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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("./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('./TSLA-Streamlit/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('./TSLA-Streamlit/images/tsla_logo.png')
reddit_logo = Image.open('./TSLA-Streamlit/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)
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