Backtesting momentum factor strategy
Investment strategies are essential in today’s financial markets, and their success relies heavily on thorough testing and analysis. Backtesting provides an excellent way to evaluate and refine investment and trading ideas, which can lead to increased profitability and lower risk. In this custom research sample, we demonstrate in short how the backtesting process goes on the example of momentum factor strategy using our advisor’s own-developed library, Portfolio Quantitative Research (PQR) in Python. This library enables easy and efficient testing of trading ideas by providing a simple and intuitive framework to handle large amounts of financial data. It includes a set of customizable functions for data preparation, filtering, and portfolio construction.
The momentum factor strategy is a popular trading strategy that involves buying securities that have shown positive momentum over a given period. The strategy relies on the belief that securities that have performed well in the past are likely to continue to perform well in the future. Our momentum strategy considers building a portfolio of stocks that demonstrate growth according to the selection conditions set.
To conduct a backtest of our momentum strategy, we followed the following steps: (a) searching and importing needed for analysis data; (b) cleansing and bringing it to the required format; (c) conducting a backtest algorithm and illustrating the results. We used daily returns data covering the 2010-2022 period. For demonstration purposes, we selected the stocks and benchmark data randomly.
The PQR library provides customizable parameters that enable users to fine-tune their backtesting. These parameters include filters on price and trade volume, setting a benchmark to compare backtesting results, choosing to buy and hold periods, and splitting results into a defined number of portfolios with winners, average (neutral), and losers stocks samples.
In this case, backtesting results show that the winners and average portfolios (the first 3 split quantiles) outperformed the market over the period under analysis. The figure below illustrates the backtesting results, and the embedded dashboard provides key performance metrics such as Annualized Mean Return, Annualized Volatility, Alpha, Sharpe Ratio, Win Rate, Max Drawdown, and Compounded Returns. But we wouldn’t stick to it over time it looks explicitly risky adjusted to potential returns with Sharpe Ration lower 1.
Figure 1: Backtesting results
Backtesting is a critical component of investment strategy development and refinement. It is essential to note that this article serves as a sample of the framework we use in our backtesting projects and is published for demonstration purposes only.
