remove twitter functions

7 files changed, 0 insertions, 542 deletions

diff --git a/openbb_terminal/common/behavioural_analysis/twitter_model.py b/openbb_terminal/common/behavioural_analysis/twitter_model.py
deleted file mode 100644
index 034cc834113..00000000000
--- a/openbb_terminal/common/behavioural_analysis/twitter_model.py
+++ /dev/null
@@ -1,212 +0,0 @@
-"""Twitter Model"""
-__docformat__ = "numpy"
-
-import logging
-from datetime import datetime, timedelta
-from typing import Optional
-
-import pandas as pd
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
-from openbb_terminal.core.session.current_user import get_current_user
-from openbb_terminal.decorators import check_api_key, log_start_end
-from openbb_terminal.helper_funcs import clean_tweet, get_data, request
-from openbb_terminal.rich_config import console
-
-logger = logging.getLogger(__name__)
-
-analyzer = SentimentIntensityAnalyzer()
-
-
-@log_start_end(log=logger)
-@check_api_key(["API_TWITTER_BEARER_TOKEN"])
-def load_analyze_tweets(
-    symbol: str,
-    limit: int = 100,
-    start_date: Optional[str] = "",
-    end_date: Optional[str] = "",
-) -> pd.DataFrame:
-    """Load tweets from twitter API and analyzes using VADER.
-
-    Parameters
-    ----------
-    symbol: str
-        Ticker symbol to search twitter for
-    limit: int
-        Number of tweets to analyze
-    start_date: Optional[str]
-        If given, the start time to get tweets from
-    end_date: Optional[str]
-        If given, the end time to get tweets from
-
-    Returns
-    -------
-    df_tweet: pd.DataFrame
-        Dataframe of tweets and sentiment
-    """
-    params = {
-        "query": rf"(\${symbol}) (lang:en)",
-        "max_results": str(limit),
-        "tweet.fields": "created_at,lang",
-    }
-
-    if start_date:
-        # Assign from and to datetime parameters for the API
-        params["start_time"] = start_date
-    if end_date:
-        params["end_time"] = end_date
-
-    # Request Twitter API
-    response = request(
-        "https://api.twitter.com/2/tweets/search/recent",
-        params=params,  # type: ignore
-        headers={
-            "authorization": "Bearer "
-            + get_current_user().credentials.API_TWITTER_BEARER_TOKEN
-        },
-    )
-
-    # Create dataframe
-    df_tweets = pd.DataFrame()
-
-    # Check that the API response was successful
-    if response.status_code == 200:
-        tweets = []
-        for tweet in response.json()["data"]:
-            row = get_data(tweet)
-            tweets.append(row)
-        df_tweets = pd.DataFrame(tweets)
-    elif response.status_code == 401:
-        console.print("Twitter API Key provided is incorrect\n")
-        return pd.DataFrame()
-    elif response.status_code == 400:
-        console.print(
-            """
-            Status Code 400.
-            This means you are requesting data from beyond the API's 7 day limit"""
-        )
-        return pd.DataFrame()
-    elif response.status_code == 403:
-        console.print(
-            f"""
-            Status code 403.
-            It seems you're twitter credentials are invalid - {response.text}
-        """
-        )
-        return pd.DataFrame()
-    else:
-        console.print(
-            f"""
-            Status code {response.status_code}.
-            Something went wrong - {response.text}
-        """
-        )
-        return pd.DataFrame()
-
-    sentiments = []
-    pos = []
-    neg = []
-    neu = []
-
-    for s_tweet in df_tweets["text"].to_list():
-        tweet = clean_tweet(s_tweet, symbol)
-        sentiments.append(analyzer.polarity_scores(tweet)["compound"])
-        pos.append(analyzer.polarity_scores(tweet)["pos"])
-        neg.append(analyzer.polarity_scores(tweet)["neg"])
-        neu.append(analyzer.polarity_scores(tweet)["neu"])
-    # Add sentiments to tweets dataframe
-    df_tweets["sentiment"] = sentiments
-    df_tweets["positive"] = pos
-    df_tweets["negative"] = neg
-    df_tweets["neutral"] = neu
-
-    return df_tweets
-
-
-@log_start_end(log=logger)
-def get_sentiment(
-    symbol: str,
-    n_tweets: int = 15,
-    n_days_past: int = 2,
-) -> pd.DataFrame:
-    """Get sentiments from symbol.
-
-    Parameters
-    ----------
-    symbol: str
-        Stock ticker symbol to get sentiment for
-    n_tweets: int
-        Number of tweets to get per hour
-    n_days_past: int
-        Number of days to extract tweets for
-
-    Returns
-    -------
-    df_sentiment: pd.DataFrame
-        Dataframe of sentiment
-    """
-    # Date format string required by twitter
-    dt_format = "%Y-%m-%dT%H:%M:%SZ"
-
-    # Algorithm to extract
-    dt_recent = datetime.utcnow() - timedelta(seconds=20)
-    dt_old = dt_recent - timedelta(days=n_days_past)
-    console.print(
-        f"From {dt_recent.date()} retrieving {n_tweets*24} tweets ({n_tweets} tweets/hour)"
-    )
-
-    df_tweets = pd.DataFrame(
-        columns=[
-            "created_at",
-            "text",
-            "sentiment",
-            "positive",
-            "negative",
-            "neutral",
-        ]
-    )
-    while True:
-        # Iterate until we haven't passed the old number of days
-        if dt_recent < dt_old:
-            break
-        # Update past datetime
-        dt_past = dt_recent - timedelta(minutes=60)
-
-        temp = load_analyze_tweets(
-            symbol,
-            n_tweets,
-            start_date=dt_past.strftime(dt_format),
-            end_date=dt_recent.strftime(dt_format),
-        )
-
-        if (isinstance(temp, pd.DataFrame) and temp.empty) or (
-            not isinstance(temp, pd.DataFrame) and not temp
-        ):
-            return pd.DataFrame()
-
-        df_tweets = pd.concat([df_tweets, temp])
-
-        if dt_past.day < dt_recent.day:
-            console.print(
-                f"From {dt_past.date()} retrieving {n_tweets*24} tweets ({n_tweets} tweets/hour)"
-            )
-
-        # Update recent datetime
-        dt_recent = dt_past
-
-    # Sort tweets per date
-    df_tweets.sort_index(ascending=False, inplace=True)
-    df_tweets["cumulative_compound"] = df_tweets["sentiment"].cumsum()
-    df_tweets["prob_sen"] = 1
-
-    # df_tweets.to_csv(r'notebooks/tweets.csv', index=False)
-    df_tweets.reset_index(inplace=True)
-    df_tweets["Month"] = pd.to_datetime(df_tweets["created_at"]).apply(
-        lambda x: x.month
-    )
-    df_tweets["Day"] = pd.to_datetime(df_tweets["created_at"]).apply(lambda x: x.day)
-    df_tweets["date"] = pd.to_datetime(df_tweets["created_at"])
-    df_tweets = df_tweets.sort_values(by="date")
-    df_tweets["cumulative_compound"] = df_tweets["sentiment"].cumsum()
-
-    return df_tweets
diff --git a/openbb_terminal/common/behavioural_analysis/twitter_view.py b/openbb_terminal/common/behavioural_analysis/twitter_view.py
deleted file mode 100644
index fba72770628..00000000000
--- a/openbb_terminal/common/behavioural_analysis/twitter_view.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""Twitter view."""
-__docformat__ = "numpy"
-
-import logging
-import os
-from typing import Optional, Union
-
-import numpy as np
-import pandas as pd
-from dateutil import parser as dparse
-
-from openbb_terminal import OpenBBFigure, theme
-from openbb_terminal.common.behavioural_analysis import twitter_model
-from openbb_terminal.decorators import log_start_end
-from openbb_terminal.helper_funcs import export_data, get_closing_price
-from openbb_terminal.rich_config import console
-
-logger = logging.getLogger(__name__)
-
-
-@log_start_end(log=logger)
-def display_inference(
-    symbol: str, limit: int = 100, export: str = "", sheet_name: Optional[str] = None
-):
-    """Prints Inference sentiment from past n tweets.
-
-    Parameters
-    ----------
-    symbol: str
-        Stock ticker symbol
-    limit: int
-        Number of tweets to analyze
-    sheet_name: str
-        Optionally specify the name of the sheet the data is exported to.
-    export: str
-        Format to export tweet dataframe
-    """
-    df_tweets = twitter_model.load_analyze_tweets(symbol, limit)
-
-    if (isinstance(df_tweets, pd.DataFrame) and df_tweets.empty) or (
-        not isinstance(df_tweets, pd.DataFrame) and not df_tweets
-    ):
-        return
-
-    # Parse tweets
-    dt_from = dparse.parse(df_tweets["created_at"].values[-1])
-    dt_to = dparse.parse(df_tweets["created_at"].values[0])
-    console.print(f"From: {dt_from.strftime('%Y-%m-%d %H:%M:%S')}")
-    console.print(f"To:   {dt_to.strftime('%Y-%m-%d %H:%M:%S')}")
-
-    console.print(f"{len(df_tweets)} tweets were analyzed.")
-    dt_delta = dt_to - dt_from
-    n_freq = dt_delta.total_seconds() / len(df_tweets)
-    console.print(f"Frequency of approx 1 tweet every {round(n_freq)} seconds.")
-
-    pos = df_tweets["positive"]
-    neg = df_tweets["negative"]
-
-    percent_pos = len(np.where(pos > neg)[0]) / len(df_tweets)
-    percent_neg = len(np.where(pos < neg)[0]) / len(df_tweets)
-    total_sent = np.round(np.sum(df_tweets["sentiment"]), 2)
-    mean_sent = np.round(np.mean(df_tweets["sentiment"]), 2)
-    console.print(f"The summed compound sentiment of {symbol} is: {total_sent}")
-    console.print(f"The average compound sentiment of {symbol} is: {mean_sent}")
-    console.print(
-        f"Of the last {len(df_tweets)} tweets, {100*percent_pos:.2f} % had a higher positive sentiment"
-    )
-    console.print(
-        f"Of the last {len(df_tweets)} tweets, {100*percent_neg:.2f} % had a higher negative sentiment"
-    )
-
-    export_data(
-        export,
-        os.path.dirname(os.path.abspath(__file__)),
-        "infer",
-        df_tweets,
-        sheet_name,
-    )
-
-
-@log_start_end(log=logger)
-def display_sentiment(
-    symbol: str,
-    n_tweets: int = 15,
-    n_days_past: int = 2,
-    compare: bool = False,
-    export: str = "",
-    sheet_name: Optional[str] = None,
-    external_axes: bool = False,
-) -> Union[OpenBBFigure, None]:
-    """Plots sentiments from symbol
-
-    Parameters
-    ----------
-    symbol: str
-        Stock ticker symbol to get sentiment for
-    n_tweets: int
-        Number of tweets to get per hour
-    n_days_past: int
-        Number of days to extract tweets for
-    compare: bool
-        Show corresponding change in stock price
-    sheet_name: str
-        Optionally specify the name of the sheet the data is exported to.
-    export: str
-        Format to export tweet dataframe
-    external_axes: bool, optional
-        Whether to return the figure object or not, by default False
-    """
-
-    df_tweets = twitter_model.get_sentiment(symbol, n_tweets, n_days_past)
-
-    if df_tweets.empty:
-        return None
-
-    if compare:
-        plots_kwargs = dict(
-            rows=3,
-            cols=1,
-            shared_xaxes=True,
-            vertical_spacing=0.05,
-            row_heights=[0.2, 0.6, 0.2],
-        )
-    else:
-        plots_kwargs = dict(
-            rows=2,
-            cols=1,
-            shared_xaxes=True,
-            vertical_spacing=0.05,
-            row_heights=[0.6, 0.4],
-        )
-
-    fig = OpenBBFigure.create_subplots(**plots_kwargs)  # type: ignore
-
-    fig.add_scatter(
-        x=pd.to_datetime(df_tweets["created_at"]),
-        y=df_tweets["cumulative_compound"].values,
-        row=1,
-        col=1,
-    )
-
-    fig.set_yaxis_title("<br>Cumulative<br>VADER Sentiment", row=1, col=1)
-
-    for _, day_df in df_tweets.groupby(by="Day"):
-        day_df["time"] = pd.to_datetime(day_df["created_at"])
-        day_df = day_df.sort_values(by="time")
-        fig.add_scatter(
-            x=day_df["time"],
-            y=day_df["sentiment"].cumsum(),
-            row=1,
-            col=1,
-        )
-        fig.add_bar(
-            x=df_tweets["date"],
-            y=df_tweets["positive"],
-            row=2,
-            col=1,
-            marker_color=theme.up_color,
-        )
-
-    fig.add_bar(
-        x=df_tweets["date"],
-        y=-1 * df_tweets["negative"],
-        row=2,
-        col=1,
-        marker_color=theme.down_color,
-    )
-    fig.set_yaxis_title("VADER Polarity Scores", row=2, col=1)
-
-    if compare:
-        # get stock end price for each corresponding day if compare == True
-        closing_price_df = get_closing_price(symbol, n_days_past)
-        fig.add_scatter(
-            x=closing_price_df["Date"],
-            y=closing_price_df["Close"],
-            name=pd.to_datetime(closing_price_df["Date"]).iloc[0].strftime("%Y-%m-%d"),
-            row=3,
-            col=1,
-        )
-        fig.set_yaxis_title("Stock Price", row=3, col=1)
-
-    fig.update_layout(
-        title=f"Twitter's {symbol} total compound sentiment over time is {round(np.sum(df_tweets['sentiment']), 2)}",
-        xaxis=dict(type="date"),
-        showlegend=False,
-    )
-
-    export_data(
-        export,
-        os.path.dirname(os.path.abspath(__file__)),
-        "sentiment",
-        df_tweets,
-        sheet_name,
-        fig,
-    )
-
-    return fig.show(external=external_axes)
diff --git a/openbb_terminal/stocks/behavioural_analysis/ba_controller.py b/openbb_terminal/stocks/behavioural_analysis/ba_controller.py
index 00953436ee6..547c5f23897 100644
--- a/openbb_terminal/stocks/behavioural_analysis/ba_controller.py
+++ b/openbb_terminal/stocks/behavioural_analysis/ba_controller.py
@@ -13,7 +13,6 @@ from openbb_terminal.common.behavioural_analysis import (
     google_view,
     reddit_view,
     stocktwits_view,
-    twitter_view,
 )
 from openbb_terminal.core.session.current_user import get_current_user
 from openbb_terminal.custom_prompt_toolkit import NestedCompleter
@@ -21,7 +20,6 @@ from openbb_terminal.decorators import log_start_end
 from openbb_terminal.helper_funcs import (
     EXPORT_BOTH_RAW_DATA_AND_FIGURES,
     EXPORT_ONLY_RAW_DATA_ALLOWED,
-    check_int_range,
     check_non_negative,
     check_positive,
     valid_date,
@@ -53,8 +51,6 @@ class BehaviouralAnalysisController(StockBaseController):
         "messages",
         "trending",
         "stalker",
-        "infer",
-        "sentiment",
         "mentions",
         "regions",
         "queries",
@@ -102,8 +98,6 @@ class BehaviouralAnalysisController(StockBaseController):
         mt.add_cmd("stalker")
         mt.add_cmd("bullbear", self.ticker)
         mt.add_cmd("messages", self.ticker)
-        mt.add_cmd("infer", self.ticker)
-        mt.add_cmd("sentiment", self.ticker)
         mt.add_cmd("mentions", self.ticker)
         mt.add_cmd("regions", self.ticker)
         mt.add_cmd("interest", self.ticker)
@@ -674,106 +668,6 @@ class BehaviouralAnalysisController(StockBaseController):
                 console.print("No ticker loaded. Please load using 'load <ticker>'\n")
 
     @log_start_end(log=logger)
-    def call_infer(self, other_args: List[str]):
-        """Process infer command."""
-        parser = argparse.ArgumentParser(
-            add_help=False,
-            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-            prog="infer",
-            description="""
-                Print quick sentiment inference from last tweets that contain the ticker.
-                This model splits the text into character-level tokens and uses vader sentiment analysis.
-                [Source: Twitter]
-            """,
-        )
-        parser.add_argument(
-            "-l",
-            "--limit",
-            action="store",
-            dest="limit",
-            type=check_int_range(10, 100),
-            default=100,
-            help="limit of latest tweets to infer from.",
-        )
-        if other_args and "-" not in other_args[0][0]:
-            other_args.insert(0, "-l")
-        ns_parser = self.parse_known_args_and_warn(
-            parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
-        )
-        if ns_parser:
-            if self.ticker:
-                twitter_view.display_inference(
-                    symbol=self.ticker,
-                    limit=ns_parser.limit,
-                    export=ns_parser.export,
-                    sheet_name=" ".join(ns_parser.sheet_name)
-                    if ns_parser.sheet_name
-                    else None,
-                )
-            else:
-                console.print("No ticker loaded. Please load using 'load <ticker>'\n")
-
-    @log_start_end(log=logger)
-    def call_sentiment(self, other_args: List[str]):
-        """Process sentiment command."""
-        parser = argparse.ArgumentParser(
-            add_help=False,
-            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-            prog="sentiment",
-            description="""
-                Plot in-depth sentiment predicted from tweets from last days
-                that contain pre-defined ticker. [Source: Twitter]
-            """,
-        )
-        # in reality this argument could be 100, but after testing it takes too long
-        # to compute which may not be acceptable
-        # TODO: use https://github.com/twintproject/twint instead of twitter API
-        parser.add_argument(
-            "-l",
-            "--limit",
-            action="store",
-            dest="limit",
-            type=check_int_range(10, 62),
-            default=15,
-            help="limit of tweets to extract per hour.",
-        )
-        parser.add_argument(
-            "-d",
-            "--days",
-            action="store",
-            dest="n_days_past",
-            type=check_int_range(1, 6),
-            default=6,
-            help="number of days in the past to extract tweets.",
-        )
-        parser.add_argument(
-            "-c",
-            "--compare",
-            action="store_true",
-            dest="compare",
-            help="show corresponding change in stock price",
-        )
-        if other_args and "-" not in other_args[0][0]:
-            other_args.insert(0, "-l")
-        ns_parser = self.parse_known_args_and_warn(
-            parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES
-        )
-        if ns_parser:
-            if self.ticker:
-                twitter_view.display_sentiment(
-                    symbol=self.ticker,
-                    n_tweets=ns_parser.limit,
-                    n_days_past=ns_parser.n_days_past,
-                    compare=ns_parser.compare,
-                    export=ns_parser.export,
-                    sheet_name=" ".join(ns_parser.sheet_name)
-                    if ns_parser.sheet_name
-                    else None,
-                )
-            else:
-                console.print("No ticker loaded. Please load using 'load <ticker>'\n")
-
-    @log_start_end(log=logger)
     def call_headlines(self, other_args: List[str]):
         """Process finbrain command."""
         parser = argparse.ArgumentParser(
diff --git a/tests/openbb_terminal/common/behavioural_analysis/test_twitter_model.py b/tests/openbb_terminal/common/behavioural_analysis/test_twitter_model.py
deleted file mode 100644
index e69de29bb2d..00000000000
--- a/tests/openbb_terminal/common/behavioural_analysis/test_twitter_model.py
+++ /dev/null
diff --git a/tests/openbb_terminal/common/behavioural_analysis/test_twitter_view.py b/tests/openbb_terminal/common/behavioural_analysis/test_twitter_view.py
deleted file mode 100644
index e69de29bb2d..00000000000
--- a/tests/openbb_terminal/common/behavioural_analysis/test_twitter_view.py
+++ /dev/null
diff --git a/tests/openbb_terminal/stocks/behavioural_analysis/test_ba_controller.py b/tests/openbb_terminal/stocks/behavioural_analysis/test_ba_controller.py
index 888a0ac6936..c966d950db7 100644
--- a/tests/openbb_terminal/stocks/behavioural_analysis/test_ba_controller.py
+++ b/tests/openbb_terminal/stocks/behavioural_analysis/test_ba_controller.py
@@ -298,27 +298,6 @@ def test_call_func_expect_queue(expected_queue, queue, func):
             ),
         ),
         (
-            "call_infer",
-            ["--limit=20", "--export=csv"],
-            "twitter_view.display_inference",
-            [],
-            dict(symbol="MOCK_TICKER", limit=20, export="csv", sheet_name=None),
-        ),
-        (
-            "call_sentiment",
-            ["--limit=20", "--days=2", "--compare", "--export=csv"],
-            "twitter_view.display_sentiment",
-            [],
-            dict(
-                symbol="MOCK_TICKER",
-                n_tweets=20,
-                n_days_past=2,
-                compare=True,
-                export="csv",
-                sheet_name=None,
-            ),
-        ),
-        (
             "call_mentions",
             ["--start=2020-12-01", "--export=csv"],
             "google_view.display_mentions",
@@ -422,8 +401,6 @@ def test_call_func(
         "call_messages",
         "call_trending",
         "call_stalker",
-        "call_infer",
-        "call_sentiment",
         "call_mentions",
         "call_regions",
         "call_queries",
@@ -456,8 +433,6 @@ def test_call_func_no_parser(func, mocker):
     "func",
     [
         "call_headlines",
-        "call_sentiment",
-        "call_infer",
         "call_rise",
         "call_queries",
         "call_regions",
diff --git a/tests/openbb_terminal/stocks/behavioural_analysis/txt/test_ba_controller/test_print_help.txt b/tests/openbb_terminal/stocks/behavioural_analysis/txt/test_ba_controller/test_print_help.txt
index e2a9a232e0b..e342867786e 100644
--- a/tests/openbb_terminal/stocks/behavioural_analysis/txt/test_ba_controller/test_print_help.txt
+++ b/tests/openbb_terminal/stocks/behavioural_analysis/txt/test_ba_controller/test_print_help.txt
@@ -12,8 +12,6 @@ Ticker: TSLA
     stalker            stalk stocktwits user's last messages                           [Stocktwits]
     bullbear           estimate quick sentiment from last 30 messages on board         [Stocktwits]
     messages           output up to the 30 last messages on the board                  [Stocktwits]
-    infer              infer about stock's sentiment from latest tweets                [Twitter]
-    sentiment          in-depth sentiment prediction from tweets over time             [Twitter]
     mentions           interest over time based on stock's mentions                    [Google]
     regions            regions that show highest interest in stock                     [Google]
     interest           interest over time of sentences versus stock price              [Google]