LuisSousaSilva

from pandas_datareader import data as pdr
import matplotlib.pyplot as plt
import yfinance as yf
import seaborn as sns
import pandas as pd
import numpy as np
import calendar

pd.options.display.max_rows = 999

LuisSousaSilva

#%%
# Optimized for use in VS Code
# importing libraries
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import plotly.offline as py
import cufflinks as cf
import seaborn as sns
import pandas as pd
import numpy as np

LuisSousaSilva

import yfinance as yf
import pandas as pd

data = yf.download("^GSPC", start="1971-02-26", end="2021-02-26",
                   group_by="ticker")[['Adj Close']]

data.columns=['S&P 500']

change = data.pct_change().dropna()

LuisSousaSilva

import pandas as pd
import investpy

IWDA = investpy.get_etf_historical_data(etf='iShares Core MSCI World UCITS',
                                      from_date='01/01/2000',
                                      to_date='01/01/2023',
                                      country='netherlands')[['Close']]

IWDA.columns=['Price']
IWDA_daily_ret = IWDA.pct_change().dropna() * 100

LuisSousaSilva

import yfinance as yf
data = yf.download("^GSPC", start="1970-01-01", end="2030-04-30")[['Open', 'High', 'Low', 'Close']]

data['Change (%)'] = data['Close'].pct_change() * 100

data["Yesterday close - Today's Low (%)"] = (((data['Close'].shift() / data['Low']) - 1) * 100) * -1

data['Range (High-Low) (%)'] = ((data['High'] / data['Low']) - 1) * 100

data = data.sort_values('Range (High-Low) (%)', ascending=False)

LuisSousaSilva

# importing libraries
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import datetime as dt
import seaborn as sns
import pandas as pd
import numpy as np
import statsmodels.api as sm

from IPython.display import Markdown, display

LuisSousaSilva

def test(a):
  assert a == 1

  return "It Worked!!"
  
def soma():
  return 2+2