Global Tactical Asset Allocation Strategy
The Global Tactical Asset Allocation Strategy (GTAA) is one of the most widely known quantitative asset allocation models. Introduced by Meb Faber in 2007, the strategy uses a simple trend-following rule to allocate capital across equities, bonds, commodities, and real estate.
Assets trading above their 200-day moving average remain in the portfolio, while assets below the trend filter are replaced with short-term Treasury bills.
Despite its simplicity, the strategy has become one of the most influential tactical allocation frameworks of the last two decades.
Updated Results
Meb Faber’s original study covered the period from 1972 through 2005.
We extended the analysis through March 2025. The out-of-sample results remain encouraging, with the strategy producing a CAGR of 6.05%, a Sharpe Ratio of 0.68, and a maximum drawdown of 11.7%.
These results were achieved despite a period that included the Global Financial Crisis, the COVID-19 pandemic, and the inflation-driven selloff of 2022.
Rebalance Timing Luck
One practical issue often overlooked in tactical asset allocation is rebalance timing luck.
To investigate this effect, we tested multiple GTAA portfolios that differed only in their rebalance date. The results showed meaningful variation in performance, suggesting that implementation details can have a larger impact than many investors expect.
To address this issue, we explore both weekly rebalancing and a tranched implementation designed to reduce timing risk while lowering portfolio turnover.
Practical Implementation with Python and IBKR
In addition to the research findings, we present a complete Python implementation of the Global Tactical Asset Allocation Strategy.
The framework supports automatic data retrieval, signal generation, portfolio rebalancing, and trade execution through Interactive Brokers. Investors can either generate basket orders for manual execution or automate the entire process using the IBKR API.
Key Takeaways
The Global Tactical Asset Allocation Strategy continues to demonstrate robust performance nearly twenty years after its original publication.
Our research highlights the importance of rebalance timing, shows how tranched rebalancing can reduce implementation risk, and provides a practical framework for investors looking to automate the strategy using Python and Interactive Brokers.
Full Research Note (Recommended)
For the complete methodology and empirical analysis, see the full research note.