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import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Define a list of stock tickers for the portfolio
tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']

# Set the date range for historical data
start_date = '2010-01-01'

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# Import necessary libraries
import quandl
import matplotlib.pyplot as plt

# Set your Quandl API key (you need to sign up on Quandl's website to get your API key)
quandl.ApiConfig.api_key = 'YOUR_API_KEY'

# Define the dataset codes for the BRICS nations' GDP
brics_gdp_datasets = {
    'Brazil': 'ODA/BRA_NGDPD',

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# Import necessary libraries
import quandl
import numpy as np
import matplotlib.pyplot as plt

# Set your Quandl API key (you need to sign up on Quandl's website to get your API key)
quandl.ApiConfig.api_key = 'YOUR_API_KEY'

# Define the dataset codes for Google and Yahoo
google_dataset = 'WIKI/GOOGL'  # Replace with the correct dataset code for Google

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# Import necessary libraries
import quandl
import matplotlib.pyplot as plt

# Set your Quandl API key (you need to sign up on Quandl's website to get your API key)
quandl.ApiConfig.api_key = 'YOUR_API_KEY'

# Define the companies and their respective dataset codes
companies = {
    'Google': 'WIKI/GOOGL',  # Replace with the correct dataset code for Google

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#Here's a full example of Perry Kaufman's market efficiency strategy implemented in Python, including backtesting capabilities 
#using historical data. Remember that this is a simplified example for educational purposes, and it does not include considerations 
#such as transaction costs or slippage.

pip install pandas numpy yfinance matplotlib

import pandas as pd
import numpy as np
import yfinance as yf
import matplotlib.pyplot as plt

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library(quantmod)

# Download SPY data from Yahoo Finance
getSymbols("SPY", src = "yahoo", from = "2007-01-01", to = "2022-04-07")

# Calculate moving average
ma_length <- 38
ma <- SMA(Cl(SPY), n = ma_length)

# Calculate OBV

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import pandas as pd
import backtrader as bt

# Define the strategy
class MyStrategy(bt.Strategy):
    params = dict(
        ma_length=50,
        obv_ma_length=200,
        target=5000,
        stop=4000

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import pandas as pd
import numpy as np
from pandas_datareader import data as pdr
import statsmodels
from statsmodels.tsa.stattools import coint
import matplotlib.pyplot as plt

# Step 1: Download the stock data from yahoo finance for all stocks in the S&P 500 Index
tickers = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")[0]["Symbol"].tolist()
data = pdr.get_data_yahoo(tickers, start="2010-01-01", end="2022-12-31")["Adj Close"]

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import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
import requests

# Load the data for the macro, fundamentals, sentiment, and valuation pillars
macro_data = pd.read_csv('macro_data.csv')
fundamentals_data = pd.read_csv('fundamentals_data.csv')
sentiment_data = pd.read_csv('sentiment_data.csv')
valuation_data = pd.read_csv('valuation_data.csv')

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import numpy as np
import pandas as pd
import yfinance as yf
from scipy.optimize import minimize

# Download the ETF data from Yahoo Finance
etf_data = {}
for etf in ['SPY', 'MDY', 'EFA', 'EEM', 'TLT']:
    etf_data[etf] = yf.Ticker(etf).history(period="10y")