TSLA stock sentiment

app.py

@@ -14,7 +14,7 @@ from plotly.subplots import make_subplots
 
 # Read CSV file into pandas and extract timestamp data
 # dfSentiment = ### YOUR LINE OF CODE HERE
-dfSentiment = pd.read_csv('../Phase_I-Proof_of_concept/TSLASentimentAnalyzer/sentiment_data.csv')
+dfSentiment = pd.read_csv('sentiment_data.csv')
 dfSentiment['timestamp'] = [datetime.strptime(dt, '%Y-%m-%d') for dt in dfSentiment['timestamp'].tolist()]
 
 # Multi-select columns to build chart

sentiment_data.csv

@@ -0,0 +1,6 @@
+,timestamp,counter,close,volume,sentiment_score,close_lag1,perc_change_close,sentiment_score_lag1,perc_change_sentiment,sentiment_SMA3mo
+1,2022-03-01,272916,1099.56995,40225400,0.8757717551892263,0.0,inf,0.8744244989083738,0.0015407348290612092,0.0
+2,2022-04-01,204020,1145.44995,45377900,0.8448942852492379,1099.56995,0.04172540364530686,0.8757717551892263,-0.035257439803270174,0.8650301797822794
+3,2022-05-01,18769,0.0,,0.8563334928811902,1145.44995,-1.0,0.8448942852492379,0.0135392176650571,0.8589998444398849
+4,2022-06-01,3721,0.0,,0.858559500975687,0.0,0.0,0.8563334928811902,0.002599464009059427,0.8532624263687051
+5,2022-07-01,8281,0.0,,0.8565136809925457,0.0,0.0,0.858559500975687,-0.0023828517194398676,0.8571355582831409