "|Adj Close_microsoft| 0.7730381448810538|0.7701511284100235|0.7711068312774227| 0.7718157786620587| 0.3441340036848946| 0.7718157786620587|\n",
      "+-------------------+-------------------+------------------+------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "========= Correlation between Microsoft and Zoom =========\n",
      "+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|      Col_microsoft|          High_zoom|           Low_zoom|          Open_zoom|         Close_zoom|        Volume_zoom|     Adj Close_zoom|\n",
      "+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "|     High_microsoft|  0.842943435742423| 0.8446807740425668| 0.8431556728082742| 0.8457305405096808| 0.4226079274105368| 0.8457305405096808|\n",
      "|      Low_microsoft| 0.8402230937604342| 0.8431835101596402| 0.8409687513000877| 0.8438095376580264| 0.3937197188025966| 0.8438095376580264|\n",
      "|     Open_microsoft| 0.8412417913229586| 0.8434883912733688| 0.8420396419988482| 0.8440685624930693| 0.4071695914114405| 0.8440685624930693|\n",
      "|    Close_microsoft| 0.8394640419527906|  0.841908807532696| 0.8399360270120861| 0.8431651966720145| 0.4081136920229053| 0.8431651966720145|\n",
      "|   Volume_microsoft|0.07381718482068282|0.06066027881974787|0.06854774110462795|0.06652733036427771|0.46433664704537064|0.06652733036427771|\n",
      "|Adj Close_microsoft|  0.841447516036464| 0.8438106800699313| 0.8418591891093383| 0.8451039311151026|0.41146927851170856| 0.8451039311151026|\n",
      "+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+\n",
      "\n",
      "The return rate of the microsoft dataframe from the 2020-01-01 on month days is: 12.160003662109375.\n",
      "\n",
      "------------ EXPLORATION OF THE <TESLA> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Tesla dataframe:\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|  Volume|         Adj Close|company_name|        daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|2017-01-03|44.066001892089844| 42.19200134277344| 42.97200012207031| 43.39799880981445|29616500| 43.39799880981445|       TESLA|  0.4259986877441406|\n",
      "|2017-01-04|45.599998474121094| 42.86199951171875| 42.95000076293945| 45.39799880981445|56067500| 45.39799880981445|       TESLA|      2.447998046875|\n",
      "|2017-01-05| 45.49599838256836| 44.38999938964844|45.284000396728516|45.349998474121094|29558500|45.349998474121094|       TESLA| 0.06599807739257812|\n",
      "|2017-01-06|  46.0620002746582| 45.09000015258789| 45.38600158691406|45.801998138427734|27639500|45.801998138427734|       TESLA|  0.4159965515136719|\n",
      "|2017-01-09| 46.38399887084961|45.599998474121094| 45.79399871826172| 46.25600051879883|19897500| 46.25600051879883|       TESLA|  0.4620018005371094|\n",
      "|2017-01-10|46.400001525878906| 45.37799835205078|46.400001525878906|  45.9739990234375|18300000|  45.9739990234375|       TESLA|-0.42600250244140625|\n",
      "|2017-01-11| 45.99599838256836| 45.33599853515625| 45.81399917602539| 45.94599914550781|18254000| 45.94599914550781|       TESLA| 0.13199996948242188|\n",
      "|2017-01-12| 46.13999938964844| 45.11600112915039|  45.8120002746582|45.917999267578125|18951000|45.917999267578125|       TESLA| 0.10599899291992188|\n",
      "|2017-01-13| 47.56999969482422|45.917999267578125|              46.0| 47.54999923706055|30465000| 47.54999923706055|       TESLA|  1.5499992370605469|\n",
      "|2017-01-17|47.992000579833984|46.874000549316406| 47.34000015258789| 47.11600112915039|23087500| 47.11600112915039|       TESLA|    -0.2239990234375|\n",
      "|2017-01-18| 47.94200134277344| 47.11600112915039| 47.33000183105469|47.672000885009766|18845000|47.672000885009766|       TESLA|  0.3419990539550781|\n",
      "|2017-01-19|49.736000061035156|48.150001525878906| 49.45000076293945| 48.75199890136719|38661500| 48.75199890136719|       TESLA| -0.6980018615722656|\n",
      "|2017-01-20| 49.20000076293945| 48.60200119018555| 49.09199905395508| 48.94599914550781|21021500| 48.94599914550781|       TESLA|-0.14599990844726562|\n",
      "|2017-01-23|50.178001403808594|49.099998474121094| 49.16999816894531|49.784000396728516|31314500|49.784000396728516|       TESLA|  0.6140022277832031|\n",
      "|2017-01-24|50.959999084472656| 49.93000030517578|              50.0|50.922000885009766|24827500|50.922000885009766|       TESLA|  0.9220008850097656|\n",
      "|2017-01-25| 51.69200134277344| 50.36000061035156| 51.46200180053711| 50.89400100708008|25713000| 50.89400100708008|       TESLA| -0.5680007934570312|\n",
      "|2017-01-26| 51.14799880981445|50.150001525878906|50.858001708984375| 50.50199890136719|15760500| 50.50199890136719|       TESLA| -0.3560028076171875|\n",
      "|2017-01-27|50.599998474121094| 49.70399856567383|  50.2760009765625| 50.59000015258789|15831500| 50.59000015258789|       TESLA|  0.3139991760253906|\n",
      "|2017-01-30| 51.05799865722656| 49.41999816894531| 50.50600051879883|50.125999450683594|19005500|50.125999450683594|       TESLA| -0.3800010681152344|\n",
      "|2017-01-31|51.178001403808594|49.540000915527344|49.847999572753906| 50.38600158691406|20580500| 50.38600158691406|       TESLA|  0.5380020141601562|\n",
      "|2017-02-01| 50.63999938964844|49.810001373291016| 50.61000061035156|49.847999572753906|19794000|49.847999572753906|       TESLA| -0.7620010375976562|\n",
      "|2017-02-02| 50.48400115966797| 49.54199981689453|49.667999267578125|50.310001373291016|12499000|50.310001373291016|       TESLA|  0.6420021057128906|\n",
      "|2017-02-03| 50.43600082397461| 49.93600082397461| 50.38199996948242| 50.26599884033203|10933500| 50.26599884033203|       TESLA|-0.11600112915039062|\n",
      "|2017-02-06| 51.56399917602539|50.125999450683594| 50.20000076293945| 51.55400085449219|17812500| 51.55400085449219|       TESLA|  1.3540000915527344|\n",
      "|2017-02-07|              52.0|51.284000396728516| 51.63800048828125| 51.49599838256836|21224000| 51.49599838256836|       TESLA|-0.14200210571289062|\n",
      "|2017-02-08|52.672000885009766|  51.2400016784668|51.470001220703125| 52.41600036621094|19665000| 52.41600036621094|       TESLA|  0.9459991455078125|\n",
      "|2017-02-09|54.236000061035156| 53.22999954223633|             53.25| 53.84000015258789|39101000| 53.84000015258789|       TESLA|  0.5900001525878906|\n",
      "|2017-02-10|54.189998626708984| 53.22200012207031| 53.95800018310547| 53.84600067138672|18098500| 53.84600067138672|       TESLA|   -0.11199951171875|\n",
      "|2017-02-13| 56.15800094604492| 54.10200119018555| 54.14799880981445|56.119998931884766|35148000|56.119998931884766|       TESLA|  1.9720001220703125|\n",
      "|2017-02-14| 57.47800064086914| 55.72200012207031|55.805999755859375| 56.19599914550781|36726000| 56.19599914550781|       TESLA|  0.3899993896484375|\n",
      "|2017-02-15|56.448001861572266| 55.28799819946289|              56.0| 55.95199966430664|24739500| 55.95199966430664|       TESLA|-0.04800033569335...|\n",
      "|2017-02-16|              56.0| 53.70000076293945| 55.52000045776367|53.790000915527344|35386500|53.790000915527344|       TESLA| -1.7299995422363281|\n",
      "|2017-02-17|54.577999114990234| 52.83000183105469| 53.15999984741211| 54.44599914550781|31285500| 54.44599914550781|       TESLA|  1.2859992980957031|\n",
      "|2017-02-21|56.279998779296875|54.801998138427734| 55.09000015258789| 55.47800064086914|28383500| 55.47800064086914|       TESLA|    0.38800048828125|\n",
      "|2017-02-22|56.689998626708984| 54.52000045776367|  56.0620002746582| 54.70199966430664|43775000| 54.70199966430664|       TESLA| -1.3600006103515625|\n",
      "|2017-02-23| 52.93199920654297| 51.11199951171875| 52.79999923706055|51.198001861572266|74576000|51.198001861572266|       TESLA| -1.6019973754882812|\n",
      "|2017-02-24|51.650001525878906|50.040000915527344| 50.53200149536133|51.400001525878906|40858000|51.400001525878906|       TESLA|  0.8680000305175781|\n",
      "|2017-02-27|49.672000885009766|48.402000427246094| 49.63399887084961| 49.24599838256836|57304000| 49.24599838256836|       TESLA|   -0.38800048828125|\n",
      "|2017-02-28| 50.20000076293945|48.779998779296875|  48.8380012512207| 49.99800109863281|30390500| 49.99800109863281|       TESLA|  1.1599998474121094|\n",
      "|2017-03-01|50.970001220703125|49.821998596191406| 50.83599853515625| 50.00400161743164|24047500| 50.00400161743164|       TESLA| -0.8319969177246094|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|       daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|2020-10-07| 429.8999938964844| 413.8500061035156| 419.8699951171875|425.29998779296875|43127700.00000000...|425.29998779296875|       TESLA|   5.42999267578125|\n",
      "|2020-10-08|             439.0|425.29998779296875|438.44000244140625| 425.9200134277344|40421100.00000000...| 425.9200134277344|       TESLA|-12.519989013671875|\n",
      "|2020-10-09| 434.5899963378906| 426.4599914550781| 430.1300048828125|             434.0|28925700.00000000...|             434.0|       TESLA|    3.8699951171875|\n",
      "|2020-10-12|  448.739990234375| 438.5799865722656|             442.0|442.29998779296875|38791100.00000000...|442.29998779296875|       TESLA|   0.29998779296875|\n",
      "|2020-10-13| 448.8900146484375| 436.6000061035156| 443.3500061035156| 446.6499938964844|34463700.00000000...| 446.6499938964844|       TESLA|   3.29998779296875|\n",
      "|2020-10-14| 465.8999938964844| 447.3500061035156| 449.7799987792969|461.29998779296875|48045400.00000000...|461.29998779296875|       TESLA| 11.519989013671875|\n",
      "|2020-10-15|456.57000732421875|             442.5|450.30999755859375| 448.8800048828125|35672400.00000000...| 448.8800048828125|       TESLA|  -1.42999267578125|\n",
      "|2020-10-16|455.95001220703125| 438.8500061035156|454.44000244140625| 439.6700134277344|32775900.00000000...| 439.6700134277344|       TESLA|-14.769989013671875|\n",
      "|2020-10-19|             447.0| 428.8699951171875|  446.239990234375| 430.8299865722656|36287800.00000000...| 430.8299865722656|       TESLA|-15.410003662109375|\n",
      "|2020-10-20|            431.75|419.04998779296875|            431.75|421.94000244140625|31656300.00000000...|421.94000244140625|       TESLA|  -9.80999755859375|\n",
      "|2020-10-21|432.95001220703125|            421.25|422.70001220703125| 422.6400146484375|32370500.00000000...| 422.6400146484375|       TESLA|  -0.05999755859375|\n",
      "|2020-10-22| 445.2300109863281|  424.510009765625| 441.9200134277344| 425.7900085449219|39993200.00000000...| 425.7900085449219|       TESLA|  -16.1300048828125|\n",
      "|2020-10-23| 422.8900146484375| 407.3800048828125| 421.8399963378906| 420.6300048828125|33717000.00000000...| 420.6300048828125|       TESLA| -1.209991455078125|\n",
      "|2020-10-26|  425.760009765625|             410.0| 411.6300048828125| 420.2799987792969|28239200.00000000...| 420.2799987792969|       TESLA|  8.649993896484375|\n",
      "|2020-10-27|             430.5| 420.1000061035156|  423.760009765625|424.67999267578125|22686500.00000000...|424.67999267578125|       TESLA|   0.91998291015625|\n",
      "|2020-10-28| 418.6000061035156|             406.0| 416.4800109863281| 406.0199890136719|25451400.00000000...| 406.0199890136719|       TESLA| -10.46002197265625|\n",
      "|2020-10-29|418.05999755859375| 406.4599914550781| 409.9599914550781| 410.8299865722656|22655300.00000000...| 410.8299865722656|       TESLA|    0.8699951171875|\n",
      "|2020-10-30| 407.5899963378906| 379.1099853515625| 406.8999938964844| 388.0400085449219|42511300.00000000...| 388.0400085449219|       TESLA|  -18.8599853515625|\n",
      "|2020-11-02| 406.9800109863281|392.29998779296875|             394.0|  400.510009765625|29021100.00000000...|  400.510009765625|       TESLA|     6.510009765625|\n",
      "|2020-11-03| 427.7699890136719|406.69000244140625| 409.7300109863281| 423.8999938964844|34351700.00000000...| 423.8999938964844|       TESLA|  14.16998291015625|\n",
      "|2020-11-04| 435.3999938964844| 417.1000061035156| 430.6199951171875| 420.9800109863281|32143100.00000000...| 420.9800109863281|       TESLA| -9.639984130859375|\n",
      "|2020-11-05|             440.0|             424.0|428.29998779296875| 438.0899963378906|28414500.00000000...| 438.0899963378906|       TESLA|  9.790008544921875|\n",
      "|2020-11-06|436.57000732421875| 424.2799987792969| 436.1000061035156|429.95001220703125|21706000.00000000...|429.95001220703125|       TESLA| -6.149993896484375|\n",
      "|2020-11-09|             452.5|             421.0|             439.5|  421.260009765625|34833000.00000000...|  421.260009765625|       TESLA|   -18.239990234375|\n",
      "|2020-11-10| 420.0899963378906| 396.0299987792969| 420.0899963378906| 410.3599853515625|30284200.00000000...| 410.3599853515625|       TESLA| -9.730010986328125|\n",
      "|2020-11-11|418.70001220703125| 410.5799865722656|416.45001220703125| 417.1300048828125|17357700.00000000...| 417.1300048828125|       TESLA|   0.67999267578125|\n",
      "|2020-11-12|             423.0| 409.5199890136719|415.04998779296875|  411.760009765625|19855100.00000000...|  411.760009765625|       TESLA|  -3.28997802734375|\n",
      "|2020-11-13| 412.5299987792969| 401.6600036621094| 410.8500061035156|             408.5|19771100.00000000...|             408.5|       TESLA| -2.350006103515625|\n",
      "|2020-11-16|412.45001220703125| 404.0899963378906|408.92999267578125| 408.0899963378906|26838600.00000000...| 408.0899963378906|       TESLA| -0.839996337890625|\n",
      "|2020-11-17|             462.0|  433.010009765625| 460.1700134277344| 441.6099853515625|61188300.00000000...| 441.6099853515625|       TESLA|-18.560028076171875|\n",
      "|2020-11-18|             496.0|             443.5| 448.3500061035156| 486.6400146484375|78044000.00000000...| 486.6400146484375|       TESLA| 38.290008544921875|\n",
      "|2020-11-19| 508.6099853515625|487.57000732421875|             492.0| 499.2699890136719|62475300.00000000...| 499.2699890136719|       TESLA|  7.269989013671875|\n",
      "|2020-11-20|             502.5|489.05999755859375|  497.989990234375| 489.6099853515625|32807300.00000000...| 489.6099853515625|       TESLA|   -8.3800048828125|\n",
      "|2020-11-23|             526.0| 501.7900085449219|             503.5| 521.8499755859375|50260300.00000000...| 521.8499755859375|       TESLA|   18.3499755859375|\n",
      "|2020-11-24|  559.989990234375| 526.2000122070312| 540.4000244140625| 555.3800048828125|53648500.00000000...| 555.3800048828125|       TESLA|     14.97998046875|\n",
      "|2020-11-25|             574.0| 545.3699951171875| 550.0599975585938|             574.0|48930200.00000000...|             574.0|       TESLA|  23.94000244140625|\n",
      "|2020-11-27|  598.780029296875| 578.4500122070312| 581.1599731445312|  585.760009765625|37561100.00000000...|  585.760009765625|       TESLA|   4.60003662109375|\n",
      "|2020-11-30| 607.7999877929688|  554.510009765625| 602.2100219726562| 567.5999755859375|63003100.00000000...| 567.5999755859375|       TESLA| -34.61004638671875|\n",
      "|2020-12-01| 597.8499755859375| 572.0499877929688| 597.5900268554688|  584.760009765625|40103500.00000000...|  584.760009765625|       TESLA| -12.83001708984375|\n",
      "|2020-12-02| 571.5399780273438| 541.2100219726562| 556.4400024414062|  564.030029296875|20055104.00000000...|  564.030029296875|       TESLA|   7.59002685546875|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "\n",
      "The Tesla dataframe contains 987 observations.\n",
      "\n",
      "The period between points of the Tesla dataset is:\n",
      "\t1 days (median)\n",
      "\t1.449290060851927 days (on average)\n",
      "\n",
      "Statistics for Tesla dataframe:\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|summary|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|       daily_return|\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|  count|               987|               987|               987|               987|                 987|               987|         987|                987|\n",
      "|   mean|109.78768801858239|104.62396354636644|107.26065877433244|107.35318730208408|       46222332.1216|107.35318730208408|        null|0.09252852775164047|\n",
      "| stddev|111.32320643662912|104.77686876247262|108.22123794487165|108.29768349070845|3.1034771241949815E7|108.29768349070845|        null|  5.629282836366072|\n",
      "|    min| 37.33599853515625| 35.39799880981445|36.220001220703125| 35.79399871826172|             7255600| 35.79399871826172|       TESLA| -34.61004638671875|\n",
      "|    max| 607.7999877929688| 578.4500122070312| 602.2100219726562|  585.760009765625|           304694000|  585.760009765625|       TESLA|  53.71002197265625|\n",
      "+-------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "\n",
      "Missing values for Tesla:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Tesla\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[1.         0.99900013 0.99935214 0.99943662 0.22260226 0.99943662]\n",
      " [0.99900013 1.         0.99919528 0.99923199 0.20065752 0.99923199]\n",
      " [0.99935214 0.99919528 1.         0.99864835 0.21072936 0.99864835]\n",
      " [0.99943662 0.99923199 0.99864835 1.         0.21256222 1.        ]\n",
      " [0.22260226 0.20065752 0.21072936 0.21256222 1.         0.21256222]\n",
      " [0.99943662 0.99923199 0.99864835 1.         0.21256222 1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Tesla dataset are:\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|company_name| mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "|       TESLA|0.09252852775164047|0.053997039794921875|   5.629282836366072|\n",
      "+------------+-------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the tesla dataframe on 31 days is:\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+-------------------+------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|  Volume|         Adj Close|company_name|       daily_return|    Moving_average|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+-------------------+------------------+\n",
      "|2017-01-03|44.066001892089844| 42.19200134277344| 42.97200012207031| 43.39799880981445|29616500| 43.39799880981445|       TESLA| 0.4259986877441406|44.066001892089844|\n",
      "|2017-01-04|45.599998474121094| 42.86199951171875| 42.95000076293945| 45.39799880981445|56067500| 45.39799880981445|       TESLA|     2.447998046875| 44.83300018310547|\n",
      "|2017-01-05| 45.49599838256836| 44.38999938964844|45.284000396728516|45.349998474121094|29558500|45.349998474121094|       TESLA|0.06599807739257812| 45.05399958292643|\n",
      "|2017-01-06|  46.0620002746582| 45.09000015258789| 45.38600158691406|45.801998138427734|27639500|45.801998138427734|       TESLA| 0.4159965515136719|45.305999755859375|\n",
      "|2017-01-09| 46.38399887084961|45.599998474121094| 45.79399871826172| 46.25600051879883|19897500| 46.25600051879883|       TESLA| 0.4620018005371094| 45.52159957885742|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+-------------------+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "========= Correlation between Tesla and Zoom =========\n",
      "+---------------+--------------------+--------------------+--------------------+------------------+-------------------+------------------+\n",
      "|      Col_tesla|           High_zoom|            Low_zoom|           Open_zoom|        Close_zoom|        Volume_zoom|    Adj Close_zoom|\n",
      "+---------------+--------------------+--------------------+--------------------+------------------+-------------------+------------------+\n",
      "|     High_tesla|  0.9406250294875865|  0.9404018441050482|  0.9394321078863493|0.9424228517598485|0.40193920283122897|0.9424228517598485|\n",
      "|      Low_tesla|   0.944307659020412|  0.9448065212915425|  0.9433937617565638|0.9464382695456939|0.39366443862963896|0.9464382695456939|\n",
      "|     Open_tesla|  0.9434341228082705|  0.9433446151435875|  0.9426038471310438|0.9451227972857537| 0.4009758983766666|0.9451227972857537|\n",
      "|    Close_tesla|   0.940800198189653|   0.941008119628639|  0.9395485713286444|0.9431541228015273| 0.3963072457433858|0.9431541228015273|\n",
      "|   Volume_tesla|-0.02976482719894...|-0.03578186879675534|-0.03452152319750033|-0.031808325448444|0.05265727715184792|-0.031808325448444|\n",
      "|Adj Close_tesla|   0.940800198189653|   0.941008119628639|  0.9395485713286444|0.9431541228015273| 0.3963072457433858|0.9431541228015273|\n",
      "+---------------+--------------------+--------------------+--------------------+------------------+-------------------+------------------+\n",
      "\n",
      "The return rate of the tesla dataframe from the 2020-01-01 on month days is: 42.11000061035156.\n",
      "\n",
      "------------ EXPLORATION OF THE <ZOOM> DATAFRAME ------------\n",
      "Showing the first and last 40 rows of the Zoom dataframe:\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|  Volume|         Adj Close|company_name|        daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "|2019-04-18|              66.0| 60.32099914550781|              65.0|              62.0|25764700|              62.0|        ZOOM|                -3.0|\n",
      "|2019-04-22|  68.9000015258789|59.939998626708984|              61.0| 65.69999694824219| 9949700| 65.69999694824219|        ZOOM|  4.6999969482421875|\n",
      "|2019-04-23| 74.16899871826172| 65.55000305175781| 66.87000274658203|              69.0| 6786500|              69.0|        ZOOM|  2.1299972534179688|\n",
      "|2019-04-24|              71.5| 63.15999984741211|  71.4000015258789| 63.20000076293945| 4973500| 63.20000076293945|        ZOOM|  -8.200000762939453|\n",
      "|2019-04-25|  66.8499984741211|62.599998474121094| 64.73999786376953|              65.0| 3863300|              65.0|        ZOOM| 0.26000213623046875|\n",
      "|2019-04-26| 66.98999786376953|63.599998474121094| 66.12000274658203| 66.22000122070312| 1527400| 66.22000122070312|        ZOOM| 0.09999847412109375|\n",
      "|2019-04-29|              68.5|             64.75| 66.52999877929688| 68.16999816894531| 1822300| 68.16999816894531|        ZOOM|  1.6399993896484375|\n",
      "|2019-04-30|  72.5199966430664| 66.66999816894531|  68.4000015258789| 72.47000122070312| 4113100| 72.47000122070312|        ZOOM|   4.069999694824219|\n",
      "|2019-05-01| 76.94999694824219| 70.81600189208984| 72.72000122070312| 72.76000213623047| 3301900| 72.76000213623047|        ZOOM| 0.04000091552734375|\n",
      "|2019-05-02| 75.88999938964844| 69.69100189208984|             72.75|              75.5| 2525300|              75.5|        ZOOM|                2.75|\n",
      "|2019-05-03|             80.25|              75.0|              75.0| 79.18000030517578| 2590300| 79.18000030517578|        ZOOM|   4.180000305175781|\n",
      "|2019-05-06| 80.79000091552734|              74.5| 75.01000213623047| 78.23999786376953| 2051800| 78.23999786376953|        ZOOM|  3.2299957275390625|\n",
      "|2019-05-07| 78.05000305175781|             73.25|  77.8499984741211| 73.33000183105469| 1975200| 73.33000183105469|        ZOOM|  -4.519996643066406|\n",
      "|2019-05-08|              78.5| 74.02999877929688| 74.61000061035156| 77.68000030517578| 2265500| 77.68000030517578|        ZOOM|  3.0699996948242188|\n",
      "|2019-05-09| 76.98999786376953|              74.0|  76.8499984741211| 75.20999908447266| 1348200| 75.20999908447266|        ZOOM| -1.6399993896484375|\n",
      "|2019-05-10| 79.73999786376953|  74.7699966430664| 75.79000091552734| 79.62999725341797| 1555100| 79.62999725341797|        ZOOM|   3.839996337890625|\n",
      "|2019-05-13| 77.38999938964844|  70.5999984741211| 77.38999938964844| 72.54000091552734| 2873200| 72.54000091552734|        ZOOM|  -4.849998474121094|\n",
      "|2019-05-14| 76.88500213623047| 73.11000061035156| 74.12000274658203| 73.13999938964844| 1950400| 73.13999938964844|        ZOOM| -0.9800033569335938|\n",
      "|2019-05-15|              80.0| 72.20999908447266|  73.4000015258789| 79.76000213623047| 2426500| 79.76000213623047|        ZOOM|  6.3600006103515625|\n",
      "|2019-05-16| 87.55000305175781|             79.25| 80.12000274658203|  83.4000015258789| 4580700|  83.4000015258789|        ZOOM|   3.279998779296875|\n",
      "|2019-05-17| 90.27999877929688| 81.87999725341797|             82.25|  89.9800033569336| 3442500|  89.9800033569336|        ZOOM|   7.730003356933594|\n",
      "|2019-05-20| 91.45999908447266|  83.2699966430664|  90.0999984741211| 84.66999816894531| 3666800| 84.66999816894531|        ZOOM|  -5.430000305175781|\n",
      "|2019-05-21| 89.69999694824219|              84.5| 86.62999725341797| 85.44000244140625| 2576000| 85.44000244140625|        ZOOM| -1.1899948120117188|\n",
      "|2019-05-22| 85.69999694824219|              82.0| 84.62999725341797| 82.43000030517578| 1596400| 82.43000030517578|        ZOOM| -2.1999969482421875|\n",
      "|2019-05-23|              81.5| 77.26000213623047|             81.25| 78.76000213623047| 2856000| 78.76000213623047|        ZOOM| -2.4899978637695312|\n",
      "|2019-05-24|             81.25| 74.19999694824219|  80.4800033569336|             76.25| 2946800|             76.25|        ZOOM|  -4.230003356933594|\n",
      "|2019-05-28| 79.38999938964844| 76.80000305175781| 76.80000305175781|  77.7699966430664| 1641300|  77.7699966430664|        ZOOM|  0.9699935913085938|\n",
      "|2019-05-29| 77.93000030517578| 73.58300018310547|              77.0|  75.7699966430664| 1512100|  75.7699966430664|        ZOOM| -1.2300033569335938|\n",
      "|2019-05-30| 80.97000122070312|  76.5999984741211| 76.68000030517578| 80.41999816894531| 1996000| 80.41999816894531|        ZOOM|  3.7399978637695312|\n",
      "|2019-05-31| 83.16999816894531| 77.77999877929688|  78.7699966430664|  79.7300033569336| 1594300|  79.7300033569336|        ZOOM|  0.9600067138671875|\n",
      "|2019-06-03| 81.94000244140625|  75.6500015258789|              80.0|  75.9000015258789| 1570500|  75.9000015258789|        ZOOM|  -4.099998474121094|\n",
      "|2019-06-04| 78.87999725341797| 76.62000274658203| 78.19999694824219| 78.73999786376953| 1134900| 78.73999786376953|        ZOOM|  0.5400009155273438|\n",
      "|2019-06-05|  80.5999984741211| 76.23999786376953| 80.13999938964844| 78.04000091552734| 1295800| 78.04000091552734|        ZOOM| -2.0999984741210938|\n",
      "|2019-06-06|             79.75| 77.02999877929688|  77.4000015258789| 79.43000030517578| 3024000| 79.43000030517578|        ZOOM|   2.029998779296875|\n",
      "|2019-06-07| 98.88999938964844|              92.5| 93.66000366210938| 94.05000305175781| 9487800| 94.05000305175781|        ZOOM|  0.3899993896484375|\n",
      "|2019-06-10|105.98500061035156|              96.0| 98.51000213623047|             102.0| 4852800|             102.0|        ZOOM|  3.4899978637695312|\n",
      "|2019-06-11|101.19999694824219| 91.56999969482422|             101.0| 94.87000274658203| 4372400| 94.87000274658203|        ZOOM|  -6.129997253417969|\n",
      "|2019-06-12|104.18499755859375|              94.0|  94.5999984741211| 102.7699966430664| 3151700| 102.7699966430664|        ZOOM|   8.169998168945312|\n",
      "|2019-06-13| 105.1719970703125| 98.55000305175781| 105.0999984741211|100.94999694824219| 3189100|100.94999694824219|        ZOOM|  -4.150001525878906|\n",
      "|2019-06-14|104.56999969482422|             99.25|100.47000122070312|100.29000091552734| 1889300|100.29000091552734|        ZOOM|-0.18000030517578125|\n",
      "+----------+------------------+------------------+------------------+------------------+--------+------------------+------------+--------------------+\n",
      "only showing top 40 rows\n",
      "\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|      Date|              High|               Low|              Open|             Close|              Volume|         Adj Close|company_name|       daily_return|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "|2020-10-07|             487.5| 477.1099853515625|             482.5| 480.6099853515625|4449500.000000000...| 480.6099853515625|        ZOOM|   -1.8900146484375|\n",
      "|2020-10-08|  489.489990234375| 475.5799865722656|             484.0|478.54998779296875|4193100.000000000...|478.54998779296875|        ZOOM|  -5.45001220703125|\n",
      "|2020-10-09| 498.8999938964844| 484.0799865722656|  487.989990234375| 492.4100036621094|8719700.000000000...| 492.4100036621094|        ZOOM|  4.420013427734375|\n",
      "|2020-10-12| 506.1099853515625| 490.4779968261719|501.42999267578125| 491.5400085449219|6979500.000000000...| 491.5400085449219|        ZOOM| -9.889984130859375|\n",
      "|2020-10-13|             520.0|             493.0|             494.0| 518.7899780273438|8941500.000000000...| 518.7899780273438|        ZOOM|  24.78997802734375|\n",
      "|2020-10-14| 519.7999877929688| 498.6000061035156|  518.780029296875|            509.25|8327700.000000000...|            509.25|        ZOOM|    -9.530029296875|\n",
      "|2020-10-15| 540.0499877929688|             506.5| 509.0799865722656| 536.4000244140625|16520600.00000000...| 536.4000244140625|        ZOOM| 27.320037841796875|\n",
      "|2020-10-16| 565.4500122070312|            543.25|             544.0|             559.0|13745800.00000000...|             559.0|        ZOOM|               15.0|\n",
      "|2020-10-19| 588.8400268554688| 562.5499877929688|             572.5| 568.3400268554688|15193200.00000000...| 568.3400268554688|        ZOOM|  -4.15997314453125|\n",
      "|2020-10-20| 575.6900024414062| 534.5900268554688| 572.3300170898438|   537.02001953125|12098200.00000000...|   537.02001953125|        ZOOM| -35.30999755859375|\n",
      "|2020-10-21| 551.6599731445312| 511.7799987792969| 535.8800048828125| 513.1900024414062|9215400.000000000...| 513.1900024414062|        ZOOM| -22.69000244140625|\n",
      "|2020-10-22| 530.4299926757812| 503.4700012207031|  521.780029296875| 520.5399780273438|8100900.000000000...| 520.5399780273438|        ZOOM|  -1.24005126953125|\n",
      "|2020-10-23|             527.0| 505.4100036621094| 522.3300170898438| 511.5199890136719|5377100.000000000...| 511.5199890136719|        ZOOM|-10.810028076171875|\n",
      "|2020-10-26|   541.10498046875|510.30999755859375|  520.489013671875| 517.7899780273438|10080800.00000000...| 517.7899780273438|        ZOOM|  -2.69903564453125|\n",
      "|2020-10-27|             540.0| 513.2999877929688|  525.219970703125|  538.989990234375|7227600.000000000...|  538.989990234375|        ZOOM|     13.77001953125|\n",
      "|2020-10-28| 553.8590087890625|             511.5|             549.5|  516.010009765625|8810100.000000000...|  516.010009765625|        ZOOM|   -33.489990234375|\n",
      "|2020-10-29| 525.4550170898438|             483.5| 521.1900024414062|489.67999267578125|9207600.000000000...|489.67999267578125|        ZOOM|   -31.510009765625|\n",
      "|2020-10-30|495.95001220703125| 457.0199890136719|             481.5| 460.9100036621094|8697800.000000000...| 460.9100036621094|        ZOOM|-20.589996337890625|\n",
      "|2020-11-02|             477.0|             440.0| 462.2900085449219|             453.0|8051900.000000000...|             453.0|        ZOOM| -9.290008544921875|\n",
      "|2020-11-03| 461.8800048828125|  445.010009765625| 456.3280029296875|  451.510009765625|6497000.000000000...|  451.510009765625|        ZOOM|   -4.8179931640625|\n",
      "|2020-11-04|484.94000244140625| 455.3999938964844| 471.9200134277344|483.70001220703125|8048500.000000000...|483.70001220703125|        ZOOM| 11.779998779296875|\n",
      "|2020-11-05| 499.3500061035156|  480.760986328125|  497.989990234375| 496.7300109863281|6379900.000000000...| 496.7300109863281|        ZOOM| -1.259979248046875|\n",
      "|2020-11-06| 505.8800048828125| 482.7300109863281| 494.6600036621094| 500.1099853515625|4266000.000000000...| 500.1099853515625|        ZOOM|  5.449981689453125|\n",
      "|2020-11-09| 446.4700012207031|             402.0|             433.0|  413.239990234375|18033500.00000000...|  413.239990234375|        ZOOM|   -19.760009765625|\n",
      "|2020-11-10|414.45001220703125| 366.2799987792969| 412.5039978027344|  376.010009765625|20751900.00000000...|  376.010009765625|        ZOOM|-36.493988037109375|\n",
      "|2020-11-11|419.79998779296875|             385.0| 391.6499938964844| 413.3399963378906|16691100.00000000...| 413.3399963378906|        ZOOM|  21.69000244140625|\n",
      "|2020-11-12|            435.75| 419.6099853515625| 427.8500061035156| 428.6400146484375|14910000.00000000...| 428.6400146484375|        ZOOM|  0.790008544921875|\n",
      "|2020-11-13|  428.468994140625| 396.8699951171875|  425.989990234375| 403.5799865722656|11732200.00000000...| 403.5799865722656|        ZOOM|-22.410003662109375|\n",
      "|2020-11-16|400.79998779296875| 375.1000061035156| 382.6000061035156| 399.1499938964844|12054400.00000000...| 399.1499938964844|        ZOOM|  16.54998779296875|\n",
      "|2020-11-17|             411.5|396.32000732421875| 406.2900085449219| 401.6300048828125|6972100.000000000...| 401.6300048828125|        ZOOM| -4.660003662109375|\n",
      "|2020-11-18|             423.5| 390.2200012207031|             399.0| 415.0400085449219|10974200.00000000...| 415.0400085449219|        ZOOM| 16.040008544921875|\n",
      "|2020-11-19| 427.8900146484375|             407.5|  408.989990234375| 414.2900085449219|8007000.000000000...| 414.2900085449219|        ZOOM|  5.300018310546875|\n",
      "|2020-11-20|445.29998779296875|415.55999755859375|418.54998779296875| 439.6000061035156|12557900.00000000...| 439.6000061035156|        ZOOM| 21.050018310546875|\n",
      "|2020-11-23|436.51300048828125| 417.8800048828125|  432.010009765625| 430.2799987792969|8486000.000000000...| 430.2799987792969|        ZOOM| -1.730010986328125|\n",
      "|2020-11-24| 438.8320007324219|  420.010009765625| 421.5050048828125|427.54998779296875|6148400.000000000...|427.54998779296875|        ZOOM|   6.04498291015625|\n",
      "|2020-11-25| 448.4800109863281| 433.3299865722656| 435.8500061035156| 443.7200012207031|8763700.000000000...| 443.7200012207031|        ZOOM|    7.8699951171875|\n",
      "|2020-11-27| 472.0400085449219|             449.0|  453.989990234375| 471.6099853515625|7276400.000000000...| 471.6099853515625|        ZOOM|   17.6199951171875|\n",
      "|2020-11-30| 486.8299865722656|             460.0|485.70001220703125| 478.3599853515625|16646500.00000000...| 478.3599853515625|        ZOOM|  -7.34002685546875|\n",
      "|2020-12-01|  434.989990234375| 401.7200012207031| 434.7200012207031|406.30999755859375|20837500.00000000...|406.30999755859375|        ZOOM|-28.410003662109375|\n",
      "|2020-12-02|424.42498779296875|394.57000732421875|             399.0| 419.6282043457031|5943384.000000000...| 419.6282043457031|        ZOOM| 20.628204345703125|\n",
      "+----------+------------------+------------------+------------------+------------------+--------------------+------------------+------------+-------------------+\n",
      "\n",
      "The Zoom dataframe contains 411 observations.\n",
      "\n",
      "The period between points of the Zoom dataset is:\n",
      "\t1 days (median)\n",
      "\t1.448780487804878 days (on average)\n",
      "\n",
      "Statistics for Zoom dataframe:\n",
      "+-------+------------------+------------------+-----------------+------------------+-----------------+------------------+------------+-------------------+\n",
      "|summary|              High|               Low|             Open|             Close|           Volume|         Adj Close|company_name|       daily_return|\n",
      "+-------+------------------+------------------+-----------------+------------------+-----------------+------------------+------------+-------------------+\n",
      "|  count|               411|               411|              411|               411|              411|               411|         411|                411|\n",
      "|   mean|182.39606543585043|172.02370037069576|177.1945766625323|177.46828020750172|     7060712.6131|177.46828020750172|        null| 0.2737035449693963|\n",
      "| stddev|142.85245242428914| 134.5095182837666|138.9019224307191|138.66359007242428|6905543.039975025|138.66359007242428|        null|  8.233562360939432|\n",
      "|    min|  63.7400016784668|59.939998626708984|             61.0|              62.0|           512600|              62.0|        ZOOM|-36.493988037109375|\n",
      "|    max| 588.8400268554688| 562.5499877929688|            572.5| 568.3400268554688|         53346800| 568.3400268554688|        ZOOM| 34.470001220703125|\n",
      "+-------+------------------+------------------+-----------------+------------------+-----------------+------------------+------------+-------------------+\n",
      "\n",
      "Missing values for Zoom:\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|Date|High|Low|Open|Close|Volume|Adj Close|company_name|daily_return|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "|   0|   0|  0|   0|    0|     0|        0|           0|           0|\n",
      "+----+----+---+----+-----+------+---------+------------+------------+\n",
      "\n",
      "Correlation matrix for Zoom\n",
      "Columns are: ['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close']\n",
      "[[1.         0.99913202 0.99929984 0.99921986 0.41942667 0.99921986]\n",
      " [0.99913202 1.         0.99914703 0.99926417 0.39512838 0.99926417]\n",
      " [0.99929984 0.99914703 1.         0.99824163 0.40555376 0.99824163]\n",
      " [0.99921986 0.99926417 0.99824163 1.         0.4103528  1.        ]\n",
      " [0.41942667 0.39512838 0.40555376 0.4103528  1.         0.4103528 ]\n",
      " [0.99921986 0.99926417 0.99824163 1.         0.4103528  1.        ]]\n",
      "\n",
      "The statistics of the daily_return column in the Zoom dataset are:\n",
      "+------------+------------------+--------------------+--------------------+\n",
      "|company_name|mean(daily_return)|median(daily_return)|stddev(daily_return)|\n",
      "+------------+------------------+--------------------+--------------------+\n",
      "|        ZOOM|0.2737035449693963|  0.0800018310546875|   8.233562360939432|\n",
      "+------------+------------------+--------------------+--------------------+\n",
      "\n",
      "The moving average of the zoom dataframe on 31 days is:\n",
      "+----------+-----------------+------------------+-----------------+-----------------+--------+-----------------+------------+-------------------+-----------------+\n",
      "|      Date|             High|               Low|             Open|            Close|  Volume|        Adj Close|company_name|       daily_return|   Moving_average|\n",
      "+----------+-----------------+------------------+-----------------+-----------------+--------+-----------------+------------+-------------------+-----------------+\n",
      "|2019-04-18|             66.0| 60.32099914550781|             65.0|             62.0|25764700|             62.0|        ZOOM|               -3.0|             66.0|\n",
      "|2019-04-22| 68.9000015258789|59.939998626708984|             61.0|65.69999694824219| 9949700|65.69999694824219|        ZOOM| 4.6999969482421875|67.45000076293945|\n",
      "|2019-04-23|74.16899871826172| 65.55000305175781|66.87000274658203|             69.0| 6786500|             69.0|        ZOOM| 2.1299972534179688|69.68966674804688|\n",
      "|2019-04-24|             71.5| 63.15999984741211| 71.4000015258789|63.20000076293945| 4973500|63.20000076293945|        ZOOM| -8.200000762939453|70.14225006103516|\n",
      "|2019-04-25| 66.8499984741211|62.599998474121094|64.73999786376953|             65.0| 3863300|             65.0|        ZOOM|0.26000213623046875|69.48379974365234|\n",
      "+----------+-----------------+------------------+-----------------+-----------------+--------+-----------------+------------+-------------------+-----------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "The return rate of the zoom dataframe from the 2020-01-01 on month days is: 5.819999694824219.\n",
      "\n",
      "------------ EXPLORATION STATS OF ALL DATAFRAMES ------------\n",
      "The best return rate from the 2020-01-01 with a 'month' period is: google with a rate of 88.469970703125.\n",
      "\n"
     ],
     "output_type":"stream"
    },
    {
     "name":"stderr",
     "text":[
      "\/opt\/python\/envs\/default\/lib\/python3.8\/site-packages\/pyspark\/sql\/context.py:125: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
      "  warnings.warn(\n"
     ],
     "output_type":"stream"
    }
   ],
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   "cell_type":"markdown",
   "source":[
    "## Regression"
   ],
   "attachments":{
    
   },
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   "source":[
    "### Summary of all the functions for the Regression\n",
    "\n",
    "**Functions for loading datasets:**\n",
    "\n",
    "* join_datasets(stocks: dict) -> DataFrame:\n",
    "    * Joins all stocks into one dataset\n",
    "\n",
    "* convert_date_to_double(df: DataFrame, colname: str) -> DataFrame:\n",
    "    * Convert a date column into a double\n",
    "\n",
    "* convert_double_to_date(df: DataFrame, colname: str) -> DataFrame:\n",
    "    * Convert a double column into a date\n",
    "\n",
    "* create_vector_assembler(features: list, output_name: str= \"features\") -> VectorAssembler:\n",
    "    * Creates a vector assembler for numerical columns\n",
    "\n",
    "* linear_regressor(features: list, pred_col: str = \"Adj Close\") -> Pipeline:\n",
    "    * Creates a linear regressor pipeline\n",
    "    \n",
    "* decision_tree_regressor(features: list, pred_col: str= \"Adj Close\", max_bins: int=MAX_BIN_DEFAULT) -> Estimator:\n",
    "    * Creates a decision tree regressor pipeline\n",
    "    \n",
    "* random_forest_regressor(features: list, pred_col: str= \"Adj Close\", max_bins: int=MAX_BIN_DEFAULT) -> Estimator:\n",
    "    * Creates a random forest regressor pipeline\n",
    "\n",
    "* tuned_random_forest_regressor(features: list, pred_col: str= \"Adj Close\", max_bins: int=MAX_BIN_DEFAULT) -> Estimator:\n",
    "    * Creates a random forest regressor pipeline tuned using CrossValidator\n",
    "    \n",
    "* plot_prediction(df: DataFrame, x_col: str, real_col: str, pred_col: str, curve_name: str, plot_title: str=None) -> None:\n",
    "    * Plots the predictions of a model\n",
    "    \n",
    "    "
   ],
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   "source":[
    "MAX_BIN_DEFAULT = 64\n",
    "\n",
    "def join_datasets(stocks: dict) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Joins all stocks into one dataset\n",
    "    \"\"\"\n",
    "    names = iter(stocks.keys())\n",
    "    df = stocks[next(names)]\n",
    "    for name in names:\n",
    "        df = df.union(stocks[name])\n",
    "    return df\n",
    "\n",
    "def date_to_double(df: DataFrame, colname: str) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Convert a date column into a double\n",
    "    \"\"\"\n",
    "    return df.withColumn(colname, col(colname).cast(\"timestamp\").cast(\"double\"))\n",
    "\n",
    "def double_to_date(df: DataFrame, colname: str) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Convert a double column into a date\n",
    "    \"\"\"\n",
    "    return df.withColumn(colname, col(colname).cast(\"timestamp\").cast(\"date\"))\n",
    "\n",
    "def create_vector_assembler(features: list, output_name: str= \"features\") -> VectorAssembler:\n",
    "    \"\"\"\n",
    "    Creates a vector assembler for numerical columns\n",
    "    \"\"\"\n",
    "    return VectorAssembler(inputCols=features, outputCol=output_name)\n",
    "\n",
    "def linear_regressor(features: list, pred_col: str = \"Adj Close\") -> Pipeline:\n",
    "    \"\"\"\n",
    "    Creates a linear regressor pipeline\n",
    "    \"\"\"\n",
    "    vec_assembler = create_vector_assembler(features)\n",
    "    lr = LinearRegression(featuresCol=\"features\", labelCol=pred_col)\n",
    "    \n",
    "    return Pipeline(stages=[vec_assembler, lr])\n",
    "\n",
    "def decision_tree_regressor(features: list, pred_col: str= \"Adj Close\", max_bins: int=MAX_BIN_DEFAULT) -> Estimator:\n",
    "    \"\"\"\n",
    "    Creates a decision tree regressor pipeline\n",
    "    \"\"\"\n",
    "    vec_assembler = create_vector_assembler(features)\n",
    "    dt = DecisionTreeRegressor(labelCol=pred_col)\n",
    "    dt.setMaxBins(max_bins)\n",
    "\n",
    "    return Pipeline(stages=[vec_assembler, dt])\n",
    "\n",
    "def random_forest_regressor(features: list, pred_col: str= \"Adj Close\", max_bins: int=MAX_BIN_DEFAULT) -> Estimator:\n",
    "    \"\"\"\n",
    "    Creates a random forest regressor pipeline\n",
    "    \"\"\"\n",
    "    vec_assembler = create_vector_assembler(features)\n",
    "    rf = RandomForestRegressor(labelCol=pred_col, maxBins=max_bins, seed=123)\n",
    "\n",
    "    return Pipeline(stages=[vec_assembler, rf])\n",
    "\n",
    "def tuned_random_forest_regressor(features: list, pred_col: str= \"Adj Close\", max_bins: int=MAX_BIN_DEFAULT) -> Estimator:\n",
    "    \"\"\"\n",
    "    Creates a random forest regressor pipeline tuned using CrossValidator\n",
    "    \"\"\"\n",
    "    vec_assembler = create_vector_assembler(features)\n",
    "    rf = RandomForestRegressor(labelCol=pred_col, maxBins=max_bins, seed=123)\n",
    "\n",
    "    pipeline = Pipeline(stages=[vec_assembler, rf])\n",
    "\n",
    "    param_grid = (ParamGridBuilder()\n",
    "                  .addGrid(rf.maxDepth, [2, 4, 6, ])\n",
    "                  .addGrid(rf.numTrees, [10, 100])\n",
    "                  .build())\n",
    "    evaluator = RegressionEvaluator(\n",
    "        labelCol=pred_col,\n",
    "        predictionCol=\"prediction\",\n",
    "        metricName=\"rmse\"\n",
    "    )\n",
    "    cv = CrossValidator(\n",
    "        estimator=pipeline,\n",
    "        evaluator=evaluator,\n",
    "        estimatorParamMaps=param_grid,\n",
    "        numFolds=3,\n",
    "        seed=123\n",
    "    )\n",
    "    return cv\n",
    "\n",
    "def plot_prediction(trainDF, testDF, x_col: str, real_col: str, pred_col: str, model_name: str, title: str) -> None:\n",
    "    \"\"\"\n",
    "    Plots the predictions of a model\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(20,10))\n",
    "    plt.title(title)\n",
    "\n",
    "\n",
    "    get_x = lambda df: df.select(x_col).collect()\n",
    "\n",
    "    if trainDF is not None:\n",
    "        trainDF = trainDF.orderBy(x_col)\n",
    "        x_val_train = get_x(trainDF)\n",
    "        plt.plot(x_val_train, trainDF.select(real_col).collect(), label=f\"train({real_col})\")\n",
    "        plt.plot(x_val_train, trainDF.select(pred_col).collect(), label=f\"train({model_name})\")\n",
    "\n",
    "    if testDF is not None:\n",
    "        testDF = testDF.orderBy(x_col)\n",
    "        x_val_test = get_x(testDF)\n",
    "        plt.plot(x_val_test, testDF.select(real_col).collect(), label=f\"test({real_col})\")\n",
    "        plt.plot(x_val_test, testDF.select(pred_col).collect(), label=f\"test({model_name})\")\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ],
   "execution_count":13,
   "outputs":[
    
   ],
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   "source":[
    "\n",
    "def predict_and_plot_stock(name, stock, models, features, split_ratio=0.2):\n",
    "    stock = date_to_double(stock, \"Date\")\n",
    "\n",
    "    stock_len = stock.count()\n",
    "    testDF = spark.createDataFrame(stock.tail(int(stock_len * split_ratio)))\n",
    "    trainDF = stock.subtract(testDF)\n",
    "    # trainDF, testDF = stock.randomSplit([8., 2.], seed=123) is not used because we want to predict the future.\n",
    "\n",
    "    for model in models:\n",
    "        pipeline = model(features, \"Adj Close\")\n",
    "        pipelineModel = pipeline.fit(trainDF)\n",
    "        pred_test = double_to_date(pipelineModel.transform(testDF), \"Date\")\n",
    "        pred_train = double_to_date(pipelineModel.transform(trainDF), \"Date\")\n",
    "        plot_prediction(pred_train, pred_test, \"Date\", \"Adj Close\", \"prediction\", model.__name__, f\"{name.capitalize()}: {model.__name__}\")\n",
    "\n",
    "        pred_test.unpersist(False)\n",
    "        pred_train.unpersist(False)\n",
    "\n",
    "def main_regression():\n",
    "    stocks = load_stocks()\n",
    "    # We do not use tuned_random_forest_regressor since random forest is not a good model for this data\n",
    "    # and the algorithm is very expensive to train and compute\n",
    "    models = [linear_regressor, decision_tree_regressor, random_forest_regressor]\n",
    "    features = [\"Date\", \"Open\"]\n",
    "\n",
    "    for name, stock in stocks.items():\n",
    "        predict_and_plot_stock(name, stock, models, features)"
   ],
   "execution_count":14,
   "outputs":[
    
   ],
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   "cell_type":"code",
   "source":[
    "if __name__ == \"__main__\":\n",
    "    main_regression()"
   ],
   "execution_count":15,
   "outputs":[
    {
     "data":{
      "image\/png":[
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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "type":"CODE",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    
   ],
   "execution_count":null,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "type":"CODE",
     "hide_input_from_viewers":false,
     "hide_output_from_viewers":false
    }
   }
  }
 ],
 "metadata":{
  "datalore":{
   "version":1,
   "computation_mode":"JUPYTER",
   "package_manager":"pip",
   "base_environment":"default",
   "packages":[
    
   ]
  }
 },
 "nbformat":4,
 "nbformat_minor":4
}