trading-strategy-backtest
# Backtesting trading strategies #### Last Update February 25, 2021 #### #### Matteo Bottacini, [[email protected]](mailto:[email protected]) #### # Project description This project is to backtest different trading strategies applying different approaches from the Modern Portfolio Tehory (MPT) in `Python 3`. The strategies backtested are: * The Optimal Markowitz Portfolio; * The Global Minimum Variance Portfolio; * The Risk-Parity Portfolio; * The Equally Weighted Portfolio The ETFs considered are: * EEM: iShares MSCI Emerging Markets ETF * EMLC: VanEck Vectors J.P. Morgan EM Local Currency Bond ETF * IAU: iShares Gold Trust * IEF: iShares 7-10 Year Treasury Bond ETF * IWM: iShares Russell 2000 ETF * SPY: SPDR S&P 500 ETF Trust * TIP: iShares TIPS Bond ETF * TLT: iShares 20+ Year Treasury Bond ETF * VGK: Vanguard FTSE Europe Index Fund ETF Shares The scripts do the following: * Download and analyse financial data; * Find the optimal Markowtiz portfolio (mean-variance); * Find the Global Minimum Variance (GMV) portfolio (minimum variance); * Find the risk-parity portfolio (risk parity); * Find the equally weighted portfolio (equally weighted); * Backtest a trading strategy with monthly rebalance with out-of-the-sample results; * Compare the results. Folder structure: ~~~~ trading-strategy-backtest/ deliverable/ run_backtest.py src/ equally_weighted_portfolio.py mean_variance_portfolio.py minimum_variance_portfolio.py risk_parity_portoflio.py README.md ~~~~ # Trading strategy configuration Each model is setup in its specific scripts. ### Equally weighted portfolio ### ```python def equally_weighted_portfolio(ret): init_weights = [1 / len(ret.columns)] * len(ret.columns) opt_weights = init_weights return opt_weights ``` ### Risk parity portfolio ### ```python def risk_parity_portfolio(ret): init_guess = 1 / ret.std() opt_weights = list(init_guess / init_guess.sum()) return opt_weights ``` ### Minimum variance portfolio ### ```python import numpy as np from scipy.optimize import minimize def minimum_variance_portfolio(ret): # define objective function to minimize: variance def get_portfolio_variance(weights): weights = np.array(weights) # check cov_mat = ret.cov() port_variance = np.dot(weights.T, np.dot(cov_mat, weights)) return port_variance # equality constraint: sum of the weights = 1 def weight_cons(weights): return np.sum(weights) - 1 # model set-up # - long only portfolio # - initial guess # - constraints bounds_lim = ((0, 1),) * len(ret.columns) init_weights = [1 / len(ret.columns)] * len(ret.columns) constraint = {'type': 'eq', 'fun': weight_cons} # find optimal portfolio opt_port = minimize(fun=get_portfolio_variance, x0=init_weights, bounds=bounds_lim, constraints=constraint, method='SLSQP') # find optimal weights opt_weights = list(opt_port['x']) return opt_weights ``` ### Mean-variance portfolio ### ```python # import modules import numpy as np from scipy.optimize import minimize def mean_variance_portfolio(ret): # define objective function to minimize: sharpe ratio def get_portfolio_sr(weights): weights = np.array(weights) # check # expected returns port_ret = np.dot(ret, weights) mean_ret = port_ret.mean() # volatility cov_mat = ret.cov() port_std = np.sqrt(np.dot(weights.T, np.dot(cov_mat, weights))) # sharpe ratio port_sr = mean_ret / port_std return port_sr def objective_fun(weights): neg_sr = get_portfolio_sr(weights) * (-1) return neg_sr # equality constraint: sum of the weights = 1 def weight_cons(weights): return np.sum(weights) - 1 # model set-up # - long only portfolio # - initial guess # - constraints bounds_lim = ((0, 1),) * len(ret.columns) init_weights = [1 / len(ret.columns)] * len(ret.columns) constraint = {'type': 'eq', 'fun': weight_cons} # find optimal portfolio opt_port = minimize(fun=objective_fun, x0=init_weights, bounds=bounds_lim, constraints=constraint, method='SLSQP') # find optimal weights opt_weights = list(opt_port['x']) return opt_weights ``` # Backtest configuration In the script `../deliverable/run_backtest.py` you can change the main variables to spot your ideal asset allocation and strategy. The parameters you can change are the following: * Assets; * Length of the training set ```python # feel free to change the following parameters: tickers = [] months_training_set = 12 * 5 ``` # Results  # Supported versions This configuration has been tested against Python 3.8