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.

The rules and strategy file are presented in an E-book, which you will receive for free by claiming the Bonus you can read about HERE. The ready-to-use algorithmic strategy will be available to you directly on the Algocloud platform.

Is the strategy worth adding to your strategy portfolio? Form an opinion based on our research.

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 May 2024. 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 (survival 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:

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.

Equity chart for this test and for the benchmark (yellow line):

Basic statistics resulting from the test:

Net Profit and CAGR

Net profit above $19.7 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 $1.8 million. The CAGR was 19.28% vs 10.37% 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 Max Drawdown in the analyzed strategy was 24% vs 55% in the benchmark, resulting in a better Return/Drawdown ratio of 7.57 vs 4.39, 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 68% 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 does not use typical stop-loss and take profit. According to our tests, for most stock strategies, these settings worsen results (see why). Instead, the strategy has one exit signal or additional safety exit after X bars (time-based stop-loss). Position diversification within one strategy and across the portfolio serves as protection against the strong impact of a single stock price change on the entire portfolio.

Market regime

The strategy has been tested in all basic market regimes and includes filters implemented based on this.

Trading Costs

Trading costs and slippage that occurred in real account tests for the Alpaca broker were taken into account in backtests (detailed study). 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.

Strategy robustness

Robustness was tested by practically executing all possible transactions in stocks (max open positions 100) from 1994 to May 2024 for the S&P500 (45'987 transactions) and Russell1000 (61'733 transactions) indices at %MM. This strategy passed our manual parameter modification tests. We adhere to the principle that the fewer parameters, the greater the strategy's robustness. Therefore, we make efforts for our strategies to have as few parameters as possible and to choose only those parameters that have a significant impact on strategy effectiveness while also aligning with its character.

• S&P500 max transactions: 45'987

• Russell1000 max transactions: 61'733

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.

Correlation

To check the correlation of this strategy with others, visit the dedicated correlation page.

Summary & Strengths and weaknesses of the strategy

Strengths of the strategy:

• Profit Stability: In the analyzed strategy, Net Profit was above $19.7 million, compared to the benchmark of $1.8 million. The CAGR was 19.28% vs. 10.37% in the Benchmark.

• Low Drawdown: Max Drawdown in the analyzed strategy was 24% compared to 55% in the Benchmark. This shows that the strategy was less risky and more stable.

• High Winning Percent: 67.8% of trades ended in profit, highlighting the effectiveness of the strategy and increasing comfort in its use.

• Robustness: The strategy was tested on the S&P 500 and Russell 1000 indices, totaling over 100,000 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 analyzed 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 $19.7 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.
  

If you need the code for this strategy in formats such as MultiCharts, MT4, or MT5 (MQL), please contact us on this topic.