Unveiling Stocksimpy’s Core: Backtesting Strategies in Python

This article is the sixth installment in the “Building Stocksimpy” series, chronicling the development of a lightweight Python library for financial backtesting. Previous discussions introduced the Portfolio class, designed for efficient organization and workload distribution. This post will delve into the critical aspects of the backtesting loop and strategy implementation.

Crafting the Trading Strategy

The Backtester class is intentionally streamlined, with the core trading strategy—determining buy, sell, or hold signals—provided during initialization. Currently, the strategy is a simple function that returns one of these three actions.

However, complex strategies often demand intricate calculations and multiple function calls. To accommodate this, future iterations may allow for either a function or a class to define the strategy. A class-based approach could standardize a signal_generator() method, offering greater flexibility while maintaining the project’s lightweight philosophy. This enhancement remains a consideration for future development.

Inside the Main Backtesting Loop

The heart of Stocksimpy is a straightforward for loop that iterates through the entire dataset. Initial concerns about performance with large datasets were mitigated when tests showed that processing 5-10 years of daily data took approximately two minutes—a respectable time for a pure Python implementation. While high-performance backtesting libraries typically leverage C/C++ bindings for speed, Stocksimpy aims for accessibility and ease of use.

A potential area for optimization lies in leveraging Pandas’ built-in functionalities. Precomputing signals using the defined strategy and storing them in a Pandas.Series could allow the loop to execute only on dates where a trade occurs, significantly enhancing efficiency. This optimization is planned for future implementation.

Fixed vs. Dynamic Backtesting: Offering Flexibility

To cater to diverse testing needs, Stocksimpy offers two distinct run_backtest() functions. The run_backtest_fixed() function allows users to trade a predetermined, fixed amount, simplifying basic testing scenarios.

However, recognizing the limitations of fixed-amount trading for more sophisticated strategies—such as selling a specific percentage of an owned stock based on a dynamic logic—run_backtest_dynamic() was introduced. This function provides the flexibility to specify the exact number of stocks to buy or sell, empowering users to implement more nuanced trading strategies.

The Road Ahead: Enhancing Stocksimpy

Future development for Stocksimpy will prioritize the integration of crucial risk-adjusted performance metrics. Without measures like the Sharpe ratio or drawdown analysis, the library remains primarily a conceptual tool. Implementing these robust metrics will be the immediate focus, transforming Stocksimpy into a more comprehensive and practical backtesting solution.

While visualization tools and thorough documentation cleanup are still on the roadmap, the addition of risk-adjusted calculations takes precedence to provide meaningful insights into strategy performance. Stay tuned for further updates as Stocksimpy evolves.