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| import pandas as pd | |
| import streamlit as st | |
| import numpy as np | |
| from streamlit_card import card | |
| import yfinance as yf | |
| import altair as alt | |
| # Set the background color and opacity for the container | |
| container_style = """ | |
| background-color: rgba(55, 65, 82, 0.7); | |
| padding: 100px; | |
| border-radius: 10px; | |
| margin-top: 20px; | |
| margin-bottom: 20px; | |
| """ | |
| st.markdown("<h1 style='text-align: center;'>Volatility Indicator</h1>", unsafe_allow_html=True) | |
| st.write(""" ### Economic Volitility Examination""") | |
| # Create a translucent container | |
| x = card(title="", text = "When we talk about stock volitility, we typically need fundamental data like company earning reports, interest rates, and technical analysis trends", | |
| styles = { | |
| "card": { | |
| "width": "650px", | |
| "height": "200px", | |
| "background-color": "rgba(55, 65, 82, 1)", | |
| "padding": "20px", | |
| "margin-top": "20px", | |
| #"margin-bottom": "20px", | |
| }, | |
| } | |
| ) | |
| # Download historical stock data for Tesla | |
| ticker = "TSLA" | |
| start_date = "2021-09-29" | |
| end_date = "2022-09-29" | |
| stock_data = yf.download(ticker, start=start_date, end=end_date) | |
| # Calculate daily returns | |
| stock_data["Daily_Return"] = stock_data["Close"].pct_change() | |
| # Calculate historical volatility (standard deviation) | |
| historical_volatility = stock_data["Daily_Return"].std() | |
| # Streamlit app | |
| st.markdown("<h1 style='text-align: center;'>Tesla Stock Volatility Analysis</h1>", unsafe_allow_html=True) | |
| # Display historical stock data | |
| st.subheader("Historical Stock Data") | |
| st.write(stock_data) | |
| stock_data = yf.download(ticker, start=start_date, end=end_date) | |
| # Calculate daily returns | |
| stock_data["Daily_Return"] = ((stock_data["Close"] / stock_data["Open"]) - 1) | |
| notable = [] | |
| days = [] | |
| for day in stock_data["Daily_Return"]: | |
| if day > 0.1: | |
| notable.append(day) | |
| days.append("Date") | |
| elif day < -0.1: | |
| notable.append(day) | |
| days.append("Date") | |
| # Line chart for stock prices | |
| st.subheader("Tesla Stock Prices Over Time") | |
| line_chart = alt.Chart(stock_data.reset_index()).mark_line().encode( | |
| x="Date:T", | |
| y="Daily_Return", | |
| tooltip=["Date", "Daily_Return"] | |
| ).properties(width=800, height=400) | |
| st.altair_chart(line_chart, use_container_width=True) | |
| st.write("2021-11-09 00:00:00: \"Telsa fire in Stanford took 42 minutes to extinguish\" ") | |
| st.write("2022-01-27 00:00:00: \"Tesla drops more than 11% as investors digest new vehicle delays\"") | |
| st.write("2022-02-23 00:00:00: \"Tesla model Y wins EV award\"") | |
| st.write("2022-04-26 00:00:00: \"Elon Musk says people might download their personalities onto a human robot constructed by Tesla\"") | |
| st.markdown("<h1 style='text-align: center;'>Our Approach</h1>", unsafe_allow_html=True) | |
| x = card(title="", | |
| text = "How can we predict the potential social impact on stock volitility? Qualitative tabular data poses a challenge concerning data processing resources", | |
| styles = { | |
| "card": { | |
| "width": "650px", | |
| "height": "200px", | |
| "background-color": "rgba(55, 65, 82, 1)", | |
| "padding": "50px", | |
| "margin-top": "10px", | |
| "margin-bottom": "10px", | |
| }, | |
| } | |
| ) | |
| st.write("") | |
| # Streamlit app | |
| st.title('First Model') | |
| model1 = card(title="", | |
| text = "", | |
| styles = { | |
| "card": { | |
| "width": "650px", | |
| "height": "200px", | |
| "margin-top": "10px", | |
| "margin-bottom": "10px", | |
| }, | |
| }, | |
| image="https://i.postimg.cc/Bn8q0Ddy/XBoost.png", | |
| on_click=lambda: st.write("The model generating embeddings represent the data in the prompt. Each embedding captures an immense amount of training data that is then used to project desired data") | |
| ) | |
| st.title('Second Model') | |
| mod2 = card(title="", | |
| text = "", | |
| styles = { | |
| "card": { | |
| "width": "700px", | |
| "height": "400px", | |
| "margin-top": "20px", | |
| "margin-bottom": "20px", | |
| } | |
| }, | |
| image="https://miro.medium.com/v2/resize:fit:976/1*oc1gaCFvgWXq_gHQFM63UQ.png", | |
| on_click=lambda: st.write("A neural network learns to map input data to output by adjusting the strengths of connections (weights) between nodes during a training process. This enables the network to recognize patterns and make predictions on new data.") | |
| ) | |