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.
We are testing the period of the last 30 years from 1994 to February 2025.
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: 30
Tested years: 1994-02.2025
Tested Index: Nasdaq 100
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 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 of over 20 million USD in the analyzed strategy is 10 times higher than the Benchmark (S&P 500 Index via SPY ETF, marked in yellow on the chart), which is over 2 million USD. This translates to a CAGR of 18.8% compared to 10.5%. This means the analyzed strategy achieves a much higher net profit and average annual return rate, highlighting its exceptional efficiency in generating long-term profits.
Drawdown and Return/Drawdown ratio
The max open drawdown in the analyzed strategy was -18.5% vs -55.19% in the benchmark, resulting in a significantly better Return/Open Drawdown ratio of 16.20 vs 5.07. This means that the analyzed strategy is less risky and more stable, as the maximum capital drawdown is smaller, leading to better risk management compared to the benchmark.
Exposure
The average exposure in the analyzed strategy was only 24% vs 100% in the benchmark. The study was conducted on the underlying instrument, which are stocks of the Nasdaq 100 index. Exposure is measured by a dedicated study, which you can read about here.
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 71.48%. This means that 71% of transactions were profitable, emphasizing the effectiveness of the strategy in generating positive results, giving the user greater confidence in the frequency of profit generation.
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 100 open positions) from 1994 to February 2025. This included 30'752 transactions for the S&P 500 index and 48'937 transactions for the Russell 1000 index at %MM. 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: 30'752
Russell 1000 max transactions: 48'937
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
Pattern Day Trader
The strategy statistically closed 8.77% 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 30 years on the S&P500 index, there have been only 4 cases of meeting PDT conditions.
Here are the details of this study:
Of course, after combining it in the wallet with other strategies, 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 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:
Outstanding profits and stability. In the analyzed strategy R2 Turbo, Net Profit was over $20 million USD, while the Benchmark achieved over $2 million USD. The CAGR was 18.8%, almost 2x higher than the 10.5% for the Benchmark.
Low capital engagement. The strategy involves relatively small capital Engagement (24%).
Low drawdown. The Max Open Drawdown in the analyzed strategy was 18.5% compared to 55.19% in the Benchmark. This shows that the strategy is less risky and more stable.
High winning percent ratio. 71.48% of trades ended in profit, highlighting the effectiveness and user-friendliness of the strategy.
Robustness. The strategy was tested on the S&P 500 and Russell 1000 indices, achieving a maximum of 30'7521 and 48'937 trades, respectively.
Weaknesses of the strategy:
Percentage of same-day trades. A downside of the strategy is the percentage of trades closed on the same day, which is over 8%. This may lead to meeting the Day Trader conditions mentioned above.
Summary
Over 30 years, the analyzed strategy had two losing years, which is an outstanding result for a Stockpicker strategy. The Max Drawdown was a very low level in the context of 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 weakness, which is the challenge of applying it 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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