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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('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) | |