GrovesD2

import time
import random
import numba as nb
import numpy as np
import pandas as pd
from copy import deepcopy

# For type hinting
from typing import Tuple

GrovesD2

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

from typing import Tuple
from copy import deepcopy

import plotly.io as pio
import plotly.graph_objects as go
pio.renderers.default='browser'

GrovesD2

import numpy as np
import matplotlib.pyplot as plt

from scipy import sparse
from typing import Tuple
from copy import deepcopy
from scipy.ndimage import gaussian_filter


IMAGE = 'PATH_TO_JPG_IMAGE'  # Include JPG image here!

GrovesD2

import time
import random
import numba as nb
import numpy as np
import pandas as pd
import datetime as dt
from tqdm import tqdm
from copy import deepcopy

# For type hinting

GrovesD2

'''
This code is a demonstration to show how you can accidentally slip in future
results to a time-series predicting neural network.

DO NOT USE THIS CODE FOR MAKING PREDICTIONS, IT'S FAULTY ON PURPOSE.
'''

import numba as nb
import numpy as np
import pandas as pd

GrovesD2

'''
Simple Neural Network attempt for price prediction - it does not do a good job
at all and therefore SHOULD NOT BE USED FOR ANY REAL TRADING/INVESTING
'''

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

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__':

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 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 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'
    '''