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// === INPUTS ===
Inputs:
    ORStart(0930),
    OREnd(0950),
    ARangeTicks(8),
    CRangeTicks(8),
    ConfirmBars(3),
    MaxBarsLookback(30);

// === VARIABLES ===

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Inputs:
    Length(14);  // Optimizable lookback period

Vars:
    UpCount(0),
    DownCount(0),
    UpSum(0),
    DownSum(0),
    i(0),
    PriceChange(0),

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import requests
import json
import time
from bs4 import BeautifulSoup

# ================================================
# CONFIGURATION
# ================================================

# Your SEC EDGAR user agent string

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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