mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-01 23:12:21 +00:00
ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
146 lines
4.8 KiB
Python
146 lines
4.8 KiB
Python
""" Comparison Analysis FinBrain Model """
|
|
__docformat__ = "numpy"
|
|
|
|
import logging
|
|
from typing import List, Tuple
|
|
|
|
import pandas as pd
|
|
|
|
from openbb_terminal.decorators import log_start_end
|
|
from openbb_terminal.helper_funcs import request
|
|
from openbb_terminal.rich_config import console
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@log_start_end(log=logger)
|
|
def find_smallest_num_data_point(results_list: List[dict]) -> int:
|
|
"""Helper function to find the number for the smallest total number of data points
|
|
out of all the tickers. This is necessary because if one ticker has more or less
|
|
data points than another then it will throw an indexing error. The solution is to
|
|
have each ticker have the same number of data points as to graph and view properly.
|
|
We chose to set each ticker to the minimum of number data points out of all the
|
|
tickers.
|
|
|
|
Parameters
|
|
----------
|
|
results_list : List[json]
|
|
List of dicts storing ticker data
|
|
|
|
Returns
|
|
-------
|
|
int
|
|
Value of smallest total number of sentiment data points
|
|
"""
|
|
small_list = list()
|
|
for result_json in results_list:
|
|
if (
|
|
"ticker" in result_json
|
|
and "sentimentAnalysis" in result_json
|
|
and len(result_json["sentimentAnalysis"].values()) > 0
|
|
):
|
|
small_list.append(len(result_json["sentimentAnalysis"].values()))
|
|
return min(small_list)
|
|
|
|
|
|
@log_start_end(log=logger)
|
|
def get_sentiments(symbols: List[str]) -> pd.DataFrame:
|
|
"""Gets Sentiment analysis from several symbols provided by FinBrain's API.
|
|
|
|
Parameters
|
|
----------
|
|
symbols : List[str]
|
|
List of tickers to get sentiment.
|
|
Comparable companies can be accessed through
|
|
finnhub_peers(), finviz_peers(), polygon_peers().
|
|
|
|
Returns
|
|
-------
|
|
pd.DataFrame
|
|
Contains sentiment analysis from several tickers
|
|
"""
|
|
|
|
df_sentiment = pd.DataFrame()
|
|
dates_sentiment = []
|
|
symbols_to_remove = list()
|
|
results_list = list()
|
|
for ticker in symbols:
|
|
result = request(f"https://api.finbrain.tech/v0/sentiments/{ticker}")
|
|
# Check status code, if its correct then convert to dict using .json()
|
|
if result.status_code == 200:
|
|
result_json = result.json()
|
|
results_list.append(result_json)
|
|
else:
|
|
console.print(
|
|
f"Request error in retrieving {ticker} sentiment from FinBrain API"
|
|
)
|
|
symbols_to_remove.append(ticker)
|
|
|
|
# Finds the smallest amount of data points from any of the tickers as to not run
|
|
# into an indexing error when graphing
|
|
smallest_num_data_point = find_smallest_num_data_point(results_list)
|
|
|
|
num = 0
|
|
for result_json in results_list:
|
|
ticker = symbols[num]
|
|
# Checks to see if sentiment data in results_json
|
|
if (
|
|
"ticker" in result_json
|
|
and "sentimentAnalysis" in result_json
|
|
and len(result_json["sentimentAnalysis"].values()) > 0
|
|
):
|
|
# Storing sentiments and dates in list
|
|
sentiments = list(result_json["sentimentAnalysis"].values())
|
|
dates_sentiment = list(result_json["sentimentAnalysis"].keys())
|
|
|
|
# If there are more sentiment data points for one ticker compared to the
|
|
# smallest amount of data points, then remove that data points from that
|
|
# ticker as to be able to graph properly
|
|
if len(sentiments) > smallest_num_data_point:
|
|
sentiments = sentiments[0:smallest_num_data_point]
|
|
dates_sentiment = dates_sentiment[0:smallest_num_data_point]
|
|
|
|
df_sentiment[ticker] = [float(val) for val in sentiments]
|
|
|
|
# If sentiment data not in results_json remove it
|
|
else:
|
|
console.print(
|
|
f"Unexpected data format or no data from FinBrain API for {ticker}"
|
|
)
|
|
symbols_to_remove.append(ticker)
|
|
|
|
num = num + 1
|
|
|
|
for ticker in symbols_to_remove:
|
|
symbols.remove(ticker)
|
|
|
|
if not df_sentiment.empty:
|
|
df_sentiment.index = dates_sentiment
|
|
df_sentiment.sort_index(ascending=True, inplace=True)
|
|
|
|
return df_sentiment
|
|
|
|
|
|
@log_start_end(log=logger)
|
|
def get_sentiment_correlation(
|
|
similar: List[str],
|
|
) -> Tuple[pd.DataFrame, pd.DataFrame]:
|
|
"""Get correlation sentiments across similar companies. [Source: FinBrain].
|
|
|
|
Parameters
|
|
----------
|
|
similar : List[str]
|
|
Similar companies to compare income with.
|
|
Comparable companies can be accessed through
|
|
finnhub_peers(), finviz_peers(), polygon_peers().
|
|
|
|
Returns
|
|
-------
|
|
Tuple[pd.DataFrame,pd.DataFrame]
|
|
Contains sentiment analysis from several tickers
|
|
"""
|
|
df_sentiment = get_sentiments(similar)
|
|
corrs = df_sentiment.corr()
|
|
|
|
return corrs, df_sentiment
|