SPY Trading Algo - Kaufmann Market Efficiency

Perry Kaufmann Market Efficiency Algo.py

#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

# Function to calculate the efficiency ratio
def calculate_efficiency_ratio(data, period=10):
    movement_speed = data['Close'].diff(periods=period)
    volatility = data['Close'].diff().abs().rolling(window=period).sum()
    efficiency_ratio = movement_speed / volatility
    return efficiency_ratio

# Function to generate entry signals
def generate_entry_signals(data, entry_threshold=0.6):
    data['Entry Signal'] = np.where(data['Efficiency Ratio'] > entry_threshold, 1, 0)
    return data['Entry Signal']

# Function to backtest the strategy
def backtest_strategy(data, entry_threshold=0.6):
    data['Entry Signal'] = generate_entry_signals(data, entry_threshold)
    data['Position'] = data['Entry Signal'].shift()
    data['Return'] = data['Close'].pct_change()
    data['Strategy Return'] = data['Return'] * data['Position']
    data['Cumulative Return'] = (1 + data['Strategy Return']).cumprod()
    return data['Cumulative Return']

# Download historical data for SPY using Yahoo Finance
ticker = "SPY"
start_date = "2019-01-01"
end_date = "2022-12-31"
data = yf.download(ticker, start=start_date, end=end_date)

# Calculate the efficiency ratio
data['Efficiency Ratio'] = calculate_efficiency_ratio(data)

# Backtest the strategy
cumulative_returns = backtest_strategy(data)

# Plot the cumulative returns
plt.plot(cumulative_returns)
plt.title("Perry Kaufman's Market Efficiency Strategy - Cumulative Returns")
plt.xlabel("Date")
plt.ylabel("Cumulative Return")
plt.show()