In this article, we develop a mean reversion strategy applied to a basket of stocks. The trading logic is straightforward: buy after short-term bearish moves and close the position when price returns toward an equilibrium level.

The underlying assumption is that the U.S. equity market has displayed a long-term bullish bias for decades, supported by earnings growth, innovation, and economic expansion. This does not imply that prices move in a straight line, but rather that short-term phases of weakness often tend to revert.

Therefore, within a structurally bullish environment, short-term pullbacks can turn into opportunities.

How To Build a Nasdaq Basket Without Survivorship Bias

As our reference universe, we use a basket of stocks belonging to the Nasdaq, one of the most widely followed equity indices globally, particularly due to its strong concentration of technology and growth companies.

In this type of analysis, it is essential to avoid survivorship bias, which occurs when only the stocks currently included in the index are considered, extending their historical data back to periods when they were not yet components. This would result in including in the test stocks that entered the index only after a significant growth phase, thereby overstating the robustness of the strategy.

Consider, for example, a stock that remained outside the index for years and was added to the Nasdaq only after a period of particularly strong performance. If, in the backtest, we used its full historical series as if it had always been part of the basket, we would be incorporating a growth path that would not actually have been accessible to an investor replicating the index at that time.

For this reason, the basket is reconstructed by including, for each historical period, only the components that were effectively part of the index at that moment, thereby more faithfully replicating the real-world conditions an investor would have faced.

What Is the IBS (Internal Bar Strength) Indicator and How Is It Calculated?

To identify short-term weakness conditions, we use the IBS (Internal Bar Strength), an indicator introduced in quantitative trading specifically to measure where the close is positioned within the bar’s range.

The formula is very simple:

IBS = (Close – Low) / (High – Low)

The indicator’s value ranges between 0 and 1:

• Values close to 0 indicate that the closing price is positioned near the lower end of the bar, signaling a potential weakness condition.
• Values close to 1 indicate that the close is near the upper end of the bar, signaling short-term strength.

In essence, IBS tells us where the close is located relative to the total range of the bar. It does not measure trend, it does not rely on moving averages, and it does not incorporate volatility or external parameters. It is simply a ratio between three data points of the bar. This simplicity is one of its key strengths, as it reduces the risk of overfitting that often affects more complex indicators.

Trading Rules: Entry, Exit, Stop Loss and Time-Based Exit

The strategy applies IBS on a daily time frame. The entry logic is straightforward: buy the stock at the close of the session when the indicator returns a value below 0.1. In practical terms, this means the close is positioned at the extreme lower end of the daily range, indicating pronounced very short-term weakness.

The primary exit also occurs at the close, when IBS rises above 0.9, meaning the close is positioned near the top of the bar. The concept is consistent with a mean reversion approach: enter on a bearish excess and exit once that excess has been absorbed.

To avoid holding positions for too long, a time-based exit is also included. If after 10 days the trade has not reached the indicator-based exit condition, the position is closed anyway. Since this is a short-term strategy, this filter helps limit exposure in situations where the market does not rebound as expected.

In terms of risk management, each trade allocates a fixed capital of $10,000 and applies a 10% stop loss as an additional safety measure. While the stop loss is not the core component of the strategy, it helps prevent excessive losses in the event of abnormal price movements.

Figure 1 shows a concrete example of a trade generated using the Internal Bar Strength (IBS) indicator.

Figure 1 – Example of a trade generated by the Internal Bar Strength (IBS) indicator.

Backtest Results: Equity Curve, Returns and Drawdown

The strategy was tested over a long historical sample starting in 1990. Assuming 100 stocks in the index and allocating $10,000 per trade, the total capital deployed by the portfolio amounts to $1 million.

Figure 2 shows the overall equity curve. Despite the simplicity of the logic, growth appears steady and well distributed across the entire historical sample. The system clearly experiences stress during periods when the equity market undergoes significant shocks, such as during the dot-com bubble. However, the overall structure remains solid, and subsequent recoveries unfold progressively.

Figure 3 presents the performance summary. The maximum drawdown is approximately $305,000, equivalent to about a 30% decline on the capital allocated to the portfolio. This figure is meaningful because it is significantly lower than the drawdowns experienced by the Nasdaq index over the same period. The annual return is approximately 13%, broadly in line with the benchmark, but achieved with a more contained risk profile.

Figure 4 shows the Total Trade Analysis, which allows us to examine the quality of individual trades. The total number of trades is very high, and the percentage of profitable trades is around 60%. The average trade amounts to $77, representing a solid starting point for a short-term strategy. However, it is not particularly large, and therefore it is a metric that must be monitored carefully: an average trade that is too small may make the strategy more sensitive to transaction costs.

Overall, the results confirm that a very simple mean reversion approach, applied systematically and to a broad basket free from bias, can generate robust and consistent performance over the long term.

Figure 2 – Equity curve of the Nasdaq portfolio with IBS strategy (1990-2026)

Figure 3 – Performance Summary of the IBS mean-reversion strategy on the Nasdaq.

Figure 4 – Total Trade Analysis of the IBS mean-reversion strategy on the Nasdaq.

How To Improve the Strategy: Volatility Filters and Capital Allocation

The results show that the IBS-based mean reversion logic works reasonably well across the entire basket. Once the robustness of the base structure has been verified, the next step is to make the approach more selective and efficient.

One possible improvement is to filter the stocks on which the strategy is applied. For example, one could focus only on the most volatile stocks, only on the least volatile ones, or restrict trading to stocks that are in an uptrend. This would reduce the number of signals and make the strategy more suitable for smaller portfolios, avoiding simultaneous exposure to a large number of stocks.

Another area of improvement concerns capital allocation. In the test, a fixed value of $10,000 per trade was used, but a more efficient approach could involve adjusting exposure based on each stock’s volatility: allocating more capital to less volatile stocks and less capital to more volatile ones. This would help standardize the risk of individual trades and produce a smoother equity curve.

The same reasoning can be applied to the stop loss. The 10% level was used as an indicative threshold and does not represent the same price movement across very different stocks. A 10% move in a highly volatile stock like Tesla is not equivalent to a 10% move in a defensive stock. A more consistent approach could be based on a relative measure, such as using the Average True Range to define a threshold better aligned with the behavior of each individual asset.

Finally, during drawdown phases, one might consider introducing a hedging strategy, for example by using Nasdaq futures. This would allow exposure to be reduced during periods when the broader market is under stress, limiting the magnitude of declines without altering the core logic of the strategy.

Conclusions: Can A Simple Strategy Beat The Nasdaq?

The results show that an extremely simple strategy, based on a parameter-free indicator and a straightforward mean reversion logic, can generate solid performance over a very long time horizon. Thanks to its long-term bullish bias, the equity market lends itself well to this type of approach, and IBS proves to be an effective indicator for capturing short-term excesses.

The strategy presented here can be considered a solid starting point, and the extensions discussed represent potential directions to make the model more robust and better suited for real-world trading applications.

See you next time, happy trading!

Feature Image credit: Author

Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.