IBS Master draws inspiration from the experiences of Linda Raschke described in the book Street Smarts: High Probability Short-Term Trading.
Inspiration
The original strategy was used by the Linda Raschke for intraday trading, but here we have an implementation that has successfully stood the test of time in stocks. The foundation of the strategy is the IBS (Internal Bar Strength) indicator, but the strategy includes a specific way of using this indicator and filters that increase its effectiveness.
Key Components
Reversal in an uptrend. The essence of this strategy is the ability to detect specific pullbacks in an uptrend. The strategy utilizes typical market behavior in stocks for reversals and temporary pullbacks, expecting the trend to continue.
IBS as the foundation. As the name suggests, Internal Bar Strength is a key element of the IBS Master strategy. It helps identify patterns leading to price reversals and finds potential reversal points. The IBS Master strategy includes additional conditions and filters that activate and deactivate based on certain 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 (15). This results in a capital commitment of up to $6.7k per position.
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
Test period (years): 30
Tested years: 1994-05.2024
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 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. The rest of the parameters remain unchanged.
The Equity Chart for this test looks as follows:
Basic statistics resulting from the test:
Trading strategy analysis
Net Profit and CAGR
The net profit above $17 million in the analyzed period is almost 10x higher than the benchmark (S&P 500 Index in the form of SPY ETF marked in yellow on the chart), which is above $1'774'929. This translates to a CAGR of 18.70% vs 10.28%. This means that the analyzed strategy achieves significantly higher net profit and a higher average annual return, indicating its exceptional efficiency in generating profits over the long term.
Drawdown and Return/Drawdown Ratio
The max drawdown in the analyzed strategy was 23.90% vs 55.19% in the benchmark, resulting in a much better Return/Drawdown ratio of 16.90 vs 4.4. This indicates that the analyzed strategy is less risky and more stable because the maximum capital drawdown is smaller, leading to better risk management compared to the benchmark.
Exposure
The average exposure in the analyzed strategy was 64% 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. The analyzed strategy used less capital on average and therefore was 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 64.4%. This means that most of transactions were profitable, highlighting the effectiveness of the strategy in generating positive results and giving the user greater confidence in the frequency of profit generation.
SL & TP
The strategy does not use typical stop-loss and take profit orders. According to our tests, for most stock strategies, these settings worsen results. Instead, the strategy has one exit signal or additional safety exit after X bars (time-based stop-loss). Diversification of positions within one strategy and across the portfolio serves as protection against the strong impact of a single stock price change on the entire portfolio. Click for more about stop loss order.
Market Regime
The strategy was tested in all major market regimes and includes filters implemented based on this analysis.
Trading Costs
Trading costs and slippage were taken into account in the backtests, which occurred in real account tests for the Alpaca broker. With a diversified portfolio of stocks and strategies, transaction costs can significantly impact your profit or loss, so take the time to thoroughly test and choose a broker.
Strategy Robustness
The robustness was tested by practically executing all possible stock transactions (max open positions 100) for the period from 1994-2023 for the S&P500 (37,895 transactions) and Russell1000 (55'872 transactions) indexes 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 to ensure our strategies have as few parameters as possible and to select only those parameters that have a significant impact on strategy effectiveness while aligning with its character.
S&P500 max transactions: 37'895
Russell1000 max transactions: 55'872
The results are as follows:
Recommended Instruments
The recommended primary instrument for this strategy in Algocloud Stockpicker are companies in the S&P 500 index, which have shown the best historical results. However, the strategy also performs well with stocks in the Nasdaq 100.
Pattern Day Trader
Statistically, the strategy closed 11.97% of trades on the same day, meeting the Pattern Day Trader (PDT) criteria. This means that a real account with a minimum of $25k is required for the entire portfolio.
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 Size and Stability: In the analyzed strategy, the Net Profit was alost 10x higher than the benchmark ($17 mln vs. $1,77 mln). The CAGR was 18.70%, significantly higher than the benchmark's 10.28%.
Low Drawdown: The Max Drawdown in the analyzed strategy was 23.9% compared to 55.19% in the benchmark. This shows that the strategy was less risky and more stable.
Low Correlation: The strategy has a unique driver and gives a relatively low correlation to other reversal strategies.
High Winning Percent: 64.4% of trades ended in profit, highlighting the effectiveness of the strategy and increasing comfort in using it.
Robustness: The strategy was tested on the S&P 500 and Russell 1000 indices, achieving a maximum of 37'895 and 55'872 trades respectively.
Weaknesses of the strategy:
Capital Engagement: The strategy requires a relatively higher capital engagement compared to other strategies (64%).
Percentage of Same-Day Trades: A downside of the strategy is the percentage of trades closed on the same day, which is almost 12%. This may lead to meeting the Pattern Day Trader conditions mentioned above.
Summary
Over 30 years, the IBS Master strategy being analyzed only had 3 losing years, which is a very solid result. It demonstrates high efficiency and, importantly, a unique profile = low correlation compared to most reversal strategies. Historically, it has carried relatively low risk and high stability. It can definitely be a driver for a good portfolio of strategies.
What you receive in the package for this strategy:
An e-book presenting detailed rules and results of the strategy.
An SQX file ready to use on platforms like Algocloud and StrategyQuant.
Pseudocode describing all rules in an easy-to-understand manner.
IBS indicator for TradingView.
If you need the strategy code in formats such as MultiCharts, MT4, or MT5 (MQL), please contact us regarding this matter.
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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