GrovesD2

GrovesD2

GrovesD2

GrovesD2

import numpy as np
import pandas as pd
import yfinance as yf

import matplotlib.pyplot as plt

def heat_smooth(
        ts: np.array,
        h: float,
        k: float,

GrovesD2

import numpy as np
import pandas as pd
import yfinance as yf

from dtaidistance import dtw
from plotly import graph_objects as go
from plotly.subplots import make_subplots

import plotly.io as pio
pio.renderers.default='svg'

GrovesD2

import time
import numpy as np
import numba as nb
import pandas as pd
import yfinance as yf
import plotly.graph_objects as go

import plotly.io as pio
pio.renderers.default='svg'

GrovesD2

import numpy as np
from scipy.optimize import minimize, LinearConstraint

def find_grad_intercept(case, x, y):
    '''
    Find the granient and intercept terms for the envelope trend line.

    Note: case = 'above' or 'below'
    '''
    

GrovesD2

import numpy as np
import yfinance as yf
import scipy.stats as stats
from scipy.integrate import quad
from scipy.optimize import minimize

# Get the monthly price data for the SPY ticker
df = yf.download(
    'SPY', 
    interval='1mo',

GrovesD2

import streamlit as st
import streamlit.components.v1 as components

st.set_page_config(layout="wide")

def get_info_widget(
        ticker: str = "AAPL",
        theme: str = "dark",   
    ):
    

GrovesD2

import pandas as pd
import yfinance as yf

GROWTH_SINCE = '2021-12-01' # The lower date to calculate the stock performance
GROUPBY_COL = 'GICS Sector' # Use 'GICS Sector' or 'GICS Sub-Industry'
S_AND_P_URL = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
NUM_PER_GROUP = 3 # The top n winning stocks per group


if __name__ == '__main__':