OpenBB/openbb_terminal/common/behavioural_analysis/finbrain_view.py

"""FinBrain View Module"""
__docformat__ = "numpy"

import logging
import os
from typing import Optional, List

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

from openbb_terminal.config_terminal import theme
from openbb_terminal.common.behavioural_analysis import finbrain_model
from openbb_terminal.config_plot import PLOT_DPI
from openbb_terminal.decorators import log_start_end
from openbb_terminal.helper_funcs import (
    export_data,
    plot_autoscale,
    print_rich_table,
    is_valid_axes_count,
)
from openbb_terminal.rich_config import console
from openbb_terminal import rich_config


logger = logging.getLogger(__name__)


def lambda_sentiment_coloring(val: float, last_val: float) -> str:
    if float(val) > last_val:
        return f"[green]{val}[/green]"
    return f"[red]{val}[/red]"


@log_start_end(log=logger)
def display_sentiment_analysis(
    symbol: str,
    raw: bool = False,
    export: str = "",
    external_axes: Optional[List[plt.Axes]] = None,
):
    """Plots Sentiment analysis from FinBrain. Prints table if raw is True. [Source: FinBrain]

    Parameters
    ----------
    symbol: str
        Ticker symbol to get the sentiment analysis from
    raw: False
        Display raw table data
    export: str
        Format to export data
    external_axes: Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """
    sentiment = finbrain_model.get_sentiment(symbol)
    if sentiment.empty:
        console.print("No sentiment data found.\n")
        return

    if not raw:
        # This plot has 1 axis
        if external_axes is None:
            _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
        elif is_valid_axes_count(external_axes, 1):
            (ax,) = external_axes
        else:
            return

        for index, row in sentiment.iterrows():
            if float(row["Sentiment Analysis"]) >= 0:
                ax.scatter(
                    index, float(row["Sentiment Analysis"]), s=100, color=theme.up_color
                )
            else:
                ax.scatter(
                    index,
                    float(row["Sentiment Analysis"]),
                    s=100,
                    color=theme.down_color,
                )
        ax.axhline(y=0, linestyle="--")
        ax.set_xlabel("Time")
        ax.set_ylabel("Sentiment")
        start_date = sentiment.index[-1].strftime("%Y/%m/%d")
        ax.set_title(
            f"FinBrain's Sentiment Analysis for {symbol.upper()} since {start_date}"
        )
        ax.set_ylim([-1.1, 1.1])
        senValues = np.array(pd.to_numeric(sentiment["Sentiment Analysis"].values))
        senNone = np.array(0 * len(sentiment))
        ax.fill_between(
            sentiment.index,
            pd.to_numeric(sentiment["Sentiment Analysis"].values),
            0,
            where=(senValues < senNone),
            alpha=0.30,
            color=theme.down_color,
            interpolate=True,
        )
        ax.fill_between(
            sentiment.index,
            pd.to_numeric(sentiment["Sentiment Analysis"].values),
            0,
            where=(senValues >= senNone),
            alpha=0.30,
            color=theme.up_color,
            interpolate=True,
        )
        theme.style_primary_axis(ax)

        if external_axes is None:
            theme.visualize_output()

    else:
        if rich_config.USE_COLOR:
            color_df = sentiment["Sentiment Analysis"].apply(
                lambda_sentiment_coloring, last_val=0
            )
            color_df = pd.DataFrame(
                data=color_df.values,
                index=pd.to_datetime(sentiment.index).strftime("%Y-%m-%d"),
            )
            print_rich_table(
                color_df,
                headers=["Sentiment"],
                title="FinBrain Ticker Sentiment",
                show_index=True,
            )
        else:
            print_rich_table(
                pd.DataFrame(
                    data=sentiment.values,
                    index=pd.to_datetime(sentiment.index).strftime("%Y-%m-%d"),
                ),
                headers=["Sentiment"],
                title="FinBrain Ticker Sentiment",
                show_index=True,
            )

    export_data(
        export, os.path.dirname(os.path.abspath(__file__)), "headlines", sentiment
    )