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("../TSLASentimentAnalyzer/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 = 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='Stocks v Sentiments', 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 = "sentiment_score" stock_select = "close" # 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 st.title("Stocks vs Sentiment") ## 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'][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: yaxis_label = "Perc" elif sentiment_select in sentiment_cols: yaxis_label = "Sentiment" 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 st.write(fig) st.markdown('### Detailed Data View') st.dataframe(dfSentiment.iloc[:, 1:][0:i]) time.sleep(1)