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150 lines
4.4 KiB
Python
150 lines
4.4 KiB
Python
"""FinBrain View Module"""
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__docformat__ = "numpy"
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import logging
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import os
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from typing import Optional, List
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.config_terminal import theme
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from gamestonk_terminal.common.behavioural_analysis import finbrain_model
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from gamestonk_terminal.config_plot import PLOT_DPI
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from gamestonk_terminal.decorators import log_start_end
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from gamestonk_terminal.helper_funcs import (
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export_data,
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plot_autoscale,
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print_rich_table,
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)
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from gamestonk_terminal.rich_config import console
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logger = logging.getLogger(__name__)
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def lambda_sentiment_coloring(val: float, last_val: float) -> str:
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if float(val) > last_val:
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return f"[green]{val}[/green]"
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return f"[red]{val}[/red]"
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@log_start_end(log=logger)
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def plot_sentiment(
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sentiment: pd.DataFrame, ticker: str, external_axes: Optional[List[plt.Axes]] = None
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) -> None:
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"""Plot Sentiment analysis provided by FinBrain's API
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Parameters
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----------
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sentiment : pd.DataFrame
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Dataframe with sentiment data to plot
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ticker : str
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Ticker to get the sentiment analysis from
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"""
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# This plot has 1 axis
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if external_axes is None:
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_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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else:
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if len(external_axes) != 1:
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logger.error("Expected list of one axis item.")
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console.print("[red]Expected list of one axis item./n[/red]")
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return
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(ax,) = external_axes
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for index, row in sentiment.iterrows():
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if float(row["Sentiment Analysis"]) >= 0:
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ax.scatter(
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index, float(row["Sentiment Analysis"]), s=100, color=theme.up_color
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)
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else:
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ax.scatter(
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index, float(row["Sentiment Analysis"]), s=100, color=theme.down_color
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)
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ax.axhline(y=0, linestyle="--")
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ax.set_xlabel("Time")
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ax.set_ylabel("Sentiment")
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start_date = sentiment.index[-1].strftime("%Y/%m/%d")
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ax.set_title(
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f"FinBrain's Sentiment Analysis for {ticker.upper()} since {start_date}"
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)
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ax.set_ylim([-1.1, 1.1])
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senValues = np.array(pd.to_numeric(sentiment["Sentiment Analysis"].values))
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senNone = np.array(0 * len(sentiment))
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ax.fill_between(
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sentiment.index,
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pd.to_numeric(sentiment["Sentiment Analysis"].values),
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0,
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where=(senValues < senNone),
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alpha=0.30,
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color=theme.down_color,
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interpolate=True,
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)
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ax.fill_between(
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sentiment.index,
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pd.to_numeric(sentiment["Sentiment Analysis"].values),
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0,
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where=(senValues >= senNone),
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alpha=0.30,
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color=theme.up_color,
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interpolate=True,
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)
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theme.style_primary_axis(ax)
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if external_axes is None:
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theme.visualize_output()
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@log_start_end(log=logger)
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def display_sentiment_analysis(
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ticker: str, export: str = "", external_axes: Optional[List[plt.Axes]] = None
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):
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"""Sentiment analysis from FinBrain
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Parameters
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----------
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ticker : str
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Ticker to get the sentiment analysis from
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export : str
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Format to export data
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"""
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df_sentiment = finbrain_model.get_sentiment(ticker)
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if df_sentiment.empty:
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console.print("No sentiment data found.\n")
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return
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plot_sentiment(sentiment=df_sentiment, ticker=ticker, external_axes=external_axes)
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df_sentiment.sort_index(ascending=True, inplace=True)
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if gtff.USE_COLOR:
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color_df = df_sentiment["Sentiment Analysis"].apply(
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lambda_sentiment_coloring, last_val=0
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)
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color_df = pd.DataFrame(
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data=color_df.values,
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index=pd.to_datetime(df_sentiment.index).strftime("%Y-%m-%d"),
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)
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print_rich_table(
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color_df,
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headers=["Sentiment"],
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title="FinBrain Ticker Sentiment",
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show_index=True,
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)
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else:
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print_rich_table(
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pd.DataFrame(
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data=df_sentiment.values,
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index=pd.to_datetime(df_sentiment.index).strftime("%Y-%m-%d"),
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),
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headers=["Sentiment"],
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title="FinBrain Ticker Sentiment",
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show_index=True,
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)
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console.print("")
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export_data(
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export, os.path.dirname(os.path.abspath(__file__)), "headlines", df_sentiment
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)
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