The R2 Turbo strategy is inspired by Larry Connors' experiences. It uses the Relative Strength Index (RSI) indicator in a unique way, along with filters to boost its effectiveness. This trend reversal strategy waits for a specific pullback during an uptrend.
Inspiration
The R2 Turbo strategy draws inspiration from Larry Connors' experiences. Reversal strategies are effective in the stock market, where opportunities for profit often arise during pullbacks. In this article, we will discuss the details of the R2 Turbo strategy, covering its key components, historical results, and recommended trading instruments.
Key components
Detecting pullbacks in an uptrend. The essence of this strategy is the ability to detect pullbacks in an uptrend. The strategy utilizes typical reversal behavior in the stock market and temporary pullbacks, expecting the trend to continue.
While RSI is at the core of the strategy, it is worth noting that the R2 Turbo strategy includes a more advanced application of this indicator and 2 filters that turn it on and off depending on the conditions.
The Stockpicker mechanism searches and automatically selects stocks that meet the entry criteria.
Backtest 1 - $ Money Management
In this variant, we invest a constant amount of $100k, which is divided by the maximum number of open positions (10). This results in a capital commitment of up to $10k per position.
The backtest automatically selects stocks that meet the criteria from the Nasdaq 100 index. It is important to note that the list of stocks included in the index changed over the years, which is taken into account in the Stockpicker data (survivorship bias).
Invested capital: $100k
Number of positions: 10
Maximum investment in 1 position: $10k
Test period in years: 31
Tested years: 1995 - 2025
Tested Index: Nasdaq 100
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 are investing 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. All other parameters remain unchanged.
The equity chart for this test is shown below. It includes a benchmark, represented by a faint yellow line at the bottom.
Basic statistics resulting from the test:
Trading Strategy Analysis
Net profit and CAGR
The net profit over $10M in the analyzed strategy is more than 4x higher than the Benchmark (S&P 500 Index via SPY ETF, marked in yellow on the chart), which achieved over $2,4M. This translates to a CAGR of 16.17% compared to 10.97%. This means the analyzed strategy still achieves a much higher net profit and average annual return rate than the benchmark, highlighting its strong long-term efficiency.
Drawdown and Return/Drawdown ratio
The maximum open drawdown in the analyzed strategy was 18.55%, compared to 55.19% in the benchmark, indicating a significantly better risk profile than the index. The strategy achieved a Return/Open Drawdown ratio of 32, more than five times the benchmark's 5.76.
Exposure
The average exposure in the analyzed strategy was only 24.17%, compared with 100% in the benchmark.
The analyzed strategy used one-fourth of the capital and, therefore, was much less exposed to market risk, with the remaining capital available for use in other strategies.
Winning percent
The Winning percent in the analyzed strategy was 70.75%. This means that around 71% of transactions were profitable, underscoring the strategy's effectiveness in generating positive results and giving the user greater confidence in how often trades end in profit.
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 the robustness by conducting all possible stock transactions (up to 40 open positions) from 1995 to the end of 2025. The strategy successfully passed our parameter modification tests. We believe that fewer parameters lead to greater robustness. Therefore, we make efforts to ensure that our strategies have as few parameters as possible and to only select parameters that have a significant impact on the strategy's effectiveness while also aligning with its nature.
S&P 500 max transactions: 22'088
S&P 100 max transactions: 6'275
The results are as follows:
Recommended Instruments
The recommended primary instrument for this strategy in Algocloud Stockpicker is the Nasdaq 100 index, which has shown the best historical results. However, the strategy also yields stable results on S&P 500 stocks.
Primary Instrument: Nasdaq 100
Supplementary Instrument: S&P 500, S&P 100
Pattern Day Trader
The strategy statistically closed 7.72% of trades on the same day, so theoretically it should meet the Pattern Day Trader (PDT) criteria that we write about here.
However, the strategy trades relatively less compared to other stockpicker-type strategies, and in the last 31 years on the Nasdaq 100 index, there have been only 3 cases of meeting PDT conditions.
Of course, after combining it with other strategies in the wallet, there may be more such cases, so we suggest you familiarize yourself with our PDT Finder and Exposure Master tools, which you can receive for free as part of our BONUS (see the Bonus section at www.algohubb.com).
Correlation
To check the correlation of the strategy with others, visit the correlations page.
Summary & Strengths and Weaknesses
Strengths of the strategy:
Strong long-term growth profile. Over the full backtest period, the strategy clearly outperformed a passive index approach, while maintaining a relatively smooth equity curve. This makes it attractive as a core, long-term component of an equity portfolio.
Efficient use of capital. The system keeps a substantial portion of capital in cash whenever there are no qualified signals, which leaves room to run additional strategies in parallel or to hold other investments without over-leveraging the account.
Controlled drawdowns. Historical tests show that losses during market stress were much shallower than in the broad index, so the strategy limited deep equity declines even in difficult environments.
High quality of trades. A clear majority of trades historically closed with a profit, which supports trader discipline and makes the strategy psychologically easier to follow through normal drawdown phases.
Proven robustness across universes. The rules were tested on different large-cap US stock universes and continued to generate consistent, positive results, which increases confidence that the edge does not depend on a single index composition.
Weaknesses of the strategy:
Suitability for smaller accounts. Because a noticeable part of trades closes on the same day and position sizing assumes diversification across many stocks, the strategy can more easily approach Pattern Day Trader thresholds and requires capital that is prepared for PDT rules; this makes it less convenient for very small accounts.
Uneven distribution of signals. As a reversal system that waits for specific pullbacks in uptrends, the strategy tends to generate clusters of entries followed by quieter periods, which may feel less comfortable for traders expecting a more regular trading rhythm.
Summary
Over a 31-year period, the analyzed strategy experienced only two losing years, which is an exceptional outcome for a Stockpicker strategy. The maximum drawdown remained very low, particularly when considering long-term investment strategies.
The R2 Turbo strategy is a Stockpicker strategy that demonstrates high efficiency, low risk, and great stability over the long term. Its strengths outweigh its weaknesses, making it challenging to apply to smaller accounts. Such reversal strategies should form the foundation of a good portfolio for trading US stocks.
What do you receive in the package for this strategy?
An SQX file ready for use on the Algocloud and StrategyQuant platforms.
An eBook presenting detailed rules and results for the strategy.
Pseudocode describing all the rules in an easy-to-understand manner.
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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