If J. Welles Wilder knew that the indicator he described in 1978 was still performing so well, he would be very proud. It is a matter of matching a powerful indicator to the nature of the instrument, that is US stocks.
Inspirations
The concept of the RSI indicator was introduced in 1978 by J. Welles Wilder in the book "New Concepts in Technical Trading Systems," and it's hard to believe how well it has stood the test of time, still providing a fantastic foundation for creating algorithmic strategies for the stock market. For us, this is also an opportunity to obtain a reliable test over a 45-year Out Of Sample period.
The RSI Range Rider strategy combines the best of RSI by combining this indicator with a trend filter and effective entry and exit methods, providing you with an efficient strategy suitable even for smaller accounts.
Backtest 1, Fixed $ Money Management
In this variant, we invest a fixed amount of $100k, which is divided by the maximum number of open positions (15). This results in a capital commitment per position of up to $6.7k.
We are testing the period of the last 30 years, covering years from 1994 to February 2025. The backtest automatically selects stocks that meet the criteria from the S&P 500 index. It's important to note that the list of stocks in the index changed over the years, which is taken into account in the Stockpicker data (survivorship bias).
Invested Capital: $100k
Tested Period (years): 30
Tested Index: S&P 500
Equity chart for this test:
Basic statistics and results month by month:
Click the button to see the latest backtest:
Backtest 2, % Money Management
In this backtest, we invest in a strategy that constantly uses 100% of the current capital (starting with $100k capital). This means that as the capital grows or decreases, the position value changes proportionally. The rest of the parameters remain unchanged.
The equity chart for this test looks as follows. The chart includes a benchmark (!) - a faint yellow line at the bottom.
Basic statistics resulting from the test:
Trading Strategy Analysis
Net Profit and CAGR
Net profit above $21 million in the analyzed strategy is significantly higher than the benchmark (S&P 500 Index in the form of SPY ETF marked in yellow on the chart), which is above $2 million. The CAGR was 18.97% vs 10.53% in the benchmark.
A CAGR of almost 2 times higher resulted in over 10 times higher income in the analyzed period. That's the magic of compound interest.
Drawdown and Return/Drawdown Ratio
The maximum open drawdown in the analyzed strategy was -27.48% vs -55.19% in the benchmark, resulting in a better Return/Open Drawdown ratio of 7.87 vs 5.07, respectively. This means that the analyzed strategy was not only more profitable but also less risky compared to the benchmark.
Exposure
The average exposure in the analyzed strategy was 68% vs 100% in the benchmark. The study was conducted on the underlying instrument, which is the S&P 500 index stocks. Exposure is measured by a dedicated study, which you can read about here.
Winning Percent
Close to 67.74% of transactions were profitable, providing stable control and psychological comfort in using the strategy, giving the user greater confidence in the frequency of achieving profits.
SL & TP
The strategy doesn't use a typical stop-loss and relies on an exit condition. But you can add an SL if it makes you more comfortable. Instead, diversifying positions within a single strategy and across the whole portfolio helps protect against the significant impact of a potential price change in one stock on the entire portfolio. Visit the stop loss order page.
Market regime
The strategy was tested in all basic market regimes and includes filters implemented based on this. Read more about market regimes.
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
We tested robustness by executing all possible stock transactions from 1994 to February 2025 for the Nasdaq 100 and Russell 1000 indices. This involved 10,427 transactions with a maximum of 50 open positions for Nasdaq 100, and 63,402 transactions with a maximum of 100 open positions for Russell 1000 at %MM. Our strategy successfully passed manual parameter modification tests.
We believe that fewer parameters lead to greater robustness. Therefore, we strive to keep our strategies simple, using only parameters that significantly impact effectiveness and align with the strategy's character.
Nasdaq 100 max transactions: 10'427
Russell 1000 max transactions: 63'402
The results of the robustness tests are as follows:
Recommended Instruments
The recommended primary instrument for this strategy on Algocloud Stockpicker is the S&P 500 index companies, which have shown the best historical results. However, the strategy also yields stable results on Nasdaq 100 stocks.
Pattern Day Trader
The strategy statistically closed 1.93% of transactions on the same day, and over the 30 years of research, it never met the PDT conditions, which means that the strategy can be used on real accounts below $25k, and it can be used in a more flexible manner without PDT-related limitations.
Here are the details of our research:
After combining in the portfolio with other strategies, such cases may overlap, so we suggest you familiarize yourself with our tools PDT Finder and Exposure Master, which we will provide to you for free as a BONUS (see Bonus section at www.algohubb.com.
Correlation
To check the correlation of the strategy with others, visit the correlations page.
Summary & Strengths and Weaknesses of the strategy
Strengths of the strategy:
Profit stability. In the analyzed strategy, Net Profit was above $21 million, compared to the benchmark of $2 million. The CAGR was 18.97% vs. 10.53% in the Benchmark.
Low drawdown. The Max Open Drawdown in the analyzed strategy was 27% compared to 55% in the Benchmark. This shows that the strategy was less risky and more stable.
High winning percent. 67% of trades ended in profit, highlighting the effectiveness of the strategy and increasing comfort in its use.
Robustness. The strategy was tested on the Nasdaq 100 and Russell 1000 indices, totaling nearly 74,000 trades, ensuring its high resilience.
Ability to apply the strategy on smaller accounts. The strategy does not meet PDT requirements, so it can be successfully used on accounts below $25k.
Weaknesses of the strategy:
Exposure. The average exposure of around 70% limits the ability to utilize allocated capital by other strategies.
Summary
Over 30 years, the RSI Range Rider strategy only had 2 losing years, which is a very good result for a Stockpicker type strategy. In tests, the strategy achieved an impressive net profit of over $21 million, significantly outperforming the benchmark. Its strengths are very strong for accounts of any size.
I hope you will consider it as an attractive part of your strategy portfolio. We recommend RSI Range Rider as a free BONUS, which you can read more about here.
What you receive in the package for this strategy:
An e-book presenting detailed rules and results of the strategy.
SQX file ready to be used on the Algocloud and StrategyQuant platforms.
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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