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 31 years, covering the years from 1995-2025.
Invested Capital: $100k
Tested Index: S&P 500
Equity chart for this test:
Basic statistics and results month by month:
In the table, we highlighted the moment when the strategy was published.
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
The net profit of $8.23M in the analyzed strategy remains significantly higher than the benchmark (the S&P 500 Index, represented by the SPY ETF, marked in yellow on the chart), which achieved $2.46M. The CAGR was 15.34% vs 11% in the benchmark.
The higher CAGR in the strategy still translates into significantly higher cumulative income over the analyzed period – that's the magic of compound interest.
Drawdown and Return/Drawdown Ratio
The maximum open drawdown in the strategy was 28.11% vs 55.19% in the benchmark, which confirms a substantially smaller equity decline in deep stress periods. While the benchmark shows a Return/Open Drawdown ratio of 5.77, the strategy also stands out with an exposure-adjusted return of 22.68%, reflecting a clearly more favourable risk-return profile than the index.
Exposure
The average exposure in the analyzed strategy was 67.61% vs 100% in the benchmark.
Winning Percent
Close to 66.81% 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 evaluated the robustness of our strategy by executing all possible stock transactions from 1995 to 2025 for the Nasdaq 100 and Russell 1000 indices. This testing included 9,717 transactions with a maximum of 40 open positions for the Nasdaq 100, and 30,682 transactions with a maximum of 40 open positions for the Russell 1000 at percentage money management. Our strategy successfully passed the 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: 9'717
Russell 1000 max transactions: 30'682
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, which has shown the best historical results. However, the strategy also yields stable results on Nasdaq 100 stocks.
Pattern Day Trader
The strategy statistically closed about 1.83% of transactions on the same day (216 day trades over the full history), and over the 31-year research period, it triggered the PDT conditions only 1 time. This means that, in practice, the strategy can still be used on real accounts below $25k, with PDT-related limitations appearing only in very rare edge cases. Pattern Day Trader
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 strategy's correlation with others, visit the correlations page.
Summary & Strengths and Weaknesses
Strengths of the strategy:
Clear edge over the market. Over the long term, the strategy clearly outperforms the broad equity market index, showing that it exploits periods of advantage better than a simple buy‑and‑hold approach.
Controlled capital drawdowns. The maximum declines in portfolio value remain noticeably lower than in the case of the index itself, which translates into less psychological pressure during weaker market phases.
High trade success rate. A large majority of trades end in profit, which supports a smoother equity curve and makes it easier to stick to the system rules.
Proven robustness. The strategy has been tested on many thousands of trades and across different equity indices, confirming its stable behaviour in varied market conditions.
Suitable for smaller accounts. The construction of the strategy and the way trades are executed make it appropriate also for lower‑value accounts, without the need to meet the most restrictive regulatory requirements.
Weaknesses of the strategy:
Moderate level of exposure. The strategy keeps a noticeable share of capital invested for a significant portion of the time, which limits the possibility of simultaneously using the same funds fully in other systems in the portfolio.
Summary
In the long test horizon, the strategy recorded only a very small number of losing years while at the same time clearly outpacing the broad market in terms of cumulative performance. The combination of a high trade success rate, controlled drawdowns, and resilience to different market phases means it can serve as a solid pillar of an equity‑strategy portfolio.
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 eBook presenting detailed rules and results of the strategy.
The SQX file is 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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