Refactor BA Menu (#768)

* Finnhub and finbrain fns

* Twitter menu (forex/ba  adjusted)

* Google menu

* Reddit menu pt1

* Reddit commands

* Stocktwits commands

* Sentimentinvestor commands

* Update crypto-dd

* Review pt 1

* Hugo server

* Review comments - get rid of all prints/errors in model...

28 files changed, 3752 insertions, 2521 deletions

diff --git a/gamestonk_terminal/common/behavioural_analysis/finbrain_model.py b/gamestonk_terminal/common/behavioural_analysis/finbrain_model.py
new file mode 100644
index 00000000000..7b08659c93d
--- /dev/null
+++ b/gamestonk_terminal/common/behavioural_analysis/finbrain_model.py
@@ -0,0 +1,31 @@
+"""FinBrain Model"""
+__docformat__ = "numpy"
+
+import pandas as pd
+import requests
+
+
+def get_sentiment(ticker: str) -> pd.DataFrame:
+    """Gets Sentiment analysis provided by FinBrain's API [Source: finbrain]
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker to get the sentiment analysis from
+
+    Returns
+    -------
+    DataFrame()
+        Empty if there was an issue with data retrieval
+    """
+    result = requests.get(f"https://api.finbrain.tech/v0/sentiments/{ticker}")
+    sentiment = pd.DataFrame()
+    if result.status_code == 200:
+        if "sentimentAnalysis" in result.json():
+            sentiment = pd.DataFrame.from_dict(
+                result.json()["sentimentAnalysis"], orient="index"
+            )
+            sentiment.index = pd.to_datetime(sentiment.index).to_pydatetime()
+            sentiment.index.name = "date"
+            sentiment.columns = ["Sentiment Analysis"]
+    return sentiment
diff --git a/gamestonk_terminal/common/behavioural_analysis/finbrain_view.py b/gamestonk_terminal/common/behavioural_analysis/finbrain_view.py
index 14004542dd3..1cfb189fa64 100644
--- a/gamestonk_terminal/common/behavioural_analysis/finbrain_view.py
+++ b/gamestonk_terminal/common/behavioural_analysis/finbrain_view.py
@@ -1,20 +1,16 @@
 """FinBrain View Module"""
 __docformat__ = "numpy"
 
-import argparse
-from typing import List
-import requests
+import os
 from matplotlib import pyplot as plt
 import matplotlib.dates as mdates
 import pandas as pd
 from pandas.plotting import register_matplotlib_converters
 import numpy as np
 from colorama import Fore, Style
-
-from gamestonk_terminal.helper_funcs import (
-    parse_known_args_and_warn,
-    plot_autoscale,
-)
+from tabulate import tabulate
+from gamestonk_terminal.helper_funcs import plot_autoscale, export_data
+from gamestonk_terminal.common.behavioural_analysis import finbrain_model
 from gamestonk_terminal.config_plot import PLOT_DPI
 from gamestonk_terminal import feature_flags as gtff
 
@@ -23,44 +19,10 @@ register_matplotlib_converters()
 
 
 def sentiment_coloring(val: float, last_val: float) -> str:
-    if float(val) > last_val:
-        color = Fore.GREEN
-    else:
-        color = Fore.RED
+    color = Fore.GREEN if float(val) > last_val else Fore.RED
     return f"{color}{val}{Style.RESET_ALL}"
 
 
-def get_sentiment(ticker: str) -> pd.DataFrame:
-    """Gets Sentiment analysis provided by FinBrain's API
-
-    Parameters
-    ----------
-    ticker : str
-        Ticker to get the sentiment analysis from
-
-    Returns
-    -------
-    DataFrame()
-        Empty if there was an issue with data retrieval
-    """
-    result = requests.get(f"https://api.finbrain.tech/v0/sentiments/{ticker}")
-    sentiment = pd.DataFrame()
-    if result.status_code == 200:
-        if "sentimentAnalysis" in result.json():
-            sentiment = pd.DataFrame.from_dict(
-                result.json()["sentimentAnalysis"], orient="index"
-            )
-            sentiment.index = pd.to_datetime(sentiment.index).to_pydatetime()
-            sentiment.index.name = "date"
-            sentiment.columns = ["Sentiment Analysis"]
-        else:
-            print("Unexpected data format from FinBrain API")
-    else:
-        print("Request error in retrieving sentiment from FinBrain API")
-
-    return sentiment
-
-
 def plot_sentiment(sentiment: pd.DataFrame, ticker: str):
     """Plot Sentiment analysis provided by FinBrain's API
 
@@ -71,27 +33,29 @@ def plot_sentiment(sentiment: pd.DataFrame, ticker: str):
     ticker : str
         Ticker to get the sentiment analysis from
     """
-    plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
+    fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
     for index, row in sentiment.iterrows():
         if float(row["Sentiment Analysis"]) >= 0:
-            plt.scatter(index, float(row["Sentiment Analysis"]), s=100, c="green")
+            ax.scatter(index, float(row["Sentiment Analysis"]), s=100, c="green")
         else:
-            plt.scatter(index, float(row["Sentiment Analysis"]), s=100, c="red")
-    plt.axhline(y=0, color="k", linestyle="--", lw=2)
-    plt.xlabel("Time")
-    plt.ylabel("Sentiment")
-    plt.grid(b=True, which="major", color="#666666", linestyle="-")
-    plt.minorticks_on()
-    plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
+            ax.scatter(index, float(row["Sentiment Analysis"]), s=100, c="red")
+    ax.axhline(y=0, color="k", linestyle="--", lw=2)
+    ax.set_xlabel("Time")
+    ax.set_ylabel("Sentiment")
+    ax.grid(b=True, which="major", color="#666666", linestyle="-")
+    ax.minorticks_on()
+    ax.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
     start_date = sentiment.index[-1].strftime("%Y/%m/%d")
-    plt.title(f"FinBrain's Sentiment Analysis for {ticker.upper()} since {start_date}")
+    ax.set_title(
+        f"FinBrain's Sentiment Analysis for {ticker.upper()} since {start_date}"
+    )
     plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
     plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))
     plt.gcf().autofmt_xdate()
-    plt.ylim([-1.1, 1.1])
+    ax.set_ylim([-1.1, 1.1])
     senValues = np.array(pd.to_numeric(sentiment["Sentiment Analysis"].values))
     senNone = np.array(0 * len(sentiment))
-    plt.fill_between(
+    ax.fill_between(
         sentiment.index,
         pd.to_numeric(sentiment["Sentiment Analysis"].values),
         0,
@@ -100,7 +64,7 @@ def plot_sentiment(sentiment: pd.DataFrame, ticker: str):
         color="red",
         interpolate=True,
     )
-    plt.fill_between(
+    ax.fill_between(
         sentiment.index,
         pd.to_numeric(sentiment["Sentiment Analysis"].values),
         0,
@@ -111,53 +75,57 @@ def plot_sentiment(sentiment: pd.DataFrame, ticker: str):
     )
     if gtff.USE_ION:
         plt.ion()
+    fig.tight_layout()
     plt.show()
 
 
-def sentiment_analysis(other_args: List[str], ticker: str):
+def display_sentiment_analysis(ticker: str, export: str = ""):
     """Sentiment analysis from FinBrain
 
     Parameters
     ----------
-    other_args : List[str]
-        Command line arguments to be processed with argparse
     ticker : str
         Ticker to get the sentiment analysis from
+    export : str
+        Format to export data
     """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-        prog="finbrain",
-        description="""FinBrain collects the news headlines from 15+ major financial news
-                    sources on a daily basis and analyzes them to generate sentiment scores
-                    for more than 4500 US stocks.FinBrain Technologies develops deep learning
-                    algorithms for financial analysis and prediction, which currently serves
-                    traders from more than 150 countries all around the world.
-                    [Source:  https://finbrain.tech]""",
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        df_sentiment = get_sentiment(ticker)
-
-        if not df_sentiment.empty:
-            plot_sentiment(df_sentiment, ticker)
-
-        df_sentiment.sort_index(ascending=True, inplace=True)
-
-        if gtff.USE_COLOR:
+    df_sentiment = finbrain_model.get_sentiment(ticker)
+    if df_sentiment.empty:
+        print("No sentiment data found.\n")
+        return
+
+    plot_sentiment(df_sentiment, ticker)
+
+    df_sentiment.sort_index(ascending=True, inplace=True)
+
+    if gtff.USE_COLOR:
+        color_df = df_sentiment["Sentiment Analysis"].apply(
+            sentiment_coloring, last_val=0
+        )
+        if gtff.USE_TABULATE_DF:
+            color_df = pd.DataFrame(
+                data=color_df.values,
+                index=pd.to_datetime(df_sentiment.index).strftime("%Y-%m-%d"),
+            )
+            print(tabulate(color_df, headers=["Sentiment"], tablefmt="fancy_grid"))
+        else:
+            print(color_df.to_string())
+    else:
+        if gtff.USE_TABULATE_DF:
             print(
-                df_sentiment["Sentiment Analysis"]
-                .apply(sentiment_coloring, last_val=0)
-                .to_string()
+                tabulate(
+                    pd.DataFrame(
+                        data=df_sentiment.values,
+                        index=pd.to_datetime(df_sentiment.index).strftime("%Y-%m-%d"),
+                    ),
+                    headers=["Sentiment"],
+                    tablefmt="fancy_grid",
+                )
             )
+
         else:
             print(df_sentiment.to_string())
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
-        print("")
+    print("")
+    export_data(
+        export, os.path.dirname(os.path.abspath(__file__)), "headlines", df_sentiment
+    )
diff --git a/gamestonk_terminal/common/behavioural_analysis/finnhub_model.py b/gamestonk_terminal/common/behavioural_analysis/finnhub_model.py
new file mode 100644
index 00000000000..95ff851ae52
--- /dev/null
+++ b/gamestonk_terminal/common/behavioural_analysis/finnhub_model.py
@@ -0,0 +1,28 @@
+"""Finnhub Model"""
+__docformat__ = "numpy"
+
+from typing import Dict
+import requests
+from gamestonk_terminal import config_terminal as cfg
+
+
+def get_sentiment_stats(ticker: str) -> Dict:
+    """Get sentiment stats [Source: finnhub]
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker to get sentiment stats
+
+    Returns
+    -------
+    Dict
+        Get sentiment stats
+    """
+    response = requests.get(
+        f"https://finnhub.io/api/v1/news-sentiment?symbol={ticker}&token={cfg.API_FINNHUB_KEY}"
+    )
+    if response.status_code == 200:
+        return response.json()
+
+    return {}
diff --git a/gamestonk_terminal/common/behavioural_analysis/finnhub_view.py b/gamestonk_terminal/common/behavioural_analysis/finnhub_view.py
index ab0c42ea933..63e652387f4 100644
--- a/gamestonk_terminal/common/behavioural_analysis/finnhub_view.py
+++ b/gamestonk_terminal/common/behavioural_analysis/finnhub_view.py
@@ -1,83 +1,50 @@
-import argparse
-from typing import List, Dict
-import requests
-from gamestonk_terminal import config_terminal as cfg
-from gamestonk_terminal.helper_funcs import (
-    parse_known_args_and_warn,
-)
+"""Finnhub View"""
+__docformat__ = "numpy"
 
+import os
 
-def get_sentiment_stats(ticker: str) -> Dict:
-    """Get sentiment stats
+import pandas as pd
 
-    Parameters
-    ----------
-    ticker : str
-        Ticker to get sentiment stats
-
-    Returns
-    -------
-    Dict
-        Get sentiment stats
-    """
-    response = requests.get(
-        f"https://finnhub.io/api/v1/news-sentiment?symbol={ticker}&token={cfg.API_FINNHUB_KEY}"
-    )
-    if response.status_code == 200:
-        return response.json()
+from gamestonk_terminal.helper_funcs import export_data
+from gamestonk_terminal.common.behavioural_analysis import finnhub_model
 
-    return {}
 
-
-def sentiment_stats(other_args: List[str], ticker: str):
+def display_sentiment_stats(ticker: str, export: str = ""):
     """
     Sentiment stats which displays buzz, news score, articles last week, articles weekly average,
     bullish vs bearish percentages, sector average bullish percentage, and sector average news score
 
     Parameters
     ----------
-    other_args : List[str]
-        Command line arguments to be processed with argparse
     ticker : str
         Ticker to get sentiment stats
+    export : str
+        Format to export data
     """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-        prog="stats",
-        description="""
-            Sentiment stats which displays buzz, news score, articles last week, articles weekly average,
-            bullish vs bearish percentages, sector average bullish percentage, and sector average news score.
-            [Source: https://finnhub.io]
-        """,
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        d_stats = get_sentiment_stats(ticker)
-
-        if d_stats:
-            print(f"Buzz: {round(100*d_stats['buzz']['buzz'],2)} %")
-            print(f"News Score: {round(100*d_stats['companyNewsScore'],2)} %")
-            print("")
-            print(f"Articles Last Week: {d_stats['buzz']['articlesInLastWeek']}")
-            print(f"Articles Weekly Average: {d_stats['buzz']['weeklyAverage']}")
-            print("")
-            print(f"Bullish: {round(100*d_stats['sentiment']['bullishPercent'],2)} %")
-            print(f"Bearish: {round(100*d_stats['sentiment']['bearishPercent'],2)} %")
-            print("")
-            print(
-                f"Sector Average Bullish: {round(100*d_stats['sectorAverageBullishPercent'],2)} %"
-            )
-            print(
-                f"Sector Average News Score: {round(100*d_stats['sectorAverageNewsScore'],2)} %"
-            )
-        else:
-            print("No sentiment stats found.")
-        print("")
-
-    except Exception as e:
-        print(e, "\n")
+    d_stats = finnhub_model.get_sentiment_stats(ticker)
+
+    if d_stats:
+        print(
+            f"""
+Buzz: {round(100*d_stats['buzz']['buzz'],2)} %
+News Score: {round(100*d_stats['companyNewsScore'],2)} %
+
+Articles Last Week: {d_stats['buzz']['articlesInLastWeek']}
+Articles Weekly Average: {d_stats['buzz']['weeklyAverage']}
+
+Bullish: {round(100*d_stats['sentiment']['bullishPercent'],2)} %
+Bearish: {round(100*d_stats['sentiment']['bearishPercent'],2)} %
+
+Sector Average Bullish: {round(100*d_stats['sectorAverageBullishPercent'],2)}
+Sector Average News Score: {round(100*d_stats['sectorAverageNewsScore'],2)} %"""
+        )
+        export_data(
+            export,
+            os.path.dirname(os.path.abspath(__file__)),
+            "stats",
+            pd.DataFrame(d_stats),
+        )
+
+    else:
+        print("No sentiment stats found.")
+    print("")
diff --git a/gamestonk_terminal/common/behavioural_analysis/google_model.py b/gamestonk_terminal/common/behavioural_analysis/google_model.py
new file mode 100644
index 00000000000..407892fc865
--- /dev/null
+++ b/gamestonk_terminal/common/behavioural_analysis/google_model.py
@@ -0,0 +1,78 @@
+"""Google Model"""
+__docformat__ = "numpy"
+
+import pandas as pd
+from pytrends.request import TrendReq
+
+
+def get_mentions(ticker: str) -> pd.DataFrame:
+    """Get interest over time from google api [Source: google]
+
+    Parameters
+    ----------
+    ticker : str
+        Stock ticker
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe of interest over time
+    """
+    pytrend = TrendReq()
+    pytrend.build_payload(kw_list=[ticker])
+    return pytrend.interest_over_time()
+
+
+def get_regions(ticker: str) -> pd.DataFrame:
+    """Get interest by region from google api [Source: google]
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker to look at
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe of interest by region
+    """
+    pytrend = TrendReq()
+    pytrend.build_payload(kw_list=[ticker])
+    return pytrend.interest_by_region()
+
+
+def get_queries(ticker: str) -> pd.DataFrame:
+    """Get related queries from google api [Source: google]
+
+    Parameters
+    ----------
+    ticker : str
+        Stock ticker to compare
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe of related queries
+    """
+    pytrend = TrendReq()
+    pytrend.build_payload(kw_list=[ticker])
+    return pytrend.related_queries()
+
+
+def get_rise(ticker: str) -> pd.DataFrame:
+    """Get top rising related queries with this stock's query [Source: google]
+
+    Parameters
+    ----------
+    ticker : str
+        Stock ticker
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe containing rising related queries
+    """
+    pytrend = TrendReq()
+    pytrend.build_payload(kw_list=[ticker])
+    df_related_queries = pytrend.related_queries()
+    return df_related_queries[ticker]["rising"]
diff --git a/gamestonk_terminal/common/behavioural_analysis/google_view.py b/gamestonk_terminal/common/behavioural_analysis/google_view.py
index 74f198a221e..f6df2b24405 100644
--- a/gamestonk_terminal/common/behavioural_analysis/google_view.py
+++ b/gamestonk_terminal/common/behavioural_analysis/google_view.py
@@ -1,217 +1,160 @@
-import argparse
-from typing import List
+"""Google View"""
+__docformat__ = "numpy"
+
+import os
 from datetime import datetime
-from pytrends.request import TrendReq
+
+from tabulate import tabulate
 import matplotlib.pyplot as plt
-from gamestonk_terminal.helper_funcs import (
-    check_positive,
-    valid_date,
-    parse_known_args_and_warn,
-)
 
+from gamestonk_terminal.helper_funcs import plot_autoscale, export_data
+from gamestonk_terminal.common.behavioural_analysis import google_model
+from gamestonk_terminal import config_plot as cfp, feature_flags as gtff
 
-def mentions(other_args: List[str], ticker: str, start: datetime):
+
+def display_mentions(ticker: str, start: datetime, export: str = ""):
     """Plot weekly bars of stock's interest over time. other users watchlist. [Source: Google]
 
     Parameters
     ----------
-    other_args : List[str]
-        Command line arguments to be processed with argparse
     ticker : str
         Ticker
-    start : str
+    start : datetime
         Start date
+    export: str
+        Format to export data
     """
-    parser = argparse.ArgumentParser(
-        add_help=False,
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-        prog="mentions",
-        description="""
-            Plot weekly bars of stock's interest over time. other users watchlist. [Source: Google]
-        """,
-    )
-    parser.add_argument(
-        "-s",
-        "--start",
-        type=valid_date,
-        dest="start",
-        default=start,
-        help="starting date (format YYYY-MM-DD) from when we are interested in stock's mentions.",
-    )
-
-    try:
-        ns_parser = parse_known_args_and_warn(parser, other_args)
-        if not ns_parser:
-            return
-
-        pytrend = TrendReq()
-        pytrend.build_payload(kw_list=[ticker])
-        df_interest = pytrend.interest_over_time()
-
-        plt.title(f"Interest over time on {ticker}")
-        if ns_parser.start:
-            df_interest = df_interest[ns_parser.start :]
-            plt.bar(df_interest.index, df_interest[ticker], width=2)
-            plt.bar(
-                df_interest.index[-1],
-                df_interest[ticker].values[-1],
-                color="tab:orange",
-                width=2,
-            )
-        else:
-            plt.bar(df_interest.index, df_interest[ticker], width=1)
-            plt.bar(
-                df_interest.index[-1],
-                df_interest[ticker].values[-1],
-                color="tab:orange",
-                width=1,
-            )
-
-        plt.grid(b=True, which="major", color="#666666", linestyle="-")
-        plt.ylabel("Interest [%]")
-        plt.xlabel("Time")
-        plt.show()
-        print("")
+    df_interest = google_model.get_mentions(ticker)
+
+    fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
+    ax.set_title(f"Interest over time on {ticker}")
+    if start:
+        df_interest = df_interest[start:]  # type: ignore
+        ax.bar(df_interest.index, df_interest[ticker], width=2)
+        ax.bar(
+