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import pandas as pd |
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import streamlit as st |
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import numpy as np |
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from streamlit_card import card |
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import yfinance as yf |
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import altair as alt |
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container_style = """ |
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background-color: rgba(55, 65, 82, 0.7); |
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padding: 100px; |
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border-radius: 10px; |
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margin-top: 20px; |
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margin-bottom: 20px; |
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""" |
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st.markdown("<h1 style='text-align: center;'>Volatility Indicator</h1>", unsafe_allow_html=True) |
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st.write(""" ### Economic Volitility Examination""") |
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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", |
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styles = { |
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"card": { |
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"width": "650px", |
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"height": "200px", |
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"background-color": "rgba(55, 65, 82, 1)", |
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"padding": "20px", |
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"margin-top": "20px", |
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}, |
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} |
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) |
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ticker = "TSLA" |
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start_date = "2021-09-29" |
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end_date = "2022-09-29" |
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stock_data = yf.download(ticker, start=start_date, end=end_date) |
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stock_data["Daily_Return"] = stock_data["Close"].pct_change() |
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historical_volatility = stock_data["Daily_Return"].std() |
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st.markdown("<h1 style='text-align: center;'>Tesla Stock Volatility Analysis</h1>", unsafe_allow_html=True) |
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st.subheader("Historical Stock Data") |
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st.write(stock_data) |
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stock_data = yf.download(ticker, start=start_date, end=end_date) |
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stock_data["Daily_Return"] = ((stock_data["Close"] / stock_data["Open"]) - 1) |
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notable = [] |
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days = [] |
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for day in stock_data["Daily_Return"]: |
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if day > 0.1: |
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notable.append(day) |
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days.append("Date") |
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elif day < -0.1: |
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notable.append(day) |
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days.append("Date") |
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st.subheader("Tesla Stock Prices Over Time") |
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line_chart = alt.Chart(stock_data.reset_index()).mark_line().encode( |
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x="Date:T", |
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y="Daily_Return", |
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tooltip=["Date", "Daily_Return"] |
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).properties(width=800, height=400) |
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st.altair_chart(line_chart, use_container_width=True) |
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st.write("2021-11-09 00:00:00: \"Telsa fire in Stanford took 42 minutes to extinguish\" ") |
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st.write("2022-01-27 00:00:00: \"Tesla drops more than 11% as investors digest new vehicle delays\"") |
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st.write("2022-02-23 00:00:00: \"Tesla model Y wins EV award\"") |
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st.write("2022-04-26 00:00:00: \"Elon Musk says people might download their personalities onto a human robot constructed by Tesla\"") |
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st.markdown("<h1 style='text-align: center;'>Our Approach</h1>", unsafe_allow_html=True) |
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x = card(title="", |
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text = "How can we predict the potential social impact on stock volitility? Qualitative tabular data poses a challenge concerning data processing resources", |
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styles = { |
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"card": { |
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"width": "650px", |
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"height": "200px", |
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"background-color": "rgba(55, 65, 82, 1)", |
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"padding": "50px", |
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"margin-top": "10px", |
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"margin-bottom": "10px", |
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}, |
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} |
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) |
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st.write("") |
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st.title('First Model') |
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model1 = card(title="", |
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text = "", |
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styles = { |
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"card": { |
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"width": "650px", |
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"height": "200px", |
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"margin-top": "10px", |
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"margin-bottom": "10px", |
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}, |
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}, |
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image="https://i.postimg.cc/Bn8q0Ddy/XBoost.png", |
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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") |
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) |
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st.title('Second Model') |
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mod2 = card(title="", |
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text = "", |
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styles = { |
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"card": { |
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"width": "700px", |
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"height": "400px", |
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"margin-top": "20px", |
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"margin-bottom": "20px", |
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} |
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}, |
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image="https://miro.medium.com/v2/resize:fit:976/1*oc1gaCFvgWXq_gHQFM63UQ.png", |
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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.") |
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) |
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