Enhanced version of the Master thesis project. This version also features a module that automatically downloads and stores new intraday data from Yahoo Finance, to serve as a real robo-advisor for investments. The code lets you choose between U.S. and European market, represented by stocks listed in the SP500 and STOXXE600 indices.

Once new data are downloaded, the code backtests a long-only portfolio strategy based on a two-step procedure: in the first one, the algorithm screens the stock data in a rolling window to select a user-defined number of optimal ones, and, in the second, computes the portfolio allocation, using either Markowitz' mean-variance analysis or empirics. In this way, a potentially very large asset universe is shrunk to a few tens of assets, which dramatically reduces the estimation error of the covariance matrix of asset returns.

I perform the asset selection by recasting the Markowitz’ risk minimization problem as a regression problem, as explained in: Fan, J. and Zhang, J. and Yu, K. (2021). Vast Portfolio Selection with Gross-Exposure Constraints. Journal of the American Statistical Association, 107(498):592-606. This enables to access the vast statistical toolbox associated to regression.

I build upon the approach presented in: Dan Wang, C. and Chen, Z. and Lian Y. and Chen M. (2020). Asset Selection based on High Frequency Sharpe Ratio. Journal of Econometrics, and I further enhance it by adopting the improved covariance matrix estimator, tested in the ImprovingMVPortfolio repository.

The performance of the strategy is compared against the widely-used momentum strategy, its risk-managed counterpart and Dan Wang's method, which are all implemented in the code. The momentum strategy is presented in: Jegadeesh, N. and Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48:65–91. For risk-managed momentum, please refer to: Barroso, P. and Santa-Clara, P. (2015). Momentum Has Its Moments. Journal of Financial Economics, 116(1):111–120.

They are located in the main() method in main.py

dataset: Investment universe. String. Possible choices are 'SP500' or 'STOXXE600'

endow: (optional) Initial amount of money to be invested. Float

n_stk: Number of stocks to hold in the portfolio. Integer

n_obs_ar: Lengths of rolling window (in trading days). Array of integers

n_reb_ar: Portfolio rebalancing interval (in trading days). Array of integers

algo: Algorithm to use for stock selection. String. Possible choices are: 'mtm', 'rmmtm', 'sev' or 'sev+'

• mtm: Momentum. Pick stocks with largest value of average return
• rmmtm: Risk-managed Momentum. Pick stocks with largest value of average Sharpe ratio
• sev: Dan Wang's stock selection method
• sev+: Improved Dan Wang's method

wght_mtd: Stock weighting methods for portfolio allocation. List of strings. Possible choices are: 'equal', 'metric', 'mktcap', 'riskpar', 'lotp', 'tp'

• equal: Equally-weighted portfolio
• metric: Weights are proportional to momentum or SEV
• mktcap: Weights are proportional to market capitalization
• riskpar: Risk-parity portfolio
• tp: Tangency Portfolio (obtained via mean-variance analysis)
• lotp: Long-Only Tangency Portfolio (obtained via mean-variance analysis)

trsctn_fee_fix: (optional) Fixed transaction fees

trsctn_fee_prop: (optional) Proportional transaction fees