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Fixing scorr and sentiment dataframe indexing bug (#1947)

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* Fixing scorr and sentiment dataframe indexing bug

* Fixing type hint and did some slight refactoring

Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt>
This commit is contained in:
James Simmons
2022-06-20 16:07:56 -04:00
committed by GitHub
parent ac50ab0f57
commit 7ea577e142
1 changed files with 68 additions and 10 deletions
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78
openbb_terminal/stocks/comparison_analysis/finbrain_model.py
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@@ -13,6 +13,36 @@ 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(tickers: List[str]) -> pd.DataFrame:
"""Gets Sentiment analysis from several tickers provided by FinBrain's API
@@ -31,26 +61,54 @@ def get_sentiments(tickers: List[str]) -> pd.DataFrame:
df_sentiment = pd.DataFrame()
dates_sentiment = []
tickers_to_remove = list()
results_list = list()
for ticker in tickers:
result = requests.get(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()
if "ticker" in result_json and "sentimentAnalysis" in result_json:
df_sentiment[ticker] = [
float(val)
for val in list(result_json["sentimentAnalysis"].values())
]
dates_sentiment = list(result_json["sentimentAnalysis"].keys())
else:
console.print(f"Unexpected data format from FinBrain API for {ticker}")
tickers_to_remove.append(ticker)
results_list.append(result_json)
else:
console.print(
f"Request error in retrieving {ticker} sentiment from FinBrain API"
)
tickers_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 = tickers[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}"
)
tickers_to_remove.append(ticker)
num = num + 1
for ticker in tickers_to_remove:
tickers.remove(ticker)