• Raw relative strength can be misleading, as high-volatility shares often appear strong but are prone to sharp reversals. Adjusting for volatility helps avoid these “high-beta traps”.
• VARS normalises momentum by Average True Range (ATR), comparing a share’s risk-adjusted performance against a benchmark and offering clearer signals on whether strength or weakness is genuine.
• Interpreting VARS with trend structure enhances timing, as divergences around the moving average identify when a trend is gaining or losing true momentum, validated by Apple’s contrasting case studies.
• Traders can use VARS for smarter stock selection, ranking shares on a risk-adjusted basis, and building more stable trend-following or rotation strategies that avoid chasing unstable volatility leaders.

Trend-following trading strategies can be better refined with momentum filters to determine whether an uptrending share still has the “strength” to rise further, and inversely for a downtrending share to decline further.

In this guide, we will use relative strength as a momentum filter by comparing the share price with its benchmark stock index exchange-traded fund, for example, by plotting the price actions of Apple (AAPL) against the S&P 500 ETF (SPY), which gives a ratio (relative strength performance) of Apple’s share price in comparison with the SPY (see Fig. 1).

Momentum strategies typically rank shares by performance over a lookback period. The downside is that high-beta names often dominate these rankings.

Hence, this “raw form” of relative strength measurement has a flaw that does not take into account the volatility of the share price. This matters because high-volatility shares can appear strong but often reverse violently, giving rise to the “momentum crash” phenomenon when the trend reverses.

Also, consider two stocks that both move in response to SPY's movements. If SPY moves significantly more than its average past volatility (measured by its ATR), and the share does the same, traditional relative strength calculations might show strength when, in fact, the share is just mirroring SPY's increased volatility.

For instance, if SPY typically moves $0.25 an hour (ATR) but suddenly moves $1, and a share typically moves $0.50 (ATR) but moves $2, the share's apparent relative strength of $1 ($2 - $1) over the SPY might be overstated when in reality there is no relative strength for the share when adjusted for volatility (ATR).

Hence, to reduce the risk of “high beta traps”, we need to filter out this noise by adjusting the “raw form” of relative strength with volatility, which is termed as volatility-adjusted relative strength (VARS).

VARS blends relative performance with risk normalisation to determine which shares are delivering stronger gains per unit of volatility, giving us a clearer picture of whether a trending share is truly strong potentially on a risk-adjusted basis

Compute the daily change in the share using the absolute price difference of closing prices: Close Price t – Close Price t-1

Compute the volatility of the share price using a smoothed Average True Range (ATR) of either 14 or 20 periods, where ATR t measures the rate of price change by considering the high, low, and closing values of the share for the chosen period t.

Compute the volatility-adjusted absolute daily change of the share:

(Close Price t – Close Price t-1) / ATR t = Share Change t

Compute the daily change in the benchmark stock index ETF using the absolute price difference of closing prices: Close Index t – Close Index t-1

Compute the volatility of the benchmark stock index ETF using a smoothed Average True Range (ATR) of either 14 or 20 periods, where ATR t measures the rate of price change by considering the high, low, and closing values of the stock index ETF for the chosen period t.

Compute the volatility-adjusted absolute daily change of the benchmark stock index ETF: (Close Index t – Close Index t-1) / ATR t = Index Change t

Define a look-back period to sum up the volatility-adjusted absolute daily change of the share and the benchmark stock index ETF:

Cumulative change of share = sum (Share Change t, lookback)

Cumulative change of the benchmark stock index ETF = sum (Index Change t, lookback)

VARS = Cumulative change of share - Cumulative change of the benchmark stock index ETF

Thanks to TradingView's open-source Pine Script coding community, we can obtain a volatility-adjusted relative strength (VARS) indicator from Mattishenner in the TradingView charting platform.

Its key inputs are as follows (see Fig. 2):

Comparison instrument => user choice of benchmark stock index ETF, S&P 500 (SPY) by default

Look-back length => how far back (N-periods) to measure the cumulative relative strength of both the share and user choice of benchmark stock index ETF

Moving average type and length => smooth the volatility-adjusted relative strength line (VARS)

ATR length => how volatility is measured for normalisation of the relative strength

Identifying shares with actual strength or weakness compared to the market (benchmark stock index ETF).

1. When the VARS line is above zero and above the moving average, it indicates a share with relative strength that is still likely to gain more strength.
2. When the VARS line is above zero but below the moving average or shows a bearish divergence above the moving average with the corresponding share price, it indicates a share with relative strength that is currently losing strength.
3. When the VARS line is below zero and below the moving average, it indicates a stock with relative weakness that is still likely losing strength.
4. When the VARS line is below zero but above the moving average or shows a bullish divergence below the moving average with the corresponding share price, it indicates a share with relative weakness that is starting to gain back some strength.

Let’s now do some case studies on the application of the VARS indicator using Apple (AAPL) share and the S&P 500 exchange-traded fund (SPY) as the reference benchmark stock index

In the case studies, we will use daily charts, a look-back period of 60 days (approximately three trading months), a simple moving average (SMA) with a length of 50 days, and the ATR length is set at 20 days as an example.

On the observed day of 3 January 2025, the share price of Apple was trading in a medium-term uptrend phase as its price actions were above the 50-day moving average, and the 20-day moving average was above the 50-day moving average.

Hence, we will deploy a bullish trend-following strategy to establish a bullish opportunity near or at the support of 237.27 (also close to the 20-day moving average), as its prior decline from 26 December 2024 high to 3 January 2025 low had shown bearish exhaustion condition, where the daily price action of 3 January 2025 formed a small-body, “Doji” candlestick after its prior day, 2 January 2025’s long-body bearish candlestick.

Interestingly, the VARS of Apple against the S&P 500 ETF had shown a bearish divergence condition (VARS formed a “lower high” above its 50-day moving average) in contrast with a higher high seen in the price action of Apple in the same corresponding period (see Fig. 3).

Hence, this observation falls under point (2) under the interpretation of VARS, which suggests that Apple’s volatility-adjusted relative strength against the S&P 500 ETF was losing strength, and the bullish trend-following opportunity should be avoided.

Thereafter, the share price of Apple staged a decline of 11% (high to low) in the next four weeks from 6 January 2025 to 21 January 2025 (see Fig. 4).

An interesting point to take note of as well, the raw relative strength (RS) of Apple against the S&P 500 ETF (a ratio that does not consider volatility) did not flash a bearish divergence condition and misinterpreted the continuation of Apple’s outperformance against the S&P 500 ETF at that juncture.

Hence, this case study illustrated the efficiency of VARS over the raw RS indicator.

The share price of Apple had managed to stage a major bullish breakout on 7 August 2025 after being capped below a major descending trendline resistance for the past seven months since 26 December 2024.

In conjunction, the VARS of Apple against the S&P 500 ETF had shown a bullish divergence condition (VARS formed a “high low” above its 50-day moving average) in contrast with a lower low seen in the price action of Apple in the same corresponding period (see Fig. 5).

This observation falls under point (4) under the interpretation of VARS, which suggests that Apple’s volatility-adjusted relative weakness against the S&P 500 ETF was starting to gain back some relative strength. Hence, it supported the bullish breakout trend-following opportunity, coupled with the share price of Apple, which managed to trade back above its 20-day and 50-day moving averages

Thereafter, the share price of Apple rallied by 26% (low to high) in the next two months from 8 August 2025 to 31 October 2025 (see Fig. 6).

Share traders can incorporate VARS into a watchlist to rank shares on a daily and weekly basis and focus on those shares that are ranked higher according to their respective VARS.

By adjusting for volatility, traders can build a better diversified portfolio that avoids chasing high-volatility leaders without adjusting for risk (volatility).

VARS helps traders stay aligned with strong, stable trends while avoiding unnecessary volatility. Whether you’re learning momentum for the first time or refining a rotation model, this indicator gives a more realistic view of market leadership.

OANDA Asia Pacific Pte Ltd is a CFD broker and offers CFD trading on shares as an underlying asset. You do not own or have any rights to the underlying assets when trading CFDs.