def display_sentiment_analysis(ticker: str, export: str = ""):
"""Sentiment analysis from FinBrain
Parameters
----------
ticker : str
Ticker to get the sentiment analysis from
export : str
Format to export data
"""
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(
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("")
export_data(export, os.path.dirname(os.path.abspath(__file__)),
"headlines", df_sentiment)
def display_sentiment_analysis(ticker: str,
export: str = "",
external_axes: Optional[List[plt.Axes]] = None):
"""Sentiment analysis from FinBrain
Parameters
----------
ticker : str
Ticker to get the sentiment analysis from
export : str
Format to export data
"""
df_sentiment = finbrain_model.get_sentiment(ticker)
if df_sentiment.empty:
console.print("No sentiment data found.\n")
return
plot_sentiment(sentiment=df_sentiment,
ticker=ticker,
external_axes=external_axes)
df_sentiment.sort_index(ascending=True, inplace=True)
if gtff.USE_COLOR:
color_df = df_sentiment["Sentiment Analysis"].apply(
lambda_sentiment_coloring, last_val=0)
color_df = pd.DataFrame(
data=color_df.values,
index=pd.to_datetime(df_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=df_sentiment.values,
index=pd.to_datetime(df_sentiment.index).strftime("%Y-%m-%d"),
),
headers=["Sentiment"],
title="FinBrain Ticker Sentiment",
show_index=True,
)
console.print("")
export_data(export, os.path.dirname(os.path.abspath(__file__)),
"headlines", df_sentiment)
def display_crypto_sentiment_analysis(coin: str, export: str) -> None:
"""Sentiment analysis from FinBrain for Cryptocurrencies
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]
Parameters
----------
coin: str
Cryptocurrency
export : str
Export dataframe data to csv,json,xlsx file
"""
df_sentiment = get_sentiment(
f"{coin}-USD") # Currently only USD pairs are available
if df_sentiment.empty:
console.print(f"Couldn't find Sentiment Data for {coin}\n")
return
plot_sentiment(df_sentiment, coin)
df_sentiment.sort_index(ascending=True, inplace=True)
if gtff.USE_COLOR:
console.print(
df_sentiment["Sentiment Analysis"].apply(sentiment_coloring,
last_val=0).to_string(),
"\n",
)
else:
console.print(df_sentiment.to_string(), "\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"finbrain",
df_sentiment,
)
def test_get_sentiment(recorder):
df = finbrain_model.get_sentiment(ticker="PM")
recorder.capture(df)
