Refactor EDA -> Qa (#686)

* Ignore nft of the day test

* Rename eda -> qa

* Refactor rolling into view/model

* Refactored commands to new convention

* updated menu layout + add load

* Add ra functions into qa menu + fix portfolio paths

* fix menu help string

* Update qa with target idea that indicates variable to do statistics on

* Review changes except todos

* Make qa indepedent of stock menu

23 files changed, 1832 insertions, 2474 deletions

diff --git a/exports/ta/.gitkeep b/exports/stocks/quantitative_analysis/.gitkeep
index e69de29bb2d..e69de29bb2d 100644
--- a/exports/ta/.gitkeep
+++ b/exports/stocks/quantitative_analysis/.gitkeep
diff --git a/gamestonk_terminal/common/exploratory_data_analysis/eda_api.py b/gamestonk_terminal/common/exploratory_data_analysis/eda_api.py
deleted file mode 100644
index b2e9ad4b14f..00000000000
--- a/gamestonk_terminal/common/exploratory_data_analysis/eda_api.py
+++ /dev/null
@@ -1,706 +0,0 @@
-""" Exploratory Data Analysis API """
-__docformat__ = "numpy"
-
-import argparse
-from typing import List
-from datetime import datetime
-from detecta import detect_cusum
-from matplotlib import pyplot as plt
-import pandas as pd
-from pandas.plotting import register_matplotlib_converters
-import numpy as np
-import seaborn as sns
-import statsmodels.api as sm
-
-# pylint: disable=R0402
-import matplotlib.gridspec as gridspec
-from statsmodels.tsa.seasonal import seasonal_decompose
-from gamestonk_terminal.helper_funcs import (
-    parse_known_args_and_warn,
-    plot_autoscale,
-    check_positive,
-)
-from gamestonk_terminal.config_plot import PLOT_DPI
-from gamestonk_terminal import feature_flags as gtff
-
-
-register_matplotlib_converters()
-
-
-def summary(other_args: List[str], stock: pd.DataFrame):
-    """Print summary statistics
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="summary",
-        description="""
-            Summary statistics
-        """,
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        df_stats = stock.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9])
-        df_stats.loc["var"] = df_stats.loc["std"] ** 2
-        print(df_stats.round(2).to_string())
-
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def hist(other_args: List[str], ticker: str, stock: pd.DataFrame, start: datetime):
-    """Plot histogram and density
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="hist",
-        description="""
-            Histogram with depicted density and rug
-        """,
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
-
-        stock = stock["Adj Close"]
-
-        sns.distplot(
-            stock,
-            bins=35,
-            color="blue",
-            hist=True,
-            hist_kws={"edgecolor": "black"},
-            kde=True,
-            kde_kws={"color": "black", "lw": 3, "label": "KDE"},
-            rug=True,
-            rug_kws={"edgecolor": "orange"},
-        )
-        plt.title(
-            f"Histogram with Density of {ticker} from {start.strftime('%Y-%m-%d')}"
-        )
-        plt.ylabel("Density")
-        plt.xlabel("Share Price")
-        plt.grid(True)
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def cumulative_distribution_function(
-    other_args: List[str], ticker: str, stock: pd.DataFrame, start: datetime
-):
-    """Plot cumulative distribution function
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="cdf",
-        description="""
-            Cumulative distribution function
-        """,
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
-
-        stock = stock["Adj Close"]
-
-        cdf = stock.value_counts().sort_index().div(len(stock)).cumsum()
-        cdf.plot(lw=2)
-        plt.title(
-            f"Cumulative Distribution Function of {ticker} from {start.strftime('%Y-%m-%d')}"
-        )
-        plt.ylabel("Probability")
-        plt.xlabel("Share Price")
-        minVal = stock.values.min()
-        q25 = np.quantile(stock.values, 0.25)
-        medianVal = np.quantile(stock.values, 0.5)
-        q75 = np.quantile(stock.values, 0.75)
-        data = [
-            (minVal, q25),
-            (0.25, 0.25),
-            "r",
-            (q25, q25),
-            (0, 0.25),
-            "r",
-            (minVal, medianVal),
-            (0.5, 0.5),
-            "r",
-            (medianVal, medianVal),
-            (0, 0.5),
-            "r",
-            (minVal, q75),
-            (0.75, 0.75),
-            "r",
-            (q75, q75),
-            (0, 0.75),
-            "r",
-        ]
-        plt.plot(*data, ls="--")
-        plt.text(minVal + (q25 - minVal) / 2, 0.27, "Q1", color="r", fontweight="bold")
-        plt.text(
-            minVal + (medianVal - minVal) / 2,
-            0.52,
-            "Median",
-            color="r",
-            fontweight="bold",
-        )
-        plt.text(minVal + (q75 - minVal) / 2, 0.77, "Q3", color="r", fontweight="bold")
-        plt.xlim(cdf.index[0], cdf.index[-1])
-        plt.grid(True)
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def bwy(other_args: List[str], ticker: str, stock: pd.DataFrame, start: datetime):
-    """Box and Whisker plot yearly
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="bwy",
-        description="""
-            Box and Whisker plot yearly
-        """,
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
-
-        stock = stock["Adj Close"]
-
-        sns.set(style="whitegrid")
-        box_plot = sns.boxplot(x=stock.index.year, y=stock)
-        box_plot.set(
-            xlabel="Year",
-            ylabel="Share Price",
-            title=f"Box-plot per Year of {ticker} from {start.strftime('%Y-%m-%d')}",
-        )
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def bwm(other_args: List[str], ticker: str, stock: pd.DataFrame, start: datetime):
-    """Box and Whisker plot monthly
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="bwm",
-        description="""
-            Box and Whisker plot monthly
-        """,
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
-
-        stock = stock["Adj Close"]
-
-        sns.set(style="whitegrid")
-        box_plot = sns.boxplot(x=stock.index.month, y=stock)
-        box_plot.set(
-            xlabel="Month",
-            ylabel="Share Price",
-            title=f"Box-plot per Month of {ticker} from {start.strftime('%Y-%m-%d')}",
-        )
-        l_months = [
-            "Jan",
-            "Feb",
-            "Mar",
-            "Apr",
-            "May",
-            "Jun",
-            "Jul",
-            "Aug",
-            "Sep",
-            "Oct",
-            "Nov",
-            "Dec",
-        ]
-        l_ticks = []
-        for val in box_plot.get_xticklabels():
-            l_ticks.append(l_months[int(val.get_text()) - 1])
-        box_plot.set_xticklabels(l_ticks)
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def rolling(other_args: List[str], ticker: str, stock: pd.DataFrame):
-    """Rolling mean and std deviation
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="rolling",
-        description="""
-            Rolling mean and std deviation
-        """,
-    )
-    parser.add_argument(
-        "-w",
-        "--window",
-        dest="rolling_window",
-        type=check_positive,
-        default=10,
-        help="rolling window",
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        stock = stock["Adj Close"]
-
-        rolling_mean = stock.rolling(
-            ns_parser.rolling_window, center=True, min_periods=1
-        ).mean()
-        rolling_std = stock.rolling(
-            ns_parser.rolling_window, center=True, min_periods=1
-        ).std()
-
-        _, axMean = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
-
-        axMean.plot(stock.index, stock.values, label=ticker, linewidth=2, color="black")
-        axMean.plot(rolling_mean, linestyle="--", linewidth=3, color="blue")
-        axMean.set_xlabel("Time")
-        axMean.set_ylabel("Share Price", color="blue")
-        axMean.legend(["Real values", "Rolling Mean"], loc=2)
-        axMean.tick_params(axis="y", labelcolor="blue")
-        axStd = axMean.twinx()
-        axStd.plot(
-            rolling_std, label="Rolling std", linestyle="--", color="green", linewidth=3
-        )
-        axStd.set_ylabel("Std Deviation")
-        axStd.legend(["Rolling std"], loc=1)
-        axStd.set_ylabel("Share Price standard deviation", color="green")
-        axStd.tick_params(axis="y", labelcolor="green")
-        axMean.set_title(
-            "Rolling mean and std with window "
-            + str(ns_parser.rolling_window)
-            + " applied to "
-            + ticker
-        )
-        plt.xlim([stock.index[0], stock.index[-1]])
-        plt.grid(b=True, which="major", color="#666666", linestyle="-")
-        plt.minorticks_on()
-        plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def decompose(other_args: List[str], ticker: str, stock: pd.DataFrame):
-    """Decompose time series as:
-        - Additive Time Series = Level + CyclicTrend + Residual + Seasonality
-        - Multiplicative Time Series = Level * CyclicTrend * Residual * Seasonality
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="decompose",
-        description="""
-            Decompose time series as:
-              - Additive Time Series = Level + CyclicTrend + Residual + Seasonality
-              - Multiplicative Time Series = Level * CyclicTrend * Residual * Seasonality
-        """,
-    )
-    parser.add_argument(
-        "-m",
-        "--multiplicative",
-        action="store_true",
-        default=False,
-        dest="multiplicative",
-        help="decompose using multiplicative model instead of additive",
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        stock = stock["Adj Close"]
-
-        seasonal_periods = 5
-        # Hodrick-Prescott filter
-        # See Ravn and Uhlig: http://home.uchicago.edu/~huhlig/papers/uhlig.ravn.res.2002.pdf
-        lamb = 107360000000
-
-        fig = plt.figure(
-            figsize=plot_autoscale(), dpi=PLOT_DPI, constrained_layout=True
-        )
-        spec = gridspec.GridSpec(ncols=4, nrows=5, figure=fig)
-
-        fig.add_subplot(spec[0, :])
-        plt.plot(stock)
-
-        plt.title(ticker + " (Time-Series)")
-
-        if ns_parser.multiplicative:
-            resultMul = seasonal_decompose(
-                stock, model="multiplicative", period=seasonal_periods
-            )
-            cycleMul, trendMul = sm.tsa.filters.hpfilter(
-                resultMul.trend[resultMul.trend.notna().values], lamb=lamb
-            )
-
-            # Multiplicative model
-            fig.add_subplot(spec[1, :4])
-            plt.plot(resultMul.trend, lw=2, c="purple")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Multiplicative Cyclic-Trend")
-
-            fig.add_subplot(spec[2, 0:2])
-            plt.plot(trendMul, lw=2, c="tab:blue")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Multiplicative Trend component")
-
-            fig.add_subplot(spec[2, 2:])
-            plt.plot(cycleMul, lw=2, c="green")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Multiplicative Cycle component")
-
-            fig.add_subplot(spec[3, :])
-            plt.plot(resultMul.seasonal, lw=2, c="orange")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Multiplicative Seasonal effect")
-
-            fig.add_subplot(spec[4, :])
-            plt.plot(resultMul.resid, lw=2, c="red")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Multiplicative Residuals")
-
-        else:
-            resultAdd = seasonal_decompose(
-                stock, model="additive", period=seasonal_periods
-            )
-            cycleAdd, trendAdd = sm.tsa.filters.hpfilter(
-                resultAdd.trend[resultAdd.trend.notna().values], lamb=lamb
-            )
-
-            # Additive model
-            fig.add_subplot(spec[1, :4])
-            plt.plot(resultAdd.trend, lw=2, c="purple")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Additive Cyclic-Trend")
-
-            fig.add_subplot(spec[2, 0:2])
-            plt.plot(trendAdd, lw=2, c="tab:blue")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Additive Trend component")
-
-            fig.add_subplot(spec[2, 2:])
-            plt.plot(cycleAdd, lw=2, c="green")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Additive Cycle component")
-
-            fig.add_subplot(spec[3, :])
-            plt.plot(resultAdd.seasonal, lw=2, c="orange")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Additive Seasonal effect")
-
-            fig.add_subplot(spec[4, :])
-            plt.plot(resultAdd.resid, lw=2, c="red")
-            plt.xlim([stock.index[0], stock.index[-1]])
-            plt.title("Additive Residuals")
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-        # From  # https://otexts.com/fpp2/seasonal-strength.html
-
-        print("Time-Series Level is " + str(round(stock.mean(), 2)))
-
-        if ns_parser.multiplicative:
-            FtMul = max(0, 1 - np.var(resultMul.resid)) / np.var(
-                resultMul.trend + resultMul.resid
-            )
-            print("Strength of Trend: %.4f" % FtMul)
-            FsMul = max(
-                0,
-                1
-                - np.var(resultMul.resid)
-                / np.var(resultMul.seasonal + resultMul.resid),
-            )
-            print("Strength of Seasonality: %.4f" % FsMul)
-
-        else:
-            FtAdd = max(
-                0,
-                1 - np.var(resultAdd.resid) / np.var(resultAdd.trend + resultAdd.resid),
-            )
-            print("Strength of Trend: %.4f" % FtAdd)
-            FsAdd = max(
-                0,
-                1
-                - np.var(resultAdd.resid)
-                / np.var(resultAdd.seasonal + resultAdd.resid),
-            )
-            print("Strength of Seasonality: %.4f" % FsAdd)
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def cusum(other_args: List[str], stock: pd.DataFrame):
-    """Cumulative sum algorithm (CUSUM) to detect abrupt changes in data
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="cusum",
-        description="""
-            Cumulative sum algorithm (CUSUM) to detect abrupt changes in data
-        """,
-    )
-    parser.add_argument(
-        "-t",
-        "--threshold",
-        dest="threshold",
-        type=float,
-        default=(max(stock["Adj Close"].values) - min(stock["Adj Close"].values)) / 40,
-        help="threshold",
-    )
-    parser.add_argument(
-        "-d",
-        "--drift",
-        dest="drift",
-        type=float,
-        default=(max(stock["Adj Close"].values) - min(stock["Adj Close"].values)) / 80,
-        help="drift",
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        stock = stock["Adj Close"]
-
-        detect_cusum(stock.values, ns_parser.threshold, ns_parser.drift, True, True)
-
-        if gtff.USE_ION:
-            plt.ion()
-
-        plt.show()
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        return
-
-
-def acf(other_args: List[str], ticker: str, stock: pd.DataFrame, start: datetime):
-    """Auto-Correlation and Partial Auto-Correlation Functions for diff and diff diff stock data
-
-    Parameters
-    ----------
-    other_args : str
-        Command line arguments to be processed with argparse
-    ticker : str
-        Ticker of the stock
-    stock : pd.DataFrame
-        Stock data
-    """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        prog="acf",
-        description="""
-            Auto-Correlation and Partial Auto-Correlation Functions for diff and diff diff stock data
-        """,
-    )
-    parser.add_argument(
-        "-l",
-        "--lags",
-        dest="lags",
-        type=check_positive,
-        default=15,
-        help="maximum lags to display in plots",
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        print(stock.head())
-        stock = stock["Adj Close"]
-
-        fig = plt.figure(
-            figsize=plot_autoscale(), dpi=PLOT_DPI, constrained_layout=True
-        )
-        spec = gridspec.GridSpec(ncols=2, nrows=2, figure=fig)
-
-        # Diff Auto-correlation function for original time series
-        ax_acf = fig.add_subplot(spec[0, 0])
-        sm.graphics.tsa.plot_acf(
-            np.diff(np.diff(stock.values)), lags=ns_parser.lags, ax=ax_acf
-        )
-        plt.title(f"Diff({ticker}) Auto-Correlation from {start.strftime('%Y-%m-%d')}")
-        # Diff Partial auto-correlation function for origin