first commit

app.py

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+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('data/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.tolist()
+
+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 = close_list + volume_list
+
+# Config for page
+st.set_page_config(
+    page_title="Tesla Reddit Sentiment",
+    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(
+        'Start 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("Sentiment", sentiment_cols)
+    stock_select = st.selectbox("Stock", stocks_cols)
+
+# Banner with TSLA and Reddit images
+company_logo = Image.open('./images/tsla_logo.png')
+reddit_logo = Image.open('./images/reddit_logo.png')
+st.image([company_logo, reddit_logo], width=200)
+
+# dashboard title
+st.title("Tesla Reddit Sentiment and Stock Price")
+
+## 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-1]
+    last_sentiment_lag1 =  dfSentiment['sentiment_score'][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:
+                y_axis_label = 'Percentage'
+
+            elif sentiment_select in sentiment_cols:
+                y_axis_label = 'Sentiment'
+
+            elif sentiment_select in post_list:
+                y_axis_label = 'Volume'
+
+            fig.layout.yaxis.title=y_axis_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
+            st.write(fig)
+
+
+        st.markdown('### Detailed Data View')
+        st.dataframe(dfSentiment.iloc[:, 1:][0:i], width=500)
+        time.sleep(1)

data/sentiment_data.csv

@@ -0,0 +1,18 @@
+,timestamp,counter,close,volume,sentiment_score,close_lag1,perc_change_close,sentiment_score_lag1,perc_change_sentiment,sentiment_SMA3mo
+1,2021-03-01,3519376,667.92999,942452400,0.8834596920305732,675.5,-0.011206528497409362,0.8913090358317738,-0.008806534530277223,0.0
+2,2021-04-01,682276,709.44,678539700,0.886186366941392,667.92999,0.06214724689933459,0.8834596920305732,0.003086360289456685,0.8869850316012463
+3,2021-05-01,619369,625.21997,625175800,0.8932396112130588,709.44,-0.1187133936626072,0.886186366941392,0.007959098147730066,0.8876285567283414
+4,2021-06-01,56169,679.70001,519921900,0.8921351734595963,625.21997,0.08713739581926667,0.8932396112130588,-0.0012364406365305593,0.890520383871349
+5,2021-07-01,19044,687.20001,448449800,0.8929486063079558,679.70001,0.011034279666996032,0.8921351734595963,0.0009117820623585036,0.8927744636602037
+6,2021-08-01,64,735.71997,381324900,0.8806141242384911,687.20001,0.07060529582937575,0.8929486063079558,-0.01381320490600653,0.8885659680020144
+7,2021-09-01,7921,775.47998,390171300,0.8813562647680219,735.71997,0.054042314496370085,0.8806141242384911,0.0008427533798331643,0.8849729984381564
+8,2021-10-01,155236,1114.0,528934600,0.8845597780900558,775.47998,0.4365296703081878,0.8813562647680219,0.0036347541284875644,0.8821767223655229
+9,2021-11-01,1343281,1144.76001,649111500,0.8937287590337067,1114.0,0.027612217235188478,0.8845597780900558,0.010365586555889553,0.8865482672972614
+10,2021-12-01,122500,1056.78003,510055900,0.890013679265976,1144.76001,-0.07685451905329928,0.8937287590337067,-0.004156831398988991,0.8894340721299128
+11,2022-01-01,498436,936.71997,638668800,0.9025504858399248,1056.78003,-0.11360931943424396,0.890013679265976,0.014086083018733339,0.8954309747132024
+12,2022-02-01,96100,870.42999,463708900,0.8847938576052266,936.71997,-0.07076819340149225,0.9025504858399248,-0.01967383377803372,0.8924526742370423
+13,2022-03-01,38416,1077.59998,576424300,0.8887287633759635,870.42999,0.23800879149396034,0.8847938576052266,0.004447257106177405,0.8920243689403716
+14,2022-04-01,198916,870.76001,506986600,0.8823608437995739,1077.59998,-0.19194503882600294,0.8887287633759635,-0.007165200271228026,0.8852944882602546
+15,2022-05-01,320356,758.26001,649407200,0.9085272860190051,870.76001,-0.1291974811750944,0.8823608437995739,0.029655035582443468,0.8932056310648475
+16,2022-06-01,107584,673.41998,670166000,0.8913517594337463,758.26001,-0.11188778107921049,0.9085272860190051,-0.01890479994334424,0.8940799630841084
+17,2022-07-01,19600,752.28998,137580300,0.8903145917824337,673.41998,0.11711859217482677,0.8913517594337463,-0.00116358961581179,0.8967312124117283

requirements.txt

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+numpy==1.23.0
+pandas==1.4.2
+Pillow==9.2.0
+plotly==5.9.0
+streamlit==1.10.0