The strategy explores growth patterns at the end of the month in the price of US Treasury bonds. The strategies from the Monthly Mover series utilize the best periods of the month for trading selected instruments.
Inspirations
Today, another interesting strategy from the Monthly Mover category, this time on US bonds. We explore the observation that this instrument owes practically all its returns to the last part of the month.
From the perspective of an international investor, the American debt market seems quite complicated, mainly due to the specific terminology of bonds.
The entire American debt market is referred to as "Bonds." However, it's important to know about its internal division into three categories:
Short-term, called Treasury Bills (T-Bills), with a maturity of less than a year. A popular ETF is TBIL (3M)
Medium-term, called Treasury Notes (T-Notes), with a maturity of 2 to 10 years. A popular ETF is IEF (7-10Y)
Long-term, called Treasury Bonds (T-Bonds), with a maturity of 20 to 30 years. Example ETFs are TLT (20Y)
Today, we will focus on a strategy exploring a pattern in long-term bonds, specifically on TLT.
Why bother with bonds at all? For decades, they have been cited as an example of a stabilizing instrument for a stock portfolio, i.e., an instrument that is lowly correlated or even inversely correlated with stock prices. Just read about the 60/40 portfolio.
We won't delve into the details of treating bonds as an investment here. Statistically, it is an investment with a much lower return rate than stocks, and additionally, as shown in the years 2020-2023, this stable haven turned out to be a nightmare for many financial institutions (a price drop of over 48%), contributing to the banking crisis in the USA at the beginning of 2023.
However, since we are looking for instruments and strategies that are lowly or inversely correlated with stocks, today we are tackling TLT. Let's get to work!
Is it true that there is a negative correlation between TLT and S&P500 (SPY)?
The chart below shows that in the long term, YES.
Throughout most of the period from 2003 to 2023, bonds were inversely correlated with stocks (an increase in one was accompanied by a decrease in the other). The year 2022 was exceptional because stocks and long-term bonds fell simultaneously, which was painfully felt by investors using the 60/40 model. The average correlation (the purple line on the bottom chart) places us in the middle today, indicating a lack of correlation, which is precisely what we aim for.
Bond Monthly Mover withstood the challenges of this instrument from 2020 to 2024 and closed this very negative period for bonds with profits.
Key Components
Exploration of the monthly pattern on TLT.
The strategy includes two simple filters to improve its effectiveness.
Backtest 1 - Fixed $ Money Management
In this variant, we invest a fixed amount of $100k. We test the period from 2002 to 2025.
Invested Capital: $100,000
Tested Period in Years: 23
Tested Years: 2002-2025
Tested Instrument: TLT
Equity Chart for this test:
Basic statistics and results month by month:
In the table, we highlighted the moment when the strategy was published.
Examples of transactions on the chart:
Click the button to see the latest backtest:
Backtest 2 - % Money Management
In this backtest, we invest 100% of the current capital (initial capital of $100,000). This causes the position value to change proportionally with changes in capital. The rest of the parameters remain unchanged.
The equity chart for this test is shown below (the yellow line is a benchmark).
Basic statistics resulting from the test:
Trading Strategy Analysis
Net Profit and CAGR
The net profit of $185,706 from the studied strategy is significantly lower than the Benchmark (Buy & Hold SPY, marked in yellow on the chart), which stands at $1,005,059. This translates to a CAGR of 4.67% compared to 11.00%. These figures indicate that the studied strategy yields a substantially lower net profit and average annual return rate, suggesting it is less effective in generating long-term profits.
Drawdown and Return/Drawdown Ratio
The maximum open drawdown in the analyzed strategy was 7.8%, compared to 55.19% in the benchmark. This results in a much better return/open drawdown ratio of 13.8 versus 5.43. This indicates that the analyzed strategy is less risky and more stable.
Exposure
The exposure in the analyzed strategy was only 22.88% vs 100% in the benchmark. This means that the analyzed strategy was "in the market" only 23% of the time. This results in less market risk exposure, and the Risk Adjusted Return was approximately 20.41%. You can read more about exposure here.
Winning Percent
The analyzed strategy achieved a winning percentage of 62.29%, indicating that this proportion of transactions was profitable—a fairly comfortable outcome. Furthermore, the average profit was 1.29 times greater than the average loss, as reflected in the Win/Loss ratio.
SL & TP
The strategy does not use a typical stop loss and take profit, although there are no obstacles to introducing them. According to our tests, for most strategies on ETFs/stocks, these settings worsen results (see why). The protection against a very strong impact of a potential price change of a single stock on the entire portfolio is the diversification within the portfolio of various strategies. The strategy uses a Time Exit. Visit the stop loss order page.
Market Regime
The strategy has been tested in all basic market regimes and includes filters implemented based on this. You can find more on this topic here.
Trading Costs
Trading costs and slippage were taken into account in the backtests. You can check our last research about trading costs using Alpaca Broker here. With a diversified portfolio of stocks and strategies, transaction costs can determine your profit or loss, so take the time to thoroughly test and choose a broker.
Robustness
The number of historical transactions 175, is significantly lower than Stockpicker-type strategies, which can provide even over 100,000 transactions in robustness tests. We adhere to the principle that the fewer parameters, the greater the strategy's robustness. Therefore, we strive to ensure our strategies have as few parameters as possible and to select only those parameters that significantly impact the strategy's effectiveness while aligning with its nature.
Recommended Instruments
The recommended primary instrument for this strategy in Algocloud is TLT.
Pattern Day Trader
The strategy statistically did not close any trades on the same day, so it does not meet the criteria of a Pattern Day Trader (PDT). This means it can also be used on smaller accounts.
Correlation
The strategy is inversely correlated with most other strategies, which helps balance a stock strategy portfolio. This is a significant advantage. To check the strategy's correlation with others, visit the correlations page.
Summary & Strengths and Weaknesses
Strengths of the strategy:
Very favorable profit-to-risk profile – high Return/Open Drawdown ratio compared to the benchmark, with relatively small maximum capital drawdown.
Low average exposure (about 23% of the time in the market), which limits market risk and allows for comfortably combining the strategy with other systems in the portfolio.
Stability of results over the long term – over 23 years, only a few years ended in a loss, confirming the approach's resilience to changes in market conditions.
High percentage of winning trades (over 60%), supported by a favorable average profit to average loss ratio, which translates into a statistical advantage in a series of trades.
Exposure to long-term treasury bonds (TLT) provides diversification compared to equity market-based strategies and helps smooth the capital path of the entire portfolio.
Weaknesses of the strategy:
Lower CAGR than a simple "buy and hold" approach on a stock index – the strategy is designed more as a risk stabilizer and portfolio component rather than a standalone source of maximum capital growth.
Relatively low exposure and a limited number of signals may result in longer periods without new trades, which can be psychologically challenging for some investors (the strategy "works in the background" but does not generate continuous activity).
Summary
Bonds are not, and never will be, growth leaders. Compared with other stock strategies, this approach's annual return is not particularly impressive. We achieve a return of just under 4% annually, but with capital employed for only about 14% of the time, this results in a 20% annualized return.
The main advantage of this strategy is the inverse correlation with stocks that bonds statistically provide, as demonstrated in the test results. Although the decline in bond and stock prices in 2022 was unusually parallel, historical data show this is the exception rather than the rule. Unlike bonds alone, this strategy emerged as defensive during that period, delivering positive returns in all turbulent years. Thanks to the inverse correlation with stocks, it can provide stability to a portfolio.
If you are considering bonds in your portfolio, this strategy is worth considering as an alternative to simply buying and holding them.
What you get in the package for this strategy:
The SQX file is ready to use on the Algocloud and StrategyQuant platforms.
An eBook describing detailed rules and results of the strategy.
Pseudocode that describes all the rules in an easy-to-understand way.
Disclaimer
The results obtained from historical data do not guarantee future outcomes. The effectiveness of a strategy can change over time. Backtesting is a tool that allows for the analysis and evaluation of an investment strategy based on historical data. Various factors, such as market changes or economic conditions, can influence the effectiveness of a strategy over time.
Investing always involves risk. This material is not investment advice. We share our experience and algorithms for educational purposes. We make efforts to ensure that our algorithms are error-free, but neither we nor the tools we use guarantee the absence of technical issues. Any decisions to use a particular strategy are made at your own risk and should be preceded by careful understanding and verification. You should always carefully consider your investment goals and risk tolerance before making investment decisions.
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