Annual Long-Short Strategy Based on Asset Growth

Summary

This algorithmic implementation describes an annual asset-growth factor strategy for U.S. equities. At the end of June, it calculates each eligible company’s change in total assets from the prior observation, sorts stocks into ten groups, buys the lowest-growth group, and shorts the highest-growth group. Holdings are equally weighted and refreshed annually. The implementation narrows its universe to the largest 3,000 qualifying stocks by market capitalization and includes a custom transaction-fee assumption and leverage setting.

The code illustrates universe filtering, fundamental-data handling, portfolio selection, and order placement in a QuantConnect-style framework. It does not provide backtest results or discuss the empirical evidence for the asset-growth effect, so the strategy’s return and risk characteristics cannot be inferred from this file. Its stored asset values update only when the universe is selected, and the selection schedule and execution logic should be checked carefully to confirm that the intended annual rebalance occurs as expected. Shorting constraints, survivorship and data timing, and transaction costs beyond the specified fee also affect practical results.

Key ideas

• Stocks are ranked by the change in reported total assets and divided into ten groups.
• The strategy buys the lowest asset-growth group and shorts the highest-growth group with equal weighting.
• The implementation limits its universe to the largest qualifying stocks by market capitalization.
• It includes a custom fee model and applies leverage to newly added securities.
• No performance results are supplied, and data timing and rebalance behavior need scrutiny.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.