add initial app files and data

app.py

@@ -0,0 +1,204 @@
+from datetime import date
+from datetime import datetime
+import re
+
+import numpy as np  
+import pandas as pd 
+from PIL import Image
+import plotly.express as px  
+import plotly.graph_objects as go
+import streamlit as st  
+import time
+
+from plotly.subplots import make_subplots
+
+# Read CSV file into pandas and extract timestamp data
+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
+col_list = dfSentiment.columns
+
+r_sentiment = re.compile(".*sentiment")
+sentiment_cols = list(filter(r_sentiment.match, col_list))
+
+r_post = re.compile(".*post")
+post_list = list(filter(r_post.match, col_list))
+
+r_perc= re.compile(".*perc")
+perc_list = list(filter(r_perc.match, col_list))
+
+r_close = re.compile(".*close")
+close_list = list(filter(r_close.match, col_list))
+
+r_volume = re.compile(".*volume")
+volume_list = list(filter(r_volume.match, col_list))
+
+sentiment_cols = sentiment_cols + post_list
+stocks_cols = perc_list + close_list + volume_list
+
+# Config for page
+st.set_page_config(
+    page_title= "TSLA Sentiment Analyzer",
+    page_icon='✅',
+    layout='wide',
+)
+
+with st.sidebar:
+    # FourthBrain logo to sidebar
+    fourthbrain_logo = Image.open('./images/fourthbrain_logo.png')
+    st.image([fourthbrain_logo], width=300)
+
+    # Date selection filters
+    start_date_filter = st.date_input(
+        "State Date",
+        min(dfSentiment['timestamp']),
+        min_value=min(dfSentiment['timestamp']),
+        max_value=max(dfSentiment['timestamp'])
+        )
+        
+
+    end_date_filter = st.date_input(
+        'End Date',
+        max(dfSentiment['timestamp']),
+        min_value=min(dfSentiment['timestamp']),
+        max_value=max(dfSentiment['timestamp'])
+        )
+
+    sentiment_select = st.selectbox('Pick one', ['sentiment_score', 'sentiment_score_lag1','perc_change_sentiment','sentiment_SMA3mo'])
+    stock_select = st.selectbox("Pick one",['close','close_lag1','perc_change_close','volume'])
+
+# Banner with TSLA and Reddit images
+tsla_logo = Image.open('./images/tsla_logo.png')
+reddit_logo = Image.open('./images/reddit_logo.png')
+st.image([tsla_logo, reddit_logo], width=200)
+
+# dashboard title
+### YOUR LINE OF CODE HERE
+st.title('TSLA Sentiment Analyzer (r/TSLA)')
+
+## dataframe filter
+# start date
+dfSentiment = dfSentiment[dfSentiment['timestamp'] >= datetime(start_date_filter.year, start_date_filter.month, start_date_filter.day)]
+    
+# end date
+dfSentiment = dfSentiment[dfSentiment['timestamp'] <= datetime(end_date_filter.year, end_date_filter.month, end_date_filter.day)]
+dfSentiment = dfSentiment.reset_index(drop=True)
+
+
+# creating a single-element container
+placeholder = st.empty()
+
+# near real-time / live feed simulation
+for i in range(1, len(dfSentiment)-1):
+
+    # creating KPIs
+    last_close =  dfSentiment['close'][i]
+    last_close_lag1 = dfSentiment['close'][i-1]
+    last_sentiment = dfSentiment['sentiment_score'][i]
+    last_sentiment_lag1 = dfSentiment['sentiment_score_lag1'][i-1]
+
+
+    with placeholder.container():
+
+        # create columns
+        kpi1, kpi2, kpi3 = st.columns(3)
+
+        # fill in those three columns with respective metrics or KPIs
+        kpi1.metric(
+            label='Sentiment Score',
+            value=round(last_sentiment, 3),
+            delta=round(last_sentiment_lag1, 3),
+        )
+        
+        kpi2.metric(
+            label='Last Closing Price',
+            value=round(last_close, 3),
+            delta=round(last_close_lag1, 3),
+        )
+        
+
+        # create two columns for charts
+        fig_col1, fig_col2 = st.columns(2)
+        
+        with fig_col1:
+            # Add traces
+            fig=make_subplots(specs=[[{"secondary_y":True}]])
+
+
+            fig.add_trace(                               
+                go.Scatter(                          
+                x=dfSentiment['timestamp'][0:i],
+                y=dfSentiment[sentiment_select][0:i],
+                name=sentiment_select,
+                mode='lines',                            
+                hoverinfo='none',                        
+                )              
+            )
+
+            if sentiment_select.startswith('perc') == True:
+                ### YOUR LINE OF CODE HERE
+                yaxis_label = 'Percent'
+
+            elif sentiment_select in sentiment_cols:
+                ### YOUR LINE OF CODE HERE
+                yaxis_label = 'Sentiment Score'
+
+            elif sentiment_select in post_list:
+                yaxis_label = 'Volume'
+
+            fig.layout.yaxis.title=yaxis_label
+                                          
+            if stock_select.startswith('perc') == True:
+                fig.add_trace(                               
+                    go.Scatter(                          
+                    x=dfSentiment['timestamp'][0:i],
+                    y=dfSentiment[stock_select][0:i],
+                    name=stock_select,
+                    mode='lines',                            
+                    hoverinfo='none', 
+                    yaxis='y2',                    
+                    ) 
+                )
+                fig.layout.yaxis2.title='% Change Stock Price ($US)'
+            
+            elif stock_select == 'volume':
+                fig.add_trace(                               
+                    go.Scatter(                          
+                    x=dfSentiment['timestamp'][0:i],
+                    y=dfSentiment[stock_select][0:i],
+                    name=stock_select,
+                    mode='lines',                            
+                    hoverinfo='none', 
+                    yaxis='y2',                    
+                    ) 
+                )
+                
+                fig.layout.yaxis2.title="Shares Traded"
+
+
+            else:
+                fig.add_trace(                               
+                    go.Scatter(                          
+                    x=dfSentiment['timestamp'][0:i],
+                    y=dfSentiment[stock_select][0:i],
+                    name=stock_select,
+                    mode='lines',                            
+                    hoverinfo='none', 
+                    yaxis='y2',                    
+                    ) 
+                )
+
+                fig.layout.yaxis2.title='Stock Price ($USD)'
+
+
+            fig.layout.xaxis.title='Timestamp'
+
+            # write the figure throught streamlit
+            ### YOUR LINE OF CODE HERE
+            st.write(fig)
+
+
+        st.markdown('### Detailed Data View')
+        st.dataframe(dfSentiment.iloc[:, 1:][0:i])
+        time.sleep(1)

requirements.txt

@@ -0,0 +1,5 @@
+numpy==1.23.1
+pandas==1.4.3
+Pillow==9.2.0
+plotly==5.9.0
+streamlit==1.10.0

sentiment_data.csv

@@ -0,0 +1,97 @@
+,timestamp,counter,close,volume,sentiment_score,close_lag1,perc_change_close,sentiment_score_lag1,perc_change_sentiment,sentiment_SMA3mo
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